
==== Front
Redox Biol
Redox Biol
Redox Biology
2213-2317
Elsevier

S2213-2317(24)00315-X
10.1016/j.redox.2024.103337
103337
Research Paper
Comprehensive modulatory effects of whole grain consumption on immune-mediated inflammation in middle-aged and elderly community residents: A real-world randomized controlled trial
Li Cheng
Li Yaru
Wang Nan
Ge Zhiwen
Wang Jia
Ding Bingjie
Bi Yanxia
Wang Yuxia
Wang Yisi
Peng Zebin
Yang Xinli
Wang Congcong
Hong Zhongxin hongzhongxin@vip.sina.com
⁎
Department of Clinical Nutrition, Beijing Friendship Hospital, Capital Medical University, Beijing, China
⁎ Corresponding author. hongzhongxin@vip.sina.com
05 9 2024
10 2024
05 9 2024
76 1033378 8 2024
30 8 2024
30 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background and aims

Whole grain consumption is widely recognized as a vital component of a balanced diet. Dietary fiber has been well-documented to play a crucial role in these health benefits attributed to whole grain intake. However, population-based evidence directly linking whole grain consumption to anti-inflammatory effects, especially in the context of immune-mediated inflammation, remains limited. We hypothesized that whole grain consumption promotes health by modulating immune-mediated inflammation.

Methods and results

This study was designed as a real-world, population-based randomized controlled trial. We compared the effects of whole grain versus refined grain consumption on immune-mediated inflammation through staple food substitution, while participants maintained their usual dietary practices. The results demonstrated that whole grain consumption significantly reduced circulating levels of pro-inflammatory cytokines IL-22 and IL-23 compared to refined grain consumption. These reductions were associated with optimized short-chain fatty acid profiles and changes in CD4+ T cell subset distributions.

Conclusions

The findings suggest that the anti-inflammatory effects of whole grain consumption in middle-aged and elderly populations are mediated by targeting specific CD4+ T cell subsets, in addition to modulating both upstream short-chain fatty acid composition and downstream expression of the pro-inflammatory cytokines IL-22 and IL-23.

Highlights

• In this investigation, we employed a method of substituting staple foods to examine the impact of varying grain consumption on immune-mediated inflammation and its associated metabolic endpoints.

• Whole grains exhibit a comprehensive anti-inflammatory effect mediated by immune-induced inflammatory responses, involving T cell subsets, upstream short-chain fatty acids, and downstream inflammatory cytokines.

• Whole grains may exert anti-inflammatory effects by reducing the circulating levels of IL-22 and IL-23. This effect may be linked to improvements in short-chain fatty acid composition and the distribution of CD4+T cells.

Keywords

Whole grain
Inflammation
Immune function
SCFAs
IL-23
Abbreviations

ANCOVA (analysis of covariance)

CI (confidence interval)

CKD (chronic kidney disease)

CVD (cardiovascular disease)

DBP (diastolic blood pressure)

FBG (fasting blood glucose)

GLM (generalized linear model)

HsCRP (high-sensitivity C-reactive protein)

IL-6 (interleukin-6)

NCD (non-communicable disease)

PBG (post-prandial blood glucose)

SBP (systolic blood pressure)

SCFA (short-chain fatty acid)

Th cell (T helper cell)

Treg (T regulatory cell)

T2DM (type 2 diabetes mellitus)
==== Body
pmc1 Introduction

Inflammation is a complex biological response essential for host defense and tissue repair. Chronic low-grade inflammation has been recognized as a critical factor in the development of various non-communicable diseases (NCDs), including cardiovascular diseases (CVDs), type 2 diabetes mellitus (T2DM), and certain cancers [1]. T lymphocytes, key players in adaptive immunity, are crucial in mediating inflammatory responses through their diverse subsets and intricate regulatory mechanisms [2]. Among these, T cell subsets, including CD4+ helper T cells and CD8+ cytotoxic T cells, exhibit distinct functions in inflammatory processes [2]. Specifically, Th1 and Th17 cells are generally considered pro-inflammatory, as they produce cytokines that promote inflammation and tissue damage [2,3]. In contrast, regulatory T cells (Tregs) often play a vital role in maintaining immune homeostasis by suppressing excessive inflammatory responses through the production of anti-inflammatory cytokines [4,5]. The balance among these multiple T cell subsets is essential for regulating inflammatory responses and maintaining immune homeostasis [6]. Dysregulation of this balance has been linked to various inflammatory disorders. For instance, an overabundance of Th17 cells relative to Treg cells has been implicated in the pathogenesis of autoimmune diseases such as psoriasis, rheumatoid arthritis, and inflammatory bowel disease [7].

Recent evidence suggests that dietary factors can modulate T cell differentiation and function, thereby influencing immune-mediated inflammation [8]. Several nutrients and bioactive compounds found in natural foods have been shown to affect T cell polarization, cytokine production, and epigenetic regulation [8,9]. Previous studies have demonstrated that diets high in salt [10,11] or fat [12,13] can induce pro-inflammatory responses of T cells, promote the expression of pro-inflammatory T cell subsets, and then trigger the pathogenesis of inflammation-related diseases. Our previous study also found that an irrational dietary pattern characterized by refined grains, animal foods, and alcoholic beverages may elevate circulating levels of pro-inflammatory Th17 cells, increasing the risk of hypertension in middle-aged and older individuals [14]. Although several studies have demonstrated that unhealthy dietary factors can promote inflammatory responses and related diseases by disrupting the balance of T cell subsets [8], there is a lack of research on how natural food can exert anti-inflammatory effects through T cell subsets.

Previous studies have suggested that daily consumption of whole grains may exert specific anti-inflammatory effects by modulating T cell subsets [15,16]. Commonly, whole grains are defined as intact, ground, cracked, or flaked kernels that retain the bran, germ, and endosperm in their natural compounds. They are rich sources of dietary fiber, vitamins, minerals, and various bioactive compounds, including phenolic acids, flavonoids, and lignin [16]. These components, individually and synergistically, may contribute to the anti-inflammatory effects of whole grains. For example, short-chain fatty acids (SCFAs), produced through the fermentation of dietary fiber, have been demonstrated to promote Treg cell differentiation and metabolic function [17]. Acetic acid, butyric acid, and propionic acid, as the three predominant SCFAs, were found to ameliorate inflammatory responses by regulating Treg function and frequency [8]. Additionally, SCFAs could also regulate T-cell differentiation by inhibiting histone deacetylases, mediating the regulation of immune-related inflammation [17]. Beyond fiber-derived SCFAs, polyphenols found in whole grains have also been reported to suppress Th1 and Th17 responses while enhancing Treg activity [18]. However, despite these findings and the potential mechanisms identified in vitro or animal studies, there remains insufficient evidence from population-based studies on how whole grains regulate immune-mediated inflammatory responses.

This study employed a staple food substitution approach to evaluate the fundamental health implications of whole grains intervention in middle-aged and older people. Although several population studies have investigated the effects of whole grain intervention, the diversity in intervention methods has made it challenging to isolate the independent health effects of whole grains. In the present study, we utilized a staple food replacement method, directly providing participants with raw food materials without altering their cooking and eating habits, to observe the health impacts of whole grains consumption. Furthermore, we assessed its influence on circulating T cell subsets, associated inflammatory markers, and fecal SCFAs to elucidate the extent of immune-mediated inflammatory response and potential mechanisms underlying whole grain consumption.

2 Methods

2.1 Participants and study design

The study was a randomized, single-blinded, parallel controlled trial (Fig. 1). Middle-aged and older participants were recruited from the Zhangfang Community, Fangshan District, Beijing. Hypertensive patients aged 45 years and older from the local community matched 1:1 with non-hypertensive individuals based on age and gender. Hypertension diagnoses and medical histories of all study participants were verified using the electronic health records at Zhangfang Community Health Service Center. Local community physicians conducted routine medical examinations and offered health management services, including medication guidance, to assist patients with hypertension in controlling their condition. In this study, our research team included eight experienced physicians from Beijing Friendship Hospital, who collaborated with local community doctors. Together, they conducted health assessments and monitoring for hypertensive patients enrolled in the study and provided disease consultation services. Each pair consisted of one hypertensive and one non-hypertensive individual. The pairs were then randomly allocated to either the intervention or control group using a random number table generated by a statistician with SPSS version 22.0 (IBM Corp., USA).Fig. 1 Study design and flow chart.

Fig. 1

The research team provided all raw staple food materials throughout the whole study in batches and specified quantities. Before the intervention, a comprehensive assessment of local dietary patterns was conducted through preliminary research and a pre-intervention survey. Based on these findings and the Chinese Dietary Guidelines, participants were provided with a daily allocation of staple foods, with a maximum limit of 300 g per person per day. This allocation consisted of equal parts rice and flour (150 g/d each), allowing flexibility in daily diet. Participants were allowed to prepare and consume these allocated staple foods according to their individual dietary preferences and culinary practices. To mitigate potential intra-household food sharing and ensure adequate individual intake, an additional 600 g of staple food was allocated for household members, maintaining the habitual staple food consumption patterns for a three-member household (the most typical family structure in the local area). Based on the study design and sample size calculation (Supplementary File 1), 144 participants were enrolled. This study adhered to the ethical principles outlined in the Declaration of Helsinki. All participants provided written informed consent after receiving comprehensive information about the study. The study received approval from the Ethics Committee of Beijing Friendship Hospital, Capital Medical University, and was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2300072978).

2.2 Food intervention and dietary adherence

A pre-intervention survey in the community revealed that the daily staple foods of local residents predominantly consisted of home-cooked rice, steamed buns, flatbreads, or noodles. Consequently, the intervention strategy was tailored to align with these dietary habits. Participants in whole grain (WG) group was provided with whole grains, including brown rice and whole grain flour. Meanwhile, those in the refined grain (RG) group was provided with refined grains, including wheat flour and refined rice. All staple food items were distributed in commercially available raw form with standardized packaging and were delivered personally to each participant by community workers in an anonymized manner. Online follow-up and support groups were established via WeChat (10.13039/100015803 Tencent , China). Instructional cooking videos demonstrating the preparation of rice and wheat-based foods were produced and disseminated through this platform. Licensed clinical dietitians provided comprehensive guidance on staple food preparation techniques and general dietary recommendations. To facilitate precise data collection, each participant was equipped with a dietary diary and an electronic scale by the research team, to record their detailed daily food intake, focusing mainly on quantifying their consumption of staple foods.

2.3 Baseline investigation

A field investigation was conducted in June 2023. Local community workers and researchers informed all participants of the specific timing, location, and procedural details of the investigation through telephonic or in-person communication two weeks before its official commencement. One day before the formal investigation, local community workers provided participants with a reminder regarding the precise start time and specific procedures. On the initiation day of the formal investigation, researchers once again familiarized all participants with survey items and elaborated on investigative procedures. Since all participants were local residents, the local community center was chosen as the field investigation site for convenient transportation of all participants. The site was located within a 1.5 km radius from all participants. Well-trained investigators collected data, including basic demographic information such as age, gender, physical activity, dietary preferences, dining habits, history of common NCDs including T2DM, chronic kidney disease (CKD), gout, and hyperlipidemia. A food frequency questionnaire was used to investigate the daily diet of the participants at baseline.

2.4 Measurements of general metabolic indicators

Height (m) and weight (kg) were measured by trained investigators, and body mass index (BMI) was calculated. To account for potential dietary influences, blood pressure and blood glucose measurements were conducted in two phases: fasting in the morning and postprandially. Participants were instructed to report to the centralized investigation site in a fasting state early in the morning. Fasting blood glucose (FBG) levels were measured using the ACCU-CHEK glucometer (Roche, Germany), with the time of measurement recorded by investigators. Fasting systolic blood pressure (SBP) and diastolic blood pressure (DBP) were assessed using the Omron HEM-907 electronic sphygmomanometer (Omron Healthcare, Japan). After completing the fasting assessments, participants were provided with a standardized breakfast. Two hours later, post-prandial blood glucose (PBG) and blood pressure were re-measured using the same methodology by the same investigators as the fasting measurements.

2.5 Collection and detection of circulating indicators and fecal SCFAs

Fasting venous blood samples were collected by certified nurses. Inflammatory markers, including high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), IL-10, IL-17A, IL-22, and IL-23, were quantified using enzyme-linked immunosorbent assay kits according to the standardized protocols, measurements taken using the Thermo Multiskan EX Microplate photometer (Thermo Scientific, USA). T cell subsets were analyzed using flow cytometry. Briefly, peripheral blood mononuclear cells were isolated by density gradient centrifugation, stained with fluorochrome-conjugated monoclonal antibodies, and analyzed on the BD FACSCanto II flow cytometer (BD Biosciences, USA). Data were processed using the FACSDiVa version 8.0 software (BD Biosciences, USA). Fresh fecal samples were collected by participants. At the initiation of the study, researchers instructed participants on fecal collection and submission procedures and distributed standardized sterile tools. Fecal SCFA content was determined using high-performance liquid chromatography.

2.6 Statistical analysis

Quantitative data are presented as means with standard deviations (SDs) for normally distributed variables or medians with interquartile ranges for non-normally distributed variables. Categorical data are reported as counts and percentages. Intergroup comparisons for categorical variables were conducted using the Chi-square test or Fisher's exact test, as appropriate. Between-group comparisons for continuous variables were performed using analysis of covariance (ANCOVA), with and without baseline values as covariates. For variables that did not adhere to a normal distribution, log transformation was applied prior to analysis. The mean change of each outcome measure within the intervention is presented as a mean with 95 % confidence intervals (CIs). Crude and adjusted generalized linear models (GLMs) were used to analyze the difference in changes between the two groups, respectively. Changes in outcome measures were further adjusted for potential confounding factors, including age, gender, physical activity, smoking and drink status, daily consumption of salt and oil, out dinner, NCDs, and baseline energy intake. Paired t-tests were used to compare baseline and post-intervention values of each indicator. Multivariate linear regression models were employed to assess the association between grain intake and health indicators. Estimated β coefficients and 95 % CIs were calculated, with adjustments for the aforementioned confounding factors. Multiple imputation was conducted to replace the values of missing data. Each dataset was analyzed individually for the sensitivity analysis. Rubin's rules were applied to combine the relevant results, yielding the final estimates and their variances. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 22.0 (IBM Corp., USA) and SAS version 9.3 (SAS Institute, USA).

3 Results

3.1 Baseline characteristics

Following the application of inclusion and exclusion criteria and completion of the study, a total of 120 subjects were included in the final analysis. The baseline characteristics of these subjects are presented in Table 1. The RG group comprised 62 individuals with an average age of 63.9 years; among them, 27 were male, accounting for 43.55 % of the group. The WG group consisted of 58 individuals with an average age of 63.5 years; 24 participants were male, representing 41.38 % of the group. No significant differences were observed between the two groups regarding their demographic profiles or clinical characteristics, such as daily physical activities, dietary preferences, and habits related to dining out. The prevalence rate for hypertension was similar between both groups (RG:51.61 %, WG:51.72 %), indicating no significant difference in this regard. Furthermore, no significant differences were found between the two groups regarding the prevalence rates for chronic diseases, including T2DM, CKD, gout, and hyperlipidemia.Table 1 General characteristics of participants at baseline.

Table 1Characteristics	Refined grain group	Whole grain group	Statistics	P value	
(RG group)	(WG group)	
Age (y)	63.9 ± 5.2	63.5 ± 6.3	0.17	0.680	
Gender (%)			0.06	0.810	
Male	27 (43.55)	24 (41.38)			
Female	35 (56.45)	34 (58.62)			
Daily exercise level (n, %)			2.02	0.364	
Light	37 (59.68)	28 (48.28)			
Moderate	4 (6.45)	7 (12.07)			
Heavy	21 (33.87)	23 (39.66)			
Daily salt intake (n, %)			4.04	0.133	
Light	20 (32.26)	11 (18.97)			
Moderate	22 (35.48)	19 (32.76)			
Heavy	20 (32.26)	28 (48.28)			
Daily oil intake (n, %)			5.87	0.053	
Light	23 (37.1)	14 (24.14)			
Moderate	24 (38.71)	18 (31.03)			
Heavy	15 (24.19)	26 (44.83)			
Out dinner	14 (22.58)	16 (27.59)	0.40	0.527	
Current smoke	14 (22.58)	12 (20.69)	0.06	0.802	
Current drink	16 (25.81)	11 (18.97)	0.80	0.370	
Hypertension (n, %)	32 (51.61)	30 (51.72)	0	0.990	
T2DM (n, %)	20 (32.26)	14 (24.14)	0.97	0.324	
CKD (n, %)	7 (11.29)	2 (3.45)	0.08	0.165	
Gout (n, %)	3 (4.84)	3 (5.17)	0.32	1	
Hyperlipidemia (n, %)	21 (33.87)	16 (27.59)	0.56	0.456	

3.2 The effects of different grains on conventional metabolic markers

In the present study, measurements and comparisons of conventional metabolic markers, including BMI, blood pressure, and blood glucose levels, were conducted before and after the intervention (Table 2). The results indicated that no significant differences were observed in BMI, blood pressure, and blood glucose levels between the two groups, both before and after the intervention. Although a significant reduction in post-meal SBP was observed compared to baseline levels (P < 0.01), the mean change was −4.2 (−8.2, −0.2) mmHg in the RG group, and −4.0 (−8.2, 0.1) mmHg in the WG group, while, there no significant changes between the two groups with and without adjustments (P > 0.05). Following the 6-week intervention, a significant decrease in both FBG (P < 0.01) and PBG (P < 0.01) was observed among participants. Nevertheless, no significant differences were found between the two groups in the GLMs with and without adjustments (P > 0.05). An additional 6-week intervention and follow-up after the initial intervention, and the results showed that the 12-week intervention with different types of grains had no significant differences in the impact on levels of BMI, blood pressure, and blood glucose (Supplementary Table 1).Table 2 Comparisons of BMI, blood pressure, and blood glucose levels between two groups with 6-week intervention.

Table 2Indicators	Time	Refined grain group	Whole grain group	Baseline-ende	
(RG group)	(WG group)	Statistics	P value	Statistics	P value	
BMI (kg/mb)	Baselinea	25.8 ± 3.6	26.2 ± 3.9	−0.49	0.627	0.82	0.413	
Endb	25.8 ± 3.5	26.1 ± 4.0	−0.17	0.865			
Changec	−0.1 (-0.2,0)	−0.1 (-0.1,0)	−0.08	0.940			
Adjustedd			0.05	0.958			
Pre-meal SBP (mmHg)	Baselinea	144.0 ± 25.5	143.2 ± 18.9	0.20	0.844	0.79	0.429	
Endb	142.2 ± 22.1	142.3 ± 20.6	−0.18	0.856	
Changec	−1.8 (-6.4,2.9)	−0.9 (-5.7,3.9)	−0.25	0.800	
Adjustedd			−0.75	0.452	
Post-meal SBP (mmHg)	Baselinea	136.7 ± 20.3	137.9 ± 18.0	−0.37	0.715	2.85	0.005	
Endb	132.4 ± 19.9	133.9 ± 18.4	−0.24	0.807	
Changec	−4.2 (-8.2,-0.2)	−4.0 (-8.2,0.1)	−0.07	0.943	
Adjustedd			−0.63	0.531	
Pre-meal DBP (mmHg)	Baselinea	79.5 ± 9.4	81.3 ± 10.9	−1.00	0.322	−1.50	0.138	
Endb	81.0 ± 12.5	82.3 ± 13.6	0.16	0.875	
Changec	1.6 (-0.9,4)	1.1 (-1.5,3.6)	0.30	0.767	
Adjustedd			−0.09	0.930	
Post-meal DBP (mmHg)	Baselinea	75.4 ± 10.2	77.0 ± 11.8	−0.77	0.442	1.78	0.078	
Endb	74.2 ± 11.6	74.9 ± 11.8	0.14	0.891	
Changec	−1.3 (-3.8,1.3)	−2.1 (-4.7,0.6)	0.43	0.670	
Adjustedd			−0.25	0.802	
FBG (mmol/L)	Baselinea	6.4 (5.8,7.9)	6.3 (5.8,7.3)	0.81	0.421	7.97	<0.001	
Endb	5.5 (4.9,7.0)	5.6 (4.9,6.4)	0.28	0.782	
Changec	−0.8 (-0.9,-0.6)	−0.8 (-0.9,-0.7)	0.30	0.766	
Adjustedd			−0.49	0.628	
PBG (mmol/L)	Baselinea	9.0 (7.4,12.3)	7.7 (6.6,11.4)	1.52	0.129	9.13	<0.001	
Endb	7.4 (6.0,9.9)	6.7 (5.8,8.9)	0.17	0.866	
Changec	−1.8 (-2.0,-1.6)	−1.6 (-1.9,-1.4)	−0.30	0.763	
Adjustedd			0.24	0.809	
a Data were presented as mean ± std or median and range interquartile according to the distribution.

b Between-group comparisons were conducted using ANCOVA with adjusted baseline values. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

c Between-group comparisons were conducted using GLM and the data were presented as means and 95%CIs.

d Change values were adjusted by age, gender, physical activity, smoking and drink status, daily consumption of salt and oil, out dinner, NCDs, and baseline energy intake.

e Paired t-test was conducted for the comparison between baseline and end of the intervention. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

3.3 The effects of different grains on circulating inflammatory cytokines

The circulating levels of inflammatory cytokines between the two groups were compared and presented in Table 3, with changes observed over a 6-week intervention period. The analysis revealed no significant differences in serum levels of hsCRP and IL-17A between the two groups at any stage of the intervention. Following the intervention, both groups showed a significant increase in IL-6 (P < 0.01), while there was no significant difference in the extent of these changes between the two groups (P > 0.05). After the 6-week intervention, both groups exhibited a significant decrease in IL-10 levels. However, after the intervention, the WG group demonstrated a significantly lower median level of IL-10 (1.4 pg/mL) compared to the RG group (1.9 pg/mL, P < 0.05). After the intervention, the mean level of IL-22 in the WG group (9.9 ± 6.2 pg/mL) was significantly lower than that in the RG group (12.4 ± 6.3 pg/mL, P < 0.05). Compared to the baseline, the mean increase of IL-22 levels in the RG group was 2.2 pg/mL, while the mean decrease in the WG group was −1.4 pg/mL. Additionally, after the 6-week intervention, significantly lower IL-23 levels were observed in the WG group (23.5 ± 12.2 pg/mL) compared to the RG group (29.6 ± 12.9 pg/mL, P < 0.01). The mean decrease of IL-23 was −0.2 pg/mL in the RG group, while the mean decrease of IL-23 in the WG group was −5.3 pg/mL.Table 3 Comparisons of inflammatory cytokines between two groups with 6-week intervention.

Table 3Indicators	Time	Refined grain group	Whole grain group	Baseline-ende	
(RG group)	(WG group)	Statistics	P value	Statistics	P value	
HsCRP (mg/L)	Baselinea	1.7 (1.3,2.9)	1.8 (1.4,2.7)	−0.53	0.594	0.62	0.536	
Endb	1.7 (1.4,2.4)	1.7 (1.5,2.8)	0.50	0.615	
Changec	0.2 (-0.1,0.5)	−0.7 (-1.1,-0.3)	1.50	0.132	
Adjustedd			1.55	0.122	
IL-6 (pg/mL)	Baselinea	0.6 (0.3,1.4)	0.8 (0.5,1.3)	−0.62	0.536	−3.65	<0.001	
Endb	1.0 (0.6,2.0)	1.2 (0.7,1.9)	−0.79	0.431	
Changec	0 (-0.4,0.4)	0.6 (0.4,0.8)	−1.04	0.301	
Adjustedd			−0.72	0.473	
IL-10 (pg/mL)	Baselinea	2.9 (2.3,3.8)	4.1 (3.1,6.9)	−2.97	0.003	7.17	<0.001	
Endb	1.9 (1.4,2.6)	1.4 (0.8,2.4)	1.98	0.047	
Changec	−1.8 (-2.3,-1.3)	−3.7 (-4.3,-3.2)	2.01	0.044	
Adjustedd			1.64	0.101	
IL-17A (pg/mL)	Baselinea	6.2 ± 3.1	6.3 ± 4.0	−0.26	0.798	1.68	0.093	
Endb	5.7 ± 2.9	5.5 ± 2.6	0.49	0.627	
Changec	−0.5 (-0.9,0)	−0.9 (-1.3,-0.4)	0.51	0.611	
Adjustedd			0.90	0.369	
IL-22 (pg/mL)	Baselinea	10.2 ± 6.9	11.3 ± 6.6	−0.91	0.366	−0.57	0.567	
Endb	12.4 ± 6.3	9.9 ± 6.2	2.29	0.024	
Changec	2.2 (1.3,3.1)	−1.4 (-2.3,-0.5)	2.29	0.024	
Adjustedd			1.97	0.051	
IL-23 (pg/mL)	Baselinea	29.7 ± 16.0	28.8 ± 15.7	0.33	0.742	1.39	0.163	
Endb	29.6 ± 12.9	23.5 ± 12.2	2.65	0.009	
Changec	−0.2 (-2.2,1.9)	−5.3 (-7.4,-3.1)	1.36	0.175	
Adjustedd			0.54	0.593	
a Data were presented as mean ± std or median and range interquartile according to the distribution.

b Between-group comparisons were conducted using ANCOVA with adjusted baseline values. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

c Between-group comparisons were conducted using GLM and the data were presented as means and 95%CIs.

d Change values were adjusted by age, gender, physical activity, smoking and drink status, daily consumption of salt and oil, out dinner, NCDs, and baseline energy intake.

e Paired t-test was conducted for the comparison between baseline and end of the intervention. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

3.4 The effects of different grains on the subsets of circulating CD4+ T cells

Circulating CD4+ T cells were collected and analyzed during the 6-week intervention (Table 4). Compared to the baseline, the frequencies of CD4+ and Th0 significantly decreased by the end of the 6-week intervention (P < 0.01). However, no statistical differences were observed between the two groups with and without the adjustment of potential covariates (P > 0.05). After the intervention, the mean frequency of Th1 in the WG group (19.3 ± 5.9 %) was significantly higher than in the RG group (17.2 ± 5.8 %, P < 0.05). Compared to the baseline, the frequency of Th1 was significantly decreased at the end of the intervention (P < 0.01), while no statistical difference was observed between the two groups (P > 0.05). In contrast, circulating Th2 was significantly increased during the 6-week intervention. The mean increment of Th2 was 10.1 % in the RG group and 9.4 % in the WG group, while no statistical difference was observed between the two groups (P > 0.05). Opposite trends were observed in circulating Th17 and Th1/17. Compared to the baseline, the frequency of Th17 increased significantly after the intervention (P < 0.01), while the frequency of Th1/17 decreased significantly (P < 0.01). No significant differences were observed between the two groups at baseline or after the intervention. Circulating Tregs was significantly increased within the 6-week intervention (P < 0.01). No significant difference was observed between the groups at baseline. In contrast, after the intervention, the mean frequency of Tregs in the WG group (5.0 ± 1.1 %) was significantly lower than in the RG group (5.7 ± 1.6 %, P < 0.01).Table 4 Comparisons of CD4+ T-cell subsets in two groups with 6-week intervention.

Table 4Frequency of CD4 (%)	Time	Refined grain group	Whole grain group	Baseline-ende	
(RG group)	(WG group)	Statistics	P value	Statistics	P value	
CD4 (CD3+CD4+)	Baselinea	60.9 ± 11.2	58.3 ± 9.4	1.37	0.175	6.49	<0.001	
Endb	58.5 ± 12.0	55.6 ± 9.4	0.45	0.653	
Changec	−2.5 (-3.6,-1.4)	−2.7 (-3.8,-1.6)	0.31	0.759	
Adjustedd			0.49	0.626	
Th0 (CD4+CC45RA+)	Baselinea	32.9 ± 12.5	32.8 ± 12.4	0.02	0.985	16.00	<0.001	
Endb	28.8 ± 12.1	28.0 ± 11.5	1.50	0.136	
Changec	−4.1 (-4.8,-3.3)	−4.9 (-5.6,-4.1)	1.42	0.160	
Adjustedd			1.92	0.058	
Th1 (CD4+CC45RA-CXCR3+CCR6-)	Baselinea	22.4 ± 6.7	23.3 ± 6.1	−0.74	0.460	12.01	<0.001	
Endb	17.2 ± 5.8	19.3 ± 5.9	−2.21	0.029	
Changec	−5.2 (-6.3,-4.2)	−4 (-5.1,-2.9)	−1.65	0.101	
Adjustedd			−1.91	0.059	
Th2 (CD4+CC45RA-CXCR3-CCR6-)	Baselinea	16.0 ± 6.2	15.5 ± 5.0	0.53	0.598	−21.14	<0.001	
Endb	26.1 ± 9.0	24.9 ± 7.3	0.61	0.540	
Changec	10.1 (8.8,11.3)	9.4 (8.1,10.7)	0.70	0.487	
Adjustedd			0.77	0.446	
Th17 (CD4+CC45RA-CXCR3-CCR6+)	Baselinea	12.8 ± 4.7	12.6 ± 4.2	0.22	0.826	−8.49	<0.001	
Endb	14.9 ± 5.6	14.0 ± 4.2	1.81	0.073	
Changec	2.1 (1.5,2.7)	1.4 (0.8,1.9)	1.82	0.071	
Adjustedd			1.93	0.057	
Th1/17 (CD4+CC45RA-CXCR3+CCR6+)	Baselinea	14.7 ± 6.7	14.5 ± 5.8	0.10	0.920	12.26	<0.001	
Endb	11.2 ± 5.5	11.8 ± 5.5	−1.58	0.116	
Changec	−3.5 (-4.2,-2.8)	−2.7 (-3.4,-2.0)	−1.45	0.151	
Adjustedd			−1.75	0.083	
Treg (CD25+Foxp3+)	Baselinea	1.9 ± 0.8	2.0 ± 0.6	−0.66	0.508	−31.07	<0.001	
Endb	5.7 ± 1.6	5.0 ± 1.1	3.96	<0.001	
Changec	3.8 (3.5,4.1)	3.0 (2.7,3.3)	3.96	<0.001	
Adjustedd			3.23	0.002	
a Data were presented as mean ± std or median and range interquartile according to the distribution.

b Between-group comparisons were conducted using ANCOVA with adjusted baseline values. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

c Between-group comparisons were conducted using GLM and the data were presented as means and 95%CIs.

d Change values were adjusted by age, gender, physical activity, smoking and drink status, daily consumption of salt and oil, out dinner, NCDs, and baseline energy intake.

e Paired t-test was conducted for the comparison between baseline and end of the intervention. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

3.5 The effects of different grains on the proportion of fecal SCFAs

The composition of fecal SCFAs between the two groups and their changes were analyzed and presented in Table 5. After the 6-week intervention, the proportion of acetic acid in the WG group (50.4 ± 8.0 %) was significantly lower than the RG group (56.1 ± 8.5 %, P < 0.01). Compared to baseline, the mean increased proportion of acetic acid was 8.3 % in the RG group and 0.8 % in the WG group. The increase of acetic acid in the RG group was significantly higher than the WG group, with and without adjustment (P < 0.01). Compared to the baseline, within the 6-week intervention, the mean change of propionic acid in the RG group was −0.4 %, and 3.0 % in the WG group. The mean change of propionic acid in the RG group was significantly lower than the WG group in the crude model (P < 0.05), while the significant difference disappeared after the adjustment of potential covariates (P > 0.05). Conversely, the proportion of butyric acid was significantly decreased from baseline to the end. The mean decrease in RG was −7.2 %, which was statistically more extensive than the decrease of butyric acid in WG group (−3.7 %, P < 0.05). And then, after the 6-week intervention, the mean level of fecal butyric acid in the RG group (16.9 ± 5.6 %) was significantly lower than the WG group (20.5 ± 6.3, P < 0.05). As for the other SCFAs, including isobutyric acid, valeric acid, isovaleric acid, and caproic acid, no statistical difference was observed between the two groups at baseline or after the 6-week intervention.Table 5 Comparisons of the compositions of fecal SCFAs in two groups with 6-weeks intervention.

Table 5SCFAs (%)	Time	Refined grain group	Whole grain group			Baseline-ende	
(RG group)	(WG group)	Statistics	P value	Statistics	P value	
Acetic acid	Baselinea	47.8 ± 8.2	49.6 ± 7.9	−1.23	0.219	−4.67	<0.001	
Endb	56.1 ± 8.5	50.4 ± 8.0	3.98	<0.001	
Changec	8.3 (7.4,9.3)	0.8 (-0.4,1.9)	3.99	<0.001	
Adjustedd			2.80	0.005	
Propionic acid	Baselinea	20.4 ± 7.2	18.2 ± 5.7	1.82	0.069	−1.48	0.141	
Endb	20.0 ± 6.3	21.2 ± 7.0	−1.38	0.170	
Changec	−0.4 (-1.2,0.5)	3.0 (2.2,3.8)	−2.16	0.032	
Adjustedd			−0.83	0.409	
Butyric acid	Baselinea	24.0 ± 8.7	24.1 ± 8.8	−0.04	0.966	7.35	<0.001	
Endb	16.9 ± 5.6	20.5 ± 6.3	−3.46	0.001	
Changec	−7.2 (-7.9,-6.5)	−3.7 (-4.5,-2.9)	−2.35	0.020	
Adjustedd			−2.55	0.013	
Isobutyric acid	Baselinea	1.8 (0.9,4.1)	2.6 (1.0,4.3)	−1.00	0.316	1.99	0.047	
Endb	1.7 (0.7,2.7)	1.7 (1.1,3.7)	−1.65	0.099	
Changec	−0.6 (-0.8,-0.4)	−0.6 (-0.9,-0.4)	0.10	0.921	
Adjustedd			0.63	0.532	
Valeric acid	Baselinea	2.8 (0.8,4.6)	2.5 (0.9,3.4)	−0.065	0.949	−1.03	0.307	
Endb	2.5 (1.1,4.0)	3.0 (1.4,4.3)	−1.34	0.182	
Changec	−0.3 (-0.5,-0.1)	0.5 (0.3,0.7)	−2.07	0.038	
Adjustedd			−1.67	0.095	
Isovaleric acid	Baselinea	1.2 (0.5,2.7)	1.5 (0.6,3.2)	−0.77	0.440	−0.34	0.731	
Endb	1.3 (0.4,2.5)	1.5 (0.8,3.4)	−1.56	0.120	
Changec	0.1 (-0.1,0.2)	0.1 (-0.1,0.3)	−0.10	0.920	
Adjustedd			0.27	0.787	
Caproic acid	Baselinea	0 (0,0.4)	0.1 (0,0.9)	−0.58	0.561	−2.82	0.005	
Endb	0.2 (0,0.4)	0.1 (0.1,0.8)	−0.51	0.612	
Changec	0 (-0.1,0.1)	−0.1 (-0.2,0.1)	0.29	0.772	
Adjustedd			0.19	0.852	
a Data were presented as mean ± std or median and range interquartile according to the distribution.

b Between-group comparisons were conducted using ANCOVA with adjusted baseline values. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

c Between-group comparisons were conducted using GLM and the data were presented as means and 95%CIs.

d Change values were adjusted by age, gender, physical activity, smoking and drink status, daily consumption of salt and oil, out dinner, NCDs, and baseline energy intake.

e Paired t-test was conducted for the comparison between baseline and end of the intervention. Log transformation was applied as a preliminary step for the data did not adhere to a normal distribution.

3.6 Association between grain intake during the 6-week intervention and changes in related indicators

To further investigate the dose-response relationship between grain consumption and associated indicators, this study employed multivariate linear regression models to examine the association between different types of grain intake and health outcome indicators among two groups of participants (Fig. 2). The grain consumption during the intervention period and relevant data were presented in supplementary files (Supplementary Tables 2 and 3). After adjusting for potential confounding factors, it was observed that in the RG group (Fig. 2-A), during the 6-week intervention period, there was a significantly inverse association between daily consumption of refined grains with circulating Th1 levels (β = −0.022, 95%CI = −0.043∼-0.001, P < 0.05). No significant associations were detected between refined grain consumption and the other indicators. In contrast, in the WG group (Fig. 2-B), during the 6-week intervention period, a significantly inverse association was found between daily consumption of whole grains and circulating Th1 levels (β = −0.021, 95%CI = −0.039∼-0.004, P < 0.05). Additionally, a notable positive association was observed between whole grain consumption and circulating Th2 levels (β = 0.028, 95%CI = 0.008∼-0.049, P < 0.01).Fig. 2 Estimated β and 95%CI of grain intake during 6-weeks intervention and changes in related indicators in multiple linear regression model. (a) Subgroup analysis of RG group; (b) Subgroup analysis of WG group.

Fig. 2

4 Discussion

Previous studies have demonstrated that an irrational diet high in refined grain, animal-based foods, and low in dietary fiber, can lead to alterations in the distribution of T cell subsets, thereby increasing the risk of inflammation, obesity, and related NCDs [8,9,14]. In the present study, we significantly increased the intake of whole grains and dietary fiber in the intervention group through whole grain substitution of staple foods, without altering the habitual dietary patterns of the participants in the present study. No significant differences were found between the two groups regarding BMI, blood pressure, blood glucose, and body composition (Supplementary Table 4). Meanwhile, our findings reveal the comprehensive health effects of whole grains, including anti-inflammatory properties, modulation of T cell subset, and optimization of SCFA composition. The distinctive feature of the present study lies in its approach to observing and comparing the health effects of whole grains through staple food substitution, without altering the habitual dietary patterns of participants.

Before the present study, our preliminary investigations and research had already identified the potential health risks associated with excessive refined grain consumption among local residents [14]. Building on these findings, we designed this study in alignment with the Chinese Dietary Guidelines, tailoring our approach to address the specific nutritional challenges of the target population. To ensure the effectiveness of the intervention, we implemented a family-based intervention strategy, providing staple food ingredients to participants and their family members. This approach allowed subjects to flexibly consume rice, noodles, and other grain-cooked foods according to their daily requirements, thereby enhancing the authenticity and feasibility of the study. This methodological approach facilitated a more accurate assessment of the health impacts of whole grain consumption in a real-world setting. To the best of our knowledge, our study, which employs a staple food substitution method in a real-world intervention setting, provides a valuable contribution to the existing body of evidence in population-based nutritional epidemiology related to whole grains. This approach not only effectively controls for the aforementioned confounding factors but also offers new scientific evidence for developing more precise and targeted dietary guidelines.

4.1 Regulatory effects of whole grain consumption on T cell subsets and inflammation

The health benefits of whole grains are often attributed to their high dietary fiber content, which ferments in the gut to produce beneficial metabolites such as SCFAs [16]. However, whole grains also contain other functional components like gluten proteins, polyphenols, vitamins, and minerals, which may exert both positive and adverse health effects on metabolism. Consequently, the overall health impact of whole grains may differ from that of simple dietary fiber supplementation [19,20]. A previous systematic review has demonstrated that whole grain intake has an inhibitory effect on CRP levels, but no significant impact on other inflammatory markers such as IL-6 [21]. A previous cross-over study, which included 32 healthy individuals with habitually low consumption of whole grains, showed lower trends of ex vivo activation of CD4+ T cells, and circulating IL-10 was found in the arm of higher whole-grain intake. However, no significant change was observed among the circulating IL-6, IL-8, and IL-1β [22]. In our study, we similarly did not observe a significant modulatory effect of whole grain consumption on IL-6 levels. However, compared to refined grains, whole grains intake significantly downregulated the circulating levels of IL-22 and IL-23. IL-23 has emerged as a pro-inflammatory cytokine in recent years, playing a crucial role in the development of immune-mediated inflammatory diseases [23].

To our knowledge, there is a paucity of research on the direct modulatory effects of whole grains on pro-inflammatory IL-23. A diet high in salt and fat could significantly activate and enhance the expression of IL-23 via activating Th17, leading to the secretion of inflammatory cytokines such as IL-22 and IL-17A, thereby exacerbating the development of related inflammatory diseases [24,25]. A previous in-vitro study showed that food-borne Lpb. Plantarum could decrease ROS production and then significantly inhibit the IL-23/IL-17 axis in inflamed mucosa intestinal cells [26]. Whole grains generally provide a good fermentation substrate for Lpb. plantarum, especially when making fermented foods [27]. Moreover, an animal study found that gallic acid, which is abundant in whole grains [28], could improve the inflammatory response levels in mice with ulcerative colitis by inhibiting the NF-kB pathway and suppressing the expression levels of pro-inflammatory cytokines such as IL-23 and IL-17 both in vivo and in vitro [29]. Those preliminary studies provide indirect evidence for whole-grain intervention in inhibiting IL-23 from the perspectives of anti-inflammation and antioxidation. Our results contribute to the growing evidence on the anti-inflammatory properties of whole grains, particularly about IL-23. Furthermore, the differential effects observed between whole grains and refined grains underscore the importance of considering the entire nutrient profile of foods, rather than focusing solely on individual components like dietary fiber and related SCFAs.

In contrast to the well-established pro-inflammatory role of IL-23, IL-22 exhibits a more complex, multifaceted effect in inflammatory responses. Previous research has shown that IL-22 can exert both pro-inflammatory and anti-inflammatory actions, depending on the specific physiological context and disease state. In the processes of tissue repair and cell regeneration, IL-22 primarily exerts anti-inflammatory and protective effects [30]. However, in certain Th17-associated immune responses and specific stages of inflammatory bowel disease, IL-22 may exhibit pro-inflammatory characteristics [30]. Notably, the role of IL-22 is highly dependent on the specific microenvironment and disease stage. Zou et al. demonstrated that the fermentable fiber inulin could enhance gut epithelial cell proliferation and improve metabolic health in mice fed a high-fat diet. Notably, these beneficial effects were primarily attributed to the elevated expression of IL-22, which was significantly increased in the colon but not detected in serum [31]. Additionally, an animal study demonstrated that modified apple polysaccharide could attenuate drug-induced colitis in a murine model. This effect was mediated through the suppression of circulating IL-22 levels and the upregulation of IL-22-neutralizing antibodies in intestinal cells [32]. In our present study, we observed that whole grain consumption significantly downregulated IL-22 levels in the study population. This finding further underscores that the overall health effects of whole grains are more complex than those of dietary fiber alone. The complex interplay between various bioactive components in whole grains and their effects on inflammatory mediators like IL-22 and IL-23 highlights the need for a more nuanced approach to nutritional recommendations. Subsequent investigations should focus on monitoring the fluctuations in intestinal IL-22 and IL-23 levels, elucidate the mechanisms underlying these effects, and investigate how they may be tailored to benefit specific populations or disease states.

4.2 SCFAs mediated the regulatory effects of whole grain on T cells and inflammatory markers

While the exact mechanisms underlying the immunomodulatory effects of whole grains remain to be fully elucidated, SCFAs have been hypothesized to play a crucial role. Whole grains are rich in dietary fiber, which is fermented by gut microbiota to produce SCFAs, primarily acetic acid, propionic acid, and butyric acid. These SCFAs can modulate various physiological processes, including intestinal epithelial barrier function, immune cell activation, and inflammatory responses [33,34]. The high dietary fiber content in whole grains provides substrates for gut microbial fermentation, and the resulting SCFAs may mediate, at least in part, the immunomodulatory and anti-inflammatory effects of whole grain consumption.

A previous randomized dietary intervention in 20 healthy young adults demonstrated that diets rich in fermentable fiber suppressed circulating pro-inflammatory Th1 cells, potentially by increasing levels of SCFAs [35]. In contrast, our present intervention found that consuming refined and whole grains downregulated Th1 and Th1/17 cells while upregulating anti-inflammatory Th2 cells (Table 4). This discrepancy may be attributable to the different fiber compositions and fermentation patterns between the interventions. High-amylose maize starch and inulin fermentable fibers were the primary sources of fermentable fibers and SCFAs in the previous study [35], the resulting SCFA profiles and their immunomodulatory effects may differ from those observed with whole-grain foods. In addition, our study also revealed that grain intervention influenced fecal SCFA composition. After 6-week intervention, compared to the refined grain group, the whole grain group showed a lower proportion of acetic acid and a higher proportion of butyric acid (Table 5). Commonly, acetate is the most abundant SCFA, while butyrate is an important energy source for colonocytes and has the most substantial trophic effects on gastrointestinal epithelium among SCFAs [36]. Compared with acetate, butyrate and propionate not only signal to various receptors but also have epigenetic effects by inhibiting histone deacetylase activity [37,38]. In addition, previous research has indicated that acetate has relatively weaker anti-inflammatory effects compared to butyrate and propionate [39]. Moreover, the treatment of CD4+ T cells with butyrate has been shown to promote the differentiation of mucosal Tregs [40]. The shift towards a higher composition of butyric acid in the whole grain group aligns with the observed immunomodulatory effects, potentially explaining the anti-inflammatory outcomes. This SCFA profile change further emphasizes the complex interplay between dietary components and gut microbiota metabolism. It highlights that health effects of whole grains extend beyond their fiber content, involving intricate interactions with gut microbiota and subsequent metabolite production. However, this study is not without its limitations. Firstly, the daily grain intake was recorded by participants using electronic scales, which lacked more precise and convenient methods. Additionally, it is suggested that future research should further investigate the health effects of other dietary nutrients abundant in whole grains, such as polyphenols and fatty acids, beyond dietary fiber, following such interventions.

5 Conclusion

In this study, we investigated the health effects of different grains by substituting staple foods with raw grain materials without altering the overall dietary habits of middle-aged and older participants. After a short-term intervention, we observed no significant differences in BMI, blood pressure, and blood glucose levels between the group consuming whole grains and refined grains. Significantly lower levels of circulating inflammatory factors IL-22 and IL-23, along with a higher proportion of butyric acid, were observed in the whole grain group. Furthermore, whole grain consumption showed a significant association with CD4+ helper T cell distribution, contributing to comprehensive anti-inflammatory health effects. These findings suggest that whole grains exert broad anti-inflammatory effects through various mechanisms mediated by the upstream and downstream inflammatory pathways of T cell subsets. Our findings not only strengthen the scientific evidence supporting the health benefits of whole grains but also offer a feasible approach to improving suboptimal dietary patterns.

Funding

This work was supported by Beijing Friendship Hospital, Capital Medical University (BFHOS20230001 , YYZZ202137 ).

CRediT authorship contribution statement

Cheng Li: Writing – original draft, Methodology, Investigation, Funding acquisition. Yaru Li: Methodology, Investigation. Nan Wang: Investigation. Zhiwen Ge: Investigation. Jia Wang: Supervision, Investigation. Bingjie Ding: Supervision, Investigation. Yanxia Bi: Investigation. Yuxia Wang: Investigation. Yisi Wang: Investigation. Zebin Peng: Investigation. Xinli Yang: Investigation. Congcong Wang: Investigation. Zhongxin Hong: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

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Data availability

Data will be made available on request.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.redox.2024.103337.
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