METHODS AND STUDY DESIGN: A randomized controlled study was conducted on obese women with high breast adiposity (<0.1 Sm-1), aged 40-60 years in Klang Valley, Malaysia. Subjects were assigned to intervention (n=16) and control group (n=15). Intervention group received a home based health education package with close monitoring weekly, personal diet consultation and physical training in group. Assessment was ascertained at three time points; baseline, weeks 8 and 16. Outcome measures were the energy intake, physical activity, body composition, blood tests, blood biomarkers and electrical impedance tomography (EIT) quantitative values. Analyses were done using 2-way repeated measures ANOVA.
RESULTS AND CONCLUSIONS: All subjects completed the program without any drop-out. The HSI group had 100% compliance towards the intervention program; their energy intake was reduced for approximately 35% and their activity score was increased for approximately 11%. A significant interaction effect was found in body weight, body mass index (BMI), total cholesterol/HDL, vitamin C intake and matrix metallopeptidase 9 (MMP-9) (p<0.05). Interestingly, their EIT extremum values were also significantly increased indicating a reduction of breast adiposity. The intervention program was successful in improving body composition, physical activities, MMP9 and breast adipose tissue composition.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
METHODOLOGY: Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.
RESULTS: In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.
CONCLUSION: Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.
Objective: The objective of this study was to use IHC to compare leptin and leptin receptor expressions in clear cell renal cell carcinomas (ccRCC) in non-obese and obese patients to determine the association between these proteins with the clinicopathological features and prognosis of ccRCC. Patients and Methods. The study involved 60 patients who underwent nephrectomy of which 34 were obese, as assessed using body mass index (BMI). Nephrectomy samples provided tissues of ccRCC and adjacent non-cancerous kidney. The intensity and localization of leptin and leptin receptor protein expressions were evaluated using IHC and correlated with clinicopathological features and clinical outcomes. Aperio ImageScope morphometry and digital pathology were applied to assess the IHC results. The chi-square test was used to determine if there was any significant association between the proteins and the clinicopathological features. The Kaplan-Meier test was used to determine the overall survival, disease-free survival, and recurrence-free survival. A value of p < 0.05 was considered significant.
Results: There was neither significant difference in the overall cellular and nuclear expressions of leptin and leptin receptor between non-cancerous kidney and ccRCC tissues nor in non-obese and obese individuals with ccRCC.
Conclusion: In this present study, it was revealed that leptin and leptin receptor were not associated with tumour characteristics and progression of ccRCC patients. Interestingly, nuclear expression of leptin was significantly associated with overall survival. However, the significance of these proteins as biomarkers in other RCC histotypes is still unclear.
Methods: We assessed study-related records to determine the pace of data collection, response from potential participants, and feedback following data and sample collection. Overall and stratified measures of data and sample availability were summarised. Crude prevalence of key risk factors was examined.
Results: Approximately half (49.5%) of invited individuals consented to participate in this study, for a final sample size of 203 (161 adults and 42 children). Women were more likely to consent to participate compared with men, whereas children, young adults and individuals of Malay ethnicity were less likely to consent compared with older individuals or those of any other ethnicity. At least one biological sample (blood from all participants - finger-prick and venous [for serum, plasma and whole blood samples], hair or urine for adults only) was successfully collected from all participants, with blood test data available from over 90% of individuals. Among adults, urine samples were most commonly collected (97.5%), followed by any blood samples (91.9%) and hair samples (83.2%). Cardiometabolic risk factor burden was high (prevalence of elevated HbA1c among adults: 23.8%; of elevated triglycerides among adults: 38.1%; of elevated total cholesterol among children: 19.5%).
Conclusions: In this study, we show that it is feasible to create biodata resources using existing HDSS frameworks, and identify a potentially high burden of cardiometabolic risk factors that requires further evaluation in this population.