MATERIALS AND METHODS: This was an investigator-initiated, single-center, randomized, controlled, clinical trial in patients with T2DM and DKD, comparing 12-weeks of low carbohydrate diet (<20g daily intake) versus standard low protein (0.8g/kg/day) and low salt diet. Patients in the VLCBD group underwent 2-weekly monitoring including their 3-day food diaries. In addition, Dual-energy x-ray absorptiometry (DEXA) was performed to estimate body fat percentages.
RESULTS: The study population (n = 30) had a median age of 57 years old and a BMI of 30.68kg/m2. Both groups showed similar total calorie intake, i.e. 739.33 (IQR288.48) vs 789.92 (IQR522.4) kcal, by the end of the study. The VLCBD group showed significantly lower daily carbohydrate intake 27 (IQR25) g vs 89.33 (IQR77.4) g, p<0.001, significantly higher protein intake per day 44.08 (IQR21.98) g vs 29.63 (IQR16.35) g, p<0.05 and no difference in in daily fat intake. Both groups showed no worsening of serum creatinine at study end, with consistent declines in HbA1c (1.3(1.1) vs 0.7(1.25) %) and fasting blood glucose (1.5(3.37) vs 1.3(5.7) mmol/L). The VLCBD group showed significant reductions in total daily insulin dose (39(22) vs 0 IU, p<0.001), increased LDL-C and HDL-C, decline in IL-6 levels; with contrasting results in the control group. This was associated with significant weight reduction (-4.0(3.9) vs 0.2(4.2) kg, p = <0.001) and improvements in body fat percentages. WC was significantly reduced in the VLCBD group, even after adjustments to age, HbA1c, weight and creatinine changes. Both dietary interventions were well received with no reported adverse events.
CONCLUSION: This study demonstrated that dietary intervention of very low carbohydrate diet in patients with underlying diabetic kidney disease was safe and associated with significant improvements in glycemic control, anthropometric measurements including weight, abdominal adiposity and IL-6. Renal outcomes remained unchanged. These findings would strengthen the importance of this dietary intervention as part of the management of patients with diabetic kidney disease.
MATERIALS AND METHODS: An extensive systematic electronic review (PUBMED, CINAHL, PsyINFO and Ovid) and handsearch were carried out to retrieve published articles up to November 2012, using Depression OR Dysthymia AND (Cancer OR Tumor OR Neoplasms as the keywords. Information about the design of the studies, measuring scale, characteristics of the participants, prevalence of depression and its associated factors from the included studies were extracted and summarized.
RESULTS: We identified 32 eligible studies that recruited 10,826 breast cancer survivors. Most were cross-sectional or prospective designed. The most frequent instrument used to screen depression was the Center for Epidemiological Studies for Depression (CES-D, n=11 studies) followed by the Beck Depression Inventory (BDI, n=6 studies) and the Hospital Anxiety and Depression Scale (HADS, n=6 studies). CES-D returned about similar prevalence of depression (median=22%, range=13-56%) with BDI (median=22%, range=17-48%) but higher than HADS (median=10%, range=1-22%). Depression was associated with several socio-demographic variables, cancer-related factors, treatment-related factors, subject psychological factors, lifestyle factors, social support and quality of life.
CONCLUSIONS: Breast cancer survivors are at risk for depression so that detection of associated factors is important in clinical practice.
MATERIALS AND METHODS: This cross sectional study involved 123 subjects from Temiar subtribe in Kuala Betis, Gua Musang, Kelantan. MetS criteria were measured according to standard protocol by modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guideline. Anthropometric and biochemical measurements were performed including serum adiponectin and resistin for every study subjects.
RESULTS: Serum adiponectin was significantly lower in MetS subjects (7.98 ± 5.65 ng/ml) but serum resistin was found to be significantly higher in MetS subjects (11.22 ± 6.34 ng/ml) compared to non-MetS subjects with p
METHOD: This cross-sectional study involved eighty-three (n=83) adults attending a health screening program at Universiti Putra Malaysia (UPM). Demographic data, anthropometric measurements and blood samples for fasting blood glucose (FBG), fasting lipid profile (FSL), glycated haemoglobin (HbA1c) and hsCRP were taken. Respondents were grouped according to FRS and the Joint Interim Statement into 10-year CVD risk categories (low, intermediate and high) and MetS, respectively.
RESULTS: hsCRP was significantly increased in patients with high body mass index (BMI) (p=0.001), at-risk waist circumference (WC) (p=0.001) and MetS (p=0.009). Spearman's correlation coefficient showed a significant positive correlation between hsCRP level and total FRS score (r=0.26, p<0.05) and HDL-C score (r=0.22, p<0.05).
CONCLUSION: The significant difference of hsCRP levels across obesity levels and MetS with its modest correlation with FRS scores supported the adjunctive role of hsCRP in CVD risk prediction, most likely capturing the inflammatory pathological aspect and thus partly accounting for the residual CVD risk.
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: We examined associations of body mass index (BMI), waist circumference (WC), and waist-hip ratio (WHR) with lung cancer risk among 1.6 million Americans, Europeans, and Asians. Cox proportional hazard regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment for potential confounders. Analyses for WC/WHR were further adjusted for BMI. The joint effect of BMI and WC/WHR was also evaluated.
RESULTS: During an average 12-year follow-up, 23 732 incident lung cancer cases were identified. While BMI was generally associated with a decreased risk, WC and WHR were associated with increased risk after controlling for BMI. These associations were seen 10 years before diagnosis in smokers and never smokers, were strongest among blacks, and varied by histological type. After excluding the first five years of follow-up, hazard ratios per 5 kg/m2 increase in BMI were 0.95 (95% CI = 0.90 to 1.00), 0.92 (95% CI = 0.89 to 0.95), and 0.89 (95% CI = 0.86 to 0.91) in never, former, and current smokers, and 0.86 (95% CI = 0.84 to 0.89), 0.94 (95% CI = 0.90 to 0.99), and 1.09 (95% CI = 1.03 to 1.15) for adenocarcinoma, squamous cell, and small cell carcinoma, respectively. Hazard ratios per 10 cm increase in WC were 1.09 (95% CI = 1.00 to 1.18), 1.12 (95% CI = 1.07 to 1.17), and 1.11 (95% CI = 1.07 to 1.16) in never, former, and current smokers, and 1.06 (95% CI = 1.01 to 1.12), 1.20 (95% CI = 1.12 to 1.29), and 1.13 (95% CI = 1.04 to 1.23) for adenocarcinoma, squamous cell, and small cell carcinoma, respectively. Participants with BMIs of less than 25 kg/m2 but high WC had a 40% higher risk (HR = 1.40, 95% CI = 1.26 to 1.56) than those with BMIs of 25 kg/m2 or greater but normal/moderate WC.
CONCLUSIONS: The inverse BMI-lung cancer association is not entirely due to smoking and reverse causation. Central obesity, particularly concurrent with low BMI, may help identify high-risk populations for lung cancer.