A body shape index (ABSI) is associated inversely with post-menopausal progesterone-receptor-negative breast cancer risk in a large European cohort

Background Associations of body shape with breast cancer risk, independent of body size, are unclear because waist and hip circumferences are correlated strongly positively with body mass index (BMI). Methods We evaluated body shape with the allometric “a body shape index” (ABSI) and hip index (HI), which compare waist and hip circumferences, correspondingly, among individuals with the same weight and height. We examined associations of ABSI, HI, and BMI (per one standard deviation increment) with breast cancer overall, and according to menopausal status at baseline, age at diagnosis, and oestrogen and progesterone receptor status (ER+/-PR+/-) in multivariable Cox proportional hazards models using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Results During a mean follow-up of 14.0 years, 9011 incident breast cancers were diagnosed among 218,276 women. Although there was little evidence for association of ABSI with breast cancer overall (hazard ratio HR = 0.984; 95% confidence interval: 0.961–1.007), we found borderline inverse associations for post-menopausal women (HR = 0.971; 0.942-1.000; n = 5268 cases) and breast cancers diagnosed at age ≥ 55 years (HR = 0.976; 0.951–1.002; n = 7043) and clear inverse associations for ER + PR- subtypes (HR = 0.894; 0.822–0.971; n = 726) and ER-PR- subtypes (HR = 0.906; 0.835–0.983 n = 759). There were no material associations with HI. BMI was associated strongly positively with breast cancer overall (HR = 1.074; 1.049–1.098), for post-menopausal women (HR = 1.117; 1.085–1.150), for cancers diagnosed at age ≥ 55 years (HR = 1.104; 1.076–1.132), and for ER + PR + subtypes (HR = 1.122; 1.080–1.165; n = 3101), but not for PR- subtypes. Conclusions In the EPIC cohort, abdominal obesity evaluated with ABSI was not associated with breast cancer risk overall but was associated inversely with the risk of post-menopausal PR- breast cancer. Our findings require validation in other cohorts and with a larger number of PR- breast cancer cases.World Health OrganizationDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonCancer Research UK 14136 C8221/A29017UK Research & Innovation (UKRI) Medical Research Council UK (MRC) 1000143 MR/M012190/

Sex differences in the associations of body size and body shape with platelets in the UK Biobank cohort

Background: Obesity is accompanied with low-grade inflammation and leucocytosis and increases the risk of venous thromboembolism. Associations with platelet count, however, are unclear because several studies have reported positive associations only in women. Associations with body shape are also unclear, because waist and hip circumferences reflect overall body size, as well as body shape, and are correlated strongly positively with body mass index (BMI). Methods: We evaluated body shape with the allometric body shape index (ABSI) and hip index (HI), which reflect waist and hip size among individuals with the same weight and height and are uncorrelated with BMI. We examined the associations of BMI, ABSI, and HI with platelet count, mean platelet volume (MPV), and platelet distribution width (PDW) in multivariable linear regression models for 125,435 UK Biobank women and 114,760 men. We compared men with women, post-menopausal with pre-menopausal women, and older (≥52 years) with younger (<52 years) men. Results: BMI was associated positively with platelet count in women, more strongly in pre-menopausal than in post-menopausal, and weakly positively in younger men but strongly inversely in older men. Associations of BMI with platelet count were shifted towards the inverse direction for daily alcohol consumption and current smoking, resulting in weaker positive associations in women and stronger inverse associations in men, compared to alcohol≤3 times/month and never smoking. BMI was associated inversely with MPV and PDW in pre-menopausal women but positively in post-menopausal women and in men. ABSI was associated positively with platelet count, similarly in women and men, while HI was associated weakly inversely only in women. ABSI was associated inversely and HI positively with MPV but not with PDW and only in women. Platelet count was correlated inversely with platelet size and positively with leucocyte counts, most strongly with neutrophils. Conclusions: Competing factors determine the associations of BMI with platelet count. Factors with sexually-dimorphic action (likely thrombopoietin, inflammatory cytokines, or cortisol), contribute to a positive association, more prominently in women than in men, while age-dependent factors (likely related to liver damage and fibrosis), contribute to an inverse association, more prominently in men than in women

A Body Shape Index (ABSI), hip index and risk of cancer in the UK Biobank cohort

Abdominal size is associated positively with the risk of some cancers but the influence of body mass index (BMI) and gluteofemoral size is unclear because waist and hip circumference are strongly correlated with BMI. We examined associations of 33 cancers with A Body Shape Index (ABSI) and hip index (HI), which are independent of BMI by design, and compared these with waist and hip circumference, using multivariable Cox proportional hazards models in UK Biobank. During a mean follow up of seven years, 14,682 incident cancers were ascertained in 200,289 men and 12,965 cancers in 230,326 women. In men, ABSI was associated positively with cancers of the head and neck (hazard ratio HR=1.14; 95% confidence interval 1.03-1.26 per one standard deviation increment), oesophagus (adenocarcinoma, HR=1.27; 1.12-1.44), gastric cardia (HR=1.31; 1.07-1.61), colon (HR=1.18; 1.10-1.26), rectum (HR=1.13; 1.04-1.22), lung (adenocarcinoma, HR=1.16; 1.03-1.30; squamous-cell carcinoma (SCC), HR=1.33; 1.17-1.52), and bladder (HR=1.15; 1.04-1.27), while HI was associated inversely with cancers of the oesophagus (adenocarcinoma, HR=0.89; 0.79-1.00), gastric cardia (HR=0.79; 0.65-0.96), colon (HR=0.92; 0.86-0.98), liver (HR=0.86; 0.75-0.98), and multiple myeloma (HR=0.86; 0.75-1.00). In women, ABSI was associated positively with cancers of the head and neck (HR=1.27; 1.10-1.48), oesophagus (SCC, HR=1.37; 1.07-1.76), colon (HR=1.08; 1.01-1.16), lung (adenocarcinoma, HR=1.17; 1.06-1.29; SCC, HR=1.40; 1.20-1.63; small-cell, HR=1.39; 1.14-1.69), kidney (clear-cell, HR=1.25; 1.03-1.50), and post-menopausal endometrium (HR=1.11; 1.02-1.20), while HI was associated inversely with skin SCC (HR=0.91; 0.83-0.99), post-menopausal kidney cancer (HR=0.77; 0.67-0.88) and post-menopausal melanoma (HR=0.90; 0.83-0.98). Unusually, ABSI was associated inversely with melanoma in men (HR=0.89; 0.82-0.96) and pre-menopausal women (HR=0.77; 0.65-0.91). Waist and hip circumference reflected associations with BMI, when examined individually, and provided biased risk estimates, when combined with BMI. In conclusion, preferential positive associations of ABSI or inverse of HI with several major cancers indicate an important role of factors determining body shape in cancer development

Associations of body shape index (ABSI) and hip index with liver, metabolic, and inflammatory biomarkers in the UK Biobank cohort

Associations of liver, metabolic, and inflammatory biomarkers in blood with body shape are unclear, because waist circumference (WC) and hip circumference (HC) are dependent on overall body size, resulting in bias. We have used the allometric “a body shape index” (ABSI = WC(mm)∗Weight(kg)-2/3∗Height(m)5/6) and hip index (HIwomen = HC(cm)∗Weight(kg)-0.482∗Height(cm)0.310, HImen = HC(cm)∗Weight(kg)-2/5∗Height(cm)1/5), which are independent of body mass index (BMI) by design, in multivariable linear regression models for 121,879 UK Biobank men and 135,559 women. Glucose, glycated haemoglobin (HbA1c), triglycerides, low-density-lipoprotein cholesterol, apolipoprotein-B, alanine aminotransferase (ALT), gamma-glutamyltransferase, and lymphocytes were associated positively with BMI and ABSI but inversely with HI. High-density-lipoprotein cholesterol and apolipoprotein-A1 were associated inversely with BMI and ABSI but positively with HI. Lipid-related biomarkers and ALT were associated only with HI in obese men. C-reactive protein, neutrophils, monocytes, and alkaline phosphatase were associated positively, while bilirubin was associated inversely, with BMI and ABSI but not with HI. Associations were consistent within the clinical reference ranges but were lost or changed direction for low or high biomarker levels. Our study confirms associations with waist and hip size, independent of BMI, for metabolic biomarkers but only with waist size for inflammatory biomarkers, suggesting different contribution of the mechanistic pathways related to body shape

