Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

Kengne, André Pascal; Beulens, Joline W.J.; Peelen, Linda M.; Moons, Karel G.M.; Schouw, Yvonne T. van der; Schulze, Matthias B.; Spijkerman, Annemieke M.W.; Griffin, Simon J.; Grobbee, Diederick E.; Palla, Luigi; Tormo, María José; Arriola, Larraitz; Barengo, Noël C.; Barricarte, Aurelio; Bonet, Catalina; Clavel‐Chapelon, Françoise; Dartois, Laureen; Fagherazzi, Guy; Franks, Paul W.; Huerta, José María; Kaaks, Rudolf; Key, Timothy J.; Khaw, Kay Tee; Li, Kuanrong; Mühlenbruch, Kristin; Nilsson, Peter M.; Overvad, Kim; Overvad, Thure Filskov; Palli, Domenico; Panico, Salvatore; Quirós, J. Ramón; Rolandsson, Olov; Roswall, Nina; Sacerdote, Carlotta; Sánchez, María José; Slimani, Nadia; Tagliabue, Giovanna; Tjønneland, Anne; Tumino, Rosario; A, Daphne L. van der; Forouhi, Nita G.; Sharp, Stephen J.; Langenberg, Claudia; Riboli, Elio; Wareham, Nicholas J.

doi:10.1016/s2213-8587(13)70103-7

Cited by 148 publications

(155 citation statements)

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“…Finally, primary prevention should be addressed at high-risk subjects when they are still in a normoglycemic state, and interventions should prevent their transition from normoglycemia to IFG and IGT. For these reasons, inexpensive, easily administered, cost-effective, and validated non-invasive risk scores (based on non-laboratory clinical variables) have been made available [41,42]. These non-invasive scores identify a high risk of T2DM (C statistics C 0.8) with acceptable to good discriminatory power across diverse settings in Europe [42][43][44][45][46][47].…”

Section: Ethnic Origin and The Risk Of Type 2 Diabetesmentioning

confidence: 99%

“…For these reasons, inexpensive, easily administered, cost-effective, and validated non-invasive risk scores (based on non-laboratory clinical variables) have been made available [41,42]. These non-invasive scores identify a high risk of T2DM (C statistics C 0.8) with acceptable to good discriminatory power across diverse settings in Europe [42][43][44][45][46][47]. Five non-invasive scores, the ARIC 2005, ARIC 2009, AUSDRISK, DPoRT, and QD Score, include the ethnic origin in the model [48][49][50][51][52].…”

Section: Ethnic Origin and The Risk Of Type 2 Diabetesmentioning

confidence: 99%

“…A study performed in the USA compared three risk scores in a multiethnic population living in the same country [53]. In Europe, non-invasive tools were validated in different settings, although none of the diverse setting included ethic minority groups [42].…”

Section: Ethnic Origin and The Risk Of Type 2 Diabetesmentioning

confidence: 99%

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Lifestyle interventions in preventing new type 2 diabetes in Asian populations

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et al. 2015

Intern Emerg Med

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The aim of this study was to review current evidence on interventional studies aimed at the prevention of type 2 diabetes in Asian population with lifestyle interventions. Prevalence of type 2 diabetes sharply increased in most Asian countries during the last decades. This issue has now also relevant implication for Europe where different surveys are also consistently revealing an higher prevalence of type 2 diabetes and other and major CVD risk factors among subjects originating from Asian Countries than in the native population. Nutrition and lifestyle transition seem to play a role in disclosing the predisposition for the development of type 2 diabetes and great interest is now shown toward the possibility to intervene with lifestyle intervention on at risk populations. A meta-analysis of Randomized Controlled Trials showed that lifestyle interventions are highly effective also in the Asian population. All studies were, however, conducted with an individual approach based on the identification of high-risk individuals. When ethnic minority groups have to be addressed, an approach directed to the community rather than to the individual might, however, be more effective. This review reinforces the importance for policy-makers to consider the involvement of the whole community of minority immigrant groups with lifestyle intervention programs.

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Section: Ethnic Origin and The Risk Of Type 2 Diabetesmentioning

confidence: 99%

Section: Ethnic Origin and The Risk Of Type 2 Diabetesmentioning

confidence: 99%

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Lifestyle interventions in preventing new type 2 diabetes in Asian populations

Modesti

Galanti

Calà³

et al. 2015

Intern Emerg Med

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“…The evidence shows that models are generally acceptable to identify high-risk individuals for T2D over 10 years in white middle-aged adults (2)(3)(4). Moreover, all prediction models were demonstrated to provide better prediction in people younger than 60 years (4). The worldwide epidemic is shifting prevalence of T2D to younger ages (5); therefore, the burden of T2D will rise as the duration of diabetes is elongated.…”

mentioning

confidence: 99%

“…If the picture still was not clear, one would proceed with an assessment of additional biochemical tests, typically from blood or urine collected during the same or a next visit. This differs from how prediction models are currently being envisioned for clinical utility (2,4). Current evaluation is being done as if all data are available at the same time (6).…”

