Unstable bodyweight and incident type 2 diabetes mellitus: A meta-analysis

Smoking Cessation, Weight Gain, and Risk for Type 2 Diabetes: A Prospective Study

Objectives: To examine the association between smoking cessation and risk of type 2 diabetes with emphasis on post-cessation weight gain.

Methods: In total, 8,951 participants from the China Health and Retirement Longitudinal Study at the baseline (2011) were included. Diabetes incidence was accessed at the third survey (2015). Current smokers were treated as the reference and odds ratios (OR) of type 2 diabetes for never smokers, recent, and long-term quitters were computed using multivariable logistic regression. Stratified analysis was further conducted by weight gain after smoking cessation.

Results: There were 712 cases of type 2 diabetes identified. Compared with current smokers, the fully multivariable-adjusted ORs were 1.55 (1.02, 2.36) for recent quitters, 0.88 (0.61, 1.28) for long-term quitters, and 0.75 (0.59, 0.95) for never smokers. Stratified analysis showed recent quitters with weight gain of ≥2.0 kg had a significantly higher odds of type 2 diabetes [2.25 (1.02, 4.95)].

Conclusion: The present study of the Chinese population suggested recent quitters with weight gain of ≥2.0 kg, compared with current smokers, had a significantly increased odds of type 2 diabetes.

Increased Visit-to-Visit Liver Enzyme Variability Is Associated with Incident Diabetes: A Community-Based 12-Year Prospective Cohort Study

BACKGROUND

Fatty liver and/or increased liver enzyme values have been reported to be associated with incident diabetes. We sought to determine whether increased visit-to-visit liver enzyme variability is associated with incident diabetes.

METHODS

Study participants were recruited from the Korean Genome and Epidemiologic Study (KoGES). A total of 4,151 people aged 40 to 69 years was recruited and tested every 2 years for up to 12 years. Visit-to-visit aspartate aminotransferase (AST) and alanine aminotransferase (ALT) variability was evaluated in first the 6-year period through the use of various variability measurements: standard deviation (SD), average successive variability, coefficient of variation (CV), and variation independent of mean (VIM). Oral glucose tolerance test was performed at every visit.

RESULTS

During the 6-year follow-up appointments, 13.0% (538/4,151) of people developed incident diabetes. Visit-to-visit AST variability was associated with an increased risk of diabetes independent of conventional risk factors for diabetes (hazard ratio per 1-SD increment [95% confidence interval]: 1.06 [1.00 to 1.11], 1.12 [1.04 to 1.21], and 1.13 [1.04 to 1.22] for SD, CV, and VIM, respectively; all P<0.05); however, no such associations were observed in the visit-to-visit ALT variability. According to alcohol consumption status, both AST and ALT variability were independent predictors for incident diabetes in subjects with heavy alcohol consumption; however, neither AST nor ALT variability was associated with diabetes risk in subjects who did not drink alcohol heavily.

CONCLUSION

Visit-to-visit liver enzyme variability is an independent predictor of incident diabetes. Such association was more evident in those who consumed significant amounts of alcohol.

The impact of early body-weight variability on long-term weight maintenance: exploratory results from the NoHoW weight-loss maintenance intervention

BACKGROUND

Weight-loss programmes often achieve short-term success though subsequent weight regain is common. The ability to identify predictive factors of regain early in the weight maintenance phase is crucial.

OBJECTIVE

To investigate the associations between short-term weight variability and long-term weight outcomes in individuals engaged in a weight-loss maintenance intervention.

METHODS

The study was a secondary analysis from The NoHoW trial, an 18-month weight maintenance intervention in individuals who recently lost ≥5% body weight. Eligible participants (n = 715, 64% women, BMI = 29.2 (SD 5.0) kg/m², age = 45.8 (SD 11.5) years) provided body-weight data by smart scale (Fitbit Aria 2) over 18 months. Variability in body weight was calculated by linear and non-linear methods over the first 6, 9 and 12 weeks. These estimates were used to predict percentage weight change at 6, 12, and 18 months using both crude and adjusted multiple linear regression models.