Associations of body shape phenotypes with sex steroids and their binding proteins in the UK Biobank cohort

Associations of sex steroids and their binding proteins with body shape are unclear, because waist and hip circumference are correlated strongly with body size. We defined body shape using “a body shape index” (ABSI) and hip index (HI), which are independent of weight and height by design, and examined associations in multivariable generalised linear models for the UK Biobank cohort (179,902 men, 207,444 women). Total testosterone was associated inversely with ABSI, especially in men. Free testosterone was lowest for large-ABSI-large-HI (“wide”) and highest for small-ABSI-small-HI (“slim”) in men, but lowest for small-ABSI-large-HI (“pear”) and highest for large-ABSI-small-HI (“apple”) in women. Oestradiol was associated inversely with ABSI in obese pre-menopausal women but positively with HI in obese men and post-menopausal women not using hormone replacement therapy. Sex-hormone binding globulin (SHBG) was associated inversely with ABSI but positively with HI and was lowest for “apple” and highest for “pear” phenotype in both sexes. Albumin was associated inversely with HI in women, but matched the pattern of free testosterone in obese men (lowest for “wide”, highest for “slim” phenotype). In conclusion, sex steroids and their binding proteins are associated with body shape, including hip as well as waist size, independent of body size

Interactions of platelets with obesity in relation to lung cancer risk in the UK Biobank cohort

Background: Platelet count (PLT) is associated positively with lung cancer risk but has a more complex association with body mass index (BMI), positive only in women (mainly never smokers) and inverse in men (mainly ever smokers), raising the question whether platelets interact with obesity in relation to lung cancer risk. Prospective associations of platelet size (an index of platelet maturity and activity) with lung cancer risk are unclear. Methods: We examined the associations of PLT, mean platelet volume (MPV), and platelet distribution width (PDW) (each individually, per one standard deviation increase) with lung cancer risk in UK Biobank men and women using multivariable Cox proportional hazards models adjusted for BMI and covariates. We calculated Relative Excess Risk from Interaction (RERI) with obese (BMI ≥30 kg/m2), dichotomising platelet parameters at ≥median (sex-specific), and multiplicative interactions with BMI (continuous scale). We examined heterogeneity according to smoking status (never, former, current smoker) and antiaggregant/anticoagulant use (no/yes). Results: During a mean follow-up of 10.4 years, 1620 lung cancers were ascertained in 192,355 men and 1495 lung cancers in 218,761 women. PLT was associated positively with lung cancer risk in men (hazard ratio HR=1.14; 95% confidence interval (CI): 1.09–1.20) and women (HR=1.09; 95%CI: 1.03–1.15) but interacted inversely with BMI only in men (RERI=-0.53; 95%CI: -0.80 to -0.26 for high-PLT-obese; HR=0.92; 95%CI=0.88–0.96 for PLTBMI). Only in men, MPV was associated inversely with lung cancer risk (HR=0.95; 95%CI: 0.90–0.99) and interacted positively with BMI (RERI=0.27; 95%CI=0.09–0.45 for high-MPV-obese; HR=1.08; 95%CI=1.04–1.13 for MPVBMI), while PDW was associated positively (HR=1.05; 95%CI: 1.00–1.10), with no evidence for interactions. The associations with PLT were consistent by smoking status, but MPV was associated inversely only in current smokers and PDW positively only in never/former smokers. The interactions with BMI were retained for at least eight years of follow-up and were consistent by smoking status but were attenuated in antiaggregant/anticoagulant users. Conclusions: In men, PLT was associated positively and MPV inversely with lung cancer risk and these associations appeared hindered by obesity. In women, only PLT was associated positively, with little evidence for interaction with obesity

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Comprehensive 2‐dimensional gas chromatography fast quadrupole mass spectrometry (GC × GC‐qMS) for urinary steroid profiling: Mass spectral characteristics with chemical ionization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89453/1/dta380.pd

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation