Effects of Oats (Avena sativa L.) on Inflammation: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Oat and its compounds have been found to have anti-inflammatory effects. Through this systematic review and meta-analysis, we aimed to determine an evidence-based link between oat consumption and inflammatory markers. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. By the end of April 2021, we included randomized controlled trials (RCTs) that investigated the anti-inflammatory effect of oat and oat-related products through screening PubMed, Embase, Web of Science, ClinicalTrial.gov, and CENTRAL. Meta-analysis was conducted with a random-effect model on the standardized mean difference (SMD) of the change scores of inflammatory markers, including C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8). Subgroup analyses were conducted to stratify confounding variables. The risk of bias was evaluated using the Cochrane risk of bias tool and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was applied to report the quality of evidence. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021245844). Results: Systematic screening of five databases yielded 4,119 studies, of which 23 RCTs were finally selected. For the four systemic inflammatory markers analyzed, no significant alterations were found after oat consumption. However, oat intake was found to significantly decrease CRP levels in subjects with one or more health complications (SMD: −0.18; 95% CI: −0.36, 0.00; P = 0.05; I2 = 10%). Furthermore, IL-6 levels were significantly decreased in subjects with dyslipidemia (SMD = −0.34; 95% CI: −0.59, −0.10; P = 0.006; I2 = 0%). These beneficial effects might be attributed to the effects of avenanthramide and β-glucan. Conclusions: Overall evidence supporting the alleviation of inflammatory response by oat intake was poor, calling for future studies including a larger sample size to confirm the findings.


INTRODUCTION
Inflammation plays a pivotal role in the body's immune response to infection. Moreover, it maintains physiological homeostasis under a variety of abnormal conditions (1). However, excessive inflammation can cause various acute and chronic diseases, including atherosclerosis (2), autoimmune diseases (3), cancer (4), and depression (5). High-calorie diets, diets high in saturated fatty acids, and overeating increase the likelihood of abnormal inflammatory reactions (6,7). The effects of diet control on inflammation are gaining research attention because diet constitutes a modifiable risk factor for inflammatory disorders. Thus, several studies have investigated the correlation between dietary habits and inflammation (8,9).
Oats (Avena sativa L.) contain specific components, including avenanthramide, avenacoside, avenasterol, and β-glucan as major fiber (10,11). Oats are widely consumed in the form of porridge and dietary supplements. Although several processing methods yield various commercial oat products, most products constitute whole grains (WG), because the processing methods for oat mostly preserve the germ and bran (12). Sufficient intake of WG is considered one of the cornerstones of a healthy diet owing to its numerous beneficial effects (13). For instance, WG supports maintaining physiological homeostasis by modulating inflammatory reactions (12,14). Hence, oat and its components have been investigated and recognized as beneficial anti-inflammatory agents (15,16). However, some studies have reported that oats actually have no anti-inflammatory effects (17,18). Although there have been several meta-analyses on the antiinflammatory properties of overall WG consumption (14,19,20), there is a lack of robust evidence in the absence of meta-analyses on the effects of oats and oat products on inflammation.
Therefore, we aimed to provide clinical evidence for the effects of oats on the modulation of inflammation by systematically inspecting randomized controlled trials (RCTs). To provide comprehensive information about oat effects, we included all data, regardless of their basal status, and all inflammatory markers or measures. Unprocessed oat, processed oat products, and oat-specific compounds were considered as appropriate intervention, whereas placebo diet, other ingredients, and a marginal amount of oat intake were considered as control.

Literature Search
This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (21) (Supplementary Table 1) and was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration no. CRD42021245844). Detailed review protocol can be accessed in PROSPERO. All procedures were independently performed by at least two reviewers and the discrepancies were managed by a third-party reviewer. English literature was collected from five databases: PubMed, Embase, Web of Science, ClinicalTrial.gov, and CENTRAL to retrieve eligible studies.  Table 1. Those who consumed a substantially small amount of oats and those who did not consume oats at all served as the controls. There was no limitation on the participants, allowing broad coverage of the discovery of inflammatory markers.

Inclusion and Exclusion Criteria
A study was considered eligible if it satisfied all the following items: (i) the study was designed as a RCT; (ii) oats, oat-related products, or oat-specific compounds were consumed orally in the treatment group; (iii) oats, oat-related products, or oatspecific compounds were absent or insignificantly consumed in the control group; and (iv) any inflammatory markers or measures thereof were evaluated. The exclusion criteria were as follows: (i) inappropriate intervention for the treatment group (oats were mixed with other ingredients not related to oats) and/or inappropriate intervention for the control group (diet contained a significant amount of oats); (ii) outcomes unrelated to inflammatory outcomes; (iii) not a RCT; (iv) duplicated or a part of a more extensive research included beforehand; and (v) irrelevant publication type such as a review, conference, abstract, or any other secondary scientific reports.

