Evaluation of Anthropometric Indices and Biochemical Markers in Iranian Prediabetics in Hoveyzeh Located in Southwest Iran: A Cross-sectional Study

authors:

avatar Majed Meripour 1 , avatar Hashem Mohamadian 1 , avatar Morteza Abdullatif Khafaie ORCID 2 , *

Department of Health Education and Promotion, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Social Determinants of Health Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

how to cite: Meripour M , Mohamadian H , Khafaie M A. Evaluation of Anthropometric Indices and Biochemical Markers in Iranian Prediabetics in Hoveyzeh Located in Southwest Iran: A Cross-sectional Study. Jundishapur J Chronic Dis Care. 2021;10(4):e117444. https://doi.org/10.5812/jjcdc.117444.

Abstract

Background:

The number of prediabetic individuals is rising rapidly in Iran and other parts of the world. This complication seriously affects life quality and public health.

Objectives:

This study aimed to evaluate the anthropometric indices and biochemical measures among the prediabetic population.

Methods:

This cross-sectional study was conducted on prediabetic subjects in the Hoveyzeh population, Iran, during 2018 - 2019. A sample of 240 prediabetic individuals within the age range of 30 - 75 years enrolled in this study. The demographic characteristics, anthropometric measurements, and participants’ clinical parameters were checked out in this study. Moreover, this study investigated important demographic variables associated with anthropometric and clinical parameters.

Results:

The results showed that the mean values of fasting plasma glucose, triglyceride, and total cholesterol were 113.13 ± 6.8, 193.64 ± 115.44, and 193.64 ± 40.67 mg/dl, respectively. The mean values of systolic blood pressure and diastolic blood pressure (DBP) were 114.75 ± 15.25 and 73.25 ± 8.81 mmHg, respectively. The results showed no statistically significant difference between clinical and demographic variables except between DBP and marital status (P < 0.05). The prevalence of prediabetes in Hoveyzeh was closely associated with some demographic, anthropometric, and clinical variables. This study documented significant associations between height, hip circumference, and body mass index with both genders (P < 0.05). Furthermore, there was no statistical relationship between other demographic and anthropometric variables (P > 0.05).

Conclusions:

Based on the findings, abdominal obesity indicators may be related to prediabetes. Therefore, preventive strategies and suitable educational programs should be based on influential factors.

1. Background

Prediabetes is an intermediate condition of hyperglycemia with fasting plasma glucose (FPG) concentration above the normal range but below the diabetes cut-offs (1). According to the American Diabetes Association, prediabetes includes several parameters, namely impaired glucose tolerance (IGT), impaired fasting glucose (IFG), and hemoglobin A1c (HbA1c) value (range: 5.7 - 6.4%) (2, 3). If the hyperglycemic status is not controlled, prediabetic individuals will be advanced into type 2 diabetes after 5 - 10 years, which is established as a major risk factor of cardiovascular diseases (4).

Diabetes mellitus is considered a serious metabolic disorder with a major impact on the physical, psychological, and social health of individuals, families, and communities worldwide (5). It is characterized by hyperglycemia, resulting in micro-and macro-vascular complications, organ failure, and lipid disorders (5, 6). In 2019, at least 463 million subjects suffered from diabetes, and this number will reach 578 million individuals by 2030 according to the predictions (7). The World Health Organization estimated that in 2025 about 5.2 million individuals would have diabetes mellitus in Iran (8).

In recent years, the prevalence of prediabetes is increasing worldwide, and probably more than 470 million individuals will have prediabetes symptoms by 2030 (6). However, most of these individuals are unaware of their prediabetic status. A recent study performed on prediabetic individuals reported that 50 - 80% of these patients did not have enough basic information to manage this disease. These individuals require self-management educational programs to learn how to control and improve their condition (9, 10).

Monitoring biochemical indices, such as FPG, triglyceride (TG), total cholesterol (TC), and other anthropometric measures is an essential need of continuous monitoring of the pre-diabetic patients and their risk factors to control them (11, 12). However, identifying these factors is recommended as an effective and low-cost strategy to control prediabetes or delay the progression of type 2 diabetes among high-risk individuals (13).

2. Objectives

This study aimed to evaluate anthropometric indices and biochemical measurements among the prediabetic population in Hoveyzeh, Iran.

3. Methods

3.1. Study Setting

This cross-sectional population-based study was conducted on prediabetic subjects in Hoveyzeh located in southwest Iran during 2018 - 2019. The present study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences, Khouzestan Province, Iran (IR.AJUMS.REC.1397.689). A total of 240 subjects were randomly selected out of 4,000 prediabetic individuals covered by five comprehensive health service centers in Hoveyzeh. The study objectives were clearly explained to all the participants, and their consent was obtained by signing a consent form.

