Body-Weight Fluctuation and Incident Diabetes Mellitus, Cardiovascular Disease, and Mortality: A 16-Year Prospective Cohort Study

Oh, Tae Jung; Moon, Jae Hoon; Choi, Sung Hee; Lim, Soo; Park, Kyong Soo; Cho, Nam H.; Jang, Hak Chul

doi:10.1210/jc.2018-01239

Cited by 66 publications

(59 citation statements)

References 32 publications

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“…The risk of DM decreased with an increase in the body weight fluctuation in subjects with a baseline BMI > 25 kg/m 2 . In that study, a greater reduction in body weight (weight loss) was observed with higher body weight fluctuation in obese subgroup [37]. In our study, we have examined the effect of weight changes, not cycling, on cardiovascular events and mortality.…”

Section: Discussionmentioning

confidence: 93%

“…Several prospective studies have reported that those who fluctuate in their body weight, with weight loss followed by weight gain or vice versa (weight cycling), have an increased risk of cardiovascular and all-cause mortality [35–37]. The effect of body weight fluctuation on incident DM depended on the presence of obesity at baseline [37]. The risk of DM decreased with an increase in the body weight fluctuation in subjects with a baseline BMI > 25 kg/m 2 .…”

Section: Discussionmentioning

confidence: 99%

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Weight change and mortality and cardiovascular outcomes in patients with new-onset diabetes mellitus: a nationwide cohort study

Kim

Han

Koh

et al. 2019

Cardiovasc Diabetol

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BackgroundBecause weight control is a cornerstone of diabetes management, it is important to understand the relationship of weight change to risk of cardiovascular disease (CVD) among patients with type 2 diabetes mellitus (DM). We aimed to investigate whether changes in weight early after diagnosis influence the incidence of CVD and all-cause mortality in patients with type 2 DM.MethodsUsing nationally representative data from the Korean National Health Insurance System, 173,246 subjects with new-onset DM who underwent health examinations during 2007–2012 were included. Weight was measured at the time of diabetes diagnosis and 2 years later. Weight change over 2 years was divided into five categories of 5% weight change, from weight loss ≥ − 10% to weight gain ≥ 10%.ResultsThere were 3113 deaths (1.8%), 2060 cases of stroke (1.2%), and 1767 myocardial infarctions (MIs) (1.0%) during a median follow-up of 5.5 years. Subjects with weight gain ≥ 10% had a significantly higher risk of stroke (hazard ratio [HR] 1.51, 95% confidence interval [CI] 1.23–1.84), compared with the group with stable weight. There was no significant association between weight change after diagnosis of DM and incident MI. All-cause mortality showed a U-shaped curve according to weight change. The group with weight loss ≥ − 10% had the highest HR for all-cause mortality (HR 1.86; 95% CI 1.61–2.14) and the HR for weight gain ≥ 10% was 1.61 (95% CI 1.37–1.89).ConclusionsWeight changes of more than 10% after diabetes diagnosis were associated with higher mortality and over 10% weight gain was associated with increased risk of stroke.Electronic supplementary materialThe online version of this article (10.1186/s12933-019-0838-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Weight change and mortality and cardiovascular outcomes in patients with new-onset diabetes mellitus: a nationwide cohort study

Kim

Han

Koh

et al. 2019

Cardiovasc Diabetol

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“…Individuals who were overweight or obese and had high fluctuations showed a 2.7-fold higher risk for diabetes compared with those who were lean and had low fluctuations in body weight. The result of another long-term study with a 16-year follow-up suggests that the effect of body weight fluctuation on incident diabetes depends on the presence of obesity at baseline [27]. To summarize, there is no clear consensus regarding the effect of weight fluctuation on the risk of diabetes mellitus because of the large discrepancies among the results of various studies performed with various study populations.…”

Section: Body Weight Variabilitymentioning

confidence: 99%

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

2020

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Innumerable studies have suggested "the lower, the better" for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.

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“…Body weight variability (BWV), that is, the variability around the overall trend in body weight, can be quantified from frequent body weight measures. Several recent studies have associated BWV with outcomes such as all-cause mortality [ 9 - 11 ], type 2 diabetes incidence [ 12 ], cardiovascular morbidity or mortality [ 13 , 14 ], and cancer [ 15 ]. Further indications suggest that BWV may serve as a potential prognostic tool for obesity [ 16 , 17 ] and as a risk factor in patients with heart failure [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Turicchi¹,

O’Driscoll²,

Finlayson³

et al. 2020

JMIR Mhealth Uhealth

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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.

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Body-Weight Fluctuation and Incident Diabetes Mellitus, Cardiovascular Disease, and Mortality: A 16-Year Prospective Cohort Study

Cited by 66 publications

References 32 publications

Weight change and mortality and cardiovascular outcomes in patients with new-onset diabetes mellitus: a nationwide cohort study

Weight change and mortality and cardiovascular outcomes in patients with new-onset diabetes mellitus: a nationwide cohort study

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

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

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