Cumulative muscle strength and risk of diabetes: A prospective cohort study with mediation analysis

Individual and combined effects of muscle strength and visceral adiposity on incident prediabetes and type 2 diabetes in a longitudinal cohort of midlife Asian women

AIM

To investigate the independent and combined effects of muscle strength and visceral adiposity on prediabetes and type 2 diabetes incidence among midlife women.

MATERIALS AND METHODS

In this prospective study of midlife women (mean age 56.4 years), visceral adiposity, defined as visceral adipose tissue (VAT) >131 cm2 measured by dual energy X-ray absorptiometry, and poor combined muscle strength, defined as handgrip strength <18 kg and/or five-time repeated chair stand test performance ≥12 s, were determined at baseline between 2014 and 2016. After 6.6 years, the effects of VAT and muscle strength on risk of incident prediabetes (fasting blood glucose 5.6-6.9 mmol/L) and type 2 diabetes (fasting blood glucose levels ≥7 mmol/L, medication use, or physician diagnosis) were examined using modified Poisson regression analysis.

RESULTS

Among the 733 initially normoglycaemic participants, 150 (20.5%) developed prediabetes or type 2 diabetes. Women with both poor combined muscle strength and high VAT had the highest risk for both prediabetes and type 2 diabetes (adjusted relative risk [aRR] 2.63, 95% confidence interval [CI] 1.81-3.82). In comparison, high VAT alone increased risk by 1.78-fold (95% CI 1.12-2.84). Stratification analyses showed that among women with low muscle strength, high VAT demonstrated increased risks of prediabetes and type 2 diabetes (aRR 2.84, 95% CI 1.95-4.14) compared to those with normal strength (aRR 1.66, 95% CI 1.04-2.65).

CONCLUSIONS

Low combined muscle strength with high VAT poses a greater risk for the development of prediabetes and type 2 diabetes than high VAT alone. Muscle strengthening should be promoted alongside weight loss in diabetes prevention.

A Systematic Review and Meta-Analysis of the Effect of Caloric Restriction on Skeletal Muscle Mass in Individuals with, and without, Type 2 Diabetes

BACKGROUND

Severe caloric restriction interventions (such as very-low-calorie diets) are effective for inducing significant weight loss and remission of type 2 diabetes (T2DM). However, suggestions of associated significant muscle mass (MM) loss create apprehension regarding their widespread use. We conducted a systematic review and meta-analysis to provide a quantitative assessment of their effect on measures of MM in individuals with, or without, T2DM.

METHODS

EMBASE, Medline, Pubmed, CINAHL, CENTRAL and Google Scholar were systematically searched for studies involving caloric restriction interventions up to 900 kilocalories per day reporting any measure of MM, in addition to fat mass (FM) or body weight (BW).

RESULTS

Forty-nine studies were eligible for inclusion, involving 4785 participants. Individuals with T2DM experienced significant reductions in MM (WMD -2.88 kg, 95% CI: -3.54, -2.22; p < 0.0001), although this was significantly less than the reduction in FM (WMD -7.62 kg, 95% CI: -10.87, -4.37; p < 0.0001). A similar pattern was observed across studies involving individuals without T2DM. MM constituted approximately 25.5% of overall weight loss in individuals with T2DM, and 27.5% in individuals without T2DM. Subgroup analysis paradoxically revealed greater BW and FM reductions with less restrictive interventions.

CONCLUSIONS

Our review suggests that caloric restriction interventions up to 900 kilocalories per day are associated with a significant reduction in MM, albeit in the context of a significantly greater reduction in FM. Furthermore, MM constituted approximately a quarter of the total weight loss. Finally, our data support the use of less restrictive interventions, which appear to be more beneficial for BW and FM loss.

Associations of muscle mass, strength, and quality with diabetes and the mediating role of inflammation in two National surveys from China and the United states

AIMS

The evidence for joint and independent associations of low muscle mass and low muscle strength with diabetes is limited and mixed. The study aimed to determine the associations of muscle parameters (muscle mass, strength, quality, and sarcopenia) and sarcopenia obesity with diabetes, and the previously unstudied mediating effect of inflammation.

MATERIALS AND METHODS

A total of 13,420 adults from the 2023 China National Health Survey (CNHS) and 5,380 adults from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) were included in this study. Muscle mass was determined using bioelectrical impedance analysis (BIA) in the CNHS, and whole-body dual X-ray absorptiometry (DXA) in the NHANES. Muscle strength was assessed using digital hand dynamometer. Multivariate logistic regression models were used to evaluate the associations of muscle parameters and sarcopenia obesity with diabetes. Inflammatory status was assessed using blood cell counts and two systemic inflammation indices (platelet-to-lymphocyte ratio (PLR) and system inflammation response index (SIRI)). Mediation analysis was conducted to examine inflammation's role in these associations.

RESULTS

Low muscle mass and strength were independently related to diabetes. Low muscle quality was associated with elevated diabetes risk. Sarcopenia has a stronger association with diabetes compared to low muscle strength alone or mass alone (CNHS, odds ratio (OR) = 1.93, 95 % confidence interval (CI):1.64-2.27; NHANES, OR = 3.80, 95 %CI:2.58-5.58). Participants with sarcopenia obesity exhibit a higher risk of diabetes than those with obesity or sarcopenia alone (CNHS, OR = 2.21, 95 %CI:1.72-2.84; NHANES, OR = 6.06, 95 %CI:3.64-10.08). Associations between muscle parameters and diabetes were partially mediated by inflammation (mediation proportion: 1.99 %-36.64 %, P < 0.05).

CONCLUSION

Low muscle mass and muscle strength are independently or jointly associated with diabetes, and inflammation might be a potential mechanism underlying this association. Furthermore, the synergistic effects of sarcopenia and obesity could significantly increase diabetes risk.

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study

AIMS/INTRODUCTION

Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.

METHODS

This cross-sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (n = 3,586) and the testing dataset (n = 896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30-s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other.

RESULTS

Of the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs >0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30-s chair stand test to this approach did not increase the AUC further (P = 0.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (Pcomparison < 0.01), regardless of the addition of handgrip strength or the 30-s chair stand test.

CONCLUSIONS

The ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.