3.2. Participant Selection

The inclusion criteria were prediabetic individuals aged 30 - 75 years residing in Hoveyzeh. The exclusion criteria were not living in Hoveyzeh, reluctance to continue participating in the study, and acute or chronic illnesses (eg, mental illness, mental retardation, chronic infectious diseases, and motor disabilities). The required data for this study were documented in three categories, including demographic characteristics (eg, age, gender, family members, educational level, occupational status, and marital status), anthropometric indices (eg, height, weight, waist circumference [WC], hip circumference [HC], and body mass index [BMI]), and biochemical and physiological parameters (eg, blood lipids, FPS, and blood pressure).

3.3. Outcome Measures

Prediabetes: This status is defined as IFG (FPG: 100 - 125 mg/dL and 2-hour plasma glucose level < 140 mg/dL) or IGT (FPG < 126 mg/dL, but with 140 ≤ 2-hour plasma glucose concentration < 200 mg/dL) or HbA1c 6.0 - 6.49%. Hypertension: This status is defined as systolic blood pressure (SBP) of > 140 mmHg and diastolic blood pressure (DBP) ≥ 90 mmHg. BMI (kg/m2): This status is defined as a normal weight of < 25 kg/m2. Overweight: This status is defined as 25 ≤ BMI < 30 kg/m2. Obesity: This status is defined as a BMI of ≥ 30 kg/m2. Central obesity: This status is defined as WC of ≥ 80 cm for females and WC of ≥ 94 cm for males (14).

3.4. Statistical Analysis

Statistical analyses were carried out using SPSS software (version 22.0). The data were appropriately reported as number (percentage) or mean ± standard deviation. The Chi-square test and t-test were applied to compare categorical and continuous variables, and a P-value of less than 0.05 was set as a statistically significant level.

4. Results

4.1. Demographic Characteristics of Prediabetic Subjects

The demographic characteristics of all the participants are summarized in Table 1. Almost half of the participants were male, and most participants were married (97.5%) and within the middle-age range of 45 - 70 years (69.17%). Additionally, the illiteracy rate was reported as 55%.

Table 1. Demographic Characteristics of Participants (n = 240)
VariableFrequency, No. (%)
Age (y)
< 4574 (30.83)
45 - 70166 (69.17)
Family members (n)
< 440 (16.67)
≥ 4200 (83.33)
Gender
Male119 (49.58)
Female121 (50.42)
Marital status
Single/Divorced6 (2.5)
Married234 (97.5)
Occupational status
Farmer55 (22.91)
Housewife117 (48.75)
Employee34 (14.16)
Others34 (14.16)
Educational level
Illiterate131 (54.58)
Elementary school51 (21.25)
Secondary school18 (7.5)
Diploma14 (5.83)
Associate degree16 (6.66)
Bachelor’s degree and higher10 (4.16)

4.2. Anthropometric Measurements of Prediabetic Subjects

The results of Table 2 show that the prediabetic subjects are in the overweight category. Table 3 tabulates the association between each variable of subjects’ anthropometric measurements and demographic characteristics. This study documented significant associations between height, HC, and BMI with both genders (P < 0.05). Moreover, there was no statistical relationship between other demographic and anthropometric variables (P > 0.05).

Table 2. Anthropometric Measurements of Participants (n = 240)
VariableWeight (kg)Height (cm)Waist Circumference (cm)Hip Circumference (cm)Body Mass Index (kg/m2)
Individuals (Mean ± standard deviation)76.69 ± 11.94166.35 ± 996.41 ± 10.15102.58 ± 12.2527.84 ± 4.71
Table 3. Association Between Demographic and Anthropometric Variables in the Study Population (n = 240) a,b
Weight (kg)Height (cm)Waist Circumference (cm)Hip Circumference (cm)Body Mass Index (kg/m2)
Age (y)
< 4576.36 ± 12.94165.28 ± 7.796.21 ± 10.2102.8 ± 11.1228.01 ± 4.77
≥ 4576.83 ± 11.51166.83 ± 9.5196.5 ± 10.26102.8 ± 11.4227.77 ± 4.71
P-value0.780.180.840.860.72
Educational level
Diploma76.47 ± 11.47165.92 ± 9.0496.44 ± 10.32102.8 ± 11.4927.93 ± 4.7
Academic degree78.44 ± 15.5169.92 ± 8.0296.23 ± 9.52100.88 ± 9.6827.14 ± 4.91
P-value0.430.03*0.920.420.42
Marital status
Single76.9 ± 11.93166.41 ± 9.0896.58 ± 10.24102.69 ± 11.3727.9 ± 4.73
Married68.5 ± 10.42164.17 ± 5.8190 ± 7.0798.67 ± 8.6225.45 ± 3.99
P-value0.090.550.120.390.21
Gender
Male77.61 ± 11.62170.34 ± 8.1996.06 ± 9.19101.07 ± 9.4826.85 ± 4.32
Female75.78 ± 12.25162.43 ± 8.0296.76 ± 11.16104.07 ± 12.7228.82 ± 4.9
P-value0.24< 0.001***0.590.04*0.001**

4.3. Distribution of Clinical Features of Participants

The findings of FPG (mg/dL), TG (mg/dL), TC (mg/dL), and blood pressure variables (mmHg) of prediabetic subjects are presented in Table 4. The mean values of FPG, TG, and TC were 113.13 ± 6.8, 193.64 ± 115.44, and 193.64 ± 40.67 mg/dL, respectively. The mean values of SBP and DBP were 114.75 ± 15.25 and 73.25 ± 8.81 mmHg, respectively. The results in Table 5 show no statistically significant difference between clinical and demographic variables except between DBP and marital status (P < 0.05).

Table 4. Clinical Features of Participants (n = 240)
VariableFPG (mg/dL)TG (mg/dL)TC (mg/dL)SBP (mmHg)DBP (mmHg)
Individuals (Mean ± standard deviation)113.13 ± 6.8193.64 ± 115.44193.64 ± 40.67114.75 ± 15.2573.25 ± 8.81
Table 5. Association Between Demographic and Clinical Variables in the Study Population (n = 240) a,b
VariableFPG (mg/dL)TG (mg/dL)TC (mg/dL)SBP (mmHg)DBP (mmHg)
Age (y)
< 45113.38 ± 6.23188.8 ± 117.87195.09 ± 39.45112.5 ± 14.6572.16 ± 8.64
≥ 45113.03 ± 7.08177.89 ± 115.39192.99 ± 41.35115.75 ± 15.5673.73 ± 8.90
P-value0.70.500.710.130.2
Educational level
Diploma113.24 ± 6.88181.69 ± 117.09192.87 ± 40.82115 ± 14.7473.64 ± 8.65
Academic degree112.27 ± 6.40177.69 ± 108.98199.92 ± 39.94112.69 ± 19.7170 ± 9.8
P-value0.490.870.400.470.08
Marital status
Single113.26 ± 6.83181.52 ± 116.79193.25 ± 40.52115 ± 15.3973.33 ± 8.93
Married108.33 ± 4.59170.83 ± 87.90208.67 ± 48.77105 ± 8.3770 ± 0.00
P-value0.080.820.820.11< 0.001***
Gender
Male113.15 ± 6.73191.84 ± 13.91194.62 ± 36.61115.67 ± 13.5373.70 ± 8.42
Female113.12 ± 6.93170.84 ± 94.66192.67 ± 41.89113.84 ± 16.9272.81 ± 9.24
P-value0.970.160.710.360.44

5. Discussion

This population-based study was designed to evaluate anthropometric indices and biochemical parameters in a prediabetic population in Hoveyzeh in southwest Iran. This study was performed on 240 individuals aged 30 - 75 years, and the majority of the prediabetic population were housewives, married, and illiterate. The data obtained from this study showed abnormal anthropometric indices in the whole prediabetic population. The aforementioned results documented significant associations between HC and BMI with both genders. There was no statistical relationship between clinical and demographic variables except between DBP and marital status.

The prevalence of prediabetes and type 2 diabetes has been rising dramatically in Iran in recent years. It is estimated that type 2 diabetes incidence will reach approximately 9.2 million in the Iranian population by 2030 (8). Hyperglycemia has been associated with abnormal lipid metabolism, and improving glycemic status in hyperglycemia subjects is related to the enhancement of lipid profile and various complications (15).

The primary and rapid modifications in lifestyle in many societies have raised the prevalence of obesity, particularly abdominal obesity, and other risk factors (eg, high blood pressure), which have been documented to be the severe risk factors for the rising prevalence of prediabetes and type 2 diabetes (16). The lower abdominal obesity and higher physical activity make individuals, mainly urban populations, less likely to have prediabetes (17).

Some previous studies have reported that obesity and abdominal obesity indicators, such as BMI > 30 kg/m2, WC, and HC, may be independent predictors for prediabetes and type 2 diabetes mellitus (18-20). A meta-analysis of the published literature has suggested that the incidence of diabetes was associated with BMI and WC (21). Similar associations between abdominal obesity indicators and diabetes incidence were reported in an Iranian population (22). However, the current study demonstrated abnormal anthropometric indices in prediabetic individuals.

In the present study, prediabetic subjects were overweight and had abnormal FPG tests. According to some studies, prediabetes and type 2 diabetes mellitus are also associated with some of the Iranian population’s demographical factors. The association between prediabetes and education level was reported in an Iranian urban population. Low educational level was higher in prediabetic women than normal glycemic subjects (4). The data from a cross-sectional study conducted in the northeast of Iran, Khorasan province, showed that diabetes was not related to educational level and marital status (23). However, most prediabetic populations (age range: 30 - 75 years) were housewives, married, and illiterate in the present study.

The current study showed the effects of demographic variables on clinical parameters and anthropometric measures. A significant association was observed between marital status and DBP. A similar association was reported between gender with HC and BMI. The collected data in this study indicated that only 10.8% of adults with academic degrees suffer from prediabetics, demonstrating low educational levels and poor awareness among prediabetic individuals. In this study, inadequate awareness among prediabetic individuals may be a significant barrier to effective prevention and control of glycemic status. Therefore, a suitable educational program is proposed to inform these individuals.

5.1. Conclusions

The results of this study showed that abdominal obesity indicators, including increasing WC and BMI, are observed in prediabetic subjects, and there was no statistical relationship between other demographic and anthropometric variables. According to the obtained findings, most prediabetic individuals were illiterate; possibly, further awareness and educational programs may lead to adopting a healthy lifestyle associated with the management and control of prediabetic status. Therefore, it is recommended to implement educational programs in patients with prediabetes in future investigations.

References

  • 1.

    Glechner A, Keuchel L, Affengruber L, Titscher V, Sommer I, Matyas N, et al. Effects of lifestyle changes on adults with prediabetes: A systematic review and meta-analysis. Prim Care Diabetes. 2018;12(5):393-408. doi: 10.1016/j.pcd.2018.07.003. [PubMed: 30076075].

  • 2.

    Gholi Z, Heidari-Beni M, Feizi A, Iraj B, Askari G. The characteristics of pre-diabetic patients associated with body composition and cardiovascular disease risk factors in the Iranian population. J Res Med Sci. 2016;21:20. doi: 10.4103/1735-1995.179888. [PubMed: 27904566]. [PubMed Central: PMC5122248].

  • 3.

    Bansal N. Prediabetes diagnosis and treatment: A review. World J Diabetes. 2015;6(2):296-303. doi: 10.4239/wjd.v6.i2.296. [PubMed: 25789110]. [PubMed Central: PMC4360422].

  • 4.

    Rahmanian K, Shojaei M, Sotoodeh Jahromi A, Madani A. The association between pre-diabetes with body mass index and marital status in an Iranian urban population. Glob J Health Sci. 2015;8(4):95-101. doi: 10.5539/gjhs.v8n4p95. [PubMed: 26573038]. [PubMed Central: PMC4873576].

  • 5.

    Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40-50. doi: 10.1016/j.diabres.2017.03.024. [PubMed: 28437734].

  • 6.

    Nayak VKR, Raghurama Nayak K, Vidyasagar S, Kamath A. Body composition analysis, anthropometric indices and lipid profile markers as predictors for prediabetes. PLoS One. 2018;13(8). e0200775. doi: 10.1371/journal.pone.0200775. [PubMed: 30114196]. [PubMed Central: PMC6095495].

  • 7.

    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. doi: 10.1016/j.diabres.2019.107843. [PubMed: 31518657].

  • 8.

    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. doi: 10.1038/s41598-017-13379-z. [PubMed: 29044139]. [PubMed Central: PMC5647418].

  • 9.

    Nejhaddadgar N, Darabi F, Rohban A, Solhi M, Kheire M. The effectiveness of self-management program for people with type 2 diabetes mellitus based on PRECEDE-PROCEDE model. Diabetes Metab Syndr. 2019;13(1):440-3. doi: 10.1016/j.dsx.2018.08.016. [PubMed: 30641740].

  • 10.

    Hosseini SG, Shojaeizadeh D, Sanagu A, Vakili M, Mirkarimi K, Jahanshahi R. Effect of educational intervention on self-care behaviors among patients with diabetes: An application of PRECEDE model. Ann Trop Med Public Health. 2017.

  • 11.

