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. 2026 Jul 20;17(9):100709. doi: 10.1016/j.advnut.2026.100709

Dietary Intake Modifies the Association between Air Pollution and Chronic Diseases: A Systematic Review and Meta-analysis

Ye Chen 1,2,†, Bangqin Cai 1,2,†, Fei Wang 3,†, Hongyu Xuan 1,2, Yongqiang Sun 4, Yang Xia 1,2, Yuhong Zhao 1,2, Lishen Shan 3,⁎, Hehua Zhang 1,2,5,⁎⁎
PMCID: PMC13486347  PMID: 42476230

Abstract

Interaction effects between dietary factors and air pollutants remain inconsistent. This systematic review and meta-analysis examined whether dietary intake modifies the air pollution–chronic disease risk association. This study searched PubMed, Embase, Web of Science, and the Cochrane from inception to May 2026. A total of 51 observational studies (cohort, case-control, and cross-sectional), comprising 9,956,549 participants, that examined the modifying effect of dietary intake on the association between air pollution exposure and chronic disease risk through stratified analyses were included. Stratum-specific pooled odds ratios (pORs) per 5 μg/m3 pollutant increase were estimated using random-effects models, whereas effect modification was assessed using deft analysis by pooling study-specific within-study interaction estimates. Particulate matter (PM) increased cardiovascular disease risk by 7%, attenuated by greater adherence to healthy dietary patterns (HDPs, P-int < 0.001, high compared with low intake pOR: 1.05 compared with 1.11), grain (P-int < 0.001, pOR: 1.04 compared with 1.06), and dairy (P-int < 0.001, pOR: 1.03 compared with 1.08), and amplified by sugar-sweetened beverage (SSB). PM and nitrogen oxides (NOX) increased chronic respiratory disease risk by 20% and 7%, respectively, attenuated by omega-3 (n–3) fatty acids (P-int = 0.002, pOR 1.05 compared with 1.09), and amplified by unhealthy dietary patterns (UDPs) and ω-6 fatty acids. PM and NOX increased metabolic disorder risk by 19% and 13%, respectively, attenuated by HDPs (PM: P-int < 0.001, pOR 1.04 compared with 1.24; NOX: P-int < 0.001, pOR 1.01 compared with 1.07), and amplified by UDPs, SSB, and sodium. PM increased neuropsychiatric and digestive disease risk by 9% and 17%, respectively, attenuated by vitamin B (dementia: P-int < 0.001, pOR: 0.95 compared with 1.18). These findings suggest that greater adherence to HDPs, grain, dairy, ω-3 fatty acids, and vitamin B may attenuate the association between PM and NOX exposure and chronic disease risk. This review was registered in PROSPERO as CRD420251148066.

Keywords: air pollution, dietary factor, modification, particulate matter, chronic diseases, nitrogen oxides, meta-analysis

Introduction

Statement of Significance.

This systematic review and meta-analysis is the first quantitative review to assess interaction effects, offering potential dietary prevention strategies to mitigate the harmful impact of air pollution on public health at 3 levels: dietary patterns, food items, and dietary nutrients.

Chronic diseases, defined as conditions lasting ≥1 y that require ongoing medical management or result in limitations in daily activities [1], account for nearly two-thirds of global disability-adjusted life years. Cardiovascular diseases (CVDs) (e.g., ischemic heart disease and stroke), cancers, chronic respiratory diseases (CRDs) [e.g., chronic obstructive pulmonary disease (COPD) and asthma], metabolic disorders (e.g., obesity and diabetes mellitus), neuropsychiatric disorders (e.g., depression and dementia), and digestive diseases (e.g., Crohn’s disease) are common chronic diseases that constitute a significant global health burden and underline the urgent need for prevention [2,3].

