Patent Application
20160026770
1. Field of the Invention
The present invention relates to a method of evaluating lifestyle-related disease indicator, a lifestyle-related disease indicator-evaluating apparatus, a lifestyle-related disease indicator-evaluating method, a lifestyle-related disease indicator-evaluating program product, a lifestyle-related disease indicator-evaluating system, and an information communication terminal apparatus.
2. Description of the Related Art
Biomarker testing has rapidly been developed with the recent progress of genome analysis and post-genome testing and is widely utilized for, for example, prevention, diagnosis, and prognosis estimation of diseases. Examples of biomarker testing actively performed include genomics and transcriptomics based on gene information, proteomics based on protein information, and metabolomics based on metabolite information.
Genomics and transcriptomics reflect genetic factors but do not reflect environmental factors. Proteomics requires analysis of a number of proteins and still has many problems in analytical methods and comprehensive analysis methods. Metabolomics is promising in that it is a biomarker that reflects environmental factors in addition to genetic factors but, because of a large number of metabolites, still has many problems in comprehensive analysis methods.
Amino acids, which play a dominant role in metabolic pathways among metabolites in living bodies, are drawing attention as a novel biomarker.
It is reported that amino acid concentrations vary with diseases such as liver failure and renal failure (“Rosen H M, Yoshimura N, Hodgman J M, et al., “Plasma amino acid patterns in hepatic encephalopathy of differing etiology”, Gastroenterology, 1977, 72, 483-487” and “Suliman M E, Qureshi A R, Stenvinkel P, et al., “Inflammation contributes to low plasma amino acid concentrations in patients with chronic kidney disease”, Am. J. Olin. Nutr., 2005, 82, 342-349”).
WO 2004/052191, WO 2006/098192, and WO 2009/054351 related to a method of relating an amino acid concentration and a biological state are disclosed as previous patents. WO 2008/015929 related to a method of evaluating a state of metabolic syndrome using an amino acid concentration, WO 2009/001862 related to a method of evaluating a state of visceral fat accumulation using an amino acid concentration, WO 2009/054350 related to a method of evaluating a state of impaired glucose tolerance using an amino acid concentration, WO 2010/095682 related to a method of evaluating a state of at least one of apparent obesity, non-apparent obesity, and obesity that are defined by BMT (Body Mass Index) and VFA (Visceral Fat Area), using an amino acid concentration, and WO 2013/002381 related to a method of evaluating a state of fatty liver related disease including at least one of fatty liver, NAFLD (non-alcoholic fatty liver disease), and NASH (non-alcoholic steatohepatitis), using an amino acid concentration are disclosed as previous patents.
However, no search has been conducted for amino acids that are clinically useful for evaluating the states of indicators of lifestyle-related diseases (for example, the risk factors of lifestyle-related diseases that may be caused mainly by metabolic syndrome (for example, visceral fat accumulation, insulin resistance, and fatty liver)) in light of preventive medicine. Hence, no method has been developed of accurately and systematically evaluating the states of indicators of lifestyle-related diseases using amino acid concentrations. For example, although it is known that the progress of metabolic syndrome causes serious diseases such as cardiovascular events and cerebrovascular events in the future, no search has been conducted for a method of preventing these events using the profiles of amino acids in blood (see “Despres J P, Lemieux I, “Abdominal obesity and metabolic syndrome”, Nature, 2006, 444, 881-887” and “Van Gaal L F, Mertens I L, DeBlock C E, “Mechanisms linking obesity with cardiovascular disease”, Nature, 2006, 444, 873-880”).
It is an object of the present invention to at least partially solve the problems in the conventional technology.
The present invention has been made in view of the problems described above, and an object of the present invention is to provide a method of evaluating lifestyle-related disease indicator, a lifestyle-related disease indicator-evaluating apparatus, a lifestyle-related disease indicator-evaluating method, a lifestyle-related disease indicator-evaluating program product, a lifestyle-related disease indicator-evaluating system, and an information communication terminal apparatus, which can provide reliable information that may be helpful in knowing a state of an indicator of lifestyle-related disease.
To solve the problem and achieve the object described above, a method of evaluating lifestyle-related disease indicator according to one aspect of the present invention includes an obtaining step of obtaining amino acid concentration data on concentration values of amino acids in blood collected from a subject to be evaluated, and an evaluating step of evaluating a state of an indicator of lifestyle-related disease for the subject using the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject obtained at the obtaining step.
In the present specification, various amino acids are mainly written in abbreviations, the formal names of these are as follows.
The method of evaluating lifestyle-related disease indicator according to another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the concentration values of the amino acids of Gly, Tyr, and Asn, the concentration values of the amino acids of Gly, Tyr, and Ala, the concentration values of the amino acids of Gly, Tyr, and Val, or the concentration values of the amino acids of Gly, Tyr, and Trp are used. The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the concentration values of the amino acids of Gly, Tyr, Asn, and Ala are used.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, a state of at least one of fatty liver, visceral fat, and insulin is evaluated. The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the states of at least two of fatty liver, visceral fat, and insulin are evaluated. The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the states of fatty liver, visceral fat, and insulin are evaluated.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, a degree of a possibility of developing lifestyle-related disease (a risk of developing lifestyle-related disease) is evaluated using (i) the concentration values of the amino acids of Gly and Tyr, (ii) the concentration values of the amino acids of Gly, Tyr, and Asn, (iii) the concentration values of the amino acids of Gly, Tyr, and Ala, (iv) the concentration values of the amino acids of Gly, Tyr, and Val, (v) the concentration values of the amino acids of Gly, Tyr, and Trp, or (vi) the concentration values of the amino acids of Gly, Tyr, Asn, and Ala.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the state of insulin is evaluated by calculating a value of a formula (hereinafter referred sometimes as a value of an evaluation formula or an evaluation value) using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula (hereinafter referred sometimes as the evaluation formula) including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the state of visceral fat is evaluated by calculating a value of a formula using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, a previously obtained BMI (Body Mass Index) value of the subject, and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the state of fatty liver is evaluated by calculating a value of a formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, the states of insulin and visceral fat are evaluated by calculating a value of a formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention is the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, (i) the state of insulin is evaluated by calculating a value of a formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Tip, (ii) the state of visceral fat is evaluated by calculating a value of a formula using (a) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (b) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, a previously obtained BMI value of the subject, and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject, and (iii) the state of fatty liver is evaluated by calculating a value of a formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
The method of evaluating lifestyle-related disease indicator according to still another aspect of the present invention may be the method of evaluating lifestyle-related disease indicator, wherein at the evaluating step, a degree of a possibility of developing lifestyle-related disease (a risk of developing lifestyle-related disease) is evaluated by calculating a value of a formula using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, a previously obtained BMI (Body Mass Index) value of the subject, and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject, or (iii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
A lifestyle-related disease indicator-evaluating apparatus according to one aspect of the present invention is a lifestyle-related disease indicator-evaluating apparatus including a control unit and a memory unit to evaluate a state of an indicator of lifestyle-related disease for a subject to be evaluated. The control unit includes an evaluating unit that evaluates the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) concentration values of the amino acids of Gly and Tyr included in previously obtained amino acid concentration data of the subject on the concentration values of the amino acids and (ii) the formula previously stored in the memory unit including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr.
A lifestyle-related disease indicator-evaluating method according to one aspect of the present invention is a lifestyle-related disease indicator-evaluating method of evaluating a state of an indicator of lifestyle-related disease for a subject to be evaluated. The method is carried out with an information processing apparatus including a control unit and a memory unit. The method includes an evaluating step of evaluating the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) concentration values of the amino acids of Gly and Tyr included in previously obtained amino acid concentration data of the subject on the concentration values of the amino acids and (ii) the formula previously stored in the memory unit including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr. The evaluating step is executed by the control unit.
A lifestyle-related disease indicator-evaluating program product according to one aspect of the present invention is a lifestyle-related disease indicator-evaluating program product having a non-transitory computer readable medium including programmed instructions for making an information processing apparatus including a control unit and a memory unit execute a method of evaluating a state of an indicator of lifestyle-related disease for a subject to be evaluated. The method includes an evaluating step of evaluating the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) concentration values of the amino acids of Gly and Tyr included in previously obtained amino acid concentration data of the subject on the concentration values of the amino acids and (ii) the formula previously stored in the memory unit including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr. The evaluating step is executed by the control unit.
A recording medium according to one aspect of the present invention is a non-transitory computer-readable recording medium including the programmed instructions for making an information processing apparatus execute the lifestyle-related disease indicator-evaluating method.
A lifestyle-related disease indicator-evaluating system according to one aspect of the present invention is a lifestyle-related disease indicator-evaluating system including (I) a lifestyle-related disease indicator-evaluating apparatus including a control unit and a memory unit to evaluate a state of an indicator of lifestyle-related disease in a subject to be evaluated and (II) an information communication terminal apparatus including a control unit to provide amino acid concentration data of the subject on concentration values of amino acids that are connected to each other communicatively via a network. The control unit of the information communication terminal apparatus includes (I) an amino acid concentration data-sending unit that transmits the amino acid concentration data of the subject to the lifestyle-related disease indicator-evaluating apparatus and (II) a result-receiving unit that receives an evaluation result on the state of the indicator of lifestyle-related disease for the subject, transmitted from the lifestyle-related disease indicator-evaluating apparatus. The control unit of the lifestyle-related disease indicator-evaluating apparatus includes (I) an amino acid concentration data-receiving unit that receives the amino acid concentration data of the subject transmitted from the information communication terminal apparatus, (II) an evaluating unit that evaluates the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject received by the amino acid concentration data-receiving unit and (ii) the formula previously stored in the memory unit including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr, and (III) a result-sending unit that transmits the evaluation result of the subject obtained by the evaluating unit to the information communication terminal apparatus.
An information communication terminal apparatus according to one aspect of the present invention is an information communication terminal apparatus including a control unit to provide amino acid concentration data of a subject to be evaluated on concentration values of amino acids. The control unit includes a result-obtaining unit that obtains an evaluation result on a state of an indicator of lifestyle-related disease for the subject. The evaluation result is the result of evaluating the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject and (ii) the formula including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr.
The information communication terminal apparatus according to another aspect of the present invention is the information communication terminal apparatus, wherein the apparatus is communicatively connected via a network to a lifestyle-related disease indicator-evaluating apparatus that evaluates the state of the indicator of lifestyle-related disease for the subject. The control unit further includes an amino acid concentration data-sending unit that transmits the amino acid concentration data of the subject to the lifestyle-related disease indicator-evaluating apparatus. The result-obtaining unit receives the evaluation result transmitted from the lifestyle-related disease indicator-evaluating apparatus.
A lifestyle-related disease indicator-evaluating apparatus according to one aspect of the present invention is a lifestyle-related disease indicator-evaluating apparatus including a control unit and a memory unit to evaluate a state of an indicator of lifestyle-related disease for a subject to be evaluated, being connected communicatively via a network to an information communication terminal apparatus that provides amino acid concentration data of the subject on concentration values of amino acids. The control unit includes (I) an amino acid concentration data-receiving unit that receives the amino acid concentration data of the subject transmitted from the information communication terminal apparatus, (II) an evaluating unit that evaluates the state of the indicator of lifestyle-related disease for the subject by calculating a value of a formula using (i) the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject received by the amino acid concentration data-receiving unit and (ii) the formula previously stored in the memory unit including explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr, and (III) a result-sending unit that transmits an evaluation result obtained by the evaluating unit to the information communication terminal apparatus.
According to the present invention, (I) the amino acid concentration data on the concentration values of the amino acids in blood collected from the subject is obtained, and (II) the state of the indicator of lifestyle-related disease for the subject is evaluated using the concentration values of the amino acids of Gly and Tyr included in the obtained amino acid concentration data of the subject. Thus, the present invention achieves the effect of being able to provide reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease.
According to the present invention, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly and Tyr and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr.
According to the present invention, “the concentration values of the amino acids of Gly, Tyr, and Asn”, “the concentration values of the amino acids of Gly, Tyr, and Ala”, “the concentration values of the amino acids of Gly, Tyr, and Val”, or “the concentration values of the amino acids of Gly, Tyr, and Trp” are used. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the state of the indicator of lifestyle-related disease.
According to the present invention, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, and Asn and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Asn”, “the concentration values of the amino acids of Gly, Tyr, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Ala”, “the concentration values of the amino acids of Gly, Tyr, and Val and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Val”, or “the concentration values of the amino acids of Gly, Tyr, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Trp”.
According to the present invention, the concentration values of the amino acids of Gly, Tyr, Asn, and Ala are used. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the state of the indicator of lifestyle-related disease.
According to the present invention, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, and Ala.
According to the present invention, the state of at least one of fatty liver, visceral fat, and insulin is evaluated. Thus, the present invention achieves the effect of being able to provide reliable information that may be helpful in knowing the state of at least one of “fatty liver, visceral fat, and insulin” that are the indicators of lifestyle-related disease.
According to the present invention, the states of at least two of fatty liver, visceral fat, and insulin are evaluated. Thus, the present invention achieves the effect of being able to provide reliable information that may be helpful in knowing the states of at least two of fatty liver, visceral fat, and insulin.
According to the present invention, the states of fatty liver, visceral fat, and insulin are evaluated. Thus, the present invention achieves the effect of being able to provide reliable information that may be helpful in knowing the three states of fatty liver, visceral fat, and insulin.
According to the present invention, the state of insulin is evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the state of insulin.
According to the present invention, the state of insulin may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
According to the present invention, the state of visceral fat is evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the state of visceral fat.
According to the present invention, the state of visceral fat may be evaluated using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the previously obtained BMI value of the subject.
According to the present invention, the state of fatty liver is evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the state of fatty liver.
According to the present invention, the state of fatty liver may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
According to the present invention, the states of insulin and visceral fat are evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the two states of insulin and visceral fat.
According to the present invention, the states of insulin and visceral fat may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
According to the present invention, (i) the state of insulin is evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the state of visceral fat is evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”, and (iii) the state of fatty liver is evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu. Thus, the present invention achieves the effect of being able to achieve further improvement in reliability of information that may be helpful in knowing the three states of fatty liver, visceral fat, and insulin.
According to the present invention, (i) the state of insulin may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the state of visceral fat may be evaluated using (a) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (b) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the previously obtained BMI value of the subject, and (iii) the state of fatty liver may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
According to the present invention, “evaluating the state of the indicator of lifestyle-related disease for the subject” may refer to qualitatively or quantitatively evaluating the degree of the state of the indicator of lifestyle-related disease in the subject. In this manner, reliable information that may be helpful in knowing the degree of the state of the indicator of lifestyle-related disease can be provided.
According to the present invention, qualitatively evaluating the degree of the state of the indicator of lifestyle-related disease in the subject may refer to classifying the subject into any one of a plurality of categories defined at least considering the degree of the state of the indicator of lifestyle-related disease, using “the concentration value of an amino acid and one or more preset thresholds” or “the concentration value of an amino acid, a formula including an explanatory variable to be substituted with the concentration value of an amino acid, and one or more preset thresholds”. In this manner, reliable information that may be helpful in knowing the degree of the state of the indicator of lifestyle-related disease can be provided in easily understandable form.
According to the present invention, quantitatively evaluating the degree of the state of the indicator of lifestyle-related disease in the subject may refer to estimating the value of the indicator of lifestyle-related disease in the subject, using the concentration value of an amino acid and a formula including an explanatory variable to be substituted with the concentration value of an amino acid, if the indicator of lifestyle-related disease can be measured with successive numerical values. In this manner, reliable numerical information that may be helpful in knowing the value of the indicator of lifestyle-related disease can be provided.
