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Michael Berk

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Review Open access Aug 2026

Associations between metabolic dysfunction-associated steatotic liver disease and physical and mental health-related quality of life in older adults.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common, often overlooked liver condition. Its impact on quality-of-life in older adults is not well understood. We examined the association between MASLD and physical and mental quality of life in a well-characterized cohort of Australians aged 70 and older. MASLD was identified using Fatty Liver Index ≥ 60 and consensus lifestyle and cardiometabolic criteria and assessed at baseline and at 3 years. PCS and MCS scores were measured annually using the 12-Item Short Form Health Survey. Generalized estimating equations adjusted for key confounders were used to evaluate the association of baseline and time-updated MASLD with longitudinal HRQoL outcomes. Analysis of 8880 participants (median follow-up 5.5 years) showed that baseline MASLD was associated with lower, then declining PCS over follow-up compared with no MASLD (mean difference -1.3; 95% CI -1.8, -0.9). Exploratory analysis showed stronger associations among women and individuals with frailty. Findings were similar when time-updated MASLD was modelled. While the presence of MASLD was associated with a deterioration in PCS, the difference was below the minimal clinically important difference (MCDI) of 4.2, indicating limited clinical implications at the individual level. For MCS, associations with MASLD were inconsistent and did not show a clear linear pattern, whereas MASLD was associated with lower mental HRQoL. Although the differences in PCS and MCS were small and below the MCID, these findings, at the population level, support integrating metabolic liver risk assessment with geriatric and psychiatric care, including screening for frailty, functional limitations, and depression.

Marzieh Nasr Azadani, M. Lotfaliany, Najmeh Davoodian et al. · 0 citations
Open access Aug 2026

Circulating biomarkers and polygenic scores for cardiovascular risk prediction in healthy older adults: differential and complementary contributions.

BACKGROUND AND AIMS Atherothrombotic cardiovascular disease (CVD) risk prediction in older adults remains suboptimal. The relative contributions of circulating protein biomarkers and polygenic scores (PGS) are uncertain. METHODS In 10,433 older individuals aged ≥70 years without prior CVD events, we evaluated traditional risk factors alongside three circulating biomarkers (high-sensitivity C-reactive protein, hsCRP; N-terminal pro-b-type natriuretic peptide, NT proBNP; and high-sensitivity troponin I, hsTnI), and two PGSs (coronary artery disease, ischemic stroke) for prediction of major adverse cardiovascular events (MACE). Associations were assessed using Cox proportional hazards models. Model performance was evaluated using the C-index, calibration, and continuous net reclassification improvement (NRI). RESULTS Over a median follow-up of 4.5 years (interquartile range 3.4-5.5), 359 MACE occurred. Each biomarker and both PGSs were independently associated with MACE, with NT-proBNP showing the strongest association (adjusted HR per SD 1.50, 95% CI 1.36-1.65). Compared with the base clinical model, the addition of the three circulating biomarkers (hsCRP, NT-proBNP, and hsTnI) resulted in a greater improvement in discrimination than the addition of the two PGSs (ΔC-index +0.041 vs. +0.016). The fully combined model achieved the highest discrimination (C-index 0.734) with good calibration. Circulating biomarkers improved reclassification primarily through correct identification of non-cases (NRI 0.34), whereas PGSs contributed relatively more to identification of cases (NRI 0.31). CONCLUSIONS In older adults, circulating biomarkers and PGSs provide cumulative information for CVD risk prediction, contributing differently to discrimination and risk reclassification. Integrating biomarkers and genetic risk may improve CVD risk prediction in older people beyond traditional risk factors.

Cheng-Long Yu, C. Tran, J. Neumann et al. · 0 citations

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