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  • CagA+ Helicobacter pylori infection and gastric cancer risk in the EPIC-EURGAST study.

    11 December 2018

    Helicobacter pylori (H. pylori), atrophic gastritis, dietary and life-style factors have been associated with gastric cancer (GC). These factors have been evaluated in a large case-control study nested in the European Prospective Investigation into Cancer and Nutrition carried out in 9 countries, including the Mediterranean area. Participants, enrolled in 1992-1998, provided life-style and dietary information and a blood sample (360,000; mean follow-up: 6.1 years). For 233 GC cases diagnosed after enrolment and their 910 controls individually-matched by center, gender, age and blood donation date H. pylori antibodies (antilysate and antiCagA) and plasma Pepsinogen A (PGA) were measured by ELISA methods. Severe chronic atrophic gastritis (SCAG) was defined as PGA circulating levels <22 microg/l. Overall, in a conditional logistic regression analysis adjusted for education, smoke, weight and consumption of total vegetables, fruit, red and preserved meat, H. pylori seropositivity was associated with GC risk. Subjects showing only antibodies anti-H. pylori lysate, however, were not at increased risk, while those with antiCagA antibodies had a 3.4-fold increased risk. Overall, the odds ratio associated with SCAG was 3.3 (95% CI 2.2-5.2). According to site, the risk of noncardia GC associated with CagA seropositivity showed a further increase (OR 6.5; 95% CI 3.3-12.6); on the other hand, a ten-fold increased risk of cardia GC was associated with SCAG (OR 11.0; 95% CI 3.0-40.9). These results support the causal relationship between H. pylori CagA+ strains infection, and GC in these European populations even after taking into account dietary habits. This association was limited to distal GC, while serologically defined SCAG was strongly associated with cardia GC, thus suggesting a divergent risk pattern for these 2 sites.

  • Body size and risk of renal cell carcinoma in the European Prospective Investigation into Cancer and Nutrition (EPIC).

    11 December 2018

    Previous studies suggest that obesity is related to increased risk of renal cell carcinoma (RCC); however, only a few studies report on measures of central vs. peripheral adiposity. We examined the association between anthropometric measures, including waist and hip circumference and RCC risk among 348,550 men and women free of cancer at baseline from 8 countries of the European Prospective Investigation into Cancer and Nutrition (EPIC). During 6.0 years of follow-up we identified 287 incident cases of RCC. Relative risks were calculated using Cox regression, stratified by age and study center and adjusted for smoking status, education, alcohol consumption, physical activity, menopausal status, and hormone replacement therapy use. Among women, an increased risk of RCC was conferred by body weight (relative risk [RR] in highest vs. lowest quintile = 2.13; 95% confidence interval [CI] = 1.16-3.90; p-trend = 0.003), body mass index (BMI) (RR = 2.25; 95% CI = 1.14-4.44; p-trend = 0.009), and waist (RR = 1.67; 95% CI = 0.94-2.98; p-trend = 0.003) and hip circumference (RR = 2.30; 95% CI = 1.22-4.34; p-trend = 0.01); however, waist and hip circumference were no longer significant after controlling for body weight. Among men, hip circumference (RR = 0.44; 95% CI = 0.20-0.98; p-trend = 0.03) was related significantly to decreased RCC risk only after accounting for body weight. Height was not related significantly to RCC risk. Our findings suggest that obesity is related to increased risk of RCC irrespective of fat distribution among women, whereas low hip circumference is related to increased RCC risk among men. Our data give further credence to public health efforts aiming to reduce the prevalence of obesity to prevent RCC, in addition to other chronic diseases.

  • Folate intake and colorectal cancer risk: a meta-analytical approach.

    11 December 2018

    Adequate consumption of folate may reduce the risk of colorectal cancer. We performed a meta-analysis of 7 cohort and 9 case-control studies that examined the association between folate consumption and colorectal cancer risk. In cohort studies, the association between folate consumption and colorectal cancer risk was stronger for dietary folate (folate from foods alone; relative risk for high vs. low intake = 0.75; 95% CI = 0.64-0.89) than for total folate (folate from foods and supplements; relative risk for high vs. low intake = 0.95; 95% CI = 0.81-1.11) and there was no significant heterogeneity between studies. There was significant heterogeneity between case-control studies. These results offer some support for the hypothesis that folate has a small protective effect against colorectal cancer but confounding by other dietary factors cannot be ruled out.

  • Anti-Müllerian hormone and risk of ovarian cancer in nine cohorts.

    11 December 2018

    Animal and experimental data suggest that anti-Müllerian hormone (AMH) serves as a marker of ovarian reserve and inhibits the growth of ovarian tumors. However, few epidemiologic studies have examined the association between AMH and ovarian cancer risk. We conducted a nested case-control study of 302 ovarian cancer cases and 336 matched controls from nine cohorts. Prediagnostic blood samples of premenopausal women were assayed for AMH using a picoAMH enzyme-linked immunosorbent assay. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable-adjusted conditional logistic regression. AMH concentration was not associated with overall ovarian cancer risk. The multivariable-adjusted OR (95% CI), comparing the highest to the lowest quartile of AMH, was 0.99 (0.59-1.67) (Ptrend : 0.91). The association did not differ by age at blood draw or oral contraceptive use (all Pheterogeneity : ≥0.26). There also was no evidence for heterogeneity of risk for tumors defined by histologic developmental pathway, stage, and grade, and by age at diagnosis and time between blood draw and diagnosis (all Pheterogeneity : ≥0.39). In conclusion, this analysis of mostly late premenopausal women from nine cohorts does not support the hypothesized inverse association between prediagnostic circulating levels of AMH and risk of ovarian cancer.

  • Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank.

    11 December 2018

    UK Biobank is a UK-wide cohort of 502,655 people aged 40-69, recruited from National Health Service registrants between 2006-10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request.We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank.For prevalent diabetes, 0.001% and 0.002% of people classified as "diabetes unlikely" in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as "probable" type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.

  • Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

    11 December 2018

    OBJECTIVES:1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS:This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS:Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION:Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

  • Accuracy of Patient Self-Report of Stroke: A Systematic Review from the UK Biobank Stroke Outcomes Group.

    11 December 2018

    We performed a systematic review of the accuracy of patient self-report of stroke to inform approaches to ascertaining and confirming stroke cases in large prospective studies.We sought studies comparing patient self-report against a reference standard for stroke. We extracted data on survey method(s), response rates, participant characteristics, the reference standard used, and the positive predictive value (PPV) of self-report. Where possible we also calculated sensitivity, specificity, negative predictive value (NPV), and stroke prevalence. Study-level risk of bias was assessed using the Quality Assessment of Diagnostic Studies tool (QUADAS-2).From >1500 identified articles, we included 17 studies. Most asked patients to report a lifetime history of stroke but a few limited recall time to ≤5 years. Some included questions for transient ischaemic attack (TIA) or stroke synonyms. No study was free of risk of bias in the QUADAS-2 assessment, the most frequent causes of bias being incomplete reference standard data, absence of blinding of adjudicators to self-report status, and participant response rates (<80%). PPV of self-report ranged from 22-87% (17 studies), sensitivity from 36-98% (10 studies), specificity from 96-99.6% (10 studies), and NPV from 88.2-99.9% (10 studies). PPV increased with stroke prevalence as expected. Among six studies with available relevant data, if confirmed TIAs were considered to be true rather than false positive strokes, PPV of self-report was >75% in all but one study. It was not possible to assess the influence of recall time or of the question(s) asked on PPV or sensitivity.Characteristics of the study population strongly influence self-report accuracy. In population-based studies with low stroke prevalence, a large proportion of self-reported strokes may be false positives. Self-report is therefore unlikely to be helpful for identifying cases without subsequent confirmation, but may be useful for case ascertainment in combination with other data sources.

  • Ovarian cancer and body size: individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies.

    11 December 2018

    Only about half the studies that have collected information on the relevance of women's height and body mass index to their risk of developing ovarian cancer have published their results, and findings are inconsistent. Here, we bring together the worldwide evidence, published and unpublished, and describe these relationships.Individual data on 25,157 women with ovarian cancer and 81,311 women without ovarian cancer from 47 epidemiological studies were collected, checked, and analysed centrally. Adjusted relative risks of ovarian cancer were calculated, by height and by body mass index. Ovarian cancer risk increased significantly with height and with body mass index, except in studies using hospital controls. For other study designs, the relative risk of ovarian cancer per 5 cm increase in height was 1.07 (95% confidence interval [CI], 1.05-1.09; p<0.001); this relationship did not vary significantly by women's age, year of birth, education, age at menarche, parity, menopausal status, smoking, alcohol consumption, having had a hysterectomy, having first degree relatives with ovarian or breast cancer, use of oral contraceptives, or use of menopausal hormone therapy. For body mass index, there was significant heterogeneity (p<0.001) in the findings between ever-users and never-users of menopausal hormone therapy, but not by the 11 other factors listed above. The relative risk for ovarian cancer per 5 kg/m(2) increase in body mass index was 1.10 (95% CI, 1.07-1.13; p<0.001) in never-users and 0.95 (95% CI, 0.92-0.99; p=0.02) in ever-users of hormone therapy.Ovarian cancer is associated with height and, among never-users of hormone therapy, with body mass index. In high-income countries, both height and body mass index have been increasing in birth cohorts now developing the disease. If all other relevant factors had remained constant, then these increases in height and weight would be associated with a 3% increase in ovarian cancer incidence per decade. Please see later in the article for the Editors' Summary.

  • Dietary fiber, carbohydrate quality and quantity, and mortality risk of individuals with diabetes mellitus.

    11 December 2018

    Dietary fiber, carbohydrate quality and quantity are associated with mortality risk in the general population. Whether this is also the case among diabetes patients is unknown.To assess the associations of dietary fiber, glycemic load, glycemic index, carbohydrate, sugar, and starch intake with mortality risk in individuals with diabetes.This study was a prospective cohort study among 6,192 individuals with confirmed diabetes mellitus (mean age of 57.4 years, and median diabetes duration of 4.4 years at baseline) from the European Prospective Investigation into Cancer and Nutrition (EPIC). Dietary intake was assessed at baseline (1992-2000) with validated dietary questionnaires. Cox proportional hazards analysis was performed to estimate hazard ratios (HRs) for all-cause and cardiovascular mortality, while adjusting for CVD-related, diabetes-related, and nutritional factors.During a median follow-up of 9.2 y, 791 deaths were recorded, 306 due to CVD. Dietary fiber was inversely associated with all-cause mortality risk (adjusted HR per SD increase, 0.83 [95% CI, 0.75-0.91]) and CVD mortality risk (0.76[0.64-0.89]). No significant associations were observed for glycemic load, glycemic index, carbohydrate, sugar, or starch. Glycemic load (1.42[1.07-1.88]), carbohydrate (1.67[1.18-2.37]) and sugar intake (1.53[1.12-2.09]) were associated with an increased total mortality risk among normal weight individuals (BMI≤25 kg/m(2); 22% of study population) but not among overweight individuals (P interaction≤0.04). These associations became stronger after exclusion of energy misreporters.High fiber intake was associated with a decreased mortality risk. High glycemic load, carbohydrate and sugar intake were associated with an increased mortality risk in normal weight individuals with diabetes.