METHODS: Biomarkers of internal exposure were measured in red blood cells (collected at baseline) by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) . In this cross-sectional analysis, four dependent variables were evaluated: HbAA, HbGA, sum of total adducts (HbAA + HbGA), and their ratio (HbGA/HbAA). Simple and multiple regression analyses were used to identify determinants of the four outcome variables. All dependent variables (except HbGA/HbAA) and all independent variables were log-transformed (log2) to improve normality. Median (25th-75th percentile) HbAA and HbGA adduct levels were 41.3 (32.8-53.1) pmol/g Hb and 34.2 (25.4-46.9) pmol/g Hb, respectively.
RESULTS: The main food group determinants of HbAA, HbGA, and HbAA + HbGA were biscuits, crackers, and dry cakes. Alcohol intake and body mass index were identified as the principal determinants of HbGA/HbAA. The total percent variation in HbAA, HbGA, HbAA + HbGA, and HbGA/HbAA explained in this study was 30, 26, 29, and 13 %, respectively.
CONCLUSIONS: Dietary and lifestyle factors explain a moderate proportion of acrylamide adduct variation in non-smoking postmenopausal women from the EPIC cohort.
METHODS: This study includes 235,880 participants, 25-70 years old, recruited between 1992 and 2000 in 10 European countries. Intakes of 23 nutrients were estimated from country-specific validated dietary questionnaires using the harmonized EPIC Nutrient DataBase. Four nutrient patterns, explaining 67 % of the total variance of nutrient intakes, were previously identified from principal component analysis. Body weight was measured at recruitment and self-reported 5 years later. The relationship between nutrient patterns and annual weight change was examined separately for men and women using linear mixed models with random effect according to center controlling for confounders.
RESULTS: Mean weight gain was 460 g/year (SD 950) and 420 g/year (SD 940) for men and women, respectively. The annual differences in weight gain per one SD increase in the pattern scores were as follows: principal component (PC) 1, characterized by nutrients from plant food sources, was inversely associated with weight gain in men (-22 g/year; 95 % CI -33 to -10) and women (-18 g/year; 95 % CI -26 to -11). In contrast, PC4, characterized by protein, vitamin B2, phosphorus, and calcium, was associated with a weight gain of +41 g/year (95 % CI +2 to +80) and +88 g/year (95 % CI +36 to +140) in men and women, respectively. Associations with PC2, a pattern driven by many micro-nutrients, and with PC3, a pattern driven by vitamin D, were less consistent and/or non-significant.
CONCLUSIONS: We identified two main nutrient patterns that are associated with moderate but significant long-term differences in weight gain in adults.
METHODS: This is an observational, cross-sectional analysis of 486 women who presented to a tertiary urogynecological center between May 2013 and August 2014. They underwent a standardized interview and an examination that involved 3-dimensional/4-dimensional TPUS. The SMIS and VAS were administered if they answered positively to a question on AI. The association between defects of the EAS and symptoms of AI was evaluated using bivariate tests, as well as adjusting for pertinent covariates using multiple linear regression modeling.
RESULTS: Of the included patients, 17.1% reported AI, and 15.2% had significant EAS defects (≥4 slices) on TPUS imaging. A significant sonographic defect was diagnosed in 23% of women with AI versus 14% of those without (P = 0.033). Women with symptoms of AI were more likely to have a significant defect on TPUS (odds ratio, 1.878; 95% confidence interval, 1.05-3.37). No significant findings were seen when analyzing SMIS, its components, and VAS against sonographic EAS defects.
CONCLUSIONS: The symptom of AI is associated with significant EAS defects detected on TPUS. However, this study failed to show an association between significant EAS defects and the SMIS and VAS.
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: A total of 2322 Malaysian older adults aged 60 years and older were recruited using multistage random sampling in a population-based cross-sectional study. Out of 2322 older adults recruited, 2309 (48% men) completed assessments on cognitive function and body composition. Cognitive functions were assessed using the Malay version of the Mini-Mental State Examination, the Bahasa Malaysia version of Montreal Cognitive Assessment, Digit Span Test, Digit Symbol Test and Rey Auditory Verbal Learning Test. Body composition included body mass index, mid-upper arm circumference, waist circumference, calf circumference, waist-to-hip ratio, percentage body fat and skeletal muscle mass.
RESULTS: The association between body composition and cognitive functions was analyzed using multiple linear regression. After adjustment for age, education years, hypertension, hypercholesterolemia, diabetes mellitus, depression, smoking status and alcohol consumption, we found that calf circumference appeared as a significant predictor for all cognitive tests among both men and women (P