Data Extraction
Study characteristics, including the first author's name, country, year of publication, design of RCT (crossover or parallel), health status and age range of participants, details of treatment and control intervention (sample size, formula, and dose), and treatment duration, were extracted from the selected RCTs. If a study did not specify the type of processed oats, it was automatically deemed as whole oats. In terms of outcome, any inflammation-related markers or measurements were extracted along the direction of alteration. If available, values of data distribution, such as mean, standard deviation (SD), standard error (SE), and 95% confidence interval (CI), were obtained.

Meta-Analysis
Review Manager 5.4 (Nordic Cochrane Center, The Cochrane Collaboration) was used for the overall meta-analysis. The metaanalysis was conducted on commonly reported inflammatory markers, including C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8). Markers reported in fewer than three studies were not examined due to the lack of statistical power. The overall effect sizes were calculated by synthesizing the difference in change scores. Accordingly, studies that provided either change scores within each group or both baseline and post-treatment levels were considered eligible. For crossover RCTs, the difference of two post-treatment values was used to infer intergroup differences based on the assumption that a wash-out period eliminated all carry-over effects and resulted in an identical baseline status. If the SD was not explicitly presented in the study, it was calculated by transforming the values of either SE or CI, considering that the data were normally distributed. Notably, the SE of the mean difference (MD) was determined depending on the presented data type (change score vs. baseline/post-treatment) and RCT design (parallel vs. crossover). To incorporate crossover RCT into the meta-analysis, the correlation coefficient between change scores was imputed to approximate a paired analysis. Meanwhile, the SD of each change score was calculated by imputing a correlation between baseline and post-treatment in the case of parallel RCT. Since no study presented necessary correlation information, all the unknown correlation coefficients were set to 0.5. This was in accordance with previous studies (22,23).
Because of an observable heterogeneity in the magnitude of values, the effect sizes were expressed as the standardized mean difference (SMD). The equations for the SE of MD, SMD, and SE of SMD calculation are presented in Table 2. The randomeffects method with an inverse-variance approach was applied. Heterogeneity between studies was estimated using Cochran's Q test and I 2 . Robust statistics were examined by sensitivity analyses using the leave-one-out method and by imputing correlation coefficients (ρ) of 0.2 and 0.8. The most representative oat and control groups were chosen over others if multiple groups were presented. An intervention period of at least 2 weeks was considered eligible to observe treatment-induced effects. If the measurement of markers was performed at multiple time points, the most approximate period to those of the other studies was selected. Subgroup analyses were conducted to stratify confounding variables, including the type of measurement (CRP vs. hs-CRP), basal condition (healthy vs. unhealthy), type of oat product (whole vs. fiber-rich fraction), and type of control (placebo or no intervention vs. other materials, such as wheat). However, subgroup analyses of TNF-α and IL-8 were not conducted because the number of included RCTs was limited (n = 3 for both). Studies that provided only median over the mean or interquartile range over SD were excluded. A funnel plot was generated for each meta-analyzed marker to visualize the potential publication bias. Two-tailed p-values were estimated following Begg's rank correlation test and Egger's regression test to evaluate funnel plot asymmetry. A p-value <0.1 was considered an inherent risk for publication bias.

Risk of Bias and Quality of Evidence Evaluation
The risk of bias in individual studies was evaluated using the second version of the Cochrane risk of bias tool for randomized trials (RoB2) (24). The tool had five bias domains, which TABLE 2 | Calculation of the standard error (SE) of the mean difference (MD), the standardized mean difference (SMD), and the SE of SMD based on the type of study design (parallel vs. crossover randomized controlled trials).

Parallel
Crossover Change score † : data presented as change score value.
Pre-/post-: data presented as pre-treatment (baseline) and post-treatment value.  contained at least three signaling questions that could address almost all the important aspects possibly influencing the results of a trial. In detail, the five bias domains evaluate bias that can arise from the randomization process, due to deviations from the intended intervention, missing outcome data, or occur in the measurement of the outcome and in the selection of the reported result. Each domain was regarded as either high risk, some concerns, low risk of bias. Finally, overall risk was determined based on evaluated domains of individual trials. The evaluation was independently conducted by two reviewers. The quality of evidence on each meta-analyzed marker was evaluated based on the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) (25). Since the study design of all the included studies was RCT, the quality estimate started from high quality and downgraded following the judgements to risk of bias, inconsistency, indirectness, imprecision, and publication bias.
A total of 76 results on inflammatory markers from 23 studies were obtained, among which 53 showed no significant change, 22 revealed reductions, and only 1 showed an increase (Figure 2). Detailed information on the extracted data is provided in Table 3.

