RETRACTED: Association of Polyunsaturated Fatty Acid Intake on Inflammatory Gene Expression and Multiple Sclerosis: A Systematic Review and Meta-Analysis

Abstract

The health benefits of omega-3 fatty acid (FA) supplementation on inflammatory gene expression (IGE) and multiple sclerosis (MS) are becoming more evident. However, an overview of the results from randomized controlled trials is lacking. This study aimed to conduct a meta-analysis to evaluate the effect of omega-3 fatty acid intake on MS (based on the criteria of the Expanded Disability Status Scale (EDSS)) and inflammatory gene expression (IGE). A search was conducted of PubMed, EMBASE, and Web of Science for cohort studies published from the inception of the database up to May 2022 that assessed the associations of omega-3 polyunsaturated fatty acids (n-3 PUFAs), docosahexaenoic acid (DHA), α-linolenic acid (ALA), and eicosapentaenoic acid (EPA) with EDSS and inflammatory gene expression (peroxisome proliferator-activated receptor gamma (PPAR-γ), tumor necrosis factor-alpha (TNF-α), interleukin-1 (IL-1), interleukin-6 (IL-6), and interleukin-8 (IL-8)) outcomes. For the highest vs. lowest comparison, the relative risk (RR) estimates with a 95% confidence interval (CI) were pooled using the random-effect model. In total, 13 cohort studies with 1353 participants were included in the meta-analysis during periods of 3 to 144 weeks. A significant inverse relationship was found between DHA and EDSS scores (RR: 1.05; 95% CI: 0.62, 1.48; p < 0.00001). Our results also showed that omega-3 FAs significantly upregulated the gene expression of PPAR-γ (RR: 0.95; 95% CI: 0.52, 1.38; p < 0.03) and downregulated the expression of TNF-α (RR: −0.15; 95% CI: −0.99, 0.70; p < 0.00001) and IL-1 (RR: −0.60; 95% CI: −1.02, −0.18; p < 0.003). There was no clear evidence of publication bias with Egger’s tests for inflammatory gene expression (p = 0.266). Moreover, n-3 PUFAs and EPA were not significantly associated with EDSS scores (p > 0.05). In this meta-analysis of cohort studies, blood omega-3 FA concentrations were inversely related to inflammatory gene expression (IGE) and EDSS score, which indicates that they may hold great potential markers for the diagnosis, prognosis, and management of MS. However, further clinical trials are required to confirm the potential effects of the omega-3 FAs on MS disease management.

