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Abstract
Background
Handgrip strength is a simple, convenient and economic tool measuring the muscle strength. A few studies investigated the relationship between diabetes and handgrip strength but the results are conflicting. This study investigated the association of handgrip strength with diabetes among the adult Koreans.
Methods
This cross-sectional study analyzed data from participants aged 20 years or more (n=8,082) who measured height, weight, handgrip strength and fasting blood glucose in the 2014–2015 Korea National Health and Nutrition Examination Survey. Relative handgrip strength (RHGS) was defined as the sum of the greatest handgrip strengths in both hands divided by body mass index. To investigate the association of diabetes with handgrip strength, complex sample multivariate logistic regression analyses were done after adjusting for socioeconomic (age, sex, education), lifestyle (smoking, alcohol drinking, physical activity, obesity) and comorbid (chronic obstructive pulmonary disease, stroke, coronary artery disease, arthritis) variables. Stratified analysis were done according to socioeconomic and lifestyle variables.
Results
The prevalence of diabetes was 8.3% (standard error, 0.4). After adjusting for socioeconomic, lifestyle, and comorbid variables, the risk of diabetes increased according to the decrease in sex-specific quartile of RHGS (Ptrend<0.001). Individuals with lower RHGS (per 1 standard deviation decrease) had higher odds of diabetes (adjusted odds ratio, 1.6; 95% confidence interval, 1.3–2.0). Furthermore, lower RHGS was associated with higher odds for diabetes throughout the strata of socioeconomic and lifestyle variables.
Figures and Tables
Table 1
Characteristics of participants according to the sex-specific relative handgrip strength quartile (lowest vs. those with higher)
Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; HGS, handgrip strength; RHGS, relative handgrip strength.
Values are represented as weighted means or weighted proportions with standard error for continuous or for categorical variables.
P using complex sample cross-tab analysis or linear regression analysis.
aDefined as those consuming alcohol more than once per month in the past year.
bDefined as those engaging in moderate-intensity physical activity at least 150 minutes per week, or high-intensity physical activity for 75 minutes or combination of both (1min of high-intensity physical activity equaled 2 minutes of moderate-intensity activity).
Table 2
Odds ratios for diabetes mellitus with the change in relative handgrip strength and the sex-specific relative handgrip strength quartiles
Abbreviations: SD, standard deviation; RHGS, relative handgrip strength.
Values are represented as odds ratio (95% confidence interval).
P by complex sample logistic regression model after adjusting age group, sex, education, physical activity, alcohol use, smoking status, comorbidity (chronic obstructive pulmonary disease, stroke, coronary artery disease, arthritis) in model I; confounding factors in model I and obesity in Model II.
Table 3
Odds ratios for diabetes mellitus with the change in relative handgrip strength and the sex-specific relative handgrip strength quartiles according to demographic characteristics and health behaviors
Abbreviations: SD, standard deviation; RHGS, relative handgrip strength.
aValues are represented as odds ratio (95% confidence interval) after adjusting for confounding factors (age group, sex, education, physical activity, alcohol use, smoking status, obesity, COPD, stroke, coronary artery disease, arthritis) except for sex.
bAll confounding factors except for age group using complex logistic regression model.
cAll confounding factors except for obesity using complex logistic regression model.
dAll confounding factors except for education using complex logistic regression model.
eAll confounding factors except for smoking using complex logistic regression model.
fAll confounding factors except for alcohol drinking using complex logistic regression model.
gAll confounding factors except for physical activity using complex logistic regression model.
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