What this is
- This research investigates the relationship between (), (), and () in older adults.
- It analyzes data from 69,388 participants aged 60 and above in Xinzheng, China.
- The study compares the predictive abilities of various anthropometric indicators for .
Essence
- Increased and are positively associated with in older adults. is the strongest predictor for men, while shows a similar predictive ability for women.
Key takeaways
- Higher and quintiles correlate with increased odds of . Odds ratios for range from 1.416 to 2.156 for and from 1.322 to 2.169 for across quintiles.
- is the best predictor of in men, while , , and body adiposity estimator (BAE) have similar predictive power in women.
- The study confirms that the relationships between , , and persist even after adjusting for various potential confounding factors.
Caveats
- The cross-sectional design limits the ability to infer causation between obesity and . Additionally, the study focuses solely on individuals over 60, which may not represent younger populations.
- Some potential confounding factors may not have been accounted for, and the lack of hip circumference data restricts a fuller analysis of obesity's predictive power.
Definitions
- Type 2 diabetes mellitus (T2DM): A chronic condition characterized by insulin resistance and high blood sugar levels.
- Body Mass Index (BMI): A measure calculated from a person's weight and height, used to classify underweight, normal weight, overweight, and obesity.
- Waist circumference (WC): A measurement of the circumference of the waist, used as an indicator of central obesity.
AI simplified
Background
In recent years, diabetes has been a substantial public health burden worldwide. The global prevalence of diabetes has reached 10.5% (536.6 million people) and will reach 12.2% (783.2 million people) by 2045. Middle-income countries had the largest increase in the prevalence of diabetes. The economic cost was $966 billion in 2021 and will be $1,054 billion in 2045 [1]. The prevalence of diabetes in Chinese adults was 12.8% (ADA criteria including the addition of HbA1c) in 2017. The total number of diabetes patients in mainland China was approximately 129.8 million (70.4 million men and 59.4 million men). The prevalence of diabetes was 28.8% in individuals 60–69 years old and 31.8% in individuals over 70 years old [2].
Diabetes can cause many complications: macrovascular complications, including coronary heart disease, stroke and peripheral vascular disease, and microvascular complications, such as end-stage renal disease, retinopathy and neuropathy, and lower-extremity amputations [3]. The risks of all-cause [4] and cardiovascular disease (CVD) mortality [5] were significantly increased in patients with diabetes. Meanwhile, the quality of life of people with diabetes may decrease [6].
Diabetes has multiple risk factors, such as overweight, obesity, unhealthy diet, poor lifestyle habits, and increased age [7–10]. In recent years, the proportion of people with obesity (BMI ≥ 30 kg/m2) has been rising substantially worldwide [11]. The prevalence of overweight (25 kg/m2 ≤ BMI < 30 kg/m2) and obesity (BMI ≥ 30 kg/m2) among Chinese adults was 28.1% and 5.2%, respectively [12]. The prevalence of central obesity [waist circumference (WC) ≥ 90 in males and ≥ 85 in females] in Chinese adults was 29.1% (28.6% in males and 29.6% in females), and the estimated total number was 277.8 million (140.1 million males and 137.7 females) [13].
Some articles have studied the relationship between obesity and type 2 diabetes mellitus (T2DM). However, the relationship between obesity and T2DM has always been estimated by grouping body mass index (BMI) or waist circumference (WC), so it is difficult to obtain the precise dose–response relationship. Few studies have examined the interaction between BMI and WC for T2DM. Therefore, this research studied the relationship between BMI and WC and T2DM in elderly individuals, including the dose–response relationship based on restricted cubic spline plots and the additive interaction between WC and BMI for T2DM. The receiver operating characteristic curve (ROC) was used to compare the predictive ability of WC, BMI and other anthropometric indicators for T2DM, including WHtR, BAE and BRI. These indicators have recently been used to study cardiovascular disease, hypertension, mortality and so on [14, 15], so we calculated and analyzed these indicators and compared these indicators with BMI and WC.
Method
Population
The subjects of this cross-sectional study were adults aged ≥ 60 years in Xinzheng, Henan Province, Central China. The data came from the residents' electronic health records in the Xinzheng Hospital Information System from January to December 2020. Doctors set up health records for each resident at their first hospital visit or health examination, and participants aged 60 or older could receive free health examinations. At the start of the study, 70,161 elderly adults were eligible for the study. We excluded participants with the following conditions: (1) missing information for marital status, drinking, smoking, exercise, resting heart rate (RHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), WC or BMI (n = 532); (2) the values of the variables above were illogical (n = 241). Ultimately, the study included 69,388 participants. The data screening flow chart is presented in Supplementary Fig. 1. Informed consent was obtained from the subjects, and this study was approved by the Ethics Committee of Zhengzhou University (Reference Number: ZZUIRB2019-019).
