Background: 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 degrees 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