Anthropometric Measures, Cardiometabolic and Hepatic Indices, and Cut-off Points for Predicting Type 2 Diabetes Mellitus in Southwest Iran: A Cross-Sectional Study from the Enrolment Phase of the Hoveyzeh Cohort Study

authors:

avatar Batoul Farhadi 1 , avatar Mehrnoosh Zakerkish 1 , * , avatar Meysam Alipour ORCID 2 , ** , avatar Homeira Rashidi 1

Diabetes Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Department of Nutrition, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran
Corresponding Authors:
Journal of Nursing and Midwifery Sciences: Vol.11, issue 3; e148315
published online: August 31, 2024
article type: Research Article
received: April 27, 2024
revised: July 16, 2024
accepted: August 3, 2024
DOI: https://doi.org/10.5812/jnms-148315

how to cite: Farhadi B, Zakerkish M, Alipour M, Rashidi H. Anthropometric Measures, Cardiometabolic and Hepatic Indices, and Cut-off Points for Predicting Type 2 Diabetes Mellitus in Southwest Iran: A Cross-Sectional Study from the Enrolment Phase of the Hoveyzeh Cohort Study. J Nurs Midwifery Sci. 2024;11(3):e148315. https://doi.org/10.5812/jnms-148315.

Abstract

Background:

Cut-off points for anthropometric measures associated with diabetes may vary across different ethnic groups.

Objectives:

This study aimed to identify the optimal cut-off values for these measures and their relationship with cardiometabolic and hepatic indices to predict type 2 diabetes mellitus (T2DM) in southwest Iran.

Methods:

This cross-sectional study was conducted in Hoveyzeh, Iran, and included a total of 7,300 individuals (1,607 with T2DM and 5,693 healthy participants). Anthropometric, cardiometabolic, and hepatic indices were calculated.

Results:

The visceral adiposity index (VAI) showed the highest correlation with cardiometabolic indices, such as the cardiometabolic index and lipid accumulation product. The conicity index (CI) had the strongest correlation with hepatic indices, including the hepatic steatosis index and the Alcoholic Liver Disease/Non-alcoholic Fatty Liver Disease (ALD/NAFLD) index. The atherogenic index of plasma was the most significant predictor of T2DM in the Hoveyzeh population for both men (OR: 3.39; CI: 2.38 - 4.81) and women (OR: 5.68; CI: 4.20 - 7.68). The optimal cut-off values for several anthropometric measures were higher in men than in women: BMI (28.0 vs. 25.48), Waist/Height ratio (0.64 vs. 0.56), Weight-adjusted waist index (11.83 vs. 10.76), CI (1.35 vs. 1.29), body roundness index (8.43 vs. 7.33), relative fat mass (44.63 vs. 28.5), and lipid accumulation product (67.23 vs. 67.04). Conversely, the cut-off values for optimal waist circumference (99.45 vs. 98.35), Waist/Hip ratio (0.99 vs. 0.96), VAI (2.22 vs. 2.19), and abdominal volume index (20 vs. 19.6) were higher in women than in men.

Conclusions:

The atherogenic index of plasma is a highly predictive indicator of T2DM. Our results suggest that novel anthropometric and cardiometabolic indices could serve as accessible and cost-effective strategies for assessing health outcomes related to T2DM.

1. Background

Type 2 diabetes mellitus (T2DM) is a chronic illness that can be largely prevented and is one of the four primary non-communicable diseases (1). Diabetes is a widespread endocrine disorder and a major global health issue. According to the latest statistics, approximately 9.3% (463 million adults) were diagnosed with diabetes in 2019. The number of people with impaired glucose tolerance worldwide is projected to increase to 700 million (10.9%) by 2045 (2). In Iran, from 2014 to 2020, diabetes and pre-diabetes affected 15% and 25.4% of the population, respectively (3).

Excess obesity can lead to the development of hypertension, dyslipidemia, and diabetes (4). In the cardiovascular system, excess adipose tissue results in the release of free fatty acids from fats (5). Activation of the diacylglycerol-protein kinase C pathway may result in the accumulation of lipids in cells, which could lead to insulin resistance (6). Insulin resistance is a contributing factor to changes in lipid metabolism and the development of atherogenic dyslipidemia. Inhibition of microsomal triglyceride transfer protein and lipoprotein lipase activation in insulin resistance results in hypertriglyceridemia (7). Blood lipids, including cholesterol, triglycerides, and lipoproteins, are considered the most important indicators of metabolic control in diabetic patients (8). Android excess fat mass is associated with high triglyceride levels and low high-density lipoprotein cholesterol (HDL) in men, and high low-density lipoprotein cholesterol (LDL) and low HDL cholesterol levels in women. In men, there is a positive correlation between excess gynoid fat mass and total cholesterol (TC) and TG, and HDL cholesterol in women (9).

Anthropometry is a method of quantitatively measuring the physical characteristics of the body (10). Since the structural characteristics of the human body vary under the influence of factors such as age, gender, and type of nutrition, and since the data of each population is specific to that population, anthropometric databases have been established in advanced countries (11). Anthropometric information from each region can be used for diagnostic and treatment purposes for the same population (12).

Body Mass Index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) as anthropometric indicators are strongly linked to diabetes (13). New anthropometric indices have been developed to overcome the limitations of BMI. Body Shape Index (ABSI), Body Roundness Index (BRI), and Body Adiposity Index (BAI) are important factors for estimating body fat distribution (14, 15). One limitation of older anthropometric indices is that they cannot determine the amount of fat or differentiate between weight components (16). They also cannot distinguish visceral adipose tissue from subcutaneous adipose tissue because they measure BMI and WC (17). The risk of T2DM can be influenced by the location of fat accumulation in the body relative to the total body fat volume (18). Visceral adipose tissue is a hormonally active component of body fat, and the risk of developing diabetes is higher in people with this type of obesity, with or without visible obesity (19, 20).

There is also a strong significant relationship between liver enzymes, including alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and BMI, blood serum lipids, lipoproteins, glucose, insulin, and blood pressure (21).

Considering the importance of diabetes and its relationship with anthropometric indicators, various studies have shown different predictive factors for diabetes across different races and countries, indicating that the cut-off points related to anthropometric indices vary for different ethnic groups (22, 23).

2. Objectives

This study aimed to identify the optimal cut-off values of anthropometric measures and their relationship with cardiometabolic and hepatic indices to predict T2DM in southwest Iran.

