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This brief is about weight change among veterans before, during, and after discharge from the military. In policy and practice, physicians should discuss strategies to prevent unhealthy weight gain with veteran patients; the VA should better tailor its MOVE! weight management program to younger veterans. Suggestions for future research include looking at the reasons behind weight gain in discharged servicemembers, analyzing the strategies used by members of the Reserve/National Guard to limit weight gain, and using more samples of younger veterans to generalize results.

Objective: To examine factors associated with selfreported unintended weight change in (ex-) military personnel of the UK Armed Forces. Design: A cohort study whereby a self-report questionnaire was administered at baseline (2003–2005) and follow-up (2007–2009). Participants: A total of 6352 former and serving military personnel of the UK Armed Forces. Setting: United Kingdom. Main outcome measures: Personnel were asked about socio-demographic, military and (mental) health characteristics, including screening measures for common mental health disorders. Further, participants were asked to report unintended weight fluctuations (none, than 10 lbs in the past month). Multinomial regression analyses were used to identify factors associated with self-reported weight change at follow-up. Results: Out of the 6352 former and serving military personnel, 123 (2.1%) reported unintended weight loss and 577 (9.0%) reported unintended weight gain in the past month. At follow-up, multivariable analyses indicated that unintended weight loss or weight gain was more likely to be reported by those who screened positive for mental health problems and those reporting weight changes at baseline. Reported weight loss was more common among smokers than non-smokers, whereas reported weight gain at follow-up was associated with having a higher BMI at baseline. Conclusions: At follow-up, self-reported unintended weight changes in former and serving military personnel of the UK Armed Forces were found to be associated with mental health problems, body mass index

IntroductionDepression has consistently been associated with an increased risk of diabetes in recent meta-analyses. However, depression is a highly heterogeneous construct and people with specific symptoms of depression, such as weight gain and increased sleep, may be at a higher risk of diabetes.
Objectives and ApproachThis work will compare incident diabetes in Ontario adults with recent depressive episodes that included symptoms of weight gain, weight loss, or no weight change and in those with no recent depressive episodes. Participants will be drawn from several waves of the Canadian Community Health Survey and the National Population Health Survey. Past 12-month depressive episodes and weight change during most recent or worst episodes was measured using the CIDI/CIDI short form. Time to incident diabetes will be ascertained through linkage with the Ontario Diabetes Database. Cox proportional hazards regression will assess diabetes incidence by depression and weight change characteristics.
ResultsThis study will include 106 084 Ontario adults who participated in the Canadian Community Health Survey (2000/2001, 2002, 2003, 2012) and the National Population Health Survey (1996). Follow-up time will range from 4 to 19 years (until March 2017). Study covariates will include demographic and lifestyle factors, comorbidities, and health care use and will be extracted from the surveys above and from administrative health data. The dataset for this study is currently being prepared by the Institute for Clinical Evaluative Sciences (ICES) and the findings of this analysis will be presented at this conference.
Conclusion/ImplicationsThe results of this work will provide insight into who, among those with depression, is at highest risk of new-onset diabetes. These results will be relevant to the development of both personalized and population-level diabetes screening and prevention strategies.

Weight-related beliefs, attitudes, and patterns of weight change were analyzed for 144 registered nurses followed for one year in a self-help smoking cessation study. Smoking history and outcome status, physical descriptors, weight orientation, and use of self-help smoking cessation materials were examined in relation to weight change. A multiple regression analysis which included all subjects yielded three predictors of weight gain: continuous abstainer smoking status, lower body mass index, and greater fear of weight gain. A logistic regression confirmed the influence of smoking status at outcome on weight change. Abstinence was associated with weight gain; continuous abstainers were more likely to gain weight (88.2%) than noncontinuous abstainers (50%) and never quitters (35.9%). Weight variables were found to be interrelated, forming an "eating orientation" linked to smoking behavior.

The association of dietary energy density (ED) and overweight is not clear in the literature. Our aim was to study in 4259 of the PREDIMED trial whether an increase in dietary ED based on a higher adherence to a Mediterranean dietary pattern was associated with 3-year weight gain. A validated 137-item food-frequency questionnaire was administered. Multivariable-adjusted models were used to analyze the association between 3-year ED change and the subsequent 3-year body weight change. The most important weight reduction after 3-year follow-up was observed in the two lowest quintiles and the highest quintile of ED change. The highest ED increase was characterized by an increased intake of extra virgin olive oil (EVOO) and nuts and a decreased intake of other oils, vegetable and fruit consumption (p < .001). In conclusion, increased 3-year ED in the PREDIMED study, associated with a higher EVOO and nuts consumption, was not associated with weight gain. ; The PREDIMED trial was supported by the Instituto de Salud Carlos III (ISCIII), the official funding agency for Biomedical Research of the Spanish Government, through grants provided to research networks specifically developed for the trial: RTIC G03/140, CIBERobn and RTIC RD06/0045. The authors also acknowledge grants received from Centro Nacional de Investigaciones Cardiovasculares CNIC 06/2007, Fondo de Investigación Sanitaria – Fondo Europeo de Desarrollo Regional (PI04-2239, PI05/2584, CP06/00100, PI07/0240, PI07/1138, PI07/0954, PI07/0473; PI10/01407, PI11/01647, PI11/02505, PI13/00615; PI13/01090, PI14/01668, PI14/01798, and PI14/01764), Ministerio de Ciencia e Innovación (AGL-2009-13906-C02, AGL2010-22319-C03), Fundación Mapfre 2010, Public Health Division of the Department of Health of the Autonomous Government of Catalonia and Generalitat Valenciana (ACOMP06109, CS2010-AP-111 and CS2011-AP-042; GVACOMP2010-181 and GVACOMP2011-151), and a joint contract (CES09/030) with the ISCIII and the Health Department of the Catalan Government (Generalitat de Catalunya). The roles of the sponsors are as follows: the supplemental foods used in the study were generously donated by Patrimonio Comunal Olivarero and Hojiblanca from Spain (extra-virgin olive oil); the California Walnut Commission from Sacramento, CA (walnuts); and Borges S.A. (almonds) and La Morella Nuts (hazelnuts), both from Reus, Spain. However, the sponsors played no role in the design, collection, analysis, or interpretation of the data or in the decision to submit the manuscript for publication. E. Ros received research grants from the California Walnut Commission (Sacramento, CA) and is a non-paid member of its Scientific Advisory Committee. J. Salas-Salvadó received research grants from the International Nut Council (Reus, Spain) and is a non-paid member of its Scientific Advisory Committee. R. Estruch reports serving on the board of and receiving lecture fees from the Research Foundation on Wine and Nutrition (FIVIN); serving on the boards of the Beer and Health Foundation and the European Foundation for Alcohol Research (ERAB); receiving lecture fees from Cerveceros de España. R.M. Lamuela-Raventos reports serving on the board of and receiving lecture fees from FIVIN; receiving lecture fees from Cerveceros de España; and receiving lecture fees and travel support from PepsiCo. L. Serra-Majem reports serving on the boards of the Mediterranean Diet Foundation and the Beer and Health Foundation. X. Pinto´ reports serving on the board of Omegafort; receiving lecture fees from Danone; receiving payment for the development of educational presentations from Menarini; and receiving grant support through his institution from Kowa Life Science Foundation, Unilever, and Karo Bio.

Abstract. The "Freshman-15" refers to an expected average weight gain of 15 pounds during the first year at college in US students. Although an overall weight gain during this period can be observed, most studies found that students gain less than 15 pounds on average. Studies in countries other than the US, however, are scarce. In the current study, 120 female freshmen at a German university were tested at the start of the first semester and again at the start of the second semester (after approximately 6 months). Body mass index (BMI) did not differ between measurements, but participants had 0.2% more body fat at the second measurement. Participants with higher BMI at the first measurement lost weight and participants with higher weight suppression (i.e., the difference between an individual's highest previous weight and current weight) at the first measurement gained weight. Participants who reported to exercise regularly at the first measurement gained weight, but this effect was driven by those who reduced their amount of physical exercise during the first semester. Dietary habits and eating styles at the first measurement were not associated with weight change. To conclude, no evidence was found for an overall weight gain during the first semester in female, German students. Furthermore, weight change was exclusively predicted by BMI, weight suppression, and regular exercise, while eating behaviors were unrelated to weight change. Thus, it appears that variables influencing energy expenditure are more robust predictors of future weight gain than variables influencing energy intake in female freshmen.

Aims: To describe weight-change pathways in patients with type 2 diabetes (T2D) and associated healthcare costs using repeated BMI measurements and healthcare utilization data. Methods: Patients with newly-diagnosed T2D with body mass index (BMI, kg/m(2)) at diagnosis and subsequent measures at year 1-3 were identified. Based on three-year BMI change, patients were assigned to one of 27 BMI change pathways defined by annual BMI change: BMI NE arrow (>= 1 BMI unit increase), BMI -> (= 1 BMI unit decrease). Mean annual and three-year cumulative healthcare costs were estimated for each pathway by combining Swedish unit costs with resource use from primary care and national patient registers. Results: Cohort consisted of 15,819 patients; 44% women, mean age of 61 years, HbA1c of 6.7% (50 mmol/mol), BMI of 30.6 kg/m(2). Most common BMI pathways (mean costs): BMI ->->-> ((sic)5,311), BMI SE arrow ->->((sic)5,461), and BMI ->->SE arrow((sic)6,281). General trends: BMI)->->-> linked to lowest, BMI NE arrow ->NE arrow linked to highest costs. Conclusion: In patients with newly -diagnosed T2D, weight stability was the most common BMI change pattern over 3 years and associated with lowest healthcare costs. Relationship between weight change and healthcare costs appears complex warranting further investigation.