Fish Oil-Derived Fatty Acids in Pregnancy and Brain Metabolism in Middle Childhood: Results From a Randomized Controlled Trial

Fish Oil-Derived Fatty Acids in Pregnancy and Brain Metabolism in Middle Childhood: Results From a Randomized Controlled Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fish Oil-Derived Fatty Acids in Pregnancy and Brain Metabolism in Middle Childhood: Results From a Randomized Controlled Trial María Lorca, Mark Vestergaard, Karen Sandø Ambrosen, Jayachandra Raghava, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6678778/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Maternal blood levels of polyunsaturated fatty acids (PUFA) during pregnancy have been associated with brain health outcomes in offspring, but causal effects of PUFA on brain metabolism is unexplored. We examined the effect of maternal PUFA supplementation on brain metabolism in children at age 10, measured as cerebral blood flow (CBF), oxygen consumption (CMRO₂), and brain lactate concentration. We used data from the COPSAC2010 cohort (n = 700), where pregnant women were randomized to PUFA supplementation or placebo. At age 10, the children underwent psychopathological and cognitive assessments as well as MRI scans to obtain CBF, CMRO₂, and brain lactate. We estimated the effect of maternal PUFA intake on brain metabolism and examined the associations between those metabolic measurements with psychopathological symptoms and cognitive outcomes in the offspring. Lastly, we investigated the association of age with brain metabolic outcomes by comparing with data from adult participants from five previous studies. 487 children (51.7% male, 10.3 ± 0.32 years old) underwent 3 Tesla MRI scan. Children whose mothers received PUFA supplementation exhibited significantly lower CBF and CMRO₂; however, these parameters were not associated with psychopathology or cognition. The age-related analysis, with additional data of 248 adults, showed that higher age was associated with lower CBF, CMRO₂, and lactate concentration. Our findings suggest that maternal PUFA supplementation influences brain maturation in the offspring at age 10, although PUFA supplementation does not directly translate into psychopathological status and cognitive performance. Our results highlight the need for further large-scale studies on maternal nutrition and brain development. Biological sciences/Neuroscience Biological sciences/Psychology Figures Figure 1 Figure 2 INTRODUCTION Compared to adults, children have a more demanding brain energy metabolism, characterized by higher cerebral blood flow (CBF), higher cerebral metabolic rate of oxygen (CMRO₂), and higher cerebral lactate production. This heightened metabolic demand during neurodevelopment and brain maturation in childhood is driven by multiple processes involving synaptic formation, pruning, and myelination 1 , 2 and declines as the brain matures. CBF and CMRO₂ follow parallel lifespan trajectories, peaking in middle childhood and declining towards adulthood as an age dependent maturation process. During early childhood, CBF undergoes significant changes closely associated with cognitive and structural brain development increasing until approximately age 7 years 3 , and declining from this point 4 , 5 through adolescence to senescence 6 – 8 . The variations in CBF have been associated with changes in metabolism 9 , 10 , cognitive function 11 – 13 , and/or neuropsychiatric disorders 14 – 16 . Similarly, CMRO₂ has been shown to increase from the neonatal period during early childhood, peaking at approximately age 7, and decline during adolescence into adulthood 17 . Cerebral lactate concentration is also elevated in children. In childhood, the brain exhibits a marked oxygen-independent glycolytic activity, occurring despite sufficient oxygen availability, a process therefore often referred to as aerobic glycolysis (AG). AG will lead to the cerebral production of lactate. While lactate production yields significantly less adenosine triphosphate (ATP) production than glucose oxidation, AG is essential for neurodevelopment, particularly in processes like myelin production by myelinoligodendrocytes 18 , 19 . AG activity varies throughout the lifespan from around 30–40% of the glucose used in the newborn brain to approximately 10–12% in the adult brain 20 – 22 . The physiological explanation underlying this discrepancy in AG between infancy and adulthood is unclear, however, lactate has been shown to be crucial for supporting neuronal functions during development, such as long-term memory formation in juvenile animals 23 . Beyond the physiological variations in brain metabolism and function throughout the lifespan, brain metabolism has been associated with fish oil intake, which contains polyunsaturated fatty acids (PUFA) 24 . PUFAs are essential nutrients that have been proven to serve a vital role in brain development 25 and brain structure and function 26 . Indirectly, supporting the importance of PUFA accumulation in the brain during the critical pre- and perinatal growth phase 27 , 28 , prior studies have shown associations of maternal PUFA supplementation during pregnancy with better neurodevelopmental outcomes in the offspring 29 – 32 . However, to our knowledge, no studies have investigated the direct impact of PUFA intake during pregnancy on the brain metabolism in childhood, and the associations of brain metabolic markers with psychopathological and cognitive outcomes in a pediatric population. In the current study, we leverage data from the COPSAC2010 33 cohort with results from a randomized controlled trial (RCT) on PUFA supplementation during the third trimester of pregnancy. At the 10-year visit, in the COPSYCH study 34 , the children underwent comprehensive assessments including evaluations of psychopathology, cognitive function, and magnetic resonance imaging (MRI) brain scans. The current analyses characterize three brain metabolic measurements (total average CBF, CMRO₂, and cerebral lactate concentration) of the children at age 10 and investigate their relationship to maternal PUFA levels and PUFA supplementation during pregnancy. We hypothesize that higher maternal PUFA levels in pregnancy and PUFA supplementation during pregnancy would be associated with higher brain physiology maturation, reflected in lower measurements of the three investigated metabolic parameters. We further hypothesize that lower brain metabolic measurements will be reflected in lower psychopathology and better cognitive function. As an independent age-validation analysis, we present the association of age with the brain metabolism parameters including participants of young and older adults from prior studies using identical MRI scanner and sequences. METHODS Study design and sample Participants in this study are part of the COPSAC2010 cohort, a longitudinal clinical study involving 700 mother-child pairs based on the background population. Pregnant women were recruited at 24 weeks of gestation and participated in a RCT of PUFA supplementation in the third trimester of pregnancy 33 . At recruitment, i.e., pregnancy week 24, the women were randomly assigned in a 1:1 ratio to either receive 2.4 g per day of n − 3 LCPUFA (55% eicosapentaenoic acid -EPA- and 37% docosahexaenoic acid -DHA-), in triacylglycerol form (Incromega TG33/22, Croda Health Care) or a placebo, in the form of olive oil, containing 72% n–9 oleic acid and 12% n − 6 linoleic acid (Pharma-Tech A/S). Supplementation continued until one week postpartum and adherence to the intervention was measured as the difference between the number of capsules returned and the number expected to be returned. The RCT was blinded for the families and researchers until the youngest child turned three years old. At age 10, the families were invited to participate in the COpenhagen Prospective Study on Neuro-PSYCHiatric Development (COPSYCH) study, a comprehensive assessment of cognition and dimensional and categorical psychopathology, including an MRI scan of the brain examining both structure and physiology 34 , 35 . For the age-validation analysis, we compared data from the current study with data from healthy adult control subjects from four previously published studies 7 , 36 – 39 . These studies were performed at the same site, using the same MRI scanner and sequences as the children of the COPSAC2010 cohort, enhancing the reliability and validity of the analysis. The method descriptions (recruitment, inclusion and exclusion criteria, social and clinical characteristics, and brain outcomes) related to the studies used for the age-validation analysis are described elsewhere 7 , 36 – 39 . Measurements Maternal pre-intervention PUFA Since the RCT intervention consisted of supplementation of DHA and EPA, maternal whole-blood levels of DHA and EPA (in µg/100µL) were measured at pregnancy week 24 prior to randomization. Brain metabolism MRI scans of the whole brain were acquired with a Philips Achieva 3.0 T scanner (Philips Healthcare, Best, The Netherlands) with a 32-channel SENSE Head Coil. We obtained distinct images and measurements derived from the MRI acquisition to account for the following parameters: i) Cerebral blood flow (CBF) was obtained using a phase-contrast mapping (PCM) MRI sequence, which measures the blood velocity through the feeding basilar and carotid arteries supplying the brain. Blood-velocity sensitive images were measured using a turbo field echo sequence (1 slice, FOV = 240 x 240 mm 2 ; voxel size = 0.75 x 0.75 x 8 mm 3 ; Echo time (TE) = 7.4 ms; Repetition time (TR) = 12.3 ms; flip angle = 10 o ; velocity encoding = 100 cm/s, without cardiac gating; 5 dynamics). The sequence was acquired twice to get optimal perpendicular slice-positions on both the internal carotid arteries and the basilar artery (second scan). After manually delineating each artery, the total CBF to the brain was calculated as mean blood velocity times the cross-sectional area of the delineated arteries, and it was normalized to brain weight to obtain values in ml/100g/min providing total average CBF. The post-processing of the data was performed using publicly available in-house developed software (see Data Availability). ii) Total average cerebral metabolic rate of oxygen (CMRO₂) was calculated using Fick’s Principle, \(\:CM{RO}_{2}=Hgb\cdot\:CBF\cdot\:(Sa{O}_{2}-Sv{O}_{2})\) . The hemoglobin concentration (Hgb) was acquired from venous blood sampling. The arterial oxygen saturation (SaO 2 ) was assumed to be 98%. The venous oxygen saturation (SvO 2 ) of the blood leaving the brain through the sagittal sinus was measured using a susceptibility-based oximetry (SBO) MRI sequence (Jain et al., 2010). From the technique the differences in magnetic susceptibility between venous blood and surrounding tissue can be related to oxygen saturation. Susceptibility-weighted phase maps were acquired using a dual-echo gradient-echo sequence (1 slice, field of view = 220 x 190 mm 2 ; voxel size = 0.69 x 0.69 x 8 mm 3 ; repetition time = 23.1 ms; TE 1 / TE 2 = 8.02/17.72 ms; flip angle = 30 o ; SENSE-factor = 2; 5 dynamics). The imaging plane was placed orthogonal to the sagittal sinus. Regions of interests covering the sagittal sinus and surrounding tissue were manually delineated and susceptibility values from these regions were used to calculate SvO 2 . In-depth description of the postprocessing has been previously published (“Cerebral Metabolism and Vascular Reactivity during Breath-Hold and Hypoxic Challenge in Freedivers and Healthy Controls Mark B Vestergaard et Al,” 2019). The post-processing of the data was performed using publicly available in-house developed software (see Data Availability). iii) Cerebral lactate concentrations were measured using magnetic resonance spectroscopy (MRS) by a single voxel point-resolved 1 H-spectroscopy (PRESS) sequence (TE = 288; TR = 2000 ms; voxel size = 30 x 35 x 30 mm 3 ; 112 averages). A long TE of 288 ms was used to optimize the sequence for lactate measurement, as this TE allows for more reliable distinction of the lactate peak from overlapping lipid signals in the spectrum. The voxel was placed in precuneus because of its relatively stable metabolism. Precuneus is part of the default mode network and is therefore expected to exhibit consistent activity during resting-state MRI scans. The water peak acquired in the spectrum was used to quantify lactate concentration as reference 40 . The water concentration in the voxel was estimated based on the composition of gray matter, white matter, and cerebrospinal fluid in the voxel using tissue segmentations from the structural MRI images 41 . The quantification was corrected for T2-decay of the lactate and water peaks. Figure S1 provides an overview of the MRI techniques utilized in the study. Psychopathological and cognitive measurements Details on the instruments used for assessment during the COPSYCH visit, including the inter-rater reliability of agreement, have been described elsewhere 34 , 35 . In brief, assessments included are: i) Categorical psychopathology : The examiner conducted the clinical diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version (K-SADS-PL 42 )that either yielded a clinical research diagnosis or no diagnosis. Diagnoses were established according to both the International Classification of Diseases 10th Revision 43 , and the Diagnostic and Statistical Manual of Mental Disorders 44 ; ii) Dimensional psychopathology : We collected ratings of global psychopathology with the total scores of the Child Behavior Checklist (CBCL 45 ) and the Strengths and Difficulties Questionnaire (SDQ 46 ) questionnaires rated by the parents, global functioning with the Children's Global Assessment Scale (CGAS 47 ) rated by the examiner, ratings of ADHD symptoms with the ADHD-Rating Scale (ADHD-RS 48 ), autism traits with the Social Responsiveness Scale-2 (SRS-2 49 ) completed by the parents, and rating of emotional and behavioral problems rated by the examiner with the Test Observation Form (TOF 50 ) iii) Cognition : The cognitive assessment included an evaluation of executive function with the total score of the Behavior Rating Inventory of Executive Functions – Second Edition (BRIEF-2 51 ) rated by the parents and intelligence with the General Ability Index as the total sum of the prorated scaled Vocabulary and Matrix reasoning tests from the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV 52 ). Baseline characteristics Baseline characteristics and information regarding factors potentially associated with the child’s neurodevelopment were obtained prospectively throughout the scheduled visits to the COPSAC clinic. These include child sex, maternal age at birth, World Health Organization age and sex specific body mass index z-scores (zBMI 53 ) at age 10, socioeconomic status (SES), i.e., highest maternal/paternal education (four levels: elementary, high school, professional certificate, university), trimestral household income at birth (5 levels: 250 000 DKK), gestational age. Statistical analysis We performed all statistical analyses with R, version 4.3.2 54 and the packages included in tidyverse 55 . To compare demographic characteristics and the maternal pre-intervention levels of PUFA between the placebo and intervention groups, we employed the independent samples t-test and chi-square test for continuous and dichotomous measurements, respectively. As for the main analysis, we investigated the effects of the RCT intervention and the observational associations between maternal pre-intervention EPA + DHA and brain metabolic outcomes using adjusted linear regression models. Adjustments were made in three steps for the effects of the RCT (basic adjustment, basic + SES adjustment, and full adjustment), and two steps for the observational associations of maternal EPA + DHA levels (basic adjustment, and basic + SES adjustment). The covariates included in each step are: i) basic adjustment , sex, age at scan, and Hgb as covariates for the outcome CBF; sex and age at scan for the outcome CMRO₂; and sex, age at scan, and composition of gray matter, white matter and cerebrospinal fluid in the MRS voxel for the outcome lactate; ii) basic + SES adjustment, covariates in the basic adjustment and those referring to SES, i.e., household income, mother education at birth, father education at birth, and gestational age; and iii) full adjustment, all covariates in steps i) and ii) and pre-intervention maternal levels of EPA + DHA. Association between brain metabolic measurements and psychopathology and cognitive function were estimated using adjusted logistic and linear regression analysis for categorical diagnoses and continuous scores, respectively. Covariates for these analyses are the same as those described in the basic + SES adjustment step. The association between brain metabolic measurements and diagnostic outcomes, where only performed for diagnoses with more than 10 cases. Children receiving more than one diagnosis were included under each label. In the age-validation analysis, examining the correlation between age and brain metabolic outcomes by including data from adults from prior studies, an adjusted linear regression model was used, including the covariates in the basic adjustment step described above. Statistical significance was determined at p < 0.05 (two-sided test). RESULTS Of the 700 children invited to participate, we scanned 487 (69.57%) and after MRI processing and quality control, data from 460 children (48.3% females; mean ± SD age 10.3 ± 0.32 years; 49.6% PUFA group) were eligible for analysis. Gestational age was higher for the children in the PUFA intervention group, as we have previously reported 56 . We did not observe other statistically significant differences in demographic characteristics between the two groups, Table 1 . Table 1 Baseline demographics of participants in both RCT groups. Placebo N = 232 PUFA N = 228 P DEMOGRAPHICS Sex, male: N (%) 126 (54.3) 112 (49.1) 0.31 Race, non-caucasian: N (%) 14 (6) 8 (3.5) 0.29 Maternal age at birth: mean (SD) 32.47 (4.36) 32.39 (4.06) 0.83 Paternal age at birth: mean (SD) 34.73 (5.17) 34.43 (4.94) 0.53 Highest education mother: N (%) 0.77 high 67 (28.9) 72 (31.6) medium 145 (62.5) 139 (61.0) low 20 (8.6) 17 (7.5) Highest education father: N (%) 0.58 high 66 (29.1) 65 (29.0) medium 139 (61.2) 142 (63.4) low 22 (9.7) 16 (7.1) Household Income at birth (DKK per trimester): N (%) 0.13 Below 100 000 16 (6.9) 25 (11.0) 100 000–150 000 61 (26.3) 45 (19.8) 150 000–200 000 69 (29.7) 67 (29.5) 200 000–250 000 48 (20.7) 61 (26.9) Above 250 000 38 (16.4) 29 (12.8) BASELINE CHARACTERISTICS Age (in years) at scan: mean (SD) 10.35 (0.29) 10.33 (0.35) 0.57 Gestational age (in days) at birth: median [IQR] 280 [272.75, 286] 282 [274, 289] 0.03 Maternal pre intervention DHA + EPA level (in µg/100µL): mean (SD) 13.64 (3.62) 13.21 (3.46) 0.22 zBMI at age 10: mean (SD) -0.01 (1.06) 0.15 (1.05) 0.101 Table 1 . Descriptive statistics and pairwise comparisons in baseline characteristics of the two groups of children, the group of children exposed to PUFA, i.e. EPA +DHA, during the third trimester of pregnancy and the group of children exposed to placebo. zBMI refers to the World Health Organization age and sex specific body mass index z-scores Statistically significant results (p < 0.05) are highlighted in bold. The RCT showed significant effects of PUFA supplementation on lowering CBF (adj β=-2.3 ml/100g/min, 95%CI=-4.42; -0.25, p = 0.029) and CMRO₂ (adj β=-17.4 µmol/100g/min, 95%CI=-33.15; -1.60, p = 0.032) in children at age 10 years, after full covariate adjustment. PUFA supplementation did not significantly alter lactate concentration (adj β = 0.01 mmol/l, 95%CI=-0.02;0.04, p = 0.54), Table 2 and Fig. 1 . Observational adjusted linear regression results pertaining maternal pre-intervention PUFA (DHA + EPA) level and brain metabolic outcomes in middle childhood did not show any significant associations (all p > 0.05) on CBF, CMRO₂ or lactate concentration, Table 3 . Table 2 Effect of PUFA supplementation in pregnancy on brain metabolic parameters in middle childhood N Placebo N, Mean (SD) PUFA N, Mean (SD) Beta estimate [95% CI] p CBF [ml/100g/min] Basic* adj 373 193, 80.3 (10.1) 180, 78.4 (8.39) -1.953 [-3.83; -0.07] 0.04 Basic* + SES adj 365 188, 80.3 (10.1) 177, 78.5 (8.44) -2.171 [-4.13; -0.22] 0.03 Full adj 325 175, 80.2 (10.3) 150, 78.2 (8.07) -2.333 [-4.42; -0.25] 0.03 CMRO₂ [µmol/100g/min] Basic** adj 286 151, 263 (67) 135, 247 (57.5) -15.817 [-30.47; -1.17] 0.04 Basic** + SES adj 280 147, 265 (66.8) 133, 247 (57.7) -15.074 [-30.32; 0.17] 0.05 Full adj 271 145, 264 (67.2) 126, 244 (57.8) -17.377 [-33.15; -1.60] 0.03 Lactate [mmol/l] Basic*** adj 443 226, 0.929 (0.162) 216, 0.937 (0.153) 0.008 [-0.02; 0.04] 0.59 Basic*** + SES adj 433 221, 0.929 (0.161) 211, 0.937 (0.154) 0.009 [-0.02; 0.04] 0.53 Full adj 380 205, 0.929 (0.165) 175, 0.943 (0.158) 0.01 [-0.02; 0.04] 0.54 Table 2 . Results from adjusted linear regressions estimating the effect of PUFA supplementation (EPA+DHA) during the third trimester of pregnancy on brain metabolic parameters in the offspring at age 10 years. Statistically significant results (p < 0.05) are highlighted in bold. Basic* adjustment includes sex, age at scan and hemoglobin as covariates Basic** adjustment includes sex and age at scan as covariates Basic*** adjustment includes sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel as covariates +SES includes the basic adjustment and household income, mother education at birth, father education at birth, and gestational age Full adjustment includes all the previous covariates specific for each model and pre-intervention EPA+DHA maternal levels at pregnancy week 24 Abbreviations: CBF: cerebral blood flow; CMRO₂: cerebral metabolic rate of oxygen. Table 3 Association of pre-intervention EPA + DHA maternal levels with brain metabolic parameters in the offspring at age 10 N Beta estimate [95% CI] p CBF [ml/100g/min] Basic adj* 332 0.227 [-0.06;0.52] 0.12 Basic adj* + SES 325 0.236 [-0.07;0.54] 0.14 CMRO₂ [µmol/100g/min] Basic adj** 277 0.155 [-2.02;2.33] 0.89 Basic adj** + SES 271 0.317 [-2.06;2.69] 0.79 Lactate [mmol/l] Basic adj*** 388 − 0.00019 [-0.00;0.00] 0.93 Basic adj*** + SES 380 -0.001 [-0.01;0.00] 0.80 Table 3 . Observational results from adjusted linear regressions associating pre-intervention EPA+DHA maternal levels at pregnancy week 24 with brain metabolic parameters in the offspring at age 10. Basic* adjustment includes group in RCT, sex, age at scan and hemoglobin as covariates. Basic** adjustment includes group in RCT, sex and age at scan as covariates. Basic*** adjustment includes group in RCT, sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel as covariates. +SES includes the basic adjustment and household income, mother education at birth, father education at birth, and gestational age. Out of the 460 children with brain metabolic data, a total of 63 children (13.70%, 46 male) fulfilled diagnostic criteria for at least one psychiatric clinical research diagnosis. The most prevalent diagnosis was attention-deficit/hyperactivity disorder (ADHD, ICD-10 codes F90, F90.8, F98.8), identified in 46 children (10%, 36 male), followed by autism spectrum disorder, diagnosed in 10 children (2.18%, 7 male). Stratified by ADHD presentation, we diagnosed 26 children (21 male) with the combined presentation (ICD-10 codes F90, F98.8) and 20 children (15 male) with the predominantly inattentive presentation (ICD-10 code F98.8). Other diagnoses found in the cohort in smaller numbers were chronic motor or vocal tics (N = 10, ICD10 code F95.1), conduct disorders (N = 7, ICD-10 codes F91.1, F91.2, F91.3), Tourette’s syndrome (N = 4, ICD-10 code F95.2), obsessive-compulsive disorder (N = 3, ICD-10 codes F42 F42.1 F42.2) and psychotic disorder (N = 1, ICD-10 code F28). Results from logistic regression analyses on ADHD (both total and stratified by presentation) and autism did not reveal any significant associations between brain metabolic factors and diagnoses. Likewise, analyses of dimensional psychopathology scores and cognitive parameters did not yield any significant associations with brain metabolic factors, Table 4 . Table 4 Cross-sectional associations between brain metabolic measurements and psychopathology and cognition at age 10 years. CBF [ml/100g/min] adj* est [95% CI] p N = 370 CMRO₂ [µmol/100g/min] adj** est [95% CI] p N = 283 Lactate [mmol/l] adj*** est [95% CI] p N = 438 CATEGORICAL PSYCHOPATHOLOGY (DIAGNOSIS) ADHD (all presentations) 1.01 (0.967,1.05) 0.73 1.00 (0.996,1.01) 0.45 1.49 (0.15,13.7) 0.73 combined presentation 1.01 (0.959,1.06) 0.73 1.01 (0.997,1.01) 0.18 2.33 (0.141,33.9) 0.54 predominantly inattentive presentation 1.01 (0.947,1.07) 0.83 0.999 (0.989,1.01) 0.79 0.617 (0.016,22.2) 0.79 Autism 0.930 (0.842,1.01) 0.13 1.00 (0.987, 1.01) 0.98 0.436 (0.002, 55.5) 0.75 DIMENSIONAL PSYCHOPATHOLOGY Global psychopathology (CBCL total problems score) 0.097 (-0.064,0.258) 0.24 0.017 6 (-0.011,0.04 6) 0.22 1.23 (-8.75,11.2) 0.81 Global psychopathology (SDQ total score) 0.011 (-0.042,0.063) 0.69 0.005 (-0.004,0.014) 0.32 1.48 (-1.64,4.59) 0.35 Global Functioning (CGAS score) 0.033 (-0.1,0.163) 0.62 -0.003 (-0.026,0.02) 0.83 6.09 (-1.35,1351) 0.11 ADHD symptoms (ADHD-RS total score) 0.061 (-0.056,0.179) 0.31 0.017 (-0.004,0.038) 0.11 3.20 (-3.49,9.90) 0.35 Autism traits (SRS-2 total score) 0.056 (-0.124,0.236) 0.54 0.017 (-0.015,0.045 ) 0.3 2.48 (-8.02,13) 0.64 Emotional and Behavioral problems (TOF total score) 0.03 (-0.168,0.228) 0.77 0.018 (-0.016,0.051 ) 0.31 -6.21 (-17.7,5.25) 0.29 COGNITIVE FUNCTION Executive Function (BRIEF-2 general executive composite score) 0.058 (-0.164,0.281) 0.61 0.02 (-0.019,0.059) 0.32 -1.97 (-14.9,10.9) 0.77 Intelligence (WISC general ability index) 0.015 (-0.141,0.17) 0.86 0.006 (-0.021,0.033) 0.66 0.194 (-8.58,8.97) 0.97 Table 4 . Results from adjusted regressions associating brain metabolic parameters with psychopathology and cognitive functioning. Covariates in the models include: *sex, age at scan and hemoglobin, household income and gestational age; **sex, age at scan, household income and gestational age; ***sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel, household income and gestational age. Abbreviations: Attention-Deficit/Hyperactivity Disorder; K-SADS-PL: Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version; CGAS: Children’s Global Assessment Scale; ADHD-RS: ADHD-Rating Scale; SRS-2: Social Responsiveness Scale-2; CBCL: Child Behaviour Checklist; SDQ: Strengths and Difficulties Questionnaire; TOF: Test Observation Form Descriptive statistics of the COPSAC2010 cohort and the adult participants from the prior studies used to examine age effect are listed in Table S1 . Linear regression analysis confirmed that higher age is significantly associated with lower CBF (adj β = 0.49 ml/100g/min, 95%CI=-0.53;-0.45, p = 3.34e-82), CMRO₂ (adj β=-2.215 µmol/100g/min, 95%CI=-2.43;-2.00, p = 1.45e-69), and lactate (adj β=-0.006 mmol/l, 95%CI=-0.01;-0.00, p = 1.43e-12), Fig. 2 . DISCUSSION To our knowledge, this is the first study to directly examine the effect of maternal PUFA supplementation, i.e., EPA and DHA, during pregnancy on brain metabolic outcomes, CBF, CMRO₂, and lactate, in the offspring. Furthermore, we were able to investigate the association between these brain metabolic outcomes and brain function (i.e., psychopathology and cognition). Finally, the age-validation analysis, including data from adult participants from previous studies, collected using the same MRI scanner and sequence as used for the COPSAC2010 cohort, enables us to compare the level of these parameters across the lifespan in an age-dependent manner and validate a metabolic peak in childhood. In this population based single-center RCT study, our findings, in line with our hypothesis, demonstrate that PUFA intake in pregnancy reduces CBF and CMRO₂ in the offspring at age 10, however, no significant effect on lactate concentration was observed. The disparity between the effects on CMRO₂ and brain lactate may reflect their involvement into two different aspects of brain metabolism, and brain energy consumption. While CMRO₂ represents the rate of oxygen consumption indicating aerobic metabolic activity, lactate is primarily a byproduct of glycolysis. These contrasting effects could therefore be attributed to PUFA, particularly EPA and DHA, being more directly linked to CMRO₂’s role in modulating the membrane fluidity and function, as well as anti-inflammatory processes 57 , rather than influencing the metabolic processes that generate lactate. Furthermore, lactate has been identified as the primary energy source for the brain during the perinatal stage 58 , peaking during the neonatal period 59 , and while it may remain elevated in middle childhood compared to adulthood, this difference could be subtle given the earlier peak 21 . Although previous studies in adults have demonstrated a relationship between brain metabolism and brain function 60 – 63 , we did not observe such an association in children. Investigating the brain during the developmental period when both metabolic parameters and brain function are still maturing provides a different perspective on the interpretation of these changes, which do not seem of clinical relevance in the pediatric population according to our results. The psychopathological conditions that are more strongly linked to alterations in brain metabolism, such as psychosis or dementia, typically emerge later in life, either post-adolescence or during senescence. This developmental timing may partly explain the absence of detectable associations in middle childhood. The age-validation analysis, including adult data from prior studies, confirms a marked decrease in these brain metabolic parameters from childhood into adult age. This decrease does not appear linear but rather as a dramatic drop from middle childhood, to then decline in a moderate manner in adulthood. This pattern confirms the one described in previous studies, showing how the demands tied to the first years of neurodevelopment yield a high brain metabolic demand, reflected in CBF and CMRO₂ then starts decreasing after middle childhood 4 – 7 , 17 . Prior studies have also suggested increased brain lactate production in children, based on observations of higher glucose consumption relative to oxygen utilization 21 . Here we confirm that this translates to significantly higher brain lactate concentration in brain tissue in children compared to adults. Overall, our data support that brain metabolism markedly decreases when going from childhood into adult age and further, we observe that PUFA supplementation during pregnancy has a lowering effect on CBF and CMRO₂ in children at age 10. Thus, this RCT suggests that the PUFA supplementation altered the brain metabolic maturation in middle childhood. Furthermore, since the parallel observational model of maternal pre-interventional PUFA levels did not yield significant results, our data indicate that we observe a causal effect of the maternal PUFA supplementation on CBF and CMRO₂ in offspring. The strengths of this study include its RCT design embedded within the population based COPSAC2010 cohort, allowing for unbiased investigation of the effects of the PUFA intervention from the 24th week of pregnancy on brain metabolic outcomes in middle childhood. Moreover, the comprehensive COPSYCH study provides an exhaustive two-day assessment of psychopathology, cognition and brain function at age 10, allowing for evaluation of both cognitive and psychopathological outcomes in relation to brain metabolism; and the extensive data collection of the COPSAC2010 cohort allows for rich statistical models and checking additional factors. Specifically, PUFA levels are commonly associated with lifestyle and socioeconomic status (SES) factors, which can lead to a diet incorporating more sources of PUFA, such as fatty fish 64 . These factors were accounted for in our models, including maternal pre-intervention levels of PUFA. Furthermore, the methods used in this study regarding the MRI technique for measuring CBF have been validated using gold-standard 15 O-water PET imaging in humans and animals across various perfusion states 65 – 67 . The SBO MRI technique for measuring cerebral venous oxygen and calculating CMRO₂ has been verified by comparing it to blood samples collected directly from the jugular vein during MRI scans conducted under different perfusion and arterial oxygen saturation conditions 68 . The reproducibility of the MRI measurements has previously been examined by the authors using the same scanner and sequences, showing low coefficient of variation for both CBF, CMRO₂ and lactate 69 . Nevertheless, we recognize the single brain measurement time point as a limitation refraining us from further interpretation of the maternal PUFA intake in the childhood brain physiological outcomes. Furthermore, while statistically significant, the effect sizes were relatively small. This limitation might be emphasized due to a large variance in the data at this age. A follow-up assessment later, e.g., after adolescence, might show a decrease in data variance and the difference between groups, if still present, could become clearer. In addition, functional neural activation or similar physiological perturbation of the brain tissue could augment sensitivity to detect subtle changes not detected under resting state, as in the current study. In the current analyses on this RCT, we demonstrate that PUFA intake in the third trimester of pregnancy influences brain metabolic outcomes in the offspring, reflected in lower CBF and lower CMRO₂ at age 10. However, changes in these brain metabolic parameters appear not to translate directly into psychopathological status and cognitive performance at age 10. The comparison with older participants, which clearly shows a decrease from childhood to adulthood, suggests that PUFA intake may accelerate the maturation process. This effect could not be explained by potential confounders related to maternal pre-interventional blood level of PUFA or SES factors. Collectively our results encourage large-scale studies on maternal nutrition intake and brain metabolic outcomes. Abbreviations ADHD Attention Deficit/Hyperactivity Disorder ADHD-RS ADHD-Rating Scale Questionnaire AG Aerobic glycolysis CBCL Child Behaviour Checklist CBF Cerebral blood flow CGAS Children’s Global Assessment Scale CMRO₂ Cerebral metabolic rate of oxygen COPSAC 2010 Copenhagen Prospective Studies on Asthma in Childhood 2010 COPSYCH COpenhagen Prospective Study on Neuro-PSYCHiatric Development DHA Docosahexaenoic acid EPA Eicosapentaenoic acid HGB Hemoglobin K-SADS-PL Kiddie-Schedule for Affective Disorders and Schizophrenia for School-age Children Present and Lifetime Version PUFA Polyunsaturated fatty acids RCT Randomized controlled trial SDQ Strengths and Difficulties Questionnaire SES Socioeconomic status SRS-2 Social Responsiveness Scale-2 TOF Test Observation Form Declarations Ethics: The Local Ethics Committee (H-B-2008-093), and the Danish Data Protection Agency (2015-41-3696) approved the study. All families provided written informed consent before enrolment