Weight Change and Incident Distal Colorectal Adenoma Risk in the PLCO Cancer Screening Trial

BACKGROUND: Although obesity is a known risk factor, the impact of weight change on colorectal adenoma risk is less clear and could have important implications in disease prevention. We prospectively evaluated weight change in adulthood and incident colorectal adenoma. METHODS: We assessed weight change during early-late (age 20 years to baseline, ie, ages 55-74 years), early-middle (20-50 years), and middle-late (50 years-baseline) adulthood using self-reported weight data in relation to incident distal adenoma in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (cases = 1053; controls = 16 576). For each period, we defined stable weight as greater than −0.5 kg to less than or equal to 1 kg/5 years, weight loss as less than or equal to −0.5 kg/5 years, and weight gain as greater than 1-2, greater than 2-3, or greater than 3 kg/5 years. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) using logistic regression; all tests were 2-sided. RESULTS: Compared with stable weight, weight loss during early-late adulthood was associated with reduced adenoma risk (OR = 0.54, 95% CI = 0.34 to 0.86), particularly among those who were overweight or obese at age 20 years (OR = 0.39, 95% CI = 0.18 to 0.84). Results were similar for early-middle adulthood but less pronounced for middle-late adulthood. Weight gain greater than 3 kg/5 years during early-late adulthood was associated with increased risk (OR = 1.30, 95% CI = 1.07 to 1.58, P(trend) 3 kg/5 years = 1.41, 95% CI = 1.11 to 1.80) than women (OR = 1.09, 95% CI = 0.79 to 1.50, P(interaction) = .21). CONCLUSIONS: Weight loss in adulthood was associated with reduced adenoma risk, particularly for those who were overweight or obese, whereas weight gain greater than 3 kg/5 years increased risk. Findings underscore the importance of healthy weight maintenance throughout adulthood in preventing colorectal adenoma

Prevalence, control, and treatment of diabetes, hypertension, and high cholesterol in the Amish

Background The burden of diabetes and cardiovascular risk is not uniform across the USA, with much of this disparity tracking differences in socioeconomic status, cultural practices and lifestyle. To further evaluate disparities in these disorders, we assessed the prevalence of diabetes, hypertension, and hypercholesterolemia in an Old Order Amish community that is characterized by distinctive sociocultural practices that include a very cohesive social structure and limited use of modern technologies and medications. We compared prevalence of these conditions with that of the overall US population.Method We performed a community-wide survey in 5377 Amish individuals aged 18 years and older from the Lancaster County, Pennsylvania, Amish settlement that included a basic physical examination and fasting blood draw during the period 2010–2018. We then compared the prevalence of diabetes, hypertension, and high cholesterol, defined using standard criteria, between the Amish and the European Caucasian subsample of the 2013–2014 US National Health and Nutrition Examination Survey (NHANES).Results Prevalence rates for diabetes, hypertension and hypercholesterolemia were 3.3%, 12.7%, and 26.2% in the Amish compared with 13.2%, 37.8% and 35.7% in NHANES (p<0.001 for all). Among individuals with these disorders, Amish were less likely to be aware that they were affected, and among those aware, were less likely to be treated with a medication for their disorder.Conclusion There is substantially lower prevalence of diabetes, hypertension and hypercholesterolemia in the Amish compared with non-Amish Caucasians in the USA. Possible factors contributing to this disparity include higher physical activity levels in the Amish or other protective sociocultural factors, a greater understanding of which could inform risk reduction interventions for these chronic diseases

Identification of race-associated metabolite biomarkers for hepatocellular carcinoma in patients with liver cirrhosis and hepatitis C virus infection

<div><p>Disparities in hepatocellular carcinoma (HCC) incidence and survival have been observed between ethnic groups including African-Americans (AA) and European-Americans (EA). The evaluation of the changes in the levels of metabolites in samples stratified by race could provide a snapshot of ethnically diverse disease related pathways and identify reliable biomarkers. In this study, we considered AA and EA to investigate metabolites that may be associated with HCC in a race-specific manner. The levels of 46 metabolites in plasma samples, collected from patients recruited at MedStar Georgetown University Hospital, were analyzed by Agilent GC-qMS in selected ion monitoring (SIM) mode. A least absolute shrinkage and selection operator (LASSO) regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA separately; and (3) EA separately. In addition, metabolites that distinguish HCC cases from cirrhosis in these three groups were selected by excluding those without HCV infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the receiver operating characteristics (ROC) curve (AUC) compared to alpha-fetoprotein (AFP), the serological marker widely used for the diagnosis of HCC. This study sheds light on metabolites that could potentially be used as biomarkers for HCC by monitoring their levels in high-risk population of cirrhotic patients in a race-specific manner.</p></div

Performance of AFP as a classifier for HCC.

<p>The panels show dot plots (circle for liver cirrhotic, diamond for HCC; the horizontal line represents median) and ROC curve (AUC; 95% CI) for AFP for AA and EA combined, AA only, and EA only groups. The left panels (A, B, C) correspond to HCV +/- and the right panels (A₁, B₁, C₁) to HCV+ only.</p

Individual and combined with AFP ROC curves for alpha tocopherol, valine and glycine in each group.

<p>ROC curves for alpha tocopherol, valine and glycine in AA and EA combined, AA, and EA groups are shown in Fig 4A, 4B and 4C respectively (black square dot line for AFP, red triangle dot line for alpha tocopherol, valine and glycine and blue circle dot line for the combination of AFP and the corresponding metabolite).</p

Plot of the AUC values for the ROC curves corresponding to selected top n metabolites (n = 1,…,10), using MSVM-RFE, for AA and EA combined, AA only, and EA only.

<p>Plot of the AUC values for the ROC curves corresponding to selected top n metabolites (n = 1,…,10), using MSVM-RFE, for AA and EA combined, AA only, and EA only.</p

Prediction performance in leave-one-out cross validation based on metabolites selected by LASSO or top five metabolites ranked by MSVM-RFE.

<p>Prediction performance in leave-one-out cross validation based on metabolites selected by LASSO or top five metabolites ranked by MSVM-RFE.</p

Characteristics of HCV +, AA and EA.

<p>Characteristics of HCV +, AA and EA.</p

Characteristics of AA and EA.

<p>Characteristics of AA and EA.</p