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Original Article
Metabolic Risk/Epidemiology Association of Body Composition Changes with the Development of Diabetes Mellitus: A Nation-Wide Population Study
Hyung Jun Kim1orcid, Hyung-Woo Lee2, Min-Kyoung Kang2, Gwang Hyun Leem2, Min-Ho Kim3, Tae-Jin Song2orcid

DOI: https://doi.org/10.4093/dmj.2023.0243
Published online: May 21, 2024
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1Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

2Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Korea

3Medical Research Institute, Ewha Womans University, Seoul, Korea

Corresponding author: Tae-Jin Song orcid Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, 260 Gonghang-daero, Gangseo-gu, Seoul 07804, Korea E-mail: knstar@ewha.ac.kr
• Received: August 31, 2023   • Accepted: January 26, 2024

Copyright © 2024 Korean Diabetes Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • Background
    We investigated the association between body composition changes and new-onset diabetes mellitus (DM) development according to the body mass index (BMI) in a longitudinal setting in the general Korean population.
  • Methods
    From 2010 to 2011 (1st) and 2012 to 2013 (2nd), we included 1,607,508 stratified random sample participants without DM from the National Health Insurance Service-Health Screening dataset of Korean. The predicted appendicular skeletal muscle mass index (pASMMI), body fat mass index (pBFMI), and lean body mass index (pLBMI) were calculated using pre-validated anthropometric prediction equations. A prediction equation was constructed by combining age, weight, height, waist circumference, serum creatinine levels, alcohol consumption status, physical activity, and smoking history as variables affecting body composition.
  • Results
    Decreased pASMMI (men: hazard ratio [HR], 0.866; 95% confidence interval [CI], 0.830 to 0.903; P<0.001; women: HR, 0.748; 95% CI, 0.635 to 0.881; P<0.001), decreased pLBMI (men: HR, 0.931; 95% CI, 0.912 to 0.952; P<0.001; women: HR, 0.906; 95% CI, 0.856 to 0.959; P=0.007), and increased pBFMI (men: HR, 1.073; 95% CI, 1.050 to 1.096; P<0.001; women: HR, 1.114; 95% CI, 1.047 to 1.186; P=0.007) correlated with the development of new-onset DM. Notably, only in the overweight and obese BMI categories, decreases in pASMMI and pLBMI and increases in pBFMI associated with new-onset DM, regardless of gender.
  • Conclusion
    Decreased pASMMI and pLBMI, and increased pBFMI with excess fat accumulation may enhance the risk of newonset DM. Therefore, appropriate changes in body composition can help prevent new-onset DM.
• This study explores the association between body composition changes and new-onset DM.
• Decreased ASMMI and LBMI, and increased BFMI may enhance the risk of new-onset DM.
• There was an association with new-onset DM in the group with excessive fat.
• Optimizing body composition may help prevent new-onset DM.
Diabetes mellitus (DM) is a major global health concern [1,2]. In addition to the microvascular complications of DM, such as nephropathy, neuropathy, and retinopathy, macrovascular complications, such as myocardial infarction and ischemic stroke, are being increasingly reported [3,4]. Currently, preventive strategies for DM include controlling factors related to a healthy lifestyle, such as maintaining proper waist circumference and body weight, physical activity, and dietary habits [5]. Nevertheless, considering the disease burden of DM, further investigations are required to identify risk factors associated with DM, particularly those that are remediable.
Obesity is defined as abnormal or excessive fat accumulation, commonly with a body mass index (BMI) ≥30 kg/m2 [6]. In recent decades, the prevalence of health-impairing obesity, defined by a high BMI, has increased steadily in East Asia, along with an explosive increase in the national health and socioeconomic burdens of obesity [7]. Obesity, particularly excessive fat accumulation, develops and worsens comorbidities, including glucose intolerance, insulin resistance, hypertension, dyslipidemia, endothelial dysfunction, and inflammation [8,9]. Aging causes excessive fat accumulation and decreases skeletal muscle mass [10,11]. Excessive fat mass and low skeletal muscle mass are bidirectionally correlated with DM [10,12]. However, BMI cannot reflect the muscle or fat mass [13]. Therefore, the correlation of DM with body composition rather than BMI should be analyzed.
While various studies have examined the association between body composition and new-onset DM [14,15], no longitudinal studies have been conducted with large sample sizes targeting the general population. Moreover, no studies have investigated the relationship between changes in body composition indices and new-onset DM in large general population. Therefore, we aimed to investigate the association between changes in the predicted appendicular skeletal muscle mass index (pASMMI), predicted body fat mass index (pBFMI), and predicted lean body mass index (pLBMI), which were derived from an equation previously validated in the Korean general population [13] with the development of new-onset DM in a longitudinal setting.
Participants
This nation-wide population study was conducted using the National Health Insurance Service-Health Screening (NHISHEALS) dataset provided by the NHIS Corporation of South Korea. In Korea, the NHIS supports free health screening biennially for employed citizens aged ≥40 or ≥20 years and combines the results of the national health screening and national health insurance claim data (national health screening data: age, sex, weight, height, waist circumference, smoking history, alcohol consumption status, laboratory results, and sociodemographic data; national health insurance claim data: household income, diagnostic codes, medication prescriptions, hospitalization, procedure information, and date of death) [16,17]. We included 2,040,094 people with a BMI >18.5 kg/m2 who had undergone consecutive national health examinations from 2010 to 2011 (1st) and 2012 to 2013 (2nd) (dataset number NHIS-2021-1-715) by stratified random sampling. The dataset was subjected to predetermined identification and verification processes [18-20]. Of the 2,130,184 participants, those with any missing value (fasting blood glucose n=481, total cholesterol n=13, creatinine n=32, blood pressure n=536, BMI n=366, waist circumference n=717, smoking history n=1,108, alcohol consumption status n=1,057, physical activity n=960, household income tertiles n=84,820; total: n=90,090), a history of DM (n=369,631) or a fasting blood glucose level of 126 mg/dL or higher during at least one health examination period (n=62,955) were excluded. Finally, 1,607,508 participants were included in the study (Fig. 1). The baseline characteristics of the 90,090 excluded for missing values and the 1,607,508 included in the study did not differ with a standardized mean difference of less than 0.1 (Supplementary Table 1). The study protocol was approved by the Institutional Review Board of the Ewha Womans University Seoul Hospital (approval number: SEUMC 2022-02-018). Written informed consent by the patients was waived due to a retrospective nature of our study.
Predicted body composition and covariates
Body composition indices, including pASMMI, pBFMI, and pLBMI, were assessed using validated prediction equations based on anthropometric measurements from the Korean National Health and Nutrition Examination Survey 2008 to 2011 [13]. In a previous cohort study, fat-free, body fat, and appendicular skeletal muscle masses were identified using dual-energy X-ray absorptiometry, and a prediction equation was constructed by combining age, weight, height, waist circumference, serum creatinine levels, alcohol consumption status, physical activity, and smoking history as variables affecting body composition [13]. Equations that added other factors such as serum creatinine levels, alcohol consumption status, physical activity, and smoking history had greater predictive power for ASMMI, BFMI, and LBMI than equations that included age, height, weight, and waist circumference alone. These equations were verified to have low bias, high predictive power, and moderate agreement rate in a study involving the Korean general population. The predictive indices for appendicular skeletal muscle, body fat, and lean body masses (weight [kg] divided by height squared [m2]) are presented as pASMMI, pBFMI, and pLBMI, respectively (Part 1 in Supplementary Methods). Changes in pASMMI, pBFMI, and pLBMI were calculated by subtracting the pASMMI, pBFMI, and pLBMI obtained during the primary health examination (2010 to 2011) from the pASMMI, pBFMI, and pLBMI obtained during the secondary health examination (2012 to 2013).
Detailed definitions of the covariates are provided in Part 2 in Supplementary Methods and in previous studies [21-24]. Variables, including age, sex, BMI, hypertension, dyslipidemia, DM, cancer, atrial fibrillation, renal disease, household income tertiles, alcohol consumption status (none, moderate, or heavy), smoking history (never, former, or current), physical activity (low or moderate), and Charlson comorbidity index scores (0, 1, or ≥2) were collected from the secondary health examination [22,25-27]. Comorbidities were defined by combining the International Classification of Diseases, 10th Revision (ICD10) codes, laboratory results, and medication history, differently for each comorbidity [22,25].
Outcomes
The index date was the date of the most recent health screening. New-onset DM was defined as a primary or secondary DM diagnosis (ICD-10 codes E10–E14), and the diagnostic criteria included at least one claim per year for visiting an outpatient clinic and admission, accompanied by prescription records of any hypoglycemic agents use. Alternatively, at least one fasting plasma glucose result of ≥126 mg/dL from the NHIS-HEALS before the index date led to a diagnosis of new-onset DM [28]. The participants were followed up from the index date until the date of new-onset DM, date of death, or December 2020, whichever came first.
