Use of GDF-15 in the Diagnosis and Treatment of Frailty and Conditions Associated with Altered Physiological Reserve, Physical Fitness and Exercise Capacity

Information

  • Patent Application
  • 20220065877
  • Publication Number
    20220065877
  • Date Filed
    August 30, 2021
    2 years ago
  • Date Published
    March 03, 2022
    2 years ago
Abstract
Provided herein are biomarkers useful for determining frailty, a biomarker signature for frailty, and methods of using the biomarkers to identify, classify, and treat a subject having frailty. Provided herein are also biomarkers useful for determining, identifying, classifying, and treating conditions associated with altered physical reserve, physical fitness, and exercise capacity.
Description
FIELD

This application relates to the identification, stratification, and treatment of frailty and conditions associated with altered physiological reserve, physical fitness and exercise capacity.


BACKGROUND

Frailty is a complex multidomain syndrome characterized by a decline in systemic physiological reserve, reduced physical health and fitness, and an accumulation of multiple medical comorbidities (‘multimorbidity’), including hypertension and diabetes mellitus. Frailty encompasses a conglomerate of signs, symptoms and clinical findings, including skeletal muscle wasting (sarcopenia), reduced muscle strength, progressive unintentional weight loss (cachexia), anorexia, systemic inflammation (‘inflammaging’), neurohormonal maladaptation, immune dysfunction, and depression. Frailty is a serious problem because it is difficult to detect, yet once it appears, can quickly lead to increased morbidity in a patient. However, studies have shown that frailty is potentially reversible and may even transition between non-frail/pre-frail and frail states through time. For all of the above reasons, there is a strong need for new methods of identifying and treating frailty in patients.


SUMMARY OF THE INVENTION

Provided herein is one or more blood biomarkers that can detect, diagnose, gauge, profile, classify or stratify frailty. In some embodiments, provided is a method of using one or more circulating blood biomarkers optionally in combination with metabolites or metabolomics to detect, diagnose, gauge, profile, classify or stratify frailty. Some embodiments provide a method of monitoring frailty status over time. Another embodiment provides a methodological process using one or more blood biomarkers for profiling, classifying, diagnosing and risk stratifying conditions associated with altered physical reserve, physical fitness or exercise capacity. In some embodiments, the blood biomarker is growth differentiation factor 15 (GDF-15), also known as macrophage inhibitory cytokine 1 (MIC-1).


Another embodiment provides a methodological process for profiling, classifying, diagnosing and risk stratifying the clinical syndrome of frailty with/without cardiac dysfunction (CD) through the use of one or more biomarkers, including but not limited to GDF-15 and NT-proBNP (N-terminal prohormone of B-type (brain) natriuretic peptide), coupled with metabolic/metabolomic profiling.


Another embodiment provides a method of determining frailty severity in a subject comprising the steps of

    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, and GlycA.


Some embodiments further comprise the step of measuring the levels of one or more biomarkers selected from the group consisting of phosphoglycerides, glycine, and alanine.


Some embodiments further comprise the step of determining frailty severity according to methods described herein, and treating the subject determined to have severe frailty.


Yet another embodiment provides a method of using of a biomarker in combination with metabolic or metabolomic profiling to provide a comprehensive high-dimensional picture of a subject's total health condition (e.g. internal milieu). In some embodiments, the total health conditions include one or more pathophysiological diagnoses and/or monitoring of such conditions.


Another embodiment provides the use of GDF-15 and NT-proBNP, optionally with the metabolome, to identify, define, characterize, diagnose and profile the frailty and non-frailty spectrum from robust to frail status, particularly in the subphenotyping or classification of individuals with and without CD.


Up until now there has been no available blood biomarker or imaging test that can reliably detect, diagnose, classify or risk stratify frailty. Accordingly, the biomarkers and methods provided herein solve such a problem and furthermore have several advantages over current solutions.


In some embodiments the use of circulating biomarker(s) and a broad array of metabolic measures/features (metabolomics) provides quantitation of differences between disease states and/or disorders, and serves as objective measures over time.


In some embodiments the provided methods provide objective testing, diagnosis and monitoring of frailty which are improvements over frailty assessment schemes based on point scoring along multidomain scales (e.g. Fried phenotype assessment [Fried 2001]) which are limited by subjectivity, recall bias, interobserver variability, require the subject to have a certain level of auditory, cognitive and mental competence, and pertain to domains that are under the influence of metabolic, neurohormonal and circulating factors in the bloodstream.


In some embodiments, the use of the 1H-nuclear magnetic resonance (NMR) Nightingale or similar platform coupled with functional biomarkers (e.g. NT-proBNP, GDF-15) provides more accurate and advanced frailty-related diagnostics, classification of health status, and health and disease management.


In some embodiments, the combination of biomarkers with metabolomics (biomarker-guided metabolomics) provides a comprehensive high-dimensional picture of the internal milieu.





BRIEF DESCRIPTION OF THE FIGURES

The disclosure will be readily understood by the following detailed description in conjunction with the accompanying figures.



FIG. 1 shows receiver operative characteristic (ROC) curves for CD for different biomarkers.



FIG. 2A shows metabolomic biosignatures of NT-proBNP according to CD status for 250 metabolites/metabolic features and the strength of the association between NT-proBNP and the metabolites measured using the β coefficient values.



FIG. 2B shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2C shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2D shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2E shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2F shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2G shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2H shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2I shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 2J shows continued metabolomic biosignatures of NT-proBNP of FIG. 2A thereof.



FIG. 3A shows metabolic profiles of paired comparisons among frailty status.



FIG. 3B shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3C shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3D shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3E shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3F shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3G shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3H shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3I shows continued metabolic profiles of FIG. 3A thereof.



FIG. 3J shows continued metabolic profiles of FIG. 3A thereof.



FIG. 4A(i) shows a discovery set of metabolic profiles of GDF-15 shifted with frailty status.



FIG. 4A(ii) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(iii) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(iv) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(v) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(vi) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(vii) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(viii) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(ix) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4A(x) shows a continued discovery set of metabolic profiles of GDF-15 of FIG. 4A(i) thereof.



FIG. 4B(i) shows a replication/validation set of metabolic profiles of GDF-15 shifted with frailty status.



FIG. 4B(ii) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(iii) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(iv) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(v) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(vi) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(vii) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(viii) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(ix) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4B(x) shows a continued replication/validation set of metabolic profiles of GDF-15 of FIG. 4B(i) thereof.



FIG. 4C(i) shows a combined set of discovery and replication/validation of metabolic profiles of GDF-15 shifted with frailty status.



FIG. 4C(ii) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(iii) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(iv) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(v) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(vi) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(vii) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(viii) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(ix) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 4C(x) shows a continued combined set of discovery and replication/validation of metabolic profiles of GDF-15 of FIG. 4C(i) thereof.



FIG. 5A shows receiver operating characteristic (ROC) curve for a discovery set of the combined classifiers GDF-15, albumin, glutamine, glycoprotein actetylation marker of inflammation (GlycA), and phosphoglycerides), age and sex in predicting frailty (area under the curve AUC).



FIG. 5B shows receiver operating characteristic (ROC) curve for a set of discovery and replication/validation of the combined classifiers GDF-15, albumin, glutamine, glycoprotein actetylation marker of inflammation (GlycA), and phosphoglycerides), age and sex in predicting frailty (area under the curve AUC).



FIG. 6 shows a workflow of logistic regression analysis with adjustment for age and sex.



FIG. 7 shows a workflow of age- and sex-adjusted linear regression.





DETAILED DESCRIPTION

Throughout this description for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the many aspects and embodiments disclosed herein. It will be apparent, however, to one skilled in the art that the many aspects and embodiments may be practiced without some of these specific details. In other instances, known biological and biochemical entities, mechanisms and analyses are shown herein to avoid obscuring the underlying principles of the described aspects and embodiments. The present invention is in the technical field of diagnostics, classification of health status, and human health and disease management.


Definitions and Abbreviations

As used herein and in the claims, the terms “comprise” (or any related form such as “comprises” and “comprising”), “include” (or any related forms such as “includes” or “including”), “contain” (or any related forms such as “contains” or “containing”), means including the following elements but not excluding others. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Where a range is referred in the specification, the range is understood to include each discrete point within the range. For example, 1-7 means 1, 2, 3, 4, 5, 6, and 7.


As used herein and in the claims, an “effective amount”, is an amount that is effective to achieve at least a measurable amount of a desired effect. For example, the amount may be effective to elicit an immune response, and/or it may be effective to elicit a protective response, against a pathogen bearing the polypeptide of interest. In some embodiments, the amount may be effective to maintain stable health, increase mobility, improved ability to retain nutrients, or improve FRAIL test results.


As used herein and in the claims, a “subject” refers to animals such as mammals and vertebrates, including, but not limited to, primates (e.g. humans), cows, sheep, goats, horses, pigs, dogs, cats, rabbits, rats, mice, frogs, zebrafish and the like.


As used herein, the term “treat,” “treating” or “treatment” refers to methods of alleviating, abating or ameliorating a disease or condition symptoms, preventing additional symptoms, ameliorating or preventing the underlying metabolic causes of symptoms, inhibiting the disease or condition, arresting the development of the disease or condition, relieving the disease or condition, causing regression of the disease or condition, relieving a condition caused by the disease or condition, or stopping the symptoms of the disease or condition either prophylactically and/or therapeutically.


“GDF-15” means growth differentiation factor 15.


“NT-proBNP” means N-terminal prohormone of B-type (brain) natriuretic peptide, a biomarker of cardiac dysfunction.


“CD” means cardiac dysfunction.


“CI” means confidence interval.


“NS” means not significant.


“OR” means odds ratio.


“GlycA” means glycoprotein acetylation marker of inflammation measured clinically in blood by the presence of certain characteristic N-acetyl methyl group protons which are detectable by 1H-NMR.


“Albumin” is a globular protein detectable in blood.


“Phosphoglycerides” is glycerol-based phospholipids.


