Dietary inflammatory index and cardiovascular risk and mortality: an updated systematic review and meta-analysis

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally, and chronic inflammation is pivotal in CVDs development. Pro-inflammatory diets may exacerbate inflammation and thus increase CVDs risk. The Dietary Inflammatory Index (DII) is a validated measure of the inflammatory potential of diet. This updated systematic review and meta-analysis was conducted to clarify the association between DII and CVDs incidence and mortality.

Methods: A comprehensive search was conducted in Pub Med, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI) until February 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Risk ratios (HR) and 95% confidence intervals (CI) were pooled using Review Manager 5.4, with subgroup analyses performed. Sensitivity and publication bias analyses were conducted using Stata 18.0.

Results: Thirty cohort studies (NOS ≥7) from nine countries, involving 669,205 participants, were included. Compared with the lowest DII category, the highest category was associated with increased risks of CVD incidence [HR = 1.23, 95% CI (1.14–1.33); I² = 54%] and mortality [HR = 1.29, 95% CI (1.24–1.35); I² = 16%]. Stratified analyses indicated higher incidence risk among men (HR = 1.51) and higher mortality risk among women (HR = 1.25). Subgroup analyses further revealed a significant positive association between elevated DII and myocardial infarction (HR = 1.41). In models stratified by diabetes history, unadjusted associations were stronger (HR = 1.40), while adjusted associations were attenuated but remained significant, with a significant interaction (P = 0.002). Sensitivity and trim-and-fill analyses confirmed the robustness of these associations (all P < 0.001).

Conclusion: Higher DII scores, reflecting pro-inflammatory dietary patterns, are significantly associated with increased risks of CVD incidence and mortality. These findings underscore the clinical and public health importance of promoting anti-inflammatory dietary strategies to mitigate the global CVD burden.

Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420250654615, PROSPERO, CRD420250654615.

1 Introduction

With the continuous intensification of societal aging and the significant increase in the consumption of ultra-processed foods, the incidence and mortality rates of cardiovascular diseases (CVDs) have been showing a rising trend (1). The Global Burden of Disease Study 2021 (GBD 2021) reported that between 1990 and 2021, the number of new CVD cases rose from 34.74 million to 66.81 million, while deaths increased from 12.33 million to 19.42 million. Although age-standardized incidence and mortality rates declined overall, absolute numbers grew substantially, with marked regional disparities (2) 2021, dietary risk factors were linked to 6.58 million CVD deaths, highlighting the considerable potential of dietary prevention and intervention to reduce the global burden (1). Moreover, evidence from multiple countries indicates that exposure to ultra-processed foods is independently associated with elevated CVD risk, further emphasizing the interplay among diet quality, inflammation, and cardiovascular health, and their public health implications (3, 4).

Based on previous research findings, the pathogenesis of cardiovascular diseases is extremely complex, involving a process of multi-factor interaction and multi-mechanism synergistic effects. In this complex pathological process, chronic inflammatory responses play a key role (5). Inflammatory biomarkers such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) not only promote the formation of atherosclerotic plaques but also promote plaque instability and thrombosis, thereby triggering acute cardiovascular events (6) a modifiable factor of inflammation, diet's role mechanism has been increasingly focused on. Studies have shown that daily diet can directly influence the body's inflammatory state and can also establish a close link with cardiovascular diseases through the gut microbiota (7). As the “second genome” of humans, the composition and function of the gut microbiota are significantly influenced by daily dietary patterns (8). Long-term consumption of a diet rich in pro-inflammatory nutrients alters the gut microbiota structure, leading to impaired gut barrier function, which in turn triggers chronic inflammatory responses in the body, increasing the risk of cardiovascular disease incidence and mortality, In contrast, diets rich in ω-3 polyunsaturated fatty acids and polyphenols, which are anti-inflammatory nutrients, provide metabolic substrates for beneficial gut bacteria, promoting their proliferation and the production of short-chain fatty acids and other substances. Previous studies have shown that these substances not only regulate immune function, alleviate inflammation, but also improve endothelial cell function and reduce cardiovascular disease risk (9).

With the continuous deepening and expansion of nutritional science, the Dietary Inflammatory Index (DII), a tool designed to reflect the pro- or anti-inflammatory properties of diet, emerged. Constructed by researchers at the University of South Carolina through analyzing and integrating human, animal, and cell experiments, it includes 45 dietary nutrients and 6 inflammatory markers. To reflect specific nutrients' impacts on body inflammatory markers, the authors weighted related studies, assigned each dietary nutrient an inflammatory effect score via different weights, and calculated the impact of an individual's overall diet on body inflammation (10).

Currently, the DII has been widely used to explore links between diet and various inflammation—related diseases (especially cancer, digestive tract diseases, and cardiovascular diseases), becoming a research hotspot in recent years (11). Although existing evidence supports the DII-CVDs association (12), cohort studies published in the past five years have not been systematically evaluated. Thus, conducting a comprehensive and timely Meta-analysis is essential to provide a medical evidence—based basis for constructing precise dietary intervention strategies.

2 Methods

The reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (13), with the protocol registered on PROSPERO (ID: CRD420250654615).

2.1 Search strategy

We systematically searched PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang Data, and VIP databases for studies investigating the association between the DII and the risk of CVDs incidence or mortality. The search timeframe spanned from database inception to February 2025. To minimize omissions, we manually reviewed the reference lists of relevant articles. The detailed search strategy is provided in Supplementary Material 1.

2.2 Study selection

Table 1 outlines the PICOS criteria for study inclusion. Studies meeting the following criteria were eligible: (1) Study type: Cohort studies (retrospective or prospective); (2) Population: Adults aged ≥18 years; (3) Exposure: DII as the primary exposure variable; (4) Outcome: Incident CVDs or CVDs-related mortality; (5) Statistical reporting: Full effect estimates, including hazard ratios (HRs) with 95% confidence intervals (CIs). Exclusion criteria included: (1) Non-original research (e.g., reviews, conference abstracts, book chapters) or secondary evidence (e.g., systematic reviews); (2) Unavailable full-text articles; (3) Duplicate publications; (4) Insufficient data for extraction or conversion; (5) Low methodological quality. Two investigators (YN and QY) independently performed study selection, with discrepancies resolved through team discussions.