Ambient particulate matter (PM) air pollution is the second leading risk factor for total disease burden worldwide, as measured by disability-adjusted life years [2]. Both acute and chronic exposure to air pollution increase morbidity and mortality across a range of chronic diseases, including CVD, cancer, CRD, metabolic disorders, neuropsychiatric disorders, and digestive diseases [[4], [5], [6], [7], [8], [9], [10], [11], [12]]. Mechanistically, inhaled pollutants trigger primary responses such as oxidative stress, inflammation, autonomic dysregulation, and direct particle translocation [13,14]. In the cardiovascular system, these primary responses initiate secondary cascades—including endothelial dysfunction, thrombosis, hypothalamic–pituitary–adrenal axis overactivation, and epigenetic changes—that elevate CVD risk [15,16]. In the respiratory tract, pollutant-induced inflammation and epithelial injury promote COPD and asthma [17]. In metabolic tissues, impaired insulin signaling and dysregulated lipid metabolism increase susceptibility to obesity and type 2 diabetes [18]. Moreover, systemic and neural inflammation increase risk of neuropsychiatric disorders and cognitive decline [19], whereas gut inflammation and oxidative stress may contribute to digestive diseases [20]. Given the ubiquity of air pollutants, effective measures to reduce personal exposure remain limited, emphasizing the urgent need for individualized intervention strategies to mitigate the adverse effects of air pollution on chronic diseases.

Dietary intake, encompassing dietary patterns, food items, and specific nutrients, is a major modifiable contributor to the burden of leading chronic diseases, including CVD, type 2 diabetes, and site-specific cancers [[21], [22], [23]]. Adherence to healthy dietary patterns (HDPs) such as the Mediterranean diet (MED) and Dietary Approaches to Stop Hypertension diet; greater adherence to fruits, vegetables, whole grains, and nuts; and adequate consumption of antioxidants, PUFAs, and vitamins have all been associated with reduced proinflammatory biomarkers and a lower incidence of CVD, CRD, metabolic disorders, neuropsychiatric disorders, and digestive diseases [[24], [25], [26]]. Mechanistically, healthy dietary intake can reduce risk of chronic diseases by lowering systemic inflammation and oxidative stress, improving immune function, modulating the gut microbiota, and supporting metabolic and endothelial health [[27], [28], [29], [30]]. Because the reduction in chronic disease risk conferred by healthy dietary intake and the elevation induced by air pollution exposure are mediated by overlapping molecular pathways, dietary intake has the potential to act as an effect modifier of the air pollution–chronic disease risk association.

Previous studies have indicated that dietary intake may modify the impact of pollution on most chronic diseases [[31], [32], [33], [34], [35], [36]]. However, the findings are inconsistent; some studies have reported that healthy dietary factors [e.g., MED, whole grain, and omega-3 (n–3) fatty acids] can mitigate the negative impact of pollution on chronic diseases [[31], [32], [33],36], whereas others have not demonstrated a significant modification [34,35]. Therefore, the aim of this study was to systematically review and meta-analyze the evidence on whether dietary factors modify the association between air pollution and chronic disease risk and to identify possible dietary patterns, specific food items, and nutrients that may attenuate or exacerbate these effects, with the goal of providing evidence-based dietary strategies to reduce the health hazards of air pollution.

Methods

This study was prospectively registered in PROSPERO (CRD420251148066) and followed the 2020 PRISMA reporting guidelines [37]. Two independent investigators (Y.C. and B.C.) performed the bibliographic search, study screening, quality evaluation, and information extraction. Discordant assessments were resolved through discussion with a third reviewer (H.Z.).

Search strategy

This study systematically searched the PubMed, Embase, Web of Science, and Cochrane Library databases from inception to May 2026. The complete search strategy is presented in Supplemental Table 1. In addition, the citation lists of all eligible investigations and relevant reviews were manually examined to prevent omissions.

