Key result
A three-parameter prediction model for metabolic syndrome achieved an AUC of 0.81 in the NHANES training dataset, and 0.87 and 0.79 in Chinese and Indian validation datasets, respectively.
Why the study?
Metabolic syndrome poses significant cardiovascular risk, and early-stage detection and prevention are critical for effective management strategies.
Can a three-parameter risk score based on pathophysiological determinants accurately predict Metabolic Syndrome across different ethnicities?
Observational
Yes
Can a three-parameter risk score based on pathophysiological determinants accurately predict Metabolic Syndrome across different ethnicities?
A novel three-parameter risk score based on pathophysiological determinants accurately predicts Metabolic Syndrome across diverse ethnic populations.
Metabolic syndrome (MetS), constituted by obesity, hyperglycemia, hypertension, and dyslipidemia, is a growing public health concern due to its associated risk with cardiovascular and other metabolic disorders. Early-stage detection and prevention of MetS are key factors in an effective management strategy. Thus, the current study aimed to develop a risk score for MetS progression based on features pertaining to its pathophysiology, that can facilitate early detection and treatment strategies. A multivariate logistic regression model was developed using a representative feature from MetS pathophysiological pathways of inflammation, endothelial dysfunction, and hepatic dysregulation. The model was built on NHANES dataset and validated in Chinese and Indian datasets. The model performance evaluated using ROC resulted in an AUC of 0.81 in training and 0.87 and 0.79 for Chinese and Indian validation datasets, respectively. In conclusion, a MetS predictive model built on three MetS pathophysiological determinants was developed and validated in distinct datasets. Highlights A MetS prediction model, trained on NHANES data, is validated with two datasets of varied ethnicity. The three parameter model, independent of MetS diagnostic criteria, is reflective of inflammation, endothelial dysfunction, and hepatic dysregulation. The model achieved an AUC of 0.81 in NHANES data; 0.87 and 0.79 for Chinese and Indian validation datasets respectively.
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Desai et al. (2025) conducted an observational in Metabolic syndrome. MetS prediction model was evaluated on MetS prediction. A three-parameter prediction model for metabolic syndrome achieved an AUC of 0.81 in the NHANES training dataset, and 0.87 and 0.79 in Chinese and Indian validation datasets, respectively.
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