Composite polyphenols mitigate microplastic exposure-related immune disturbances: a two-phase population trial

Domain: neuroscience
Published: 2026-04-03
Editorial outcome: major_revision

Abstract
Microplastics (MPs) are widespread, making it urgent to elucidate their toxicity and identify intervention strategies. Here, we designed a two-phase population trial, comprising a baseline pilot population (n = 151) and a 28-day randomized, double-blind, placebo-controlled trial (n = 98). Primary outcomes include fecal MP concentration and blood parameters (complete blood count, glycemic and lipid, and cytokines), with exploratory outcomes comprising fecal metagenomics and plasma metabolomics. The median MP concentration in 151 participants’ fecal samples is 158.28 μg/g dry weight, correlating with levels of 7 inflammatory indexes, 4 cytokines, and 2 lipid indicators. Composite polyphenols (CP) significantly reduced plasma levels of IL-1β (P = 0.045, effect sizes = -0.463), IL-6 (P = 0.023, effect sizes = -0.576) and IL-8 (P = 0.022, effect sizes = -0.529). 507 differentially expressed microbiotas (DEMs; P < 0.05) and 144 significantly different metabolites (SDMs; P-FDR < 0.25, VIP ≥ 1) are observed between the high and low MP exposure groups; 108 DEMs and 85 SDMs are identified following CP intervention. Notably, CP could mitigate the pro-inflammatory effects of high MP exposure by modulating gut microbiota and up-regulating glycerophospholipid metabolism and arginine biosynthesis. The gut bacteria Staphylococcus and the plasma metabolite PC (22:5/0:0) are identified as potential mediators in this protective effect. Trial registration: ClinicalTrials.gov: NCT06437119.

Introduction
The extensive global production and utilization of plastic materials have led to the pervasive accumulation of microplastics (MPs) in the environment, establishing them as a critical emerging contaminant of public health concern[1]. Recent advancements have demonstrated the utility of pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) in detecting MP accumulation in human tissues, including testes[2], brain[3], blood[4], and semen[2]. Nevertheless, knowledge gaps remain concerning population-level MP exposure and its correlations with adverse health outcomes.

Growing evidence suggests that MPs may disrupt gut microbiota homeostasis, as demonstrated by animal studies showing significant alterations in gut microbiota composition and diversity following exposure[5]. While most studies have relied on 16S rRNA sequencing (limited to prokaryotic profiling), metagenomic analysis of total DNA allows in-depth genetic and functional studies based on 16S rRNA analysis[6]. Only one human study has employed metagenomic sequencing, identifying significant associations between MP exposure and microbial virulence factors, transporter systems, and biodegradation pathways[7]. Furthermore, emerging research indicates that MPs-induced gut dysbiosis may perturb multiple metabolic pathways, involving amino acid and lipid metabolism[8, 9]. However, these mechanistic insights remain confined to animal models, with critical gaps in human data.

Given the inevitability of MP exposure, the potential of orally administered phytonutrients to counteract MP-induced toxicity has emerged as a key research focus. Polyphenolic phytonutrients have excellent anti-inflammatory and antioxidant properties, which have been widely validated to protect the intestinal barrier integrity and intestinal ecology[10]. Several preclinical studies have demonstrated the potential of polyphenols to mitigate the toxic effects of MPs in mouse and Caenorhabditis elegans models[11, 12]. Our previous research revealed that quercetin intervention significantly ameliorates MP-induced intestinal damage and immune dysregulation[11]. Meanwhile, cyanidin-3-O-glucoside (C3G) could enhance fecal MP excretion by modulating gut microbiota composition and microbial-derived xenobiotic metabolites[12, 13]. Gallic acid, a ubiquitous plant polyphenol, and its esterified derivative epigallocatechin gallate (EGCG) exhibit benefits by increasing probiotic abundance and suppressing MP-triggered inflammatory response[14, 15]. Notably, other polyphenols, such as rutin and ellagic acid, have been shown to exert protective effects against various environmental toxicants, including heavy metals and pesticides[16, 17].

Current research indicates that the combined intervention of multiple polyphenols exhibits synergistic protective effects, showing greater efficacy in counteracting environmental pollutants compared to the single[18, 19]. Composite polyphenol (CP) interventions could exert synergistic effects through enhanced bioavailability, potentiated anti-inflammatory and antioxidant capacity, gut microbiota modulation, and coordinated regulation of metabolic pathways[20]. For instance, ethanol extracts from Psychotria microphylla leaves—rich in polyphenols such as quercetin, gallic acid, and rutin—have demonstrated significant antioxidant activity in both in vitro and in vivo studies, effectively reversing heavy metal-induced redox imbalance in rats[21]. Similarly, combined intervention with quercetin and ellagic acid has shown enhanced antioxidant, metal-chelating, and anti-inflammatory effects[22, 23]. Additionally, co-administration of gallic acid and quercetin could alleviate lipid peroxidation and metabolic alterations induced by persistent organic pollutants[24]. Therefore, the combined intervention of quercetin, C3G, gallic acid, rutin, and ellagic acid may have the potential to exert a synergistic protective effect against the toxic effects associated with environmental pollutants. Moreover, interventions with CP are more relevant to the real situation, as phenolic compounds naturally coexist and are commonly consumed in combination. However, no existing studies have investigated whether CP could mitigate MPs' toxicity, and the key targets underlying their potential mechanisms remain unclear.

In this work, we design a two-phase sequential study: First, a pilot population study is conducted to investigate the potential toxic mechanisms and targets of MP exposure. This involves (1) quantifying internal MP exposure levels, (2) assessing blood routine, blood glycemic and lipid indicators, and inflammatory indicators. and (3) performing integrative multi-omics analyses (fecal metagenomics and plasma metabolomics); Subsequently, 101 participants with relatively high-risk plastic usage behavior from the pilot cohort are enrolled in a randomized, double-blind, placebo-controlled intervention trial (RCT). This study reevaluates MP levels after the intervention ended to ensure that the health benefits brought by the intervention are not caused by a reduction in MP levels. By employing an integrated phenotypic and multi-omics analysis, we comprehensively evaluate the efficacy of CP interventions in mitigating MP-induced toxicity and preliminarily explore their potential mechanistic targets.

Results
2.1 The detection of Fecal MPs levels
Among the first-stage baseline participants (FSBPs), detailed demographic information is provided in Table S1. The median total concentration of detected MPs in the feces samples was 158.28 μg/g dw (dry weight). Specifically, Polyvinylchloride (PVC), Nylon-66 (PA66), Polyethylene (PE), Polyethylene Terephthalate (PET), and Polystyrene (PS) were identified with detection rates of 100%, 100%, 92.72%, 70.86%, and 72.19%, respectively. PVC and PA66 exhibited the highest detection rates and concentrations, followed by PE (Table S1). Other types of MPs (PP, polymethyl methacrylate (PMMA), PC, PA6, polylactic acid (PLA), and polybutylene terephthalate (PBAT)) were not detected or had a detection rate of less than 70%. The H-MPs group exhibited significantly elevated fecal MP concentrations (P = 0.01) relative to the L-MPs group (Table S1). Concentrations of MPs in human samples reported in recent studies have been systematically compiled in Table S2.

The detailed demographic and fecal MP detection information for RCT participants is presented in Table S3. No significant difference was observed in MP concentration between the intervention group and the placebo group either before or after the 28-day intervention. Therefore, it can be reasonably inferred that the protective effects attributed to CP in this study were independent of alterations in MP exposure levels.

2.2 The Relationships of MP exposure with inflammatory index and cytokines
As shown in Figure 1, the fecal total MP level was significantly negatively correlated with the plasma expressions of IL-1β, IL-2, IL-10, and IL-13, while being significantly positively correlated with IFN-γ, cholesterol (CHO), and high-density lipoprotein (HDL). Regarding inflammatory indicators, it was significantly negatively correlated with monocyte count (MXD) and monocyte percentage (MXDP).

Regression analysis of individual MPs revealed that various MP components were significantly associated with changes in cytokine levels, primarily influencing IL-1β, IL-2, IL-4, IL-10, IL-13, and IFN-γ (Table S4). The Quantile-based g computation (QGcomp) analysis (Figure S1) was examined to identify the contribution of each individual MP to these overall effects. The mixed exposure to MPs exhibited a significant positive correlation with neutrophil count (NEU), neutrophil percentage (NEUP), and systemic immune-inflammation index (SII), and a significant negative correlation with MXD, MXDP, lymphocyte percentage (LYMP), as well as the cytokines IL-1β and IL-10. PA66 was identified as the primary driver of the significant associations. Specifically, PA66 contributed the largest positive weight to the models for NEU, NEUP, and SII. It also contributed the largest negative weight for the models examining MXD, MXDP, lymphocyte count (LYM), LYMP, IL-1β, IL-2, IL-10, and IL-13. The other components (PS, PVC, and PET) generally showed smaller and more mixed contributions for these specific endpoints.

Furthermore, the results of Bayesian Kernel Machine Regression (BKMR) analysis were consistent with those of Qgcomp analysis (Figure S2). The MP mixture was significantly positively associated with NEU, NEUP, and SII. These outcomes demonstrated a clear positive dose-response trend as exposure quantiles increased. Conversely, the MP mixture was significantly negatively correlated with the inflammatory indices MXD, MXDP, and LYMP, as well as the cytokines IL-1β, IL-2, IL-10, and IL-13.

2.3 The CP intervention regulates the levels of specific cytokines in plasma
Table 1 and Table S5 show that the Base-Int group exhibited significantly higher levels of IL-6 (P = 0.005) and triglycerides (TG) (P = 0.04) compared to the Base-Pla group. Interestingly, following the intervention, the elevated levels of IL-6 in the After-Int group were alleviated, with a notably substantial effect size (P = 0.023, effect size = -0.576). Compared with the placebo group, the levels of proinflammatory cytokines (IL-1β, IL-6, and IL-8) in the intervention group were significantly reduced following the intervention (P = 0.045, P = 0.023, and P = 0.022, respectively), with effect sizes of -0.463, -0.576, and -0.529, respectively.

2.4 Fecal metagenome alterations of MPs exposure and CP intervention
In the FSBPs, the H-MPs group showed a significantly higher Shannon diversity index and a significantly lower Simpson diversity index (D) in terms of the alpha diversity compared to the L-MPs group, both changes indicating that exposure to higher MPs increases the diversity level of the microbial community (Figure 2A-B). No significant differences were observed in the beta diversity (Figure 2E). In the RCT population, no significant statistical differences were observed in alpha diversity indices among the four groups (Figure 2C-D). Significant differences were detected in beta diversity among the four RCT groups (Figure 2F).

For the FSBPs and RCT populations, the taxonomic composition of the gut microbiota is presented in Figure S3A and S3B. At the genus level, attention was paid to the significantly differentially expressed genera with the highest relative abundance. Figure S3C shows that the relative abundance of Ruminococcus and Escherichia in the H-MPs was significantly lower than that in the L-MPs; However, there was no significant difference after false discovery rate (FDR) correction. The linear discriminant analysis effect size (LEfSe) analysis also revealed that the Ruminococcus and Escherichia were the two bacterial genera that had the greatest impact on the differential effects between the H-MPs and L-MPs groups (Figure S3E). Furthermore, Figure S3D indicates that the relative abundances of bacterial genera such as Bacteroides, Faecalibacterium, and Phocaeicola significantly differed among the four groups. Further multiple comparisons revealed that the After-Int group had a significantly higher relative abundance of Faecalibacterium and Phocaeicola compared to the Base-Int group, whereas no significant difference in Bacteroides abundance was observed between these two groups (Figure S3G-I). The LEfSe analysis revealed that the Foetidibacter had the greatest impact on the differential effects between the four groups (Figure S3F).

Furthermore, significant alterations were observed in the abundance of 507 gut microbial species between the H-MPs and L-MPs groups; However, there was no significant difference after false discovery rate (FDR) correction. In the RCT, a total of 1463 differentially expressed microbiotas (DEMs) were identified. A series test of cluster was conducted on 1463 identified DEMs to screen for DEMs exhibiting significant changes exclusively in the After-Int group, and a total of 108 were obtained (Figure S4).

Finally, KEGG functional enrichment analysis was performed on the 507 and 108 DEMs mentioned above. As shown in Figure 2G, several pathways related to chemical carcinogens, neuromuscular disorders, and neurodegenerative diseases were significantly enriched in the H-MPs group. Interestingly, Figure 2H indicates that CP intervention significantly reduced the above disease-related pathways and up-regulated the organism's metabolic pathways compared to the Base-Pla group. Moreover, Figure 2I demonstrates that the After-Int group showed significantly upregulated pathways related to amino acid biosynthesis, metabolism of diverse amino acids, and protein synthesis compared to the After-Pla group, suggesting that these changes may be associated with CP intervention.

2.5 Plasma metabolome alterations of MP exposure and CP intervention
The results from the FSBPs showed that elevated MP exposure demonstrated significant metabolic alterations in human subjects (Figure 3A). Comparative analysis revealed 144 significantly different metabolites (SDMs) between the L-MPs and H-MPs groups (P-FDR < 0.25, VIP ≥ 1), with 136 exhibiting downregulation and 8 showing upregulation (Figure S5).

Figure 3B demonstrates that CP intervention exerted a significant influence on overall metabolic profile modifications (P-FDR < 0.25, VIP ≥ 1). Cluster analysis of 242 metabolites displaying significant intervention-associated variations identified four distinct metabolic clusters comprising 85 target SDMs that exhibited pronounced changes in the After-Int group (Figure S6). These clusters were subsequently prioritized for further investigation as potential biomarkers of CP intervention efficacy.

Among SDMs identified between the L-MPs and H-MPs groups, as well as among the four RCT groups, lipid and lipid-like molecules, organoheterocyclic compounds, and organic acids and derivatives emerged as the three most predominant HMDB superclass categories in proportional representation (Figure 3C-D).

Finally, KEGG functional enrichment analysis was performed on the 144 and 85 SDMs mentioned above. As shown in Figure 3E, the H-MPs group showed significant enrichment in lipid metabolism pathways such as alpha-linolenic acid metabolism, Choline metabolism in cancer, multiple amino acid metabolism pathways, and various immune cell-related pathways. Interestingly, Figure 3F revealed that the metabolites altered by CP interventions were also enriched in glycerophospholipid metabolism pathways, Choline metabolism in cancer, and amino acid biosynthesis pathways.

2.6 Integrated multi-omics between plasma metabolome, fecal metagenome, and host phenotype
In the FSBPs, the subsequent integrated analysis was performed using 144 SDMs and 507 DEMs obtained through the aforementioned screening process. For the RCT population, 85 SDMs and 108 DEMs derived from cluster analysis were utilized for further analysis.

Firstly, the KEGG pathways were separately annotated for the screened SDMs and DEMs, and the common annotation pathways shared by the two omics were identified (Figures 4A and 4B). KEGG pathways associated with lipid metabolism and amino acid metabolism, which are frequently observed, were selected for subsequent correlation analysis.

In the FSBPs, correlation analysis was performed between 11 metabolites, the top 150 abundant bacterial species selected from 507 DEMs, and phenotypic indicators (Figure S7). This analysis revealed 142 significant microbe-metabolite pairs and 43 metabolite-phenotype pairs. The correlation network integrating metabolites, gut microbiota, and host phenotypes is presented in Figure 4C.

For the RCT study, correlation analysis of 16 metabolites, the 108 DEMs, and phenotypic indicators identified 209 significant microbe-metabolite pairs and 85 metabolite-phenotype pairs (Figure S8). Figure 4D revealed a potential correlation network among key metabolites, gut microbiota, and host phenotypes.

2.7 The microbiota and metabolite targets of CP intervention
Based on Figures 4A and 4C, the altered gut microbial genera and plasma metabolic pathways associated with MP exposure are shown in Figure 5A. Similarly, Figure 5B, derived from Figures 4B and 4D, demonstrated that the above bacteria and metabolic pathways were influenced by CP intervention. Pathways significantly correlated with both MP exposure and CP intervention, which changed concurrently, were displayed in Figure 5C.

As illustrated in Figure 5A, the H-MPs group exhibited significant alterations in gut microbiota abundance, with significant upregulation of Sutterella wadsworthensis, Clostridium bornimense, Bacteroides acidifaciens, and Parabacteroides bouchesdurhonensis coupled with downregulation of Enterococcus columbae, Staphylococcus succinus, Lactobacillus pasteurii, and Yersinia enterocolitica. These microbial shifts demonstrated significant correlations with circulating metabolite levels, particularly the diminished expression of Stearidonic acid, 13(S)-Hydroperoxylinolenic acid, Phenethylamine glucuronide, Phosphatidylcholine (PC) (22:5/0:0), 3a,6b,7a,12a-Tetrahydroxy-5b-cholanoic acid, 5-Methoxyindoleacetate, L-Methionine, O-Acetyl-L-homoserine, and DL-O-Phosphoserine, indicative of suppressed amino acid and lipid metabolism pathways. Concomitantly, the downregulation of metabolites led to changes in cytokines, characterized by elevated pro-inflammatory cytokines (IL-6, IL-8, and IFN-γ), reduced anti-inflammatory IL-13, disturbed blood lipid indexes (increased CHO, low-density lipoprotein cholesterol (LDL), and HDL), and altered inflammatory indexes (upregulated WBC, NEU, NEUP, and SII; downregulated LYMP, MXD, and MXDP).

Following CP intervention (Figure 5B), comparative analysis between placebo and intervention groups revealed dynamic gut microbiota abundance, including enrichment of Enterococcus faecium, Clostridium saccharoperbutylacetonicum, Parabacteroides genus (Parabacteroides chongii, Parabacteroides goldsteinii, and Parabacteroides gordonii), and Bacteroides genus (Bacteroides faecis and Bacteroides rhinocerotis), alongside depletion of Sutterella sp. Staphylococcus epidermidis, Parabacteroides acidifaciens, Bacteroides togonis, and Bacteroides koreensis in the intervention group. These changes correlated with metabolic alterations, marked by upregulation of a series of glycerophospholipid-associated metabolites (LysoPC(17:0/0:0), LysoPC(22:5(7Z,10Z,13Z,16Z,19Z)/0:0), PC (22:5/0:0), SN-glycero-3-phosphocholine, Triethanolamine, and Choline glycerophosphate), and amino acid biosynthesis-related metabolites (3-Dehydroquinic acid, N-epsilon-acetyl-l-lysine, N2-acetyl-l-ornithine, and O-Acetyl-L-homoserine), contrasted by downregulation of Pc(22:5(4z,7z,10z,13z,16z)/p-18:1(9z)), GPEtn(14:0/22:4), GPEtn (T16:1/22:4), Pe(p-16:0/22:6) and Pe(16:1(9z)/p-18:0). Such metabolic shifts were associated with reduced inflammation (lower levels of IL2, IL-6, IL-10, IL-12p70, TNF-α and IFN-γ), decreased blood glucose (GLU) and lipid levels (including glycated serum protein (GSP), RBG, CHO, TG, HDL and LDL), as well as regulated inflammatory indicators (increased WBC, MXD, LYM and LYMP, and decreased NEU, NEUP and SII).

Notably, as shown in Figure 5C, both high MP exposure and CP intervention selectively modulated the Staphylococcus genus (Staphylococcus succinus and Staphylococcus epidermidis), whose abundances were associated with PC (22:5/0:0) in glycerophospholipid metabolism. Shared phenotypes across MP exposure and interventions included IL-6, IL-13, and MXDP.

Methods
4.1 Experimental model and subject details
4.1.1 Trial registration and ethical review
The study protocol has been reviewed and approved by the Ethics Committee of the School of Public Health, Fudan University (IRB#2024-03-1102) and is registered in the U.S. Clinical Trials Registry (NCT06437119). The trial was conducted at the School of Public Health, Fudan University. All participants were informed of the study procedures prior to participation, including the background, objectives, process, duration, methods, potential benefits, and possible risks. All participants signed an informed consent form. The study officially commenced only after participants agreed to and signed the informed consent form. Sex was not factored into the study design, and the sex of the participants was ascertained based on self-report.

4.1.2 First-stage baseline participant
This study initially enrolled a cohort of 371 participants. Following rigorous screening procedures, 220 individuals were excluded based on predetermined inclusion and exclusion criteria. 151 eligible participants with complete sample and information collection were subsequently included as the FSBPs (Figure 6).

Inclusion Criteria:
1. Participants aged 18–65 years;
2. Body mass index (BMI) < 35 kg/m².
3. On-campus residence for one year and no need to leave the province during the trial period.
4. Agreeing to and signing the informed consent form.

Exclusion Criteria:
1. Documented diagnosis of congenital or acquired immunodeficiency disorders, allergic diseases, gastrointestinal pathologies, or other acute/chronic conditions requiring therapeutic intervention;
2. Administration of immunosuppressive agents, antibiotics, probiotics, prebiotics, synbiotics, or gastrointestinal motility-active medications within three months prior to the trial;
3. Consumption of nutritional supplements within three months preceding the study;
4. Underlying disease, including hypertension or diabetes mellitus;
5. Habitual substance use (tobacco smoking or alcohol consumption);
6. Influenza vaccination within 12 months prior to the trial;
7. pregnancy or lactation status;
8. Body weight fluctuation exceeding 5% within three months before the study;
9. Concurrent or planned enrollment in alternative clinical investigations.

Among the FSBPs, 36 were male (23.84%), and 115 were female (76.16%). The median age of the study population was 24 years, with a height of 1.65 m and a weight of 57 kg. Most participants had BMI measurements within the range of 19.15–22.52 kg/m² (Table S1).

4.1.3 RCT participant
Subsequently, 101 individuals with high-risk plastic usage behaviors in the baseline population were enrolled in a double-blind, randomized controlled trial. The risk assessment criteria are as follows: consuming takeout, packaged meals, or hot beverages at least once a day, or only drinking bottled or barrel water as regular drinking water. Their fecal detection levels of MPs were significantly higher than those of excluded participants (n=50) (Table S6).

Participants were randomly allocated via computer-generated randomization sequence to either the placebo-controlled group (n=50) or the intervention cohort (n=51). Three participants discontinued participation during the trial period, resulting in final analytical cohorts of 48 in the placebo group (Base-Pla) and 50 subjects in the intervention group (Base-Int) who completed the 28-day intervention protocol (Figure 6). Among the RCT participants, there were no significant differences in demographic indicators such as the male-to-female ratio, age, height, weight, and BMI between the placebo group and the control group (Table S3).

In order to confirm that the participants maintained their basic lifestyle habits unchanged, we conducted dietary questionnaire surveys and MP detection two times at the first-stage baseline and following the 28-day intervention. Among the RCT participants, there were no significant differences in dietary intake between the placebo group and the intervention group (Table S3). There was no significant difference in the MP concentration between the Base-Pla and Base-Int groups prior to intervention, and no significant difference was observed between the two groups after the intervention either (Table 1).

4.1.4 Information and sample collection
On April 23-24, 2024, basic demographic information, food frequency questionnaire (FFQ), and 24-hour daily recall (24h-DR) were collected for the FSBPs, and fecal and blood samples were collected.

The questionnaire information and samples of participants on the day of the first stage sampling were used as baseline information, and the 28-day intervention began on April 25, 2024, and ended on May 22, 2024. The FFQ and 24h-DR after the intervention were conducted on May 23, 2024, and fecal and blood samples were collected.

Blood samples were collected by trained professionals using EDTA anticoagulant tubes through venipuncture to obtain 20 mL of blood. Participants were asked to rest for at least 5 minutes before blood collection. After collection, the plasma was separated by centrifugation at 1509 × g for 10 minutes at 4 °C.

Stool samples were self-collected by participants under the guidance of researchers. Participants have been instructed to squat and naturally defecate into a clean aluminum foil tray. They then took approximately 20 g of stool from the middle section that had not been exposed to air or the bottom of the tray using a metal spoon, placing it into a sampling glass bottle. The collected stool samples were immediately placed in a freezer and kept cold with ice packs during submission. Later, the samples were transferred and stored at -80 °C.

4.2 Method details
4.2.1 Intervention materials
The main active components of the CP intervention agent include a mixture of 43 types of polyphenols. The CP intervention agent is powders encapsulated in sachets, each containing 155 mg CP. The dosage was three times daily, with a total intake of 465mg of CP. The intervention agent should be taken one sachet each time, dissolved in warm water and taken during or after meals in the morning, afternoon, and evening. The ingredients of the CP were detailed in Table S7.

4.2.2 Placebo materials
The placebo mainly consists of resistant dextrin and erythritol. Apart from the contents, the appearance, color, and consumption method of the placebo are the same as those of the intervention agent. Participants were not informed of their group allocation in any way. The ingredients of the placebo were detailed in Table S7.

4.2.3 Randomization and allocation concealment
Random numbers were generated using IBM SPSS Statistics 26.0, with the random seed set to the date of the day. Eligible participants were sorted based on the size of the generated random numbers, with the first 50% of the ranked list assigned to the placebo group and the last 50% assigned to the intervention group. Participants were allocated randomly in equal proportions, and the random list was validated by a statistician.

4.2.4 Blinding
During the experiment, the random list generated was kept confidential, and neither participants nor researchers were aware of the group assignments. Participants were divided into the placebo group and the CP intervention group. The random codes were sealed in double-layered opaque envelopes and managed by researchers responsible for the blind method.

4.2.5 Sample collection
A specialized fecal sample collection kit (Figure S9) was provided to the participants, containing essential tools such as a tin foil plate, a metal spoon, a glass bottle with a metal cap, a sterile fecal collector, a pair of disposable nitrile gloves, and an ice pack. To standardize the sampling process, we pre-instructed the participants to simulate real-world scenarios using clay models. The entire sampling process strictly adhered to the no-plastic principle.

4.2.6 Adverse events reporting and safety monitoring
The plant-based nutrients and placebo used in this trial have undergone food safety testing and obtained the relevant production licenses, effectively controlling the safety risks for participants. During the intervention period, participants must report any adverse events to the researchers every day, and the researchers must also document any observed adverse events. All adverse events were collected and promptly thoroughly submitted to the study's principal investigator, who determined the subsequent course of action, including close monitoring, additional medical management, or early termination of participation.

4.2.7 Adherence to study interventions
This study employs multiple strategies to monitor participants' adherence to the intervention. During the 28-day RCT period, all participants were provided with clear oral and written instructions and were reminded to take the intervention or placebo three times daily via WeChat. Unused medications were required to be returned and recorded. Generally, adherence of ≥ 80% is considered good. In this study, overall medication adherence was 95.51%, and participants had good adherence. MPs detection and dietary information collection were conducted before and after the intervention to ensure that participants did not change their eating habits or exposure to MPs during the study. Participants were required to submit food pictures to the researchers for three days to ensure that 24h-DR was accurate and reliable.

4.2.8 Fecal MPs detection
The detailed steps for detecting MPs in fecal samples are identical to those described in the previous study by Ke et al[25]. The brief steps are as follows:

A proper amount of fecal sample is placed in a beaker, dried at 60 °C until it reaches a constant weight, and the weight is subsequently recorded. Then add 10 g of filtered concentrated nitric acid and let the sample digest for 48 hours. After complete digestion with no visible impurities remaining, the sample is concentrated on a heating plate at 110 °C. Finally, the sample is transferred to a Py-GC/MS sample crucible using a glass pipette, and the solvent is allowed to evaporate before analysis.

The pretreated samples are analyzed using a Frontier Lab EGA/PY-3030D instrument (Shimadzu, Japan) equipped with an AS1020E automatic sampler for thermal pyrolysis. The pyrolysis products are then separated on an Rtx-5MS (30 m*0.25 mm*0.25 μm) Thermo Fisher TG-5 SilMS column and analyzed using a GCMS-QP2020 (Shimadzu). Helium is used as the carrier gas. The oven program is as follows: initial hold at 40 °C for 2 minutes, followed by a temperature ramp of 20 °C/min to 320 °C, and a final hold at 320 °C for 14 minutes, with a total program time of 30 minutes. A GCMS-QP2020 is used for mass spectrometric analysis, with an ionization voltage of 70 eV and a scan time of 30 minutes.

Qualitative and quantitative analysis of MPs in the samples is performed using LabSolutions 4.45 software. Target polymers include PVC, PET, PE, PS, PP, PC, PA6, PA66, PLA, PMMA, and PBAT. Each polymer was identified based on its specific characteristic components and ions. Table S8 lists the detection limits of the Py-GC/MS method and the pre-treatment recovery rates of low, medium, and high concentrations of MPs spiking amounts. Fecal samples from all 151 participants were tested for the presence of these 11 types of polymers.

All reagents are filtered three times through a 0.45 μm PTFE membrane before use. All consumables used in the experiment are made of glass and are rinsed with ethanol prior to use. Operators clean their hands before the experiment, wear disposable acetone gloves, and wear cotton lab coats. Process blank samples are prepared according to a 1:30 dilution procedure. Additionally, all types of MPs were also undetected in the process blank samples collected post-intervention.

The estimated daily intake (EDI, μg/kg-bw/d) was extrapolated from MP concentrations in fecal matter, and this estimation should be considered with caution due to its speculative nature. The calculated equation was as follows [25]:
(1) EDI= (fecal concentration (μg/g) × excretion rate (g/day)) / body weight (kg)

The median value of the MP concentrations was taken as the fecal concentration. It is reported that the average dry weight of feces excreted by adults per day is about 87 g.

4.2.9 Physical assessment
Height and weight were measured using the InBody 770 instrument (Biospace China Co., Ltd.), and the BMI was calculated based on these measurements.

4.2.10 Blood parameters detection
Blood parameters were measured using the Dima DH36X fully automatic blood cell analyzer for routine blood examination (three-part differential) and the Hitachi 7180 fully automatic biochemical analyzer for blood plasma samples, including glycemic (GSP and GLU) and lipid (CHO, TG, HDL, and LDL) indicators. The levels of 10 inflammatory cytokines in blood plasma samples were detected using Luminex technology, including IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-α, and IFN-γ.

The SII is a comprehensive indicator that integrates three independent white blood cell subsets and platelets, calculated as follows[26]:
(2) SII= (Platelet count (×10^9/L) × Neutrophil count (×10^9/L)) / Lymphocyte count (×10^9/L)

4.2.11 Plasma Metabolomics
100 μL of plasma sample was added to a 1.5 mL centrifuge tube, followed by 400 μL of extraction solution (acetonitrile: methanol = 1:1). The mixture was vortexed and subjected to low-temperature ultrasonic extraction for 30 minutes (5 °C, 40 KHz). The sample was then incubated at -20 °C for 30 minutes, centrifuged at 4 °C for 15 minutes at 13000 g, and the supernatant was transferred. The supernatant was dried under nitrogen gas and reconstituted with 100 μL of reconstitution solution (acetonitrile: water = 1:1). After another 5 minutes of ultrasonic extraction (5 °C, 40 KHz), the sample was centrifuged at 4 °C for 10 minutes at 13000 g, and the supernatant was collected for subsequent analysis.

The pretreated samples were analyzed using an Ultra-high Performance Liquid Chromatography with quadrupole time-of-flight mass spectrometry (UPLC-TripleTOF) system (AB SCIEX). The samples were separated using a BEH C18 chromatography column (100 mm*2.1 mm i.d., 1.8 µm) and detected via mass spectrometry. The mass spectrometry signal was acquired in positive and negative ion scanning modes, with a mass-to-charge ratio (m/z) range of 50–1000.

Raw data were imported into the Progenesis QI software (Waters Corporation, Milford, USA) for processing, generating a data matrix containing retention time, m/z, and peak intensity. The MS and MS/MS spectra were matched with the HMDB (http://www.hmdb.ca/) and Metlin (https://metlin.scripps.edu/) databases to identify metabolites. The data matrix was preprocessed, including missing value recoding and normalization. After quality control (QC) validation, the metabolite data were log-transformed to generate a data matrix for subsequent analysis.

4.2.12 Fecal Metagenomics
DNA was extracted from fecal samples using the FastPure Stool DNA Isolation Kit (Magnetic bead) (MJYH, Shanghai, China). After DNA quality control, the DNA was fragmented using the Covaris M220 (Gene Company, China), and fragments of approximately 350 bp were selected for paired-end (PE) library construction. The DNA fragments were amplified via bridge PCR and sequenced using the Illumina NovaSeq/HiSeq Xten platform (Illumina, USA).

Raw data were processed using Fastp and BWA software for quality control and removal of host DNA sequences. The cleaned sequences were assembled using the MEGAHIT software, and contigs ≥ 300 bp were selected as the final assembly results. Open reading frames (ORFs) were predicted using the MetaGene software, and all predicted gene sequences from the samples were clustered using CD-HIT to construct a non-redundant gene set. Genes with nucleotide lengths ≥100 bp were translated into amino acid sequences. Finally, the filtered reads from each sample were aligned to the non-redundant gene set using SOAPaligner (95% identity), and the abundance of genes in each sample was determined.

The amino acid sequences of the non-redundant gene set were compared to the NR database using BLASTP to obtain species annotations at various taxonomic levels (Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species). BLASTP was also used to compare the non-redundant gene set with the KEGG gene database (GENES) to obtain functional annotations of the genes.

4.2.13 Statistics & Reproducibility
The experiment was designed as a randomized, double-blind study. Due to the lack of previous literature, no statistical methods were employed to pre-determine the sample size. The sample size range was merely estimated based on the hypothesized outcome. During the analysis, no data were excluded.

Data were processed and visualized using R 4.3.0, IBM SPSS Statistics 26.0, and GraphPad Prism 10. Results are expressed as the median (P50, P25, P75). The Shapiro-Wilk test was used to assess data normality. For non-normal data, the skewness and kurtosis Z-values were calculated. Data with absolute Z-values for skewness and kurtosis <1.96 were considered approximately normally distributed. In phenotypic and omics analyses, significant differences between the two groups were analyzed using independent sample T-tests or Wilcoxon rank-sum tests. Significant differences among multiple groups were analyzed using one-way ANOVA or Kruskal-Wallis test, followed by Tukey-Kramer post hoc tests. The pre-post differences within each group were calculated, and significant differences in change values between the two groups were analyzed using independent samples t-tests or Wilcoxon rank-sum tests. The associations between total MPs and individual MP concentrations with cytokines, blood lipids, and inflammatory indices were evaluated using the generalized linear model (GLM). The BKMR model, with 10,000 Markov Chain Monte Carlo iterations, was utilized to study combined exposure effects, considering variable interactions. The QGcomp model provided estimation of overall mixture effects, accounting for non-linear and non-additive effects of single and mixed exposures.

The alpha diversity was evaluated by calculating the Shannon diversity index and the Simpson diversity index (D). The beta diversity was assessed using permutational multivariate analysis of variance (PERMANOVA) based on the Bray-Curtis distances. The LEfSe analysis was used to identify the differentially abundant taxa. For plasma metabolomics, the ropls package (Version 1.6.2) in R was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) on the preprocessed data matrix. Significant differential metabolites were screened based on variable importance in projection (VIP) values and Student’s t-test from the OPLS-DA model. Differential metabolites were screened using the criteria: P-FDR < 0.25 and VIP > 1. Differential metabolites were annotated using the KEGG database (https://www.kegg.jp/kegg/pathway.html) for metabolic pathway enrichment analysis. The gut bacteria with P < 0.05, as well as the plasma metabolites with P-FDR < 0.25 and VIP > 1, were used for subsequent correlation analysis. Pathway enrichment analysis was performed using the Python package scipy.stats, and Fisher’s exact test was used to identify the most relevant biological pathways associated with the experimental treatment. Spearman’s correlation coefficients were calculated to assess correlations between metabolites, species, and phenotypes, with coefficients > 0.2 and P < 0.05 considered significant. For omics research, the P-values were all corrected using the FDR method (Benjamini & Hochberg). All other statistical analyses with P < 0.05 were considered statistically significant. Effect sizes (Cohen’s d method) were presented along with statistical significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents one of the first human studies examining the relationship between microplastic (MP) exposure and immune–metabolic disturbances, and it further tests a dietary intervention with composite polyphenols (CP) in a randomized controlled trial (RCT). The integration of metagenomic and metabolomic analyses with clinical phenotyping is ambitious and generates potentially important insights. The study is clearly written, methodologically detailed, and addresses a pressing issue in environmental health.\nThis study has several key strengths: it is the first population-based trial to test whether polyphenols can help reduce immune effects linked to microplastic exposure; it uses a multi-omics approach that combines microbiome and metabolomic data with clinical measures to give deeper insight; it includes a randomized, placebo-controlled design, which is stronger than the mostly observational studies done before; and it gives clear details on how microplastics were measured, how randomization and blinding were done, and how adherence was monitored,\nThat said, several aspects of the study design and interpretation limit the strength of the conclusions. In particular, issues around exposure assessment, population representativeness, statistical rigor, and the scope of interpretation should be addressed to ensure the findings are presented with appropriate caution.\nMajor Concerns\nExposure assessment\nMPs were quantified in feces using Py-GC/MS, but fecal levels may not reflect systemic burden; tissue retention could differ in type and concentration. The relatively high concentrations reported compared with previous studies may reflect methodological differences, which complicates cross-study comparisons. The calculation of estimated daily intake (EDI) from fecal data is speculative.\nPopulation representativeness\nThe study cohort was young (median age 24), largely healthy, and predominantly female. This narrow profile restricts the generalizability of findings to the broader population.Extensive exclusion criteria (smoking, alcohol use, chronic diseases, supplement use) create a homogeneous group but also one that does not fully reflect real-world vulnerability.\nIntervention trial\nThe 28-day trial duration is too short to evaluate sustained effects of polyphenols on MP-related health outcomes. The sample size (~100) is modest, limiting power for subgroup and multi-omics analyses.\nStatistical analysis\nThe handling of multiple testing in large omics datasets is not clearly described. Without appropriate correction, the risk of false positives is high.Some baseline imbalances (e.g., higher IL-6 and triglycerides in the intervention group) complicate interpretation of intervention effects. Many results are reported primarily through p-values. Greater emphasis on effect sizes and clinical relevance would improve interpretation.\nInterpretation\nSome discussion points overstate causality (e.g., MPs “leading to” neurodegenerative pathways). These should be presented as associations or hypotheses. Proposed mechanistic chains (e.g., Sutterella abundance → lipid metabolism changes → systemic inflammation) remain speculative and should be presented as such. Statements about CP as a “protective strategy” are premature given the short timeframe, modest effect sizes, and absence of clinical endpoints.\nMinor Concerns\nThe description of CP composition is inconsistent across sections (five components vs. inclusion of tannic acid) and should be standardized.\nSome figures and tables are overly dense (e.g., volcano plots, KEGG enrichment) and would benefit from simplification, with secondary analyses moved to supplementary material.\nCytokine data reported as quartiles obscure potentially important changes; mean ± SD or effect size plots might better illustrate findings.\nThe manuscript is generally well written, but certain sections are redundant and could be streamlined for clarity and balance.\nIn conclsuion, this study provides some of the first human evidence linking microplastic exposure with immune and metabolic disturbances and suggests that composite polyphenols may help mitigate these effects via microbiome and metabolomic pathways. The short intervention period and narrow cohort limit generalizability. The conclusions are supported by the data but should be framed as preliminary and hypothesis-generating. Overall, the work is original and timely, though confirmation is needed.", "reviewer_2": "This manuscript addresses an important and timely question: the population-level health impacts of microplastics (MP) exposure and a potential dietary mitigation strategy using a composite polyphenol (CP) formulation. The combination of a pilot population study, a randomized double-blind placebo-controlled trial, and an integrated multi-omics approach is innovative and of high potential impact. However, several important methodological clarifications and additional analyses/interpretation are required before the manuscript is acceptable for publication.\n1. The manuscript states CP consists of five compounds (quercetin, C3G, gallic acid, rutin, ellagic acid), yet Supplementary Table S1 lists botanical extracts (Goji Berry Extract, Hawthorn Flower Extract, Wild Cherry Berry Extract, Dendrobium officinale Flower Extract, Pomegranate Extract). Please provide a detailed, quantitative breakdown of the CP product:\n- mg of each extract per daily dose and the percentage (or mg) of each named polyphenol per gram of extract and per daily dose.\n- Information on standardization (e.g., percent total polyphenols, HPLC/LC–MS quantification certificates) and batch-to-batch variability.\n- A full list of other abundant bioactive compounds in these extracts (other polyphenols, sugars, fibers, alkaloids, etc.) and their approximate abundances or whether such analyses were performed.\nWithout this information it is impossible to attribute effects to the named polyphenols specifically, to replicate the study, or to compare dosing to prior literature.\n2. Explain why 15 g/day was chosen (e.g., manufacturer recommendation, prior human clinical data, preclinical efficacy/tolerability data, or pilot tolerability studies) and provide supporting references or pilot data. If the dose is based on the manufacturer’s recommended serving, state this explicitly.\n3. Selection and detection of targeted microplastics (MPs):\no Explain the criteria used to select the MPs in Table S2 (representativeness, prevalence in environment/human samples, polymer classes, particle size, expected exposure routes).\no Clarify which of the targeted MPs were actually detected in participants’ samples and at what concentrations. Table S6 shows five of eleven, please disclose whether the other six were below detection limits or not analyzed.\no If some targeted MPs were not detected, discuss implications for exposure–effect analyses and whether analyses should focus on the detected subset.\n4. Exposure route and source attribution. The manuscript mentions PVC is widely used in construction materials. Provide discussion and (if available) rationale or evidence on likely exposure pathways for the MPs measured (oral/dietary ingestion, inhalation, dermal). State whether the measured MPs in the biological matrix are most plausibly reflecting dietary intake, inhalation, dust ingestion, or mixed routes. Discuss how non-dietary exposure routes could influence gut microbiome-mediated effects (for example, inhaled MPs that are swallowed via mucociliary clearance).\n5. The authors report gut microbiome changes (notably Sutterella) associated with MP exposure and CP effects. Explicitly discuss whether microbiome alterations are more likely a cause or a consequence of MP exposure (or of host response to MPs). Clarify limitations of cross-sectional associations and the RCT for causal inference, and propose approaches to test directionality.\n6. The manuscript states plasma cholesterol and LDL tended to increase with MP exposure, citing supporting studies. However, some preclinical reports (e.g., Lu L, Wan Z, Luo T, Fu Z, Jin Y. Polystyrene microplastics induce gut microbiota dysbiosis and hepatic lipid metabolism disorder in mice. Sci Total Environ. 2018 Aug 1;631-632:449-458. doi: 10.1016/j.scitotenv.2018.03.051.) show different or opposite lipid effects. Please:\n- Acknowledge conflicting evidence explicitly,\n- Offer possible reasons for discrepancies (species differences, MP polymer type, particle size, dose, exposure route, diet, duration), and\n- Clarify how the present human data fit within this heterogeneous literature.\n7. Intervention duration and biological relevance of significant endpoints. A 28-day intervention is short for many cardiometabolic outcomes. The authors already note lack of changes in many clinical indicators. Please discuss whether the significant molecular/microbiome/metabolite changes (e.g., Sutterella and PC (22:5/0:0)) are likely to be transient biomarkers or plausibly predictive of longer-term clinical effects. State explicitly that these molecular changes require replication and longer-term follow-up for clinical interpretation.", "reviewer_3": "The submitted work assessed the effects of dietary supplementation with composite polyphenols on microplastic exposure-related immune disturbances by a two-phase population trial. Overall, the 2 clinical studies were conducted rigorously, avoiding any major biases. The chosen methods are up-to -date, relevant, and well-described. The advanced statistical analyses require review by a qualified biostatistician, which I am not. The manuscript is clear, well-structured, and easy to understand, except for the difficulty in reading the figures which are very small and of poor quality (they are rarely readable even when enlarged).\nFirstly, the authors performed a pilot population study with 75 participants in low MP exposure group and 76 participants in high MP exposure group. Microplastic were quantified in feces samples by Py-GC/MS. This study provides the first population-based evidence that MP exposure is associated with decreased blood levels of Il1b, Il-2, Il-10, Il-13, monocytes and increased levels of IFN-g, cholesterol and HDL. The weight of each MP polymer on the immune-inflammatory parameters was also analyzed. Differentially expressed fecal bacterial strains and pathways, and differential blood metabolites and pathways, associated with MP exposure in this pilot population study were identified. The mechanisms potentially involved are highlighted by integrated multi-omic analysis between plasma metabolome, fecal metagenome and host phenotype. These results are extremely important because they start to fill the knowledge gap on the effects of microplastic exposure in humans directly. This type of clinical data is highly anticipated by the scientific community and by society.\nSecond, the authors performed a randomized, double-blind, placebo-controlled intervention trial to evaluate the protective effects of composite polyphenol supplementation on the adverse effects induced by microplastic exposure. As the adverse effects of MP exposure in healthy population are low, this part was really challenging but the in-depth analyses performed allowed the authors to identify Sutterella and plasma metabolite phosphatidylcholine (22:5/0:0) as key targets of mitigation of MP associated toxic effects by CP intervention.\nGlobally, the conclusions are supported by the results but I have few comments aiming to improve the manuscript.\nLines 183-205-206-207-222-223 Please correct the use of the future tense.\nLine 185: please separate better intervention from placebo materials.\nLine 243: Please remove “The sample is then left to digest for 48 hours”\nLine 245: Does using a 110-degree plate lead to the degradation and loss of some microplastics? Is this a limitation of your method?\nWhat is the size range of MP detected in feces samples? This is information that will become increasingly important as methodologies progress.\nLine 395: please define the systemic immune- inflammation index\nLine 397. The results of the figure S2 are very interesting and should be if possible explained and better highlighted.\nLines 410-411: Interestingly, following the intervention, the elevated levels of IL-6 and TG in the After-Int group were alleviated. This is true for Il-6 but not for TG. Please correct.\nLines 411-413: the data showing this statement are not provided. The statistics between After-Int and After-Pla groups are not provided. So please remove or provide statistical evidence. In addition, the target effect here is the one observed between Base-Int and After-Int, i.e. reduction of IL-1b and IL-6 levels and increase of IL-8 levels, which should be highlighted in the text. Still, based on only theses 3 significant variations attributable to CP, the title on line 408 “The CP intervention regulates the plasma cytokine levels” is not supported.\nFigure 3B and 3D. The title axis is Shannon but, in the text and legend, the authors noted observed species index and Sobs indices. Please correct.\nLines 421-422. I rather see a reduction of the Simpson index and an increase of the second alpha-diversity index. Please clarify.\nLines 428-430: why focusing on these 4 genera, are they the only ones significantly dysregulated?\nLines 443-445 and figure 3K: is the comparison between After-Pla and After-Int groups appropriate?\nFigure 4 D-E: the axis titles should be revised.\nFigure 5 caption: repetitions should be reduced.\nLine 526: Please correct metabonomic by metabolome as well as in figure 6 caption\nFigure 6 For the DEM, a similar graphical representation should be added in order to allow the reader to see if the variation is a decrease or a decrease and how strong it is. The complete name of the strains should appear in the figure\nLine 565: TG levels are increased\nLine 614: Il-8 levels are increased.\nLine 621. The increase of inflammation by MP is weak and the reversion by CP is also weak. I think that the authors should be more cautious on their interpretation of their results.\nLines 627-641. Again the authors focused on Ruminoccocus as in the results but this required further justification.\nSimilarly the reader needs to understand why the authors focused to Phocaeicola and Bacteroides for CP intervention."}, "author_response": "We appreciate very much the reviewers’ comments on our manuscript entitled \"Composite polyphenols mitigate microplastic exposure-related immune disturbances: a two-phase population trial\". Those comments are very helpful for revising and improving our paper. We have studied the comments carefully and made corrections that we hope to meet with approval. We have highlighted all changes in the manuscript and the responds to the reviewers’ comments are as follows.\nReviewer #1: This manuscript presents one of the first human studies examining the relationship between microplastic (MP) exposure and immune–metabolic disturbances, and it further tests a dietary intervention with composite polyphenols (CP) in a randomized controlled trial (RCT). The integration of metagenomic and metabolomic analyses with clinical phenotyping is ambitious and generates potentially important insights. The study is clearly written, methodologically detailed, and addresses a pressing issue in environmental health.\nThis study has several key strengths: it is the first population-based trial to test whether polyphenols can help reduce immune effects linked to microplastic exposure; it uses a multi-omics approach that combines microbiome and metabolomic data with clinical measures to give deeper insight; it includes a randomized, placebo-controlled design, which is stronger than the mostly observational studies done before; and it gives clear details on how microplastics were measured, how randomization and blinding were done, and how adherence was monitored.\nThat said, several aspects of the study design and interpretation limit the strength of the conclusions.\nIn particular, issues around exposure assessment, population representativeness, statistical rigor, and the scope of interpretation should be addressed to ensure the findings are presented with appropriate caution.\nResponse:\nThank you for your comments. We sincerely appreciate your time and effort in reviewing our manuscript, as well as your positive evaluation of our work. We have carefully revised the manuscript accordingly, and our detailed point-by-point responses are provided below.\nMajor Concerns:\n1. Comments:\nExposure assessment: MPs were quantified in feces using Py-GC/MS, but fecal levels may not reflect systemic burden; tissue retention could differ in type and concentration. The relatively high concentrations reported compared with previous studies may reflect methodological differences, which complicates cross-study comparisons. The calculation of estimated daily intake (EDI) from fecal data is speculative.\nResponse:\nThank you for your insightful comments regarding the exposure assessment in our study. We appreciate the opportunity to address your concerns.\nFirstly, you rightly pointed out that microplastics (MPs) quantified in feces may not fully reflect the systemic burden, as tissue retention could differ in type and concentration. We agree that this is an important consideration. Indeed, MPs have been detected in various human tissues and body fluids, including testes, brain, blood, and semen, and the types and concentrations retained in tissues may indeed differ from those excreted in feces[1].\nResearch suggests that oral intake is the primary route of human exposure to MPs[2]. MPs degraded by digestive enzymes may become more hydrophilic and subsequently be excreted via feces[3].\nFurthermore, due to the inherent biological inertness of most polymers, a significant portion of ingested MPs may pass through the gastrointestinal tract unchanged and be directly eliminated in feces. MPs entering the body through non-oral routes have also been observed to translocate to the digestive tract via biliary secretion or intestinal wall permeation, ultimately leading to fecal excretion. Therefore, to a certain extent, MPs in feces could reflect the level of human intake[4].\nMoreover, due to ethical constraints, obtaining samples from other human tissues through invasive procedures is not feasible. Among the samples that can be collected non-invasively (such as urine, sputum, and feces), fecal samples offer notable advantages: the high detection rate of MPs (95.8% in our study) and the feasibility of both quantitative and qualitative analyses make fecal detection an effective approach for reflecting the types and relative abundance of MPs to which humans are actually exposed. Therefore, the measurement of MPs in feces is commonly used to assess internal exposure in population studies [5-6]. Importantly, since our research aims to investigate the effects of MPs on the intestinal tract and the gut microbiota, and employs polyphenols as an intervention whose metabolism primarily occurs in the intestine, fecal MP levels may provide the most appropriate and biologically relevant metric for assessing internal exposure in this context.\nSecondly, you also noted that the concentrations of MPs reported in our study are relatively high compared to previous studies. We acknowledge that such discrepancies may arise from differences in methodological approaches, which may complicate cross-study comparisons. The differences brought about by this methodology may stem from two aspects: one is the disparity in sample pretreatment methods, and the other is the variance in sample selection. Therefore, to clarify the comparison of MP internal exposure levels among different studies, we have revised the \"Discussion\" section of this manuscript: \"This discrepancy may be attributed to differences in MPs’ detecting methods. Specifically, the units used for quantifying MP detection levels differed. Song et al. expressed the concentration of MPs per gram of wet fecal weight, while our study reported MPs per gram of dry fecal weight. The fecal water content varies among populations consuming different dietary patterns. Given that the average fecal water content in humans is approximately 74.6%, this discrepancy may be a primary contributor to the elevated values observed in our study[29]. Secondly, Song et al. utilized organic extraction to isolate MPs from fecal samples, whereas our study adopted nitric acid digestion and concentration. Additionally, the limited sample size of the study by Song et al. (26 individuals per region) compared to our 151 participants may contribute to the different findings. Thus, the variability in microplastic detection methodologies complicates inter-study comparisons. There is an urgent need to develop standardized protocols for microplastic quantification to ensure reliable cross-study validation.\" (Page: 22, Line: 624)\nRegarding the relatively high concentrations you mentioned compared to previous studies, we speculate that this might be due to geographical location and lifestyle factors, which result in a higher intake of MPs. Therefore, we have added the following content in the \"Discussion\" section of this manuscript: \"These data reveal that the MPs' exposure burden in the Shanghai population is significantly elevated compared to the Changsha and Changchun cohorts. This observation is strongly correlated with regional geographic and dietary factors[30]. Geographically, Shanghai's estuarine location confers an increased risk of exposure to marine MPs reservoirs, contrasting with the inland setting of Changsha and Changchun. Dietarily, the established bioaccumulation of MPs in seafood, coupled with the high aquatic product intake by Shanghai residents, presents a compounding exposure risk that likely accounts for the observed disparity in exposure levels[31].\" (Page: 22, Line: 637)\nFinally, we recognize that the method of calculating the estimated daily intake (EDI) based on fecal data is to some extent speculative, but it represents a preliminary attempt to estimate human exposure doses of MPs. EDI is typically estimated based on the concentration of specific substances in food, dietary intake, and body weight, which inherently involves a certain degree of uncertainty. For MPs, due to the absence of standardized detection methods across existing studies, accurate quantitative data on MPs in different foods are currently lacking. In our research, it is relatively difficult to measure MP concentrations in all consumed foods over a given period for all subjects. Therefore, using fecal MP concentrations is a relatively appropriate alternative method.\nThe methods employed in our manuscript are based on the exposure assessment methods used in the previous research[5-6]. Specifically, the concentrations of MPs detected in the infant feces are extrapolated to represent their dietary exposure doses. This method is not solely utilized in studies concerning the assessment of MP exposure; it is also applied to other environmental pollutants, such as phthalates (PAEs)[7]. Consequently, we posit that, despite being a conjectural conclusion, this aspect of the results maintains a certain degree of significance for referencing the evaluation of population exposure levels to MPs. In our future work, we aim to refine and enhance the methodologies for assessing MP exposure doses. In light of your suggestion, we have incorporated the aforementioned content into the \"Method\" section: \"The estimated daily intake (EDI, μg/kg-bw/d) was extrapolated from MP concentrations in fecal matter, and this estimation should be considered with caution due to its speculative nature.\" (Page: 7, Line: 274)\nReference:\n1. Zuri G, Karanasiou A, Lacorte S. Human biomonitoring of microplastics and health implications: A review. Environ Res. 2023 Nov 15;237(Pt 1):116966.\n2. Zuri G, Karanasiou A, Lacorte S. Microplastics: Human exposure assessment through air, water, and food. Environ Int. 2023 Sep;179:108150.\n3. Wu P, Lin S, Cao G, et al. Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications. J Hazard Mater. 2022 Sep 5;437:129361.\n4. Zhu L, Wu Z, Dong J, et al. Unveiling Small-Sized Plastic Particles Hidden behind Large-Sized Ones in Human Excretion and Their Potential Sources. Environ Sci Technol. 2024 Jul 9;58(27):11901-11911.\n5. Zhang N, Li YB, He HR, et al. You are what you eat: Microplastics in the feces of young men living in Beijing. Sci Total Environ. 2021 May 1;767:144345.\n6. Ke D, Zheng J, Liu X, et al. Occurrence of microplastics and disturbance of gut microbiota: a pilot study of preschool children in Xiamen, China. EBioMedicine. 2023 Nov;97:104828.\n7. Hua L, Guo S, Xu J, et al. Phthalates in dormitory dust and human urine: A study of exposure characteristics and risk assessments of university students. Sci Total Environ. 2022 Nov 1;845:157251.\n2. Comments:\nPopulation representativeness: The study cohort was young (median age 24), largely healthy, and predominantly female. This narrow profile restricts the generalizability of findings to the broader population. Extensive exclusion criteria (smoking, alcohol use, chronic diseases, supplement use) create a homogeneous group but also one that does not fully reflect real-world vulnerability.\nResponse:\nWe sincerely thank the reviewer for this insightful comment. We fully acknowledge that the relatively young, healthy, and predominantly female composition of our study cohort.\nThis population characteristic is a direct result of conducting recruitment within a university campus environment. We chose this setting for several methodological advantages that enhance the study's internal validity. They are generally healthy, which reduces the influence of pre-existing conditions, and demonstrate high adherence to study protocols. Furthermore, confounding factors like education and socioeconomic status are more controlled in this group. Many clinical intervention studies choose college students as their research subjects[1-3].\nWe agree with the reviewer that the high proportion of female participants is a notable limitation. This imbalance likely reflects a common phenomenon whereby female students might show a greater propensity to volunteer for nutritional intervention studies. This certainly impacts the extent to which our results can be generalized. Therefore, we have revised the \"Limitations\" section in our manuscript to state this more explicitly: \"Additionally, for the RCT study, the intervention period was relatively short (28 days), the sample size was relatively limited (101 participants), and the participants were generally young, in good health, and predominantly female. This might account for the lack of significant changes observed in other clinical indicators, which limited the depth of our analysis and might also restrict the general applicability of the research results.\" (Page: 28, Line: 915)\nSecondly, we agree that our stringent exclusion criteria—prohibiting smoking, alcohol use, chronic diseases, and recent supplement intake—led to a highly homogeneous sample, which may limit the direct applicability of our findings to more heterogeneous, real-world populations. These criteria were implemented primarily to control for confounding variables and enhance internal validity, though we recognize this comes at the cost of reduced external validity.\nFor instance, to isolate the specific effects of our intervention from those of other dietary aids, it is imperative to exclude individuals with a history of recent supplement use. Numerous nutritional supplements, particularly protein supplements, have been shown to significantly alter gut microbiota composition[4]. Similarly, supplements containing probiotics or prebiotics are known to modulate intestinal flora[5]. Likewise, cigarette smoke exerts immunosuppressive effects and disrupts immune-inflammatory systems[6, 7]. Constituents of tobacco, such as nicotine, can directly reach the gastrointestinal tract and interact with the gut microbiota. These components induce persistent alterations in microbial composition and function[8], while potentially perturbing bile acid metabolism, activating the MAPK/ERK signaling pathway, and compromising gut barrier function [9]. Furthermore, alcohol-induced microbial shifts can deprive the host of the protective effects conferred by physiological gut microbiota[10], increase intestinal permeability[11], and elicit pro-inflammatory responses[12, 13]. Finally, the well-established association between chronic diseases and low-grade inflammation[6] necessitates the exclusion of individuals suffering from such conditions to prevent potential confounding of our results. Therefore, the exclusion of these factors is essential to ensure the acquisition of clear and interpretable data signals. By controlling for these potent variables, we aim to minimize experimental error and bolster confidence in the validity of our conclusions.\nAddressing the above important limitation will be a key priority for our future research, where we aim to include older individuals, those with comorbid conditions, and a more balanced distribution of sexes to improve the translational relevance of our results.\nReference:\n1. Kypri K, Vater T, Bowe SJ, et al. Web-based alcohol screening and brief intervention for university students: a randomized trial. JAMA. 2014 Mar 26;311(12):1218-24.\n2. Lai H, Li Y, He Y, et al. Effects of dietary fibers or probiotics on functional constipation symptoms and roles of gut microbiota: a double-blinded randomized placebo trial. Gut Microbes. 2023 Jan-Dec;15(1):2197837.\n3. Galante J, Dufour G, Vainre M, et al. A mindfulness-based intervention to increase resilience to stress in university students (the Mindful Student Study): a pragmatic randomised controlled trial. Lancet Public Health. 2018 Feb;3(2):e72-e81.\n4. Blakeley-Ruiz JA, Bartlett A, McMillan AS, et al. Dietary protein source alters gut microbiota composition and function. ISME J. 2025 Jan 2;19(1):wraf048.\n5. HHughes RL, Holscher HD. Fueling Gut Microbes: A Review of the Interaction between Diet, Exercise, and the Gut Microbiota in Athletes. Adv Nutr. 2021 Dec 1;12(6):2190-2215.\n6. Chen J, Wang X, Schmalen A, et al. Antiviral CD8+ T-cell immune responses are impaired by cigarette smoke and in COPD. Eur Respir J. 2023 Aug 3;62(2):2201374.\n7. Awad W, Mayall JR, Xu W, et al. Cigarette smoke components modulate the MR1-MAIT axis. J Exp Med. 2025 Feb 3;222(2):e20240896.\n8. Fan J, Zhou Y, Meng R, et al. Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study. BMC Med. 2023 Apr 28;21(1):163.\n9. Bai X, Wei H, Liu W, et al. Cigarette smoke promotes colorectal cancer through modulation of gut microbiota and related metabolites. Gut. 2022 Dec;71(12):2439-2450.\n10. Sosnowski K, Przybyłkowski A. Ethanol-induced changes to the gut microbiome compromise the intestinal homeostasis: a review. Gut Microbes. 2024 Jan-Dec;16(1):2393272.\n11. Yan X, Wang Y, Ren XY, et al. Gut dysbiosis correction contributes to the hepatoprotective effects of Thymus quinquecostatus Celak extract against alcohol through the gut-liver axis. Food Funct. 2021 Oct 19;12(20):10281-10290.\n12. MMartino C, Zaramela LS, Gao B, et al. Acetate reprograms gut microbiota during alcohol consumption. Nat Commun. 2022 Aug 8;13(1):4630.\n13. Parker R, Kim SJ, Gao B. Alcohol, adipose tissue and liver disease: mechanistic links and clinical considerations. Nat Rev Gastroenterol Hepatol. 2018 Jan;15(1):50-59.\n3. Comments:\nIntervention trial: The 28-day trial duration is too short to evaluate sustained effects of polyphenols on MP-related health outcomes. The sample size (~100) is modest, limiting power for subgroup and multi-omics analyses.\nResponse:\nWe sincerely thank the reviewer for their insightful comments on the trial duration and sample size. These specific parameters were determined based on practical constraints, including resource availability and feasibility for an initial investigation. We fully acknowledge that the 28-day intervention is a limitation for observing long-term health outcomes. To address this, we proactively increased the daily polyphenol dosage within the safe range to maximize the potential for a detectable effect within the study period. It should be noted that a larger or longer study would have incurred prohibitive costs, and we chose to invest our resources in deep, mechanistic analyses (such as multi-omics) rather than spreading them more thinly across a larger cohort. Furthermore, the 28-day timeframe was chosen to promote high participant compliance and minimize the risk of attrition, which often compromises longer-term studies.\nDue to the high costs associated with conducting RCT, most of the similar studies have relied on smaller cohorts (n < 20) [1-2]. Although some of these studies adopted crossover designs (such designs are not suitable for this investigation because the prolonged effects of polyphenols may persist into the placebo phase and bias the results), their theoretical sample sizes were still smaller than that of our study[3]. During the research design phase, we estimated the required sample size using PASS 2025 software, which yielded a target of 48 participants. However, due to limited data in the current literature regarding the specific outcomes and effect indicators of MPs on human immune function, key parameters could only be approximated (Power = 0.8, Alpha = 0.05, Superiority Difference = 0.05, Expected Effect Size = 0.2, Allocation Ratio = 0.8, Dropout Rate = 0.1). Consequently, to maintain scientific rigor and transparency, we decided not to include the sample size calculation in the manuscript. Therefore, considering both budget constraints and the need for more reliable outcome detection, we ultimately enrolled 50 participants. Most importantly, we acknowledge the validity of your observation: a larger sample size would be necessary to adequately account for inter-individual variability. While our current sample size is sufficient for analyzing primary outcomes, it does impose limitations on statistical power when conducting more granular analyses, such as subgroup comparisons.We have explicitly acknowledged this limitation in the revised manuscript.\nIn this situation, although a 28-day trial period and the selection of 50 participants in each group are reasonable, they do indeed limit the implementation of subgroup analysis and multi-omics analysis. To ensure transparency, we have now added discussion of these limitations to the manuscript:\n\"Additionally, no significant improvements were observed in plasma CHO and LDL indicators following CP intervention. This weakens the interpretability of these findings, which may potentially be attributed to the relatively brief duration of our intervention.\" (Page: 23, Line: 696);\n\"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896)\nReference:\n1. van Galen KA, Schrantee A, Ter Horst KW, et al. Brain responses to nutrients are severely impaired and not reversed by weight loss in humans with obesity: a randomized crossover study. Nat Metab. 2023 Jun;5(6):1059-1072.\n2. Basolo A, Hohenadel M, Ang QY, et al. Effects of underfeeding and oral vancomycin on gut microbiome and nutrient absorption in humans. Nat Med. 2020 Apr;26(4):589-598.\n3. Dijkgraaf MGW, Haukoos J, Itani KMF. Practical Guide to Design Choice of Randomized Clinical Trials in Surgery. JAMA Surg. 2022 Dec 1;157(12):1154-1155.\n4. Comments:\nStatistical analysis: The handling of multiple testing in large omics datasets is not clearly described. Without appropriate correction, the risk of false positives is high.Some baseline imbalances (e.g., higher IL-6 and triglycerides in the intervention group) complicate interpretation of intervention effects. Many results are reported primarily through p-values. Greater emphasis on effect sizes and clinical relevance would improve interpretation.\nResponse:\nWe thank the reviewer for these critical and constructive comments regarding the statistical analysis.\nFor the statistical methods in the omics analysis section, we first used the Wilcoxon rank sum test to analyze the differences between H-MPs and L-MPs in the baseline population. For the RCT population, the Kruskal-Wallis test was used to analyze the differences among the four groups (the pre-intervention and post-intervention groups of the placebo group and the intervention group), and then the Tukey-Kramer post hoc test was conducted. The above content has been revised simultaneously in the section \"Qualification and statistical analysis\" of the manuscript: \"In phenotypic and omics analyses, significant differences between the two groups were analyzed using independent sample T-tests or Wilcoxon rank-sum tests. Significant differences among multiple groups were analyzed using one-way ANOVA or Kruskal-Wallis test, followed by Tukey-Kramer post hoc tests.\" (Page: 9, Line: 347)\nSecondly, in the metagenomic analysis, the Wilcoxon rank sum test revealed that there were 23 significantly different bacterial genera between the H-MPs group and the L-MPs group (P < 0.05). However, after false discovery rate (FDR) correction (using the Benjamini-Hochberg method), the P-FDR of these 23 significantly different genera were not significant. Therefore, in the analysis of metagenomic data, in order to identify the significantly different bacteria between different groups while reducing the risk of false positives, we added the results of LEfSe differential discrimination analysis (Figure S4E and S4F). For plasma metabolomics, based on your suggestion, we used the FDR-corrected P-value instead of the original P-value to screen the differential metabolites between different groups. The detailed criteria are as follows: P-FDR < 0.25 and VIP > 1. The above content has been revised simultaneously in the \"Qualification and statistical analysis\" section of the manuscript: \"In phenotypic and omics analyses, significant differences between the two groups were analyzed using independent sample T-tests or Wilcoxon rank-sum tests. Significant differences among multiple groups were analyzed using one-way ANOVA or Kruskal-Wallis test, followed by Tukey-Kramer post hoc tests.\" (Page: 9, Line: 347); \"The alpha diversity was evaluated by the calculated Shannon and Simpson indices. The beta diversity was assessed using permutational multivariate analysis of variance (PERMANOVA) based on the Bray-Curtis distances. Linear discriminant analysis effect size (LEfSe) was used to identify the differentially abundant taxa. For plasma metabolomics, the ropls package (Version 1.6.2) in R was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) on the preprocessed data matrix. Significant differential metabolites were screened based on variable importance in projection (VIP) values and Student’s t-test from the OPLS-DA model. Differential metabolites were screened using the criteria: P-FDR < 0.25, and VIP > 1. Differential metabolites were annotated using the KEGG database (https://www.kegg.jp/kegg/pathway.html) for metabolic pathway enrichment analysis. The gut microorganisms with P < 0.05, as well as the plasma metabolites with P-FDR < 0.25 and VIP > 1, were used for subsequent correlation analysis. Pathway enrichment analysis was performed using the Python package scipy.stats, and Fisher’s exact test was used to identify the most relevant biological pathways associated with the experimental treatment. Spearman’s correlation coefficients were calculated to assess correlations between metabolites, species, and phenotypes, with coefficients > 0.2 and P < 0.05 considered significant. For omics research, the P-values were all corrected using FDR method (Benjamini & Hochberg). All other statistical analyses with P < 0.05 were considered statistically significant. Effect sizes (Cohen’s d method) were presented along with statistical significance.\" (Page: 9, Line: 360);\n\"For the FSBPs and RCT populations, the taxonomic composition of the gut microbiota is presented in Figure S4A and S4B. At the genus level, attention was paid to the significantly differentially expressed genera with the highest relative abundance. Figure S4C shows that the relative abundance of Ruminococcus and Escherichia in the H-MPs was significantly lower than that in the L-MPs; However, there was no significant difference after false discovery rate (FDR) correction. The LEfSe analysis also revealed that the Ruminococcus and Escherichia were the two bacterial genera that had the greatest impact on the differential effects between the H-MPs and L-MPs groups (Figure S4E). Furthermore, Figure S4D indicates that the relative abundances of bacterial genera such as Bacteroides, Faecalibacterium, and Phocaeicola significantly differed among the four groups. Further multiple comparisons revealed that the After-Int group had a significantly higher relative abundance of Faecalibacterium and Phocaeicola compared to the Base-Int group, whereas no significant difference in Bacteroides abundance was observed between these two groups (Figure S4G-I). The LEfSe analysis revealed that the Foetidibacter had the greatest impact on the differential effects between the four groups (Figure S4F).\" (Page: 15, Line: 458)\nThe most important part is the multi-omics combined analysis section. We used a stepwise dimension reduction method to screen out potential key bacteria and metabolites. In the metagenomic analysis, the Wilcoxon rank sum test revealed that there were 507 significant differences in bacterial genera between the H-MPs group and the L-MPs group (P < 0.05). However, after false discovery rate (FDR) correction (using the Benjamini-Hochberg method), the P-FDR of these 507 significant differences in genera was all > 0.7. The possible reason might be that when annotating the metagenomic data to the \"species\" level, the types of intestinal microorganisms can reach over ten thousand. And the P values of the significant differences in bacteria between the H-MPs group and the L-MPs group were not particularly low, which might lead to the P values of the significant differences becoming very large after correction.\nOur previous research also found this phenomenon[1]. Furthermore, for the significantly different bacterial species mentioned above, we will perform subsequent dimensionality reduction through the following steps: (1) conducting Spearman correlation analyses among the significantly altered bacterial species, metabolites, and host phenotypes, using a correlation coefficient (r) >0.2 and P < 0.05 as the screening criterion; and (2) integrating common correlation networks identified in both the baseline population and the RCT population. Therefore, in the first step of the metagenomic data analysis, as mentioned above, the P values of the significant differences in bacteria between the H-MPs group and the L-MPs group after FDR correction were all greater than 0.7. This led to our inability to perform the subsequent dimension reduction steps. Moreover, considering that in this step our aim was to screen out the bacteria with more significant relative differences under high exposure to MPs from the over ten thousand bacterial species annotated in the baseline population, for subsequent analysis steps. Therefore, in the multi-omics combined analysis process, we did not perform FDR correction for the metagenomic data. For the serum metabolome, the updated screening criteria were also P-FDR < 0.25 and VIP > 1, and a total of 144 significant difference metabolites were screened out.\nBased on your comments and the above content, the \"Results\" sections of this manuscript have been re-written as follows: \"Furthermore, significant alterations were observed in the abundance of 507 gut microbial species between the H-MPs and L-MPs groups; However, there was no significant difference after false discovery rate (FDR) correction.\" (Page: 15, Line: 473)\nRegarding the phenomenon of baseline imbalances, we acknowledge that the baseline imbalances in certain parameters (e.g., IL-6 and triglycerides) pose a challenge for interpreting the intervention effects. As this was an observational baseline characteristic in our recruited cohort, we could not intervene to alter this distribution post-randomization. To reduce the impact of this cause in the analysis, we calculated the difference between the post-intervention and pre-intervention to analyze these outcome measures: \"The pre-post differences within each group were calculated, and significant differences in change values between the two groups were analyzed using independent samples t-tests or Wilcoxon rank-sum tests.\" (Page: 9, Line: 350)\nRegarding the results report section. We believe that your suggestion that the results should not be reported solely based on the P-value is very reasonable. The results should not only focus on whether there is a significant impact, but also on how significant this impact is. Effect sizes can compensate for the result misjudgments caused by over-reliance on the P-value. Therefore, we have revised the results section of the manuscript to ensure that effect sizes will be presented prominently along with statistical significance: \"All other statistical analyses with P < 0.05 were considered statistically significant. Effect sizes (Cohen’s d method) were presented along with statistical significance.\" (Page: 9, Line: 376) At the same time, we agree with your point that the importance of effect size and clinical relevance cannot be overstated. Therefore, based on your opinion and the modifications made to the above results, we have revised and expanded the discussion in this section, providing a more in-depth interpretation of the key correlations discovered, and not just focusing on the P-value: \"Interestingly, following the intervention, the elevated levels of IL-6 in the After-Int group were alleviated, with a notably substantial effect size (P < 0.05, effect size = -0.576). Compared with the placebo group, the levels of proinflammatory cytokines (IL-1β, IL-6, and IL-8) in the intervention group were significantly reduced following the intervention (P < 0.05), with effect sizes of -0.463, -0.576, and -0.529, respectively.\" (Page: 12, Line: 438)\nThank you again for these valuable suggestions, which have substantially improved the rigor and clarity of our manuscript.\nReference:\n1. Liu S, Liu X, Guo J, et al. The Association Between Microplastics and Microbiota in Placentas and Meconium: The First Evidence in Humans. Environ Sci Technol. 2023 Nov 21;57(46):17774-17785.\n5. Comments:\nInterpretation: Some discussion points overstate causality (e.g., MPs \"leading to\" neurodegenerative pathways). These should be presented as associations or hypotheses. Proposed mechanistic chains (e.g., Sutterella abundance → lipid metabolism changes → systemic inflammation) remain speculative and should be presented as such. Statements about CP as a \"protective strategy\" are premature given the short timeframe, modest effect sizes, and absence of clinical endpoints.\nResponse:\nWe thank the reviewer for this valuable feedback concerning the interpretation of our results. We fully agree that the expression of the observed relationships in our study should be more precise and better reflect the correlational and exploratory nature of our research. In response to this comment, we have carefully revised the discussion section to ensure greater clarity and caution in interpreting findings, explicitly avoiding any language that might imply a direct causal relationship.\nFirstly, during the revision process, we adopted a more stringent statistical approach for the microbiome analysis to address your concern in Comments 4. Under this optimized analytical framework, the significance of Sutterella diminished, while Staphylococcus was identified as the primary target genus. To ensure our conclusions are built on the most robust evidence, we have shifted our focus to Staphylococcus in the revised manuscript.\nSecondly, regarding your points of \"MPs leading to neurodegenerative pathways\" and \"Staphylococcus abundance → lipid metabolism changes → systemic inflammation\", we have rephrased the \"Abstract\" and \"Discussion\" sections to reduce the emphasis on the causal relationships described therein: \"The gut microorganism Staphylococcus and the plasma metabolite PC (22:5/0:0) were identified as relatively important potential targets in this protective effect.\" (Page: 1, Line: 27); \"Notably, it could be speculated that intestinal Staphylococcus genus and plasma metabolite PC (22:5/0:0) were the important targets for CP interventions to mitigate the toxic effects associated with the high MP exposure, which deserve further research validation.\" (Page: 27, Line: 878); \"These results suggest that exposure to higher MPs may be associated with alterations in gut microbiota function and the development of various cognitive-related diseases.\" (Page: 24, Line: 720); \"Therefore, this study speculates that MP exposure may disrupt lipid and amino acid metabolism by altering the composition of gut microbiota, leading to the down-regulation of metabolites associated with the above pathways (primarily PC (22:5/0:0)). This disruption may be related to an imbalance in lipid metabolism and the promotion of systemic inflammation.\" (Page: 26, Line: 822)\nThirdly, we have toned down the conclusive language regarding the protective strategy of the intervention. The relevant statements in the \"Discussion\" section of this manuscript have been revised to clarify that, while our results show promising associative changes, the short-term duration, modest effect sizes, and lack of direct clinical endpoints mean that these findings are preliminary: \"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896) It is also described in the section on \"Limitations\": \"Firstly, in the cross-sectional baseline study, measuring exposures and outcomes simultaneously prevents establishing temporality, thus precluding causal inference. The results obtained from strictly controlled RCT studies may have limited external validity. The potential intervention targets identified through correlation analysis are also speculative and still require further validation via in vivo or in vitro experiments.\"(Page: 28, Line: 911)\nFurthermore, we also made careful revisions to other parts of the manuscript to avoid using causal language throughout the entire discussion. Specifically, we have replaced phrases such as \"leading to\" with more accurate terms like \"associated with,\" \"may contribute to,\" or \"could suggest a potential link\". For the unproven mechanism pathways proposed in the manuscript, the descriptive sections all indicated their speculative nature and were presented as a model for generating hypotheses for future research.\nThese changes have been implemented in the relevant sections of the manuscript to ensure our interpretations are appropriately cautious and aligned with the evidence presented.\nMinor Concerns\n1. Comments:\nThe description of CP composition is inconsistent across sections (five components vs. inclusion of tannic acid) and should be standardized.\nResponse:\nThank you for your suggestion. We apologize for the inconvenience caused to you due to the non-standard description of the CP composition in the manuscript. Based on your suggestions, we have revised and standardized the description of the CP composition throughout the text to ensure that the aforementioned issue does not occur again: \"The main active components of the CP intervention agent include a mixture of 44 types of polyphenols.\" (Page: 5, Line: 187) The formulations of the CP were detailed in Table S1.\n2. Comments:\nSome figures and tables are overly dense (e.g., volcano plots, KEGG enrichment) and would benefit from simplification, with secondary analyses moved to supplementary material.\nResponse:\nThank you for your comments. We have simplified our figures as per your request. Specifically, we have moved the parts that play a relatively minor role in the results of this manuscript, such as the Taxonomic composition and relative abundance of gut microbiota and volcano plots, to the supplementary materials. (Figure S4 and Figure S6) We believe that the revised figures can more clearly present the main results of this study and be more convenient for readers to read.\n3. Comments:\nCytokine data reported as quartiles obscure potentially important changes; mean ± SD or effect size plots might better illustrate findings.\nResponse:\nWe sincerely thank the reviewer for this insightful comment and valuable suggestion. In our initial analysis, we found that the cytokine data violated the assumptions of normality. Consequently, we chose to report the data as median and interquartile range (IQR) in the main text, as this is a more robust and non-parametric method for describing such data [1].\nHowever, we fully acknowledge the reviewer's point that supplementing this with mean ± standard deviation (SD) or effect size can provide a complementary perspective on the data's central tendency and spread. The aforementioned Comment 3 also suggests that the use of P-values in conjunction with effect sizes is necessary to improve the interpretation of the results. In direct response to this comment, we have now added a new table (Table S8) to the Supplementary Materials. This table presents all cytokine data as Mean ± SD, providing a clear visual representation of the findings. Furthermore, for the statistical analysis results of health outcomes across multiple groups, we also included the results of effect sizes (Table 1 and S8).\nWe believe these additions significantly enhance the transparency and clarity of our results, and we thank the reviewer for prompting this improvement.\nReference:\n1. Holmes, DT. Chapter 2 - Statistical methods in laboratory medicine. Contemporary Practice in Clinical Chemistry (Fourth Edition). 2020; 15-35. ISBN 9780128154991.\n4. Comments:\nThe manuscript is generally well written, but certain sections are redundant and could be streamlined for clarity and balance.\nResponse:\nThank you for this helpful feedback. We agree that improving the clarity and balance of the manuscript is important. In response to your suggestion, we have carefully reviewed the text and streamlined several sections to reduce redundancy and improve overall conciseness. Specific changes include eliminating repetitive statements (such as the uniformity of CP components, the elaboration of results, the Note description of the chart, and the presentation method of Figure 6), and ensuring a more logical flow between ideas (such as the caption of Figure 5). We believe these revisions have significantly enhanced the readability and focus of the manuscript.\nReviewer #2: This manuscript addresses an important and timely question: the population-level health impacts of microplastics (MP) exposure and a potential dietary mitigation strategy using a composite polyphenol (CP) formulation. The combination of a pilot population study, a randomized double-blind placebo-controlled trial, and an integrated multi-omics approach is innovative and of high potential impact. However, several important methodological clarifications and additional analyses/interpretation are required before the manuscript is acceptable for publication.\nResponse:\nThank you for your comments. We really appreciate your efforts in reviewing our manuscript. We have revised the manuscript accordingly. Our point-by-point responses are detailed below.\n1. Comments:\nThe manuscript states CP consists of five compounds (quercetin, C3G, gallic acid, rutin, ellagic acid), yet Supplementary Table S1 lists botanical extracts (Goji Berry Extract, Hawthorn Flower Extract, Wild Cherry Berry Extract, Dendrobium officinale Flower Extract, Pomegranate Extract). Please provide a detailed, quantitative breakdown of the CP product:\na. mg of each extract per daily dose and the percentage (or mg) of each named polyphenol per gram of extract and per daily dose.\nb. Information on standardization (e.g., percent total polyphenols, HPLC/LC–MS quantification certificates) and batch-to-batch variability.\nc. A full list of other abundant bioactive compounds in these extracts (other polyphenols, sugars, fibers, alkaloids, etc.) and their approximate abundances or whether such analyses were performed.\nWithout this information it is impossible to attribute effects to the named polyphenols specifically, to replicate the study, or to compare dosing to prior literature.\nResponse:\nWe sincerely thank the reviewer for this insightful comment and for highlighting this crucial omission. We agree that a precise quantitative description of the investigational product is essential. We have now refined the description of the CP intervention agent, comprehensively revised Supplementary Table S1, and provided detailed explanations below to address your comments.\na. Quantitative Breakdown of Polyphenols per Daily Dose\nThe composite polyphenol (CP) supplement is indeed a blend of the five specified botanical extracts. As per the reviewer's request, we conducted UPLC/MS analysis on the Intervention Agent. Based on the above results, in the Table S1 of the Supplementary Material , we have described the types of polyphenols that may be contained in the CP intervention agents and the total polyphenol mass. (Table S1) We have also refined and standardized the descriptions of the CP intervention agents in our manuscript: \"The main active components of the CP intervention agent include a mixture of 44 types of polyphenols. The CP intervention agent is powders encapsulated in sachets, each containing 155 mg CP. The dosage was three times daily, with a total intake of 465mg of CP.\" (Page: 5, Line: 187)\n[redacted]\nb. Standardization and Quality Control\nWe conducted UPLC/MS analysis on the Intervention Agent. The UPLC analysis was performed using an Agilent 1290 UPLC system (California, USA) combined with an Agilent Q-TOF 6545 LC/MS system, a sample manager, a PDA detector, and a binary solvent manager, and was controlled using MassHunter Workstation Software. We detected a mixture of 44 types of polyphenols in each package of the CP intervention agent. Each package of CP intervention agent contains 155mg of total polyphenols. These information have been revised in the \"Methods\" section of this manuscript as mentioned above. (Page: 5, Line: 187) [redacted]\nc. Other bioactive compounds\nTo configure the CP intervention agent, based on previous studies, we selected plant extracts rich in polyphenolic bioactive compounds, including Sophora flower extract, Phyllanthus emblica extract, Aronia melanocarpa extract, Dendrobium officinale flower extract, and red pomegranate extract[1-5]. However, due to the constraints of commercial secrets, the specific formulas of these extracts cannot be presented in the main text of the article. Therefore, we detected all possible polyphenolic substances in the intervention agent through UPLC/MS analysis.\n[redacted]\nThe complete list of the 44 polyphenols detected in the intervention agent and the excipients it cont ains is presented in Table S1. (Table S1) We acknowledge that the intervention agent may contain tr ace amounts of a variety of other bioactive compounds. In light of your comments and the aforemen tioned content, we have supplemented the description in the \"Methods\" and \"Limitations\" sections of this manuscript: \"The main active components of the CP intervention agent include a mixture of 44 types of polyphenols. The CP intervention agent is powders encapsulated in sachets, each contai ning 155 mg CP.\" (Page: 5, Line: 187); \"Finally, apart from the 44 kinds of polyphenols identified b y UPLC/MS, there might be trace amounts of other bioactive substances in the intervention agent th at could potentially influence the results.\" (Page: 28, Line: 924)\nWe hope that these additional details and the revised supplementary tables will address the reviewers' concerns. Now, the observed effects in our study should mainly be attributed to the specific doses of polyphenols, which enables repeat experiments for verification and comparison with other studies.\nReference:\n1. Ma Y, Zhang C, Li J, et al. Inhibitory mechanism on tyrosinase activity of flavonoids from flower buds of Sophora japonica L. Heliyon. 2024 Sep 21;10(19):e38252.\n2. Saini R, Sharma N, Oladeji OS, et al. Traditional uses, bioactive composition, pharmacology, and toxicology of Phyllanthus emblica fruits: A comprehensive review. J Ethnopharmacol. 2022 Jan 10;282:114570.\n3. Sidor A, Gramza-Michałowska A. Black Chokeberry Aronia melanocarpa L.-A Qualitative Composition, Phenolic Profile and Antioxidant Potential. Molecules. 2019 Oct 15;24(20):3710.\n4. Zhao M, Fan J, Liu Q, et al. Phytochemical profiles of edible flowers of medicinal plants of Dendrobium officinale and Dendrobium devonianum. Food Sci Nutr. 2021 Oct 4;9(12):6575-6586.\n5. Skrt M, Albreht A, Vovk I, et al. Extraction of Polyphenols and Valorization of Fibers from Istrian-Grown Pomegranate (Punica granatum L.). Foods. 2022 Sep 6;11(18):2740.\n2. Comments:\nExplain why 15 g/day was chosen (e.g., manufacturer recommendation, prior human clinical data, preclinical efficacy/tolerability data, or pilot tolerability studies) and provide supporting references or pilot data. If the dose is based on the manufacturer’s recommended serving, state this explicitly.\nResponse:\nWe thank the reviewer for this important question regarding our dosage selection. The chosen dose was determined through a combination of factors, as detailed below:\nThe 15 g/day dose mentioned in the previous manuscript refers to the total amount of the intervention product administered to participants, which was taken three times a day, with each dose being one sachet of 5 g. The scientific rationale for this dose was based on its content of active compounds (mixed polyphenols). We conducted UPLC/MS analysis on the intervention product and determined that each sachet of 5 g contained 165 mg of polyphenols. That is, the 15 g of daily dosage can provide 465 mg of mixed polyphenols.\nOur selection of the 465 mg/day target dose for mixed polyphenols was a deliberate decision based on several factors. First, we referred to the daily minimum intake recommended by the manufacturer, which is 2 sachets, containing 310 mg of polyphenols. This figure has been generally regarded as safe. However, to ensure a dose robust enough to produce an observable and clinically meaningful effect within our 28-day intervention period, we also reviewed the existing clinical literature. This review confirmed that polyphenol doses exceeding 1000 mg/day have been safely administered in human trials while demonstrating significant physiological effects[1,2]. Therefore, in consultation with the manufacturer, we selected a target dose of 465 mg/day. This dose was determined to be optimal, as it is higher than the minimum recommended (310 mg/day), which has the potential to enhance the beneficial effects, while remaining well within the established safety margins identified in the literature.\nIn summary, the intervention dose was not arbitrary. It was a scientifically justified decision derived from our target active dose of 465 mg/day of mixed polyphenols, which itself was informed by manufacturer guidelines, scientific literature, and expert consultation to balance the pursuit of efficacy with a paramount commitment to participant safety.\nReference:\n1. Del Bo' C, Bernardi S, Cherubini A, et al. A polyphenol-rich dietary pattern improves intestinal permeability, evaluated as serum zonulin levels, in older subjects: The MaPLE randomised controlled trial. Clin Nutr. 2021 May;40(5):3006-3018.\n2. Condezo-Hoyos L, Gazi C, Pérez-Jiménez J. Design of polyphenol-rich diets in clinical trials: A systematic review. Food Res Int. 2021 Nov;149:110655.\n3. Comments:\nSelection and detection of targeted microplastics (MPs):\na. Explain the criteria used to select the MPs in Table S2 (representativeness, prevalence in environment/human samples, polymer classes, particle size, expected exposure routes).\nb. Clarify which of the targeted MPs were actually detected in participants’ samples and at what concentrations. Table S6 shows five of eleven, please disclose whether the other six were below detection limits or not analyzed.\nc. If some targeted MPs were not detected, discuss implications for exposure–effect analyses and whether analyses should focus on the detected subset.\nResponse:\nWe appreciate the reviewer’s comments regarding the selection and detection of targeted MPs. We have addressed your points as follows:\na. Explanation of the criteria used to select the target MPs:\nOur selection of the 11 target polymers (PVC, PET, PE, PS, PP, PC, PA6, PA66, PLA, PMMA, and PBAT) was based on a comprehensive assessment of the following criteria:\nRepresentativeness: The selected list is highly representative of global plastic production and consumption. It covers the five major commodity plastics (PE, PP, PVC, PS, and PET), which constitute the bulk of plastic waste. It also includes key engineering plastics (PC, PA6, PA66, and PMMA) valued for their performance in durable goods, and incorporates emerging bio-based and biodegradable plastics (PLA and PBAT) to reflect current market trends and their environmental relevance.\nPrevalence in Environmental and Human Samples: These polymers, particularly PE, PP, PET, and PS, are among the most frequently identified MPs in diverse environmental matrices (e.g., water, sediment, air)[1, 2]and have been widely detected in human samples, including feces, blood, and lung tissue[3-5]. Targeting these prevalent MPs ensures our findings are comparable with the broader scientific literature and relevant to common human exposures.\nExpected exposure pathways: The selected MPs are directly related to the main human exposure pathways. Oral intake may be the main pathway for MPs[6, 7]. The research has found that MPs have been widely detected in many of the foods we consume daily, such as drinking water, tea, salt, beer, beverages, seafood, sugar, and fruits. The main types of MPs are PET, PS, PA, and PE[8-11]. Furthermore, the processing, packaging, and handling stages of modern diets also extensively use plastics, which may also release additional MPs[11]. Beverage bottles, takeout boxes, plastic baby bottles, milk storage bags, and other food packaging containers all release MPs during use, mainly including PS, PE, PET, and PP[12-14]. It is equally important to note that the fibers and other material fragments (such as PET, PA6, PA66) present in textiles can also easily adhere to surfaces like food, thereby leading to ingestion through the mouth[15, 16]. Apart from oral exposure, inhalation is also one of the important ways for the population to come into contact with MPs. Current research has found that the MPs detected in indoor air and bronchoalveolar lavage fluid of non-smokers mainly include PE, PET, and PP[17, 18]. Regarding the skin contact aspect for MPs, the research indicates that humans may be exposed to types of MPs such as PE and PS through the use of skin-cleaning products, medical applications, and other means[19, 20]. Nevertheless, many countries and organizations have issued regulations prohibiting the addition of MPs in cosmetics[21]. The risk of absorbing MPs or nanoplastics (NPs) through the skin exposure route is still unclear. It should be noted that the aforementioned types of MPs were also mainly detected in the population sample of this study.\nFurthermore, it should be clarified that the 11 types of MPs we have selected for detection are also the upper limit that the current Py-GC/MS method can reach for detection (ASTM D8401-24). One limitation of the Py-GC/MS method is that it cannot provide information on the particle size of MPs. Since this is a destructive study, we also look forward to the development of more comprehensive Py-GC/MS detection methods for various polymer types, as well as combined detection methods.\nb. Clarification of which targeted MPs were detected in participant samples:\nWe analyzed all 11 targeted MP types as planned. However, the detection frequencies varied significantly among the polymers. Five of the MPs were consistently detected in participant samples, each with a detection frequency greater than 70%. These were: polyethylene (PE), polyamide 66 (PA66), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) 56.34 μg/g dw, with an median value of 23.51 (11.48, 42.66) μg/g dw, 63.95 (37.01, 94.03) μg/g dw, 0.85 (0.42, 1.61) μg/g dw, 9.64 (3.83, 27.34) μg/g dw and 56.34 (39.72, 72.13) μg/g dw, respectively. The above results are also described in Table S2 and Table S3 of this manuscript. The remaining six targeted MPs were either not detected or were detected at very low frequencies. To enhance transparency, we have updated Table S2 in the supplementary materials to include the specific detection frequencies for all 11 targeted MPs.\nc. Discussion of implications for undetected MPs in exposure-effect analyses:\nWe agree that the handling of non-detected compounds is a critical aspect of the analysis. Given that the six MPs with very low detection rates constitute highly censored data, including them in the exposure-effect models would introduce substantial statistical bias and could lead to unreliable conclusions. For the undetected data, we usually have two approaches. One is to use multiple interpolations. However, when the proportion of missing data exceeds 70%, the reliability and accuracy of the data will be severely compromised[1, 2]. Secondly, the undetected data can be replaced by the detection limit or the minimum value. However, too much duplicate data in the statistical model may cause deviations in the analysis results[3]. Therefore, to ensure the statistical robustness and validity of our findings, we focused the subsequent statistical analyses exclusively on the five MPs with reliable detection rates of 70%.\nThe important point is that the absence of detection of other types of MPs in our sample might indicate that exposure to these polymers was negligible within this cohort, or that their concentrations were below the limit of detection of our analytical method. Therefore, this suggests that they may have limited relevance to the health outcomes examined in this specific study population.\nWe want to emphasize that all 11 MPs were included in the initial analytical screening, and none were arbitrarily omitted. We have revised the \"Methods\" and \"Results\" sections to provide a clearer description of our analytical process and the criteria used for including MPs in the final statistical analyses: \"Fecal samples from all 151 participants were tested for the presence of these 11 types of polymers.\" (Page: 7, Line: 264); \"Other types of MPs (PP, PMMA, PC, PA6, PLA and PBAT) were not detected or had a detection rate of less than 70%.\" (Page: 10, Line: 387)\nThank you once again for your constructive review, which has significantly strengthened the quality and clarity of our manuscript.\nReference:\n1. Yu F, Yan C, Ji Y, et al. Microplastic pollution in water and sediment in the Henan section of the Yellow River based on the MultiMP comprehensive evaluation method. J Hazard Mater. 2025 Sep 5;495:139059.\n2. Parham RL, Ayala AM, Meagher L, et al. Identifying Microplastics in Laboratory and Atmospheric Aerosol Mixtures via Optical Photothermal Infrared and Raman Microspectroscopy. Anal Chem. 2025 Aug 26;97(33):18136-18143.\n3. Zhang N, Li YB, He HR, et al. You are what you eat: Microplastics in the feces of young men living in Beijing. Sci Total Environ. 2021 May 1;767:144345.\n4. Gao B, Chen L, Wu L, et al. Association between microplastics and the functionalities of human gut microbiome. Ecotoxicol Environ Saf. 2025 Jan 15;290:117497.\n5. Amato-Lourenço LF, Carvalho-Oliveira R, Júnior GR, et al. Presence of airborne microplastics in human lung tissue. J Hazard Mater. 2021 Aug 15;416:126124.\n6. Zuri G, Karanasiou A, Lacorte S. Microplastics: Human exposure assessment through air, water, and food. Environ Int. 2023 Sep;179:108150.\n7. Fang M, Liao Z, Ji X, et al. Microplastic ingestion from atmospheric deposition during dining/drinking activities. J Hazard Mater. 2022 Jun 15;432:128674.\n8. Wright SL, Kelly FJ. Plastic and Human Health: A Micro Issue? Environ Sci Technol. 2017 Jun 20;51(12):6634-6647.\n9. Lusher AL, McHugh M, Thompson RC. Occurrence of microplastics in the gastrointestinal tract of pelagic and demersal fish from the English Channel. Mar Pollut Bull. 2013 Feb 15;67(1-2):94-9.\n10. Meng J, Zhang L, Zhao S, et al. Micro- and nano-plastic contamination in foods and potential risk to human health. Ecotoxicol Environ Saf. 2025 Sep 15;303:119021.\n11. Thompson RC, Courtene-Jones W, Boucher J, et al. Twenty years of microplastic pollution research-what have we learned? Science. 2024 Oct 25;386(6720):eadl2746.\n12. Yet Yin Hee, Keith Weston, Suhaimi Suratman. The effect of storage conditions and washing on microplastic release from food and drink containers. Food Packaging and Shelf Life. 2022 32, 2214-2894.\n13. Long Zhao, Yiying Gu, Hua Cai, et al. Microplastics release from infant feeding bottles and milk storage bags. Food Control. 2025 168. 0956-7135.\n14. Li D, Shi Y, Yang L, et al. Microplastic release from the degradation of polypropylene feeding bottles during infant formula preparation. Nat Food. 2020 Nov;1(11):746-754.\n15. Meier P, Zabara M, Hirsch C, et al. Evaluation of fiber and debris release from protective COVID-19 mask textiles and in vitro acute cytotoxicity effects. Environ Int. 2022 Sep;167:107364.\n16. Perera K, Ziajahromi S, Bengtson Nash S, et al. Airborne Microplastics in Indoor and Outdoor Environments of a Developing Country in South Asia: Abundance, Distribution, Morphology, and Possible Sources. Environ Sci Technol. 2022 Dec 6;56(23):16676-16685.\n17. O'Brien S, Rauert C, Ribeiro F, et al. There's something in the air: A review of sources, prevalence and behaviour of microplastics in the atmosphere. Sci Total Environ. 2023 May 20;874:162193.\n18. Qiu L, Lu W, Tu C, et al. Evidence of Microplastics in Bronchoalveolar Lavage Fluid among Never-Smokers: A Prospective Case Series. Environ Sci Technol. 2023 Feb 14;57(6):2435-2444.\n19. Napper IE, Bakir A, Rowland SJ, Thompson RC. Characterisation, quantity and sorptive properties of microplastics extracted from cosmetics. Mar Pollut Bull. 2015 Oct 15;99(1-2):178-85.\n20. Duis K, Coors A. Microplastics in the aquatic and terrestrial environment: sources (with a specific focus on personal care products), fate and effects. Environ Sci Eur. 2016;28(1):2.\n21. Woźniak-Budych M, Staszak K, Wieszczycka K, et al. Microplastic label in microencapsulation field - Consequence of shell material selection. J Hazard Mater. 2024 Mar 5;465:133000.\n4. Comments:\nExposure route and source attribution. The manuscript mentions PVC is widely used in construction materials. Provide discussion and (if available) rationale or evidence on likely exposure pathways for the MPs measured (oral/dietary ingestion, inhalation, dermal). State whether the measured MPs in the biological matrix are most plausibly reflecting dietary intake, inhalation, dust ingestion, or mixed routes. Discuss how non-dietary exposure routes could influence gut microbiome-mediated effects (for example, inhaled MPs that are swallowed via mucociliary clearance).\nResponse:\nThank you for your guidance. We believe that your opinion is extremely important for the structure improvement of the risk assessment section of this manuscript. The responses and revisions we have made are as follows:\nFirstly, concerning the possible exposure pathways of the detected MPs that you raised, we have addressed this in Comment 3. The MPs we selected are directly relevant to the major human exposure pathways. We analyzed all 11 targeted types of MPs as planned. In the fecal samples of the participants, five types—PE, PA66, PS, PET, and PVC—were predominantly detected, with detection frequencies exceeding 70% for each. The exact origins of these MPs remain difficult to trace; potential sources could be cautiously hypothesized. Among them, PS, PET, and PE are commonly used in food packaging and disposable products, suggesting that dietary intake is likely the primary exposure route. These polymers have been frequently detected in various food items and during the use of plastic food containers[1-7]. Furthermore, PA66 is widely used in textiles, industrial components, and electronic devices. Research indicates that fibers and fragments from synthetic textiles (e.g., PA6, PA66) could become airborne or adhere to food surfaces, leading to ingestion via ingestion and inhalation pathways[8-9]. Additionally, PVC is extensively employed in construction materials, while PET is also utilized in engineering plastics and household goods. Studies report that PET and PVC are among the most prevalent polymer types in indoor air, increasing the risk of inhalation exposure[10]. PE and PS are often included in personal care products and medical formulations, raising concerns about dermal exposure[11-12]. Nevertheless, many countries and organizations have issued regulations prohibiting the addition of MPs to cosmetics[13]. The risk of absorbing MPs or nanoplastics (NPs) through the skin exposure route is still unclear and warrants further investigation.\nSecondly, concerning the most likely sources of MPs measured in biological samples—whether they derive from dietary intake, inhalation, dust intake, or a combination of these pathways—we hypothesize that the mixed pathway is the primary route of exposure. The reasons are as follows: First, MPs have been detected in various foods consumed daily by humans, which suggests humans are continuously ingesting large amounts of MPs orally[1-4]. Research further suggests that the majority of MPs detected in feces originate from the fraction that is ingested but not absorbed[18]. Moreover, the levels of MPs detected in feces also show a significant correlation with human diet and the degree of food processing[14]. Second, humans breathe constantly, and it has been widely proven that the air is filled with MPs[15]. For MPs inhaled through the respiratory system, some may enter the cardiovascular system and be transported via the bloodstream[16]. Subsequently, some MPs in the blood can be filtered by the spleen and cleared into the intestinal tract. Furthermore, studies have shown that MPs deposited in the lungs may be transferred to the esophagus through the mucosal cilia clearance mechanism[17]. Crucially, the types of MPs detected in large quantities in our fecal samples correspond to the types reported to be potentially inhaled. Finally, although some studies suggest that skin exposure is a potential route for MPs, due to the protective function of the skin, it is not as widespread as ingestion and inhalation. Only a small portion of nanoplastics, synthetic fibers, or monomers can penetrate (with the exception of damaged skin)[18].\nTherefore, based on these findings, we reasonably speculate that the MPs detected in the feces of the population in this study likely originate from multiple pathways. However, since this is not the main research direction and result of this study, this content remains speculative. We plan to conduct further research in this direction in the future. The above was also described in detail in the \"Discussion\" section of our manuscript:\n\"In addition, PVC and PA66 exhibited the highest detection rates and concentrations in our study, followed by PE, PS, and PET. Consistently, Song et al. and Ke et al. also found that the top three polymers with the highest detection rates of MPs in human fecal samples included PVC and PE[25]. Detection of MPs in feces could be associated with the specific types of plastics to which individuals are routinely exposed. PE, PS, and PET is commonly employed in food containers, disposable products, etc. Many of the foods and beverages consumed daily—such as drinking water, tea, salt, beer, soft drinks, seafood, sugar, and fruits—are found to contain MPs, primarily in the types of PET, PS, PA, and PE[32, 33].\nIn addition, modern food processing, packaging, and handling practices may release MPs, including PS, PE, PET, and PP[34, 35]. Furthermore, PA66 is widely used in textiles, industrial components, and electronic devices. Research indicates that fibers and fragments from synthetic textiles (e.g., PA6, PA66) could become suspended in the air or adhere to food surfaces, leading to ingestion via ingestion and inhalation pathways[36, 37]. PVC is extensively employed in construction materials, while PET is also utilized in engineering plastics and household goods. Studies report that PET and PVC are among the most prevalent polymer types detected in indoor air, increasing the risk of inhalation exposure[38]. PE and PS are often included in personal care products and medical formulations, raising concerns about dermal exposure[39, 40]. Collectively, these findings suggest that the MPs detected in the fecal samples of this study likely originate from multiple exposure pathways. However, this conclusion remains preliminary and warrants further investigation to enable a more comprehensive understanding.\" (Page: 22, Line: 645)\nFinally, regarding the discussion on how non-dietary exposure methods affect the effects mediated by the intestinal microbiome, after our literature review and discussion, we agree that this perspective is highly important for enhancing the depth of the manuscript. As you mentioned, and as stated above, MPs accumulated in the human digestive tract may partially originate from respiratory intake. During the migration of MPs, their shape and the loads of other microorganisms/pollutants will change[18, 20], which could impact the intestinal microbiome. However, such studies are currently lacking, and the above results remain speculative. Consequently, further in-depth research is still required. Therefore, we have added a description of the possible effects of non-dietary exposure methods on the intestinal microbiome to the \"Discussion\" section of this manuscript:\n\"Regarding the aforementioned MPs that come into contact with the gut microbial in the intestines, non-dietary exposure routes are as critical as oral intake. MPs detected in human feces may partly originate from inhalation via the respiratory tract, subsequently entering the gastrointestinal tract through systemic circulation or mucociliary clearance mechanisms[51]. During their translocation, the physical characteristics of MPs and their associated loads of microorganisms or pollutants may change, potentially exerting additional impacts on the composition and function of the gut microbiota[51, 52].\" (Page: 24, Line: 724)\nReference:\n1. Wright SL, Kelly FJ. Plastic and Human Health: A Micro Issue? Environ Sci Technol. 2017 Jun 20;51(12):6634-6647.\n2. Lusher AL, McHugh M, Thompson RC. Occurrence of microplastics in the gastrointestinal tract of pelagic and demersal fish from the English Channel. Mar Pollut Bull. 2013 Feb 15;67(1-2):94-9.\n3. Meng J, Zhang L, Zhao S, et al. Micro- and nano-plastic contamination in foods and potential risk to human health. Ecotoxicol Environ Saf. 2025 Sep 15;303:119021.\n4. Thompson RC, Courtene-Jones W, Boucher J, Pahl S, Raubenheimer K, Koelmans AA. Twenty years of microplastic pollution research-what have we learned? Science. 2024 Oct 25;386(6720):eadl2746.\n5. Yet Yin Hee, Keith Weston, Suhaimi Suratman. The effect of storage conditions and washing on microplastic release from food and drink containers. Food Packaging and Shelf Life. 2022 32, 2214-2894.\n6. Long Zhao, Yiying Gu, Hua Cai, et al. Microplastics release from infant feeding bottles and milk storage bags. Food Control. 2025 168. 0956-7135.\n7. Li D, Shi Y, Yang L, et al. Microplastic release from the degradation of polypropylene feeding bottles during infant formula preparation. Nat Food. 2020 Nov;1(11):746-754.\n8. Meier P, Zabara M, Hirsch C, et al. Evaluation of fiber and debris release from protective COVID-19 mask textiles and in vitro acute cytotoxicity effects. Environ Int. 2022 Sep;167:107364.\n9. Perera K, Ziajahromi S, Bengtson Nash S, et al. Airborne Microplastics in Indoor and Outdoor Environments of a Developing Country in South Asia: Abundance, Distribution, Morphology, and Possible Sources. Environ Sci Technol. 2022 Dec 6;56(23):16676-16685.\n10. Ageel HK, Harrad S, Abdallah MA. Microplastics in indoor air from Birmingham, UK: Implications for inhalation exposure. Environ Pollut. 2024 Dec 1;362:124960.\n11. Napper IE, Bakir A, Rowland SJ, et al. Characterisation, quantity and sorptive properties of microplastics extracted from cosmetics. Mar Pollut Bull. 2015 Oct 15;99(1-2):178-85.\n12. Duis K, Coors A. Microplastics in the aquatic and terrestrial environment: sources (with a specific focus on personal care products), fate and effects. Environ Sci Eur. 2016;28(1):2.\n13. Woźniak-Budych M, Staszak K, Wieszczycka K, et al. Microplastic label in microencapsulation field - Consequence of shell material selection. J Hazard Mater. 2024 Mar 5;465:133000.\n14. Hartmann C, Lomako I, Schachner C, et al. Assessment of microplastics in human stool: A pilot study investigating the potential impact of diet-associated scenarios on oral microplastics exposure. Sci Total Environ. 2024 Nov 15;951:175825.\n15. Wieland S, Balmes A, Bender J, et al. From properties to toxicity: Comparing microplastics to other airborne microparticles. J Hazard Mater. 2022 Apr 15;428:128151.\n16. Zuri G, Karanasiou A, Lacorte S. Microplastics: Human exposure assessment through air, water, and food. Environ Int. 2023 Sep;179:108150.\n17. Triantafyllaki M, Chalvatzaki E, Torres-Agullo A, et al. The fate of airborne microfibers in the human respiratory tract in different microenvironments. Sci Total Environ. 2024 Nov 25;953:176000.\n18. Wu P, Lin S, Cao G, et al. Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications. J Hazard Mater. 2022 Sep 5;437:129361.\n5. Comments:\nThe authors report gut microbiome changes (notably Sutterella) associated with MP exposure and CP effects. Explicitly discuss whether microbiome alterations are more likely a cause or a consequence of MP exposure (or of host response to MPs). Clarify limitations of cross-sectional associations and the RCT for causal inference, and propose approaches to test directionality.\nResponse:\nThank you for your guidance. Regarding the questions you raised, our response and the revisions made to the manuscript are as follows:\nFirstly, during the revision process, we adopted a more stringent statistical approach for the microbiome analysis to address concerns raised by another reviewer. Under this optimized analytical framework, the significance of Sutterella diminished, while Staphylococcus was identified as the primary target genus. To ensure our conclusions are built on the most robust evidence, we have shifted our focus to Staphylococcus in the revised manuscript. Regarding the question of whether the changes in the microbial community are more likely to be the cause or the result of MP exposure, our discussion is as follows. On one hand, there is already extensive research evidence on the impact of MP exposure on the intestinal microbiome. Animal studies have shown that MP exposure can affect the structure and function of the intestinal microbiota, altering the abundance of beneficial and opportunistic pathogenic bacteria[1]. Furthermore, in vitro experiments have provided additional verification of the impact of MP exposure on the intestinal microbiome[2].\nOn the other hand, the intestinal microbiome has also been found to potentially affect the absorption and excretion of MPs. Studies have discovered that specific bacterial species in the intestine have the potential to degrade MPs, such as PET and ester-based polyurethanes (PUR), consequently influencing the size, type, and abundance of the accumulated MPs[3,4]. Crucially, research suggests that MPs may affect the communication between the host's intestines and the intestinal microbiota, thereby altering the expression of intestinal genes and the composition and structure of the intestinal microbiota[5]. Moreover, existing studies highlight that the intestinal microbiota can influence the host, and the host can also influence the intestinal microbiota[6-8]. Therefore, based on these results, we reasonably speculate that the relationship among MP exposure, changes in the intestinal microbiota, and the host's response is not a simple one-way transmission. Instead, there is a bidirectional communication mechanism at play.\nIn line with your comments and the above findings, we have revised and detailedly described this \"Discussion\" section of the manuscript: \"The relationship between MP exposure, alterations in the gut microbiota, and host responses is likely not a unidirectional process, but rather involves a bidirectional interaction. Evidence from animal and in vitro studies has demonstrated that MP exposure could disrupt the composition and function of the gut microbiota[53, 54]. Conversely, the gut microbiota may influence the absorption, distribution, and excretion of MPs. Certain bacterial species in the gut have been shown to possess the capacity to degrade MPs[12, 55, 56]. Research reports indicate that MPs may interfere with host-microbiota crosstalk in the gut, leading to altered expression of intestinal genes and shifts in microbial community structure[57]. However, further investigation is required to confirm these findings and elucidate the underlying mechanisms.\" (Page: 24, Line: 732)\nSecondly, we have, as per your instructions, incorporated relevant descriptions regarding the limitations of cross-sectional correlational studies and the role of randomized controlled trials in causal inference into the “Limitations” section of this manuscript: \"Firstly, in the cross-sectional baseline study, measuring exposures and outcomes simultaneously prevents establishing temporality, thus precluding causal inference. The results obtained from strictly controlled RCT studies may have limited external validity. The potential intervention targets identified through correlation analysis are also speculative and still require further validation via in vivo or in vitro experiments. Additionally, for the RCT study, the intervention period was relatively short (28 days), the sample size was relatively limited (101 participants), and the participants were generally young, in good health, and predominantly female. This might account for the lack of significant changes observed in other clinical indicators, which limited the depth of our analysis and might also restrict the general applicability of the research results.\" (Page: 28, Line: 911)\nFurthermore, in the \"Discussion\" section of the manuscript, we have added methods for testing the directionality of causal inferences in future research: \"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896)\nReference:\n1. Hirt N, Body-Malapel M. Immunotoxicity and intestinal effects of nano- and microplastics: a review of the literature. Part Fibre Toxicol. 2020 Nov 12;17(1):57.\n2. Fournier E, Leveque M, Ruiz P, et al. Microplastics: What happens in the human digestive tract? First evidences in adults using in vitro gut models. J Hazard Mater. 2023 Jan 15;442:130010.\n3. Danso D, Chow J, Streit WR. Plastics: Environmental and Biotechnological Perspectives on Microbial Degradation. Appl Environ Microbiol. 2019 Sep 17;85(19):e01095-19.\n4. Tamargo A, Molinero N, Reinosa JJ, et al. PET microplastics affect human gut microbiota communities during simulated gastrointestinal digestion, first evidence of plausible polymer biodegradation during human digestion. Sci Rep. 2022 Jan 11;12(1):528.\n5. Hsu WH, Chen YZ, Chiang YT, et al. Polystyrene nanoplastics disrupt the intestinal microenvironment by altering bacteria-host interactions through extracellular vesicle-delivered microRNAs. Nat Commun. 2025 Jun 10;16(1):5026.\n6. Zhang C, Liu H, Sun L, et al. An overview of host-derived molecules that interact with gut microbiota. Imeta. 2023 Feb 13;2(2):e88.\n7. Khan R, Di Gesù CM, Lee J, et al. The contribution of age-related changes in the gut-brain axis to neurological disorders. Gut Microbes. 2024 Jan-Dec;16(1):2302801.\n8. Yang H, Wang T, Qian C, et al. Gut microbial-derived phenylacetylglutamine accelerates host cellular senescence. Nat Aging. 2025 Mar;5(3):401-418.\n6. Comments:\nThe manuscript states plasma cholesterol and LDL tended to increase with MP exposure, citing supporting studies. However, some preclinical reports (e.g., Lu L, Wan Z, Luo T, Fu Z, Jin Y. Polystyrene microplastics induce gut microbiota dysbiosis and hepatic lipid metabolism disorder in mice. Sci Total Environ. 2018 Aug 1;631-632:449-458. doi: 10.1016/j.scitotenv.2018.03.051.) show different or opposite lipid effects. Please:\na. Acknowledge conflicting evidence explicitly,\nb. Offer possible reasons for discrepancies (species differences, MP polymer type, particle size, dose, exposure route, diet, duration), and\nc. Clarify how the present human data fit within this heterogeneous literature.\nResponse:\nThank you for this excellent suggestion. We agree that a more nuanced discussion is needed. To address this, we have revised the manuscript to explicitly acknowledge the conflicting evidence, offer potential explanations for the discrepancies, and better position our human data within the existing literature.\na. Based on your suggestions, we have made revisions to the \"Discussion\" section of this manuscript, explicitly acknowledging the existence of contradictory evidence: \"Several in vivo studies have also shown that exposure to both PS- and PE-MPs could lead to elevated serum CHO and LDL levels[42, 43]. It is noteworthy that the animal study conducted by Liu et al. reported divergent lipid-related outcomes compared to our findings, as both hepatic triglyceride (TG) and total cholesterol (TCH) levels were reduced[44].\" (Page: 23, Line: 673)\nb. The reasons you have proposed for the aforementioned contradictions might stem from species differences, polymer types, particle size, dosage, exposure route, diet, duration, which are very reasonable and important. Extrapolating the results of animal experiments to humans involves risks.\n1. The doses, types, and particle sizes of the particles used in animal experiments are significantly different from the real situation. 2. We are exposed to mixed types of exposure (through the mouth, through breathing, etc.) every day, but the exposure methods used in animal experiments are usually oral. 3. The duration of animal experiments is generally only one month, but an adult may have been exposed to low doses continuously for a longer period of time. 4. Due to cost reasons, the sample size of most animal experiments is smaller than that of this study, and so on. Therefore, these differences may be the reason why our research results cannot be fully verified with animal studies. The above content is also described in the \"Discussion\" section of this manuscript: \"These discrepancies may stem from multiple factors, such as variations in experimental models, exposure doses, and the types of exposure MPs. It is important to recognize the potential inconsistency between animal and human studies, which underscores the need for cautious interpretation of the results when extrapolating across species.\" (Page: 23, Line: 677)\nc. It should be noted that currently, there are no similar studies conducted in a population setting. Therefore, our research results can only be compared with those of some animal studies or in vitro studies. As mentioned earlier, this may lead to the discovery of significantly different results. we believe that although there are differences between human data and previous animal study results, these pieces of evidence together indicate that MPs exposure will continuously lead to lipid metabolism disorders. The above content is also described in detail in the \"Discussion\" section of our manuscript: \"Despite these varied outcomes, the collective evidence suggests that MP exposure may potentially contribute to lipid metabolism disorders. This disruption may manifest as a pro-atherogenic profile, underscoring the need for further research to elucidate the underlying mechanisms in humans.\" (Page: 23, Line: 681)\nWe are grateful for the opportunity to improve our manuscript and believe this change has significantly strengthened the discussion.\n7. Comments:\nIntervention duration and biological relevance of significant endpoints. A 28-day intervention is short for many cardiometabolic outcomes. The authors already note lack of changes in many clinical indicators. Please discuss whether the significant molecular/microbiome/metabolite changes (e.g., Sutterella and PC (22:5/0:0)) are likely to be transient biomarkers or plausibly predictive of longer-term clinical effects. State explicitly that these molecular changes require replication and longer-term follow-up for clinical interpretation.\nResponse:\nThank you for your question. We sincerely thank the reviewer for their insightful comments on the trial duration. Reviewer 1 also raised this crucial issue in comment 3. The trial duration was determined based on practical constraints, including resource availability and feasibility for an initial investigation. We fully acknowledge that the 28-day intervention is a limitation for observing long-term health outcomes. To address this, we proactively increased the daily polyphenol dosage within the safe range to maximize the potential for a detectable effect within the study period. It should be noted that a larger or longer study would have incurred prohibitive costs, and we chose to invest our resources in deep, mechanistic analyses (such as multi-omics) rather than spreading them more thinly across a larger cohort. Furthermore, the 28-day timeframe was chosen to promote high participant compliance and minimize the risk of attrition, which often compromises longer-term studies. We fully agree that a 28-day intervention is generally insufficient to elicit stable clinical improvements in many cardiovascular and metabolic outcomes—such as blood lipids and blood glucose—which have been described in the \"Discussion\" and \"Limitations\" section of this manuscript:\n\"Additionally, no significant improvements were observed in plasma CHO and LDL indicators following CP intervention. This weakens the interpretability of these findings, which may potentially be attributed to the relatively brief duration of our intervention.\" (Page: 23, Line: 696);\n\"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896); \"Additionally, for the RCT study, the intervention period was relatively short (28 days), the sample size was relatively limited (101 participants), and the participants were generally young, in good health, and predominantly female. This might account for the lack of significant changes observed in other clinical indicators, which limited the depth of our analysis and might also restrict the general applicability of the research results.\" (Page: 28, Line: 915)\nAs is mentioned in response for comments 5, we have shifted our focus from Sutterella to Staphylococcus in the revised manuscript. Our research suggests that the intestinal microbe Staphylococcus and the plasma metabolite PC (22:5/0:0) may serve as potential targets for polyphenol intervention against MP-induced immune dysregulation. The observation that many clinical indicators remained unchanged, despite significant shifts in specific microbes and metabolites, warrants a discussion on whether these changes represent transient biomarkers or predictors of long-term clinical outcomes. In response to this viewpoint, we provided a detailed elaboration in the \"Discussion\" section, offering cautious interpretations of the observed molecular, microbial, and metabolite markers. We clarified that these markers may represent either transient responses to short-term exposure or early indicators of potential long-term biological pathways, while emphasizing that current evidence remains insufficient to support their use as direct predictors of long-term clinical outcomes:\n\"However, it should be noted that this study did not observe significant clinical improvements in most cardiovascular outcomes. Therefore, the interpretation of the aforementioned potential biomarkers should be approached with caution. The short-term changes observed may have two implications: on one hand, they may represent only a transient response to the intervention, reflecting reversible, short-term adjustments in gut ecology or metabolism; on the other hand, they may also serve as early biological signals that, if sustained over time, could contribute to longer-term physiological adaptations and ultimately influence clinical endpoints.\" (Page: 27, Line: 881)\nFurthermore, we believe that the findings based on multi-omics are only preliminary. To clearly identify these molecular changes and their associated clinical indicators, further in vivo and in vitro/experimental studies, repeated research, and longer-term follow-ups are needed to verify. The above content is also elaborated in the \"Discussion\" section of the manuscript: \"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896)\nReference:\n1. van Galen KA, Schrantee A, Ter Horst KW, et al. Brain responses to nutrients are severely impaired and not reversed by weight loss in humans with obesity: a randomized crossover study. Nat Metab. 2023 Jun;5(6):1059-1072.\n2. Basolo A, Hohenadel M, Ang QY, et al. Effects of underfeeding and oral vancomycin on gut microbiome and nutrient absorption in humans. Nat Med. 2020 Apr;26(4):589-598.\n3. Dijkgraaf MGW, Haukoos J, Itani KMF. Practical Guide to Design Choice of Randomized Clinical Trials in Surgery. JAMA Surg. 2022 Dec 1;157(12):1154-1155.\nReviewer #3: The submitted work assessed the effects of dietary supplementation with composite polyphenols on microplastic exposure-related immune disturbances by a two-phase population trial. Overall, the 2 clinical studies were conducted rigorously, avoiding any major biases. The chosen methods are up-to -date, relevant, and well-described. The advanced statistical analyses require review by a qualified biostatistician, which I am not. The manuscript is clear, well-structured, and easy to understand, except for the difficulty in reading the figures which are very small and of poor quality (they are rarely readable even when enlarged).\nFirstly, the authors performed a pilot population study with 75 participants in low MP exposure group and 76 participants in high MP exposure group. Microplastic were quantified in feces samples by Py-GC/MS. This study provides the first population-based evidence that MP exposure is associated with decreased blood levels of Il1b, Il-2, Il-10, Il-13, monocytes and increased levels of IFN-g, cholesterol and HDL. The weight of each MP polymer on the immune-inflammatory parameters was also analyzed. Differentially expressed fecal bacterial strains and pathways, and differential blood metabolites and pathways, associated with MP exposure in this pilot population study were identified. The mechanisms potentially involved are highlighted by integrated multi-omic analysis between plasma metabolome, fecal metagenome and host phenotype. These results are extremely important because they start to fill the knowledge gap on the effects of microplastic exposure in humans directly. This type of clinical data is highly anticipated by the scientific community and by society.\nSecond, the authors performed a randomized, double-blind, placebo-controlled intervention trial to evaluate the protective effects of composite polyphenol supplementation on the adverse effects induced by microplastic exposure. As the adverse effects of MP exposure in healthy population are low, this part was really challenging but the in-depth analyses performed allowed the authors to identify Sutterella and plasma metabolite phosphatidylcholine (22:5/0:0) as key targets of mitigation of MP associated toxic effects by CP intervention.\nGlobally, the conclusions are supported by the results but I have few comments aiming to improve the manuscript.\nResponse:\nThank you for your comments. We really appreciate your efforts in reviewing our manuscript. In response, we have carefully revised the manuscript accordingly. To enhance clarity and readability, we have improved the quality of the figures, and elements that do not significantly contribute to the main findings have been moved to the supplementary materials. Our point-by-point responses are detailed below.\n1. Comments:\nLines 183-205-206-207-222-223 Please correct the use of the future tense.\nResponse:\nWe have revised the use of the future tense in the indicated lines and have also carefully checked the entire manuscript to ensure consistent and appropriate tense usage throughout: \"Later, the samples were transferred and stored at -80 °C.\" (Page: 5, Line: 183); \"During the experiment, the random list generated was kept confidential, and neither participants nor researchers were aware of the group assignments.\" (Page: 5, Line: 207); \"Participants were divided into the placebo group and the CP intervention group.\" (Page: 5, Line: 208); \"The random codes were sealed in double-layered opaque envelopes and managed by researchers responsible for the blind method.\" (Page: 5, Line: 209); \"All adverse events were collected and promptly thoroughly submitted to the study's principal investigator, who determined the subsequent course of action, including close monitoring, additional medical management, or early termination of participation.\" (Page: 6, Line: 223)\n2. Comments:\nLine 185: please separate better intervention from placebo materials.\nResponse:\nThank you for your guidance. As per your suggestion, we have already separated the intervention materials from the placebo materials in the \"Methods\" section of the manuscript into 2.2.1 Intervention materials: \"The main active components of the CP intervention agent include a mixture of 44 types of polyphenols. The CP intervention agent is powders encapsulated in sachets, each containing 155 mg CP. The dosage was three times daily, with a total intake of 465mg of CP. The intervention agent should be taken one sachet each time, dissolved in warm water and taken during or after meals in the morning, afternoon, and evening. The ingredients of the CP was detailed in Table S1.\" and \"2.2.2 Placebo materials\": \"The placebo mainly consists of resistant dextrin and erythritol. Apart from the contents, the appearance, color, and consumption method of the placebo are the same as those of the intervention agent. Participants were not informed of their group allocation in any way. The ingredients of the placebo was detailed in Table S1.\" (Page: 5, Line: 186)\n3. Comments:\nLine 243: Please remove \"The sample is then left to digest for 48 hours\"\nResponse:\nWe apologize for the redundancy in our expression. Thank you for your reminder. We have removed \"The sample is then left to digest for 48 hours\".\n4. Comments:\nLine 245: Does using a 110-degree plate lead to the degradation and loss of some microplastics? Is this a limitation of your method? What is the size range of MP detected in feces samples? This is information that will become increasingly important as methodologies progress.\nResponse:\nThank you for your question.\nDuring the process of establishing the pre-treatment method, we used the ATR-FTIR method to conduct qualitative detection on the samples. Attenuated total reflection (ATR) is a testing method for FTIR, which has high sensitivity in detecting changes in the surface molecular groups of the samples. It is an effective means for identifying whether chemical reactions occur in MPs during the pre-treatment process. As shown in Figure 1, after the MPs PE, PP, PS, PMMA, PET, PC, PA, and PVC were immersed in 68% nitric acid at room temperature for 48 hours and then reacted at 110 °C for 3 hours, the FTIR spectra showed no significant changes. This indicates that the above reaction conditions do not affect the qualitative determination of microplastics PE, PP, PS, PMMA, PET, PC, and PVC. The pre-treatment process does not lead to the degradation of MPs. To further ensure that the degradation and loss of MPs are minimized during the pre-treatment process, we tested the recovery rate of the entire pre-treatment process to ensure the reliability of the pre-treatment method: \"Table S2 lists the detection limits of the Py-GC/MS method and the pre-treatment recovery rates of low, medium and high concentrations of MPs spiking amounts.\" (Page: 7, Line: 262) The recovery rate of the MPs pre-treatment process are detailed in Table S2 of the Supplementary materials. We also added a discussion on our detection method in the \"Limitations\" section: \"Moreover, during the pre-treatment process, despite taking various measures to minimize the loss of MPs, some MPs may still be lost, which may lead to an underestimation of the detection of fecal MPs.\" (Page: 28, Line: 920)\nWe fully agree with your emphasis on the significance of the size range of MPs detected in fecal samples. However, since we used the Py-GC/MS method for the detection, this method can obtain the mass of MPs, but as it is a destructive method, it will lose the information about the particle size of MPs and thus cannot obtain the size range of MPs in human feces. Based on your suggestion, we have discussed this point in the \"Limitations\" section of the discussion: \"Due to the application of the Py-GC/MS method, it is also unable to obtain certain key information about MPs, such as particle size, color, and shape, which may be crucial in assessing their toxicity.\" (Page: 28, Line: 922)\nFigure 1. FTIR spectra of MPs before and after pretreatment\nNote: (A-H) The infrared comparison spectra of (A) PE-MP, (B) PP-MP, (C) PS-MP, (D) PMMA-MP, (E) PET-MP, (F) PC-MP, (G) PA-MP, (H) PVC-MP before and after reaction. The upper part is the original sample, and the lower part is the infrared spectrum after the reaction.\n5. Comments:\nLine 395: please define the systemic immune- inflammation index\nResponse:\nWe are grateful to you for pointing out our oversight. In response, we have provided a clear definition of the systemic immune-inflammation index (SII) within \"2.2.9 Blood parameters detection\" of the \"Methods\" section: \"The systematic immune-inflammation index (SII) is a comprehensive indicator that integrates three independent white blood cell subsets and platelets, calculated as follows[26]:\n\" (Page: 7, Line: 294)\n6. Comments:\nLine 397. The results of the figure S2 are very interesting and should be if possible explained and better highlighted.\nResponse:\nThank you for your guidance. We also strongly hope to present the results of our mixed exposure model in the main text, including Qgcomp and BKMR. However, given the considerable size of these charts, they are not conducive to direct inclusion in the main text. To effectively highlight these interesting findings, in \"3.2 The Relationship between MP Exposure and Inflammatory Indicators and Cytokines\" of the \"Results\" section, we have included a detailed description of the results shown in Figure S2: \"The Qgcomp analysis (Figure S2) were examined to identify the contribution of each individual MP to these overall effects. The mixed exposure to MPs exhibited a significant positive correlation with neutrophil count (NEU), neutrophil percentage (NEUP), and SII, and a significant negative correlation with MXD, MXDP, lymphocyte percentage (LYMP), as well as the cytokines IL-1β and IL-10 (P < 0.05). PA66 was identified as the primary driver of the significant associations. Specifically, PA66 contributed the largest positive weight to the models for NEU, NEUP, and SII. It also contributed the largest negative weight for the models examining MXD, MXDP, lymphocyte count (LYM), LYMP, IL-1β, IL-2, IL-10, and IL-13. The other components (PS, PVC, and PET) generally showed smaller and more mixed contributions for these specific endpoints.\nFurthermore, the results of BKMR analysis were consistent with those of Qgcomp analysis (Figure S3). the MP mixture was significantly positively associated with NEU, NEUP, and SII. These outcomes demonstrated a clear positive dose-response trend as exposure quantiles increased. Conversely, the MP mixture was significantly negatively correlated with the inflammatory indices MXD, MXDP, and LYMP, as well as the cytokines IL-1β, IL-2, IL-10, and IL-13.\" (Page: 10, Line: 410)\n7. Comments:\nLines 410-411: Interestingly, following the intervention, the elevated levels of IL-6 and TG in the After-Int group were alleviated. This is true for Il-6 but not for TG. Please correct.\nResponse:\nThank for your correction. The statement regarding TG has been deleted to accurately reflect that the intervention only alleviated IL-6 levels: \"Interestingly, following the intervention, the elevated levels of IL-6 in the After-Int group were alleviated, with a notably substantial effect size (P < 0.05, effect size = -0.576).\" (Page: 12, Line: 438)\n8. Comments:\nLines 411-413: the data showing this statement are not provided. The statistics between After-Int and After-Pla groups are not provided. So please remove or provide statistical evidence. In addition, the target effect here is the one observed between Base-Int and After-Int, i.e. reduction of IL-1b and IL-6 levels and increase of IL-8 levels, which should be highlighted in the text. Still, based on only theses 3 significant variations attributable to CP, the title on line 408 “The CP intervention regulates the plasma cytokine levels” is not supported.\nResponse:\nThank you for pointing out the errors in our manuscript. You are correct. Thank you for pointing out the errors in our manuscript. You are correct. Our study compared the differences between the Base-Pla and Base-Int groups, and used a paired T-test to compare the differences in changes between the placebo group and the intervention group before and after the intervention. However, our original description confused this method. Therefore, in the note of Table 1, we have revised the original description: \"P indicates the P-value assessing the difference in change values 2 between the placebo group and the intervention group before and after the intervention;\" (Page: 14, Line: 446) At the same time, the corresponding result descriptions have also been modified: \"Compared with the placebo group, the levels of proinflammatory cytokines (IL-1β, IL-6, and IL-8) in the intervention group were significantly reduced following the intervention (P < 0.05), with effect sizes of -0.463, -0.576, and -0.529, respectively.\" (Page: 12, Line: 440)\nSecondly, indeed, using such a title based solely on three significant cytokines caused by CP is a bit exaggerated. Therefore, after our discussion, we have revised the title according to your suggestion: \"3.3 The CP intervention regulates the levels of specific cytokines in plasma\" (Page: 12, Line: 436)\n9. Comments:\nFigure 3B and 3D. The title axis is Shannon but, in the text and legend, the authors noted observed species index and Sobs indices. Please correct.\nResponse:\nThank you for your careful review and valuable feedback. We appreciate you pointing out the inconsistency in our manuscript. Regarding your comment on Figure 3B and 3D, you are correct that we had mistakenly referred to the \"Shannon\" index as \"observed species index\" and \"Sobs indices\" in the text and legend. We have now corrected this error throughout the manuscript to ensure consistency. The revised text now reads: \"In the FSBPs, the H-MPs group showed significantly higher Shannon indices and lower Simpson indices of alpha diversity compared to the L-MPs group, suggesting that exposure to higher MPs increases microbial richness (Figure 3A-B).\" (Page: 15, Line: 451)\n10. Comments:\nLines 421-422. I rather see a reduction of the Simpson index and an increase of the second alpha-diversity index. Please clarify.\nResponse:\nThank you for your keen observation and for requesting clarification. We appreciate the opportunity to improve the accuracy of our manuscript. You are correct in your observation of a reduction in the Simpson index and an increase in the second alpha-diversity index, which is the Shannon index. Both a smaller Simpson index and a larger Shannon index indicate a higher alpha-diversity of the gut microbiota community[1-3]. We have corrected this description in the \"Results\" section: \"In the FSBPs, the H-MPs group showed significantly higher Shannon indices and lower Simpson indices of alpha diversity compared to the L-MPs group, suggesting that exposure to higher MPs increases microbial richness (Figure 3A-B).\" (Page: 15, Line: 451)\nFurthermore, the interpretation of this finding is elaborated in detail in the \"Discussion\" section of this manuscript: \"High MP exposure significantly increased the alpha diversity of gut microbiota and decreased the relative abundance of core intestinal genera, such as Ruminococcus. Animal experiments employing PS-MPs have similarly demonstrated a significant increase in the alpha diversity of gut microbiota in mice exposed to high concentrations of MPs[5, 46]. This alteration may result from MP-induced disruption of intestinal homeostasis or from the substantial microbial load carried by the administered MPs, which can be transferred to the gut.\" (Page: 23, Line: 703)\nWe believe this correction makes it clear which panel corresponds to which diversity index and directly addresses your concern. Thank you again for your valuable feedback.\nReference:\n1. Ordinola-Zapata R, Mansour D, Saavedra F, et al. In vitro efficacy of a non-instrumentation technique to remove intracanal multispecies biofilm. Int Endod J. 2022 May;55(5):495-504.\n2. Alcazar M, Escribano J, Ferré N, et al. Gut microbiota is associated with metabolic health in children with obesity. Clin Nutr. 2022 Aug;41(8):1680-1688.\n3. Garcia-Mauriño C, Shao Y, Miltz A, et al. Investigation of associations between the neonatal gut microbiota and severe viral lower respiratory tract infections in the first 2 years of life: a birth cohort study with metagenomics. Lancet Microbe. 2025 Aug;6(8):101072.\n11. Comments:\nLines 428-430: why focusing on these 4 genera, are they the only ones significantly dysregulated?\nResponse:\nThank you for your question. We admit that we made an error in the description of the results, which led to your confusion. In fact, there are a large number of differentially expressed bacterial genera between the H-MPs and L-MPs groups, as well as among the four RCT intervention groups. It is difficult to directly determine which genera are relatively more important. Therefore, referring to other relevant studies, we selected the differentially expressed genera with the highest relative abundance among all the significantly different genera. It can be reasonably assumed that these bacteria may be the most significantly affected under the conditions of MPs exposure and CP intervention[1-4]. These genera were included in our key research targets in subsequent studies.\nRegarding the above content, we have revised the \"3.4 Fecal metagenome alterations of MPs exposure and CP intervention\" section of the \"Results\" and Figure S4 to prevent readers from having the above-mentioned confusion again: \"For the FSBPs and RCT populations, the taxonomic composition of the gut microbiota is presented in Figure S4A and S4B. At the genus level, attention was paid to the significantly differentially expressed genera with the highest relative abundance. Figure S4C shows that the relative abundance of Ruminococcus and Escherichia in the H-MPs was significantly lower than that in the L-MPs; However, there was no significant difference after false discovery rate (FDR) correction. The LEfSe analysis also revealed that the Ruminococcus and Escherichia were the two bacterial genera that had the greatest impact on the differential effects between the H-MPs and L-MPs groups (Figure S4E). Furthermore, Figure S4D indicates that the relative abundances of bacterial genera such as Bacteroides, Faecalibacterium, and Phocaeicola significantly differed among the four groups. Further multiple comparisons revealed that the After-Int group had a significantly higher relative abundance of Faecalibacterium and Phocaeicola compared to the Base-Int group, whereas no significant difference in Bacteroides abundance was observed between these two groups (Figure S4G-I). The LEfSe analysis revealed that the Foetidibacter had the greatest impact on the differential effects between the four groups (Figure S4F).\" (Page: 15, Line: 458)\nReference:\n1. Cheng M, Zhang X, Zhu J, et al. A metagenomics approach to the intestinal microbiome structure and function in high fat diet-induced obesity mice fed with oolong tea polyphenols. Food Funct. 2018 Feb 21;9(2):1079-1087.\n2. Wu G, Xu T, Zhao N, et al. A core microbiome signature as an indicator of health. Cell. 2024 Nov 14;187(23):6550-6565.e11.\n3. Zhao L, Wang C, Peng S, et al. Pivotal interplays between fecal metabolome and gut microbiome reveal functional signatures in cerebral ischemic stroke. J Transl Med. 2022 Oct 8;20(1):459.\n4. Wallen ZD, Demirkan A, Twa G, et al. Metagenomics of Parkinson's disease implicates the gut microbiome in multiple disease mechanisms. Nat Commun. 2022 Nov 15;13(1):6958.\n12. Comments:\nLines 443-445 and figure 3K: is the comparison between After-Pla and After-Int groups appropriate?\nResponse:\nThank you for your question. Following the suggestion of Reviewer 1, we have updated Figure 3. The Figure 3K you mentioned is now referred to as Figure 3I. For Figure 3I and its description, the explanation we provide for the KEGG functional enrichment analysis between \"After-Pla\" and \"After-Int\" groups is as follows: Firstly, in the baseline population, compared with the low-exposure group of MPs, we found that the high-exposure group of MPs was enriched in multiple pathways related to chemical carcinogens, neuromuscular disorders, and neurodegenerative diseases. Moreover, compared with the baseline group, the enrichment levels of the above disease-related pathways significantly decreased after CP intervention, while certain metabolic pathways of the organism were significantly enriched in the CP intervention group. Therefore, we speculate that the beneficial effects brought by CP intervention may be related to the changes in certain metabolic pathways of the organism. To explore whether these metabolic pathways are related to CP intervention (and not related to other factors), we have additionally added a comparison between the After-Pla group and the After-Int group.\nWe believe that your question is very reasonable. The original statement in our manuscript is indeed difficult for people to understand easily. Therefore, we have made revisions to the \"Results\" section of the manuscript: \"Moreover, Figure 3I demonstrates that the After-Int group showed significantly upregulated pathways related to amino acid biosynthesis, metabolism of diverse amino acids, and protein synthesis compared to the After-Pla group, suggesting that these changes may be associated with CP intervention.\" (Page: 15, Line: 485)\n13. Comments:\nFigure 4 D-E: the axis titles should be revised.\nResponse:\nThank you for your valuable suggestion. As per your recommendation, to enhance the clarity of Figure 4D and 4E, we have revised the x-axis title to \"HMDB Super Class\" and the y-axis title to \"Number of Differential Metabolites.\" These changes have been updated in the revised manuscript. (Page: 17, Line: 527)\n14. Comments:\nFigure 5 caption: repetitions should be reduced.\nResponse:\nThank you for your valuable suggestion. We agree that the original caption for Figure 5 was repetitive. As suggested, we have revised the caption to be more concise by merging the descriptions for similar subplots. The revised caption now reads: \"Note: (A, B) Common KEGG-annotated pathways of differential plasma metabolites and gut microbiota between (A) the H-MPs and L-MPs groups and (B) the four RCT groups; (C, D) Correlation network analysis of fecal metabolites, gut microbiota, and host phenotypes in (C) the H-MPs and L-MPs groups and (D) the four RCT groups; In the correlation networks, red and blue lines indicate positive and negative correlations, respectively, with line thickness corresponding to the P-value.\" (Page: 19, Line: 560)\nAll other similar repetitive descriptions have also been revised.\n15. Comments:\nLine 526: Please correct metabonomic by metabolome as well as in figure 6 caption. Figure 6 For the DEM, a similar graphical representation should be added in order to allow the reader to see if the variation is a decrease or a decrease and how strong it is. The complete name of the strains should appear in the figure.\nResponse:\nThank you for your comments. At first, as per your request, we have changed \"metabonomic\" to \"metabolome\" and also modified the title of Figure 6. (Page: 21, Line: 612) We have also carefully read and revised other parts of the article. Secondly, based on your suggestions, we modified the visualization mode of the DEM in Figure 6 and added the complete names of the strains, so that readers could clearly see whether this change was a reduction or a reduction to a certain extent and how strong it was.\n16. Comments:\nLine 565: TG levels are increased\nResponse:\nThank you for pointing out the inappropriate representation of the charts in our manuscript. After our careful discussion, we found that the original expression mode of Figure 6 was that both the increase and decrease were calculated using the change ratio, with the denominator being the pre-intervention value of the control group. This might indeed lead readers to mistakenly believe that \"TG levels have increased\". Therefore, to present the results more intuitively, we have redrawn Figure 6. (Page: 21, Line: 612) We believe that the revised Figure 6 has solved this problem.\n17. Comments:\nLine 614: Il-8 levels are increased.\nResponse:\nThank you for your question. This issue corresponds to the error identified in Comments 16 of our manuscript. To rectify this, we have revised and redrawn Figure 6 accordingly. (Page: 21, Line: 612)\n18. Comments:\nLine 621. The increase of inflammation by MP is weak and the reversion by CP is also weak. I think that the authors should be more cautious on their interpretation of their results.\nResponse:\nThank you for your guidance. Indeed, the findings of this study, namely the \"enhancing effect of inflammation caused by MP\" and the \"reversing effect of inflammation produced by CP\", were both relatively weak. Therefore, we have revised the relevant interpretations in the \"Discussion\" section of the manuscript to clarify the speculative nature of the research findings and to avoid overly exaggerated descriptions.\n\"This study further verified, through a population-based investigation, the potential of polyphenol intervention in alleviating inflammatory responses associated with MPs exposure, which was previously discovered in in vivo experiments. However, both the increase of inflammation by MPs and the reversion by CP intervention were relatively weak. Additionally, no significant improvements were observed in plasma CHO and LDL indicators following CP intervention. This weakens the interpretability of these findings, which may potentially be attributed to the relatively brief duration of our intervention.\" (Page: 23, Line: 692); \"Notably, it could be speculated that intestinal Staphylococcus genus and plasma metabolite PC (22:5/0:0) were the important targets for CP interventions to mitigate the toxic effects associated with the high MP exposure, which deserve further research validation. However, it should be noted that this study did not observe significant clinical improvements in most cardiovascular outcomes. Therefore, the interpretation of the aforementioned potential biomarkers should be approached with caution. The short-term changes observed may have two implications: on one hand, they may represent only a transient response to the intervention, reflecting reversible, short-term adjustments in gut ecology or metabolism; on the other hand, they may also serve as early biological signals that, if sustained over time, could contribute to longer-term physiological adaptations and ultimately influence clinical endpoints.\" (Page: 27, Line: 878); \"Given the early stage of this research and its limitations in duration, scale, and the scope of clinical indicators, further investigation is essential. Future work should prioritize conducting large-scale, long-term, and fully randomized human trials to verify the clinical effectiveness of CP protection strategies. Simultaneously, rigorous in vivo and in vitro experiments should be performed, along with targeted lipidomics research, to precisely quantify the dynamic changes of PC (22:5/0:0) and other glycerophospholipid metabolites at different time points, thus comprehensively exploring its protective rationale and potential mechanisms. Moreover, to accurately investigate the causal relationship between CP intervention and the alleviation of immune disorders associated with MP exposure, long-term CP intervention should be implemented in populations with higher levels of MP exposure. After a specific intervention period, the intervention should be ceased, and the changes in effects before and after the cessation should be analyzed to determine the causal relationship between the intervention and the outcome.\" (Page: 28, Line: 896)\nFurthermore, just as reviewer 2 mentioned in comment 5, \"Clarify limitations of cross-sectional associations and the RCT for causal inference, and propose approaches to test directionality.\" We must admit that even RCT studies cannot provide evidence for causal inference in this study. Therefore, we rethought the descriptions of causal judgment in the full manuscript and avoided exaggerated descriptions of causal judgment. This content is also described in the \"Limitations\" section of the manuscript: \"Firstly, in the cross-sectional baseline study, measuring exposures and outcomes simultaneously prevents establishing temporality, thus precluding causal inference. The results obtained from strictly controlled RCT studies may have limited external validity. The potential intervention targets identified through correlation analysis are also speculative and still require further validation via in vivo or in vitro experiments.\" (Page: 28, Line: 911)\n19. Comments:\nLines 627-641. Again the authors focused on Ruminoccocus as in the results but this required further justification.\nSimilarly the reader needs to understand why the authors focused to Phocaeicola and Bacteroides for CP intervention.\nResponse:\nThank you very much for your valuable suggestions. We agree that in the original manuscript, we failed to adequately explain the reasons for choosing Ruminoccocus, Phocaeicola and Bacteroides as the main subjects of discussion. This indeed requires further elaboration.\nWe acknowledge that as stated in comment 11 that in both the H-MPs and L-MPs groups (involving Ruminoccocus), as well as among the four CP intervention groups (involving Phocaeicola and Bacteroides), a large number of differentially expressed bacterial genera were identified in the study.\nGiven the numerous different bacterial genera, it is difficult for us to directly determine which genera are biologically \"more important\". Therefore, we referred to the screening strategy adopted in other related metagenomic studies [1-4]: we selected the genera with the highest relative abundance among all those showing statistically significant differences for in-depth analysis.\nThe reason we adopted this standard is that these bacterial genera not only have a high abundance (possibly playing a fundamental role in the intestinal ecosystem), but also their abundance is significantly influenced by MPs exposure or CP intervention. Therefore, we can reasonably infer that these genera may be the most affected and crucial groups under our experimental conditions.\nRuminoccocus, Phocaeicola and Bacteroides were selected as the key targets for our subsequent research precisely because they had the highest relative abundance among all the different bacterial genera in their respective comparison groups.\nTo address your concerns and prevent potential confusion among readers, we have revised the \"3.4 Fecal metagenome alterations of MPs exposure and CP intervention\" section in the \"Results\" and Figure S4 to clearly clarify our experimental design: \"For the FSBPs and RCT populations, the taxonomic composition of the gut microbiota is presented in Figure S4A and S4B. At the genus level, attention was paid to the significantly differentially expressed genera with the highest relative abundance. Figure S4C shows that the relative abundance of Ruminococcus and Escherichia in the H-MPs was significantly lower than that in the L-MPs; However, there was no significant difference after false discovery rate (FDR) correction. The LEfSe analysis also revealed that the Ruminococcus and Escherichia were the two bacterial genera that had the greatest impact on the differential effects between the H-MPs and L-MPs groups (Figure S4E). Furthermore, Figure S4D indicates that the relative abundances of bacterial genera such as Bacteroides, Faecalibacterium, and Phocaeicola significantly differed among the four groups. Further multiple comparisons revealed that the After-Int group had a significantly higher relative abundance of Faecalibacterium and Phocaeicola compared to the Base-Int group, whereas no significant difference in Bacteroides abundance was observed between these two groups (Figure S4G-I). The LEfSe analysis revealed that the Foetidibacter had the greatest impact on the differential effects between the four groups (Figure S4F).\" (Page: 15, Line: 458)\nWe believe that this modification will clearly convey to the readers the basis for our selection and focus on these specific bacterial genera.\nReference:\n1. Cheng M, Zhang X, Zhu J, et al. A metagenomics approach to the intestinal microbiome structure and function in high fat diet-induced obesity mice fed with oolong tea polyphenols. Food Funct. 2018 Feb 21;9(2):1079-1087.\n2. Wu G, Xu T, Zhao N, et al. A core microbiome signature as an indicator of health. Cell. 2024 Nov 14;187(23):6550-6565.e11.\n3. Zhao L, Wang C, Peng S, et al. Pivotal interplays between fecal metabolome and gut microbiome reveal functional signatures in cerebral ischemic stroke. J Transl Med. 2022 Oct 8;20(1):459.\n4. Wallen ZD, Demirkan A, Twa G, et al. Metagenomics of Parkinson's disease implicates the gut microbiome in multiple disease mechanisms. Nat Commun. 2022 Nov 15;13(1):6958.\n[redacted]\nWe express our profound gratitude to Editor and all the reviewers for their valuable comments and recognition of our efforts on the manuscript entitled “Composite polyphenols mitigate microplastic exposure-related immune disturbances: a two-phase population trial”. We have meticulously examined the comments from the editor and all the reviewers and made corresponding revisions, which are clearly marked in the manuscript. It should be noted that we have updated a detailed name of the author's institution without changing the author's affiliation. Our responses to the reviewers' comments are presented as follows, and we sincerely hope for your approval.\nREVIEWERS' COMMENTS\nReviewer #1: Most of the questions raised during the review process have now been satisfactorily addressed, and the remaining points do not affect the overall scientific quality of the work.\nIn its current form, the manuscript is clear, coherent, and meets the standards required for publication. I therefore consider that this publication can be accepted as it stands.\nResponse: Thank you for your positive suggestions and the time you spent evaluating our manuscript throughout the revision process We are glad that the current version has met your expectations and reached the publication standards. On behalf of our research team, I sincerely wish you a happy life.\nReviewer #2: I thank the authors for their extensive revisions and for the very detailed rebuttal. The revised manuscript has improved substantially in methodological transparency, statistical rigor, interpretive caution, and organization. The authors have addressed all major concerns raised in the first review round. More specifically, the authors have made creditable efforts to respond to all reviewer comments. Many sections have been clarified, methodological limitations are now openly acknowledged, and overly causal language has been corrected throughout. The multi‑omics integration is described more transparently, and the rationale for statistical choices is now better justified. The Limitations section is substantially improved, clearly outlining unavoidable constraints while avoiding overstatements.\nMy only remaining concern is that, while scientifically sound, the Discussion is now excessively long, dense, and at times repetitive. It includes very detailed mechanistic explanations, extensive literature descriptions, and elements that resemble a mini‑review rather than a focused discussion of key findings.\nIn consequence, I strongly recommend substantial shortening of the Discussion. This could improve clarity and comply with the editorial expectations of Nature Communications. Some suggestions could be as follow: i) condense the detailed biochemical pathways (particularly in Sections 4.3 and 4.5); ii) avoid re‑explaining results already covered in the Results section; iii) streamline mechanistic details to the most relevant and robust points; iv) avoid extended descriptions of individual metabolites unless central to the main conclusions.\nResponse: Thank you for your positive evaluation of the revision of our manuscript and your new suggestions. After re-reading the discussion section of the article, we found that there were indeed problems such as excessive length, overly detailed descriptions of biochemical pathways, and repetitive descriptions. Consequently, we have performed a comprehensive \"top-to-bottom\" revision of the “Discussion” section, particularly for Sections 4.3 and 4.5. The main revisions include:\n1) Simplified detailed description of biochemical pathways: We have omitted the exhaustive discussion on overall metabolites with significant changes and instead concentrated on specific metabolites and metabolic pathways that are vital to the main conclusions, such as PC(22:5/0:0) and the glycerophospholipid metabolic pathway. The redundant details in the discussions of relevant literature have also been appropriately simplified. Moreover, for the metagenomic results, discussions less relevant to the main conclusions have been appropriately reduced.\n2) Eliminate repetitive descriptions of the study results: we have removed the numerical data and redundant restatements of significant results that were already detailed in the results section. The names of the significant metabolites in the metabolic pathways already described in the results have also been removed from the \"Discussion\" section. Currently, the discussion in our manuscript primarily focuses on the interpretation and discussion of the key findings presented in the results.\n3) Simplified detailed description of the mechanism: We prioritized the most significant mechanism connections identified in our research, specifically focusing on the correlation between intestinal Staphylococcus genus, plasma metabolite PC (22:5/0:0), and the host inflammatory phenotype. By concentrating on the discussion to infer how the changes of specific microorganisms drive the alterations of glycerophospholipid-related metabolism (especially PC (22:5/0:0)), and thereby affect the inflammatory response, while clarifying the underlying core mechanism of the protective effect exerted by CP intervention.\nIn addition, we have simplified detailed discussions on non-critical issues, such as detailed speculations on the reasons for the relatively high detection levels of MPs in our study, the high detection levels of MPs in the Shanghai region, the main exposure sources of MPs, the direction of the impact of MP exposure on host responses, and speculations on the future directions of related research, etc.\nAfter these revisions, the narrative structure of the discussion section in our manuscript has become more focused and is no longer as long-winded as a \"review\". The length has been appropriately shortened. These changes have rendered our main findings and their interpretations more distinct, and this manuscript is now more accessible to readers.\nReviewer #3: The manuscript is significantly improved. The authors have addressed most of my comments. There remains an error in interpretation in Lines 451-453.The decrease in Simpson's index indicates a decrease in microbial richness, which contradicts the increase in Shannon's index, which shows increased microbial richness. If the authors want to keep both indices, they need to explain how to interpret this apparent contradiction.\nResponse: Thank you for your new comments and the positive feedback on the revised version of our manuscript. We have re-examined our research results and relevant references based on the errors you pointed out. To address this apparent contradiction, we would like to clarify the calculation method and definition of the alpha diversity indices used in our analysis.\nThe alpha diversity indices used in our analysis were the community diversity indices, which reflect the combined status of species richness and evenness. Commonly used ones are the Shannon diversity index and the Simpson diversity index (D). Firstly, the Shannon diversity index takes into consideration both the species abundance and evenness within a community. A higher value of this index indicates a higher level of diversity in the community[1-2]. The calculation formula for the Shannon diversity index is as follows[2]:\nsobs\nn n\ni i\nH =−∑ ln\nshannon N N\ni=1\nS represents The actual number of observed species;\nobs\nn represents The number of sequences contained in the i-th species\ni\nN represents the total number of sequences.\nSecondly, the Simpson diversity index (D) is frequently employed to quantitatively describe the biodiversity of a region. What might be confusing is that there are typically two calculation formulas for the Simpson index, namely the Simpson diversity index (D) and the Simpson's index of diversity (1-D). The formula for the former (the Simpson diversity index (D)) utilized in our study is as follows[3]. Its significance lies in the probability that two sequences randomly chosen from the sample belong to different species. The smaller the value, the higher the diversity level of the community[3]. This is exactly the opposite of the meaning of Simpson's Index of Diversity (1-D)[4].\n∑\nSobsn\n(n−1)\nD = i=1 i i\nsimpson N(N−1)\nS represents The actual number of observed species;\nobs\nn represents The number of sequences contained in the i-th species\ni\nN represents the total number of sequences.\nTherefore, in the results of our manuscript, the decline of the Simpson diversity index (D) indicates an increase in the diversity level of the microbial community, which is consistent with the increase in the Shannon diversity index (also indicating an increase in the diversity level of the microbial community). We acknowledge that the original presentation of the method and results might cause confusion among reviewers and readers. To avoid these misunderstandings, we have revised the “Methods” and “Results” sections: “The alpha diversity was evaluated by calculating the Shannon diversity index and the Simpson diversity index (D).” (Page 18: , Line: 772-773); “In the FSBPs, the H-MPs group showed a significantly higher Shannon diversity index and a significantly lower Simpson diversity index (D) in terms of the alpha diversity compared to the L-MPs group, both changes indicating that exposure to higher MPs increases the diversity level of the microbial community (Figure 3A-B).” (Page: 4, Line: 159-162)\nReference:\n1. Alcazar M, Escribano J, Ferré N, et al. Gut microbiota is associated with metabolic health in children with obesity. Clin Nutr. 2022;41(8):1680-1688.\n2. Smith B and Wilson JB. A consumer's guide to evenness indices. Oikos. 1996;76(1):70-82.\n3. Simpson EH. Measurement of Diversity. Nature. 1949;163:688.\n4. Mutebi JP, Crabtree MB, Kading RC, et al. Mosquitoes of western Uganda. J Med Entomol. 2012;49(6):1289-306."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Most of the questions raised during the review process have now been satisfactorily addressed, and the remaining points do not affect the overall scientific quality of the work.\nIn its current form, the manuscript is clear, coherent, and meets the standards required for publication. I therefore consider that this publication can be accepted as it stands.", "reviewer_2": "I thank the authors for their extensive revisions and for the very detailed rebuttal. The revised manuscript has improved substantially in methodological transparency, statistical rigor, interpretive caution, and organization. The authors have addressed all major concerns raised in the first review round. More specifically, the authors have made creditable efforts to respond to all reviewer comments. Many sections have been clarified, methodological limitations are now openly acknowledged, and overly causal language has been corrected throughout. The multi‑omics integration is described more transparently, and the rationale for statistical choices is now better justified. The Limitations section is substantially improved, clearly outlining unavoidable constraints while avoiding overstatements.\nMy only remaining concern is that, while scientifically sound, the Discussion is now excessively long, dense, and at times repetitive. It includes very detailed mechanistic explanations, extensive literature descriptions, and elements that resemble a mini‑review rather than a focused discussion of key findings.\nIn consequence, I strongly recommend substantial shortening of the Discussion. This could improve clarity and comply with the editorial expectations of Nature Communications. Some suggestions could be as follow: i) condense the detailed biochemical pathways (particularly in Sections 4.3 and 4.5); ii) avoid re‑explaining results already covered in the Results section; iii) streamline mechanistic details to the most relevant and robust points; iv) avoid extended descriptions of individual metabolites unless central to the main conclusions.", "reviewer_3": "The manuscript is significantly improved. The authors have addressed most of my comments. There remains an error in interpretation in Lines 451-453.The decrease in Simpson's index indicates a decrease in microbial richness, which contradicts the increase in Shannon's index, which shows increased microbial richness. If the authors want to keep both indices, they need to explain how to interpret this apparent contradiction."}, "author_response": "We express our profound gratitude to Editor and all the reviewers for their valuable comments and recognition of our efforts on the manuscript entitled “Composite polyphenols mitigate microplastic exposure-related immune disturbances: a two-phase population trial”. We have meticulously examined the comments from the editor and all the reviewers and made corresponding revisions, which are clearly marked in the manuscript. It should be noted that we have updated a detailed name of the author's institution without changing the author's affiliation. Our responses to the reviewers' comments are presented as follows, and we sincerely hope for your approval.\nREVIEWERS' COMMENTS\nReviewer #1: Most of the questions raised during the review process have now been satisfactorily addressed, and the remaining points do not affect the overall scientific quality of the work.\nIn its current form, the manuscript is clear, coherent, and meets the standards required for publication. I therefore consider that this publication can be accepted as it stands.\nResponse: Thank you for your positive suggestions and the time you spent evaluating our manuscript throughout the revision process We are glad that the current version has met your expectations and reached the publication standards. On behalf of our research team, I sincerely wish you a happy life.\nReviewer #2: I thank the authors for their extensive revisions and for the very detailed rebuttal. The revised manuscript has improved substantially in methodological transparency, statistical rigor, interpretive caution, and organization. The authors have addressed all major concerns raised in the first review round. More specifically, the authors have made creditable efforts to respond to all reviewer comments. Many sections have been clarified, methodological limitations are now openly acknowledged, and overly causal language has been corrected throughout. The multi‑omics integration is described more transparently, and the rationale for statistical choices is now better justified. The Limitations section is substantially improved, clearly outlining unavoidable constraints while avoiding overstatements.\nMy only remaining concern is that, while scientifically sound, the Discussion is now excessively long, dense, and at times repetitive. It includes very detailed mechanistic explanations, extensive literature descriptions, and elements that resemble a mini‑review rather than a focused discussion of key findings.\nIn consequence, I strongly recommend substantial shortening of the Discussion. This could improve clarity and comply with the editorial expectations of Nature Communications. Some suggestions could be as follow: i) condense the detailed biochemical pathways (particularly in Sections 4.3 and 4.5); ii) avoid re‑explaining results already covered in the Results section; iii) streamline mechanistic details to the most relevant and robust points; iv) avoid extended descriptions of individual metabolites unless central to the main conclusions.\nResponse: Thank you for your positive evaluation of the revision of our manuscript and your new suggestions. After re-reading the discussion section of the article, we found that there were indeed problems such as excessive length, overly detailed descriptions of biochemical pathways, and repetitive descriptions. Consequently, we have performed a comprehensive \"top-to-bottom\" revision of the “Discussion” section, particularly for Sections 4.3 and 4.5. The main revisions include:\n1) Simplified detailed description of biochemical pathways: We have omitted the exhaustive discussion on overall metabolites with significant changes and instead concentrated on specific metabolites and metabolic pathways that are vital to the main conclusions, such as PC(22:5/0:0) and the glycerophospholipid metabolic pathway. The redundant details in the discussions of relevant literature have also been appropriately simplified. Moreover, for the metagenomic results, discussions less relevant to the main conclusions have been appropriately reduced.\n2) Eliminate repetitive descriptions of the study results: we have removed the numerical data and redundant restatements of significant results that were already detailed in the results section. The names of the significant metabolites in the metabolic pathways already described in the results have also been removed from the \"Discussion\" section. Currently, the discussion in our manuscript primarily focuses on the interpretation and discussion of the key findings presented in the results.\n3) Simplified detailed description of the mechanism: We prioritized the most significant mechanism connections identified in our research, specifically focusing on the correlation between intestinal Staphylococcus genus, plasma metabolite PC (22:5/0:0), and the host inflammatory phenotype. By concentrating on the discussion to infer how the changes of specific microorganisms drive the alterations of glycerophospholipid-related metabolism (especially PC (22:5/0:0)), and thereby affect the inflammatory response, while clarifying the underlying core mechanism of the protective effect exerted by CP intervention.\nIn addition, we have simplified detailed discussions on non-critical issues, such as detailed speculations on the reasons for the relatively high detection levels of MPs in our study, the high detection levels of MPs in the Shanghai region, the main exposure sources of MPs, the direction of the impact of MP exposure on host responses, and speculations on the future directions of related research, etc.\nAfter these revisions, the narrative structure of the discussion section in our manuscript has become more focused and is no longer as long-winded as a \"review\". The length has been appropriately shortened. These changes have rendered our main findings and their interpretations more distinct, and this manuscript is now more accessible to readers.\nReviewer #3: The manuscript is significantly improved. The authors have addressed most of my comments. There remains an error in interpretation in Lines 451-453.The decrease in Simpson's index indicates a decrease in microbial richness, which contradicts the increase in Shannon's index, which shows increased microbial richness. If the authors want to keep both indices, they need to explain how to interpret this apparent contradiction.\nResponse: Thank you for your new comments and the positive feedback on the revised version of our manuscript. We have re-examined our research results and relevant references based on the errors you pointed out. To address this apparent contradiction, we would like to clarify the calculation method and definition of the alpha diversity indices used in our analysis.\nThe alpha diversity indices used in our analysis were the community diversity indices, which reflect the combined status of species richness and evenness. Commonly used ones are the Shannon diversity index and the Simpson diversity index (D). Firstly, the Shannon diversity index takes into consideration both the species abundance and evenness within a community. A higher value of this index indicates a higher level of diversity in the community[1-2]. The calculation formula for the Shannon diversity index is as follows[2]:\nsobs\nn n\ni i\nH =−∑ ln\nshannon N N\ni=1\nS represents The actual number of observed species;\nobs\nn represents The number of sequences contained in the i-th species\ni\nN represents the total number of sequences.\nSecondly, the Simpson diversity index (D) is frequently employed to quantitatively describe the biodiversity of a region. What might be confusing is that there are typically two calculation formulas for the Simpson index, namely the Simpson diversity index (D) and the Simpson's index of diversity (1-D). The formula for the former (the Simpson diversity index (D)) utilized in our study is as follows[3]. Its significance lies in the probability that two sequences randomly chosen from the sample belong to different species. The smaller the value, the higher the diversity level of the community[3]. This is exactly the opposite of the meaning of Simpson's Index of Diversity (1-D)[4].\n∑\nSobsn\n(n−1)\nD = i=1 i i\nsimpson N(N−1)\nS represents The actual number of observed species;\nobs\nn represents The number of sequences contained in the i-th species\ni\nN represents the total number of sequences.\nTherefore, in the results of our manuscript, the decline of the Simpson diversity index (D) indicates an increase in the diversity level of the microbial community, which is consistent with the increase in the Shannon diversity index (also indicating an increase in the diversity level of the microbial community). We acknowledge that the original presentation of the method and results might cause confusion among reviewers and readers. To avoid these misunderstandings, we have revised the “Methods” and “Results” sections: “The alpha diversity was evaluated by calculating the Shannon diversity index and the Simpson diversity index (D).” (Page 18: , Line: 772-773); “In the FSBPs, the H-MPs group showed a significantly higher Shannon diversity index and a significantly lower Simpson diversity index (D) in terms of the alpha diversity compared to the L-MPs group, both changes indicating that exposure to higher MPs increases the diversity level of the microbial community (Figure 3A-B).” (Page: 4, Line: 159-162)\nReference:\n1. Alcazar M, Escribano J, Ferré N, et al. Gut microbiota is associated with metabolic health in children with obesity. Clin Nutr. 2022;41(8):1680-1688.\n2. Smith B and Wilson JB. A consumer's guide to evenness indices. Oikos. 1996;76(1):70-82.\n3. Simpson EH. Measurement of Diversity. Nature. 1949;163:688.\n4. Mutebi JP, Crabtree MB, Kading RC, et al. Mosquitoes of western Uganda. J Med Entomol. 2012;49(6):1289-306."}]

Decision letter