Eligibility criteria

According to the Population, Exposure, Comparison, Outcome, Study design model [38], the inclusion criteria were as follows: 1) Population: people of all ages; 2) Exposure factors: dietary factors (dietary patterns, food items, or dietary nutrients) and ambient air pollution [PM with an aerodynamic diameter of ≤1 μm (PM1), ≤2.5 μm (PM2.5), ≤10 μm (PM10), or between 2.5 μm and 10 μm (PM2.5-10); nitrogen dioxide (NO2), and nitrogen oxides (NOX), which are collectively referred to as NOX; sulfur dioxide (SO2); carbon monoxide (CO); or ozone (O3)]; 3) Outcome: any chronic disease; in accordance with the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) [39], chronic diseases were systematically classified as CVD (ICD-10: I00-I99), CRD (ICD-10: J30-J99), metabolic disorders (ICD-10: E00-E90), neuropsychiatric disorders (ICD-10: F00-F99 and G00-G99), and digestive diseases (ICD-10: K00-K93). Cancers were classified into various systemic diseases based on their primary anatomical sites [40,41]. Details of the diseases included in this study can be found in Supplemental Table 2. 4) Study design: cohort studies, cross-sectional studies, or case-control studies. 5) Studies that conducted stratified analyses by dietary factors of the association between air pollution and risk of chronic diseases, with or without assessment of interaction effects.

The exclusion criteria were as follows: 1) studies that did not report stratified analyses of dietary intake; 2) studies in which the outcome did not include chronic diseases; 3) nonoriginal research publications, such as reviews, commentaries, editorials, government reports, letters, animal and experimental studies, or conference abstracts; 4) studies in which the study population was not a natural population (i.e., the control group did not consist of healthy individuals without major chronic diseases); 5) non–English-language publications; and 6) publications lacking requisite statistical parameters [effect estimates, 95% confidence intervals (CIs), and necessary data for their derivation].

Data extraction

The following information was extracted: author, publication year, study design, follow-up time (applicable only to prospective cohort studies), sample size, age group, country, dietary intake, outcome, air pollution exposure, effect sizes (stratified effect estimates and 95% CIs, with or without P values for interaction effects), and adjusted covariates. For each study, stratum-specific effect estimates describing the association between air pollution exposure and chronic disease risk were extracted separately for the high- and low-dietary intake groups. Specifically, dietary patterns in which the main contributing foods recognized as healthy were grouped into the HDP group. In contrast, dietary patterns with main contributing foods considered detrimental to health were categorized as unhealthy dietary patterns (UDPs).

Risk of bias and quality assessment

The modified Newcastle-Ottawa Scale [42] scoring criteria were used to evaluate the quality of the cohort and case-control studies, with scores of 0 to 3, 4 to 6, and 7 to9 representing low, medium, and high quality, respectively. The quality of the cross-sectional studies was evaluated using the Agency for Healthcare Research and Quality [43] scoring criteria, with scores of 0 to 3, 4 to 7, and 8 to 11 representing low, medium, and high quality, respectively.

Statistical analysis

Stratified meta-analyses were performed for each dietary subgroup when a minimum of 3 independent effect estimates were available within each stratum. Odds ratios (ORs), risk ratios, and hazard ratios were considered equivalent measures of relative risk and pooled together [44]. For studies reporting standardized mean differences, ORs were estimated using the approximation method proposed by Chinn [45]. When effect estimates were reported as percent changes, log transformation was applied to approximate log ORs before back-transformation. All effect estimates were standardized to a 5 μg/m3 increment in air pollutant concentration whenever possible [46]. To facilitate the interpretation of the association between air pollution and chronic disease within dietary strata, pooled odds ratios (pORs) and the corresponding 95% CIs were estimated separately for high- and low-dietary intake groups using random-effects models.