Statistical analyses
Independent t-tests and chi-square tests were performed to compare continuous and categorical variables, respectively. Cox proportional hazards regression analyses with hazard ratios (HRs) and 95% confidence intervals (CIs) were used to analyze the effects of the body composition indices (pASMMI, pBFMI, and pLBMI) on the incidence of new-onset DM. The multivariate analysis adjusted for the following potential confounders: age, baseline and secondary BMIs, household income, smoking history, alcohol consumption status, physical activity, fasting serum glucose level, hypertension, dyslipidemia, atrial fibrillation, estimated glomerular filtration rate, and Charlson comorbidity index score. A sensitivity analysis was also performed according to changes from the first to the second health screening in BMI categories (normal weight 18.5–24.9 kg/m2; overweight 25–29.9 kg/m2; obesity ≥30 kg/m2). We also performed a sensitivity analysis for patients with normal fasting glucose, excluding pre-diabetics using the same statistical analyses used for all patients. Subgroup analyses of the demographic data and classic vascular risk factors were performed. Statistical analyses were performed using the R software version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria) and SAS version 9.4 (SAS Inc., Cary, NC, USA). Two-sided P values <0.05 were considered statistically significant.
Data availability
We used the research data extracted and provided by the National Health Insurance Sharing System, but with restrictions to data availability. The use of the dataset is restricted to the current research under license; therefore, public access of the data is not available. Researchers are only access the data upon reasonable request with approval from the inquiry committee of research support in National Health Insurance Corporation (Further details available at: https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do).
Demographic and clinical characteristics of the study population
We enrolled 1,607,508 participants, including 930,498 men and 844,175 women, with a mean age of 49.33±13.31 years (men 48.03±13.08 years, women 50.77±13.41 years, P<0.001). The waist circumference, BMI (health screening periods 1 and 2), household income, smoking history, alcohol consumption status, physical activity, fasting serum glucose level, hypertension, dyslipidemia, atrial fibrillation, renal disease, and Charlson comorbidity index scores differed significantly between men and women (Table 1). pASMMI, pBFMI, and pLBMI measured in health screening periods 1 (pASMMI: 8.28±0.76 kg/m2 for men and 6.24±0.55 kg/m2 for women, P<0.001; pBFMI: 5.34±1.39 kg/m2 for men and 7.54±1.86 kg/m2 for women, P<0.001; pLBMI: 18.62±1.64 kg/m2 for men and 15.33±1.34 kg/m2 for women, P<0.001) and 2 (pASMMI: 8.29±0.77 kg/m2 for men and 6.24±0.55 kg/m2 for women, P<0.001; pBFMI: 5.40±1.41 kg/m2 for men and 7.59±1.88 kg/m2 for women, P<0.001; pLBMI: 18.67±1.66 kg/m2 for men and 15.37±1.35 kg/m2 for women, P<0.001) also differed significantly between men and women (Table 1).
The median follow-up period for all patients is 7.6 years (interquartile range, 7.2 to 8.1), with a median follow-up period of 7.5 years (interquartile range, 7.2 to 8.0) for men and 7.6 years (interquartile range, 7.2 to 8.2) for women. During this period, 113,332 participants developed new-onset DM. Compared to the new-onset DM (–) group, the new-onset DM (+) group was significantly older, had a larger waist circumference, higher levels of all three body composition measures, more pre-diabetic participants, and more comorbidities such as hypertension, dyslipidemia, and atrial fibrillation (Supplementary Table 2).
Changes in body composition and new-onset DM
During the follow-up period, 144,965 (8.17%) participants developed new-onset DM, of which 79,103 (54.57%) were male participants and 65,862 (45.43%) were female participants. In the Cox proportional hazards model, the decrease in pASMMI (men: HR, 0.866; 95% CI, 0.830 to 0.903; P<0.001; women: HR, 0.748; 95% CI, 0.635 to 0.881; P<0.001), decrease in pLBMI (men: HR, 0.931; 95% CI, 0.912 to 0.952; P<0.001; women: HR, 0.906; 95% CI, 0.856 to 0.959; P=0.007), and increase in pBFMI (men: HR, 1.073; 95% CI, 1.050 to 1.096; P<0.001; women: HR, 1.114; 95% CI, 1.047 to 1.186; P=0.007) correlated with the development of new-onset DM, regardless of sex (Table 2, Fig. 2).
Sensitivity analysis by BMI categories