Effect of Oats on Other Inflammatory Markers
In addition to ILs, TNF-α, and CRP, several inflammatory markers were measured to examine the effects of oat intake on inflammation. However, we could not perform a metaanalysis on the other markers because of insufficient eligible data. Neutrophil respiratory burst (NRB), granulocyte colonystimulating factor (G-CSF), and soreness score were consistently   4 | Summary of the results of this meta-analysis on C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor alpha (TNF-α). Downgraded for a Risk of bias, b Imprecision, and c inconsistency.

Risk of Bias and Quality of Evidence
In terms of the risk of bias in individual studies, four RCTs were considered as low risk, as most of the aspects were wellmanaged (16,27,35,39). The randomization process and selection of the reported result were evaluated as the most common risks. The detailed outcomes for each bias domain are presented in Supplementary Table 4. There was no significant visual evidence of publication bias across the studies when the funnel plots were inspected (Supplementary Figure 2). Egger's regression and Begg's rank test also showed no statistical evidence of publication bias across studies on inflammatory markers (Supplementary Table 5). Quality of evidence on overall metaanalyzed markers is presented in Table 4, indicating moderate quality for CRP (downgraded by risk of bias), low quality for IL-6 and TNF-α (both downgraded by risk of bias and imprecision), and very low quality for IL-8 (downgraded by risk of bias, imprecision, and inconsistency).

