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. 2026 Jul 24;68(4):e70184. doi: 10.1002/dev.70184

Impact of Omega‐3 Fatty Acid Docosahexaenoic Acid Supplementation in Individuals During Pregnancy on Neurodevelopment and Growth of Offspring: Systematic Review and Meta‐Analysis

Linyun Xie 1, Meicen Zhou 2, Liqun Lu 3, XueMei Ning 3, Yue Song 3,✉
PMCID: PMC13397051  PMID: 42494300

ABSTRACT

Maternal prenatal docosahexaenoic acid (DHA) intake is critical for fetal brain and retinal development; however, evidence regarding its effects on offspring neurodevelopment and growth remains inconsistent. This systematic review and meta‐analysis evaluated the efficacy of prenatal DHA supplementation on offspring neurodevelopmental outcomes (attention, motor, intelligence, language, executive function, and short‐term memory) and growth parameters (birth weight, head circumference, gestational age, and birth length) in the general population. We analyzed randomized controlled trials (RCTs) from 2001 to 2023 across PubMed, Embase, and the Cochrane Library. Of 917 records, 21 RCTs were included in the systematic review, of which 19 were eligible for meta‐analysis. The meta‐analysis showed that prenatal DHA supplementation had no significant benefits on any neurodevelopmental outcome. Subgroup analyses revealed no improvements across all assessed domains (all p > 0.05). In contrast, prenatal DHA supplementation significantly increased neonatal birth weight (mean difference [MD] = 72.51 g, 95% CI: 37.55–107.48), whereas no notable improvements were observed in head circumference, gestational age, birth length, or any of the assessed neurodevelopmental outcomes. Overall, the current evidence does not support benefits of prenatal DHA supplementation for neurodevelopmental outcomes or for growth outcomes other than birth weight in the general population. Future research should focus on identifying subgroups that may be responsive to DHA based on genetic, nutritional, or environmental factors.

Keywords: docosahexaenoic acid, growth, meta‐analysis, neurodevelopment, offspring, pregnancy

1. Introduction

Docosahexaenoic acid (DHA) (C22:6n‐3) is a long‐chain polyunsaturated fatty acid (LCPUFA) of the omega‐3 family, characterized by a 22‐carbon chain with six cis double bonds, the first of which is located at the third carbon from the methyl terminus (omega‐3) (Li et al. 2025). As a key structural component of cell membranes, omega‐3 fatty acids play vital roles in the development and function of multiple physiological systems (Stillwell and Wassall 2003). DHA is particularly abundant in the phospholipid membranes of neurons and retinal cells, where it enhances membrane fluidity, facilitates signal transduction, supports synaptic plasticity, and promotes neuronal survival (Bazan 2005). Additionally, DHA serves as a precursor for neuroprotective mediators such as protectins and resolvins, which contribute to the resolution of inflammation and the maintenance of brain health (Serhan et al. 2015).

Humans lack the ability to synthesize linoleic acid (LA) and α‐linolenic acid, two important polyunsaturated fatty acids. For this reason, these essential nutrients are entirely dependent on dietary intake (Wolff et al. 2025). This requirement is especially critical during periods of rapid growth, such as infancy and childhood, when omega‐3 PUFAs support immune function, visual development, and central nervous system maturation (Ozerskaia et al. 2024). Since maternal DHA is transferred transplacentally to the fetus and is excreted in breast milk, it represents a critical nutritional factor for early brain development (Tam et al. 2016). Clinical studies have indicated that omega‐3 supplementation, particularly DHA, may enhance retinal development in preterm infants (Ozerskaia et al. 2024). Moreover, DHA is strongly correlated with brain morphometry: Higher levels are associated with increased total brain volume, including cortical gray matter, deep gray matter, cerebellar tissue, and white matter (Hortensius et al. 2021; Zou et al. 2021).

Intervention studies further support the role of DHA in neurodevelopment. For example, infants receiving DHA supplementation have demonstrated improved mental and psychomotor development scores compared with control groups (Gould et al. 2023; Hu et al. 2024). However, a meta‐analysis on DHA supplementation in infants did not reach a consistent conclusion. The results showed that while DHA supplementation did not significantly improve the Mental Development Index in infants, it did improve the Psychomotor Development Index (Hu et al. 2024). In addition, Tam et al. (2016) demonstrated that higher DHA and lower LA levels were associated with decreased intraventricular hemorrhage, improved brain microstructural development, and better long‐term outcomes in preterm children. Therefore, the authors proposed that early, and possibly antenatal, DHA intervention in high‐risk pregnancies deserves further investigation for its potential benefits on developmental outcomes in preterm infants (Tam et al. 2016). Based on this evidence, several international health authorities recommend that pregnant women consume an additional 200 mg of DHA daily to support fetal neurodevelopment and mitigate risks associated with deficiency (Koletzko et al. 2018; Basak et al. 2020). Importantly, the same guideline from Koletzko et al. (2018) explicitly acknowledges that “data on the benefit of DHA supplements in pregnancy for the child's cognitive development are inconsistent.”

Therefore, this systematic review and meta‐analysis aims to evaluate the effects of DHA supplementation during pregnancy on neurodevelopment and growth of offspring, including intelligence, executive function, motor, language, height, and weight. The study specifically focused on pregnant women and their children, as the prenatal and early postnatal periods represent the most critical stages of brain development.

2. Methods and Materials

2.1. Search Strategy

We searched the PubMed, Embase, and Cochrane Central databases and included only randomized controlled trials (RCTs). The search strategy incorporated comprehensive combinations of terms related to the study population, intervention, and outcomes. To minimize the risk of omitting relevant studies, we also expanded the search by including numerous relevant subterms under the main keywords. The detailed search syntax is provided in the Supporting Information. Prior to study selection, the protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD42024502178).

2.2. Eligibility Criteria

The titles and abstracts of the retrieved literature were screened to determine eligibility for inclusion. The numbers of included and excluded studies, along with reasons for exclusion, are summarized in Figure 1.

FIGURE 1.

FIGURE 1

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Flow chart of inclusion and exclusion of studies.

This study included pregnant women from the general population and their offspring, with only RCTs eligible for inclusion. Interventions consisted of DHA supplementation during pregnancy, with control groups. Outcomes included offspring neurological development, assessed across attention, short‐term memory, executive function, language, motor, and intelligence as well as growth parameters (birth weight, head circumference, gestational age, and birth length). Exclusions applied to non‐DHA interventions and animal studies. The study selection process is outlined in Figure 1.

2.3. Data Extraction

Two independent reviewers performed the literature search and screened titles and abstracts according to predefined inclusion and exclusion criteria. Any discrepancies between reviewers were resolved through discussion until consensus was reached.

Data extraction included bibliographic information (e.g., year, author), sample size, intervention details, and daily dosage for both experimental and control groups. For each subcategory, the most validated and comprehensive assessment scale was prioritized. Neurological development and growth outcomes were recorded as described in the original studies. All outcome units were standardized for consistency.

Relevant data pertaining to neurodevelopment and growth were extracted. When standard deviations were not directly reported, they were imputed from interquartile ranges or standard errors of the mean using conversion formulas recommended by the Cochrane Handbook (Cochrane Collaboration 2019).

2.4. Selected Study Bias Risk Assessment

We assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool and Review Manager software (version 5.4.1), in accordance with the guidelines provided in the Cochrane Handbook. Each RCT was evaluated across the following seven domains and assigned a rating of “High,” “Low,” or “Unclear” risk of bias: (1) random sequence generation, (2) allocation concealment, (3) blinding of participants and personnel, (4) blinding of outcome assessment, (5) incomplete outcome data, (6) selective reporting, and (7) other potential sources of bias (Figure 2 and Table 2).

FIGURE 2.

FIGURE 2

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Assessment of overall risk of bias (as percentage) across selected articles.

TABLE 2.

Quality evaluation of 21 randomized controlled trials.

No. Author Random sequence generation Allocation concealment Blinding of participants and personnel Blinding of outcome assessment Incomplete outcome data Selective reporting Other bias
1 Carlson et al. (2013) 1 1 1 1 1 1 1
2 Campoy et al. (2011) 1 1 1 — 0 1 0
3 Gould et al. (2014) 1 1 1 1 1 1 1
4 Helland et al. (2001) 1 — 1 — 1 1 1
5 Hurtado et al. (2015) 1 1 1 — 1 1 —
6 Gonzalez‐Casanova et al. (2021) 1 — 1 1 1 1 1
7 Jensen et al. (2005) 1 1 1 — 1 1 1
8 Jensen et al. (2010) — — 1 — 1 1 1
9 Colombo et al. (2016) 1 — 1 1 1 1 1
10 Judge et al. (2007) — — 1 1 1 1 0
11 Keenan et al. (2016) 1 1 1 — 0 1 0
12 Mulder et al. (2018) — — 1 1 1 1 1
13 Lauritzen et al. (2004) 1 1 1 — 1 1 —
14 Lauritzen et al. (2005) — — 1 1 1 1 —
15 Makrides et al. (2010) 1 1 1 1 1 1 1
16 Makrides et al. (2014) 1 1 1 1 1 1 1
17 Ramakrishnan et al. (2015) 1 1 1 1 1 1 1
18 Ramakrishnan et al. (2016) — — 1 — 1 1 1
19 Tofail et al. (2006) — — 1 1 0 1 0
20 van Goor et al. (2010) 1 — 1 1 1 1 —
21 van Goor et al. (2011) 1 — 1 — 1 — —
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Note: 0, high risk; 1, low risk.

The overall methodological quality of the included RCTs is summarized in the risk of bias graph. The assessment revealed variability in bias risk across studies. A high risk of bias was predominantly identified in the domains of “Blinding of participants and personnel (performance bias)” and “Incomplete outcome data (attrition bias),” indicating inadequate blinding procedures in certain trials and insufficient follow‐up or handling of outcome data completeness. Most other domains were rated as “Low risk” or “Unclear risk,” suggesting a comparatively lower likelihood of bias in areas such as selective reporting.

2.5. Statistical Analyses

For the meta‐analysis, we used Review Manager (RevMan, version 5.4). Mean differences (MDs) were calculated to compare neurological and growth outcomes between study groups in the offspring. A fixed‐effect model was applied when heterogeneity was not significant; otherwise, a random‐effects model was used. Heterogeneity was quantified using I 2 statistics. If a single study reported multiple outcomes within the same cognitive domain, outcome measures were pooled by combining means and standard deviations. Standard errors were converted to standard deviations where necessary (Higgins and Thompson 2002). To evaluate the robustness of the overall effect, sensitivity analysis was performed by sequentially excluding individual trials and recalculating the effect size. Publication bias was assessed using funnel plots, with p < 0.05 indicating statistical significance.

3. Results

3.1. Literature Search

A total of 917 studies were initially identified, with 68 retrieved from PubMed, 785 from Embase, and 64 from Cochrane Central. After excluding irrelevant topics, the full texts of 31 articles were evaluated for eligibility by the same reviewers. Among these, five studies were excluded due to the following reasons: incompatible study design (n = 2), inappropriate exposure (n = 1), duplicate publication (n = 1), or lack of relevant data (n = 1). Ultimately, 21 studies were deemed suitable for inclusion in the systematic review. Of these, 19 provided data in a format amenable to meta‐analysis (Figure 1). Detailed search strategies are provided in the Supporting Information.

3.2. Characteristics and Quality of Studies

The majority of the included trials (n = 9) were conducted in industrialized countries, with only two trials reported from developing countries (Mexico and Bangladesh). The duration of the studies ranged from 9 to 90 months. Group sizes varied across studies, with participant numbers per arm ranging from 14 to 487 (Table 1). All trials involved DHA supplementation during pregnancy, with daily doses ranging between 20 and 1200 mg. Only outcomes reaching statistical significance were reported; otherwise, findings were indicated as “no difference” at the 95% CI.

TABLE 1.

Characteristics of studies included in systematic review.

Ref.

Duration

(months)

No.

intervention

group

No.

control

group

DHA/day

(mg)

 Country Include in meta Domain

Outcomes
Helland et al. (2001) 90 152 136 1183 Norway Yes

Intelligence

 At 6 and 9 months: No difference in cognitive function.

Lauritzen et al. (2004)

Lauritzen et al. (2005)

 84 53 47 900 Denmark Yes Intelligence, language, executive function, growth and development

At 9 months: Girls in intervention better in problem solving than control (p = 0.024).

At 2 years: Boys in intervention had less sentence complexity than control (p = 0.043).

Jensen et al. (2005)

Jensen et al. (2010)

 60 83 77 200 United States Yes Intelligence, language, executive function, attention, motor, growth and development

At 30 months: The PDI of the intervention group was significantly higher than that of the control group (p = 0.008).

At 4 and 8 months: No difference in visual function.

At 5 years: The intervention group performed better in the “sustained attention” test (p = 0.008), no difference was found in visual function.
Tofail et al. (2006) 14 125 124 1200 Bangladesh Yes Intelligence, motor, growth and development At 10 months: No difference.
Judge et al. (2007) 13 14 15 214 United States Yes Intelligence, growth and development

At 9 months: Infants in the intervention group achieved significantly more intentional solutions on the two‐step problem‐solving test than those in the control group (p = 0.011).

No between‐group differences were found on the Fagan Test of Infant Intelligence.

Makrides et al. (2010)

Makrides et al. (2014)

Gould et al. (2014)

 53 351 375 800 Australia Yes Intelligence, language, executive function, attention, motor, short‐term memory, growth and development

At 18 months: No difference.

At 27 months: No difference.

Ramakrishnan et al. (2015)

Ramakrishnan et al. (2016)

 65 487 486 400 Mexico Yes Intelligence, attention, motor, language, executive function, and short‐term memory

At 18 months: No overall effects.

At 5 years: Improved attention, no global cognitive differences.

van Goor et al. (2010)

van goor et al. (2011)

 24 42+42 36 220 Netherlands Yes Intelligence, motor At 18 months: No significant intervention effects.
Campoy et al. (2011) 90 37 45 500 Spain No Short‐term memory At 78 months: No difference.
Carlson et al. (2013) 14 154 147 600 United States Yes Attention, growth and development The subsequent results are contained within Colombo et al. (2016).
Hurtado et al. (2015) 15 38 38 320 Spain Yes Intelligence, motor, growth and development At 12 months: No difference.
Colombo et al. (2016) 12 123 107 600 United States Yes Growth and development At 6 and 9 months: No difference.
Keenan et al. (2016) 9 34 15 450 United States Yes Motor, language, growth and development At 3 months: No difference in neurodevelopment, but DHA group had lower cortisol response to stress.
Mulder et al. (2018) 66 46 52 400 Canada No Language At 60–72 months: No difference.
Gonzalez‐Casanova et al. (2021) 90 152 136 400 United States Yes Intelligence, attention, language, executive function, short‐term memory, growth and development At 60 months: No difference in MSCA Composite Score.
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According to the Cochrane risk‐of‐bias assessment, 12 of the selected articles were rated as having low to moderate risk of bias, as summarized in Figure 2 and Table 2. Four studies were judged to have a high risk of bias due to the presence of multiple potential sources of bias. Four studies exhibited various methodological limitations that raise concerns regarding their validity. Similarly, Campoy et al. (2011) was judged to be at high risk of bias due to incomplete outcome data, suggesting nonrandom missingness potentially related to treatment or outcomes, thereby introducing significant attrition bias. van Goor et al. (2010) was rated at high risk of selective reporting bias, indicating that outcomes may have been selectively disclosed based on favorability, which undermines the comprehensiveness and interpretability of the results; additional unclear risks in other domains further reduce the study's overall credibility. Although Helland et al. (2001) showed a low risk of bias in random sequence generation and incomplete outcome data, critical missing details regarding blinding and allocation concealment resulted in an unclear risk of bias in these areas, precluding definitive conclusions about its freedom from bias.

3.3. Neonatal Neurological Outcomes and DHA

A total of 16 studies were included to evaluate the effects of DHA supplementation during pregnancy on neonatal neurological outcomes.

Attention: Four studies involving 1642 children (intervention group: 811; control group: 831) assessed attention outcomes. The random‐effects meta‐analysis showed no significant difference between groups (MD = 0.45, 95% CI: −1.82 to 2.72; Z = 0.39, p = 0.70), with substantial heterogeneity (I 2 = 81%, p = 0.001). These results suggest that DHA supplementation did not significantly improve attention ability in the offspring (Figure 3A).

FIGURE 3.

FIGURE 3

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Forest plot of neurodevelopment outcomes of offspring. (A) Attention, (B) Motor, (C) Intelligence, (D) Language, (E) Executive function, (F) Short‐term memory.

Motor: Six studies comprising 2041 children (intervention: 1018; control: 1023) evaluated motor. Using a random‐effects model, no significant between‐group difference was observed (MD = 1.25, 95% CI: −0.35 to 2.84; Z = 1.53, p = 0.12), with moderate heterogeneity (I 2 = 51%, p = 0.07). Thus, DHA supplementation was not associated with improved motor (Figure 3B).

Intelligence: Nine studies involving 2393 children (intervention: 1193; control: 1200) assessed intelligence. A fixed‐effects model revealed no significant difference (MD = −0.09, 95% CI: −0.58 to 0.40; Z = 0.36, p = 0.72), with low heterogeneity (I 2 = 11%, p = 0.34), indicating that DHA did not enhance intelligence outcomes (Figure 3C).

Language: Seven studies with 2594 children (intervention: 1300; control: 1294) were included. The fixed‐effects model showed no significant benefit from DHA supplementation (MD = −0.55, 95% CI: −1.19 to 0.10; Z = 1.65, p = 0.10), with negligible heterogeneity (I 2 = 2%, p = 0.41), suggesting no improvement in language (Figure 3D).

Executive function: Three studies involving 1064 children (intervention: 538; control: 526) assessed executive function. A fixed‐effects model indicated no significant difference (MD = −0.11, 95% CI: −0.86 to 0.65; Z = 0.28, p = 0.78), with no heterogeneity (I 2 = 0%, p = 0.40), supporting no effect of DHA on executive function (Figure 3E).

Short‐term memory: Three studies encompassing 1426 children (intervention: 1030; control: 1035) evaluated short‐term memory. A random‐effects model showed no significant group difference (MD = −0.43, 95% CI: −1.35 to 0.49; Z = 0.92, p = 0.36), despite considerable heterogeneity (I 2 = 71%, p = 0.03), indicating that DHA did not improve short‐term memory (Figure 3F).

3.4. Birth Anthropometric Measures and DHA

A total of eight studies were evaluated to assess the effects of maternal DHA supplementation during pregnancy on birth anthropometric measures reflecting offspring growth.

Birth weight: Eight studies involving 3571 children (intervention group: 1802; control group: 1769) were included. A random‐effects meta‐analysis showed a statistically significant difference between groups (MD = 72.51, 95% CI: 37.55–107.48; Z = 4.06, p < 0.0001), with low heterogeneity (I 2 = 37%, p = 0.13), indicating that prenatal DHA supplementation increased birth weight (Figure 4A).

FIGURE 4.

FIGURE 4

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Forest plot of birth anthropometric measures of offspring. (A) Birth weight, (B) Head circumference, (C) Gestational age, (D) Birth length

Head circumference: Ten studies comprising 899 children (intervention: 460; control: 439) were analyzed. Using a fixed‐effects model, no significant difference was observed (MD = 0.16, 95% CI: −0.05 to 0.38; Z = 1.47, p = 0.14), with low heterogeneity (I 2 = 33%, p = 0.20), suggesting no effect of DHA on head circumference (Figure 4B).

Gestational age: Seven studies involving 3571 children (intervention: 1802; control: 1769) were included. A fixed‐effects model revealed no significant difference (MD = 0.09, 95% CI: −0.01 to 0.18; Z = 1.85, p = 0.06), with moderate heterogeneity (I 2 = 49%, p = 0.05), indicating that DHA supplementation did not prolong gestational age (Figure 4C).

Birth length: Six studies with 899 children (intervention: 460; control: 439) were evaluated. A random‐effects model showed no significant group difference (MD = 0.20, 95% CI: −0.44 to 0.83; Z = 0.62, p = 0.54), despite considerable heterogeneity (I 2 = 67%, p = 0.02), demonstrating that DHA did not improve birth length (Figure 4D).

3.5. Sensitivity Analysis

A sensitivity analysis was performed by sequentially removing each individual trial from the meta‐analysis (see Supporting Information). The results indicated that no single study significantly altered the overall effect size of the outcomes.

3.6. Funnel Plot

Funnel plots were employed to visually evaluate potential publication bias across cognitive domains, displaying results from both fixed‐effect (represented by dashed lines) and random‐effects models. These plots depict the association between effect size, expressed as MD, and its precision, measured by standard error. Among all cognitive domains (motor, executive function, attention, language, intelligence, and short‐term memory), effect estimates from individual studies mostly clustered near the top of the funnel (Figure 5). The overall distribution of points exhibited broad symmetry under both models. This symmetrical pattern suggests a low probability of substantial publication bias.

FIGURE 5.

FIGURE 5

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Funnel plot of neurodevelopment.

4. Discussion

This systematic review and meta‐analysis systematically evaluated the effects of prenatal DHA supplementation on offspring growth, development, and neurodevelopmental outcomes. The results showed that prenatal DHA supplementation increased birth weight (Figure 4A), a finding consistent with the studies by Carlson et al. (2013), Bilgundi et al. (2024), and Abdelrahman et al. (2023). Some studies reported positive effects of prenatal DHA supplementation on growth‐related outcomes such as neonatal head circumference (Carlson et al. 2013), birth length (Carlson et al. 2013), and gestational age (Wang et al. 2023), and suggested potential improvements in neurodevelopmental outcomes in offspring, including cognition (Nevins et al. 2021), intelligence (Liu, Zhong, et al. 2025), language (Liu, Zhong, et al. 2025), motor (Jensen et al. 2005), short‐term memory (Gonzalez Casanova et al. 2021), executive function (Gustafson et al. 2022), and attention (Colombo et al. 2016). However, in the present meta‐analysis, we did not observe statistically significant beneficial effects for other growth‐related outcomes or for most neurodevelopmental outcomes.

The discrepancies in the findings between studies may be attributed to variations in the source and dosage of the supplements. A systematic review and meta‐analysis conducted by Abdelrahman et al. (2023) investigated the effects of n‐3 polyunsaturated fatty acid intake during pregnancy. This study included 38 RCTs, comprising 8321 women in the n‐3 supplementation group and 8184 women in the control group, and found that n‐3 polyunsaturated fatty acid supplementation during pregnancy significantly increased neonatal birth weight. However, this analysis did not differentiate the effects of various n‐3 fatty acid supplements, such as DHA and alpha‐linolenic acid. Furthermore, the dosage of DHA in that study ranged from 80 to 2100 mg/day, representing a wide range. In contrast, the present study focused on a clearly defined type of supplement, specifically DHA, and included eight RCTs (comprising 1802 women in the intervention group and 1769 women in the control group). In addition, we restricted the prenatal DHA dosage to a range of 200–1200 mg/day, thereby further validating the conclusions of the aforementioned meta‐analysis. These findings indicate that when the supplement primarily consists of DHA, a DHA dosage of 200–1200 mg/day during pregnancy continues to exert a beneficial effect on birth weight. This result not only reduces the uncertainty associated with supplement heterogeneity in previous studies, but also provides a more targeted reference for the rational selection of specific types of n‐3 supplements in clinical practice.

Our meta‐analysis showed that prenatal DHA supplementation led to a statistically significant increase in birth weight of approximately 72.5 g. Although this increase is modest in absolute magnitude, it may still be of clinical relevance. This finding is consistent with previous research, including a meta‐analysis (Bilgundi et al. 2024) which reported that DHA supplementation at 450–800 mg/day significantly improved birth weight. Furthermore, studies in higher‐risk populations have demonstrated that DHA‐induced increases in birth weight can translate into meaningful clinical benefits. For instance, Carlson et al. (2013) found that 600 mg/day DHA during the second and third trimesters increased mean birth weight by 172 g and significantly reduced the incidence of very low birth weight and early preterm birth (< 34 weeks) (both p < 0.05). Similarly, Bilgundi et al. (2024) reported that prenatal DHA at 450–800 mg/day was associated with a significantly higher birth weight (MD: 101.71 g; 95% CI: 57.36–146.06; p = 0.00001) and a lower risk of low birth weight (OR: 0.53; 95% CI: 0.33–0.86; p = 0.01). Taken together, while the 72.5‐g increase observed in our study of predominantly term infants is modest, evidence from higher‐risk populations suggests that even modest increases in birth weight may confer clinical benefits in certain subgroups. However, the generalizability of these clinical benefits to healthy, term‐born populations requires further investigation.

The results of a meta‐analysis published in 2025 by Liu, Zhang, et al. (2025) suggested that early LCPUFA supplementation may not improve most neurodevelopmental outcomes in preterm or low birth weight infants, but may reduce the risk of intellectual disability. This finding was based on a single trial in which supplementation was initiated shortly after birth. This timing differs from the conditions of prenatal DHA supplementation in our study. Additionally, Gustafson et al. (2022) used high‐purity algal oil‐derived DHA administered at a high dose (600 mg/day) starting at approximately 14.5 weeks of gestation, and reported that prenatal DHA affects brain and behavioral responses during the performance of an inhibitory task during the preschool period. Overall inhibitory performance (i.e., errors on No‐Go trials) was improved for children from DHA‐supplemented mothers. These findings suggest that variations in the type of LCPUFA supplement, timing of supplementation, and dosage may also influence neurodevelopmental outcomes.

Differences between our findings and those of several earlier studies in terms of neurodevelopmental outcomes may also be attributed to variations in genetic background, assessment tools, and follow‐up duration. For instance, the benefits in short‐term memory reported by Gonzalez Casanova et al. (2021) were confined to a subgroup with a specific genetic profile (maternal FADS2 SNP rs174602 TT genotype), whereas our analysis encompassed a broader population. Additionally, Colombo et al. (2016), through a rigorously controlled RCT using a standardized protocol (initiation in the second trimester, 600 mg/day algal oil‐derived DHA), reported intervention effects using the visual habituation protocol. Unlike that study, the other RCTs included in our analysis varied in intervention timing (prenatal vs. postnatal), dosage (ranging from 200 to 800 mg/day), outcome measures, and assessment tools (global cognitive scales vs. domain‐specific attention tests). Among these, Jensen et al. (2010) and Ramakrishnan et al. (2016) reported positive effects on sustained attention, whereas Mulder et al. (2018) and Makrides et al. (2014) found no significant differences in cognitive outcomes. Furthermore, the study by Gustafson et al. (2022) conducted long‐term follow‐up until 5.5 years of age and employed fine‐grained measurements of inhibitory control using event‐related potentials combined with a Go/No‐Go task. In contrast, the studies synthesized in our analysis generally utilized broader developmental scales and exhibited considerable heterogeneity in dosage and intervention timing.

Limitations: This study analyzed the effects of variables such as interventions and population characteristics on outcomes. However, due to clinical and methodological heterogeneity among the included studies, the overall quality of evidence is moderate, and caution is warranted when interpreting the results. Similar to many meta‐analyses, this study is also limited by variations in trial design. Differences existed in the use of DHA supplements across trials, including variations in the source and ratio of DHA to EPA, dosage specifications, and duration of supplementation, with DHA dosages ranging from 200 to 1200 mg/day. These factors may influence the direction and magnitude of effects, thereby complicating the interpretation of pooled results. In terms of evidence strength, some trials had insufficient sample sizes, and the number of trials in certain comparison groups was limited. For instance, one study included in the analysis of birth weight data (Keenan et al. 2016) had an intervention group sample size of 30 and a control group sample size of 13, resulting in low statistical power. In addition, evidence regarding intervention adherence was incomplete, further limiting the ability to assess the reliability of the intervention effects. Another limitation is that this study only included trials with postnatal follow‐up and lacked long‐term follow‐up data, thus precluding the assessment of the dynamic trajectory of child development. As time progresses, the risk of confounding by environmental factors also increases.

5. Conclusion

Our meta‐analysis revealed a beneficial effect of prenatal DHA supplementation on offspring birth weight, with no significant impacts observed on other growth indicators or neurodevelopmental outcomes. Collectively, the current evidence does not demonstrate consistent benefits to support the universal recommendation of prenatal DHA supplementation for improving overall developmental outcomes in the general population. Nevertheless, it is important to acknowledge that several RCTs have reported improvements in specific cognitive domains, and discrepancies across studies may be attributed to differences in DHA dosage, timing of supplementation, genetic background, and assessment tools. Collectively, the evidence does not support the universal recommendation of prenatal DHA supplementation for improving overall developmental outcomes in the general population. Future research should focus on identifying specific subgroups that may benefit from supplementation based on genetic, nutritional, or environmental factors.

Author Contributions

All authors made a substantial contribution to the concept and design of the work. L.X., M.Z., X.N., and Y.S. contributed equally to the acquisition, analysis, and interpretation of data. L.X., M.Z., L.L., X.N., and Y.S. drafted the article. All authors revised it critically for important intellectual content and approved the version to be published. All authors participated sufficiently in the work to take public responsibility for appropriate portions of the content.

Funding

This study was supported by Joint Innovation Fund Project of Chengdu Municipal Health Commission and Universities (WXLHCXJJ25‐64) and Open Fund of Development and Regeneration Key Laboratory of Sichuan Province (grant no. 24FYYZS12).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supplementary Materials: dev70184‐sup‐0001‐SuppMat.doc

DEV-68-e70184-s001.doc (101.5KB, doc)

Acknowledgments

The authors have nothing to report.

Data Availability Statement

The databases, extracted data, and materials used in the present study are available upon request from the corresponding author.

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Supplementary Materials

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Data Availability Statement

The databases, extracted data, and materials used in the present study are available upon request from the corresponding author.

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