Abstract
Respiratory tract infections (RTIs) remain a major public health burden among adults, with substantial interindividual heterogeneity in susceptibility. Chronic low-grade systemic inflammation has been proposed as an important host-related determinant of infection risk, and the dietary inflammatory index (DII) provides an integrative measure of the inflammatory potential of habitual diet. However, evidence linking DII to RTI risk and the underlying immune–inflammatory mechanisms remains limited. We analyzed data from 9 cycles of the National Health and Nutrition Examination Survey, including 43,293 adults aged 18 years and older. Dietary inflammatory potential was assessed using DII scores derived from 24-hour dietary recalls, and RTIs were defined based on self-reported recent respiratory infections. Weighted logistic regression models and restricted cubic spline analyses were used to examine the association between DII and RTI risk, while mediation analyses with bootstrapping were performed to quantify the mediating roles of immune–inflammatory indices. In fully adjusted models, higher DII scores were significantly associated with an increased risk of RTIs. Each 1-unit increase in DII was associated with higher odds of RTIs (odds ratio = 1.05, 95% confidence interval: 1.02–1.08; P = .003), and participants in the highest DII quartile had a 23% higher risk of RTIs compared with those in the lowest quartile. Mediation analyses showed that systemic immune–inflammatory indices partially mediated this association. Among the mediators, the systemic immune-inflammation index accounted for the largest proportion of the total effect (10.58%), followed by the platelet-to-lymphocyte ratio (4.22%) and the neutrophil-to-lymphocyte ratio (2.99%). These findings suggest that pro-inflammatory dietary patterns, as reflected by higher DII scores, are associated with increased RTI risk among US adults, and that this relationship may be partially explained by systemic immune–inflammatory dysregulation, supporting inflammation as a biologically plausible pathway linking diet to infection susceptibility.
Keywords: dietary inflammatory index, immune–inflammatory indices, mediation analysis, NHANES, respiratory tract infections
1. Introduction
Respiratory tract infections (RTIs) remain among the most prevalent infectious diseases worldwide and continue to impose a substantial burden on population health.[1,2] In adults, RTIs account for a considerable proportion of outpatient visits and antibiotic prescriptions, whereas lower RTIs disproportionately contribute to hospitalizations, healthcare expenditures, and infection-related mortality.[3] Despite advances in vaccination strategies and antimicrobial therapies, the incidence of RTIs has remained persistently high, and substantial interindividual heterogeneity exists in susceptibility and clinical outcomes.[4,5] Collectively, these observations indicate that host-related determinants, beyond pathogen exposure alone, play a critical role in shaping the risk of RTIs.
Epidemiological studies have identified several established risk factors for RTIs, including older age, smoking, obesity, and cardiometabolic comorbidities, many of which converge on chronic low-grade systemic inflammation.[4,6] Diet is a key modifiable determinant of inflammation at the population level and influences immune function through effects on oxidative stress, metabolic homeostasis, gut microbiota composition, and inflammatory signaling.[7] To capture the cumulative inflammatory potential of diet, the dietary inflammatory index (DII) was developed as a literature-derived score integrating evidence across multiple dietary components.[8,9] Notably, the DII has been widely applied in population-based studies and has demonstrated robust associations with adverse health outcomes such as cardiometabolic disease, frailty, mortality, and impaired sleep quality.[10,11] Despite the central role of inflammation in host defense, evidence linking dietary inflammatory potential to RTI risk remains limited.[12]
Systemic immune–inflammatory dysregulation represents a biologically plausible pathway linking dietary inflammatory potential to susceptibility to RTIs.[13] In recent years, composite indices derived from routine hematological parameters – including the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyteratio (PLR), and monocyte-to-lymphocyte ratio (MLR) – have emerged as integrative markers of host immune–inflammatory status.[14,15] These indices capture complementary dimensions of innate immune activation, adaptive immune competence, and overall inflammatory burden.[14,15] In addition, elevated systemic inflammatory markers have been associated with greater disease severity, prolonged clinical course, and increased mortality in RTIs and other acute infectious diseases.[16] Nevertheless, population-based studies that simultaneously integrate the DII, systemic immune–inflammatory profiles, and clinically relevant RTI outcomes remain scarce.
