Effects of Mediterranean Diet During Pregnancy on the Onset of Overweight or Obesity in the Offspring: A Randomized Trial

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Mediterranean Diet (MD) is one of the healthiest dietary models exerting protective effects against excess weight. To date, the evidence on the MD-effects during pregnancy for the prevention of childhood overweight/obesity are scarce and based on observational studies. The Me diterranean Di et during Pre gnancy (PREMEDI) trial has been designed to evaluate the efficacy of a nutritional counseling aimed at promoting MD-adherence during pregnancy on the occurrence of overweight or obesity at 24 months in the offspring. Methods The PREMEDI was a randomized-controlled, parallel groups, prospective trial. 104 women in their first trimester of pregnancy were randomly assigned to standard obstetrical and gynecological care alone (CT group, n=52) or plus a nutritional counseling promoting MD (MD group, n=52). 5 women in the MD arm and 2 women in the CT arm were lost to follow-up. Women enrolled in the MD group were provided 3 session of nutritional counseling (one session for trimester). The primary outcome was the proportion of overweight or obesity at 24 months. Other outcomes included maternal MD-adherence, maternal weight gain, and epigenetic modulation of genes involved in metabolic pathways. Results A lower proportion of overweight or obesity was observed at 24 months in children of MD-arm mothers compared to those in the CT arm (6% vs. 33%, absolute risk difference=-27%, 95%CI -41% to -12%, intention to treat analysis, p<0.001; number needed to treat 3, 95%CI 2 to 8). This effect was associated with a higher DNA methylation rate of the leptin gene in cord blood (30.4% [1.02 SD] vs. 16.9% [2.99 SD], MD vs. CT arm, p<0.0001). Conclusions MD during pregnancy is an effective strategy to prevent pediatric overweight/obesity at 24 months. This effect could be mediated, at least in part, by an epigenetic modulation of leptin expression. Health sciences/Diseases/Nutrition disorders/Obesity Biological sciences/Physiology/Metabolism/Fat metabolism Randomized controlled trial Mediterranean Diet Pregnancy Children Pediatric Obesity Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The current epidemic of pediatric obesity is a major public health issue, advocating the need of preventive strategies to limit the associated burden of disease (1). The first stages of life represent a window of opportunity to prevent the occurrence of overweight or obesity and to achieve long-term health outcomes (2). Nutritional exposures during this critical period of life are potentially modifiable factors that can influence the future disease susceptibility (3). Maternal diet during pregnancy has been linked to offspring overweight/obesity risk and could represent a potential target of intervention for disease prevention (4). The Mediterranean Diet (MD) is one of the healthiest dietary patterns, providing considerable amounts of fiber, antioxidants, polyphenols, vitamins, and a balanced ratio of essential fatty acids, which impacts beneficially the overall health and protects against the occurrence of excess weight in adults (5). Emerging evidence suggests that MD during pregnancy may protect against overweight or obesity in the offspring (6). Maternal diet could affect fetal programming by epigenetic modulation of gene expression, with long-lasting effects on human health (6-8). To date, the evidence on the effects of MD during pregnancy for the prevention of childhood overweight/obesity are scarce and based on observational studies (9-11). The Me diterranean Di et during Pre gnancy (PREMEDI) trial is a randomized controlled trial (RCT) aimed at addressing such limitation. METHODS Trial design PREMEDI was a randomized, parallel-group, controlled trial aimed at evaluating the effects of MD during pregnancy on the prevention of overweight/obesity at 24 months in the offspring. The trial was approved by the Ethics Committee of the University of Naples “Federico II” and was performed in accordance with the Helsinki Declaration and with the relevant European and Italian privacy regulations. Written informed consent to participate in the study was obtained from the women. No financial compensation was provided. The study was registered on ClinicalTrials.gov (Identifier: NCT03337802). The study was performed at the Evangelical Hospital Villa Betania in Naples, Italy from 30/11/2017 to 31/01/2021. The study is reported following the Consolidated Standards of Reporting Trials (CONSORT) guidelines. Participants Pregnant women in their first trimester of pregnancy were eligible for the study. Inclusion criteria were Caucasian ethnicity and age range of 20 to 35 years. We excluded women with proven infections during pregnancy, twin pregnancy, malignancies, gastrointestinal tract malformations, immunodeficiency, diabetes mellitus and other chronic diseases; chronic intestinal inflammatory diseases; gastrointestinal functional disorders; celiac disease; a history of abdominal surgery; neuropsychiatric disorders; and use of a vegan diet. Intervention The women were randomly allocated in a 1:1 ratio to the control (CT) and MD arms. The CT arm received standard obstetrical and gynecological care; the MD arm received standard obstetrical and gynecological care, plus personalized MD nutritional counseling provided by certified dietitians. Women enrolled in the CT group were provided standard-of-care recommendations by the study gynecologists. Such recommendations included energy intake, physical activity, optimal weight gain during pregnancy based on pre-pregnancy weight (12), and hygiene rules for food-related illnesses (13). Women enrolled in the MD group were provided personalized nutritional counseling by certified dietitians. Nutritional counseling was performed in three face-to-face sessions at enrollment (8–13 gestational weeks), 3 months (14–28 gestational weeks) and 6 months after the enrollment (29–40 gestational weeks). Nutritional counseling was based on the following recommendations: use of extra virgin olive oil as the main cooking fat (at least 4 tablespoons/day); intake of 2 servings/day of vegetables; intake of 3 servings/day of fruit, avoiding juices; intake of 3 servings/day of wholegrain cereals; intake of 3 servings/day of skimmed dairy products; intake of 3 servings/week of legumes; intake of 3 servings/week of fish; intake of 3 servings/week of nuts and seeds; intake of 2 liters/day of water; low consumption of red meat and processed meat; and avoidance of refined grains, processed baked goods, pre-sliced bread, soft drinks, fresh juices, fast foods, and precooked meals. Outcomes The primary outcome was the proportion of overweight or obesity at 24 months in the offspring in the MD vs. the CT arm, as detected by the International Obesity Task Force (IOTF) growth charts (14). The analysis of the primary outcome was prespecified as intention to treat (ITT). The secondary outcomes were maternal adherence to MD as detected by the MedDiet Score (15) and maternal weight gain. The tertiary outcome was the evaluation of epigenetic modulation of metabolic pathways in the offspring. Non-primary outcomes were analyzed using per-protocol analysis (PPA). Sample size calculation Sample size was calculated based on the primary outcome, i.e., the proportion of offspring with overweight or obesity at 24 months. Based on prior observational studies, we expected an incidence of overweight or obesity at 24 months of 25%. To detect an absolute difference of 20% in the proportion of overweight or obesity at 24 months at an alpha level of 0.05 and with a power of 80%, 49 mother-child pairs per group are necessary (Pearson Chi-square test). Assuming a dropout rate of up to 5% as in our previous studies, we enrolled 52 mother-child pairs per group, for a total of 104 pairs. Randomization A central randomization was used to allocate women in a 1:1 ratio into treatment arms. The randomization list was generated by applying the ralloc command with block sizes of 2 in Stata version 14.2 (Stata Corporation, College Station, TX, USA) (16). Allocation concealment The treatments were consecutively numbered according to the randomization list, which was known only to the study coordinator. Blinding Blinding of the patients and of the outcome assessors was not possible because of the nature of the intervention. Study Monitoring and Data Management Study monitoring was performed by an independent clinical trial monitor and included on-site visits and telephone interviews with the investigators. The monitor reviewed the clinical forms for completeness, clarity, and consistency and instructed the researchers to make any needed corrections or additions. The clinical researchers entered data in a case report form. Such data was anonymized and