Differences in bone mineral density and lifestyle factors of postmenopausal women living in Bangkok and other provinces.

Decreased bone mineral density (BMD) with age is an increasing health problem, especially for postmenopausal women. Multiple factors have been reported to affect BMD including both genetic and environmental factors such as calcium intake and physical activity. For Thailand, people residing in different regions may differ in BMD due to these factors. However, there is a paucity of data concerning this issue. The objectives of this study were to identify the lifestyle factors which may influence BMD and to investigate the association between BMD and these factors in postmenopausal women who have been living in Bangkok and other provinces in Thailand. Subjects consisted of 466 postmenopausal women aged 46-90 years including 236 Bangkokians (116 early postmenopausals and 120 late postmenopausals) and 230 non-Bangkokians (134 early postmenopausals and 96 late postmenopausals). All were healthy and ambulatory. BMD was measured by dual energy X-ray absorptiometry (DEXA, Expert XL). Calcium intake was assessed by food-frequency questionnaire. Data were expressed by mean + /- SEM. There were 22 per cent (n=52), 5.9 per cent (n=14), and 4.2 per cent (n=10) of postmenopausal Bangkokians while 13.9 per cent (n=32), 4.3 per cent (n=10), and 2.2 per cent (n=5) of postmenopausal non-Bangkokians who had low BMD at spine, femoral neck, and at both sites, respectively. Spine BMD (SPBMD) and femoral neck BMD (FNBMD) increased significantly across the quartiles of calcium intake in both groups of subjects (P<0.05) and a significant difference was found between the lowest and the highest quartiles of calcium intake (P<0.05). Moreover, BMD at both regions was shown to be correlated with calcium intake, exercise and sunlight exposure in these subjects (P<0.001). Further analysis revealed higher BMD at spine (0.992 + 0.02 vs 0.945 +/- 0.02 g/cm2, P<0.05) and at femur (0.780 +/- 0.01 vs 0.740 +/- 0.01 g/cm2, P<0.05), calcium intake (348.9 +/- 12.7 vs 316.3 +/- 8.0 mg/day, P<0.05), exercise (2.8 +/- 0.1 vs 2.4 +/- 0.1 h/wk, P<0.001) and sunlight exposure (2.9 +/- 0.06 vs 1.9 +/- 0.04 h/day, P<0.001) were found in late postmenopausal women in other provinces than their counterparts in Bangkok. Nevertheless, no significant difference of BMD at both sites, calcium intake and exercise was found in the early postmenopausal groups of these two areas. CONCLUSIONS: There were significant differences in BMD and lifestyle factors between late postmenopausal women in Bangkok and other provinces. Environmental factors especially calcium intake, exercise and sunlight exposure, may influence BMD in late postmenopausal Thai women.

Correlation of apolipoprotein E gene polymorphism to serum lipid concentrations in healthy Thais.

This study determined the genotype distribution of apolipoprotein E (apo E) gene and its relation to serum lipids in 217 healthy Thais consisting of 79 males and 138 females. Serum total cholesterol (TC), HDL-cholesterol (HDL-C) and triglyceride (TG) concentrations were determined by enzymatic-colorimetric methods, while serum LDL-cholesterol (LDL-C) levels were calculated using Friedewald formula. Apo E genotypes were determined by PCR-RFLP. Out of 217 subjects, apo E genotype frequencies were 5.5 per cent for E2/E2, 12.4 per cent for E2/E3, 81.1 per cent for E3/E3 and 0.9 per cent for E4/E4. In men, advancing age was associated with increased serum TC (r = 0.28, P < 0.05) and LDL-C (r = 0.27, P < 0.01). Subjects having the E2 allele had lower TC (r = -0.27, P < 0.05) and LDL-C. (r = -0.25, P < 0.05). Age and apo E genotypes were not associated with HDL-C and TG in men. In women, increasing age was related to higher serum TC (r = 0.45, P < 0.001), LDL-C (r = 0.44, P < 0.001), TG (r = 0.40, P < 0.001) and lower HDL-C (r = -0.36, P < 0.001). The presence of E2 allele was related to lower TC (r = -0.24, P < 0.001), LDL-C (r = -0.26, P < 0.001), TG (r = -0.15, P < 0.05) and higher HDL-C (r = 0.20, P < 0.01) independent of age and menopausal status. We concluded that the epsilon 4 allele of apo E gene is rare in Thais. The presence of the epsilon 2 allele is associated with a more favorable lipid profile and there is a sexual dimorphism concerning the effect of apo E genotype on serum HDL-C and TG.

Serum leptin concentrations in relation to body fat, gender, sex hormones and metabolic covariates in Thais.

In the present study, the relation of serum leptin to adiposity, gender and metabolic covariates in normal Thais was examined. Subjects consisted of 224 individuals aged between 20-79 years. Eighty two were men while 142 were women. Data were expressed as mean +/- SEM. Serum leptin was associated with total body fat assessed by dual-energy X-ray absorptiometry in both men (r = 0.80, P < 0.0001) and women (r = 0.73, P < 0.0001). Compared to women, serum leptin concentrations was lower in men (P < 0.0001). The difference still persisted after controlling the adiposity. Compared to premenopausal women, postmenopausal women had higher serum leptin independent of adiposity (P < 0.0001). In men, serum free testosterone was negatively associated with serum leptin (r = -0.36, P < 0.001) while there was no association between serum estradiol and leptin. The relation between serum FT and leptin in men no longer persisted after controlling for adiposity. Body fat was associated with fasting insulin levels in both men (r = 0.26, P < 0.05) and women (r = 0.18, P < 0.05). However, the association between fasting insulin levels and body fat in both men and women no longer existed after adjusting for leptin. We concluded that serum leptin concentrations are associated with total body adiposity and serum leptin may mediate the effect of body fat on insulin sensitivity. There appears to be a sexual dimorphism of serum leptin unrelated to sex hormone status and the amount of body fat.

Prediction of low bone mineral density in postmenopausal women by artificial neural network model compared to logistic regression model.

Measuring bone mineral density (BMD) is currently the best modality to diagnose osteoporosis and predict future fractures. The use of risk factors to predict BMD and fracture risk has been considered to be inadequate for precise diagnostic purpose, but it may be helpful as a screening tool to determine who actually needs BMD assessment. Recently, artificial neural network (ANN), a nonlinear computational model, has been used in clinical diagnosis and classification. In the present study, we evaluated the risk factors associated with low BMD in Thai postmenopausal women and assessed the prediction of low BMD using an ANN model compared to a logistic regression model. The subjects consisted of 129 Thai postmenopausal women divided into 2 groups, 100 subjects in the training set and the remaining 29 subjects in the validation set. The subjects were classified as having either low BMD or normal BMD by using BMD value 1 SD lower than the mean value of young adults as the cutoff point. Decreased body weight, decreased hip circumference and increased years since menopause were found to be associated with low BMD at the lumbar spine by logistic regression. For the femoral neck, increased age and decreased urinary calcium were associated with low BMD. The models had a sensitivity of 85.0 per cent, a specificity of 11.1 per cent and an accuracy of 62.0 per cent for the diagnosis of low BMD at the lumbar spine when tested in the validation group. For the femoral neck, the sensitivity, specificity and accuracy were 90.5 per cent, 12.5 per cent, and 69.0 per cent, respectively. Models based on ANN correctly classified 65.5 per cent of the subjects in the validation group according to BMD at the lumbar spine with a sensitivity of 80.0 per cent and a specificity of 33.3 per cent while it correctly classified 58.6 per cent of the subjects at the femoral neck with a sensitivity of 76.2 per cent and a specificity of 12.5 per cent. There was no significant difference in terms of accuracy, sensitivity and specificity in the prediction of low BMD at the lumbar spine or the femoral neck between ANN model and logistic regression model. We concluded that ANN does not perform better than convention statistical methods in the prediction of low BMD. The less than perfect performance of the prediction rules used in the prediction of low BMD may be due to the lack of adequate association between the commonly used risk factors and BMD rather than the nature of the computational models.