Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics DOI Creative Commons
Yihan Chen,

Siying Lin,

Shuangyu Yang

et al.

Journal of Advanced Research, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Frailty Index (FI) is a common measure of frailty, which has been advocated as routine clinical test by many guidelines. The genetic and phenotypic relationships FI with cardiovascular indicators (CIs) behavioral characteristics (BCs) are unclear, hampered ability to monitor using easily collected data. This study designed investigate the associations frailty CIs BCs, further construct model predict FI. Genetic 288 90 BCs were assessed cross-trait LD score regression (LDSC) Mendelian randomization (MR). data these integrated machine-learning individuals in UK-biobank. predicted risks type 2 diabetes (T2D) neurodegenerative diseases tested Kaplan-Meier estimator Cox proportional hazards model. MR revealed putative causal effects seven eight on These establish for predicting significantly correlated observed (Pearson correlation coefficient = 0.660, P-value 4.96 × 10-62). prediction indicated "usual walking pace" contributes most prediction. Patients who high higher risk T2D (HR 2.635, P < 10-16) 2.307, 1.62 10-3) than other patients. supports from perspectives. that developed integrating potential disease risk.

Language: Английский

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics DOI Creative Commons
Yihan Chen,

Siying Lin,

Shuangyu Yang

et al.

Journal of Advanced Research, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Frailty Index (FI) is a common measure of frailty, which has been advocated as routine clinical test by many guidelines. The genetic and phenotypic relationships FI with cardiovascular indicators (CIs) behavioral characteristics (BCs) are unclear, hampered ability to monitor using easily collected data. This study designed investigate the associations frailty CIs BCs, further construct model predict FI. Genetic 288 90 BCs were assessed cross-trait LD score regression (LDSC) Mendelian randomization (MR). data these integrated machine-learning individuals in UK-biobank. predicted risks type 2 diabetes (T2D) neurodegenerative diseases tested Kaplan-Meier estimator Cox proportional hazards model. MR revealed putative causal effects seven eight on These establish for predicting significantly correlated observed (Pearson correlation coefficient = 0.660, P-value 4.96 × 10-62). prediction indicated "usual walking pace" contributes most prediction. Patients who high higher risk T2D (HR 2.635, P < 10-16) 2.307, 1.62 10-3) than other patients. supports from perspectives. that developed integrating potential disease risk.

Language: Английский

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