Abstract
Background
The increasing intake of sugar-sweetened (SSBs) and artificially sweetened beverages (ASBs) raises concerns over their potential impact on cardiovascular health. This umbrella meta-analysis examines their association with cardiovascular outcomes, synthesizing existing evidence comprehensively.
Methods
A systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar databases, covering studies published up to November 2024. Eligible meta-analyses were synthesized using a random-effects model to calculate pooled effect sizes and 95% confidence intervals. Subgroup and sensitivity analyses were performed, and the methodological quality of studies was evaluated using AMSTAR 2 tools.
Results
Nineteen meta-analyses encompassing 67 datasets met the inclusion criteria. ASB consumption was associated with a 10% increased risk of mortality (RR: 1.10; 95% CI: 1.07 to 1.12), including an 8% increased risk of cardiovascular disease (CVD) mortality (RR: 1.08; 95% CI: 1.05 to 1.11). SSB intake was linked to a 9% higher overall mortality risk (RR: 1.09; 95% CI: 1.07 to 1.11) and a 10% increased CVD mortality risk (RR: 1.10; 95% CI: 1.07 to 1.13). Additional cardiovascular risks associated with both beverages included hypertension (RR range: 1.12–1.19), coronary heart disease (RR range: 1.09–1.20), metabolic syndrome (RR range: 1.19–1.31), and stroke (RR range: 1.06–1.25).
Conclusion
The results underscore a clear link between the intake of sweetened beverages and elevated risks of mortality and major cardiovascular outcomes, suggesting an urgent need to reduce their consumption as part of public health initiatives. Targeted strategies, including consumer education and policy interventions, may help mitigate these risks and improve cardiovascular health outcomes.
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12937-025-01242-1.
Keywords: Sugar-sweetened beverages, Artificially sweetened beverages, Cardiovascular outcomes, Mortality, Meta-analysis
Introduction
Despite advancements in healthcare, cardiovascular related complications continue to be a leading cause of morbidity and mortality globally, representing a significant public health concern. In 2019, cardiovascular related complications accounted for approximately 17.9 million deaths worldwide, representing 32% of all global deaths, with a notable proportion occurring in low- and middle-income countries [1]. This substantial burden underscores the urgent need for implementing effective prevention and management strategies.
Dietary choices are key modifiable risk factors for cardiovascular health, with sugar-sweetened beverages (SSBs) and artificially sweetened beverages (ASBs) identified as important contributors [2]. The CARDIA study linked cumulative intake of both SSBs and ASBs to higher risk of Type 2 Diabetes (T2DM), a major cardiovascular risk factor [3]. Likewise, a meta-analysis reported significant associations between high consumption of these beverages and increased cardiovascular and all-cause mortality, with SSBs linked to an 11% higher all-cause mortality risk (highest vs lowest intake category) and ASBs to a 12% higher risk. Dose–response analysis further indicated an ~ 8% increase in all-cause mortality risk per additional daily serving of SSBs. [4]. These findings highlight the need to address beverage consumption in strategies for cardiovascular disease prevention and global health promotion.
SSBs, rich in added sugars like sucrose and high-fructose corn syrup, have been consistently linked to cardiovascular risk factors including obesity, T2DM, and metabolic syndrome [5]. Reviews indicate positive associations between SSB intake and weight gain or obesity [6, 7], though findings are not uniform. For example, a prospective study in pediatric populations reported mixed results, with associations attenuated after adjusting for total energy intake [8].
Conversely, ASBs, often marketed as healthier alternatives to SSBs, contain non-nutritive sweeteners and are generally perceived as beneficial or neutral in terms of cardiovascular risk, as they contain no sugar. However, some studies suggest potential metabolic disruptions and cardiovascular risks associated with ASBs consumption. For metabolic syndrome specifically, a dose–response meta-analysis of 24 population-based studies reported that each 250 mL/day increase in intake was associated with a 19% higher risk for SSBs and a 31% higher risk for ASBs [9]. These findings are consistent with a more recent systematic review and meta-analysis (14 studies) showing higher odds of metabolic syndrome with greater SSB consumption [10]. In contrast, the Health Professionals Follow-Up Study found no association between artificially sweetened beverage intake and coronary heart disease (CHD), whereas higher sugar-sweetened beverage intake was associated with increased CHD risk (≈20% higher risk for top vs. bottom intake; ≈19% higher risk per additional daily serving) [11].
Despite the growing body of research and recent debates, a significant knowledge gap persists in understanding the impact of ASBs and SSBs consumption on various cardiovascular risk factors, including hypertension, dyslipidemia, obesity, and insulin resistance. To address this, we aimed to conduct a comprehensive umbrella review of published systematic reviews and meta-analyses thoroughly investigating the association between ASBs and SSBs intake and major cardiovascular and metabolic outcomes, including hypertension, stroke, CHD, metabolic syndrome, cardio vascular diseases (CVDs), and mortality.
Methods
This umbrella meta-analysis adhered to the PRIOR (Preferred Reporting Items for Overviews of Reviews) guidelines [12], which are specifically developed for reporting overviews of reviews of healthcare interventions (Supplementary file 1). The study protocol has been registered in PROSPERO databases (CRD42024505091).
Database search
We conducted a comprehensive search of global scientific databases, such as PubMed, Scopus, Web of Science, and Google Scholar to identify relevant articles. The search encompassed articles in English available from database conception until November 2024. The complete search strategy for all databases is outlined in Supplementary file 2.
Eligibility criteria
This umbrella meta-analysis incorporated published meta-analyses assessing the impact of ASBs and SSBs on the risk of Cardiovascular related outcomes including hypertension, stroke, CHDs, metabolic syndrome, CVDs, and mortality. Excluded studies contained co-interventions, individuals below 18 years of age (children and adolescents), or pregnant/lactating women. The PICO criteria for this umbrella meta-analysis were as follows: Population/Patients (P: adult population); Intervention or exposure (E: ASBs and SSBs consumption); Comparison (C: control group); Outcome (O: Cardiovascular related outcomes including hypertension, stroke, CHD, metabolic syndrome, CVDs and mortality risk).