3. Methods

3.1. Research Design and Participants

This cross-sectional study analyzed data from the first phase of the Hoveyzeh Cohort Study, conducted from 2016 to 2018. The study selected adult males and females from Hoveyzeh city, focusing on a population-based sample from an Arab community in southwest Iran (24). To be eligible for this study, participants had to be between the ages of 35 and 70 and express a desire to take part. However, the study excluded pregnant or lactating women, individuals with diets under 800 kcal or over 4200 kcal, those with incomplete demographic, anthropometric, or biochemical information, individuals who had undergone surgery, lost weight in the past year following special diets, or were taking special medications, those with a BMI less than 18.5 kg/m² or more than 40 kg/m², and those who consume alcohol.

Figure 1 shows that 34,929 individuals were selected from a total of 47,032 people. After excluding individuals who lacked interest, were too busy, or had immigrated, the study included a total of 10,009 participants. Finally, the study included 7,300 participants (1,607 with T2DM and 5,693 healthy individuals).

Flow chart of participant selection
Flow chart of participant selection
Figure 1.

Flow chart of participant selection

3.2. Anthropometric, Blood Pressure, and Heart Rate Assessment

Weight (measured with an accuracy of 0.1 kg) and height (measured with an accuracy of 0.1 cm) of participants were determined after an overnight fast using a standard scale (Seca). Waist circumference (WC) was measured using a constant tension tape at the end of a normal exhale, with the arms relaxed at the sides. The measurement was taken at the midpoint between the lower part of the lowest rib and the highest point of the hip on the mid-axillary line. BMI was estimated by dividing the weight (kg) by the height (m²).

Participants' systolic and diastolic blood pressure and heart rate (HR) were measured twice in a seated position using a Reister manometer cuff and stethoscope on both arms.

The following formulas were used to calculate the anthropometric indices (14, 15, 25):

WHR=WChip circumference
WWI=WC cmweigth (kg)

AVI (Abdominal Volume Index) = (2 cm (waist)2 + 0.7 cm (waist-hip)2)/1000

CI:Waist m0.109 weight kgheight (m)

BRI (Body Roundness Index): (364.2 - 365.5) × (1-[(WC/2π)2/ (0.5 × height)2] (1/2))

HI Hip Index=HC×Wt-0.482×Ht0.310
Relative Fat Mass:Men:64-20×heightWC
Women:76-20×heightWC

3.3. Biochemical Assessments

After fasting for 12 hours, a laboratory expert drew a 10 cc blood sample from the participants. The blood samples were centrifuged at 3000 rpm for 10 minutes to extract the serum. Venous blood samples were collected to assess mean corpuscular volume (MCV), fasting blood sugar (FBS), lipid profile, ALT, AST, gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP).

The following formulas were used to calculate the metabolic and hepatic indices:

LAP lipid accumulation product=WC-65×TG in men WC-58×TG in women
AIP atherogenic index of plasma=log⁡TGHDL-C
CMI=TGHDL-C×(Waist-to-height)
TIMI risk index=heart rate bpm×age102systolic BP (mmHg)

VAI (Men) = (WC/ [39.68 + 1.89 × BMI]) × ([TG (mmol/L)]/1.03) × (1.31/ [HDL (mmol/L)])

VAI (Women) = (WC/ [36.58 + 1.89 × BMI]) × (TG/0.81) × (1.52/HDL)

TyG Index=ln⁡TG mgdL×FPG mgdL2
TyG-BMI=Triglyceride-Glucose ×BMI
TyG-WC=TyG index×WC (cm)
LCI=TC×TG×LDLHDL-C

HIS = 8 × (ALT/ [AST ratio]) + BMI (+2, if female; if diabetes mellitus)

ANI: ALD/NAFLD Index = -58.5 + 0.637 (MCV) + 3.91 (AST/ALT) -0.406 (BMI) + 6.35 for male gender

3.4. Statistical Methods

The data was analyzed using IBM SPSS Statistics software (Version 24) from IBM SPSS Statistics in Armonk, USA. The Kolmogorov-Smirnov test was used to assess the normality of the variables. Quantitative variables between the healthy and diabetic groups were compared using the Independent t-test or the Mann-Whitney test, while qualitative variables were compared using the chi-square test. Pearson's correlation coefficients were employed to assess the correlations among anthropometric, hepatic, and cardiometabolic indices.

A logistic regression test was conducted to evaluate the association between anthropometric, hepatic, and cardiometabolic indices and T2DM risk in both males and females, calculating the odds ratio (OR) and 95% confidence interval. Receiver-operating characteristic (ROC) analysis was used to determine the sensitivity, specificity, and area under the curve for anthropometric indices. A P-value of less than 0.05 was considered statistically significant.

4. Results

Table 1 presents the basic characteristics of the individuals who participated in the study. The patients with T2DM had a mean age of 53.14 years (SD = 9.25), while healthy participants had a mean age of 47.86 years (SD = 9.03). In diabetic individuals, the average weight, height, wrist measurement, systolic and diastolic blood pressure, MCV, ALT, GGT, TG, energy intake, and physical activity were higher in men compared to women. Conversely, diabetic women had higher average BMI, WC, hip circumference (HC), HR, TC, HDL, LDL, and ALP levels than diabetic men. Except for height, HDL, MCV, AST, physical activity, and energy intake, all parameters were higher in individuals with T2DM compared to healthy participants, as shown in Table 1.

Table 1.

Baseline Characteristics of the Study Population

CharacteristicsT2DMP aHealthyP bP c
Total (N = 1607)Male (n = 620)Female (n = 987)Total (N = 5693)Male (n = 2219)Female (n = 3474)
Age (y)53.14±8.9653.30±8.9453.04±8.970.57447.86 ± 9.0348.72 ± 9.2747.31 ± 8.83< 0.001< 0.001
Weight (cm)77.89 ± 13.2682.72 ± 13.1274.85 ± 12.43< 0.00176.64 ± 14.2181.19 ± 14.0773.74 ± 13.53< 0.0010.002
Height (cm)163.49 ± 8.74171.96 ± 5.85158.17 ± 5.45< 0.001164.42 ± 9.05172.92 ± 6.33158.99 ± 5.73< 0.001< 0.001
Body Mass Index (kg/m2)29.12 ± 4.4027.93 ± 3.9729.87 ± 4.49< 0.00128.34 ± 4.7527.11 ± 4.2729.12 ± 4.87< 0.001< 0.001
WC (cm)102.14 ± 10.4899.45 ± 10.23103.82 ± 10.28< 0.00198.25 ± 11.1396.00 ± 10.7599.70 ± 11.12< 0.001< 0.001
Wrist (cm)17.48 ± 1.2817.91 ± 1.1517.21 ± 1.28< 0.00117.35 ± 1.2717.81 ± 1.1817.06 ± 1.23< 0.001< 0.001
HC (cm)103.20 ± 9.04100.51 ± 7.85104.89 ± 9.34< 0.001103.66 ± 9.07100.46 ± 7.88105.70 ± 9.20< 0.0010.073
BP systolic 117.90 ± 20.01120.67 ± 18.66116.17 ± 20.64< 0.001111.63 ± 17.60115.30 ± 16.99109.29 ± 17.59< 0.001< 0.001
BP diastolic 73.09 ± 11.2574.91 ± 11.1971.95 ± 11.15< 0.00170.80 ± 11.0672.94 ± 11.0669.43 ± 10.83< 0.001< 0.001
Heart rate (n)80.22 ± 9.8679.14 ± 9.3380.89 ± 10.13< 0.00178.12 ± 9.4576.42 ± 9.3279.21 ± 9.37< 0.001< 0.001
TC (mg/dL)193.82 ± 47.781187.41 ± 45.55197.84 ± 48.73< 0.001187.57 ± 38.10185.77 ± 37.57188.72 ± 38.390.004< 0.001
TG (mg/dL)195.26 ± 143.19204.51 ± 148.06189.44 ± 139.810.04152.88 ± 91.20173.30 ± 108.71139.84 ± 75.15< 0.001< 0.001
HDL (mg/dL)49.37 ± 11.7545.20 ± 9.7451.99 ± 12.14< 0.00150.33 ± 11.8745.95 ± 10.3653.12 ± 11.93< 0.0010.004
LDL (mg/dL)106.22 ± 36.62102.45 ± 34.01108.57 ± 37.990.001106.80 ± 32.00105.42 ± 31.60107.68 ± 33.220.010.534
FBS (mg/dL)178.93 ± 72.11182.02 ± 69.98176.98 ± 73.380.17393.84 ± 10.2493.58 ± 9.9694.01 ± 10.410.125< 0.001
ALT (U/L)21.82 ± 13.7724.98 ± 14.4419.83 ± 12.95< 0.00120.79 ± 14.7126.25 ± 16.6417.30 ± 12.10< 0.0010.012
AST (U/L)17.92 ± 13.1318.24 ± 9.1817.72 ± 15.090.43818.71 ± 8.1820.74 ± 8.3717.41 ± 7.79< 0.0010.003
GGT (U/L)32.47 ± 30.5737.25 ± 38.7629.4679 ± 23.58< 0.00124.72 ± 16.5830.79 ± 19.4820.8496 ± 13.03< 0.001< 0.001
ALP (U/L)234.89 ± 68.87228.86 ± 65.89238.68 ± 70.450.005204.70 ± 58.19208.84 ± 53.36202.06 ± 60.94< 0.001< 0.001
MCV (FL)83.79 ± 6.3584.92 ± 6.0583.078 ± 6.44< 0.00184.65 ± 6.9885.90 ± 6.7683.865 ± 7.01< 0.001< 0.001
Energy (Kcal)2757.25 ± 711.783036.46 ± 656.422581.86 ± 689.13< 0.0012906.66 ± 693.833168.82 ± 631.922739.21 ± 679.71< 0.001< 0.001
Physical activity (MET)35.83 ± 5.3736.63 ± 6.8135.33 ± 4.16< 0.00137.24 ± 5.5037.98 ± 7.2136.77 ± 3.98< 0.001< 0.001

Abbreviations: WC, waist circumference; HC, hip circumference; BP, blood pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FBS, fasting blood sugar; ALT, alanine aminotransferase; AST, Aspartate transaminase; GGT, gamma-glutamyl transferase; ALP, alkaline phosphatase; MCV, mean corpuscular volume, T2DM, type 2 diabetes mellitus.

a P-value between male and female in T2DM.

b P-value between male and female in healthy.

c P-value between T2DM and healthy.

All anthropometric, hepatic, and cardiometabolic indices were significantly higher in individuals with diabetes compared to healthy individuals (P < 0.001). Among patients with T2DM, women had significantly higher anthropometric, hepatic, and cardiometabolic indices compared to men, except for Atherogenic Index of plasma (AIP), LCI, and CMI (Table 2).

Table 2.

Comparison of Anthropometric, Hepatic, and Cardiometabolic Indices Between Patients with Type 2 Diabetes Mellitus and Healthy Individuals

CharacteristicsT2DMP aHealthyP bP c
Total (N = 1607)Male (n = 620)Female (n = 987)Total (N = 5693)Male (n = 2219)Female (n = 3474)
WHR0.99 ± 0.060.98 ± 0.050.99 ± 0.060.4700.94 ± 0.060.95 ± 0.050.94 ± 0.06< 0.001< 0.001
WHtR0.62 ± 0.070.57 ± 0.050.65 ± 0.06< 0.0010.59 ± 0.070.55 ± 0.060.62 ± 0.06< 0.001< 0.001
WWI11.62 ± 0.8210.96 ± 0.5712.04 ± 0.66< 0.00111.27 ± 0.8210.67 ± 0.6411.65 ± .68< 0.001< 0.001
VAI3.05 ± 2.552.95 ± 2.683.11 ± 2.460.2352.30 ± 1.842.45 ± 2.192.21 ± 1.58< 0.001< 0.001
AVI21.08 ± 4.2819.99 ± 4.0921.77 ± 4.26< 0.00119.55 ± 4.3918.66 ± 4.1620.13 ± 4.45< 0.001< 0.001
CI1.36 ± 0.071.32 ± 0.071.37 ± 0.07< 0.0011.32 ± 0.071.28 ± 0.071.35 ± 0.07< 0.001< 0.001
RFM39.00 ± 8.5429.07 ± 3.6145.24 ± 3.13< 0.00137.41 ± 8.7927.53 ± 4.1843.72 ± 3.69< 0.001< 0.001
BRI8.18 ± 0.957.55 ± 0.788.58 ± 0.85< 0.0017.82 ± 0.997.25 ± 0.828.19 ± 0.92< 0.001< 0.001
LAP91.79 ± 67.2780.95 ± 61.1198.60 ± 70.04< 0.00166.02 ± 46.3763.22 ± 49.6667.81 ± 44.06< 0.001< 0.001
AIP0.54 ± 0.270.59 ± 0.270.51 ± .26< 0.0010.43 ± 0.260.52 ± 0.270.38 ± 0.25< 0.001< 0.001
CMI2.70 ± 2.302.88 ± 2.622.59 ± 2.070.0152.02 ± 1.712.34 ± 2.141.82 ± 1.33< 0.001< 0.001
TIMI risk index20.06 ± 7.2919.30 ± 6.6020.53 ± 7.660.00116.72 ± 6.6716.40 ± 6.5416.92 ± 6.740.004< 0.001
TyG-BMI278.04 ± 47.53268.63 ± 45.81283.94 ± 47.66< 0.001248.27 ± 46.54240.75 ± 43.99253.07 ± 47.50< 0.001< 0.001
LCI27.30 ± 29.2228.78 ± 31.7526.38 ± 27.490.11020.30 ± 18.1323.58 ± 18.9418.21 ± 17.28< 0.001< 0.001
HIS91.85 ± 39.8557.13 ± 24.17113.66 ± 31.40< 0.00144.09 ± 28.699.80 ± 3.3666.00 ± 10.53< 0.001< 0.001
ANI-10.94 ± 5.99-6.19 ± 4.59-13.92 ± 4.71< 0.001-9.46 ± 6.41-4.82 ± 5.15-12.42 ± 5.29< 0.001< 0.001

Abbreviations: WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WWI, Weight-adjusted Waist Index; VAI, Visceral Adiposity Index; AVI, Abdominal Volume Index; CI, Conicity Index; RFM, relative fat mass; BRI, Body Roundness Index; LAP, lipid accumulation product; AIP, Atherogenic Index of plasma; CMI; Cardiometabolic Index; TIMI Risk Index, The Thrombolysis in Myocardial Infarction risk index; TyG, the triglyceride-glucose; TyG-BMI, TyG Index × BMI; LCI, the Lipoprotein Combine Index; HIS, Hepatic Steatosis Index; ANI; ALD/NAFLD.

a P-value between male and female in T2DM.

b P-value between male and female in healthy.

c P-value between T2DM and health.

According to Pearson’s correlation coefficient, the waist-to-hip ratio (WHR) had the strongest correlation with fasting blood sugar (FBS) (r = 0.245, P < 0.001). The visceral adiposity index (VAI) consistently showed the most significant correlations with various cardiometabolic variables, including LAP, AIP, CMI, and LCI. The Conicity Index (CI) had the strongest correlation with hepatic indices such as ANI and HSI (Table 3).

Table 3.

Pearson’s Correlation Coefficients Between Anthropometric, Hepatic, and Cardiometabolic Indices Among Men and Women

VariablesWCBMIWHRWHtRWWIVAIAVICIBRIRFM
FBS
R0.1220.0490.2450.1190.1480.1800.1200.0550.1690.119
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001
LAP
R0.5050.4230.4090.4540.3010.8540.5010.2710.3610.455
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001
AIP
R0.2140.1590.2740.103-0.0030.8310.204-0.0980.0870.104
P< 0.001< 0.001< 0.001< 0.001< 0.786< 0.001< 0.001< 0.001< 0.001< 0.001
CMI
R0.2410.1970.2550.1740.0760.9840.2360.0090.1330.175
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.0010.446< 0.001< 0.001
TIMI risk index
R0.088-0.1040.3250.1460.368-0.0010.0900.1010.3780.145
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.944< 0.001< 0.001< 0.001< 0.001
LCI
R0.1360.0930.2010.0920.0540.6740.129-0.0210.0950.092
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.0010.070< 0.001< 0.001
HIS
R0.3860.4080.1640.5910.5530.1010.3860.7790.4200.589
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001
ANI
R-0.442-0.496-0.138-0.579-0.441-0.059-0.439-0.677-0.342-0.579
P< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

Abbreviations: FBS, fasting blood pressure; LAP, lipid accumulation product; AIP, atherogenic index of plasma; CMI, Cardiometabolic Index; TIMI Risk Index, The Thrombolysis in Myocardial Infarction Risk Index; LCI, The Lipoprotein Combine Index; HIS, Hepatic Steatosis Index; ANI, ALD/NAFLD Index; BMI, Body Mass Index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WWI, Weight-adjusted Waist Index; VAI, Visceral Adiposity Index; AVI, Abdominal Volume Index; CI, Conicity Index; BRI, Body Roundness Index; RFM, relative fat mass.

The multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the T2DM risk in males and females are presented in Tables 4 and 5. These models included both unadjusted (Model 1) and adjusted (Model 2) results, where adjustments were made for age, energy intake, physical activity, and wrist circumference. Among the anthropometric, cardiometabolic, and hepatic indices, the Atherogenic Index of plasma had the highest multivariate-adjusted OR for T2DM risk in both males (OR = 3.39, 95% CI: 2.38 - 4.81) and females (OR = 5.68, 95% CI: 4.20 - 7.68).

Table 4.

Logistic Regression for Investigation of The Association Between Anthropometric, Hepatic, and Cardiometabolic Indices and Type 2 Diabetes Mellitus Risk in Males

VariablesModel 1 aModel 2 b
Odd Ratio(95% CI)P-ValueOdd Ratio(95% CI)P-Value
WC1.03(1.02 - 1.04)< 0.0011.04(1.02 - 1.05)< 0.001
BMI1.05(1.02 - 1.07)< 0.0011.08(1.05 - 1.12)< 0.001
WHR1.11(1.10 - 1.13)< 0.0011.09(1.07 - 1.11)< 0.001
WHtR1.06(1.05 - 1.07)< 0.0011.06(1.04 - 1.08)< 0.001
WWI2.13(1.83 - 2.48)< 0.0011.57(1.32 - 1.86)< 0.001
VAI 1.09(1.05 - 1.13)< 0.0011.11(1.07 - 1.15)< 0.001
AVI1.08(1.05 - 1.10)< 0.0011.09(1.06 - 1.13)< 0.001
CI1.89(1.65 - 2.16)< 0.0011.13(0.90 - 1.42)< 0.001
BRI1.57(1.41 - 1.76)< 0.0011.34(0.94 - 1.92)< 0.001
RFM1.10(1.08 - 1.13)< 0.0011.10(1.07 - 1.14)< 0.001
LAP 1.01(1.01 - 1.01)< 0.0011.01(1.01 - 1.01)< 0.001
AIP2.47(1.79 - 3.41)< 0.0013.39(2.38 - 4.81)< 0.001
CMI1.10(1.05 - 1.14)< 0.0011.12(1.08 - 1.17)< 0.001
TIMI risk index1.07(1.05 - 1.08)< 0.0011.01(0.97 - 1.03)< 0.001
TyG-BMI index1.01(1.01 - 1.02)< 0.0011.03(1.02-1.03)< 0.001
LCI1.01(1.01 - 1.01)< 0.0011.01(1.01 - 1.02)< 0.001
HIS1.16(1.14 - 1.17)< 0.0011.17(1.14 - 1.19)< 0.001
ANI0.95(0.93 - 0.96)< 0.0010.94(0.92 - 0.95)< 0.001

