Malnutrition mediates the association between handgrip status and asthma risk: an observational and prospective cohort study from multiple European countries

TL;DR

This study finds that lower handgrip strength is linked to higher asthma risk in Europeans, with malnutrition partially explaining the connection.

Contribution

The study identifies malnutrition as a mediator between handgrip strength and asthma risk using large-scale observational data.

Findings

Lower handgrip strength and relative handgrip strength are associated with increased asthma risk.

Malnutrition partially mediates the relationship between low handgrip strength and asthma risk.

XGBoost machine learning model showed better predictive performance for asthma risk.

Abstract

Epidemiological investigations on the association of handgrip status and asthma risk still remain understudied. This research aims to investigate the associations of handgrip strength (HGS), relative handgrip strength (RHGS), low HGS, and asthma risk, as well as the mediating role of nutritional status, using data from the Survey of Health, Ageing and Retirement in Europe (SHARE).

This investigation included 27,185 participants for a cross-sectional study and 18,047 participants for a prospective cohort study from SHARE. Four machine learning models, the Shapley Additive Explanations (SHAP) model, restricted cubic spline (RCS), cumulative occurrence curve, logistic regression, and Cox regression were used to comprehensively evaluate the performance of handgrip status in predicting asthma risk. Finally, the mediation effect model was employed to evaluate the role of nutritional status in the relationship between grip strength and asthma risk.

The cross-sectional investigation suggested that both HGS (OR: 0.98, 95% CI: 0.98–0.99) and RHGS (OR: 0.61, 95% CI: 0.51–0.73) were negatively linked to the risk of asthma, and low HGS was a risk factor for asthma (OR: 1.52, 95% CI: 1.24–1.87). And the prospective cohort investigation with a median follow-up time of 30 months further confirmed that both HGS (HR: 0.98, 95% CI: 0.97–1.00) and RHGS (HR: 0.52, 95% CI: 0.37–0.73) were negatively linked to the risk of asthma. Among the four machine learning models used to evaluate handgrip status and the risk of asthma, eXtreme Gradient Boosting (XGBoost) showed better predictive performance. The SHAP model based on XGBoost suggested that the top five crucial indicators for predicting asthma risk were RHGS, HGS, country, age, and chronic lung disease. Finally, the mediation effect model suggested that malnutrition partially mediated the relationship between low HGS and increased risk of asthma, with a mediation proportion of 2.71%.

This investigation suggested that lower HGS and RHGS were linked to a higher risk of asthma, and handgrip status could be used as an independent marker of asthma risk in European populations. And malnutrition partially mediated the relationship between low HGS and asthma risk. Improving muscle strength could be a potential preventive strategy against asthma, with implications for public health and clinical practice.

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