Table 1

Table 1. Picos criteria of this study.

2.3 Data extraction

To ensure comprehensive data extraction, two investigators (YQ and XT) independently performed data extraction using a predefined standardized template. Any discrepancies encountered during the process were resolved through team discussions. Extracted data included: Study characteristics (first author, publication year, study location, study design, follow-up duration); Participant information (age, sex ratio, health status); Assessment methods (dietary survey methods, dietary evaluation tools, criteria for ascertaining CVDs incidence and mortality); Statistical analyses: Hazard ratios (HR) with 95% confidence intervals (CI) comparing the highest vs. lowest DII quantiles.

2.4 Quality assessment

Two investigators (YN and TX) independently evaluated study quality using the Newcastle-Ottawa Scale (NOS), a validated tool developed by the Ottawa Hospital Research Institute for assessing observational studies. The NOS comprises three domains: participant selection (4 items), comparability of groups (1 item), and outcome assessment (3 items), with a maximum score of 9. Studies scoring 7–9 were classified as high quality with low overall bias and included in the systematic review and meta-analysis. Studies scoring ≤6 were excluded due to elevated risk of bias. Discrepancies in scoring were resolved through team consensus.

2.5 Statistical analysis

Data were analyzed using Stata 18.0 and Review Manager 5.4. Pooled HRs with 95% CIs were calculated to evaluate associations between DII and CVDs incidence/mortality. Heterogeneity was assessed using Cochran's Q-test and the I² statistic (significance level α = 0.1). A fixed-effect model was applied if I² < 50% and P > 0.1; otherwise, a random-effect model was used. Descriptive analyses were conducted when insufficient data precluded meta-analysis. Subgroup analyses explored heterogeneity sources, sensitivity analyses assessed result robustness, and funnel plots with trim-and-fill adjustments evaluated publication bias.

3 Results

Figure 1 outlines the literature screening process. Initial database searches yielded 3,419 records. After excluding 1,386 duplicates and 1,938 irrelevant studies through title/abstract screening, 95 full-text articles were reviewed. Ultimately, 30 English-language cohort studies met the inclusion criteria.

Figure 1

Figure 1. Flowchart of study selection process for the meta-analysis of DII and CVDs risk/mortality.

All the studies mentioned above were evaluated using the Newcastle-Ottawa Scale (NOS), with all scores ≥7, encompassing 669,205 participants across nine countries (14–43). The basic characteristics and quality assessments of studies investigating DII—CVDs incidence and mortality are presented in Tables 2, 3, respectively.

Table 2

Table 2. Basic characteristics and quality assessment of studies on the risk of CVDs associated with DII.

Table 3

Table 3. Basic characteristics and quality assessment of studies on the risk of CVDs mortality associated with DII.

3.1 Meta-Analysis results

3.1.1 Association between DII and CVDs risk

Fourteen studies examined the relationship between DII and CVDs incidence (14–25, 41, 42). Significant heterogeneity was observed across studies (I² = 54%, P < 0.01), necessitating a random-effects model. The highest DII quantile was associated with a 23% increased risk of CVDs incidence compared to the lowest quantile [HR = 1.23, 95% CI (1.14–1.33)] (Figure 2).

Figure 2

Figure 2. Forest plot of the association between DII and CVDs risk.

Subgroup analyses stratified by outcome type, region, sex, dietary assessment method, BMI adjustment, energy adjustment, and diabetes history are summarized in Table 4. Between-group comparisons indicated significant effect modification by sex, dietary method, energy adjustment, and diabetes history. The strongest and most consistent association was observed for myocardial infarction [HR = 1.41, 95% CI (1.16–1.72)]. Associations were particularly evident among men [HR = 1.51, 95% CI (1.26–1.80)], studies using 24-hour dietary recall [HR = 1.53, 95% CI (1.19–1.94)], studies with energy adjustment [HR = 1.44, 95% CI (1.23–1.69)], and studies without diabetes history adjustment [HR = 1.40, 95% CI (1.25–1.56)].

Table 4

Table 4. Subgroup analysis of the association between DII and CVDs risk.

3.1.2 Association between DII and CVDs mortality

Besides the above-mentioned studies, the remaining 16 studies assessed CVDs-related death events (26–40, 43). As shown in Figure 3, the meta-analysis revealed a positive association between the dietary inflammatory index and the risk of CVDs death [I² = 16%, HR = 1.29, 95%CI (1.24–1.35)].

Figure 3

Figure 3. Forest plot of the association between DII and CVDs mortality.

Similarly, subgroup analyses of the included studies were conducted based on factors such as disease status of the subjects, study region, gender, BMI adjustment, physical activity adjustment, dietary assessment method, energy intake adjustment, and diabetes history adjustment to explore the impact of each factor on the study results (Table 5). The subgroup—analysis results indicated that gender significantly contributed to heterogeneity (P < 0.05), with stronger associations observed in women [HR = 1.25, 95% CI (1.12–1.39)]. Other factors (disease status, region, dietary method, BMI adjustment, physical activity adjustment, energy adjustment, diabetes history) did not significantly explain heterogeneity.

Table 5

Table 5. Subgroup analysis of the association between DII and CVDs mortality.

3.2 Sensitivity analysis and publication bias

Sensitivity analyses were performed using Stata 18.0. The pooled effect estimates for both CVDs incidence and mortality demonstrated minimal changes upon sequential exclusion of individual studies, indicating robust meta-analysis results (Figures 4, 5). Funnel plots revealed asymmetric scatter distributions, suggesting potential publication bias. Trim-and-fill adjustments were subsequently applied. For CVDs incidence analyses, imputation of 11 hypothetical missing studies under a random-effects model yielded a persistent statistically significant association [HR = 1.106, 95% CI (1.018–1.201), P < 0.001]. Similarly, imputation of 6 hypothetical missing studies in CVDs mortality analyses under a fixed-effects model maintained significance [HR = 1.268, 95% CI (1.218–1.321), P < 0.001], with no reversal in the direction of conclusions. Collectively, these findings confirm the robustness of the meta-analysis results (Figures 6, 7).

Figure 4

Figure 4. Sensitivity analysis of DII and CVDs risk.

Figure 5

Figure 5. Sensitivity analysis of DII and CVDs mortality.

Figure 6

Figure 6. Trim-and-fill funnel plot for the association between DII and CVDs risk.

Figure 7

Figure 7. Trim-and-fill funnel plot for the association between DII and CVDs mortality.

4 Discussion

This updated systematic review and meta-analysis further substantiates the consistent association between pro-inflammatory dietary patterns, as quantified by the Dietary Inflammatory Index (DII), and elevated risks of cardiovascular disease (CVDs) incidence and mortality. Individuals consuming diets with higher inflammatory potential exhibit significantly greater risks of experiencing CVDs events and related deaths compared to those adhering to diets with lower inflammatory potential. These findings align with a growing body of evidence indicating that unhealthy dietary habits—characterized by excessive intake of processed meats, sugar-sweetened beverages, and refined carbohydrates—adversely affect cardiovascular health (44). Consequently, adopting dietary patterns rich in anti-inflammatory components such as fruits, vegetables, and whole grains may serve as an effective strategy to mitigate the population burden of CVDs (45). In this context, the DII emerges as a practical tool that translates complex nutritional data into actionable indicators of dietary inflammatory potential, enabling clinicians to identify high-risk dietary patterns and provide tailored recommendations to patients.

From a mechanistic perspective, pro-inflammatory diets may promote CVD development through chronic low-grade inflammation and oxidative stress (46). Diets with high DII scores are typically rich in saturated fats, trans fatty acids, and added sugars, which can activate the IKKβ/NF-κB signaling pathway, stimulating the release of pro-inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) (47). This activation initiates pathogenic cascades leading to atherosclerotic plaque formation, vascular remodeling, and increased arterial stiffness (48). Furthermore, diet-induced inflammatory burden destabilizes the fibrous cap of plaques (49). Evidence suggests that inflammation within the fibrous cap promotes degradation of collagen and extracellular matrix, as well as smooth muscle cell apoptosis, resulting in cap thinning and heightened risk of rupture and thrombosis (50).

In addition, high DII diets are often deficient in antioxidants and phytochemicals, which impairs endogenous defense mechanisms and aggravates oxidative stress–induced vascular injury (51). Insufficient dietary fiber intake also reduces the abundance of butyrate-producing bacteria, thereby lowering short-chain fatty acid (particularly butyrate) production and compromising intestinal barrier integrity. Barrier dysfunction combined with dysbiosis facilitates translocation of gut-derived metabolites such as lipopolysaccharide and trimethylamine (TMA) into the circulation, activating Toll-like receptor 4 (TLR4) and triggering inflammatory cascades that lead to cytokine overproduction and exacerbation of vascular and myocardial injury (52, 53).

Moreover, excessive choline and carnitine in high DII diets are metabolized by the gut microbiota into TMA, which is subsequently oxidized in the liver to trimethylamine-N-oxide (TMAO) (53). This metabolite exerts multiple deleterious effects, including enhancing platelet activation to promote thrombosis, activating the NLRP3 inflammasome to aggravate plaque inflammation, facilitating foam cell formation, and contributing to vulnerable plaque characteristics such as thin fibrous caps and increased microvascularization (54–57). Clinically, TMAO levels independently predict major adverse cardiovascular events (MACE) and adverse prognosis in patients with acute coronary syndrome (58). For example, a meta-analysis by Li et al. demonstrated that each 1 μmol/L increase in TMAO was associated with an ∼11% higher risk of MACE (95% CI: 1.07–1.14; P = 0.0000104) (59). Animal studies further revealed that reducing gut-derived metabolites via antibiotic administration significantly attenuated monocyte infiltration and ventricular rupture in myocardial infarction models, supporting a causal role of gut microbiota–mediated inflammation in acute MI (60). Importantly, identical dietary exposures (or equivalent DII scores) do not necessarily result in equal TMAO loads. For instance, microbiota dominated by Firmicutes can substantially enhance TMA-to-TMAO conversion, thereby amplifying pro-inflammatory and pro-thrombotic effects under high DII conditions (61). This provides a biological explanation for interindividual variability in risk responses. Thus, future research on DII–CVD associations should incorporate gut microbiota phenotypes or metabolic capacity into models to better clarify mediating mechanisms and support precision prevention strategies.

Beyond these mechanisms, subgroup analyses in this study indicated potential sex differences in the relationship between dietary inflammation and CVD risk. Elevated DII scores were more strongly associated with CVD incidence among men, whereas the association with CVD mortality was more pronounced among women. This heterogeneity may arise from multiple interacting biological mechanisms. First, estrogen exerts anti-inflammatory and vasoprotective effects. In premenopausal women, estrogen upregulates eNOS expression, enhances nitric oxide production, and suppresses oxidative stress and inflammatory signaling, thereby mitigating vascular injury induced by chronic inflammation (62). However, after menopause, declining estrogen levels weaken this protection, potentially predisposing women to more severe outcomes under high-inflammatory dietary exposure (63). Second, the sex–microbiota–inflammation axis may modulate the strength of DII-related signaling (64). Sex hormones influence microbial composition and metabolic activity, which in turn affect short-chain fatty acid production, intestinal permeability, and endotoxin leakage (65, 66). Under such mechanisms, women may partially buffer pro-inflammatory signaling due to stronger microbial regulatory capacity, whereas men may more readily translate these signals into systemic inflammatory burden. Nonetheless, current evidence is insufficient to conclude that “men are more susceptible to incidence while women are more susceptible to mortality” at equivalent DII levels. Heterogeneity in population composition, follow-up duration, endpoint definitions (incidence vs. mortality), and covariate adjustments across studies complicates interpretation, and most studies lack concurrent assessments of sex hormones, microbiota profiles, inflammatory biomarkers, and sex interactions. Future research should incorporate these mechanistic variables and test sex interactions in larger samples to verify and quantify this heterogeneity.

In addition, methodological subgroup analyses revealed stronger associations in studies using 24HR, studies with energy adjustment, and those without diabetes history adjustment. We interpret these findings as follows: first, compared with FFQ, repeated 24HR captures within-person variation more accurately, thereby reducing non-differential exposure misclassification and biasing effect estimates toward the null (67). Second, energy adjustment “fixes” total energy intake, diminishing confounding from factors such as physical activity and metabolic efficiency, and allowing the analysis to focus on dietary composition–driven inflammatory signals (68). In contrast, adjustment for diabetes history produced weaker associations. This likely reflects the dual role of diabetes history as both a confounder and mediator. Including diabetes as a covariate may block mediating pathways and cause over-adjustment, systematically underestimating the true association (69). However, failure to adjust entirely may leave residual confounding, as diabetes diagnosis and dietary management can alter subsequent DII, while diabetes itself increases CVD risk. Thus, not adjusting could exaggerate or distort the associations. Nevertheless, even in studies adjusting for diabetes, high DII remained positively associated with CVD risk, indicating that DII influences cardiovascular outcomes through mechanisms beyond glycemic pathways, particularly chronic systemic inflammation (70).

Overall, this study demonstrates that pro-inflammatory dietary patterns significantly elevate CVD incidence and mortality, reinforcing the central role of dietary modulation in cardiovascular progression and providing evidence for personalized dietary interventions based on DII. From a practical standpoint, we recommend DII as a supplementary tool for risk stratification and dietary management in addition to traditional CVD risk assessments. Suitable applications include: primary prevention, where baseline nutritional assessments and annual follow-ups are conducted for individuals with multiple cardiometabolic risk factors or family history of early-onset CVD; secondary prevention, where dietary inflammatory load is monitored after hospital discharge and during cardiac rehabilitation follow-up; and clinical decision points such as initiation or intensification of lipid-lowering, glucose-lowering, or weight management interventions, smoking cessation, or exercise prescription. In patients with inflammatory phenotypes or metabolic comorbidities, DII can serve as additional evidence to reinforce lifestyle management. Regarding dietary assessment, repeated 24HR (≥3 non-consecutive recalls, including ≥2 weekdays and ≥1 weekend day) with energy adjustment is recommended for calculating DII (71). In resource-limited settings, simplified FFQs calibrated to local diets may serve as alternatives. For interpretation, DII should be treated as a continuous metric, where negative values indicate relatively anti-inflammatory and positive values relatively pro-inflammatory diets. Given variations in food items, assessment tools, and population characteristics, no universal clinical cutoffs exist. Thus, reporting individuals' percentile rank within a sample, alongside blood pressure, lipids, glucose/HbA1c, anthropometry, and hs-CRP, is a more prudent strategy than using fixed thresholds. Finally, DII interpretation should be linked to heart-healthy dietary patterns and translated into actionable advice. For CVD patients or high-risk individuals, clinicians should encourage increased intake of fruits, dark green leafy vegetables, whole grains, nuts, and ω-3–rich fish, preferential use of liquid plant oils, and restriction of red meat, refined sugars, and fried foods (44, 72). Given the positive correlation between DII and CRP, clinicians may consider presenting improvements in DII alongside reductions in inflammatory biomarkers, thereby helping patients recognize the modifiable “diet–inflammation–CVD event” pathway and motivating them to adopt anti-inflammatory dietary habits in daily life.

4.1 Strengths and limitations

Compared with previous meta-analyses, this study has several strengths: a larger sample size, broader geographic coverage, and stricter methods for covariate control, publication bias assessment, and robustness testing, all of which enhance the reliability and generalizability of the effect estimates. Nevertheless, some limitations should be acknowledged. First, the lack of age-stratified analyses represents a key limitation. As inflammation and dietary habits vary with age, age may act as an important effect modifier. However, incomplete reporting of mean age and standard deviation in some studies prevented subgroup analyses, potentially underestimating or masking age-related effects. Second, although energy adjustment and multiple covariate controls were applied, residual confounding remains inevitable. Potential unmeasured factors—such as medication use, genetic predisposition, and gut microbiota characteristics—may influence inflammation and cardiovascular outcomes. Third, most included studies were conducted in high-income countries, with limited representation from low- and middle-income countries, restricting the generalizability of findings to diverse socioeconomic and nutritional transition contexts.

Future research should integrate multidimensional information including inflammatory biomarkers, microbiota profiles, and genetic susceptibility to clarify biological mechanisms of dietary inflammatory load and explore differential responses across metabolic states and population subgroups.

5 Conclusion

In conclusion, higher Dietary Inflammatory Index (DII) scores are closely associated with increased risks of cardiovascular disease (CVDs) incidence and mortality. Pro-inflammatory diets may accelerate the development of CVDs through systemic inflammation and gut microbiota-mediated pathways. This association may vary by sex and metabolic status, highlighting the importance of nuanced approaches in research and dietary recommendations. Our findings support the promotion of anti-inflammatory dietary patterns as public health measures and clinical interventions to reduce overall cardiovascular risk, advocating for a shift towards more personalized dietary guidance to improve heart health outcomes.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

YN: Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing – original draft. QY: Data curation, Formal analysis, Validation, Writing – original draft. TX: Data curation, Validation, Writing – original draft. XL: Funding acquisition, Resources, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declare that no Generative AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2025.1626523/full#supplementary-material

References

1. Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The global burden of cardiovascular diseases and risk: a compass for future health. J Am Coll Cardiol. (2022) 80(25):2361–71. doi: 10.1016/j.jacc.2022.11.005

PubMed Abstract | Crossref Full Text | Google Scholar

2. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. (2024) 403(10440):2100–32. doi: 10.1016/S0140-6736(24)00367-2