Study selection and data extraction
Two independent reviewers (MJ, BH) meticulously screened articles for eligibility. Initially, the title and abstract of each article were assessed, followed by a full-text evaluation of relevant articles to determine their suitability for inclusion in the umbrella meta-analysis. Discrepancies were resolved through consensus with the senior reviewer (MZ). The following relevant information was extracted from the selected meta-analyses and recorded in an Excel spreadsheet: publication year, sample size, study type, study location, journal of study, type of study complication, health condition, type of treatment, effect sizes as reported in the included meta-analyses, preferring pooled risk ratios (RRs) or hazard ratios (HRs) comparing the highest versus lowest and corresponding CIs for hypertension, mortality risk, stroke, CVD, CHD, and metabolic syndrome. We extracted the exact results and beverage definitions as reported in each meta-analysis under the categories of ASBs and SSBs, which varied across studies; results were synthesized accordingly without reclassification.
Data synthesis and statistical analysis
Random-effects models with the restricted maximum likelihood method (REML) was employed to estimate the pooled ES and its corresponding 95% CI [13]. Heterogeneity was assessed using I2 statistics and Cochrane's Q-test, considering an I2 value greater than 50% or a p-value less than 0.1 for the Q-test as indicative of substantial between-study heterogeneity [14]. Sensitivity analysis was conducted to evaluate the influence of individual studies on the overall effect size. Subgroup analyses were performed based on age, quality of the study, type of mortality, and type of effect size where applicable. Begg’s and Egger’s tests were utilized to evaluate publication bias, and visual inspection of the funnel plot was conducted for additional assessment. If publication bias was confirmed, trim and fill analysis was executed to present a new effect size adjusted for publication bias by inserting new hypothetical studies into the model. Publication bias analyses were not conducted if the number of observations for a variable was fewer than eight. Stata, version 16 (Stata Corporation, College Station, TX, US), was used for all statistical analyses, and a p-value less than 0.05 was considered statistically significant.
Methodological quality assessment
Two independent reviewers (PA, MR) systematically evaluated the methodological robustness of the included studies utilizing the Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR2) questionnaire [15]. Discrepancies were resolved through consultation with the senior author (MZ) to establish consensus. The AMSTAR2 questionnaire, encompassing 16 items, required responses denoted as "Yes," "Partial Yes," "No," or "Not a Meta-analysis." The resulting assessments from the AMSTAR2 checklist were categorized into levels of quality, specifically "Critically low quality," "Low quality," "Moderate quality," and "High quality."
Results
Study selection
A total of 14,805 articles were retrieved through database searches. After deduplication, 8,173 records remained. Title and abstract screening led to the exclusion of 7,345 records. Of the 828 full-text articles assessed, 37 meta-analyses were excluded for the following reasons: examining unrelated outcomes such as obesity (n = 26), reviewing sugar intake from food groups rather than beverages (n = 7), or focusing on ASBs and SSBs in children (n = 4). Ultimately, 19 meta-analyses comprising 67 datasets were eligible for inclusion. The complete stepwise selection process and reasons for exclusions are presented in Fig. 1.
Fig. 1.
The flowchart shows the stepwise selection process
Characteristics of the included meta-analyses
The characteristics of 19 meta-analyses with 67 datasets are summarized in Table 1. Among them ten meta-analyses were conducted in China [4, 9, 16–23], two in USA [24, 25], two in UK [26, 27], two in Canada [28, 29], one in Brazil [30], one in Czech [31] and one in Spain [10]. All included records were meta-analyses of observational studies—predominantly prospective cohort studies, with some nested case–control, case-cohort, and occasional cross-sectional studies— published between 2010 and 2023. Specifically, four meta-analyses have assessed the association between the intake of ASB and the risk of hypertension [4, 16, 23, 24], six studies with ten data sets have investigated the association between ASBs intake and mortality risk [4, 18, 20, 21, 23, 27, 31], four studies have examined the relationship between ASBs intake and the risk of stroke [4, 20, 27, 28], two studies, the risk of CHD [4, 20], two studies, the risk of CVDs [21, 27], and two studies, investigated the association between the risk of metabolic syndrome and ASBs consumption [9, 26]. Additionally, six studies have investigated the effect of SSB intake on the risk of hypertension [4, 16, 22–24, 29], nine studies, including fifteen datasets, have studied the relationship of SSBs with mortality risk [4, 17–21, 23, 27, 31], six studies have examined the relationship between SSBs intake and stroke risk [4, 17, 20, 22, 27, 30], four and two studies have investigated the risk of CHD [4, 20, 22, 30] and CVD [21, 27], respectively, in relation to the consumption of SSBs. Finally, four studies, including six datasets, have examined the relationship between SSBs intake and the risk of metabolic syndrome [9, 10, 25, 26].
Table 1.
