Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women
Sørensen, Thorkild I A.
Huerta, José María
Quirós, J. Ramón
Bueno-de-Mesquita, H. Bas
van der A, Daphne
May, Anne M.
Peeters, Petra H. M.
Boeing, HeinerNote: Order does not necessarily reflect citation order of authors.
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CitationSteffen, A., T. I. A. Sørensen, S. Knüppel, N. Travier, M. Sánchez, J. M. Huerta, J. R. Quirós, et al. 2013. “Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women.” PLoS ONE 8 (7): e67429. doi:10.1371/journal.pone.0067429. http://dx.doi.org/10.1371/journal.pone.0067429.
AbstractBackground: Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. Methods: We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). Results: Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63–0.65) in the derivation sample and 0.57 (95% CI = 0.56–0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥200 points were 9% and 96%, respectively. Conclusion: The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.
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