Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study.

ABSTRACT:

Background

The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers.

Methods

Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical imp

SUBMITTER: Huemer MT 

PROVIDER: S-EPMC8350207 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature