Abstract
Background:
Various investigations have evaluated the predictive ability of different anthropometric indices for type 2 diabetes mellitus (T2DM) risk and the findings were inconsistent in different populations. This study investigated the relationship between anthropometric indicators and T2DM in the Rafsanjan Cohort Study.
Methods:
The present cross-sectional study included 9895 adults, aged 35–70 years, among them who have completed data, were studied. We obtained the data from the Rafsanjan Cohort Study (RCS), as a part of the prospective epidemiological research studies in Iran (PERSIAN). Fasting blood glucose >126 and use of glucose-lowering drugs were used from cohort data as T2DM. Anthropometric indices were compared for T2DM or prediabetes odds vs. normal group. Demographic characteristics and risk factors were compared in diabetes, prediabetes, and normal groups.
Results:
Of 9895 participants, about 23 (n = 2283) and 35% (n = 3455) of this population had T2DM and prediabetes, respectively. After adjusting for potential confounders, for waist-to-hip ratio (WHR) (OR = 3.25, 95% CI 2.68–3.94) and waist-to-height ratio (WHtR) (OR = 2.90, 95% CI 2.40–3.49), individuals in the highest quartile had a higher probability of developing T2DM than individuals in the lowest quartile. Also, the odds ratio of T2DM increased in participants with overweight, obesity, and abnormal waist circumference (WC) by considering a cutoff point.
Conclusions:
According to our findings, the strongest and weakest anthropometric indexes related to T2DM were WHR and WC, respectively.
Keywords: Body mass index, prospective epidemiological research studies in Iran (PERSIAN), Rafsanjan Cohort Study (RCS), type 2 diabetes mellitus, waist circumference, waist-to-height ratio, waist-to-hip ratio
Introduction
Type 2 diabetes mellitus (T2DM) prevalence has increased dramatically in the last decades and has become a major public health burden worldwide.[1,2] It is estimated that by 2045, the prevalence of diabetes in people aged 20 to 79 will increase from 10.1% to 12.2% and reach 738.2 million people in the world.[3] Age, race, family history, dietary habits, obesity, and insufficient physical activity were well-known and serious risk factors for T2DM, among which obesity was significant.[2]
Although there is a definite relationship between obesity and diabetes, it remains unknown which anthropometric index is suitable to assess obesity and evaluate the risk of T2DM in the population.[4] There are different anthropometric indices to measure body fat accumulation. Body mass index (BMI) as a general obesity indicator and waist-to-hip ratio (WHR), waist circumference (WC), and waist-to-height ratio (WHtR) are indices to assess abdominal obesity.[5]
BMI, which is suggested by the World Health Organization, is the most common index used to measure obesity in people.[6] However, some studies indicated that BMI as a marker to reflect general obesity is not able to show body fat distribution and abdominal obesity.[7,8]
On the other hand, several studies have reported that diabetes was more associated with central obesity than general obesity.[9,10,11] Hence, other measures of adiposity, such as WHR, WC, and WHtR, have been used in studies to measure abdominal fat.[12] The results were different in various studies. Some had found that indicators of abdominal obesity had better predictive abilities for diabetes risk,[12,13,14] while others had shown that BMI was better,[6,8] or they had the same predictive powers.[15,16]
In addition, other studies have shown that the predictive strength of different anthropometric indices for diabetes risk varies in different countries and races.[17,18,19] Because of the high prevalence and incidence of T2DM in Iran,[20] this study assessed the association between anthropometric indices and T2DM in the Rafsanjan Cohort Study.
Methods
Study population
The Rafsanjan cohort study (RCS)[21] as a branch of the Prospective Epidemiological Research Study in Iran (PERSIAN) cohort[22] is an ongoing population-based longitudinal study of adults, aged 35–70 years, living in urban and rural areas of Rafsanjan, a region in the southeast of Iran. RCS was initiated in August 2015. Participants were selected by multistage random sampling. Detailed protocol of the RCS has been reported previously.[21] In summary, 9991 individuals from both genders participated in RCS. After obtaining their informed written consent, medical history, personal habits, and demographic data of all participants were asked following a standardized interview using validated questionnaires by the PERSIAN.[22] Measurements of blood pressure, physical examinations, and anthropometric parameters were also carried out. Ethical approval was obtained from the Ethics Committee of Rafsanjan University of Medical Sciences (IR.RUMS.REC.1400.055). In the present cross-sectional study, subjects with incomplete data on medical history and fasting blood sugar were excluded, and finally, 9895 subjects were included. The frequency difference between the total number and some of the covariates was related to the missing data.
Anthropometric assessment
Trained healthcare providers measured anthropometric characteristics, including height, waist, hip, and wrist circumferences (cm), and weight (kg) according to the US National Institutes of Health protocols.[21] Since measurement errors were the least in the morning, anthropometric indices were measured while the participants were still fasting. In addition, before examining the anthropometric indicators, participants were asked to remove extra clothes and shoes.
Weight and height were measured with the participants in a standing position using a digital scale (SECA Digital Scale, Germany) and a stadiometer (SECA Stadiometer, Germany), respectively. WC was determined using a constant tension tape with a precision of 0.1 cm at the level of the umbilicus and hip circumference (HC) was also measured at the widest point over the hips.[21]
The abnormality of WC was evaluated in participants according to three definitions: Adult Treatment Panel III (ATP-III), International Diabetes Federation (IDF), and IDF ethnic-specific cutoff for the Iranian population (Iranian IDF). According to ATP-III, WC ≥ 102 cm in men and ≥ 88 cm in women were defined as abnormal WC. Based on IDF International, WC ≥ 94 cm in men and ≥ 80 cm in women was considered abnormal WC. To define abnormal WC based on Iranian IDF, a cutoff point of WC ≥ 95 cm in both genders was used (metabolic syndrome criteria).[23]
BMI was calculated as weight (kg) divided by height squared (m2). BMI was divided into four groups (<25, 25–30, 30–35, and ≥ 35). Also, WHR and WHtR were determined by dividing the waist to hip and height in cm, respectively. WHR, WHtR, and wrist were categorized into quartiles to assess the dose–response relationship. Also, we used the optimal cutoff points of the three anthropometric indices, including WC, WHR, and WHtR, in a sample of Iranian adults. The proposed optimal cutoffs for WC, WHtR, and WHR were 84 cm, 0.48, and 0.78 for women and 98 cm, 0.56, and 0.87 for men, respectively.[24]
Biochemical measurements
For each participant, samples of fasting blood were collected by trained technicians. Fasting blood sugar (FBS), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), and total cholesterol (TC) were measured by a biotechnical analyzer (BT 1500, Italy) at the Central Laboratory in the cohort center.