entered into an electronic database by the same researcher. The database underwent data cleaning according to standard procedures and was locked before statistical analysis, performed by a statistician. Statistical analysis Continuous variables were reported as median (50 th percentile) and interquartile range (IQR, 25 th and 75 th percentiles). Discrete variables were reported as the number and proportion of subjects with the characteristic of interest. The main outcome was the proportion of children who were overweight or obese at 24 months. The analysis of the main outcome was performed using the prespecified Pearson’s Chi-square test (see sample size calculation). The 95% confidence intervals of the risk difference between the experimental and control arms were calculated using Newcombe 10 method (17) and those of the number needed to treat using Bender’s method (18). The other outcomes, i.e., maternal adherence to treatment was analyzed using random-effect liner regression with MedDiet Score (continuous, score) or weight (continuous, kg) as response variable and treatment (discrete: 0 = CT; 1 = MD), trimester (discrete: 0 = 1 st trimester; 1 = 2 nd trimester; 2 = 3 rd trimester), and a treatmentXtime (discreteXdiscrete) interaction as predictors (19); maternal weight gain at the end of pregnancy, and the epigenetic modulation of metabolic pathways in the offspring were analyzed performing an unpaired t-test to compare the groups. Statistical analysis was performed using Stata 18.0 (Stata Corporation, College Station, TX, USA) and GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA). Data collection The following data were collected at the enrollment: anamnestic and clinical features, personal and anthropometric data, socio-demographic factors, gestational age, allergies, number of cohabitants, pets, sports activities, use of drugs, smoking exposure, education level, family and living conditions. Furthermore, a validated 14-item questionnaire on MD-adherence (MedDiet Score) was administered by a dietitian and the results were recorded (15). Each item assigned a score of 0 or 1, with a total score ranging from 0 to 14; MD adequate adherence was determined if the MedDiet score ≥ 9. The intake of drugs, dietary supplements, pre-, pro-, and symbiotics were recorded in the same chart. Then, follow-up visits at 8–13, 14–28 and 29–40 gestational weeks were scheduled. During these visits, we performed a full physical examination, anamnestic and clinical data collection, MedDiet Score assessment. At delivery, a cord blood sample of at least 10 ml was collected and the neonatal clinical features were evaluated. After delivery, for all babies a follow-up visit was planned every 3 months for the first 12 months of life and then every 6 months until the age of 2 years. At each visit, we performed complete anamnestic and clinical evaluation, body growth assessment, occurrence of allergic disorders, occurrence of other conditions, number of times of antibiotics use. The diagnosis of overweight or obesity in the offspring at 24 months of age was made using the International Obesity Task Force (IOTF) body mass index (BMI) cut-offs (14). DNA isolation from cord blood, methylome analyses and ultra-deep DNA methylation at leptin gene Cord blood (5ml) was collected from all births at the time of delivery by trained nursing staff in the EDTA tubes. Genomic DNA was extracted using the DNA Extraction Kit (GE Healthcare, Uppsala, Sweden) following the manufacturer's protocol. Methylome analyses were performed by using Epic Array Illumina 850k. Bioinformatic analyses were performed on IDAT files by applying RnBeads R-based scripts (20, 21). As a first step, the quality score was determined. According to sample annotations, batch effects and phenotype covariates were identified. DNA methylation distributions and intergroup as well as intragroup variability in methylation profiles were analyzed. Differential methylation between groups of samples was calculated. Differentially methylated CpG sites, promoters and CpG island were calculated among single samples and between groups by Mann Whitney tests. According to the dissimilarities in terms of DNA methylation at each of the 850k CpG sites a Principal Component Analysis (PCA) was performed and PCA plots were generated. To analyze DNA methylation at the Leptin gene, we generated an amplicon library for sequencing as previously described (22). Briefly, genomic DNA was submitted to bisulfite treatment and a double amplification strategy was adopted. The first PCR step was performed using bisulfite-specific Leptin primers with Hot Start Taq (Qiagen) and with the following temperature conditions: 95°C for 15 min; 36 cycles of denaturation at 95°C for 30 s, annealing at 53°C for 40 s, and elongation at 72°C for 1 min; 72°C for 10 min. The second PCR protocol was performed to add multiplexing indices to the first amplicons (forward and reverse “Nextera XT” primers, Illumina, San Diego, CA, USA). The Master Mix KAPA Uracil plus (Roche, Basel, Switzerland) was used for the second amplification and the PCRs were performed with the following temperature conditions: 95°C for 3 min; 12 cycles of denaturation at 98 °C for 20 s, annealing at 55°C for 30 s, and elongation at 72 °C for 50 s; 72°C for 5 min. Both PCR steps were followed by purification using magnetic Beads (Beckman-Coulter, Brea, CA, USA) according to the manufacturer’s instructions. All amplicons were quantified using Qubit® 2.0 Fluorometer. An equimolar amplicon library was generated and then diluted to a final concentration of 8 pM. Phix control library (Illumina) [10% (v/v)] was added to increase diversity of base calling during sequencing. The library was subjected to sequencing using V2-nano reagent kits on the Illumina MiSeq system (Illumina). RESULTS A total of 110 women were assessed for eligibility. Five women were excluded because they did not meet the inclusion criteria, and one woman because she declined to participate. The remaining 104 women were randomized in a 1:1 ratio into the MD (n = 52) or CT (n = 52) arm. Five women in the MD arm and 2 women in the CT arm were lost to follow-up ( Figure 1 ). Baseline features The women’s baseline features are given in Table 1 . Women enrolled in the two study groups were comparable for all the demographic and anamnestic features. Incidence of overweight or obesity at 24 months The incidence of overweight or obesity in the MD and CT arms is reported in Table 2 . At per-protocol (PPA) analysis, the absolute risk difference for overweight or obesity in MD vs. CT was -0.24 (95%CI -0.38 to -0.08), corresponding to an NNT of 4 (95%CI 2 to 12). At intention to treat analysis assuming a worst-case scenario , i.e., with MD children lost to follow-up in the MD group (n = 5) assigned a negative outcome and with CT children lost to follow-up (n = 2) assigned a positive outcome, the absolute risk difference for MD vs. CT was -0.27 (95%CI -0.41 to -0.12), corresponding to an NNT of 3 (95%CI 2 to 8). The main outcome is separated into its components, i.e., overweight or obesity (PPA). No child (0%) of the mothers enrolled in the MD arm had obesity as compared to 4 (8%) of those born from mothers in the CT group, with corresponding figures of 3 (6%) vs. 11 (22%) for overweight. Change of the MedDiet score Panel A of Figure 2 plots the changes of the MedDiet score in the MD and CT arms at the 1 st , 2 nd , and 3 rd trimester of pregnancy (PPA). There was a clear increase of the MedDiet Score during the trial in the MD compared to the CT arm. Panel B of Figure 2 shows that the mean (95%CI) increase of the MedDiet score was 0.7 (-0.1 to 1.6, Bonferroni corrected p-value = 0.08) at the 1 st , 3.0 (2.2 to 3.8, Bonferroni corrected p-value < 0.0001) at the 2 nd and 4.1 (3.2 to 4.9, Bonferroni corrected p-value < 0.0001) at the 3 rd trimester of pregnancy. The mean (95%CI) of the MedDiet score in the MD arm was ≥ 9, meaning excellent MD-adherence, already starting from the 2 nd trimester. Pregnancy weight gain The median weight gain during pregnancy (PPA) was similar in both groups. At the end of pregnancy, both groups gained a median of 11.00 kg, 95%CI 8.00 to 12.00 into the MD arm, and 95%CI 9.00 to 13.00 into the CT arm (p=0.09). Genome-wide DNA methylation analyses in women following MD during pregnancy To assess whether specific DNA methylation signatures are associated with MD during pregnancy, we analyzed the genome-wide methylome using the Infinium EPIC array, which evaluates the methylation status of 850 000 probes. To this aim, we randomly selected 11 women from the MD arm and 11 from the CT arm. Given the high amount of data produced by genome-wide methylation analysis, we compared the DNA methylation of MD to CT women by performing three layers of bioinformatic analyses. First, we compared DNA methylation of MD arm and CT women considering all the quality filtered probes of the methylome; second, we focused only on CpG sites, genes, and promoters with the highest between-group difference; lastly, we went in depth by analyzing differentially methylated genes containing at least 5 differentially methylated CpGs. To these aims, we analyzed IDAT files from the methylome array by using R-based RnBeads scripts (18, 19). After quality filtering, 686 509 probes were retained and subjected to the subsequent analyses. First, we evaluated whether MD women differed from CT women at the epigenome-wide level. To this aim, we performed PCA by clustering samples according to the rate of methylation at single CpG sites, genes, and promoters levels ( Figure 3A, 3B and 3C , respectively). In all analyzed regions and CpG sites, the analysis of whole methylome profiles gave similar results in the MD and CT women. This may be partially explained by the high inter-individual variability of women belonging to each group and by the small number of women (n = 22) considered in the present study. We then performed hierarchical clustering based on the methylation levels at sites/regions with the highest variance across all samples. By this way, we selected CpGs, promoters and genes that may be more discriminant between MD group and CT group. Also in this case, we did not observe differences between the two groups ( Figure 3D, 3E, 3F ). We then went in depth by analyzing CpG sites with at least 5% different methylation between MD and CT women. Among the filtered 686 509 CpG sites, we found 86 020 CpG sites hypomethylated in the MD arm and 77 471 CpG sites hypermethylated in the MD arm. Subsequently, we associated each identified CpG site to genes. We identified 9 860 hypomethylated genes and 8744 hypermethylated genes in the MD arm. We submitted the lists of genes to the DAVID Bioinformatics tool and searched for the genes that have been associated to obesity through the GAD Gene-Disease Association tool (23). We found 282 hypomethylated genes in the MD arm annotated as “Body Mass Index” and 240 hypermethylated genes in the same group annotated as associated “Body Weight”. Among these selected genes, we retained only those with at least 10 CpG sites differentially methylated between the two arms ( data not shown ) . Targeted DNA methylation at the leptin gene Since the leptin gene is the principal gene associated with obesity, we analyzed DNA methylation at the promoter region of Leptin gene in MD and CT women with a high-resolution approach through amplicon-bisulphite sequencing. We analyzed a region of 317 base pairs comprising 28 CpG sites. We found that MD women had the levels of DNA methylation at the leptin gene were significantly higher than in women of CT arm (mean [SD], 30.4% [1.02] vs. 16.9% [2.99], MD vs. CT arm, p<0.0001) ( Figure 4A ). Moreover, the higher methylation levels in MD arm characterized all the analyzed CpG sites ( Figure 4B ). The observed methylation increases at the leptin promoter may indicate that the expression level of this obesity-related gene may be lower in women following MD and thus, this may be considered a protective event likely induced by the MD. DISCUSSION The current epidemic of pediatric obesity necessitates the implementation of effective preventive measures (24). Nutrition is a major modifiable factor that could influence the development of disease throughout the lifespan (25). Nevertheless, the effects of nutritional exposures during the prenatal period are likely to be the deepest, resulting in long-term phenotypic changes in the offspring (7, 26). Thus, targeting the maternal diet during pregnancy could be a feasible strategy for the prevention of overweight and obesity later in the life. MD is considered a healthy dietary pattern and has been associated with a lower risk of non-communicable diseases, including obesity (27). In particular, MD during pregnancy has been proposed as a potential dietary strategy to prevent overweight or obesity in children (28). Previous prospective and retrospective observational studies yielded conflicting results about the effects of MD in pregnancy for the prevention of overweight or obesity in the offspring. A study of 1827 mother-child pairs of the Spanish “Infancia y Medio Ambiente” cohort reported no association between MD during pregnancy and childhood overweight and obesity. However, there was an inverse association between adherence to MD and waist circumference, a surrogate measure of abdominal obesity (29). On the contrary, a study analyzing 997 mother-child pairs from “Project Viva” in Massachusetts (USA) and one of 569 pairs from the “Rhea study” in Crete (Greece) showed that maternal MD in pregnancy was associated to lower BMI standard deviation scores in children aged 4–10 years (9). The “NEST” prospective cohort study, including 929 mother-child pairs, reported that higher adherence to MD during pregnancy was associated with lower body size at birth and that such an effect was maintained to ages 3 to 5 and 6 to 8 years in the offspring (30). Lastly, in a cross-sectional study aimed at evaluating the impact of dietary counseling promoting adherence to MD in obese pregnant women, it was shown that adhering to the MD resulted in reduced gestational weight gain, newborn birth weight, fat mass, and cord leptin level (10). The lack of RCTs aimed at evaluating the effects of MD nutritional counseling during pregnancy on overweight or obesity in the offspring is a major limitation to understanding the role of MD in obesity prevention. The PREMEDI trial was designed to evaluate the effects of MD nutritional counseling during pregnancy on the occurrence of overweight or obesity at 24 months in the offspring. We found that the adherence to MD at baseline was low in all pregnant women. Our findings agree with previous studies, showing that MD adherence during pregnancy is generally poor (31). However, our findings suggest that personalized nutritional counseling could be effective in improving MD adherence during pregnancy. Nutritional counseling appears to be central to improving MD adherence, as reported by studies performed in adults (32). Even if PREMEDI was the first RCT to evaluate the effects of MD nutritional counseling during pregnancy on the incidence of overweight/obesity in the offspring, it is not without limitations. Due to the nature of the treatment, it was not possible to blind the participants and the researchers to allocation groups. Other limitations of our study were the relatively small sample size, the small number of samples available to perform the genome-wide DNA methylation analysis, and the lack of evaluation of more objective biomarkers of MD adherence, such as plasma or urinary levels of selected nutrients. Among the mechanisms by which MD in pregnancy may affect fetal programming and exert long-term protective effects on the offspring are epigenetic mechanisms (6). Epigenetic modifications include DNA methylation, histone modification, non-coding RNA modification and hereditable changes in gene expression without changes in DNA sequencing (33). Maternal diet leads to specific epigenetic signatures that may potentially predispose to the development of late-life obesity (34). Adherence to MD pattern has been associated with increased maternal gut microbial diversity, promoting the abundance of beneficial metabolites and bacteria able to modulate epigenetic mechanisms (35). The high intake of plant foods, such as whole grains, legumes, vegetables, and fruits, is the key component driving this association (36). Once produced, these microbial metabolites, e.g., butyrate, folate and biotin, may epigenetically regulate the energy homeostasis and the substrates’ metabolism, potentially providing an anti-obesity effect (37, 38). In the present study, the proportion of overweight or obesity was lower in children of mothers enrolled in the MD arm. This effect was associated with a higher DNA methylation rate of the leptin gene in cord blood mononuclear cells, suggesting reduced gene expression. Leptin is a growth hormone that regulates appetite, metabolism, and body fat distribution; its synthesis occurs in the placenta and its cord blood levels are associated with newborn anthropometry and body fat (39, 40). Our results are in line with those of another study reporting low levels of leptin in the cord blood of women following MD during pregnancy (10). Lastly, to the best of our knowledge, there are no published data on the global epigenetic effects of MD on the offspring. In fact, most studies reported the epigenetic effects of isolated dietary ingredients typical of the MD pattern (6). Methylome data obtained from PREMEDI suggest that MD could modulate epigenetic mechanisms involved in the gene’s expression relevant to overweight or obesity. CONCLUSION In conclusion, nutritional counseling aimed at