Lifetime water arsenic, genetic susceptibility, and bladder cancer in the New England Bladder Cancer Study.

Background: Exposure to arsenic in drinking water may interact with common genetic variants in urinary bladder cancer risk.

Methods: We conducted a gene-environment interaction analysis among 1091 bladder cancer cases and 928 controls from the New England Bladder Cancer Study. Genetic variants tested as effect modifiers included those associated with bladder cancer and arsenic metabolism.

A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study.

Objective: To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D).

Research Design And Methods: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment.

Dietary Glycemic Index, Glycemic Load, Sugar, and Fiber Intake in Association With Breast Cancer Risk: An Updated Meta-analysis.

Context: Several prospective cohort studies have investigated the association between glycemic index (GI), glycemic load (GL), dietary sugar, and total dietary fiber intake, with female breast cancer (BC) risk and reported inconsistent results. In the last decade, several large epidemiological studies have investigated these associations, suggesting the need to revisit the current body of evidence.

Objective: The aim of this study was to update a systematic review and meta-analysis conducted by Schlesinger et al in 2017 using recent scientific evidence published since 2015.

Plasma Concentrations of Multiple Oxysterols and Risk of Colorectal Adenomas.

Oxysterols are metabolites of cholesterol that regulate the homeostasis of cholesterol, fatty acids, and glucose. These metabolites are generated throughout the body, either enzymatically or from oxidative stress, and are detectable in peripheral circulation. We previously reported that circulating 27-hydroxycholesterol (27-OHC), an endogenous selective estrogen receptor modulator, may be a risk factor for colorectal adenomas.

Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review.

Background: The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D.

Methods: We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier.

Who benefits most from outpatient lifestyle intervention? An IMI-SOPHIA study on pediatric individuals living with overweight and obesity.

Objective: The first-line approach for childhood obesity is lifestyle intervention (LI); however, success varies. This study aimed first to identify distinct subgroups of response in children living with overweight and obesity and second to elucidate predictors for subclusters.

Methods: Based on the obesity patient follow-up registry the APV (Adipositas-Patienten-Verlaufsdokumentation) initiative, a total of 12,453 children and adolescents (median age: 11.

Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review.

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition.

Race/Ethnicity and Gender Representation in Hematology and Oncology Editorial Boards: What is the State of Diversity?

Introduction: Women and underrepresented groups in medicine hold few academic leadership positions in the field of hematology/oncology. In this study, we assessed gender and race/ethnicity representation in editorial board positions in hematology/oncology journals.

Materials And Methods: Editorial leadership board members from 60 major journals in hematology and oncology were reviewed; 54 journals were included in the final analysis.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes.

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers.

Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women.

Aims/hypothesis: Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors.

Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality.

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person's degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual's cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as 'sensitive' to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.

Estimating the Direct Effect between Dietary Macronutrients and Cardiometabolic Disease, Accounting for Mediation by Adiposity and Physical Activity.

Assessing the causal effects of individual dietary macronutrients and cardiometabolic disease is challenging because distinguish direct effects from those mediated or confounded by other factors is difficult. To estimate these effects, intake of protein, carbohydrate, sugar, fat, and its subtypes were obtained using food frequency data derived from a Swedish population-based cohort (n~60,000). Data on clinical outcomes (i.

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits.

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits  to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits.

An investigation of causal relationships between prediabetes and vascular complications.

Prediabetes is a state of glycaemic dysregulation below the diagnostic threshold of type 2 diabetes (T2D). Globally, ~352 million people have prediabetes, of which 35-50% develop full-blown diabetes within five years. T2D and its complications are costly to treat, causing considerable morbidity and early mortality.

Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research.

Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.