RESULTS

Greater non-linear weight variability over the first 6, 9 and 12 weeks was associated with increased subsequent weight in all comparisons; as was greater linear weight variability measured over 12 weeks (up to AdjR² = 4.7%). Following adjustment, 6-week weight variability did not predict weight change in any model, though greater 9-week weight variability by non-linear methods was associated with increased body-weight change at 12 (∆AdjR² = 1.2%) and 18 months (∆AdjR² = 1.3%) and by linear methods at 18 months (∆AdjR² = 1.1%). Greater non-linear weight variability measured over 12 weeks was associated with increased weight at 12 (∆AdjR² = 1.4%) and 18 (∆AdjR² = 2.2%) months; and 12-week linear variability was associated with increased weight at 12 (∆AdjR² = 2.1%) and 18 (∆AdjR² = 3.6%) months.

CONCLUSION

Body-weight variability over the first 9 and 12 weeks of a weight-loss maintenance intervention weakly predicted increased weight at 12 and 18 months. These results suggest a potentially important role in continuously measuring body weight and estimating weight variability.

Body-weight fluctuation and risk of diabetes in older adults: The China Health and Retirement Longitudinal Study (CHARLS)

AIMS

Body-weight fluctuation is associated with an increased risk of all-cause mortality. Yet no studies investigate its association with risk of diabetes in adults aged ≥ 60 years. This study aimed to address this issue.

METHODS

A total of 1,565 participants free of diabetes at baseline in the CHARLS were followed for 4-year. Body-weight was collected at baseline and every 2-year. Body-weight fluctuation was primarily calculated as the root-mean-square-error deviation from the regression line of body-weights against years. The risk of diabetes was estimated using logistic regression analysis.

RESULTS

During the 4-year follow-up, 153 participants developed diabetes. The risk of diabetes was increased by 23% (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.06 to 1.43) per every 1-standard deviation higher of body-weight fluctuation after controlling for cardiovascular risk factors. The association appeared pronounced among participants with poor physical performance (both P < 0.03). Participants with overweight/obesity and a high body-weight fluctuation had the largest increase in the risk for diabetes (OR 3.03). Body-weight fluctuation correlated with hemoglobin A1c and white blood cells at follow-up or their change scores from baseline, especially in females (all P < 0.02).

CONCLUSIONS

Body-weight fluctuation led to an increased risk of diabetes in adults aged ≥ 60 years.

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

Background

Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available.

Objective

This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches

Methods

In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated.

Results

Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method.

Conclusions

The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

Intentional weight loss as a predictor of type 2 diabetes occurrence in a general adult population

INTRODUCTION

Observational and intervention studies have verified that weight loss predicts a reduced type 2 diabetes (T2D) risk. At the population level, knowledge on the prediction of self-report intentional weight loss (IWL) on T2D incidence is, however, sparse. We studied the prediction of self-report IWL on T2D incidence during a 15-year follow-up in a general adult population.

RESEARCH DESIGN AND METHODS

The study sample from the representative Finnish Health 2000 Survey comprised 4270 individuals, aged 30-69 years. IWL was determined with questions concerning dieting attempts and weight loss during the year prior to baseline. Incident T2D cases during a 15-year follow-up were drawn from national health registers. The strength of the association between IWL and T2D incidence was estimated with the Cox model.

RESULTS

During the follow-up, 417 incident cases of T2D occurred. IWL predicted an increased risk of T2D incidence (HR 1.44; 95% CI 1.11 to 1.87, p=0.008) in a multivariable model. In interaction analyses comparing individuals with and without IWL, a suggestively elevated risk emerged in men, the younger age group, among less-educated people and in individuals with unfavorable values in several lifestyle factors.

CONCLUSIONS

Self-report IWL may predict an increased risk of T2D in long-term, probably due to self-implemented IWL tending to fail. The initial prevention of weight gain and support for weight maintenance after weight loss deserve greater emphasis in order to prevent T2D.

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

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.

Weight cycling is associated with adverse cardiometabolic markers in a cross-sectional representative US sample

BACKGROUND

Whether weight cycling (repeated weight loss and regain) is associated with cardiometabolic health is unclear. Study objective was to examine whether weight cycling since young adulthood (ie, 25 years of age) was associated with cardiometabolic markers.

METHODS

Data from a nationally representative cross-sectional US sample (National Health and Nutrition Examination Survey, 1999-2014) were used. Weight history was based on self-reported weight at age 25, 10 years prior and 1 year prior to the survey (n=4190, 51% male). Using current self-reported weight as the anchor, participants were classified as (1) stable weight , (2) weight losers, (3) weight gainers and (4) weight cyclers. Cardiometabolic markers included fasting lipids, insulin sensitivity and blood pressure. Multiple linear regressions were used to analyse weight history (reference: stable weight) and adjusted for covariates. Analyses incorporated the sampling design and survey weights and were stratified by sex or weight status.

