Associations of grip strength with retinal and choroidal thickness in patients with type 2 diabetes mellitus without retinopathy: a cross-sectional study

Relationship between age and handgrip strength: Proposal of reference values from infancy to senescence

Introduction

Measurement of hand grip strength (HGS) has been proposed as a key component of frailty and has also been suggested as a central biomarker of healthy aging and a powerful predictor of future morbidity and mortality.

Objectives

(a) To determine whether a nonlinear relationship model could improve the prediction of handgrip strength (HGS) compared to the linear model and (b) to propose percentiles to evaluate HGS according to age and sex for a regional population of Chile from infancy to senescence.

Methods

A cross-sectional descriptive study was developed in a representative sample of the Maule region (Chile). The volunteers amounted to 5,376 participants (2,840 men and 2,536 women), with an age range from 6 to 80 years old. Weight, height, HGS (right and left hand) according to age and sex were evaluated. Percentiles were calculated using the LMS method [(L (Lambda; asymmetry), M (Mu; median), and S (Sigma; coefficient of variation)].

Results and discussion

There were no differences in HGS from 6 to 11 years of age in both sexes; however, from 12 years of age onwards, males presented higher HGS values in both hands (p < 0.05). The linear regression between age with HGS showed values of R ² = 0.07 in males and R ² = 0.02 in females. While in the non-linear model (cubic), the values were: R ² = 0.50 to 0.51 in men and R ² = 0.26 in women. The percentiles constructed by age and sex were: P5, P15, P50, P85, and P95 by age range and sex. This study demonstrated that there is a nonlinear relationship between chronological age with HGS from infancy to senescence. Furthermore, the proposed percentiles can serve as a guide to assess and monitor upper extremity muscle strength levels at all stages of life.

What can hand grip strength tell us about type 2 diabetes?: mortality, morbidities and risk of diabetes

INTRODUCTION

Hand grip strength (HGS) has been determined as a simple and cost effective method for evaluating overall skeletal muscle strength and quality. It has been used in numerous clinical studies to examine the association between skeletal muscle and type 2 diabetes (T2D). Previous studies have shown that HGS is significantly associated with mortality and cardiovascular disease (CVD) in patients with T2D. As the prevalence of T2D has seen a steady increase globally, HGS measurement can be useful and essential in managing T2D.

AREAS COVERED

This narrative review summarizes the current evidence regarding the association between HGS and T2D. HGS has been considered a useful tool for identifying risks for T2D, CVD, and mortality and evaluating skeletal muscle strength and quality.

OPINION

HGS should be measured in all patients with T2D together with height, weight, and waist circumference. However, there is yet no standard method used in measuring for HGS. T2D patients with a low HGS can engage in resistance training more effectively with appropriate advice based on HGS. Future studies clarifying the genetic association between skeletal muscle and T2D are needed to provide individualized exercise therapy.

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.