upon receiving information about the study prior to participation. Funding Statement: This work was supported by The Lundbeck Foundation (Grant no. R269-2017-5), BC has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 946228). Acknowledgements : We would like to acknowledge the huge work of late professor Hans Bisgaard, who was the founder of COPSAC and was head of the clinical research center for more than 25 years. We thank the MRI radiographers and technicians Daban Sulaiman, Mads Rostock, Eduardo Hansen, Karina Elin Segers, Robabeh Hozouri Tavangar and Helle Juhl Simonsen for their efforts in acquiring the MRI data. We would also like to express our deepest gratitude to the children and families of the COPSAC2010 cohort study for all their support and commitment, and the members of COPSAC and CNSR for their efforts. Competing interests: All authors declare no potential, perceived, or real conflict of interest regarding the content of this manuscript. BHE is part of the Advisory Board of Boehringer Ingelheim, Lundbeck Pharma, and Orion Pharma; and has received lecture fees from Boehringer Ingelheim, Otsuka Pharma Scandinavia AB, and Lundbeck Pharma. The funding agencies did not have any role in design and conduct of the study; collection, management, and interpretation of the data; or preparation, review, or approval of the manuscript. Data availability: The MRI images are not publicly available due to privacy restrictions. Data derived from the MRI images can be made available with a data sharing agreement upon reasonable request. Code availability: Software to calculate CBF with PCM technique is available at https://github.com/MarkVestergaard/PCMCalculator/ and software to calculate SvO2 with susceptibility-based oximetry MRI technique is available at https://github.com/MarkVestergaard/SBOCalculator/. Author contributions : H.B.W.L., K.B. and B.H.E. initiated and formulated the study. M.H.L. and M.B.V. performed the data analysis and wrote the manuscript. H.B.W.L., M.B.V., J.M.R. and U.L. were responsible for the collection and processing of MRI data. K.B, B.H.E., R.K.V., B.C., P.M. and J.B.R. were responsible for the COPSAC2010 cohort. All authors reviewed the manuscript. B.Y.G, R.K.V, K.B, H.B.W.L and B.H.E supervised the project and were responsible for funding. References Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proceedings of the National Academy of Sciences 109, 16480–16485 (2012). Semple, B. D., Blomgren, K., Gimlin, K., Ferriero, D. M. & Noble-Haeusslein, L. J. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files TableS1FISHOILDERIVEDFATTYACIDSINPREGNANCYANDBRAINMETABOLISMINMIDDLECHILDHOOD.docx Table S1 FigureS1FISHOILDERIVEDFATTYACIDSINPREGNANCYANDBRAINMETABOLISMINMIDDLECHILDHOOD.docx Figure S1 Cite Share Download PDF Status: Under Review Version 1 posted Unknown event 06 Jun, 2025 Editorial decision: Reject before peer review 03 Jun, 2025 Editor assigned by journal 20 May, 2025 Submission checks completed at journal 20 May, 2025 First submitted to journal 20 May, 2025 Unknown event 19 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Abbreviations: CBF: cerebral blood flow, CMRO₂: Cerebral metabolic rate of oxygen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e Statistically different means\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6678778/v1/63ae363c64c7ac96cc6bd305.png"},{"id":83355613,"identity":"a68632ee-f63a-45b2-983e-2735db1b2a51","added_by":"auto","created_at":"2025-05-23 15:07:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94924,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between age and CMRO₂, CBF and Lactate including participants of three adult cohorts\u003c/p\u003e\n\u003cp\u003eScatter plots with loess regression lines illustrate the association of age with the three brain metabolic parameters including participants of prior studies. a) Association between CBF and age; b) Association between CMRO₂ and age; c) Association between lactate concentration and age. Dots in red and blue colors represent COPSAC2010 placebo and PUFA intervention data points, respectively. Dots in gray color represent the healthy adults’ data points (\u003cstrong\u003eTable S1\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6678778/v1/27678ba69d1d16f7ce279f8e.png"},{"id":83833359,"identity":"948fea72-778f-463d-8ddc-facfc1e290df","added_by":"auto","created_at":"2025-06-03 12:24:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6678778/v1/575e3df5-692e-4324-a195-79d678b40d41.pdf"},{"id":83355614,"identity":"03b2d613-bafd-425b-8e7f-274e7bc17613","added_by":"auto","created_at":"2025-05-23 15:07:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1833423,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1\u003c/p\u003e","description":"","filename":"TableS1FISHOILDERIVEDFATTYACIDSINPREGNANCYANDBRAINMETABOLISMINMIDDLECHILDHOOD.docx","url":"https://assets-eu.researchsquare.com/files/rs-6678778/v1/a368666850d2e45b0bcc0bce.docx"},{"id":83355619,"identity":"7a754a71-766a-4ba3-8cbe-3185535135b3","added_by":"auto","created_at":"2025-05-23 15:07:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5610795,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"FigureS1FISHOILDERIVEDFATTYACIDSINPREGNANCYANDBRAINMETABOLISMINMIDDLECHILDHOOD.docx","url":"https://assets-eu.researchsquare.com/files/rs-6678778/v1/a0fcf98c03ad20cbc03975bd.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"\u003cp\u003eFish Oil-Derived Fatty Acids in Pregnancy and Brain Metabolism in Middle Childhood: Results From a Randomized Controlled Trial\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCompared to adults, children have a more demanding brain energy metabolism, characterized by higher cerebral blood flow (CBF), higher cerebral metabolic rate of oxygen (CMRO₂), and higher cerebral lactate production. This heightened metabolic demand during neurodevelopment and brain maturation in childhood is driven by multiple processes involving synaptic formation, pruning, and myelination \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and declines as the brain matures.\u003c/p\u003e \u003cp\u003eCBF and CMRO₂ follow parallel lifespan trajectories, peaking in middle childhood and declining towards adulthood as an age dependent maturation process. During early childhood, CBF undergoes significant changes closely associated with cognitive and structural brain development increasing until approximately age 7 years \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and declining from this point\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e through adolescence to senescence \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The variations in CBF have been associated with changes in metabolism \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, cognitive function \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and/or neuropsychiatric disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Similarly, CMRO₂ has been shown to increase from the neonatal period during early childhood, peaking at approximately age 7, and decline during adolescence into adulthood \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCerebral lactate concentration is also elevated in children. In childhood, the brain exhibits a marked oxygen-independent glycolytic activity, occurring despite sufficient oxygen availability, a process therefore often referred to as aerobic glycolysis (AG). AG will lead to the cerebral production of lactate. While lactate production yields significantly less adenosine triphosphate (ATP) production than glucose oxidation, AG is essential for neurodevelopment, particularly in processes like myelin production by myelinoligodendrocytes \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. AG activity varies throughout the lifespan from around 30\u0026ndash;40% of the glucose used in the newborn brain to approximately 10\u0026ndash;12% in the adult brain \u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The physiological explanation underlying this discrepancy in AG between infancy and adulthood is unclear, however, lactate has been shown to be crucial for supporting neuronal functions during development, such as long-term memory formation in juvenile animals \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond the physiological variations in brain metabolism and function throughout the lifespan, brain metabolism has been associated with fish oil intake, which contains polyunsaturated fatty acids (PUFA) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. PUFAs are essential nutrients that have been proven to serve a vital role in brain development \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and brain structure and function \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Indirectly, supporting the importance of PUFA accumulation in the brain during the critical pre- and perinatal growth phase \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, prior studies have shown associations of maternal PUFA supplementation during pregnancy with better neurodevelopmental outcomes in the offspring \u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, no studies have investigated the direct impact of PUFA intake during pregnancy on the brain metabolism in childhood, and the associations of brain metabolic markers with psychopathological and cognitive outcomes in a pediatric population.\u003c/p\u003e \u003cp\u003eIn the current study, we leverage data from the COPSAC2010 \u003csup\u003e33\u003c/sup\u003e cohort with results from a randomized controlled trial (RCT) on PUFA supplementation during the third trimester of pregnancy. At the 10-year visit, in the COPSYCH study \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, the children underwent comprehensive assessments including evaluations of psychopathology, cognitive function, and magnetic resonance imaging (MRI) brain scans. The current analyses characterize three brain metabolic measurements (total average CBF, CMRO₂, and cerebral lactate concentration) of the children at age 10 and investigate their relationship to maternal PUFA levels and PUFA supplementation during pregnancy. We hypothesize that higher maternal PUFA levels in pregnancy and PUFA supplementation during pregnancy would be associated with higher brain physiology maturation, reflected in lower measurements of the three investigated metabolic parameters. We further hypothesize that lower brain metabolic measurements will be reflected in lower psychopathology and better cognitive function. As an independent age-validation analysis, we present the association of age with the brain metabolism parameters including participants of young and older adults from prior studies using identical MRI scanner and sequences.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and sample\u003c/h2\u003e \u003cp\u003eParticipants in this study are part of the COPSAC2010 cohort, a longitudinal clinical study involving 700 mother-child pairs based on the background population. Pregnant women were recruited at 24 weeks of gestation and participated in a RCT of PUFA supplementation in the third trimester of pregnancy \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. At recruitment, i.e., pregnancy week 24, the women were randomly assigned in a 1:1 ratio to either receive 2.4 g per day of n\u0026thinsp;\u0026minus;\u0026thinsp;3 LCPUFA (55% eicosapentaenoic acid -EPA- and 37% docosahexaenoic acid -DHA-), in triacylglycerol form (Incromega TG33/22, Croda Health Care) or a placebo, in the form of olive oil, containing 72% n\u0026ndash;9 oleic acid and 12% n\u0026thinsp;\u0026minus;\u0026thinsp;6 linoleic acid (Pharma-Tech A/S). Supplementation continued until one week postpartum and adherence to the intervention was measured as the difference between the number of capsules returned and the number expected to be returned. The RCT was blinded for the families and researchers until the youngest child turned three years old. At age 10, the families were invited to participate in the COpenhagen Prospective Study on Neuro-PSYCHiatric Development (COPSYCH) study, a comprehensive assessment of cognition and dimensional and categorical psychopathology, including an MRI scan of the brain examining both structure and physiology \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor the age-validation analysis, we compared data from the current study with data from healthy adult control subjects from four previously published studies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These studies were performed at the same site, using the same MRI scanner and sequences as the children of the COPSAC2010 cohort, enhancing the reliability and validity of the analysis. The method descriptions (recruitment, inclusion and exclusion criteria, social and clinical characteristics, and brain outcomes) related to the studies used for the age-validation analysis are described elsewhere \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMaternal pre-intervention PUFA\u003c/h2\u003e \u003cp\u003eSince the RCT intervention consisted of supplementation of DHA and EPA, maternal whole-blood levels of DHA and EPA (in \u0026micro;g/100\u0026micro;L) were measured at pregnancy week 24 prior to randomization.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBrain metabolism\u003c/h3\u003e\n\u003cp\u003eMRI scans of the whole brain were acquired with a Philips Achieva 3.0 T scanner (Philips Healthcare, Best, The Netherlands) with a 32-channel SENSE Head Coil. We obtained distinct images and measurements derived from the MRI acquisition to account for the following parameters: \u003cb\u003ei)\u003c/b\u003e \u003cb\u003eCerebral blood flow (CBF)\u003c/b\u003e was obtained using a phase-contrast mapping (PCM) MRI sequence, which measures the blood velocity through the feeding basilar and carotid arteries supplying the brain. Blood-velocity sensitive images were measured using a turbo field echo sequence (1 slice, FOV\u0026thinsp;=\u0026thinsp;240 x 240 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e; voxel size\u0026thinsp;=\u0026thinsp;0.75 x 0.75 x 8 mm \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e ; Echo time (TE)\u0026thinsp;=\u0026thinsp;7.4 ms; Repetition time (TR)\u0026thinsp;=\u0026thinsp;12.3 ms; flip angle\u0026thinsp;=\u0026thinsp;10\u003csup\u003eo\u003c/sup\u003e; velocity encoding\u0026thinsp;=\u0026thinsp;100 cm/s, without cardiac gating; 5 dynamics). The sequence was acquired twice to get optimal perpendicular slice-positions on both the internal carotid arteries and the basilar artery (second scan). After manually delineating each artery, the total CBF to the brain was calculated as mean blood velocity times the cross-sectional area of the delineated arteries, and it was normalized to brain weight to obtain values in ml/100g/min providing total average CBF. The post-processing of the data was performed using publicly available in-house developed software (see Data Availability). \u003cb\u003eii) Total average cerebral metabolic rate of oxygen (CMRO₂)\u003c/b\u003e was calculated using Fick\u0026rsquo;s Principle, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CM{RO}_{2}=Hgb\\cdot\\:CBF\\cdot\\:(Sa{O}_{2}-Sv{O}_{2})\\)\u003c/span\u003e\u003c/span\u003e. The hemoglobin concentration (Hgb) was acquired from venous blood sampling. The arterial oxygen saturation (SaO\u003csub\u003e2\u003c/sub\u003e) was assumed to be 98%. The venous oxygen saturation (SvO\u003csub\u003e2\u003c/sub\u003e) of the blood leaving the brain through the sagittal sinus was measured using a susceptibility-based oximetry (SBO) MRI sequence (Jain et al., 2010). From the technique the differences in magnetic susceptibility between venous blood and surrounding tissue can be related to oxygen saturation. Susceptibility-weighted phase maps were acquired using a dual-echo gradient-echo sequence (1 slice, field of view\u0026thinsp;=\u0026thinsp;220 x 190 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e; voxel size\u0026thinsp;=\u0026thinsp;0.69 x 0.69 x 8 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e; repetition time\u0026thinsp;=\u0026thinsp;23.1 ms; TE\u003csub\u003e1\u003c/sub\u003e / TE\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.02/17.72 ms; flip angle\u0026thinsp;=\u0026thinsp;30\u003csup\u003eo\u003c/sup\u003e; SENSE-factor\u0026thinsp;=\u0026thinsp;2; 5 dynamics). The imaging plane was placed orthogonal to the sagittal sinus. Regions of interests covering the sagittal sinus and surrounding tissue were manually delineated and susceptibility values from these regions were used to calculate SvO\u003csub\u003e2\u003c/sub\u003e. In-depth description of the postprocessing has been previously published (\u0026ldquo;Cerebral Metabolism and Vascular Reactivity during Breath-Hold and Hypoxic Challenge in Freedivers and Healthy Controls Mark B Vestergaard et Al,\u0026rdquo; 2019). The post-processing of the data was performed using publicly available in-house developed software (see Data Availability). \u003cb\u003eiii)\u003c/b\u003e \u003cb\u003eCerebral lactate\u003c/b\u003e concentrations were measured using magnetic resonance spectroscopy (MRS) by a single voxel point-resolved 1 H-spectroscopy (PRESS) sequence (TE\u0026thinsp;=\u0026thinsp;288; TR\u0026thinsp;=\u0026thinsp;2000 ms; voxel size\u0026thinsp;=\u0026thinsp;30 x 35 x 30 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e; 112 averages). A long TE of 288 ms was used to optimize the sequence for lactate measurement, as this TE allows for more reliable distinction of the lactate peak from overlapping lipid signals in the spectrum. The voxel was placed in precuneus because of its relatively stable metabolism. Precuneus is part of the default mode network and is therefore expected to exhibit consistent activity during resting-state MRI scans. The water peak acquired in the spectrum was used to quantify lactate concentration as reference \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The water concentration in the voxel was estimated based on the composition of gray matter, white matter, and cerebrospinal fluid in the voxel using tissue segmentations from the structural MRI images \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The quantification was corrected for T2-decay of the lactate and water peaks. \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e provides an overview of the MRI techniques utilized in the study.\u003c/p\u003e\n\u003ch3\u003ePsychopathological and cognitive measurements\u003c/h3\u003e\n\u003cp\u003eDetails on the instruments used for assessment during the COPSYCH visit, including the inter-rater reliability of agreement, have been described elsewhere \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In brief, assessments included are: \u003cb\u003ei) Categorical psychopathology\u003c/b\u003e: The examiner conducted the clinical diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version (K-SADS-PL \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e)that either yielded a clinical research diagnosis or no diagnosis. Diagnoses were established according to both the International Classification of Diseases 10th Revision \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and the Diagnostic and Statistical Manual of Mental Disorders \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; \u003cb\u003eii) Dimensional psychopathology\u003c/b\u003e: We collected ratings of global psychopathology with the total scores of the Child Behavior Checklist (CBCL \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e) and the Strengths and Difficulties Questionnaire (SDQ \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e) questionnaires rated by the parents, global functioning with the Children's Global Assessment Scale (CGAS \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e) rated by the examiner, ratings of ADHD symptoms with the ADHD-Rating Scale (ADHD-RS \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e), autism traits with the Social Responsiveness Scale-2 (SRS-2 \u003csup\u003e49\u003c/sup\u003e) completed by the parents, and rating of emotional and behavioral problems rated by the examiner with the Test Observation Form (TOF \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e) \u003cb\u003eiii) Cognition\u003c/b\u003e: The cognitive assessment included an evaluation of executive function with the total score of the Behavior Rating Inventory of Executive Functions \u0026ndash; Second Edition (BRIEF-2 \u003csup\u003e51\u003c/sup\u003e) rated by the parents and intelligence with the General Ability Index as the total sum of the prorated scaled Vocabulary and Matrix reasoning tests from the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eBaseline characteristics and information regarding factors potentially associated with the child\u0026rsquo;s neurodevelopment were obtained prospectively throughout the scheduled visits to the COPSAC clinic. These include child sex, maternal age at birth, World Health Organization age and sex specific body mass index z-scores (zBMI \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e) at age 10, socioeconomic status (SES), i.e., highest maternal/paternal education (four levels: elementary, high school, professional certificate, university), trimestral household income at birth (5 levels: \u0026lt;100 000/\u0026hellip;/\u0026gt;250 000 DKK), gestational age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe performed all statistical analyses with R, version 4.3.2 \u003csup\u003e54\u003c/sup\u003e and the packages included in tidyverse \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo compare demographic characteristics and the maternal pre-intervention levels of PUFA between the placebo and intervention groups, we employed the independent samples t-test and chi-square test for continuous and dichotomous measurements, respectively. As for the main analysis, we investigated the effects of the RCT intervention and the observational associations between maternal pre-intervention EPA\u0026thinsp;+\u0026thinsp;DHA and brain metabolic outcomes using adjusted linear regression models. Adjustments were made in three steps for the effects of the RCT (basic adjustment, basic\u0026thinsp;+\u0026thinsp;SES adjustment, and full adjustment), and two steps for the observational associations of maternal EPA\u0026thinsp;+\u0026thinsp;DHA levels (basic adjustment, and basic\u0026thinsp;+\u0026thinsp;SES adjustment). The covariates included in each step are: i) \u003cem\u003ebasic adjustment\u003c/em\u003e, sex, age at scan, and Hgb as covariates for the outcome CBF; sex and age at scan for the outcome CMRO₂; and sex, age at scan, and composition of gray matter, white matter and cerebrospinal fluid in the MRS voxel for the outcome lactate; ii) basic\u0026thinsp;+\u0026thinsp;SES adjustment, covariates in the basic adjustment and those referring to SES, i.e., household income, mother education at birth, father education at birth, and gestational age; and iii) full adjustment, all covariates in steps i) and ii) and pre-intervention maternal levels of EPA\u0026thinsp;+\u0026thinsp;DHA.\u003c/p\u003e \u003cp\u003eAssociation between brain metabolic measurements and psychopathology and cognitive function were estimated using adjusted logistic and linear regression analysis for categorical diagnoses and continuous scores, respectively. Covariates for these analyses are the same as those described in the basic\u0026thinsp;+\u0026thinsp;SES adjustment step. The association between brain metabolic measurements and diagnostic outcomes, where only performed for diagnoses with more than 10 cases. Children receiving more than one diagnosis were included under each label.\u003c/p\u003e \u003cp\u003eIn the age-validation analysis, examining the correlation between age and brain metabolic outcomes by including data from adults from prior studies, an adjusted linear regression model was used, including the covariates in the basic adjustment step described above.\u003c/p\u003e \u003cp\u003eStatistical significance was determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided test).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf the 700 children invited to participate, we scanned 487 (69.57%) and after MRI processing and quality control, data from 460 children (48.3% females; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age 10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 years; 49.6% PUFA group) were eligible for analysis. Gestational age was higher for the children in the PUFA intervention group, as we have previously reported \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. We did not observe other statistically significant differences in demographic characteristics between the two groups, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographics of participants in both RCT groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;232\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePUFA\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;228\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDEMOGRAPHICS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male: N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, non-caucasian: N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age at birth: mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.47 (4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.39 (4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaternal age at birth: mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.73 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.43 (4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHighest education mother: N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHighest education father: N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHousehold Income at birth (DKK per trimester): N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow 100 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 000\u0026ndash;150 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 000\u0026ndash;200 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200 000\u0026ndash;250 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 250 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBASELINE CHARACTERISTICS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years) at scan: mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.35 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.33 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (in days) at birth: median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 [272.75, 286]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 [274, 289]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal pre intervention DHA\u0026thinsp;+\u0026thinsp;EPA level (in \u0026micro;g/100\u0026micro;L): mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.64 (3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.21 (3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ezBMI at age 10: mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Descriptive statistics and pairwise comparisons in baseline characteristics of the two groups of children, the group of children exposed to PUFA, i.e. EPA +DHA, during the third trimester of pregnancy and the group of children exposed to placebo. zBMI refers to the World Health Organization age and sex specific body mass index z-scores Statistically significant results (p\u003cu\u003e\u0026lt;\u003c/u\u003e0.05) are highlighted in bold.\u003c/p\u003e\u003cp\u003eThe RCT showed significant effects of PUFA supplementation on lowering CBF (adj β=-2.3 ml/100g/min, 95%CI=-4.42; -0.25, p\u0026thinsp;=\u0026thinsp;0.029) and CMRO₂ (adj β=-17.4 \u0026micro;mol/100g/min, 95%CI=-33.15; -1.60, p\u0026thinsp;=\u0026thinsp;0.032) in children at age 10 years, after full covariate adjustment. PUFA supplementation did not significantly alter lactate concentration (adj β\u0026thinsp;=\u0026thinsp;0.01 mmol/l, 95%CI=-0.02;0.04, p\u0026thinsp;=\u0026thinsp;0.54), Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Observational adjusted linear regression results pertaining maternal pre-intervention PUFA (DHA\u0026thinsp;+\u0026thinsp;EPA) level and brain metabolic outcomes in middle childhood did not show any significant associations (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) on CBF, CMRO₂ or lactate concentration, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of PUFA supplementation in pregnancy on brain metabolic parameters in middle childhood\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003cp\u003eN, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePUFA\u003c/p\u003e \u003cp\u003eN, Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta estimate [95% CI] p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCBF [ml/100g/min]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic* adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193, 80.3 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180, 78.4 (8.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.953 [-3.83; -0.07] 0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic* + SES adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188, 80.3 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177, 78.5 (8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.171 [-4.13; -0.22] 0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175, 80.2 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150, 78.2 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.333 [-4.42; -0.25] 0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMRO₂ [\u0026micro;mol/100g/min]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic** adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151, 263 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135, 247 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-15.817 [-30.47; -1.17] 0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic** + SES adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147, 265 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133, 247 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-15.074 [-30.32; 0.17] 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145, 264 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126, 244 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-17.377 [-33.15; -1.60] 0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactate [mmol/l]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic*** adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226, 0.929 (0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216, 0.937 (0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008 [-0.02; 0.04] 0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic*** + SES adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221, 0.929 (0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211, 0.937 (0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009 [-0.02; 0.04] 0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205, 0.929 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175, 0.943 (0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01 [-0.02; 0.04] 0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Results from adjusted linear regressions estimating the effect of PUFA supplementation (EPA+DHA) during the third trimester of pregnancy on brain metabolic parameters in the offspring at age 10 years. Statistically significant results (p\u003cu\u003e\u0026lt;\u003c/u\u003e0.05) are highlighted in bold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic*\u003c/strong\u003e adjustment includes sex, age at scan and hemoglobin as covariates\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic**\u003c/strong\u003e adjustment includes sex and age at scan as covariates\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic***\u003c/strong\u003e adjustment includes sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel as covariates\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e+SES\u003c/strong\u003e includes the basic adjustment and household income, mother education at birth, father education at birth, and gestational age\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFull\u0026nbsp;\u003c/strong\u003eadjustment includes all the previous covariates specific for each model and pre-intervention EPA+DHA maternal levels at pregnancy week 24\u003c/p\u003e\n\u003cp\u003eAbbreviations: CBF: cerebral blood flow; CMRO₂: cerebral metabolic rate of oxygen.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of pre-intervention EPA\u0026thinsp;+\u0026thinsp;DHA maternal levels with brain metabolic parameters in the offspring at age 10\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta estimate [95% CI] p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCBF [ml/100g/min]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.227 [-0.06;0.52] 0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj* + SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236 [-0.07;0.54] 0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMRO₂ [\u0026micro;mol/100g/min]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155 [-2.02;2.33] 0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj** + SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317 [-2.06;2.69] 0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactate [mmol/l]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.00019 [-0.00;0.00] 0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic adj*** + SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001 [-0.01;0.00] 0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Observational results from adjusted linear regressions associating pre-intervention EPA+DHA maternal levels at pregnancy week 24 with brain metabolic parameters in the offspring at age 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic*\u003c/strong\u003e adjustment includes group in RCT, sex, age at scan and hemoglobin as covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic**\u003c/strong\u003e adjustment includes group in RCT, sex and age at scan as covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic***\u003c/strong\u003e adjustment includes group in RCT, sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel as covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e+SES\u003c/strong\u003e includes the basic adjustment and household income, mother education at birth, father education at birth, and gestational age.\u003c/p\u003e \u003cp\u003eOut of the 460 children with brain metabolic data, a total of 63 children (13.70%, 46 male) fulfilled diagnostic criteria for at least one psychiatric clinical research diagnosis. The most prevalent diagnosis was attention-deficit/hyperactivity disorder (ADHD, ICD-10 codes F90, F90.8, F98.8), identified in 46 children (10%, 36 male), followed by autism spectrum disorder, diagnosed in 10 children (2.18%, 7 male). Stratified by ADHD presentation, we diagnosed 26 children (21 male) with the combined presentation (ICD-10 codes F90, F98.8) and 20 children (15 male) with the predominantly inattentive presentation (ICD-10 code F98.8). Other diagnoses found in the cohort in smaller numbers were chronic motor or vocal tics (N\u0026thinsp;=\u0026thinsp;10, ICD10 code F95.1), conduct disorders (N\u0026thinsp;=\u0026thinsp;7, ICD-10 codes F91.1, F91.2, F91.3), Tourette\u0026rsquo;s syndrome (N\u0026thinsp;=\u0026thinsp;4, ICD-10 code F95.2), obsessive-compulsive disorder (N\u0026thinsp;=\u0026thinsp;3, ICD-10 codes F42 F42.1 F42.2) and psychotic disorder (N\u0026thinsp;=\u0026thinsp;1, ICD-10 code F28). Results from logistic regression analyses on ADHD (both total and stratified by presentation) and autism did not reveal any significant associations between brain metabolic factors and diagnoses. Likewise, analyses of dimensional psychopathology scores and cognitive parameters did not yield any significant associations with brain metabolic factors, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-sectional associations between brain metabolic measurements and psychopathology and cognition at age 10 years.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCBF [ml/100g/min]\u003c/p\u003e \u003cp\u003eadj* est [95% CI] p\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;370\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMRO₂ [\u0026micro;mol/100g/min]\u003c/p\u003e \u003cp\u003eadj** est [95% CI] p\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;283\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLactate [mmol/l]\u003c/p\u003e \u003cp\u003eadj*** est [95% CI] p\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;438\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCATEGORICAL PSYCHOPATHOLOGY (DIAGNOSIS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADHD\u003c/b\u003e (all presentations)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.967,1.05) 0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.996,1.01) 0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (0.15,13.7) 0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecombined presentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.959,1.06) 0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.997,1.01) 0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (0.141,33.9) 0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epredominantly inattentive presentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.947,1.07) 0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999 (0.989,1.01) 0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617 (0.016,22.2) 0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAutism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.930 (0.842,1.01) 0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.987, 1.01) 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.436 (0.002, 55.5) 0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDIMENSIONAL PSYCHOPATHOLOGY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal psychopathology\u003c/b\u003e (CBCL total problems score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097 (-0.064,0.258) 0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e (-0.011,0.04\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6)\u003c/span\u003e 0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (-8.75,11.2) 0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal psychopathology\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(SDQ total score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011 (-0.042,0.063) 0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005 (-0.004,0.014) 0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48 (-1.64,4.59) 0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal Functioning\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(CGAS score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033 (-0.1,0.163) 0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003 (-0.026,0.02) 0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.09 (-1.35,1351) 0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADHD symptoms\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(ADHD-RS total score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.061 (-0.056,0.179) 0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017 (-0.004,0.038) 0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20 (-3.49,9.90) 0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAutism traits\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(SRS-2 total score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056 (-0.124,0.236) 0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017 (-0.015,0.045\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e 0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48 (-8.02,13) 0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmotional and Behavioral problems\u003c/b\u003e (TOF total score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (-0.168,0.228) 0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018 (-0.016,0.051\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e 0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.21 (-17.7,5.25) 0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOGNITIVE FUNCTION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecutive Function\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(BRIEF-2 general executive composite score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058 (-0.164,0.281) 0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (-0.019,0.059) 0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.97 (-14.9,10.9) 0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntelligence\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(WISC general ability index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015 (-0.141,0.17) 0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006 (-0.021,0.033) 0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194 (-8.58,8.97) 0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. Results from adjusted regressions associating brain metabolic parameters with psychopathology and cognitive functioning. Covariates in the models include: *sex, age at scan and hemoglobin, household income and gestational age; **sex, age at scan, household income and gestational age; ***sex, age at scan and ratio of gray matter, white matter and cerebrospinal fluid in the MRS voxel, household income and gestational age. Abbreviations: Attention-Deficit/Hyperactivity Disorder; K-SADS-PL: Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version; CGAS: Children\u0026rsquo;s Global Assessment Scale; ADHD-RS: ADHD-Rating Scale; SRS-2: Social Responsiveness Scale-2; CBCL: Child Behaviour Checklist; SDQ: Strengths and Difficulties Questionnaire; TOF: Test Observation Form\u003c/p\u003e\u003cp\u003eDescriptive statistics of the COPSAC2010 cohort and the adult participants from the prior studies used to examine age effect are listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Linear regression analysis confirmed that higher age is significantly associated with lower CBF (adj β\u0026thinsp;=\u0026thinsp;0.49 ml/100g/min, 95%CI=-0.53;-0.45, p\u0026thinsp;=\u0026thinsp;3.34e-82), CMRO₂ (adj β=-2.215 \u0026micro;mol/100g/min, 95%CI=-2.43;-2.00, p\u0026thinsp;=\u0026thinsp;1.45e-69), and lactate (adj β=-0.006 mmol/l, 95%CI=-0.01;-0.00, p\u0026thinsp;=\u0026thinsp;1.43e-12), Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo our knowledge, this is the first study to directly examine the effect of maternal PUFA supplementation, i.e., EPA and DHA, during pregnancy on brain metabolic outcomes, CBF, CMRO₂, and lactate, in the offspring. Furthermore, we were able to investigate the association between these brain metabolic outcomes and brain function (i.e., psychopathology and cognition). Finally, the age-validation analysis, including data from adult participants from previous studies, collected using the same MRI scanner and sequence as used for the COPSAC2010 cohort, enables us to compare the level of these parameters across the lifespan in an age-dependent manner and validate a metabolic peak in childhood.\u003c/p\u003e \u003cp\u003eIn this population based single-center RCT study, our findings, in line with our hypothesis, demonstrate that PUFA intake in pregnancy reduces CBF and CMRO₂ in the offspring at age 10, however, no significant effect on lactate concentration was observed. The disparity between the effects on CMRO₂ and brain lactate may reflect their involvement into two different aspects of brain metabolism, and brain energy consumption. While CMRO₂ represents the rate of oxygen consumption indicating aerobic metabolic activity, lactate is primarily a byproduct of glycolysis. These contrasting effects could therefore be attributed to PUFA, particularly EPA and DHA, being more directly linked to CMRO₂\u0026rsquo;s role in modulating the membrane fluidity and function, as well as anti-inflammatory processes \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, rather than influencing the metabolic processes that generate lactate. Furthermore, lactate has been identified as the primary energy source for the brain during the perinatal stage \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, peaking during the neonatal period \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and while it may remain elevated in middle childhood compared to adulthood, this difference could be subtle given the earlier peak \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough previous studies in adults have demonstrated a relationship between brain metabolism and brain function \u003csup\u003e\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, we did not observe such an association in children. Investigating the brain during the developmental period when both metabolic parameters and brain function are still maturing provides a different perspective on the interpretation of these changes, which do not seem of clinical relevance in the pediatric population according to our results. The psychopathological conditions that are more strongly linked to alterations in brain metabolism, such as psychosis or dementia, typically emerge later in life, either post-adolescence or during senescence. This developmental timing may partly explain the absence of detectable associations in middle childhood.\u003c/p\u003e \u003cp\u003eThe age-validation analysis, including adult data from prior studies, confirms a marked decrease in these brain metabolic parameters from childhood into adult age. This decrease does not appear linear but rather as a dramatic drop from middle childhood, to then decline in a moderate manner in adulthood. This pattern confirms the one described in previous studies, showing how the demands tied to the first years of neurodevelopment yield a high brain metabolic demand, reflected in CBF and CMRO₂ then starts decreasing after middle childhood \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Prior studies have also suggested increased brain lactate production in children, based on observations of higher glucose consumption relative to oxygen utilization \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Here we confirm that this translates to significantly higher brain lactate concentration in brain tissue in children compared to adults. Overall, our data support that brain metabolism markedly decreases when going from childhood into adult age and further, we observe that PUFA supplementation during pregnancy has a lowering effect on CBF and CMRO₂ in children at age 10. Thus, this RCT suggests that the PUFA supplementation altered the brain metabolic maturation in middle childhood. Furthermore, since the parallel observational model of maternal pre-interventional PUFA levels did not yield significant results, our data indicate that we observe a causal effect of the maternal PUFA supplementation on CBF and CMRO₂ in offspring.