Dataset Information

Body weight variability is not associated with changes in risk factors for cardiometabolic disease.

ABSTRACT:

Context

Weight loss is known to improve health, however the influence of variability in body weight around the overall trajectory on these outcomes is unknown. Few studies have measured body weight frequently enough to accurately estimate the variability component.

Objective

To investigate the association of 12-month weight variability and concurrent weight change with changes in health markers and body composition.

Methods

This study was a secondary analysis of the NoHoW trial, a 2 × 2 factorial randomised controlled trial promoting evidence-based behaviour change for weight loss maintenance. Outcome measurements related to cardiometabolic health and body composition were taken at 0, 6 and 12 months. Participants were provided with Wi-Fi connected smart scales (Fitbit Aria 2) and asked to self-weigh regularly over this period. Associations of weight variability and weight change with change in outcomes were investigated using multiple linear regression with multiple levels of adjustment in 955 participants.

Results

Twelve models were generated for each health marker. Associations between weight variability and changes in health markers were inconsistent between models and showed no evidence of a consistent relationship, with all effects explaining <1% of the outcome, and most 0%. Weight loss was consistently associated with improvements in health and body composition, with the greatest effects seen in percent body fat (R² = 10.4-11.1%) followed by changes in diastolic (4.2-4.7%) and systolic (3-4%) blood pressure.

Conclusion

Over 12-months, weight variability was not consistently associated with any measure of cardiometabolic health or body composition, however weight loss consistently improved all outcomes.

Trial registration number

ISRCTN88405328.

SUBMITTER: Turicchi J

PROVIDER: S-EPMC7803052 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Body weight variability is not associated with changes in risk factors for cardiometabolic disease.

Turicchi Jake J O'Driscoll Ruairi R Horgan Graham G Duarte Cristiana C Santos Inês I Encantado Jorge J Palmeira Antonio L AL Larsen Sofus C SC Olsen Jack K JK Heitmann Berit L BL Stubbs R James RJ

International Journal of Cardiology. Hypertension 20200802

<h4>Context</h4>Weight loss is known to improve health, however the influence of variability in body weight around the overall trajectory on these outcomes is unknown. Few studies have measured body weight frequently enough to accurately estimate the variability component.<h4>Objective</h4>To investigate the association of 12-month weight variability and concurrent weight change with changes in health markers and body composition.<h4>Methods</h4>This study was a secondary analysis of the NoHoW t ...[more]

PMID: 33447771

Similar Datasets

Project description:BackgroundWorksites are potentially effective locations for obesity control because they provide opportunities for group intervention and social support. Studies are needed to identify effective interventions in these settings.ObjectiveWe examined the effects of a multicomponent lifestyle intervention on weight loss and prevention of regain in 4 worksites (2 intervention and 2 control sites).DesignOverweight and obese employees (n = 133) enrolled in this pilot worksite-randomized controlled trial with a 0-6-mo weight-loss phase and a 6-12-mo structured weight-maintenance phase. The intervention combined recommendations to consume a reduced-energy, low-glycemic load, high-fiber diet with behavioral change education. Outcome measurements included changes in body weight and cardiometabolic risk factors.ResultsThe mean ± SEM weight loss was substantial in intervention participants, whereas control subjects gained weight (-8.0 ± 0.7 compared with +0.9 ± 0.5 kg, respectively; P < 0.001), and 89% of participants completed the weight-loss phase. Intervention effects were not significant at the 0.05 level but would have been at the 0.10 level (P = 0.08) in a mixed model in which the worksite nested within group was a random factor. There were also significant improvements in cardiometabolic risk factors in intervention compared with control subjects regarding fasting total cholesterol, glucose, systolic blood pressure, and diastolic blood pressure (P ≤ 0.02 for each). No significant weight regain was observed in participants who enrolled in the structured weight-maintenance program (0.5 ± 0.7 kg; P = 0.65), and overweight and obese employees in intervention worksites who were not enrolled in the weight-loss program lost weight compared with subjects in control worksites (-1.3 ± 0.5 compared with +0.7 ± 0.2 kg, respectively; P = 0.02).ConclusionWorksites can be effective for achieving clinically important reductions in body weight and improved cardiometabolic risk factors. This trial was registered at clinicaltrials.gov as NCT01470222.

