Body fat distribution and bone mineral density in a multi-ethnic sample of postmenopausal women in The Malaysian Cohort

Summary: In this study of postmenopausal women in Malaysia, total adiposity was inversely associated with total BMD, while regional associations varied. No differences were detected across Malay, Chinese, and Indian ethnicities. Low BMD contributes substantially to morbidity and mortality, and increasing adiposity levels globally may be contributing to this. Purpose: To investigate associations of total and regional adiposity with bone mineral density (BMD) among a multi-ethnic cohort of postmenopausal women. Methods: Dual X-ray absorptiometry (DXA) imaging was undertaken for 1990 postmenopausal women without prior chronic diseases (30% Malay, 53% Chinese, and 17% Indian) from The Malaysian Cohort (TMC). The strength of the associations between standardized total and regional body fat percentages with total and regional BMD was examined using linear regression models adjusted for age, height, lean mass, ethnicity, education, and diabetes. Effect modification was assessed for ethnicity. Results: Women with a higher total body fat percentage were more likely to be Indian or Malay. Mean (SD) BMD for the whole-body total, lumbar spine, leg, and arm were 1.08 (0.11), 0.96 (0.15), 2.21 (0.22), and 1.36 (0.12) g/cm2, respectively. Total body and visceral fat percentage were inversely associated with total BMD (− 0.02 [95% CI − 0.03, − 0.01] and − 0.01 [− 0.02, − 0.006] g/cm2 per 1 SD, respectively). In contrast, subcutaneous and gynoid fat percentages were positively associated with BMD (0.007 [0.002, 0.01] and 0.01 [0.006, 0.02] g/cm2, respectively). Total body fat percentage showed a weak positive association with lumbar BMD (0.01 [0.004, 0.02]) and inverse associations with leg (− 0.04 [− 0.06, − 0.03]) and arm (− 0.02 [− 0.03, − 0.02]) BMD in the highest four quintiles. There was no effect modification by ethnicity (phetero > 0.05). Conclusion: Total adiposity was inversely associated with total BMD, although regional associations varied. There was no heterogeneity across ethnic groups suggesting adiposity may be a risk factor for low BMD across diverse populations

Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population