Statistical analysis

All statistical analysis were performed using SPSS (SPSS Inc, Chicago, IL, USA) for Windows, version 25.0.

The normality of continuous variables was checked with the Kolmogorov- Smirnov test. Variables not normally distributed were natural log transformed when necessary, to meet the necessary criteria for the application of parametric statistical tests. Descriptive characteristics comprising geometric means (95% CI) or median (P10-P90) and relative frequencies (95% CI), where applicable, are reported for all the participants. Genotype and allele frequencies (95% CI) of the studied genetic variants are also reported. Confidence intervals of categorical variables expressed in % are calculated using the “CIA” programme (Southampton, UK). The McNemar test was used to compare the prevalence of plasma and red blood cell folate deficiency between different time points of pregnancy (<12, 15, 24-27, 34 GW, labour). Post-hoc Bonferroni correction for multiple comparisons of the P values was applied. ANOVA for repeated measures was performed to compare means between different pregnancy time points (<12, 15, 24-27, 34 GW, labour) and post-hoc Bonferroni correction for multiple comparisons of the P values was applied. Maternal, paternal and cord 1C metabolism status (plasma folate, erythrocyte folate, total plasma homocysteine and plasma cobalamin) were compared between the different genotypes for MTHFR 677C>T and SLC19A1 80 G>A, as well as the combination of both. Statistical significance was accepted from p. values <0.05. Percentages (95% CI) of the outcomes included in the study and according to genotypes are also reported.

BMI was calculated as weight (kg) divided by height squared (m²). Smoking pattern was categorized by women who smoke throughout pregnancy versus never or women who smoke in the first trimester versus never. Previous

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pregnancy was categorized as confirmed previous live newborn or none. Socioeconomic status was categorized as low versus mid-high status considering maternal and paternal status together. Variables of the polymorphisms were used comparing the homozygote normal versus the heterozygote or the homozygote genotypes: CC vs CT or TT for MTHFR 677 C>T and GG vs GA or AA for SLC19A1 80G>A. The variable used as control for the three clinical outcomes was created with participants without any pregnancy complication such as gestational hypertension, gestational diabetes, IUGR, etc.

The 3 main outcomes studied were: pathological doppler of uterine arteries at 20GW, intrauterine growth restriction and pregnancy induced hypertension. Multiple linear and logistic regressions were used to examine the associations between maternal and paternal genetic, nutritional and metabolic components of 1C metabolism and continuous and categorical variables associated with the development of pregnancy outcomes and complications respectively.

Interaction terms were calculated to assess possible interactions between independent variables, in their relationship with the dependent variable of interest. The product of the independent variables being tested was calculated and included in the linear or logistic regression analysis. When the interaction term was significant, we proceeded to perform the regression analysis on stratified sets of data to account for the interaction.

Multiple linear regression analysis was performed to identify maternal and paternal predictors of pulsatility index of uterine arteries (dependent variable, natural log transformed). The models will be described in detail in the results section. Generally, for each analysis we designed 2 models. The first model explored the association of the 1CM variables of interest and pulsatility index.

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For the genotype models, this included the MTHFR C677T and SLC19A1 G80A genotypes as well as first trimester plasma cobalamin and red blood cell folate status (low tertile versus the others). Subsequently maternal clinical and lifestyle factors (smoking during pregnancy versus never, socioeconomic status, age, BMI, late pregnancy anaemia (3rd _{trimester haemoglobin <11} g/dL), previous pregnancies longer than 20 GW versus none, gestational age (weeks at birth and sex of the baby) were added to form the complete model. In the tHcy models, only tHcy (representing overall 1CM status) at the corresponding time of pregnancy was included in the first model. Subsequently, the next model was adjusted for the same maternal clinical and lifestyle factors described for the complete 1CM model above.

To study the involvement of paternal factors, we added the paternal variables to the complete maternal models described above. Therefore, paternal

MTHFR 677C>T genotype, cobalamin and red blood cell folate status and

paternal tHcy ≥P90 were included in the corresponding models.

Multiple logistic regression analysis was used to identify maternal and paternal factors associated with Pathological doppler of uterine arteries at 20 GW (dependent variable). The models to examine the association between genotypes and impaired placentation included MTHFR 677C>T and SLC19A1 80G>A genotype, red blood cell folate and plasma cobalamin concentrations at <12 and 15 GW. In the models focused on the association between tHcy and impaired placentation, the categorical tHcy variable (≥P90 tHcy in early pregnancy versus other percentiles) replaced the genotypes and red blood cell folate and plasma cobalamin variables in each model, at <12 and 15 GW. The models were adjusted for smoking habit, socioeconomic status, 1st _trimester BMI, age, parity and gestational age at doppler measurement. Adjusting for study centre did not change the results in any of the models so it was not included.

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We checked for outliers and influential cases (Cook’s distance >4/n) and excluded them from the models when identified.

Paternal factors were also studied in the logistic regressions. Paternal MTHFR 677C>T genotype, smoking habit, age, red blood cell folate concentrations and elevated paternal tHcy levels (≥P90) were included in the same models used for the mothers.

Multiple linear regression analysis was performed to identify maternal and paternal predictors of birthweight (dependent variable) with a similar strategy to those described for uterine artery pulsatility index above. In all multiple linear regression analyses, participants with gestational diabetes were excluded due to its potential effect on birth weight.

Multiple logistic regression analysis was used to identify maternal and paternal factors associated with IUGR (dependent variable) applying a similar strategy as described for pathological doppler above. In this case we did not exclude participants with gestational diabetes to avoid loss of statistical power.

Multiple logistic regressions analysis was used to study the association between maternal and paternal genetic and 1CM related factors and gestational hypertension. We excluded pregnancies affected by gestational diabetes or intrauterine growth retardation from the analysis and used similar model designs as described above for pathological Doppler and IUGR.

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111 4. Results