METHODS: This is a follow-up study among 84 obese housewives without co-morbidities aged 18 to 59 years old who previously participated as a control group (delayed intervention, G1) in the My Body is Fit and Fabulous at Home (MyBFF@home) Phase II. Baseline data were obtained from 12 month data collection for this group. A new group of 42 obese housewives with co-morbidities (G2) were also recruited. Both groups received a 6 month intervention (July-December 2015) consisting of dietary counselling, physical activity (PA) and self-monitoring tools (PA diary, food diary and pedometer). Study parameters included weight, height, waist circumference, blood pressure and body compositions. Body compositions were measured using a bioelectrical impedance analysis device, Inbody 720. Descriptive and repeated measures ANOVA analyses were performed using SPSS 21.
RESULTS: There were reductions in mean body fat, fat mass and visceral fat area, particularly among obese women without co-morbidities. There were also decreases fat and skeletal muscle from baseline to month six with mean difference - 0.12 (95% CI: -0.38, 0.14) and visceral fat area from month three to month six with mean difference - 9.22 (- 17.87, - 0.56) for G1. G2 showed a decreasing pattern of skeletal muscle from baseline to month six with mean difference - 0.01(95% CI: -0.38, 0.37). There was a significant difference for group effect of visceral fat area (p
METHODS: Baseline and sixth month data from the MyBFF@home study were used for this purpose. A total of 169 of overweight and obese respondents answered the IPAQ-SF and were asked to use a pedometer for 7 days. Data from IPAQ-SF were categorised as inactive and active while data from pedometer were categorised as insufficiently active and sufficiently active by standard classification. Data on sociodemographic and anthropometry were also obtained. Cohen's kappa was applied to measure the agreement of IPAQ-SF and pedometer in determining the physical activity level. Pre-post cross tabulation table was created to evaluate the changes in physical activity over 6 months.
RESULTS: From 169 available respondents, 167 (98.8%) completed the IPAQ-SF and 107 (63.3%) utilised the pedometer. A total of 102 (61.1%) respondents were categorised as active from the IPAQ-SF. Meanwhile, only 9 (8.4%) respondents were categorised as sufficiently active via pedometer. Cohen's κ found there was a poor agreement between the two methods, κ = 0.055, p > 0.05. After sixth months, there was + 9.4% increment in respondents who were active when assessed by IPAQ-SF but - 1.3% reductions for respondents being sufficiently active when assessed by pedometer. McNemar's test determined that there was no significant difference in the proportion of inactive and active respondents by IPAQ-SF or sufficiently active and insufficiently active by pedometer from the baseline and sixth month of intervention.
CONCLUSION: The IPAQ-SF and pedometer were both able to measure physical activity. However, poor agreement between these two methods were observed among overweight and obese women.
Methods: Analyses were performed on 243 women (mean body mass index 31.27 ± 4.14 kg/m2) who completed a 12-month lifestyle intervention in low socioeconomic communities in Klang Valley, Malaysia. Analysis of covariance (ANCOVA) was used to compare changes of cardiometabolic risk factors across weight change categories (2% gain, ±2% maintain, >2 to <5% loss, and 5 to 20% loss) within intervention and control group.
Results: A graded association for changes in waist circumference, fasting insulin, and total cholesterol (p=0.002, for all variables) across the weight change categories were observed within the intervention group at six months postintervention. Participants who lost 5 to 20% of weight had the greatest improvements in those risk markers (-5.67 cm CI: -7.98 to -3.36, -4.27 μU/mL CI: -7.35, -1.19, and -0.59 mmol/L CI: -.99, -0.19, respectively) compared to those who did not. Those who lost >2% to <5% weight reduced more waist circumference (-4.24 cm CI: -5.44 to -3.04) and fasting insulin (-0.36 μU/mL CI: -1.95 to 1.24) than those who maintained or gained weight. No significant association was detected in changes of risk markers across the weight change categories within the control group except for waist circumference and adiponectin.
Conclusion: Weight loss of >2 to <5% obtained through lifestyle intervention may represent a reasonable initial weight loss target for women in the low socioeconomic community as it led to improvements in selected risk markers, particularly of diabetes risk.
METHODS: The study involved 235 Malaysian subjects who were randomly selected (66 normal weight subjects, 97 overweight, 59 obese subjects, and 13 subjects who were underweight). Serum sDPP4 and active GLP-1 levels were examined by enzyme-linked immunosorbent assay (ELISA). Also, body mass index kg/m(2) (BMI), lipid profiles, insulin and glucose levels were evaluated. Insulin resistance (IR) was estimated via the homeostasis model assessment for insulin resistance (HOMA-IR).
RESULTS: Serum sDPP4 levels were significantly higher in obese subjects compared to normal weight subjects (p=0.034), whereas serum levels of active GLP-1 were lower (p=0.021). In obese subjects, sDPP4 levels correlated negatively with active GLP-1 levels (r(2)=-0.326, p=0.015). Furthermore, linear regression showed that sDPP4 levels were positively associated with insulin resistance (B=82.28, p=0.023) in obese subjects.
CONCLUSION: Elevated serum sDPP4 levels and reduced GLP-1 levels were observed in obese subjects. In addition, sDPP4 levels correlated negatively with active GLP-1 levels but was positively associated with insulin resistance. This finding provides evidence that sDPP4 and GLP-1 may play an important role in the pathogenesis of obesity, suggesting that sDPP4 may be valuable as an early marker for the augmented risk of obesity and insulin resistance.
Materials and Methods: Sixty postmenopausal female patients aged 51-68 years were included in the study to assess the relationship between tooth loss and the level of blood pressure. The information including sociodemographics, last menstruation period, hypertension history, and the duration of having tooth loss was recorded. Blood pressure was measured using sphygmomanometer and the number of tooth loss was determined.
Results: The results showed a more significant tooth loss in hypertension (median: 23 + 4; interquartile range [IQR]: 6) compared to the normotension postmenopausal women (median: 18 + 6; IQR: 12; P < 0.05). Furthermore, obese patients had more tooth loss (median: 23 + 5; IQR: 8) than the overweight patients (median: 19 + 8; IQR: 8).
Conclusion: Tooth loss is associated with the increase of hypertension in postmenopausal women which may have a role in the development of vascular diseases.
METHODS: Out of the 7247 students in the ten selected schools studied, a total of 6248 students (2928 males, 3320 females) took part. A validated self-administered questionnaire was used. Data was analysed using SPSS version 22. Multivariable logistic regression was used to determine the adjusted odd ratio.
RESULTS: The prevalence of overweight and obesity was 16.0% and 11.5% respectively. Obesity/overweight was significantly (p<0.05) associated with gender, age, ethnicity, education level of father, education level of mother, physical activity, disordered eating, smoking status, body size perception and body part satisfaction. The multivariable analysis results showed that the odds of being overweight/obesity were higher in males compared to females (OR 1.56, 95%CI: 1.37, 1.77). The results also showed that the odds of being overweight/obesity were highest among those in age group 12 and 13 years and among Malay ethnicity. The odds of overweight/obesity were higher in those who was dissatisfied with their body parts, (OR 1.96, 95%CI: 1.71, 2.25), dissatisfied with their body size (OR: 4.25, 95%CI: 3.60, 5.02), low physical activity (OR 1.23, 95%CI: 1.06, 1.44), current smokers (OR 1.38, 95%CI: 1.07, 1.78) and at risk of having eating disorder (OR: 1.39, 95%CI 1.22, 1.59).
CONCLUSION: The overall prevalence of overweight and obesity is high. The findings from this study can be used by policy makers to plan an integrated intervention program in schools.
METHODS: A total of 4005 diabetic patients who had a history of ischemic stroke were identified in a retrospective cross-sectional dataset from the Malaysian National Neurology Registry. Patients were classified based on BMI, and multivariable regression analysis was used to evaluate the association between risk factors and recurrent ischemic stroke.
RESULTS: Among obese patients, those with ischemic heart disease (aOR, 1.873; 95% CI, 1.131-3.103), received formal education (aOR, 2.236; 95% CI, 1.306-3.830), and received anti-diabetic medication (aOR, 1.788; 95% CI, 1.180-2.708) had a higher stroke recurrence risk, while receiving angiotensin receptors blockers (aOR, 0.261; 95% CI, 0.126-0.543) lowered the odds of recurrence. Overweight patients with hypertension (aOR, 1.011; 95% CI, 1.002-1.019) for over 10 years (aOR, 3.385; 95% CI, 1.088-10.532) and diabetes prior to the first stroke (aOR, 1.823; 95% CI, 1.020-3.259) as well as those received formal education (aOR, 2.403; 95% CI, 1.126-5.129) had higher odds of stroke recurrence, while receiving angiotensin-converting enzyme inhibitors (aOR, 0.244; 95% CI, 0.111-0.538) lowered the recurrence risk. Normal weight East Malaysians (aOR, 0.351; 95% CI, 0.164-0.750) receiving beta-blockers (aOR, 0.410; 95% CI, 0.174-0.966) had lower odds of stroke recurrence.
CONCLUSIONS: Ischemic heart disease, hypertension, receiving anti-hypertensive agents, and educational level were independent predictors of recurrent stroke in obese patients. Managing the modifiable risk factors can decrease the odds of stroke recurrence.
METHODS: This meta-analysis was performed based on the PRISMA recommendations. PubMed, Web of Science, Scopus, Embase, and Google Scholar databases were searched for all published observational studies that reported the risk of UTI based on BMI categories up to March 2020.
RESULTS: Fourteen (n = 14) articles comprising 19 studies in different populations met our inclusion criteria. The overall analysis showed a significant increased risk of UTI in subjects affected by obesity vs. individuals without obesity (RR = 1.45; 95% CI: 1.28 - 1.63; I2 = 94%), and a non-significant increased risk of UTI in subjects who were overweight (RR = 1.03; 95% CI: 0.98 - 1.10; I2 = 49.6%) and underweight (RR = 0.99; 95% CI: 0.81 - 21; I2 = 0.0%) when compared to subjects who had normal weight. In the stratified analysis, we showed that obesity increased the risk of UTI in females (RR = 1.63; 95% CI: 1.38 - 1.93) and in subjects below 60 years old (RR = 1.53; 95% CI: 1.33 - 1.75).
CONCLUSION: This systematic review and meta-analysis recognized a significant relationship between BMI and incidence of UTI in obese vs. non-obese subjects, as well as in females and in individuals below 60 years old.