key: cord-0744737-2jo1x4w1
authors: Zheng, Jia; Zhang, Lu; Jiang, Min
title: Lower handgrip strength levels probably precede triglyceride glucose index and associated with diabetes in men not in women
date: 2021-07-27
journal: J Diabetes Investig
DOI: 10.1111/jdi.13626
sha: 89337713dc98e8f930521a2e625711e4988d4676
doc_id: 744737
cord_uid: 2jo1x4w1

AIMS/INTRODUCTION: To explore the relationship between handgrip strength per weight (HGS/W), triglyceride glucose index (TyG) and diabetes, and whether lower HGS levels precede TyG in the Chinese elderly population. MATERIALS AND METHODS: Two linear regression models were used to explore the association of whether baseline HGS/W predicted follow‐up variation of TyG or baseline TyG predicted follow‐up variation of HGS/W. The logistic regression model was used to examine the relationship between baseline HGS/W and future diabetes. RESULTS: A total of 4,561 participants in the China Health and Retirement Longitudinal Study were enrolled, of which 47.0% were men, and the mean age was 58.7 years (standard deviation 8.68 years). A lower baseline HGS/W significantly correlated with a higher level of follow‐up TyG (β = −0.173, P = 0.002). The baseline level of HGS/W was significantly negatively associated with the incidence risk of diabetes (rate ratio 0.375, P = 0.004). However, in sex stratification, the statistical association between HGS/W and TyG and diabetes was only in men. CONCLUSIONS: Our results showed that HGS/W was inversely associated with TyG and diabetes, and lower HGS/W levels preceded TyG levels in the elderly population. However, the effect was inconsistent between men and women, and the possible mechanism would require further clarification.

Diabetes, as one of the top causes of world death, is a chronic disease because of the resistance of insulin or the lack of enough insulin 1 . Over time, diabetes might develop into systemic diseases 2 . The number of patient with diabetes is growing globally 3 . China ranked among the top five globally in morbidity and mortality of diabetes in 2017 4 , and these numbers are increasing 5 . However, more than half of diabetes (or prediabetes) patients are undiagnosed 6 . In addition, under the coronavirus disease 2019 pandemic background, it was reported that diabetes patients have a higher chance of developing severe coronavirus symptoms and have a poor prognosis 7 . However, because of coronavirus disease 2019, hospital resources are redistributed, and citizens must reduce social interactions, restricting medical testing and treatment for diabetes or prediabetes patients. Hence, it will reduce the future diabetes burden if there is a simple method for predicting diabetes, and patients or potential patients can self-test at home and report to their family doctor online.

The handgrip strength (HGS) test is a non-invasive, simple upper limb muscle function test 8 . HGS is becoming a predictor of heart disease, vascular disease, peripheral artery disease, nerve damage and malnutrition 9 . Furthermore, some studies have explored the relationship between HGS and diabetes in people of different ages and races 10, 11 . Nevertheless, the relationship is still unknown among the Chinese elderly. Therefore, the present study aimed to examine the correlation between HGS and diabetes in Chinese older people.

Muscle movement depends on glucose metabolism 12 . However, insulin resistance (IR) reduces the muscle's ability to process glucose and weakens muscle strength 13, 14 . In addition, IR is one of the central mechanisms of diabetes progression 15 . Most diabetes patients, especially obese patients, are resistant to insulin. Hence, IR testing is used in early diabetes screening 16 . Therefore another goal of our research was to explore the temporal relationship between HGS and IR. The triglyceride glucose index (TyG) can replace standard methods of IR measurement, such as the euglycemic-hyperinsulinemic clamp test and the homeostasis model assessment of insulin resistance index 17, 18 . Furthermore, the more accessible and affordable price gives TyG a greater advantage in large-scale screening. Hence, the TyG index was used to identify IR in the present study.