    Wang R, Zhang P, Li Z, Lv X, Cai H, Gao C, et al. The prevalence of pre-diabetes and diabetes and their associated factors in Northeast China: a cross-sectional study. Sci Rep. 2019;9(1):2513. doi: 10.1038/s41598-019-39221-2. [PubMed: 30792436]. [PubMed Central: PMC6385189].

  • 12.

    Siddiqui S, Zainal H, Harun SN, Ghadzi SMS. Diet quality and its association with glycemic parameters in different diabetes progression stages. A cross-sectional questionnaire study at a primary care clinic. Clin Nutr ESPEN. 2020;39:165-72. doi: 10.1016/j.clnesp.2020.06.022. [PubMed: 32859312].

  • 13.

    Roberts S, Barry E, Craig D, Airoldi M, Bevan G, Greenhalgh T. Preventing type 2 diabetes: Systematic review of studies of cost-effectiveness of lifestyle programmes and metformin, with and without screening, for pre-diabetes. BMJ Open. 2017;7(11). e017184. doi: 10.1136/bmjopen-2017-017184. [PubMed: 29146638]. [PubMed Central: PMC5695352].

  • 14.

    Esteghamati A, Meysamie A, Khalilzadeh O, Rashidi A, Haghazali M, Asgari F, et al. Third national Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007) in Iran: methods and results on prevalence of diabetes, hypertension, obesity, central obesity, and dyslipidemia. BMC Public Health. 2009;9:167. doi: 10.1186/1471-2458-9-167. [PubMed: 19480675]. [PubMed Central: PMC2697989].

  • 15.

    Gillies NA, Pendharkar SA, Singh RG, Asrani VM, Petrov MS. Lipid metabolism in patients with chronic hyperglycemia after an episode of acute pancreatitis. Diabetes Metab Syndr. 2017;11 Suppl 1:S233-41. doi: 10.1016/j.dsx.2016.12.037. [PubMed: 28065464].

  • 16.

    Hassan Lotfi M. Association between anthropometric parameters (WC, BMI, WHR) and type 2 diabetes in the adult Yazd population, Iran. J Diabetes Metab. 2014;5(10). doi: 10.4172/2155-6156.1000444.

  • 17.

    Alam DS, Talukder SH, Chowdhury MA, Siddiquee AT, Ahmed S, Pervin S, et al. Overweight and abdominal obesity as determinants of undiagnosed diabetes and pre-diabetes in Bangladesh. BMC Obes. 2016;3:19. doi: 10.1186/s40608-016-0099-z. [PubMed: 27004127]. [PubMed Central: PMC4797177].

  • 18.

    Boles A, Kandimalla R, Reddy PH. Dynamics of diabetes and obesity: Epidemiological perspective. Biochim Biophys Acta Mol Basis Dis. 2017;1863(5):1026-36. doi: 10.1016/j.bbadis.2017.01.016. [PubMed: 28130199]. [PubMed Central: PMC5429876].

  • 19.

    Reyes-Munoz E, Ortega-Gonzalez C, Martinez-Cruz N, Arce-Sanchez L, Estrada-Gutierrez G, Moran C, et al. Association of obesity and overweight with the prevalence of insulin resistance, pre-diabetes and clinical-biochemical characteristics among infertile Mexican women with polycystic ovary syndrome: a cross-sectional study. BMJ Open. 2016;6(7). e012107. doi: 10.1136/bmjopen-2016-012107. [PubMed: 27449893]. [PubMed Central: PMC4964199].

  • 20.

    Ahmad N, Adam SI, Nawi AM, Hassan MR, Ghazi HF. Abdominal obesity indicators: Waist circumference or waist-to-hip ratio in Malaysian adults population. Int J Prev Med. 2016;7:82. doi: 10.4103/2008-7802.183654. [PubMed: 27330688]. [PubMed Central: PMC4910307].

  • 21.

    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. doi: 10.1093/epirev/mxm008. [PubMed: 17494056].

  • 22.

    Haghighatdoost F, Amini M, Feizi A, Iraj B. Are body mass index and waist circumference significant predictors of diabetes and prediabetes risk: Results from a population based cohort study. World J Diabetes. 2017;8(7):365-73. doi: 10.4239/wjd.v8.i7.365. [PubMed: 28751960]. [PubMed Central: PMC5507834].

  • 23.

    Azimi-Nezhad M, Ghayour-Mobarhan M, Parizadeh MR, Safarian M, Esmaeili H, Parizadeh SM, et al. Prevalence of type 2 diabetes mellitus in Iran and its relationship with gender, urbanisation, education, marital status and occupation. Singapore Med J. 2008;49(7):571-6. [PubMed: 18695867].

Copyright © 2021, Jundishapur Journal of Chronic Disease Care. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.