1. Introduction

The chronic demyelinating autoimmune illness known as multiple sclerosis (MS), which is still incurable, is a leading cause of disability [1]. Although patients with early stage multiple sclerosis typically recover partially following each attack, a persistent advanced course eventually takes hold, characterized by central nervous system (CNS) degeneration and gradually worsening disability [2], creating difficulties for effective treatment [3]. Multiple sclerosis results from genetic [4] and environmental and behavioral factors [5,6]. Among these, interest in fatty acids (FAs) to discover treatments for multiple sclerosis and its causes dates back to the 1950s [7], when epidemiologic and autopsy data confirmed the lack of sufficient polyunsaturated fatty acids (PUFAs) in patients with multiple sclerosis (polyunsaturated fatty acid supplementation) [8] and low saturation and elongation of fatty acids in the central nervous system [9]. Furthermore, the Expanded Disability Status Scale (EDSS) criteria are used for the clinical inspection of functional and neurological impairment in patients with MS. In clinical care, rehabilitation, and scientific investigation, the EDSS is regarded as the de facto measurement tool for disabilities. Currently, clinical MS research uses the EDSS to assess the severity and development of the MS disease [10,11]. Unfortunately, despite the many encouraging results from animal and in vitro studies, multiple randomized controlled trials (RCTs) carried out between the 1980s and 2010s have failed to demonstrate the benefits of providing polyunsaturated fatty acids (FAs) in multiple sclerosis therapy [12,13,14]. Nevertheless, there is still a lack of understanding regarding the part fatty acids play in multiple sclerosis. Additionally, one of the primary milestones has been the clinical assessment of autoimmune disease hazard factors [15]. Some genetic factors may be utilized in disease prevention, treatment, and diagnosis [16]. According to D’Onofrio et al. [17], tumor necrosis factor-alpha (TNF-α), interleukins, and peroxisome proliferator-activated receptors (PPARs) are all genetic factors that contribute to inflammation. Additionally, the impact of diet on associated gene expression has garnered interest [18]. Therefore, past studies have assessed how essential nutrients, including polyunsaturated fatty acids, affect the expression of inflammatory genes [19]. Furthermore, our daily dietary habits may play an essential role in modulating the risk of developing MS [20,21]. The long-chain omega-3 polyunsaturated fatty acids (n-3 PUFAs), α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), mainly present in adipose fish, are promising candidates. The effect of long-chain n-3 PUFAs against MS may be explained by their major structural and functional roles in maintaining neuron membrane fluidity [22], their vascular and anti-inflammatory properties [23], as well as their potential capacity to modulate neuroinflammation and the expression of neuronal plasticity-related genes [24]. Omega-3 polyunsaturated fatty acid intake, particularly fatty fish, and the intake of marine n-3 PUFAs, DHA, EPA, and ALA are thought to play protective roles [25]. Furthermore, blood biomarkers are probably more objective and accurate in quantifying levels of FAs [26]. The literature has produced inconclusive findings regarding blood fatty acid biomarkers as an exposure variable. For example, previous studies have related high levels of fatty acids to decreased risk of MS [27,28].

Moreover, a previous meta-analysis focused on fatty acids in MS, which included only studies of a cross-sectional design without considering inflammatory gene expression (IGE). In contrast, there is considerable interest in the role of n-3 PUFAs, DHA, ALA, and EPA in inflammatory gene expression. To the best of our knowledge, no previous meta-analysis has remarkably evaluated the effect of n-3 PUFA intake on MS disease severity (as characterized by EDSS) and IGE from the evidence of prospective cohort studies. Summarizing the evidence from longitudinal studies on n-3 PUFA intake and MS and IGE will help elucidate the relationships and establish a potential predictive role for n-3 PUFAs in MS disease. Therefore, we performed a meta-analysis to pool the evidence from cohort studies on the relationship between n-3 PUFAs and the score of EDSS and inflammatory gene expression (IGE).

2. Materials and Methods

2.1. Search Strategy and Inclusion Criteria

This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [29]. The study protocol was registered prospectively in the International Prospective Register of Systematic Reviews (PROSPERO) (Centre for Reviews and Dissemination: CRD42022363071). The articles indexed in three databases (Web of Science, PubMed, and Excerpta Medica Database (EMBASE)) and the Cochrane Register of Controlled Trials (CENTRAL) published between January 1990 and May 2022 were searched. The search strategy for subject headings and keywords was as follows: (1) omega-3 fatty acid, docosahexaenoic acid (DHA), α-arachidonic acid (ALA), eicosapentaenoic acid (EPA), omega-3, n-3 fatty acid, and n-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFAs); (2) multiple sclerosis, autoimmune diseases, central nervous system, immune system, multiple sclerosis disease, cognition and cognitive; (3) reported any gene expression levels of inflammatory biomarkers; (4) blood, serum, plasma, and blood level. No restrictions were imposed. Studies were enclosed if they completed all the subsequent criteria:

• Studies were of prospective design (prospective cohort, case–cohort, or nested case–control);
• Participants were selected from general populations;
• Multiple sclerosis (MS) and inflammatory gene expression (IGE) were recorded by use of well-defined criteria;
• The association of MS and IGE with n-3 PUFAs was evaluated by the odds ratio (OR), risk ratio (RR), hazard ratio (HR), or standard mean difference (SMD) with the corresponding confidence intervals (CI);
• The blood, serum, or plasma omega-3 levels were considered as the exposure of interest.

We excluded those articles that met the following standards: (1) letter, review, editorial, or supplement; (2) studies of animal models alone. If data were replicated in more than one study, the article with complete results was included in the meta-analysis.

2.2. Data Extraction

All authors reviewed all the articles independently and discussed the articles until a consensus was reached. For each included study, we extracted the first author, publication year, region (or country), demographic characteristics (age, gender, case, and sample size), mean follow-up duration, diagnostic criteria, adjusted covariates, and multivariate-adjusted effects (RR and 95% confidence interval (95% CI)). We used the WebPlotDigitizer software (v2.6.8, http://arohatgi.info/WebPlotDigitizer/ (accessed on 12 May 2022)) to capture the data from figures when necessary. The authors of the articles were reached for more information in cases in which the data were shown in graphical form and could not be directly transcribed. The most adjusted models were extracted if the risk estimates were documented with several multivariate-adjusted models in the original studies.

2.3. Quality Assessment

The Newcastle–Ottawa Scale (NOS) Quality Assessment was used to assess the study’s quality [30]. Each prospective study may receive a maximum of eight points: four for selection, one for comparability, and three for evaluating results. High (6–8 *), moderate (3–5 *), and poor (1–2 *) quality ratings were given.

2.4. Data Analysis

In this meta-analysis, the standard mean difference (SMD), RRs, and 95% CIs were considered to establish the standard association’s measure between each class of plasma omega-3 FAs and MS or IGE across the studies. Hazard ratios (HRs) were deemed to be equivalent to RRs. The formula RR = OR/((1 − P0) + (P0 × OR)) was used to transform the odds ratio (ORs) into RRs, where P0 is the incidence of the interesting result in the non-exposed group [31]. Some studies notified OR or hazard ratio (HR) in each classification, and if the value of P0 was relatively small, the odds ratio (or hazard ratio) was considered equal to the RR in the cohort studies.

Given the between-study heterogeneity in study design, demographic characteristics, and different measured outcomes, a random-effect model was used to pool the RRs. Knapp–Hartung adjustments were used to calculate the confidence interval around the pooled effect [32]. The heterogeneity of the results across studies was assessed using the I-square (I²) statistic and chi-squared (X²) test in each analysis.

To evaluate the potential publication bias, Egger’s test was used in this analysis. The corresponding p < 0.05 was judged to be a significant publication bias; in contrast, if the corresponding p-value of the rank correlation method was >0.05, the publication bias was considered to be not significant. When a possible publication bias was identified, we used the trim-and-fill method for adjustment. To examine the robustness of the main findings, sensitivity analyses were performed by omitting one study in turn to investigate the effect of a single study on the overall risk factors. All statistical analyses were performed using R version 4.0.4 (R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were two-tailed, and the significance level in this meta-analysis was defined as p < 0.05.

3. Results

3.1. Literature Search

Figure 1 shows the search results and a flowchart of the study selection [10,11,19,33,34,35,36,37,38,39,40,41,42]. The initial literature search yielded a total of 1814 records after duplicate exclusion. After reviewing the full text, 1786 articles were further excluded. Finally, 13 studies with 1353 participants were included in this study. All the included studies were published as full manuscripts.

3.2. Study Characteristics

Table 1. Characteristics of the identified prospective studies of omega-3 FAs and score of EDSS or inflammatory gene expression.

Study	Country	Age	Sample Size	Duration (Week)	Number and Gender (Male/Female)	BMI (kg/m2)	n-3 Fatty Acid Type	Main Outcome
Zandi et al. [33]	Iran	33.30 ± 1.2	41	48	12/29	26.4 ± 5.3	EPA DHA	↔EDSS ↓IL-1 ↔TNFα ↔IL-6
Ramirez et al. [34]	Mexico	34.9 ± 2.3	50	48	(41/9)	28.1 ± 6.3	EPA DHA	↔EDSS ↓IL-1 ↓TNFα ↓IL-6
Anderson et al. [35]	United States	65.8 ± 9.9	24	3	16/8	30.1 ± 6.0	EPA DHA	↑PPAR-γ
Hashemzadeh et al. [36]	Iran	59.2 ± 11.1	60	12	45/15	30.9 ± 4.2	ALA	↑PPAR-γ ↓TNF-α ↔IL-8
Torkildsen et al. [10]	Norway	38.6 ± 9.2	91	96	32/59	28.1 ± 5.6	EPA DHA	↔EDSS ↓IL-1
Jamilian et al. [19]	Iran	25.4 ± 1.2	40	6	0/40	27.0 ± 3.1	EPA DHA	↑PPAR-γ ↓IL-1 ↓TNF-α ↔IL-8
Weinstock et al. [11]	United States	42.5 ± 10.11	27	48	4/23	25.1 ± 5.3	EPA DHA	↓EDSS
Nasri et al. [37]	Iran	27.5 ± 5.7	60	12	0/60	28.1 ± 6.3	ALA	↑PPAR-γ
Rahmani et al. [38]	Iran	25.6 ± 4.8	40	12	0/40	25.1 ± 4.3	EPA DHA ALA	↑PPAR-γ ↔TNF-α ↓IL-8 ↓IL-1
Toupchian et al. [39]	Iran	55.9 ± 7.8	67	8	34/33	29.2 ± 2.8	EPA DHA ALA	↔TNF-α ↔IL-6
Nordvik et al. [40]	Norway	45.0 ± 6.29	16	96	8/8	26.2 ± 6.3	EPA DHA ALA	↓EDSS
Kouchaki et al. [41]	Iran	26.2 ± 6.5	53	12	53/0	27.1 ± 4.3	EPA DHA	↓EDSS
Hoare et al. [42]	Australia	30.9 ± 4.2	784	144	434/350	28.1 ± 5.1	EPA DHA ALA	↓EDSS

EDSS, Expanded Disability Status Scale; FA, fatty acids; ALA, α-linolenic acid; DHA, docosahexaenoic acid; TNF-α, tumor necrosis factor-alpha; IL-8, interleukin-8; IL-6, interleukin-6; IL-1, interleukin-1; BMI, body mass index; PPAR-γ, peroxisome proliferator-activated receptor-gamma; ↓ this symbol is an indication of reducing variables; ↑ this symbol is an indication of rising variables; ↔ this symbol indicates that there is no difference between the two groups.

exhibits a summary of the characteristics of all the included studies. Nine studies compared IGE between patients vs. cognitively normal controls, and seven investigated both IGE and MS. The mean follow-up period of all the included studies ranged from 3 to 144 weeks. The studies included two from the United States, two from Europe, one from Australia, one from Mexico, and seven from Iran. In total, 1353 participants included MS patients and inflammatory-related genes. Most studies provided risk estimates adjusted for age, gender, body mass index (BMI), smoking, alcohol consumption, and education. The mean quality score of each study ranged from 6 to 8 *, suggesting that all studies were of high quality (

Table 2. Quality assessment of cohort studies.

Study	Year	Selection				Comparability	Outcome
		Representativeness of the Exposed Cohort	Selection of the Non Exposed Cohort	Ascertainment of Exposure	Demonstration That Outcome of Interest Was Not Present at Start of Study	Comparability of Cohorts on the Basis of the Design or Analysis	Assessment of Outcome	Was Follow-Up Long Enough for Outcomes to Occur	Adequacy of Follow-Up of Cohorts
Zandi et al. [33]	2017	*	*	*	*	*	*	*	*
Anderson et al. [35]	2014	*	*	*	*	*	*	-	*
Ramirez et al. [34]	2013	*	*	*	*	*	*	-	*
Hashemzadeh et al. [36]	2017	*	*	*	*	*	-	-	*
Torkildsen et al. [10]	2012	*	*	*	*	*	*	-	*
Jamilian et al. [19]	2018	*	*	*	*	*	-	-	-
Weinstock et al. [11]	2005	*	*	*	*	*	*	-	-
Nasri et al. [37]	2017	*	*	*	*	*	-	-	*
Rahmani et al. [38]	2018	*	*	*	*	*	*	-	*
Toupchian et al. [39]	2018	*	*	*	*	*	*	-	*
Nordvik et al. [40]	2000	*	*	*	*	*	-	-	-
Kouchaki et al. [41]	2018	*	*	*	*	*	*	-	*
Hoare et al. [42]	2012	*	*	*	*	*	-	-	*
Zandi et al. [33]	2016	*	*	*	*	*	*	-	-

One star * for one score. - indicates non-score.

).

3.3. Omega-3 FA Intake and Inflammatory Gene Expression

Nine cohort studies [10,19,33,34,35,36,37,38,39] investigated the association between omega-3 FA intake and IGE, totaling 473 patients. Our results demonstrate that omega-3 fatty acid intake significantly upregulated the gene expression of PPAR-γ (RR: 0.95; 95% CI: 0.52, 1.38) (Figure 2A), with moderate heterogeneity (I² = 83.9%; p = 0.03). Our results also reveal that omega-3 fatty acid levels particularly downregulated the interleukin-1 gene expression (RR: −0.60; 95% CI: −1.02, −0.18), with moderate heterogeneity (I² = 80.4%; p = 0.003) (Figure 2B), and tumor necrosis factor-alpha (RR: −0.15; 95% CI: −0.99, 0.70) with moderate heterogeneity (I² = 90.8%; p = 0.000) (Figure 3). Our results also show that omega-3 fatty acids did not change interleukin-6 (RR: −0.30; 95% CI: −0.78, 0.18; I² = 50.4%; p = 0.133) (Figure 4A) and interleukin-8 (RR: −0.43; 95% CI: −0.74, −0.12; I² = 0.0%; p = 0.831) (Figure 4B).

3.4. Omega-3 FAs and EDSS Score

All seven studies [10,11,33,34,40,41,42] were included in the highest vs. lowest meta-analysis of the relationship between DHA levels and the risk of MS, involving 1062 participants (Table 1). The levels of DHA were significantly associated with EDSS score: the pooled RR of 1.05 (95% CI: 0.62, 1.48) (Figure 5), with moderate heterogeneity (I² = 73.9%; p = 0.000). There was no evidence of a relationship between EPA and ALA with EDSS score.

3.5. Risk of Bias across Studies

The pooled relative risks obtained in the current meta-analysis were strong and were not driven by a single study, according to sensitivity analyses for other associations. Additionally, we assessed and reported the bias between n-3 PUFA intake and MS and IGE using Egger’s test. To assess the publication bias, Egger’s tests were applied (A: Egger’s p = 0.865; B: Egger’s p = 0.326; C: Egger’s p = 0.266; D: Egger’s p = 0.916; E: Egger’s p = 0.403; F: Egger’s p = 0.616). No overt publication bias was discovered. Additionally, using the trim-and-fill method did not alter the average effect size, indicating that results were not impacted by publication bias.

4. Discussion

In the present study, we enumerated the effects of the dietary consumption of omega-3 FAs on clinical development (as measured by the EDSS) and the expression of a few inflammatory genes. The evaluation of clinical and functional changes was carried out using EDSS ratings. The Kurtzke EDSS scale is frequently employed in MS clinical trials and is recognized as a standard tool for assessing clinical disease trends. The impairment and degree of the condition are reflected by the EDSS [43,44]. To the best of our knowledge, this is the first meta-analysis of prospective cohort studies that specifically addressed the n-3 PUFA consumption and inflammatory gene expression and MS. The results of this meta-analysis, which included 13 cohort studies with 1353 participants, showed that there was no evidence of study heterogeneity (I² = 0%) and that a higher level of DHA was significantly associated with a lower score on the EDSS. Nevertheless, there was no evidence that other types of n-3 PUFAs are associated with EDSS. Our findings follow the results of several previous studies that found a significant inverse relationship was found between DHA levels and MS. For instance, several studies [11,40,41,42] reported that blood DHA had a significant protective effect against the EDSS score. According to a previous meta-analysis of metabolites linked to general cognitive function, high DHA levels are associated with lower risks of developing autoimmune and all-cause disease incidents [42]. The data from our study further support these conclusions. Other studies, however, have shown that DHA has no effect, or only a weak effect, on the EDSS score [45,46,47]. We found no association between the EPA, total omega-3 fatty acid, and MS. However, some prospective observational studies showed that high relative proportions of plasma EPA decreased the score of EDSS [40,41], although no significant statistical differences were identified in the pooled analyses.

In contrast, some studies did find beneficial effects of omega-3 or EPA and DHA separately [11,42], while others did not [10,33]. As well, there has not been any prior information about a significant inverse relationship between ALA and the risk of MS. The body cannot synthesize ALA, an essential fatty acid, but it can be converted through saturation and elongation to the long-chain n-3 fatty acid EPA and then to the omega-3 fatty acid DHA [48]. As a result, rather than reflecting the effect of ALA directly, the link we observed between ALA and MS risk may have been mediated by its derivatives. However, only a little fraction of dietary ALA (6%) is converted to EPA, and ALA may have biological effects apart from its downstream derivatives, which would be compatible with our findings given that we found no correlation between EPA or ALA and the risk of MS [49]. Indeed, a 2-year treatment regimen with 0.9 g/day of long-chain marine fatty acid supplement showed a significant reduction in the mean annual exacerbation rate and the mean EDSS, compared with the pre-study values, according to an intervention study of patients with newly diagnosed MS [40]. Patients receiving n-3 fatty acid supplements showed a substantial improvement in EDSS, compared with the placebo in a randomized, placebo-controlled clinical trial with inflammation and the EDSS score as the major endpoints [41]. Relapsing–remitting MS (RRMS) patients were treated with identical doses of n-3 PUFAs and olive oil in a double-blind, randomized experiment for a year [11]. Patients were randomly assigned to one of two groups: the n-3 group, whose daily dietary fat consumption was limited to 15%, and the olive oil group, whose intake was limited to 30%. Following the 12-month intervention, the data revealed no significant change in the relapse rate or clinical severity (EDSS) scores. However, a low-fat diet supplemented with n-3 PUFAs was beneficial for patients with relapsing–remitting MS, as the mean change in relapse rate in the n-3 group was 0.797 relapses/year (p = 0.021) vs. 0.69 (p = 0.044) in the olive oil control group [11]. However, not all clinical investigations had successful results. In a randomized, double-blind, and placebo-controlled experiment, Torkildsen et al. assessed the impact of n-3 PUFAs on the severity of MS disease [10]. For six months, they administered 1350 mg of EPA and 850 mg of DHA daily to the treatment group and a placebo to the control group. To determine the quantity of the new T1-weighted gadolinium-enhanced lesions that appeared within the first six months, magnetic resonance imaging (MRI) was used. After 9 and 24 months, the relapse rate, disability progression, exhaustion, and quality of life were assessed. The number of new gadolinium-enhancing MRI lesions during the first six months, as well as the disease activity after six months, did not differ between the n-3 PUFA and placebo groups. Although the dosage, the source and concentrations of EPA/DHA agents, the duration of the trial, and the methods of evaluation could all have contributed to the various results at the end of the trial, the reason for the variance of outcomes in different trials is still unknown.

Furthermore, in autoimmune illnesses, omega-3 fatty acids have been shown to have anti-oxidant, anti-inflammatory, and neuroprotective effects. Multiple mechanisms, including gene expression and signaling cascades, are involved in MS [50]. These mechanisms mediate different factors, such as NF-kB suppression (a crucial inflammatory transcription factor), the reduction in oxidative stress, the promotion of PPAR-γ activation, the exertion of anti-inflammatory effects, and the blocking of proinflammatory interleukins [19,36,37]. Therefore, the findings of earlier research into n-3 PUFAs’ impact on PPAR are also debatable. While some have observed no effects, others have found that DHA increases the PPAR-γ expression levels [51,52]. The effects of PPAR on controlling lipid metabolism and adipocyte differentiation are well-documented. Additionally, PPAR is crucial for protecting the vasculature [53]. The current study’s findings support that supplementing with a moderate amount of DHA-rich fish oil considerably boosted PPAR activation. As a PPAR natural ligand, DHA had no effects on the receptor’s expression, according to earlier investigations on the effects of DHA-rich fish oil on variables affecting endothelial function in diabetes patients [52]. The PPAR activity has been assessed in several investigations, predominantly in vitro [54,55]. It has been established that the activation of PPAR-γ is how omega-3 FAs produce anti-inflammatory benefits. A DNA-binding mechanism that is mediated by the inhibition of monocyte chemoattractant protein 1 (MCP-1) production by the NF-kB pathway through the activation of PPAR has been shown in earlier experimental studies to have anti-inflammatory effects [56,57]. DHA prevents the lipopolysaccharide (LPS)-induced M1 polarization of the microglia and reverses their anti-neurogenic activity, allowing them to support neural progenitor cell (NPC) survival and development by activating PPAR and inhibiting p38 mitogen-activated protein kinase (p38/MAPK) phosphorylation [58]. Acetate can modify the MAPK and NF-kB signaling pathways for LPS-induced astrocyte inflammation in vitro, inhibiting TNF-α, interleukin-1 (IL-1), and interleukin-6 (IL-6) while increasing transforming growth factor-alpha (TGF-α) and interleukin-4 (IL-4) production [59]. This effect is not limited to microglia. Through GPR120/40 and their scaffold protein arrestin-2, DHA, EPA, and ALA also inhibit macrophage-induced NLRP3 inflammasome activation and IL-1 production [60]. DPA can inhibit the transcriptional level of the LPS-induced production of proinflammatory cytokines in macrophage-like RAW264.7 cells [61] regardless of their conversion to DHA. Regarding stimulating the TLR-related proinflammatory signal pathways in neutrophils and macrophages, DHA has the opposite effect of saturated fatty acids (SFAs) [62]. As well, omega-3 has been shown to significantly impact proinflammatory cytokines such as IL-1, IL-6, and TNF-α [33,63,64]. As higher oxidative stress and cytokines are linked to disease progression in multiple sclerosis patients, a decrease in inflammatory or oxidative indicators may aid MS therapy [65,66]. However, few human studies examine how omega-3 fatty acids impact inflammatory markers in MS patients. Through a meta-analysis of two studies performed in the current investigation, it was found that omega-3 fatty acid intervention can significantly reduce serum TNF-α concentrations, with no inter-study heterogeneity. According to Ramirez-Ramirez et al. [34], four grams of fish oil per day for 12 months significantly reduced TNF-α expressions, which is consistent with the present study’s findings. In contrast to the current findings, they also discovered that the intervention of omega-3 fatty acids dramatically downregulated the IL-6 gene. The current meta-analysis found no significant reduction in the expression of IL-8 and IL-6 genes, consistent with a different study by Zandi-Esfahan et al. [33] using 1 g/day of fish oil for 12 months. The variance in these studies’ findings may be attributable to variations in omega-3 fatty acids, the quantities used during interventions, the length of MS, or cytokine levels at the baseline. Additionally, there was high heterogeneity among the included studies regarding serum IL-1 and IL-6 levels. The findings of this meta-analysis also suggest that omega-3 fatty acid intake reduces the expression of the IL-1 gene. TNF-α, IL-1, and other inflammatory cytokines were produced at lower levels in numerous studies of healthy human volunteers who received omega-3 FAs [67]. Since omega-3 FAs are the precursors of various eicosanoids, other eicosanoid-related mechanisms could explain how omega-3 FAs affect the gene expression of inflammatory mediators [68].

