Weight variability and cardiovascular outcomes: a systematic review and meta-analysis

BMI variability and cardiovascular outcomes within clinical trial and real-world environments in type 2 diabetes: an IMI2 SOPHIA study

BACKGROUND

BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability.

METHODS

We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome.

RESULTS

After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08-1.17, P < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability.

CONCLUSIONS

In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes.

Medical Nutrition Education for Health, Not Harm: BMI, Weight Stigma, Eating Disorders, and Social Determinants of Health

Effective nutrition training is fundamental to medical education. Current training is inadequate and can cause harm to students and patients alike; it leaves physicians unprepared to counsel on nutrition, places undue focus on weight and body mass index (BMI), can exacerbate anti-obesity bias, and increase risk for development of eating disorders, while neglecting social determinants of health and communication skills. Physicians and educators hold positions of influence in society; what we say and how we say it matters. We propose actionable approaches to improve nutrition education to minimize harm and pursue evidence-based, effective, and equitable healthcare.

Comparison of BMI and HbA1c changes before and during the COVID-19 pandemic in type 1 diabetes: a longitudinal population-based study

Purpose

To compare the changes in body weight and glycemic control before and during the COVID-19 pandemic in people with type 1 diabetes (T1D).

Methods

In 47,065 individuals with T1D from the German Diabetes Prospective Follow-up Registry (DPV), we compared the adjusted mean changes in BMI-Z-scores and HbA1c as well as the distribution of individual changes between four periods from March 2018 to February 2022, by sex and age group (4- < 11, 11- < 16, 16-50 years).

Results

At population level, the only significant pandemic effects were a slight increase in BMI Z-score in prepubertal children (girls: + 0.03 in the first COVID year vs. before, P < 0.01; boys: + 0.04, P < 0.01) as well as a stabilization of HbA1c in all subgroups or even improvement in women (- 0.08%, P < 0.01). At individual level, however, heterogeneity increased significantly (p < 0.01), especially in children. More prepubertal children gained weight (girls: 45% vs. 35% before COVID; boys: 39% vs. 33%). More pubertal girls lost weight (30% vs. 21%) and fewer gained weight (43% vs. 54%). More children had a decreasing HbA1c (prepubertal group: 29% vs. 22%; pubertal girls: 33% vs. 28%; pubertal boys: 32% vs. 25%) and fewer had increasing values. More women had stable HbA1c and fewer had increasing values (30% vs. 37%). In men, no significant changes were observed.

Conclusion

This real-world analysis shows no detrimental consequences of the two first COVID years on weight and HbA1c in T1D on average, but reveals, beyond the mean trends, a greater variability at the individual level.

An interpretable predictive deep learning platform for pediatric metabolic diseases

OBJECTIVES

Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications.

MATERIALS AND METHODS

No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts.

RESULTS

The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16).

DISCUSSION

Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

The association of body mass index variability with cardiovascular disease and mortality: a mediation analysis of pooled cohorts

Aim

We aimed to investigate the effect of BMI variability on CVD and mortality and to explore the mediation effects of the main cardiovascular risk factors contributing to this association.

Method

Participants aged 40-65 years were pooled from three cohort studies(ARIC [Atherosclerosis Risk in Communities], MESA [Multi-ethnic Study of Atherosclerosis], and TLGS [Tehran Lipid and Glucose Study]. We employed root mean squared error of the fractional mixed model to calculate BMI variability in the measurement period. In the event assessment period, the hazard ratios for CVD and mortality were estimated using Cox proportional hazard regression models. In the next step, the mediation and interaction effects of fasting plasma glucose, total cholesterol, and systolic blood pressure were determined.

Results

A total of 19073 participants were included in this pooled analysis. During a median of 20.7 years of follow-up, 3900 (20.44%) CVD and 6480 (33.97%) all-cause mortality events were recorded. After adjusting for potential confounders, BMI variability was linked to the 1.3 (1.2-1.4) and 1.7 (1.6-1.8) increased risk of CVD and mortality, respectively. Fasting plasma glucose mediated approximately 24% and 8% of the effect of BMI variability on CVD and mortality, respectively. However, systolic blood pressure and total cholesterol did not have mediation effects in this association.

Conclusion

High BMI variability is independently associated with the development of CVD and mortality. This association is partly mediated through fasting plasma glucose. Modern cardiometabolic therapies that lower fasting glucose may reduce the risk of future CVD and mortality in individuals with high BMI variability.

Gender differences in midlife to later-life cumulative burden and variability of obesity measures and risk of all-cause and cause-specific mortality

BACKGROUND/OBJECTIVES

Previous studies have reported the gender-specific association between general and central obesity measures, using snapshot assessments, and mortality events. This study seeks to further explore this link by examining how the longitudinal cumulative burden and variability of obesity measures from midlife to later-life impact mortality events in the Atherosclerosis Risk in Communities (ARIC) study population, specifically in relation to gender differences.

SUBJECTS/METHODS

Using data from the ARIC study, a total of 7615 (4360 women) participants free of cardiovascular disease, cancer, and early mortality events were included in the data analysis. Longitudinal cumulative burden (estimated by the area under the curve (AUC) using a quadratic mixed-effects method) and variability (calculated according to average successive variability (ASV)) were considered as exposures, separately and all together. Cox proportional hazard regression models were used to estimate multivariable-adjusted standardized hazard ratios.

RESULTS

The mean age was 62.4 and the median follow-up was 16.9 years. In men, AUCs of waist-related obesity measures, and also ASVs of all obesity measures were associated with increased all-cause mortality risk. In women, waist circumference and waist-to-height ratio AUCs were associated with increased all-cause mortality risk. Regarding cardiovascular mortality, all adiposity measures ASVs in both genders and waist-related obesity measures AUCs in men were associated with increased risk. Significant gender differences were found for the associations between cumulative and variability of waist-to-hip ratio for all-cause mortality and all adiposity measures ASVs for cardiovascular mortality risk with higher impact among men.

CONCLUSIONS

Cumulative burden and variability in general and central obesity measures were associated with higher all-cause and cardiovascular mortalities among men. In women, general obesity measures variability, as well as cumulative and variability of central adiposity measure, increased all-cause mortality risk.

Ethnic disparities in cardiovascular and renal responses to canagliflozin between Asian and White patients with type 2 diabetes mellitus: A post hoc analysis of the CANVAS Program

AIM

To assess the potential heterogeneity in cardiovascular (CV), renal and safety outcomes of canagliflozin between Whites and Asians, as well as these outcomes in each subgroup.

MATERIALS AND METHODS

The CANVAS Program enrolled 10 142 patients with type 2 diabetes, comprising 78.34% Whites and 12.66% Asians. CV, renal and safety outcomes were comprehensively analysed using Cox regression models, while intermediate markers were assessed using time-varying mixed-effects models. Racial heterogeneity was evaluated by adding a treatment-race interacion term.

RESULTS

Canagliflozin showed no significant racial disparities in the majority of the CV, renal and safety outcomes. The heterogeneity (p = .04) was observed on all-cause mortality, with reduced risk in Whites (hazard ratio 0.84; 95% confidence interval 0.71-0.99) and a statistically non-significant increased risk in Asians (hazard ratio 1.64; 95% confidence interval 0.94-2.90). There was a significant racial difference in acute kidney injury (p = .04) and a marginally significant racial heterogeneity for the composite of hospitalization for heart failure and CV death (p = .06) and serious renal-related adverse events (p = .07).

CONCLUSION

Canagliflozin reduced CV and renal risks similarly in Whites and Asians; however, there was a significant racial discrepancy in all-cause mortality. This distinction may be attributed to the fact that Asian patients exhibited diminished CV protection effects and more renal adverse events with canagliflozin, potentially resulting from the smaller reductions in weight and uric acid. These findings highlight the importance of investigating the impact of race on treatment response to sodium-glucose cotransporter-2 inhibitors and provide more precise treatment strategies.

Association of variability in metabolic parameters with the incidence of type 2 diabetes: evidence from a functional community cohort

BACKGROUND

To investigate the association of variability in metabolic parameters such as total cholesterol concentrations (TC), uric acid (UA), body mass index (BMI), visceral adiposity index (VAI) and systolic blood pressure (SBP) with incident type 2 diabetes (T2D) and whether variability in these metabolic parameters has additive effects on the risk of T2D.

METHODS

Based on the Beijing Functional Community Cohort, 4392 participants who underwent three health examinations (2015, 2016, and 2017) were followed up for incident T2D until the end of 2021. Variability in metabolic parameters from three health examinations were assessed using the coefficient of variation, standard deviation, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability index. Participants were grouped according to the number of high-variability metabolic parameters. Cox proportional hazards models were performed to assess the hazard ratio (HR) and 95% confidence interval (CI) for incident T2D.

RESULTS

During a median follow-up of 3.91 years, 249 cases of incident T2D were identified. High variability in TC, BMI, VAI and SBP was significantly associated with higher risks of incident T2D. As for UA, significant multiplicative interaction was found between variability in UA and variability in other four metabolic parameters for incident T2D. The risk of T2D significantly increased with the increasing numbers of high-variability metabolic parameters. Compared with the group with low variability for 5 parameters, the HR (95% CI) for participants with 1-2, 3, 4-5 high-variability metabolic parameters were 1.488 (1.051, 2.107), 2.036 (1.286, 3.222) and 3.017 (1.549, 5.877), respectively. Similar results were obtained in various sensitivity analyses.

CONCLUSIONS

High variability of TC, BMI, VAI and SBP were independent predictors of incident T2D, respectively. There was a graded association between the number of high-variability metabolic parameters and incident T2D.

Weight variability and diabetes complications

Body weight is a recognized risk factor for cardiovascular diseases (CVD). More recently, weight variability, i.e. the oscillation of body weight over time, has also been suggested to be independently associated with development of CVD and mortality in subjects without diabetes and in people with both type 1 and type 2 diabetes (T2D). In T2D, weight variability emerged as one of the most relevant risk factors for CVD and it was suggested to interact with the variability of other risk factors to identify people at high cardiovascular risk. In addition, weight variability seems also to confer a higher risk for microvascular complications in people with T2D. While the exact mechanism linking weight variability to CVD is unknown, evidence from experimental models suggests that weight cycling promote an enduring pro-inflammatory program in the adipose tissue. Here we review the clinical evidence relative to the association of weight variability with CVD and microvascular complications of diabetes. We then briefly summarize the alterations proposed to explain this association. Finally, we synthesize the possible strategies, e.g. specific dietetic regimens and available glucose-lowering drugs, to minimize weight fluctuations.