Dietary, lifestyle and clinico-pathological factors associated with APC mutations and promoter methylation in colorectal cancers from the EPIC-Norfolk Study

The tumour suppressor APC is the most commonly altered gene in colorectal cancer (CRC). Genetic and epigenetic alterations of APC may therefore be associated with dietary and lifestyle risk factors for CRC. Analysis of APC mutations in the extended mutation cluster region (codons 1276-1556) and APC promoter 1A methylation was performed on 185 archival CRC samples collected from participants of the European Prospective Investigation into Cancer (EPIC)-Norfolk Study, with the aim of relating these to high quality seven-day dietary and lifestyle data collected prospectively. Truncating APC mutations (APC+) and promoter 1A methylation (PM+) were identified in 43% and 23% of CRCs analysed, respectively. Distal CRCs were more likely than proximal CRCs to be APC+ or PM+ (P = 0.04). APC+ CRCs were more likely to be moderately/well differentiated and microsatellite stable than APC- CRCs (P = 0.05 and 0.03). APC+ CRC cases consumed more alcohol than their counterparts (P = 0.01) and PM+ CRC cases consumed lower levels of folate and fibre (P = 0.01 and 0.004). APC+ or PM+ CRC cases consumedhigher levels of processed meat and iron from red meat and red meat products (P=0.007 and 0.006). Specifically, CRC cases harbouring GC to AT transition mutations consumed higher levels of processed meat (35 versus 24 g/day, P = 0.04) and iron from red meat and red meat products (0.8 versus 0.6 mg/day, P = 0.05). In a logistic regression model adjusted for age, sex and cigarette smoking status, each 19g/day (1SD) increment increase in processed meat consumption was associated with cases with GC to AT mutations (OR 1.68, 95% CI 1.03-2.75). In conclusion, APC+ and PM+ CRCs may be influenced by diet and GC to AT mutations in APC are associated with processed meat consumption, suggesting a mechanistic link with dietary alkylating agents, such as N-nitroso compounds.

Possible role of aerosol transmission in a hospital outbreak of influenza.

BACKGROUND: We examined the role of aerosol transmission of influenza in an acute ward setting. METHODS: We investigated a seasonal influenza A outbreak that occurred in our general medical ward (with open bay ward layout) in 2008. Clinical and epidemiological information was collected in real time during the outbreak. Spatiotemporal analysis was performed to estimate the infection risk among patients. Airflow measurements were conducted, and concentrations of hypothetical virus-laden aerosols at different ward locations were estimated using computational fluid dynamics modeling. RESULTS: Nine inpatients were infected with an identical strain of influenza A/H3N2 virus. With reference to the index patient's location, the attack rate was 20.0% and 22.2% in the "same" and "adjacent" bays, respectively, but 0% in the "distant" bay (P = .04). Temporally, the risk of being infected was highest on the day when noninvasive ventilation was used in the index patient; multivariate logistic regression revealed an odds ratio of 14.9 (95% confidence interval, 1.7-131.3; P = .015). A simultaneous, directional indoor airflow blown from the "same" bay toward the "adjacent" bay was found; it was inadvertently created by an unopposed air jet from a separate air purifier placed next to the index patient's bed. Computational fluid dynamics modeling revealed that the dispersal pattern of aerosols originated from the index patient coincided with the bed locations of affected patients. CONCLUSIONS: Our findings suggest a possible role of aerosol transmission of influenza in an acute ward setting. Source and engineering controls, such as avoiding aerosol generation and improving ventilation design, may warrant consideration to prevent nosocomial outbreaks.

What determines the out-of-home placement of children in the USA?

Using NCANDS data of US child maltreatment reports for 2009, logistic regression, probit analysis, discriminant analysis and an artificial neural network are used to determine the factors which explain the decision to place a child in out-of-home care. As well as developing a new model for 2009, a previous study using 2005 data is replicated. While there are many small differences, the four estimation techniques give broadly the same results, demonstrating the robustness of the results. Similarly, apart from age and sexual abuse, the 2005 and 2009 results are roughly similar. For 2009, child characteristics (particularly child emotional problems) are more important than the nature of the abuse and the situation of the household; while caregiver characteristics are the least important. All these models have low explanatory power.