RESULTS

Compared with females with stable weight, female weight cyclers had worse lipids and homeostasis model assessment for insulin resistance (HOMA-IR) (all ps<0.05). Compared with males with stable weight, male weight cyclers had worse high-density lipoprotein cholesterol (HDL) and HOMA-IR (ps<0.05). Weight cyclers with normal weight had worse HDL and low-density lipoprotein cholesterol (ps<0.05), and weight cyclers with overweight or obesity had worse HOMA-IR (p=0.05). Blood pressure was not associated.

CONCLUSION

Weight cycling is adversely associated with cardiometabolic markers but associations differ by sex and weight status. While weight cycling is consistently associated with worse cardiometabolic markers among females, results are mixed among males. Weight cycling is associated with worse lipid measures for normal weight persons, and marginally worse insulin sensitivity for those with overweight/obesity.

Weekly, seasonal and holiday body weight fluctuation patterns among individuals engaged in a European multi-centre behavioural weight loss maintenance intervention

BACKGROUND

Technological advances in remote monitoring offer new opportunities to quantify body weight patterns in free-living populations. This paper describes body weight fluctuation patterns in response to weekly, holiday (Christmas) and seasonal time periods in a large group of individuals engaged in a weight loss maintenance intervention.

METHODS

Data was collected as part The NoHoW Project which was a pan-European weight loss maintenance trial. Three eligible groups were defined for weekly, holiday and seasonal analyses, resulting in inclusion of 1,421, 1,062 and 1,242 participants, respectively. Relative weight patterns were modelled on a time series following removal of trends and grouped by gender, country, BMI and age.

RESULTS

Within-week fluctuations of 0.35% were observed, characterised by weekend weight gain and weekday reduction which differed between all groups. Over the Christmas period, weight increased by a mean 1.35% and was not fully compensated for in following months, with some differences between countries observed. Seasonal patterns were primarily characterised by the effect of Christmas weight gain and generally not different between groups.

CONCLUSIONS

This evidence may improve current understanding of regular body weight fluctuation patterns and help target future weight management interventions towards periods, and in groups, where weight gain is anticipated.

HDL-Cholesterol, Its Variability, and the Risk of Diabetes: A Nationwide Population-Based Study

CONTEXT

The bidirectional relationship between low high-density lipoprotein cholesterol (HDL-C) and glucose intolerance is well established. Recent studies suggested an association of lipid variability with various health outcomes.

OBJECTIVE

To investigate the combined effect of HDL-C levels and their variability on the risk of diabetes.

DESIGN

A population-based cohort study.

SETTING AND PARTICIPANTS

In all, 5,114,735 adults without known diabetes in the Korean National Health Insurance System cohort who underwent three or more health examinations from 2009 to 2013 were included. Visit-to-visit HDL-C variability was calculated using variability independent of the mean (VIM) and the coefficient of variation (CV). Low mean and high variability groups were defined as the lowest and highest quartiles of HDL-C mean and variability, respectively.

MAIN OUTCOME MEASURES

Newly developed diabetes.

RESULTS

There were 122,192 cases (2.4%) of incident diabetes during the median follow-up of 5.1 years. Lower mean or higher variability of HDL-C was associated with higher risk of diabetes in a stepwise manner, and an additive effect of the two measures was noted. In the multivariable-adjusted model, the hazard ratios and 95% CIs for incident diabetes were 1.20 (1.18 to 1.22) in the high mean/high VIM group, 1.35 (1.33 to 1.37) in the low mean/low VIM group, and 1.40 (1.38 to 1.42) in the low mean/high VIM group compared with the high mean/low VIM group. Similar results were observed when modeling the variability using CV and in various subgroup analyses.

CONCLUSIONS

Low mean and high variability in HDL-C were independent predictors of diabetes with an additive effect. Both elevating and stabilizing HDL-C may be important goals for reducing diabetes risk.