PubMed Abstract | Crossref Full Text | Google Scholar

3. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-santé). Bmj. (2019) 365:l1451. doi: 10.1136/bmj.l1451

PubMed Abstract | Crossref Full Text | Google Scholar

4. Juul F, Vaidean G, Lin Y, Deierlein AL, Parekh N. Ultra-Processed foods and incident cardiovascular disease in the framingham offspring study. J Am Coll Cardiol. (2021) 77(12):1520–31. doi: 10.1016/j.jacc.2021.01.047

PubMed Abstract | Crossref Full Text | Google Scholar

5. Kheirouri S, Alizadeh M. Dietary inflammatory potential and the risk of neurodegenerative diseases in adults. Epidemiol Rev. (2019) 41(1):109–20. doi: 10.1093/epirev/mxz005

PubMed Abstract | Crossref Full Text | Google Scholar

6. Stark K, Massberg S. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat Rev Cardiol. (2021) 18(9):666–82. doi: 10.1038/s41569-021-00552-1

PubMed Abstract | Crossref Full Text | Google Scholar

7. Perler BK, Friedman ES, Wu GD. The role of the gut Microbiota in the relationship between diet and human health. Annu Rev Physiol. (2023) 85:449–68. doi: 10.1146/annurev-physiol-031522-092054

PubMed Abstract | Crossref Full Text | Google Scholar

8. Campaniello D, Corbo MR, Sinigaglia M, Speranza B, Racioppo A, Altieri C, et al. How diet and physical activity modulate gut microbiota, evidence, and perspectives. Nutrients. (2022) 14(12):2456. doi: 10.3390/nu14122456

PubMed Abstract | Crossref Full Text | Google Scholar

9. Damigou E, Anastasiou C, Chrysohoou C, Barkas F, Tsioufis C, Pitsavos C, et al. Prevented fractions of cardiovascular disease cases, by long-term adherence to the Mediterranean diet; the ATTICA study (2002–2022). Nutr Metab Cardiovasc Dis. (2024) 35:103777. doi: 10.1016/j.numecd.2024.10.015

PubMed Abstract | Crossref Full Text | Google Scholar

10. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. (2014) 17(8):1689–96. doi: 10.1017/S1368980013002115

PubMed Abstract | Crossref Full Text | Google Scholar

11. Geng Lan YL, Mei Z. Visualization analysis of dietary inflammatory Index research based on web of science. Food Nutr China. (2024) 30(7):5–10; 5. doi: 10.19870/j.cnki.11-3716/ts.20230807.001

Crossref Full Text | Google Scholar

12. Ji M, Hong X, Chen M, Chen T, Wang J, Zhang N. Dietary inflammatory index and cardiovascular risk and mortality: a meta-analysis of cohort studies. Medicine (Baltimore). (2020) 99(20):e20303. doi: 10.1097/MD.0000000000020303

PubMed Abstract | Crossref Full Text | Google Scholar

13. Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J. (2021) 372:n71. doi: 10.1136/bmj.n71

Crossref Full Text | Google Scholar

14. Asadi Z, Yaghooti-Khorasani M, Ghazizadeh H, Sadabadi F, Mosa-Farkhany E, Darroudi S, et al. Association between dietary inflammatory index and risk of cardiovascular disease in the mashhad stroke and heart atherosclerotic disorder study population. IUBMB Life. (2020) 72(4):706–15. doi: 10.1002/iub.2172

PubMed Abstract | Crossref Full Text | Google Scholar

15. Canto-Osorio F, Denova-Gutierrez E, Sánchez-Romero LM, Salmerón J, Barrientos-Gutierrez T. Dietary inflammatory Index and metabolic syndrome in Mexican adult population. Am J Clin Nutr. (2020) 112(2):373–80. doi: 10.1093/ajcn/nqaa135

PubMed Abstract | Crossref Full Text | Google Scholar

16. Khan I, Kwon M, Shivappa N, Hébert JR, Kim MK. Positive association of dietary inflammatory Index with incidence of cardiovascular disease: findings from a Korean population-based prospective study. Nutrients. (2020) 12(2):588. doi: 10.3390/nu12020588

PubMed Abstract | Crossref Full Text | Google Scholar

17. Liu B, Ren X, Tian W. Dietary inflammatory potential and the risk of nonfatal cardiovascular diseases in the China health and nutrition survey. Nutrition. (2024) 124:112469. doi: 10.1016/j.nut.2024.112469

PubMed Abstract | Crossref Full Text | Google Scholar

18. MacDonald C-J, Laouali N, Madika A-L, Mancini FR, Boutron-Ruault M-C. Dietary inflammatory index, risk of incident hypertension, and effect modification from BMI. Nutr J. (2020) 19(1):62. doi: 10.1186/s12937-020-00577-1

PubMed Abstract | Crossref Full Text | Google Scholar

19. Ramallal R, Toledo E, Martínez-González MA, Hernández-Hernández A, García-Arellano A, Shivappa N, et al. Dietary inflammatory Index and incidence of cardiovascular disease in the SUN cohort. PLoS One. (2015) 10(9):e0135221. doi: 10.1371/journal.pone.0135221

PubMed Abstract | Crossref Full Text | Google Scholar

20. Villaverde P, Rivera-Paredez B, Argoty-Pantoja AD, Velázquez Cruz R, Salmerón J. Dietary inflammatory Index and blood pressure levels in Mexican adults. Nutrients. (2024) 16(18):3052. doi: 10.3390/nu16183052

PubMed Abstract | Crossref Full Text | Google Scholar

21. Vissers LET, Waller MA, van der Schouw YT, Hebert JR, Shivappa N, Schoenaker DAJM, et al. The relationship between the dietary inflammatory index and risk of total cardiovascular disease, ischemic heart disease and cerebrovascular disease: findings from an Australian population-based prospective cohort study of women. Atherosclerosis. (2016) 253:164–70. doi: 10.1016/j.atherosclerosis.2016.07.929