Abbreviations: LAP, lipid accumulation product; AIP, Atherogenic index of plasma; CMI, Cardiometabolic Index; TIMI Risk Index, The Thrombolysis in Myocardial Infarction Risk Index; TyG, the triglyceride-glucose; TyG-BMI, TyG Index × BMI; LCI, The Lipoprotein Combine Index, HIS, Hepatic Steatosis Index, ANI, ALD/NAFLD Index; BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WWI, Weight-adjusted Waist Index; VAI, Visceral Adiposity Index; AVI, Abdominal Volume Index; CI, conicity Index; BRI, Body Roundness Index; RFM, relative fat mass.

a Model 1: Unadjusted.

b Model 2: Adjusted for age, energy intake, physical activity, and wrist circumference.

Table 5.

Logistic Regression for Investigation of the Association Between Anthropometric, Hepatic, and Cardiometabolic Indices and Type 2 Diabetes Mellitus Risk in Females

VariablesModel 1 aModel 2 b
Odd Ratio(95% CI)P-ValueOdd Ratio(95% CI)P-Value
WC1.03(1.03 - 1.04)< 0.0011.03(1.02 - 1.04)< 0.001
BMI1.03(1.02 - 1.05)< 0.0011.04(1.02 - 1.06)< 0.001
WHR1.11(1.10 - 1.12)< 0.0011.08(1.07 - 1.09)< 0.001
WHtR1.06(1.05 - 1.08)< 0.0011.05(1.03 - 1.06)< 0.001
WWI2.30(2.06 - 2.56)< 0.0011.64(1.45 - 1.86)< 0.001
VAI 1.27(1.22 - 1.32)< 0.0011.23(1.18 - 1.28)< 0.001
AVI1.09(1.07 - 1.10)< 0.0011.07(1.04 - 1.09)< 0.001
CI2.09(1.89 - 2.31)< 0.0011.29(1.11 - 1.49)< 0.001
BRI1.61(1.48 - 1.75)< 0.0011.41(1.10 - 1.82)< 0.001
RFM1.14(1.11 - 1.16)< 0.0011.10(1.07-1.13)< 0.001
LAP 1.01(1.01 - 1.01)< 0.0011.01(1.01 - 1.01)< 0.001
AIP7.06(5.31 - 9.39)< 0.0015.68(4.20 - 7.68)< 0.001
CMI1.34(1.28 - 1.40)< 0.0011.29(1.23 - 1.36)< 0.001
TIMI risk index1.07(1.06 - 1.08)< 0.0010.99(0.97 - 1.01)< 0.001
TyG-BMI index1.01(1.01 - 1.02)< 0.0011.02(1.02 - 1.02)< 0.001
LCI1.02(1.01 - 1.02)< 0.0011.01(1.01 - 1.01)< 0.001
HIS1.11(1.10 - 1.12)< 0.0011.12(1.11 - 1.13)< 0.001
ANI0.95(0.93 - 0.96)< 0.0010.94(0.93 - 0.96)< 0.001

Abbreviations: LAP, lipid accumulation product; AIP, atherogenic index of plasma; CMI, Cardiometabolic Index; TIMI risk index, The Thrombolysis in Myocardial Infarction Risk Index; TyG, the triglyceride-glucose; TyG-BMI, TyG Index × BMI, LCI, The Lipoprotein Combine Index; HIS, Hepatic Steatosis Index; ANI; ALD/NAFLD Index; BMI, Body Mass Index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WWI, Weight-adjusted Waist Index; VAI, Visceral Adiposity Index; AVI, Abdominal Volume Index; CI, Conicity Index; BRI, Body Roundness Index; RFM, relative fat mass.

a Model 1: Unadjusted.

b Model 2: Adjusted for age, energy intake, physical activity, and wrist circumference.

Optimal cut-off values for various indices differed between men and women. For men, the optimal cut-off values were higher for BMI (28.0 vs. 25.48), WHtR (0.64 vs. 0.56), WWI (11.83 vs. 10.76), CI (1.35 vs. 1.29), BRI (8.43 vs. 7.33), RFM (44.63 vs. 28.5), and LAP (67.23 vs. 67.04). For women, the optimal cut-off values were higher for WC (99.45 vs. 98.35), WHR (0.99 vs. 0.96), VAI (2.22 vs. 2.19), and AVI (20 vs. 19.6) (Table 6).

Table 6.

The Optimal Cut-off Value for Anthropometric Indices for the Prediction of Type 2 Diabetes Mellitus in Men and Women

VariablesT2DM
MaleFemale
Cut-offAUCSen (%)Spe (%)YICut-offAUCSen (%)Spe (%)YI
WC99.450.5951.563.50.15098.350.6171.644.60.162
BMI25.480.5672.937.40.10328.000.5465.7420.077
WHR0.990.675570.20.2520.960.7070.159.50.296
WHtR0.560.6162.356.90.1920.640.6260590.190
WWI10.760.6464.756.10.20811.830.6664.460.90.253
VAI 2.220.5851.959.90.1182.190.6560.862.70.235
AVI20.000.6050.569.50.20019.600.6169.747.20.169
CI1.290.636553.60.1861.350.6673.2500.232
BRI7.330.6163.555.20.1878.430.6258.860.10.189
RFM28.500.6162.356.90.19244.630.6263.555.40.189
LAP 67.040.6050.2660.16267.230.6763.960.40.243

Abbreviations: LAP, lipid accumulation product; BMI, Body Mass Index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WWI, Weight-adjusted Waist Index; VAI, Visceral Adiposity Index; AVI, Abdominal Volume Index; CI, Conicity Index; BRI, Body Roundness Index; RFM, relative fat mass; Sen, sensitivity; Spe, specificity; YI, Youden Index.

