Cardiometabolic Risk Factors in South Asians: An Epidemiological and Anthropological Study in an Urban Populace of Eastern India.
Yasmin, K.
Show abstract
BackgroundThis study examines cardiometabolic (CM) risk factors in an urban South Asian population, integrating medical and Anthropological perspectives to explore the effects of socio-economic, lifestyle, gender-specific factors, and cultural norms on health outcomes. ResultsAnalysis indicates a high prevalence of MetS and Pre-MetS, particularly among females, with significant predictors including BMI, triglycerides, total cholesterol, and waist circumference, alongside socio-genetic and lifestyle factors. Employing Elastic Net logistic regression, the researcher rigorously validated models to evaluate their predictive performance while also describing the associations and prevalence of known risk factors. The use of this method underlines the importance of combining traditional risk factors with socio-genetic, biological, economic and lifestyle variables, while Anthropological insights reveal the impact of urbanization and socio-cultural norms on health behaviors. ConclusionThe study advocates for a multidisciplinary approach in public health strategies, emphasizing the complex interplay between genetic, environmental, biological and socio-cultural influences on cardiometabolic health. This dual approach aligns with descriptive and predictive model goals. The future research should further integrate biomedical sciences with socio-cultural studies to develop culturally sensitive interventions, aiming to address the growing challenge of CM diseases in urban South Asian contexts.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Relationship of sociodemographic and lifestyle factors and diet habits with metabolic syndrome (MetS) in a multi-ethnic Asian population 97%
- Prevalence of cardiometabolic risk factors according to urbanization level, gender and age, in apparently healthy adults living in Gabon, Central Africa 97%
- Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: the application of latent class analysis model 96%
Similar papers in this journal
- Overweight and Obesity among Women at Reproductive Age 15-49 Years Old in Cambodia: Data Analysis of Cambodia Demographic and Health Survey 2014 95%
- Prevalence and Factors Associated with Overweight and Obesity among Women of Reproductive Age in Cambodia: Analysis of Cambodia Demographic and Health Survey 2021-2022 95%
- Changes in the prevalence of the common risk factors for non-communicable diseases in Uganda between 2014 and 2023: Informed by nationally representative cross-sectional surveys 95%
Similar papers in this journal
- Machine learning-based equations for improved body composition estimation in Indian adults 94%
- An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers 93%
- Automated Image Transcription for Perinatal Blood Pressure Monitoring Using Mobile Health Technology 92%
Similar papers in this journal
- Body Mass Index Asian Populations category and stroke and heart disease in the adult population: A longitudinal study of The Indonesia Family Life Survey (IFLS) 2007 and 2014 95%
- The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: A Prospective Study from the NHANES 95%
- Concordance of weight status between mothers and children: A secondary analysis of the Pakistan Demographic and Health Survey VII 95%
Similar papers in this journal
- Health literacy profiles correlate with participation in primary health care among patients with chronic diseases: A latent profile analysis 93%
- Knowledge, attitudes, and practices among the general population during COVID-19 outbreak in Iran: A national cross-sectional survey 93%
- A weighted quantile sum regression with penalized weights and two indices 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.