AIMS: This study was aimed to examine the association between BsmI polymorphism and risk of vitamin D deficiency, obesity and insulin resistance in adolescents living in a tropical country.
METHODS: Thirteen-year-old adolescents were recruited via multistage sampling from twenty-three randomly selected schools across the city of Kuala Lumpur, Malaysia (n = 941). Anthropometric measurements were obtained. Obesity was defined as body mass index higher than the 95th percentile of the WHO chart. Levels of fasting serum vitamin D (25-hydroxyvitamin D (25(OH)D)), glucose and insulin were measured. HOMA-IR was calculated as an indicator for insulin resistance. Genotyping was performed using the Sequenom MassARRAY platform (n = 807). The associations between BsmI and vitamin D, anthropometric parameters and HOMA-IR were examined using analysis of covariance and logistic regression.
RESULT: Those with AA genotype of BsmI had significantly lower levels of 25(OH)D (p = 0.001) compared to other genotypes. No significant differences was found across genotypes for obesity parameters. The AA genotype was associated with higher risk of vitamin D deficiency (p = 0.03) and insulin resistance (p = 0.03) compared to GG. The A allele was significantly associated with increased risk of vitamin D deficiency compared to G allele (adjusted odds ratio (OR) = 1.63 (95% Confidence Interval (CI) 1.03-2.59, p = 0.04). In those with concurrent vitamin D deficiency, having an A allele significantly increased their risk of having insulin resistance compared to G allele (adjusted OR = 2.66 (95% CI 1.36-5.19, p = 0.004).
CONCLUSION: VDR BsmI polymorphism was significantly associated with vitamin D deficiency and insulin resistance, but not with obesity in this population.
MATERIALS AND METHODS: A cross-sectional study was performed and a 13-item survey questionnaire was given to FMPs practicing in 12 different teaching hospitals in Karachi, Pakistan. The questions were aimed at exploring the knowledge of FMP's regarding the association of obesity and periodontal disease and their attitude towards the association of obesity and periodontal disease. Chi-square and Spearman co-efficient were conducted to compare subgroups and correlate factors with the knowledge score of FMPs.
RESULTS: A total of 314 questionnaires were completed (response rate = 92%). Median age of participants was 41 years and 57% were females. Almost 61% of FMPs answered all the knowledge questions correctly and 64% reported moderate understanding of the association between periodontal health and obesity. Nearly 73% FMPs inquired from obese patients regarding the periodontal disease and more than half (58%) refer patients to a dentist for evaluation. More than half of FMPs perform periodontal disease screening. Nearly all FMPs considered informing obese patients regarding periodontal disease as one of their roles.
CONCLUSIONS: FMP's play an important role in the early diagnosis, prevention and treatment of periodontal conditions in obese patients. More than two thirds of FMPs showed good knowledge of the association of obesity and periodontal disease. The attitudes of FMPs towards assessing and referring obese patients at a risk of having periodontal disease were reassuring.
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: A total of 243 participants from MyBFF@home were included in this study. Fasting blood samples at baseline, 6- and 12-month were assessed for fasting plasma glucose (FPG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides. The effect of the intervention on cardiometabolic risk markers were investigated within and between study groups using t-test and general linear model (GLM) repeated measure ANOVA.
RESULTS: Results from repeated measures ANOVA showed intervention effect only in TC where significant reduction was found in the intervention group (- 0.26 mmol/L [95% CI: - 0.47 to - 0.06], p
OBJECTIVE: To assess the chronic effects of the substitution of refined carbohydrate or MUFA for SAFA on insulin secretion and insulin sensitivity in centrally obese subjects.
METHODS: Using a crossover design, randomized controlled trial in abdominally overweight men and women, we compared the effects of substitution of 7% energy as carbohydrate or MUFA for SAFA for a period of 6 weeks each. Fasting and postprandial blood samples in response to corresponding SAFA, carbohydrate, or MUFA-enriched meal-challenges were collected after 6 weeks on each diet treatment for the assessment of outcomes.
RESULTS: As expected, postprandial nonesterified fatty acid suppression and elevation of C-peptide, insulin and glucose secretion were the greatest with high-carbohydrate (CARB) meal. Interestingly, CARB meal attenuated postprandial insulin secretion corrected for glucose response; however, the insulin sensitivity and disposition index were not affected. SAFA and MUFA had similar effects on all markers except for fasting glucose-dependent insulinotropic peptide concentrations, which increased after MUFA but not SAFA when compared with CARB.
CONCLUSION: In conclusion, a 6-week lower-fat/higher-carbohydrate (increased by 7% refined carbohydrate) diet may have greater adverse effect on insulin secretion corrected for glucose compared with isocaloric higher-fat diets. In contrast, exchanging MUFA for SAFA at 7% energy had no appreciable adverse impact on insulin secretion.
STUDY DESIGN: A cross-sectional study was conducted among 444 pregnant women (≥20 weeks gestation).
MAIN OUTCOMES MEASURES: Women completed questionnaires on sociodemographic data, maternal characteristics and pre-pregnancy weight. Height, current weight and MUAC were measured at study visit (from 1st February 2016 to 31st January 2017).
RESULTS: About a third (34.24%) of pregnant women were overweight or obese prior to pregnancy. MUAC was inversely associated with an inadequate rate of gestational weight gain (OR = 0.77; 95% CI: 0.68, 0.87) as compared to normal gestational weight gain. In contrast, a higher MUAC was associated with a higher odds ratio (OR = 1.28; 95% CI: 1.11, 1.49) of having excessive rate of gestational weight. No associations were found for pre-pregnancy BMI categories for gestational weight gain rate.
CONCLUSION: Our findings revealed that women with low MUAC were more likely to have an inadequate gestational weight gain rate during pregnancy whereas higher MUAC was associated with an excessive gestational weight gain rate. MUAC may be a useful indicator of nutritional status associated with GWG. Routine measurement of MUAC in pregnant women may help health professionals, particularly in middle-income countries, to counsel women about gestational weight gain.
METHODS: A total of 48 periodontitis subjects (obese, n = 18; normal weight, n = 30) were recruited (hereafter will be referred as participants) to participate into a prospective, before and after clinical trial. Obesity status is defined by body mass index (BMI) criteria (obese: ≥30 kg/ m2; normal weight
METHODS: 1812 biopsy-proven NAFLD patients across nine countries in Asia assessed between 2006 and 2019 were pooled into a curated clinical registry. Demographic, metabolic and histological differences between non-obese and obese NAFLD patients were evaluated. The performance of Fibrosis-4 index for liver fibrosis (FIB-4) and NAFLD fibrosis score (NFS) to identify advanced liver disease across the varying obesity subgroups was compared. A random forest analysis was performed to identify novel predictors of fibrosis and steatohepatitis in non-obese patients.
FINDINGS: One-fifth (21.6%) of NAFLD patients were non-obese. Non-obese NAFLD patients had lower proportions of NASH (50.5% vs 56.5%, p = 0.033) and advanced fibrosis (14.0% vs 18.7%, p = 0.033). Metabolic syndrome in non-obese individuals was associated with NASH (OR 1.59, 95% CI 1.01-2.54, p = 0.047) and advanced fibrosis (OR 1.88, 95% CI 0.99-3.54, p = 0.051). FIB-4 performed better than the NFS score (AUROC 81.5% vs 73.7%, p