Amino acids herein may be referred to by their full names or their abbreviated names, including, but not limited to, the below list:

















Alanine: Ala



Arginine: Arg



Asparagine: Asn



Aspartic acid: Asp



Cysteine: Cys



Glutamic acid: Glu



Glutamine: Gln



Glycine: Gly



Histidine: His



Isoleucine: Ile



Leucine: Leu



Lysine: Lys



Methionine: Met



Phenylalanine: Phe



Proline: Pro



Serine: Ser



Threonine: Thr



Tryptophan: Trp



Tyrosine: Tyr



Valine: Val










Although the description referred to particular aspects and embodiments, the disclosure should not be construed as limited to the embodiments set forth herein.


One aspect provides an in vitro method of determining frailty severity in a subject comprising the steps of

    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) determining the subject as being frail if the level of the biomarker in the biological sample is higher than about 2,000 pg/ml to 6,000 pg/ml; pre-frail if the level of the biomarker in the biological sample is higher than 500 pg/ml to 2000 pg/ml; and robust if the level of the biomarker in the biological sample is less than 500 pg/ml.


Another aspect provides an in vitro method of determining frailty severity in a subject comprising the steps of

    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;








    • d) determining the subject as being frail if the score p is a value defined in Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5.





Another aspect provides a method of determining frailty severity in a subject comprising the steps of

    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
    • c) determining the overall biosignature score p; and
    • d) determining the subject as being frail if p is Z;
      • wherein Z is a value defined in Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5;
    • wherein the score p is determined by
      • i) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;








    • or
      • ii) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:









p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(


1.20
×
GDF

-
15

)

+

(

7.26
×
Gln

)

+

(

1

0.0




×
albumin

)

+

(

6.30
×
GlycA

)

+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)

+


(


-
0.


97
×
Ala

)

.






Another aspect provides a method of determining frailty severity in a subject comprising the steps of

  • a) measuring the levels of GDF-15 in a biological sample from the subject;
  • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
  • c) determining the overall biosignature score p; and
  • d) determining the subject as being frail if p is Z;
    • wherein Z is a value defined in Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5;
      • wherein the score p is determined by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(


1.20
×
GDF

-
15

)

+

(

7.26
×
Gln

)

+

(

1

0.0




×
albumin

)

+

(

6.30
×
GlycA

)

+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)

+


(


-
0.


97
×
Ala

)

.






Another aspect provides a method of determining frailty severity in a subject comprising the steps of

  • a) measuring the levels of GDF-15 in a biological sample from the subject;
  • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
  • c) determining the overall biosignature score p; and
  • d) determining the subject as being frail if p is Z;
    • wherein Z is a value defined in Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5;
      • wherein the score p is determined by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(


1.20
×
GDF

-
15

)

+

(

7.26
×
Gln

)

+

(

1

0.0




×
albumin

)

+

(

6.30
×
GlycA

)

+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)

+


(


-
0.


97
×
Ala

)

.






In some embodiments, the method is an in vitro method.


In some embodiments, the Z is 0.15 to 0.56. In some embodiments, Z is 0.259 or Youden's J statistic. In some embodiments, the value defined by Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5 is 0.15 to 0.56; in other embodiments, 0.259 or Youden's J statistic. In some embodiments, the score p is 0.15 to 0.56. In some embodiments, the score p is 0.259 or Youden's J statistic.


Some embodiments further comprise the step of wherein if the subject is determined to be frail, treating the subject with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function. In some embodiments, the therapeutic drug is a combined angiotensin receptor blocker and neprilysin inhibitor (e.g. sacubitril/valsartan), a sodium-glucose transport protein 2 (SGLT2) inhibitor or gliflozins (e.g. dapagliflozin, empagliflozin), a beta blocker (e.g. metoprolol, carvedilol, bisoprolol), renin-angiotensin system inhibitor (e.g. enalapril, lisinopril), mineralocorticoid receptor antagonist (e.g. eplerenone, spironolactone), ivabradine, digoxin, inotropes (e.g. dobutamine, milrinone) or inodilator (e.g. levosimendan).


Some embodiments further comprise the step of measuring the levels of NT-proBNP, wherein if NT-proBNP levels are elevated but GDF-15 levels are not, determining the subject has cardiac dysfunction without frailty; if GDF-15 levels are elevated but NT-proBNP are not, determining the subject has systemic physiological injury or inflammation, hypoperfusion, or non-cardiac frailty; and if both GDF-15 and NT-proBNP levels are elevated, determining the subject has frailty and is predicted to have heart failure. In some embodiments, the subject has cardiac dysfunction, systemic tissue injury or hypoperfusion which can lead to heart failure.


Table 1 below shows thresholds and associated sensitivity, specificity values and Youden's index values (J) for the biosignature score p.













