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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 2  |  Page : 152-156

Anthropometric measures in risk prediction for type 2 diabetes mellitus? – A cross-sectional study in regular athletes


1 Department of Clinical and Experimental Pharmacology, School of Tropical Medicine, Kolkata, West Bengal, India
2 Department of Pharmacology, R G Kar Medical College and Hospital, Kolkata, West Bengal, India

Date of Submission23-Feb-2020
Date of Decision02-Jul-2020
Date of Acceptance13-Dec-2020
Date of Web Publication18-May-2021

Correspondence Address:
Dr. Shatavisa Mukherjee
Department of Clinical and Experimental Pharmacology, School of Tropical Medicine, Kolkata - 700 073, West Bengal
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijhas.IJHAS_28_20

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  Abstract 


BACKGROUND: Obesity has been an increasing problem globally, and attempts have been made to identify the best anthropometric predictor of chronic diseases in various populations. Owing to increased cost and methodological complexities, imaging diagnostics has been a challenge in resource constraint settings. Thus, anthropometric markers have been assumed to be a better predictor in this regard. Addressing the dearth of research in this arena from this part of the country, the present study was conducted in regular footballers who were assessed for their anthropometric parameters as a probable risk indicator for type 2 diabetes mellitus (T2DM).
MATERIALS AND METHODS: This cross-sectional study involved 136 athletes, who were screened for risk factors and assessed for their measures such as height, weight, body circumferences, fat level, skeletal muscle, and skinfold thickness. Baseline laboratory investigations of serum glutamic-oxaloacetic transaminase (SGOT), serum glutamic-pyruvic transaminase, fasting glucose, and insulin were done. Homeostatic model assessment of insulin resistance (HOMA-IR) was assessed for all the participants. A 3-day dietary recall history was obtained from all respondents for calculation of total nutrient intake.
RESULTS: The mean age of respondents was 13.96 ± 1.91 years. While body mass index was recorded in normal range for 41 participants, 94 were in the “under” range and 1 was overweight. The mean waist, hip, and mid-thigh circumferences were 65.8 cm, 72.15 cm, and 43.86 cm, respectively. HOMA-IR derangement was noted in 11 participants. Increased fasting glucose and SGOT levels were noted in 9 participants, respectively.
CONCLUSION: Anthropometric measures may serve as an easy and inexpensive marker for T2DM prediction. However, assessment of its utility across genders and various subgroup populations mandates further research.

Keywords: Anthropometric measures, body mass index, homeostatic model assessment of insulin resistance, skinfold thickness, waist circumference


How to cite this article:
Samajdar SS, Mukherjee S, Begum SA, Sen S, Tripathi SK. Anthropometric measures in risk prediction for type 2 diabetes mellitus? – A cross-sectional study in regular athletes. Int J Health Allied Sci 2021;10:152-6

How to cite this URL:
Samajdar SS, Mukherjee S, Begum SA, Sen S, Tripathi SK. Anthropometric measures in risk prediction for type 2 diabetes mellitus? – A cross-sectional study in regular athletes. Int J Health Allied Sci [serial online] 2021 [cited 2021 Jun 17];10:152-6. Available from: https://www.ijhas.in/text.asp?2021/10/2/152/316292




  Introduction Top


Diabetes, a major global health problem, is often characterized as a modern epidemic resulting directly from Westernization.[1] The gravity of the situation can be better mapped from the alarming figures of mortality and morbidity due to it. One of the major risk factors for type 2 diabetes mellitus (T2DM) is obesity. Clinical evidence indicates a stronger association of diabetes with obesity. Simple anthropometric measurements have been used as surrogate measurements of obesity and have more practical value in both clinical practices and for large-scale epidemiological studies. Measures such as body mass index (BMI) and waist and hip circumferences are of prime importance in this regard. In general population, BMI is used as a simple method to calculate the prevalence of overweight and obesity in terms of height and weight. However, measurement of body circumferences in terms of waist, hip, and mid-thigh is of utmost significance. Owing to increased cost and methodological complexities, imaging diagnostics has been a challenge in various resource constraint settings. Thus, anthropometric markers have been assumed to be a better predictor in this regard.[2],[3]

For various cardiovascular diseases, abdominal adiposity has been considered one of the best predictors. Waist circumference, a more accurate measure of the distribution of body fat, has been shown to be more strongly associated with morbidity and mortality. Waist circumference is the best anthropometric measure of both intra-abdominal fat mass and total fat.[4] However, in individuals with a high proportion of lean muscle mass, BMI can be misrepresentative.