A neurofuzzy classifier for two class problems

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.

A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

The prevalence and nature of prescribing and monitoring errors in English general practice – a retrospective case note review

Objective To determine the prevalence and nature of prescribing and monitoring errors in general practices in England. Design Retrospective case note review of unique medication items prescribed over a 12 month period to a 2% random sample of patients. Mixed effects logistic regression was used to analyse the data. Setting Fifteen general practices across three primary care trusts in England. Data sources Examination of 6048 unique prescription items prescribed over the previous 12 months for 1777 patients. Main outcome measures Prevalence of prescribing and monitoring errors, and severity of errors, using validated definitions. Results Prescribing and/or monitoring errors were detected in 4.9% (296/6048) of all prescription items (95% confidence interval 4.4 - 5.5%). The vast majority of errors were of mild to moderate severity, with 0.2% (11/6048) of items having a severe error. After adjusting for covariates, patient-related factors associated with an increased risk of prescribing and/or monitoring errors were: age less than 15 (Odds Ratio (OR) 1.87, 1.19 to 2.94, p=0.006) or greater than 64 years (OR 1.68, 1.04 to 2.73, p=0.035), and higher numbers of unique medication items prescribed (OR 1.16, 1.12 to 1.19, p<0.001). Conclusion Prescribing and monitoring errors are common in English general practice, although severe errors are unusual. Many factors increase the risk of error. Having identified the most common and important errors, and the factors associated with these, strategies to prevent future errors should be developed based on the study findings.

Ensemble projections for wine production in the Douro Valley of Portugal

Wine production is largely governed by atmospheric conditions, such as air temperature and precipitation, together with soil management and viticultural/enological practices. Therefore, anthropogenic climate change is likely to have important impacts on the winemaking sector worldwide. An important winemaking region is the Portuguese Douro Valley, which is known by its world-famous Port Wine. The identification of robust relationships between atmospheric factors and wine parameters is of great relevance for the region. A multivariate linear regression analysis of a long wine production series (1932–2010) reveals that high rainfall and cool temperatures during budburst, shoot and inflorescence development (February-March) and warm temperatures during flowering and berry development (May) are generally favourable to high production. The probabilities of occurrence of three production categories (low, normal and high) are also modelled using multinomial logistic regression. Results show that both statistical models are valuable tools for predicting the production in a given year with a lead time of 3–4 months prior to harvest. These statistical models are applied to an ensemble of 16 regional climate model experiments following the SRES A1B scenario to estimate possible future changes. Wine production is projected to increase by about 10 % by the end of the 21st century, while the occurrence of high production years is expected to increase from 25 % to over 60 %. Nevertheless, further model development will be needed to include other aspects that may shape production in the future. In particular, the rising heat stress and/or changes in ripening conditions could limit the projected production increase in future decades.

The politics of temporary work deregulation in Europe: solving the French puzzle

Temporary work has expanded in the last three decades with adverse implications for inequalities. Because temporary workers are a constituency that is unlikely to impose political costs, governments often choose to reduce temporary work regulations. While most European countries have indeed implemented such reforms, France went in the opposite direction, despite having both rigid labour markets and high unemployment. My argument to solve this puzzle is that where replaceability is high, workers in permanent and temporary contracts have overlapping interests, and governments choose to regulate temporary work to protect permanent workers. In turn, replaceability is higher where permanent workers’ skills are general and wage coordination is low. Logistic regression analysis of the determinants of replaceability — and how this affects governments’ reforms of temporary work regulations — supports my argument. Process tracing of French reforms also confirm that the left has tightened temporary work regulations to compensate for the high replaceability.

Dietary factors impact on the association between CTSS variants and obesity related traits

BACKGROUND/AIMS: Cathepsin S, a protein coded by the CTSS gene, is implicated in adipose tissue biology--this protein enhances adipose tissue development. Our hypothesis is that common variants in CTSS play a role in body weight regulation and in the development of obesity and that these effects are influenced by dietary factors--increased by high protein, glycemic index and energy diets. METHODS: Four tag SNPs (rs7511673, rs11576175, rs10888390 and rs1136774) were selected to capture all common variation in the CTSS region. Association between these four SNPs and several adiposity measurements (BMI, waist circumference, waist for given BMI and being a weight gainer-experiencing the greatest degree of unexplained annual weight gain during follow-up or not) given, where applicable, both as baseline values and gain during the study period (6-8 years) were tested in 11,091 European individuals (linear or logistic regression models). We also examined the interaction between the CTSS variants and dietary factors--energy density, protein content (in grams or in % of total energy intake) and glycemic index--on these four adiposity phenotypes. RESULTS: We found several associations between CTSS polymorphisms and anthropometric traits including baseline BMI (rs11576175 (SNP N°2), p = 0.02, β = -0.2446), and waist change over time (rs7511673 (SNP N°1), p = 0.01, β = -0.0433 and rs10888390 (SNP N°3), p = 0.04, β = -0.0342). In interaction with the percentage of proteins contained in the diet, rs11576175 (SNP N°2) was also associated with the risk of being a weight gainer (p(interaction) = 0.01, OR = 1.0526)--the risk of being a weight gainer increased with the percentage of proteins contained in the diet. CONCLUSION: CTSS variants seem to be nominally associated to obesity related traits and this association may be modified by dietary protein intake.

Genetic polymorphisms in the hypothalamic pathway in relation to subsequent weight change--the DiOGenes study

BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes encoding the components involved in the hypothalamic pathway may influence weight gain and dietary factors may modify their effects. AIM: We conducted a case-cohort study to investigate the associations of SNPs in candidate genes with weight change during an average of 6.8 years of follow-up and to examine the potential effect modification by glycemic index (GI) and protein intake. METHODS AND FINDINGS: Participants, aged 20-60 years at baseline, came from five European countries. Cases ('weight gainers') were selected from the total eligible cohort (n = 50,293) as those with the greatest unexplained annual weight gain (n = 5,584). A random subcohort (n = 6,566) was drawn with the intention to obtain an equal number of cases and noncases (n = 5,507). We genotyped 134 SNPs that captured all common genetic variation across the 15 candidate genes; 123 met the quality control criteria. Each SNP was tested for association with the risk of being a 'weight gainer' (logistic regression models) in the case-noncase data and with weight gain (linear regression models) in the random subcohort data. After accounting for multiple testing, none of the SNPs was significantly associated with weight change. Furthermore, we observed no significant effect modification by dietary factors, except for SNP rs7180849 in the neuromedin β gene (NMB). Carriers of the minor allele had a more pronounced weight gain at a higher GI (P = 2 x 10⁻⁷). CONCLUSIONS: We found no evidence of association between SNPs in the studied hypothalamic genes with weight change. The interaction between GI and NMB SNP rs7180849 needs further confirmation.

Association between serotonin 5-HT-2C receptor gene (HTR2C) polymorphisms and obesity- and mental health-related phenotypes in a large population-based cohort

OBJECTIVE: Studies have shown that common single-nucleotide polymorphisms (SNPs) in the serotonin 5-HT-2C receptor (HTR2C) are associated with antipsychotic agent-induced weight gain and the development of behavioural and psychological symptoms. We aimed to analyse whether variation in the HTR2C is associated with obesity- and mental health-related phenotypes in a large population-based cohort. METHOD: Six tagSNPs, which capture all common genetic variation in the HTR2C gene, were genotyped in 4978 men and women from the European Prospective Investigation into Cancer (EPIC)-Norfolk study, an ongoing prospective population-based cohort study in the United Kingdom. To confirm borderline significant associations, the -759C/T SNP (rs3813929) was genotyped in the remaining 16 003 individuals from the EPIC-Norfolk study. We assessed social and psychological circumstances using the Health and Life Experiences Questionnaire. Genmod models were used to test associations between the SNPs and the outcomes. Logistic regression was performed to test for association of SNPs with obesity- and mental health- related phenotypes. RESULTS: Of the six HTR2C SNPs, only the T allele of the -759C/T SNP showed borderline significant associations with higher body mass index (BMI) (0.23 kg m(-2); (95% confidence interval (CI): 0.01-0.44); P=0.051) and increased risk of lifetime major depressive disorder (MDD) (Odds ratio (OR): 1.13 (95% CI: 1.01-1.22), P=0.02). The associations between the -759C/T and BMI and lifetime MDD were independent. As associations only achieved borderline significance, we aimed to validate our findings on the -759C/T SNP in the full EPIC-Norfolk cohort (n=20 981). Although the association with BMI remained borderline significant (beta=0.20 kg m(-2); 95% CI: 0.04-0.44, P=0.09), that with lifetime MDD (OR: 1.01; 95% CI: 0.94-1.09, P=0.73) was not replicated. CONCLUSIONS: Our findings suggest that common HTR2C gene variants are unlikely to have a major role in obesity- and mental health-related traits in the general population.

Association of the PPARGC1A gene polymorphism with diabetic nephropathy in an Asian Indian population (CURES-41)

BACKGROUND: The aim of this study was to evaluate the association of polymorphisms of the peroxisome proliferator-activated receptor gamma (PPARG) gene and peroxisome proliferators-activated receptor gamma co-activator 1 alpha (PPARGC1A) gene with diabetic nephropathy (DN) in Asian Indians. METHODS: Six common polymorphisms, 3 of the PPARG gene [-1279G/A, Pro12Ala, and His478His (C/T)] and 3 of the PPARGC1A gene (Thr394Thr, Gly482Ser, and +A2962G) were studied in 571 normal glucose-tolerant (NGT) subjects, 255 type 2 diabetic (T2D) subjects without nephropathy, and 141 DN subjects. Genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and direct sequencing. Logistic regression analysis was performed to assess the covariables associated with DN. RESULTS: Among the 6 polymorphisms examined, only the Gly482Ser of the PPARGC1A gene was significantly associated with DN. The genotype frequency of Ser/Ser genotype of the PPARGC1A gene was 8.8% (50/571) in NGT subjects, 7.8% (20/255) in T2D subjects, and 29.8% (42/141) in DN subjects. The odds ratios (ORs) for DN for the susceptible Gly/Ser and Ser/Ser genotype after adjusting for age, sex, body mass index, and duration of diabetes were 2.14 [95% confidence interval (CI), 1.23-3.72; P = 0.007] and 8.01 (95% CI, 3.89-16.47; P < 0.001), respectively. The unadjusted OR for DN for the XA genotype of the Thr394Thr polymorphism was 1.87 (95% CI, 1.20-2.92; P = 0.006) compared to T2D subjects. However, the significance was lost (P = 0.061) when adjusted for age, sex, BMI, and duration of diabetes. The +A2962G of PPARGC1A and the 3 polymorphisms of PPARG were not associated with DN. CONCLUSION: The Gly482Ser polymorphism of the PPARGC1A gene is associated with DN in Asian Indians.

A novel association of a polymorphism in the first intron of adiponectin gene with type 2 diabetes, obesity and hypoadiponectinemia in Asian Indians

Adiponectin is an adipose tissue specific protein that is decreased in subjects with obesity and type 2 diabetes. The objective of the present study was to examine whether variants in the regulatory regions of the adiponectin gene contribute to type 2 diabetes in Asian Indians. The study comprised of 2,000 normal glucose tolerant (NGT) and 2,000 type 2 diabetic, unrelated subjects randomly selected from the Chennai Urban Rural Epidemiology Study (CURES), in southern India. Fasting serum adiponectin levels were measured by radioimmunoassay. We identified two proximal promoter SNPs (-11377C-->G and -11282T-->C), one intronic SNP (+10211T-->G) and one exonic SNP (+45T-->G) by SSCP and direct sequencing in a pilot study (n = 500). The +10211T-->G SNP alone was genotyped using PCR-RFLP in 4,000 study subjects. Logistic regression analysis revealed that subjects with TG genotype of +10211T-->G had significantly higher risk for diabetes compared to TT genotype [Odds ratio 1.28; 95% Confidence Interval (CI) 1.07-1.54; P = 0.008]. However, no association with diabetes was observed with GG genotype (P = 0.22). Stratification of the study subjects based on BMI showed that the odds ratio for obesity for the TG genotype was 1.53 (95%CI 1.3-1.8; P < 10(-7)) and that for GG genotype, 2.10 (95% CI 1.3-3.3; P = 0.002). Among NGT subjects, the mean serum adiponectin levels were significantly lower among the GG (P = 0.007) and TG (P = 0.001) genotypes compared to TT genotype. Among Asian Indians there is an association of +10211T-->G polymorphism in the first intron of the adiponectin gene with type 2 diabetes, obesity and hypoadiponectinemia.