TABLE 1






Thresholds
sensitivity
specificity
J



















1
-Inf
1
0
0


2
0.0149
1
0.00493
0.00493


3
0.0172
1
0.00985
0.00985


4
0.0194
1
0.0148
0.0148


5
0.0199
1
0.0197
0.0197


6
0.0218
1
0.0246
0.0246


7
0.0242
1
0.0296
0.0296


8
0.0247
1
0.0345
0.0345


9
0.026
1
0.0394
0.0394


10
0.0285
1
0.0443
0.0443


11
0.0304
1
0.0493
0.0493


12
0.031
1
0.0542
0.0542


13
0.0315
1
0.0591
0.0591


14
0.033
1
0.064
0.064


15
0.0339
1
0.069
0.069


16
0.0344
1
0.0739
0.0739


17
0.0353
0.989
0.0739
0.0629


18
0.0369
0.989
0.0788
0.0678


19
0.0383
0.989
0.0837
0.0727


20
0.0384
0.989
0.0887
0.0777


21
0.0387
0.989
0.0936
0.0826


22
0.0401
0.989
0.0985
0.0875


23
0.0414
0.989
0.103
0.092


24
0.0415
0.989
0.108
0.097


25
0.0417
0.989
0.113
0.102


26
0.0419
0.989
0.118
0.107


27
0.0422
0.989
0.123
0.112


28
0.0427
0.989
0.128
0.117


29
0.043
0.989
0.133
0.122


30
0.0434
0.989
0.138
0.127


31
0.0439
0.989
0.143
0.132


32
0.0444
0.989
0.148
0.137


33
0.0448
0.989
0.153
0.142


34
0.0455
0.989
0.158
0.147


35
0.0488
0.989
0.163
0.152


36
0.0533
0.989
0.167
0.156


37
0.0553
0.989
0.172
0.161


38
0.0556
0.989
0.177
0.166


39
0.0568
0.978
0.177
0.155


40
0.0581
0.978
0.182
0.16


41
0.0591
0.978
0.187
0.165


42
0.0602
0.978
0.192
0.17


43
0.0609
0.978
0.197
0.175


44
0.0621
0.968
0.197
0.165


45
0.0631
0.968
0.202
0.17


46
0.0635
0.968
0.207
0.175


47
0.0638
0.968
0.212
0.18


48
0.0642
0.968
0.217
0.185


49
0.0643
0.968
0.222
0.19


50
0.0653
0.968
0.227
0.195


51
0.0673
0.968
0.232
0.2


52
0.0697
0.968
0.236
0.204


53
0.0719
0.968
0.241
0.209


54
0.0728
0.968
0.246
0.214


55
0.0731
0.968
0.251
0.219


56
0.0736
0.968
0.256
0.224


57
0.0742
0.968
0.261
0.229


58
0.0744
0.968
0.266
0.234


59
0.0752
0.968
0.271
0.239


60
0.0767
0.968
0.276
0.244


61
0.0781
0.968
0.281
0.249


62
0.0789
0.968
0.286
0.254


63
0.0795
0.968
0.291
0.259


64
0.0807
0.968
0.296
0.264


65
0.0823
0.968
0.3
0.268


66
0.0842
0.968
0.305
0.273


67
0.0854
0.968
0.31
0.278


68
0.0869
0.968
0.315
0.283


69
0.0882
0.968
0.32
0.288


70
0.0884
0.968
0.325
0.293


71
0.0889
0.968
0.33
0.298


72
0.0893
0.968
0.335
0.303


73
0.0901
0.968
0.34
0.308


74
0.091
0.968
0.345
0.313


75
0.0918
0.968
0.35
0.318


76
0.0953
0.968
0.355
0.323


77
0.0984
0.968
0.36
0.328


78
0.0992
0.968
0.365
0.333


79
0.0996
0.968
0.369
0.337


80
0.0998
0.968
0.374
0.342


81
0.1
0.968
0.379
0.347


82
0.102
0.968
0.384
0.352


83
0.104
0.968
0.389
0.357


84
0.106
0.968
0.394
0.362


85
0.107
0.968
0.399
0.367


86
0.108
0.968
0.404
0.372


87
0.109
0.968
0.409
0.377


88
0.11
0.968
0.414
0.382


89
0.111
0.968
0.419
0.387


90
0.112
0.968
0.424
0.392


91
0.112
0.968
0.429
0.397


92
0.115
0.968
0.433
0.401


93
0.119
0.957
0.433
0.39


94
0.125
0.957
0.438
0.395


95
0.131
0.957
0.443
0.4


96
0.131
0.957
0.448
0.405


97
0.132
0.957
0.453
0.41


98
0.132
0.957
0.458
0.415


99
0.133
0.957
0.463
0.42


100
0.135
0.957
0.468
0.425


101
0.136
0.957
0.473
0.43


102
0.138
0.957
0.478
0.435


103
0.141
0.957
0.483
0.44


104
0.143
0.957
0.488
0.445


105
0.144
0.957
0.493
0.45


106
0.144
0.957
0.498
0.455


107
0.146
0.957
0.502
0.459


108
0.146
0.957
0.507
0.464


109
0.148
0.946
0.507
0.453


110
0.151
0.946
0.512
0.458


111
0.152
0.946
0.517
0.463


112
0.153
0.946
0.522
0.468


113
0.154
0.946
0.527
0.473


114
0.155
0.946
0.532
0.478


115
0.155
0.946
0.537
0.483


116
0.157
0.946
0.542
0.488


117
0.161
0.946
0.547
0.493


118
0.164
0.946
0.552
0.498


119
0.165
0.946
0.557
0.503


120
0.167
0.935
0.557
0.492


121
0.168
0.935
0.562
0.497


122
0.17
0.925
0.562
0.487


123
0.172
0.925
0.567
0.492


124
0.172
0.925
0.571
0.496


125
0.174
0.925
0.576
0.501


126
0.175
0.925
0.581
0.506


127
0.176
0.914
0.581
0.495


128
0.178
0.914
0.586
0.5


129
0.18
0.914
0.591
0.505


130
0.183
0.914
0.596
0.51


131
0.185
0.914
0.601
0.515


132
0.186
0.903
0.601
0.504


133
0.188
0.903
0.606
0.509


134
0.192
0.903
0.611
0.514


135
0.197
0.903
0.616
0.519


136
0.202
0.903
0.621
0.524


137
0.206
0.892
0.621
0.513


138
0.21
0.892
0.626
0.518


139
0.212
0.892
0.631
0.523


140
0.212
0.892
0.635
0.527


141
0.214
0.882
0.635
0.517


142
0.215
0.871
0.635
0.506


143
0.217
0.871
0.64
0.511


144
0.22
0.871
0.645
0.516


145
0.222
0.871
0.65
0.521


146
0.223
0.871
0.655
0.526


147
0.223
0.871
0.66
0.531


148
0.227
0.871
0.665
0.536


149
0.232
0.871
0.67
0.541


150
0.234
0.871
0.675
0.546


151
0.235
0.86
0.675
0.535


152
0.237
0.86
0.68
0.54


153
0.238
0.86
0.685
0.545


154
0.24
0.849
0.685
0.534


155
0.241
0.849
0.69
0.539


156
0.242
0.849
0.695
0.544


157
0.247
0.839
0.695
0.534


158
0.254
0.839
0.7
0.539


159
0.258
0.839
0.704
0.543


160
0.259
0.839
0.709
0.548


161
0.259
0.839
0.714
0.553


162
0.26
0.828
0.714
0.542


163
0.262
0.817
0.714
0.531


164
0.264
0.817
0.719
0.536


165
0.267
0.817
0.724
0.541


166
0.269
0.817
0.729
0.546


167
0.274
0.806
0.729
0.535


168
0.28
0.796
0.729
0.525


169
0.283
0.785
0.729
0.514


170
0.286
0.785
0.734
0.519


171
0.288
0.774
0.734
0.508


172
0.289
0.774
0.739
0.513


173
0.292
0.774
0.744
0.518


174
0.296
0.774
0.749
0.523


175
0.305
0.774
0.754
0.528


176
0.315
0.763
0.754
0.517


177
0.318
0.763
0.759
0.522


178
0.318
0.763
0.764
0.527


179
0.319
0.763
0.768
0.531


180
0.32
0.753
0.768
0.521


181
0.321
0.742
0.768
0.51


182
0.321
0.742
0.773
0.515


183
0.322
0.731
0.773
0.504


184
0.323
0.72
0.773
0.493


185
0.324
0.72
0.778
0.498


186
0.325
0.72
0.783
0.503


187
0.325
0.72
0.788
0.508


188
0.326
0.72
0.793
0.513


189
0.328
0.71
0.793
0.503


190
0.33
0.71
0.798
0.508


191
0.333
0.71
0.803
0.513


192
0.337
0.71
0.808
0.518


193
0.341
0.699
0.808
0.507


194
0.347
0.699
0.813
0.512


195
0.349
0.699
0.818
0.517


196
0.356
0.699
0.823
0.522


197
0.366
0.688
0.823
0.511


198
0.38
0.688
0.828
0.516


199
0.39
0.688
0.833
0.521


200
0.396
0.688
0.837
0.525


201
0.402
0.688
0.842
0.53


202
0.406
0.688
0.847
0.535


203
0.408
0.677
0.847
0.524


204
0.413
0.667
0.847
0.514


205
0.422
0.667
0.852
0.519


206
0.428
0.667
0.857
0.524


207
0.432
0.667
0.862
0.529


208
0.435
0.667
0.867
0.534


209
0.44
0.656
0.867
0.523


210
0.443
0.656
0.872
0.528


211
0.444
0.645
0.872
0.517


212
0.449
0.645
0.877
0.522


213
0.455
0.645
0.882
0.527


214
0.46
0.634
0.882
0.516


215
0.465
0.624
0.882
0.506


216
0.466
0.613
0.882
0.495


217
0.47
0.602
0.882
0.484


218
0.474
0.602
0.887
0.489


219
0.476
0.591
0.887
0.478


220
0.476
0.581
0.887
0.468


221
0.477
0.57
0.887
0.457


222
0.481
0.57
0.892
0.462


223
0.491
0.559
0.892
0.451


224
0.498
0.559
0.897
0.456


225
0.501
0.559
0.901
0.46


226
0.51
0.548
0.901
0.449


227
0.519
0.548
0.906
0.454


228
0.522
0.538
0.906
0.444


229
0.527
0.527
0.906
0.433


230
0.534
0.527
0.911
0.438


231
0.539
0.516
0.911
0.427


232
0.541
0.516
0.916
0.432


233
0.548
0.516
0.921
0.437


234
0.558
0.516
0.926
0.442


235
0.563
0.505
0.926
0.431


236
0.568
0.495
0.926
0.421


237
0.574
0.495
0.931
0.426


238
0.578
0.484
0.931
0.415


239
0.584
0.473
0.931
0.404


240
0.588
0.462
0.931
0.393


241
0.59
0.452
0.931
0.383


242
0.594
0.441
0.931
0.372


243
0.597
0.43
0.931
0.361


244
0.6
0.43
0.936
0.366


245
0.607
0.419
0.936
0.355


246
0.612
0.409
0.936
0.345


247
0.615
0.398
0.936
0.334


248
0.621
0.387
0.936
0.323


249
0.635
0.376
0.936
0.312


250
0.646
0.376
0.941
0.317


251
0.646
0.366
0.941
0.307


252
0.648
0.366
0.946
0.312


253
0.65
0.355
0.946
0.301


254
0.651
0.344
0.946
0.29


255
0.655
0.344
0.951
0.295


256
0.657
0.344
0.956
0.3


257
0.659
0.333
0.956
0.289


258
0.661
0.333
0.961
0.294


259
0.663
0.323
0.961
0.284


260
0.666
0.312
0.961
0.273


261
0.679
0.301
0.961
0.262


262
0.691
0.29
0.961
0.251


263
0.692
0.28
0.961
0.241


264
0.695
0.269
0.961
0.23


265
0.702
0.258
0.961
0.219


266
0.709
0.247
0.961
0.208


267
0.717
0.237
0.961
0.198


268
0.727
0.226
0.961
0.187


269
0.731
0.226
0.966
0.192


270
0.733
0.215
0.966
0.181


271
0.735
0.204
0.966
0.17


272
0.742
0.194
0.966
0.16


273
0.759
0.183
0.966
0.149


274
0.769
0.172
0.966
0.138


275
0.771
0.161
0.966
0.127


276
0.78
0.161
0.97
0.131


277
0.796
0.151
0.97
0.121


278
0.809
0.14
0.97
0.11


279
0.816
0.129
0.97
0.099


280
0.823
0.129
0.975
0.104


281
0.837
0.129
0.98
0.109


282
0.856
0.118
0.98
0.098


283
0.864
0.108
0.98
0.088


284
0.865
0.0968
0.98
0.0768


285
0.867
0.086
0.98
0.066


286
0.874
0.086
0.985
0.071


287
0.879
0.0753
0.985
0.0603


288
0.884
0.0645
0.985
0.0495


289
0.89
0.0538
0.985
0.0388


290
0.894
0.0538
0.99
0.0438


291
0.908
0.0538
0.995
0.0488


292
0.93
0.043
0.995
0.038


293
0.95
0.043
1
0.043


294
0.963
0.0323
1
0.0323


295
0.973
0.0215
1
0.0215


296
0.987
0.0108
1
0.0108


297
Inf
0
1
0









Some embodiments further comprise the step of treating the subject for heart failure if NT-proBNP levels are elevated but GDF-15 levels are not; treating the subject for systemic physiological injury or inflammation, hypoperfusion, or non-cardiac frailty with a drug if GDF-15 levels are elevated but NT-proBNP are not; treating the subject with a heart failure and frailty drug if both GDF-15 and NT-proBNP levels are elevated.


Some embodiments further comprise the step of treating the subject in accordance with guideline-directed medical therapy (GDMT); wherein if the subject has elevated levels of both NT-proBNP and GDF-15, treating the subject as advanced stage D in accordance with GDMT; if the subject has elevated levels of NT-proBNP but not elevated levels of GDF-15, conducting cardiac imaging to determine the causes of cardiac dysfunction and treating the cardiac dysfunction in accordance with stage B or C in accordance with GDMT; if the subject has elevated levels of GDF-15 but not elevated levels of NT-proBNP, conducting a clinical assessment of medical comorbidities and treating the subject in accordance with stage B or C in accordance with GDMT; and if the subject does not have elevated levels of either NT-proBNP or GDF-15, treating the patient with as stage A in accordance with GDMT, particularly when the subject experiences no symptoms and/or when there are no cardiac structural abnormalities identified by imaging. In some embodiments, if the subject does not have elevated levels of either NT-proBNP or GDF-15 with/without the performance of other tests to exclude functional and/or structural abnormalities in the heart as deemed appropriate by the treating physician and according to standard practice guidelines, advising the subject on lifestyle modifications and managing particular risk factors without medical or therapeutic intervention.


In some embodiments, the stages A through D of the GDMT are based on the American College of Cardiology/American Heart Association (ACC/AHA) staging framework. In some embodiments, the treatments may be selected from one or more of pharmacological, device and other interventional therapies.


Another aspect provides a method of identifying and treating frailty, altered physiological and physical reserve, aging, or aging-related inflammation in a subject comprising the steps of

    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;








    • d) determining the subject as being frail if the score p is a value defined by a threshold value in Table 1 wherein the corresponding sensitivity and specificity values of the threshold value add up to between 1.4 and 1.6; and wherein at least one of the sensitivity or specificity values is 0.5 or above.





