LEAN MASS-CORRECTION OF eGFR STRENGTHENS ASSOCIATIONS OF CKD WITH CARDIOMETABOLIC DISEASE

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LEAN MASS-CORRECTION OF eGFR STRENGTHENS ASSOCIATIONS OF CKD WITH CARDIOMETABOLIC DISEASE

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Co-author 1

Oliver Helk oliver.helk@meduniwien.ac.at Medical University of Vienna Department of Medicine III, Clinical Division Nephrology and Dialysis Vienna Austria * Austrian Academic Institute of Clinical Nutrition n/a Vienna Austria

Co-author 2

Charmaine Lim charmaine.helk-lim@leadstudy.at Sigmund Freud Private University Faculty of Medicine Vienna Austria - Ludwig Boltzmann Institute for Lung Health n/a Vienna Austria

Co-author 3

Emiel Wouters woutersemiel@gmail.com Maastricht University Nutrition and Translational Research in Metabolism (NUTRIM) Maastricht Netherlands - Ludwig Boltzmann Institute for Lung Health n/a Vienna Austria Sigmund Freud Private University Faculty of Medicine Vienna Austria

Co-author 4

Robab Breyer-Kohansal robab.breyer-kohansal@copd.lbg.ac.at Clinic Hietzing Department of Respiratory and Pulmonary Diseases Vienna Austria - Ludwig Boltzmann Institute for Lung Health n/a Vienna Austria

Co-author 5

Marie-Kathrin Breyer Marie-Kathrin.Breyer@lunghealth.lbg.ac.at Clinic Penzing Department of Respiratory and Pulmonary Diseases Vienna Austria - Ludwig Boltzmann Institute for Lung Health n/a Vienna Austria

Co-author 6

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Introduction

Serum creatinine (SCr)-based estimated glomerular filtration rate (eGFR) is the most widely applied marker of kidney function in clinical and epidemiological settings. However, its validity is limited, among other factors, by the dependence of serum creatinine levels on muscle mass. Higher lean mass leads to elevated serum creatinine levels independent of kidney function, potentially introducing systematic bias in eGFR estimation. While chronic kidney disease is a recognized and impactful cardiometabolic risk factor, confounding effects may weaken the associations of eGFRSCr with adverse metabolic and cardiovascular outcomes.

Methods

We determined lean mass using dual-energy X-ray absorptiometry (DEXA) and serum creatinine in 11143 adult (≥18 years) participants from the Lung, hEart, sociAl, boDy (LEAD) cohort, a general population observational cohort study in Austria. We calculated lean-mass index (LMI) corrected serum creatinine (LMISCr) values by adjusting for each individual’s LMI in relation to the population mean LMI by applying the following formula:

eGFR was then calculated from SCr and LMISCr separately using the CKD-EPI 2009 formula. The strength of associations between standard- and LMI corrected eGFR (eGFRSCr and eGFRLMISCr, respectively) with arterial hypertension, pre-diabetes and type II diabetes mellitus, dyslipidemia, metabolic syndrome and manifest cardiovascular disease was compared using standardized, sex- and age adjusted logistic regression analyses.

Results

eGFRLMISCr showed substantially stronger associations with dyslipidemia, arterial hypertension, pre-diabetes and diabetes as well as metabolic syndrome compared to eGFRSCr. We also detected stronger associations for manifest cardiovascular disease, albeit with overlapping confidence intervals. Corresponding odds ratios with 95% confidence intervals are illustrated in Figure 1.

Conclusion

Adjusting serum creatinine for lean body mass significantly improves epidemiological utility of eGFR. Implementation of such an approach could refine risk stratification in general populations and particularly in cohorts with high variability in body composition. Over- or underestimation of kidney function due to body composition may contribute to misclassification of risk and may thus obscure important pathophysiological relationships. Our results require validation against more accurate measurements of kidney function to confirm the validity of our approach in improving GFR estimation accuracy.

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