Intrauterine devices and endometrial cancer risk : a pooled analysis of the Epidemiology of Endometrial Cancer Consortium

Intrauterine devices (IUDs), long-acting and reversible contraceptives, induce a number of immunological and biochemical changes in the uterine environment that could affect endometrial cancer (EC) risk. We addressed this relationship through a pooled analysis of data collected in the Epidemiology of Endometrial Cancer Consortium. We combined individual-level data from 4 cohort and 14 case-control studies, in total 8,801 EC cases and 15,357 controls. Using multivariable logistic regression, we estimated pooled odds ratios (pooled-ORs) and 95% confidence intervals (CIs) for EC risk associated with ever use, type of device, ages at first and last use, duration of use and time since last use, stratified by study and adjusted for confounders. Ever use of IUDs was inversely related to EC risk (pooled-OR = 0.81, 95% CI = 0.74-0.90). Compared with never use, reduced risk of EC was observed for inert IUDs (pooled-OR = 0.69, 95% CI = 0.58-0.82), older age at first use (≥35 years pooled-OR = 0.53, 95% CI = 0.43-0.67), older age at last use (≥45 years pooled-OR = 0.60, 95% CI = 0.50-0.72), longer duration of use (≥10 years pooled-OR = 0.61, 95% CI = 0.52-0.71) and recent use (within 1 year of study entry pooled-OR = 0.39, 95% CI = 0.30-0.49). Future studies are needed to assess the respective roles of detection biases and biologic effects related to foreign body responses in the endometrium, heavier bleeding (and increased clearance of carcinogenic cells) and localized hormonal changes

Fat, protein, and meat consumption and renal cell cancer risk: a pooled analysis of 13 prospective studies

Results of several case-control studies suggest that high consumption of meat (all meat, red meat, or processed meat) is associated with an increased risk of renal cell cancer, but only a few prospective studies have examined the associations of intakes of meat, fat, and protein with renal cell cancer. We conducted a pooled analysis of 13 prospective studies that included 530 469 women and 244 483 men and had follow-up times of up to 7-20 years to examine associations between meat, fat, and protein intakes and the risk of renal cell cancer. All participants had completed a validated food frequency questionnaire at study entry. Using the primary data from each study, we calculated the study-specific relative risks (RRs) for renal cell cancer by using Cox proportional hazards models and then pooled these RRs by using a random-effects model. All statistical tests were two-sided. A total of 1478 incident cases of renal cell cancer were identified (709 in women and 769 in men). We observed statistically significant positive associations or trends in pooled age-adjusted models for intakes of total fat, saturated fat, monounsaturated fat, polyunsaturated fat, cholesterol, total protein, and animal protein. However, these associations were attenuated and no longer statistically significant after adjusting for body mass index, fruit and vegetable intake, and alcohol intake. For example, the pooled age-adjusted RR of renal cell cancer for the highest vs the lowest quintile of intake for total fat was 1.30 (95% confidence interval [CI] = 1.08 to 1.56; P(trend) = .001) and for total protein was 1.17 (95% CI = 0.99 to 1.38; P(trend) = .02). By comparison, the pooled multivariable RR for the highest vs the lowest quintile of total fat intake was 1.10 (95% CI = 0.92 to 1.32; P(trend) = .31) and of total protein intake was 1.06 (95% CI = 0.89 to 1.26; P(trend) = .37). Intakes of red meat, processed meat, poultry, or seafood were not associated with the risk of renal cell cancer. Intakes of fat and protein or their subtypes, red meat, processed meat, poultry, and seafood are not associated with risk of renal cell cancer

Going Beyond a Mean-field Model for the Learning Cortex: Second-Order Statistics

Mean-field models of the cortex have been used successfully to interpret the origin of features on the electroencephalogram under situations such as sleep, anesthesia, and seizures. In a mean-field scheme, dynamic changes in synaptic weights can be considered through fluctuation-based Hebbian learning rules. However, because such implementations deal with population-averaged properties, they are not well suited to memory and learning applications where individual synaptic weights can be important. We demonstrate that, through an extended system of equations, the mean-field models can be developed further to look at higher-order statistics, in particular, the distribution of synaptic weights within a cortical column. This allows us to make some general conclusions on memory through a mean-field scheme. Specifically, we expect large changes in the standard deviation of the distribution of synaptic weights when fluctuation in the mean soma potentials are large, such as during the transitions between the “up” and “down” states of slow-wave sleep. Moreover, a cortex that has low structure in its neuronal connections is most likely to decrease its standard deviation in the weights of excitatory to excitatory synapses, relative to the square of the mean, whereas a cortex with strongly patterned connections is most likely to increase this measure. This suggests that fluctuations are used to condense the coding of strong (presumably useful) memories into fewer, but dynamic, neuron connections, while at the same time removing weaker (less useful) memories

Dairy products and pancreatic cancer risk: A pooled analysis of 14 cohort studies

Pancreatic cancer has few early symptoms, is usually diagnosed at late stages, and has a high case-fatality rate. Identifying modifiable risk factors is crucial to reducing pancreatic cancer morbidity and mortality. Prior studies have suggested that specific foods and nutrients, such as dairy products and constituents, may play a role in pancreatic carcinogenesis. In this pooled analysis of the primary data from 14 prospective cohort studies, 2212 incident pancreatic cancer cases were identified during follow-up among 862 680 individuals. Adjusting for smoking habits, personal history of diabetes, alcohol intake, body mass index (BMI), and energy intake, multivariable study-specific hazard ratios (MVHR) and 95% confidence intervals (CIs) were calculated using the Cox proportional hazards models and then pooled using a random effects model. There was no association between total milk intake and pancreatic cancer risk (MVHR = 0.98, 95% CI = 0.82-1.18 comparing ≥500 with 1-69.9 g/day). Similarly, intakes of low-fat milk, whole milk, cheese, cottage cheese, yogurt, and icecream were not associated with pancreatic cancer risk. No statistically significant association was observed between dietary (MVHR = 0.96, 95% CI = 0.77-1.19) and total calcium (MVHR = 0.89, 95% CI = 0.71-1.12) intake and pancreatic cancer risk overall when comparing intakes ≥1300 with <500 mg/day. In addition, null associations were observed for dietary and total vitamin D intake and pancreatic cancer risk. Findings were consistent within sex, smoking status, and BMI strata or when the case definition was limited to pancreatic adenocarcinoma. Overall, these findings do not support the hypothesis that consumption of dairy foods, calcium, or vitamin D during adulthood is associated with pancreatic cancer risk. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved