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Article

Association of Maternal Diet during Pregnancy and Metabolite Profile in Cord Blood

by
Carla P. Harris
1,2,*,
Carlana Ramlochansingh
1,3,
Olaf Uhl
2,
Hans Demmelmair
2,
Joachim Heinrich
1,4,5,
Berthold Koletzko
2,
Marie Standl
1 and
Elisabeth Thiering
1,2
1
Institute of Epidemiology, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
2
Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich, 80337 Munich, Germany
3
Institute of Medical Informatics, Biometry and Epidemiology (IBE), LMU Munich, 81377 Munich, Germany
4
Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, LMU University Hospitals, 80336 Munich, Germany
5
Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Biomolecules 2022, 12(10), 1333; https://doi.org/10.3390/biom12101333
Submission received: 28 July 2022 / Revised: 15 September 2022 / Accepted: 16 September 2022 / Published: 21 September 2022
(This article belongs to the Special Issue Bioactive Lipids: Sources, Synthesis, and Biological Roles)
Versions Notes

Abstract

:
Cord blood metabolites can be predictive of long-term disease risk, but how levels of different metabolites might vary with respect to maternal diet is not well understood. The aim of this study was to evaluate the associations of different dietary patterns during pregnancy with cord blood metabolites (including glycerophospholipid fatty acids, polar lipids, non-esterified fatty acids, amino acids, and the sum of hexoses). Participants from the German LISA birth cohort study, with available data on targeted cord blood metabolomics and maternal diet, were included (n = 739). Maternal diet during the last 4 weeks of pregnancy was assessed by a non-quantitative food-frequency questionnaire. Using factor analysis, ten dietary patterns were identified, which were used in linear regression models exploring associations with cord blood metabolites. After correction for multiple hypothesis testing and adjustment for basic covariates, “fish and shellfish” was associated with higher glycerophospholipid fatty acid C20:5 n3 and lower C22:5 n6, whereas the “meat and potato” pattern was directly associated with propionylcarnitine (C3:0). The observed associations highlight potential metabolic pathways involved in the early programming of health and disease through maternal diet, as well as the potential for establishing quantitative biomarkers for dietary patterns of pregnant women.

1. Introduction

Maternal diet during pregnancy contributes to fetal growth and development, playing a crucial role in the short- and long-term health of the offspring. It is well established that certain key nutrient deficiencies in the mother can lead to health problems in the offspring, manifesting as impaired physical and/or mental capacities [1,2,3]. Furthermore, it is now widely accepted that many adult-onset chronic diseases may have their origins before birth, with maternal diet hypothesized as a primary programming stimulus [4,5]. For example, reduced birth size resulting from insufficient fetal nutrient supply is associated with a higher risk of chronic diseases in adulthood [6,7,8]. Low birth weight is often followed by increased postnatal weight gain, which in turn has been linked to asthma [9] and reduced lung function in adolescence [10]. In contrast, excessive maternal energy intake increases the risk for high birth weight, leading to increased risk of overweight in later childhood [11,12,13].
Growing evidence also highlights the importance of diet composition to ensure optimal health of the offspring [14]. The human diet typically involves combinations of different foods and nutrients, and thus, their roles must be interpreted in the context of possible correlations and interactions among them. For this, the study of dietary patterns can be highly informative [15]. A number of studies on maternal dietary patterns have observed significant associations with birth outcomes, most often reporting protective effects of vegetables, fruit, whole grains, and fish [16,17,18], and negative effects of diets high in fat, sugars, and processed foods [19,20,21]. Nevertheless, the metabolic pathways through which different dietary patterns may influence fetal development and future chronic diseases are poorly understood. We assume that the maternal diet affects metabolism, and consequently the intrauterine milieu, hence determining metabolic adaptations in the neonate. The cord blood metabolome provides information on the intrauterine environment, which involves the complex interaction of maternal metabolism, placental nutrient transfer, and fetal metabolism [22]. Studies have shown significant associations between maternal metabolic status, such as BMI or dysglycemia, and cord blood metabolites [23,24,25,26]. Others have identified metabolic profiles associated with unfavorable birth outcomes [27,28,29,30]. However, there is limited evidence on the impact of maternal diet on cord blood metabolites [31]. Thus, in the present study, we applied targeted metabolomics in order to investigate the association of maternal dietary patterns during pregnancy with cord blood metabolites. We demonstrated associations with specific metabolites with known roles in lipid signaling, beta-oxidation, and branched-chain amino acid degradation. Ultimately, if confirmed in future studies, these findings could contribute to a better understanding of early disease etiology.

2. Materials and Methods

2.1. Study Population

Data obtained from participants of the LISA (Influence of Life-Style Factors on the Development of the Immune System and Allergies in East and West Germany) study were used for the present analyses [32]. In summary, 3097 healthy full-term newborns (of which 3 later withdrew consent) were recruited from November 1997 to January 1999 from four regions of Germany (Munich, Leipzig, Wesel, and Bad Honnef). At birth, 4 mL of venous cord blood was collected and centrifuged at 1400× g for 10 min. The resulting serum was deep-frozen at −80 °C until metabolomics analysis. Information on maternal diet during the last four weeks of pregnancy was obtained by means of a non-quantitative food-frequency questionnaire, administered to the mothers after birth. Dietary assessment and metabolomics analysis are described in more detail below. Additional information was collected after birth through questionnaires, including gestational age at birth (weeks), smoking during the 3rd trimester of pregnancy (yes; no), maternal education (low: <10 years of education; medium: 10 years; high: >10 years), maternal age (years), maternal pre-pregnancy BMI (kg/m2, categorized as underweight: <18.5; normal weight: ≥18.5 and <25; and overweight: ≥25, according to WHO classification [29]), gestational weight gain (kg/month), and birth weight of the neonate (kg). Approval by the local ethics committees (Bavarian Board of Physicians, Board of Physicians of North-Rhine-Westphalia) and written consent from the parents were obtained.

2.2. Metabolomics Analysis

Glycerophospholipid fatty acids (GPL-FA), acylcarnitines (AC), diacyl-phosphatidylcholines (PCa), acyl-alkyl-phosphatidylcholines (PCe), sphingomyelins (SM), acyl-lysophosphatidylcholines (LPCa), alkyl-lysophosphatidylcholines (LPCe), non-esterified fatty acids (NEFA), amino acids, and the sum of hexoses (H1), were measured in 750 samples of cord blood serum. Measurements were performed at the laboratory of the Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-Universität München.
Analysis of GPL-FA was performed by transesterification of GPL-FA into methyl esters and their gas chromatographic separation, flame ionization detection (Agilent 7890A, Agilent Technologies, Waldbronn, Germany), and quantification as previously described [33]. AC, PC, SM, LPC, and the sum of hexoses were analyzed by flow-injection analysis tandem mass spectrometry as previously reported [31]. Briefly, 10 μL of samples were diluted with methanol for protein precipitation, containing internal standards for different lipid groups (D3-carnitine C2, D3-carnitine C8, D3-carnitine C16, 13C6-D-glucose (all Cambridge Isotope Laboratories, Tewksbury, MA, USA), acyl-lysophosphatidylcholine C13:0, and diacyl-phosphatidylcholine C28:0 (both Avanti Polar Lipids, Alabaster, AL, USA)) and ammonium acetate. After centrifugation, supernatants were injected into a high-performance liquid chromatography system (1200, Agilent, Waldbronn, Germany) coupled to a triple quadrupole mass spectrometer (QTRAP4000, Sciex, Darmstadt, Germany) with an electrospray ionization source. The system was run in multiple reaction monitoring (MRM) mode. NEFA were analyzed as previously described [34]. Briefly, 10 μL of the samples was mixed with 200 µL isopropanol containing internal standard (uniformly labeled palmitic acid U-13C16, 98%, Euriso-Top) for protein precipitation. After centrifugation, an aliquot of the supernatant was injected into a high-performance liquid chromatography system (1200, Agilent, Waldbronn, Germany) coupled to a triple quadrupole mass spectrometer (QTRAP4000, Sciex, Darmstadt, Germany) operating in multiple reaction monitoring mode after negative electrospray ionization. Unlike for GPL-FA, the analytical technique applied for AC, PC, SM, LPC, and NEFA is not capable of determining the position of the double bonds and the distribution of carbon atoms between fatty acid side chains. Therefore, these were separated according to chain length and number of double bonds, but not according to the position of double bonds. In the applied nomenclature for these metabolites, CX:Y, X is the length of the carbon chain, and Y is the number of double bonds. ‘a’ indicates that the acyl chain is bound via an ester bond to the backbone, while ‘e’ indicates binding by an ether bond.
Finally, amino acid (AA) analysis was performed as previously reported [35]. Briefly, 10 μL of samples was diluted with the corresponding internal standard reagent. A labeled AA standards set (set A) was mixed with 15N2-L-asparagine, and indole-D5-L-tryptophan (all Cambridge Isotope Laboratories, Tewksbury, MA, USA) and added to the precipitation reagent for internal standardization. After centrifugation, AA were derivatized to AA butyl esters and determined by ion-pair high-performance liquid chromatography (1100, Agilent, Waldbronn, Germany) coupled to a triple quadrupole mass spectrometer (API 2000, Applied Biosystems, Darmstadt, Germany) with an atmospheric pressure chemical ionization source operating in positive ionization mode and multiple reaction monitoring.
Six plasma quality control samples were measured twice, along with the samples per batch. The coefficient of variation (CV) was calculated for each single batch (intra-batch) and for all batches (inter-batch). Regarding intra-batch precision, batches with a CV larger than 30% were excluded for single metabolite measurements, and complete metabolite measurements with an inter-batch CV > 30% were excluded. We report all metabolite concentrations in μmol/L cord blood serum.

2.3. Dietary Assessment

Maternal dietary intake during the last 4 weeks of pregnancy was assessed by means of a non-quantitative food-frequency questionnaire (FFQ), administered shortly after childbirth [36]. For the 45 food items included in the FFQ, mothers were asked to report their average consumption frequency over the last 4 weeks. For all food items except milk and yoghurt, this was done by selecting from a choice of five frequency categories, namely: “never or less than 2 times per month”, “2–3 times per month”, “1–2 times per week”, “3–4 times per week”, and “more than 4 times per week”. Milk and yoghurt intakes were quantified according to four categories, defined for milk as “never”, “sometimes”, “up to 0.5 L per day”, and “more than 0.5 L per day”, and for yoghurt as “never”, “sometimes”, “up to 200 g per day”, and “more than 200 g per day”. Subjects were excluded (78 of 3094) if they had missing responses to more than 9 food items, which amounted to 20% of the FFQ. In accordance with recommendations by Willett for data cleaning in nutritional epidemiology [37], sporadic blanks in an otherwise carefully completed FFQ were considered as no consumption of that particular food item, defined as “never or less than 2 times per month” and as “0 L/day” or “0 g/day” for milk and yoghurt, respectively.

2.4. Statistical Analysis

All statistical analyses were performed using R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/, first accessed on 1 October 2017) [38]. As a first step, we conducted an exploratory factor analysis to identify maternal dietary patterns, which would then be used in association analysis with cord blood metabolites. Factor analysis seeks to compress several variables into a few underlying factors, based on the degree to which they are correlated with one another. It is hence a useful tool for investigating complex exposures such as habitual diet, going beyond single foods and nutrients, which facilitates interpretation and accounts for possible correlations between foods that are commonly consumed together [39]. Additionally, the method reduces the number of tests to be carried out, limiting the occurrence of chance findings. Firstly, the 45 reported food items were presented in a correlation matrix and the eigenvalues of the matrix calculated for the extraction of factors. The optimal number of factors was decided based on the Kaiser–Guttman rule that states that factors with an eigenvalue greater than 1 should be used [40,41]. The number of factors to retain following this rule was defined by a Non-Graphical Cattell’s Scree test, using the nScree function in the package “nFactors” [42]. We then performed a posteriori determination of dietary patterns by maximum likelihood factor analysis with varimax rotation, using the factanal function in the package “stats” [38]. Varimax rotation is an orthogonal rotation of the factor axes that maximizes variance of the squared loadings of a factor on all the variables in a factor matrix, which effectively differentiates the original variables by extracted factor. Factor loadings show an increasing difference between lower weights, centering them closer to zero, and higher weights, converging them to one, therefore allowing an easier interpretation of the results. In this study, food items with factor loadings of ≥ |0.3| were considered influential and were used to define a descriptive ‘pattern’ of the diet associated with the factor. Factor scores for each subject were obtained by the regression method of Thomson 1951 [43]. Their associations with potential covariates were tested using Pearson correlation for numeric variables and t-test or ANOVA for categorical variables. Following checks to confirm that the metabolomics data met the corresponding assumptions, associations between the identified maternal dietary patterns and metabolite concentrations in cord blood were analyzed using linear regression, in four differently adjusted models. The first model was a “crude model”, minimally adjusted for potential batch effects in the metabolite measurements. The “main model” included further adjustment for city, sex, gestational age, smoking during the 3rd trimester, maternal education, and maternal age. Additional models were run, adjusting for covariates of the main model plus maternal pre-pregnancy BMI and gestational weight gain (“main model +”), and finally adjusting for all above-mentioned covariates as well as child’s birth weight (“main model ++”). The main model is considered the most important model for interpretation as maternal BMI, gestational weight gain, and child’s birth weight may lie in the causal pathway (being potentially affected by diet and in turn altering metabolic processes), and hence models with these covariates could be over-adjusted. Correction for multiple hypothesis testing via false discovery rate (FDR) was applied to all models to determine significant associations.

3. Results

3.1. Study Population

The present analysis comprises 739 participants from the Munich (77%) and Bad Honnef (23%) LISA study centers, for whom complete information on maternal diet and cord blood metabolites was available (representing 39% and 18% of newborns initially recruited from Munich and Bad Honnef, respectively). Basic characteristics of the study population are described in Table 1 with means (standard deviation) for numeric variables and frequencies (%) for categorical variables. The average age of the mothers was 32.0 ± 4.0 years, and ~75% of them had a normal BMI. Among the remaining 25%, 124 were overweight and 43 underweight, all together presenting a mean maternal BMI of 22.6 ± 4.1. On average, ~90% of the mothers reported to be non-smokers during the last trimester of pregnancy. Gestational weight gain averaged 0.3 ± 0.1 kg per month over approximately 40.0 ± 1.2 weeks. The offspring had a mean birth weight of 3.5 ± 0.4 kg.

3.2. Maternal Dietary Patterns

Following factor analysis, ten dietary patterns were retained, explaining 28% of the total variance of maternal diet. This level of total explained variance is expected for food-frequency questionnaires, including a large number of items, given the multidimensionality of diet, and is comparable in magnitude to that observed in other studies [44,45]. The dietary patterns were labeled based on the most relevant food items as indicated by the highest factor loadings. The chosen labels were hence data-dependent and entirely arbitrarily defined: Factor 1 = “vegetables”, Factor 2 = “fruits”, Factor 3 = “summer vegetables”, Factor 4 = “salad and dressings”, Factor 5 = “cereal, seeds, nuts, yoghurt, cheese”, Factor 6 = “butter vs. margarine”, Factor 7 = “meat and potato”, Factor 8 = “sweets”, Factor 9 = “fish and shellfish”, and Factor 10 = “seasonal”. For the ten dietary patterns (factors 1–10), the factor loadings for each food item and the percentage of variance explained are presented in Table 2. The foods with factor loadings of ≥|0.3|, which were used to label each dietary pattern, are marked with an asterisk, as well as separately listed in Supplementary Table S1. The dietary patterns indicated the tendency for mothers to have specific eating behaviors characterized by more (or less) of certain foods.
Some of the covariates presented significant associations with the resulting dietary patterns (Supplementary Table S2). In Bad Honnef, there was a higher maternal consumption of the “vegetables” and “meat and potato” patterns (p < 0.001) and, in contrast, lower intakes of “fruits”, “summer vegetables” and “butter vs. margarine” patterns (p < 0.05). Mothers who smoked during the last trimester of pregnancy also showed a difference in their diet. Non-smokers indicated a preference for the “cereal, nut, seeds, yoghurt, cheese” and the “butter vs. margarine” patterns (p < 0.001), whereas the smokers had higher intakes of the “meat and potato” dietary pattern (p < 0.05). High maternal education was associated with a higher consumption of “fruits” (p < 0.05), “cereal, nut, seeds, yoghurt, cheese” and “butter vs. margarine” (p < 0.001) patterns, with significantly less intakes of “meat and potato” (p < 0.001); the opposite was the case for the lowest educated mothers. An increase in “cereal, nut, seeds, yoghurt, cheese”, “butter vs. margarine” (p < 0.001), and “fish and shellfish” (p < 0.05) was also observed with increasing maternal age.

3.3. Associations of Dietary Patterns with Cord Blood Metabolites

Since the many linear regression associations tested cannot be displayed in a single table, only associations with p < 0.005 in at least one model are presented in Table 3. Associations that were significant following correction for multiple hypothesis testing are shown in bold and marked with an asterisk. The full list of all associations is provided in Supplementary Table S3. Generally, a positive beta coefficient indicates that a dietary pattern is proportionately influencing the level of a metabolite, and a negative beta would imply that metabolite levels are inversely associated with the dietary pattern.
A significant positive association was seen for the “cereal, nut, seeds, yoghurt, cheese” pattern with SM C30:1 in the crude model, which was also significant in model ++. For this dietary pattern, an association with SM C43:2 was also observed in the crude model, but this association was not statistically significant in any of the further-adjusted models. For the “butter vs. margarine” pattern, the crude model resulted in eight significant associations, including positive associations with SM C39:1, SM C43:2, LPCe C18:0, LPCe C16:0, and NEFA C15:0 and C17:0, and inverse associations with LPCa C18:3 and AC C8:1. After adjustment for covariates, none of these associations were statistically significant; however, effect sizes remained stable for AC C8:1 (p = 0.001), SM C39:1 (p = 0.001) and SM C43:2 (p = 0.003) after adjustment in the main model. A significant, direct association with AC C3:0 was observed for the “meat and potato” pattern across all models. “Fish and shellfish” presented an inverse association with n-6 osbond acid (C22:5 n6) across all models except model ++. Further, this pattern had a direct association with n-3 eicosapentaenoic acid (EPA, C20:5 n3) in the crude model, which approached significance in all further-adjusted models (with p-value = 0.001).

4. Discussion

In the present study, we investigated the association of maternal diet with a range of chemically characterized cord blood metabolites. Using factor analysis, we identified ten dietary patterns describing the eating behaviors of mothers during the last four weeks of pregnancy. Dietary patterns presenting robust associations with metabolites across differently adjusted models were “fish and shellfish” and “meat and potato”. For the dietary patterns “cereals, seeds, nuts, yoghurt, cheese” and “butter vs. margarine”, associations were observed which were stable across all models in terms of effect size, but which were not statistically significant beyond the crude model.