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Comment on Vassy et al. Polygenic Type 2 Diabetes Prediction at the Limit of Common Variant Detection. Diabetes 2014;63:2172–2182

2014

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Vassy et al.(1) present data from three young adult cohorts to investigate additive predictive values of recently discovered genetic variants for type 2 diabetes (T2D). In the past 20 years, many prediction models have been developed for T2D risk over a wide range of populations (2). More accurate estimation of T2D risk is critical to allocating resources and planning health policies. So far, there is little evidence that any of epidemiologic models with or without genetic markers can accurately estimate T2D risk in different populations.To bridge the gap between the development and the utility of prediction models, recent studies have validated and compared a variety of models in prospective population-based cohorts (2,3). The evidence shows that models are generally acceptable to identify high-risk individuals for T2D over 10 years in white middle-aged adults (2-4). Moreover, all prediction models were demonstrated to provide better prediction in people younger than 60 years (4). The worldwide epidemic is shifting prevalence of T2D to younger ages (5); therefore, the burden of T2D will rise as the duration of diabetes is elongated. Current modeling approaches do not account for potential variation of the relationship between risk factors and the disease with age. Therefore, younger individuals are commonly classified as low-risk subgroups in the short-term horizon, but of course several of these subjects would be at high-risk if one considers it from a longterm perspective. Vassy et al.(1) analyzed this important aspect in which the use of genetic data led to a greater improvement in prediction among young adults. Likewise, all prediction models are based on incorporation of baseline data and do not include more information than that of one single time point from which prediction started. Consequently, the contribution of risk factors to diabetes risk and potential interactions with genetic factors has been assumed to be constant over time. It merits investigation of what extent existing models in combination with genetic markers predict risk of T2D across the life span.Yet another aspect is that the clinical utility of prediction models depends on a number of factors and has some challenges (4). For instance, if a physician would like to predict an individual's T2D risk, gathering personal health data such as age, sex, smoking history, and family history of diabetes only requires a small interview. Assessment of (central) obesity, blood pressure, and routine biochemical testing (e.g., glucose and cholesterol) already requires more effort. Testing of additional biologic and genetic markers is even further away. In clinical prediction, one would typically first come to an impression based on the interview and the simple physical measurements. If the picture still was not clear, one would proceed with an assessment of additional biochemical tests, typically from blood or urine collected during the same or a next visit. This differs from how prediction models are currently being envisioned for clinical utility (2...

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Associations Between Migraine and Type 2 Diabetes in Women

et al. 2019

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IMPORTANCE Little is known about the associations between migraine and type 2 diabetes and the temporality of the association between these 2 diseases. OBJECTIVE To evaluate the association between migraine and type 2 diabetes incidence as well as the evolution of the prevalence of active migraine before and after type 2 diabetes diagnosis. DESIGN, SETTING, AND PARTICIPANTS We used data from the E3N cohort study, a French prospective population-based study initiated in 1990 on a cohort of women born between 1925 and 1950. The E3N study participants are insured by a health insurance plan that mostly covers teachers. From the eligible women in the E3N study, we included those who completed the 2002 follow-up questionnaire with information available on migraine. We then excluded prevalent cases of type 2 diabetes, leaving a final sample of women who were followed up between 2004 and 2014. All potential occurrences of type 2 diabetes were identified through a drug reimbursement database. Statistical analyses were performed in March 2018. EXPOSURES Self-reported migraine occurrence. MAIN OUTCOMES AND MEASURES Pharmacologically treated type 2 diabetes. RESULTS From the 98 995 women in the study, 76 403 women completed the 2002 follow-up survey. Of these, 2156 were excluded because they had type 2 diabetes, leaving 74 247 women. Participants had a mean (SD) age of 61 (6) years at baseline, and all were free of type 2 diabetes. During 10 years of follow-up, 2372 incident type 2 diabetes cases occurred. A lower risk of type 2 diabetes was observed for women with active migraine compared with women with no migraine history (univariate hazard ratio, 0.80 [95% CI, 0.67-0.96], multivariable-adjusted hazard ratio, 0.70 [95% CI, 0.58-0.85]). We also observed a linear decrease in active migraine prevalence from 22% (95% CI, 16%-27%) to 11% (95% CI, 10%-12%) during the 24 years prior to diabetes diagnosis, after adjustment for potential type 2 diabetes risk factors. A plateau of migraine prevalence around 11% was then observed for 22 years after diagnosis. CONCLUSIONS AND RELEVANCE We observed a lower risk of developing type 2 diabetes for women with active migraine and a decrease in active migraine prevalence prior to diabetes diagnosis. Further targeted research should focus on understanding the mechanisms involved in explaining these findings.

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Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

Cited by 148 publications

References 41 publications

Lifestyle interventions in preventing new type 2 diabetes in Asian populations

Lifestyle interventions in preventing new type 2 diabetes in Asian populations

Comment on Vassy et al. Polygenic Type 2 Diabetes Prediction at the Limit of Common Variant Detection. Diabetes 2014;63:2172–2182

Associations Between Migraine and Type 2 Diabetes in Women

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