The proportion of women participating in the German mammography screening program (MSP) is only 50
This study aimed to investigate the association between body compositions with overall/subtype-specific HNC risks. We included participants from the UK Biobank with complete body composition data and without other cancers. Prospective analysis further excluded prevalent HNC. Multivariable logistic and Cox proportional hazards regression models were used to assess the associations. Subgroup analysis was stratified by age, sex, and smoking status. Among 399,640 participants included, 1,342 HNC cases were identified. Multiple body composition measures showed protective effects against overall HNC, including body mass index (BMI, overweight: odds ratio [OR] = 0.76, p < 0.001; obese: OR = 0.67, p < 0.001) and fat percentage of the whole-body (OR = 0.78, p < 0.001), trunk (OR = 0.81, p < 0.001), legs (OR = 0.74, p < 0.001), and arms (OR = 0.80, p < 0.001). Fat-free mass (FFM) of the whole-body (OR = 0.98, p < 0.001), trunk (OR = 0.95, p < 0.001), legs (OR = 0.88, p < 0.001) and arms (OR = 0.70, p < 0.001) also demonstrated protective effects. The majority of these associations were significant in young males and ever-smokers, and the effects of FFM remained significant in old males and females. Additionally, site-specific body composition factors showed complex relationships with HNC subtypes and the prospective analysis confirmed most observed associations. BMI, fat percentage, and FFM were associated with lower risk of overall HNC, functioning mainly in males and old females.
Colibactin-producing Escherichia coli (pks+ E. coli) has been linked to colorectal cancer (CRC) through double-strand DNA breaks and distinct mutational signatures. Whether gut colonization by pks+ E. coli is persistent and has direct implications for whether it represents a modifiable risk factor in diagnosed patients. Based on whole-genome metagenomic sequencing data from 638 individuals it was found that pks+ E. coli was significantly more prevalent in CRC patients (34.7
Colorectal cancer (CRC) is rising across Southeast Asia (SEA) and leads by incidence in Brunei Darussalam. Machine learning (ML) applications for CRC in ASEAN remain endoscopy-based and diagnostic; no risk-stratification model using routinely collected non-invasive data has been reported. This review asks not which predictors associate with CRC risk, but which can serve as valid, equitable ML inputs in multiethnic SEA populations, where lifestyle variables may encode ethnic or religious identity rather than biological risk (proxy-mediated bias). This narrative review used qualitative synthesis rather than meta-analysis. PubMed, Scopus and Google Scholar were searched to 9 July 2026 (English language). Six predictor categories were appraised for Asian evidence strength, SEA-specific data, and proxy-bias risk: dietary and lifestyle factors, anthropometric measures, faecal immunochemical test (FIT), complete blood count (CBC), medication history, and emerging predictors; 22 studies were prioritised for appraisal. FIT and BMI (Asian threshold ≥23.0 kg/m2) showed the most consistent Asian evidence; FIT, with age, sex and CBC, carried the lowest proxy-bias risk. CBC-based ML performs well in Western populations (AUC 0.736–0.82) but is unvalidated in any Asian population. The central paradox: the most available predictor has the weakest regional evidence, whilst those with strongest Asian evidence are most entangled with identity. Lifestyle predictors require explicit proxy-bias management; medication-history evidence was inconsistent. A four-criterion framework (effect size, biological plausibility, proxy-bias risk, and routine data availability) and a two-pathway delivery model provide an evidence-grounded basis for equitable, risk-stratified CRC early detection in Brunei and comparable SEA systems.
Disorders of lipid metabolism are associated with tumor development, yet their relationship with clear cell renal cell carcinoma (ccRCC) remains insufficiently elucidated. This study aimed to investigate the association between lipid parameters and the risk of ccRCC and to evaluate their utility in constructing prediction models and assessing patient prognosis. Data were collected from 401 ccRCC patients (June 2013 to June 2023) and 518 participants from the China Health and Retirement Longitudinal Study (CHARLS, 2015). Logistic regression and restricted cubic spline (RCS) models were used to assess the association between lipid parameters and ccRCC. A novel lipid ratio, defined as the Low-Density to Non-Low-Density Lipoprotein Cholesterol Ratio (LNLR), was introduced. The incremental predictive value of LNLR in machine learning (ML) models was evaluated using integrated discrimination improvement (IDI), net reclassification improvement (NRI), and area under the curve (AUC). Model interpretability was assessed using Shapley Additive Explanations (SHAP). Kaplan–Meier survival curves and Cox regression analyses were performed to evaluate the prognostic significance of LNLR. LNLR was significantly associated with ccRCC risk [odds ratio (OR): 15.446; 95
