BMI mediates the association of family medical history with self-reported hypertension and diabetes among older adults: Evidence from baseline wave of the longitudinal aging study in India

Muhammad, T.; Irshad, C. V.; Rajan, S. Irudaya

doi:10.1016/j.ssmph.2022.101175

Cited by 6 publications

(4 citation statements)

References 87 publications

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“…Our findings further indicated that there is a strong effect of family history of hypertension, diabetes, and stroke on multimorbidity among older adults in India. Previous studies also revealed a clear association between the family history of hypertension, diabetes and stroke with their diagnoses [ 57 – 61 ]. Study showed that genetic factors or family history of diseases increase the risk of single and multimorbidity mainly by metabolic disease [ 62 ].…”

Section: Discussionmentioning

confidence: 85%

Interaction between depressive symptoms and obesity-related anthropometric measures on multimorbidity among community-dwelling older adults: evidence from India

Ahmed,

Muhammad,

Irshad

2024

BMC Public Health

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Background This study aimed to examine the associations between depressive symptoms, body mass index (BMI), waist circumference, waist-hip ratio and multimorbidity among community-dwelling older adults. We also examine the interaction effects between depressive symptoms, BMI, waist circumference and waist-hip ratio on multimorbidity among older adults in India. Methods A cross-sectional study was conducted, and the data were obtained from the Longitudinal Ageing Study in India (LASI) wave-1, with a sample of 31,464 older adults aged 60 years and above (men-15,098 and women-16,366). We used multinomial logistic regression to explore the independent associations between depressive symptoms, obesity-measures, and single and multimorbidity. We also estimated the interaction effects of depressive symptoms and obesity-measures on multimorbidity. Results The prevalence of multimorbidity was higher among individuals with depressive symptoms (39.22%) than individuals with no depressive symptoms (29.94%). Adjusted models indicated that older adults with depressive symptoms had higher odds of single and multimorbidity [(AOR = 1.40, 95% CI: 1.17–1.68) and (AOR = 1.85, 95% CI: 1.58–2.16), respectively]. Similarly, in comparison to the normal BMI category, overweight and obese older adults were more likely to report single morbidity [(AOR = 1.62, 95% CI: 1.37–1.92 and (AOR = 2.14, 95% CI: 1.67–2.75), respectively] and multimorbidity [(AOR = 2.00, 95% CI: 1.72–2.33) and (AOR = 3.77, 95% CI: 2.94–4.82), respectively]. Conclusion The findings revealed that the presence of depressive symptoms, overweight or obesity, and high-risk anthropometric measures such as high-risk waist circumference and high-risk waist to hip ratio significantly increased the risk of morbidity among older adults in India. Thus, it is suggested to adopt an integrated public health policy approach to control depressive symptoms and high-risk body composition to strategically prepare against the elevated risk of multimorbidity among ageing populations.

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Section: Discussionmentioning

confidence: 85%

Interaction between depressive symptoms and obesity-related anthropometric measures on multimorbidity among community-dwelling older adults: evidence from India

Ahmed,

Muhammad,

Irshad

2024

BMC Public Health

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“…For example, health practitioners could train and test their historical patient data with the above ML models and additional BMI parameters (age, gender, or both independently), and, either serve as a validation of the results, if there is no difference with the traditional BMI results, or, in case of discrepancy, study in more detail the potential causes behind the differences between the traditional BMI formula versus the ML model prediction. Furthermore, additional data, such as body composition [24], medical history [25], and demographic and socioeconomic information [26], could help health practitioners, researchers, and scientists to provide more realistic weight management and health assessments, as well as early diagnoses, treatments, better healthcare, as well as new opportunities for R&D and scientific discovery.…”

Section: Discussionmentioning

confidence: 99%

Reinventing the Body Mass Index: A Machine Learning Approach

Peregrin-Alvarez

2024

Preprint

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This study explores the predictive capabilities of the Body Mass Index (BMI) formula across a diverse dataset, examining the potential enhancements achievable through integrating additional parameters using machine learning (ML) models. Various modern ML models were utilized (K- Nearest Neighbors, Neural Networks, Decision Trees, Support Vector Classification, Logistic Regression, and Ridge Classifiers. Ensemble models: voting Classifier, Random Forest, and Gradient Boosting), demonstrating improved accuracy and precision over the traditional BMI calculations. Incorporating age and gender into BMI calculations together with the best performing ML model such as Gradient Boosting offers promise for more accurate and personalized health assessments, with significant implications for clinical practice and public health interventions.

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“…And also, it has been set up that the influence of any kind of middling physical fitness course help to overcome with PMS problems and other problems like anger, depression and tension. By reducing these problems factors, it brings balance to the daily leaving of the women (Muhammad et al, 2022). The inactive physical state of women can cause them to go through mood swings, such as changing their moods within a short amount of time.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of Mood Swings during Menstrual Cycles between Physically active and Inactive Adolescents: A Tripura-based Study

Dhar,

Farooque,

Devi

2024

inasport

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The menstrual cycle is an important biological process in women that is associated with a range of physical symptoms that can shape how women think, feel, and participate in daily life activities. This study employed a qualitative survey design to investigate the effect of physical activity and menstrual cycle on mood variability in adolescents. Purposively, 40 participants from Tripura University (n=20, physically active and n=20 Inactive) completed the Brunel mood scale questionnaire that explored menstrual cycle events: anger, tension, depression, fatigue, confusion, happiness and calmness. The participants’ response was further analysed descriptively through percentage analysis and differences between physically active and inactive students were assessed using the Mann Whitney-U test. The descriptive results depicted a mean rank of 16.20 and 24.80 of mood swing among Physically Active Students and Physically Inactive students respectively which shows a higher mood swing in inactive women. The significant difference was observed between physically active students and physically inactive students (two groups as U-value was 114.000 which was smaller than the critical value i.e., 127). Persons involved in physical activity have less chance of mood swing compared to sedentary people. In addition, the study concluded that regular moderate exercise helps reduce factors that enhance mood swing during the menstrual cycle.

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BMI mediates the association of family medical history with self-reported hypertension and diabetes among older adults: Evidence from baseline wave of the longitudinal aging study in India

Cited by 6 publications

References 87 publications

Interaction between depressive symptoms and obesity-related anthropometric measures on multimorbidity among community-dwelling older adults: evidence from India

Interaction between depressive symptoms and obesity-related anthropometric measures on multimorbidity among community-dwelling older adults: evidence from India

Reinventing the Body Mass Index: A Machine Learning Approach

Dynamics of Mood Swings during Menstrual Cycles between Physically active and Inactive Adolescents: A Tripura-based Study

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