DISCUSSION
The aim of the current study was to provide evidence of a correlation between oat consumption and inflammatory markers. According to this meta-analysis, there were no significant alterations in systemic inflammatory markers after oat consumption, although a meaningful proportion of systematically reviewed individual studies reported otherwise. However, when stratified based on the specific type of measurement method (hs-CRP), the SMD was negatively correlated with oat consumption. There was a significant decrease in CRP among subjects with one or more health complications. Similarly, IL-6 levels were significantly lower in subjects with dyslipidemia. The physicochemical characteristics of β-glucan, such as molecular weight and structure, are related to immunomodulatory responses (46). Several laboratory experiments have investigated oat β-glucan to determine its correlation with inflammation. Kopiasz et al. (47) suggested that β-glucan potentially modulated the pathophysiology of inflammatory bowel disease in mouse models by altering the expression of pattern recognition receptors, including toll-like receptors and Dectin-1. Likewise, oat β-glucan intake inhibited a sudden surge of inflammatory markers like IL-10 and IL-12 in rats with lipopolysaccharide-induced enteritis (48). This was partially in line with our findings, i.e., unhealthy subjects, especially those with a high risk of inflammatory complications such as coronary heart disease (CHD) and type 2 diabetes mellitus, were more responsive to the effects of oats on systemic inflammatory markers. Multiple studies have also revealed the anti-inflammatory properties of avenanthramide (49)(50)(51), which allosterically suppresses the inhibitor of nuclear factor kappa B (IκB) kinase, leading to the prevention of IκB phosphorylation. This makes IκB resistant to degradation by the S26 proteasome, thereby inhibiting the nuclear factor kappa B (NF-κB) pathway.
As reviewed in the current study, avenanthramide intake significantly reduced exercise-induced CRP and TNF-α levels in clinical settings (16,27). Zhang et al. (34) further revealed that IL-1Ra, soluble vascular cell adhesion molecule-1 (sVCAM-1), G-CSF, and NRB levels significantly decreased following avenanthramide intake. Although our meta-analysis did not reveal a significant improvement in inflammatory markers, reduction considerably exceeded elevation in all reviewed RCTs. Notably, in addition to CRP and ILs, almost all inflammatory markers did not change significantly or were significantly reduced, as shown in Figure 2. Hence, further clinical trials exploring the effects of oat consumption on these markers can provide meaningful outcomes. Regarding IL-6, we observed that the change in the overall SMD was affected by the inclusion of a study by Nieman et al. (17), and the overall SMD reduction was statistically significant in the absence of this study. Notably, this was the only study with a 2-week long intervention period. Other studies had an intervention period that lasted for 3 or more weeks. This study employed a reversed outcome compared to previous meta-analyses on WG, where the longer intervention periods did not show a stronger tendency for IL-6 reduction (14,19,20). Considering that the study by Nieman et al. (17) also introduced serious heterogeneity, the reduction in IL-6 levels upon oat intake should not be negligible. In contrast to IL-6, our results corresponded with previous meta-analyses on WG regarding TNF-α levels, which remained unchanged (14,19,20).
Hs-CRP is an inflammatory marker used to evaluate and screen cardiac complications, including CHD (52). Oat intake was negatively correlated with hs-CRP serum levels according to the current meta-analysis, indicating that oat intake may attenuate the risk of cardiovascular disease (CVD). Multiple studies have reported a correlation between oat intake and reduced CVD risk. For example, oat β-glucan prevents both primary and secondary events of CHD (53,54). In addition, a longitudinal study by Xu et al. showed a negative correlation between oat consumption in both heart disease and stroke, especially in the elderly (55). These effects are in accordance with the fact that the fiber-rich fraction of oats improves blood cholesterol levels (56,57). Mechanistically, avenanthramide, a component of oats, prevents coronary plaque formation by inhibiting NF-κB in human aortic endothelial cells (HAECs) (51). Moreover, the secretion of IL-1β-induced pro-inflammatory cytokines, such as ICAM-1, VCAM-1, and E-selectin, was significantly reduced when HAECs were pre-incubated with 20 and 40 ng/mL of avenanthramide (58). The Food and Drug Administration of the United States has approved a health claim that consumption of the soluble fiber form of oat may attenuate the risk for CHD. Major evidence for this claim was based on the cumulative information on improvement in cholesterol levels (59). The current meta-analysis is in line with this health claim, as many of the reference RCTs specifically emphasize that their intervention in the oat group contains a high proportion of fiber. Moreover, our data provide more powerful evidence of the prophylactic effect of oat on CHD since hs-CRP is more highly associated with CHD than with other markers, including LDL (60).
The results of the meta-analysis on CRP and IL-6 suggested that unhealthy subjects were more responsive to the effects of oat consumption. Notably, a similar pattern was found in a previous meta-analysis on WG intake, which showed that the subgroup of unhealthy individuals showed a significant reduction in CRP and IL-6 levels after observing insignificant changes in the overall CRP and IL-6 levels (20). This indicates that oat intake does not simply reduce inflammatory markers. However, it modulates inflammatory marker level to be within the optimal range. In a Sprague Dawley rat model, oat β-glucan intake reversed the lipopolysaccharide (LPS)-induced upregulation of inflammatory markers, including IL-10 and IL-12 (48). However, there was no significant alteration in rats in a physiologically healthy state, except in the case where high-molecular-weight oat β-glucan increased IL-10 levels. Similarly, oat β-glucan intake partially reversed TNF-α, IL-6, and IL-1β levels in the liver tissue of an LPS-induced nonalcoholic steatohepatitis mouse model (61). Ex vivo experiments on a human endotoxemia model revealed that β-glucan enhanced the cytokine-producing attributes of LPS-induced tolerant monocytes, restoring the innate immune response (62). Accordingly, consumption of oats, especially the fiber-rich fraction, controls the immune response and helps sustain cytokine homeostasis.
There are several limitations worth noting in the current systematic review and meta-analysis. First, we were unable to investigate the dose-dependent correlation between the amount of oat intake and inflammatory marker levels due to limited access to the original data. This was mainly because the included studies did not specify the whole or dried weight of oats. Second, the imputed data (ρ = 0.5) were at risk of providing a distorted outcome. Although sensitivity analysis with ρ = 0.2 and 0.8 confirmed the robustness of the outcome, this issue should have been addressed when interpreting the data. The small number of meta-analyzed RCTs from those systematically reviewed overall was another limitation of our study. We also note that there may be a potential language bias, as only databases containing English literature were screened for reference collection. Finally, caution is needed regarding the heterogeneity in blood levels of outcome variables and other factors within the study design, such as population characteristics, control intervention, and type of blood sample (plasma vs. serum).
In summary, based on our meta-analysis results, there is insufficient evidence that oat intake reduces inflammatory response. However, a reduction in the levels of CRP, ILs, TNFα, and other systemic markers was observed after oat intake according to the qualitatively synthesized data. A significant reduction in hs-CRP in the subgroup analysis can be potentially explained by the fact that oat intake is negatively correlated with CVD. Moreover, CRP and IL-6 levels decline in unhealthy subjects. Nevertheless, these findings can be confirmed by more robust studies in the future by including a large sample size. Therefore, future studies with properly presented outcomes are required to reach a stronger conclusion.

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/s.

AUTHOR CONTRIBUTIONS
SunK, CJ, SungK, and SL: conceptualization. SunK, CJ, NA, and SukK: data screening and collection. SunK, CJ, and SP: data analysis. SunK and CJ: quality assessment. SunK, CJ, and SL: manuscript writing. All authors confirmed the manuscript and agreed to the submitted version.