In order to develop anaerobic endurance potential, athletes such as football, tennis, basketball, and field hockey players usually perform high-intensity intermittent exercise. Endurance-trained athletes are characterized by a high oxidative capacity and have elevated intramyocellular lipids. Such fat accumulation in skeletal muscle strongly associates with the development of muscle insulin resistance (IR), the principal risk factor in developing T2DM. Furthermore, increased intramyocellular triglycerides are associated with obesity and T2DM.[4],[5] Exploring the risk of T2DM in young endurance-trained athletes is thus of much importance, and understanding the dearth of research in this arena from this part of the country, the present study was conducted in regular footballers who were assessed for their anthropometric parameters as a probable risk indicator for T2DM.


  Material and Methods Top


A cross-sectional study was conducted involving 136 footballers in a football club. The study commenced only after obtaining institutional ethical clearance for the conduct of this study. Potential responders were briefed regarding the purpose of the study, and for those consenting, written assent/consent was obtained from the responders prior to their inclusion in the study where applicable. For responders < 18 years of age, parental consent was also obtained. Respondents were screened for risk factors through proper history taking and were then assessed for their anthropometric measures including height, weight, body circumferences (waist, hip, and mid-thigh), fat level (total and visceral), skeletal muscle, and skinfold thickness (triceps and subscapular). All the variables were measured according to the World Health Organization (WHO) guidelines[6] and Asian criteria[7] for anthropometric measures. Appropriate quality control was maintained during collection of data.

All the measurements were taken over light clothing. Weight was measured by mechanical weighing scale in kilograms to the nearest 0.5 kg, without footwear with the scale being placed on a firm flat surface. Height was measured by a measuring tape against a flat vertical surface and recorded in centimeters, to the nearest 0.1 cm. Waist circumference was measured by a measuring tape and recorded in centimeters, to the nearest 0.1 cm, at the mid-point between costal margin and iliac crest. Hip circumference was measured by a measuring tape and recorded in centimeters, to the nearest 0.1 cm, at the level of maximum circumference of the ischial tuberosity of the participant.[8]

Baseline laboratory investigations were done at the study site for serum glutamic-oxaloacetic transaminase (SGOT), serum glutamic-pyruvic transaminase (SGPT), fasting blood glucose, and homeostatic model assessment of IR (HOMA-IR). The normal reference ranges for SGOT and SGPT were 0–40 IU/L. The normal reference range for HOMA-IR is 0.5–1.4. Less than 1.0 suggests insulin sensitivity which is optimal. Measures above 1.9 indicate early IR and above 2.9 indicates significant IR.[9] Fasting blood glucose < 100 mg/dL was considered normal, while those within 100–125 mg/dL were considered prediabetic and > 125 mg/dL were diabetic.

Samples were collected by phlebotomists and were analyzed at the institution. A 3-day dietary recall history was obtained from all respondents by an experienced dietician for calculation of total nutrient intake. Data were entered and analyzed using the Statistical Package for the Social Sciences (SPSS) version 21 (SPSS Inc., Chicago, IL, USA) for Windows. Descriptive data were presented using frequency, percentages, mean, and standard deviation (SD). Association between measures was analyzed using Pearson's correlation coefficient for numerical data and Chi-square test for categorical data. Different levels will be expressed at 95% confidence interval. P < 0.05 will be considered statistically significant.


  Results Top


The present study included 136 regular footballers, with a mean age of respondents being 13.96 ± 1.91 years. BMI was assessed using both WHO criteria and Asian criteria [Table 1].
Table 1: Body mass index of the respondents

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Anthropometric measures included assessment of body circumference, body fat, and skinfold thickness [Table 2]. The mean waist, hip, and mid-thigh circumferences were 65.8 cm, 72.15 cm, and 43.86 cm, respectively. Substantially increased risk of metabolic complications in 58.8% of the study participants where the waist-to-hip ratio was > 0.90, thus crossing the WHO cutoffs.
Table 2: Observed anthropometric measures

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The mean SGPT was noted as 21.07 ± 9.64 IU/L, while deranged SGPT was observed in 1 respondent only. The mean SGOT was noted as 28.58 ± 7.93 IU/L, while deranged SGOT was observed in 7.4% (n = 10) of the respondents (7.4%). Assessing HOMA-IR, 88.23% (n = 120) of the respondents were observed to be insulin sensitive, while 16 were found to be insulin resistant. Fasting blood glucose was observed among the respondents. 95.6% of the respondents (n = 130) were found normal, while 3.7% (n = 5) were found prediabetic and 1 (0.7%) was diabetic. The mean fasting insulin level for the respondents was 4.15 ± 3.2 (0.24–19.42).