PubMed Abstract | Crossref Full Text | Google Scholar

22. Vissers LET, Waller M, van der Schouw YT, Hébert JR, Shivappa N, Schoenaker DAJM, et al. A pro-inflammatory diet is associated with increased risk of developing hypertension among middle-aged women. Nutr Metab Cardiovasc Dis. (2017) 27(6):564–70. doi: 10.1016/j.numecd.2017.03.005

PubMed Abstract | Crossref Full Text | Google Scholar

23. Wu M, Li S, Lv Y, Liu K, Wang Y, Cui Z, et al. Associations between the inflammatory potential of diets with adherence to plant-based dietary patterns and the risk of new-onset cardiometabolic diseases in Chinese adults: findings from a nation-wide prospective cohort study. Food Funct. (2023) 14(19):9018–34. doi: 10.1039/D3FO02579A

PubMed Abstract | Crossref Full Text | Google Scholar

24. Xu Z, Li X, Ding L, Zhang Z, Sun Y. The dietary inflammatory index and new-onset hypertension in Chinese adults: a nationwide cohort study. Food Funct. (2023) 14(24):10759–69. doi: 10.1039/D3FO03767C

PubMed Abstract | Crossref Full Text | Google Scholar

25. Zuercher MD, Harvey DJ, Santiago-Torres M, Au LE, Shivappa N, Shadyab AH, et al. Dietary inflammatory index and cardiovascular disease risk in hispanic women from the women’s health initiative. Nutr J. (2023) 22(1):5. doi: 10.1186/s12937-023-00838-9

PubMed Abstract | Crossref Full Text | Google Scholar

26. Bondonno NP, Lewis JR, Blekkenhorst LC, Shivappa N, Woodman RJ, Bondonno CP, et al. Dietary inflammatory index in relation to sub-clinical atherosclerosis and atherosclerotic vascular disease mortality in older women. Br J Nutr. (2017) 117(11):1577–86. doi: 10.1017/S0007114517001520

PubMed Abstract | Crossref Full Text | Google Scholar

27. Choi MK, Park Y-MM, Shivappa N, Hong O-K, Han K, Steck SE, et al. Inflammatory potential of diet and risk of mortality in normal-weight adults with central obesity. Clin Nutr. (2023) 42(2):208–15. doi: 10.1016/j.clnu.2022.11.019

PubMed Abstract | Crossref Full Text | Google Scholar

28. Deng FE, Shivappa N, Tang Y, Mann JR, Hebert JR. Association between diet-related inflammation, all-cause, all-cancer, and cardiovascular disease mortality, with special focus on prediabetics: findings from NHANES III. Eur J Nutr. (2017) 56(3):1085–93. doi: 10.1007/s00394-016-1158-4

PubMed Abstract | Crossref Full Text | Google Scholar

29. Huang J, Zhang Y, Li J, Li H, Wei Y, Sun M. Association of dietary inflammatory index with all-cause and cardiovascular disease mortality in hyperuricemia population: a cohort study from NHANES 2001 to 2010. Medicine (Baltimore). (2023) 102(51):e36300. doi: 10.1097/MD.0000000000036300

PubMed Abstract | Crossref Full Text | Google Scholar

30. Ma Q, Zhang Y, Zhang D, Liu C, Zhu W, Wang G, et al. The relationship between dietary inflammatory index and all-cause and cardiovascular disease-related mortality in adults with metabolic syndrome: a cohort study of NHANES. Front Endocrinol (Lausanne). (2024) 15:1417840. doi: 10.3389/fendo.2024.1417840

PubMed Abstract | Crossref Full Text | Google Scholar

31. Majidi A, Hughes MCB, Webb IK, Miura K, van der Pols JC. Inflammatory potential of diet and mortality in Australian adults. Public Health Nutr. (2024) 27(1):e129. doi: 10.1017/S1368980024000909

PubMed Abstract | Crossref Full Text | Google Scholar

32. Okada E, Shirakawa T, Shivappa N, Wakai K, Suzuki K, Date C, et al. Dietary inflammatory Index is associated with risk of all-cause and cardiovascular disease mortality but not with cancer mortality in middle-aged and older Japanese adults. J Nutr. (2019) 149(8):1451–9. doi: 10.1093/jn/nxz085

PubMed Abstract | Crossref Full Text | Google Scholar

33. Park S-Y, Kang M, Wilkens LR, Shvetsov YB, Harmon BE, Shivappa N, et al. The dietary inflammatory Index and all-cause, cardiovascular disease, and cancer mortality in the multiethnic cohort study. Nutrients. (2018) 10(12):1844. doi: 10.3390/nu10121844

PubMed Abstract | Crossref Full Text | Google Scholar

34. Shivappa N, Steck SE, Hussey JR, Ma Y, Hebert JR. Inflammatory potential of diet and all-cause, cardiovascular, and cancer mortality in national health and nutrition examination survey III. Study. Eur J Nutr. (2017) 56(2):683–92. doi: 10.1007/s00394-015-1112-x

Crossref Full Text | Google Scholar

35. Sun S-N, Ni S-H, Li Y, Liu X, Deng J-P, Ouyang X-L, et al. Association between dietary inflammatory index with all-cause and cardiovascular disease mortality among older US adults: a longitudinal cohort study among a nationally representative sample. Arch Gerontol Geriatr. (2024) 118:105279. doi: 10.1016/j.archger.2023.105279

PubMed Abstract | Crossref Full Text | Google Scholar

36. Veronese N, Cisternino AM, Shivappa N, Hebert JR, Notarnicola M, Reddavide R, et al. Dietary inflammatory index and mortality: a cohort longitudinal study in a Mediterranean area. J Hum Nutr Diet. (2020) 33(1):138–46. doi: 10.1111/jhn.12701

PubMed Abstract | Crossref Full Text | Google Scholar

37. Xie L, Liu J, Wang X, Liu B, Li J, Li J, et al. Role of dietary inflammatory index in the association of NT-proBNP with all-cause and cardiovascular mortality in NHANES 1999–2004. Sci Rep. (2024) 14(1):19978. doi: 10.1038/s41598-024-70506-3