In some embodiments, the score p is 0.15 to 0.56. In some embodiments, the score p is 0.259. In some embodiments, the score p is a threshold value having a maximum value of Youden's J statistic. In a further embodiment, the maximum value of Youden's J statistic is 0.553.


Another aspect provides a method of generating a biosignature for frailty comprising the steps of


Identifying a subpopulation with elevated levels of GDF-15;


Conducting a biomarker screen/metabolic/metabolomic profiling on the subpopulation and using mathematical modeling tools to identifying biomarkers that correlate with the subpopulation.


In some embodiments, the biomarker screen includes a disease-specific biomarker selected from one or more of a heart failure biomarker (NT-proBNP or BNP), a renal failure biomarker (serum creatinine alone or in combination with cystatin C (CysC), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1) and/or neutrophil-gelatinase-associated lipocalin (NGAL)), a panel of inflammatory biomarkers (proinflammatory cytokines, e.g. interleukins, chemokines), and a tissue-specific biomarker.


Another aspect provides a system for detecting frailty in a subject comprising:

    • a) a GDF-15 analyzer configured to analyze biological samples from the subject to provide a concentration of GDF-15 in the biological sample;
    • b) a computer programmed to execute the following steps:
      • i) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/l), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
Phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;










      • ii) determining the subject as being frail if the score p is 0.15 to 0.56.







Another aspect provides a system for detecting frailty in a subject comprising:

    • a) a GDF-15 analyzer configured to analyze biological samples from the subject to provide a concentration of GDF-15 in the biological sample;
    • b) a computer programmed to execute at least the following:
      • i) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;








    • or
      • ii) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:









p
=


exp


(
H
)



1
+

exp


(
H
)









wherein






H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(


1.20
×
GDF

-
15

)

+

(

7.26
×
Gln

)

+

(

1

0.0




×
albumin

)

+

(

6.30
×
GlycA

)

+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)

+

(


-
0.


97
×
Ala

)



;




and

      • iii) determining the subject as being frail if the score p is 0.15 to 0.56.


Another aspect provides a method of improving the accuracy of frailty and non-frailty classification comprising using GDF-15 as a guiding biomarker with a metabolomic panel of metabolites selected from one or more of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine; comprising the following steps:

    • a) measuring GDF-15 concentration in a blood serum or plasma sample from a subject using the Roche Elecsys Assay kit on a Roche Cobas e immunoassay analyzer;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine using 1H-NMR Nightingale metabolomic profiling;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;






and

    • d) determining the subject as being frail if the score p is 0.15 to 0.56.


Another aspect provides a method of identifying subjects who will have improved survival outcomes when treated with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function; comprising the steps of

    • a) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;








    • b) determining the subject as being frail if the score p is 0.15 to 0.56; and

    • c) treating the subjects determined as being frail with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.





Another aspect provides a method of identifying subjects who will have improved survival outcomes when treated with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function; comprising the steps of

    • a) determining the overall biosignature score p; and
    • b) determining the subject as being frail if the score p is 0.15 to 0.56;
      • wherein the score p is determined by
        • i) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)

+

(


0
.
5


71




×
albumin

)

+

(

0.526
×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)

+

(


-
0.0577

×
Ala

)


;










      • or
        • ii) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:











p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(

1.20
×
GDF


-


15

)









(


+
7.26

×
Gln

)

+

(

10.0
×
albumin

)

+

(

6.30
×
GlycA

)





+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)





+


(


-
0.


97
×
Ala

)

.







    • and

    • c) treating the subjects determined as being frail with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.





Another aspect provides a method of identifying and treating subjects who will have improved survival outcomes when treated with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function; comprising the steps of

    • a) determining the overall biosignature score p; and
    • b) determining the subject as being frail if the score p is 0.15 to 0.56;
      • wherein the score p is determined by
        • i) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=


(




-

9
.
3



1

±


0
.
8


17
×
sex


+

0.111
×
age


)

+

(

0.000121
×
GDF


-


15

)










(


+
0.355

×
Gln

)

+

(

0.571
×
albumin

)

+

(

0.526
×
GlycA

)





+

(


-
0.


256
×
phosphoglycerides

)

+

(

0.266
×
Gly

)





+

(


-
0.0577

×
Ala

)


;








      • or
        • ii) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and log10-transformed serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:











p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=



-
2



5
.
5


4

+

(


-
0.


95
×
sex

)

+

(

0.10
×
age

)

+

(


1.20
×
GDF

-
15

)

+

(

7.26
×
Gln

)

+

(

1

0.0




×
albumin

)

+

(

6.30
×
GlycA

)

+

(


-
3.


11
×
phosphoglycerides

)

+

(

3.22
×
Gly

)

+


(


-
0.


97
×
Ala

)

.








    • and

    • c) treating the subjects determined as being frail with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.





Another aspect provides a kit for evaluating frailty comprising

    • a) a test for measuring blood levels of GDF-15; and
    • b) optionally one or more tests for measuring blood levels of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine.


In some embodiments, the test for measuring albumin, glutamine, GlycA, and phosphoglyceride is the 1H-NMR Nightingale system.


Another embodiment provides the use of tissue-specific blood biomarkers (e.g. NT-proBNP) to identify impaired organ or organ system (e.g. cardiac failure) as a subclassification (or subphenotyping) of frailty. In one embodiment, elevation of both circulating NT-proBNP and GDF-15 levels indicate CD and systemic tissue injury or hypoperfusion, and increase the probability of a diagnosis of heart failure and frailty (cardiac frailty). In another embodiment, sole elevation of NT-proBNP but not GDF-15 indicates cardiac dysfunction that is not so extensive as to cause systemic physiological compromise (cardiac dysfunction without frailty). In yet another embodiment, elevation of GDF-15 alone (with normal NT-proBNP levels) indicates systemic physiological injury, hypoperfusion or abnormalities that are less likely to be attributable cardiac dysfunction (noncardiac frailty). In some embodiments, GDF-15 elevation broadly indicates systemic tissue injury, inflammation, compromised systemic physiology and impaired physical fitness that characterize frailty (e.g. reduced skeletal muscle growth, weight loss, reduced appetite, easy fatigability).


In conjunction with physical measures (e.g. 6-minute walk distance (6MWD), gait speed, handgrip strength), GDF-15 is a useful biomarker for the classification and stratification of frailty classes.


In some embodiments, GDF-15 elevation is defined as having GDF-15 blood levels greater than 1000 pg/ml. In some embodiments, the GDF-15 blood levels are in a range between 1,000 to 6,000 pg/ml, 1000 to 4000 pg/ml, 2000 to 4000 pg/ml, 2500 to 3500 pg/ml, or 3000±1000 pg/ml. In some embodiments, the GDF-15 blood level is 3,206.6±2,565.4 pg/ml.


Examples

Provided herein are examples that describe in more detail certain embodiments of the present disclosure. The examples provided herein are merely for illustrative purposes and are not meant to limit the scope of the invention in any way. All references given below and elsewhere in the present application are hereby included by reference.


Example 1

Blood levels of a number of biomarkers and metabolites were measured in 306 subjects (derivation set). Blood serum levels of GDF-15 and NT-proBNP were measured using the Roche ELECSYS® GDF-15 Assay kit and a Roche COBAS® e immunoassay analyzer, or a compatible instrument, as per manufacturer.


Subjects were also evaluated based on the FRAIL Scale [Abelian van Kan 2008; Morley 2012; Woo 2012]. The 5-point FRAIL scale is a multi-domain instrument that assesses the key deficits and risks associated with frailty. A subject is frail if the score is 3 to 5; pre-frail if the score is 1 to 2; and robust if the score is 0.


It shall be understood that frailty severity can be defined by many different types of scores or methods, and there are other known frailty scoring methods that could stand in the place of the FRAIL scale, such as the Edmonton frail scale [Rolfson 2006]), or a cumulative deficit approach whereby an index is calculated from the proportion of health and medical problems relative to a predefined inventory (e.g. Rockwood frailty index)[Mitnitski 2002; Rockwood 2011].


Logistic regression analysis was done with adjustments for age and sex. Biomarker levels were expressed as mean±standard deviation (SD). Kruskal-Wallis H test with Dunn post hoc test, Chi-square or Fisher's exact test were used to compare differences between groups. aP<0.05, pre-frail vs. robust; bP<0.05, frail vs. robust; cP<0.05, frail vs. pre-frail (Table 2). The workflow of age- and sex-adjusted linear regression is shown in FIG. 7. The continuous variable of log10-transformed NT-proBNP or GDF-15 level, was modeled on each (transformed) metabolite or metabolic feature as the dependent variable.


GDF-15 as an Indicator of Frailty

Table 2 shows that circulating blood levels of the biomarkers, NT-proBNP and GDF-15, can indicate non-frailty and frailty irrespective of the etiology. Table 2 also shows that subjects with GDF-15 blood levels in the range of 3,206.6±2,565.4 pg/ml were confirmed frail according to the FRAIL scale.













TABLE 2





Variables
Robust
Pre-frail
Frail
Overall







Sample size, n
104
107
95
306


NT-proBNP, pg/ml
180.8 ± 506.1
239.4 ± 466.4a
361.0 ± 745.5b,c
257.2 ± 582.1


GDF-15, pg/ml
1,667.4 ± 1,114.9
2,220.0 ± 1,781.3a
3,206.6 ± 2.565.4b,c
2.338.5 ± 1,986.0










GDF-15 Surprisingly Effective as a Differentiator of Frailty Vs. Non-Frailty


Table 3 shows that log10-transformed GDF-15 does predict and differentiate frailty from non-frailty (P=1.29×10−3) whereas NT-proBNP does not (P=not significant (NS)). NT-proBNP is a strong independent predictor of CD, whereas GDF-15 can independently differentiate between individuals with and without frailty.