Characteristics of the selected studies
| First author | year | Study Type | Study number | No. participation | Age | Follow-up (years) | Effect size | Evaluated cardiovascular outcomes, following ASBs and SSBs intake |
|---|---|---|---|---|---|---|---|---|
| Zhao (a) | 2023 | cohort | 3 | 223,891 | 43.09 | NR | RR | ASBs-HTN |
| Zhao (b) | 2023 | cohort and cross sectional | 16 | 396,192 | 34.25 | NR | RR | SSBs-HTN |
| Baoyu Li (a) | 2023 | cohort | 4 | 227,254 | 43 | 20.45 | RR | ASBs-HTN |
| Baoyu Li (b) | 2023 | cohort | 7 | 836,161 | 60 | 16.85 | RR | ASBs-All-cause mortality |
| Baoyu Li (c) | 2023 | cohort | 4 | 209,379 | 55 | 16 | RR | ASBs-CVD mortality |
| Baoyu Li (d) | 2023 | cohort | 2 | 5452 | 65.5 | 9.9 | RR | ASBs-Stroke |
| Baoyu Li (e) | 2023 | cohort | 3 | 133,967 | 50 | 18.6 | RR | ASBs-CHD |
| Baoyu Li (f) | 2023 | cohort | 8 | 258,721 | 45 | 15 | RR | SSBs-HTN |
| Baoyu Li (g) | 2023 | cohort | 14 | 1,131,605 | 50 | 16 | RR | SSBs-All-cause mortality |
| Baoyu Li (h) | 2023 | cohort | 9 | 729,644 | 50 | 15 | RR | SSBs-CVD mortality |
| Baoyu Li (i) | 2023 | cohort | 7 | 302,669 | 55 | 18 | RR | SSBs-Stroke |
| Baoyu Li (j) | 2023 | cohort | 7 | 590,466 | 50 | 17.5 | RR | SSBs-CHD |
| Muñoz-Cabrejas (a) | 2023 | Cross-sectional | 12 | 62,693 | 30 | NR | OR | SSBs-Metabolic syndrome |
| Muñoz-Cabrejas (b) | 2023 | cohort | 7 | 28,932 | 50 | 7.2 | OR | SSBs-Metabolic syndrome |
| Hongyi Li (a) | 2022 | cohort | 5 | NR | 60 | 16.85 | HR | ASBs-All-cause mortality |
| Hongyi Li (b) | 2022 | cohort | 3 | NR | NR | NR | HR | ASBs-CVD mortality |
| Hongyi Li (c) | 2022 | cohort | 3 | NR | NR | NR | HR | ASBs-cancer mortality |
| Hongyi Li (d) | 2022 | cohort | 11 | NR | NR | NR | HR | SSBs-All-cause mortality |
| Hongyi Li (e) | 2022 | cohort | 10 | NR | NR | NR | HR | SSBs-CVD mortality |
| Hongyi Li (f) | 2022 | cohort | 7 | NR | NR | NR | HR | SSBs-cancer mortality |
| Hongyi Li (g) | 2022 | cohort | 6 | NR | NR | NR | HR | SSBs-mortality |
| Bhagavathula (a) | 2022 | cohort | 5 | 1,112,451 | 55 | 16 | RR | ASBs-CVD mortality |
| Bhagavathula (b) | 2022 | cohort | 5 | 748,046 | 55 | 13.4 | RR | SSBs-CVD mortality |
| J. Lee | 2022 | cohort | 1 | 127,456 | 50 | 25 | RR | ASBs-Stroke |
| Yuanxin Wang (a) | 2022 | cohort | 9 | 949,023 | 55 | 12.1 | RR | SSBs-mortality |
| Yuanxin Wang (b) | 2022 | cohort | 8 | 350,684 | 45 | 18 | RR | SSBs-Stroke |
| Pozza Santos (a) | 2022 | cohort | 5 | 304,759 | 55 | 16 | RR | SSBs-Stroke |
| Pozza Santos (b) | 2022 | cohort | 3 | 114,496 | 53 | 12.5 | RR | SSBs-Stroke |
| Pozza Santos (c) | 2022 | cohort | 3 | 415,748 | 45 | 18 | RR | SSBs-CHD |
| Jiawei Yin (a) | 2021 | cohort | 4 | 445,907 | 50 | 20 | RR | ASBs-CVD mortality |
| Jiawei Yin (b) | 2021 | cohort | 5 | 213,871 | 60 | 16.5 | RR | ASBs-Stroke |
| Jiawei Yin (c) | 2021 | cohort | 4 | 215,631 | 53 | 17 | RR | ASBs-CHD |
| Jiawei Yin (d) | 2021 | cohort | 6 | 510,224 | 55 | 19.8 | RR | SSBs-CVD mortality |
| Jiawei Yin (e) | 2021 | cohort | 5 | 171,943 | 60 | 17.5 | RR | SSBs-Stroke |
| Jiawei Yin (f) | 2021 | cohort | 4 | 173,703 | 55 | 18.5 | RR | SSBs-CHD |
| Yantong Meng (a) | 2021 | cohort | 6 | 864,083 | 55 | 20 | RR | ASBs-All-cause mortality |
| Yantong Meng (b) | 2021 | cohort | 8 | 325,259 | 51.5 | NR | RR | ASBs-CVD |
| Yantong Meng (c) | 2021 | cohort | 8 | 917,975 | 50 | 17 | RR | SSBs-All-cause mortality |
| Yantong Meng (d) | 2021 | cohort | 14 | 521,084 | 50 | 12.5 | RR | SSB-CVD |
| Xiao Zhang (a) | 2021 | cohort and cross sectional | 5 | 17,386 | 45 | NR | RR | ASBs-Metabolic syndrome |
| Xiao Zhang (b) | 2021 | cohort and cross sectional | 15 | 60,558 | 55 | NR | RR | SSBs-Metabolic syndrome |
| Zhang (a) | 2021 | cohort | 11 | 965,851 | 60 | 14.5 | HR | SSBs-All-cause mortality |
| Zhang (b) | 2021 | cohort | 13 | 898,005 | 50 | 13 | HR | SSBs-CVD mortality |
| Zhang (c) | 2021 | cohort | 5 | 692,272 | 50 | 19 | HR | SSBs-cancer mortality |
| Pei Qin (a) | 2020 | cohort | 5 | 296,762 | NR | 18 | RR | ASBs-HTN |
| Pei Qin (b) | 2020 | cohort | 4 | 665,221 | NR | 19.7 | RR | ASBs-All-cause mortality |
| Pei Qin (c) | 2020 | cohort | 6 | 312,156 | NR | 16 | RR | SSB-HTN |
| Pei Qin (d) | 2020 | cohort | 10 | 1,125,834 | NR | 16.5 | RR | SSBs-All-cause mortality |
| Aditya Narain (a) | 2017 | cross sectional | 2 | 2126 | 51.5 | NR | RR | ASBs-Metabolic syndrome |
| Aditya Narain (b) | 2017 | cohort | 3 | 20,564 | 55 | 12 | RR | ASBs-Metabolic syndrome |
| Aditya Narain (c) | 2017 | cross-sectional | 12 | 35,141 | 40 | NR | RR | SSBs-Metabolic syndrome |
| Aditya Narain (d) | 2017 | cohort | 3 | 13,122 | 45 | 12 | RR | SSBs-Metabolic syndrome |
| Aditya Narain (a) | 2016 | cohort | 2 | NR | NR | NR | RR | ASBs-Mortality |
| Aditya Narain (b) | 2016 | cohort | 2 | NR | NR | NR | RR | ASBs-Stroke |
| Aditya Narain (c) | 2016 | cohort | 2 | NR | NR | NR | RR | ASBs-CVD |