The China Health and Retirement Longitudinal Study (CHARLS) is carried out by the National School of Development of Peking University and is a nationally representative survey. The national baseline survey was carried out from June 2011 to March 2012. A multistage, random cluster sampling process was carried out in 450 villages/urban areas, and 10,287 households aged ≥45 years as a representative sample were finally selected. Furthermore, baseline questionnaires were used in these households every 2 years. In addition, their blood samples were also collected in 2011 and 2015. Additional information about the CHARLS can be found on the website: http:// charls.pku.edu.cn/en.

The present study was a post-hoc analysis. Figure 1 describes the details of the sampling process. A total of 17,708 individuals were at baseline. We excluded individuals aged <45 years (n = 1,099) or those who had diabetes and cancer (n = 2,366) at baseline. Then, individuals were excluded if 2011 data were missing for ethnicity, education level and sex (n = 1,413), or for height, weight and HGS (2,989), or for systolic blood pressure (SBP), diastolic blood pressure (DBP) or pulse (n = 131), or in high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides (TG) or fasting plasma glucose (FPG; n = 2,815). Thus, 6,895 participants attended the follow up. Furthermore, individuals were excluded because of missing data in height, weight, HGS (n = 237), TG or FPG (n = 2079) in 2015. Finally, 4,561 participants (2,144 men and 2,417 women) were included in the present study.

Peking University's ethical review committee (IRB 00001052-11015) approved the study protocol on 20 January 2011. The procedures followed the ethical standards of the responsible committee of Peking University and the Chinese Center for Disease Control and Prevention, and written informed consent was obtained from all participants or their proxies.

Demographics (age, sex, education and ethnicity), lifestyle factors (drinking and smoking status) and diseases (diabetes and cancer) were obtained through the structured home interview by CHARLS trained health staff. In addition, health behaviors information was collected from a self-reported health questionnaire: (i) frequency of alcohol consumption (never, once a month and more than once a month); and (ii) smoking status in the past year (never, former and current smoker).

Furthermore, CHARLS describes the blood pressure indexes (cholesterol indexes, FPG, biochemical blood indexes and others) collection and measurement methods on their website (http://charls.pku.edu.cn/en). We used an auto-analyzer (Olympus AU640 Auto-Analyzer; Olympus Corp., Kobe, Japan) to measure TG, low-density lipoprotein cholesterol and highdensity lipoprotein cholesterol, and Omron TM HEM-7200 Monitor (Omron Co., Ltd., Dalian, China) to measure SBP, DBP and pulse in the sitting position in a 5-min rest interval.

We used a mechanical handgrip meter (WL-1000 Mechanical Handgrip Meter; Nantong, China) to measure HGS. The participant held a handgrip meter suitable for their hands' size with their elbows at a 90°angle on the body sides. Then, the participant used the maximum strength to hold the handgrip meter for a few seconds and then release it. There were four measurements: first and third with the dominant hand, and second and fourth with the other hand. After each measurement, the research interviewer recorded the result and handed the meter to the participant. Participants whose hands had surgery, or had swelling, inflammation, severe pain or injury in the past 6 months were excluded. Participants whose hand had the symptoms as aforementioned only measured the other 16 hand. In addition, the HGS per weight (HGS/W) was calculated by HGS/W = HGS (kg) / weight (kg). We used the TyG index to replace the standard methods of IR measurement. TyG index was calculated through the following formula: TyG = ln [fasting TG (mmol/L) 9 FPG (mmol/L) 9 0.5 9 159.37].

According to the American Diabetes Association criteria, in the present study, diabetes patients are defined as: self-reported diabetes diagnosed by a doctor during an individual's interview or the person whose FPG was ≥126 mg/dL (7.0 mmol/mol), or glycated hemoglobin ≥6.5% (48 mmol/mol) from blood test reports 19 .

Body mass index (BMI) was calculated as the weight divided by the square of height (kg/m 2 ), and those who had BMI ≥28 were defined as obese.

Percentiles, mean (standard deviation) and median (interquartile range) describe the central and discrete trends for categorical variables, normally distributed continuous variables and non-normally distributed continuous variables, respectively. Simultaneously, t-test, Mann-Whitney U-test or Pearson's v 2tests were used to compare the statistical significance between men and women.