4.1. Fish and Shellfish

For “fish and shellfish”, an inverse association was observed with osbond acid, and a positive association was observed with EPA (although the latter was only significant in the crude model, the effect size was barely reduced in all further-adjusted models, and these all had p = 0.001). In particular, oily fish and seafood are rich sources of EPA and docosahexaenoic acid (DHA, C22:6 n3) [46], thus a positive association with EPA is to be expected. A significant positive association with DHA was however not observed in our analyses. Incorporation of DHA in membranes is reported to be slower and more erratic than that of EPA [47]. Some [48,49] but not all [50,51] studies have reported increased cord blood DHA levels with DHA supplementation during pregnancy. The importance of DHA for optimal neurodevelopment of the fetus is now widely recognized [52,53]; however, research on DHA during pregnancy was at very early stages during the time of dietary assessment (1995–1998) [48], and so residual confounding due to DHA supplementation is unlikely. It has been previously highlighted that only a small proportion of the variation in cord blood DHA levels is explained by maternal DHA intake [54]. It is postulated that in the last trimester of pregnancy there is a preferential transfer of DHA from the mother to the fetus, leading to a depletion of maternal stores [50]. Differences in dietary intake at this stage may hence have a more appreciable effect on maternal DHA status than on cord blood levels. In line with our results, a study of maternal diet in coastal and inland China reported higher EPA levels in cord blood in the coastal population, but no significant differences in DHA [55]. Finally, osbond acid is synthesized when there is a lack of DHA, and is hence considered a marker of DHA functional shortage [56]. The observed lower osbond acid related to “fish and shellfish” intake may thus provide a better indication of diet-induced changes to cord blood DHA status than DHA itself.

4.2. Meat and Potato

The dietary pattern “meat and potato” was significantly and consistently positively associated with AC C3:0 across all models. In preliminary descriptive analyses, this dietary pattern was correlated with maternal education and smoking during the third trimester (Table S2). However, adjusting for such variables in the main model did not reduce the magnitude of effect. The short-chain AC C3:0 is a by-product of branched-chain amino acid (BCAA) catabolism [57]. In line with our findings, observational studies in adults have shown this metabolite to be associated with higher meat intakes [58,59], and increased levels were observed in infants provided with high-protein formula milk [60]. Short-chain ACs have been associated with high birth weight [61], as well as with obesity and metabolic syndrome in adults [62]. Considering that meat is the richest dietary source of BCAA [63], these results add to the evidence suggesting an involvement of the BCAA degradation pathway in linking maternal diets high in red and processed meat to metabolic disturbances in the offspring [64,65]. We did not observe differences in levels of BCAA; however, the aromatic amino acid tryptophan was slightly increased in association with this dietary pattern (p = 0.002 in the main model). BCAA are known to elevate aromatic amino acid concentrations, suggested to be the most potent promoters of IGF-1 secretion [66], thereby enhancing infant weight gain according to the early protein hypothesis [67]. Finally, the observed inverse association with palmitoleate (C16:1n7), which was not significant but consistent across all models at p ≤ 0.002, may reflect reduced adipocyte de novo lipogenesis [68]. Palmitoleate from adipose tissue is suggested to promote insulin sensitivity [69,70], and lower levels could reflect unfavorable fetal metabolism. Indeed, the same opposing associations of AC C3:0 and palmitoleate have been previously reported in relation to type 2 diabetes mellitus and fasting glucose sensitivity [71]. It is suggested that chronic elevations in BCAA and related metabolites synergize with a rise in circulating fatty acids to drive a state of chronic hyperinsulinemia and metabolic disease [72]. Taken together, our results indicate that a maternal diet high in meat might already promote such metabolic disruptions in the offspring in utero.

4.3. Butter vs. Margarine

In relation to the “butter vs. margarine” pattern, an inverse association with AC C8:1 and positive associations with SM C39:1 and SM C43:2 were observed in the crude model, and although not statistically significant (p ≤ 0.003), the magnitude of these effects remained stable following further adjustment in the main model. The inverse association observed with AC C8:1 could be an indication of better mitochondrial function, as medium-chain ACs are suggested to result from a mismatch between beta-oxidation and tricarboxylic acid (TCA) cycle capacity [73] and have also been associated with gestational diabetes [74]. Indeed, this specific metabolite has been associated with increased body fat [75] and pre-diabetic conditions [76], so lower levels would suggest a beneficial effect in the offspring by consuming butter at the expense of margarine during pregnancy. A similar dietary pattern was described by Floegel et al. in adults, for which an inverse cross-sectional association with AC C8:1 was also observed [77].
The positive associations of butter with SM C39:1 and 43:2 have to our knowledge not been previously reported. Dairy products are important dietary sources of SM; however, during butter manufacture, the milk fat globule membrane that contains the SM is separated from the triglyceride-rich core and removed [78]. The associated metabolites are therefore more likely to be the result of the endogenous combination of ceramides and sphingomyelins with very long-chain saturated and monounsaturated fatty acids derived from fatty acid elongation [79,80]. Most saturated and monounsaturated very long-chain fatty acids are sphingolipid components and thus play important roles in skin barrier formation and neural functions [81]. Higher SM C39:1 has been observed within LDL in pregnant women [82], and cholesterol levels are known to increase during pregnancy [83]. LDL receptors in the placenta allow the uptake of LDL, providing important cholesterol and fatty acids for fetal development [84]. The direct associations observed in our study suggest a specific relevance of these metabolites for the fetus. Indeed, SM with saturated acyl chains are the main constituents of lipid rafts, and are important for membrane function and in the regulation of cell signaling pathways, especially in the brain [85]. The biological importance of SM for brain development has been shown in a trial that reported neurobehavioral benefits in preterm infants given formula enriched with SM [86].

4.4. Cereals, Nuts, Seeds, Yoghurt, Cheese

The association of “cereals, seeds, nuts, yoghurt, cheese” with SM C30:1 could be explained by the high intakes of dairy that characterize this dietary pattern. We expect that the fatty acyl chain in SM C30:1 is a medium-chain saturated fatty acid (MCFA, C12:0), assuming a sphingosine backbone (C18:1). Although most common in coconut milk, this fatty acid can also be found in bovine milk and is suggested to have antibacterial properties [87], as well as modulating cellular signaling and regulating key glucose and lipid metabolism [88]. Saturated MCFA are used in mitochondrial energy production independent of the carnitine transport system, thus allowing valuable ATP molecules to be saved for other cellular processes in the developing fetus [89]. Indeed, a study observed lower levels of MCFA in the diet of women giving birth to preterm and small for gestational age infants [90], highlighting their value for fetal development.
From the present findings, it seems that effects of maternal diet are overruled in some cases by the corresponding placental transfer mechanisms. In general, given the predominance of significant associations with phospholipids, our findings suggest that facilitated diffusion is of greater importance for these than for amino acids. Diffusion leads to proportional concentrations, whereas the active transport of amino acids works against the proportionality of maternal and cord blood concentrations (higher amino acid concentrations in fetal than in maternal circulation) [91], and may explain why maternal diet was reflected to a lesser extent in cord blood amino acids. In line with this principle is the lack of association for fish intake with DHA, which could be explained by the selective maternal–fetal transfer of DHA being more reliant on existing maternal stores than on dietary DHA intake [54]. Additionally, an unexpected observation is that associations between fish and long-chain PUFA were seen for GPL-FA but not for PC species. This could signify insufficient choline availability and thus limited phosphatidylethanolamine-N-methyltransferase activity, reducing placental transport of PCs carrying long-chain PUFA [92].

4.5. Strengths and Limitations

A major strength of this study is the cord blood metabolomics data available in a large sample of 739 participants, which entails a tremendous effort and is highly unusual. Our study provides a data-driven description of dietary patterns, defined on the basis of foods most commonly associated with one another (consumed together or at the expense of each other). This takes into account the complexities of habitual diet, such as potential interactions or synergistic effects within the food complex or correlations among different foods and nutrients, the individual effects of which are often difficult to disentangle. This method also ensures enough power in the analysis of associations with a large number of metabolites, whereas a focus on individual foods would signify a much larger number of tests and an increased potential for chance findings. Total explained variance by the different factors was indeed low; however, this is not uncommon for factor analysis of dietary data given its multidimensionality, and is comparable to that observed in other studies [44].
The applied method for dietary assessment entails some limitations. A non-quantitative FFQ was used, which was part of the main questionnaire completed upon recruitment, and was not previously validated. The lack of more detailed portion size information limits our ability to precisely estimate associations of metabolites with intake levels of individual foods. Thus, with the available data, we cannot show which specific food items and intake levels within the derived dietary patterns underlie the observed associations. The FFQ was however not intended for an estimation of exact quantities, or nutrient content of the reported diet. Rather, the aim was to provide a general estimation of dietary intakes of common foods to allow the ranking of individual intakes with respect to the overall study population, for which frequency alone should provide sufficient information [93]. Potential reporting errors common to all dietary assessment methods may however limit the accuracy of the collected dietary data. On the other hand, we cannot know to what extent reports of diet during the last four weeks of pregnancy are representative of usual diet. Despite this, we observed plausible and robust associations between dietary patterns and a select few metabolites. Nonetheless, residual confounding cannot be ruled out and given the cross-sectional nature of the study, a causal effect of diet cannot be assumed. We also cannot exclude possible variation arising due to the length of time until storage of samples or the duration of storage [29].

5. Conclusions

This study demonstrated clear associations between maternal dietary patterns consumed during pregnancy and cord blood metabolites. Significant associations were observed specifically in relation to a pattern high in fish and shellfish and to a pattern high in meat and potatoes. The identified associated metabolites highlight possible mechanisms through which consuming certain types of dietary patterns during pregnancy may influence both short- and long-term health of the offspring. In this context, the relevance of lipid signaling, beta-oxidation, and branched-chain amino acid degradation are discussed. This study provides novel findings that, if confirmed in further studies, could greatly aid our understanding of the role of diet during pregnancy and hence improve dietary recommendations. Our findings also indicate the great potential for establishing biomarkers for dietary patterns of pregnant women, which could overcome the inherent imprecision of current available methods of assessing dietary intakes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biom12101333/s1, Table S1: List of foods with factor loadings of ≥|0.3| used to label dietary patterns; Table S2: Association between dietary patterns and covariates; Table S3: Association of dietary patterns with cord blood metabolites (all results).

Author Contributions

Conceptualization, J.H., M.S., and E.T.; Formal analysis, C.R. and E.T.; Methodology, O.U., H.D. and B.K.; Supervision, E.T.; Writing—original draft preparation, C.P.H.; Writing—review and editing, C.P.H., O.U., H.D., J.H., B.K., M.S., and E.T. All authors have read and agreed to the published version of the manuscript.

Funding

The LISA study was mainly supported by grants from the Federal Ministry for Education, Science, Research and Technology and in addition from Helmholtz Zentrum Munich (former GSF), Helmholtz Centre for Environmental Research—UFZ, Leipzig, Research Institute at Marien-Hospital Wesel, Pediatric Practice, Bad Honnef for the first 2 years. The 4 year, 6 year, 10 year and 15 year follow-up examinations of the LISA study were covered from the respective budgets of the involved partners (Helmholtz Zentrum Munich (former GSF), Helmholtz Centre for Environmental Research—UFZ, Leipzig, Research Institute at Marien-Hospital Wesel, Pediatric Practice, Bad Honnef, IUF—Leibniz-Research Institute for Environmental Medicine at the University of Düsseldorf) and in addition by a grant from the Federal Ministry for Environment (IUF Düsseldorf, FKZ 20462296). Further, the 15-year follow-up examination of the LISA study was supported by the Commission of the European Communities, the 7th Framework Program: MeDALL project. The metabolomics analyses were financially supported in part by the Commission of the European Communities 7th Framework Programme, contract FP7–289346-EARLY NUTRITION, the European Research Council Advanced Grant ERC-2012-AdG—No. 322605 META-GROWTH and German Research Council/Deutsche Forschungsgemeinschaft (INST 409/224-1 FUGG). BK is the Else Kröner Seniorprofessor of Paediatrics at LMU—University of Munich, financially supported by Else Kröner-Fresenius-Foundation, LMU Medical Faculty and LMU University Hospitals. This paper does not necessarily reflect the views of the European Commission and in no way anticipates the future policy in this area. The APC was funded by MDPI Biomolecules (invitation to contribute a featured paper).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the local ethics committees (Bavarian Board of Physicians, Board of Physicians of North-Rhine-Westphalia).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Restrictions apply to the availability of these data. The datasets generated and/or analyzed during the current study are not publicly available due data protection but are available from the corresponding author on reasonable request and acceptance of a data transfer agreement from the legal department of the Helmholtz Zentrum München.

Acknowledgments

We thank Stefanie Winterstetter and Stefan Stromer (Division of Metabolic and Nutritional Medicine, von Hauner Children’s Hospital, Ludwigs-Maximilians-Universität München), who prepared the plasma samples for GC and LC-MS/ MS analysis. Furthermore, we thank all members of the LISA Study Group for their excellent work. The LISA Study group consists of the following: Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology, Munich (Heinrich J, Schnappinger M, Brüske I, Ferland M, Schulz H, Zeller C, Standl M, Thiering E, Tiesler C, Flexeder C); Department of Pediatrics, Municipal Hospital “St. Georg”, Leipzig (Borte M, Diez U, Dorn C, Braun E); Marien Hospital Wesel, Department of Pediatrics, Wesel (von Berg A, Berdel D, Stiers G, Maas B); Pediatric Practice, Bad Honnef (Schaaf B); Helmholtz Centre of Environmental Research—UFZ, Department of Environmental Immunology/Core Facility Studies, Leipzig (Lehmann I, Bauer M, Röder S, Schilde M, Nowak M, Herberth G, Müller J); Technical University Munich, Department of Pediatrics, Munich (Hoffmann U, Paschke M, Marra S); Clinical Research Group Molecular Dermatology, Department of Dermatology and Allergy, Technische Universität München (TUM), Munich (Ollert M, J. Grosch).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jägerstad, M. Folic Acid Fortification Prevents Neural Tube Defects and May Also Reduce Cancer Risks: Folate Fortification, NTD and Cancer. Acta Paediatr. 2012, 101, 1007–1012. [Google Scholar] [CrossRef] [PubMed]
  2. Allen, L.H. Anemia and Iron Deficiency: Effects on Pregnancy Outcome. Am. J. Clin. Nutr. 2000, 71, 1280S–1284S. [Google Scholar] [CrossRef]
  3. Trumpff, C.; Vandevijvere, S.; Moreno-Reyes, R.; Vanderpas, J.; Tafforeau, J.; Van Oyen, H.; De Schepper, J. Neonatal Thyroid-Stimulating Hormone Level Is Influenced by Neonatal, Maternal, and Pregnancy Factors. Nutr. Res. 2015, 35, 975–981. [Google Scholar] [CrossRef] [PubMed]
  4. Harding, J. The Nutritional Basis of the Fetal Origins of Adult Disease. Int. J. Epidemiol. 2001, 30, 15–23. [Google Scholar] [CrossRef] [PubMed]
  5. Demmelmair, H.; von Rosen, J.; Koletzko, B. Long-Term Consequences of Early Nutrition. Early Hum. Dev. 2006, 82, 567–574. [Google Scholar] [CrossRef]
  6. Ravelli, A.; van der Meulen, J.; Michels, R.; Osmond, C.; Barker, D.; Hales, C.; Bleker, O. Glucose Tolerance in Adults after Prenatal Exposure to Famine. Lancet 1998, 351, 173–177. [Google Scholar] [CrossRef]
  7. Roseboom, T.J. Coronary Heart Disease after Prenatal Exposure to the Dutch Famine, 1944–1945. Heart 2000, 84, 595–598. [Google Scholar] [CrossRef]
  8. Barker, D.J.P.; Godfrey, K.M.; Gluckman, P.D.; Harding, J.E.; Owens, J.A.; Robinson, J.S. Fetal Nutrition and Cardiovascular Disease in Adult Life. Lancet 1993, 341, 938–941. [Google Scholar] [CrossRef]
  9. Flexeder, C.; Thiering, E.; Brüske, I.; Koletzko, S.; Bauer, C.-P.; Wichmann, H.-E.; Mansmann, U.; von Berg, A.; Berdel, D.; Krämer, U.; et al. Growth Velocity during Infancy and Onset of Asthma in School-Aged Children: Growth Velocity and Onset of Asthma. Allergy 2012, 67, 257–264. [Google Scholar] [CrossRef]
  10. Flexeder, C.; Thiering, E.; von Berg, A.; Berdel, D.; Hoffmann, B.; Koletzko, S.; Bauer, C.-P.; Koletzko, B.; Heinrich, J.; Schulz, H. Peak Weight Velocity in Infancy Is Negatively Associated with Lung Function in Adolescence: Growth and Lung Function. Pediatr Pulmonol. 2016, 51, 147–156. [Google Scholar] [CrossRef] [Green Version]
  11. Pei, Z.; Fuertes, E.; Thiering, E.; Koletzko, B.; Cramer, C.; Berdel, D.; Lehmann, I.; Bauer, C.-P.; Heinrich, J. Early Life Risk Factors of Being Overweight at 10 Years of Age: Results of the German Birth Cohorts GINIplus and LISAplus. Eur. J. Clin. Nutr. 2013, 67, 855–862. [Google Scholar] [CrossRef] [PubMed]
  12. Von Kries, R.; Ensenauer, R.; Beyerlein, A.; Amann-Gassner, U.; Hauner, H.; Rosario, A.S. Gestational Weight Gain and Overweight in Children: Results from the Cross-Sectional German KiGGS Study. Int. J. Pediatric Obes. 2011, 6, 45–52. [Google Scholar] [CrossRef] [PubMed]
  13. Voerman, E.; Santos, S.; Patro Golab, B.; Amiano, P.; Ballester, F.; Barros, H.; Bergström, A.; Charles, M.-A.; Chatzi, L.; Chevrier, C.; et al. Maternal Body Mass Index, Gestational Weight Gain, and the Risk of Overweight and Obesity across Childhood: An Individual Participant Data Meta-Analysis. PLoS Med. 2019, 16, e1002744. [Google Scholar] [CrossRef] [PubMed]
  14. Lowensohn, R.I.; Stadler, D.D.; Naze, C. Current Concepts of Maternal Nutrition. Obstet. Gynecol. Surv. 2016, 71, 413–426. [Google Scholar] [CrossRef]
  15. Michels, K.B.; Schulze, M.B. Can Dietary Patterns Help Us Detect Diet–Disease Associations? Nutr. Res. Rev. 2005, 18, 241–248. [Google Scholar] [CrossRef]
  16. Hillesund, E.R.; Bere, E.; Haugen, M.; Øverby, N.C. Development of a New Nordic Diet Score and Its Association with Gestational Weight Gain and Fetal Growth—A Study Performed in the Norwegian Mother and Child Cohort Study (MoBa). Public Health Nutr. 2014, 17, 1909–1918. [Google Scholar] [CrossRef]
  17. Thompson, J.M.D.; Wall, C.; Becroft, D.M.O.; Robinson, E.; Wild, C.J.; Mitchell, E.A. Maternal Dietary Patterns in Pregnancy and the Association with Small-for-Gestational-Age Infants. Br. J. Nutr. 2010, 103, 1665–1673. [Google Scholar] [CrossRef]
  18. Englund-Ogge, L.; Brantsaeter, A.L.; Sengpiel, V.; Haugen, M.; Birgisdottir, B.E.; Myhre, R.; Meltzer, H.M.; Jacobsson, B. Maternal Dietary Patterns and Preterm Delivery: Results from Large Prospective Cohort Study. BMJ 2014, 348, g1446. [Google Scholar] [CrossRef]
  19. Knudsen, V.K.; Orozova-Bekkevold, I.M.; Mikkelsen, T.B.; Wolff, S.; Olsen, S.F. Major Dietary Patterns in Pregnancy and Fetal Growth. Eur. J. Clin. Nutr. 2008, 62, 463–470. [Google Scholar] [CrossRef]
  20. Okubo, H.; Miyake, Y.; Sasaki, S.; Tanaka, K.; Murakami, K.; Hirota, Y.; The Osaka Maternal and Child Health Study Group. Dietary Patterns during Pregnancy and the Risk of Postpartum Depression in Japan: The Osaka Maternal and Child Health Study. Br. J. Nutr 2011, 105, 1251–1257. [Google Scholar] [CrossRef] [Green Version]
  21. Martin, C.L.; Siega-Riz, A.M.; Sotres-Alvarez, D.; Robinson, W.R.; Daniels, J.L.; Perrin, E.M.; Stuebe, A.M. Maternal Dietary Patterns during Pregnancy Are Associated with Child Growth in the First 3 Years of Life. J. Nutr. 2016, 146, 2281–2288. [Google Scholar] [CrossRef] [PubMed]
  22. Fanos, V.; Atzori, L.; Makarenko, K.; Melis, G.B.; Ferrazzi, E. Metabolomics Application in Maternal-Fetal Medicine. BioMed. Res. Int. 2013, 2013, 720514. [Google Scholar] [CrossRef]
  23. Schlueter, R.J.; Al-Akwaa, F.M.; Benny, P.A.; Gurary, A.; Xie, G.; Jia, W.; Chun, S.J.; Chern, I.; Garmire, L.X. Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring. J. Proteome Res. 2020, 19, 1361–1374. [Google Scholar] [CrossRef] [PubMed]
  24. Lowe, W.L.; Bain, J.R.; Nodzenski, M.; Reisetter, A.C.; Muehlbauer, M.J.; Stevens, R.D.; Ilkayeva, O.R.; Lowe, L.P.; Metzger, B.E.; Newgard, C.B.; et al. Maternal BMI and Glycemia Impact the Fetal Metabolome. Dia Care 2017, 40, 902–910. [Google Scholar] [CrossRef] [PubMed]
  25. Patel, N.; Hellmuth, C.; Uhl, O.; Godfrey, K.; Briley, A.; Welsh, P.; Pasupathy, D.; Seed, P.T.; Koletzko, B.; Poston, L.; et al. Cord Metabolic Profiles in Obese Pregnant Women: Insights into Offspring Growth and Body Composition. J. Clin. Endocrinol. Metab. 2018, 103, 346–355. [Google Scholar] [CrossRef]
  26. Shokry, E.; Marchioro, L.; Uhl, O.; Bermúdez, M.G.; García-Santos, J.A.; Segura, M.T.; Campoy, C.; Koletzko, B. Impact of Maternal BMI and Gestational Diabetes Mellitus on Maternal and Cord Blood Metabolome: Results from the PREOBE Cohort Study. Acta Diabetol. 2019, 56, 421–430. [Google Scholar] [CrossRef]
  27. Ivorra, C.; García-Vicent, C.; Chaves, F.J.; Monleón, D.; Morales, J.M.; Lurbe, E. Metabolomic Profiling in Blood from Umbilical Cords of Low Birth Weight Newborns. J. Transl. Med. 2012, 10, 142. [Google Scholar] [CrossRef] [PubMed]
  28. Favretto, D.; Cosmi, E.; Ragazzi, E.; Visentin, S.; Tucci, M.; Fais, P.; Cecchetto, G.; Zanardo, V.; Viel, G.; Ferrara, S.D. Cord Blood Metabolomic Profiling in Intrauterine Growth Restriction. Anal. Bioanal. Chem. 2012, 402, 1109–1121. [Google Scholar] [CrossRef]
  29. Hellmuth, C.; Uhl, O.; Standl, M.; Demmelmair, H.; Heinrich, J.; Koletzko, B.; Thiering, E. Cord Blood Metabolome Is Highly Associated with Birth Weight, but Less Predictive for Later Weight Development. Obes. Facts 2017, 10, 85–100. [Google Scholar] [CrossRef]
  30. Sanz-Cortés, M.; Carbajo, R.J.; Crispi, F.; Figueras, F.; Pineda-Lucena, A.; Gratacós, E. Metabolomic Profile of Umbilical Cord Blood Plasma from Early and Late Intrauterine Growth Restricted (IUGR) Neonates with and without Signs of Brain Vasodilation. PLoS ONE 2013, 8, e80121. [Google Scholar] [CrossRef]
  31. Marchioro, L.; Geraghty, A.A.; Uhl, O.; Shokry, E.; O’Brien, E.C.; Koletzko, B.; McAuliffe, F.M. Effect of a Low Glycaemic Index Diet during Pregnancy on Maternal and Cord Blood Metabolomic Profiles: Results from the ROLO Randomized Controlled Trial. Nutr. Metab. 2019, 16, 59. [Google Scholar] [CrossRef]
  32. Heinrich, J.; Bolte, G.; Holscher, B.; Douwes, J.; Lehmann, I.; Fahlbusch, B.; Bischof, W.; Weiss, M.; Borte, M.; Wichmann, H.-E. Allergens and Endotoxin on Mothers’ Mattresses and Total Immunoglobulin E in Cord Blood of Neonates. Eur. Respir. J. 2002, 20, 617–623. [Google Scholar] [CrossRef] [PubMed]
  33. Glaser, C.; Demmelmair, H.; Koletzko, B. High-Throughput Analysis of Total Plasma Fatty Acid Composition with Direct In Situ Transesterification. PLoS ONE 2010, 5, e12045. [Google Scholar] [CrossRef] [PubMed]
  34. Hellmuth, C.; Weber, M.; Koletzko, B.; Peissner, W. Nonesterified Fatty Acid Determination for Functional Lipidomics: Comprehensive Ultrahigh Performance Liquid Chromatography–Tandem Mass Spectrometry Quantitation, Qualification, and Parameter Prediction. Anal. Chem. 2012, 84, 1483–1490. [Google Scholar] [CrossRef] [PubMed]
  35. Harder, U.; Koletzko, B.; Peissner, W. Quantification of 22 Plasma Amino Acids Combining Derivatization and Ion-Pair LC–MS/MS. J. Chromatogr. B 2011, 879, 495–504. [Google Scholar] [CrossRef]
  36. Sausenthaler, S.; Schaaf, B.; Lehmann, I.; Borte, M.; Herbarth, O.; von Berg, A.; Wichmann, H.-E.; Heinrich, J. Maternal Diet during Pregnancy in Relation to Eczema and Allergic Sensitization in the Offspring at 2 y of Age. Am. J. Clin. Nutr. 2007, 85, 530–537. [Google Scholar] [CrossRef]
  37. Willet, W. Nutritional Epidemiology, 2nd ed.; Oxford University Press: New York, NY, USA, 1998. [Google Scholar]
  38. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021; Available online: https://www.R-Project.Org/ (accessed on 1 October 2017).
  39. Hu, F.B. Dietary Pattern Analysis: A New Direction in Nutritional Epidemiology. Curr. Opin. Lipidol. 2002, 13, 3–9. [Google Scholar] [CrossRef]
  40. Guttman, L. Some Necessary Conditions for Common-Factor Analysis. Psychometrika 1954, 19, 149–161. [Google Scholar] [CrossRef]
  41. Kaiser, H.F. A Second Generation Little Jiffy. Psychometrika 1970, 35, 401–415. [Google Scholar] [CrossRef]
  42. Raiche, G.; Magis, D. An R Package for Parallel Analysis and Non Graphical Solutions to the Cattell Scree Test. In R Package Version 2.3.3. Edn. 2010. Available online: https://cran.r-project.org/package=nFactors (accessed on 1 October 2017).
  43. Thomson, G.H. The Factorial Analysis of Human Ability; University Press: London, UK, 1951. [Google Scholar]
  44. Newby, P.; Muller, D.; Hallfrisch, J.; Andres, R.; Tucker, K.L. Food Patterns Measured by Factor Analysis and Anthropometric Changes in Adults. Am. J. Clin. Nutr. 2004, 80, 504–513. [Google Scholar] [CrossRef] [Green Version]
  45. Schulze, M.B.; Hoffmann, K.; Kroke, A.; Boeing, H. Dietary Patterns and Their Association with Food and Nutrient Intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study. Br. J. Nutr 2001, 85, 363–373. [Google Scholar] [CrossRef]
  46. Simopoulos, A.P. Omega-3 Fatty Acids in Health and Disease and in Growth and Development. Am. J. Clin. Nutr. 1991, 54, 438–463. [Google Scholar] [CrossRef]
  47. Katan, M.B.; Deslypere, J.P.; van Birgelen, A.P.; Penders, M.; Zegwaard, M. Kinetics of the Incorporation of Dietary Fatty Acids into Serum Cholesteryl Esters, Erythrocyte Membranes, and Adipose Tissue: An 18-Month Controlled Study. J. Lipid Res. 1997, 38, 2012–2022. [Google Scholar] [CrossRef]
  48. Van Houwelingen, A.C.; Søsrensen, J.D.; Hornstra, G.; Simonis, M.M.G.; Boris, J.; Olsen, S.F.; Secher, N.J. Essential Fatty Acid Status in Neonates after Fish-Oil Supplementation during Late Pregnancy. Br. J. Nutr. 1995, 74, 723–731. [Google Scholar] [CrossRef] [PubMed]
  49. Dunstan, J.A.; Mori, T.A.; Barden, A.; Beilin, L.J.; Holt, P.G.; Calder, P.C.; Taylor, A.L.; Prescott, S.L. Effects of N-3 Polyunsaturated Fatty Acid Supplementation in Pregnancy on Maternal and Fetal Erythrocyte Fatty Acid Composition. Eur. J. Clin. Nutr. 2004, 58, 429–437. [Google Scholar] [CrossRef]
  50. Montgomery, C.; Speake, B.K.; Cameron, A.; Sattar, N.; Weaver, L.T. Maternal Docosahexaenoic Acid Supplementation and Fetal Accretion. Br. J. Nutr. 2003, 90, 135–145. [Google Scholar] [CrossRef]
  51. Malcolm, C.A. Maternal Docosahexaenoic Acid Supplementation during Pregnancy and Visual Evoked Potential Development in Term Infants: A Double Blind, Prospective, Randomised Trial. Arch. Dis. Child.—Fetal Neonatal Ed. 2003, 88, 383F–390. [Google Scholar] [CrossRef] [PubMed]
  52. Calder, P.C. Docosahexaenoic Acid. Ann. Nutr. Metab. 2016, 69, 8–21. [Google Scholar] [CrossRef]
  53. Koletzko, B.; Cetin, I.; Thomas Brenna, J. Dietary Fat Intakes for Pregnant and Lactating Women. Br. J. Nutr. 2007, 98, 873–877. [Google Scholar] [CrossRef]
  54. Muhlhausler, B.S.; Gibson, R.A.; Yelland, L.N.; Makrides, M. Heterogeneity in Cord Blood DHA Concentration: Towards an Explanation. Prostaglandins Leukot. Essent. Fat. Acids 2014, 91, 135–140. [Google Scholar] [CrossRef] [Green Version]
  55. Peng, Y.; Zhou, T.; Wang, Q.; Liu, P.; Zhang, T.; Zetterström, R.; Strandvik, B. Fatty Acid Composition of Diet, Cord Blood and Breast Milk in Chinese Mothers with Different Dietary Habits. Prostaglandins Leukot. Essent. Fat. Acids 2009, 81, 325–330. [Google Scholar] [CrossRef] [PubMed]
  56. Hornstra, G. Essential Fatty Acids in Mothers and Their Neonates. Am. J. Clin. Nutr. 2000, 71, 1262S–1269S. [Google Scholar] [CrossRef] [PubMed]
  57. Platell, C.; Kong, S.; McCauley, R.; Hall, J.C. Branched-chain Amino Acids. J. Gastroenterol. Hepatol. 2000, 15, 706–717. [Google Scholar] [CrossRef] [PubMed]
  58. Cheung, W.; Keski-Rahkonen, P.; Assi, N.; Ferrari, P.; Freisling, H.; Rinaldi, S.; Slimani, N.; Zamora-Ros, R.; Rundle, M.; Frost, G.; et al. A Metabolomic Study of Biomarkers of Meat and Fish Intake. Am. J. Clin. Nutr. 2017, 105, 600–608. [Google Scholar] [CrossRef]
  59. Bouchard-Mercier, A.; Rudkowska, I.; Lemieux, S.; Couture, P.; Vohl, M.-C. The Metabolic Signature Associated with the Western Dietary Pattern: A Cross-Sectional Study. Nutr. J. 2013, 12, 158. [Google Scholar] [CrossRef]
  60. Kirchberg, F.F.; Harder, U.; Weber, M.; Grote, V.; Demmelmair, H.; Peissner, W.; Rzehak, P.; Xhonneux, A.; Carlier, C.; Ferre, N.; et al. Dietary Protein Intake Affects Amino Acid and Acylcarnitine Metabolism in Infants Aged 6 Months. J. Clin. Endocrinol. Metab. 2015, 100, 149–158. [Google Scholar] [CrossRef]
  61. Sánchez-Pintos, P.; de Castro, M.-J.; Roca, I.; Rite, S.; López, M.; Couce, M.-L. Similarities between Acylcarnitine Profiles in Large for Gestational Age Newborns and Obesity. Sci. Rep. 2017, 7, 16267. [Google Scholar] [CrossRef]
  62. Rousseau, M.; Guénard, F.; Garneau, V.; Allam-Ndoul, B.; Lemieux, S.; Pérusse, L.; Vohl, M.-C. Associations Between Dietary Protein Sources, Plasma BCAA and Short-Chain Acylcarnitine Levels in Adults. Nutrients 2019, 11, 173. [Google Scholar] [CrossRef]
  63. McNeill, S.H. Inclusion of Red Meat in Healthful Dietary Patterns. Meat Sci. 2014, 98, 452–460. [Google Scholar] [CrossRef]
  64. Maslova, E.; Hansen, S.; Grunnet, L.G.; Strøm, M.; Bjerregaard, A.A.; Hjort, L.; Kampmann, F.B.; Madsen, C.M.; Baun Thuesen, A.; Bech, B.H.; et al. Maternal Protein Intake in Pregnancy and Offspring Metabolic Health at Age 9–16 y: Results from a Danish Cohort of Gestational Diabetes Mellitus Pregnancies and Controls. Am. J. Clin. Nutr. 2017, 106, 623–636. [Google Scholar] [CrossRef] [Green Version]
  65. Maslova, E.; Rytter, D.; Bech, B.H.; Henriksen, T.B.; Rasmussen, M.A.; Olsen, S.F.; Halldorsson, T.I. Maternal Protein Intake during Pregnancy and Offspring Overweight 20 y Later. Am. J. Clin. Nutr. 2014, 100, 1139–1148. [Google Scholar] [CrossRef] [PubMed]
  66. Dawson-Hughes, B.; Harris, S.S.; Rasmussen, H.M.; Dallal, G.E. Comparative Effects of Oral Aromatic and Branched-Chain Amino Acids on Urine Calcium Excretion in Humans. Osteoporos Int. 2007, 18, 955–961. [Google Scholar] [CrossRef] [PubMed]
