Methods: We examined whether (a) PA and (b) selected nsSNPs are associated with adiposity parameters and whether PA interacts with these nsSNPs on these outcomes in adolescents from the Malaysian Health and Adolescents Longitudinal Research Team study (n = 1,151). Body mass indices, waist-hip ratio, and percentage body fat (% BF) were obtained. PA was assessed using Physical Activity Questionnaire for Older Children (PAQ-C). Five nsSNPs were included: beta-3 adrenergic receptor (ADRB3) rs4994, FABP2 rs1799883, GHRL rs696217, MC3R rs3827103, and vitamin D receptor rs2228570, individually and as combined genetic risk score (GRS). Associations and interactions between nsSNPs and PAQ-C scores were examined using generalized linear model.
Results: PAQ-C scores were associated with % BF (β = -0.44 [95% confidence interval -0.72, -0.16], p = 0.002). The CC genotype of ADRB3 rs4994 (β = -0.16 [-0.28, -0.05], corrected p = 0.01) and AA genotype of MC3R rs3827103 (β = -0.06 [-0.12, -0.00], p = 0.02) were significantly associated with % BF compared to TT and GG genotypes, respectively. Significant interactions with PA were found between ADRB3 rs4994 (β = -0.05 [-0.10, -0.01], p = 0.02) and combined GRS (β = -0.03 [-0.04, -0.01], p = 0.01) for % BF.
Conclusion: Higher PA score was associated with reduced % BF in Malaysian adolescents. Of the nsSNPs, ADRB3 rs4994 and MC3R rs3827103 were associated with % BF. Significant interactions with PA were found for ADRB3 rs4994 and combined GRS on % BF but not on measurements of weight or circumferences. Targeting body fat represent prospects for molecular studies and lifestyle intervention in this population.
METHODS AND FINDINGS: Stratified random sampling design was used to select adolescents from 15 urban and rural secondary schools in Selangor, Perak and Kuala Lumpur, Malaysia. Data collection was carried out from 1st April 2014 to 30th June 2014. Information regarding socio-demographic characteristics, sun exposure and sun protective behaviours, clinical data and environmental factors were collected. Blood for total vitamin D was sampled. Descriptive and multivariate logistic regressions were performed. Total 1061 participants were analyzed (62% were female; mean age 15.1 ± 0.4 years). The prevalence of vitamin D deficiency was 33%. Mean vitamin D was lower in female (53 ± 15 nmol), obese (body fat percentage (≥25%m; ≥33.8%f) (56 ± 16 nmol/L), Malays (58 ± 18 nmol/L) and Indians (58 ± 15 nmol/L). In multivariate analysis, female (OR = 5.5; 95% CI: 3.4-7.5), Malay (OR = 3.2; 95% CI: 1.3-8.0), Indian (OR = 4.3; 95% CI: 1.6-12.0) and those always wearing long sleeve (OR = 2.4; 95% CI: 1.1-5.4) were more likely to have vitamin D deficiency. For female participants, ethnicity {Malays (OR = 6.7; 95% CI: 2.0-18.5), Indian (OR = 4.5; 95% CI: 1.8-19.3)} was an important risk factors. Cloud cover, school residence, skin pigmentation, sun-exposure and sun-protective behaviours were not significant risk factors. The limitation of this study was recall bias as it relied on self-reported on the sun exposure and protective behaviours. The diet factors were not included in this analysis.
CONCLUSIONS: The prevalence of Vitamin D deficiency among Malaysian adolescents was considerable. Gender, ethnicity and clothing style were important risk factors.
METHODS: Participants were identified from the Department of Statistics Malaysia sampling frame. Surveys were carried out with individual households aged 18 years and older through self-administered questionnaires. Information was collected on demographics, household income, employment status, number of diseases, and HRQOL assessed using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) tool.
RESULTS: Out of a total of 1899 participants, 620 (32.6%) were female and 328 (17.3%) were aged 60 years and above. The mean (SD) age was 45.2 (14.1) and mean (SD) household income was RM2124 (1356). Compared with younger individuals, older respondents were more likely to experience difficulties in mobility (32.1% vs 9.7%, p<0.001), self-care (11.6% vs 3.8%, p<0.001), usual activities (24.5% vs 9.1%, p<0.001), pain/discomfort (38.8% vs 16.5%, p<0.001) and anxiety/depression (21.4% vs 13.5%, p<0.001). The mean (SD) EQ-5D index scores were lower among older respondents, 0.89 (0.16) vs 0.95 (0.13), p = 0.001. After adjusting for covariates, age was a significant influencing factor (p = 0.001) for mobility (OR = 2.038, 95% CI:1.439-2.885), usual activities (OR = 1.957, 95% CI:1.353-2.832) and pain or discomfort (OR = 2.241, 95% CI:1.690-2.972).
CONCLUSION: Lower-income older adults had poorer HRQOL compared to their younger counterparts. This has important implications concerning intervention strategies that incorporate active ageing concepts on an individual and policy-making level to enhance the QOL and wellbeing, particularly among the older lower-income population.
METHODS: This is a cross-sectional study involved interviewing newly diagnosed breast cancer patients in the University Malaya Medical Centre (UMMC) using a structured questionnaire. Eligible respondents were interviewedduring a routine clinical visit.
RESULTS: A total of 400 patients were interviewed, of whom 139 (34.8%) were CAM users. Dietary supplementation (n = 107, 77.0%) was the most frequently used type of CAM, followed by spiritual healing (n = 40, 28.8%) and traditional Chinese medicine (n = 32, 23.0%). Malay ethnic group (n = 61, 43.9%) was the largest group of CAM users, followed by Chinese (n = 57, 41.0%) and Indian (n = 20, 14.4%). Majority of these CAM users (n = 87, 73.1%) did not disclose the use of CAM to their doctors. Most of them used remedies based on the recommendation of family and friends. Malay ethnicity and patients with 3 or more comorbidities were more likely to use CAM.
CONCLUSION: There is substantial use of CAM among breast cancer patients in UMMC prior to seeking hospital treatment, and the most popular CAM modality is dietary supplements. Since, the majority of CAM users do not disclose the use of CAM to their physicians, therefore health care providers should ensure that those patients who are likely to use CAM are appropriately counseled and advised.
DESIGN: This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations.
SETTING: General Intensive Care Unit, University of Malaya Medical Centre.
PATIENTS: Mechanically ventilated critically ill patients.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during acute phase (1,765 ± 456 kcal) (p = 0.037). The predictive equations with the best agreement and accuracy for acute phase was Swinamer (1990), for late phase was Brandi (1999) and Swinamer (1990), and for chronic phase was Swinamer (1990). None of the resting energy expenditure calculated from predictive equations showed very good agreement or accuracy.
CONCLUSIONS: Predictive equations tend to either over- or underestimate resting energy expenditure at different phases. Predictive equations with "dynamic" variables and respiratory data had better agreement with resting energy expenditure measured by indirect calorimetry compared with predictive equations developed for healthy adults or predictive equations based on "static" variables. Although none of the resting energy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appears to provide relatively good agreement across three phases and could be used to predict resting energy expenditure when indirect calorimetry is not available.