TABLE 3







Log10 NT-proBNP
Log10 GDF-15






















Comparisons
OR
(95% CI)
P
OR
(95% CI)
P


Frailty vs. non-
1.07
(0.95-1.20)
NS
1.38
(1.13-1.67)
1.29 × 10−3


frailty


CD vs. non-CD
1.47
(1.33-1.62)
5.49 × 10−13
1.02
(0.85-1.23)
NS










Comparison Data: GDF-15 vs. NT-proBNP in Prediction of Frailty & CD


Table 4 shows multiple linear regression analysis of phenotypic variables modeling on log10 NT-proBNP and log10 GDF-15 levels as dependent variables identifying CD and frailty as their respective explanatory factors. Table 4 further confirms that elevated GDF-15 levels are predictive of subjects with CD and frailty.











TABLE 4







Independent
Log10 NT-proBNP
Log10 GDF-15















variables
β
SE
t
P-values
β
SE
t
P-values


















Age, y
0.02
0.00
7.27
3.23 × 10−12
0.01
0.00
7.33
2.18 × 10−12


Sex, % female
−0.03
0.05
−0.65
NS
0.02
0.03
0.49
NS


BMI
0.00
0.01
0.05
NS
0.01
0.00
1.94
NS


Frailty status
0.05
0.03
1.47
NS
0.07
0.02
3.38
8.36 × 10−4 


CD
0.41
0.06
7.37
1.71 × 10−12
−0.01
0.03
−0.22
NS









R2
0.39
0.27





BMI, body mass index;


CD, cardiac dysfunction;


GDF-15, growth differentiation factor 15;


NS, not significant;


NT-proBNP, N-terminal prohormone of B-type natriuretic peptide;


SE, standard error






NT-proBNP


FIG. 1 shows how NT-proBNP independently distinguishes older adults with and without CD (n=306 subjects). Adding additional variables including frailty (FRAIL score) and/or GDF-15 did not further improve the predictive performance of NT-proBNP for CD. DeLong test was used to test for statistical difference between classifiers.



FIGS. 2A-J show metabolomic biosignatures of NT-proBNP generated using linear regression for all 250 metabolites/metabolic features and the strength of the association between NT-proBNP and the metabolites measured using the β coefficient values. Individuals with CD and without CD (non-CD) were classified according to whether or not echocardiographic CD was present. Metabolomic biosignature of NT-proBNP classified according to whether or not echocardiographic CD is present. Linear regression with adjustment for age and sex was used to estimate the strength of association (β coefficient) between each metabolite/metabolic feature and NT-proBNP in non-CD and CD groups. Refer to Table 8 for identity of metabolite number.


Pairwise Comparisons without GDF-15 Guidance Leads to Weaker Predictive Abilities



FIGS. 3A-J show that without biomarker guidance, pairwise comparisons between frail and non-frail groups (frail vs. robust; pre-frail vs. robust; frail vs. pre-frail) are possible. In FIGS. 3A-J, logistic regression with adjustment for age and sex is used to model each metabolite/metabolic feature on frailty status. Odds ratios are used to estimate the direction, size, and strength of the association between the metabolite/metabolic feature and the frailty or non-frailty phenotype. No biomarker is used in this analysis. Statistically significant variables are highlighted in blue. Refer to Table 8 for identity of metabolite number.



FIGS. 4A(i)-4A(x), FIGS. 4B(i)-4B(x) and FIGS. 4C(i)-4C(x) show the metabolomic biosignature of GDF-15 classified according to frailty status (robust, pre-frail or frail). The metabolomic biosignature of GDF-15 was generated using linear regression for all 250 metabolites/metabolic features (see Table 8 for metabolites). Linear regression with adjustment for age and sex was used to estimate the strength of association 03 coefficient) between each metabolite/metabolic feature and GDF-15 in robust, pre-frail or frail groups. β coefficient values were calculated from correlating each metabolite/metabolic feature against its respective GDF-15 level. Refer to Table 8 for identity of metabolite number. Of note, the GDF-15-guided metabolomic biosignature for the robust and pre-frail (collectively, non-frail) groups are not markedly similar, whereas a plethora of statistically different metabolites/metabolic features highlighted as shown is evident. At the individual metabolite/metabolic feature level, the identities are shown in Table 6.









TABLE 6







Frailty-related metabolic biosignatures in different studies.[Fung 2018; Pujos-Guillot 2018; Marron 2019],











NU-AGE study
Health ABC study
UFO study









Study population










Community-dwelling black
Community-dwelling elderly











Free-living elderly in Europe
men in America
in HK (China)









Frailty assessment











Fried et al. (2001)
SAVE score
FRAIL score









Subgroups






















Vigorous
Average
Frail






Robust
Pre-frail
Robust
Pre-frail
(range:
(range:
(range:






male
male
female
female
0-3)
4-5)
6-10)
Robust
Pre-frail
Frail





Sample size
60
31
 67
 54
73
105
109
104
107
95


Age, y
71 ± 4
73 ± 4
71 ± 4
72 ± 4
74 ± 3
75 ± 3
75 ± 3
71 ± 6
74 ± 8
79 ± 8


Sex, % female
 0
 0
100
100
 0
 0
 0
 42
 81
84










Metabolomics
UPLC coupled to QTOF-MS
LC-MS

1H-NMR



platform





Blood sample
Serum
Overnight-fasting plasma
Non-fasting serum


types





Significant
Significant metabolites for pre-frailty
8 metabolites positively correlated
Compared with robust, the frail group:


metabolites or
at baseline in males (stable):
with SAVE scores:
Total concentration of lipoprotein


metabolic
Pipecolic acid ↑
Glucoronate
particles↑


features
2,3-dihydromethylpyrrole ↑
N-carbamoyl-beta-alanine
Phosphoglycerides ↑



Proline ↑
Isocitrate
Cholines ↑



Butyrylcarnitine ↑
Creatinine
Phosphatidylcholines ↑



Significant metabolites for pre-frailty at
C4—OH carnitine
Sphingomyelins ↑



baseline in females (stable):
Cystathionine
Docosahexaenoic acid ↑



Amino-octanoic acid ↓
Hydroxyphenylacetate
Alanine ↑



Significant metabolites for pre-frailty at
Putrescine
Glutamine ↑



baseline in males (improved):
6 metabolites negatively correlated
Glycerol ↑



Glutamine ↑
with SAVE scores:
Creatinine ↑



Mannose ↓
Tryptophan
Albumin ↑



Gly-Phe ↓
Methionine
GlycA ↑



Significant metabolites for pre-frailty at
Tyrosine
Concentrations of particles in the



baseline in females (improved):
C14:0 sphingomyelin
S_HDL ↑



Threonine ↑
1-methylnicotinamide
Concentrations of free cholesterol in



Fructose ↓
asparagine
the S_HDL ↑



Phenylalanine ↓

Ratio of cholesterol to total lipids





in S_LDL ↑





Ratio of cholesterol esters to total





lipids in XL_HDL ↑





Ratio of free cholesterol to total





lipids in XL_HDL ↓





Ratio of phospholipids to total lipids





in M_HDL ↓






1H-NMR, proton nuclear magnetic resonance;



LC-MS, liquid chromatography-mass spectrometry;


QTOF-MS, quadrupole time-of-flight mass spectrometry;


UPLC, ultra-performance liquid chromatography






GDF-15 Predictive of Reduced Physical Activity

Table 5 below shows how NT-proBNP or GDF-15 levels impact other physical fitness measures (recognized surrogate markers of frailty and physical fitness) using Spearman's test with adjustment for age and sex. Both NT-proBNP or GDF-15 are markers of functional and physical domains of frailty. The data show that GDF-15 is significantly and inversely correlated with physical fitness and strength.


GDF-15 Guided Metabolomic Signature Most Predictive of Frailty


FIGS. 5A-B show the validation of the combined classifier of metabolites/metabolic features (albumin, glutamine, and glycoprotein actetylation marker of inflammation (GlycA) [Bell 1987; Otyos 2015; Ritchie 2015] that met the FDR 5% (from 4A(i)-4A(x), 4B(i)-4B(x) and 4C(i)-4C(x)) with addition of phosphoglycerides (Table 7), age, sex and GDF-15 to demonstrate an excellent predictive capacity of AUC 0.841 for prediction of frailty against non-frailty.



FIG. 6 shows a workflow of logistic regression analysis with adjustment for age and sex. A binary variable is modelled on each (transformed) metabolite or metabolic feature as the dependent variable.












TABLE 5









Model with adjustment for age and sex












Physical fitness
NT-proBNP

GDF-15












measures
Rho
P
Rho
P





6-minute walk
−0.13
0.02
−0.29
4.71 &times 10−7


distance


Gait speed
−0.10
NS
−0.26
3.14 &times 10−6


HGS/BMI
−0.06
NS
−0.20
3.80 &times 10−4





HGS/BMI, handgrip strength indexed to body mass index;


NS, not significant.






Example 2: Metabolic Profiling

Metabolomic profiling of blood samples from 306 subjects was done using 1H-NMR (Nightingale Health Ltd (Helsinki, Finland) [Soininen P, et al. Circ Cardiovasc Genet 2015; 8:192-206]). Table 8 shows the biomarkers profiled. Fresh blood serum or newly thawed specimens retrieved from −80° C. storage (or in transit on dry ice) were processed on the Nightingale proprietary platform and a proprietary Nightingale algorithm was used to identify and quantify levels of GlycA, phosphoglycerides, albumin and glutamine based on 1H-NMR spectral data.


Univariate regression, adjusted logistic (FIG. 6) and linear regression analyses (FIG. 7) was done to identify associations represented by β values between the biomarker (e.g. NT-proBNP, GDF-15) and each metabolite/metabolic feature and the dependent variable for the different clinical phenotypes or subphenotypes under study (FIGS. 2A-J, 3A-J, 4A(i)-4A(x), 4B(i)-4B(x) and 4C(i)-4C(x)).


A series of biosignatures were generated for each group or subgroup using a biomarker-guided metabolomic profiling strategy (FIGS. 6-7 and Table 7), to show the significant correlations 03 values) between the biomarker and the metabolites/metabolic feature (FIGS. 2A-J, 3A-J, 4A(i)-4A(x), 4B(i)-4B(x) and 4C(i)-4C(x)). The differences in the patterns of each group's metabolome can be visualized in a forest plot and the statistically significant findings are highlighted in the respective figures for the particular metabolites/metabolic features that reach the stringent false discovery rate (FDR) cut-off of 0.05 (5%).


Table 7 below shows how subjects who had GDF-15 levels that highly correlated with the following three or six biomarkers also showed the phenotype of frailty according to the FRAIL scale. Significant metabolites/metabolic features and area under the receiver operating curve (AUC) values at the respective false-discovery rates (FDR) are shown. Incremental lowering of the FDR threshold from 0.05 (standard) to 0.135 and beyond yields a greater number of metabolites/metabolic features.











TABLE 7






Significant metabolic features (along with age,



FDR
sex, GDF-15)
AUC

















0.05
Albumin, Gln, GlycA
0.8181


0.075
Albumin, Gln, GlycA
0.8181


0.1
Albumin, Gln, GlycA
0.8181


0.125
Albumin, Gln, GlycA
0.8181


0.135
Albumin, Gln, GlycA, Phosphoglycerides, Gly, Ala
0.8438


0.15
Albumin, Gln, GlycA, Phosphoglycerides, Gly, Ala
0.8438


0.175
Albumin, Gln, GlycA, Phosphoglycerides, Gly, Ala
0.8438


0.2
Albumin, Gln, GlycA, Phosphoglycerides, Gly, Ala
0.8438










Table 8 shows a list of 250 metabolites/metabolic features analyzed by Nightingale's 1H-NMR platform.











TABLE 8





No.

Units








CHOLESTEROL



001
Total cholesterol
mmol/l


002
Total cholesterol minus HDL-C
mmol/l


003
Remnant cholesterol (non-HDL, non-LDL-cholesterol)
mmol/l


004
VLDL cholesterol
mmol/l


005
Clinical LDL cholesterol
mmol/l


006
LDL cholesterol
mmol/l


007
HDL cholesterol
mmol/l



TRIGLYCERIDES


008
Total triglycerides
mmol/l


009
Triglycerides in VLDL
mmol/l


010
Triglycerides in LDL
mmol/l


011
Triglycerides in HDL
mmol/l



PHOSPHOLIPIDS


012
Total phospholipids in lipoprotein particles
mmol/l


013
Phospholipids in VLDL
mmol/l


014
Phospholipids in LDL
mmol/l


015
Phospholipids in HDL
mmol/l



CHOLSTERYL ESTERS


016
Total esterified cholesterol
mmol/l


017
Cholesteryl esters in VLDL
mmol/l


018
Cholesteryl esters in LDL
mmol/l


019
Cholesteryl esters in HDL
mmol/l



FREE CHOLESTEROL


020
Total free cholesterol
mmol/l


021
Free cholesterol in VLDL
mmol/l


022
Free cholesterol in LDL
mmol/l


023
Free cholesterol in HDL
mmol/l



TOTAL LIPIDS


024
Total lipids in lipoprotein particles
mmol/l


025
Total lipids in VLDL
mmol/l


026
Total lipids in LDL
mmol/l


027
Total lipids in HDL
mmol/l



LIPOPROTEIN PARTICLE CONCENTRATIONS


028
Total concentration of lipoprotein particles
mmol/l


029
Concentration of VLDL particles
mmol/l


030
Concentration of LDL particles
mmol/l


031
Concentration of HDL particles
mmol/l



LIPOPROTEIN PARTICLE SIZES


032
Average diameter for VLDL particles
nm


033
Average diameter for LDL particles
nm


034
Average diameter for HDL particles
nm


035
Phosphoglycerides
mmol/l


036
Ratio of triglycerides to phosphoglycerides
ratio (%)


037
Total cholines
mmol/l


038
Phosphatidylcholines
mmol/l


039
Sphingomyelins
mmol/l



APOLIPOPROTEINS


040
Apolipoprotein B
g/l


041
Apoliproprotein A1
g/l


042
Ratio of apolipoprotein B to apolipoprotein A1
ratio (%)



FATTY ACIDS


043
Total fatty acids
mmol/l


044
Degree of unsaturation
degree


045
Omega-3 fatty acids
mmol/l


046
Omega-6 fatty acids
mmol/l


047
Polyunsaturated fatty acids
mmol/l


048
Monosaturated fatty acids
mmol/l


049
Saturated fatty acids
mmol/l


050
Linoleic acid
mmol/l


051
Docosahexaenoic acid
mmol/l



FATTY ACID RATIOS


052
Ratio of omega-3 fatty acids to total fatty acids
ratio (%)


053
Ratio of omega-6 fatty acids to total fatty acids
ratio (%)


054
Ratio of polyunsaturated fatty acids to total fatty acids
ratio (%)


055
Ratio of monounsaturated fatty acids to total fatty acids
ratio (%)


056
Ratio of saturated fatty acids to total fatty acids
ratio (%)


057
Ratio of linoleic acid to total fatty acids
ratio (%)


058
Ratio of docosahexaenoic acid to total fatty acids
ratio (%)


059
Ratio of polyunsaturated fatty acids to monosaturated
ratio (%)



fatty acids


060
Ratio of omega-6 fatty acids to omega-3 fatty acids
ratio (%)



AMINO ACIDS


061
Alanine
mmol/l


062
Glutamine
mmol/l


063
Glycine
mmol/l


064
Histidine
mmol/l



BRANCHED-CHAIN AMINO ACIDS


065
Total concentration of branched-chain amino acids
mmol/l


066
Isoleucine
mmol/l


067
Leucine
mmol/l


068
Valine
mmol/l



AROMATIC AMINO ACIDS


069
Phenylalanine
mmol/l


070
Tyrosine
mmol/l



GLYCOLYSIS-RELATED METABOLITES


071
Glucose
mmol/l


072
Lactate
mmol/l


073
Pyruvate
mmol/l


074
Citrate
mmol/l


075
Glycerol
mmol/l



KETONE BODIES


076
3-Hydroxybutyrate
mmol/l


077
Acetate
mmol/l


078
Acetoacetate
mmol/l


079
Acetone
mmol/l



FLUID BALANCE


080
Creatinine
mmol/l


081
Albumin
g/l



INFLAMMATION


082
Glycoprotein acetyls
mmol/l



LIPOPROTEIN SUBCLASSES



Chylomicrons and extremely large VLDL (diameter 75 nm



upwards)


083
Concentrations of chylomicron and extremely large VLDL
mmol/l



particles


084
Total lipids in chylomicron and extremely large VLDL
mmol/l


085
Phospholipids in chylomicrons and extremely large VLDL
mmol/l


086
Cholesterol in chylomicrons and extremely large VLDL
mmol/l


087
Cholesteryl esters in chylomicrons and extremely large VLDL
mmol/l


088
Free cholesterol in chylomicrons and extremely large VLDL
mmol/l


089
Triglycerides in chylomicrons and extremely large VLDL
mmol/l



Very large VLDL (average diameter 64 nm)


090
Concentration of very large VLDL particles
mmol/l


091
Total lipids in very large VLDL
mmol/l


092
Phospholipids in very large VLDL
mmol/l


093
Cholesterol in very large VLDL
mmol/l


094
Cholesteryl esters in very large VLDL
mmol/l


095
Free cholesterol in very large VLDL
mmol/l


096
Triglycerides in very large VLDL
mmol/l



Large VLDL (average diameter 53.6 nm)


097
Concentration of large VLDL particles
mmol/l


098
Total lipids in large VLDL
mmol/l


099
Phospholipids in large VLDL
mmol/l


100
Cholesterol in large VLDL
mmol/l


101
Cholesteryl esters in large VLDL
mmol/l


102
Free cholesterol in large VLDL
mmol/l


103
Triglycerides in large VLDL
mmol/l



Medium VLDL (average diameter 44.5 nm)


104
Concentration of medium VLDL particles
mmol/l


105
Total lipids in medium VLDL
mmol/l


106
Phospholipids in medium VLDL
mmol/l


107
Cholesterol in medium VLDL
mmol/l


108
Cholesteryl esters in medium VLDL
mmol/l


109
Free cholesterol in medium VLDL
mmol/l


110
Triglycerides in medium VLDL
mmol/l



Small VLDL (average diameter 36.8 nm)


111
Concentration of small VLDL particles
mmol/l


112
Total lipids in small VLDL
mmol/l


113
Phospholipids in small VLDL
mmol/l


114
Cholesterol in small VLDL
mmol/l


115
Cholesteryl esters in small VLDL
mmol/l


116
Free cholesterol in small VLDL
mmol/l


117
Triglycerides in small VLDL
mmol/l



Very small VLDL (average diameter 31.3 nm)


118
Concentration of small VLDL particles
mmol/l


119
Total lipids in small VLDL
mmol/l


120
Phospholipids in small VLDL
mmol/l


121
Cholesterol in small VLDL
mmol/l


122
Cholesteryl esters in small VLDL
mmol/l


123
Free cholesterol in small VLDL
mmol/l


124
Triglycerides in small VLDL
mmol/l



IDL (average diameter 28.6 nm)


125
Concentration of IDL particles
mmol/l


126
Total lipids in IDL
mmol/l


127
Phospholipids in IDL
mmol/l


128
Cholesterol in IDL
mmol/l


129
Cholesteryl esters in IDL
mmol/l


130
Free cholesterol in IDL
mmol/l


131
Triglycerides in IDL
mmol/l



Large LDL (average diameter 25.5 nm)


132
Concentration of large LDL particles
mmol/l


133
Total lipids in large LDL
mmol/l


134
Phospholipids in large LDL
mmol/l


135
Cholesterol in large LDL
mmol/l


136
Cholesteryl esters in large LDL
mmol/l


137
Free cholesterol in large LDL
mmol/l


138
Triglycerides in large LDL
mmol/l



Medium LDL (average diameter 23 nm)


139
Concentration of medium LDL particles
mmol/l


140
Total lipids in medium LDL
mmol/l


141
Phospholipids in medium LDL
mmol/l


142
Cholesterol in medium LDL
mmol/l


143
Cholesteryl esters in medium LDL
mmol/l


144
Free cholesterol in medium LDL
mmol/l


145
Triglycerides in medium LDL
mmol/l



Small LDL (average diameter 18.7 nm)


146
Concentration of small LDL particles
mmol/l


147
Total lipids in small LDL
mmol/l


148
Phospholipids in small LDL
mmol/l


149
Cholesterol in small LDL
mmol/l


150
Cholesteryl esters in small LDL
mmol/l


151
Free cholesterol in small LDL
mmol/l


152
Triglycerides in small LDL
mmol/l



Very large HDL (average diameter 14.3 nm)


153
Concentration of very large HDL particles
mmol/l


154
Total lipids in very large HDL
mmol/l


155
Phospholipids in very large HDL
mmol/l


156
Cholesterol in very large HDL
mmol/l


157
Cholesteryl esters in very large HDL
mmol/l


158
Free cholesterol in very large HDL
mmol/l


159
Triglycerides in very large HDL
mmol/l



Large HDL (average diameter 12.1 nm)


160
Concentration of large HDL particles
mmol/l


161
Total lipids in large HDL
mmol/l


162
Phospholipids in large HDL
mmol/l


163
Cholesterol in large HDL
mmol/l


164
Cholesteryl esters in large HDL
mmol/l


165
Free cholesterol in large HDL
mmol/l


166
Triglycerides in large HDL
mmol/l



Medium HDL (average diameter 10.9 nm)


167
Concentration of large HDL particles
mmol/l


168
Total lipids in large HDL
mmol/l


169
Phospholipids in large HDL
mmol/l


170
Cholesterol in large HDL
mmol/l


171
Cholesteryl esters in large HDL
mmol/l


172
Free cholesterol in large HDL
mmol/l


173
Triglycerides in large HDL
mmol/l



Small HDL (average diameter 8.7 nm)


174
Concentration of small HDL particles
mmol/l


175
Total lipids in small HDL
mmol/l


176
Phospholipids in small HDL
mmol/l


177
Cholesterol in small HDL
mmol/l


178
Cholesteryl esters in small HDL
mmol/l


179
Free cholesterol in small HDL
mmol/l


180
Triglycerides in small HDL
mmol/l



RELATIVE LIPOPROTEIN LIPID CONCENTRATIONS



Chylomicrons and extremely large VLDL ratios


181
Phospholipids to total lipids ratio in chylomicrons and
ratio (%)



extremely large VLDL


182
Cholesterol to total lipids ratio in chylomicrons and
ratio (%)



extremely large VLDL


183
Cholesteryl esters to total lipids ratio in chylomicrons and
ratio (%)



extremely large VLDL


184
Free cholesterol to total lipids ratio in chylomicrons and
ratio (%)



extremely large VLDL


185
Triglycerides to total lipids ratio in chylomicrons and
ratio (%)



extremely large VLDL



Very large VLDL ratios


186
Phospholipids to total lipids ratio in very large VLDL
ratio (%)


187
Cholesterol to total lipids ratio in very large VLDL
ratio (%)


188
Cholesteryl esters to total lipids ratio in very large VLDL
ratio (%)


189
Free cholesterol to total lipids ratio in very large VLDL
ratio (%)


190
Triglycerides to total lipids ratio in very large VLDL
ratio (%)



Large VLDL ratios


191
Phospholipids to total lipids ratio in large VLDL
ratio (%)


192
Cholesterol to total lipids ratio in large VLDL
ratio (%)


193
Cholesteryl esters to total lipids ratio in large VLDL
ratio (%)


194
Free cholesterol to total lipids ratio in large VLDL
ratio (%)


195
Triglycerides to total lipids ratio in large VLDL
ratio (%)



Medium VLDL ratios


196
Phospholipids to total lipids ratio in medium VLDL
ratio (%)


197
Cholesterol to total lipids ratio in medium VLDL
ratio (%)


198
Cholesteryl esters to total lipids ratio in medium VLDL
ratio (%)


199
Free cholesterol to total lipids ratio in medium VLDL
ratio (%)


200
Triglycerides to total lipids ratio in medium VLDL
ratio (%)



Small VLDL ratios


201
Phospholipids to total lipids ratio in small VLDL
ratio (%)


202
Cholesterol to total lipids ratio in small VLDL
ratio (%)


203
Cholesteryl esters to total lipids ratio in small VLDL
ratio (%)


204
Free cholesterol to total lipids ratio in small VLDL
ratio (%)


205
Triglycerides to total lipids ratio in small VLDL
ratio (%)



Very small VLDL ratios


206
Phospholipids to total lipids ratio in very small VLDL
ratio (%)


207
Cholesterol to total lipids ratio in very small VLDL
ratio (%)


208
Cholesteryl esters to total lipids ratio in very small VLDL
ratio (%)


209
Free cholesterol to total lipids ratio in very small VLDL
ratio (%)


210
Triglycerides to total lipids ratio in very small VLDL
ratio (%)



IDL ratios


211
Phospholipids to total lipids ratio in IDL
ratio (%)


212
Cholesterol to total lipids ratio in IDL
ratio (%)


213
Cholesteryl esters to total lipids ratio in IDL
ratio (%)


214
Free cholesterol to total lipids ratio in IDL
ratio (%)


215
Triglycerides to total lipids ratio in IDL
ratio (%)



Large LDL ratios


216
Phospholipids to total lipids ratio in large LDL
ratio (%)


217
Cholesterol to total lipids ratio in large LDL
ratio (%)


218
Cholesteryl esters to total lipids ratio in large LDL
ratio (%)


219
Free cholesterol to total lipids ratio in large LDL
ratio (%)


220
Triglycerides to total lipids ratio in large LDL
ratio (%)



Medium LDL ratios


221
Phospholipids to total lipids ratio in medium VLDL
ratio (%)


222
Cholesterol to total lipids ratio in medium VLDL
ratio (%)


223
Cholesteryl esters to total lipids ratio in medium VLDL
ratio (%)


224
Free cholesterol to total lipids ratio in medium VLDL
ratio (%)


225
Triglycerides to total lipids ratio in medium VLDL
ratio (%)



Small LDL ratios


226
Phospholipids to total lipids ratio in small LDL
ratio (%)


227
Cholesterol to total lipids ratio in small LDL
ratio (%)


228
Cholesteryl esters to total lipids ratio in small LDL
ratio (%)


229
Free cholesterol to total lipids ratio in small LDL
ratio (%)


230
Triglycerides to total lipids ratio in small LDL
ratio (%)



Very large HDL ratios


231
Phospholipids to total lipids ratio in very large HDL
ratio (%)


232
Cholesterol to total lipids ratio in very large HDL
ratio (%)


233
Cholesteryl esters to total lipids ratio in very large HDL
ratio (%)


234
Free cholesterol to total lipids ratio in very large HDL
ratio (%)


235
Triglycerides to total lipids ratio in very large HDL
ratio (%)



Large HDL ratios


286
Phospholipids to total lipids ratio in large HDL
ratio (%)


237
Cholesterol to total lipids ratio in large HDL
ratio (%)


238
Cholesteryl esters to total lipids ratio in large HDL
ratio (%)


239
Free cholesterol to total lipids ratio in large HDL
ratio (%)


240
Triglycerides to total lipids ratio in large HDL
ratio (%)



Medium HDL ratios


241
Phospholipids to total lipids ratio in medium HDL
ratio (%)


242
Cholesterol to total lipids ratio in medium HDL
ratio (%)


243
Cholesteryl esters to total lipids ratio in medium HDL
ratio (%)


244
Free cholesterol to total lipids ratio in medium HDL
ratio (%)


245
Triglycerides to total lipids ratio in medium HDL
ratio (%)



Small HDL ratios


246
Phospholipids to total lipids ratio in small HDL
ratio (%)


247
Cholesterol to total lipids ratio in small HDL
ratio (%)


248
Cholesteryl esters to total lipids ratio in small HDL
ratio (%)


249
Free cholesterol to total lipids ratio in small HDL
ratio (%)


250
Triglycerides to total lipids ratio in small HDL
ratio (%)









Numbered Embodiments



  • 1. A method of determining frailty severity in a subject comprising the steps of
    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) determining the subject as being frail if the level of the biomarker in the biological sample is higher than about 2,000 pg/ml to 6,000 pg/ml; pre-FRAIL if the level of the biomarker in the biological sample is higher than 500 pg/ml to 2000 pg/ml; and robust if the level of the biomarker in the biological sample is less than 500 pg/ml.

  • 2. A method of determining frailty severity in a subject comprising the steps of
    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:







p
=


exp


(
H
)



1
+

exp


(
H
)









wherein






H
=


exp


(




-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

0.0001

21
×
GDF


-


15


)


+

(


0
.
3


55
×
Gln

)

+





(

0.571
×
albumin

)

+

(


0
.
5


26




×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(


0
.
2


66
×
Gly

)

+





(


-

0
.
0



577
×
Ala

)



;




and

    • d) determining the subject as being frail if the score p is a value defined by a threshold value in Table 1 wherein the corresponding sensitivity and specificity values of the threshold value add up to between 1.4 and 1.6; and wherein at least one of the sensitivity or specificity values is 0.5 or above.
  • 3. The method of embodiment 2, wherein the score p is 0.15 to 0.56.
  • 4. The method of embodiment 3, wherein the score p is 0.259 or a threshold value with the maximum value of Youden's J statistic according to Table 1.
  • 5. The method of embodiment 4, wherein the maximum value of Youden's J statistic is 0.553.
  • 6. The method of any one of embodiments 1-4, wherein if the subject is determined to be frail, treating the subject with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.
  • 7. The method of embodiment 5, wherein the therapeutic drug is sacubitril/valsartan, dapagliflozin, empagliflozin, beta blocker (e.g. metoprolol, carvedilol, bisoprolol), renin-angiotensin system inhibitor (e.g. enalapril, lisinopril), mineralocorticoid receptor antagonist (e.g. eplerenone, spironolactone), ivabradine, digoxin, inotropes (e.g. dobutamine, milrinone) or inodilator (e.g. levosimendan).
  • 8. The method of any one of embodiments 1-7, wherein the method is an in vitro method.
  • 9. A method of determining frailty severity in a subject comprising the steps of
    • a) measuring the levels of GDF-15 in a biological sample from the subject; and
    • b) measuring the levels of NT-proBNP, wherein if NT-proBNP levels are elevated but GDF-15 levels are not, determining the subject has cardiac dysfunction without frailty; if GDF-15 levels are elevated but NT-proBNP are not, determining the subject has systemic physiological injury, hypoperfusion, diabetes mellitus, inflammatory disorders, or non-cardiac frailty; if both GDF-15 and NT-proBNP levels are elevated, determining the subject has frailty and is predicted to have heart failure and if neither NT-proBNP levels nor GDF-15 levels are elevated, determining the subject is at low risk for frailty and low risk for heart failure.
  • 10. The method of embodiment 9, further comprising the step of treating the subject with guideline-directed medical therapy (GDMT); wherein if the subject has elevated levels of both NT-proBNP and GDF-15, treating the subject as advanced stage Din accordance with GDMT; if the subject has elevated levels of NT-proBNP but not elevated levels of GDF-15, conducting cardiac imaging to determine the causes of cardiac dysfunction and treating the cardiac dysfunction in accordance with stage B or C in accordance with GDMT; if the subject has elevated levels of GDF-15 but not elevated levels of NT-proBNP, conducting a clinical assessment of medical comorbidities and treating the subject in accordance with stage B or C in accordance with GDMT; and if the subject does not have elevated levels of either NT-proBNP or GDF-15, treating the patient with as stage A in accordance with GDMT.
  • 11. A method of identifying and treating frailty, altered physiological and physical reserve, aging, or aging-related inflammation in a subject comprising the steps of
    • a) measuring the levels of GDF-15 in a biological sample from the subject;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

exp
(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(

0.000121
×
GDF


-


15

)

+

(


0
.
3


55
×
Gln

)

+





(

0.571
×
albumin

)

+

(


0
.
5


26




×
GlycA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(


0
.
2


66
×
Gly

)

+





(


-

0
.
0



577
×
Ala

)


;






and

    • d) determining the subject as being frail if the score p is 0.15 to 0.56.
  • 12. A system for detecting frailty in a subject comprising:
    • a) a GDF-15 analyzer configured to analyze biological samples from the subject to provide a concentration of GDF-15 in the biological sample;
    • b) a computer programmed to execute the following steps:
      • i) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

exp
(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(

0.000121
×
GDF


-


15

)

+

(

0.355
×




Gln

)

+

(

0.571
×
albumin

)

+

(


0
.
5


26
×
GlycA

)

+

(


-

0
.
2



56
×
phosphoglycerides

)

+





(

0.266
×
Gly

)

+

(


-

0
.
0



577
×
Ala

)


;






and

      • ii) determining the subject as being frail if the score p is 0.15 to 0.56.
  • 13. A method of improving the accuracy of frailty and non-frailty classification comprising using GDF-15 as a guiding biomarker with a metabolomic panel of metabolites selected from one or more of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine; comprising the following steps:
    • a) measuring GDF-15 concentration in a blood serum or plasma sample from a subject using the Roche Elecsys Assay kit on a Roche Cobas e immunoassay analyzer;
    • b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine using 1H-NMR Nightingale metabolomic profiling;
    • c) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein





H
=

exp
(





-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(

0.000121
×
GDF


-


15

)

+

(


0
.
3


55
×
Gln

)

+





(

0.571
×
albumin

)

+

(


0
.
5


26




×
GlycaA

)

+

(


-
0.


256
×
phosphoglycerides

)

+

(


0
.
2


66
×
Gly

)

+





(


-

0
.
0



577
×
Ala

)


;






and

    • d) determining the subject as being frail if the score p is 0.15 to 0.56.
  • 14. A method of identifying subjects who will have improved survival outcomes when treated with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function; comprising the steps of
    • a) determining the overall biosignature score p by inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:






p
=


exp


(
H
)



1
+

exp


(
H
)









wherein






H
=


exp


(




-

9
.
3



1

±


0
.
8


17
×
sex


+

(

0.111
×
age

)

+

(


0.000121
×
GDF

-
15

)

+

(

0.355
×
Gln

)





+

(

0.571
×
albumin

)

+

(


0
.
5


26




×
GlycA

)

+

(


-
0.


256
×




phosphoglycerides

)

+

0.266
×
Gly


)


+

(

0.0577
×
Ala

)



;






    • b) determining the subject as being frail if the score p is 0.15 to 0.56; and

    • c) treating the subjects determined as being frail with exercise therapy, physical therapy, physiotherapy, nutritional supplementation (amino acid(s)/leucine (in non-cardiac failure)/protein), or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.



  • 15. A kit for evaluating frailty comprising
    • a) a test for measuring blood levels of GDF-15; and
    • b) optionally one or more tests for measuring blood levels of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine.

  • 16. The kit of embodiment 15 wherein the test for measuring albumin, glutamine, GlycA, and phosphoglyceride is the 1H-NMR Nightingale system.



The exemplary embodiments of the present invention are thus fully described. Although the description referred to particular embodiments, it will be clear to one skilled in the art that the present invention may be practiced with variation of these specific details. Hence this invention should not be construed as limited to the embodiments set forth herein.

Claims
  • 1. A method of treating frailty in a subject comprising the steps of a) measuring the levels of GDF-15 in a biological sample from the subject; andb) determining the subject as being frail if the level of the biomarker in the biological sample is higher than about 2,000 pg/ml to 6,000 pg/ml; pre-frail if the level of the biomarker in the biological sample is higher than 500 pg/ml to 2000 pg/ml; and robust if the level of the biomarker in the biological sample is less than 500 pg/ml;wherein if the subject is determined to be frail, treating the subject.
  • 2. A method of treating frailty in a subject comprising the steps of: a) measuring the levels of GDF-15 in a biological sample from the subject;b) measuring the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine;c) determining the overall biosignature score p; andd) determining the subject as being frail if p is Z; wherein Z is a value defined in Table 1 wherein the corresponding sensitivity and specificity values add up to between 1.4 and 1.5;wherein the score p is determined by i) inputting the subject's age in years; sex as value of 0 if female, 1 if male; and serum concentrations of biomarkers GDF-15 (pg/ml), albumin (g/1), glutamine (mmol/1), GlycA (mmol/1), phosphoglycerides (mmol/1), glycine (mmol/1), and alanine (mmol/1) of the subject into the following equation:
  • 3. The method of claim 2, wherein Z is 0.15 to 0.56.
  • 4. The method of claim 3, wherein Z is 0.259 or a threshold value with the maximum value of Youden's J statistic according to Table 1.
  • 5. The method of claim 4, wherein the maximum value of Youden's J statistic is 0.553.
  • 6. The method of claim 2, wherein treating the subject comprises exercise therapy, physical therapy, physiotherapy, nutritional supplementation, or administering a therapeutic drug for treating impaired cardiovascular or cardiopulmonary function.
  • 7. The method of claim 6, wherein the therapeutic drug is sacubitril/valsartan, dapagliflozin, empagliflozin, beta blocker (e.g. metoprolol, carvedilol, bisoprolol), renin-angiotensin system inhibitor (e.g. enalapril, lisinopril), mineralocorticoid receptor antagonist (e.g. eplerenone, spironolactone), ivabradine, digoxin, inotropes (e.g. dobutamine, milrinone) or inodilator (e.g. levosimendan).
  • 8. (canceled)
  • 9. A method of treating frailty in a subject comprising the steps of a) measuring the levels of GDF-15 in a biological sample from the subject;b) measuring the levels of NT-proBNP,wherein if NT-proBNP levels are elevated but GDF-15 levels are not, determining the subject has cardiac dysfunction without frailty; if GDF-15 levels are elevated but NT-proBNP are not, determining the subject has systemic physiological injury, hypoperfusion, diabetes mellitus, inflammatory disorders, or non-cardiac frailty; if both GDF-15 and NT-proBNP levels are elevated, determining the subject has frailty and is predicted to have heart failure and if neither NT-proBNP levels nor GDF-15 levels are elevated, determining the subject is at low risk for frailty and low risk for heart failure; andc) treating the subject with a guideline-directed medical therapy (GDMT); wherein if the subject has elevated levels of both NT-proBNP and GDF-15, treating the subject as advanced stage D in accordance with GDMT; if the subject has elevated levels of NT-proBNP but not elevated levels of GDF-15, conducting cardiac imaging to determine the causes of cardiac dysfunction and treating the cardiac dysfunction in accordance with stage B or C in accordance with GDMT; if the subject has elevated levels of GDF-15 but not elevated levels of NT-proBNP, conducting a clinical assessment of medical comorbidities and treating the subject in accordance with stage B or C in accordance with GDMT; and if the subject does not have elevated levels of either NT-proBNP or GDF-15, treating the patient with as stage A in accordance with GDMT.
  • 10-11. (canceled)
  • 12. A system for detecting frailty in a subject comprising: a) a GDF-15 analyzer configured to analyze biological samples from the subject to provide a concentration of GDF-15 in the biological sample;b) a computer programmed to execute at least the steps i) and ii) of claim 2.
  • 13. The method of claim 2, wherein a) the biological sample is a blood serum or plasma sample from a subject and is measured using the ROCHE Elecsys Assay kit on a ROCHE Cobas e immunoassay analyzer;b) the levels of one or more biomarkers selected from the group consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine are measured using 1H-NMR Nightingale metabolomic profiling.
  • 14-16. (canceled)
CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to, and the benefit of, U.S. Provisional Application Ser. No. 63/072,917 filed Aug. 31, 2020, entitled “Use of GDF-15 in the Diagnosis and Treatment of Frailty and Conditions Associated with Altered Physiological Reserve, Physical Fitness and Exercise Capacity”. The entire contents of the foregoing application are hereby incorporated by reference for all purposes.

Provisional Applications (1)
Number Date Country
63072917 Aug 2020 US