| Aditya Narain (d) | 2016 | cohort | 1 | NR | NR | NR | RR | ASBs-CVD |
| Aditya Narain (e) | 2016 | cohort | 3 | NR | NR | NR | RR | SSBs-mortality |
| Aditya Narain (f) | 2016 | cohort | 6 | NR | NR | NR | RR | SSBs-Stroke |
| Aditya Narain (g) | 2016 | cohort | 4 | NR | NR | NR | RR | SSBs-CVD |
| Aditya Narain (h) | 2016 | cohort | 1 | NR | NR | NR | RR | SSBs-CVD |
| Bo Xi (a) | 2015 | cohort | 6 | 240,726 | 40 | 18 | RR | SSBs-HTN |
| Bo Xi (b) | 2015 | cohort | 4 | 259,176 | 45 | 17.5 | RR | SSBs-Stroke |
| Bo Xi (c) | 2015 | cohort | 4 | 194,611 | 50 | 18 | RR | SSBs-CHD |
| Viranda H Jayalath | 2015 | cohort | 6 | 240,508 | 40 | 18 | RR | SSBs-HTN |
| Wisit Cheungpasitporn (a) | 2015 | cohort and cross sectional | 4 | 382 588 | NR | NR | RR | ASBs-HTN |
| Wisit Cheungpasitporn (b) | 2015 | cohort and cross sectional | 8 | 414,328 | NR | NR | RR | SSBs-HTN |
| MALIK | 2010 | cohort | 3 | 19,431 | 50 | 5.5 | RR | SSBs-Metabolic syndrome |
NR Not reported, SSBs Sugar sweetened beverages, ASBs Artificially sweetened beverages, HTN Hypertension, CVD Cardiovascular diseases, CHD Coronary heart disease
Methodological quality assessment
The result of quality assessment of meta-analyses according to the AMSTAR2 questionnaire is shown in Supplementary File 3. Specifically, five studies were classified as high quality [10, 21, 26, 27, 31], four studies as moderate quality [17, 18, 23, 24], nine studies as low quality [4, 9, 16, 19, 20, 22, 28–30] and one study was considered critically low quality [25].
Relationship between ASBs and SSBs intake and mortality risk
Based on our findings, both ASBs and SSBs intake were associated with a significant increase in mortality risk (all-cause, CVD, and cancer mortality) when comparing the highest versus lowest categories of consumption, with ASBs showing a 10% increase (RR: 1.10; 95% CI: 1.07 to 1.12; p = 0.008) (Fig. 2A), and SSBs showing a 9% increase (RR: 1.09; 95% CI: 1.07 to 1.11; p = 0.006) (Fig. 2B). Heterogeneity among studies was found to be insignificant for both ASBs (I2 = 19.2%, P-heterogeneity = 0.26), and SSBs (I2 = 21.6%, P-heterogeneity = 0.21). Sensitivity analysis did not yield any statistically significant difference upon excluding individual study. Subgroup analysis revealed that ASB consumption was more strongly associated with mortality risk in individuals aged over 60 years (RR: 1.12; 95% CI: 1.06–1.18; p < 0.001) (Table 2). Additionally, ASBs intake was associated with a higher risk of all-cause mortality compared to CVD mortality ((RR: 1.13; p < 0.001, compared (RR: 1.08; p = 0.007)), whereas, SSBs consumption was associated with a higher risk of CVD mortality compared to all-cause mortality ((RR: 1.10; p = 0.005, compared (RR: 1.09; p = 0.006)). A significant small-study effect for ASBs was detected using Egger’s test (p = 0.02), but not Begg’s test (p = 0.1). Visual inspection of the funnel plot revealed an uneven distribution of meta-analyses, and trim and fill analysis confirmed the significant association with ASBs (RR: 1.07; 95% CI: 1.04 to 1.10) (Fig. 2C). Regarding SSBs, no significant small-study effect was detected using Egger’s (p = 0.26) and Begg’s (p = 0.06) tests. However, visual inspection of the funnel plot revealed an uneven distribution of meta-analyses, and trim and fill analysis indicated a significant association with SSBs (RR: 1.08; 95% CI: 1.07 to 1.10) (Fig. 2D).
Fig. 2.
A) Forest plot representing the risk ratio and 95% confidence intervals (CIs) pertaining the association between ASBs intake and mortality; and B) SSBs intake and mortality. C) Funnel plot related to the association between ASBs and mortality risk; and D) SSBs and mortality risk
Table 2.
Subgroup analyses of ASBs and SSBs intake in relation to various cardiovascular outcomes
|
Effect size number |
ES (95% CI)1 | P-within2 | I2(%)3 | P-heterogeneity4 | |
|---|---|---|---|---|---|
| Association between ASBs intake and mortality risk | |||||
| Overall | 10 | 1.10 (1.07, 1.12) | 0.008 | 19.2 | 0.26 |
| Age | |||||
| ≥ 60 | 2 | 1.12 (1.06, 1.18) | < 0.001 | 0.00 | 1.00 |
| < 60 | 4 | 1.09 1.05, 1.13) | 0.003 | 31.1 | 0.22 |
| NR | 4 | 40 | 0.17 | ||
| Study quality | |||||
| Low | 6 | 1.08 (1.05, 1.10) | 0.008 | 0.00 | 0.46 |
| high | 3 | 1.14 (1.08, 1.21) | < 0.001 | 0.00 | 0.84 |
| Moderate | 1 | 1.15 (1.07, 1.23) | < 0.001 | 0.00 | - |
| Type of mortality | |||||
| All-cause mortality | 5 | 1.13 (1.09, 1.17) | < 0.001 | 0.00 | 0.95 |
| CVD mortality | 4 | 1.08 (1.05, 1.11) | 0.007 | 8.5 | 0.35 |
| Cancer mortality | 1 | 1.04 (0.97, 1.11) | 0.187 | 0.00 | - |
| Association between SSBs intake and HTN risk | |||||
| Overall | 6 | 1.13 (1.10, 1.16) | < 0.001 | 45.6 | 0.10 |
| Study quality | |||||
| Low | 4 | 1.14 (1.09, 1.19) | < 0.001 | 67.1 | 0.02 |
| Moderate | 2 | 1.13 (1.10, 1.16) | < 0.001 | 0.00 | 0.85 |
