A newly developed prediction tool may help clinicians identify older adults at risk for low handgrip strength—a marker associated with frailty, functional decline, and increased mortality.

The model was developed using data from 1,138 individuals aged 45 years and older from a nationally representative dataset in China. Among them, 37% had low handgrip strength; this group tended to be older and had more chronic conditions, such as stroke and cancer, along with lower levels of physical activity and poorer nutritional and cognitive status.

Using logistic regression with Firth adjustment to address rare events and small sample bias, researchers examined 33 variables and identified 12 significant predictors. These included age, marital status, physical activity, education, chronic disease history, and laboratory values such as glycated hemoglobin.

Key risk factors for low grip strength included age over 65, stroke history, cancer history, limitations in daily activities, and elevated glycated hemoglobin. Protective factors included being married, partaking in regular moderate physical activity, and higher education levels.

Body mass index (BMI) followed a U-shaped pattern—individuals with a BMI between 23.8 and 26.4 kg/m² had the lowest risk, while those with a very low or high BMI had greater risk.

The model demonstrated strong predictive accuracy with an area under the curve of 0.78. At a risk threshold of 0.40, it achieved 72.5% sensitivity and 69.8% specificity.

To support clinical use, the researchers developed a nomogram—a visual scoring tool that helps providers estimate risk by assigning points to each variable. Higher total scores correspond to greater risk of low grip strength.

Validation through cross-validation and bootstrapping confirmed consistent model performance across subgroups by sex and age.

This tool can be applied in primary care, geriatrics, and community health to support early screening and intervention, according to the study's authors. It relies on standard demographic, lifestyle, and laboratory data typically collected during routine checkups, requiring no specialized equipment, they emphasized.

By identifying high-risk individuals early, clinicians may more effectively allocate resources and implement interventions to help maintain functional independence in older adults, the investigators concluded.

The authors reported no competing interests.

Source: Scientific Reports