According to the present invention, the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the value of the indicator of lifestyle-related disease in the subject. In this manner, reliable information that may be helpful in knowing the degree of the state of the indicator of lifestyle-related disease can be provided in more easily understandable form, and reliability of numerical information that may be helpful in knowing the value of the indicator of lifestyle-related disease can be further improved.
According to the present invention, evaluating the state of insulin for the subject may refer to qualitatively or quantitatively evaluating the degree of the amount of insulin in the subject (for example, the amount of insulin in the subject's blood). In this manner, reliable information that may be helpful in knowing the degree of the amount of insulin can be provided.
According to the present invention, qualitatively evaluating the degree of the amount of insulin in the subject may refer to classifying the subject into any one of a plurality of categories defined at least considering the degree of the amount of insulin, using “the concentration value of an amino acid and one or more preset thresholds” or “the concentration value of an amino acid, a formula including an explanatory variable to be substituted with the concentration value of an amino acid, and one or more preset thresholds”. In this manner, reliable information that may be helpful in knowing the degree of the amount of insulin can be provided in easily understandable form. The categories may include (i) a category to which a subject whose amount of insulin (for example, a 120-minute OGTT (oral glucose tolerance test) insulin level (insulin level after the OGTT) is large belongs, (ii) a category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is small belongs, and (iii) a category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is medium belongs. The categories may include (i) a category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is equal to or greater than a criterion value (for example, 40 μU/ml) belongs and (ii) a category to which a subject whose amount of insulin (for example, a 120-minute OGTT insulin level) is equal to or smaller than the criterion value (for example, 40 μU/ml) belongs. The categories may include (i) a category to which a subject with whom a possibility that the 120-minute OGTT insulin level is equal to or greater than 40 U/ml is high belongs, (ii) a category to which a subject with whom the possibility is low belongs, and (iii) a category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) a category to which a subject with whom a possibility that the 120-minute OGTT insulin level is equal to or greater than 40 μU/ml is high belongs and (ii) a category to which a subject with whom the possibility is low belongs.
According to the present invention, quantitatively evaluating the degree of the amount of insulin in the subject may refer to estimating the amount of insulin in the subject using the concentration value of an amino acid and a formula including an explanatory variable to be substituted with the concentration value of an amino acid. In this manner, reliable numerical information that may be helpful in knowing the amount of insulin can be provided.
According to the present invention, the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of insulin in the subject. In this manner, reliable information that may be helpful in knowing the degree of the amount of insulin can be provided in more easily understandable form, and reliability of numerical information that may be helpful in knowing the amount of insulin can be further improved.
According to the present invention, evaluating the state of visceral fat for the subject may refer to qualitatively or quantitatively evaluating the degree of the amount of visceral fat in the subject (for example, the value of the fat area in the axial section of the abdomen). In this manner, reliable information that may be helpful in knowing the degree of the amount of visceral fat can be provided.
According to the present invention, qualitatively evaluating the degree of the amount of visceral fat in the subject may refer to classifying the subject into any one of a plurality of categories defined at least considering the degree of the amount of visceral fat, using “the concentration value of an amino acid and one or more preset thresholds” or “the concentration value of an amino acid, a formula including an explanatory variable to be substituted with the concentration value of an amino acid, and one or more preset thresholds”. In this manner, reliable information that may be helpful in knowing the degree of the amount of visceral fat can be provided in easily understandable form. The categories may include (i) a category to which a subject who has a large amount of visceral fat (for example, a visceral fat area value) belongs, (ii) a category to which a subject who has a small amount of visceral fat (for example, the visceral fat area value) belongs, and (iii) a category to which a subject who has a medium amount of visceral fat (for example, the visceral fat area value) belongs. The categories may include (i) a category to which a subject whose amount of visceral fat (for example, the visceral fat area value) is equal to or greater than a criterion value (for example, 100 cm2) belongs and (ii) a category to which a subject whose amount of visceral fat (for example, the visceral fat area value) is equal to or smaller than the criterion value (for example, 100 cm2) belongs. The categories may include (i) a category to which a subject with whom a possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs, (ii) a category to which a subject with whom the possibility is low belongs, and (iii) a category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) a category to which a subject with whom a possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs and (ii) a category to which a subject with whom the possibility is low belongs.
According to the present invention, quantitatively evaluating the degree of the amount of visceral fat in the subject may refer to estimating the amount of visceral fat in the subject using the concentration value of an amino acid and a formula including an explanatory variable to be substituted with the concentration value of an amino acid. In this manner, reliable numerical information that may be helpful in knowing the amount of visceral fat can be provided.
According to the present invention, the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of visceral fat in the subject. In this manner, reliable information that may be helpful in knowing the degree of the amount of visceral fat can be provided in more easily understandable form, and reliability of numerical information that may be helpful in knowing the amount of visceral fat can be further improved.
According to the present invention, when the amount of visceral fat is evaluated, the subject's BMI value and the formula further including an explanatory variable to be substituted with the BMI value may be further used. In this manner, reliability of information that may be helpful in knowing the degree of the amount of visceral fat can be further improved.
According to the present invention, evaluating the state of fatty liver for the subject may refer to evaluating the degree of the possibility of fatty liver, that is, the degree of the possibility that the subject's liver is in a state of having a certain amount or more of fat (for example, the amount of fat exceeding 5% of the weight of the liver, the amount of fat equivalent to 30% or more of hepatocytes, or the amount of fat determined by doctors to be a fatty liver). In this manner, reliable information can be provided that may be helpful in knowing the degree of the possibility of fatty liver, that is, the degree of the possibility that the liver is in a state of having a certain amount or more of fat.
According to the present invention, evaluating the degree of the possibility that the subject's liver is in a state of having a certain amount or more of fat may refer to classifying the subject into any one of a plurality of categories defined at least considering the degree of the possibility that the liver is in the state above, using “the concentration value of an amino acid and one or more preset thresholds” or “the concentration value of an amino acid, a formula including an explanatory variable to be substituted with the concentration value of an amino acid, and one or more preset thresholds”. In this manner, reliable information that may be helpful in knowing the degree of the possibility that the liver is in a state of having a certain amount or more of fat can be provided in easily understandable form. The categories may include (i) a category to which a subject with whom a possibility that the liver is in the state above is high belongs, (ii) a category to which a subject with whom the possibility that the liver is in the state above is low belongs, and (iii) a category to which a subject with whom the possibility that the liver is in the state above is intermediate belongs. The categories may include (i) a category to which a subject with whom a possibility that the liver is in the state above is high belongs and (ii) a category to which a subject with whom the possibility that the liver is in the state above is low belongs.
According to the present invention, the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories. In this manner, reliable information that may be helpful in knowing the degree of the possibility that the liver is in a state of having a certain amount or more of fat can be provided in more easily understandable form.
According to the present invention, the formula may be any one of a logistic regression equation, a fractional expression, a linear discriminant, a multiple regression equation, a formula prepared by a support vector machine, a formula prepared by a Mahalanobis' generalized distance method, a formula prepared by canonical discriminant analysis, and a formula prepared by a decision tree. Thus, further improvement in reliability of information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be achieved.
According to the present invention, the formula used for evaluating the state of insulin may be Formula 1, the formula used for evaluating the state of visceral fat may be Formula 2, and the formula used for evaluating the state of fatty liver may be Formula 3. In this manner, reliability of information that may be helpful in knowing each of insulin, visceral fat, and fatty liver can be further improved.
a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1 (Formula 1)
a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2 (Formula 2)
a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3 (Formula 3)
In Formula 1, a1, b1, c1, d1, e1, and f1 each are any given real number other than zero, and g1 is any given real number.
In Formula 2, a2, b2, c2, d2, e2, f2, and g2 each are any given real number other than zero, and h2 is any given real number.
In Formula 3, a3, b3, c3, d3, e3, f3 each are any given real number other than zero, and g3 is any given real number.
According to the present invention, among a plurality of items defined as diagnosis criteria items for metabolic syndrome, the number of items applicable to the subject may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3. In this manner, reliable information that may be helpful in knowing the number of applicable diagnosis criteria items for metabolic syndrome can be provided.
According to the present invention, the number of lifestyle-related diseases that the subject has may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3. In this manner, reliable information that may be helpful in knowing the number of lifestyle-related diseases that the subject has can be provided.
According to the present invention, the degree of the possibility that the subject is affected by lifestyle-related disease may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3. In this manner, reliable information that may be helpful in knowing the degree of the possibility of being affected by lifestyle-related disease can be provided.
According to the present invention, evaluating the state of the indicator of lifestyle-related disease for the subject may refer to determining that the value of the formula reflects the state of the indicator of lifestyle-related disease for the subject. In this manner, reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be provided.
According to the present invention, the value of the formula may be converted by a predetermined method, and it may be determined that the converted value reflects the state of the indicator of lifestyle-related disease for the subject. In this manner, reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be provided.
According to the present invention, positional information about a position of a predetermined mark (for example, a circle sign or a star sign) corresponding to the value of the formula or the converted value may be generated on a predetermined scale (for example, a graduated scale at least marked with graduations corresponding to the upper limit value and the lower limit value in the possible range of the value of the formula or the converted value, or part of the range) visually presented on a display device such as a monitor or a physical medium such as paper for evaluating the state of the indicator of lifestyle-related disease, using the value of the formula or the converted value, and it may be decided that the generated positional information reflects the state of the indicator of lifestyle-related disease for the subject. Hence, reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be provided.
According to the present invention, the evaluation formula stored in the memory unit may be prepared based on index state information previously stored in the memory unit including the amino acid concentration data and lifestyle-related disease index data on a state of an index (risk factor) of lifestyle-related disease. Specifically, (i) a candidate formula that is a candidate of the evaluation formula may be prepared based on a predetermined formula-preparing method from the index state information, (ii) the prepared candidate formula may be verified based on a predetermined verifying method, (iii) an explanatory variable of the candidate formula may be selected based on a predetermined explanatory variable-selecting method, thereby selecting a combination of the amino acid concentration data included in the index state information used in preparing the candidate formula, and (iv) the candidate formula used as the evaluation formula may be selected from a plurality of the candidate formulae based on the verification results accumulated by repeatedly executing the (1), (ii) and (iii), thereby preparing the evaluation formula. Hence, the evaluation formula most appropriate for evaluating the state of the indicator of lifestyle-related disease can be prepared.
According to the present invention, (I) the amino acid concentration data on the concentration values of the amino acids in blood collected from the subject to which a desired substance group consisting of one or more substances has been administered may be obtained, (II) the state of the indicator of lifestyle-related disease for the subject may be evaluated using the concentration values of the amino acids of Gly and Tyr included in the obtained amino acid concentration data of the subject, and (III) whether or not the desired substance group ameliorates the state of the indicator of lifestyle-related disease may be judged using the obtained evaluation result. Hence, reliable information on a substance ameliorating the state of the indicator of lifestyle-related disease can be provided by applying the method of evaluating lifestyle-related disease indicator which can provide reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease.
In the present invention, when the state of the indicator of lifestyle-related disease is evaluated, the concentration value of the amino acid other than the 19 amino acids above may be additionally used. In the present invention, when the state of the indicator of lifestyle-related disease is evaluated, the value related to other biological information (for example, values listed in 1. to 4. below) may further be used in addition to the concentration value of the amino acid. In the present invention, the formulae above may additionally include one or more explanatory variables to be substituted with the concentration value of the amino acid other than the 19 amino acids. In the present invention, the formulae above may additionally include one or more explanatory variables to be substituted with the value related to other biological information (for example, values listed in 1. to 4. below) in addition to the explanatory variable to be substituted with the concentration value of the amino acid.
1. Concentration values of metabolites in blood other than amino acids (amino acid metabolites, carbohydrates, lipids, and the like), proteins, peptides, minerals, hormones, and the like.
2. Blood test values such as albumin, total protein, triglyceride, HbA1c, glycoalbumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid.
3. Values obtained from image information such as ultrasonic echo, X ray, CT, and MRI.
4. Values of biological indices such as age, height, weight, BMT, abdominal girth, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleeping information, family medical history information, and disease history information (for example, diabetes).
In the present invention, lifestyle-related disease refers to a group of diseases of which onset and progress are associated with lifestyle including dietary habit, exercise habit, rest, smoking, and drinking. Examples include metabolic syndrome, disorder of carbohydrate metabolism (for example, diabetes, prediabetes, impaired glucose tolerance), cerebral vascular disorder (for example, stroke, arteriolosclerosis), heart disease (for example, myocardial infarction), dyslipidemia, hypertension, obesity, nephropathy (for example, chronic nephropathy), hepatic disease, and hyperuricemia.
The present invention can be utilized to know the indicator of lifestyle-related disease and to know the risk at preclinical stages of lifestyle-related disease or at earlier stages of lifestyle-related disease. The present invention therefore can evaluate the risk of developing lifestyle-related disease (the degree of the possibility of developing lifestyle-related disease) or the risk of future progress of lifestyle-related disease (the degree of the possibility that lifestyle-related disease progress in the future), leading to prevention of lifestyle-related disease.
Formulae 1 to 3 can be used to evaluate the number of applicable diagnosis criteria items for metabolic syndrome and evaluate the number of lifestyle-related diseases that the subject has, so that the seriousness of lifestyle-related disease (the degree of progress of lifestyle-related disease (the degree of the possibility that lifestyle-related disease progresses)) can be evaluated using the values of Formulae 1 to 3.
The above and other objects, features, advantages and technical and industrial significance of this invention will be better understood by reading the following detailed description of presently preferred embodiments of the invention, when considered in connection with the accompanying drawings.
Hereinafter, an embodiment (first embodiment) of the method of evaluating lifestyle-related disease indicator according to the present invention and an embodiment (second embodiment) of the lifestyle-related disease indicator-evaluating apparatus, the lifestyle-related disease indicator-evaluating method, the lifestyle-related disease indicator-evaluating program, the recording medium, the lifestyle-related disease indicator-evaluating system, and the information communication terminal apparatus according to the present invention are described in detail with reference to the drawings. The present invention is not limited to these embodiments.
1-1. Outline of First Embodiment
Here, an outline of the first embodiment will be described with reference to
The amino acid concentration data on the concentration values of the amino acids in blood (including, for example, plasma or serum) collected from the subject to be evaluated (for example, an individual such as animal or human) is obtained (step S11).
In step S11, for example, the amino acid concentration data determined by a company or the like that performs amino acid concentration value measurements may be obtained, or the amino acid concentration data may be obtained by determining the concentration value of an amino acid by a measurement method such as, for example, the following method (A) or (B) from blood collected from the subject. Here, the unit of the concentration value of an amino acid may be, for example, a molar concentration, a weight concentration, or one obtained by addition, subtraction, multiplication, and division of any constant with these concentrations.
(A) Plasma is separated from blood by centrifuging a collected blood sample. All plasma samples are frozen and stored at −80° C. until an amino acid concentration value is measured. At the time of measuring an amino acid concentration value, acetonitrile is added to perform a protein removal treatment, pre-column derivatization is then performed using a labeled reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and an amino acid concentration value is analyzed by liquid chromatograph mass spectrometer (LC/MS) (see International Publication WO 2003/069328 and International Publication WO 2005/116629).
(B) Plasma is separated from blood by centrifuging a collected blood sample. All plasma samples are frozen and stored at −80° C. until an amino acid concentration value is measured. At the time of measuring an amino acid concentration value, sulfosalicylic acid is added to perform a protein removal treatment, and an amino acid concentration value is analyzed by an amino acid analyzer based on post-column derivatization using a ninhydrin reagent.
The state of the indicator of lifestyle-related disease for the subject is evaluated using, as evaluation values for evaluating the state of the indicator of lifestyle-related disease, the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data obtained in step S11 (step S12). Before step S12 is executed, data such as defective and outliers may be removed from the amino acid concentration data obtained in step S11.
According to the first embodiment described above, the amino acid concentration data of the subject is obtained in step S11, and in step S12, the state of the indicator of lifestyle-related disease for the subject is evaluated using, as the evaluation values, the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject obtained in step S11. Hence, reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be provided.
In step S12, the state of the indicator of lifestyle-related disease for the subject may be evaluated using “the concentration values of the amino acids of Gly, Tyr, and Asn”, “the concentration values of the amino acids of Gly, Tyr, and Ala”, “the concentration values of the amino acids of Gly, Tyr, and Val”, or “the concentration values of the amino acids of Gly, Tyr, and Trp”.
In step S12, the state of the indicator of lifestyle-related disease for the subject may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, and Ala.
In step S12, the state of at least one of fatty liver, visceral fat, and insulin may be evaluated. For example, the state of insulin may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp. The state of visceral fat may be evaluated using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the previously obtained BMT value of the subject. The state of fatty liver may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S12, the states of at least two of fatty liver, visceral fat, and insulin may be evaluated. For example, the states of insulin and visceral fat may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
In step S12, the states of fatty liver, visceral fat, and insulin may be evaluated. For example, (i) the state of insulin may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the state of visceral fat may be evaluated using (a) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (b) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the previously obtained BMT value of the subject, and (iii) the state of fatty liver may be evaluated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S12, it may be decided that the concentration values of the amino acids of at least Gly and Tyr reflect the state of the indicator of lifestyle-related disease for the subject. The concentration values may be converted, for example, by the methods listed below, and it may be decided that the converted values reflect the state of the indicator of lifestyle-related disease for the subject. In other words, in step S12, the concentration values or the converted values may be treated per se as the evaluation result on the state of the indicator of lifestyle-related disease for the subject.
The concentration value may be converted such that the possible range of the concentration value falls within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from −10.0 to 10.0), for example, by addition, subtraction, multiplication, and division of any given value with the concentration value, by conversion of the concentration value by a predetermined conversion method (for example, index transformation, logarithm transformation, angular transformation, square root transformation, probit transformation, or reciprocal transformation), or by performing a combination of these computations on the concentration value. For example, the value of an exponential function with the concentration value as an exponent and Napier constant as the base may be further calculated (specifically, the value of p/(1−p) where a natural logarithm ln(p/(1−p)) is equal to the concentration value when the probability p that the indicator of lifestyle-related disease has a predetermined state is defined (for example, a state of exceeding a criterion value)), and a value (specifically, the value of probability p) may be further calculated by dividing the calculated value of exponential function by the sum of 1 and the value of exponential function.
The concentration value may be converted such that the converted value is a particular value when a particular condition is met. For example, the concentration value may be converted such that the converted value is 4.0 when the sensitivity is 80% and the converted value is 8.0 when the sensitivity is 60%.
In step S12, the positional information about the position of the predetermined mark (for example, a circle sign or a star sign) corresponding to the concentration value or the converted value may be generated on the predetermined scale (for example, a graduated scale at least marked with graduations corresponding to the upper limit value and the lower limit value in the possible range of the concentration value or the converted value, or part of the range) visually presented on the display device such as the monitor or the physical medium such as paper for evaluating the state of the indicator of lifestyle-related disease, using the concentration values of the amino acids of at least Gly and Tyr or, if the concentration values are converted, the converted values. Then it may be decided that the generated positional information reflects the state of the indicator of lifestyle-related disease for the subject.
In step S12, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly and Tyr and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr.
In step S12, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, and Asn and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Asn”, “the concentration values of the amino acids of Gly, Tyr, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Ala”, “the concentration values of the amino acids of Gly, Tyr, and Val and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Val”, or “the concentration values of the amino acids of Gly, Tyr, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Trp”.
In step S12, the state of the indicator of lifestyle-related disease in the subject may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, and Ala.
In step S12, the state of at least one of fatty liver, visceral fat, and insulin may be evaluated. For example, the state of insulin may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp. The state of visceral fat may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”. The state of fatty liver may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S12, the states of at least two of fatty liver, visceral fat, and insulin may be evaluated. For example, the states of insulin and visceral fat may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
In step S12, the states of fatty liver, visceral fat, and insulin may be evaluated. For example, (i) the state of insulin may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the state of visceral fat may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”, and (iii) the state of fatty liver may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S12, it may be decided that the calculated value of the formula reflects the state of the indicator of lifestyle-related disease for the subject. The value of the formula may be converted, for example, by the methods listed below, and it may be decided that the converted value reflects the state of the indicator of lifestyle-related disease for the subject. In other words, in step S12, the value of the formula or the converted value may be treated per se as the evaluation result on the state of the indicator of lifestyle-related disease for the subject.
The value of the evaluation formula may be converted such that the possible range of the value of the evaluation formula falls within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from −10.0 to 10.0), for example, by addition, subtraction, multiplication, and division of any given number with the value of the evaluation formula, by conversion of the value of the evaluation formula by a predetermined conversion method (for example, index transformation, logarithm transformation, angular transformation, square root transformation, probit transformation, or reciprocal transformation), or by performing a combination of these computations on the value of the evaluation formula. For example, the value of an exponential function with the value of the evaluation formula as an exponent and Napier constant as the base may be further calculated (specifically, the value of p/(1−p) where a natural logarithm ln(p/(1−p)) is equal to the value of the evaluation formula when the probability p that the indicator of lifestyle-related disease has a predetermined state is defined (for example, a state of exceeding a criterion value)), and a value (specifically, the value of probability p) may be further calculated by dividing the calculated value of exponential function by the sum of 1 and the value of exponential function.
The value of the evaluation formula may be converted such that the converted value is a particular value when a particular condition is met. For example, the value of the evaluation formula may be converted such that the converted value is 4.0 when the sensitivity is 80% and the converted value is 8.0 when the sensitivity is 60%.
The evaluation value in the present description may be the value of the evaluation formula per se or may be the value obtained by converting the value of the evaluation formula.
In step S12, the positional information about the position of the predetermined mark (for example, a circle sign or a star sign) corresponding to the value of the formula or the converted value may be generated on the predetermined scale (for example, a graduated scale at least marked with graduations corresponding to the upper limit value and the lower limit value in the possible range of the value of the formula or the converted value, or part of the range) visually presented on the display device such as the monitor or the physical medium such as paper for evaluating the state of the indicator of lifestyle-related disease, using the value of the formula or, if the value of the formula is converted, the converted value. Then it may be decided that the generated positional information reflects the state of the indicator of lifestyle-related disease for the subject.
In step S12, the degree of the state of the indicator of lifestyle-related disease in the subject may be qualitatively or quantitatively evaluated.
In step S12, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the state of the indicator of lifestyle-related disease, using “the concentration value of the amino acid and the one or more preset thresholds” or “the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds”.
In step S12, the value of the indicator of lifestyle-related disease in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid, if the indicator of lifestyle-related disease can be measured with successive numerical values.
In step S12, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the value of the indicator of lifestyle-related disease in the subject.
In step S12, the degree of the amount of insulin in the subject (for example, the amount of insulin in the subject's blood) may be qualitatively or quantitatively evaluated.
In step S12, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the amount of insulin, using “the concentration value of the amino acid and the one or more preset thresholds” or “the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds”. The categories may include (i) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is large belongs, (ii) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is small belongs, and (iii) a category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is medium belongs. The categories may include (i) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is equal to or greater than the criterion value (for example, 40 μU/mL) belongs and (ii) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is equal to or smaller than the criterion value (for example, 40 μU/ml) belongs. The categories may include (i) the category to which a subject with whom the possibility that the 120-minute OGTT insulin level is equal to or greater than 40 μU/ml is high belongs, (ii) the category to which a subject with whom the possibility is low belongs, and (iii) the category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the 120-minute OGTT insulin level is equal to or greater than 40 μU/ml is high belongs and (ii) the category to which a subject with whom the possibility is low belongs.
In step S12, the amount of insulin in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid.
In step S12, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of insulin in the subject.
In step S12, the degree of the amount of visceral fat in the subject (for example, the value of the fat area in the axial section of the abdomen) may be qualitatively or quantitatively evaluated.
In step S12, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the amount of visceral fat, using “the concentration value of the amino acid and the one or more preset thresholds” or “the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds”. The categories may include (i) the category to which a subject who has a large amount of visceral fat (for example, a visceral fat area value) belongs, (ii) the category to which a subject who has a small amount of visceral fat (for example, a visceral fat area value) belongs, and (iii) the category to which a subject who has a medium amount of visceral fat (for example, a visceral fat area value) belongs. The categories may include (i) the category to which a subject whose amount of visceral fat (for example, a visceral fat area value) is equal to or greater than the criterion value (for example, 100 cm2) belongs and (ii) the category to which a subject whose amount of visceral fat (for example, a visceral fat area value) is equal to or smaller than the criterion value (for example, 100 cm2) belongs. The categories may include (i) the category to which a subject with whom the possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs, (ii) the category to which a subject with whom the possibility is low belongs, and (iii) the category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs and (ii) the category to which a subject with whom the possibility is low belongs.
In step S12, the amount of visceral fat in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid.
In step S12, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of visceral fat in the subject.
When the classification or the estimation is conducted, the BMI value of the subject and the formula further including the explanatory variable to be substituted with the BMT value may be used.
In step S12, the degree of the possibility of fatty liver, that is, the degree of the possibility that the subject's liver is in a state of having a certain amount or more of fat (for example, the amount of fat exceeding 5% of the weight of the liver, the amount of fat equivalent to 30% or more of hepatocytes, or the amount of fat determined by doctors to be a fatty liver) may be evaluated.
In step S12, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the possibility that the liver is in the state above, using “the concentration value of the amino acid and the one or more preset thresholds” or “the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds”. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs, (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs, and (iii) the category to which a subject with whom the possibility that the liver is in the state above is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs and (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs.
In step S12, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories.
The formula may be any one of the logistic regression equation, the fractional expression, the linear discriminant, the multiple regression equation, the formula prepared by the support vector machine, the formula prepared by the Mahalanobis' generalized distance method, the formula prepared by the canonical discriminant analysis, and the formula prepared by the decision tree.
The formula used for evaluating the state of insulin may be Formula 1, the formula used for evaluating the state of visceral fat may be Formula 2, and the formula used for evaluating the state of fatty liver may be Formula 3.
a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1 (Formula 1)
a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2 (Formula 2)
a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3 (Formula 3)
In Formula 1, a1, b1, c1, d1, e1, and f1 each are any given real number other than zero, and g1 is any given real number.
In Formula 2, a2, b2, c2, d2, e2, f2, and g2 each are any given real number other than zero, and h2 is any given real number.
In Formula 3, a3, b3, c3, d3, e3, f3 each are any given real number other than zero, and g3 is any given real number.
In step S12, among the plurality of items defined as diagnosis criteria items for metabolic syndrome, the number of items applicable to the subject may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In step S12, the number of lifestyle-related disease that the subject has may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In step S12, the degree of the possibility that the subject is affected by lifestyle-related disease may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In addition to the formulae described in the present specification, the formulae described in the international patent applications, filed by the present applicant, WO 2008/016111, WO 2008/075662, WO 2008/075663, WO 2009/099005, WO 2009/154296, and WO 2009/154297 can be additionally employed as evaluation formulae to evaluate the state of the indicator of lifestyle-related disease.
The formula employed as the evaluation formula may be prepared by a method described in WO 2004/052191 that is an international application filed by the present applicant or by a method described in WO 2006/098192 that is an international application filed by the present applicant. Any formulae obtained by these methods can be preferably used in the evaluation of the state of the indicator of lifestyle-related disease, regardless of the unit of the amino acid concentration value in the amino acid concentration data as input data.
The formula employed as the evaluation formula refers to a form of equation used generally in multivariate analysis and includes, for example, fractional expression, multiple regression equation, multiple logistic regression equation, linear discriminant function, Mahalanobis' generalized distance, canonical discriminant function, support vector machine, decision tree, and an equation shown by the sum of different forms of equations. In the multiple regression equation, the multiple logistic regression equation, and the canonical discriminant function, a coefficient and a constant term are added to each explanatory variable, and the coefficient and the constant term may be preferably real numbers, more preferably values in the range of 99% confidence interval for the coefficient and the constant term obtained from data for the various kinds of classifications described above, more preferably in the range of 95% confidence interval for the coefficient and the constant term obtained from data for the various kinds of classifications described above. The value of each coefficient and the confidence interval thereof may be those multiplied by a real number, and the value of the constant term and the confidence interval thereof may be those having an arbitrary actual constant added or subtracted or those multiplied or divided by an arbitrary actual constant. When an expression such as a logistic regression, a linear discriminant, and a multiple regression analysis is used as an evaluation formula, a linear transformation of the expression (addition of a constant and multiplication by a constant) and a monotonic increasing (decreasing) transformation (for example, a logit transformation) of the expression do not alter evaluation performance and thus are equivalent to before transformation. Therefore, the expression includes an expression that is subjected to a linear transformation and a monotonic increasing (decreasing) transformation.
In the fractional expression, the numerator of the fractional expression is expressed by the sum of the amino acids A, B, C etc. and the denominator of the fractional expression is expressed by the sum of the amino acids a, b, c etc. The fractional expression also includes the sum of the fractional expressions α, β, γ etc. (for example, α+β) having such constitution. The fractional expression also includes divided fractional expressions. The amino acids used in the numerator or denominator may have suitable coefficients respectively. The amino acids used in the numerator or denominator may appear repeatedly. Each fractional expression may have a suitable coefficient. A value of a coefficient for each explanatory variable and a value for a constant term may be any real numbers. In a fractional expression and the one in which explanatory variables in the numerator and explanatory variables in the denominator in the fractional expression are switched with each other, the positive and negative signs are generally reversed in correlation with objective explanatory variables, but because their correlation is maintained, the evaluation performance can be assumed to be equivalent. The fractional expression therefore also includes the one in which explanatory variables in the numerator and explanatory variables in the denominator in the fractional expression are switched with each other.
In the first embodiment, when the state of the indicator of lifestyle-related disease is evaluated, the concentration value of the amino acid other than the 19 amino acids above may be additionally used. In the first embodiment, when the state of the indicator of lifestyle-related disease is evaluated, the value related to other biological information (for example, values listed in 1. to 4. below) may further be used in addition to the concentration value of the amino acid. In the first embodiment, the formulae above may additionally include one or more explanatory variables to be substituted with the concentration value of the amino acid other than the 19 amino acids. In the first embodiment, the formulae above may additionally include one or more explanatory variables to be substituted with the value related to other biological information (for example, values listed in 1. to 4. below) in addition to the explanatory variable to be substituted with the concentration value of the amino acid.
1. Concentration values of metabolites in blood other than amino acids (amino acid metabolites, carbohydrates, lipids, and the like), proteins, peptides, minerals, hormones, and the like.
2. Blood test values such as albumin, total protein, triglyceride, HbA1c, glycoalbumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid.
3. Values obtained from image information such as ultrasonic echo, X ray, CT, and MRI.
4. Values of biological indices such as age, height, weight, BMI, abdominal girth, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleeping information, family medical history information, and disease history information (for example, diabetes).
When, before step S11 is executed, the desired substance group consisting of one or more substances is administered to the subject, and then blood is collected from the subject, and in step S11, the amino acid concentration data of the subject is obtained, a substance ameliorating the state of the indicator of lifestyle-related disease may be searched by judging whether or not the administered substance group ameliorates the state of the indicator of lifestyle-related disease, using the evaluation result obtained in step S12.