Therefore, using nationally representative data from the National Health and Nutrition Examination Survey (NHANES), the present study aimed to examine the association between DII and RTI among US adults. Meanwhile, we further sought to evaluate the potential mediating roles of multiple systemic immune–inflammatory indices, including SII, NLR, PLR, and MLR, in the association between DII and RTI risk.
2. Methods
2.1. Study population
NHANES is a nationally representative, cross-sectional survey conducted in 2-year cycles, designed to examine the relationships between nutrition, health promotion, and disease prevention using structured interviews and examinations based on a multistage probability sampling framework with oversampling of key populations.[17] Additional information on NHANES is available at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
This retrospective cross-sectional analysis utilized publicly available data from 9 consecutive cycles (2001–2018, 91,351 participants) of the NHANES. We first excluded 37,595 individuals under 18 years of age. From the remaining 53,756 adults, participants were sequentially excluded for the following reasons: incomplete data for calculating the DII (n = 6021), missing values for any of the 4 systemic inflammation markers (n = 2115), and undocumented status on RTIs (n = 2327). The final analytical sample thus comprised 43,293 eligible participants, as detailed in Figure 1. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for cross-sectional studies.
Figure 1.
Flowchart of participant selection. DII = dietary inflammatory index, MLR = monocyte-to-lymphocyte ratio, NHANES = National Health and Nutrition Examination Survey, NLR = neutrophil-to-lymphocyte ratio, PLR = platelet-to-lymphocyte ratio, RTIs = respiratory tract infections, SII = systemic immune-inflammation index.
2.2. Assessment of DII
Dietary intake data in NHANES were collected at Mobile Examination Centers using 24-hour dietary recall interviews. In this study, participants’ nutrient intakes from foods and beverages, excluding dietary supplements and medications, were estimated using the mean of 2 reliable 24-hour recalls or a single recall when only one was deemed reliable. The DII is a literature-derived scoring system developed by Shivappa et al that incorporates up to 45 dietary parameters.[8] Importantly, valid DII scores can be calculated even when fewer than 30 dietary components are available.[8] Owing to limitations in the dietary variables assessed in NHANES, the following 27 components were included in the DII calculation in the present study: carbohydrates, protein, total fat, dietary fiber, cholesterol, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, n − 3 fatty acids, n − 6 fatty acids, vitamin C, vitamin A, carotene, vitamin D, vitamin E, niacin, thiamin, riboflavin, vitamin B6, vitamin B12, folate, iron, magnesium, zinc, selenium, caffeine, and alcohol.
2.3. Assessment of blood inflammatory indicators
Based on the NHANES protocols, lymphocyte, monocyte, neutrophil, and platelet counts were measured using automated hematology analyzers as part of a complete blood count and are reported as ×103 cells/μL.[17] Based on the peripheral blood cell counts, we calculated 4 systemic inflammation markers: SII, NLR, PLR, and MLR. Calculations were as follows: SII = (neutrophils × platelets)/lymphocytes, NLR = neutrophils/lymphocytes, PLR = platelets/lymphocytes, and MLR = monocyte count/lymphocyte count.[15,18] The 4 indices were evaluated in parallel to explore whether dietary inflammatory potential was associated with different dimensions of systemic immune–inflammatory status and to assess their potential mediating roles in the association between DII and RTIs.
2.4. Assessment of RTIs
RTIs were defined based on participant responses to 2 items in the current health status questionnaire (HSQ). Specifically, HSQ500 asked, “Have you caught a cold in the past month?” (yes/no), and HSQ520 asked, “Have you had an ear infection, pneumonia, or flu in the past month?” (yes/no).[19] Participants who responded “yes” to either question were classified as having an RTI.