PubMed Abstract | Crossref Full Text | Google Scholar

38. Yang M, Miao S, Hu W, Yan J. Association between the dietary inflammatory index and all-cause and cardiovascular mortality in patients with atherosclerotic cardiovascular disease. Nutr Metab Cardiovasc Dis. (2024) 34(4):1046–53. doi: 10.1016/j.numecd.2023.11.015

PubMed Abstract | Crossref Full Text | Google Scholar

39. Yuan S, Song C, Zhang R, He J, Dou K. Dietary inflammation Index and its association with long-term all-cause and cardiovascular mortality in the general US population by baseline glycemic Status. Nutrients. (2022) 14(13):2556. doi: 10.3390/nu14132556

PubMed Abstract | Crossref Full Text | Google Scholar

40. Zhou M, Cai B, Xiao Q, Zou H, Zeng X, Zhao J, et al. Higher dietary inflammatory Index and increased mortality rate of adults with hyperuricemia: findings from the national health and nutritional examination survey (2001–2018). Arthritis Care Res (Hoboken). (2024) 76(8):1179–86. doi: 10.1002/acr.25336

PubMed Abstract | Crossref Full Text | Google Scholar

41. Garcia-Arellano A, Ramallal R, Ruiz-Canela M, Salas-Salvadó J, Corella D, Shivappa N, et al. Dietary inflammatory Index and incidence of cardiovascular disease in the PREDIMED study. Nutrients. (2015) 7(6):4124–38. doi: 10.3390/nu7064124

PubMed Abstract | Crossref Full Text | Google Scholar

42. Neufcourt L, Assmann KE, Fezeu LK, Touvier M, Graffouillere L, Shivappa N, et al. Prospective association between the dietary inflammatory Index and cardiovascular diseases in the SUpplémentation en VItamines et Minéraux AntioXydants (SU.VI.MAX). Cohort. J Am Heart Assoc. (2016) 5(3):e002735. doi: 10.1161/JAHA.115.002735

Crossref Full Text | Google Scholar

43. Ganbaatar G, Okami Y, Kadota A, Ganbaatar N, Yano Y, Kondo K, et al. Association of pro-inflammatory diet with long-term risk of all-cause and cardiovascular disease mortality: NIPPON DATA80. J Atheroscler Thromb. (2024) 31(3):326–43. doi: 10.5551/jat.64330

PubMed Abstract | Crossref Full Text | Google Scholar

44. da Silva A, Felício MB, Caldas APS, Miranda Hermsdorff HH, Bersch-Ferreira ÂC, Torreglosa CR, et al. Pro-inflammatory diet is associated with a high number of cardiovascular events and ultra-processed foods consumption in patients in secondary care. Public Health Nutr. (2021) 24(11):3331–40. doi: 10.1017/S136898002000378X

PubMed Abstract | Crossref Full Text | Google Scholar

45. Georgousopoulou EN, Kouli G-M, Panagiotakos DB, Kalogeropoulou A, Zana A, Chrysohoou C, et al. Anti-inflammatory diet and 10-year (2002–2012) cardiovascular disease incidence: the ATTICA study. Int J Cardiol. (2016) 222:473–8. doi: 10.1016/j.ijcard.2016.08.007

PubMed Abstract | Crossref Full Text | Google Scholar

46. Steven S, Frenis K, Oelze M, Kalinovic S, Kuntic M, Bayo Jimenez MT, et al. Vascular inflammation and oxidative stress: major triggers for cardiovascular disease. Oxid Med Cell Longev. (2019) 2019:7092151. doi: 10.1155/2019/7092151

PubMed Abstract | Crossref Full Text | Google Scholar

47. Chai W, Morimoto Y, Cooney RV, Franke AA, Shvetsov YB, Le Marchand L, et al. Dietary red and processed meat intake and markers of adiposity and inflammation: the multiethnic cohort study. J Am Coll Nutr. (2017) 36(5):378–85. doi: 10.1080/07315724.2017.1318317

PubMed Abstract | Crossref Full Text | Google Scholar

48. Madan M, Bishayi B, Hoge M, Amar S. Atheroprotective role of interleukin-6 in diet- and/or pathogen-associated atherosclerosis using an ApoE heterozygote murine model. Atherosclerosis. (2008) 197(2):504–14. doi: 10.1016/j.atherosclerosis.2007.02.023

PubMed Abstract | Crossref Full Text | Google Scholar

49. Zhao Z, Li L, Gao X, Hu G, Liu G, Tao H, et al. High dietary inflammatory index is associated with decreased plaque stability in patients with coronary heart disease. Nutr Res. (2023) 119:56–64. doi: 10.1016/j.nutres.2023.08.007

PubMed Abstract | Crossref Full Text | Google Scholar

50. Bentzon JF, Otsuka F, Virmani R, Falk E. Mechanisms of plaque formation and rupture. Circ Res. (2014) 114(12):1852–66. doi: 10.1161/CIRCRESAHA.114.302721

PubMed Abstract | Crossref Full Text | Google Scholar

51. Muscolo A, Mariateresa O, Giulio T, Mariateresa R. Oxidative stress: the role of antioxidant phytochemicals in the prevention and treatment of diseases. Int J Mol Sci. (2024) 25(6):3264. doi: 10.3390/ijms25063264

PubMed Abstract | Crossref Full Text | Google Scholar

52. Barillà F, Cammisotto V, Bartimoccia S, Loffredo L, Nocella C, Bruno N, et al. Toll-like receptor 4 activation in platelets from myocardial infarction patients. Thromb Res. (2022) 209:33–40. doi: 10.1016/j.thromres.2021.11.019

PubMed Abstract | Crossref Full Text | Google Scholar

53. Gatarek P, Kaluzna-Czaplinska J. Trimethylamine N-oxide (TMAO) in human health. Excli J. (2021) 20:301–19. doi: 10.17179/excli2020-3239