| Association between SSBs intake and mortality risk | |||||
| Overall | 15 | 1.09 (1.07, 1.11) | 0.006 | 21.6 | 0.21 |
| Study quality | |||||
| Low | 6 | 1.08 (1.06, 1.10) | 0.005 | 17.9 | 0.29 |
| Moderate | 6 | 1.09 (1.05, 1.14) | 0.007 | 38 | 0.15 |
| High | 3 | 1.12 (1.06, 1.18) | 0.002 | 4.8 | 0.35 |
| Type of mortality | |||||
| All-cause mortality | 8 | 1.09 (1.07, 1.11) | 0.006 | 0.00 | 0.66 |
| CVD mortality | 5 | 1.10 (1.07, 1.13) | 0.005 | 12.6 | 0.33 |
| Cancer mortality | 2 | 1.01 (0.95, 1.07) | 0.263 | 0.00 | 0.41 |
| Type of effect size | |||||
| RR | 8 | 1.09 (1.07, 1.11) | 0.006 | 0.00 | 0.61 |
| HR | 7 | 1.08 (1.04, 1.12) | 0.005 | 47.4 | 0.07 |
| Association between SSBs intake and metabolic syndrome risk | |||||
| Overall | 6 | 1.32 (1.19, 1.46) | < 0.001 | 46.9 | 0.09 |
| Age | |||||
| < 50 | 3 | 1.39 (1.22, 1.56) | < 0.001 | 0.00 | 0.83 |
| ≥ 50 | 3 | 1.29 (1.08, 1.50) | < 0.001 | 71.5 | 0.03 |
| Study quality | |||||
| High | 4 | 1.28 (1.14, 1.42) | < 0.001 | 24.1 | 0.26 |
| Low | 1 | 1.56 (1.31, 1.81) | < 0.001 | 0.00 | - |
| Critically low | 1 | 1.20 (1.00, 1.40) | 0.048 | 0.00 | - |
| Type of effect size | |||||
| OR | 2 | 1.24 (1.08, 1.40) | < 0.001 | 43.1 | 0.18 |
| RR | 4 | 1.39 (1.19, 1.60) | < 0.001 | 43.2 | 0.15 |
| Association between SSBs intake and stroke risk | |||||
| Overall | 7 | 1.10 (1.05, 1.14) | 0.010 | 0.00 | 0.85 |
| Age | |||||
| ≥ 50 | 4 | 1.09 (1.03, 1.15) | 0.009 | 0.00 | 0.51 |
| < 50 | 2 | 1.11 (1.04, 1.18) | 0.008 | 0.00 | 0.78 |
| NR | 1 | 1.10 (0.96, 1.24) | 0.319 | 0.00 | - |
| Study quality | |||||
| Low | 5 | 1.09 (1.04, 1.14) | 0.034 | 0.00 | 0.67 |
| Moderate | 1 | 1.12 (1.02, 1.22) | 0.042 | 0.00 | - |
| High | 1 | 1.10 (0.96. 1.24) | 0.297 | 0.00 | - |
Relationship between ASBs and SSBs intake and hypertension risk
The consumption of ASBs and SSBs was associated with a significant increase in the risk of hypertension, with ASBs showing a 14% increase (RR: 1.14; 95% CI: 1.12 to 1.15; p < 0.001) (Fig. 3A), and SSB showing a 13% increase (RR: 1.13; 95% CI: 1.10 to 1.16; p < 0.001) (Fig. 3B). Heterogeneity among studies was found to be insignificant for both ASBs (I2 = 0.00%, P-heterogeneity = 0.86) and SSBs (I2 = 45.6%, P-heterogeneity = 0.1). Moreover, sensitivity analysis indicated that excluding individual meta-analyses did not change the overall results.
Fig. 3.
A) Forest plot illustrating the risk ratio and 95% confidence intervals (CIs) regarding the association between ASBs intake and hypertension; B) SSBs intake and hypertension; C) ASBs intake and CHDs; and D) ASBs intake and CVDs
Relationship between ASBs and SSBs intake and the risk of CVDs and CHDs
The ASBs consumption was found to significantly increase the risk of CHDs by 6% (RR: 1.06; 95% CI: 1.02 to 1.11; p = 0.03) (Fig. 3C) and CVDs by 14% (RR: 1.14; 95% CI: 1.00 to 1.27; p = 0.04) (Fig. 3D). The heterogeneities for these two outcomes were not significant (CHD: I2 = 0.00%, P-heterogeneity = 0.66; CVD: I2 = 35.7%, P-heterogeneity = 0.21). Similarly, SSB consumption was also associated with a 16% higher risk of CHD (RR: 1.16; 95% CI: 1.12 to 1.20; p < 0.001) (Fig. 4A) and CVDs by 17% (RR: 1.17; 95% CI: 1.12 to 1.22; p < 0.001) (Fig. 4B). The obtained between-study heterogeneities were not significant for CHDs (I2 = 0.00%, P-heterogeneity = 0.98) and CVDs (I2 = 0.00%, P-heterogeneity = 0.83). However, sensitivity analysis revealed that by excluding two studies (Meng et. al [21]. and Narain et. al [27].), the association between ASBs intake and the risk of CVDs returned insignificant.
Fig. 4.
A) Forest plot depicting the risk ratio and 95% confidence intervals (CIs) to visualize the association between SSBs intake and CHDs; B) SSB intake and CVDs; C) ASBs intake and metabolic syndrome; and D) SSBs intake and metabolic syndrome
Relationship between ASBs and SSBs intake and the risk of metabolic syndrome
According to our data, ASB consumption was also associated with a 34% higher risk of metabolic syndrome (RR: 1.34; 95% CI: 1.23 to 1.45; p < 0.001) (Fig. 4C). No significant between-study heterogeneity was observed (I2 = 0.00%, P-heterogeneity = 0.40).
Nevertheless, after conducting sensitivity analysis, it was found that excluding the study by Zhang et al.[9], resulted in the overall findings becoming statistically insignificant. Moreover, the analysis of six datasets showed that SSBs consumption significantly increased the risk of metabolic syndrome by 32% (RR: 1.32; 95% CI: 1.19 to 1.46; p < 0.001) (Fig. 4D). Although there was some heterogeneity observed (I2 = 46.9%, P-heterogeneity = 0.09), sensitivity analysis showed that the overall result was not changed by removing individual studies. Furthermore, subgroup analysis results highlighted a significant increase in the risk of metabolic syndrome associated with SSB consumption, particularly among individuals below 50 years old (Table 2).