Before step S11 is executed, a suitable combination of an existing drug, amino acid, food and supplement capable of administration to humans (for example, a suitable combination of drugs known to be effective in amelioration of the indicator of lifestyle-related disease (for example, gemcitabine, erlotinib, and TS-1)) may be administered over a predetermined period (for example in the range of 1 day to 12 months) in a predetermined amount at predetermined frequency and timing (for example 3 times per day, after food) by a predetermined administration method (for example, oral administration). The administration method, dose, and dosage form may be suitably combined depending on the condition of a patient. The dosage form may be determined based on known techniques. The dose is not particularly limited, and for example, a drug containing 1 μg to 100 g active ingredient may be given.
When the judgement result that the administered substance group ameliorates the state of the indicator of lifestyle-related disease is obtained, the administered substance group may be searched as a substance ameliorating the state of the indicator of lifestyle-related disease. The substance group searched by the searching method includes, for example, the amino acid group including the amino acids of at least Gly and Tyr of the 19 kinds of amino acids.
Substances that restore normal value to the concentration values of the amino acid group including the amino acids of at least Gly and Tyr of the 19 kinds of amino acids or the value of the evaluation formula can be selected using the method of evaluating lifestyle-related disease indicator in the first embodiment or the lifestyle-related disease indicator-evaluating apparatus in the second embodiment.
Searching for a substance ameliorating the state of the indicator of lifestyle-related disease includes not only discovery of a novel substance effective in ameliorating the indicator of lifestyle-related disease, but also (i) new discovery of use of a known substance in ameliorating the indicator of lifestyle-related disease, (ii) discovery of a novel composition consisting of a combination of existing drugs, supplements etc. having efficacy expectable for amelioration of the indicator of lifestyle-related disease, (iii) discovery of the suitable usage, dose and combination described above to form them into a kit, (iv) presentation of a preventing and therapeutic menu including a diet, exercise etc., and (v) presentation of a necessary change in menu for each individual by monitoring the effect of the preventing and therapeutic menu.
1-2. Specific Example of the First Embodiment
Here, a specific example of the first embodiment will be described with reference to
The amino acid concentration data on the concentration values of the amino acids in blood collected from an individual such as animal or human is obtained (step SA11). In step SA11, for example, the amino acid concentration data determined by a company or the like that performs amino acid concentration value measurements may be obtained, or the amino acid concentration data may be obtained by determining the concentration values of the amino acids by the measurement method such as, for example, the above described (A) or (B) from blood collected from the individual.
Data such as defective and outliers is then removed from the amino acid concentration data of the individual obtained in step SA11 (step SA12).
The 120-minute OGTT insulin level, the visceral fat area value, and the degree of the possibility that the individual's liver is in a state of having a certain amount or more of fat are evaluated for the individual using the amino acid concentration data of the individual from which the data such as the defective and the outliers have been removed in step SA12 (step SA13).
Specifically, the 120-minute OGTT insulin level of the individual is estimated using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and Formula 1.
a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1 (Formula 1)
In Formula 1, a1, b1, c1, d1, e1, and f1 each are any given real number other than zero, and g1 is any given real number.
The visceral fat area value of the individual is estimated using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the previously obtained BMI value of the individual or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the individual, and Formula 2.
a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2 (Formula 2)
In Formula 2, a2, b2, c2, d2, e2, f2, and g2 each are any given real number other than zero, and h2 is any given real number.
The individual is classified into any one of the plurality of categories defined at least considering the degree of the possibility that the individual's liver is in a state of having a certain amount or more of fat, using (i) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu or (ii) the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and Formula 3. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs, (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs, and (iii) the category to which a subject with whom the possibility that the liver is in the state above is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs and (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs.
a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3 (Formula 3)
In Formula 3, a3, b3, c3, d3, e3, f3 each are any given real number other than zero, and g3 is any given real number.
2-1. Outline of the Second Embodiment
Here, outlines of the second embodiment will be described in detail with reference to
A control device evaluates the state of the indicator of lifestyle-related disease for the subject to be evaluated (for example, an individual such as animal or human) by calculating the value of the formula using (i) the concentration values of the amino acids of Gly and Try included in the previously obtained amino acid concentration data of the subject on the concentration values of the amino acids and (ii) the formula previously stored in a memory device including the explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr (step S21).
According to the second embodiment described above, in step S21, the state of the indicator of lifestyle-related disease for the subject is evaluated by calculating the value of the evaluation formula using (i) the concentration values of the amino acids of Gly and Tyr included in the amino acid concentration data of the subject and (ii) the formula stored in the memory device as the evaluation formula, including the explanatory variables to be substituted with the concentration values of the amino acids of Gly and Tyr. Hence, reliable information that may be helpful in knowing the state of the indicator of lifestyle-related disease can be provided.
In step S21, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, and Asn and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Asn”, “the concentration values of the amino acids of Gly, Tyr, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Ala”, “the concentration values of the amino acids of Gly, Tyr, and Val and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Val”, or “the concentration values of the amino acids of Gly, Tyr, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, and Trp”.
In step S21, the state of the indicator of lifestyle-related disease for the subject may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, and Ala and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, and Ala.
In step S21, the state of at least one of fatty liver, visceral fat, and insulin may be evaluated. For example, the state of insulin may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp. The state of visceral fat may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”. The state of fatty liver may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S21, the states of at least two of fatty liver, visceral fat, and insulin may be evaluated. For example, the states of insulin and visceral fat may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp.
In step S21, the states of fatty liver, visceral fat, and insulin may be evaluated. For example, (i) the state of insulin may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, (ii) the state of visceral fat may be evaluated by calculating the value of the formula using “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp” or “the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the previously obtained BMI value of the subject, and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and the BMI value of the subject”, and (iii) the state of fatty liver may be evaluated by calculating the value of the formula using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and the formula including the explanatory variables to be substituted with the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu.
In step S21, it may be decided that the calculated value of the formula reflects the state of the indicator of lifestyle-related disease for the subject. The value of the formula may be converted, for example, by the methods listed below, and it may be decided that the converted value reflects the state of the indicator of lifestyle-related disease for the subject. In other words, in step S12, the value of the formula or the converted value may be treated per se as the evaluation result on the state of the indicator of lifestyle-related disease for the subject.
The value of the evaluation formula may be converted such that the possible range of the value of the evaluation formula falls within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from −10.0 to 10.0), for example, by addition, subtraction, multiplication, and division of any given number with the value of the evaluation formula, by conversion of the value of the evaluation formula by a predetermined conversion method (for example, index transformation, logarithm transformation, angular transformation, square root transformation, probit transformation, or reciprocal transformation), or by performing a combination of these computations on the value of the evaluation formula. For example, the value of an exponential function with the value of the evaluation formula as an exponent and Napier constant as the base may be further calculated (specifically, the value of p/(1−p) where a natural logarithm ln(p/(1−p)) is equal to the value of the evaluation formula when the probability p that the indicator of lifestyle-related disease has a predetermined state is defined (for example, a state of exceeding a criterion value)), and a value (specifically, the value of probability p) may be further calculated by dividing the calculated value of exponential function by the sum of 1 and the value of exponential function.
The value of the evaluation formula may be converted such that the converted value is a particular value when a particular condition is met. For example, the value of the evaluation formula may be converted such that the converted value is 4.0 when the sensitivity is 80% and the converted value is 8.0 when the sensitivity is 60%.
The evaluation value in the present description may be the value of the evaluation formula per se or may be the value obtained by converting the value of the evaluation formula.
In step S21, the positional information about the position of the predetermined mark (for example, a circle sign or a star sign) corresponding to the value of the formula or the converted value may be generated on the predetermined scale (for example, a graduated scale at least marked with graduations corresponding to the upper limit value and the lower limit value in the possible range of the value of the formula or the converted value, or part of the range) visually presented on the display device such as the monitor or the physical medium such as paper for evaluating the state of the indicator of lifestyle-related disease, using the value of the formula or, if the value of the formula is converted, the converted value. Then it may be decided that the generated positional information reflects the state of the indicator of lifestyle-related disease for the subject.
In step S21, the degree of the state of the indicator of lifestyle-related disease in the subject may be qualitatively or quantitatively evaluated.
In step S21, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the state of the indicator of lifestyle-related disease, using the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds.
In step S21, the value of the indicator of lifestyle-related disease in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid, if the indicator of lifestyle-related disease can be measured with successive numerical values.
In step S21, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the value of the indicator of lifestyle-related disease in the subject.
In step S21, the degree of the amount of insulin in the subject (for example, the amount of insulin in the subject's blood) may be qualitatively or quantitatively evaluated.
In step S21, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the amount of insulin, using the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds. The categories may include (i) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is large belongs, (ii) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is small belongs, and (iii) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is medium belongs. The categories may include (i) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is equal to or greater than the criterion value (for example, 40 μU/ml) belongs and (ii) the category to which a subject whose amount of insulin (for example, the 120-minute OGTT insulin level) is equal to or smaller than the criterion value (for example, 40 μU/ml) belongs. The categories may include (i) the category to which a subject with whom the possibility that the 120-minute OGTT insulin level is equal to or greater than 40 μU/ml is high belongs, (ii) the category to which a subject with whom the possibility is low belongs, and (iii) the category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the 120-minute OGTT insulin level is equal to or greater than 40 W/ml is high belongs and (ii) the category to which a subject with whom the possibility is low belongs.
In step S21, the amount of insulin in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid.
In step S21, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of insulin in the subject.
In step S21, the degree of the amount of visceral fat in the subject (for example, the value of the fat area in the axial section of the abdomen) may be qualitatively or quantitatively evaluated.
In step S21, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the amount of visceral fat, using the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds. The categories may include (i) the category to which a subject who has a large amount of visceral fat (for example, a visceral fat area value) belongs, (ii) the category to which a subject who has a small amount of visceral fat (for example, a visceral fat area value) belongs, and (iii) the category to which a subject who has a medium amount of visceral fat (for example, a visceral fat area value) belongs. The categories may include (i) the category to which a subject whose amount of visceral fat (for example, a visceral fat area value) is equal to or greater than the criterion value (for example, 100 cm2) belongs and (ii) the category to which a subject whose amount of visceral fat (for example, a visceral fat area value) is equal to or smaller than the criterion value (for example, 100 cm2) belongs. The categories may include (i) the category to which a subject with whom the possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs, (ii) the category to which a subject with whom the possibility is low belongs, and (iii) the category to which a subject with whom the possibility is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the visceral fat area value is equal to or greater than 100 cm2 is high belongs and (ii) the category to which a subject with whom the possibility is low belongs.
In step S21, the amount of visceral fat in the subject may be estimated using the concentration value of the amino acid and the formula including the explanatory variable to be substituted with the concentration value of the amino acid.
In step S21, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories or estimate the amount of visceral fat in the subject.
When the classification or the estimation is conducted, the BMI value of the subject and the formula further including the explanatory variable to be substituted with the BMI value may be used.
In step S21, the degree of the possibility of fatty liver, that is, the degree of the possibility that the subject's liver is in a state of having a certain amount or more of fat (for example, the amount of fat exceeding 5% of the weight of the liver, the amount of fat equivalent to 30% or more of hepatocytes, or the amount of fat determined by doctors to be a fatty liver) may be evaluated.
In step S21, the subject may be classified into any one of the plurality of categories defined at least considering the degree of the possibility that the liver is in the state above, using the concentration value of the amino acid, the formula including the explanatory variable to be substituted with the concentration value of the amino acid, and the one or more preset thresholds. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs, (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs, and (iii) the category to which a subject with whom the possibility that the liver is in the state above is intermediate belongs. The categories may include (i) the category to which a subject with whom the possibility that the liver is in the state above is high belongs and (ii) the category to which a subject with whom the possibility that the liver is in the state above is low belongs.
In step S21, the concentration value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the subject into any one of the plurality of categories.
The formula may be any one of the logistic regression equation, the fractional expression, the linear discriminant, the multiple regression equation, the formula prepared by the support vector machine, the formula prepared by the Mahalanobis' generalized distance method, the formula prepared by the canonical discriminant analysis, and the formula prepared by the decision tree.
The formula used for evaluating the state of insulin may be Formula 1, the formula used for evaluating the state of visceral fat may be Formula 2, and the formula used for evaluating the state of fatty liver may be Formula 3.
a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1 (Formula 1)
a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2 (Formula 2)
a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3 (Formula 3)
In Formula 1, a1, b1, c1, d1, e1, and f1 each are any given real number other than zero, and g1 is any given real number.
In Formula 2, a2, b2, c2, d2, e2, f2, and g2 each are any given real number other than zero, and h2 is any given real number.
In Formula 3, a3, b3, c3, d3, e3, f3 each are any given real number other than zero, and g3 is any given real number.
In step S21, among the plurality of items defined as diagnosis criteria items for metabolic syndrome, the number of items applicable to the subject may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In step S21, the number of lifestyle-related disease that the subject has may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In step S21, the degree of the possibility that the subject is affected by lifestyle-related disease may be evaluated using the concentration value of the amino acid and any one of Formula 1, Formula 2, and Formula 3.
In addition to the formulae described in the present specification, the formulae described in the international patent applications, filed by the present applicant, WO 2008/016111, WO 2008/075662, WO 2008/075663, WO 2009/099005, WO 2009/154296, and WO 2009/154297 can be additionally employed as evaluation formulae to evaluate the state of the indicator of lifestyle-related disease.
The formula employed as the evaluation formula may be prepared by a method described in WO 2004/052191 that is an international application filed by the present applicant or by a method described in WO 2006/098192 that is an international application filed by the present applicant. Any formulae obtained by these methods can be preferably used in the evaluation of the state of the indicator of lifestyle-related disease, regardless of the unit of the amino acid concentration value in the amino acid concentration data as input data.
The formula employed as the evaluation formula refers to a form of equation used generally in multivariate analysis and includes, for example, fractional expression, multiple regression equation, multiple logistic regression equation, linear discriminant function, Mahalanobis' generalized distance, canonical discriminant function, support vector machine, decision tree, and an equation shown by the sum of different forms of equations. In the multiple regression equation, the multiple logistic regression equation, and the canonical discriminant function, a coefficient and a constant term are added to each explanatory variable, and the coefficient and the constant term may be preferably real numbers, more preferably values in the range of 99% confidence interval for the coefficient and the constant term obtained from data for the various kinds of classifications described above, more preferably in the range of 95% confidence interval for the coefficient and the constant term obtained from data for the various kinds of classifications described above. The value of each coefficient and the confidence interval thereof may be those multiplied by a real number, and the value of the constant term and the confidence interval thereof may be those having an arbitrary actual constant added or subtracted or those multiplied or divided by an arbitrary actual constant. When an expression such as a logistic regression, a linear discriminant, and a multiple regression analysis is used as an evaluation formula, a linear transformation of the expression (addition of a constant and multiplication by a constant) and a monotonic increasing (decreasing) transformation (for example, a logit transformation) of the expression do not alter evaluation performance and thus are equivalent to before transformation. Therefore, the expression includes an expression that is subjected to a linear transformation and a monotonic increasing (decreasing) transformation.
In the fractional expression, the numerator of the fractional expression is expressed by the sum of the amino acids A, B, C etc. and the denominator of the fractional expression is expressed by the sum of the amino acids a, b, c etc. The fractional expression also includes the sum of the fractional expressions α, β, γ etc. (for example, α+β) having such constitution. The fractional expression also includes divided fractional expressions. The amino acids used in the numerator or denominator may have suitable coefficients respectively. The amino acids used in the numerator or denominator may appear repeatedly. Each fractional expression may have a suitable coefficient. A value of a coefficient for each explanatory variable and a value for a constant term may be any real numbers. In a fractional expression and the one in which explanatory variables in the numerator and explanatory variables in the denominator in the fractional expression are switched with each other, the positive and negative signs are generally reversed in correlation with objective explanatory variables, but because their correlation is maintained, the evaluation performance can be assumed to be equivalent. The fractional expression therefore also includes the one in which explanatory variables in the numerator and explanatory variables in the denominator in the fractional expression are switched with each other.
