title: Classifying Gestational Weight Gain Trajectories Using the SITAR Growth Model.
abstract: Gestational weight gain is often characterized by the total amount of weight gained during pregnancy, however, the pattern of gain may be an important determinant of health outcomes. The SITAR (Super Imposition by Translation And Rotation) model has been used to describe childhood growth trajectories and has appeal because of the biological interpretability of its parameters. The objective of this study was to determine the feasibility of applying this model to gestational weight gain trajectories.
  
  
  
  The study cohort included 3470 normal-weight, overweight, and obese women delivering at Magee-Womens Hospital in Pittsburgh, Pennsylvania, 1998 to 2010. We applied the SITAR model, a non-linear mixed effects model, to serial prenatal weight gain measurements in each pre-pregnancy body mass index (BMI) category. We fit models of varying complexity, and chose the best-fitting model to describe the pattern of weight gain (by its absolute amount, timing, and acceleration) for each BMI group.
  
  
  
  The most complex SITAR models failed to converge, but reduced models could successfully be fit by specifying fewer random effects and simplifying the modelling of gestational age. Best-fitting models for each BMI group explained between 95% and 97% of the variation in weight gain trajectories. Peak rates of weight gain were reached between the 20th and 22nd weeks, and were higher for normal and overweight women (0.59 kg/week and 0.57 kg/week, respectively) than obese women (0.46 kg/week).
  
  
  
  Following some modifications, the SITAR model can be used to characterize pregnancy weight gain patterns.
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
authors.forename: Corinne A
authors.identifier: 0000-0001-9517-0739
authors.initials: CA
authors.lastname: Riddell
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Platt
authors.affiliation: Departments of Epidemiology and of Obstetrics, Gynecology, and Reproductive Sciences, Graduate School of Public Health and School of Medicine, University of Pittsburgh, Magee-Womens Research Institute, Pittsburgh, PA.
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Bodnar
authors.affiliation: Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Hutcheon
mesh_terms: D015992:Body Mass Index; D015331:Cohort Studies; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D009765:Obesity / Q000503:physiopathology; D050177:Overweight / Q000503:physiopathology*; D010414:Pennsylvania; D011247:Pregnancy; D011264:Pregnancy Trimesters; D015430:Weight Gain / Q000502:physiology*
keywords: gestational weight gain; growth modelling; pregnancy