Assessment of clinical metabolic risk score and its correlation with anthropometric and biochemical parameters in adults aged over 30 years: a cross-sectional study in a rural setting

INTRODUCTION

Metabolic syndrome represents a cluster of interrelated risk factors such as central obesity, dyslipidemia, hyperglycemia, and elevated blood pressure, which substantially increase the risk of cardiovascular disease and type 2 diabetes mellitus.^1,2

Rural populations, traditionally thought to be at lower risk, are increasingly showing trends parallel to urban counterparts due to changing lifestyles, decreased physical activity, and nutritional transitions.³

Clinical Metabolic Risk Score (CMRS) is a simplified metric that aggregates multiple risk parameters into a single score, helping in early diagnosis and treatment.^4,5 This study aims to assess CMRS and explore its correlation with key anthropometric and biochemical indicators in adult population at rural setting.

METHODS

Study design and setting

This was a community-based, cross-sectional study conducted in the Outpatient Department (OPD) of a rural Community Health Centre in District Hospital Dhar, M.P. The study aimed to assess adults attending the General Medicine OPD, with particular focus on individuals above 30 years of age.

Study population

The study population comprised adults aged more than 30 years who visited the General Medicine OPD during the study period. Participants were selected from among the routine attendees of the centre, ensuring a representative sample of the rural community.

Sample size and sampling technique

A universal sampling technique was adopted, whereby all eligible individuals attending the OPD during the data collection period were invited to participate. A total of 300 individuals fulfilling the eligibility criteria were enrolled in the study.

Study duration

The data collection was carried out over a period of three months from June 2025 to August 2025, starting from the date of obtaining permission from the Institutional Ethics Committee (IEC) until the compilation of the study report.

Inclusion criteria

Participants included in the study were: Adults aged more than 30 years, and individuals willing to participate and provide written informed consent.

Exclusion criteria

The following individuals were excluded from the study: Known cases of metabolic syndrome, patients diagnosed with chronic illnesses such as malignancy or renal failure.

Data collection tools and techniques

Data were collected using a pre-tested, structured questionnaire designed to capture detailed sociodemographic information and relevant clinical history. The questionnaire covered variables such as age, sex, occupation, education level, lifestyle factors, and medical history. Clinical examination and relevant investigations were performed as per standard protocols to supplement the information obtained through the questionnaire.

Anthropometric measurements included weight (in kilograms), height (in meters), and waist circumference (cm), hip circumference (cm).

Blood pressure measured using a standard mercury sphygmomanometer. Biochemical tests (fasting blood sample) included fasting blood glucose, serum triglycerides, and HDL cholesterol.

Scoring

CMRS calculation criteria

Each risk parameter was assigned a score of 1 based on the following thresholds: Waist circumference >90 cm (men)/>80 cm (women), Fasting glucose ≥100 mg/dL, Triglycerides ≥150 mg/dL, HDL cholesterol <40 mg/dL (men)/<50 mg/dL (women), Blood pressure ≥130/85 mmHg, and Total CMRS Range: 0-5.

The total CMRS score ranges from 0 to 5, where scores of 0-1 indicate low metabolic risk, 2-3 indicate moderate risk, and 4-5 indicate high risk.

Statistical analysis

Data was entered into a MS excel and analysed using appropriate statistical software, and a p value of less than 0.05 was considered statistically significant.

RESULTS

Out of the 300 participants, the mean age was 46.5±9.2 years, with a slight female predominance (54%). The average BMI was 25.3±3.8 kg/m². The distribution of CMRS showed that 22% of participants had a score of 0 or 1, while 38% had a score of 2 or 3, and 40% scored 4 or 5.

Pearson’s correlation analysis revealed a strong positive association between CMRS and body mass index (BMI) (r=0.62, p<0.001) and waist circumference (r=0.57, p<0.001). Moderate positive correlations were observed with fasting glucose levels (r=0.49, p<0.001) and serum triglycerides (r=0.44, p<0.001).

In contrast, CMRS demonstrated a moderate negative correlation with HDL cholesterol levels (r=-0.39, p<0.01), indicating that higher CMRS values were associated with lower HDL concentrations.