2.5. Covariates
The selection of covariates for analysis was based on previous studies and data availability, encompassing demographic, socioeconomic, lifestyle, and clinical factors.[20,21] Specifically, demographic factor variables included age, gender, and race. Socioeconomic factors comprised education level, marital status, and poverty-income ratio (PIR). Lifestyle and behavioral variables included body mass index (BMI), smoking status, drinking status, and energy intake. Clinical factors included hypertension, diabetes, and dyslipidemia.
2.6. Statistical analysis
Weighted analyses incorporated NHANES sampling weights, stratification, and clustering to account for the survey’s complex design. Categorical variables were summarized as counts with percentages, and continuous variables as means with standard errors. Group differences were assessed using Rao–Scott chi-square tests and t tests. Multivariable logistic regression models were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between DII and RTI risk. DII was categorized into quartiles, with the lowest quartile serving as the reference. Model 1 was unadjusted; Model 2 accounted for demographic and socioeconomic factors (age, sex, race/ethnicity, education level, marital status, and PIR); and the fully adjusted model additionally included lifestyle variables (BMI, smoking status, alcohol status, and calorie intake). Building upon Model 3, restricted cubic spline analyses with knots at the 5th, 35th, 65th, and 95th percentiles were used to evaluate potential nonlinear associations between DII and RTI risk.
In the subgroup analysis, we stratified participants by age (<40, 40–60, and ≥60 years), sex, and race/ethnicity to assess the consistency of the associations. To assess the robustness of this study, several sensitivity analyses were conducted as follows: additional analyses were conducted after adjusting for hypertension, diabetes, and dyslipidemia; and a complete reanalysis was conducted after performing multiple imputation for missing covariates, using predictive mean matching for continuous variables and logistic regression for categorical variables. In addition, mediation analysis (1000 bootstraps samples) evaluated whether inflammatory markers mediated DII’s impact on RTI. The total effect estimated the effect of DII on RTI. Path A assessed the association between DII and systemic inflammation markers. Path B evaluated the association between systemic inflammation markers and RTI. Path C (direct effect) provided an estimate of the direct effect of DII and RTI after controlling for systemic inflammation markers. The mediated effect was calculated as (mediated effect/total effect) × 100%.
All statistical analyses were performed using R statistical software (version 4.3.1; R Foundation for Statistical Computing), and a two-sided P value < .05 was deemed statistically significant.
3. Results
Table 1 presents the weighted baseline characteristics of 43,293 participants according to RTI status. A total of 8365 participants (19.32%) were identified as RTI cases, while 34,928 (80.68%) were classified as non-cases. Significant differences were observed between the 2 groups across inflammatory, sociodemographic, and lifestyle factors. Participants with RTIs had significantly higher DII scores compared with those without RTIs (P < .001). With respect to sociodemographic factors, RTI cases were generally younger and had lower PIRs than non-cases (both P < .001). Significant differences were also observed in race/ethnicity, educational attainment, marital status, and smoking status between the 2 groups (all P < .05). In terms of lifestyle and anthropometric measures, participants with RTIs exhibited slightly higher BMI and total daily energy intake compared with non-cases (both P < .05).
Table 1.
Baseline characteristics of the study population from NHANES 2001 to 2018 (weighted).
