Obesity is a well-known risk factor for cardiovascular disease (CVD) and mortality (1, 2). Overweight and obese individuals with additional cardiovascular risk factors are recommended to lose weight through lifestyle modifications (3). However, weight loss maintenance is challenging and is followed by weight regain in the long term (4). In a systematic review of observational studies, 42% of the general population reported personal weight control attempts (5). Adherence to a weight loss strategy plan, metabolic adaptation, energy homeostasis, and pregnancy are the factors that may lead to recurrent cycles of weight loss and regain and, thus, unsuccessful attempts at sustained weight loss (6).

The obesity paradox refers to the seemingly counterintuitive notion that body mass index (BMI) is not a consistent cardiovascular risk factor in overweight and obese individuals (7, 8), and questions the cardiovascular health benefits of weight loss in the long term (9–11). Body weight variability has been examined as an additional risk factor to explain the controversial findings on the effect of weight loss on CVD and mortality (12, 13). Although several studies suggested that weight variability independently increases the risk of CVD and mortality (12), some studies found no association or decreased risk for future CVD (14–16). In addition, the relationship between weight variability and CVD is inconsistent among different subpopulations (17, 18). The mechanisms through which weight variability increases the risk of CVD and mortality are not understood, and a few studies have explored the correlation of weight variability with cardiovascular risk factors (19, 20).

To address the gap in the literature, the current study investigated the association of BMI variability with CVD incidence and all-cause mortality in a large pooled sample and in different subpopulations. We also delved deeper into the underlying mechanisms driving the link between BMI variability and CVD and mortality by performing mediation analyses. This would help to determine the specific cardiovascular risk factors that act as intermediaries in this relationship, shedding new light on the potentially complex relationship between BMI variability and CVD and mortality.

Table 1 presents the baseline characteristics of the study participants according to the BMI variability tertiles. This pooled cohort comprised 19073 participants (51.89% women) with a mean age of 53 ± 6 years at baseline. The value of BMI variability ranged from 0.02-0.63 Kg/m² in the first, 0.63-1.07 Kg/m² in the second, and 1.07-9.32 Kg/m² in the third tertiles. Toward the highest tertile, the mean values of baseline BMI, SBP, FPG, TG, TC, and BMI change increased while the mean age decreased. The highest BMI variability tertile had a higher prevalence of women, individuals with obesity, and lower education levels, while the lowest BMI variability tertile showed a higher prevalence of current smokers.

During a median of 20.7 years of follow-up, 3900 (20.45%) incident CVD events and 6480 (33.97%) deaths were recorded. Table 2 displays the association of BMI variability with CVD events and mortality. After adjusting for age, sex, education, smoking status, and family history of CVD, the highest BMI variability tertile had increased risk for future CVD (HR:1.3; 95% CI:1.2 - 1.4) and mortality (HR: 1.7, 95% CI:1.6 - 1.8). In the fully adjusted model, the participants in the highest tertile had persistently greater risk of CVD (HR: 1.3, 95% CI: 1.2 - 1.4) and mortality (HR: 1.6, 95% CI: 1.5-1.7). In the continuous approach, for each 1-SD increment in BMI variability, the HRs for CVD incidence and mortality were 1.3 (95%CI: 1.2 - 1.4) and 1.7 (95%CI: 1.6 - 1.8), respectively. The subgroup analyses exhibited no difference in the association of BMI variability with CVD and mortality between the sexes and subpopulations based on obesity, diabetes, hypertension, and smoking status ( Supplementary Tables 1 , 2 ).

