Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), has become a major public health challenge worldwide, with a rising prevalence in India. Urbanization, sedentary lifestyles, and dietary changes contribute to increasing incidence, especially in South India [1]. A significant complication of T2DM is dyslipidemia, a cluster of lipid abnormalities characterized by elevated triglycerides, low high-density lipoprotein cholesterol (HDL-C), and sometimes elevated low-density lipoprotein cholesterol (LDL-C), which accelerates atherosclerosis and cardiovascular disease (CVD) risk [2].

Anthropometric measurements such as Body Mass Index (BMI) and Waist Hip Ratio (WHR) are often used as simple, non-invasive markers to assess obesity and central adiposity, which are components of metabolic syndrome and associated with cardiovascular risk [3]. The "deadly quartet" — obesity, glucose intolerance, hypertension, and dyslipidemia — emphasizes the interplay of these factors in diabetes complications [4]. While these anthropometric indices have been correlated with metabolic disturbances in many populations, their predictive accuracy for dyslipidemia among South Indian type 2 diabetics remains uncertain [5].

South Indians have a unique phenotype often characterized by a relatively lower BMI but higher visceral fat, termed as the “thin-fat Indian” phenotype, which may underestimate metabolic risk when using conventional anthropometric cut-offs [6]. The National Cholesterol Education Program (NCEP) and Adult Treatment Panel III (ATP III) guidelines, largely derived from Western populations, may underestimate metabolic syndrome prevalence and dyslipidemia risk in Indian subjects [7]. This mismatch highlights the need for region-specific evaluation of the utility of anthropometric measures.

Multiple Indian studies have shown that even individuals with normal or near-normal BMI may harbor metabolic abnormalities including dyslipidemia, insulin resistance, and hypertension [8]. Urban South Indian diabetics are particularly vulnerable due to lifestyle changes, dietary patterns, and genetic predisposition [9].

Aim

To evaluate whether anthropometric measurements (BMI and Waist Hip Ratio) can predict dyslipidemia in urban South Indian type 2 diabetic patients.

Objectives

1. To measure and classify anthropometric parameters including BMI and Waist Hip Ratio in type 2 diabetic and non-diabetic urban South Indian adults aged 35-65 years.
2. To assess the prevalence of dyslipidemia among these groups according to ATP III criteria.
3. To analyze the correlation between anthropometric measurements and lipid profile abnormalities in type 2 diabetics and compare with non-diabetic controls.

Source of Data

Data were collected from urban residents attending Sri Venkateswaraa Medical College Hospital & Research Institute, Chennai, Tamil Nadu.

Study Design

A cross-sectional observational study.

Study Location

Sri Venkateswaraa Medical College Hospital & Research Institute, Chennai, Tamil Nadu, India.

Study Duration

One year

Sample Size

Total of 1887 subjects screened: 1500 non-diabetics and 387 type 2 diabetics.

Inclusion Criteria

 Adults aged 35–65 years of either sex.

 Urban South Indian residents.

 Subjects consenting to anthropometric measurements and lipid profile estimation.

Exclusion Criteria

 Type 2 diabetics on hypolipidemic therapy.

 Use of oral contraceptive pills.

 Presence of thyroid disorders.

 Nephrotic syndrome or liver cirrhosis.

Procedure and Methodology

Anthropometric measurements including height, weight, waist circumference, and hip circumference were taken using standardized techniques. BMI was calculated as weight (kg) / height (m²). Waist circumference was measured at the narrowest point below the costal margin; hip circumference at the widest part below the waist. Waist Hip Ratio (WHR) was computed as waist circumference divided by hip circumference. BMI classification followed WHO criteria: underweight (<18.5), normal (18.5–24.9), overweight (25.0–29.9), obese Grade 1 (30.0–34.9), Grade 2 (35.0–39.9), morbid obesity (>40). Lipid profiles were estimated after a minimum of 10 hours overnight fasting by enzymatic colorimetric method using a spectrophotometer. Parameters included Total Cholesterol, Triglycerides, HDL, and LDL cholesterol. LDL cholesterol was calculated by Friedewald’s formula: LDL = Total cholesterol – (HDL + VLDL), where VLDL = triglycerides/5. Dyslipidemia was defined as per ATP III guidelines: Total cholesterol >200 mg/dL, triglycerides >150 mg/dL, LDL >100 mg/dL, HDL <40 mg/dL. Diabetes diagnosis was confirmed as per 1997 ADA guidelines using fasting plasma glucose and oral glucose tolerance test results.

Sample Processing

Fasting blood samples were collected and processed in the hospital biochemistry laboratory using standard protocols.

Statistical Methods

Data were entered and analyzed using appropriate statistical software - SPSS. Descriptive statistics summarized anthropometric and lipid parameters. Comparison of means between diabetics and non-diabetics was done using t-tests. Correlation analysis was conducted to evaluate relationships between anthropometric indices and lipid parameters. Significance level was set at p < 0.01.

Data Collection

Data on demographics, anthropometry, fasting lipid profile, and diabetic status were collected from all participants.

Table 1: Prevalence of Dyslipidemia across BMI Categories in Urban South Indian Non-Diabetics and Type 2 Diabetics

The table illustrates the prevalence of dyslipidemia among 1500 non-diabetic and 387 type 2 diabetic urban South Indian subjects, stratified by Body Mass Index (BMI) categories. Among the non-diabetic group, the overall prevalence of dyslipidemia was relatively low at 2.2%. When analyzed according to BMI, only 0.5% of individuals with ideal body weight had dyslipidemia, which gradually increased with BMI categories—1.13% in the overweight group, 3.49% in obese Grade 1, 4.8% in obese Grade 2, and reaching 10.16% in the morbidly obese subgroup. This trend indicates a positive correlation between increasing BMI and dyslipidemia prevalence in non-diabetics.

In contrast, type 2 diabetics exhibited a significantly higher prevalence of dyslipidemia across all BMI categories, with an overall rate of 42.63%. Notably, even among diabetics with ideal body weight, nearly half (47.36%) had dyslipidemia, and this high prevalence was consistently observed across overweight (41.66%), obese Grade 1 (41.86%), and obese Grade 2 (36.36%) categories. This data suggests that in urban South Indian type 2 diabetics, dyslipidemia is highly prevalent regardless of anthropometric status, highlighting that diabetes itself is a stronger predictor of lipid abnormalities than BMI.

The presented data reveal a stark contrast in the prevalence of dyslipidemia between urban South Indian non-diabetic and type 2 diabetic populations across different BMI categories. Among non-diabetics, dyslipidemia prevalence was low (2.2% overall) and showed a clear upward trend with increasing BMI—from 0.5% in individuals with ideal body weight to 10.16% among the morbidly obese. This aligns well with existing literature that correlates obesity with increased risk of

dyslipidemia due to insulin resistance, altered lipid metabolism, and chronic inflammation [10].

Conversely, the type 2 diabetic group showed a markedly higher prevalence of dyslipidemia across all BMI strata, with nearly half (47.36%) of those with ideal body weight affected. Dyslipidemia prevalence remained consistently high (above 35%) across overweight and obese categories. These findings suggest that in type 2 diabetes, dyslipidemia is largely independent of anthropometric measures such as BMI, reinforcing the concept that diabetes mellitus itself is a strong driver of lipid abnormalities [11].

Similar observations have been reported in several studies from India and other populations. For example, Mohan et al. observed a high prevalence of dyslipidemia among urban South Indian diabetics regardless of BMI, indicating that traditional anthropometric risk factors underestimate metabolic abnormalities in this group [12]. The “thin-fat” Indian phenotype, characterized by a higher percentage of body fat and visceral adiposity even at lower BMI, may partly explain this discrepancy [13].

A study by Misra et al. emphasized that BMI cut-offs derived from Western populations are not fully applicable to Indians, as many individuals with “normal” BMI may still possess significant metabolic risk factors including dyslipidemia [14]. Furthermore, the Chennai Urban Rural Epidemiology Study (CURES) demonstrated that metabolic syndrome components like dyslipidemia frequently present in diabetics across weight categories, supporting our findings [15].

The higher prevalence of dyslipidemia in diabetic patients is pathophysiologically linked to insulin resistance, which impairs lipoprotein lipase activity, increases hepatic very low-density lipoprotein (VLDL) synthesis, and reduces HDL cholesterol levels [16]. These metabolic derangements are often observed even in diabetics with normal BMI, thus anthropometric measures alone are insufficient for risk stratification.

The data also show a progressive increase of dyslipidemia with BMI in non-diabetics, which is consistent with global evidence linking obesity to lipid abnormalities [17]. This underscores that while anthropometric measurements can serve as useful markers in the general population, their predictive value diminishes in the diabetic subset, where biochemical assessment remains crucial.

The study demonstrates that anthropometric measurements such as Body Mass Index (BMI) and Waist Hip Ratio (WHR) do not reliably predict dyslipidemia in urban South Indian type 2 diabetic patients. Despite variations in BMI categories, the prevalence of dyslipidemia remained consistently high across all groups, including those with ideal body weight. This indicates that diabetes mellitus itself is a predominant factor influencing lipid abnormalities, independent of conventional anthropometric indicators. Therefore, reliance on anthropometric measures alone may underestimate cardiovascular risk in this population. Comprehensive biochemical screening for dyslipidemia and aggressive metabolic control should be prioritized in type 2 diabetics irrespective of their body composition to prevent cardiovascular morbidity and mortality.

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