| Variable | Total (n = 43,293) | Respiratory tract infections | P | |
|---|---|---|---|---|
| No (n = 34,928) | Yes (n = 8365) | |||
| Age, mean (SE) | 46.29 (0.22) | 46.89 (0.23) | 43.69 (0.30) | <.001 |
| Gender, n (%) | .149 | |||
| Male | 21,267 (48.62) | 17,327 (48.85) | 3940 (47.59) | |
| Female | 22,026 (51.38) | 17,601 (51.15) | 4425 (52.41) | |
| Race, n (%) | <.001 | |||
| Mexican American | 7556 (8.34) | 5760 (7.89) | 1796 (10.31) | |
| Non-Hispanic White | 19,412 (69.19) | 16,028 (69.92) | 3384 (66.05) | |
| Non-Hispanic Black | 9022 (10.70) | 7217 (10.49) | 1805 (11.63) | |
| Other race | 7303 (11.76) | 5923 (11.71) | 1380 (12.01) | |
| Education, n (%) | <.001 | |||
| Less than high school | 11,318 (16.69) | 8708 (15.81) | 2610 (20.50) | |
| High school graduate | 10,438 (24.68) | 8519 (24.64) | 1919 (24.87) | |
| College or above | 21,500 (58.63) | 17,670 (59.56) | 3830 (54.63) | |
| Marital status, n (%) | .005 | |||
| Married/living with partner | 24,568 (62.50) | 19,930 (62.87) | 4638 (60.90) | |
| Single | 8964 (18.50) | 7185 (18.55) | 1779 (18.26) | |
| Never married | 8292 (19.00) | 6595 (18.57) | 1697 (20.84) | |
| Poverty-to-income ratio, mean (SE) | 3.00 (0.03) | 3.05 (0.03) | 2.77 (0.04) | <.001 |
| Body mass index, mean (SE) | 28.71 (0.07) | 28.64 (0.07) | 28.98 (0.13) | .006 |
| Calorie intake, mean (SE) | 2132.86 (6.89) | 2124.76 (7.68) | 2167.93 (14.99) | .011 |
| Smoking status, n (%) | <.001 | |||
| Never | 22,411 (53.67) | 18,328 (54.42) | 4083 (50.39) | |
| Former | 10,206 (24.98) | 8372 (25.40) | 1834 (23.11) | |
| Current | 8454 (21.35) | 6508 (20.17) | 1946 (26.49) | |
| Alcohol status, n (%) | .524 | |||
| No | 13,070 (27.00) | 10,577 (26.91) | 2493 (27.41) | |
| Yes | 27,723 (73.00) | 22,391 (73.09) | 5332 (72.59) | |
| Hypertension, n (%) | .151 | |||
| No | 25,679 (63.29) | 20,708 (63.09) | 4971 (64.16) | |
| Yes | 17,471 (36.71) | 14,112 (36.91) | 3359 (35.84) | |
| Diabetes, n (%) | .753 | |||
| No | 35,487 (87.29) | 28,698 (87.26) | 6789 (87.43) | |
| Yes | 7247 (12.71) | 5785 (12.74) | 1462 (12.57) | |
| Dyslipidemia, n (%) | .075 | |||
| No | 13,053 (30.13) | 10,498 (29.85) | 2555 (31.30) | |
| Yes | 30,239 (69.87) | 24,430 (70.15) | 5809 (68.70) | |
| Systemic immune-inflammation index, mean (SE) | 564.02 (3.16) | 554.87 (3.21) | 603.65 (6.90) | <.001 |
| Neutrophil-to-lymphocyte ratio, mean (SE) | 2.21 (0.01) | 2.20 (0.01) | 2.26 (0.02) | .006 |
| Platelet-to-lymphocyte ratio, mean (SE) | 129.96 (0.52) | 129.45 (0.55) | 132.19 (1.01) | .010 |
| Monocyte-to-lymphocyte ratio, mean (SE) | 0.29 (0.00) | 0.29 (0.00) | 0.29 (0.00) | .991 |
| Dietary inflammatory index, mean (SE) | 0.93 (0.03) | 0.90 (0.03) | 1.07 (0.04) | <.001 |
NHANES = National Health and Nutrition Examination Survey, SE = standard error.
Table 2 summarizes the associations between DII and the prevalence of RTIs based on weighted logistic regression analyses. In the fully adjusted model (Model 3), each 1-unit increase in DII was associated with 5% higher odds of RTIs (OR: 1.05, 95% CI: 1.02–1.08, P = .003). When DII was analyzed in quartiles, a clear dose-response relationship emerged. Compared with participants in the lowest quartile (Q1), those in the highest quartile (Q4) exhibited a 23% increased risk of RTIs in the fully adjusted model (Model 3, OR: 1.23, 95% CI: 1.06–1.44, P = .008). Although ORs for the intermediate quartiles (Q2 and Q3) did not reach statistical significance, the overall trend across quartiles remained significant (P for trend = .011), further supporting a graded relationship between higher dietary inflammatory potential and elevated RTI risk. Additionally, restricted cubic spline analysis revealed a significant association between DII and RTI risk (P < .001; Fig. 2).
Table 2.
Associations of the dietary inflammatory index with the risk of respiratory infections using multivariable logistic regression models.
| Variables | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| DII (continuous) | 1.05 (1.03–1.07) | <.001 | 1.03 (1.01–1.06) | .005 | 1.05 (1.02–1.08) | .003 |
| DII (quartiles) | ||||||
| Quartile 1 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Quartile 2 | 1.12 (1.01–1.24) | .032 | 1.09 (0.98–1.22) | .113 | 1.11 (0.99–1.25) | .072 |
| Quartile 3 | 1.10 (1.01–1.21) | .040 | 1.05 (0.95–1.15) | .355 | 1.09 (0.98–1.21) | .111 |
| Quartile 4 | 1.28 (1.14–1.43) | <.001 | 1.17 (1.04–1.32) | .011 | 1.23 (1.06–1.44) | .008 |
| P for trend | <.001 | .023 | .011 | |||
Model 1: crude.
Model 2: adjusted for age, gender, race, education, marital status, and poverty-to-income ratio.
Model 3: adjusted for age, gender, race, education, marital status, poverty-to-income ratio, body mass index, smoking status, alcohol status, and calorie intake.
CI = confidence interval, DII = dietary inflammatory index, OR = odds ratio.
Figure 2.
Observational association of dietary inflammatory index with respiratory tract infections risk using a restricted cubic spline model. CI = confidence interval, DII = dietary inflammatory index, OR = odds ratio, RTIs = respiratory tract infections.
Subgroup analyses revealed a consistent positive association between DII and RTI risk across key demographic strata, with significant dose-response trends observed in younger adults (<40 years), older adults (≥60 years), males, and non-Hispanic White individuals (all P for trend < .05; Supplementary Table 1, Supplemental Digital Content 1). However, no significant multiplicative interactions were observed between these subgroups and RTI risk (all P for interaction > .05; Supplementary Table 1, Supplemental Digital Content 1). In sensitivity analyses, after adjusting for hypertension, diabetes, and dyslipidemia, and after multiple imputation for missing covariates, the positive association between DII and RTI risk remained statistically significant (Supplementary Tables 2 and 3, Supplemental Digital Content 2).
We further conducted mediation analyses to assess the extent to which systemic immune–inflammatory biomarkers mediated the association between DII and RTI. Significant mediation effects were observed for the SII, NLR, and PLR after adjustment for multiple sociodemographic, behavioral, and clinical covariates (Fig. 3). In addition, elevated levels of systemic inflammatory markers were significantly associated with a higher prevalence of RTIs. Among the immune–inflammatory indices, SII accounted for the largest proportion of the mediated effect (10.58%), followed by PLR (4.22%) and NLR (2.99%).
Figure 3.
Mediation effects of systemic inflammation markers on the association between dietary inflammatory index and respiratory tract infections.(A) Mediating effects of SII on the association between DII and RTIs. (B) Mediating effects of NLR on the association between DII and RTIs. (C) Mediating effects of PLR on the association between DII and RTIs. (D) Mediating effects of MLR on the association between DII and RTIs. DII = dietary inflammatory index, MLR = monocyte-to-lymphocyte ratio, NLR = neutrophil-to-lymphocyte ratio, PLR = platelet-to-lymphocyte ratio, RTIs = respiratory tract infections, SII = systemic immune-inflammation index.
4. Discussion
To our knowledge, this is the first population-based study to simultaneously examine the association between DII and the risk of RTIs while formally quantifying the mediating roles of multiple systemic immune–inflammatory indices. We observed that higher DII scores, indicative of more pro-inflammatory dietary patterns, were associated with an increased risk of RTIs. Although the association per 1-unit increase in DII was modest, participants with the highest dietary inflammatory potential had a 23% higher risk of RTIs than those with the lowest levels. Importantly, this association was partially mediated by several immune–inflammatory markers, including the SII, NLR, and PLR. These findings should be interpreted primarily in a public health context, as DII is a potentially modifiable population-level dietary characteristic rather than a stand-alone clinical prediction tool.
A growing body of observational and interventional evidence has highlighted the role of inflammatory dietary patterns in shaping immune function and susceptibility to respiratory infections. For example, a large prospective cohort study including 196,154 participants found that individuals in the highest quintile of the DII had a higher risk of incident COVID-19 (relative risk [RR] 1.10, P < .001) and severe disease requiring hospitalization (RR 1.40, P < .001) compared with those in the lowest quintile.[22] In a multicenter, double-blind randomized controlled trial among 1072 free-living older adults, consumption of fermented dairy containing Lactobacillus casei DN-114001 was associated with a shorter duration of upper RTIs compared with the control group (6.5 vs 8.0 days).[23] Consistently, a meta-analysis of 22 randomized trials involving 10,190 participants reported that probiotic fermented dairy products were associated with a lower overall risk of RTIs (RR 0.81, P < .001).[24] These findings are consistent with our results, thereby lending further support to the robustness and reliability of our observations.
Several biological mechanisms may underlie the association between pro-inflammatory dietary patterns and increased risk of RTIs through systemic inflammation. First, diets rich in saturated fats and refined carbohydrates can activate key inflammatory signaling pathways, including nuclear factor kappa B and inflammasome pathways, resulting in increased production of pro-inflammatory cytokines.[25,26] These cytokines may compromise mucosal barrier integrity, impair ciliary function, and dysregulate local immune responses within the respiratory tract.[27] In contrast, anti-inflammatory dietary components – such as dietary fiber, polyphenols, omega-3 fatty acids, and antioxidants – may enhance immune resilience by reducing oxidative stress, promoting regulatory immune responses, and modulating gut microbiota composition.[28,29] The gut–lung axis has emerged as an important mechanistic link between diet and respiratory immunity, whereby diet-induced alterations in the gut microbiota influence systemic immune responses and pulmonary inflammation.[30,31] Furthermore, systemic inflammation may interact with established RTI risk factors, including obesity, smoking, and metabolic disorders, thereby amplifying susceptibility to infection.[16] Further research is needed to delineate the underlying biological pathways and to determine how systemic immune–inflammatory dysregulation mediates the effects of pro-inflammatory diets on RTI risk.
Notably, the SII, NLR, PLR, and MLR capture distinct yet interrelated dimensions of systemic immune–inflammatory regulation. SII integrates neutrophil-, lymphocyte-, and platelet-related signals, reflecting the balance between innate immune activation and adaptive immune regulation, and NLR primarily indexes the relative dominance of innate over adaptive immunity.[32,33] PLR incorporates platelet-driven inflammatory and thromboinflammatory processes.[14,34] The consistent associations observed across these indices underscore the robustness of the link between dietary inflammatory potential and systemic immune dysregulation. Beyond association, mediation analyses demonstrated that these indicators partially mediated the relationship between the DII and RTI risk, supporting a possible biologically plausible inflammatory pathway. Chronic systemic inflammation may impair host defense through persistent innate immune activation, compromised adaptive immune responses, and amplified platelet-driven inflammatory signaling.[35] The partial mediation observed indicates that inflammation-related mechanisms explain a meaningful proportion of the association between diet and RTI risk, consistent with the multifactorial etiology of RTI. Nevertheless, additional biological pathways linking dietary inflammatory potential to RTI susceptibility remain incompletely understood and warrant further investigation.