Data collection
Demographic and clinical information was collected at the health checkup for participants. Demographic information included sex (male/female), age, place of residence (urban/rural), marital status, alcohol consumption, smoking, and physical exercise. Smoking included never smokers, former smokers and current smokers. Alcohol consumption and physical exercise were divided into four categories: never, once in a while, more than once a week and every day. Clinical data included anthropometric measurements, laboratory investigations, and self-reported disease history. Participants wearing light clothing took off their shoes, and then their weight and height were measured. BMI was calculated as weight in kilograms divided by the square of height in meters. WC was measured at the midpoint of the distance between the lowest costal ridge and the upper border of the iliac crest.
Other anthropometric indicators included WHtR, BAE and BRI.
WHtR was calculated by dividing WC by height, BAE = -44.988 + (0.503 × age) + (10.689 × sex) + (3.172 × BMI)-(0.026 × (BMI)2) + (0.181 × BMI × sex)-(0.02 × BMI × age)-(0.005 × (BMI)2 × sex) + (0.00021 × (BMI)2 × age), male = 0 and female = 1, and age is in years [16]. BRI = 364.2–365.5 × {1-[(WC/2π)2/(0.5height)2]}1/2 [17].
After participants fasted for 8 h, blood samples were collected to measure blood lipids and blood sugar. After participants had remained sitting for at least five minutes at rest, the SBP, DBP and radial pulse rate of the participants were measured twice by an electronic sphygmomanometer (Omron HEM-7125, Kyoto, Japan), and the mean value was recorded as the final result.
Definition of T2DM
T2DM was defined as having a self-reported T2DM history, using insulin or oral hypoglycemic agents, or having FPG ≥ 7.0 mmol/L [18].
Statistical analysis
Continuous variables were described as the means and standard deviations (SDs). Categorical variables are presented as numbers and proportions. The chi-square test or the Kruskal–Wallis test for categorical variables and ANOVA for continuous variables was used to compare the difference between quintiles of BMI or WC. The associations of BMI and WC with T2DM were analyzed in sex-specific quintiles by a logistic regression model, and ORs with 95% CIs of BMI and WC in categories and continuous variables were expressed in separate models. Model 1 was unadjusted. Model 2 adjusted for age and sex. Model 3 adjusted for the potential confounders, including age, sex, place of residence, alcohol consumption, smoking, physical exercise, SBP, RHR, because these potential confounders might affect the true relationships between the corresponding indicators and diabetes. Some studies have suggested a possible link between alcohol consumption and obesity and diabetes [19, 20], so we adjusted for that in our analysis. The dose–response association and the potentially nonlinear relationship of continuous BMI and WC with T2DM were explored by restricted cubic spline models with four knots. The stratified analysis was performed by sex subgroup using a logistic regression model to test the consistency of these relationships. We also performed additive interaction analysis between BMI and WC for T2DM with BMI and WC analyzed in two categories (BMI: BMI < 25 kg/m2 and BMI ≥ 25 kg/m2 [21]; WC: WC < 85 cm in females and < 90 cm in males, WC ≥ 85 cm in females and ≥ 90 in males [22]). We evaluated the existence of additive interactions by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (S). RERI > 0, AP > 0 or S > 1 was considered a statistically significant additive interaction. Finally, the receiver operating characteristic (ROC) curve and related area under the ROC curve (AUC) were used to compare the capability of BMI, WC and other anthropometric indicators, including WHtR, BAE and BRI, to diagnosis T2DM, and the logistic regression model was used to estimate the related ORs and 95% CIs of WHtR, BAE and BRI for T2DM after adjusting for age, sex, place of residence, alcohol consumption, smoking, physical exercise, SBP, RHR. The Cohen’s d was utilized to estimate the effect size of anthropometric indicators [23]. Statistical analyses were performed using SPSS V 21 and R V 4.0.3. P < 0.05 with two-sided tests was considered statistically significant.
Results
Relationship of BMI and WC with the risk of T2DM for all participants and subgroups of males and females. ORs are adjusted for age, sex (not for sex subgroup analysis), alcohol consumption, place of residence, smoking, physical exercise, SBP, RHR. Abbreviations:body mass index;confidential interval;odd ratio;waist circumference;systolic blood pressure;resting heart rate BMI CI OR WC SBP RHR
The receiver operating characteristic curve of anthropometric indicators after adjusting for age, sex, alcohol consumption, place of residence, smoking, physical exercise, SBP, RHR. Abbreviations:body mass index;waist circumference;waist-to-height ratio;body adiposity estimator;body roundness index;systolic blood pressure;resting heart rate BMI WC WHtR BAE BRI SBP RHR
| Characteristics | .BMI, kg/m2 | PValue | ||||
|---|---|---|---|---|---|---|
| First quintile | Second quintile | Third quintile | Fourth quintile | Fifth quintile | ||
| Male | BMI < 22.04 | 22.04 ≤ BMI < 23.75 | 23.75 ≤ BMI < 25.38 | 25.38 ≤ BMI < 27.36 | BMI ≥ 27.36 | |
| Female | BMI < 22.22 | 22.22 ≤ BMI < 24.06 | 24.06 ≤ BMI < 25.89 | 25.89 ≤ BMI < 28.13 | BMI ≥ 28.13 | |
| Number of participants | 13,843 | 13,886 | 13,905 | 13,908 | 13,846 | |
| Diabetes, % | 2501 (18.1) | 3393 (24.4) | 3881 (27.9) | 4271 (30.7) | 4710 (34.0) | < 0.001 |
| Age, years | 73.3 ± 7.5 | 71.6 ± 7.2 | 71.0 ± 6.7 | 70.6 ± 6.4 | 70.4 ± 6.2 | < 0.001 |
| Women, % | 7463 (53.9) | 7503 (54.0) | 7522 (54.1) | 7527 (54.1) | 7464 (53.9) | 0.994 |
| BMI, kg/m.2 | 20.4 ± 1.4 | 23.1 ± 0.5 | 24.8 ± 0.5 | 26.6 ± 0.7 | 30.1 ± 2.3 | < 0.001 |
| WC, cm | 78.0 ± 6.5 | 82.8 ± 6.0 | 86.3 ± 6.1 | 89.7 ± 6.4 | 96.2 ± 8.3 | < 0.001 |
| BRI | 3.32 ± 0.84 | 3.83 ± 0.82 | 4.28 ± 0.88 | 4.75 ± 0.95 | 5.73 ± 1.34 | < 0.001 |
| BAE | 28.84 ± 6.62 | 31.95 ± 6.38 | 33.85 ± 6.40 | 35.94 ± 6.40 | 39.52 ± 6.75 | < 0.001 |
| WHtR | 0.50 ± 0.04 | 0.52 ± 0.04 | 0.55 ± 0.04 | 0.57 ± 0.04 | 0.61 ± 0.06 | < 0.001 |
| Smoking, % | < 0.001 | |||||
| Never smokers | 11,974 (86.5) | 12,351 (88.9) | 12,408 (89.2) | 12,495 (89.8) | 12,510 (90.4) | |
| Former smokers | 211 (1.5) | 208 (1.5) | 225 (1.6) | 220 (1.6) | 219 (1.6) | |
| Current smokers | 1658 (12.0) | 1327 (9.6) | 1272 (9.1) | 1193 (8.6) | 1117 (8.1) | |
| Alcohol consumption, % | < 0.001 | |||||
| Never | 13,152 (95.0) | 13,208 (95.1) | 13,202 (94.9) | 13,137 (94.5) | 13,007 (93.9) | |
| Once in a while | 378 (2.7) | 406 (2.9) | 413 (3.0) | 445 (3.2) | 492 (3.6) | |
| More than once a week | 86 (0.6) | 94 (0.7) | 95 (0.7) | 122 (0.9) | 122 (0.9) | |
| Every day | 227 (1.6) | 178 (1.3) | 195 (1.4) | 204 (1.5) | 225 (1.6) | |
| Physical exercise, % | < 0.001 | |||||
| Never | 9690 (70.0) | 9272 (66.8) | 8745 (62.9) | 8579 (61.7) | 8680 (52.7) | |
| Once in a while | 284 (2.1) | 329 (2.4) | 340 (2.4) | 382 (2.7) | 395 (2.9) | |
| More than once a week | 628 (4.5) | 809 (5.8) | 968 (7.0) | 1008 (7.2) | 1006 (7.3) | |
| Every day | 3241 (23.4) | 3476 (25.0) | 3852 (27.7) | 3939 (28.3) | 3765 (27.2) | |