Relationship between ASBs and SSBs intake and the risk of stroke
The analysis of four studies revealed that ASB consumption was also associated with a 9% higher risk of stroke (RR: 1.09; 95% CI: 1.02 to 1.15; p = 0.04) (Fig. 5A), with significant heterogeneity observed (I2 = 59.9%, P-heterogeneity = 0.05). However, sensitivity analysis indicated that excluding Yin et. al. [20] and Narain et. al. [27] resulted in the overall outcome becoming statistically insignificant. Similarly, SSBs consumption significantly increased the risk of stroke by 10% (RR: 1.10; 95% CI: 1.05 to 1.14; p = 0.01) (Fig. 5B). There was no significant heterogeneity observed (I2 = 0.00%, P-heterogeneity = 0.85), and the overall result didn't change after sensitivity analysis. Furthermore, subgroup analysis revealed a substantial increase in the risk of stroke among individuals under 50 years of age compared to those over 50 years following SSBs consumption. No significant small-study effect was observed based on Egger’s (p = 0.67) and Begg’s (p = 1.00) tests.
Fig. 5.
A) Forest plot showing the risk ratio and 95% confidence intervals (CIs) for assessing the association between ASBs intake and stroke; as well as B) SSBs intake and stroke
Overlap of primary studies across meta-analyses
To assess redundancy across the included meta-analyses, we quantified the overlap of primary studies using the Corrected Covered Area (CCA) index, and implemented in the GROOVE (Graphical Representation of Overlap for OVErviews) tool [32, 33]. The CCA was calculated separately for each outcome of interest, and the detailed graphical outputs are presented in Supplementary File 4 (Figures S1–S12).
ASBs: Mortality 19.5% (very high); CVD 10.0% (high); CHD 16.7% (very high); Hypertension 14.8% (high); Metabolic syndrome 25.0% (very high); Stroke 8.3% (moderate).
SSBs: Mortality 14.8% (high); CVD 11.8% (high); CHD 11.9% (high); Hypertension 12.8% (high); Metabolic syndrome 9.4% (moderate); Stroke 13.7% (high).
Discussion
This umbrella review synthesizes evidence on clinical cardiovascular outcomes—all-cause mortality, CVD, CHD, and stroke—and on intermediate cardiometabolic profiles (hypertension and Mets) for both SSBs and ASBs, allowing side-by-side appraisal of risk patterns across beverage types. Considering the prevalent presence of these beverages in modern dietary patterns and their potential ramifications for public health, our review holds significant relevance and novelty. Furthermore, while prior umbrella reviews have separately evaluated SSBs across broad health domains [34] or ASBs/non-SSB across multiple outcomes [35], our work provides a cardiovascular-focused, side-by-side synthesis of ASBs and SSBs, applies a formal overlap quantification (CCA via GROOVE) to transparently address study redundancy, and reports pre-specified sensitivity analyses. Together, these features refine inference for CVD-related endpoints beyond the scope of earlier umbrellas.
Long-term consumption of both was positively associated with mortality (10% and 9%), hypertension (14% and 13%), CVDs (14% and 17%), CHDs (6% and 16%), metabolic syndrome (34% and 32%), and stroke (9% and 10%). These findings underscore the significant impact of sweetened beverages on cardiovascular health and highlight the need for their consideration in dietary guidelines and public health strategies.
Our SSB findings (higher risks of CVD, CHD, stroke, mortality) are directionally consistent with Lane et al. [34], who reported broadly adverse associations of SSBs across health outcomes, including cardiovascular endpoints. For ASBs, our associations for mortality/HTN (and more uncertain links for CVD and stroke) align with Díaz et al., [35] who graded evidence for ASBs as highly suggestive for some outcomes but generally weaker across others. Also, Compared with Beigrezaei et al. [36], who reported per-serving/day associations for non-SSB (e.g., stroke and CHD per 250 mL/day; HTN high vs low), our categorical contrasts and age-stratified analyses reach similar directions yet emphasize the high/very high CCA overlap, underscoring cautious interpretation.
In our sensitivity analyses, we uncovered the significant influence of specific studies on the overall conclusions. For instance, although our umbrella meta-analysis initially pointed to a notable rise in the risk of metabolic syndrome linked to ASBs consumption, the exclusion of the study by Zhang et al.[37] led to statistically insignificant results. Compared with the other meta-analyses, Zhang et al. pooled both prospective cohorts and cross-sectional studies (and one case–control) and standardized exposure using dose–response (per 250 mL/day) in addition to high-vs-low contrasts. Their dataset also included studies from mixed age groups and employed different quality tools for longitudinal versus cross-sectional designs. In contrast, earlier syntheses either restricted to prospective cohorts [25] or separated results by design and found no significant association in cohort-only analyses (e.g., Narain et al. [26]). Accordingly, when Zhang et al. is removed, the cohort-dominant evidence remaining in our umbrella attenuates and becomes non-significant, consistent with the notion that inclusion of cross-sectional evidence in Zhang’s pooled estimate likely contributed to the stronger association signal.
Moreover, our umbrella meta-analysis revealed a significant increase in CVD risk associated with ASBs consumption. However, sensitivity analysis demonstrated that excluding studies Meng et. al. [21] and Narain et. al. [38] returned this association statistically insignificant. The limited power of this analysis, considering only three pooled effect sizes, each based on a restricted number of studies, there's a clear need for additional research with a more extensive dataset to ensure result reliability. The interpretation should be approached cautiously, recognizing the limitations and the necessity for comprehensive interpretation of the results.
Analysis of four studies suggested a 9% higher stroke risk associated with ASB intake; however, sensitivity analysis showed that excluding Yin et al. [20] and Narain et. al [38]. rendered this association non-significant. Notably, the Narain et al. study [38], although the only high-quality review, may have disproportionately influenced the outcome, while the Yin et al. study [20], despite its lower quality, contributed the largest sample size. This reliance on a few influential studies and the substantial between-study variation highlights the need for higher-quality evidence and cautious interpretation of the association between ASB intake and stroke risk.