Consistent with recommendations from the Cochrane Handbook [47] and the meta-analysis interaction approach described by Fisher et al. [48], the modifying effect of dietary intake was formally evaluated using the deft approach [48]. For each study, a study-specific interaction estimate was calculated as the difference between the log-transformed effect estimates in the high and low-dietary intake strata, corresponding to the log ratio of ORs (log ROR): log (ROR) = log (ORhigh) – log (ORlow). The variance of each interaction estimate was derived from the reported 95% CIs of the stratum-specific effect estimates. Study-specific interaction estimates were subsequently pooled using a random-effects model to obtain the pooled within-study interaction effect (deft estimate), which was expressed as the pooled log ROR (95% CI) and the corresponding interaction P value (P-int). A significant modification effect of a given dietary factor was defined as P-int < 0.05. A positive and statistically significant pooled log ROR reflected a strengthening effect of dietary intake on the air pollution–outcome association, whereas a negative and significant value reflected a weakening effect.

Considering the potential differences in dietary intake patterns and susceptibility to air pollution between adults and children, separate meta-analyses were conducted when sufficient data were available. All analyses were performed using R software (version 4.5.2; R Core Team, 2025). Statistical significance was defined as a 2-sided P < 0.05.

Results

Study characteristics

A total of 51 studies [[31], [32], [33], [34], [35], [36],[49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93]] involving 9,956,549 participants were included. The study selection workflow is shown in Figure 1. The included studies comprised 30 cohort studies, 1 case-control study, and 20 cross-sectional studies. The disease outcomes included CVD (19 studies), CRD (9 studies), metabolic disorders (19 studies), neuropsychiatric disorders (5 studies), and digestive diseases (2 studies). Air pollutant exposures included PM (47 studies), NOX (22 studies), SO2 (8 studies), CO (3 studies), and O3 (6 studies). Dietary exposure was assessed based on nutrient intake (19 studies), dietary patterns (22 studies), and individual food intake (19 studies). The study populations were recruited from Europe (19 studies), Asia (28 studies), and the Americas (4 studies). The full details of the included studies are presented in Supplemental Table 3, and dietary exposure by disease system is listed in Supplemental Table 4.

FIGURE 1.

FIGURE 1

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PRISMA flow diagram of the study selection and screening process. (A) Definition of natural population: control groups in included observational studies comprised healthy individuals free of major chronic diseases.

Quality assessment

All 51 included studies underwent risk of bias assessment, with 41 rated as high quality and 10 rated as moderate quality. The detailed risk of bias results for the cohort, case-control, and cross-sectional studies are provided in Supplemental Tables 5 to 7.

Analysis of CVD

As shown in Figure 2, each 5 μg/m3 increase in PM concentration was associated with a 7% higher risk of CVD. This association was significantly attenuated by greater adherence to HDPs [P-int < 0.001; high compared with low intake: pOR 1.05 (95% CI: 1.02, 1.09) compared with 1.11 (1.06, 1.17)], grain intake [hypertension: P-int < 0.001; high compared with low intake: pOR 1.04 (95% CI: 1.01, 1.06) compared with 1.06 (1.02, 1.10)], and dairy intake [hypertension: P-int < 0.001; 1.03 (1.00, 1.06) compared with 1.08 (1.04, 1.12)] and significantly amplified by greater adherence to sugar-sweetened beverage (SSB) intake [hypertension: P-int = 0.008; 1.08 (1.03, 1.12) compared with 1.02 (1.01, 1.04)].

FIGURE 2.

FIGURE 2

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The modification of dietary intake on the association between air pollution and cardiovascular disease risk. ∗Deft analysis represents the pooled within-study interaction effect, expressed as the meta-analyzed log ratio of odds ratios with corresponding 95% confidence intervals; superscript a indicates that only hypertension (a single cardiovascular disease outcome) was included in the corresponding meta-analysis. CI, confidence interval; FV, fruits and vegetables; HDP, healthy dietary patterns; N, number of independent effect estimates included in the meta-analysis; NOX, nitrogen oxides; PM, particulate matter; pOR, pooled odds ratio; P-int, P value for interaction; SSB, sugar-sweetened beverages; UDP, unhealthy dietary patterns.

