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Walking Pace Optimizes Conventional Cardiovascular Disease Risk Prediction Models Among Vulnerable Subpopulations: A
Li Wang1,2, Xueqin Li3,4, Xueqing Jia3
1The Fourth Affiliated Hospital, International School of Medicine, International Institutes of Medicine, and School of Nursing and Institute of Nursing Research Zhejiang University School of Medicine Zhejiang China.
Insights
Walking pace, not grip strength, enhances cardiovascular disease risk prediction in vulnerable groups. This finding emphasizes the importance of physical function in primary cardiovascular disease prevention strategies.
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.
Background:
Grip strength and walking pace are potential indicators of adverse health outcomes for the general population. However, their role in primary cardiovascular disease (CVD) prevention among various subpopulations remains uncertain.
Methods:
A total of 206 371 individuals without CVD (aged 40-69 years) from the UK Biobank were included. Four conventional CVD prediction models (Framingham, Reynolds, ASSessing cardiovascular risk using Scottish Intercollegiate Guidelines Network [ASSIGN], and pooled cohort equations [PCEs]) were used to estimate 10-year CVD risk. Model performances were compared across diverse subpopulations defined by age, grip strength, or walking pace using a C index and calibration plot in the UK Biobank. Added predictive value was further validated in ELSA (English Longitudinal Study of Ageing).
Results:
During the follow-up period 19 664 cases of incident CVD were registered (mean 12.85 years [SD 2.74]). Vulnerable subpopulations were characterized by advanced age, low grip strength, or slow walking pace. The ASSIGN model showed lower C indexes in vulnerable subpopulations: 0.659 (95% CI, 0.646-0.672) for low grip strength versus 0.702 (95% CI, 0.699-0.706) for normal grip strength, 0.646 (95% CI, 0.635-0.657) for slow walking pace versus 0.701 (95% CI, 0.698-0.705) for normal walking pace, and 0.624 (95% CI, 0.614-0.634) for older versus 0.701 (95% CI, 0.689-0.712) for young subpopulation. Adding walking pace to the ASSIGN model improved its discriminative ability in the older subpopulation with low grip strength (C index change, 0.027 [95% CI, 0.009-0.045]). The finding was similar in ELSA.
Conclusions:
Walking pace, but not grip strength, improved the performance of conventional CVD prediction models among vulnerable subpopulations. This highlights the role of physical function in primary prevention of CVD.
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