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. 2025 Jul 20;17(7):e88376.
doi: 10.7759/cureus.88376. eCollection 2025 Jul.

Influence of Age and Sex on the Clinical Profile of Metabolic Syndrome in Diabetic Patients

Affiliations

Influence of Age and Sex on the Clinical Profile of Metabolic Syndrome in Diabetic Patients

Rakesh Uppara Kadiyala et al. Cureus. .

Abstract

Background and aim In the 21st century, the rise in metabolic syndrome (MetS) prevalence has made it a global concern due to a higher risk of coronary artery disease and overall mortality, especially in India. Diabetic patients are more susceptible to metabolic syndrome. Therefore, it is crucial to determine the various risk factors associated with diabetes that contribute to the development of MetS. This study aimed to determine the prevalence of MetS and assess the risk factors and individual components of MetS in diabetic patients across both sexes and various age groups. Material and methods This cross-sectional prospective study was conducted in diabetic adults who visited the general medicine outpatient department at All India Institute of Medical Sciences (AIIMS), Mangalagiri. Patients who met the inclusion criteria were enrolled in this study. The collected data were analyzed according to the guidelines of the International Diabetes Federation (IDF) and the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) Joint Scientific Statement (2009) for MetS diagnosis. Results A total of 388 patients with diabetes mellitus were included in this study. The mean age was 52.55±11.82 years, with 20-59 years accounting for 69.6% (n=270) and ≥60 years for 30.4% (n=118). The proportion of males was 52.3% (n=203) of the study population. A high prevalence of MetS was reported (n=316; 81.4%). The prevalence of MetS was slightly higher in the 20-59 years age group (82.6%, n=223) compared to those ≥60 years (78.8%, n=93). A strong correlation between female sex and central obesity (n=168, 90.8%; p<0.001) and lower HDL-C levels (n=146, 78.9%; p<0.001) was present. A significant correlation was found between comorbidities, including hypertension (n=164, 86.3%, p=0.016), dyslipidemia (n=92, 88.5%, p=0.031), and the prevalence of MetS. Multivariable logistic regression analysis revealed that waist circumference (odds ratio {OR}: 1.1; p=0.016), systolic blood pressure (OR: 1.09; p=0.016), fasting blood glucose (OR: 1.02; p=0.044), and triglycerides (OR: 1.02; p=0.025) were independent predictors of MetS. Conclusion This study shows a high prevalence of metabolic syndrome (MetS) among diabetic patients, with females showing higher prevalence of MetS and its components, particularly central obesity and low HDL-C levels. The study also found statistically significant associations between MetS and glycemic parameters such as postprandial blood sugar and hemoglobin A1c (HbA1c). These findings emphasize the need for targeted screening and early intervention, particularly among female and younger diabetic patients. Public health initiatives focusing on lifestyle modifications and glycemic control are essential to mitigate MetS complications. Due to sociocultural diversities, region-specific studies are needed to develop localized guidelines for the effective management and prevention of metabolic syndrome.

Keywords: cardiovascular disease; diabetes mellitus; dyslipidemia; hypertension; lifestyle modifications; metabolic syndrome; obesity.

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Conflict of interest statement

Human subjects: Informed consent for treatment and open access publication was obtained or waived by all participants in this study. Institutional Ethics Committee, All India Institute of Medical Sciences, Mangalagiri issued approval #AIIMS/MG/IEC/2020-21/54. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Trends of metabolic syndrome in both sexes and various age groups.
Figure 2
Figure 2. Metabolic syndrome and comorbidities.
Figure 3
Figure 3. Comparison of lifestyle factors in patients with and without MetS.
MetS: metabolic syndrome
Figure 4
Figure 4. Biochemical profile and BMI in metabolic syndrome.
P<0.05 is statistically significant. PPBS: postprandial blood sugar; LDL-C: low-density lipoprotein cholesterol; BMI: body mass index
Figure 5
Figure 5. Forest plot depicting the possible predictors of metabolic syndrome.
HDL-C: high-density lipoprotein cholesterol
Figure 6
Figure 6. Number of components associated with MetS criteria.
MetS: metabolic syndrome

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