  67. Koletzko, B.; Brands, B.; Grote, V.; Kirchberg, F.F.; Prell, C.; Rzehak, P.; Uhl, O.; Weber, M. Long-Term Health Impact of Early Nutrition: The Power of Programming. Ann. Nutr. Metab. 2017, 70, 161–169. [Google Scholar] [CrossRef]
  68. Cao, H.; Gerhold, K.; Mayers, J.R.; Wiest, M.M.; Watkins, S.M.; Hotamisligil, G.S. Identification of a Lipokine, a Lipid Hormone Linking Adipose Tissue to Systemic Metabolism. Cell 2008, 134, 933–944. [Google Scholar] [CrossRef]
  69. Song, Z.; Xiaoli, A.; Yang, F. Regulation and Metabolic Significance of De Novo Lipogenesis in Adipose Tissues. Nutrients 2018, 10, 1383. [Google Scholar] [CrossRef] [PubMed]
  70. Stefan, N.; Kantartzis, K.; Celebi, N.; Staiger, H.; Machann, J.; Schick, F.; Cegan, A.; Elcnerova, M.; Schleicher, E.; Fritsche, A.; et al. Circulating Palmitoleate Strongly and Independently Predicts Insulin Sensitivity in Humans. Diabetes Care 2010, 33, 405–407. [Google Scholar] [CrossRef]
  71. Menni, C.; Fauman, E.; Erte, I.; Perry, J.R.B.; Kastenmuller, G.; Shin, S.-Y.; Petersen, A.-K.; Hyde, C.; Psatha, M.; Ward, K.J.; et al. Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach. Diabetes 2013, 62, 4270–4276. [Google Scholar] [CrossRef]
  72. Newgard, C.B. Interplay between Lipids and Branched-Chain Amino Acids in Development of Insulin Resistance. Cell Metab. 2012, 15, 606–614. [Google Scholar] [CrossRef]
  73. Koves, T.R.; Ussher, J.R.; Noland, R.C.; Slentz, D.; Mosedale, M.; Ilkayeva, O.; Bain, J.; Stevens, R.; Dyck, J.R.B.; Newgard, C.B.; et al. Mitochondrial Overload and Incomplete Fatty Acid Oxidation Contribute to Skeletal Muscle Insulin Resistance. Cell Metab. 2008, 7, 45–56. [Google Scholar] [CrossRef]
  74. Batchuluun, B.; Al Rijjal, D.; Prentice, K.J.; Eversley, J.A.; Burdett, E.; Mohan, H.; Bhattacharjee, A.; Gunderson, E.P.; Liu, Y.; Wheeler, M.B. Elevated Medium-Chain Acylcarnitines Are Associated with Gestational Diabetes Mellitus and Early Progression to Type 2 Diabetes and Induce Pancreatic β-Cell Dysfunction. Diabetes 2018, 67, 885–897. [Google Scholar] [CrossRef] [Green Version]
  75. Newgard, C.B.; An, J.; Bain, J.R.; Muehlbauer, M.J.; Stevens, R.D.; Lien, L.F.; Haqq, A.M.; Shah, S.H.; Arlotto, M.; Slentz, C.A.; et al. A Branched-Chain Amino Acid-Related Metabolic Signature That Differentiates Obese and Lean Humans and Contributes to Insulin Resistance. Cell Metab. 2009, 9, 311–326. [Google Scholar] [CrossRef] [PubMed]
  76. Mai, M.; Tönjes, A.; Kovacs, P.; Stumvoll, M.; Fiedler, G.M.; Leichtle, A.B. Serum Levels of Acylcarnitines Are Altered in Prediabetic Conditions. PLoS ONE 2013, 8, e82459. [Google Scholar] [CrossRef] [PubMed]
  77. Floegel, A.; von Ruesten, A.; Drogan, D.; Schulze, M.B.; Prehn, C.; Adamski, J.; Pischon, T.; Boeing, H. Variation of Serum Metabolites Related to Habitual Diet: A Targeted Metabolomic Approach in EPIC-Potsdam. Eur. J. Clin. Nutr. 2013, 67, 1100–1108. [Google Scholar] [CrossRef] [PubMed]
  78. Dewettinck, K.; Rombaut, R.; Thienpont, N.; Le, T.T.; Messens, K.; Van Camp, J. Nutritional and Technological Aspects of Milk Fat Globule Membrane Material. Int. Dairy J. 2008, 18, 436–457. [Google Scholar] [CrossRef]
  79. Gaspardo, B.; Lavrenčič, A.; Levart, A.; Del Zotto, S.; Stefanon, B. Use of Milk Fatty Acids Composition to Discriminate Area of Origin of Bulk Milk. J. Dairy Sci. 2010, 93, 3417–3426. [Google Scholar] [CrossRef] [PubMed]
  80. Lemaitre, R.N.; King, I.B. Very Long-Chain Saturated Fatty Acids and Diabetes and Cardiovascular Disease. Curr. Opin. Lipidol. 2022, 33, 76–82. [Google Scholar] [CrossRef]
  81. Ohno, Y.; Suto, S.; Yamanaka, M.; Mizutani, Y.; Mitsutake, S.; Igarashi, Y.; Sassa, T.; Kihara, A. ELOVL1 Production of C24 Acyl-CoAs Is Linked to C24 Sphingolipid Synthesis. Proc. Natl. Acad. Sci. USA 2010, 107, 18439–18444. [Google Scholar] [CrossRef]
  82. Rauschert, S.; Gázquez, A.; Uhl, O.; Kirchberg, F.F.; Demmelmair, H.; Ruíz-Palacios, M.; Prieto-Sánchez, M.T.; Blanco-Carnero, J.E.; Nieto, A.; Larqué, E.; et al. Phospholipids in Lipoproteins: Compositional Differences across VLDL, LDL, and HDL in Pregnant Women. Lipids Health Dis 2019, 18, 20. [Google Scholar] [CrossRef]
  83. Brizzi, P.; Tonolo, G.; Esposito, F.; Puddu, L.; Dessole, S.; Maioli, M.; Milia, S. Lipoprotein Metabolism during Normal Pregnancy. Am. J. Obstet. Gynecol. 1999, 181, 430–434. [Google Scholar] [CrossRef]
  84. Herrera, E. Lipid Metabolism in Pregnancy and Its Consequences in the Fetus and Newborn. ENDO 2002, 19, 43–56. [Google Scholar] [CrossRef]
  85. van Echten-Deckert, G.; Herget, T. Sphingolipid Metabolism in Neural Cells. Biochim. Biophys. Acta (BBA)—Biomembr. 2006, 1758, 1978–1994. [Google Scholar] [CrossRef]
  86. Tanaka, K.; Hosozawa, M.; Kudo, N.; Yoshikawa, N.; Hisata, K.; Shoji, H.; Shinohara, K.; Shimizu, T. The Pilot Study: Sphingomyelin-Fortified Milk Has a Positive Association with the Neurobehavioural Development of Very Low Birth Weight Infants during Infancy, Randomized Control Trial. Brain Dev. 2013, 35, 45–52. [Google Scholar] [CrossRef] [PubMed]
  87. Haug, A.; Høstmark, A.T.; Harstad, O.M. Bovine Milk in Human Nutrition—A Review. Lipids Health Dis 2007, 6, 25. [Google Scholar] [CrossRef] [PubMed]
  88. Roopashree, P.G.; Shetty, S.S.; Suchetha Kumari, N. Effect of Medium Chain Fatty Acid in Human Health and Disease. J. Funct. Foods 2021, 87, 104724. [Google Scholar] [CrossRef]
  89. Visentin, S.; Crotti, S.; Donazzolo, E.; D’Aronco, S.; Nitti, D.; Cosmi, E.; Agostini, M. Medium Chain Fatty Acids in Intrauterine Growth Restricted and Small for Gestational Age Pregnancies. Metabolomics 2017, 13, 54. [Google Scholar] [CrossRef]
  90. Bobiński, R.; Mikulska, M.; Mojska, H.; Ulman-Włodarz, I. The Dietary Composition of Women Who Delivered Healthy Full-Term Infants, Preterm Infants, and Full-Term Infants Who Were Small for Gestational Age. Biol. Res. Nurs. 2015, 17, 495–502. [Google Scholar] [CrossRef]
  91. Jansson, T. Amino Acid Transporters in the Human Placenta. Pediatr Res. 2001, 49, 141–147. [Google Scholar] [CrossRef]
  92. Mun, J.G.; Legette, L.L.; Ikonte, C.J.; Mitmesser, S.H. Choline and DHA in Maternal and Infant Nutrition: Synergistic Implications in Brain and Eye Health. Nutrients 2019, 11, 1125. [Google Scholar] [CrossRef]
  93. Willett, W.C. Future Directions in the Development of Food-Frequency Questionnaires. Am. J. Clin. Nutr. 1994, 59, 171S–174S. [Google Scholar] [CrossRef]
Table
Table 1. Study population characteristics.
Table 1. Study population characteristics.
Nn (%) or Mean ± SD
Maternal age (years)73932.4 ± 4.1
Maternal, pre-pregnancy BMI (kg/m2)72522.5 ± 4.1
  Overweight (≥25)725124 (17.1)
  Normal (≥18.5 and <25)725558 (77)
  Underweight (<18.5)72543 (5.9)
Maternal education
  Low (<10 years)73371 (9.7)
  Medium (10 years)733232 (31.7)
  High (>10 years)733430 (58.7)
Gestational age (weeks)73040 ± 1.2
Gestational weight gain (kg/month)7150.4 ± 0.1
Birth weight (kg)7393.5 ± 0.4
City
  Munich739568 (76.9)
  Bad Honnef739171 (23.1)
Sex
  Female739395 (53.5)
  Male739344 (46.5)
Smoking, during 3rd trimester
  Yes70574 (10.5)
  No705631 (89.5)
Table
Table 2. Food item factor loadings from factor analysis.
Table 2. Food item factor loadings from factor analysis.
Food Items in FFQFactor
1		Factor
2		Factor
3		Factor
4		Factor
5		Factor
6		Factor
7		Factor
8		Factor
9		Factor
10
Milk, buttermilk 0.183
Yoghurt 0.203 0.388 *
Cheese 0.129 0.406 * 0.146
Cream, sour cream, crème fraiche, coffee cream 0.2260.1240.1720.2140.138
Butter 0.834 *
Margarine −0.155 −0.679 *0.241
Vegetable oil (not olive) 0.349 *0.179 0.109
Oily fruits and seeds 0.1940.400 * −0.140 0.212
Vegetable cooking fat 0.146
Nuts0.116 0.334 * 0.1310.238
Chocolate 0.2060.287−0.1480.218
Liver 0.179
Liver sausage, pate −0.101 0.368 *
Pork 0.488 *0.109
Fish0.152 0.1330.203 0.225 0.398 *
Seafood, shellfish 0.124 −0.109 0.490 *
Canned fish, smoked fish 0.122 0.192 0.332 *
Boiled potatoes0.391 * 0.109 0.393 *
Fried potatoes, chips0.116 0.147−0.129 0.2260.245
Vegetable juice0.2340.135 −0.115 0.198
Raw carrots0.358 *0.1720.154 0.254 −0.101−0.131
Carrots0.659 *0.136
Spinach, Swiss chard0.471 * 0.183
Cooked vegetables 0.482 * 0.198
Celery0.304 * 0.132 0.112 0.2120.113
Vegetables 0.370 * 0.117 0.169
Raw tomatoes 0.1560.479 *0.1860.219 0.119−0.222
Raw sweet pepper 0.1400.887 *0.1070.137
Cooked sweet pepper0.285 0.500 * 0.1090.141
Lettuce 0.1120.1580.719 *0.1410.145−0.109
Mayonnaise, salad dressing 0.637 *