PubMed Abstract | Crossref Full Text | Google Scholar

54. Witkowski M, Witkowski M, Friebel J, Buffa JA, Li XS, Wang Z, et al. Vascular endothelial tissue factor contributes to trimethylamine N-oxide-enhanced arterial thrombosis. Cardiovasc Res. (2022) 118(10):2367–84. doi: 10.1093/cvr/cvab263

PubMed Abstract | Crossref Full Text | Google Scholar

55. Zhang X, Li Y, Yang P, Liu X, Lu L, Chen Y, et al. Trimethylamine-N-Oxide promotes vascular calcification through activation of NLRP3 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3) inflammasome and NF-κB (nuclear factor κB) signals. Arterioscler Thromb Vasc Biol. (2020) 40(3):751–65. doi: 10.1161/ATVBAHA.119.313414

PubMed Abstract | Crossref Full Text | Google Scholar

56. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, DuGar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. (2011) 472(7341):57–63. doi: 10.1038/nature09922

PubMed Abstract | Crossref Full Text | Google Scholar

57. Liu X, Xie Z, Sun M, Wang X, Li J, Cui J, et al. Plasma trimethylamine N-oxide is associated with vulnerable plaque characteristics in CAD patients as assessed by optical coherence tomography. Int J Cardiol. (2018) 265:18–23. doi: 10.1016/j.ijcard.2018.04.126

PubMed Abstract | Crossref Full Text | Google Scholar

58. Tang TWH, Chen H-C, Chen C-Y, Yen CYT, Lin C-J, Prajnamitra RP, et al. Loss of gut Microbiota alters immune system composition and cripples postinfarction cardiac repair. Circulation. (2019) 139(5):647–59. doi: 10.1161/CIRCULATIONAHA.118.035235

PubMed Abstract | Crossref Full Text | Google Scholar

59. Li D, Lu Y, Yuan S, Cai X, He Y, Chen J, et al. Gut microbiota-derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis. Am J Clin Nutr. (2022) 116(1):230–43. doi: 10.1093/ajcn/nqac074

PubMed Abstract | Crossref Full Text | Google Scholar

60. Rivera K, Gonzalez L, Bravo L, Manjarres L, Andia ME. The gut-heart axis: molecular perspectives and implications for myocardial infarction. Int J Mol Sci. (2024) 25(22):12465. doi: 10.3390/ijms252212465

PubMed Abstract | Crossref Full Text | Google Scholar

61. Cho CE, Taesuwan S, Malysheva OV, Bender E, Tulchinsky NF, Yan J, et al. Trimethylamine-N-oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: a randomized controlled trial. Mol Nutr Food Res. (2017) 61(1):1600324. doi: 10.1002/mnfr.201600324

Crossref Full Text | Google Scholar

62. Xing D, Nozell S, Chen Y-F, Hage F, Oparil S. Estrogen and mechanisms of vascular protection. Arterioscler Thromb Vasc Biol. (2009) 29(3):289–95. doi: 10.1161/ATVBAHA.108.182279

PubMed Abstract | Crossref Full Text | Google Scholar

63. Tabung FK, Steck SE, Zhang J, Ma Y, Liese AD, Agalliu I, et al. Construct validation of the dietary inflammatory index among postmenopausal women. Ann Epidemiol. (2015) 25(6):398–405. doi: 10.1016/j.annepidem.2015.03.009

PubMed Abstract | Crossref Full Text | Google Scholar

64. Li S, Kararigas G. Role of biological sex in the cardiovascular-gut microbiome axis. Front Cardiovasc Med. (2021) 8:759735. doi: 10.3389/fcvm.2021.759735

PubMed Abstract | Crossref Full Text | Google Scholar

65. Peters BA, Lin J, Qi Q, Usyk M, Isasi CR, Mossavar-Rahmani Y, et al. Menopause is associated with an altered gut microbiome and estrobolome, with implications for adverse cardiometabolic risk in the hispanic community health study/study of latinos. mSystems. (2022) 7(3):e0027322. doi: 10.1128/msystems.00273-22

PubMed Abstract | Crossref Full Text | Google Scholar

66. Mayneris-Perxachs J, Arnoriaga-Rodríguez M, Luque-Córdoba D, Priego-Capote F, Pérez-Brocal V, Moya A, et al. Gut microbiota steroid sexual dimorphism and its impact on gonadal steroids: influences of obesity and menopausal status. Microbiome. (2020) 8(1):136. doi: 10.1186/s40168-020-00913-x

PubMed Abstract | Crossref Full Text | Google Scholar

67. Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, et al. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. (2006) 106(10):1640–50. doi: 10.1016/j.jada.2006.07.011

PubMed Abstract | Crossref Full Text | Google Scholar

68. McCullough LE, Byrd DA. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol. (2023) 192(11):1801–5. doi: 10.1093/aje/kwac071

PubMed Abstract | Crossref Full Text | Google Scholar

69. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. (2009) 20(4):488–95. doi: 10.1097/EDE.0b013e3181a819a1

PubMed Abstract | Crossref Full Text | Google Scholar

70. Hua R, Liang G, Yang F. Meta-analysis of the association between dietary inflammation index and C-reactive protein level. Medicine (Baltimore). (2024) 103(19):e38196. doi: 10.1097/MD.0000000000038196

PubMed Abstract | Crossref Full Text | Google Scholar

71. Shamah-Levy T, Rodríguez-Ramírez S, Gaona-Pineda EB, Cuevas-Nasu L, Carriquiry AL, Rivera JA. Three 24-hour recalls in comparison with one improve the estimates of energy and nutrient intakes in an urban mexican population. J Nutr. (2016) 146(5):1043–50. doi: 10.3945/jn.115.219683

PubMed Abstract | Crossref Full Text | Google Scholar

72. Bagheri S, Zolghadri S, Stanek A. Beneficial effects of anti-inflammatory diet in modulating gut Microbiota and controlling obesity. Nutrients. (2022) 14(19):3985. doi: 10.3390/nu14193985

PubMed Abstract | Crossref Full Text | Google Scholar