In the sensitivity analysis, decreases in pASMMI and pLBMI correlated with the development of new-onset DM in participants with overweight and obese BMI, regardless of sex. In contrast, an increase in pBFMI correlated with the development of new-onset DM in participants with overweight and obese BMI, regardless of sex (Table 3). In multivariable analysis according to overall obesity status and each obesity status, there were no significant interaction between baseline body composition index and obesity status.
Subgroup analysis of the differential effect of covariates on new-onset DM
Regardless of age, household income, smoking history, fasting serum glucose level, hypertension, and Charlson comorbidity index score, decreases in pASMMI and pLBMI and an increase in pBFMI were significantly correlated with the development of new-onset DM in both sexes (Supplementary Table 3).
Sensitivity analysis of participants with normal fasting glucose excluding pre-diabetics
Similar to the analysis in all participants, there were significant sex differences in all variables (Supplementary Table 4), and in the Cox proportional hazards model, decrease in pASMMI, decrease in pLBMI, and increase in pBFMI were associated with the development of new-onset DM (Supplementary Table 5). In analyses by BMI category, similar to the all participants, decreases in pASMMI and pLBMI, and increases in pBFMI were associated with new-onset DM only in overweight and obese BMI participants (Supplementary Table 6).
The key findings of the present study were that decreases in pASMMI and pLBMI and an increase in pBFMI correlated with the development of new-onset DM, regardless of sex. An analysis of the BMI subgroups showed that decreases in pASMMI and pLBMI and an increase in pBFMI correlated with the development of new-onset DM in participants with overweight and obese BMI but not in those with normal weight BMI.
Muscle cells play a key role in glucose metabolism and insulin sensitivity [29,30]. Adequate muscle mass is important for proper insulin sensitivity because muscle cells are the largest glucose consumers in the body [29,30]. Therefore, low muscle mass leads to low glucose uptake by muscle cells, which leads to increased insulin resistance [31]. Consequently, elevated glucose levels remain in the bloodstream, leading to type 2 diabetes mellitus (T2DM). Sarcopenia, the age-related loss of muscle mass and function, is associated with an increased risk of developing new-onset DM [10]. In addition to decreased muscle mass, levels of pro-inflammatory cytokines, such as tumor necrosis factor-alpha, interleukin-6, and C-reactive protein, are also increased [32,33]. These cytokines interfere with insulin signaling and promote insulin resistance [32]. A decreased muscle mass may also lead to a decrease in the production of hormones, such as testosterone [34], which can affect insulin sensitivity and glucose metabolism, and decrease physical activity, which is a risk factor for developing T2DM [35]. Participants of this study who exhibited sarcopenia characteristics, such as a decreased muscle mass, demonstrated a higher likelihood of developing new-onset DM. However, the present results show that a decreased muscle mass did not affect the development of new-onset DM in any BMI category. New-onset DM was not affected in the normal weight BMI subgroup, suggesting that only participants with excessive fat accumulation and characteristics of sarcopenic obesity are associated with new-onset DM, supporting the hypothesis that a muscle mass decrease with an excessive fat mass affect new-onset DM, rather than the muscle mass decrease alone.
The relationship between weight gain and the development of new-onset DM is well established [36-38]. In a population-based study conducted in the South Korea, an increasing BMI was associated with an increased risk of T2DM [39]. However, BMI is not always indicative of body composition, and from this perspective, it is significant that our study showed an association between changes in fat mass and the development of new-onset DM. Nevertheless, there have been rare studies of the association between changes in fat mass and the development of new-onset DM in other study populations. Therefore, further study should be needed particularly in other ethnicities. Studies of the association of quantitative fat mass or fat mass distribution at a particular time, rather than changes in fat mass over time, with new-onset DM have been conducted in a variety of ethnicities and populations, and strong associations for new-onset DM have been observed. This suggests that the association between increased fat mass and new-onset DM identified in our study may be applicable to other populations [40-42].
