Novel plasma biomarkers improve discrimination of metabolic health independent of weight

Stephen Ellison,Nathan A Bihlmeyer,William E Kraus,Lydia Coulter Kwee,Robert Mcgarrah,James R Bain,Neha Pagidipati,Jawan W Abdulrahim,Svati H Shah

doi:10.1038/s41598-020-78478-w

Stephen Ellison, Nathan A Bihlmeyer + Show 7 more

Open Access

https://doi.org/10.1038/s41598-020-78478-w

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Abstract

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10−4–3.6 × 10−123), as well as within most of the individual BMI categories (p = 8.1 × 10−3–1.4 × 10−49). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

Highlights

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight
The increasing prevalence of poor MH across increasing BMI categories was driven by all components of the metabolic health measure including higher prevalence of hypertension and diabetes, higher TG, and lower HDL
In models inclusive of clinical covariates, we found modest improvement of model performance beyond that of traditionally defined poor MH, with further improvement in performance with addition of any individual plasma biomarker other than low-density lipoprotein (LDL-P) in the overall cohort: traditionally defined poor MH (AIC 19,674, AUC 0.74), LDL-P (AIC 19,673, AUC 0.74), lipoprotein-insulin resistance (LP-IR) (AIC 19,658, AUC 0.74), GlycA (AIC 19,598, AUC 0.75), HMSP (AIC 19,560, AUC 0.75), branched-chain amino acids (BCAA) (AIC 19,672, AUC 0.75), short-chain dicarboxylacylcarnitines (SCDA) (AIC 19,649, AUC 0.75) (Table 3)

Summary

Introduction

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10−4–3.6 × 10−123), as well as within most of the individual BMI categories (p = 8.1 × 10−3–1.4 × 10−49). We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI. Plasma biomarkers reflect dysregulated systemic and tissue-specific metabolism, serving as downstream read-outs of genetic, transcriptomic and proteomic variation, and may serve as these better

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