Assessment of other variables
Socioeconomic status was determined using the wealth score index (WSI). Physical activity was reported as the metabolic equivalent of task (MET) calculated for 24 hours. Personal habits, including cigarette smoking, opium use, and alcohol consumption, were self-reported. Methods regarding the collection of these data had been previously described in detail.[25] Individuals who reported hypertension diagnosed by a physician were considered to have hypertension. T2DM was defined as a fasting blood glucose level equal to or higher than 126 mmol/L, or the use of glucose-lowering drugs.[26] Based on the American Diabetes Association (ADA), a fasting blood glucose level of 100–125 mg/dl in subjects without the use of glucose-lowering drugs was considered prediabetes.[27]
Statistical analyses
Frequency (%) for categorical variables and mean ± standard deviation for the quantitative variables were used and baseline characteristics were compared across the groups of our study using Chi-square (χ²) for categorical variables and t-test for continuous variables.
In addition, crude and two adjusted models were used in the multinomial logistic regression analysis to assess the association, calculated odds ratio, and 95% confidence intervals, between diabetes status and anthropometric indices as dependent and in-depended variables, respectively. Based on subject matter knowledge and epidemiological research, we identified confounder’s variables. Potential confounding variables were sequentially entered into models according to their hypothesized strengths of association with anthropometric indices and T2DM or prediabetes. Then, variables with a P value < 0.25 were selected as confounders. The baseline model (crude model) has been stratified on anthropometric indices.
Adjusted model 1 included anthropometric indices and basic sociodemographic characteristics (age, gender, education, wealth score index (WSI), cigarette smoking, alcohol consumption, opium consumption, physical activity level, first-degree family history of diabetes, and second-degree family history of diabetes). Adjusted model 2 adjusted including variables in adjusted model 1 and hypertension, LDL, TG, and HDL. Data of the present study were analyzed by STATA 14.0 (STATA Corp, College Station, TX) software. All P values are two-sided, and P values < 0.05 and 95% confidence intervals were considered statistically significant.
Results
The study population included 4605 males (46.54%) and 5290 females (53.46%), aged 35–70 years, as well as 2283 participants who had T2DM (23.07%), and also 3455 participants who had prediabetes (34.92%). The mean age ± SD of participants was 49.9 ± 9.56 and there was a statistically significant difference in the mean age of T2DM, prediabetes, and normal groups (P < 0.001). About 48.5% of the participants had an education level between 6 and 12 years, 41% had BMI between 25 and 29.99, 54% had WC APT III abnormal, 53% had WC Iranian IDF abnormal, and 73% had WC IDF abnormal. Also, there were differences in gender, educational level, physical activity, BMI, wealth score index, cigarette smoking, opium and alcohol consumption, hypertension, HDL, LDL, TG, cholesterol, wrist, WC, WHtR, WHR, and family history of diabetes between diabetes, prediabetes, and normal groups [Table 1].
Table 1.
Demographic selected medical and laboratory characteristics of study participants
| Characteristic | Total (n=9895) | Normal (n=4157) | Prediabetes (n=3455) | T2DM (n=2283) | P |
|---|---|---|---|---|---|
| Age- yr. n (%) | <0.001 | ||||
| 35–45 | 3672 (37.11) | 2206 (53.07) | 1161 (33.60) | 305 (13.36) | |
| 46–55 | 3051 (30.83) | 1147 (27.59) | 1180 (34.15) | 724 (31.71) | |
| ≥56 | 3172 (32.06) | 804 (19.34) | 1114 (32.24) | 1254 (54.93) | |
| Mean±SD | 49.96±9.56 | 46.54±8.92 | 50.35±9.20 | 55.61±8.35 | <0.001 |
| Gender n (%) | <0.001 | ||||
| Male | 4605 (46.54) | 1929 (41.89) | 1784 (38.74) | 892 (19.37) | |
| Female | 5290 (53.46) | 2228 (42.12) | 1671 (31.59) | 1391 (26.29) | |
| Education n (%) | <0.001 | ||||
| ≤5 years | 3476 (35.15) | 1129 (27.18) | 1215 (35.19) | 1132 (49.58) | |
| 6–12 years | 4795 (48.48) | 2229 (53.66) | 1663 (48.16) | 903 (39.55) | |
| ≥ 13 years | 1619 (16.37) | 796 (19.16) | 575 (19.65) | 248 (10.86) | |
| Mean±SD | 8.52±5.05 | 9.5±4.80 | 8.50±4.97 | 6.75±5.14 | <0.001 |
| Physical activity n (%) | <0.001 | ||||
| Mean±SD | 38.79±6.32 | 39.23±6.53 | 39.09±6.61 | 37.56±5.23 | |
| WSI n (%) | <0.001 | ||||
| Mean±SD | -0.001±1.00 | 0.076±0.997 | 0.005±0.999 | -0.150±0.989 | |
| Alcohol consumption n (%) | <0.001 | ||||
| No | 8889 (90.02) | 3674 (88.68) | 3081 (89.23) | 2134 (93.64) | |
| Yes | 986 (9.98) | 469 (11.32) | 372 (10.77) | 145 (6.36) | |
| Cigarette smoking n (%) | <0.001 | ||||
| No | 7342 (74.35) | 3068 (74.05) | 2484 (71.94) | 1790 (78.54) | |
| Yes | 2533 (25.65) | 1075 (25.95) | 969 (28.06) | 489 (21.46) | |
| Opium consumption n (%) | <0.001 | ||||
| No | 7546 (76.42) | 3206 (77.38) | 2560 (74.14) | 1780 (78.10) | |
| Yes | 2329 (23.58) | 937 (22.62) | 893 (25.86) | 499 (21.90) | |
| BMI n (%) | <0.001 | ||||
| <25 | 2847 (28.79) | 1501 (36.13) | 915 (26.49) | 431 (18.90) | |
| 25–29.99 | 4055 (41.01) | 1677 (40.37) | 1435 (41.55) | 943 (41.36) | |
| 30–34.99 | 2268 (22.94) | 778 (18.73) | 842 (24.38) | 648 (28.42) | |
| ≥35 | 718 (7.26) | 198 (4.77) | 262 (7.59) | 258 (11.32) | |
| Mean±SD | 27.84±4.89 | 26.82±4.69 | 28.13±4.88 | 29.23±4.87 | <0.001 |
| Wrist | <0.001 | ||||
| Quartile 1 | 2416 (24.43) | 1317 (31.70) | 706 (20.44) | 393 (17.23) | |
| Quartile 2 | 2618 (26.47) | 1160 (27.92) | 894 (25.88) | 564 (24.73) | |
| Quartile 3 | 2430 (24.57) | 901 (21.69) | 910 (26.35) | 619 (27.14) | |
| Quartile 4 | 2425 (24.52) | 776 (18.68) | 944 (27.33) | 705 (30.91) | |
| Mean±SD | 17.29±1.44 | 16.98±1.40 | 17.47±1.41 | 17.57±1.45 | <0.001 |
| WC APT III n (%) | <0.001 | ||||
| Normal | 4548 (45.99) | 2171 (52.26) | 1622 (46.96) | 755 (33.10) | |
| Abnormal | 5341 (54.01) | 1983 (47.74) | 1832 (53.04) | 1526 (66.90) | |
| WC Iranian IDF n (%) | <0.001 | ||||
| Normal | 4623 (46.75) | 2358 (56.78) | 1539 (44.56) | 726 (31.83) | |
| Abnormal | 5265 (53.25) | 1795 (43.22) | 1915 (55.44) | 1555 (68.17) | |
| WC IDF n (%) | <0.001 | ||||
| Normal | 2700 (27.30) | 1330 (32.02) | 985 (28.52) | 385 (16.88) | |
| Abnormal | 7189 (72.70) | 2824 (67.98) | 2469 (71.48) | 1896 (83.12) | |
| WC Iranian cutoff point | <0.001 | ||||
| Normal | 3594 (36.34) | 1751 (42.15) | 1289 (37.32) | 554 (24.29) | |
| Abnormal | 6295 (63.66) | 2403 (57.85) | 2165 (62.68) | 1727 (75.71) | |
| WHtR | <0.001 | ||||
| Quartile 1 | 2469 (24.97) | 1462 (35.19) | 736 (21.31) | 271 (11.88) | |
| Quartile 2 | 2465 (24.93) | 1073 (25.83) | 879 (25.45) | 513 (22.49) | |
| Quartile 3 | 2481 (25.09) | 929 (22.36) | 937 (27.13) | 615 (26.96) | |
| Quartile 4 | 2474 (25.02) | 690 (16.61) | 902 (26.11) | 882 (38.67) | |
| WHtR Iranian cutoff point | <0.001 | ||||
| Normal | 3183 (32.19) | 1522 (36.64) | 1187 (34.37) | 474 (20.78) | |
| Abnormal | 6706 (67.81) | 2632 (63.36) | 2267 (65.63) | 1807 (79.22) | |
| WHR | <0.001 | ||||
| Quartile 1 | 2495 (25.23) | 1455 (35.03) | 802 (23.22) | 238 (10.43) | |
| Quartile 2 | 2447 (24.74) | 1105 (26.60) | 884 (25.59) | 458 (20.08) | |
| Quartile 3 | 2464 (24.92) | 926 (22.29) | 886 (25.65) | 652 (28.58) | |
| Quartile 4 | 2483 (25.11) | 668 (16.08) | 882 (25.54) | 933 (40.90) | |
| WHR Iranian cutoff point | <0.001 | ||||
| Normal | 815 (8.24) | 498 (11.99) | 266 (7.70) | 51 (2.24) | |
| Abnormal | 9074 (91.76) | 3656 (88.01) | 3188 (92.30) | 2.230 (97.76) | |
| Hypertension n (%) | <0.001 | ||||
| No | 7660 (77.41) | 3684 (88.62) | 2734 (79.13) | 1242 (54.40) | |
| Yes | 2235 (22.59) | 473 (11.38) | 721 (20.87) | 1041 (45.60) | |
| First-degree family history of T2 Diabetes n (%) | <0.001 | ||||
| No | 5016 (50.69) | 2379 (57.23) | 1801 (52.13) | 836 (36.62) | |
| Yes | 4879 (49.31) | 1778 (42.77) | 1654 (47.87) | 1447 (63.38) | |
| Second-degree family history of T2 Diabetes n (%) | <0.001 | ||||
| No | 7465 (75.44) | 3050 (73.37) | 2692 (77.92) | 1723 (75.47) | |
| Yes | 2430 (24.56) | 1107 (26.63) | 763 (22.08) | 560 (24.53) | |
| TG | <0.001 | ||||
| Mean±SD | 169.16±110.74 | 147.50±77.70 | 175.43±112.38 | 199.14±145.78 | |
| HDL n (%) | <0.001 | ||||
| Mean±SD | 57.75±10.87 | 58.50±11.04 | 57.30±10.55 | 57.07±10.98 | |
| LDL n (%) | <0.001 | ||||
| Mean±SD | 108.18±30.29 | 107.59±27.96 | 111.99±29.87 | 103.47±34.04 | |
| Cholesterol | <0.001 | ||||
| Mean±SD | 198.66±38.04 | 195.23±35.32 | 203.16±36.65 | 198.11±43.80 |
Wealth score index (WSI); body mass index (BMI); triglyceride (TG); high-density lipoprotein (HDL); low-density lipoprotein (LDL); type 2 diabetes mellitus (T2DM)
Table 2 shows the odds ratio of prediabetes and T2DM groups by anthropometric indices. The results showed that odds of prediabetes group in crude model increased 1.40 time in participants with BMI 25–29.99 (95% CI 1.26–1.56), 1.78 time in participants with BMI 30–34.99 (95% CI 1.56–2.02), 2.17 time in participant with BMI ≥ 35 (95% CI 1.77–2.66), and 1.06 time in BMI continues (95% CI 1.05–1.07), even after adjusted confounding variables in adjusted model 1 (BMI 25–29.99: OR = 1.52, 95% CI 1.35–1.70; BMI 30–34.99: OR = 2.00, 95% CI 1.74–2.30; BMI ≥ 35: OR = 2.60, 95% CI 2.10–3.22; BMI continues: OR = 1.08, 95% CI 1.07–1.09) the risk of prediabetes group increased significantly and in the adjusted model 2 (BMI 25–29.99: OR = 1.31, 95% CI 1.16–1.47; BMI 30–34.99: OR = 1.65, 95% CI 1.44–1.91; BMI ≥ 35: OR = 2.07, 95% CI 1.66–2.57; BMI continues: OR = 1.06, 95% CI 1.05–1.07), this relationship was still significant.
Table 2.
The odds ratio of prediabetes and type 2 diabetes disease by anthropometric indices
| Anthropometric indices | Prediabetes vs. normal | T2DM vs. normal | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Crude model | Adjusted model 1 | Adjusted model 2 | Crude model | Adjusted model 1 | Adjusted model 2 | |
| BMI | ||||||
| <25 | 1 | 1 | 1 | 1 | 1 | 1 |
| 25–29.99 | 1.40 (1.26–1.56) | 1.52 (1.35–1.70) | 1.31 (1.16–1.47) | 1.96 (1.71–2.24) | 2.03 (1.75–2.37) | 1.56 (1.33–1.83) |
| 30–34.99 | 1.78 (1.56–2.02) | 2.00 (1.74–2.30) | 1.65 (1.44–1.91) | 2.90 (2.50–3.37) | 2.81 (2.37–3.35) | 1.98 (1.65–2.38) |
| ≥35 | 2.17 (1.77–2.66) | 2.60 (2.10–3.22) | 2.07 (1.66–2.57) | 4.54 (3.66–5.62) | 4.37 (3.42–5.60) | 2.76 (2.13–3.57) |
| BMI continues | 1.06 (1.05–1.07) | 1.08 (1.07–1.09) | 1.06 (1.05–1.07) | 1.11 (1.10–1.12) | 1.11 (1.10–1.13) | 1.08 (1.06–1.09) |
| WC ATP III | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.24 (1.13–1.35) | 1.76 (1.54–2.00) | 1.50 (1.31–1.71) | 2.21 (1.99–2.46) | 2.41 (2.04–2.84) | 1.77 (1.49–2.10) |
| WC Iranian IDF | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.63 (1.49–1.79) | 1.62 (1.47–1.78) | 1.40 (1.26–1.54) | 2.81 (2.53–3.13) | 2.29 (2.02–2.58) | 1.75 (1.54–1.99) |
| WC IDF | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.18 (1.07–1.30) | 1.54 (1.35–1.74) | 1.28 (1.12–1.46) | 2.32 (2.04–2.63) | 2.39 (2.02–2.84) | 1.73 (1.45–2.07) |
| WC Iranian cutoff point | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.22 (1.12–1.34) | 1.74 (1.53–1.98) | 1.48 (1.30–1.69) | 2.27 (2.03–2.55) | 2.63 (2.23–3.11) | 1.95 (1.64–2.32) |
| WHR | ||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 1.45 (1.28–1.64) | 1.32 (1.17–1.50) | 1.16 (1.02–1.32) | 2.53 (2.13–3.02) | 2.01 (1.67–2.42) | 1.73 (1.42–2.10) |
| Quartile 3 | 1.74 (1.53–1.97) | 1.47 (1.29–1.68) | 1.22 (1.07–1.40) | 4.30 (3.63–5.10) | 2.85 (2.37–3.42) | 2.21 (1.83–2.67) |
| Quartile 4 | 2.40 (2.10–2.73) | 1.82 (1.59–2.10) | 1.46 (1.27–1.69) | 8.54 (7.21–10.11) | 4.52 (3.76–5.44) | 3.25 (2.68–3.94) |
| WHR Iranian cutoff point | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.63 (1.40–1.91) | 1.78 (1.50–2.11) | 1.39 (1.17–1.66) | 5.96 (4.44–7.98) | 4.45 (3.22–6.13) | 2.94 (2.12–4.06) |
| WHtR | ||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 1.63 (1.44–1.85) | 1.50 (1.32–1.71) | 1.31 (1.15–1.49) | 2.58 (2.18–3.05) | 2.15 (1.79–2.58) | 1.74 (1.44–2.10) |
| Quartile 3 | 2.00 (1.76–2.27) | 1.73 (1.52–1.97) | 1.42 (1.24–1.63) | 3.57 (3.03–4.21) | 2.47 (2.06–2.96) | 1.80 (1.49–2.17) |
| Quartile 4 | 2.60 (2.27–2.97) | 2.15 (1.87–2.46) | 1.72 (1.49–1.99) | 6.90 (5.86–8.12) | 4.31 (3.60–5.16) | 2.90 (2.40–3.49) |
| WHtR Iranian cutoff point | ||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.10 (1.00–1.21) | 1.52 (1.32–1.75) | 1.29 (1.12–1.50) | 2.20 (1.96–2.48) | 2.33 (1.95–2.79) | 1.77 (1.47–2.13) |
| Wrist | ||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 1.44 (1.27–1.63) | 1.42 (1.25–1.61) | 1.31 (1.15–1.49) | 1.63 (1.40–1.90) | 1.58 (1.34–1.87) | 1.36 (1.14–1.62) |
| Quartile 3 | 1.88 (1.65–2.15) | 1.83 (1.60–2.09) | 1.60 (1.40–1.83) | 2.30 (1.98–2.68) | 2.21 (1.86–2.62) | 1.72 (1.44–2.05) |
| Quartile 4 | 2.27 (1.99–2.59) | 2.16 (1.89–2.47) | 1.82 (1.58–2.09) | 3.04 (2.62–3.54) | 2.71 (2.28–3.21) | 1.95 (1.64–2.33) |
The crude model is stratified on the status of anthropometric indices. The adjusted model 1 is adjusted for confounding variables age (continuous variable), gender (male/female), education (continuous variable), WSI (continuous variable), cigarette smoking (yes/no), alcohol consumption (yes/no), opium consumption (yes/no), physical activity level (continuous variable), first-degree family history of diabetes (yes/no), and second-degree family history of diabetes (yes/no). The adjusted model 2 is adjusted for confounding variables in adjusted model 1 and hypertension (yes/no), LDL (continuous variable), TG (continuous variable), and HDL (continuous variable). BMI: Body mass index; WC: waist circumference; WHR: waist-to-hip ratio; WHtR: waist-to-height ratio; WSI: wealth score index; T2DM: type 2 diabetes mellitus
Also, odds of prediabetes increased in a participant who had abnormal WC (ATP III: OR = 1.76, 95% CI 1.54–2.00; Iranian IDF: OR = 1.62, 95% CI 1.47–1.78; IDF: OR = 1.54, 95% CI 1.35–1.74 and WC Iranian cutoff point: OR = 1.74, 95% CI 1.53–1.98) in adjusted model 1 and adjusted model 2 (ATP III: OR = 1.50, 95% CI 1.31–1.71; Iranian IDF: OR = 1.40, 95% CI 1.26–1.54; IDF: OR = 1.28, 95% CI 1.12–1.46 and WC Iranian cutoff point: OR = 1.48, 95% CI 1.30–1.69), also in the second, third, and fourth quartiles of WHR, WHtR, and wrist after adjusted confounding variables in adjusted models 1 and 2, all of them remain significant. Odds of prediabetes increased in participant who had abnormal WHR Iranian cutoff point (adjusted model 1: OR = 1.78, 95% CI 1.50–2.11, and adjusted model 2: OR = 1.39, 95% CI 1.17–1.66) and abnormal WHtR Iranian cutoff point (adjusted model 1: OR = 1.52, 95% CI 1.32–1.75, and adjusted model 2: OR = 1.29, 95% CI 1.12–1.50) [Table 2].
Results in the crude model indicated that odds of T2DM significantly increased in participants with BMI ≥ 25, BMI continues (OR: 1.11, 95% CI 1.10–1.12), abnormal WC ATP III (OR: 2.21, 95% CI 1.99–2.46), WC Iranian IDF (OR: 2.81, 95% CI 2.53–3.13), WC IDF (OR: 2.32, 95% CI 2.04–2.63), WC Iranian cutoff point (OR: 2.27, 95% CI 2.03–2.55), and all quartiles of WHR, WHtR, wrist, and WHR Iranian cutoff point and WHtR Iranian cutoff point compared reference groups in crud model. After adjusting confounding variables in models 1 and 2, there was a significant relationship between the increased risk of T2DM and all anthropometric indices [Table 2].
Table 3 shows the association between prediabetes and T2DM with anthropometric indices in age groups of both genders. The odds of prediabetes dose–response increased for both age groups and both gender, men and women, in BMI > 30. Also, a dose–response increase in the odds of T2DM was observed in all age and gender groups with increase in BMI. According to Table 3, in other indices, the odds of T2DM and prediabetes increased with increase in anthropometric indices compared to normal levels.
Table 3.
The association between prediabetes and type 2 diabetes disease with anthropometric indices in age groups of both genders
| Anthropometric indices | Adjusted OR (95% CI) | |||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| Male | Female | |||||||
|
|
|
|||||||
| Prediabetes | T2DM | Prediabetes | T2DM | |||||
|
|
|
|
|
|||||
| 35–50 years old | >50 years old | 35–50 years old | >50 years old | 35–50 years old | >50 years old | 35–50 years old | >50 years old | |
| BMI | ||||||||
| <25 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 25–29.99 | 1.12 (0.91–1.37) | 1.47 (1.17–1.85) | 2.19 (1.50–3.20) | 1.66 (1.27–2.17) | 1.21 (0.95–1.55) | 1.64 (1.20–2.25) | 1.12 (0.75–1.68) | 1.54 (1.12–2.12) |
| 30–34.99 | 1.50 (1.13–1.98) | 1.60 (1.13–2.26) | 2.51 (1.50–3.20) | 2.08 (1.42–3.05) | 1.50 (1.16–1.96) | 1.98 (1.43–2.74) | 1.79 (1.19–2.70) | 1.68 (1.21–2.35) |
| ≥35 | 2.08 (1.20–3.59) | 1.62 (0.69–3.79) | 4.36 (2.03–9.35) | 1.44 (0.57–3.66) | 1.89 (1.33–2.67) | 2.42 (1.56–3.75) | 2.33 (1.43–3.79) | 2.41 (1.56–3.74) |
| BMI continues | 1.05 (1.02–1.07) | 1.07 (1.04–1.10) | 1.10 (1.06–1.14) | 1.09 (1.06–1.13) | 1.05 (1.03–1.07) | 1.07 (1.04–1.09) | 1.07 (1.05–1.10) | 1.06 (1.04–1.09) |
| WC ATP III | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.51 (1.18–1.93) | 1.21 (0.91–1.61) | 2.46 (1.73–3.50) | 1.34 (0.98–1.82) | 1.43 (1.13–1.82) | 1.87 (1.32–2.64) | 1.39 (0.94–2.06) | 2.10 (1.44–3.05) |
| WC Iranian IDF | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.41 (1.16–1.70) | 1.19 (0.95–1.47) | 1.87 (1.36–2.55) | 1.57 (1.23–2.01) | 1.37 (1.14–1.65) | 1.56 (1.24–1.97) | 1.79 (1.35–2.38) | 1.71 (1.35–2.18) |
| WC IDF | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.25 (1.04–1.52) | 1.23 (0.99–1.52) | 1.65 (1.21–2.25) | 1.76 (1.38–2.25) | 1.52 (0.95–2.43) | 1.54 (0.75–3.20) | 2.20 (0.86–5.62) | 1.83 (0.80–4.18) |
| WC Iranian cutoff point | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.57 (1.28–1.92) | 1.20 (0.95–1.52) | 2.31 (1.69–3.17) | 1.61 (1.25–2.08) | 1.54 (1.11–2.14) | 1.77 (1.10–2.83) | 1.69 (0.95–3.03) | 2.57 (1.48–4.46) |
| WHR | ||||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 1.28 (1.01–1.63) | 1.23 (0.91–1.66) | 1.68 (1.06–2.66) | 2.08 (1.38–3.13) | 1.01 (0.81–1.26) | 1.16 (0.83–1.62) | 1.73 (1.18–2.51) | 1.37 (0.94–1.99) |
| Quartile 3 | 1.29 (1.01–1.67) | 1.44 (1.06–1.94) | 1.90 (1.18–3.05) | 2.94 (1.98–4.36) | 1.04 (0.815–1.32) | 1.21 (0.87–1.68) | 2.28 (1.55–3.34) | 1.75 (1.22–2.51) |
| Quartile 4 | 1.50 (1.13–2.00) | 1.85 (1.37–2.51) | 2.78 (1.73–4.47) | 4.34 (2.93–6.42) | 1.14 (0.86–1.51) | 1.48 (1.06–2.07) | 3.78 (2.54–5.64) | 2.55 (1.77–3.65) |
| WHR Iranian cutoff point | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.43 (1.12–1.81) | 1.50 (1.12–2.00) | 2.40 (1.44–4.00) | 3.38 (2.18–5.22) | 2.17 (0.63–7.43) | 0.43 (0.04–4.21) | N | N |
| WHtR | ||||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 1.16 (0.91–1.49) | 1.64 (1.21–2.22) | 1.89 (1.17–3.06) | 2.03 (1.40–2.97) | 1.05 (0.82–1.31) | 1.76 (1.27–2.45) | 1.52 (1.04–2.22) | 1.77 (1.25–2.52) |
| Quartile 3 | 1.14 (0.87–1.48) | 1.77 (1.32–2.39) | 1.68 (1.02–2.77) | 2.37 (1.65–3.40) | 1.40 (1.11–1.78) | 1.56 (1.12–2.16) | 1.80 (1.23–2.65) | 1.47 (1.05–2.07) |
| Quartile 4 | 1.56 (1.18–2.06) | 2.01 (1.48–2.72) | 3.34 (2.05–5.45) | 3.05 (2.12–4.40) | 1.42 (1.08–1.87) | 2.13 (1.53–2.96) | 2.90 (1.96–4.29) | 2.66 (1.88–2.52) |
| WHtR Iranian cutoff point | ||||||||
| Normal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Abnormal | 1.38 (1.12–1.69) | 1.20 (0.96–1.50) | 2.07 (1.51–2.83) | 1.66 (1.29–2.12) | 1.34 (0.74–2.43) | 1.88 (0.63–5.58) | 6.26 (0.84–46.56) | 1.36 (0.44–4.25) |
| Wrist | ||||||||
| Quartile 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Quartile 2 | 0.99 (0.77–1.27) | 1.83 (1.38–2.43) | 1.23 (0.76–1.97) | 1.48 (1.06–2.06) | 1.32 (1.03–1.67) | 1.24 (0.92–1.68) | 1.26 (0.86–1.83) | 1.45 (1.06–1.98) |
| Quartile 3 | 1.40 (1.09–1.81) | 1.76 (1.31–2.36) | 2.14 (1.36–3.37) | 1.62 (1.16–2.26) | 1.49 (1.16–1.92) | 1.79 (1.30–2.46) | 1.23 (0.83–1.82) | 2.03 (1.46–2.81) |
| Quartile 4 | 1.44 (1.11–1.87) | 1.82 (1.35–2.45) | 2.21 (1.40–3.49) | 1.61 (1.15–2.27) | 1.97 (1.52–2.56) | 1.99 (1.45–2.73) | 2.05 (1.41–3.00) | 1.98 (1.43–2.75) |
The adjusted model is adjusted for confounding variables age (continuous variable), gender (male/female), education (continuous variable), WSI (continuous variable), cigarette smoking (yes/no), alcohol consumption (yes/no), opium consumption (yes/no), physical activity level (continuous variable), first-degree family history of diabetes (yes/no), second-degree family history of diabetes (yes/no), Hypertension (yes/no), LDL (continuous variable), TG (continuous variable), and HDL (continuous variable). BMI: Body mass index; WC: waist circumference; WHR: waist-to-hip ratio; WHtR: waist-to-height ratio; WSI: wealth score index; T2DM: type 2 diabetes mellitus
Discussion
In this cross-sectional study, we aimed to assess different anthropometric indices to predict the odds of T2DM among participants of the Rafsanjan Cohort Study. Our study’s results indicated that all anthropometric variables were significantly related to T2DM in diabetic patients and prediabetic ones after controlling the potential confounders.
Our findings demonstrated that WHR has the best ability to predict T2DM risk compared to the other indices. This finding is in line with the findings of other studies among different populations. Similar to our results, the Guangzhou Biobank Cohort Study,[28] the RODAM study among Ghanaian migrants and nonmigrants,[29] a study in the Iraqi population,[30] an investigation among the Taiwanese population,[31] and two other studies among Iranian adults[13,32] have shown the superiority of WHR to other indicators. It was also shown that WHtR as another indicator of abdominal obesity is also a better index than BMI. It was consistent with the findings of some investigations,[4,5,12,18] while the superiority of BMI over WHR and WHtR has been mentioned in some other studies that were inconsistent with our results.[6,8] A possible reason for this inconsistency is that indices, like WHR and WHtR, could reflect visceral obesity and body fat distribution better than BMI.[5] It has been revealed by different studies that diabetes is more related to abdominal obesity than general obesity. This might be due to different factors released by adipose tissue and leads to insulin resistance in tissues and increased insulin demands[13,17,33]
In our study, WC appeared to be the weakest predictor of T2DM risk compared to other indicators which was in line with the results of the Jinchang Cohort Study.[34] This finding was not in line with some other investigations. In a study in Malaysia, both WC and BMI had the same but better discriminatory abilities than WHR[8] In three other studies, WC and WHtR were more relative to T2DM than WHR and BMI[17,35], including a study in Iran.[5] This difference in our results could be because WC does not include the participants’ height nor the hip circumference; therefore, people with the same WC but taller have a lower risk and people with shorter height have a higher risk for diabetes.[12] In addition, participants with the same WC but a higher WHR have lower hip circumference. The hip area is made up of various components, including bone, fat tissue, and gluteal muscles. The muscle mass in this area is one of the main regions of insulin receptors; therefore, low muscle mass in this region may be related to the risk of insulin resistance and diabetes.[4,36] Furthermore, another investigation by Manolopoulos et al.[37] suggested that the adipose tissue in this area might have a protective role in T2DM risk by trapping the excess serum fatty acids and secreting adiponectin.
On the other hand, some other studies have shown that WHR, WHtR, WC, and BMI have the same values to predict T2DM risk.[15] Similarly, an investigation in Mauritius, demonstrated that WHR, BMI, and WC performed similarly in predicting diabetes risk.[16] Another study in Bangladesh showed that WHtR, WHR, and WC have the same predictive abilities in women.[38] The possible explanation for the difference in the results of the studies can be due to the difference in race, age, and gender. Another possible reason can be the difference in the study design or different methods.[18]
We also found that all anthropometric indices were positively associated with the presence of prediabetes. BMI showed the strongest association with prediabetic state among others; however, this finding was consistent with the results of the Jinchang Cohort Study,[34] while some other studies have shown the superiority of abdominal obesity indicators.[39,40] It should also be mentioned that some prediabetic participants may have received positive suggestions on weight loss and lifestyle changes, which could have affected the results, so more research is required on this issue.[40] Furthermore, wrist circumference was shown to be associated with T2DM risk and prediabetic state in our results. Similarly, it was shown by some other studies that wrist circumference is associated with T2DM among men and women[4,41]
The main strength of our study is the large sample size and detailed information about variable anthropometric indicators. However, there were some limitations in the present investigation. First, the present study is a cross-sectional study, and therefore, the causal relationship cannot be determined in this investigation. Second, each of the anthropometric indicators was measured only once in this cross-sectional study for each participant, and these indicators, which were our independent variables, may change over time due to the disease process. Third, it is possible that the results were influenced by treatments that were not adjusted, such as oral antidiabetic drugs and corticosteroids, or medical recommendations for weight loss.
Conclusion
In summary, the findings of our study showed that all anthropometric indices were associated with T2DM odds. Among these indices, WHR had the highest correlation with diabetes. In addition, WHtR also had a higher association with T2DM than BMI as an indicator of general obesity, and WC had the weakest association among them.
Ethics approval and consent to participate
The Ethics Committee of Rafsanjan University of Medical Sciences approved this study (Code of Ethics: IR.RUMS.REC.1400.055). Written informed consent was obtained from the participants. The data of participants were kept confidential and were only accessible to the study investigators. All methods were performed by the relevant guidelines and regulations.
Consent for publication
Not applicable.
Availability of data and materials
The datasets used during the current study are available on the Persian Adult Cohort Study Center, Rafsanjan University of Medical Sciences, Iran. The data are not available publicly. However, upon a reasonable request, the data can be obtained from the corresponding author.
Authors’ contributions
FA and AV designed the study and supervised the project. FR and SMP collected the data. FA prepared Tables 1, 2, 3. PK and ZK performed the statistical analysis. FA, ZJ, PK, MK, and AV wrote the main manuscript text. AV revised the paper. All the authors have read and approved the final manuscript.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
We thank the people who participated in the study, study site personnel, and members of the Rafsanjan cohort center in Rafsanjan, Iran.
Funding Statement
The Iranian Ministry of Health and Medical Education has contributed to the funding used in the PERSIAN cohort through grant no 700/534. This study has also been supported by the Vice Counselor for Research and Technology of Rafsanjan University of Medical Sciences. The context of this article is the authors’ view, and the funder had no role in the design of the study and collection, analysis, and interpretation of data, decision to publish, and writing the manuscript.
References
- 1.Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of type 2 diabetes–global burden of disease and forecasted trends. J Epidemiol Global Health. 2020;10:107–11. doi: 10.2991/jegh.k.191028.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Glovaci D, Fan W, Wong ND. Epidemiology of diabetes mellitus and cardiovascular disease. Curr Cardiol Rep. 2019;21:21. doi: 10.1007/s11886-019-1107-y. [DOI] [PubMed] [Google Scholar]
- 3.Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. doi: 10.1016/j.diabres.2021.109119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zafari N, Lotfaliany M, Mansournia MA, Khalili D, Azizi F, Hadaegh F. Optimal cut-points of different anthropometric indices and their joint effect in prediction of type 2 diabetes: Results of a cohort study. BMC Public Health. 2018;18:1–12. doi: 10.1186/s12889-018-5611-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hajian-Tilaki K, Heidari B. Is waist circumference a better predictor of diabetes than body mass index or waist-to-height ratio in Iranian adults? Int J Prev Med. 2015;6:5. doi: 10.4103/2008-7802.151434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Yang J, Wang F, Wang J, Han X, Hu H, Yu C, et al. Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: A 5 year prospective study. BMC Geriatr. 2018;18:1–9. doi: 10.1186/s12877-018-0912-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hadaegh F, Zabetian A, Harati H, Azizi F. The prospective association of general and central obesity variables with incident type 2 diabetes in adults, Tehran lipid and glucose study. Diabetes Res Clin Pract. 2007;76:449–54. doi: 10.1016/j.diabres.2006.09.030. [DOI] [PubMed] [Google Scholar]
- 8.Cheong KC, Ghazali SM, Hock LK, Subenthiran S, Huey TC, Kuay LK, et al. The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race. Diabetes Metab Syndr. 2015;9:74–8. doi: 10.1016/j.dsx.2015.02.006. [DOI] [PubMed] [Google Scholar]
- 9.Tyrovolas S, Koyanagi A, Garin N, Olaya B, Ayuso-Mateos JL, Miret M, et al. Diabetes mellitus and its association with central obesity and disability among older adults: A global perspective. Exp Gerontol. 2015;64:70–7. doi: 10.1016/j.exger.2015.02.010. [DOI] [PubMed] [Google Scholar]
- 10.Muche Ewunie T, Sisay D, Kabthymer RH. Diabetes mellitus and its association with central obesity, and overweight/obesity among adults in Ethiopia. A systematic review and meta-analysis. PLos One. 2022;17:e0269877. doi: 10.1371/journal.pone.0269877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Cao C, Hu H, Zheng X, Zhang X, Wang Y, He Y. Association between central obesity and incident diabetes mellitus among Japanese: A retrospective cohort study using propensity score matching. Sci Rep. 2022;12:13445. doi: 10.1038/s41598-022-17837-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mirzaei M, Khajeh M. Comparison of anthropometric indices (body mass index, waist circumference, waist to hip ratio and waist to height ratio) in predicting risk of type II diabetes in the population of Yazd, Iran. Diabetes Metab Syndr. 2018;12:677–82. doi: 10.1016/j.dsx.2018.04.026. [DOI] [PubMed] [Google Scholar]
- 13.Esmaillzadeh A, Mirmiran P, Azizi F. Waist-to-hip ratio is a better screening measure for cardiovascular risk factors than other anthropometric indicators in Tehranian adult men. Int J Obes. 2004;28:1325–32. doi: 10.1038/sj.ijo.0802757. [DOI] [PubMed] [Google Scholar]
- 14.Shah A, Bhandary S, Malik S, Risal P, Koju R. Waist circumference and waist-hip ratio as predictors of type 2 diabetes mellitus in the Nepalese population of Kavre District. Nepal Med Coll J. 2009;11:261–7. [PubMed] [Google Scholar]
- 15.Vazquez G, Duval S, Jacobs DR, Jr, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol Reviews. 2007;29:115–28. doi: 10.1093/epirev/mxm008. [DOI] [PubMed] [Google Scholar]
- 16.Nyamdorj R, Qiao Q, Söderberg S, Pitkäniemi JM, Zimmet PZ, Shaw JE, et al. BMI compared with central obesity indicators as a predictor of diabetes incidence in Mauritius. Obesity. 2009;17:342–8. doi: 10.1038/oby.2008.503. [DOI] [PubMed] [Google Scholar]
- 17.Zhang F-L, Ren J-X, Zhang P, Jin H, Qu Y, Yu Y, et al. Strong Association of waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) with diabetes: A population-based cross-sectional study in Jilin province, China. J Diabetes Res 2021. 2021:8812431. doi: 10.1155/2021/8812431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Khader Y, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. The performance of anthropometric measures to predict diabetes mellitus and hypertension among adults in Jordan. BMC Public Health. 2019;19:1–9. doi: 10.1186/s12889-019-7801-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Luo J, Hendryx M, Laddu D, Phillips LS, Chlebowski R, LeBlanc ES, et al. Racial and ethnic differences in anthropometric measures as risk factors for diabetes. Diabetes Care. 2019;42:126–33. doi: 10.2337/dc18-1413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Derakhshan A, Sardarinia M, Khalili D, Momenan AA, Azizi F, Hadaegh F. Sex specific incidence rates of type 2 diabetes and its risk factors over 9 years of follow-up: Tehran lipid and glucose study. PLoS One. 2014;9:e102563. doi: 10.1371/journal.pone.0102563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hakimi H, Ahmadi J, Vakilian A, Jamalizadeh A, Kamyab Z, Mehran M, et al. The profile of Rafsanjan cohort study. Eur J Epidemiol. 2021;36:243–52. doi: 10.1007/s10654-020-00668-7. [DOI] [PubMed] [Google Scholar]
- 22.Poustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar A-A, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. Am J Epidemiol. 2018;187:647–55. doi: 10.1093/aje/kwx314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Bahar A, Kashi Z, Kheradmand M, Hedayatizadeh-Omran A, Moradinazar M, Ramezani F, et al. Prevalence of metabolic syndrome using international diabetes federation, National Cholesterol Education Panel-Adult Treatment Panel III and Iranian criteria: Results of Tabari cohort study. J Diabetes Metab Disord. 2020;19:205–11. doi: 10.1007/s40200-020-00492-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Tutunchi H, Ebrahimi-Mameghani M, Ostadrahimi A, Asghari-Jafarabadi M. What are the optimal cut-off points of anthropometric indices for prediction of overweight and obesity?Predictive validity of waist circumference, waist-to-hip and waist-to-height ratios. Health Promot Perspect. 2020;10:142–7. doi: 10.34172/hpp.2020.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Jamali Z, Noroozi Karimabad M, Khalili P, Sadeghi T, Sayadi A, Mohammadakbari Rostamabadi F, et al. Prevalence of dyslipidemia and its association with opium consumption in the Rafsanjan cohort study. Sci Rep. 2022;12:11504. doi: 10.1038/s41598-022-13926-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Noroozi Karimabad M, Khalili P, Ayoobi F, Esmaeili-Nadimi A, La Vecchia C. Serum liver enzymes and diabetes from the Rafsanjan cohort study. BMC Endocr Disord. 2022;22:127. doi: 10.1186/s12902-022-01042-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Beulens J, Rutters F, Ryden L, Schnell O, Mellbin L, Hart H, et al. Risk and management of pre-diabetes. Eur J Prev Cardiol. 2019;26((2 Suppl)):47–54. doi: 10.1177/2047487319880041. [DOI] [PubMed] [Google Scholar]
- 28.Qin L, Corpeleijn E, Jiang C, Thomas GN, Schooling CM, Zhang W, et al. Physical activity, adiposity, and diabetes risk in middle-aged and older Chinese population: The Guangzhou Biobank Cohort Study. Diabetes Care. 2010;33:2342–8. doi: 10.2337/dc10-0369. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Darko SN, Meeks KA, Owiredu WK, Laing EF, Boateng D, Beune E, et al. Anthropometric indices and their cut-off points in relation to type 2 diabetes among Ghanaian migrants and non-migrants: The RODAM study. Diabetes Res Clin Pract. 2021;173:108687. doi: 10.1016/j.diabres.2021.108687. [DOI] [PubMed] [Google Scholar]
- 30.Mansour AA, Al-Jazairi MI. Cut-off values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hypertension in Iraq. Arch Med Res. 2007;38:253–8. doi: 10.1016/j.arcmed.2006.09.014. [DOI] [PubMed] [Google Scholar]
- 31.Cheng C-H, Ho C-C, Yang C-F, Huang Y-C, Lai C-H, Liaw Y-P. Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population. Nutr Res. 2010;30:585–93. doi: 10.1016/j.nutres.2010.08.007. [DOI] [PubMed] [Google Scholar]
- 32.Mirmiran P, Esmaillzadeh A, Azizi F. Detection of cardiovascular risk factors by anthropometric measures in Tehranian adults: Receiver operating characteristic (ROC) curve analysis. Eur J Clin Nutr. 2004;58:1110–8. doi: 10.1038/sj.ejcn.1601936. [DOI] [PubMed] [Google Scholar]
- 33.Radzevičienė L, Ostrauskas R. Body mass index, waist circumference, waist–hip ratio, waist–height ratio and risk for type 2 diabetes in women: A case–control study. Public Health. 2013;127:241–6. doi: 10.1016/j.puhe.2012.12.001. [DOI] [PubMed] [Google Scholar]
- 34.Ding J, Chen X, Bao K, Yang J, Liu N, Huang W, et al. Assessing different anthropometric indices and their optimal cutoffs for prediction of type 2 diabetes and impaired fasting glucose in Asians: The Jinchang cohort study. J Diabetes. 2020;12:372–84. doi: 10.1111/1753-0407.13000. [DOI] [PubMed] [Google Scholar]
- 35.Mbanya V, Kengne A, Mbanya J, Akhtar H. Body mass index, waist circumference, hip circumference, waist–hip-ratio and waist–height-ratio: Which is the better discriminator of prevalent screen-detected diabetes in a Cameroonian population? Diabetes Res Clin Pract. 2015;108:23–30. doi: 10.1016/j.diabres.2015.01.032. [DOI] [PubMed] [Google Scholar]
- 36.Janghorbani M, Momeni F, Dehghani M. Hip circumference, height and risk of type 2 diabetes: Systematic review and meta-analysis. Obes Rev. 2012;13:1172–81. doi: 10.1111/j.1467-789X.2012.01030.x. [DOI] [PubMed] [Google Scholar]
- 37.Manolopoulos K, Karpe F, Frayn K. Gluteofemoral body fat as a determinant of metabolic health. Int J Obes. 2010;34:949–59. doi: 10.1038/ijo.2009.286. [DOI] [PubMed] [Google Scholar]
- 38.Bhowmik B, Munir SB, Diep LM, Siddiquee T, Habib SH, Samad MA, et al. Anthropometric indicators of obesity for identifying cardiometabolic risk factors in a rural Bangladeshi population. J Diabetes Investig. 2013;4:361–8. doi: 10.1111/jdi.12053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Xu Z, Qi X, Dahl A, Xu W. Waist-to-height ratio is the best indicator for undiagnosed Type 2 diabetes. Diabet Med. 2013;30:e201–7. doi: 10.1111/dme.12168. [DOI] [PubMed] [Google Scholar]
- 40.Sangrós FJ, Torrecilla J, Giraldez-Garcia C, Carrillo L, Mancera J, Mur T, et al. Association of general and abdominal obesity with hypertension, dyslipidemia and prediabetes in the PREDAPS study. Rev Esp Cardiol (Engl Ed) 2018;71:170–7. doi: 10.1016/j.rec.2017.04.035. [DOI] [PubMed] [Google Scholar]
- 41.Jahangiri Noudeh Y, Hadaegh F, Vatankhah N, Momenan AA, Saadat N, Khalili D, et al. Wrist circumference as a novel predictor of diabetes and prediabetes: Results of cross-sectional and 8.8-year follow-up studies. J Clin Endocrinol Metab. 2013;98:777–84. doi: 10.1210/jc.2012-2416. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used during the current study are available on the Persian Adult Cohort Study Center, Rafsanjan University of Medical Sciences, Iran. The data are not available publicly. However, upon a reasonable request, the data can be obtained from the corresponding author.