Analysis of CRD

As shown in Figure 3, each 5 μg/m3 increase in PM and NOX was associated with 20% and 7% higher risks of CRD, respectively. These adverse associations were significantly attenuated by greater adherence to ω-3 fatty acids [PM: P-int = 0.002; high compared with low intake: pOR 1.05 (0.96, 1.14) compared with 1.09 (1.02, 1.16)]. Conversely, they were significantly amplified by greater adherence to UDPs [PM: P-int < 0.001; pOR: 1.14 (1.07, 1.23) compared with 0.93 (0.85, 1.02); NOX: P-int = 0.003; 1.04 (1.02, 1.06) compared with 1.00 (0.98, 1.02)] and ω-6 fatty acids [P-int = 0.025; 2.00 (1.04, 3.82) compared with 1.01 (0.75, 1.37)].

FIGURE 3.

FIGURE 3

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The modification of dietary intake on the association between air pollution and chronic respiratory disease risk. ∗Deft analysis represents the pooled within-study interaction effect, expressed as the meta-analyzed log ratio of odds ratios with corresponding 95% confidence intervals. CI, confidence interval; HDP, healthy dietary patterns; N, number of independent effect estimates included in the meta-analysis; NOX, nitrogen oxides; PM, particulate matter; pOR, pooled odds ratio; P-int, P value for interaction; UDP, unhealthy dietary patterns.

Analysis of metabolic disorders

As shown in Figure 4, each 5 μg/m3 increase in PM and NOX was associated with 19% and 13% higher risks of metabolic disorders, respectively. These adverse associations were significantly attenuated by greater adherence to HDPs [PM: P-int < 0.001; high compared with low intake: pOR 1.04 (1.03, 1.06) compared with 1.24 (1.17, 1.32); NOX: P-int < 0.001; 1.01 (1.00, 1.02) compared with 1.07 (1.06, 1.09)]. Conversely, they were significantly amplified by greater adherence to UDPs [PM: P-int = 0.011; 1.07 (1.04, 1.09) compared with 1.02 (0.99, 1.05), NOX: P-int = 0.001; 1.08 (1.04, 1.13) compared with 1.05 (0.98, 1.12)], SSB intake [P-int = 0.006; 1.08 (1.07, 1.10) compared with 1.05 (1.03, 1.08)], and sodium intake [P-int = 0.030; 1.02 (1.01, 1.03) compared with 1.00 (0.98, 1.01)]. Although the overall association of CO with metabolic disorder risk was not statistically significant, greater adherence to UDPs significantly amplified this association [P-int = 0.040; 1.15 (1.00, 1.34) compared with 1.07 (0.96, 1.19)].

FIGURE 4.

FIGURE 4

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The modification of dietary intake on the association between air pollution and chronic metabolic system disease risk. ∗Deft analysis represents the pooled within-study interaction effect, expressed as the meta-analyzed log ratio of odds ratios with corresponding 95% confidence intervals. CI, confidence interval; FV, fruits and vegetables; HDP, healthy dietary patterns; N, number of independent effect estimates included in the meta-analysis; NOX, nitrogen oxides; PM, particulate matter; pOR, pooled odds ratio; P-int, P value for interaction; SSB, sugar-sweetened beverages; UDP, unhealthy dietary patterns.

Analysis of neuropsychiatric disorders and digestive diseases

As shown in Figure 5, each 5 μg/m3 increase in PM was associated with 9% and 17% higher risks of neuropsychiatric disorders and digestive diseases, respectively. Greater adherence to vitamin B intake [dementia: P-int < 0.001; high compared with low: pOR 0.95 (0.87, 1.04) compared with 1.18 (1.08, 1.29)] attenuated this association, whereas fat intake strengthened the association with metabolic dysfunction-associated fatty liver disease (MAFLD) risk [P-int < 0.001; 1.23 (1.10, 1.37) compared with 1.06 (1.02, 1.10)]. Although the overall association of NOX with neuropsychiatric disorder risk was not statistically significant, greater adherence to meat intake significantly amplified this association [depression: P-int < 0.001; 1.01 (1.01, 1.01) compared with 1.00 (1.00, 1.00)].