In our study, there was an association between increased predicted fat mass and new-onset DM only in the overweight and obese groups and no association in the normal weight group, which is in line with previous studies showed that high BMI, high predicted fat mass, high lipid accumulation products, and abdominal fat accumulation were associated with new-onset DM [37,40-43]. Although these studies showed that a high absolute amount of fat mass or excessive accumulation of fat mass in the abdomen was associated with the development of new-onset DM, they did not explain whether fat mass was associated with the development of new-onset DM in the normal weight group. However, a recent Korean study showed an association between waist circumference and the development of new-onset DM, but no association was observed in participants with a waist circumference of less than 90/85 cm (male/female), suggesting that the amount of fat mass below a certain threshold may not affect the development of new-onset DM [44]. The complexity in the development of DM involves various factors, leaving the possibility that the normal weight group’s lifestyle may have had less association with the development of new-onset DM. However, our results showed that increased fat mass was associated with the development of new-onset DM only in the group with excessive fat accumulation, suggesting that excessive fat accumulation and increased fat mass work together to influence the development of new-onset DM. Fat cells, particularly those in the abdominal region, release free fatty acids into the bloodstream, which can impair insulin sensitivity and glucose metabolism [45]. As a result, individuals with excess abdominal fat are at a higher risk of developing insulin resistance, which is a key characteristic of T2DM [45,46]. Additionally, abdominal obesity is associated with other metabolic abnormalities, such as high blood pressure, high cholesterol levels, and inflammation, which can further increase the risk of T2DM [45]. Therefore, it is reasonable to assume that increased body fat mass in participants with excessive fat accumulation is more strongly associated with new-onset DM.
In addition, patients with T2DM are also at a higher risk of decreased muscle mass, as the disease can impair the function of muscle cells and reduce the production of hormones that promote muscle growth and maintenance [47]. T2DM also promotes fat accumulation in the abdominal region [47,48]. This can lead to a vicious cycle, as reduced muscle and increased fat masses exacerbate insulin resistance and the progression and development of T2DM. Muscle mass, fat mass, and T2DM are closely related and can influence one another bidirectionally.
The present study had several limitations. First, instead of dual-energy X-ray absorptiometry, we used a pre-validated equation to predict body composition indices in the Korean population to conduct a large-scale study. This equation provides the value of body composition for the whole body; therefore, we could not analyze the body composition of any specific body part. In addition, because this equation was estimated for the South Korean population, these results should be interpreted with caution when generalizing them to other ethnicities. Second, this was a retrospective longitudinal study. Therefore, a causal relationship or exclusion of confounding variables could not be confirmed. Finally, this epidemiological study could not demonstrate the underlying mechanisms of the correlation between changes in body composition indices and new-onset DM.
In conclusion, in this large epidemiological study involving the general Korean population, decreases in pASMMI and pLBMI and an increase in pBFMI correlated with heightened risk of new-onset DM, particularly in overweight and obese population. Therefore, the prevention of new-onset diabetes, particularly in overweight and obese populations, is closely linked to the effective management or reduction of fat mass, coupled with the preservation or augmentation of muscle mass.
Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2023.0243.
Supplementary Table 1.
Comparison of demographics and comorbidities between included and excluded participants
dmj-2023-0243-Supplementary-Table-1.pdf
Supplementary Table 2.
Baseline characteristics of the study participant according to occurrence for new-onset DM
dmj-2023-0243-Supplementary-Table-2.pdf
Supplementary Table 3.
Subgroup analysis of the association between changes in predicted body composition index and new-onset diabetes mellitus
dmj-2023-0243-Supplementary-Table-3.pdf
Supplementary Table 4.
Baseline characteristics of the study participant (participants with normal fasting glucose, excluding prediabetics)
dmj-2023-0243-Supplementary-Table-4.pdf
Supplementary Table 5.
HRs and 95% CIs of new-onset diabetes mellitus with variables and change in predicted body composition index (per kg/m2 increase) (participants with normal fasting glucose, excluding pre-diabetics)
dmj-2023-0243-Supplementary-Table-5.pdf
Supplementary Table 6.
HRs and 95% CIs of new-onset diabetes mellitus per kg/m2 increase in predicted body composition index stratified by BMI subgroup of BMI (participants with normal fasting glucose, excluding pre-diabetics)
dmj-2023-0243-Supplementary-Table-6.pdf

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: all authors.

Acquisition, analysis, or interpretation of data: H.J.K., G.H.L., M.H.K., T.J.S.

Drafting the work or revising: H.J.K., H.W.L., T.J.S.

Final approval of the manuscript: H.J.K., T.J.S.

FUNDING

This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (grant number: 2022-0-00621 to Tae-Jin Song, Development of artificial intelligence technology that provides dialog-based multi-modal explainability), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-00262087 to Tae-Jin Song). The funding source had no role in the design, conduct, or reporting of this study.

Acknowledgements
None
Fig. 1.
Flowchart of participant selection. BMI, body mass index.
dmj-2023-0243f1.jpg
Fig. 2.
Association of changes in the predicted body composition indices with new-onset diabetes mellitus by sex and body mass index subgroup. Association of changes in the predicted appendicular skeletal muscle mass index (pASMMI), body fat mass index (pBFMI), and lean body mass index (pLBMI) with diabetes mellitus. The solid lines indicate hazard ratios, and shaded regions show 95% confidence intervals from restricted cubic spline regression. The restricted cubic splines were constructed with four knots placed at the 5th, 35th, 65th, and 95th percentiles of changes in pASMMI, pBFMI, and pLBMI. The hazard ratios (95% confidence intervals) were calculated using the Cox proportional hazards regression analysis after adjusting for the age, baseline and secondary body mass indices, household income, smoking history, alcohol consumption status, physical activity, fasting serum glucose level, hypertension, dyslipidemia, atrial fibrillation, estimated glomerular filtration rate, and Charlson comorbidity index score. (A) Men. (B) Women.
dmj-2023-0243f2.jpg
dmj-2023-0243f3.jpg
Table 1.
Baseline characteristics of the study participant
Variable Total Male Female P value
Number 1,607,508 837,151 770,357 <0.0001
Age, yr 48.66±13.14 47.35±12.91 50.09±13.24 <0.0001
Waist circumference, cm 79.67±8.59 83.45±7.28 75.56±7.99 <0.0001
Health screening period 1 (2010–2011)
 Body mass index, kg/m2 23.54±3.14 24.10±2.99 22.94±3.18 <0.0001
 pASMMI, kg/m2 7.29±1.22 8.28±0.75 6.22±0.54 <0.0001
 pBFMI, kg/m2 6.34±1.95 5.30±1.38 7.48±1.83 <0.0001
 pLBMI, kg/m2 17.01±2.22 18.59±1.62 15.29±1.32 <0.0001
Health screening period 2 (2012–2013)
 Body mass index, kg/m2 23.66±3.18 24.23±3.04 23.04±3.22 <0.0001
 pASMMI, kg/m2 7.30±1.23 8.28±0.76 6.23±0.55 <0.0001
 pBFMI, kg/m2 6.41±1.96 5.37±1.4 7.53±1.86 <0.0001
 pLBMI, kg/m2 17.06±2.24 18.65±1.65 15.33±1.33 <0.0001
Household income <0.0001
 T1, lowest 261,619 (16.27) 91,969 (10.99) 169,650 (22.02)
 T2 333,102 (20.72) 154,063 (18.40) 179,039 (23.24)
 T3 453,010 (28.18) 258,627 (30.89) 194,383 (25.23)
 T4, highest 559,777 (34.82) 332,492 (39.72) 227,285 (29.50)
Smoking <0.0001
 Never 994,100 (61.84) 256,702 (30.66) 737,398 (95.72)
 Former 252,411 (15.70) 239,982 (28.67) 12,429 (1.61)
 Current 360,997 (22.46) 340,467 (40.67) 20,530 (2.66)
Alcohol consumption, day/wk <0.0001
 <1 828,178 (51.52) 261,080 (31.19) 567,098 (73.61)
 1–2 570,010 (35.46) 397,260 (47.45) 172,750 (22.42)
 3–4 154,939 (9.64) 130,827 (15.63) 24,112 (3.13)
 ≥5 54,381 (3.38) 47,984 (5.73) 6,397 (0.83)
Physical activity, day/wk <0.0001
 <1 906,977 (56.42) 405,951 (48.49) 501,026 (65.04)
 1–2 428,164 (26.64) 273,755 (32.70) 154,409 (20.04)
 3–4 176,569 (10.98) 102,963 (12.30) 73,606 (9.55)
 ≥5 95,798 (5.96) 54,482 (6.51) 41,316 (5.36)
Fasting serum glucose, mg/dL 92.68±8.94 93.92±9.09 91.33±8.59 <0.0001
 <100 1,018,523 (63.36) 479,362 (57.26) 539,161 (69.99)
 100–126 588,985 (36.64) 357,789 (42.74) 231,196 (30.01)
Comorbidity
 Hypertension 438,088 (27.25) 246,555 (29.45) 191,533 (24.86) <0.0001
 Dyslipidemia 366,322 (22.79) 174,227 (20.81) 192,095 (24.94) <0.0001
 Atrial fibrillation 8,072 (0.50) 5,168 (0.62) 2,904 (0.38) <0.0001
Estimated glomerular filtration rate, mL/min/1.73 m2 <0.0001
 <30 1,054 (0.07) 535 (0.06) 519 (0.07)
 30–60 73,679 (4.58) 34,906 (4.17) 38,773 (5.03)
 60–90 1,006,174 (62.59) 537,837 (64.25) 468,337 (60.79)
 ≥90 526,601 (32.76) 263,873 (31.52) 262,728 (34.10)
Charlson comorbidity index <0.0001
 0 1,190,350 (74.05) 659,622 (78.79) 530,728 (68.89)
 1 289,391 (18.00) 128,027 (15.29) 161,364 (20.95)
 ≥2 127,767 (7.95) 49,502 (5.91) 78,265 (10.16)

Values are presented as mean±standard deviation or number (%). P value by independent t-test or chi-square test.

pASMMI, predicted appendicular skeletal muscle mass index; pBFMI, predicted body fat mass index; pLBMI, predicted lean body mass index.

Table 2.
HRs and 95% CIs of new-onset diabetes mellitus by baseline information at health screening period 1 and change in predicted body composition index between health screening period 1 and health screening period 2 (per kg/m2 increase)
Variable Male
Female
HR (95% CI) P value HR (95% CI) P value
Age, yr 1.037 (1.036–1.038) <0.0001 1.029 (1.028–1.030) <0.0001
Baseline body mass index, kg/m2 1.030 (1.017–1.042) <0.0001 1.004 (0.974–1.034) 0.8166
Secondary body mass index, kg/m2 1.113 (1.099–1.126) <0.0001 1.119 (1.086–1.153) <0.0001
Household income
 T1, lowest 1 (ref) 1 (ref)
 T2 0.98 (0.955–1.004) 0.1058 1.014 (0.991–1.039) 0.2364
 T3 0.919 (0.898–0.940) <0.0001 0.981 (0.960–1.003) 0.0974
 T4, highest 0.893 (0.874–0.912) <0.0001 0.941 (0.921–0.961) <0.0001
Smoking
 Never 1 (ref) 1 (ref)
 Former 1.068 (1.048–1.087) <0.0001 1.215 (1.137–1.298) <0.0001
 Current 1.318 (1.294–1.342) <0.0001 1.436 (1.371–1.504) <0.0001
Alcohol consumption, day/wk
 <1 1 (ref) 1 (ref)
 1–2 0.997 (0.980–1.014) 0.7308 0.886 (0.866–0.907) <0.0001
 3–4 1.036 (1.014–1.058) 0.0013 0.884 (0.838–0.932) <0.0001
 ≥5 1.063 (1.035–1.093) <0.0001 0.964 (0.885–1.050) 0.3975
Physical activity, day/wk
 <1 1 (ref) 1 (ref)
 1–2 0.933 (0.917–0.949) <0.0001 0.979 (0.957–1.001) 0.0533
 3–4 0.964 (0.943–0.986) 0.0015 1.053 (1.025–1.082) 0.0002
 ≥5 0.959 (0.933–0.985) 0.0024 1.047 (1.014–1.082) 0.0054
Fasting serum glucose, mg/dL 1.019 (1.019–1.019) <0.0001 1.021 (1.021–1.022) <0.0001
Comorbidity
 Hypertension 1.404 (1.382–1.427) <0.0001 1.403 (1.378–1.428) <0.0001
 Dyslipidemia 1.461 (1.439–1.484) <0.0001 1.51 (1.485–1.535) <0.0001
 Atrial fibrillation 0.939 (0.855–1.032) 0.1907 0.927 (0.804–1.068) 0.2928
Charlson comorbidity index
 0 1 (ref) 1 (ref)
 1 1.131 (1.112–1.151) <0.0001 1.169 (1.148–1.190) <0.0001
 ≥2 1.249 (1.217–1.282) <0.0001 1.225 (1.196–1.254) <0.0001
Change in predicted body composition index, kg/m2
 pASMMI 0.866 (0.830–0.903) <0.0001 0.748 (0.635–0.881) 0.0005
 pBFMI 1.073 (1.050–1.096) <0.0001 1.114 (1.047–1.186) 0.0007
 pLBMI 0.931 (0.912–0.952) <0.0001 0.906 (0.856–0.959) 0.0007

Multivariate analysis adjusted for age, baseline and secondary body mass index, household income, smoking, alcohol consumption, regular exercise, fasting serum glucose, hypertension, dyslipidemia, atrial fibrillation, estimated glomerular filtration rate, and Charlson comorbidity index.

HR, hazard ratio; CI, confidence interval; pASMMI, predicted appendicular skeletal muscle mass index; pBFMI, predicted body fat mass index; pLBMI, predicted lean body mass index.

Table 3.
HRs and 95% CIs of new-onset diabetes mellitus (per kg increase) in predicted body composition index stratified by BMI subgroup of body mass index
Baseline BMI group Event Person-years pASMMI
pBFMI
pLBMI
HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
Male
 Overall 78,407 306,260.70 0.866 (0.830–0.903) <0.001 1.073 (1.050–1.096) <0.001 0.931 (0.912–0.952) <0.001
 Normal 35,013 137,047.66 0.978 (0.915–1.044) 0.501 1.010 (0.977–1.045) 0.560 0.990 (0.957–1.024) 0.564
 Overweight 36,739 143,476.86 0.786 (0.738–0.836) <0.001 1.126 (1.091–1.162) <0.001 0.887 (0.859–0.916) <0.001
 Obese 6,655 25,736.18 0.877 (0.778–0.990) 0.033 1.066 (1.003–1.133) 0.040 0.937 (0.881–0.997) 0.041
Female
 Overall 65,019 257,211.82 0.748 (0.635–0.881) 0.001 1.114 (1.047–1.186) 0.001 0.906 (0.856–0.959) 0.001
 Normal 33,607 135,081.78 0.812 (0.636–1.035) 0.093 1.079 (0.984–1.184) 0.108 0.933 (0.857–1.015) 0.109
 Overweight 26,133 102,040.65 0.576 (0.446–0.744) <0.001 1.233 (1.119–1.360) <0.001 0.826 (0.755–0.903) <0.001
 Obese 5,279 20,089.39 0.506 (0.312–0.822) 0.006 1.292 (1.074–1.554) 0.007 0.792 (0.669–0.937) 0.007

Multivariate analysis adjusted for age, baseline and secondary body mass index, household income, smoking, alcohol consumption, regular exercise, fasting serum glucose, hypertension, dyslipidemia, atrial fibrillation, estimated glomerular filtration rate, and Charlson comorbidity index.

HR, hazard ratio; CI, confidence interval; BMI, body mass index; pASMMI, predicted appendicular skeletal muscle mass index; pBFMI, predicted body fat mass index; pLBMI, predicted lean body mass index.

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      Kim HJ, Lee HW, Kang MK, Leem GH, Kim MH, Song TJ. Association of Body Composition Changes with the Development of Diabetes Mellitus: A Nation-Wide Population Study. Diabetes Metab J. 2024 May 21. doi: 10.4093/dmj.2023.0243. Epub ahead of print.
      Received: Aug 31, 2023; Accepted: Jan 26, 2024
      DOI: https://doi.org/10.4093/dmj.2023.0243.

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