In the second embodiment, when the state of the indicator of lifestyle-related disease is evaluated, the concentration value of the amino acid other than the 19 amino acids above may be additionally used. In the second embodiment, when the state of the indicator of lifestyle-related disease is evaluated, the value related to other biological information (for example, values listed in 1. to 4. below) may further be used in addition to the concentration value of the amino acid. In the second embodiment, the formulae above may additionally include one or more explanatory variables to be substituted with the concentration value of the amino acid other than the 19 amino acids. In the second embodiment, the formulae above may additionally include one or more explanatory variables to be substituted with the value related to other biological information (for example, values listed in 1. to 4. below) in addition to the explanatory variable to be substituted with the concentration value of the amino acid.
1. Concentration values of metabolites in blood other than amino acids (amino acid metabolites, carbohydrates, lipids, and the like), proteins, peptides, minerals, hormones, and the like.
2. Blood test values such as albumin, total protein, triglyceride, HbA1c, glycoalbumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid.
3. Values obtained from image information such as ultrasonic echo, X ray, CT, and MRI.
4. Values of biological indices such as age, height, weight, BMT, abdominal girth, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleeping information, family medical history information, and disease history information (for example, diabetes).
Here, the summary of the evaluation formula-preparing processing (steps 1 to 4) is described in detail. The processing described below is merely one example, and the method of preparing the evaluation formula is not limited thereto.
First, the control device prepares a candidate formula (e.g., y=a1x1+a2x2+ . . . +anxn, y: lifestyle-related disease index data, xi: amino acid concentration data, ai: constant, i=1, 2, . . . , n) that is a candidate for the evaluation formula, based on a predetermined formula-preparing method from index state information previously stored in the memory device containing the amino acid concentration data and lifestyle-related disease index data on the state of the index of lifestyle-related disease (step 1). Data containing defective and outliers may be removed in advance from the index state information.
In step 1, a plurality of the candidate formulae may be prepared from the index state information by using a plurality of the different formula-preparing methods (including those for multivariate analysis such as principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, and decision tree). Specifically, a plurality of the candidate formulae may be prepared simultaneously and concurrently by using a plurality of different algorithms with the index state information which is multivariate data composed of the amino acid concentration data and the lifestyle-related disease index data obtained by analyzing blood obtained from a large number of healthy groups and groups having the index of lifestyle-related disease of being a predetermined state (for example, a state of exceeding a criterion value). For example, the two different candidate formulae may be formed by performing discriminant analysis and logistic regression analysis simultaneously with the different algorithms. Alternatively, the candidate formula may be formed by converting the index state information with the candidate formula prepared by performing principal component analysis and then performing discriminant analysis of the converted index state information. In this way, it is possible to finally prepare the most suitable evaluation formula.
The candidate formula prepared by principal component analysis is a linear expression including each amino acid explanatory variable maximizing the variance of all amino acid concentration data. The candidate formula prepared by discriminant analysis is a high-powered expression (including exponential and logarithmic expressions) including each amino acid explanatory variable minimizing the ratio of the sum of the variances in respective groups to the variance of all amino acid concentration data. The candidate formula prepared by using support vector machine is a high-powered expression (including kernel function) including each amino acid explanatory variable maximizing the boundary between groups. The candidate formula prepared by using multiple regression analysis is a high-powered expression including each amino acid explanatory variable minimizing the sum of the distances from all amino acid concentration data. The candidate formula prepared by using logistic regression analysis is a linear model expressing logarithmic odds of probability, and a linear expression including each amino acid explanatory variable maximizing the likelihood of the probability. The k-means method is a method of searching k pieces of neighboring amino acid concentration data in various groups, designating the group containing the greatest number of the neighboring points as its data-belonging group, and selecting the amino acid explanatory variable that makes the group to which input amino acid concentration data belong agree well with the designated group. The cluster analysis is a method of clustering (grouping) the points closest in entire amino acid concentration data. The decision tree is a method of ordering amino acid explanatory variables and predicting the group of amino acid concentration data from the pattern possibly held by the higher-ordered amino acid explanatory variable.
Returning to the description of the evaluation formula-preparing processing, the control device verifies (mutually verifies) the candidate formula prepared in step 1 based on a particular verifying method (step 2). The verification of the candidate formula is performed on each other to each candidate formula prepared in step 1.
In step 2, at least one of discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (area under the curve in a receiver operating characteristic curve), and the like of the candidate formula may be verified by at least one of the bootstrap method, holdout method, N-fold method, leave-one-out method, and the like. In this way, it is possible to prepare the candidate formula higher in predictability or reliability, by taking the index state information and the evaluation condition into consideration.
The discrimination rate is the rate in which the negative state of the indicator of lifestyle-related disease evaluated as a true state in the present embodiment (for example, the result of definite diagnosis) is correctly evaluated as being negative and the positive state as a true state is correctly evaluated as being positive. The sensitivity refers to a rate in which the positive state of the indicator of lifestyle-related disease evaluated as a true state in the present embodiment is correctly evaluated as being positive. The specificity refers to a rate in which the negative state of the indicator of lifestyle-related disease evaluated as a true state in the present embodiment is correctly evaluated as being negative. The Akaike information criterion is a criterion representing how observation data agrees with a statistical model, for example, in regression analysis, and it is determined that the model in which the value defined by “−2×(maximum log-likelihood of statistical model)+2×(the number of free parameters of statistical model)” is smallest is the best. ROC_AUC (the area under the receiver operating characteristics curve) is defined as the area under the receiver operating characteristics curve (ROC) created by plotting (x, y)=(1-specificity, sensitivity) on two-dimensional coordinates. The value of ROC_AUC is 1 in perfect discrimination, and the closer this value is to 1, the higher the discriminative characteristic. The predictability is the average of discrimination rates, sensitivities, or specificities obtained by repeating the validation of a candidate formula. The robustness refers to the variance of discrimination rates, sensitivities, or specificities obtained by repeating the validation of a candidate formula.
Returning to the description of the evaluation formula-preparing processing, the control device selects a combination of the amino acid concentration data contained in the index state information used in preparing the candidate formula, by selecting an explanatory variable of the candidate formula based on a predetermined explanatory variable-selecting method (step 3). The selection of the amino acid explanatory variable may be performed on each candidate formula prepared in step 1. In this way, it is possible to select the amino acid explanatory variable of the candidate formula properly. The step 1 is executed once again by using the index state information including the amino acid concentration data selected in step 3.
In step 3, the amino acid explanatory variable of the candidate formula may be selected based on at least one of the stepwise method, best path method, local search method, and genetic algorithm from the verification result obtained in step 2.
The best path method is a method of selecting an amino acid explanatory variable by optimizing an evaluation index of the candidate formula while eliminating the amino acid explanatory variables contained in the candidate formula one by one.
Returning to the description of the evaluation formula-preparing processing, the control device prepares the evaluation formula by repeatedly performing the steps 1, 2 and 3, and based on verification results thus accumulated, selecting the candidate formula used as the evaluation formula from a plurality of the candidate formulae (step 4). In the selection of the candidate formula, there are cases where the optimum formula is selected from the candidate formulae prepared in the same formula-preparing method or the optimum formula is selected from all candidate formulae.
As described above, in the evaluation formula-preparing processing, the processing for the preparation of the candidate formulae, the verification of the candidate formulae, and the selection of the explanatory variables in the candidate formulae are performed based on the index state information in a series of operations in a systematized manner, whereby the evaluation formula most appropriate for evaluating the state of the indicator of lifestyle-related disease can be prepared. In other words, in the evaluation formula-preparing processing, the amino acid concentration is used in multivariate statistical analysis, and for selecting the optimum and robust combination of the explanatory variables, the explanatory variable-selecting method is combined with cross-validation to extract the evaluation formula having high evaluation performance. Logistic regression equation, linear discriminant, support vector machine, Mahalanobis' generalized distance method, multiple regression analysis, cluster analysis, Cox proportional-hazards model, and the like can be used as the evaluation formula.
2-2. System Configuration
Hereinafter, the configuration of the lifestyle-related disease indicator-evaluating system according to the second embodiment (hereinafter referred to sometimes as the present system) will be described with reference to
First, an entire configuration of the present system will be described with reference to
In the present system as shown in
Now, the configuration of the lifestyle-related disease indicator-evaluating apparatus 100 in the present system will be described with reference to
The lifestyle-related disease indicator-evaluating apparatus 100 includes (I) a control device 102, such as CPU (Central Processing Unit), that integrally controls the lifestyle-related disease indicator-evaluating apparatus, (II) a communication interface 104 that connects the lifestyle-related disease indicator-evaluating apparatus to the network 300 communicatively via communication apparatuses such as a router and wired or wireless communication lines such as a private line, (III) a memory device 106 that stores various databases, tables, files and others, and (IV) an input/output interface 108 connected to an input device 112 and an output device 114, and these parts are connected to each other communicatively via any communication channel. The lifestyle-related disease indicator-evaluating apparatus 100 may be present together with various analyzers (e.g., amino acid analyzer) in a same housing.
The memory device 106 is a storage means, and examples thereof include memory apparatuses such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk drives such as a hard disk, a flexible disk, an optical disk, and the like. The memory device 106 stores computer programs giving instructions to the CPU for various processings, together with OS (Operating System). As shown in the figure, the memory device 106 stores the user information file 106a, the amino acid concentration data file 106b, the index state information file 106c, the designated index state information file 106d, an evaluation formula-related information database 106e, and the evaluation result file 106f.
The user information file 106a stores user information on users.
Returning to
1. Concentration values of metabolites in blood other than amino acids (amino acid metabolites, carbohydrates, lipids, and the like), proteins, peptides, minerals, hormones, and the like.
2. Blood test values such as albumin, total protein, triglyceride, HbA1c, glycoalbumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid.
3. Values obtained from image information such as ultrasonic echo, X ray, CT, and MRI.
4. Values of biological indices such as age, height, weight, BMI, abdominal girth, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleeping information, family medical history information, and disease history information (for example, diabetes).
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The candidate formula file 106e1 stores the candidate formulae prepared in the candidate formula-preparing part 102h1 described below.
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The communication interface 104 allows communication between the lifestyle-related disease indicator-evaluating apparatus 100 and the network 300 (or communication apparatus such as a router). Thus, the communication interface 104 has a function to communicate data via a communication line with other terminals.
The input/output interface 108 is connected to the input device 112 and the output device 114. A monitor (including a home television), a speaker, or a printer may be used as the output device 114 (hereinafter, the output device 114 may be described as a monitor 114). A keyboard, a mouse, a microphone, or a monitor functioning as a pointing device together with a mouse may be used as the input device 112.
The control device 102 has an internal memory storing control programs such as OS (Operating System), programs for various processing procedures, and other needed data, and performs various information processings according to these programs. As shown in the figure, the control device 102 includes mainly a request-interpreting part 102a, a browsing processing part 102b, an authentication-processing part 102c, an electronic mail-generating part 102d, a Web page-generating part 102e, a receiving part 102f, the index state information-designating part 102g, the evaluation formula-preparing part 102h, the evaluating part 102i, a result outputting part 102j and a sending part 102k. The control device 102 performs data processings such as removal of data including defective, removal of data including many outliers, and removal of explanatory variables for the defective-including data in the index state information transmitted from the database apparatus 400 and in the amino acid concentration data transmitted from the client apparatus 200.
The request-interpreting part 102a interprets the requests transmitted from the client apparatus 200 or the database apparatus 400 and sends the requests to other parts in the control device 102 according to results of interpreting the requests. Upon receiving browsing requests for various screens transmitted from the client apparatus 200, the browsing processing part 102b generates and transmits web data for these screens. Upon receiving authentication requests transmitted from the client apparatus 200 or the database apparatus 400, the authentication-processing part 102c performs authentication. The electronic mail-generating part 102d generates electronic mails including various kinds of information. The Web page-generating part 102e generates Web pages for users to browse with the client apparatus 200.
The receiving part 102f receives, via the network 300, information (specifically, the amino acid concentration data, the index state information, the evaluation formula, etc.) transmitted from the client apparatus 200 and the database apparatus 400. The index state information-designating part 102g designates objective lifestyle-related disease index data and objective amino acid concentration data in preparing the evaluation formula.
The evaluation formula-preparing part 102h generates the evaluation formula based on the index state information received in the receiving part 102f and the index state information designated in the index state information-designating part 102g. Specifically, the evaluation formula-preparing part 102h generates the evaluation formula by selecting the candidate formula used as the evaluation formula from a plurality of the candidate formulae, based on verification results accumulated by repeating processings in the candidate formula-preparing part 102h1, the candidate formula-verifying part 102h2, and the explanatory variable-selecting part 102h3 from the index state information.
If the evaluation formulae are stored previously in a predetermined region of the memory device 106, the evaluation formula-preparing part 102h may generate the evaluation formula by selecting the desired evaluation formula out of the memory device 106. Alternatively, the evaluation formula-preparing part 102h may generate the evaluation formula by selecting and downloading the desired evaluation formula from the evaluation formulae previously stored in another computer apparatus (e.g., the database apparatus 400).
Hereinafter, a configuration of the evaluation formula-preparing part 102h will be described with reference to
Returning to
Hereinafter, a configuration of the evaluating part 102i will be described with reference to
The calculating part 102i1 calculates the value of the evaluation formula using (i) the concentration values of the amino acids of at least Gly and Tyr and (ii) the evaluation formula including the explanatory variables to be substituted with the concentration values of the amino acids of at least Gly and Tyr. The evaluating part 102i may store the value of the evaluation formula calculated by the calculating part 102i1 as an evaluation result in a predetermined region of the evaluation result file 106f. The evaluation formula may be any one of a logistic regression equation, a fractional expression, a linear discriminant, a multiple regression equation, a formula prepared by a support vector machine, a formula prepared by a Mahalanobis' generalized distance method, a formula prepared by canonical discriminant analysis, and a formula prepared by a decision tree. If the indicator of lifestyle-related diseases can be measured with successive numerical values, the evaluating part 102i may regard the value of the evaluation formula calculated by the calculating part 102i1 as an estimation value of the indicator.
The converting part 102i2 converts the value of the evaluation formula calculated by the calculating part 10211, for example, by the conversion method described above. The evaluating part 102i may store the converted value by the converting part 102i2 as an evaluation result in a predetermined region of the evaluation result file 106f. If the indicator of lifestyle-related diseases can be measured with successive numerical values, the evaluating part 102i may regard the converted value by the converting part 10212 as an estimation value of the indicator.
The generating part 102i3 generates the positional information about the position of the predetermined mark (for example, a circle sign or a star sign) corresponding to the value of the formula or the converted value on the predetermined scale (for example, a graduated scale at least marked with graduations corresponding to the upper limit value and the lower limit value in the possible range of the value of the formula or the converted value, or part of the range) visually presented on the display device such as the monitor or the physical medium such as paper for evaluating the state of the indicator of lifestyle-related disease, using the value of the formula calculated by the calculating part 102i1 or the converted value by the converting part 102i2. The evaluating part 102i may store the positional information generated by the generating part 102i3 as an evaluation result in a predetermined region of the evaluation result file 106f.
The classifying part 102i4 classifies the individual into any one of the plurality of categories previously defined at least considering the degree of the state of the indicator of lifestyle-related disease, using the value of the evaluation formula calculated by the calculating part 10211 or the converted value by the converting part 102i2.
Returning to
The sending part 102k transmits the evaluation results to the client apparatus 200 that is a sender of the amino acid concentration data of the individual, and transmits the evaluation formulae prepared in the lifestyle-related disease indicator-evaluating apparatus 100 and the evaluation results to the database apparatus 400.
Hereinafter, a configuration of the client apparatus 200 in the present system will be described with reference to
The client apparatus 200 includes a control device 210, ROM 220, HD (Hard Disk) 230, RAM 240, an input device 250, an output device 260, an input/output IF 270, and a communication IF 280 that are connected communicatively to one another through a communication channel.
The control device 210 has a Web browser 211, an electronic mailer 212, a receiving part 213, and a sending part 214. The Web browser 211 performs browsing processings of interpreting Web data and displaying the interpreted Web data on a monitor 261 described below. The Web browser 211 may have various plug-in softwares, such as stream player, having functions to receive, display and feedback streaming screen images. The electronic mailer 212 sends and receives electronic mails using a particular protocol (e.g., SMTP (Simple Mail Transfer Protocol) or POP3 (Post Office Protocol version 3)). The receiving part 213 receives various kinds of information, such as the evaluation results transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, via the communication IF 280. The sending part 214 sends various kinds of information such as the amino acid concentration data of the individual, via the communication IF 280, to the lifestyle-related disease indicator-evaluating apparatus 100.
The input device 250 is for example a keyboard, a mouse or a microphone. The monitor 261 described below also functions as a pointing device together with a mouse. The output device 260 is an output means for outputting information received via the communication IF 280, and includes the monitor 261 (including home television) and a printer 262. In addition, the output device 260 may have a speaker or the like additionally. The input/output IF 270 is connected to the input device 250 and the output device 260.
The communication IF 280 connects the client apparatus 200 to the network 300 (or communication apparatus such as a router) communicatively. In other words, the client apparatuses 200 are connected to the network 300 via a communication apparatus such as a modem, TA (Terminal Adapter) or a router, and a telephone line, or a private line. In this way, the client apparatuses 200 can access to the lifestyle-related disease indicator-evaluating apparatus 100 by using a particular protocol.
The client apparatus 200 may be realized by installing softwares (including programs, data and others) for a Web data-browsing function and an electronic mail-processing function to an information processing apparatus (for example, an information processing terminal such as a known personal computer, a workstation, a family computer, Internet TV (Television), PHS (Personal Handyphone System) terminal, a mobile phone terminal, a mobile unit communication terminal or PDA (Personal Digital Assistants)) connected as needed with peripheral devices such as a printer, a monitor, and an image scanner.
All or a part of processings of the control device 210 in the client apparatus 200 may be performed by CPU and programs read and executed by the CPU. Computer programs for giving instructions to the CPU and executing various processings together with the OS (Operating System) are recorded in the ROM 220 or HD 230. The computer programs, which are executed as they are loaded in the RAM 240, constitute the control device 210 with the CPU. The computer programs may be stored in application program servers connected via any network to the client apparatus 200, and the client apparatus 200 may download all or a part of them as needed. All or any part of processings of the control device 210 may be realized by hardware such as wired-logic.
The control device 210 may include an evaluating part 210a (including a calculating part 210a1, a converting part 210a2, a generating part 210a3, and a classifying part 210a4) having the same functions as the functions of the evaluating part 102i in the control device 102 of the lifestyle-related disease indicator-evaluating apparatus 100. When the control device 210 includes the evaluating part 210a, the evaluating part 210a may convert the value of the formula in the converting part 210a2, generate the positional information corresponding to the value of the formula or the converted value in the generating part 210a3, and classify the individual into any one of the categories using the value of the formula or the converted value in the classifying part 210a4, in accordance with information included in the evaluation result transmitted from the lifestyle-related disease indicator-evaluating apparatus 100.
Hereinafter, the network 300 in the present system will be described with reference to
Hereinafter, the configuration of the database apparatus 400 in the present system will be described with reference to
The database apparatus 400 has functions to store, for example, (i) the index state information used in preparing the evaluation formulae in the lifestyle-related disease indicator-evaluating apparatus 100 or in the database apparatus, (ii) the evaluation formulae prepared in the lifestyle-related disease indicator-evaluating apparatus 100, and (iii) the evaluation results obtained in the lifestyle-related disease indicator-evaluating apparatus 100. As shown in
The memory device 406 is a storage means, and may be, for example, memory apparatus such as RAM or ROM, a fixed disk drive such as a hard disk, a flexible disk, an optical disk, and the like. The memory device 406 stores, for example, various programs used in various processings. The communication interface 404 allows communication between the database apparatus 400 and the network 300 (or a communication apparatus such as a router). Thus, the communication interface 404 has a function to communicate data via a communication line with other terminals. The input/output interface 408 is connected to the input device 412 and the output device 414. A monitor (including a home television), a speaker, or a printer may be used as the output device 414 (hereinafter, the output device 414 may be described as a monitor 414). A keyboard, a mouse, a microphone, or a monitor functioning as a pointing device together with a mouse may be used as the input device 412.
The control device 402 has an internal memory storing control programs such as OS (Operating System), programs for various processing procedures, and other needed data, and performs various information processings according to these programs. As shown in the figure, the control device 402 includes mainly a request-interpreting part 402a, a browsing processing part 402b, an authentication-processing part 402c, an electronic mail-generating part 402d, a Web page-generating part 402e, and a sending part 402f.
The request-interpreting part 402a interprets the requests transmitted from the lifestyle-related disease indicator-evaluating apparatus 100 and sends the requests to other parts in the control device 402 according to results of interpreting the requests. Upon receiving browsing requests for various screens transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, the browsing processing part 402b generates and transmits web data for these screens. Upon receiving authentication requests transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, the authentication-processing part 402c performs authentication. The electronic mail-generating part 402d generates electronic mails including various kinds of information. The Web page-generating part 402e generates Web pages for users to browse with the client apparatus 200. The sending part 402f transmits various kinds of information such as the index state information and the evaluation formulae to the lifestyle-related disease indicator-evaluating apparatus 100.
2-3. Specific Example of the Second Embodiment
Here, a specific example of the second embodiment will be described with reference to
The amino acid concentration data used in the present processing is data concerning the concentration values of amino acids obtained by analyzing, by professionals or ourselves, blood (including, for example, plasma or serum) previously collected from an individual by the measurement method such as the following (A) or (B). Here, the unit of the amino acid concentration may be, for example, a molar concentration, a weight concentration, or one obtained by addition, subtraction, multiplication, and division of any constant with these concentrations.
(A) Plasma is separated from blood by centrifuging a collected blood sample. All plasma samples are frozen and stored at −80° C. until an amino acid concentration is measured. At the time of measuring an amino acid concentration, acetonitrile is added to perform a protein removal treatment, pre-column derivatization is then performed using a labeled reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and an amino acid concentration is analyzed by liquid chromatograph mass spectrometer (LC/MS) (see International Publication WO 2003/069328 and International Publication WO 2005/116629).
(B) Plasma is separated from blood by centrifuging a collected blood sample. All plasma samples are frozen and stored at −80° C. until an amino acid concentration is measured. At the time of measuring an amino acid concentration, sulfosalicylic acid is added to perform a protein removal treatment, and an amino acid concentration is analyzed by an amino acid analyzer based on post-column derivatization using a ninhydrin reagent.
First, the client apparatus 200 accesses the lifestyle-related disease indicator-evaluating apparatus 100 when the user specifies the Web site address (such as URL) provided from the lifestyle-related disease indicator-evaluating apparatus 100, via the input device 250 on the screen displaying the Web browser 211. Specifically, when the user instructs update of the Web browser 211 screen on the client apparatus 200, the Web browser 211 sends the Web site address provided from the lifestyle-related disease indicator-evaluating apparatus 100 by a particular protocol to the lifestyle-related disease indicator-evaluating apparatus 100, thereby transmitting requests demanding a transmission of Web page corresponding to an amino acid concentration data transmission screen (including the transmission of the BMI value) to the lifestyle-related disease indicator-evaluating apparatus 100 based on a routing of the address.
Then, upon receipt of the request transmitted from the client apparatus 200, the request-interpreting part 102a in the lifestyle-related disease indicator-evaluating apparatus 100 analyzes the transmitted requests and sends the requests to other parts in the control device 102 according to analytical results. Specifically, when the transmitted requests are requests to send the Web page corresponding to the amino acid concentration data transmission screen, mainly the browsing processing part 102b in the lifestyle-related disease indicator-evaluating apparatus 100 obtains the Web data for display of the Web page stored in a predetermined region of the memory device 106 and sends the obtained Web data to the client apparatus 200. More specifically, upon receiving the requests to transmit the Web page corresponding to the amino acid concentration data transmission screen by the user, the control device 102 in the lifestyle-related disease indicator-evaluating apparatus 100 demands inputs of user ID and user password from the user. If the user ID and password are input, the authentication-processing part 102c in the lifestyle-related disease indicator-evaluating apparatus 100 examines the input user ID and password by comparing them with the user ID and user password stored in the user information file 106a for authentication. Only when the user is authenticated, the browsing processing part 102b in the lifestyle-related disease indicator-evaluating apparatus 100 sends the Web data for displaying the Web page corresponding to the amino acid concentration data transmission screen to the client apparatus 200. The client apparatus 200 is identified with the IP (Internet Protocol) address transmitted from the client apparatus 200 together with the transmission requests.
Then, the client apparatus 200 receives, in the receiving part 213, the Web data (for displaying the Web page corresponding to the amino acid concentration data transmission screen) transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, interprets the received Web data with the Web browser 211, and displays the amino acid concentration data transmission screen on the monitor 261.
When the user inputs and selects, via the input device 250, for example the amino acid concentration data and the BMI value of the individual on the amino acid concentration data transmission screen displayed on the monitor 261, the sending part 214 of the client apparatus 200 transmits an identifier for identifying input information and selected items to the lifestyle-related disease indicator-evaluating apparatus 100, thereby transmitting the amino acid concentration data and the BMI value of the individual to the lifestyle-related disease indicator-evaluating apparatus 100 (step SA21). In step SA21, the transmission of the amino acid concentration data may be realized for example by using an existing file transfer technology such as FTP (File Transfer Protocol).
Then, the request-interpreting part 102a of the lifestyle-related disease indicator-evaluating apparatus 100 interprets the identifier transmitted from the client apparatus 200 thereby interpreting the requests from the client apparatus 200, and requests the database apparatus 400 to send the evaluation formula.
Then, the request-interpreting part 402a in the database apparatus 400 interprets the transmission requests from the lifestyle-related disease indicator-evaluating apparatus 100 and transmits, to the lifestyle-related disease indicator-evaluating apparatus 100, the evaluation formula (for example, the updated newest evaluation formula) stored in a predetermined region of the memory device 406 (step SA22). Specifically, in step SA22, one or more evaluation formulae (for example, any one of a logistic regression equation, a fractional expression, a linear discriminant, a multiple regression equation, a formula prepared by a support vector machine, a formula prepared by a Mahalanobis' generalized distance method, a formula prepared by canonical discriminant analysis, and a formula prepared by a decision tree) are transmitted to the lifestyle-related disease indicator-evaluating apparatus 100. In step SA22, Formula 1 for estimating the 120-minute OGTT insulin level, Formula 2 for estimating the visceral fat area value, and Formula 3 for evaluating the degree of the possibility that the individual's liver is in a state of having a certain amount or more of fat are transmitted.
a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1 (Formula 1)
a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2 (Formula 2)
a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3 (Formula 3)
In Formula 1, a1, b1, c1, d1, e1, and f1 each are any given real number other than zero, and g1 is any given real number.
In Formula 2, a2, b2, c2, d2, e2, f2, and g2 each are any given real number other than zero, and h2 is any given real number.
In Formula 3, a3, b3, c3, d3, e3, f3 each are any given real number other than zero, and g3 is any given real number.
Then, the lifestyle-related disease indicator-evaluating apparatus 100 receives, in the receiving part 102f, the amino acid concentration data and the BMI value of the individual transmitted from the client apparatuses 200 and the evaluation formula transmitted from the database apparatus 400, and stores the received amino acid concentration data and BMI value in a predetermined memory region of the amino acid concentration data file 106b and the received evaluation formula in a predetermined memory region of the evaluation formula file 106e4 (step SA23).
Then, the control device 102 in the lifestyle-related disease indicator-evaluating apparatus 100 removes data such as defective and outliers from the amino acid concentration data of the individual received in step SA23 (step SA24).
The evaluating part 102i then uses (i) the amino acid concentration data of the individual from which the data such as defective and outliers has been removed in step SA24 and the BMI value and (ii) Formula 1, Formula 2, and Formula 3 received in step SA23 to calculate the values of the evaluation formulae in the calculating part 102i1 (step SA25).
Specifically, the value of Formula 1 is calculated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp and Formula 1.
The value of Formula 2 is calculated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Val, and Trp, the BMI value of the individual, and Formula 2.
The value of Formula 3 is calculated using the concentration values of the amino acids of Gly, Tyr, Asn, Ala, Cit, and Leu and Formula 3.
The evaluating part 102i estimates the 120-minute OGTT insulin level of the individual using the value of Formula 1 calculated in step SA25. The evaluating part 102i estimates the visceral fat area value of the individual using the value of Formula 2 calculated in step SA25. The evaluating part 102i classifies the individual into any one of the plurality of categories defined at least considering the degree of the possibility that the individual's liver is in a state of having a certain amount or more of fat, using the value of the evaluation formula (the evaluation value) and the preset thethold(s) in the classifying part 102i4. The evaluating part 102i stores the evaluation results including the obtained estimation results and the classification result in a predetermined memory region of the evaluation result file 106f (step SA26).
Returning to the description of
In step SA27, the control device 102 in the lifestyle-related disease indicator-evaluating apparatus 100 may notify the evaluation results to the user client apparatus 200 by electronic mail. Specifically, the electronic mail-generating part 102d in the lifestyle-related disease indicator-evaluating apparatus 100 first acquires the user electronic mail address by referencing the user information stored in the user information file 106a based on the user ID and the like at the transmission timing. The electronic mail-generating part 102d in the lifestyle-related disease indicator-evaluating apparatus 100 then generates electronic mail data with the acquired electronic mail address as its mail address, including the user name and the evaluation results. The sending part 102k in the lifestyle-related disease indicator-evaluating apparatus 100 then sends the generated electronic mail data to the user client apparatus 200.
Also in step SA27, the lifestyle-related disease indicator-evaluating apparatus 100 may send the evaluation results to the user client apparatus 200 by using, for example, an existing file transfer technology such as FTP.
Returning to the description of
The receiving part 213 of the client apparatus 200 receives the Web data transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, and the received Web data is interpreted with the Web browser 211, to display on the monitor 261 the Web page screen displaying the evaluation results of the individual (step SA29). When the evaluation results are sent from the lifestyle-related disease indicator-evaluating apparatus 100 by electronic mail, the electronic mail transmitted from the lifestyle-related disease indicator-evaluating apparatus 100 is received at any timing, and the received electronic mail is displayed on the monitor 261 with the known function of the electronic mailer 212 in the client apparatus 200.
In this way, the user can confirm the evaluation results by browsing the Web page displayed on the monitor 261. The user may print out the content of the Web page displayed on the monitor 261 by the printer 262.
When the evaluation results are transmitted by electronic mail from the lifestyle-related disease indicator-evaluating apparatus 100, the user reads the electronic mail displayed on the monitor 261, whereby the user can confirm the evaluation results. The user may print out the content of the electronic mail displayed on the monitor 261 by the printer 262.
As described in details above, the client apparatus 200 transmits individual amino acid concentration data (including the BMI value) to the lifestyle-related disease indicator-evaluating apparatus 100. The database apparatus 400 transmits the evaluation formulae (Formula 1, Formula 2, and Formula 3) to the lifestyle-related disease indicator-evaluating apparatus 100 in response to a request from the lifestyle-related disease indicator-evaluating apparatus 100. The lifestyle-related disease indicator-evaluating apparatus 100 then (i) receives the amino acid concentration data from the client apparatus 200 and receives the evaluation formulae from the database apparatus 400, (ii) calculates the evaluation values using the received amino acid concentration data and evaluation formulae, (iii) estimates the individual 120-minute OGTT insulin level and visceral fat area value using the calculated evaluation values and classifies the individual into any one of the plurality of categories for fatty liver, using the calculated evaluation values and the threshold(s), and (iv) transmits the obtained evaluation results to the client apparatus 200 and the database apparatus 400. The client apparatus 200 then receives and displays the evaluation results transmitted from the lifestyle-related disease indicator-evaluating apparatus 100, and the database apparatus 400 receives and stores the evaluation results transmitted from the lifestyle-related disease indicator-evaluating apparatus 100.
Given the foregoing description, the explanation of the lifestyle-related disease indicator evaluation service process is finished.
In the present description, the lifestyle-related disease indicator-evaluating apparatus 100 executes the reception of the amino acid concentration data, the calculation of the values of the evaluation formulae, the estimation of the insulin level and the visceral fat area value, the classification of the individual into the category, and the transmission of the evaluation results, while the client apparatus 200 executes the reception of the evaluation results, described as an example. However, when the client apparatus 200 includes the evaluating unit 210a, the lifestyle-related disease indicator-evaluating apparatus 100 only has to execute the calculation of the values of the evaluation formulae. For example, the conversion of the values of the evaluation formulae, the generation of the positional information, the estimation of the insulin level and the visceral fat area value, and the classification of the individual into the category may be appropriately shared between the lifestyle-related disease indicator-evaluating apparatus 100 and the client apparatus 200.
For example, when the client apparatus 200 receives the value of the formula from the lifestyle-related disease indicator-evaluating apparatus 100, the evaluating unit 210a may convert the value of the formula in the converting unit 210a2, estimate the insulin level and the visceral fat area value using the value of the formula or the converted value, generate the positional information corresponding to the value of the formula or the converted value in the generating unit 210a3, and classify the individual into any one of the plurality of categories for fatty liver using the value of the formula or the converted value in the classifying unit 210a4.
When the client apparatus 200 receives the converted value from the lifestyle-related disease indicator-evaluating apparatus 100, the evaluating unit 210a may estimate the insulin level and the visceral fat area value using the converted value, generate the positional information corresponding to the converted value in the generating unit 210a3, and classify the individual into any one of the plurality of categories for fatty liver using the converted value in the classifying unit 210a4.
When the client apparatus 200 receives the value of the formula or the converted value and the positional information from the lifestyle-related disease indicator-evaluating apparatus 100, the evaluating unit 210a may estimate the insulin level and the visceral fat area value using the value of the formula or the converted value and classify the individual into any one of the plurality of categories for fatty liver using the value of the formula or the converted value in the classifying unit 210a4.
2-4. Other Embodiments
In addition to the second embodiment described above, the lifestyle-related disease indicator-evaluating apparatus, the lifestyle-related disease indicator-evaluating method, the lifestyle-related disease indicator-evaluating program product, the lifestyle-related disease indicator-evaluating system, and the information communication terminal apparatus according to the present invention can be practiced in various different embodiments within the technological scope of the claims.
Of the processings described in the second embodiment, all or a part of the processings described as automatically performed ones may be manually performed, or all or a part of the processings described as manually performed ones may be also automatically performed by known methods.
In addition, the processing procedures, the control procedures, the specific names, the information including parameters such as registered data of various processings and retrieval conditions, the screen examples, and the database configuration shown in the description and the drawings may be arbitrarily modified unless otherwise specified.
The components of the lifestyle-related disease indicator-evaluating apparatus 100 shown in the figures are functionally conceptual and therefore not be physically configured as shown in the figures.
For example, for the operational functions provided in the lifestyle-related disease indicator-evaluating apparatus 100, in particular, for the operational functions performed in the control device 102, all or part thereof may be implemented by a CPU (Central Processing Unit) and programs interpreted and executed in the CPU, or may be implemented by wired-logic hardware. The program is recorded in a non-transitory computer-readable recording medium including programmed instructions for making an information processing apparatus execute the lifestyle-related disease indicator-evaluating method according to the present invention, and is mechanically read as needed by the lifestyle-related disease indicator-evaluating apparatus 100. More specifically, computer programs to give instructions to the CPU in cooperation with an OS (operating system) to perform various processes are recorded in the memory device 106 such as ROM or a HDD (hard disk drive). The computer programs are executed by being loaded to RAM, and form the control unit in cooperation with the CPU.
The computer programs may be stored in an application program server connected to the lifestyle-related disease indicator-evaluating apparatus 100 via an arbitrary network, and all or part thereof can be downloaded as necessary.
The lifestyle-related disease indicator-evaluating program according to the present invention may be stored in the non-transitory computer-readable recording medium, or can be configured as a program product. The “recording medium” mentioned here includes any “portable physical medium” such as a memory card, a USB (universal serial bus) memory, an SD (secure digital) card, a flexible disk, a magneto-optical disc, ROM, EPROM (erasable programmable read only memory), EEPROM (registered trademark) (electronically erasable and programmable read only memory), CD-ROM (compact disk read only memory), MO (magneto-optical disk), DVD (digital versatile disk), and a Blu-ray (registered trademark) Disc.
The “program” mentioned here is a data processing method described in an arbitrary language or description method, and therefore any form such as a source code and a binary code is acceptable. The “program” is not necessarily limited to a program configured as a single unit, and, therefore, includes those dispersively configured as a plurality of modules and libraries and those in which the function of the program is achieved in cooperation with separate programs represented as OS (operating system). Any known configuration and procedures can be used as a specific configuration and reading procedure to read a recording medium by each apparatus shown in the embodiments or as an installation procedure after the reading, or the like.
The various databases and the like stored in the memory device 106 is a storage unit which is a memory device such as RAM and ROM, a fixed disk drive such as a hard disk, a flexible disk, and an optical disc, or the like. The memory device 106 stores therein various programs, tables, databases, files for Web (World Wide Web) pages, and the like used to perform various processes and to provide Web sites.
The lifestyle-related disease indicator-evaluating apparatus 100 may be configured as an information processing apparatus such as known personal computer and work station, or may be configured as the information processing apparatus connected to an arbitrary peripheral device. The lifestyle-related disease indicator-evaluating apparatus 100 may be provided by installing software (including the programs and the data, etc.) to cause the information processing apparatus to implement the lifestyle-related disease indicator-evaluating method according to the present invention.
Furthermore, a specific configuration of dispersion or integration of the apparatuses is not limited to the shown one. The apparatuses can be configured by functionally or physically dispersing or integrating all or part of the apparatuses in arbitrary units according to various types of additions or the like or according to functional loads. In other words, the embodiments may be implemented in arbitrary combinations thereof or an embodiment may be selectively implemented.
Finally, an example of the evaluation formula-preparing processing performed in the lifestyle-related disease indicator-evaluating apparatus 100 is described in detail with reference to
In the present description, the lifestyle-related disease indicator-evaluating apparatus 100 stores the index state information previously obtained from the database apparatus 400 in a predetermined memory region of the index state information file 106c. The lifestyle-related disease indicator-evaluating apparatus 100 shall store, in a predetermined memory region of the designated index state information file 106d, the index state information including the lifestyle-related disease index data and the amino acid concentration data (the one including the concentration values of the 19 kinds of amino acids) designated previously in the index state information-designating part 102g.
The candidate formula-preparing part 102h1 in the evaluation formula-preparing part 102h first prepares the candidate formulae based on a predetermined formula-preparing method from the index state information stored in a predetermine memory region of the designated index state information file 106d, and stores the prepared candidate formulae in a predetermined memory region of the candidate formula file 106e1 (step SB21). Specifically, the candidate formula-preparing part 102h1 in the evaluation formula-preparing part 102h first selects a desired method out of a plurality of different formula-preparing methods (including those for multivariate analysis such as principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, and decision tree) and determines the form of the candidate formula to be prepared (the form of formula) based on the selected formula-preparing method. The candidate formula-preparing part 102h1 in the evaluation formula-preparing part 102h then performs various calculation corresponding to the selected formula-selecting method (e.g., average or variance), based on the index state information. The candidate formula-preparing part 102h1 in the evaluation formula-preparing part 102h then determines the parameters for the calculation result and the determined candidate formula. In this way, the candidate formula is generated based on the selected formula-preparing method. When the candidate formulae are generated simultaneously and concurrently (in parallel) by using a plurality of different formula-preparing methods in combination, the processings described above may be executed concurrently for each selected formula-preparing method. Alternatively when the candidate formulae are generated in series by using a plurality of different formula-preparing methods in combination, for example, the candidate formulae may be generated by converting the index state information with the candidate formulae prepared by performing principal component analysis and performing discriminant analysis of the converted index state information.
The candidate formula-verifying part 102h2 in the evaluation formula-preparing part 102h verifies (mutually verifies) the candidate formula prepared in step SB21 according to a particular verifying method and stores the verification result in a predetermined memory region of the verification result file 106e2 (step SB22). Specifically, the candidate formula-verifying part 102h2 in the evaluation formula-preparing part 102h first generates the verification data to be used in verification of the candidate formula, based on the index state information stored in a predetermined memory region of the designated index state information file 106d, and verifies the candidate formula according to the generated verification data. If a plurality of the candidate formulae is generated by using a plurality of different formula-preparing methods in step SB21, the candidate formula-verifying part 102h2 in the evaluation formula-preparing part 102h verifies each candidate formula corresponding to each formula-preparing method according to a particular verifying method. Here in step SB22, at least one of the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (area under the curve in a receiver operating characteristic curve), and the like of the candidate formula may be verified based on at least one method of the bootstrap method, holdout method, N-fold method, leave-one-out method, and the like. Thus, it is possible to select the candidate formula higher in predictability or reliability, by taking the index state information and evaluation condition into consideration.
Then, the explanatory variable-selecting part 102h3 in the evaluation formula-preparing part 102h selects a combination of the amino acid concentration data contained in the index state information used in preparing the candidate formula by selecting an explanatory variable of the candidate formula according to a predetermined explanatory variable-selecting method, and stores the index state information including the selected combination of the amino acid concentration data in a predetermined memory region of the selected index state information file 106e3 (step SB23). When a plurality of the candidate formulae is generated by using a plurality of different formula-preparing methods in step SB21 and each candidate formula corresponding to each formula-preparing method is verified according to a predetermined verifying method in step SB22, the explanatory variable-selecting part 102h3 in the evaluation formula-preparing part 102h may select the explanatory variable of the candidate formula for each candidate formula according to a predetermined explanatory variable-selecting method in step SB23. Here in step SB23, the explanatory variable of the candidate formula may be selected from the verification results according to at least one of the stepwise method, best path method, local search method, and genetic algorithm. The best path method is a method of selecting an explanatory variable by optimizing an evaluation index of the candidate formula while eliminating the explanatory variables contained in the candidate formula one by one. In step SB23, the explanatory variable-selecting part 102h3 in the evaluation formula-preparing part 102h may select a combination of the amino acid concentration data based on the index state information stored in a predetermined memory region of the designated index state information file 106d.
The evaluation formula-preparing part 102h then judges whether all combinations of the amino acid concentration data contained in the index state information stored in a predetermined memory region of the designated index state information file 106d are processed, and if the judgment result is “End” (Yes in step SB24), the processing advances to the next step (step SB25), and if the judgment result is not “End” (No in step SB24), it returns to step SB21. The evaluation formula-preparing part 102h may judge whether the processing is performed a predetermined number of times, and if the judgment result is “End” (Yes in step SB24), the processing may advance to the next step (step SB25), and if the judgment result is not “End” (No in step SB24), it may return to step SB21. The evaluation formula-preparing part 102h may judge whether the combination of the amino acid concentration data selected in step SB23 is the same as the combination of the amino acid concentration data contained in the index state information stored in a predetermined memory region of the designated index state information file 106d or the combination of the amino acid concentration data selected in the previous step SB23, and if the judgment result is “the same” (Yes in step SB24), the processing may advance to the next step (step SB25) and if the judgment result is not “the same” (No in step SB24), it may return to step SB21. If the verification result is specifically the evaluation value for each candidate formula, the evaluation formula-preparing part 102h may advance to step SB25 or return to step SB21, based on the comparison of the evaluation value with a particular threshold corresponding to each formula-preparing method.
Then, the evaluation formula-preparing part 102h determines the evaluation formula by selecting the candidate formula used as the evaluation formula based on the verification results from a plurality of the candidate formulae, and stores the determined formula (the selected candidate formula) in particular memory region of the evaluation formula file 106e4 (step SB25). Here, in step SB25, for example, there are cases where the optimal evaluation formula is selected from the candidate formulae prepared in the same formula-preparing method or the optimal evaluation formula is selected from all candidate formulae.
Given the foregoing description, the explanation of the evaluation formula-preparing processing is finished.
Blood samples taken from examinees in health screening and visceral fat area values of the examinees measured in abdominal CT image diagnosis conducted in health screening are obtained (in total, 865 people). The concentration values (nmol/ml) of 19 amino acids (Ala, Arg, Asn, Cit, Gin, Gly, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Ser, Thr, Trp, Tyr, and Val) in blood are measured from the blood samples using the aforementioned amino acid analysis method.
In the test on the null hypothesis of “population correlation coefficient=0”, the amino acids with a significant correlation coefficient (the p value is less than 0.05) are Thr, Ser, Pro, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, His, Trp, Orn, and Lys. In the test on the null hypothesis of “ROC_AUC=0.5” under a non-parametric assumption, the amino acids with a significant ROC_AUC (the p value is less than 0.05) are Ser, Pro, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, His, Trp, Orn, and Lys.
Blood samples taken from examinees in health screening, the insulin resistance indices (HOMA-R. the value obtained by multiplying the fasting blood glucose level (mg/dl) by the blood insulin concentration (μU/ml) and divided by 405) of the examinees measured in health screening, the 120-minute OGTT (oral glucose tolerance test) blood glucose levels, and the 120-minute OGTT insulin levels are obtained (in total, 1,160 people). The concentration values (nmol/ml) of the 19 amino acids in blood are measured from the blood samples using the aforementioned amino acid analysis method.
In the test on the null hypothesis of “population correlation coefficient=0”, the amino acids with a significant correlation coefficient for the insulin resistance index (the p value is less than 0.05) are Ser, Asn, Pro, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, Trp, Orn, and Lys. In the test on the null hypothesis of population correlation coefficient=0, the amino acids with a significant correlation coefficient for the 120-minute OGTT blood glucose level (the p value is less than 0.05) are Pro, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, Trp, Orn, and Lys. In the test on the null hypothesis of population correlation coefficient=0, the amino acids with a significant correlation coefficient for the 120-minute OGTT insulin level (the p value is less than 0.05) are Ser, Asn, Pro, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, Trp, Orn, and Lys.
Blood samples taken from examinees in health screening and the diagnosis results as to fatty liver by ultrasonography conducted in health screening (the diagnosis results of fatty liver patients (964) or non-fatty liver subjects (3196)) are obtained (in total, 4160 people). The concentration values (nmol/ml) of the 19 amino acids in blood are measured from the blood samples using the aforementioned amino acid analysis method.
In the test on the null hypothesis of “ROC_AUC=0.5” under a non-parametric assumption, the amino acids with a significant ROC_AUC (the p value is less than 0.05) are Thr, Ser, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Tyr, Phe, His, Trp, Orn, Lys, and Arg.
