Why the study?
Serum creatinine-based estimates of glomerular filtration rate are influenced by muscle mass, and it was unclear whether normalizing serum creatinine to DEXA-derived lean mass index alters associations with cardiometabolic phenotypes.
Population
11,141 adults from the Austrian LEAD cohort (7487 with complete follow-up measurements)
Comparison
LMI-adjusted eGFR (eGFRLMISCr) vs standard serum creatinine-based eGFR (eGFRSCr)
Design
Cohort study
Answers are grounded in this paper and cite their sources.
Should not yet alter eGFR interpretation in practice; hypothesis-generating for refined cardiometabolic phenotyping pending prospective validation.
Background/Objectives: Serum creatinine (SCr)-based estimates of glomerular filtration rate (eGFRSCr) are influenced by muscle mass. This study investigated whether normalizing SCr to dual-energy X-ray absorptiometry (DEXA)-derived lean mass index (LMI) alters associations with cardiometabolic phenotypes. Methods: We analyzed 11,141 adults (mean age 46.0 ± 17.2 years) from the Austrian LEAD (Lung, hEart, sociAl, boDy) cohort. SCr was normalized to sex- and age-specific LMI reference values to calculate LMI-adjusted eGFR (eGFRLMISCr) within individuals. Associations with prevalent cardiometabolic conditions were compared using age- and sex-adjusted logistic regression with paired bootstrap resampling. Annualized eGFR change was compared in 7487 participants with complete follow-up measurements. Results: The estimates were closely correlated (R2 = 0.897), but LMI adjustment changed GFR-category assignment in both directions. Higher eGFRLMISCr was positively associated with prevalent dyslipidemia, hypertension, pre-diabetes, diabetes, metabolic syndrome and obesity. Differences in annualized change depended on age and sex, with lower LMI-adjusted slopes in women, particularly at younger ages, and higher slopes in men. Conclusion: LMI adjustment influences eGFR slopes in a sex- and age-specific manner and changes both filtration function category assignment and associations with cardiometabolic phenotypes. As these associations may partly reflect the direct incorporation of body composition into the estimate, our findings will require validation against measured GFR or an independent filtration marker.
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Helk et al. (2026) studied this question.
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