Three regression models were built to examine associations between baseline HGS/W, future TyG and diabetes (x = HGS/ W in 2011 and y = diabetes cases and y = TyG variation in 2015, respectively), and between baseline TyG, future HGS/W and diabetes (x = TyG in 2011 and y = diabetes cases and y = HGS/W variation in 2015, respectively). Baseline HGS/W and TyG were, respectively, adjusted in model 1. In addition, model 2 was adjusted in baseline HGS/W, baseline TyG, sex, ethnicity and age. Based on model 2, model 3 added the level of education in 2011, current smoking, alcohol drinking, SBP, DBP, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol variables. Then, the three models were constructed in groups of men, women, obese participants and non-obese participants separately. In addition, the variance expansion factor was examined for the multicollinearity among independent variables. The variance expansion factor >10 means variables significant in multicollinearity. Statistical significance was accepted when P ≤ 0.05. All statistical analyses were carried out by IBM SPSS version 25.0 (IBM Corp., Armonk, NY, USA).

Of the 4,561 participants (mean age 58.7 years, standard deviation 8.68 years ), 2,144 were men, and 2,417 were women. Population characteristics of baseline and follow up are shown in Table 1 . baseline SBP, DBP, HR and FPG, and future diabetes showed no significant sex differences. Table 2 showed the prospective association of baseline HGS/ W with follow-up TyG and diabetes. From the result of model 3, we found that the level of HGS/W was inversely associated with the level of follow-up TyG (b = -0.173, P = 0.002). At the same time, we found that a higher level of HGS/W was related to a lower incidence risk of diabetes (rate ratio [RR] 0.375, P = 0.004). Table 3 showed the prospective correlation between baseline TyG, follow-up HGS/W and diabetes. We found that the level of TyG was positively correlated with the incidence risk of follow-up diabetes (RR 1.712, P < 0.001). However, the level of baseline TyG was not significantly related to the level of followup HGS/W (b = -0.007, P = 0.057). Table 4 showed the results of subgroup analysis by sex. For men, the level of HGS/W was inversely associated with the level of follow-up TyG (b = -0.204, P = 0.010), and a higher level of HGS/W was related to a lower incidence risk of diabetes (RR 0.250, P = 0.004). However, the level of baseline HGS/W variation was not related with the level of follow-up TyG variation (b = -0.150, P = 0.055) and the incidence risk of diabetes (RR 0.577, P = 0.270) for women. Table 5 showed the results of subgroup analysis by obese or non-obese. For non-obese participants, the level of HGS/W was inversely associated with the level of follow-up TyG (b = -0.192, P = 0.001), and a higher level of HGS/W was related to a lower incidence risk of diabetes (RR 0.416, P = 0.021). However, the level of baseline HGS/W variation was positively related to the level of follow-up TyG variation (b = 0.615, P = 0.009), and the level of HGS/W was not related to incidence risk of diabetes (RR 7.463, P = 0.084) for obese participants.

Until now, the conclusions of HGS and diabetes have been conflicting. Furthermore, little research data concentrate on Chinese data. Therefore, we used the CHARLS data to explore the relationship between GS, IR and diabetes. The results of the present study were fivefold: (i) confirming that the baseline HGS/W was negatively related to future diabetes; (ii) finding HGS/W was correlated with TyG; (iii) verifying baseline HGS/ W levels preceded follow-up TyG levels; (iv) finding the effect of HGS/W on TyG and diabetes in men, not in women; and (v) finding the effect of HGS/W on TyG and diabetes in nonobese participants, not in obese participants.