The methodological quality of the included meta-analyses warrants careful consideration. A substantial proportion were rated as low or critically low quality, which may have introduced bias or limited the robustness of pooled estimates. To address this, we performed subgroup analyses stratified by study quality. Notably, the observed associations were generally consistent across quality strata, though effect sizes tended to be slightly stronger in higher-quality studies. These findings suggest that, while methodological limitations remain an important concern, the overall conclusions appear robust.
Subgroup analyses indicated that the association between ASB consumption and mortality was more pronounced among adults aged ≥ 60 years. This pattern is plausibly explained by reverse causation and confounding by indication: individuals with pre-existing cardiometabolic disease may adopt ASBs following diagnosis or clinical advice, rendering ASB intake a proxy for underlying morbidity or prior SSB exposure rather than a causal determinant. Residual time-varying confounding may also persist when diet is assessed at baseline or infrequently and early events are not lagged. Consistent with this, ASBs showed stronger associations with all-cause than with CVD mortality, potentially reflecting multimorbidity [4, 19]. By contrast, SSBs were more strongly associated with CVD than with all-cause mortality, with higher relative risks of metabolic syndrome and stroke in adults < 50 years, suggesting greater susceptibility to SSB-related cardiometabolic risk in younger populations. These observations support age-stratified analyses, targeted SSB-reduction strategies in younger adults, and analytic approaches that mitigate reverse causation (e.g., time-updated exposures, lagged analyses, substitution models) [39].
Several included meta-analyses that modeled dose–response generally indicated graded increases in risk with higher SSB intake [17, 20, 23, 29], whereas ASB associations were smaller and more method-dependent (often non-linear with excess risk at higher intakes) [9, 19, 20, 23]. Because increments (e.g., per 250 mL vs. per serving), modeling choices (linear vs. spline), beverage definitions, and effect measures varied substantially—and many reviews reported only categorical contrasts—we synthesized these findings narratively. Overall, the dose–response patterns reinforce our primary categorical results- graded increase in risk with higher SSB exposure and a more heterogeneous, method-sensitive pattern for ASBs- and supporting a monotonic risk gradient for SSBs and a more heterogeneous pattern for ASBs.
Several factors may help explain the findings of this umbrella meta-analysis. SSB consumption is often accompanied by higher intake of saturated and trans fats, greater caloric consumption, and lower dietary fiber [40, 41]. It has also been linked to lower physical activity levels [42, 43], and a higher body mass index (BMI) [44, 45]. These interconnected factors likely contribute to the increased cardiovascular risk observed with SSB intake. Understanding this multifaceted relationship can help guide public health strategies aimed at reducing SSB consumption and promoting healthier dietary patterns. Importantly, most studies report consistent associations between SSB intake and cardiovascular risk factors even after adjusting for sociodemographic, clinical, lifestyle, and dietary variables, suggesting a direct effect. However, incomplete adjustment for all potential confounders means residual confounding cannot be ruled out, and may overestimate the strength of the association.
Secondly, the consumption of SSBs may heighten CVD risk through increased risks of future obesity and T2DM development [46]. As liquid carbohydrates, SSBs often fail to provide a sense of fullness or satiety comparable to the equivalent amount of carbohydrates from solid food, which can result in incomplete energy compensation during subsequent meals [47]. Additionally, the sweetening of SSBs with high-fructose corn syrup introduces excessive hepatic fructose metabolism byproducts, potentially leading to increased lipogenesis, and subsequent visceral and intramuscular fat deposition, dyslipidemia, obesity, and metabolic syndrome [48]. Additionally, fructose, distinct among sugars, has been associated with elevated uric acid levels, which further exacerbates insulin resistance and amplifies the risk of CVD [49]. Furthermore, the high glycemic load following the consumption of SSBs may trigger the release of pro-inflammatory cytokines in response to hyperglycemia [50], potentially contributing to metabolic disorders and atherosclerosis [51].
Similarly, the risks associated with ASBs consumption can be explained through various mechanisms. Reverse causality might play a significant role, as individuals with existing cardio-metabolic risk factors may intentionally switch to ASBs [38]. Notably, recent high-quality evidence indicates that ASB consumption does not acutely alter postprandial glucose or endocrine responses compared with water. A 2023 systematic review and network meta-analysis of randomized and non-randomized acute trials (36 trials, 472 participants) demonstrated that beverages sweetened with single or blended non-nutritive sweeteners had no meaningful effects on postprandial glucose, insulin, or gut hormone responses, whereas SSBs significantly increased these responses [52]. These findings suggest that proposed mechanisms such as impaired sweet–calorie coupling are unlikely to operate in the short term. Instead, the adverse associations observed in prospective cohort studies may be driven by long-term behavioral, metabolic, or residual confounding factors, rather than acute glycemic effects of ASBs. Additionally, recent research suggests an association between ASBs consumption and altered gut microbiota, which may contribute to increased insulin resistance, as observed in both animal and human studies [53]. Another important point is the comparator dependence of associations. The associations observed for ASBs vary substantially depending on the comparator. When ASBs are compared with SSBs or assessed in substitution models (e.g., replacing SSBs with ASBs), estimates often suggest lower risk [2], whereas comparisons with water or unsweetened beverages generally yield null or weaker associations [34]. By contrast, comparisons with non-consumers may exaggerate risk owing to confounding by indication and lifestyle differences [35, 36]. Given that included meta-analyses employed heterogeneous comparators, this likely contributed to between-study variability and should temper causal interpretation. Future work should priorities substitution models alongside standard category contrasts to better clarify cardiometabolic risk.
Importantly, most of the included meta-analyses are based on observational studies, which inherently limits causal inference. Despite adjustments for lifestyle and dietary factors, residual confounding by variables such as overall diet quality, socioeconomic status, BMI, or pre-existing cardiometabolic conditions cannot be ruled out. Reverse causation is also a major concern for ASBs, as individuals at higher baseline risk (e.g., overweight or with diabetes or hypertension) may substitute ASBs for SSBs. This behavioral change may lead to spurious associations, making ASBs appear harmful when they may instead reflect underlying health risks. Consequently, the interpretation of ASB findings requires particular caution, and well-designed prospective studies with repeated dietary measures are needed to minimize these biases.
