Long-chain PUFA (LC-PUFA) are essential nutrients for human brain development and neurotransmitter functions. These fatty acids (FA) are involved in various physiological processes, including the formation and maintenance of cell membranes, neuronal signalling, and regulation of inflammation. Notably, LC-PUFA such as docosahexaenoic acid (DHA) and arachidonic acid (ARA) are particularly important during critical periods of brain growth and development, including fetal development and early childhood(Reference Lauritzen, Brambilla and Mazzocchi1,Reference Lauritzen, Sørensen and Harsløf2) . The LC-PUFA content in the blood and other tissues is closely associated with a number of health conditions, including cardiovascular diseases and psychiatric disorders(Reference Glaser, Lattka and Rzehak3–Reference Rangel-Huerta and Gil5). An adequate intake and balanced levels of LC-PUFA are crucial for promoting optimal cognitive function and maintaining overall brain health throughout life.
Notably, PUFA are mainly classified as n-3 and n-6 FAs, and α-linolenic acid (ALA) and linoleic acid (LA) have been identified as their precursors; therefore, LA and ALA are considered essential FAs (EFA)(Reference Anez-Bustillos, Dao and Fell6). The EFA are obtained from the diet because they cannot be manufactured by cells (double bonds can be introduced into all positions of the fatty acid chain, except at the n-3 and n-6 positions). Notably, ALA is an n-3 fatty acid that is converted to EPA, EPA is converted to docosapentaenoic acid (DPA) and then to DHA. Whereas, LA is the parent fatty acid of the n-6 FA class. Notably, LA is converted to γ-linoleic acid (GLA), which is further converted to arachidonic acid (AA), a precursor of several classes of eicosanoids. In the nervous system, cell membranes contain relatively high concentrations of PUFA, such as DHA(Reference Scholtz, Kerling and Shaddy7).
Furthermore, the same enzyme desaturates and elongates both the n-3 and n-6 pathways(Reference Marquardt, Stöhr and White8). The synthesis process involves a series of elongation and desaturation steps catalysed by fatty acid desaturases (FADS) and requires a sequential series of desaturations with FADS(Reference Marquardt, Stöhr and White8–Reference Cho, Nakamura and Clarke10). Notably, FADS family members (FADS) play key roles in the conservation of the dynamic balance of FAs(Reference Zhao, Gao and Li11). Mammalian FADS include FADS1, FADS2, FADS3, FADS4, FADS5, FADS6, FADS7 (DEGS1) and FADS8 (DEGS2)(Reference Mihelic, Winter and Powers12). The human body can synthesis various LC-PUFA using FADS1 known as Δ5-fatty acid desaturases (D5D), FADS2 known as Δ6-fatty acid desaturases (D6D) and elongase(Reference Park, Kothapalli and Reardon13). Therefore, genetic variation in FADS appears to be important for modulating LC-PUFA status.
Evidence from epidemiological and observational studies supports the association between LC-PUFA synthesis and genetic variations in the FADS cluster located on chromosome 11 (11q12-q13·1)(Reference Moltó-Puigmartí, Plat and Mensink14–Reference Conway, McSorley and Mulhern19). For instance, FADS2 expression is determined by the FADS2 gene, which is located on chromosome 11q12-q13·1. SNP in FADS2 can affect PUFA production, which can subsequently affect LC-PUFA levels(Reference Reynolds, Howard and Ruczinski20). Cribb found that LA and ALA are associated with major depressive disorder and are potentially modulated by genetic variations in the FADS gene cluster(Reference Cribb, Murphy and Froud17). Observations indicate that the FADS genotypes of both mothers and children were associated with LC-PUFA concentrations in the cord(Reference Conway, McSorley and Mulhern19). In addition, rs174575 is situated in the promoter region of the FADS2 gene. These studies specifically focused on the effect of the rs174575 SNP in FADS2 on LC-PUFA levels. In this study, we conducted a comprehensive meta-analysis to investigate the effects of the rs174575 SNP in FADS2 on LC-PUFA levels.
This meta-analysis has been registered on PROSPERO (ID: CRD42023470562).
Methods
Literature retrieval
Keywords such as PUFA, SNP, FADS2 and rs174575 were used to search six databases (China National Knowledge Infrastructure, PubMed, Web of Science, Science Direct, EMBASE and Scopus) published in English and Chinese before 15 July 2024. In total, 3570 articles were retrieved from the searches.
Assessment of eligibility
Studies were suggested to be eligible if they meet the following inclusion criteria:
(1) Reported in Chinese or English
(2) Available in full text with access to the necessary materials and data from the authors and
(3) Had good design quality.
Studies were excluded if they were
(1) Duplicate releases
(2) Reviews, meta-analysis, animal/plant/cell studies, abstracts, commentaries, case reports or editorials; and
(3) Contained missing data that had not been presented in the literature and could not be obtained from the authors of the published studies.
Data extraction and quality assessment
Literature quality was assessed using the Newcastle-Ottawa scale(Reference Stang21), which was developed to assess the quality of non-randomised studies based on three broad perspectives: selection of the study group (four items), comparability of groups (two items) and ascertainment of either exposure or outcome (two items). The total Newcastle-Ottawa scale score ranges from zero to eight, based on the assessment items. Studies with a score of six were considered to have high methodological quality.
The author, year of publication, measurement method, genotypes, tissue type, country, age, sample size and LC-PUFA content were extracted from the included studies. Two groups (CC and CG + GG) were formed from the three genotypes (CC, CG and GG). Two reviewers independently extracted data from the included studies, and any disagreements were discussed and resolved.
Statistical analysis
Meta-analysis was performed using Stata 17.0. Effect indices of each study were calculated as standardised mean difference (SMD) and 95 % CI. The heterogeneity of the included studies was assessed using the I2 test. If I2 was ≤ 50 %, a fixed effects model was used, otherwise a random effects model was used. Publication bias was assessed using funnel plots and Egger’s and Begg’s tests, and statistical significance was set at P < 0·05. In addition, sensitivity analysis was performed by excluding one study at a time (online Supplementary Figure 1). A subgroup analysis was used to assess the heterogeneity of the studies. Subgroup analyses were performed according to the regions, tissues, and quality scores of the studies.
Result
Literature description
Literature search
Figure 1 illustrates the selection process implemented according to our literature search strategy. Finally, ten articles(Reference Scholtz, Kerling and Shaddy7,Reference Moltó-Puigmartí, Plat and Mensink14,Reference Harsløf, Larsen and Ritz16,Reference Cribb, Murphy and Froud17,Reference Conway, McSorley and Mulhern19,Reference Xie and Innis22–Reference de la Garza Puentes, Montes Goyanes and Chisaguano Tonato26) were selected for the meta-analysis.
Fig. 1. Flow chart of the selection process of the literature search.
Study characteristics
The characteristics of the included studies are presented in Table 1. Twelve studies were extracted from the ten articles (one article simultaneously focused on plasma and breast milk and another had two cohorts of normal weight and overweight). A total of 3048 participants, representing different ethnic populations from ten countries, were included in the analysis.
Table 1. Characteristics of studies included in the meta-analysis
C, major allele; G, minor allele.
Quality assessment
The methodological quality of the studies evaluated was based on the Newcastle-Ottawa scale, and all evaluated studies showed a high level of methodological quality.
Meta-analysis results
Effects of rs174575 gene variations on the n-6 PUFA levels
The effects of rs174575 on the AA levels are shown in Fig. 2(a). The AA levels were significantly lower in minor G allele carriers than in the CC genotype group (SMD: –0·7, 95 % CI (–1·3, –0·1), P = 0·033), and significant heterogeneity (I2 = 98·1 %, 95 % CI (87·7 %, 99·3 %), P < 0·001) was observed.
Fig. 2. Forest plots showing n-6 PUFA levels difference between minor G allele carriers and CC genotype of rs174575 in FADS2. (a) Forest plot of arachidonic acid (AA); (b) forest plot of dihomo-γ-linolenic acid (DGLA); (c) forest plot of docosatetraenoic acid (DTA); (d) forest plot of linoleic acid (LA).
The effects of rs174575 on DGLA levels are shown in Fig. 2(b). The DGLA levels were significantly higher in minor G allele carriers than in the CC genotype group (SMD: 0·6, 95 % CI (0·2, 1·0), P = 0·005), and significant heterogeneity (I2 = 93·0 %, 95 % CI (56·4 %, 97·3 %), P < 0·001) was observed.