Moreover, in earlier research, the anti-inflammatory properties of omega-3 FAs have been extensively discussed [69]. The decreased levels of inflammatory eicosanoids such as leukotriene B4 (LTB4) and prostaglandin E2 (PGE2) have been used to explain how omega-3 FAs have anti-inflammatory properties. Omega-FAs also control the activity of the enzymes cyclooxygenase (COX) and lipoxygenase (LOX) to modify the expression of anti-inflammatory eicosanoids and reduce the creation of inflammatory ones [70]. For instance, LTB4 is a powerful leukocyte chemoattractant made from arachidonic acid. It has been demonstrated that a reduction in LTB4 caused by an omega-3 FA may have the ability to lessen the number of inflammatory markers generated by leukocytes [71]. Omega-3 FAs may also lessen the expression of chemoattractant receptors on leukocyte membranes [72].

With different inclusion criteria with a census date of December 2018, our study included only four cross-sectional design studies, of which only two reported data for the associations between DHA and EDSS, and none between EPA and EDSS or MS. Our search ended on May 2022, and some new information has been published since then, which greatly affects the pooled relative risks. Thus, our study provided an update to support the omega-3 fatty acid’s role in IGE and MS. A limited range of n-3 PUFAs in human-based studies, below the level necessary to supply a protective effect against MS and IGE, might be the first reasonable reason for the contradictory results. The second reason is the different cut-offs used in the included studies. A third explanation is the poly-medication used with autoimmune diseases in many older adults: Some drugs may prevent proper absorption and affect the levels of n-3 PUFAs. Finally, there is evidence that the ratio of DHA to EPA is under no consideration. These factors present obstacles to the comparison and extrapolation of the study results. There is perhaps a counter-intuitive difference between the result of this review and some randomized controlled trials focusing on omega-3 fatty acid consumption. Such inconsistent results may be explained by the weak association between n-3 PUFA intake and their bioavailability. Therefore, biological data may be more accurate than dietary data in studying the putative protective effect of PUFAs on MS and IGE. Overall, a balanced level of omega-3 fatty acids is necessary to reduce the score of EDSS, which is also suggested by the biological mechanisms of omega-3 fatty acids.

Furthermore, long-chain n-3 PUFAs are a major component of neuronal membranes, including docosahexaenoic acids (DHAs), which are important to membrane fluidity [73]. Regarding the mechanism of action, n-3 PUFAs (especially DHA) have anti-inflammatory, anti-thrombotic, anti-oxidant, and anti-amyloid properties [74,75]. They reduce inflammation, modifying the fluidity and composition of cell membranes through direct effects on receptor function and the conductance of ion channels involved in immune activation. DHA has been shown to regulate the expression of neuroplasticity-related genes [76]. Taken together, all these results supported that n-3 PUFAs (and especially DHA) can display a beneficial effect on the score of EDSS. In order to decrease the bias, we included only high-quality studies. We also performed sensitivity analyses to examine heterogeneity sources and to assess potential residual confounding. Sensitivity analyses indicated that excluding any single study did not substantially alter the preliminary overall RRs. However, we could not perform subgroup and meta-regression analyses because of a limited number of studies.

5. Conclusions

In conclusion, this meta-analysis offers dependable proof that a higher DHA level may be linked to a decreased score of EDSS. However, there was no negative correlation between the total omega-3 fatty acids and MS. These results indicate that dietary adjustments can raise blood DHA levels, which have significant implications for preventing disability. The impact of omega-3 FAs on the expression of genes linked to inflammation was examined using all published RCTs. According to the findings, omega-3 FAs resulted in a notable upregulation of peroxisome proliferator-activated receptor-gamma (PPAR-γ) gene expression and downregulation of tumor necrosis factor-alpha (TNF-α) and interleukin-1 (IL-1) gene expression. Omega-3 fatty acids’ favorable impact on inflammation can be explained by how they affect gene expression. However, long-term, more extensive, and well-designed RCTs are still required to understand these impacts fully.