Association of anthropometric parameters with SGPT, SGOT, IR, and fasting glucose was probed. Association was also assessed with BMI categories of the respondents [Table 3] and [Table 4].
Table 3: Association measures for anthropometric variables (using Pearson's correlation)

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Table 4: Association of body mass index and waist-hip ratio with various laboratory parameters

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The average nutrient intake from 3-day dietary recall was calculated:

  • Mean carbohydrate intake = 330.22 ± 112.44 g (149.8–912.64) (expressed as mean ± SD [range])
  • Mean protein intake = 74.8 ± 26.26 g (27–146.35) (expressed as mean ± SD [range])
  • Mean fat intake = 50.21 ± 21.95 g (19.68–114) (expressed as mean ± SD [range])
  • Mean energy = 2090.02 ± 471.50 kcal (1073–4078.88) (expressed as mean ± SD [range]).



  Discussion Top


Diabetes is a major global health problem which the world is facing today. India is regarded as the diabetic capital of the world. The gravity of the situation in India is much evident from the rising figures in which T2DM has been a prime contributor to mortality and morbidity. The number of diabetics in this country is soaring high, with around 380 million people being expected to have diabetes in 2025 worldwide.[10] One of the major contributing risk factors for T2DM is obesity. One of the major risk factors for T2DM is obesity. Obesity has been an increasing problem in both developing and developed countries across the globe and has been a prime contributing factor in most of the chronic diseases in various populations. Attempts have thus been made to identify the best anthropometric predictor in this regard. However, the major challenges lie in the cutoff points, which varies from population to population based on their food habits and other lifestyle factors.

As for India, South Asians tend to have larger waist measurements and waist-to-hip ratios, indicating a greater degree of central obesity, which is associated with a characteristic metabolic profile with higher insulin levels, a greater degree of IR, and a higher prevalence of diabetes.[11] Various clinical evidences direct a stronger association of diabetes with central body obesity than general obesity. A concept under Y-Y paradox suggests a unique phenotype called the “Asian Indian Phenotype” which is described as a higher degree of central body obesity (higher abdominal girth) and an increased amount of total body fat despite relatively low rates of generalized obesity. This concept suggests that an Indian person has a higher rate of both total and visceral fat than a Caucasian with similar BMI. At a normal BMI, there is higher fat percentage, which explains the faster deterioration of beta-cell function and early diabetes development.[12],[13],[14] Although interventions involving physical activity have been shown to reduce the risk of developing T2DM by over 50% in high-risk groups, there are postulations that endurance-trained athletes have elevated intramyocellular lipids, which strongly associates with obesity and T2DM. In a quest to explore this postulation, the present study was conducted in regular footballers who were assessed for their anthropometric parameters as a probable risk indicator for T2DM. Despite being in well-regulated lifestyle with regulated dietary intakes, 58.8% of the participants demonstrated a substantially increased risk of metabolic complications, with a waist-to-hip ratio being above cutoffs. This is in line with previous studies.[15],[16] SGPT and HOMA-IR were significantly associated with BMI, body circumference, and fat. Fasting insulin also showed a significant negative association with waist circumference and skinfold thickness.

There exist conflicting views on the preferred anthropometric measures. While various systematic reviews and prospective cohort studies have established waist circumference as a significant predictor, some systematic reviews and multiethnic cohort research have identified waist-to-hip ratio as a more useful screening tool in clinical settings.[17],[18] Despite the small sample size, undisputedly waist circumference may be endorsed as the single most convenient, feasible measure that could be used across communities for its significant association with IR.[8]

Smaller sample size and marked disparities in various cutoffs defining obesity add to the limitation of this study in terms of its generalizability. A cohort study with a larger sample size is recommended to determine the optimal cutoff points for various anthropometric measurements specific for the Indian population.[8]


  Conclusion Top


The present study postulates using anthropometric measures as an easy and inexpensive marker for T2DM prediction. However, assessment of its utility across genders and various subgroup populations mandates further research.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Weaver LJ, Narayan KM. Reconsidering the history of type 2 diabetes in India: Emerging or re-emerging disease? Natl Med J India 2008;21:288-91.  Back to cited text no. 1
    
2.
Valsamakis G, Chetty R, Anwar A, Banerjee AK, Barnett A, Kumar S, et al. Association of simple anthropometric measures of obesity with visceral fat and the metabolic syndrome in male caucasian and Indo-Asian subjects. Diabet Med 2004;21:1339-45.  Back to cited text no. 2
    
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Kamadjeu RM, Edwards R, Atanga JS, Kiawi EC, Unwin N, Mbanya JC, et al. Anthropometry measures and prevalence of obesity in the urban adult population of cameroon: An update from the cameroon burden of diabetes baseline survey. BMC Public Health 2006;6:228.  Back to cited text no. 3
    
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Cree MG, Newcomer BR, Katsanos CS, Sheffield-Moore M, Chinkes D, Aarsland A, et al. Intramuscular and liver triglycerides are increased in the elderly. J Clin Endocrinol Metab 2004;89:3864-71.  Back to cited text no. 4
    
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Lara-Castro C, Garvey WT. Intracellular lipid accumulation in liver and muscle and the insulin resistance syndrome. Endocrinol Metab Clin North Am 2008;37:841-56.  Back to cited text no. 5
    
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World Health Organization. Physical Status: The Use and Interpretation of Anthropometry. Available from: https://apps.who.int/iris/bitstream/handle/10665/37003/WHO_TRS_854.pdf; jsessionid=CDE3C452C7D641F895F03927BD0CDB2E?sequence=1. [Last accessed on 2020 Feb 03].  Back to cited text no. 6
    
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Snehalatha C, Viswanathan V, Ramachandran A. Cutoff values for normal anthropometric variables in Asian Indian adults. Diabetes Care 2003;26:1380-4.  Back to cited text no. 7
    
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Awasthi A, Rao CR, Hegde DS, Rao KN. Association between type 2 diabetes mellitus and anthropometric measurements – A case control study in South India. J Prev Med Hyg 2017;58:E56-62.  Back to cited text no. 8
    
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Singh Y, Garg MK, Tandon N, Marwaha RK. A study of insulin resistance by HOMA-IR and its cut-off value to identify metabolic syndrome in urban Indian adolescents. J Clin Res Pediatr Endocrinol 2013;5:245-51.  Back to cited text no. 9
    
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Tran NTT, Blizzard CL, Luong KN, Truong NLV, Tran BQ, Otahal P, et al. The importance of waist circumference and body mass index in cross-sectional relationships with risk of cardiovascular disease in Vietnam. PLoS One 2018;13:e0198202.  Back to cited text no. 10
    
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Tabish SA. Is diabetes becoming the biggest epidemic of the twenty-first century? Int J Health Sci (Qassim) 2007;1:V-VIII.  Back to cited text no. 11
    
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Gujral UP, Pradeepa R, Weber MB, Narayan KM, Mohan V. Type 2 diabetes in south asians: Similarities and differences with white caucasian and other populations. Ann N Y Acad Sci 2013;1281:51-63.  Back to cited text no. 12
    
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Unnikrishnan R, Anjana RM, Mohan V. Diabetes in South Asians: Is the phenotype different? Diabetes 2014;63:53-5.  Back to cited text no. 13
    
14.
Yajnik CS, Yudkin JS. The Y-Y paradox. Lancet 2004;363:163.  Back to cited text no. 14
    
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Lim SM, Choi DP, Rhee Y, Kim HC. Association between obesity indices and insulin resistance among healthy Korean adolescents: The JS high school study. PLoS One 2015;10:e0125238.  Back to cited text no. 15
    
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Manios Y, Kourlaba G, Kafatos A, Cook TL, Spyridaki A, Fragiadakis GA, et al. Associations of several anthropometric indices with insulin resistance in children: The children study. Acta Paediatr 2008;97:494-9.  Back to cited text no. 16
    
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Ghesmaty Sangachin M, Cavuoto LA, Wang Y. Use of various obesity measurement and classification methods in occupational safety and health research: A systematic review of the literature. BMC Obes 2018;5:28.  Back to cited text no. 17
    
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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;8:365-73.  Back to cited text no. 18
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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