Juices 0.363 *
Citrus fruits 0.547 * 0.1320.339 *
Apple0.1580.441 * 0.243
Kiwi, pineapple, mango0.1820.547 * 0.115 0.168
Banana0.1080.463 * 0.200 −0.103
Strawberry 0.1680.159 0.2340.106−0.493 *
Fruit syrup, juice concentrate 0.233
Cake 0.357 * 0.150
Fruit cake 0.1160.478 *
Gingerbread (Lebkuchen) 0.131 0.1010.486 *
Sweet dairy foods 0.143 0.460 * −0.144
Eggs 0.152 0.2720.114
Soy milk, soy products 0.108 −0.110 0.105
Cereals 0.145 0.1310.468 * −0.202
Variance Explained (%)3.93.33.23.13.02.92.62.22.01.9
Cumulative Variance (%)3.97.210.413.516.519.422.024.226.228.2
Values less than 0.1 were omitted in order to observe noteworthy factor loadings. * Factor loadings ≥ |0.3|. Factor 1 = Vegetables; Factor 2 = Fruits; Factor 3 = Summer vegetables; Factor 4 = Salad and dressings; Factor 5 = Cereal, seeds, nuts, yoghurt, cheese; Factor 6 = Butter vs. margarine; Factor 7 = Meat and potato; Factor 8 = Sweets; Factor 9 = Fish and shellfish; Factor 10 = Seasonal.
Table
Table 3. Association of dietary patterns with cord blood metabolites.
Table 3. Association of dietary patterns with cord blood metabolites.
Crude ModelMain ModelMain Model +Main Model ++
Dietary PatternMetabolitenbetasepnbetasepnbetasepnbetasep
VegetablesPCa C38:57340.0560.0170.0016890.0600.0170.0016740.0530.0170.0036740.0520.0180.003
PCe C36:37350.0680.0200.0016900.0710.0210.0016750.0670.0210.0026750.0650.0210.002
PCe C36:47370.0600.0180.0016920.0630.0190.0016770.0570.0190.0036770.0560.0190.004
Val7340.0330.0110.0046890.0380.0120.0016740.0390.0120.0016740.0390.0120.001
His7370.0550.015<0.0016920.0510.0160.0016770.0440.0160.0066770.0410.0160.009
C22:5 n67340.0660.0210.0026900.0660.0220.0026750.0610.0220.0066750.0560.0220.010
Lys6100.0400.0150.0075730.0420.0150.0055600.0370.0150.0135600.0380.0150.013
PCe C34:17370.0590.0190.0026920.0560.0200.0066770.0500.0210.0156770.0490.0210.018
SMa C30:15860.1060.0340.0025500.0930.0350.0085390.0860.0360.0175390.0880.0360.014
FruitsPCa C42:1385−0.1210.033<0.001365−0.1090.0340.002357−0.1040.0350.003357−0.1050.0350.003
SMa C42:5434−0.1120.0390.004410−0.1220.0410.003404−0.1160.0410.005404−0.1170.0410.005
H1719−0.1690.0650.010674−0.1860.0680.006659−0.2090.0690.002659−0.2060.0690.003
C20:4 n6732−0.0360.0120.003688−0.0340.0130.007673−0.0350.0130.006673−0.0350.0130.006
AC C10:1650−0.0840.0270.002616−0.0750.0280.008602−0.0730.0290.011602−0.0750.0290.009
C18:2 n6733−0.0400.0140.006689−0.0370.0150.014674−0.0420.0150.007674−0.0440.0150.004
LPCa C20:4735−0.0710.0210.001690−0.0520.0220.017675−0.0520.0220.018675−0.0460.0220.032
Met7360.0470.0170.0046910.0400.0170.0196760.0370.0170.0326760.0360.0170.035
PCa C38:4737−0.0520.0180.004692−0.0420.0180.024677−0.0400.0190.032677−0.0420.0190.026
SummerPCa C40:5738−0.0650.0230.004693−0.0670.0230.004678−0.0640.0240.007678−0.0620.0230.008
VegetablesPCe C38:3730−0.0760.0240.001685−0.0670.0240.006670−0.0630.0240.011670−0.0640.0240.009
PCe C38:2726−0.0930.0320.004681−0.0810.0320.012666−0.0760.0320.020666−0.0760.0320.019
SaladGln715−0.0970.027<0.001674−0.0810.0280.004660−0.0770.0280.006660−0.0770.0280.006
andLPCa C18:27360.0680.0220.0026910.0610.0220.0066760.0640.0230.0056760.0680.0220.002
DressingsC18:2 n67330.0470.0140.0016890.0400.0150.0076740.0380.0150.0146740.0370.0150.016
AC C16:0738−0.0850.0280.003693−0.0770.0290.008678−0.0670.0300.024678−0.0670.0300.024
PCa C36:27350.0640.0190.0016900.0520.0200.0106750.0530.0210.0106750.0530.0210.011
LPCa C18:36040.0920.0350.0095760.0870.0360.0165640.0950.0360.0105640.1050.0360.004
Orn736−0.0640.0220.004691−0.0400.0210.056676−0.0430.0210.045676−0.0430.0210.044
Gly738−0.0480.0170.005693−0.0320.0170.059678−0.0310.0170.070678−0.0310.0170.070
Met736−0.0510.0170.002691−0.0300.0170.071676−0.0300.0170.078676−0.0300.0170.075
Cereals,SMa C30:15860.1280.036<0.001 *5500.1360.038<0.0015390.1410.039<0.0015390.1430.039<0.001 *
Seeds, Nuts,SMa C43:26890.0870.025<0.001 *6500.0800.0270.0036370.0740.0270.0076370.0740.0270.007
Yogurt,AC C2:0735−0.0730.0220.001690−0.0650.0240.006675−0.0610.0240.012675−0.0610.0240.012
CheeseSMa C35:17370.0560.0190.0046920.0570.0210.0086770.0510.0210.0176770.0510.0210.017
SMa C33:17370.0550.0190.0046920.0510.0200.0136770.0460.0210.0266770.0460.0210.026
LPCe C16:06920.0800.0260.0026540.0600.0280.0326410.0540.0280.0566410.0520.0280.061
ButterAC C8:1736−0.1430.038<0.001 *692−0.1420.0410.001677−0.1200.0420.004677−0.1180.0420.005
vs.SMa C39:17340.0780.0230.001 *6890.0810.0240.0016740.0750.0250.0036740.0760.0250.002
MargarineNEFA C22:3565−0.0540.0260.035534−0.0810.0270.002523−0.0700.0270.010523−0.0710.0270.010
SMa C43:26890.0790.0250.002 *6500.0800.0270.0036370.0740.0270.0076370.0740.0270.007
LPCe C18:07330.0980.026<0.001 *6880.0800.0280.0046730.0660.0280.0196730.0660.0280.019
C15:17250.0770.0270.0046810.0800.0280.0046660.0660.0280.0206660.0670.0280.019
LPCa C18:3604−0.1170.0360.001 *576−0.1060.0380.005564−0.1180.0390.002564−0.1220.0380.001
LPCe C16:06920.0920.0260.001 *6540.0720.0280.0106410.0720.0280.0116410.0710.0280.012
NEFA C17:07340.0760.021<0.001 *6900.0570.0220.0116750.0570.0230.0126750.0580.0230.010
NEFA C15:07340.1100.0320.001 *6900.0800.0330.0166750.0770.0340.0236750.0780.0340.022
NEFA C19:17360.0790.0260.0026910.0600.0270.0286760.0610.0280.0276760.0620.0280.025
MeatAC C3:07370.1100.026<0.001 *6920.1270.027<0.001 *6770.1220.027<0.001 *6770.1220.027<0.001 *
andC16:1 n7732−0.0520.0160.001689−0.0570.0170.001674−0.0540.0180.002674−0.0540.0180.002
PotatoTrp7330.0420.0140.0026880.0450.0150.0026730.0430.0150.0046730.0430.0150.004
Gln715−0.0890.0290.003674−0.0600.0300.045660−0.0570.0300.060660−0.0570.0300.060
C18:1 n9732−0.0330.0110.003688−0.0330.0120.007673−0.0340.0120.006673−0.0340.0120.006
SMa C32:2737−0.0730.0250.004692−0.0540.0260.039677−0.0450.0270.087677−0.0440.0260.093
SweetsNEFA C24:2540−0.0660.0220.003505−0.0690.0240.004494−0.0650.0240.007494−0.0680.0240.004
FishC22:5 n6734−0.0840.022<0.001 *690−0.0990.024<0.001 *675−0.0950.024<0.001 *675−0.0860.024<0.001
andC20:5 n37290.1180.028<0.001 *6850.1050.0310.0016700.1090.0320.0016700.1030.0310.001
ShellfishC22:6 n37340.0510.0170.0036900.0480.0180.0086750.0500.0180.0076750.0470.0180.011
AC C18:16930.0910.0320.0046550.0830.0330.0136420.0770.0330.0216420.0750.0330.025
NEFA C12:0736−0.1190.0420.005691−0.0980.0460.034676−0.0910.0460.049676−0.0960.0460.039
SeasonalNEFA C22:26350.0740.0240.0035980.0860.0250.0015860.0910.025<0.0015860.0910.025<0.001
Ser7360.0570.0200.0046910.0680.0200.0016760.0690.0200.0016760.0690.0200.001
NEFA C26:36370.0530.0210.0115980.0640.0210.0035850.0610.0210.0045850.0610.0210.004
NEFA C20:26430.0710.0290.0156060.0860.0300.0045930.0910.0300.0035930.0910.0300.003
C20:1 n96940.0790.0270.0036510.0730.0280.0086360.0700.0280.0136360.0700.0280.013
Values are presented as beta coefficients (beta) and their corresponding standard error (se). Crude model: adjusted for batch; Main model: Crude model further adjusted for city, sex, gestational age, smoking, maternal education, and maternal age; Main model +: Main model further adjusted for maternal BMI and maternal gestational weight gain; Main model ++: Main model + further adjusted for birth weight. p = p-value. * Significant p-values after FDR correction for multiple hypothesis testing are highlighted in bold and marked with an asterisk.
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Harris, C.P.; Ramlochansingh, C.; Uhl, O.; Demmelmair, H.; Heinrich, J.; Koletzko, B.; Standl, M.; Thiering, E. Association of Maternal Diet during Pregnancy and Metabolite Profile in Cord Blood. Biomolecules 2022, 12, 1333. https://doi.org/10.3390/biom12101333

AMA Style

Harris CP, Ramlochansingh C, Uhl O, Demmelmair H, Heinrich J, Koletzko B, Standl M, Thiering E. Association of Maternal Diet during Pregnancy and Metabolite Profile in Cord Blood. Biomolecules. 2022; 12(10):1333. https://doi.org/10.3390/biom12101333

Chicago/Turabian Style

Harris, Carla P., Carlana Ramlochansingh, Olaf Uhl, Hans Demmelmair, Joachim Heinrich, Berthold Koletzko, Marie Standl, and Elisabeth Thiering. 2022. "Association of Maternal Diet during Pregnancy and Metabolite Profile in Cord Blood" Biomolecules 12, no. 10: 1333. https://doi.org/10.3390/biom12101333

APA Style

Harris, C. P., Ramlochansingh, C., Uhl, O., Demmelmair, H., Heinrich, J., Koletzko, B., Standl, M., & Thiering, E. (2022). Association of Maternal Diet during Pregnancy and Metabolite Profile in Cord Blood. Biomolecules, 12(10), 1333. https://doi.org/10.3390/biom12101333

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