Blood samples taken from examinees in health screening and the 120-minute OGTT blood glucose level of the examinees measured in health screening are obtained (in total, 650 people). Blood samples taken from examinees in health screening and the visceral fat area values of the examinees measured in abdominal CT image diagnosis conducted in health screening are obtained (in total, 650 people). Blood samples taken from examinees in health screening and the diagnosis results as to fatty liver by ultrasonography conducted in health screening (the diagnosis results of fatty liver patients (465) and non-fatty liver subjects (1,535)) are obtained (in total, 2,000 people). Two or more and six or less amino acids are selected from the 19 amino acids using the explanatory variable coverage method, and “the multiple regression equation correlated with the 120-minute OGTT insulin level”, “the multiple regression equation correlated with the visceral fat area value”, and “the logistic regression equation for discriminating between fatty liver and non-fatty liver” including the selected amino acids as explanatory variable are searched for. The BMI is also included as an explanatory variable in the multiple regression equation correlated with the visceral fat area value, in addition to the selected amino acids.
In the top 1,000 multiple regression equations having a high correlation coefficient that are correlated with the 120-minute OGTT insulin level, the amino acids Gly, Ala, Val, and Tyr appear as explanatory variables 500 or more times. In the top 1,000 multiple regression equations having a high correlation coefficient that are correlated with the visceral fat area value, the amino acids Gly, Val, Tyr, and Trp and the BMI appear as explanatory variables 500 or more times. In the top 1,000 logistic regression equations with a high ROC_AUC for discriminating between fatty liver and non-fatty liver, the amino acids Asn, Gly, Ala, and Tyr appear as explanatory variables 500 or more times. In particular, it is found that the amino acids Gly and Tyr appear 500 or more times as explanatory variables in all of the formulae.
The sample data used in Example 2 is used.
It is found that the multiple regression equation using a plurality of amino acids found to appear frequently in Example 2 as explanatory variables has a higher correlation coefficient than the one using one amino acid as an explanatory variable and therefore is useful for evaluating the state of the visceral fat area value.
The sample data used in Example 2 is used.
It is found that the multiple regression equation using a plurality of amino acids found to appear frequently in Example 2 and the BMI as explanatory variables has a higher correlation coefficient than the one using one amino acid as an explanatory variable and therefore is useful for evaluating the state of the visceral fat area value.
The sample data used in Example 2 is used.
It is found that the multiple regression equation using a plurality of amino acids found to appear frequently in Example 2 as explanatory variables has a higher correlation coefficient than the one using one amino acid as an explanatory variable and therefore is useful for evaluating the state of the 120-minute OGTT insulin level.
The sample data used in Example 2 is used.
It is found that the logistic regression equation using a plurality of amino acids found to appear frequently in Example 2 as explanatory variables has a higher correlation coefficient than the one using one amino acid as an explanatory variable and therefore is useful for discriminating between fatty liver and non-fatty liver.
The sample data used in Example 2 is used. From a plurality of multiple regression equations that include, as explanatory variables, “four amino acids Gly, Tyr, Asn, and Ala” and “two amino acids” selected from 15 amino acids excluding the four amino acids from the 19 amino acids using the explanatory variable coverage method in light of correlation to the 120-minute OGTT insulin level and in which the p value in the covariant (age) likelihood ratio test is greater than 0.05, a multiple regression equation with the highest adjusted R-squared is selected. As a result, Index Formula 1 below is selected. From a plurality of multiple regression equations that include, as explanatory variables, “four amino acids Gly, Tyr, Asn, and Ala”, “BMT”, and “two amino acids” selected from the 15 amino acids using the explanatory variable coverage method in light of correlation to the visceral fat area value and in which the p value in the covariant (age) likelihood ratio test is greater than 0.05, a multiple regression equation with the highest adjusted R-squared is selected. As a result, Index Formula 2 below is selected. From a plurality of logistic regression equations that include, as explanatory variables, “four amino acids Gly, Tyr, Asn, and Ala” and “two amino acids” selected from the 15 amino acids using the explanatory variable coverage method in light of discriminating between fatty liver and non-fatty liver and in which the p value in the covariant (age) likelihood ratio test is greater than 0.05, a logistic regression equation with the lowest Akaike information criterion is selected. As a result, Index Formula 3 below is selected. Index Formula 1:
“a1×Asn+b1×Gly+c1×Ala+d1×Val+e1×Tyr+f1×Trp+g1” Index Formula 2:
“a2×Asn+b2×Gly+c2×Ala+d2×Val+e2×Tyr+f2×Trp+g2×BMI+h2” Index Formula 3:
“a3×Asn+b3×Gly+c3×Ala+d3×Cit+e3×Leu+f3×Tyr+g3”
The correlation coefficient between the 120-minute OGTT insulin level and Index Formula 1 is 0.46, the correlation coefficient between the visceral fat area value and Index Formula 2 is 0.74, and the ROC_AUC serving as an index for evaluating the discrimination capability of Index Formula 3 in discriminating between fatty liver and non-fatty liver is 0.84. Thus, it is found that Index Formulae 1, 2, and 3 are useful indices with high evaluation capability. The value of each coefficient in Index Formulae 1, 2, and 3 may be a value multiplied by a real number, and the value of a constant term in Index Formulae 1, 2, and 3 may be a value obtained by addition, subtraction, multiplication, or division with any given real constant.
The sample data used in Example 1 is used.
It is thus found that Index Formulae 1, 2, and 3 can be used to evaluate not only the state of any one of “visceral fat area, insulin, and fatty liver” as the indicators of lifestyle-related diseases but also the state of two or all of the visceral fat area, insulin, and fatty liver.
The sample data used in Example 1 is used. For the examinees from whom the insulin resistance index, the 120-minute OGTT blood glucose level, and the 120-minute OGTT insulin level are obtained (people who fit into none of the diagnosis criteria items for metabolic syndrome: 361, people who fit into one item: 335, people who fit into two items: 272, people who fit into three items: 158, people who fit into four items: 34. In total, 1,160 people), analysis of correlation between the value of Index Formula 1 and the number of applicable diagnosis criteria items for metabolic syndrome is conducted. For the examinees from whom the visceral fat area value is obtained (people who fit into none of diagnosis criteria items for metabolic syndrome: 255, people who fit into one item: 244, people who fit into two items: 220, people who fit into three items: 119, people who fit into four items: 27. In total, 865 people), analysis of correlation between the value of Index Formula 2 and the number of applicable diagnosis criteria items for metabolic syndrome is conducted. For the examinees from whom the diagnosis result as to fatty liver is obtained (people who fit into none of the diagnosis criteria items for metabolic syndrome: 1,617, people who fit into one item: 1,162, people who fit into two items: 831, people who fit into three items: 436, people who fit into four items: 114. In total, 4,160 people), analysis of correlation between the value of Index Formula 3 and the number of applicable diagnosis criteria items for metabolic syndrome is conducted. The diagnosis criteria items for metabolic syndrome are Items 1 to 4 below, and the diagnosis criterion is that “if Item 1 below is applicable, when at least two of Items 2 to 4 below are applicable, metabolic syndrome is diagnosed”.
Item 1: “Waist equal to or greater than 85 cm for males, equal to or greater than 90 cm for females” (guideline for the visceral fat area value being equal to or greater than 100 cm2) or “BMT equal to or greater than 25”.
Item 2: “Triglyceride equal to or greater than 150 mg/dl” and/or “HDL cholesterol less than 40 mg/dl”.
Item 3: “Systolic blood pressure equal to or greater than 130 mmHg” and/or “diastolic blood pressure equal to or greater than 85 mmHg”.
Item 4: “Fasting blood glucose equal to or greater than 110 mg/dl”.
As shown in
It is thus found that Index Formulae 1, 2, and 3 can be used to evaluate the number of applicable diagnosis criteria items for metabolic syndrome.
The sample data used in Example 1 is used. For the examinees from whom the insulin resistance index, the 120-minute OGTT blood glucose level, and the 120-minute OGTT insulin level are obtained (people who have no concurrent lifestyle-related diseases: 368, people who have one: 430, people who have two: 263, people who have three: 77, people who have four: 22. In total, 1,160 people), analysis of correlation between the value of Index Formula 1 and the number of concurrent lifestyle-related diseases (the number of diseases corresponding to lifestyle-related diseases that the examinee has) is conducted. For the examinees from whom the visceral fat area value is obtained (people who have no concurrent lifestyle-related diseases: 266, people who have one: 318, people who have two: 205, people who have three: 58, people who have four: 18. In total, 865 people), analysis of correlation between the value of Index Formula 2 and the number of concurrent lifestyle-related diseases is conducted. For the examinees from whom the diagnosis result as to fatty liver is obtained (people who have no concurrent lifestyle-related diseases: 1,527, people who have one: 1,503, people who have two: 827, people who have three: 255, people who have four: 48. In total, 4,160 people), analysis of correlation between the value of Index Formula 3 and the number of concurrent lifestyle-related diseases is conducted. In the present Example 10, five diseases, that is, chronic nephropathy, hyperuricemia, hypertension, dyslipidemia, and disorder of carbohydrate metabolism are considered as the diseases corresponding to lifestyle-related diseases. A diagnosis criterion for chronic nephropathy is that “if the estimated glomerular filtration rate (eGFR) is less than 60, chronic nephropathy is diagnosed”. A diagnosis criterion for hyperuricemia is that “if the uric acid level is equal to or greater than 7 mg/dl, hyperuricemia is diagnosed”. A diagnosis criterion for hypertension is that “if systolic blood pressure is equal to or greater than 140 mmHg or if diastolic blood pressure is equal to or greater than 90 mmHg, hypertension is diagnosed”. A diagnosis criterion for dyslipidemia is that “if triglyceride (TG) is equal to or greater than 150 mg/dl, if HDL cholesterol is less than 40 mg/dl, or if LDL cholesterol is equal to or greater than 140 mg/dl, dyslipidemia is diagnosed”. A diagnosis criterion for disorder of carbohydrate metabolism is that “if the fasting blood glucose is equal to or greater than 126 mg/dl or if HbA1c (JDS) is equal to or greater than 6.1%, disorder of carbohydrate metabolism is diagnosed”.
As shown in
It is thus found that Index Formulae 1, 2, and 3 can be used to evaluate the number of lifestyle-related diseases that the subject has.
The sample data used in Example 1 is used. For the examinees from whom the insulin resistance index, the 120-minute OGTT blood glucose level, and the 120-minute OGTT insulin level are obtained (examinees who receive a definite diagnosis of diabetes: 143, prediabetes: 256, chronic nephropathy: 142, arteriolosclerosis: 68, stroke: 25, myocardial infarction: 8), the discrimination capability of Index Formula 1 in discrimination of 1. to 6. below is evaluated with the ROC_AUC.
For the examinees from whom the visceral fat area value is obtained (examinees who receive a definite diagnosis of diabetes: 135, prediabetes: 187, chronic nephropathy: 126, arteriolosclerosis: 67, stroke: 23, myocardial infarction: 8), the discrimination capability of Index Formula 2 in discrimination of 1. to 6. below is evaluated with the ROC_AUC.
For the examinees from whom the diagnosis result as to fatty liver is obtained (examinees who receive a definite diagnosis of diabetes: 394, prediabetes: 243, chronic nephropathy: 452, arteriolosclerosis: 201, stroke: 64, myocardial infarction: 16), the discrimination capability of Index Formula 3 in discrimination of 1. to 6. below is evaluated with the ROC_AUC.
1. Discrimination as to whether a definite diagnosis of diabetes is made.
2. Discrimination as to whether a definite diagnosis of prediabetes is made (specifically, impaired glucose tolerance (the 120-minute 75 g-OGTT blood glucose level is equal to or greater than 140 mg/dl and equal to or smaller than 199 mg/dl) and/or fasting blood glucose disorder (the fasting blood glucose level is equal to or greater than 110 mg/dl and equal to or smaller than 125 mg/dl).
3. Discrimination as to whether a definite diagnosis of chronic nephropathy is made.
4. Discrimination as to whether a definite diagnosis of arteriolosclerosis is made.
5. Discrimination as to whether a definite diagnosis of stroke is made.
6. Discrimination as to whether a definite diagnosis of myocardial infarction is made.
It is thus found that Index Formulae 1, 2, and 3 can be used to evaluate not only the state of an indicator of lifestyle-related diseases such as visceral fat area, insulin, and fatty liver but also the states of lifestyle-related diseases such as diabetes, prediabetes, chronic nephropathy, arteriolosclerosis, stroke, and myocardial infarction.
From sample data used in Example 1, those who take health screening consecutive five years are targeted (2,996 people). From among the targeted examinees, examinees who do not develop a disease event on the first year are extracted for each disease event shown in 1. to 15. below. The values of Index Formulae 1, 2, and 3 are calculated for each disease event using the sample data of the extracted examinees. For each disease event and for each of Index Formulae 1, 2, and 3, quintiles are defined in ascending order of calculated value (five ranks, namely, “1st Quintile”, “2nd Quintile”, “3rd Quintile”, “4th” Quintile and “5th Quintile”). For each disease event, for each of Index Formulae 1, 2, and 3, and for each quantile, the incidence of disease event (absolute risk and relative risk) is calculated by the person-years method, and the calculated values are compared. Whether a disease event occurs is determined based on the diagnosis criterion below.
Incidence of disease event (“absolute risk”)=total number of occurrences of disease event/sum of observation years (“person-year”)
Relative risk=incidence of disease event in “n-th Quintile”/incidence of disease event in “1st Quintile”
1. Insulin Resistance
2. High Blood Pressure
3. Hypertension
4. Fatty Liver
5. High Risk Fatty Liver
6. Diabetes
Item 1: Early morning fasting blood glucose level equal to or greater than 126 mg/dl.
Item 2: 120-minute 75 g-OGTT blood glucose level equal to or greater than 200 mg/dl.
Item 3: Casual blood glucose level equal to or greater than 200 mg/dl.
Item 4: HbA1C (JDS value) equal to or greater than 6.1% [HbA1C (international standard) equal to or greater than 6.5%].
7. Impaired glucose tolerance
8. Obesity
9. Morbid Obesity
10. Dyslipidemia
11. Chronic Nephropathy
12. Arteriosclerosis
13. Cerebral Infarction
14. Risk of Heart Disease
15. Metabolic Syndrome
Item 1: “Waist equal to or greater than 85 cm for males, equal to or greater than 90 cm for females” (guideline for the visceral fat area value equal to or greater than 100 cm2) or “BMI equal to or greater than 25”.
Item 2: “Triglyceride equal to or greater than 150 mg/dl” and/or “HDL cholesterol less than 40 mg/dl”.
Item 3: “Systolic blood pressure equal to or greater than 130 mmHg” and/or “diastolic blood pressure equal to or greater than 85 mmHg”.
Item 4: “Fasting blood glucose equal to or greater than 110 mg/dl”.
It is thus found that Index Formulae 1, 2, and 3 can be used to evaluate the future risk of developing disease events shown in 1. to 15. above.
Although the invention has been described with respect to specific embodiments for a complete and clear disclosure, the appended claims are not to be thus limited but are to be construed as embodying all modifications and alternative constructions that may occur to one skilled in the art that fairly fall within the basic teaching herein set forth.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2013-081568 | Apr 2013 | JP | national |
This application is based upon and claims the benefit of priority from PCT Application PCT/JP2014/060129, filed Apr. 7, 2014, which claims priority from Japanese Patent Application No. 2013-081568, filed Apr. 9, 2013, the entire contents of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2014/060129 | Apr 2014 | US |
| Child | 14877083 | US |
