Methods and tools for predicting cardiometabolic health by combining blood T2 parameters with other measures

1 . A method of assessing the cardiometabolic health of a subject, said method comprising:

receiving a plurality of parameters of the subject,

wherein the plurality of parameters comprises a combination of:

a T 2 relaxation time constant of the subject's blood (T 2 value), and

one or more additional parameters, wherein the one or more additional parameters is selected from the group consisting of body-mass index, age, serum uric acid/creatinine ratio, resting pulse rate, dynamic light scattering diffusion times of the subject's blood, dynamic light scattering diffusion intensities of the subject's blood, or combinations thereof;

feeding the plurality of parameters into an algorithm, wherein the algorithm comprises a multi-variable regression algorithm that utilizes multi-variable regression (MR) analyses to correlate the received T 2 relaxation time and the one or more additional parameters to the subject's cardiometabolic health, wherein the correlation comprises a prediction of the subject's susceptibility to one or more cardiometabolic health-related conditions; and

implementing a treatment decision based on the subject's predicted susceptibility to the one or more cardiometabolic health-related conditions, wherein the treatment decision comprises administering a therapeutic agent to the subject.

2 . The method of claim 1 , wherein the T 2 value represents the spin-spin relaxation time constant of the isolated plasma component of the blood sample (plasma water T 2 ).

3 . The method of claim 1 , wherein the T 2 value represents the spin-spin relaxation time constant of the isolated serum component of the blood sample (serum water T 2 ).

4 . The method of claim 1 , wherein the T 2 value represents the spin-spin relaxation time constant of the settled blood cell pellet component of an anti-coagulated whole blood sample (T 2P ).

5 . The method of claim 1 , wherein the T 2 value represents the spin-spin relaxation time constant of the supernatant liquid component of an anti-coagulated whole blood sample (T 2S ).

6 . The method of claim 1 , wherein the T 2 value represents the spin-spin relaxation time constant of a mixed and unsettled whole blood sample (whole blood T 2 ).

7 . The method of claim 1 , wherein the T 2 value is represented in the following formula:

I

⁡

(

t

)

=

∑

i

A

i

⁢

e

-

t

/

T

2

⁢

i

wherein I(t) represents the nuclear magnetic resonance (NMR) signal intensity, t represents time, A i represents the signal amplitude, and T 2i represents the transverse relaxation time constant of ith proton microenvironment or mobility domain.

8 . The method of claim 1 , wherein the one or more additional parameters comprises: age, body-mass index, and pulse rate; uric acid/creatinine ratio; or dynamic light scattering diffusion times of the subject's blood, dynamic light scattering diffusion intensities of the subject's blood.

9 . The method of claim 1 , wherein the plurality of parameters comprise a T 2 value, age, body-mass index, and resting pulse rate.

10 . The method of claim 1 , wherein the plurality of parameters comprise a T 2 value and serum uric acid/creatinine ratio.

11 . The method of claim 1 , wherein the plurality of parameters comprise a T 2 value and at least one of dynamic light scattering diffusion intensities of the subject's blood and/or dynamic light scattering diffusion times of the subject's blood.

12 . The method of claim 1 , wherein the algorithm comprises a machine-learning algorithm, or an artificial intelligence algorithm trained on the plurality of parameters.

13 . The method of claim 1 , wherein the correlation further comprises a quantitative estimation of a subject's current cardiometabolic health status, diagnosis of a cardiometabolic health-related condition in the subject, or combinations thereof.

14 . The method of claim 1 , wherein the correlation comprises a prediction of the subject's current cardiometabolic health status and susceptibility to one or more future cardiometabolic health-related conditions.

15 . The method of claim 1 , wherein the one or more cardiometabolic health-related conditions is selected from the group consisting of metabolic syndrome, early metabolic syndrome, metabolic dysregulation, early metabolic dysregulation, metabolic imbalance, early metabolic imbalance, diabetes, prediabetes, type 2 diabetes, gestational diabetes, insulin resistance, dyslipidemia, oxidative stress, subclinical inflammation, hypoxemia, subclinical hypoxemia, hypoxia, subclinical hypoxia, cardiovascular disease, endocrine disorders, hormonal disorders, kidney dysfunction, kidney failure, metabolism-associated fatty liver disease (MAFLD), steatohepatitis (MASH), cognition decline, dementia, or combinations thereof.

16 . The method of claim 1 , further comprising a step of communicating cardiometabolic health risk.

17 . The method of claim 1 , wherein the treatment decision further comprises a personalized treatment plan for the subject based on the prediction of the subject's susceptibility to one or more cardiometabolic health-related conditions.

18 . The method of claim 1 , wherein the method is repeated after implementing the treatment decision.

19 . The method of claim 1 , wherein the subject is a human being.

20 . The method of claim 1 , wherein the subject shows no visible signs or symptoms related to cardiometabolic health-related conditions.