After controlling for confounding factors, we found that HGS/W was inversely associated with future diabetes among Chinese older people. Previous studies support this finding. Mainous et al. showed that people in the USA aged >45 years with diabetes had a significant relationship with lower combined HGS 20 . In addition, in a Korea nationwide survey, HGS was negatively related to type 2 diabetes 11 . A 40-69 years age group multi-ethnic UK study confirmed that a high HGS was associated with a low diabetes prevalence, and this correlation had ethnic differences 10, 21 . Hence, the present research complements supplementing the relationship between HGS and future diabetes in the Chinese older population. In addition, some studies showed that there was no relationship between incident diabetes and HGS 22, 23 . The diabetes patients in both articles were identified by self-report. Therefore, we believe the null findings could be caused by underestimating the number of diabetes patients. Another extensive multicentric study showed that HGS was not associated with the incidence of type 2 diabetes mellitus 24 . A possible explanation for this study is the relatively short follow-up time and young age. As evidence, one study showed baseline HGS had a significant inverse relationship with the incidence of type 2 diabetes mellitus during a 10-year follow-up period 25 .

Although the potential mechanisms responsible for the association of HGS with diabetes have not been fully understood, the following findings could explain the relationship between HGS and the incidence of diabetes. First, defective glucose sensing at the b-cell and IR are two crucial pathophysiological factors for abnormal glucose metabolism 26 . Second, the levels of IR and the protein content of glucose transporter-4 can be increased by muscle strength training 27 . IR participates in the relationship between diabetes and grip strength, which can be explained by the inactivation of the insulin receptor substrate-1 (IRS-1). Pro-inflammatory cytokines promote the phosphorylation of an IRS-1 by relevant protein kinases, such as c-JUN N-terminal kinase and kappa-B kinase b 28 . Then phosphorylated IRS-1 inhibits insulin sensitivity 28 . In addition, can directly reduce IRS-1 activity 29 . Skeletal muscle is the principal place where insulin regulates glucose absorption 30 . Thus, abnormal glucose and energy metabolisms cause the change of HGS. The present results have also shown that the baseline HGS/W level associated with the future IR level, and the IR level positively correlated with the diabetes incidence risk. Hence, the IR mechanism might explain the negative correlations between HGS and diabetes. Furthermore, the temporal correlation results only showed that baseline HGS/W was related to future IR. The possible cause is that IR is an impaired response in the whole body, primarily in the liver, adipose tissue and muscle 31 . Hence, early IR will not necessarily appear in muscles. However, if decreased HGS levels occur early, IR will occur in the future. Hence, the present result verifies that baseline HGS/W level preceded the future IR level. Therefore, HGS can be a predictor of future IR and diabetes in older people 20 .

Considering the sex dimorphism of HGS, we stratified sex. We only found that low GS levels were associated with IR and diabetes in the older male group. Similarly, a USA study of older adults found a negative correlation between GS and FPG in men 32 . Furthermore, white and black men had a stronger association between GS and diabetes than white and black women 21 . The same result was also found in a Korean group aged 65-80 years 33 . Another study showed that HGS was inversely related to type 2 diabetes incidence risk among 1,632 men 34 . That study reported that low HGS could cause a 27% attributable population fraction in men 34 . In addition, they found that the factor for incidence risk of type 2 diabetes is reduced muscle strength in men 34 . The mechanism might be due to the chromosomes and sociocultural differences; men have relatively more substantial muscle mass 35 , higher inflammatory levels 36 and lower insulin sensitivity 37 than women.

In obese individuals, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other factors in developing insulin resistance 38 . Therefore, we carried out subgroup analysis according to obese or non-obese in our research regarding the inconsistent results between non-obese and obese participants. We believe this inconsistent research result might be due to the small number (480) of obese participants. In addition, one study that was carried out in the Korean population aged between 30 and 79 years showed similar findings that muscle strength was independently associated with diabetes in the nonobese population, but not in the obese population 39 . The World Health Organization uses BMI calculated by height and weight as a measure of nutritional status. Therefore, BMI does not reflect the ratio of muscle to fat 40 . This might be another reason why there is no direct statistical correlation between the HGS/W level of obese people and the risk of diabetes.

Two assumptions were crucial during the present study. As our research was a strictly observational cohort study, the association between GS/W and diabetes might not be causal. Therefore, to minimize the potential bias of reverse causality, we excluded some individuals with a history of diabetes at baseline. Second, we can infer the temporal relationship between grip strength (GS/W) and incident diabetes as the prospective cohort design of CHARLS.

At the same time, the present study also had some limitations. First, the crucial confounding factors, such as dietary patterns and physical activity, were not considered in the present study. Regular physical exercise could prevent age-related muscle strength loss 41 and increase insulin sensitivity 42 . On this basis, we are more confident that HGS could be a valuable predictor of the risk of diabetes. Another limitation is that we did not distinguish type 1 and type 2 diabetes in the present study. Type 1 diabetes mainly appears during adolescence, rarely in older age. Hence, the present study is dominated by type 2 diabetes. HGS/W can be used as a simple, easy to measure and inexpensive indicator to predict IR and diabetes by combining all related research. HGS/W is more comfortable to detect and monito than biochemical indicators. In addition, HGS/W might help primary care physicians stratify possible patients requiring further blood test screening. Furthermore, we require further studies to clarify the pathophysiological mechanisms underlying the relationship.

World Health Organization. Diabetes

Epidemiology of type 2 diabetes -global burden of disease and forecasted trends

Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025

Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study

Prevalence of diabetes among men and women in China

Diabetes epidemiology in the COVID-19 pandemic

A brief review of handgrip strength and sport performance

Prognostic value of handgrip strength in people aged 60 years and older: a systematic review and meta-analysis

The association of handgrip strength and type 2 diabetes mellitus in six ethnic groups: an analysis of the HELIUS study

Association between muscle strength and type 2 diabetes mellitus in adults in Korea: data from the Korea national health and nutrition examination survey (KNHANES) VI

Insulin resistance and muscle strength in older persons

Cytokines and abnormal glucose and lipid metabolism

The inflammation highway: metabolism accelerates inflammatory traffic in obesity

Pathogenesis of type 2 diabetes mellitus

Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: developments in research and prospects for the future

The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp

The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects

Association between social determinants of health and direct economic burden on middle-aged and elderly individuals living with diabetes in China

Grip strength as a marker of hypertension and diabetes in healthy weight adults

Association between grip strength and diabetes prevalence in black, South-Asian, and white European ethnic groups: a crosssectional analysis of 418 656 participants in the UK Biobank study

Adiposity, physical fitness and incident diabetes: the physical activity longitudinal study

Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study

Grip strength is not associated with incident type 2 diabetes mellitus in healthy adults: The CoLaus study

Greater hand-grip strength predicts a lower risk of developing type 2 diabetes 154

Potential role of skeletal muscle glucose metabolism on the regulation of insulin secretion

Phosphorylation of IRS proteins, insulin action, and insulin resistance

Metabolic inflammation: Connecting obesity and insulin resistance

Muscle strength is a marker of insulin resistance in patients with type 2 diabetes: a pilot study

Insulin Resistance

Sex differences in the association of fasting and postchallenge glucose levels with grip strength among older adults: the Rancho Bernardo Study

Association of hand grip strength and Cardiometabolic markers in Korean adult population: The Korea national health and nutrition examination survey 2015-2016

Muscle grip strength predicts incident type 2 diabetes: Population-based cohort study

Normative data and associated factors of hand grip strength among elderly individuals: The Yilan Study

Relationship of interleukin-6 and tumor necrosis factor-a with muscle mass and muscle strength in elderly men and women: the health ABC study

Sex differences in metabolic regulation and diabetes susceptibility

Mechanisms linking obesity to insulin resistance and type 2 diabetes

The differential association between muscle strength and diabetes mellitus according to the presence or absence of obesity

Limits of body mass index to detect obesity and predict body composition

Bone metabolism and hand grip strength response to aerobic versus resistance exercise training in non-insulin dependent diabetic patients

The effects of physical training on insulin secretion and effectiveness and on glucose metabolism in obesity and type 2 (noninsulin-dependent) diabetes mellitus

The Authors

The authors are grateful to the CHARLS research team for providing the data. The authors thank Roland Ligetv ari for providing medical editing support (proofreading, technical editing).

The authors declare no conflict of interest.