Recent evidence also suggests a dose-dependent relationship between sweetened beverage consumption and chronic kidney disease, obesity-related cancers, T2DM, hypertension, and all-cause mortality [23, 54, 55]. Thus, sweetened beverages appear to contribute not only to cardiovascular mortality but also to a range of chronic diseases, with risks increasing proportionally to intake. Careful monitoring of consumption levels is therefore essential to mitigate adverse outcomes.
Although most studies report elevated cardiovascular risk, some discrepancies exist, often due to methodological differences such as reliance on self-reported versus confirmed diagnoses [56], or variation in follow-up duration, which may influence the detection of long-term effects. Age is another important factor, as the impact of ASB and SSB consumption appears to differ across age groups.
Inconclusive findings in certain studies may stem from limitations such as poor study quality, small sample sizes, substantial heterogeneity in design and exposure assessment, and inconsistent definitions of SSBs, ASBs, and fruit juices. In addition, insufficient adjustment for confounders and potential publication bias have been cited as sources of uncertainty. These complexities highlight the need for greater standardization in study design and more rigorous methodologies to ensure accurate assessment of the health effects of sweetened beverages.
Strength and limitation
This study has notable strengths. As the first umbrella meta-analysis on ASB and SSB consumption and cardiovascular risk, it synthesizes evidence from cross-sectional and cohort meta-analyses, helping reconcile discrepancies. Rigorous quality assessment using AMSTAR 2 enhances credibility, while the inclusion of numerous recent studies strengthens comprehensiveness and generalizability.
However, some limitations must be acknowledged. Variability in serving definitions restricted comparisons to extreme versus lowest intake, likely underestimating associations. Changes in consumption patterns introduced exposure uncertainty, and limited study numbers for some outcomes precluded subgroup analyses. Finally, although publication bias was suggested for mortality outcomes, trim-and-fill analysis yielded similar estimates, indicating results should be interpreted cautiously. An additional limitation is that definitions of SSBs and ASBs varied across included meta-analyses. Some reviews included fruit juices or other sweetened drinks within the SSB category, while others restricted definitions to carbonated soft drinks; similarly, ASB definitions ranged from diet sodas only to all low-calorie ASBs. This definitional heterogeneity may contribute to between-study variation and should be considered when interpreting pooled estimates. Although covariates were adjusted to varying extents in all included studies, the substantial variation may contribute to inconsistencies among the results. These limitations underscore the complexity of studying dietary factors and their associations with health outcomes and emphasize the importance of considering these factors in future research endeavors. Notably, no large-scale randomized controlled trials or long-term interventions specifically assessing ASBs and SSBs in relation to cardiovascular outcomes were identified. This gap highlights the reliance on observational data, where residual confounding and reverse causality remain concerns. While RCTs could provide more definitive evidence, their long-term implementation faces major logistical and ethical challenges, reinforcing the need for cautious interpretation of current findings. Another important limitation concerns the extent of overlap across the included reviews. The CCA analyses demonstrated that most outcomes—both for ASBs and SSBs—fell into the high or very high overlap range, reflecting substantial reuse of the same primary cohorts across multiple reviews. This redundancy may artificially increase precision and contribute to biased effect estimates. Although we mitigated this issue by restricting our synthesis to meta-analytic summary estimates and conducting sensitivity analyses, the findings should nevertheless be interpreted with caution in light of the pervasive overlap. Addressing these limitations will be crucial for enhancing the validity and reliability of future studies in this field.
Conclusion and recommendations
By conducting a comprehensive umbrella meta-analysis, the current study highlights significant associations between long-term SSB intake and increased risks of mortality, hypertension, CHDs, CVDs, metabolic syndrome, and stroke. These associations were generally robust across sensitivity and subgroup analyses and supported by multiple meta-analyses. In contrast, the evidence for ASBs was less consistent: while positive associations were observed with mortality, hypertension, and metabolic syndrome, the links with stroke and CVDs often lost statistical significance in sensitivity analyses and were derived largely from low- to moderate-quality studies and also many outcomes showed moderate or high overlap across reviews. Therefore, findings related to ASBs should be regarded as suggestive rather than conclusive and interpreted with caution until confirmed by well-designed, high-quality prospective studies or randomized controlled trials. Importantly, most included evidence is based on observational data, which may be influenced by residual confounding and reverse causation. Our study underscores the urgent need for additional high-quality research to clarify these relationships and better inform clinical and public health recommendations. In the meantime, public health strategies should continue to prioritize reducing SSB consumption—given its consistent and well-documented risks—through education campaigns, product labelling regulations, sugar taxation, and the promotion of healthier alternatives such as water in schools, workplaces, and public spaces. Healthcare providers should also be trained to counsel at-risk individuals on the adverse effects of excessive SSBs and the uncertain but potentially harmful associations with ASBs. By integrating these strategies, meaningful steps can be taken toward mitigating the burden of cardiovascular and metabolic diseases and improving overall population health.
Supplementary Information
Acknowledgements
None.
Clinical trial number
Not applicable.
Authors’ contributions
**MJ**: Drafting of the manuscript, Acquisition of data; **PA:** Acquisition of data; drafting of the manuscript; **BH:** drafting of the manuscript; critical revision of the manuscript for important intellectual content, **MR:** drafting of the manuscript; critical revision of the manuscript for important intellectual content, **LK:** drafting of the manuscript; critical revision of the manuscript for important intellectual content, **EA:** Drafting of the manuscript; critical revision of the manuscript for important intellectual content, **MZ:** Interpretation of data, critical revision of the manuscript for important intellectual content, Study concept and design, Data analysis, **MA:** critical revision of the manuscript for important intellectual content and adherence to the appropriate academic writing conventions **, AS:** Critical revision of the manuscript for important intellectual content; Study supervision, Study concept and design.
Funding
None.
Data availability
All the analyses are available within an institutional repository and can be provided upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
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Contributor Information
Meysam Zarezadeh, Email: Meysam.za93@gmail.com, Email: zarezadehm@tbzmed.ac.ir.
Ahmad Saedisomeolia, Email: ahmad.saedisomeolia@chs.edu.au.
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Associated Data
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Supplementary Materials
Data Availability Statement
All the analyses are available within an institutional repository and can be provided upon request.