Body Weight Fluctuation as a Risk Factor for Type 2 Diabetes: Results from a Nationwide Cohort Study

We aimed to investigate how body weight fluctuation affects the risk of developing type 2 diabetes by conducting a nationwide cohort study. A total of 3,855,884 participants from the National Health Insurance System health check-up data from 2012 were included in this study, and follow-up continued until 2016. Body weight was measured at least thrice between 2009 and 2012. Body weight variability (BWV) was estimated using average successive variability (ASV) indices. Cox proportional hazards regression models were used to evaluate the association of BWV with the risk of type 2 diabetes using hazard ratios (HRs) and 95% confidence intervals (CIs). Body weight fluctuation was associated with a higher risk of incident diabetes after adjustment for confounders (HR 1.10, 95% CI 1.07, 1.12 in the highest BWV quartile compared to the lowest). Regardless of the weight change status, the highest ASV quartile of BWV increased the risk for diabetes. Even subjects with a normal glucose tolerance status and those aged under 65 years had a higher risk of diabetes if their body weight highly fluctuated during the follow-up years. Our results suggest that body weight variability is an independent risk factor for diabetes. It is important to pay attention to frequent body weight fluctuations.

Validity of a triaxial accelerometer and simplified physical activity record in older adults aged 64-96 years: a doubly labeled water study

BACKGROUND

The aim was to examine the validity of a triaxial accelerometer (ACCTRI) and a simplified physical activity record (sPAR) in estimating total energy expenditure (TEE) and physical activity level (PAL) in older adults with the doubly labeled water (DLW) method.

METHODS

A total of 44 Japanese elderly individuals (64-96 years), of which 28 were community-dwelling healthy adults with or without sporting habits (S or NS group) and 16 were care home residents with frailty (F group), were included in the study. Basal metabolic rate (BMR) was measured by indirect calorimetry, TEE was obtained by the DLW method, and PAL was calculated as TEE/BMR. Daily step count was monitored by a pedometer (Lifecorder). The 24-h average metabolic equivalent was assessed by ACCTRI and sPAR.

RESULTS

The TEE_DLW in men was 2704 ± 353, 2308 ± 442, and 1795 ± 338 kcal d^-1, and that in women was 2260 ± 208, 1922 ± 285, and 1421 ± 274 kcal d^-1 for the S, NS, and F groups, respectively. ACCTRI and sPAR systematically underestimated actual TEE (- 14.2 ± 11.6 and - 15.3 ± 12.3% for ACCTRI and sPAR, respectively). After diet-induced thermogenesis was taken into account for ACCTRI and sPAR, TEE_DLW was significantly correlated with TEE_ACCTRI (R² = 0.714) and TEE_sPAR (R² = 0.668). PAL_DLW was also significantly correlated with PAL_ACCTRI (R² = 0.438) and PAL_sPAR (R² = 0.402).

CONCLUSIONS

Age, living conditions, frailty, and sporting habits contribute to TEE and PAL in the elderly population. ACCTRI and sPAR underestimated TEE and PAL, and adequate corrections are required. The corrected ACCTRI and sPAR are both useful tools to estimate TEE and PAL.

Unstable bodyweight and incident type 2 diabetes mellitus: A meta-analysis

AIMS/INTRODUCTION

The present meta-analysis aimed to clarify the association of unstable bodyweight with the risk of type 2 diabetes mellitus, an association that has been controversial among longitudinal studies.

MATERIALS AND METHODS

An electronic literature search using EMBASE and MEDLINE was followed up to 31 August 2016. The relative risks (RRs) of type 2 diabetes mellitus in individuals with unstable bodyweight were pooled using the inverse variance method.

RESULTS

Eight studies were eligible for the meta-analysis. The median duration of measurements of weight change and follow-up years for ascertaining type 2 diabetes mellitus were 13.5 and 9.4 years, respectively. The pooled RR for the least vs most stable category was 1.33 (95% confidence interval 1.12-1.57). Between-study heterogeneity was statistically significant (P = 0.048). Whether type 2 diabetes mellitus was ascertained by blood testing explained 66.0% of the variance in the logarithm of RR (P = 0.02). In three studies in which blood testing was carried out, type 2 diabetes mellitus risk was not significant (RR 1.06, 95% confidence interval 0.91-1.25). Furthermore, publication bias that inflated type 2 diabetes mellitus risk was statistically detected by Egger's test (P = 0.09).

CONCLUSIONS

Unstable bodyweight might be modestly associated with the elevated risk of type 2 diabetes mellitus; although serious biases, such as diagnostic suspicion bias and publication bias, made it difficult to assess this association.