This study has several notable strengths. First, we used data from a large, nationally representative sample of US adults, enhancing the generalizability of our findings. Second, the use of the DII allowed for a comprehensive assessment of dietary inflammatory potential, rather than reliance on individual nutrients or food groups. Third, by incorporating multiple systemic immune-inflammation indices and formal mediation analyses, we provided mechanistic insight beyond simple exposure–outcome associations. Nevertheless, several limitations should be acknowledged. First, dietary intake was assessed using 1 or 2 24-hour dietary recalls, which may not fully reflect long-term habitual diet because of within-person variation and recall error. Second, the cross-sectional nature of dietary and biomarker assessments limits causal inference, although the mediation framework provides supportive evidence for biologically plausible pathways. Third, RTI outcomes were defined using binary case–control classifications, precluding assessment of infection frequency, recurrence, or severity. In addition, despite retaining over 80% of age-eligible adults and obtaining consistent results after covariate imputation, selection bias due to missing dietary, laboratory, or RTI data cannot be excluded. Finally, residual confounding by unmeasured factors, such as specific pathogen exposure or vaccination status, cannot be fully excluded.
5. Conclusion
Higher pro-inflammatory dietary patterns, as reflected by elevated DII scores, were associated with an increased risk of RTIs among US adults. This association was partially mediated by systemic immune–inflammatory indices, including the SII, NLR, and PLR, highlighting immune dysregulation as a potential pathway linking diet to infection susceptibility. These findings suggest that reducing dietary inflammatory potential may help mitigate respiratory infection risk at the population level. Further prospective studies are needed to determine whether dietary modification can reduce RTI occurrence and provide clinically meaningful benefits.
Acknowledgments
The data used in this research were obtained from NHANES. The authors would like to thank the workers, researchers, and participants involved in these projects.
Author contributions
Conceptualization: Weiliang Jiang, Yinshan Wu, Tao Zhu.
Data curation: Weiliang Jiang.
Methodology: Weiliang Jiang, Yang Lv, Yang Yang, Bin Wen, Yinshan Wu, Tao Zhu.
Visualization: Weiliang Jiang, Bin Wen.
Formal analysis: Yang Lv, Yujiao Liu.
Project administration: Tao Zhu.
Supervision: Tao Zhu.
Software: Yang Lv.
Validation: Yang Lv, Yang Yang, Yujiao Liu.
Writing – original draft: Weiliang Jiang.
Writing – review & editing: Yang Lv, Yang Yang, Bin Wen, Yujiao Liu, Yinshan Wu, Tao Zhu.
Abbreviations:
- BMI
- body mass index
- CI
- confidence interval
- DII
- dietary inflammatory index
- MLR
- monocyte-to-lymphocyte ratio
- NHANES
- National Health and Nutrition Examination Survey
- NLR
- neutrophil-to-lymphocyte ratio
- OR
- odds ratio
- PIR
- poverty-income ratio
- PLR
- platelet-to-lymphocyte ratio
- RTIs
- respiratory tract infections
- SII
- systemic immune-inflammation index
TZ and YW contributed to this article equally.
The studies involving humans were approved by the National Center for Health Statistics Ethics Review Board (ERB). The studies were conducted in accordance with local legislation and institutional requirements. The participants provided written informed consent to participate in the study.
The authors have no funding and conflicts of interest to disclose.
The datasets generated and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000049895).
How to cite this article: Jiang W, Lv Y, Yang Y, Wen B, Liu Y, Wu Y, Zhu T. Associations of dietary inflammatory index with respiratory tract infections among US adults and the mediating roles of systemic inflammation markers. Medicine 2026;105:31(e49895).
Contributor Information
Weiliang Jiang, Email: 184288@zju.edu.cn.
Yang Lv, Email: yangyang199814@126.com.
Yang Yang, Email: yangyang199814@126.com.
Bin Wen, Email: medwenbin@126.com.
Yujiao Liu, Email: 931301161@qq.com.
Tao Zhu, Email: zhutao@zju.edu.cn.
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