Chi-square analysis showed that participants with a CMRS score of 3 or higher had a significantly greater prevalence of central obesity (p<0.01), impaired fasting glucose (p<0.01), and low HDL cholesterol levels (p<0.05) compared to those with lower CMRS scores. These associations highlight the clustering of key metabolic risk factors in individuals with elevated CMRS.

DISCUSSION

This study highlights the growing burden of metabolic risk among rural adults, as reflected by the CMRS. The strong positive correlations between CMRS and parameters like BMI and waist circumference reinforce their predictive value.⁶ The inverse relationship with HDL supports its protective role in cardiometabolic health.⁷

While rural populations were previously considered low risk, the findings indicate a shift toward metabolic derangements commonly seen in urban areas.³ These changes can be attributed to evolving dietary habits, sedentary behaviour, and poor health awareness.

The utility of CMRS in this context is noteworthy due to its simplicity, objectivity, and ability to capture multi-dimensional risk in a single score.⁸ This can aid in early risk stratification and guide public health interventions.⁹

As this study is conducted at a single rural community health centre, the findings may have limited generalizability to other populations with different demographic or lifestyle characteristics.

Furthermore, the study did not assess dietary habits or physical activity levels due to the rush in opd setting, which are also important determinants of metabolic risk, thereby limiting the comprehensiveness of the analysis.

CONCLUSION

The clinical metabolic risk score is an effective tool to assess metabolic risk among adults aged over 30 years in rural areas. Its strong correlation with anthropometric and biochemical parameters makes it a valuable indicator for early detection and preventive strategies.

Recommendations

Routine screening for metabolic risk using CMRS in rural OPDs. Community-based lifestyle modification programs targeting diet, physical activity, and weight management.

ACKNOWLEDGEMENTS

Authors would like to thank the community health staff and all participants for their cooperation and contribution to this study.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: The study was approved by the Institutional Ethics Committee

REFERENCES

1. 1. Gogia A, Agarwal P. Metabolic syndrome. Ind J Medi Sci. 2006;60(2).
2. 2. Alshehri AM. Metabolic syndrome and cardiovascular risk. J Fam Commu Medi. 2010;17(2):73-8.
3. 3. Prabhakaran D, Chaturvedi V, Shah P, Manhapra A, Jeemon P, Shah B, et al. Differences in the prevalence of metabolic syndrome in urban and rural India: a problem of urbanization. Chronic Illness. 2007;3(1):8-19.
4. 4. Malmefeldt E, Rao GH. Non-invasive diagnostic tools: Cardiometabolic risk assessment and prediction. C J Clin Cardiol Diagnost. 2019;2(1):1-0.
5. 5. Gupta MK, Dutta G, G S, Raghav P, Goel AD, Bhardwaj P, et al. Application of Indian diabetic risk score (IDRS) and community based assessment checklist (CBAC) as metabolic syndrome prediction tools. PLoS One. 2023;18(3):e0283263.
6. 6. Kamińska MS, Lubkowska A, Panczyk M, Walaszek I, Grochans S, Grochans E, et al. Relationships of body mass index, relative fat mass index, and waist circumference with serum concentrations of parameters of chronic inflammation. Nutrients. 2023;15(12):2789.
7. 7. Pappa E, Elisaf MS, Kostara C, Bairaktari E, Tsimihodimos VK. Cardioprotective properties of HDL: structural and functional considerations. Curr Medi Chemist. 2020;27(18):2964-78.
8. 8. Lampignano L, Zupo R, Donghia R, Guerra V, Castellana F, Murro I, et al. Cross-sectional relationship among different anthropometric parameters and cardio-metabolic risk factors in a cohort of patients with overweight or obesity. PLoS One. 2020;15(11):e0241841.
9. 9. Leon GD, Melnick G, Kressel D, Jainchill N. Circumstances, motivation, readiness, and suitability (the CMRS scales): Predicting retention in therapeutic community treatment. Am J Drug Alcohol Abuse. 1994;20(4):495-515.