scholarly journals Predictors of Long-Term Weight Loss in Adults With Modest Initial Weight Loss, by Sex and Race

Obesity ◽  
2012 ◽  
Vol 20 (9) ◽  
pp. 1820-1828 ◽  
Author(s):  
Laura P. Svetkey ◽  
Jamy D. Ard ◽  
Victor J. Stevens ◽  
Catherine M. Loria ◽  
Deb Y. Young ◽  
...  
Keyword(s):  
Weight Loss ◽  
Initial Weight ◽  
Initial Weight Loss

scholarly journals MON-LB101 5-Year Data of an Aggressive Pharmacological Approach to Moderate and Morbid Obesity: Is Prevention of Bariatric Surgery Feasible in the Long Run?

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Flavio Cadegiani
Keyword(s):  
Bariatric Surgery ◽  
Morbid Obesity ◽  
Weight Loss ◽  
Body Weight ◽  
Weight Regain ◽  
Morbidly Obese ◽  
Biochemical Profile ◽  
Initial Weight ◽  
Initial Weight Loss

Abstract Background: Maintenance of weight loss in patients that undergo weight loss interventions is highly challenging, irrespective of the type of approach to obesity (whether surgical, pharmacological, or non-pharmacological). We proposed a protocol of an aggressive clinical treatment for obesity aiming to prevent the need of bariatric surgery, in patients unwilling to undergo this procedure, by proposing a protocol that included the combination of different anti-obesity medications and non-pharmacological modalities, for longer duration, and with an active approach to prevent weight regain. Our initial 2-year data showed that 93% (40 of 43 patients) with moderate and morbid obesity were able to avoid the need of bariatric surgery, with concomitant improvements of the biochemical profile. However, whether these patients would maintain their successful rates after five years was uncertain. Our objective is to describe the efficacy and safety of a long term (5-year data) pharmacological and multi-modal treatment for moderate and severe obesity. Methods: The 40 patients that were successful in the two-year approach in our obesity center (Corpometria Institute, Brasilia, DF, Brazil) were enrolled. A long-term anti-obesity protocol was employed, with continuous or intermittent use of anti-obesity drugs, trimestral body composition analysis, psychotherapy, visit to a nutritionist every four months, and both resistance and endurance exercises at least four times a week. Body weight (BW), total weight excess (TWE), body fat, markers of lipid and glucose metabolism, liver function, and inflammation were analyzed. Subjects that dropped out were considered as weight regain. Therapeutic success for the 5-year follow-up included as the maintenance of >20% loss of the initial BW loss, and no weight regain (or < 20% of the initial weight loss). Results: A total of 27 patients (67.5%) were able to maintain the body weight, seven dropped out, and six regained more than 20% of the initial weight loss. Of these, 21 (77.8%) had significant further increase of muscle mass and decrease of fat loss, while 17 (63.0%) had further weight loss (p < 0.05), compared to the 2-year data. Improvements on the biochemical profile persisted in all 27 patients, and had significant further improvements in 24 (88.9%) of these patients. Conclusion: The risk of weight regain five years after a weight loss treatment for obesity was significantly lower compared to previous literature, and comparable to the long-term outcomes of bariatric procedures. An aggressive, structured, and long-term clinical weight loss approach has been shown to be feasible, even for morbidly obese patients.

553: Pregnancy reduces initial weight loss after bariatric surgery but does not influence long-term outcomes

2014 ◽  
Vol 210 (1) ◽  
pp. S273
Author(s):  
Thu Quyên Pham ◽  
Philippe Deruelle ◽  
Marie Pigeyre ◽  
Eric Loridan ◽  
Julien Couster ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Weight Loss ◽  
Initial Weight ◽  
Long Term Outcomes ◽  
Initial Weight Loss

scholarly journals Improvements in Long-Term Appetite-Regulating Hormones in Response to a Combined Lifestyle Intervention for Obesity

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A489-A490
Author(s):  
Susanne Kuckuck
Keyword(s):  
Weight Loss ◽  
Lifestyle Intervention ◽  
Weight Regain ◽  
Initial Weight ◽  
Short Term ◽  
Initial Weight Loss ◽  
Hormone Levels ◽  
Adiposity Signals ◽  
Term Energy

Abstract Altered levels of hormonal appetite regulators have been observed in obesity (BMI ≥ 30.0 kg/m2), most prominently increases of insulin and leptin (indicating resistance) as well as decreases of adiponectin - all of which are long-term energy regulators and adiposity signals. Disrupted signaling of these hormones may have detrimental effects on metabolism, but may also promote weight gain. Weight loss is often accompanied by normalizations of long-term adiposity signals, but findings concerning short-term appetite regulators after weight loss vary across interventions (e.g. very low calorie diets vs. exercise). Moreover, it is debated whether such weight-loss-induced hormonal changes may reflect a disposition for weight regain. Here, we investigated changes of long- and short-term appetite signals in response to an intensive 75-week combined lifestyle intervention (CLI) comprising a normocaloric healthy diet, physical activity and psychotherapy to promote improved long-term weight management. For 39 patients, data on fasting serum levels of appetite-regulating hormones (leptin, insulin, adiponectin, GIP, PP, PYY, CCK, FGF21) were available. Hormone levels were correlated to BMI at baseline (T0) and compared across three time points: T0, T1 (after 10 weeks; initial weight loss) and T2 (after 75 weeks; weight loss maintenance). T0-T1 hormone changes were correlated to BMI changes between T1 and T2 to investigate whether hormonal alterations during initial weight loss are associated with weight regain. At T0, hormone levels were not associated with BMI. BMI decreased significantly from T0 (40.13 kg/m2 ± 5.7) to T1 (38.2 ± 5.4, p < .001) which was maintained at T2 (38.2 kg/m2 ± 5.9, p < .001). There were no significant changes in GIP, PP, PYY, CCK and FGF21. Leptin decreased from T0 (44.9 ng/nl ± 15.3) to T1 (33 ng/nl ± 14.8, p < .001) and T2 (38.6 ng/nl ± 16.0, p < .01), just like insulin which was significantly decreased at T1 (123 pmol/l ± 65, p < .05) and T2 (128 pmol/l ± 64, p < .05) compared to T0 (160 pmol/l ± 80). Adiponectin did not change between T0 (3.36 ug/ml ± 2.1) and T1 (3.2 ug/ml ± 2.1), but was increased at T2 (3.7 ug/ml ± 2.9, p < .01) compared to T1. T0-T2 BMI decrease correlated positively with T0-T2 decreases in leptin (r = .667, p < .001), insulin (rho = .535, p < .001) and increases of adiponectin (r = .412, p < .01), but no other hormone. T0-T1 hormone changes did not predict T1-T2 BMI changes. Thus, a 75-week CLI was associated with beneficial changes in the long-term energy regulators adiponectin, leptin and insulin, but no changes in short-term appetite-regulating hormones were observed despite significant weight loss. Initial changes in appetite-regulating hormones were not associated with subsequent weight regain. Overall, our data suggest that a CLI does not lead to adverse changes in appetite regulation, but rather long-term improvements such as e.g. increased leptin and insulin sensitivity.

Abstract MP003: Development and Validation of a Prediction Model of Weight Loss Maintenance or Regain at 4 Years in the Look AHEAD Trial

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Samantha E Berger ◽  
Gordon S Huggins ◽  
Jeanne M McCaffery ◽  
Alice H Lichtenstein
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Weight Loss ◽  
Logistic Regression ◽  
Prediction Model ◽  
Weight Regain ◽  
Initial Weight ◽  
Initial Weight Loss ◽  
Look Ahead

Introduction: The development of type 2 diabetes is strongly associated with excess weight gain and can often be partially ameliorated or reversed by weight loss. While many lifestyle interventions have resulted in successful weight loss, strategies to maintain the weight loss have been considerably less successful. Prior studies have identified multiple predictors of weight regain, but none have synthesized them into one analytic stream. Methods: We developed a prediction model of 4-year weight regain after a one-year lifestyle-induced weight loss intervention followed by a 3 year maintenance intervention in 1791 overweight or obese adults with type 2 diabetes from the Action for Health in Diabetes (Look AHEAD) trial who lost ≥3% of initial weight by the end of year 1. Weight regain was defined as regaining <50% of the weight lost during the intervention by year 4. Using machine learning we integrated factors from several domains, including demographics, psychosocial metrics, health status and behaviors (e.g. physical activity, self-monitoring, medication use and intervention adherence). We used classification trees and stochastic gradient boosting with 10-fold cross validation to develop and internally validate the prediction model. Results: At the end of four years, 928 individuals maintained ≥50% of their initial weight lost (maintainers), whereas 863 did not met that criterion (regainers). We identified an interaction between age and several variables in the model, as well as percent initial weight loss. Several factors were significant predictors of weight regain based on variable importance plots, regardless of age or initial weight loss, such as insurance status, physical function score, baseline BMI, meal replacement use and minutes of exercise recorded during year 1. We also identified several factors that were significant predictors depending on age group (45-55y/ 56-65y/66-76y) and initial weight loss (lost 3-9% vs. ≥10% of initial weight). When the variables identified from machine learning were added to a logistic regression model stratified by age and initial weight loss groups, the models showed good prediction (3-9% initial weight loss, ages 45-55y (n=293): ROC AUC=0.78; ≥10% initial weight loss, ages 45-55y (n=242): ROC AUC=0.78; (3-9% initial weight loss, ages 56-65y (n=484): ROC AUC=0.70; ≥10% initial weight loss, ages 56-65y (n=455): ROC AUC = 0.74; 3-9% initial weight loss, ages 66-76y (n=150): ROC AUC=0.84; ≥10% initial weight loss, ages 66-76y (n=167): ROC AUC=0.86). Conclusion: The combination of machine learning methodology and logistic regression generates a prediction model that can consider numerous factors simultaneously, can be used to predict weight regain in other populations and can assist in the development of better strategies to prevent post-loss regain.

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