ABSTRACT
BACKGROUND AND AIMS: Obesity is a risk factor for colorectal neoplasia. We examined the influence of obesity and metabolic syndrome (MetS) on prevalence of neoplasia at screening colonoscopy. METHODS: We evaluated 2020 subjects undergoing first screening colonoscopy. Body mass index (BMI) was calculated at enrolment. Hyperlipidemia (HL), hypertension (HT), and diabetes mellitus (DM) were identified. Details of colonoscopy, polypectomy, and histology were recorded. Odds for adenomas (A) and advanced adenomas (ADV) in overweight (BMI 25.1-30) and obese (BMI > 30) subjects were assessed by multinomial regression, adjusted for covariates. Analyses included relationships between HL, HT, DM, age, tobacco usage, and neoplasia. Discriminatory power of HT, HL, DM, and BMI for neoplasia was assessed by binary logistic regression. Odds were calculated for neoplasia in each colonic segment related to BMI. RESULTS: A and ADV were commoner in overweight and obese males, obese females, older subjects, and smokers. HL, HT, and DM were associated with increased odds for neoplasia, significantly for A with hypertension. BMI alone predicted neoplasia as well as HT, HL, DM, or combinations thereof. All segments of the colon were affected. Multiple polyps were particularly prevalent in the obese. CONCLUSIONS: Obesity and MetS are risk factors for colonic neoplasia in a Canadian population.
Subject(s)
Adenoma/epidemiology , Colonic Neoplasms/epidemiology , Metabolic Syndrome/complications , Obesity/complications , Adenoma/diagnosis , Adenoma/etiology , Aged , Body Mass Index , Canada/epidemiology , Colonic Neoplasms/diagnosis , Colonic Neoplasms/etiology , Colonoscopy/methods , Diabetes Mellitus/epidemiology , Female , Humans , Hyperlipidemias/epidemiology , Hypertension/epidemiology , Logistic Models , Male , Mass Screening/methods , Metabolic Syndrome/epidemiology , Middle Aged , Obesity/epidemiology , Prevalence , Prospective Studies , Risk FactorsABSTRACT
Contemporary high-density electroencephalographic systems (hd-EEG) comprising up to 256 electrodes have inter-electrode separations of 2-4 cm. Because electric currents of the brain are believed to strongly diffuse before reaching the scalp surface, higher-density electrode coverage is often deemed unnecessary. We used an ultra-dense electroencephalography (ud-EEG) sensor array to reveal strong potential variation at 1cm scale and discovered that it reflects functional brain activity. A new classification paradigm demonstrates that ud-EEG provides twice the signal to noise ratio for brain-response classification compared with contemporary hd-EEG. These results suggest a paradigm shift from current thinking by showing that higher spatial resolution sampling of EEG is required and leads to increased functional brain information that is useful for diverse neurological applications.