The effects of rs174575 on DTA levels are shown in Fig. 2(c). The DTA levels were not significantly different between minor G allele carriers and the CC genotype group (SMD: –0·2, 95 % CI (–0·4, 0·0), P = 0·050), although heterogeneity (I2 = 68·2 %, 95 % CI (0·0 %, 88·1 %), P = 0·008) was observed.
Effects of rs174575 gene variations on the n-3 PUFA levels
Figure 3 shows that the levels of ALA, DHA, DPA and EPA were not significantly different between minor G allele carriers and the CC genotype group, although significant heterogeneity was observed.
Fig. 3. Forest plots showing n-3 PUFA levels difference between minor G allele carriers and CC genotype of rs174575 in FADS2. (a) Forest plot of α-linolenic acid (ALA); (b) forest plot of docosahexaenoic acid (DHA); (c) forest plot of docosapentaenoic acid (DPA); (d) forest plot of eicosapentaenoic acid (EPA).
Effects of rs174575 in FADS2 on D5D levels
The effects of rs174575 on D5D levels are shown in Fig. 4. The D5D levels were not significantly different between minor G allele carriers and the CC genotype group (SMD: –1·6, 95 % CI (–3·6, 0·4), P = 0·116), although significant heterogeneity (I2 = 99·3 %, 95 % CI (67·7 %, 99·8 %), P < 0·001) was observed.
Fig. 4. Forest plot showing Δ5-desaturase (D5D) activity difference between minor G allele carriers and CC genotype of rs174575 in FADS.
Risk of bias evaluation
Online Supplementary Figure 2 shows funnel plots of the correlation between FADS2 rs174575 and PUFA levels. Judgements regarding the risk of bias for each included study are shown in Supplemental Table 4. There was no significant evidence that a publication bias affected the results.
Subgroup analysis
The results of the subgroup analyses are presented in Table 2. The LA levels in the minor G allele carriers were significantly higher than that in the CC genotype group in the European population (I2 = 41·4 %, 95 % CI (0·0 %, 81·3 %), P = 0·002), erythrocyte (I2 = 0·0 %, 95 % CI (0·0 %, 0·0 %), P = 0·003) and seven-score groups (I2 = 26·5 %, 95 % CI (0·0 %, 71·6 %), P = 0·001). The DGLA levels in the minor G allele carriers were significantly higher than that in the CC genotype group in the European population (I2 = 65·0 %, 95 % CI (0·0 %, 88·4 %), P < 0·001), plasma (I2 = 32·8 %, 95 % CI (0·0 %, 81·9 %), P = 0·001), breast milk (I2 = 25·6 %, 95 % CI (0·0 %, 79·9 %), P = 0·050) and the seven-score groups (I2 = 73·6 %, 95 % CI (0·0 %, 90·1 %), P = 0·001). The DTA levels in the minor G allele carriers were significantly lower than that in the CC genotype group in the European population (I2 = 56·3 %, 95 % CI (0·0 %, 88·2 %), P = 0·001) and seven-score groups (I2 = 63·5 %, 95 % CI (0·0 %, 87·5 %), P = 0·021). The AA levels in the minor G allele carriers were significantly lower than that in the CC genotype group in the European population (I2 = 85·3 %, 95 % CI (0·0 %, 95·3 %), P = 0·004), breast milk (I2 = 68·1 %, 95 % CI (0·0 %, 91·7 %), P = 0·030), erythrocyte (I2 = 0·0 %, 95 % CI (0·0 %, 5·9 %), P = 0·022) and seven-score groups (I2 = 78·8 %, 95 % CI (0·0 %, 92·2 %), P = 0·002).
Table 2. Subgroup meta-analysis of the SNP rs174575 in FADS2 on the levels of LC-PUFA (Standardised mean difference, I2 and 95 % confidence intervals)
LC-PUFA, long-chain PUFA; LA, linoleic acid; DGLA, dihomo-γ-linolenic acid; DTA, docosatetraenoic acid; AA, arachidonic acid; DPA, docosapentaenoic acid; ALA, α-linolenic acid. Boldface indicates significance in subgroups (P ≤ 0·05).
Discussion
In this meta-analysis, we evaluated the relationship between rs174575 in FADS2 and eight types of PUFA, as well as D5D activity. These assessments were conducted by pooling the results of ten studies.
The desaturation of LC-PUFA is derived from LA and ALA and is regulated by D5D and D6D(Reference Juan, Huang and Jiang27). We propose that SNP rs174575 affects the levels of DGLA and AA by altering the activity of certain desaturases in the pathway for the synthesis of LC-PUFA. In the common n-6 LC-PUFA pathway, D6D acts on LA, leading to GLA synthesis. This step is followed by elongation to DGLA and D5D conversion, resulting in AA production. We observed a lower rate of DGLA to AA conversion in these individuals, and the increased levels of DGLA and decreased levels of AA demonstrated lower D5D activity in the n-6 LC-PUFA synthesis pathway. The minor G allele of rs174575 has been associated with decreased D5D activity, leading to a decreased conversion rate of DGLA to AA and other n-6 PUFA. This effect is believed to be associated with loss of function. A meta-analysis of genome-wide association studies also provided new evidence for the genetic regulation of LC-PUFA by the FADS2 cluster, suggesting that the minor alleles of SNP rs174548 may reflect lower FADS1/2 activity, resulting in lower AA conversion(Reference Mozaffarian, Kabagambe and Johnson28). Recent studies have shown that eicosadienoic acid (EDA) is associated with FADS2 and may modulate PUFA metabolism(Reference Huang, Huang and Li29,Reference Tanaka, Shen and Abecasis30) . Furthermore, EDA is involved in the alternative AA synthesis pathways by the sequential action of a Δ8-fatty acid desaturases (D8D) and a D5D(Reference Park, Kothapalli and Lawrence31). In addition to coding for D6D, FADS2 also influences D8D activity. Notably, D8D catalyses the conversion of EDA to DGLA, which is followed by a D5D conversion, resulting in AA production. Our results suggest that the rs174575 minor G allele is associated with increased D8D activity and improved DGLA conversion.
Our meta-analysis showed that minor G allele carriers of rs174575 had lower AA and higher DGLA levels than major CC allele homozygotes. In addition to a possible association with the minor G alleles of rs174575, which have been suggested to be associated with reduced D5D activity, this phenomenon may be explained by the fact that human tissues contain relatively high AA and DGLA (AA, 6·09 % and 0·44 % of total PUFA in erythrocytes and breast milk; DGLA, 1·38 % and 0·33 % of total PUFA in erythrocytes and breast milk)(Reference Koletzko, Lattka and Zeilinger32,Reference Lattka, Rzehak and Szabó33) . Because the normal AA and DGLA contents in blood and breast milk is high, the AA reduction and DGLA increase may be significant(Reference Wu, Wu and Chan34). A single SNP in the FADS gene cluster has been suggested to have a greater tendency to affect n-6 PUFA than n-3 PUFA in breast milk, plasma and erythrocyte membranes across all age groups(Reference Lauritzen, Brambilla and Mazzocchi1). This may be explained by the fact that human tissues have a relatively higher n-6 PUFA content than n-3 PUFA. In contrast, some n-3 PUFA such as DHA and EPA originally existed in relatively low amounts in the tissues. Therefore, an accurate detection may be difficult. In addition, because of the wide range of n-3 PUFA levels among individuals, researchers often perform logarithmic transformations of n-3 PUFA levels. This may also have influenced the measurement of changes in n-3 PUFA levels.
A meta-analysis of genome-wide association studies found a significant positive direct effect of SNP rs174575 on weight during pregnancy. The rs174561 and rs174575 SNPs have direct adverse effects on plasma n-3 PUFA (EPA and ALA). Regarding the influence of SNPs on the plasma levels of PUFA, only rs174575 had positive direct effects on the plasma levels of ARA and the ARA/LA ratio of n-6 products. The rs3834458 SNP negatively affected the plasma concentrations of EPA(Reference Santana, Pereira and Carvalho35). Although these results were not entirely consistent with the results of our meta-analysis, they did indicate that in the presence of a mutation in the FADS2 SNP, the composition of the precursor and product FA were different. To date, few studies have explored the effects of dietary PUFA and FADS2 interactions, suggesting that the associated effects of SNP in FADS2 on LC-PUFA concentrations may be modified by dietary PUFA intake(Reference Yeates, Love and Engström36–Reference Porenta, Ko and Gruber38). This suggests that LC-PUFA levels may also be influenced by dietary substrates. The balance between n-3 and n-6 PUFA is an important aspect of a healthy diet(Reference Virtanen, Mursu and Voutilainen39), with evidence suggesting that n-3 PUFA have anti-inflammatory properties and n-6 PUFA have pro-inflammatory properties(Reference Poggioli, Hirani and Jogani40–Reference Jerkic, Bächle and Duecker43). However, another study suggested that a high intake of n-6 PUFA was not associated with an increase in inflammation in healthy men. Additionally, n-3 LC-PUFA are involved in inflammatory diseases(Reference Best, Gold and Kennedy44). Concomitant dietary patterns such as the Mediterranean diet, Nordic diet, Western diet, Chinese diet, or PUFA supplements may also change PUFA levels in different populations. Therefore, more research is needed to better understand the influence of dietary patterns on changes in PUFA levels in the minor G allele carriers of rs174575.
Due to the high heterogeneity observed, we performed a subgroup analysis by region, tissue, and study quality. We found that the level of heterogeneity has diminished in subgroup analyses and different regions, types of tissues and quality scores of the studies may contribute to the observed heterogeneity. In the region subgroup analysis, we found that carriers of the minor G allele had higher levels of LA and DGLA and lower AA levels in European populations than in the CC genotype group. Studies have shown ethnic discrepancies in the allele frequencies of FADS2(Reference Sergeant, Hugenschmidt and Rudock45,Reference Yang, Veenstra and Bartz46) . Most of the studies included in the meta-analysis included European populations as the main participants, and fewer participants from other regions were included. This could have been a source of heterogeneity. We further divided the studies into subgroups based on plasma, erythrocyte, and breast milk. The results indicated that the rs174575 variant had a greater effect on PUFA levels in the breast milk group. The G allele of rs174575 was significantly positively associated with breast milk DGLA and DPA concentrations and significantly negatively associated with AA. These findings are consistent with those of the overall analysis. A similar trend was observed in the groups of blood samples; a high level of DGLA was found in the plasma of the G allele group, and a low level of AA was found in the erythrocyte group. Notably, heterogeneity in each subgroup was reduced compared with overall heterogeneity, suggesting that different tissues may be the source of heterogeneity in this meta-analysis. It is important to acknowledge that both dietary intake and genetic predisposition play a role in determining the PUFA composition, so further investigation into the effects of rs174575 and its interaction with dietary patterns on fatty acid composition is required to elucidate this heterogeneity(Reference Rzehak, Thijs and Standl15). The results of our research may be less representative, primarily because of the low percentage of positive results observed in the plasma or erythrocyte group compared to the breast milk group included in the subgroup analysis. Furthermore, due to the limited number of relevant studies, we included articles with scores of six and seven. In the subgroup analyses, heterogeneity was reduced in studies with scores of seven, indicating that the quality of the included studies is also a source of heterogeneity.
Our meta-analysis had some limitations. First, we focused exclusively on the rs174575 variant within the FADS2 gene cluster and its association with LC-PUFA. This narrow focus may have resulted in the potential omission of studies that used different SNP markers, thereby introducing a selection bias. Second, our results were based on raw data without any adjustment for possible confounding factors owing to incomplete information. This lack of adjustment might have influenced the observed associations. Third, only a few studies have been conducted on this topic, indicating the need for more extensive and diverse research to establish stronger evidence of the relationship between variations in the FADS2 gene cluster and LC-PUFA levels.
To establish a relationship between genetic variations and LC-PUFA levels, further research, especially intervention studies, should focus on the effects of ethnic groups with different FADS2 variants and dietary habits on LC-PUFA levels(Reference Chen, Wu and Zhang47).
Conclusion
Analysis of observational studies showed that the minor G allele of rs174575 in FADS2 was associated with higher DGLA levels and lower AA levels than those homozygous for the major allele.
Acknowledgments
None.
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
L. X., H. Y., J. G. and M. L. planned the research; J. G., Y. C. and S. N. searched the literatures; M. L., Y. W., Y. H. and S. C. extracted the data and assessed the quality; M. L., Y. W. and Y. C. analysed the data and wrote the paper; Y. H., M. L. and S. C. drew figures. All authors have read and agreed to the published version of the manuscript.
The author(s) declare none.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114524001624