5. Discussion

Our study showed that among the anthropometric indices, the VAI had the highest correlation with cardiometabolic indices, while CI had the highest correlation with hepatic indices. In the Hoveyzeh population, the atherogenic index of plasma was the most significant predictor of T2DM in both men and women compared to other indicators.

In our study, the odds ratios in model 2 indicated that among the anthropometric indices, the Weight-Adjusted Waist Index (WWI) and VAI, and among the cardiometabolic indices, AIP, CI, Body Roundness Index (BRI), and Cardiometabolic Index (CMI), as well as Hepatic Steatosis Index (HSI) among the liver indices, were predictors of T2DM. Additionally, men and women in the study showed similar results.

The average energy intake and physical activity levels in healthy individuals were higher than in patients with T2DM, indicating the impact of diabetes on daily life and activity. In our study, systolic and diastolic blood pressure in T2DM patients was higher than in the healthy group, which aligns with findings from other studies in different Iranian populations. Therefore, early diagnosis of T2DM using anthropometric indicators can reduce the complications of diabetes, such as diabetic nephropathy, cardiovascular diseases, and cerebrovascular diseases, over time (26).

In this study, the average of all conventional anthropometric indices (WC, WHR, BMI, and WHtR) and new anthropometric indices, including WWI, VAI, abdominal volume index (AVI), CI, relative fat mass (RFM), and BRI, were higher in T2DM patients than in healthy individuals. Among the conventional and new anthropometric indices, only WHR and VAI did not show a significant difference between diabetic men and women. The conventional and new anthropometric indices measured in our research, except for WHR and VAI, were higher in healthy women than in healthy men.

Different populations have different predictive factors for diabetes, and there are also differences between male and female groups. In studies conducted in British, Chinese, German, European, and Australian native populations, WC was identified as a predictor of T2DM (27-31). Some studies have reported that WHtR is the most sensitive predictive factor for diabetes (32-34). Khader et al. reported that WHtR was the most effective predictor of diabetes in the Jordanian adult population (34). Waist-to-hip ratio is commonly used to determine abdominal obesity, distinguishing between gynoid (buttock) and android (abdominal) obesity (35). In a study conducted in Iran, it was shown that the risk of T2DM is higher in individuals with high WHR compared to those with high BMI and WC, with the increase being greater in women than in men (OR 2.79 vs. 2.36) (32). In our study, the multivariate-adjusted OR for T2DM risk in males for WHR, WC, BMI, and WHtR was 1.09, 1.04, 1.08, and 1.06, respectively, while for women, it was 1.08, 1.03, 1.04, and 1.05, respectively. According to the odds ratio greater than one (OR > 1), common anthropometric indices, including WC, BMI, WHR, and WHtR, and new anthropometric indices, including WWI, VAI, AVI, CI, RFM, and BRI, increase the risk of T2DM.

In our study population, AIP was an important predictor of T2DM compared to other indicators. The AIP is composed of two important parameters, triglycerides (TG) and HDL. The simultaneous use of TG and HDL in this ratio indicates multiple interactions between the metabolism of different lipoproteins, which can be used to predict plasma atherogenicity (36). Atherogenic Index of plasma is an atherogenic factor that can predict atherosclerosis and the risk of cardiovascular events (37, 38). Atherogenic Index of plasma indices are associated with visceral fat area in patients with T2DM in some studies (39).

In our study, BMI, WHtR, WWI, CI, BRI, RFM, and LAP had higher optimal cut-off point values in men than in women. Women had higher optimal cut-off point values for WC, WHR, VAI, and AVI than men. The optimal cut-offs for anthropometric indices to predict T2DM differ among different populations (40). The differences in study results may be largely influenced by the ethnicity, age, gender, and background diseases of the participants, as well as the anthropometric indicators chosen for analysis.

In our research, we found that TC and TG levels were higher in subjects with T2DM compared to healthy subjects. Conversely, HDL levels were higher in healthy subjects than in those with T2DM. We did not observe a significant difference in mean LDL levels between people with T2DM and healthy individuals in our study. This could be due to the use of statin drugs in the diabetic group, which may have reduced their LDL levels and acted as a confounding factor.

One of the limitations of our study was the narrow age range of 35 to 70 years. Expanding the age range to include individuals over 70 years old and under 30 years old could provide a better understanding of the impact of risk factors. Additionally, it's important to note that statin consumption in patients with dyslipidemia and T2DM could influence the results of the lipid profile and could be a confounding factor.

Our research was a cross-sectional study, which has limitations in establishing causal relationships between variables. Since data is collected at a single point in time, it is challenging to determine the temporal sequence of events or to ascertain whether a particular variable directly influences another.

5.1. Conclusions

In our study population, the Atherogenic Index of Plasma is a highly predictive indicator of T2DM. Our results suggest that novel anthropometric and cardiometabolic indices could serve as accessible and cost-effective strategies for assessing health outcomes related to T2DM.

According to previous research, differences in estrogen levels may lead to varying ANI index results in non-menopausal and menopausal women, indicating the need for further investigation to compare this index (41-43).

The study utilized the TyG index to assess insulin resistance. It is recommended to use the insulin resistance model or other standard indices of insulin resistance in future studies to allow for comparison of results, as the TyG Index primarily represents the level of insulin resistance.

Several factors, such as the number of hours slept per day and depression, contribute to the incidence and prevalence of diabetes. Therefore, it would be beneficial to conduct more extensive studies aimed at identifying predictive risk factors for type 2 diabetes, taking these factors into account.

Acknowledgements

References

  • 1.

    Miranda S, Marques A. Pilates in noncommunicable diseases: A systematic review of its effects. Complement Ther Med. 2018;39:114-30. [PubMed ID: 30012382]. https://doi.org/10.1016/j.ctim.2018.05.018.

  • 2.

    Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. [PubMed ID: 31518657]. https://doi.org/10.1016/j.diabres.2019.107843.

  • 3.

    Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A, et al. Diabetes in Iran: Prospective analysis from first nationwide diabetes report of national program for prevention and control of diabetes (NPPCD-2016). Sci Rep. 2017;7(1):13461. [PubMed ID: 29044139]. [PubMed Central ID: PMC5647418]. https://doi.org/10.1038/s41598-017-13379-z.

  • 4.

    Bazyar H, Adibmanesh A, Javid AZ, Maghsoumi-Norouzabad L, Gravand E, Alipour M, et al. The relationship between metabolic factors and anthropometric indices with periodontal status in type 2 diabetes mellitus patients with chronic periodontitis. Obesity Medicine. 2019;16:100138. https://doi.org/10.1016/j.obmed.2019.100138.

  • 5.

    D'Oria R, Genchi VA, Caccioppoli C, Calderoni I, Marrano N, Biondi G, et al. Impact of dysfunctional adipose tissue depots on the cardiovascular system. Int J Mol Sci. 2022;23(22):14296. [PubMed ID: 36430774]. [PubMed Central ID: PMC9695168]. https://doi.org/10.3390/ijms232214296.

  • 6.

    Yung JHM, Giacca A. Role of c-Jun N-terminal Kinase (JNK) in obesity and Type 2 diabetes. Cells. 2020;9(3):706. [PubMed ID: 32183037]. [PubMed Central ID: PMC7140703]. https://doi.org/10.3390/cells9030706.

  • 7.

    Au WS, Kung HF, Lin MC. Regulation of microsomal triglyceride transfer protein gene by insulin in HepG2 cells: Roles of MAPKerk and MAPKp38. Diabetes. 2003;52(5):1073-80. [PubMed ID: 12716735]. https://doi.org/10.2337/diabetes.52.5.1073.

  • 8.

    Yousefipoor P, Tadibi V, Behpoor N, Parnow A, Delbari M, Rashidi S. [Effects of aerobic exercise on glucose control and cardiovascular risk factor in type 2 diabetes patients]. Med J Mashhad Univ Med Sci. 2015;57(9):976-84. Persian. https://doi.org/10.22038/mjms.2015.3882.

  • 9.

    Min KB, Min JY. Android and gynoid fat percentages and serum lipid levels in United States adults. Clin Endocrinol (Oxf). 2015;82(3):377-87. [PubMed ID: 24974911]. https://doi.org/10.1111/cen.12505.

  • 10.

    Fryar CD, Gu Q, Ogden CL, Flegal KM. Anthropometric Reference Data for Children and Adults: United States, 2011-2014. Vital Health Stat 3 Anal Stud. 2016;(39):1-46. [PubMed ID: 28437242].

  • 11.

    Del Prado-Lu JL. Anthropometric measurement of Filipino manufacturing workers. Int J Industrial Ergonomics. 2007;37(6):497-503. https://doi.org/10.1016/j.ergon.2007.02.004.

  • 12.

    de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: Meta-regression analysis of prospective studies. Eur Heart J. 2007;28(7):850-6. [PubMed ID: 17403720]. https://doi.org/10.1093/eurheartj/ehm026.

  • 13.

    Zhang FL, Ren JX, 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. [PubMed ID: 34056007]. [PubMed Central ID: PMC8147550]. https://doi.org/10.1155/2021/8812431.

  • 14.

    Zakerkish M, Hoseinian A, Alipour M, Payami SP. The Association between Cardio-metabolic and hepatic indices and anthropometric measures with metabolically obesity phenotypes: A cross-sectional study from the Hoveyzeh Cohort Study. BMC Endocr Disord. 2023;23(1):122. [PubMed ID: 37246210]. [PubMed Central ID: PMC10226206]. https://doi.org/10.1186/s12902-023-01372-9.

  • 15.

    Hadi S, Momenan M, Cheraghpour K, Hafizi N, Pourjavidi N, Malekahmadi M, et al. Abdominal volume index: A predictive measure in relationship between depression/anxiety and obesity. Afr Health Sci. 2020;20(1):257-65. [PubMed ID: 33402914]. [PubMed Central ID: PMC7750042]. https://doi.org/10.4314/ahs.v20i1.31.

  • 16.

    Burkhauser RV, Cawley J. Beyond BMI: The value of more accurate measures of fatness and obesity in social science research. J Health Econ. 2008;27(2):519-29. [PubMed ID: 18166236]. https://doi.org/10.1016/j.jhealeco.2007.05.005.

  • 17.

    Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: Sex and race differences. Obesity (Silver Spring). 2011;19(2):402-8. [PubMed ID: 20948514]. [PubMed Central ID: PMC3960785]. https://doi.org/10.1038/oby.2010.248.

  • 18.

    Brahimaj A, Rivadeneira F, Muka T, Sijbrands EJG, Franco OH, Dehghan A, et al. Novel metabolic indices and incident type 2 diabetes among women and men: The Rotterdam Study. Diabetologia. 2019;62(9):1581-90. [PubMed ID: 31183505]. [PubMed Central ID: PMC6677703]. https://doi.org/10.1007/s00125-019-4921-2.

  • 19.

    Kojta I, Chacinska M, Blachnio-Zabielska A. Obesity, bioactive lipids, and adipose tissue inflammation in insulin resistance. Nutrients. 2020;12(5):1305. [PubMed ID: 32375231]. [PubMed Central ID: PMC7284998]. https://doi.org/10.3390/nu12051305.

  • 20.

    Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Cardiovasc Med. 2020;7:22. [PubMed ID: 32158768]. [PubMed Central ID: PMC7052117]. https://doi.org/10.3389/fcvm.2020.00022.

  • 21.

    Chen L, Zhang K, Li X, Wu Y, Liu Q, Xu L, et al. Association between aspartate aminotransferase to alanine aminotransferase ratio and incidence of type 2 diabetes mellitus in the Japanese population: A secondary analysis of a retrospective Cohort Study. Diabetes Metab Syndr Obes. 2021;14:4483-95. [PubMed ID: 34785918]. [PubMed Central ID: PMC8590482]. https://doi.org/10.2147/DMSO.S337416.

  • 22.

    Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL, et al. Optimal cut-off values for obesity: Using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord. 2002;26(9):1232-8. [PubMed ID: 12187401]. https://doi.org/10.1038/sj.ijo.0802040.

  • 23.

    Vazquez G, Duval S, Jacobs DJ, Silventoinen K. Comparison of Body Mass Index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol Rev. 2007;29:115-28. [PubMed ID: 17494056]. https://doi.org/10.1093/epirev/mxm008.

  • 24.

    Cheraghian B, Hashemi SJ, Hosseini SA, Poustchi H, Rahimi Z, Sarvandian S, et al. Cohort profile: The Hoveyzeh Cohort Study (HCS): A prospective population-based study on non-communicable diseases in an Arab community of Southwest Iran. Med J Islam Repub Iran. 2020;34:141. [PubMed ID: 33437737]. [PubMed Central ID: PMC7787022]. https://doi.org/10.34171/mjiri.34.141.

  • 25.

    Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013;21(11):2264-71. [PubMed ID: 23519954]. [PubMed Central ID: PMC3692604]. https://doi.org/10.1002/oby.20408.

  • 26.

    Thipsawat S. Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: A review of the literature. Diab Vasc Dis Res. 2021;18(6):14791641211058900. [PubMed ID: 34791910]. [PubMed Central ID: PMC8606936]. https://doi.org/10.1177/14791641211058856.

  • 27.

    Taylor AE, Ebrahim S, Ben-Shlomo Y, Martin RM, Whincup PH, Yarnell JW, et al. Comparison of the associations of Body Mass Index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: A study using data from 4 UK cohorts. Am J Clin Nutr. 2010;91(3):547-56. [PubMed ID: 20089729]. https://doi.org/10.3945/ajcn.2009.28757.

  • 28.

    Schulze MB, Heidemann C, Schienkiewitz A, Bergmann MM, Hoffmann K, Boeing H. Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes Care. 2006;29(8):1921-3. [PubMed ID: 16873804]. https://doi.org/10.2337/dc06-0895.

  • 29.

    Mamtani MR, Kulkarni HR. Predictive performance of anthropometric indexes of central obesity for the risk of type 2 diabetes. Arch Med Res. 2005;36(5):581-9. [PubMed ID: 16099342]. https://doi.org/10.1016/j.arcmed.2005.03.049.

  • 30.

    InterAct C, Langenberg C, Sharp SJ, Schulze MB, Rolandsson O, Overvad K, et al. Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: The EPIC-InterAct case-cohort study. PLoS Med. 2012;9(6):e1001230. [PubMed ID: 22679397]. [PubMed Central ID: PMC3367997]. https://doi.org/10.1371/journal.pmed.1001230.

  • 31.

    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5. [PubMed ID: 19805654]. https://doi.org/10.1161/CIRCULATIONAHA.109.192644.

  • 32.

    Azizi T, Harati HADI, Mirbolooki MR, Saadat N, Azizi F. [Association of different anthropometric measures and type 2 diabetes in an Iranian urban population]. Iran J Endocrinol Metab. 2005;7(2):103-10. Persian.

  • 33.

    Moosaie F, Fatemi Abhari SM, Deravi N, Karimi Behnagh A, Esteghamati S, Dehghani Firouzabadi F, et al. Waist-to-height ratio is a more accurate tool for predicting hypertension than waist-to-hip circumference and BMI in patients with type 2 Diabetes: A prospective study. Front Public Health. 2021;9:726288. [PubMed ID: 34692623]. [PubMed Central ID: PMC8529190]. https://doi.org/10.3389/fpubh.2021.726288.

  • 34.

    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):1416. [PubMed ID: 31664979]. [PubMed Central ID: PMC6820979]. https://doi.org/10.1186/s12889-019-7801-2.

  • 35.

    Kimiagar M, Noori N. [The effect of weight loss on waist circumference and hip circumference of overweight and obese women]. Iran J Endocrinol Metabol. 2005;7(3):255-61. Persian.

  • 36.

    Kavey RE, Daniels SR, Lauer RM, Atkins DL, Hayman LL, Taubert K, et al. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. Circulation. 2003;107(11):1562-6. [PubMed ID: 12654618]. https://doi.org/10.1161/01.cir.0000061521.15730.6e.

  • 37.

    Cai G, Shi G, Xue S, Lu W. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine (Baltimore). 2017;96(37):e8058. [PubMed ID: 28906400]. [PubMed Central ID: PMC5604669]. https://doi.org/10.1097/MD.0000000000008058.

  • 38.

    Dobiasova M, Frohlich J, Sedova M, Cheung MC, Brown BG. Cholesterol esterification and atherogenic index of plasma correlate with lipoprotein size and findings on coronary angiography. J Lipid Res. 2011;52(3):566-71. [PubMed ID: 21224290]. [PubMed Central ID: PMC3035693]. https://doi.org/10.1194/jlr.P011668.

  • 39.

    Song P, Xu L, Xu J, Zhang HQ, Yu CX, Guan QB, et al. Atherogenic index of plasma is associated with body fat level in Type 2 diabetes mellitus patients. Curr Vasc Pharmacol. 2018;16(6):589-95. [PubMed ID: 29299987]. https://doi.org/10.2174/1570161116666180103125456.

  • 40.

    Jayedi A, Soltani S, Motlagh SZ, Emadi A, Shahinfar H, Moosavi H, et al. Anthropometric and adiposity indicators and risk of type 2 diabetes: Systematic review and dose-response meta-analysis of cohort studies. BMJ. 2022;376:e067516. [PubMed ID: 35042741]. [PubMed Central ID: PMC8764578]. https://doi.org/10.1136/bmj-2021-067516.

  • 41.

    Florentino GS, Cotrim HP, Vilar CP, Florentino AV, Guimaraes GM, Barreto VS. Nonalcoholic fatty liver disease in menopausal women. Arq Gastroenterol. 2013;50(3):180-5. [PubMed ID: 24322188]. https://doi.org/10.1590/S0004-28032013000200032.

  • 42.

    Ryu S, Suh BS, Chang Y, Kwon MJ, Yun KE, Jung HS, et al. Menopausal stages and non-alcoholic fatty liver disease in middle-aged women. Eur J Obstet Gynecol Reprod Biol. 2015;190:65-70. [PubMed ID: 25988514]. https://doi.org/10.1016/j.ejogrb.2015.04.017.

  • 43.

    Bertolotti M, Lonardo A, Mussi C, Baldelli E, Pellegrini E, Ballestri S, et al. Nonalcoholic fatty liver disease and aging: Epidemiology to management. World J Gastroenterol. 2014;20(39):14185-204. [PubMed ID: 25339806]. [PubMed Central ID: PMC4202348]. https://doi.org/10.3748/wjg.v20.i39.14185.