Project description:BackgroundIncreased physical activity (PA), reduced time spent sedentary (SED), healthier diet and reduced body weight may all have a positive impact on cardiometabolic risk. The relative importance of change in each of these variables on cardiometabolic risk, however, is unclear. We therefore sought to investigate the relative contributions of changes in PA, SED, diet and body weight on cardiometabolic risk.MethodsThis is a secondary analysis of data collected from the EuroFIT randomised controlled trial, which was a 12-week group-based lifestyle intervention for overweight middle-aged men delivered by coaches in football club stadia aiming to improve PA, SED, diet, and body weight. PA and SED were assessed by accelerometry, diet using the Dietary Instrument for Nutrition Education (DINE). An overall cardiometabolic risk score was derived from combining z-scores for glucose, HbA1c, insulin, lipids and blood pressure. In total, 707 men (from the overall cohort of 1113) with complete data for these variables at baseline and 12-month follow-up were included in the multivariable linear regression analyses.ResultsIn multivariable analyses, change in number of steps (explaining 5.1% of R2) and dietary factors (less alcohol, fatty and sugary food, and more fruit and vegetables) (together explaining 4.5% of R2), but not changes in standing time or SED, were significantly associated with change in body weight. Changes in number of steps (R2 = 1.7%), fatty food score (R2 = 2.4%), and sugary food score (R2 = 0.4%) were significantly associated with change in cardiometabolic risk score in univariable models. However, in multivariable models which included changes in weight as well as changes in steps and dietary variables, change in weight explained a substantially larger proportion of the change in cardiometabolic risk score, explaining 14.1% of R2 (out of an overall model R2 of 19.0%). When baseline (as well as change) values were also included in the model, 38.8% of R2 for change in cardiometabolic risk score was explained overall, with 14.1% of R2 still explained by change in weight.ConclusionChange in body weight, together with baseline cardiometabolic risk explained most of the change in cardiometabolic risk. Thus, the benefits of increasing physical activity and improving diet on cardiometabolic risk appear to act largely via an effect on changes in body weight.Trial registrationInternational Standard Randomised Controlled Trials, ISRCTN-81935608. Registered 06052015. https://www.isrctn.com/ISRCTN81935608?q=&filters=recruitmentCountry:Portugal&sort=&offset=7&totalResults=92&page=1&pageSize=10&searchType=basic-search.

Project description:Background Metabolic syndrome is associated with high risk of cardiovascular disease, although risk may differ according to the specific conditions present and variability in those conditions. Methods and Results We defined obesity (body mass index [BMI] ≥30 kg/m2) and metabolic health (<2 nonobesity National Cholesterol Education Program Adult Treatment Panel III conditions) among 3632 Framingham Heart Study offspring cohort participants (mean age, 50.8 years; 53.8% women) who were followed up from 1987 to 2014. We defined participants whose variance independent of the mean for a metabolic syndrome-associated measure was in the top quintile as being "variable" for that measure. Variable metabolic health was defined as ≥2 variable nonobesity metabolic syndrome components. We investigated the interaction between obesity and metabolic health in their associations with cardiometabolic disease and cardiovascular disease using Cox proportional hazards regression. In addition, we estimated the associations of BMI variability and variable metabolic health with study outcomes within categories of obesity and metabolic health status, respectively. We observed 567 incident obesity (41 439 person-years), 771 incident metabolically unhealthy state (25 765 person-years), 272 incident diabetes mellitus (56 233 person-years), 503 incident hypertension (12 957 person-years), 589 cardiovascular disease (60 300 person-years), and 195 chronic kidney disease (47 370 person-years) events on follow-up. Obesity and being metabolically unhealthy were independently and positively associated with all outcomes. BMI variability, compared with stable BMI, was associated with 163%, 67%, 58%, and 74% higher risks of having obesity, becoming metabolically unhealthy, having diabetes mellitus, and having hypertension, respectively, among nonobese participants. Variable metabolic health, compared with stable metabolic health, was associated with a 28% higher risk of cardiovascular disease, among metabolically healthy participants. Conclusions We did not observe evidence for a positive interaction between obesity and metabolic health status with regard to study outcomes. BMI and metabolic health variability are associated with adverse health outcomes.

Dataset Information

Body weight variability is not associated with changes in risk factors for cardiometabolic disease.

Context

Objective

Methods

Results

Conclusion

Trial registration number

Publications

Body weight variability is not associated with changes in risk factors for cardiometabolic disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets