SYSTEMATIC REVIEW article

Front. Nutr., 14 June 2022

Sec. Nutrition and Metabolism

Volume 9 - 2022 | https://doi.org/10.3389/fnut.2022.929343

Effects of Flavonoid Supplementation on Nanomaterial-Induced Toxicity: A Meta-Analysis of Preclinical Animal Studies

    DX

    Dongli Xie 1

    JH

    Jianchen Hu 1

    TW

    Tong Wu 2

    WX

    Wei Xu 3

    QM

    Qingyang Meng 4

    KC

    Kangli Cao 5

    XL

    Xiaogang Luo 1*

  • 1. College of Textile and Clothing Engineering, Soochow University, Suzhou, China

  • 2. Shanghai Jing Rui Yang Industrial Co., Ltd, Shanghai, China

  • 3. Shanghai Nutri-woods Bio-Technology Co., Ltd, Shanghai, China

  • 4. Shanghai Pechoin Daily Chemical Co., Ltd, Shanghai, China

  • 5. Shanghai Institute of Spacecraft Equipment, Shanghai, China

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Abstract

Background:

Nanomaterials, widely applied in various fields, are reported to have toxic effects on human beings; thus, preventive or therapeutic measures are urgently needed. Given the anti-inflammatory and antioxidant activities, supplementation with flavonoids that are abundant in the human diet has been suggested as a potential strategy to protect against nanomaterial-induced toxicities. However, the beneficial effects of flavonoids remain inconclusive. In the present study, we performed a meta-analysis to comprehensively explore the roles and mechanisms of flavonoids for animals intoxicated with nanomaterials.

Methods:

A systematic literature search in PubMed, EMBASE, and Cochrane Library databases was performed up to April 2022. STATA 15.0 software was used for meta-analyses.

Results:

A total of 26 studies were identified. The results showed that flavonoid supplementation could significantly increase the levels of antioxidative enzymes (superoxide dismutase, catalase, glutathione, glutathione peroxidase, and glutathione-S-transferase), reduce the production of oxidative agents (malonaldehyde) and pro-inflammatory mediators (tumor necrosis factor-α, interleukin-6, IL-1β, C-reactive protein, immunoglobulin G, nitric oxide, vascular endothelial growth factor, and myeloperoxidase), and alleviate cell apoptosis (manifested by decreases in the mRNA expression levels of pro-apoptotic factors, such as caspase-3, Fas cell surface death receptor, and Bax, and increases in the mRNA expression levels of Bcl2), DNA damage (reductions in tail length and tail DNA%), and nanomaterial-induced injuries of the liver (reduced alanine aminotransferase and aspartate aminotransferase activities), kidney (reduced urea, blood urea nitrogen, creatinine, and uric acid concentration), testis (increased testosterone, sperm motility, 17β-hydroxysteroid dehydrogenase type, and reduced sperm abnormalities), and brain (enhanced acetylcholinesterase activities). Most of the results were not changed by subgroup analyses.

Conclusion:

Our findings suggest that appropriate supplementation of flavonoids may be effective to prevent the occupational detriments resulting from nanomaterial exposure.

Introduction

The rapid advancement of nanotechnology has promoted the wide application of nanomaterials in many fields, such as the manufacturing industry, biomedicine, agriculture, water treatment, food additive, and cosmetics (15). The increased use makes nanomaterials unavoidably enter human bodies via various routes (including inhalation, skin absorption, ingestion, or injection) and then cause toxicities to different organs (e.g., lung, liver, kidney, brain, prostate, and testis) (6, 7). Therefore, it is urgently imperative to develop strategies to prevent and treat harmful effects of nanomaterials on human health.

Since pharmacological interventions are always prone to induce adverse side effects, nutrition modification has recently gained more attention as an important method for the prevention and treatment of diseases. Flavonoids that include six main subclasses [flavanones, flavones, flavonols, flavanols (monomer flavan-3-ols and polymer proanthocyanidins), anthocyanidins, and isoflavones] based on their molecular structures belong to a group of polyphenolic secondary metabolites in the plants (8, 9). Flavonoids can be ingested into the human diet by consuming fruits, vegetables, seeds, bark, roots, stems, leaves, flowers, and beverages (e.g., fruit juice, wine, beer, tea, coffee, and chocolate) (8, 9). Increasing evidence supports that flavonoid intake prevents the development and progression of diseases by exerting antioxidant and anti-inflammatory activities (1012). It is also well known that activations of oxidative stress and inflammation are crucial mechanisms associated with tissue injuries induced by nanomaterials (13, 14). Therefore, daily dietary supplementation of flavonoids may represent a potential approach for the prevention and treatment of nanomaterial-induced toxicities; this hypothesis had been demonstrated by numerous animal experiments (1518). However, the negative effects of flavonoids were also reported in some preclinical animal studies: Ali et al. found that, compared with rats that only received nickel oxide nanoparticles (NiONPs), the levels of hepatic oxidant malonaldehyde (MDA), antioxidant glutathione (GSH), and renal superoxide dismutase (SOD) were not significantly changed in rats undergoing a combined administration of flavones (apigenin) for 7 days, which was accompanied by no effects on liver (aspartate aminotransferase, AST; alanine aminotransferase, ALT) and renal (creatinine, urea, and blood urea nitrogen) functional biomarkers (19). Shahin et al. observed that the co-administration of flavonols (morin) for 1 or 2 weeks could not significantly alleviate titanium dioxide nanoparticles (TIO2NPs)-induced lipid peroxidation (MDA) in the testicular tissues and sperm abnormalities (20). The study of Dora et al. showed that, compared with iron oxide nanoparticle (IONP)-exposed rats, the levels of GSH and serotonin were not significantly increased in the brain homogenates of rats that were orally supplemented with flavonols (quercetin) at the dosages of 25 and 50 mg/kg. The oral additional administration of flavonols (quercetin) was also proved not to significantly reduce the levels of serum cytokine interleukin-6 (IL-6) relative to the zinc oxide nanoparticle (ZnONP)-intoxicated group (21). Abdelkarem et al. even found that the level of pro-inflammatory mediator tumor necrosis factor (TNF-α) was increased in the serum of the ZnONP-exposed rats after treatment with flavonols (quercetin) (22). Therefore, whether flavonoid supplementation exerts a protective role against nanomaterial-induced toxicities remains inconclusive.

In the present study, we performed a meta-analysis of all the available published preclinical studies to comprehensively explore the effects of flavonoid supplementation on oxidant stress-, inflammation-, and organ function-related indicators. Our study may provide a theoretical basis for clinical recommendation of flavonoid supplementation, especially for workers with occupational exposure to nanomaterials.

Materials and Methods

Literature Retrieval

This meta-analysis followed the guidelines of the preferred reporting items for systematic reviews and meta-analysis (PRISMA). Three databases, including PubMed, EMBASE, and Cochrane Library, were searched to compile relevant studies published up to April 2022. No limits were set on language. The search terms were designed by referring to the previous reports on flavonoids (2325), including (“nanoparticle” OR “carbon nanotube” OR “graphene”) AND (“flavonoids” OR “flavonols” OR “flavanols” OR “flavan-3-ols” OR “flavanones” OR “flavones” OR “isoflavones” OR “isoflavanones” OR “anthocyanins” OR “proanthocyanidins” OR “anthocyanidin” OR “polyphenol” OR “cyanidin” OR “delphinidin” OR “malvidin” OR “pelargonidin” OR “catechins” OR “epicatechin” OR “epigallocatechin” OR “gallocatechin” OR “theaflavins” OR “thearubigins” OR “quercetin” OR “kaempferol” OR “myricetin” OR “myricitrin” OR “isorhamnetin” OR “galangin” OR “morin” OR “fisetin” OR “luteolin” OR “apigenin” OR “hesperetin” OR “naringenin” OR “naringin” OR “hesperidin” OR “isoflavonoid” OR “genistein” OR “daidzein” OR “baicalin” OR “silibinin” OR “taxifolin” OR “silymarin”) AND (animal). Additionally, the reference lists of the included studies and reviews were scanned to retrieve potential eligible studies.

Inclusion and Exclusion Criteria

The participants, interventions, comparisons, outcomes, and study design (PICOS) criteria were used to include the eligible studies: (1) participants (P): murine; (2) intervention (I): the experimental group was administrated with nanomaterials and any kind of flavonoids (the dosage of which should be clearly reported); (3) comparison (C): the control group was only given nanomaterials; (4) outcomes (O): oxidative stress-related (MDA, SOD, and GSH; glutathione peroxidase, GPx; catalase, CAT; glutathione-S-transferase, GST; glutathione reductase, GR; nuclear factor erythroid 2-related factor 2, Nrf2), inflammation-related (TNF-α, IL-6, and IL-1β; C-reactive protein, CRP; immunoglobulin G, IgG; myeloperoxidase, MPO; nitric oxide, NO; vascular endothelial growth factor, VEGF), global health issues-related (body weight), organ injury-related (liver function: AST and ALT; ALP, alkaline phosphatase; renal function: albumin, urea, creatinine, uric acid, blood urea nitrogen; testis function: sperm count, sperm motility, sperm abnormalities, live sperm, and testosterone; luteinizing hormone, LH; follicle-stimulating hormone, FSH; 17β-hydroxysteroid dehydrogenase type, 17β-HSD; brain function: serotonin; acetylcholinesterase, AChE), apoptosis-related (TUNEL apoptotic index; mRNA expression levels of caspase-3; Fas cell surface death receptor, FAS; B-cell lymphoma-2, Bcl2; BCL2-associated X, apoptosis regulator, Bax), and DNA damage-related (tail length, tail DNA%) indicators; and (5) study design (S): controlled trials.

The exclusion criteria were as follows: (1) duplicate articles; (2) non-original research, such as case reports, reviews, editorials, letters, comments, and expert opinion papers; (3) in vitro and non-murine in vivo experiments; (4) interventions with plant extracts in which flavonoids were included, but detailed concentrations and dosages were unclear; (5) data could not be obtained or combined with other publications; and (6) irrelevant to the study topic. Two authors independently screened the literature, and any disagreements were resolved through discussion with a third reviewer.

Data Extraction

Two reviewers independently extracted the following data from each eligible article: the first author, publication year, country of origin, animal species, sample size, type/dose of nanomaterials, type/dose/administration route/intervention duration of flavonoids, sample source for the analysis of outcomes, and data of outcomes of interest (mean ± standard deviation). The data presented in the bar graphs were estimated by using the digitizing software Engauge Digitizer 4.11. Any discrepancies were resolved through discussion with a third reviewer.

Quality Assessment

SYRCLE’s risk of bias tool was used to assess the quality of the included animal intervention studies (26). This tool consisted of 10 questions to reflect the source of bias from selection, performance, detection, attrition, reporting, and other aspects. The risk of bias was ranked as low, unclear, or high in each item when it was labeled as yes, no, or unknown to selected articles. The methodological quality was independently evaluated by two authors. Reviewers resolved discrepancies through discussion with a third reviewer.

Statistical Analysis

STATA 15.0 software (STATA Corporation, College Station, TX, United States) was used for all statistical analyses. The effect size for all outcomes was expressed as standardized mean difference (SMD) and 95% confidence interval (CI). The heterogeneity between studies was assessed by using the Cochrane’s Q-square test and I2 statistic. A fixed-effects model was chosen to calculate the pooled results if obvious heterogeneity was present (p < 0.1 and I2 > 50%); otherwise, a random-effects model was selected. To explore the potential sources of between-study heterogeneity, subgroup analyses were performed based on nanomaterial types, subclasses, dosages, intervention durations, administration routes of flavonoids, sample sources, and animal species. Egger’s linear regression test was employed to investigate the publication bias. A trim-and-fill method was used to correct the pooled results in the presence of the publication bias (p < 0.05). The stability of the meta-analysis results was assessed by using a sensitivity analysis, which was based on the removal of one study at a time.

Results

Study Selection

As shown in Figure 1, 3,436 articles were initially retrieved after a literature search in three electronic databases, of which 1,926 were duplicates. By browsing the titles and abstracts, 1,464 were excluded since 102 of them were reviews or meta-analyses, two of them were abstracts, and 1,360 studies were irrelevant to the study topic. The full text of the remaining 46 studies was downloaded and reviewed, after which 20 studies were removed because of the following reasons: in vitro studies (n = 12); non-murine in vivo experiments (n = 3); and plant extract mixture (n = 5). Finally, 26 studies were included in this meta-analysis (1522, 2744).

FIGURE 1

FIGURE 1

Flow chart of the literature search.

Study Characteristics

The basic characteristics of the included articles are presented in Table 1. These 26 studies were published between 2012 and 2022. They were conducted in seven countries, including 12 in Egypt; six in Saudi Arabia; two in Iran, Nigeria, and China; and one in Korea and México. Twenty-two preclinical studies were carried out in rats, and the other four were conducted in mice. Three flavonoid subclasses were assessed in these studies, including flavonols (quercetin, n = 13; rutin, n = 2; morin, n = 3), flavanones (naringenin, n = 1; kolaviron, n = 2; hesperidin, n = 2; silibinin, n = 1), and flavones (apigenin, n = 3). The dosage of quercetin ranged from 5 to 200 mg/kg daily; two supplementation dosages were designed for rutin and kolaviron (50 and 100 mg/kg); three were designed for apigenin (20, 25, and 40 mg/kg); and only one dosage regimen was set for other flavonoid types (morin, 100 mg/kg; naringenin, 53 mg/kg; hesperidin, 100 mg/kg; silibinin, 20 mg/kg). Supplementation with flavonoids was used to protect the injuries induced by TiO2NPs (n = 7), ZnONPs (n = 6), silver nanoparticles (AgNPs, n = 3), carbon nanotubes (CNTs, n = 2), copper oxide nanoparticles (CuONPs, n = 1), gold nanoparticles (GNPs, n = 1), IONPs (n = 1), NiONPs (n = 1), silica dioxide nanoparticles (SiONPs, n = 2), and mesoporous silica nanoparticles (MSNPs, n = 1). Flavonoids were administered orally (n = 17), intraperitoneally (n = 5), or intragastrically (n = 4). The duration of intervention ranged from 1 to 12 weeks. The risk of bias was unclear or low for domains of all studies, indicating that the quality of included studies was acceptable (Table 2).

TABLE 1

AuthorYearCountryAnimal speciesNo.Nanomaterial typeNanomaterial
dose
Flavonoid typeFlavonoid subclassFlavonoid doseFlavonoid administration
route
Flavonoid treatment
duration
Outcomes
Elblehi et al. (42)2022EgyptRats20AgNPs50 mg/kgQuercetinFlavonols50 mg/kgOrally4.3 weeksMDA, SOD, CAT, GSH, GPx, Nrf2, TNF-α, IL-6, Bax, Bcl2, AchE
Wang et al. (43)2022ChinaMice6SiO2NPs7 mg/kgApigeninFlavones20 mg/kgOrally2.4 weeksMDA, SOD, Bax, Bcl2, Nrf2
El-Wafaey and Nafea (39)2022EgyptRats16ZnONPs300 mg/kgNaringeninFlavanones53 mg/kgOrally2 weeksSOD, MDA, TNF-α, body weight, ALT, AST
Dora et al. (28)2021EgyptRats16IONPs50 mg/kgQuercetinFlavonols25 mg/kgOrally4.3 weeksMDA, GSH, AchE, serotonin
Wang et al. (44)2021ChinaMice6MSNPS300 mg/kgApigeninFlavones40 mg/kgIntraperitoneally7 daysTNF-α, IL-6, creatinine, blood urea nitrogen
Ali et al. (19)2021EgyptRats12NiONPs100 mg/kgApigeninFlavones25 mg/kgOrally1, 2, 4 weeksMDA, GSH, SOD, AST, ALT, creatinine, blood urea nitrogen, urea
Abdelazeim et al. (27)2020EgyptRats20CuONPs100 mg/kgQuercetinFlavonols150 mg/kgIntraperitoneally3 weeksALT, AST, albumin, tail length, tail DNA%
Awogbindin (71)2020NigeriaRats20CNTs1 mg/kgKolavironFlavanones50, 100 mg/kgOrally2 weeksMDA, SOD, CAT, GST, GPx, GSH, NO, MPO, TNF-α, ALT, AST, ALP, creatinine, urea
Adedara et al. (36)2020NigeriaRats20CNTs1 mg/kgKolavironFlavanones50, 100 mg/kgOrally2 weeksMDA, SOD, CAT, GSH, GST, GPx, MPO, NO, TNF-α, AchE
Lim et al. (41)2020KoreaMice12SiONPs20 mg/kgSilibininFlavanones20 mg/kgOrally2 weeksTNF-α, IL-6, IL-1β
Arisha et al. (37)2019EgyptRats24AgNPs50 mg/kgMorinFlavonols30 mg/kgOrally8 weeksMDA, SOD, CAT, GPx, ALT, AST, ALP, creatinine, blood urea nitrogen, uric acid, sperm count, live sperm, sperm motility, sperm abnormalities, LH, FSH, testosterone, FAS, caspase-3, Bax, Bcl2
Hussein et al. (38)2019EgyptRats20TiO2NPs300 mg/kgMorin, rutin, morin + rutinFlavonols30 mg/kg, 100 mg/kg, 30 + 100 mg/kgIntragastrically4.3 weeksMDA, SOD, CAT, GSH, sperm count, live sperm, sperm motility, sperm abnormalities, LH, FSH, testosterone
Abdelhalim et al., (29)2018Saudi ArabiaRats12GNPs50 μlQuercetinFlavonols100 mg/kgIntraperitoneally1 weekMDA, GSH, creatinine, blood urea nitrogen, uric acid
Abdelhalim et al., (30)2018Saudi ArabiaRats10GNPs50 μlQuercetinFlavonols200 mg/kgIntraperitoneally1 weekMDA, GSH, ALP, ALT
Alidadi et al. (32)2018IranRats16TiO2NPs50 mg/kgQuercetinFlavonols75 mg/kgOrally3 weeksMDA, SOD, CAT, body weight, creatinine, blood urea nitrogen, uric acid, TUNEL apoptotic index
Fadda et al. (16)2018Saudi ArabiaRats20TiO2NPs1000 mg/kgQuercetinFlavonols200 mg/kgOrally3 weeksMDA, TNF-α, IL-6, CRP, NO, IgG, VEGF, ALT, tail length, tail DNA%
Ansar et al. (18)2018Saudi ArabiaRats12ZnONPs600 mg/kgHesperidinFlavanones100 mg/kgOrally1 weekCAT, GPx, SOD, MDA, GSH
Shahin and Mohamed (20)2017EgyptRats12TiO2NPs50 mg/kgMorinFlavonols30 mg/kgIntragastrically1, 2, 3 weeksMDA, GSH, TNF-α, body weight, testosterone, 17β-HSD, LH, FSH, FAS,
Caspase 3, Bax, Bcl2
Ahmed and Hussein (40)2017EgyptRats18AgNPs30 mg/kgRutinFlavonols50 mg/kgOrally8 weeksMDA, GSH, CAT, SOD, GPx, serotonin
Khorsandi et al. (31)2017IranMice16TiO2NPs300 mg/kgQuercetinFlavonols75 mg/kgIntragastrically6 weeksMDA, SOD, CAT, body weight, testosterone, sperm count, sperm abnormality, TUNEL apoptotic index
Ansar et al. (17)2017Saudi ArabiaRats12ZnONPs600 mg/kgHesperidinFlavanones100 mg/kgOrally1 weekCAT, GPx, SOD, MDA, GSH, TNF-α, IL-6, IL-1β, CRP
Abdelkarem et al. (22)2016EgyptRats20ZnO NPs600 mg/kgQuercetinFlavonols200 mg/kgOrally3 weeksNO, IgG, TNF-α, IL-6, CRP, tail length, tail DNA%
Hussein et al. (33)2016EgyptRats20ZnONPs100, 400 mg/kgQuercetinFlavonols100 mg/kgIntragastrically12 weeksMDA, GSH, GPx, SOD, CAT, sperm count, live sperm, sperm motility, testosterone, 17β-HSD
González-Esquivel et al. (34)2015MéxicoRats10TiO2NPs5 mg/kgQuercetinFlavonols5 mg/kgIntraperitoneally2, 4 weeksAST, ALT, MDA, Gpx, GR
Al-Rasheed et al. (15)2013EgyptRats20TiO2NPs600, 1000 mg/kgQuercetinFlavonols200 mg/kgOrally3 weeksGSH, TNF-α, IL-6, CRP, NO, IgG, VEGF, creatinine, urea, uric acid
Faddah et al. (21)2012Saudi ArabiaRats20ZnONPs600, 1000 mg/kgQuercetinFlavonols200 mg/kgOrally3 weeksGSH, TNF-α, IL-6, CRP, NO, IgG, VEGF, body weight, creatinine, urea, uric acid

Basic characteristics of the included articles.

TiO2NPs, titanium dioxide nanoparticles; CuONPs, copper oxide nanoparticles; IONPs, iron oxide nanoparticles; ZnONPs, zinc oxide nanoparticles; GNPs, gold nanoparticles; NiONPs, nickel oxide nanoparticles; AgNPs, silver nanoparticles; CNTs, carbon nanotubes; SiONPs, silica dioxide nanoparticles; MSNPs, mesoporous silica nanoparticles; MDA, malonaldehyde; SOD, superoxide dismutase; GSH, glutathione; GPx, glutathione peroxidase; CAT, catalase; GST, Glutathione-S-transferase; GR, glutathione reductase; Nrf2, nuclear factor erythroid 2-related factor 2; TNF, tumor necrosis factor; IL, interleukin; CRP, C-reactive protein; IgG, immunoglobulin G; MPO, myeloperoxidase; NO, nitric oxide; VEGF, vascular endothelial growth factor; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; LH, luteinizing hormone; FSH, follicle-stimulating hormone; 17β-HSD, 17β-hydroxysteroid dehydrogenase type; AChE, acetylcholinesterase.

TABLE 2

ReferencesSelection bias
Performance bias
Detection bias
Attrition bias
Reporting bias
SGBCACRHBIROABOAIODSOROther
Elblehi et al. (42)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Wang et al. (43)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
El-Wafaey and Nafea (39)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Wang et al. (44)UnclearUnclearUnclearUnclearUnclearUnclearLowLowLowLow
Dora et al. (28)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Ali et al. (19)UnclearUnclearUnclearLowUnclearLowUnclearLowLowLow
Abdelazeim et al. (27)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Awogbindin (71)UnclearUnclearUnclearLowUnclearLowUnclearLowLowLow
Adedara et al. (36)UnclearUnclearUnclearLowUnclearLowUnclearLowLowLow
Lim et al. (41)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
Arisha et al. (37)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Hussein et al. (38)UnclearLowUnclearLowUnclearUnclearUnclearLowLowLow
Abdelhalim et al. (2018) (29)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
Abdelhalim et al. (2018) (30)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
Alidadi et al. (32)UnclearLowUnclearLowLowUnclearLowLowLowLow
Fadda et al. (16)UnclearUnclearUnclearUnclearUnclearLowUnclearLowLowLow
Ansar et al. (18)UnclearLowUnclearLowUnclearUnclearUnclearLowLowLow
Shahin and Mohamed (20)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Ahmed and Hussein (40)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Khorsandi et al. (31)UnclearLowUnclearLowLowLowLowLowLowLow
Ansar et al. (17)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
Abdelkarem et al. (22)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Hussein et al. (33)UnclearUnclearUnclearUnclearUnclearUnclearUnclearLowLowLow
González-Esquivel et al. (34)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Al-Rasheed et al. (15)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow
Faddah et al. (21)UnclearUnclearUnclearLowUnclearUnclearUnclearLowLowLow

Quality assessments based on SYRCLE’s risk of bias tool.

SG, sequence generation; BC, baseline characteristics; AC, allocation concealment; RH, random housing; BI, blinding of investigators; ROA, random outcome assessment; BOA, blinding of outcome assessor; IOD, incomplete outcome data; SOR, selective outcome reporting.

Meta-Analysis

Since multiple dosages, intervention durations, and tissue samples were collected for some studies, the number of datasets in our meta-analysis was larger than the actual number of included studies. The detailed data that were extracted for each variable are summarized in Supplementary Table 1.

Effects of Flavonoid Supplementation on Oxidative Stress

The effects of flavonoid supplementation on MDA, SOD, CAT, GSH, GPx, GST, GR, and Nrf2 were reported in 50, 30, 22, 40, 25, 10, 9, and 2 datasets, respectively (Supplementary Table 1). The overall meta-analysis results showed that, compared with the nanomaterial-exposed group, nutritional interventions with flavonoids could significantly reduce the levels of pro-oxidant MDA (SMD = − 7.07; 95%CI, −8.06 to −5.96; p < 0.001; Figure 2) but increase the levels of antioxidant SOD (SMD = 6.06; 95%CI, 5.26 – 6.87; p < 0.001; Figure 3), CAT (SMD = 6.70; 95%CI, 5.52 – 7.88; p < 0.001; Figure 4), GSH (SMD = 5.64; 95%CI, 4.59 – 6.70; p < 0.001), GPx (SMD = 3.79; 95%CI, 2.57 – 5.00; p < 0.001), GST (SMD = 6.98; 95%CI, 5.37 – 8.60; p < 0.001), and Nrf2 (SMD = 7.27; 95%CI, 4.97 – 9.58; p < 0.001) (Table 3). The level of GR was not significantly different between two groups (p = 0.092) (Table 3).

FIGURE 2

FIGURE 2

Forest plots to show the effects of flavonoid supplementation on MDA levels compared with the nanomaterial exposure group. MDA, malonaldehyde; SMD, standardized mean difference; CI, confidence interval.

FIGURE 3

FIGURE 3

Forest plots to show the effects of flavonoid supplementation on SOD levels compared with the nanomaterial exposure group. SOD, superoxide dismutase; SMD, standardized mean difference; CI, confidence interval.

FIGURE 4

FIGURE 4

Forest plots to show the effects of flavonoid supplementation on CAT levels compared with the nanomaterial exposure group. CAT, catalase; SMD, standardized mean difference; CI, confidence interval.

TABLE 3

VariableNo.SMD95%CIPE-valueI2PH-valueModelEgger p
Oxidative stress
MDA50–7.07–8.06,–5.96<0.00189.2<0.001R<0.001
SOD306.065.26,6.87<0.00171.1<0.001R<0.001
CAT226.705.52,7.88<0.00183.5<0.001R<0.001
GSH405.644.59,6.70<0.00188.4<0.001R<0.001
GPx253.792.57,5.00<0.00191.9<0.001R0.001
GST106.985.37,8.60<0.00178.8<0.001R<0.001
GR91.63–0.26,3.520.09288.0<0.001R0.502
Nrf227.274.97,9.58<0.0010.00.380F
Inflammation
NO16–10.69–12.88,–8.51<0.00186.7<0.001R<0.001
TNF-α28–6.61–8.08,–5.15<0.00191.8<0.001R<0.001
IL-611–4.10–5.38,–2.82<0.00183.5<0.001R<0.001
IL-1β3–4.20–5.45,–2.94< 0.0010.00.657F0.008
CRP7–5.24–7.35,–3.14<0.00190.1<0.001R<0.001
IgG6–7.33–9.81,–4.84<0.00184.4<0.001R<0.001
VEGF5–8.51–11.50,–5.52<0.00182.6<0.001R<0.001
MPO10–13.58–16.98,–10.18<0.00186.6<0.001R<0.001
Apoptosis
TUNEL apoptotic index2–10.98–17.63,–4.330.00177.30.036R
FAS mRNA7–11.14–14.01,–8.27<0.00159.50.022R0.010
Caspase 3 mRNA7–10.46–12.14,–8.79<0.0015.90.383F0.157
Bax mRNA12–9.98–11.73,–8.23<0.00154.90.011R<0.001
Bcl2 mRNA99.195.92,12.46<0.00188.7<0.001R0.001
DNA damage
Tail length3–13.77–16.45,–11.08<0.00134.40.218F<0.001
Tail DNA%3–8.59–10.31,–6.87<0.00125.40.262F0.003
Body weight81.110.26,1.970.01176.5<0.001R0.003
Liver function
ALT14–6.15–8.77,–3.53<0.00194.8<0.001R<0.001
AST12–5.25–7.44,–3.07<0.00192.5<0.001R<0.001
ALP4–11.26–17.71,–4.800.00194.3<0.001R0.031
Albumin48.132.20,14.070.00793.3<0.001R0.008
Renal function
Urea9–3.56–5.08,–2.04<0.00189.0<0.001R<0.001
Blood urea nitrogen7–5.76–8.67,–2.84<0.00191.6<0.001R0.001
Creatinine13–5.22–7.05,–3.38<0.00191.9<0.001R<0.001
Uric acid7–3.63–5.68,–1.580.00193.3<0.001R<0.001
Testis function
Testosterone106.974.48,9.46<0.00192.3<0.001R<0.001
FSH72.61–1.50,6.710.21395.9<0.001R0.784
LH73.09–1.60,7.780.19696.4<0.001R0.239
Sperm motility67.644.06,11.22<0.00193.4<0.001R<0.001
Sperm count75.952.93,8.97<0.00194.9<0.001R0.001
Sperm abnormalities5–7.73–9.87,–5.60<0.00168.20.014R0.007
Live sperm67.051.32,12.780.01696.9<0.001R0.151
17β-HSD55.353.21,7.49<0.00184.6<0.001R<0.001
Brain function
Serotonin42.18–0.57,4.930.12091.0<0.001R0.005
AChE104.380.49,8.280.02796.5<0.001R0.004

Meta-analysis results.

MDA, malonaldehyde; SOD, superoxide dismutase; GSH, glutathione; GPx, glutathione peroxidase; CAT, catalase; GST, Glutathione-S-transferase; GR, glutathione reductase; TNF, tumor necrosis factor; IL, interleukin; CRP, C-reactive protein; IgG, immunoglobulin G; MPO, myeloperoxidase; NO, nitric oxide; VEGF, vascular endothelial growth factor; Nrf2, nuclear factor erythroid 2-related factor 2; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; LH, luteinizing hormone; FSH, follicle-stimulating hormone; 17β-HSD, 17β-hydroxysteroid dehydrogenase type; AChE, acetylcholinesterase; SMD, standardized mean difference; CI, confidence interval; F, fixed-effects; R, random-effects; PH-value, significance for heterogeneity; PE-value, significance for treatment effects.

Bold values indicate the outcomes significantly changed by flavonoids.

As shown in Table 3, a random-effects model was selected for the meta-analyses because there was evidence of heterogeneity in the analysis of all oxidative stress-related indicators. Therefore, subgroup analyses were performed for them. The pooled results showed that the significant beneficial effects of flavonoid supplementation on MDA (Figure 2), SOD (Figure 3), and CAT (Figure 4) were still found in all subgroups regardless of nanomaterial types, subclasses, dosages, intervention durations, administration routes of flavonoids, sample sources, and animal species (Supplementary Table 2). Although supplementation with all three flavonoid subclasses at various dosages and durations was found to reverse the decrease in GSH induced by nanomaterials (except for GNPs), the beneficial effects for brain, liver, kidney, and prostate tissues (except of testis) were mainly exerted by oral or intragastrical administration of quercetin, rutin, kolaviron, and apigenin but not morin (p = 0.377) and hesperidin (p = 0.176) (Supplementary Table 2). The significant effects on GPx of brain, liver, and testis tissues (not kidney) were only found in the subgroups with oral or intragastrical supplementation of flavanone (not flavonols) and flavonoid interventions for more than 2 weeks (Supplementary Table 2). Flavanone (kolaviron) supplementation was shown to significantly increase the levels of GST in brain, liver, and kidney tissues regardless of the used dosages (Supplementary Table 2). Flavanone (hesperidin) supplementation was found to increase the level of GR (SMD = 3.68; 95%CI, 1.71 – 5.65; p < 0.001) (Supplementary Table 2), but only one study reported this result, and thus, further confirmation was still needed.

Effects of Flavonoid Supplementation on Inflammation

The levels of NO, TNF-α, IL-6, IL-1β, CRP, IgG, VEGF, and MPO were measured in 16, 28, 11, 3, 7, 6, 5, and 10 datasets, respectively (Supplementary Table 1). The overall meta-analysis results indicated that co-administration of flavonoids to animals intoxicated with nanomaterials significantly ameliorated the elevation in the levels of pro-inflammatory NO (SMD = − 10.69; 95%CI, − 12.88 to − 8.51; p < 0.001; Figure 5), TNF-α (SMD = − 6.61; 95%CI, −8.08 to −5.15; p < 0.001; Figure 6), IL-6 (SMD = − 4.10; 95%CI, −5.38 to − 2.82; p < 0.001; Figure 7), IL-1β (SMD = −4.20; 95%CI, −5.45 to −2.94; p < 0.001), CRP (SMD = −5.24; 95%CI, −7.35 to − 3.14; p < 0.001), IgG (SMD = − 7.33; 95%CI, −9.81 to −4.84; p < 0.001), VEGF (SMD = −8.51; 95%CI, −11.50 to −5.52; p < 0.001), and MPO (SMD = − 13.58; 95%CI, −16.98 to −10.18; p < 0.001) compared with their levels in nanomaterial-treated animals (Table 3).

FIGURE 5

FIGURE 5

Forest plots to show the effects of flavonoid supplementation on NO levels compared with the nanomaterial exposure group. NO, nitric oxide; SMD, standardized mean difference; CI, confidence interval.

FIGURE 6

FIGURE 6

Forest plots to show the effects of flavonoid supplementation on TNF-α levels compared with the nanomaterial exposure group. TNF-α, tumor necrosis factor-α; SMD, standardized mean difference; CI, confidence interval.

FIGURE 7

FIGURE 7

Forest plots to show the effects of flavonoid supplementation on IL-6 levels compared with the nanomaterial exposure group. IL, interleukin; SMD, standardized mean difference; CI, confidence interval.

Except for IL-1β which was analyzed with a fixed-effects model, other outcomes were analyzed with a random-effects model because of the presence of significant heterogeneity. Thus, subgroup analyses were performed for them. The pooled results showed that, compared with controls, all these pro-inflammatory mediators were still significantly reduced after the administration of flavonoids regardless of which subgroups they belonged to (Supplementary Table 3 and Figures 57).

Effects of Flavonoid Supplementation on Apoptosis

TUNEL apoptotic index and the mRNA expression levels of apoptosis-related genes (FAS, caspase-3, Bax, and Bcl2) were measured in 2, 7, 7, 12, and 9 datasets, respectively. The pooled analysis of these indicators showed that flavonoid (morin or morin + rutin) supplementation decreased the TUNEL apoptotic index (SMD = − 10.98; 95%CI, −17.63 to –4.33; p = 0.001), downregulated the mRNA expression levels of pro-apoptotic factors, such as FAS (SMD = −11.14; 95%CI, −14.01 to −8.27; p < 0.001), caspase-3 (SMD = − 10.46; 95%CI, −12.14 to −8.79; p < 0.001), and Bax (SMD = − 9.98; 95%CI, −11.73 to −8.23; p < 0.001), and upregulated the mRNA expression level of anti-apoptotic Bcl2 (SMD = 9.19; 95%CI, 5.92 – 12.46; p < 0.001) compared with the nanomaterial-exposed groups (Table 3). These results of FAS, caspase-3, and Bax indicators were not changed by subgroup factors. The mRNA expression level of Bcl2 seemed to be only significantly increased by quercetin and morin but not by apigenin (Supplementary Table 4).

Effects of Flavonoid Supplementation on DNA Damage

Comet assay was performed to evaluate the DNA damage in two studies with three datasets, after which the data about tail DNA content and tail length were obtained. The pooled analysis of these data found that flavonoid (quercetin) supplementation led to significant decreases in the tail length (SMD = −13.77; 95%CI, −16.45 to −11.08; p < 0.001) and the tail DNA% (SMD = −8.59; 95%CI, −10.31 to −6.87; p < 0.001) (Table 3).

Effects of Flavonoid Supplementation on Body Weight

Eight datasets recorded the body weight of the animals treated with nanomaterials alone or co-administered with flavonoids (Supplementary Table 1). The meta-analysis of these eight datasets under a random-effects model revealed that, relative to nanomaterial-intoxicated animals, flavonoid supplementation significantly increased the body weight of the animals (SMD = 1.11; 95%CI, 0.26–1.97; p = 0.011) (Table 3). The subgroup analysis indicated that only morin (p = 0.019) and naringenin (p = 0.008; this conclusion needed further confirmation because only one study was included) were effective interventions for improving the body weight of animals, but not quercetin (p = 0.397). Also, the improvement effect on the body weight was only limited to the subgroups with TiO2NP exposure, with flavonoid supplementation for less than 2 weeks and with an intervention dosage of ≤ 100 mg/kg (Supplementary Table 5).

Effects of Flavonoid Supplementation on Liver Function

Alanine aminotransferase, AST, ALP, and albumin were determined in our included studies (involving 14, 12, 4, and 4 datasets, respectively) to explore whether flavonoid supplementation could protect against nanomaterial-induced liver injuries (Supplementary Table 1). As shown in Table 3, the levels of ALT (SMD = − 6.15; 95%CI, −8.77 to −3.53; p < 0.001), AST (SMD = −5.25; 95%CI, −7.44 to −3.07; p < 0.001), and ALP (SMD = −11.26; 95%CI, −17.71 to −4.80; p = 0.001) were found to be significantly decreased; the level of albumin (SMD = 8.13; 95%CI, 2.20 – 14.07; p = 0.007) was significantly increased in the animals that received flavonoids in addition to nanomaterials.

Subgroup analyses demonstrated that the effects on ALT, AST, and albumin were particularly significant in the subgroups treated with flavonol and flavanone subclasses at a high dosage (>100 mg/kg) or a low dosage (≤50 mg/kg) and undergoing flavonoid treatment for more than 2 weeks. The level of ALP seemed not to be significantly improved in the subgroups stratified by flavonoid subclasses (Supplementary Table 6). Furthermore, flavonoid supplementation prevented ZnONP-, AgNP-, CuONP-, CNT-, and TiO2NP-induced increases in the levels of ALT and/or AST and a NiONP-induced increase in the level of albumin (Supplementary Table 6).

Effects of Flavonoid Supplementation on Renal Function

Renal function biomarkers (urea, blood urea nitrogen, creatinine, and uric acid) were, respectively, detected in 9, 7, 13, and 7 datasets (Supplementary Table 1). The combined results from a random-effects model indicated that supplementation with flavonoids to the nanomaterial-exposed animals resulted in significant decreases in the levels of urea (SMD = −3.56; 95%CI, −5.08 to −2.04; p < 0.001), blood urea nitrogen (SMD = − 5.76; 95%CI, −8.67 to −2.84; p < 0.001), creatinine (SMD = −5.22; 95%CI, −7.05 to −3.38; p < 0.001), and uric acid (SMD = − 3.63; 95%CI, − 5.68 to −1.58; p = 0.001) (Table 3).

Subgroup analyses demonstrated that supplementation with all these three flavonoid subclasses (without dosage requisition) provided statistically significant effects on four renal function biomarkers, especially when the interventions were performed for more than 2 weeks. Additionally, flavonoid supplementation only prevented TiO2NP- and AgNP-induced increases in the level of uric acid; the beneficial effects of flavonoids on the levels of urea and blood urea nitrogen were applicable to all studied nanomaterials; MSNP-induced creatinine was not significantly improved by flavonoids (Supplementary Table 7).

Effects of Flavonoid Supplementation on Testis Function

Sex hormones (testosterone, FSH, and LH), semen profile (sperm motility, sperm cell count, sperm abnormalities, and live sperm), and steroidogenesis pathway gene (17β-HSD) were reported in 10, 7, 7, 6, 7, 5, 6, and 5 datasets, respectively (Supplementary Table 1). Overall, we found that the intake of flavonoids could trigger marked increases in the serum testosterone level (SMD = 6.97; 95%CI, 4.48 – 9.46; p < 0.001), sperm motility (SMD = 7.64; 95%CI, 4.06 – 11.22; p < 0.001), cell count (SMD = 5.95; 95%CI, 2.93 – 8.97; p < 0.001), live percentage (SMD = 7.05; 95%CI, 1.32 – 12.78; p = 0.016), and 17β-HSD expression levels (SMD = 5.35; 95%CI, 3.21 – 7.49; p < 0.001) but induce a decrease in the percentage of abnormal sperms (SMD = − 7.73; 95%CI, −9.87 to −5.60; p < 0.001) compared with the nanomaterial-exposed group (Table 3). The FSH and LH levels were not significantly changed by flavonoids.

Only the flavonol subclass was investigated in the included studies; thus, the overall meta-analysis results of flavonoid supplementation represented the effects of flavonols on the testis function. Quercetin, rutin, and morin were all shown to significantly improve the testosterone levels, the percentage of abnormal sperms, and the expression levels of 17β-HSD; sperm motility, count, and the percentage of live sperm seemed to be only enhanced by rutin and morin but not by quercetin (Supplementary Table 8). Flavonoid dosage, intervention duration, and route factors did not influence the meta-analysis results (except for live sperm which was only significantly increased when flavonoids were given at a dosage of > 100 mg/kg). Although supplementation with rutin and rutin + morin was shown to increase the FSH and LH levels in the subgroup analyses, there was only one study to demonstrate this conclusion (Supplementary Table 8), and thus, further confirmation was still needed.

Effects of Flavonoid Supplementation on Brain Function

Serotonin and AchE were two biomarkers to reflect the nanomaterial-induced neurotoxicity in the brain and the effects of flavonoid supplementation. They were reported in four and ten datasets, respectively. The overall meta-analysis results showed that dietary supplementation with flavonoids was not significantly associated with alterations in the serotonin level (p = 0.120). The AChE activity was significantly enhanced by flavonoids (p = 0.027) (Table 3), and this significance was amplified in the flavanone (kolaviron) subgroup (p < 0.001) (Supplementary Table 9).

Publication Bias and Sensitivity Analysis

Egger’s linear regression test revealed the presence of the publication bias for the analysis of several indicators (except of GR, p = 0.502; FSH, p = 0.784; LH, p = 0.239; live sperm, p = 0.151; caspase-3, p = 0.157) (Table 3). Thus, a trim-and-fill method was applied to adjust the effects of the publication bias for these indicators. As a result, beneficial effects of flavonoid supplementation on MDA, NO, TNF-a, IL-6, TNF-α, CRP, IgG, VEGF, MPO, ALT, ALP, AST, urea, blood urea nitrogen, creatinine, uric acid, FAS, Bax mRNA, sperm abnormalities, tail length, and tail DNA% were found not to be altered after correction; significant results were still obtained for SOD (SMD = 4.77; 95%CI, 3.86 – 5.67; p < 0.001; Figure 8), CAT (SMD = 4.41; 95%CI, 3.20 – 5.76; p < 0.001), GSH (SMD = 3.66; 95%CI, 2.53 – 4.79; p < 0.001), GPx (SMD = 1.91; 95%CI, 0.64 – 3.19; p < 0.001), GST (SMD = 5.23; 95%CI, 3.53 – 6.94; p < 0.001), Bcl2 (SMD = 4.07; 95%CI, 0.77 – 7.37; p = 0.016), testosterone (SMD = 2.88; 95%CI, 0.26 – 5.49; p = 0.031), sperm motility (SMD = 3.60; 95%CI, 0.17 – 7.03; p = 0.04), and 17β-HSD (SMD = 3.58; 95%CI, 1.28 – 5.87; p = 0.002); the results of the body weight (SMD = 1.93; 95%CI, 0.77 – 4.86; p = 0.160), albumin (SMD = −15.51; 95%CI, −2.85 to 8.34; p = 0.337), sperm count (SMD = 2.09; 95%CI, −0.96 to 5.14; p = 0.180), live sperm (SMD = 0.83; 95%CI, −4.51 to 6.16; p = 0.304), and AChE (SMD = −0.55; 95%CI, −4.42 to 3.31; p = 0.780) were no longer significant after correction. The sensitivity analysis results also showed that the exclusion of any single study did not affect the synthesized results (Figure 9).

FIGURE 8

FIGURE 8

Publication bias analysis for SOD. (A) Egger’s publication bias plot; (B) the trim-and-fill analysis to adjust estimates. SND, standard normal deviation; CI, confidence interval.

FIGURE 9

FIGURE 9

Sensitivity analysis for SOD. CI, confidence interval.

Discussion

To our knowledge, this is the first study to systematically evaluate the effects of flavonoid supplementation on nanomaterial-induced toxicities and potential mechanisms. Our meta-analysis of data from 26 preclinical animal studies showed that flavonoid supplementation could significantly increase the levels of anti-oxidative enzymes (SOD, CAT, GSH, GPx, and GST), reduce the production of oxidative agents (MDA) and pro-inflammatory mediators (NO, TNF-α, IL-6, IL-1β, CRP, IgG, VEGF, and MPO), and alleviate cell apoptosis (manifested by decreases in the mRNA expression levels of pro-apoptotic factors, such as caspase-3, FAS, and Bax, and increases in the mRNA expression levels of Bcl2), DNA damage (reductions in tail length and tail DNA%), and nanomaterial-induced injuries of the liver (reduced ALT and AST activities), kidney (reduced urea, blood urea nitrogen, creatinine, and uric acid concentration), testis (increased testosterone, sperm motility, 17β-HSD, and reduced sperm abnormalities), and brain (enhanced AChE activities). Our conclusions were in line with previous meta-analyses that reported the protective roles of flavonoids on other oxidative stress- and inflammation-related diseases (11, 45). These findings suggest that appropriate supplementation of flavonoids may be a potential measure to prevent occupational detriments resulting from nanomaterial exposure.

Eight flavonoid types belonging to three subclasses [flavonols (quercetin, rutin, and morin), flavanones (naringenin, kolaviron, hesperidin, and silibinin), and flavones (apigenin)] were contained in our included studies. As shown in Supplementary Figure 1, all these flavonoids possess phenolic hydroxyl groups structurally. Thus, they can scavenge free radicals directly by donating hydrogen or electron (46) and then inhibit lipid peroxidation to prevent the formation of the end products of lipid peroxidation (MDA) (47). This conclusion was confirmed in our subgroup analyses (i.e., the level of MDA induced by nanomaterials was significantly reduced by flavonoids regardless of flavonoid types, durations, and dosages). Furthermore, flavonoids [e.g., grape seed procyanidin extract (48), apigenin (43), and quercetin (42)] were reported to enhance the expression level of Nrf2, which could subsequently bind with the antioxidant response element to stimulate the transcription of antioxidant enzymes. In accordance with this theory, we found that the levels of SOD, CAT, GSH, GPx, and GST were significantly increased (accompanied by upregulation of Nrf2) after supplementation with overall flavonoids in nanomaterial-intoxicated animals. However, interestingly, subgroup analyses showed that the levels of SOD and CAT changed by various nanomaterials could be influenced by all flavonoid types; morin and hesperidin had no effects on the level of GSH; quercetin did not significantly induce the increase in the level of GPx. These results may be explained by the following reasons: (1) the sample size (especially hesperidin with only two data analyzed) was small, which may cause deviation in the results; (2) SOD and CAT are the first-line enzymes of cellular defense against oxidative injuries, and they can be rapidly induced to decompose O2 and H2O2 to prevent the formation of more reactive hydroxyl radicals (49); and (3) oral quercetin supplementation was found to be superior to intraperitoneal administration (the main route for studies with GPx analyzed, as given in Supplementary Table 1) in the protection against the oxidative stress (50).

There was evidence that nanomaterials caused an inflammatory response (increases in the levels of TNF-α, IL-6, and IL-1β) by triggering the generation of reactive oxygen species (ROS), which then activated mitogen-activated protein kinase–nuclear factor kappa B/activator protein-1 pathways (41, 5153). Thus, blocking the production of ROS may be a potential reason to explain the anti-inflammatory roles of flavonoid supplementation, which was demonstrated by the study of Lim et al. (41). ChIP-seq analysis identified that Nrf2 directly bound to the upstream regions of pro-inflammatory cytokine genes (IL-6 and IL-1) to inhibit their expressions in M1-type macrophages, which was independent of the redox conditions (54). Therefore, upregulation of Nrf2 expression may be another potential mechanism of flavonoids to alleviate nanomaterial-induced inflammation as demonstrated by Elblehi et al. (42). Moreover, epigenetic regulation was considered to be important for nanomaterial-mediated toxicities (55). Sirtuin 1 (SIRT1), a nicotinamide adenosine dinucleotide-dependent deacetylase, was found to suppress the transcription of high-mobility group box 1 by direct deacetylation regulation (56, 57), which then inhibited cells to release pro-inflammatory mediators (IL-6, TNF-α, CRP, and IgG) by binding to toll-like receptor 4 (5759). Hereby, the activation of SIRT1 signaling pathways also could protect against nanomaterial-induced inflammation (60). In line with these studies, we found that the levels of TNF-α, IL-6, IL-1β, CRP, and IgG were significantly reduced by flavonoids compared with the nanomaterial-exposed group, and these results were not influenced by subgroup factors.

It had been widely accepted that ROS accumulation induced DNA damage (61) and activated mitochondria-dependent apoptotic signaling pathways (including increasing the expression levels of caspase-3 and Bax and decreasing the expression levels of Bcl2) (62). The addition of pro-inflammatory cytokines TNF-α and IL-1β was demonstrated to increase the Fas expression and Fas-mediated caspase activation and apoptosis (63, 64). Theoretically, the alleviation of oxidative stress and inflammation by flavonoids may correspondingly suppress cell apoptosis and DNA damage. As expected, we found that, compared with the nanomaterial-intoxicated animals, the tail length, tail DNA%, and the mRNA expression levels of caspase-3, Bax, and FAS, as well as the TUNEL apoptotic index, were decreased, and the mRNA expression level of Bcl2 was increased after supplementation with flavonoids. Also, these results on apoptotic indicators were almost not changed by subgroup factors. In addition to apoptosis and DNA damage, previous studies showed that high amounts of NO (65), VEGF (66), and MPO (67) could be synthesized and released in cells in response to ROS and inflammation stimuli. All the enhanced NO (68), VEGF (69), and MPO (70) could mediate an increase in the permeability of vascular endothelium, which may result in the leakage of plasma albumin to tissues and cause hypoxia and edema of organs, ultimately triggering the function dysfunction and exacerbating the disease progression. Thus, NO, VEGF, and MPO were also therapeutic targets to prevent and treat nanomaterial-induced organ injuries. This hypothesis was confirmed by our study which showed that the levels of NO, VEGF, and MPO were significantly reduced by flavonoids. All these alterations in the oxidative stress, inflammation, cell apoptosis, DNA damage, and endothelial dysfunction via supplementation with flavonoids may ultimately lead to the attenuation of organ injuries and function failure consequences. As anticipated, our meta-analysis results showed that some liver, kidney, testis, and brain functional biomarkers were significantly improved by flavonoids.

Several limitations should be acknowledged. The first was a limited number of included studies, which led to fewer data available to evaluate the influence on some indicators (e.g., Nrf2, IL-1β, etc.) and no data obtainable to determine the effects of other flavonoid types (except for three subclasses we studied) and other organ injuries. Second, the design was variable for included studies, with different nanomaterial types, subclasses, dosages, intervention durations, routes of flavonoids, sample sources, and animal species, all of which contributed to the presence of considerably high heterogeneity among studies and thus may weaken the robustness of our conclusions. Third, unlike clinical trials where randomization, allocation concealment, and blinding of outcome assessment were mandatory, these details were rarely mentioned in our included animal experiments, resulting in an unclear risk of bias present in the quality assessment. Fourth, some data were extracted from the bar graphs, which may be slightly different from the real data. Accordingly, more experiments and evidence-based studies are needed to confirm the effects of flavonoids on nanomaterial-induced toxicities.

Conclusion

This meta-analysis suggests that supplementation with flavonoids may be an effective measure to prevent nanomaterial-induced organ injuries by attenuation of oxidative stress, inflammation, apoptosis, DNA damage, and endothelial dysfunction.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Statements

Data availability statement

The original contributions presented in this study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author contributions

DX, JH, and XL conceived the idea and designed the study. DX and JH collected the data and performed the statistical analysis. DX wrote the manuscript. TW, WX, QM, and KC contributed to the interpretation of the results. XL revised the manuscript and was the guarantor of the overall content. All authors approved the final version of the manuscript.

Funding

The financial support from the National Key Research and Development Program of China (2017YFA0204600) and the Project funded by the China Postdoctoral Science Foundation (2017M621322 and 2018T110324) is greatly acknowledged.

Conflict of interest

TW was employed by Shanghai Jing Rui Yang Industrial Co., Ltd. WX was employed by Shanghai Nutri-woods Bio-Technology Co., Ltd. QM was employed by Shanghai Pechoin Daily Chemical Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2022.929343/full#supplementary-material

Supplementary Figure 1

The structure of each flavonoid.

Abbreviations

  • NiONPs

    nickel oxide nanoparticles

  • MDA

    malonaldehyde

  • GSH

    glutathione

  • SOD

    superoxide dismutase

  • AST

    aspartate aminotransferase

  • ALT

    alanine aminotransferase

  • TIO2NPs

    titanium dioxide nanoparticles

  • IONP

    iron oxide nanoparticle

  • IL-6

    interleukin-6

  • TNF-α

    tumor necrosis factor-α

  • ZnONP

    zinc oxide nanoparticle

  • PRISMA

    preferred reporting items for systematic reviews and meta-analysis

  • PICOS

    participants, interventions, comparisons, outcomes, and study design

  • GPx

    glutathione peroxidase

  • CAT

    catalase

  • GST

    glutathione-S-transferase

  • GR

    glutathione reductase

  • Nrf2

    nuclear factor erythroid 2-related factor 2

  • CRP

    C-reactive protein

  • IgG

    immunoglobulin G

  • MPO

    myeloperoxidase

  • NO

    nitric oxide

  • VEGF

    vascular endothelial growth factor

  • ALP

    alkaline phosphatase

  • LH

    luteinizing hormone

  • FSH

    follicle-stimulating hormone

  • 17β-HSD

    17β-hydroxysteroid dehydrogenase type

  • AChE

    acetylcholinesterase

  • FAS

    Fas cell surface death receptor

  • Bcl2

    B-cell lymphoma-2

  • Bax

    BCL2-associated X, apoptosis regulator

  • SMD

    standardized mean difference

  • CI

    confidence interval

  • AgNPs

    silver nanoparticles

  • CNTs

    carbon nanotubes

  • CuONPs

    copper oxide nanoparticles

  • GNPs

    gold nanoparticles

  • SiONPs

    silica dioxide nanoparticles

  • MSNPs

    mesoporous silica nanoparticles

  • ROS

    reactive oxygen species

  • SIRT1

    sirtuin 1.

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Summary

Keywords

nanomaterials, oxidative stress, inflammation, flavonoids, meta-analysis

Citation

Xie D, Hu J, Wu T, Xu W, Meng Q, Cao K and Luo X (2022) Effects of Flavonoid Supplementation on Nanomaterial-Induced Toxicity: A Meta-Analysis of Preclinical Animal Studies. Front. Nutr. 9:929343. doi: 10.3389/fnut.2022.929343

Received

27 April 2022

Accepted

19 May 2022

Published

14 June 2022

Volume

9 - 2022

Edited by

Dongmin Liu, Virginia Tech, United States

Reviewed by

Fatma Mohamady El-Demerdash, Alexandria University, Egypt; Hongwei Si, Tennessee State University, United States

Updates

Copyright

*Correspondence: Xiaogang Luo,

†These authors have contributed equally to this work

This article was submitted to Nutrition and Metabolism, a section of the journal Frontiers in Nutrition

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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