\u003c/p\u003e \u003cp\u003eThe strengths of this study include its RCT design embedded within the population based COPSAC2010 cohort, allowing for unbiased investigation of the effects of the PUFA intervention from the 24th week of pregnancy on brain metabolic outcomes in middle childhood. Moreover, the comprehensive COPSYCH study provides an exhaustive two-day assessment of psychopathology, cognition and brain function at age 10, allowing for evaluation of both cognitive and psychopathological outcomes in relation to brain metabolism; and the extensive data collection of the COPSAC2010 cohort allows for rich statistical models and checking additional factors. Specifically, PUFA levels are commonly associated with lifestyle and socioeconomic status (SES) factors, which can lead to a diet incorporating more sources of PUFA, such as fatty fish \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. These factors were accounted for in our models, including maternal pre-intervention levels of PUFA. Furthermore, the methods used in this study regarding the MRI technique for measuring CBF have been validated using gold-standard\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e O-water PET imaging in humans and animals across various perfusion states \u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The SBO MRI technique for measuring cerebral venous oxygen and calculating CMRO₂ has been verified by comparing it to blood samples collected directly from the jugular vein during MRI scans conducted under different perfusion and arterial oxygen saturation conditions \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The reproducibility of the MRI measurements has previously been examined by the authors using the same scanner and sequences, showing low coefficient of variation for both CBF, CMRO₂ and lactate \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Nevertheless, we recognize the single brain measurement time point as a limitation refraining us from further interpretation of the maternal PUFA intake in the childhood brain physiological outcomes. Furthermore, while statistically significant, the effect sizes were relatively small. This limitation might be emphasized due to a large variance in the data at this age. A follow-up assessment later, e.g., after adolescence, might show a decrease in data variance and the difference between groups, if still present, could become clearer. In addition, functional neural activation or similar physiological perturbation of the brain tissue could augment sensitivity to detect subtle changes not detected under resting state, as in the current study.\u003c/p\u003e \u003cp\u003eIn the current analyses on this RCT, we demonstrate that PUFA intake in the third trimester of pregnancy influences brain metabolic outcomes in the offspring, reflected in lower CBF and lower CMRO₂ at age 10. However, changes in these brain metabolic parameters appear not to translate directly into psychopathological status and cognitive performance at age 10. The comparison with older participants, which clearly shows a decrease from childhood to adulthood, suggests that PUFA intake may accelerate the maturation process. This effect could not be explained by potential confounders related to maternal pre-interventional blood level of PUFA or SES factors. Collectively our results encourage large-scale studies on maternal nutrition intake and brain metabolic outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD Attention Deficit/Hyperactivity Disorder\u003c/p\u003e\n\u003cp\u003eADHD-RS ADHD-Rating Scale Questionnaire\u003c/p\u003e\n\u003cp\u003eAG Aerobic glycolysis\u003c/p\u003e\n \u003cp\u003eCBCL Child Behaviour Checklist\u003c/p\u003e\n \u003cp\u003eCBF Cerebral blood flow\u003c/p\u003e\n \u003cp\u003eCGAS Children\u0026rsquo;s Global Assessment Scale\u003c/p\u003e\n \u003cp\u003eCMRO₂ Cerebral metabolic rate of oxygen\u003c/p\u003e\n \u003cp\u003eCOPSAC\u003csub\u003e2010\u003c/sub\u003e Copenhagen Prospective Studies on Asthma in Childhood 2010\u003c/p\u003e\n \u003cp\u003eCOPSYCH COpenhagen Prospective Study on Neuro-PSYCHiatric Development\u003c/p\u003e\n \u003cp\u003eDHA Docosahexaenoic acid\u003c/p\u003e\n \u003cp\u003eEPA Eicosapentaenoic acid\u003c/p\u003e\n \u003cp\u003eHGB Hemoglobin\u003c/p\u003e\n \u003cp\u003eK-SADS-PL Kiddie-Schedule for Affective Disorders and Schizophrenia for School-age Children Present and Lifetime Version\u003c/p\u003e\n \u003cp\u003ePUFA Polyunsaturated fatty acids\u003c/p\u003e\n \u003cp\u003eRCT Randomized controlled trial\u003c/p\u003e\n \u003cp\u003eSDQ Strengths and Difficulties Questionnaire\u003c/p\u003e\n \u003cp\u003eSES Socioeconomic status\u003c/p\u003e\n \u003cp\u003eSRS-2 Social Responsiveness Scale-2\u003c/p\u003e\n \u003cp\u003eTOF Test Observation Form\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics:\u0026nbsp;\u003c/strong\u003eThe Local Ethics Committee (H-B-2008-093), and the Danish Data Protection Agency (2015-41-3696) approved the study. All families provided written informed consent before enrolment upon receiving information about the study prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis work was supported by The Lundbeck Foundation (Grant no. R269-2017-5), BC has received funding from the European Research Council (ERC) under the European Union\u0026rsquo;s Horizon 2020 research and innovation programme (grant agreement No. 946228).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: We would like to acknowledge the huge work of late professor Hans Bisgaard, who was the founder of COPSAC and was head of the clinical research center for more than 25 years. We thank the MRI radiographers and technicians Daban Sulaiman, Mads Rostock, Eduardo Hansen, Karina Elin Segers, Robabeh Hozouri Tavangar and Helle Juhl Simonsen for their efforts in acquiring the MRI data. We would also like to express our deepest gratitude to the children and families of the COPSAC2010 cohort study for all their support and commitment, and the members of COPSAC and CNSR for their efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors declare no potential, perceived, or real conflict of interest regarding the content of this manuscript. BHE is part of the Advisory Board of Boehringer Ingelheim, Lundbeck Pharma, and Orion Pharma; and has received lecture fees from Boehringer Ingelheim, Otsuka Pharma Scandinavia AB, and Lundbeck Pharma. The funding agencies did not have any role in design and conduct of the study; collection, management, and interpretation of the data; or preparation, review, or approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The MRI images are not publicly available due to privacy restrictions. Data derived from the MRI images can be made available with a data sharing agreement upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Software to calculate CBF with PCM technique is available at \u0026nbsp;https://github.com/MarkVestergaard/PCMCalculator/ and software to calculate SvO2 with susceptibility-based oximetry MRI technique is available at https://github.com/MarkVestergaard/SBOCalculator/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: H.B.W.L., K.B. and B.H.E. initiated and formulated the study. M.H.L. and M.B.V. performed the data analysis and wrote the manuscript. H.B.W.L., M.B.V., J.M.R. and U.L. were responsible for the collection and processing of MRI data. K.B, B.H.E., R.K.V., B.C., P.M. and J.B.R. were responsible for the COPSAC2010 cohort. All authors reviewed the manuscript. B.Y.G, R.K.V, K.B, H.B.W.L and B.H.E supervised the project and were responsible for funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiller, D. J. \u003cem\u003eet al.\u003c/em\u003e Prolonged myelination in human neocortical evolution. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 109, 16480\u0026ndash;16485 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemple, B. D., Blomgren, K., Gimlin, K., Ferriero, D. M. \u0026amp; Noble-Haeusslein, L. J. Brain development in rodents and humans: Identifying benchmarks of maturation and vulnerability to injury across species. \u003cem\u003eProg Neurobiol\u003c/em\u003e 106\u0026ndash;107, 1\u0026ndash;16 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaniukov, D., Lebel, R. M., Giesbrecht, G. \u0026amp; Lebel, C. 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S. \u003cem\u003eet al.\u003c/em\u003e Reproducibility of cerebral blood flow, oxygen metabolism, and lactate and N-acetyl-aspartate concentrations measured using magnetic resonance imaging and spectroscopy. \u003cem\u003eFront Physiol\u003c/em\u003e 14, (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6678778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6678778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaternal blood levels of polyunsaturated fatty acids (PUFA) during pregnancy have been associated with brain health outcomes in offspring, but causal effects of PUFA on brain metabolism is unexplored. We examined the effect of maternal PUFA supplementation on brain metabolism in children at age 10, measured as cerebral blood flow (CBF), oxygen consumption (CMRO₂), and brain lactate concentration. We used data from the COPSAC2010 cohort (n\u0026thinsp;=\u0026thinsp;700), where pregnant women were randomized to PUFA supplementation or placebo. At age 10, the children underwent psychopathological and cognitive assessments as well as MRI scans to obtain CBF, CMRO₂, and brain lactate. We estimated the effect of maternal PUFA intake on brain metabolism and examined the associations between those metabolic measurements with psychopathological symptoms and cognitive outcomes in the offspring. Lastly, we investigated the association of age with brain metabolic outcomes by comparing with data from adult participants from five previous studies. 487 children (51.7% male, 10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 years old) underwent 3 Tesla MRI scan. Children whose mothers received PUFA supplementation exhibited significantly lower CBF and CMRO₂; however, these parameters were not associated with psychopathology or cognition. The age-related analysis, with additional data of 248 adults, showed that higher age was associated with lower CBF, CMRO₂, and lactate concentration. Our findings suggest that maternal PUFA supplementation influences brain maturation in the offspring at age 10, although PUFA supplementation does not directly translate into psychopathological status and cognitive performance. Our results highlight the need for further large-scale studies on maternal nutrition and brain development.\u003c/p\u003e","manuscriptTitle":"Fish Oil-Derived Fatty Acids in Pregnancy and Brain Metabolism in Middle Childhood: Results From a Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 15:07:23","doi":"10.21203/rs.3.rs-6678778/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"transferred","content":"Translational Psychiatry","date":"2025-06-06T13:21:31+00:00","index":"","fulltext":""},{"type":"decision","content":"Reject before peer review","date":"2025-06-03T12:11:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T12:19:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-20T12:10:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-05-20T08:35:55+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-05-19T10:35:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65ffcc3e-e7ec-401d-beea-3d707860f3c0","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48891036,"name":"Biological sciences/Neuroscience"},{"id":48891037,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-07-18T19:25:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 15:07:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6678778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6678778","identity":"rs-6678778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}