FIGURE 5.

FIGURE 5

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The modification of dietary intake on the association between air pollution and neuropsychiatric disorders and digestive disease risk. ∗Deft analysis represents the pooled within-study interaction effect, expressed as the meta-analyzed log ratio of odds ratios with corresponding 95% confidence intervals; Superscripts a, b, and c indicate that only dementia, depression, and metabolic-associated fatty liver disease, respectively, were included in the corresponding meta-analyses. CI, confidence interval; HDP, healthy dietary patterns; N, number of independent effect estimates included in the meta-analysis; NOX, nitrogen oxides; PM, particulate matter; pOR, pooled odds ratio; P-int, P value for interaction.

Sensitivity analyses

Supplemental Figure 1 shows the results of the meta-analysis in children. Compared with the overall population, the modification by sodium [PM-metabolic disorders: P-int = 0.030; 1.02 (1.01, 1.03) compared with 1.00 (0.98, 1.01)] remained significant, whereas no modification by dairy products, grain, SSB, ω-3 fatty acids, or ω-6 fatty acids was observed. Supplemental Figure 2 shows the results for adults. Consistent with the overall population, the modification by grain [PM-CVD: P-int < 0.001; 1.03 (1.01, 1.06) compared with 1.06 (1.02, 1.10)], dairy products [PM-CVD: P-int < 0.001; 1.03 (0.99, 1.06) compared with 1.07 (1.02, 1.11)], SSB [PM-CVD: P-int = 0.008; 1.07 (1.03, 1.12) compared with 1.02 (1.01, 1.04)], and ω-3 fatty acids [PM-CRD: P-int = 0.002; 1.01 (0.94, 1.08) compared with 1.07 (1.00, 1.14)] remained significant, whereas no significant modification by ω-6 fatty acids was observed. Notably, a modest modifying effect of legume intake was observed among adults [PM-CVD: P-int = 0.048; 1.05 (1.02, 1.08) compared with 1.07 (1.03, 1.12)], although no significant effect modification was detected in the overall population. A few studies explored the interactions between other pollutants and dietary intake; however, <3 studies were available for any given exposure–outcome pair, precluding a meta-analysis (Supplemental Table 8 for details).

Discussion

Principal findings of this study

Through the systematic synthesis of stratified effect estimates from a large body of original studies, accompanied by within-study interaction analyses using the deft approach, this study found that HDPs attenuated the harmful effects of PM and NOX on CVD and metabolic disorder risks, whereas UDPs amplified the impact of PM, NOX, and SO2 on CRD and metabolic disorder risks. Grain and dairy product intake reduced the effect of PM on hypertension, whereas SSB intake exacerbated the effects of PM on hypertension and risk of metabolic disorders. Meat intake exacerbated the effects of NOX on depression. Among these nutrients, ω-3 fatty acids and vitamin B mitigated the effects of PM on CRD and dementia, respectively, whereas sodium, ω-6 fatty acids, and fat exacerbated the corresponding harmful effects. Notably, sodium exerted modifying effects primarily in children, whereas other dietary factors exerted modifying effects in adults.

Dietary modulation of PM-associated CVD risk

Greater adherence to HDPs, grain intake, and dairy product intake reduced the adverse effects of PM on CVD, including heart failure, hypertension, stroke, and ventricular arrhythmias. In contrast, SSB intake increases the adverse effects of PM on CVD risk. A previous nonsystematic review similarly suggested that adherence to the MED may ameliorate the adverse health effects of air pollution; however, that review included only 2 primary studies on CVD mortality and provided no supporting quantitative synthesis [94]. Evidence from previous meta-analyses suggests that higher whole-grain consumption is associated with a reduced risk of hypertension, whereas refined-grain intake is not significantly associated with hypertension risk [95,96]. Additionally, 2 meta-analyses of prospective cohort studies reported that consumption of whole-fat and low-fat dairy products was associated with lower hypertension risk in adults [97,98]. Previous meta-analyses have consistently shown that higher consumption of SSB is associated with an increased risk of hypertension [[99], [100], [101]]. However, none of these studies have investigated the interaction between grain, dairy, and SSB intake and PM exposure. Sensitivity analyses did not replicate the modifying effects of grain, dairy, and SSB intake in children, which may reflect age-related physiological differences.

Collectively, HDPs, grains, and dairy products may act as effect modifiers of air pollution-related CVD risk in adults, driven by shared antioxidant and anti-inflammatory mechanisms. Bioactive compounds from HDPs improve endothelial function and inhibit free radical production and systemic inflammation, thereby alleviating PM-induced oxidative stress, inflammation, blood pressure elevation, autonomic dysfunction, and cardiovascular stress responses, ultimately reducing CVD risk [102]. Dairy bioactive compounds may exert parallel protection by mitigating PM-induced oxidative damage through antioxidant and anti-inflammatory pathways [103]. Because both SSB consumption and PM exposure promote oxidative stress, systemic inflammation, and endothelial dysfunction, high SSB intake may potentiate the hypertensive effects of PM via shared pathophysiological pathways [104].

Dietary modulation of PM and NOX-associated CRD risk

This study found that ω-3 fatty acids mitigated the adverse effects of PM on risk of CRD (including COPD and lung cancer), whereas UDPs and ω-6 fatty acids exacerbated PM-related harm. Previous systematic reviews have linked UDPs to impaired lung function [105] and elevated lung cancer risk [106]. However, no studies have explored the interactions between dietary intake and air pollution. A previous review reported protective effects of ω-3 fatty acids against PM-induced damage [107]; however, it offered only a qualitative synthesis of randomized controlled trials, addressed symptom improvement rather than disease risk, and lacked quantitative data. Other reviews have found no significant association between ω-6 fatty acid intake and cancer or allergic disease risk [108,109] and have not investigated its interaction with PM. Sensitivity analyses further showed that the protective effects of ω-3 fatty acids were confined to adults (with COPD and lung cancer), likely because of the limited number of primary studies. Therefore, higher-quality original studies are needed to validate the effect modification of ω-3 and ω-6 fatty acids on air pollution-related CRD risk.

Mechanistically, ω-3 fatty acids may mitigate the adverse respiratory effects of PM by enhancing antioxidant defenses in airway epithelial cells and alveolar macrophages. This reduces local oxidative stress and NF-κB-mediated inflammatory cascades, thereby attenuating DNA damage, airway remodeling, and chronic lung inflammation, ultimately lowering COPD and lung cancer risk [[110], [111], [112]]. Conversely, proinflammatory dietary intake and ω-6 fatty acids may upregulate proinflammatory markers, including IL-6, amplify PM-induced airway injury, and synergistically increase risk of COPD progression, allergic rhinitis, and lung cancer [34,113,114].

Dietary modulation of PM and NOX-associated metabolic disorder risk

HDPs mitigated the adverse effects of PM and NOX on risk of metabolic disorders (including diabetes and metabolic syndrome), whereas SSB intake amplified the adverse effects of PM exposure on risk of metabolic syndrome. Previous systematic reviews have summarized the independent associations between HDPs and SSB intake and metabolic disorders, demonstrating that HDPs are associated with a lower risk of metabolic diseases [115,116] and that SSB intake is associated with a higher risk of metabolic syndrome [117,118]. However, the potential interactions between dietary intake and air pollution exposure have not been investigated. Further high-quality studies are required to verify the modification effects of HDPs and SSB. Mechanistically, HDPs may improve gut microbiota and lipid metabolism, enhance anti-inflammatory capacity, and alleviate insulin resistance and metabolic disturbances, thereby buffering risk of diabetes and metabolic syndrome associated with air pollution [119].

Dietary modulation of PM-associated neuropsychiatric disorders and digestive disease risk

This study showed that vitamin B intake may attenuate the PM-related risk of dementia. A prior review indicated that B vitamins protect against PM-associated damage [107] and counteract PM-induced epigenetic modifications [120]; however, no quantitative meta-analysis was conducted. A high-fat diet amplified the effects of PM on MAFLD. A systematic review showed that high-fat dietary intake is positively associated with MAFLD risk [121]; however, no prior review has investigated fat–PM interactions. Mechanistically, PM activates hepatic Kupffer cells to initiate inflammation and oxidative DNA damage in the liver [122], whereas a high-fat diet exacerbates oxidative stress by altering fatty acid metabolism and lipid accumulation. Coexposure to PM and a high-fat diet promotes hepatic lipid deposition, inflammation, and hepatocellular injury. Given the limited number of original studies included, further high-quality investigations are warranted to verify these findings.

Strengths and limitations

This study provides quantitative evidence for assessing interaction effects, offering potential dietary prevention strategies to mitigate the harmful impacts of air pollution on public health at 3 levels: dietary patterns, food items, and dietary nutrients. This study has certain limitations that must be acknowledged. First, substantial heterogeneity was observed in the meta-analysis, likely because of variability in study populations, geographic locations, disease outcomes, and inherent recall bias in dietary assessments. Second, research on CO, SO2, and O3 exposure, as well as on neuropsychiatric disorders and digestive disease outcomes, remains limited, which may affect the accuracy and comprehensiveness of the findings. Third, although sensitivity analyses were conducted separately for children and adults, the conclusions for these subgroups diverged from those for the overall population; because of the small number of studies, these results should be interpreted cautiously. Additionally, although the deft approach minimizes ecological bias by focusing on within-study interaction effects, the present analysis was based on published aggregate data rather than individual participant data; therefore, it may still be affected by residual confounding, inconsistent definitions of dietary strata, and limited reporting of stratum-specific effect estimates across studies.

Implications

This meta-analysis suggests that dietary factors, including HDPs, grains, dairy products, ω-3 fatty acids, and vitamin B, may attenuate the adverse association between air pollution and risk of CVD, CRD, metabolic disorders, neuropsychiatric disorders, and digestive diseases. Future research should prioritize well-designed experimental studies and randomized controlled trials to further validate the effects of modifying dietary intake on the health impacts of air pollution and to elucidate the biological mechanisms underlying these modifying effects. Notably, current evidence regarding dietary modification primarily focuses on the adverse effects of PM and NOX. However, the potential modifying effects of dietary factors on other air pollutants require further investigation.

Author contribution

The authors’ responsibilities were as follows – YC, BC: completion of interpretation of the data and the manuscript; FW, HX, YS: selection of literature and the extraction of data; YX, YZ: critical revision of the manuscript for important intellectual content; LS, HZ: concept, design, interpretation of the data, completion of the study and manuscript; and all authors: read and approved the final manuscript.

Data availability

Data described in the manuscript and analytic code will be made available on proper application.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors declare that no generative AI or AI-assisted technologies were used in the writing of this manuscript.

Funding

This study was supported by the 345 Talent Project of Shengjing Hospital of China Medical University (grant number M0287 to Hehua Zhang) and the Natural Science Foundation of Liaoning Province (grant number 2024-MS-074 to Hehua Zhang). The sponsor contributed substantially to the concept, design, interpretation of the data, and completion of the study and manuscript. There are no restrictions regarding the submission or publication of this work.

Conflict of interest

The authors report no conflicts of interest.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.advnut.2026.100709.

Contributor Information

Lishen Shan, Email: shanls@sj-hospital.org.

Hehua Zhang, Email: sj_zhanghh@126.com.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (895.2KB, docx)

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Data described in the manuscript and analytic code will be made available on proper application.

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