In the preliminary analysis, we test the mediation to see how the relationships works between the variables. So, we investigated the association between BMI variability (independent variable) and mediators (FPG, TC, and SBP), as well as the association between these mediators and CVD and mortality (dependent variable). Using the linear mixed model, the estimated beta coefficients for BMI variability as exposure and FPG, TC, and SBP as outcomes were 14.1 (95% CI: 12.8-15.4), 0.6 (95% CI: -0.6-1.9), and 3.3 (95% CI: 2.7-3.9) per 1 SD increase in BMI variability, respectively ( Table 3 ). The highest BMI variability tertile was significantly associated with mean FPG and SBP with beta coefficients of 12.2 (95%CI: 11.1-13.2) and 3.1 (95%CI: 2.5-3.6), respectively. However, BMI variability was not associated with higher values of TC in the quantile approach (P value=0.2). We also performed independent risk calculations to evaluate the association of mean FPG, TC, and SBP (from first to fourth visits during the measurement period) with CVD and mortality outcomes ( Table 4 ). After adjusting for potential confounders, the risk of future CVD increased for each unit increase in the mean of FPG (HR: 1.2; 95%CI: 1.1-1.3), TC (HR: 1.1; 95%CI: 1.0-1.2), and SBP (HR: 1.2; 95%CI: 1.1-1.3). The HRs for the association between the mean of FPG, TC, and SBP measurements and mortality were 1.2 (95%CI: 1.1-1.3), 1.0 (95%CI: 0.99-1.1), and 1.2 (95%CI: 1.1-1.2), respectively.

The relationship between BMI variability and CVD and mortality was determined considering the mediating effects of the mean values of FPG, TC, and SBP using the four-way effect decomposition ( Table 5 , Figure 2 ). After considering both the mediations and interactions, BMI variability was associated significantly with CVD (HR: 1.10, 95% CI: 1.05-1.14) and mortality (HR: 1.28, 95% CI: 1.24-1.33). For the outcome of mortality, when investigating FPG as a mediator, BMI variability was responsible for 92%, and FPG accounted for 8% of the total effect (P value<0.001), while TC and SBP were not significant mediators in the relationship (P value=0.5). The HR of the direct effect of the BMI variability on CVD was 1.08 (95% CI: 1.03-1.12), explaining 75.7% of the overall effect. The indirect effect of BMI variability via FPG as the only significant mediator was 1.02 (95%CI: 1.02-1.03) indicating 24.3% of the relationship between BMI variability and future CVD.

This pooled analysis of cohort studies investigated the association between BMI variability and CVD and mortality over two decades. After adjusting for potential confounders, highest compared to the lowest BMI variability tertile was associated with a 30% and 60% increased risk of CVD and mortality, respectively. There was no significant difference in this association based on sex, obesity, smoking status, presence of diabetes, or hypertension. Subsequently, the mediating and interaction effects of the three important cardiometabolic factors, including FPG, SBP, and TC, were explored. We estimated that FPG mediated 24% of the excess risk for CVD and 8% of the excess risk for mortality. On the other hand, SBP and TC were not significant mediators in the association of BMI variability with CVD and mortality.

There is an ongoing controversy surrounding the impact of weight variability on CVD and mortality, especially among different subgroups. Several studies have suggested a link between weight fluctuation and a higher likelihood of future CVD and mortality (17), while others have not found any association (26, 27). Additionally, contrary findings have also emerged, suggesting that weight fluctuation could provide protection against CVD (14, 18). The underlying pathophysiology in the relationship between weight variability and CVD and mortality is not yet understood. Weight variability may be an indicator of underlying metabolic dysfunction (e.g., insulin resistance and inflammation) (28–30). It is also suggested that high variations in weight may lead to loss of muscle mass and an increase in fat mass (31, 32), increasing the risk of chronic diseases such as CVD.

Our findings suggested that high BMI variability was associated with an increased risk of future CVD and mortality, and this association was not significantly different among subpopulations ( Supplementary Tables 1 , 2 ), which is consistent with the latest report by Zou et al. (17). However, it is noteworthy that high BMI fluctuation did not significantly increase the risk of future CVD in obese participants, while it increased the risk of all-cause mortality. BMI variability was associated with CVD and mortality in participants with and without diabetes.

In the current study, we determined the direct effect of BMI variability on CVD and mortality and quantified how much of the effect is mediated through FPG, SBP, and TC. Using a linear mixed model, after adjusting for confounders, we found a significant relationship between BMI variability and the time-serial values of FPG, TC, and SBP. Recent animal studies support our findings by demonstrating the adverse impact of weight cycling on glucose, insulin, and inflammatory markers’ levels (29, 33, 34). Weight variability is suggested to contribute to adipocyte enlargement and an increase in lipogenic enzymes, myristic acid, palmitic acid, palmitoleic acid, and stearic acid, resulting in glucose metabolism impairment (35, 36). Previous studies have found an independent association between high weight variability and incident diabetes (37–39), although only a few prospective studies have examined the correlation of BMI variability with the slope and alterations in cardiovascular risk factors using multiple linear regression (19, 20, 40). These studies suggested that BMI variability was associated with HbA1C levels but not SBP or TC. Notably, the impact of BMI variability on FPG was not explored. The discrepancy in the results regarding the association of BMI variability with TC and SBP may stem from differences in BMI variability calculation methods, study populations, analytical approaches, and follow-up duration. Our study, in contrast to previous ones, consisted of a large sample size.

The results of the mediation analysis revealed that 24% of the excess risk of BMI variability for CVD and 8% for mortality was mediated by FPG, while TC and SBP did not demonstrate significant mediation effects. A pooled cohort analysis conducted by the global burden of diseases reported that more than 44% of the excess risk of high baseline BMI (being overweight or obese) was mediated through FPG, TC, and SBP (41). Our study is the first to analyze the mediation effect of these risk factors in the association of BMI variability and adverse health outcomes. The non-significant mediation effect of TC and SBP in our study may be due to their weaker association with BMI variability compared to FPG ( Table 3 ). These findings provide new insight into understanding the complex relationship between BMI variability, major cardiovascular risk factors, and CVD and mortality.

In the current study, for the first time, the direct and indirect effect of BMI variability on CVD and mortality was investigated in a pooled cohort analysis of three large longitudinal population-based studies with long follow-ups. Many studies resort to simple statistical methods to measure BMI variability, overlooking the non-linear nature of BMI trend and mistakenly perceiving it as fluctuation (24). To address this, we employed the RMSE within a random coefficient model to differentiate between true non-linear changes in BMI and BMI fluctuation. There are also some limitations in this study. Our study included the BMI measurements taken in the clinic and the BMI measurements were not self-reported although it is noteworthy that the measurements in the clinic are done with unified standard protocols. We did not assess whether the participants had intentions of losing or gaining weight. However, the primary goal was that unintentional weight loss may have been an indicator of underlying diseases. Also, our findings solely pertained to BMI variability and lacked information regarding alterations in fat mass, muscle mass, and change in body composition. Future research should prioritize lifelong maintenance of body weight and reduction of sporadic weight fluctuations, especially in the context of primary cardiac prevention. This approach can help in sustaining healthy weight levels and minimizing associated health risks.

In conclusion, BMI variability is an independent predictor for incident CVD and mortality and there is no significant difference in this association across subpopulations. FPG mediates 24% and 8% of excess risk of BMI variability for the development of CVD and mortality, respectively; however, SBP and TC are not significant mediators in this association. These findings present new potential implications for preventing CVD and mortality in individuals with high BMI variability. Modern cardiometabolic interventions focusing on glycemic monitoring and management might help reduce the risk of future CVD and mortality during frequent weight loss attempts.

The data analyzed in this study is subject to the following licenses/restrictions: Datasets generated during and/or analyzed during the current study are not publicly available due to institutional policies but are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to amouzegar@endocrine.ac.ir.

The studies involving humans were approved by the National Research Council of the Islamic Republic of Iran (IR.SBMU.ENDOCRINE.REC.1402.060), the Human Research Review Committee of the Endocrine Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

LM: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. MH: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. SM: Data curation, Formal Analysis, Methodology, Visualization, Writing – original draft. DK: Methodology, Writing – review & editing. FA: Supervision, Writing – review & editing. MB: Supervision, Writing – review & editing. AA: Project administration, Writing – review & editing.