| Rural areas, % | 1550 (11.2) | 2203 (15.9) | 2479 (17.8) | 2600 (18.7) | 2544 (18.4) | < 0.001 |
| RHR, beat | 74.0 ± 11.8 | 73.3 ± 10.5 | 73.0 ± 10.7 | 73.2 ± 10.6 | 73.7 ± 10.8 | < 0.001 |
| SBP, mmHg | 138.5 ± 20.0 | 139.3 ± 18.2 | 141.3 ± 18.4 | 142.2 ± 18.6 | 144.5 ± 18.7 | < 0.001 |
| DBP, mmHg | 80.8 ± 10.8 | 82.4 ± 9.8 | 83.3 ± 9.9 | 84.1 ± 10.1 | 85.4 ± 10.4 | < 0.001 |
| Variables | No. of cases | Model 1OR (95% CI) | Model 2OR (95% CI) | Model 3OR (95% CI) |
|---|---|---|---|---|
| BMI (kg/m.)2 | ||||
| Category | ||||
| Q1 | 2501 | Reference | Reference | Reference |
| Q2 | 3393 | 1.466 (1.384, 1.554) | 1.436 (1.354, 1.522) | 1.416 (1.335, 1.502) |
| Q3 | 3881 | 1.756 (1.659, 1.859) | 1.707 (1.612, 1.808) | 1.664 (1.570, 1.764) |
| Q4 | 4271 | 2.010 (1.900, 2.126) | 1.946 (1.838, 2.059) | 1.879 (1.774, 1.990) |
| Q5 | 4710 | 2.338 (2.211, 2.472) | 2.261 (2.137, 2.392) | 2.156 (2.037, 2.283) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 18,756 | 1.333 (1.311, 1.356) | 1.306 (1.284, 1.329) | 1.287 (1.265, 1.309) |
| WC (cm) | ||||
| Category | ||||
| Q1 | 2364 | Reference | Reference | Reference |
| Q2 | 3255 | 1.296 (1.221, 1.376) | 1.326 (1.249, 1.408) | 1.322 (1.244, 1.404) |
| Q3 | 3706 | 1.573 (1.484, 1.668) | 1.577 (1.487, 1.673) | 1.549 (1.459, 1.643) |
| Q4 | 4307 | 1.745 (1.648, 1.847) | 1.752 (1.654, 1.855) | 1.705 (1.609, 1.807) |
| Q5 | 5124 | 2.236 (2.114, 2.365) | 2.251 (2.127, 2.381) | 2.169 (2.048, 2.297) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 18,756 | 1.299 (1.277, 1.321) | 1.316 (1.294, 1.339) | 1.299 (1.277, 1.322) |
| Variables | No. of cases | Model 1OR (95% CI) | Model 2OR (95% CI) | Model 3OR (95% CI) |
|---|---|---|---|---|
| BMI (kg/m.)2 | ||||
| Male | ||||
| Q1 | 997 | Reference | Reference | Reference |
| Q2 | 1338 | 1.432 (1.308, 1.568) | 1.412 (1.289, 1.546) | 1.370 (1.250, 1.501) |
| Q3 | 1561 | 1.748 (1.600, 1.910) | 1.713 (1.567, 1.872) | 1.631 (1.491, 1.785) |
| Q4 | 1771 | 2.074 (1.901, 2.263) | 2.029 (1.859, 2.214) | 1.893 (1.732, 2.069) |
| Q5 | 2023 | 2.506 (2.300, 2.730) | 2.443 (2.241, 2.663) | 2.255 (2.064, 2.463) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 7690 | 1.359 (1.324, 1.394) | 1.348 (1.314, 1.384) | 1.344 (1.306, 1.383) |
| Female | ||||
| Q1 | 1504 | Reference | Reference | Reference |
| Q2 | 2055 | 1.495 (1.385, 1.612) | 1.452 (1.346, 1.568) | 1.450 (1.343, 1.566) |
| Q3 | 2320 | 1.767 (1.640, 1.904) | 1.703 (1.579, 1.836) | 1.686 (1.563, 1.819) |
| Q4 | 2500 | 1.970 (1.830, 2.122) | 1.885 (1.749, 2.031) | 1.859 (1.724, 2.005) |
| Q5 | 2687 | 2.229 (2.070, 2.399) | 2.130 (1.977, 2.294) | 2.072 (1.922, 2.234) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 11,066 | 1.298 (1.270, 1.327) | 1.279 (1.251, 1.309) | 1.253 (1.226, 1.280) |
| WC (cm) | ||||
| Male | ||||
| Q1 | 742 | Reference | Reference | Reference |
| Q2 | 1426 | 1.405 (1.275, 1.549) | 1.395 (1.266, 1.538) | 1.393 (1.263, 1.537) |
| Q3 | 1499 | 1.720 (1.561, 1.896) | 1.697 (1.540, 1.187) | 1.641 (1.487, 1.811) |
| Q4 | 1809 | 1.994 (1.813, 2.192) | 1.967 (1.789, 2.163) | 1.886 (1.713, 2.077) |
| Q5 | 2214 | 2.656 (2.420, 2.916) | 2.623 (2.389, 2.879) | 2.475 (2.250, 2.722) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 7690 | 1.374 (1.339, 1.410) | 1.370 (1.335, 1.406) | 1.355 (1.318, 1.393) |
| Female | ||||
| Q1 | 1622 | Reference | Reference | Reference |
| Q2 | 1829 | 1.330 (1.232, 1.436) | 1.309 (1.212, 1.414) | 1.304 (1.207, 1.408) |
| Q3 | 2207 | 1.560 (1.449, 1.681) | 1.524 (1.414, 1.641) | 1.508 (1.399, 1.626) |
| Q4 | 2498 | 1.676 (1.558, 1.802) | 1.638 (1.523, 1.762) | 1.608 (1.494, 1.731) |
| Q5 | 2910 | 2.083 (1.939, 2.237) | 2.043 (1.902, 2.195) | 1.992 (1.853, 2.142) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |
| Continuous (per SD) | 11,066 | 1.283 (1.254, 1.311) | 1.276 (1.248, 1.305) | 1.261 (1.233, 1.289) |
| Variable | AUC (95%CI) | Sensitivity | Specificity | Youden index |
|---|---|---|---|---|
| Male | ||||
| BMI (kg/m.) + other factors2 | 0.627 (0.620, 0.634) | 0.641 | 0.539 | 0.18 |
| WC (cm) + other factors | 0.629 (0.622, 0.636) | 0.532 | 0.651 | 0.183 |
| WHtR + other factors | 0.623 (0.616, 0.630) | 0.555 | 0.622 | 0.177 |
| BRI + other factors | 0.623 (0.616, 0.630) | 0.582 | 0.594 | 0.176 |
| BAE + other factors | 0.627 (0.620, 0.634) | 0.642 | 0.539 | 0.181 |
| Female | ||||
| BMI (kg/m.) + other factors2 | 0.600 (0.594, 0.606) | 0.612 | 0.533 | 0.145 |
| WC (cm) + other factors | 0.600 (0.593, 0.606) | 0.523 | 0.623 | 0.146 |
| WHtR + other factors | 0.593 (0.586, 0.599) | 0.625 | 0.51 | 0.135 |
| BRI + other factors | 0.592 (0.586, 0.599) | 0.618 | 0.517 | 0.135 |
| BAE + other factors | 0.600 (0.594, 0.607) | 0.663 | 0.482 | 0.145 |
Discussion
Logistic regression was used in our study to examine the relationship between obesity and T2DM in the elderly, and increased BMI and WC were found to be associated with an increased risk of T2DM. The same results were found in the sex subgroup analysis. The dose–response relationship analyzed by restricted cubic splines found a nonlinear relationship between BMI and T2DM, while the relationship between WC and T2DM was nonlinear only in women. There was no additive interaction between BMI and WC for T2DM. Both BMI and WC were positively associated with T2DM. Meanwhile, we found that WC was the best predictor in elderly males, while BMI, WC and BAE had similar predictive abilities in females. When models were adjusted for arbitrary 0–8 variables of age, sex, place of residence, alcohol consumption, smoking, physical exercise, SBP and RHR, the results did not change significantly and the effect of potential confounding factors was eliminated, so the findings were independent of the covariates considered, in other words, the relationships of these anthropometric indicators with diabetes always exist no matter what covariates were adjusted in models.
Our study found that BMI and WC were positively associated with T2DM, and those positive associations were also found in some other studies [9, 24, 25]. There are several explanations for this positive association. First, people with genetic susceptibility to T2DM have a higher risk of obesity because the skeletal muscle and pancreas islet α-cells of those people are more prone to insulin resistance, and this insulin resistance leads to increased glucose production of the liver, raising insulin levels, which leads to obesity [26]. Second, macrophages in adipose tissue produce proinflammatory cytokines that influence insulin-dependent tissues and beta cells [27]. Third, the adipokine hypothesis [28] suggests that stressed adipokines release various secretory products that can affect insulin insensitivity and beta cells. Fourth, Martin G Myers Jr et al. [29] suggested that a high energy and fat diet can lead to dysfunction of the mitochondria and endoplasmic reticulum of the hypothalamus, resulting in leptin and insulin resistance. Increased leptin leads to the release of multiple inflammatory factors. Several studies have shown that some treatments for obesity, such as lifestyle changes, drug interventions, and surgery, not only lead to weight loss but also improve type 2 diabetes [30–32]. This also suggested that obesity increases the risk of T2DM.
In this study, the positive relationship between WC and T2DM was stronger than that of BMI in males. In contrast, the association between BMI and T2DM was stronger in females. Similar results were found by Qiwei Ge et al. [10]. This situation may be due to differences in fat distribution between the sexes [33, 34]. The study of Xuefeng Ni et al. [35] showed that women over 50 years of age had significantly less visceral abdominal fat than men, while men had more muscle mass than women. This difference in fat distribution may result in BMI becoming a better indicator of the amount of fat in women, while WC is a better indicator in men. Hormonal differences between men and women may be one reason for the difference in fat distribution [36].
In this study, ROC curves and AUC were used to compare the predictive power of BMI, WC and other anthropometric indicators, including WHtR, BAE and BRI, for T2DM. WC was the strongest predictor of T2DM in men. This result is similar to several studies [9, 24, 37]. Qiwei Ge et al. [10] also found that WC was the strongest indicator to predict T2DM in elderly men. However, these studies were not identical to the indicators of our study. In women, BMI, BAE and WC had similar predictive power for T2DM. In contrast to our study, a cohort study in Japan [38] found that BRI was better than BMI and WC in predicting T2DM in both men and women. Ye Chang et al. (20) also found that BRI had better predictive ability than BMI and WC. This discrepancy may be because the age range of the participants was different. All participants in our study were older than 60 years. We noted that the AUCs in our study were relatively low even after adjusting for some potential confounding factors, which may be because our study subjects were elderly. Qiwei Ge et al. [10] looked at people aged 18–60 and over. The AUCs of all indicators in people over 60 years old were lower than those in groups between 18 and 60 years old.
There are some advantages of our study. First, the data of this study were obtained from a large-scale health check in Henan, China. Demographic and laboratory data were collected, and the sample size and statistical power were adequate. Second, height and weight in this study were objectively measured rather than self-reported, which can avoid discrepancies between participants' own reports and the actual situation. Third, the AUCs were used to compare the predictive ability of BMI and WC with other anthropometric indicators, including WHtR, BAE and BRI, for T2DM in elderly individuals, which is of practical value to improving related studies.
However, some limitations of this study should be noted. First, the subjects of this study were older than 60 years, so we could not compare the relationship between obesity and T2DM in other age groups or the predictive ability of these indicators for diabetes. Second, this study was a cross-sectional study, making it difficult to examine the causal relationship between exposure and outcome. Third, we adjusted for potential confounders, including age, sex, alcohol consumption, smoking, physical exercise, place of residence, SBP, RHR, but some potential factors may exist that we did not adjust for, and since we did not have hip circumference information, we could not study its predictive power.
Conclusion
Overall, this study found that the increased BMI and WC were associated with an increased risk of T2DM. The same results were found in the sex subgroup analysis. There was no additive interaction between BMI and WC for T2DM. WHtR, BRI, BAE were positively associated with T2DM in both men and women. Meanwhile, we found that WC was the best predictor in older males, while BMI, WC and BAE had similar predictive abilities in females.
Supplementary Information
Additional file 1: Supplementary Figure 1. Screening flowchart of participants. Supplementary Table 1. Baseline characteristics of the included participants according to different levels of WC. Supplementary Table 2. The Pearson correlations of all anthropometric indices. Supplementary Table 3. The effect size of anthropometric indices between groups. Supplementary Table 4. Associations between anthropometric measures and T2DM.