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  • DKFZ Publication Database  (3)
  • MELLITUS  (3)
  • 1
    Keywords: CANCER ; COHORT ; DISEASE ; prevention ; WOMEN ; nutrition ; LIFE-STYLE ; MELLITUS ; ENERGY-INTAKE ; FIBER INTAKE
    Abstract: The association of glycemic index (GI) and glycemic load (GL) with the risk of type 2 diabetes remains unclear. We investigated associations of dietary GI, GL, and digestible carbohydrate with incident type 2 diabetes. We performed a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition Study, including a random subcohort (n = 16,835) and incident type 2 diabetes cases (n = 12,403). The median follow-up time was 12 y. Baseline dietary intakes were assessed using country-specific dietary questionnaires. Country-specific HR were calculated and pooled using random effects meta-analysis. Dietary GI, GL, and digestible carbohydrate in the subcohort were (mean +/- SD) 56 +/- 4, 127 +/- 23, and 226 +/- 36 g/d, respectively. After adjustment for confounders, GI and GL were not associated with incident diabetes [HR highest vs. lowest quartile (HR(Q4)) for GI: 1.05 (95% CI = 0.96, 1.16); HR(Q4) for GL: 1.07 (95% CI = 0.95, 1.20)]. Digestible carbohydrate intake was not associated with incident diabetes [HR(Q4): 0.98 (95% CI = 0.86, 1.10)]. In additional analyses, we found that discrepancies in the GI value assignment to foods possibly explain differences in GI associations with diabetes within the same study population. In conclusion, an expansion of the GI tables and systematic GI value assignment to foods may be needed to improve the validity of GI values derived in such studies, after which GI associations may need reevaluation. Our study shows that digestible carbohydrate intake is not associated with diabetes risk and suggests that diabetes risk with high-GI and -GL diets may be more modest than initial studies suggested.
    Type of Publication: Journal article published
    PubMed ID: 23190759
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  • 2
    Keywords: RISK ; AGE ; WOMEN ; PROSPECTIVE COHORT ; PREVALENCE ; PROJECT ; LIFE-STYLE ; MELLITUS ; ALL-CAUSE MORTALITY ; PREMATURE
    Abstract: STUDY QUESTION: Do women who have diabetes before menopause have their menopause at an earlier age compared with women without diabetes? SUMMARY ANSWER: Although there was no overall association between diabetes and age at menopause, our study suggests that early-onset diabetes may accelerate menopause. WHAT IS KNOWN ALREADY: Today, more women of childbearing age are being diagnosed with diabetes, but little is known about the impact of diabetes on reproductive health. STUDY DESIGN, SIZE, DURATION: We investigated the impact of diabetes on age at natural menopause (ANM) in 258 898 women from the European Prospective Investigation into Cancer and Nutrition (EPIC), enrolled between 1992 and 2000. PARTICIPANTS/MATERIALS, SETTING, METHODS: Determinant and outcome information was obtained through questionnaires. Time-dependent Cox regression analyses were used to estimate the associations of diabetes and age at diabetes diagnosis with ANM, stratified by center and adjusted for age, smoking, reproductive and diabetes risk factors and with age from birth to menopause or censoring as the underlying time scale. MAIN RESULTS AND THE ROLE OF CHANCE: Overall, no association between diabetes and ANM was found (hazard ratio (HR) = 0.94; 95% confidence interval (CI) 0.89-1.01). However, women with diabetes before the age of 20 years had an earlier menopause (10-20 years: HR = 1.43; 95% CI 1.02-2.01, 〈10 years: HR = 1.59; 95% CI 1.03-2.43) compared with non-diabetic women, whereas women with diabetes at age 50 years and older had a later menopause (HR = 0.81; 95% CI 0.70-0.95). None of the other age groups were associated with ANM. LIMITATIONS, REASONS FOR CAUTION: Strengths of the study include the large sample size and the broad set of potential confounders measured. However, results may have been underestimated due to survival bias. We cannot be sure about the sequence of the events in women with a late age at diabetes, as both events then occur in a short period. We could not distinguish between type 1 and type 2 diabetes. WIDER IMPLICATIONS OF THE FINDINGS: Based on the literature, an accelerating effect of early-onset diabetes on ANM might be plausible. A delaying effect of late-onset diabetes on ANM has not been reported before, and is not in agreement with recent studies suggesting the opposite association. STUDY FUNDING/COMPETING INTERESTS: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Sante et de la Recherche Medicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ) and Federal Ministry of Education and Research (BMMF) (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucia, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skane and Vasterbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (UK). None of the authors reported a conflict of interest.
    Type of Publication: Journal article published
    PubMed ID: 25779698
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  • 3
    Keywords: FOLLOW-UP ; TOOL ; COHORT ; prevention ; VALIDITY ; MELLITUS ; METAANALYSIS ; EXTERNAL VALIDATION ; IDENTIFYING INDIVIDUALS ; LIFE-STYLE INTERVENTIONS
    Abstract: BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (〈60 years vs 〉/=60 years), BMI (〈25 kg/m(2)vs 〉/=25 kg/m(2)), and waist circumference (men 〈102 cm vs 〉/=102 cm; women 〈88 cm vs 〉/=88 cm). FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0.76 (95% CI 0.72-0.80) to 0.81 (0.77-0.84) overall, from 0.73 (0.70-0.76) to 0.79 (0.74-0.83) in men, and from 0.78 (0.74-0.82) to 0.81 (0.80-0.82) in women. We noted significant heterogeneity in discrimination (pheterogeneity〈0.0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity〉0.05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of 〈25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity. FUNDING: The European Union.
    Type of Publication: Journal article published
    PubMed ID: 24622666
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