Walking Pace Optimizes Conventional Cardiovascular Disease Risk Prediction Models Among Vulnerable Subpopulations: A...

Area of Science:

• Cardiology
• Preventive Medicine
• Gerontology

Background:

• Grip strength and walking pace are recognized indicators of health outcomes.
• Their specific utility in primary cardiovascular disease (CVD) prevention across diverse populations needs further clarification.

Purpose of the Study:

• To evaluate the predictive value of grip strength and walking pace in cardiovascular disease (CVD) risk assessment.
• To determine if these physical function measures improve existing CVD prediction models in vulnerable subpopulations.

Main Methods:

• Utilized UK Biobank data from 206,371 individuals (aged 40-69) without prior CVD.
• Assessed performance of four conventional CVD prediction models (Framingham, Reynolds, ASSIGN, PCEs) across subpopulations stratified by age, grip strength, and walking pace.
• Validated findings in the English Longitudinal Study of Ageing (ELSA).

Main Results:

• Over 19,664 incident CVD cases were recorded during a mean 12.85-year follow-up.
• Vulnerable groups were identified by older age, low grip strength, or slow walking pace.
• The ASSIGN model demonstrated reduced accuracy in these vulnerable groups.
• Incorporating walking pace into the ASSIGN model significantly improved its predictive ability in older individuals with low grip strength (C index increase of 0.027), a finding consistent in ELSA.

Conclusions:

• Walking pace, unlike grip strength, enhances the performance of conventional cardiovascular disease (CVD) prediction models, particularly in vulnerable populations.
• Physical function, specifically walking pace, plays a crucial role in the primary prevention of CVD.
• These findings suggest that incorporating walking pace assessment could refine CVD risk stratification and preventive strategies.