Absence of Association of Metabolic Syndrome with PPARGC1A, PPARG and UCP1 Gene Polymorphisms in Asian Indians

The objective of this study was to evaluate the association of PPARG coactivator1 alpha (PPARGC1A), peroxisome proliferator activated receptor gamma (PPARG), and uncoupling protein1 (UCP1) gene polymorphisms with the metabolic syndrome (MS) in an Asian Indian population. Nine common polymorphisms were genotyped via polymerase chain reaction restriction fragment length polymorphism and direct sequencing in 950 normal glucose-tolerant subjects and 550 type 2 diabetic subjects, chosen randomly from the Chennai Urban Rural Epidemiological Study, an ongoing population based study in Southern India. Among the 9 polymorphisms examined, only the Thr394Thr variant of the PPARGC1A gene was significantly associated with diabetes and obesity. The genotype frequency of GA of Thr394Thr variant was 16% (138/887) in the nonMS group and 22% (136/613) in the MS group, and this genotype frequency was significantly higher with MS both in males (p = 0.01) and females (p = 0.05), compared to the without-MS group. Logistic regression analysis revealed that the odds ratio for MS for the susceptible genotype GA of Thr394Thr was 1.411 [95% CI: 1.03-1.84, p = 0.012]. In the multiple logistic regression analysis, however, there was no association of this polymorphism as an independent factor with MS. Hence, the study shows that the polymorphisms in the PPARGC1A, PPARG and UCP1 genes are not associated with MS in Asian Indians.

Association of low adiponectin levels with the metabolic syndrome--the Chennai Urban Rural Epidemiology Study (CURES-4)

The aim of the study was to assess the relation of adiponectin levels with the metabolic syndrome in Asian Indians, a high-risk group for diabetes and premature coronary artery disease. The study was conducted on 100 (50 men and 50 women) type 2 diabetic subjects and 100 age and sex matched subjects with normal glucose tolerance selected from the Chennai Urban Rural Epidemiology Study, an ongoing population study in Chennai in southern India. Metabolic syndrome was defined using modified Adult Treatment Panel III (ATPIII) guidelines. Adiponectin values were significantly lower in diabetic subjects (men: 5.2 vs 8.3 microg/mL, P=.00l; women: 7.6 vs 11.1 microg/mL, P<.00l) and those with the metabolic syndrome (men: 5.0 vs 6.8 microg/mL, P=.01; women: 6.5 vs 9.9 microg/mL, P=.001) compared with those without. Linear regression analysis revealed adiponectin to be associated with body mass index (P<.05), waist circumference (P<.01), fasting plasma glucose (P=.001), glycated hemoglobin (P<.001), triglycerides (P<.00l), high-density lipoprotein (HDL) cholesterol (P<.001), cholesterol/HDL ratio (P<.00l), and insulin resistance measured by homeostasis assessment model (P<.00l). Factor analysis identified 2 factors: factor 1, negatively loaded with adiponectin and HDL cholesterol and positively loaded with triglycerides, waist circumference, and insulin resistance measured by homeostasis assessment model; and factor 2, with a positive loading of waist circumference and systolic and diastolic blood pressure. Logistic regression analysis revealed adiponectin to be negatively associated with metabolic syndrome (odds ratio [OR], 0.365; P<.001) even after adjusting for age (OR, 0.344; P<.00l), sex (OR, 0.293; P<.001), and body mass index (OR, 0.292; P<.00l). Lower adiponectin levels are associated with the metabolic syndrome per se and several of its components, particularly, diabetes, insulin resistance, and dyslipidemia in this urban south Indian population.