promoting MD adherence during pregnancy may protect the offspring against overweight or obesity at the age of 24 months. This effect could be mediated, at least in part, by an epigenetic modulation of leptin expression. Our findings support the role of MD during pregnancy as a safe, effective, and potentially cost-saving strategy against the pediatric obesity pandemic. Declarations ACKNOWLEDGEMENT We thank women and families for their participation in this study. We thank all physicians, nurses, technicians, and all the staff members for the big support during the study. The data underlying this article will be shared on reasonable request to the corresponding author. The study was supported by the Department of Translational Medical Science of the University of Naples Federico II (Naples, Italy), which received funding from the National Recovery and Resilience Plan, European Union-Next-Generation EU (On Foods-Research and Innovation Network on Food and Nutrition Sustainability, Safety and Security-Working on Foods, code PE0000003) and from the Italian Ministry of Health-Health Operational Plan Trajectory 5-Line of action “Creation of an action program for the fight against malnutrition in all its forms and for the dissemination of the principles of the diet Mediterranean” (Mediterranean Diet for Human Health Lab, “MeDiHealthLab”, code T5-AN-07). AUTHOR CONTRIBUTIONS R.B.C., S.C., L.P., and R.N. study design; S.C., L.C., A.A., M.N., F.M., A.P., and R.B.C. data acquisition; G.B. statistical analysis of clinical outcomes; L.P., D.C., M.C., and L.C. laboratory analysis and statistical analysis of laboratory outcomes; S.C., L.P., G.B., M.C., and R.B.C. drafting of manuscript; All authors critical revision and approval of the manuscript. COMPETING INTERESTS The authors declared no competing interests. DATA AVAILABILITY STATEMENT The data underlying this article will be shared on reasonable request to the corresponding author. References World Health Organization. Obesity and overweight. Updated June 9, 2021. Accessed January 3, 2024. https://www.who.int/mediacentre/factsheets/fs311/en/ Lioret, S, Harrar, F, Boccia, D, Hesketh, KD, Kuswara, K, Van Baaren, C, et al. 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Maderuelo-Fernandez, JA, Recio-Rodríguez, JI, Patino-Alonso, MC, Pérez-Arechaederra, D, Rodriguez-Sanchez, E, Gomez-Marcos, MA, et al. Effectiveness of interventions applicable to primary health care settings to promote Mediterranean diet or healthy eating adherence in adults: A systematic review. Prev Med. 2015;76 Suppl:S39-55. Egger, G, Liang, G, Aparicio, A, Jones, PA. Epigenetics in human disease and prospects for epigenetic therapy. Nature. 2004;429:457–63. Tamburini, S, Shen, N, Wu, HC, Clemente, JC. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22:713–22. Miller, CB, Benny, P, Riel, J, Boushey, C, Perez, R, Khadka, V, et al. Adherence to Mediterranean diet impacts gastrointestinal microbial diversity throughout pregnancy. BMC Pregnancy Childbirth. 2021;21:558. Coppola, S, Avagliano, C, Calignano, A, Berni Canani, R. The Protective Role of Butyrate against Obesity and Obesity-Related Diseases. Molecules. 2021;26:682. Henagan, TM, Stefanska, B, Fang, Z, Navard, AM, Ye, J, Lenard, NR, et al. Sodium butyrate epigenetically modulates high-fat diet-induced skeletal muscle mitochondrial adaptation, obesity and insulin resistance through nucleosome positioning. Br J Pharmacol. 2015;172:2782–98. Li, Y. Epigenetic Mechanisms Link Maternal Diets and Gut Microbiome to Obesity in the Offspring. Front Genet. 2018;9:342. Hoggard, N, Haggarty, P, Thomas, L, Lea, RG. Leptin expression in placental and fetal tissues: does leptin have a functional role? Biochem Soc Trans. 2001;29:57–63. Donnelly, JM, Lindsay, KL, Walsh, JM, Horan, M, Molloy, EJ, McAuliffe, FM. Fetal metabolic influences of neonatal anthropometry and adiposity. BMC Pediatr. 2015;15:175. Tables Table 1 – Baseline features of the MD and CT arms. MD arm CT arm N 52 52 Age (years) 31 (28;34) 32 (29;34) Pre-pregnancy weight (kg) 63.0 (56.0-69.0) 60.0 (54.0-72.0) Pre-pregnancy height (m) 1.65 (1.62-1.68) 1.62 (1.60-1.68) Pre-pregnancy BMI (kg/m 2 ) 23.6 (20.6;25.4) 23.0 (20.0;27.0) Pre-pregnancy BMI-NIH Underweight 5 (9.6%) 6 (11.5%) Normal weight 32 (61.5%) 27 (51.9%) Overweight 12 (23.1%) 16 (30.8%) Obesity 3 (5.8%) 3 (5.8%) Smoker 7 (13.5%) 7 (13.5%) Education Middle school 4 (7.7%) 9 (17.3%) High school 27 (51.9%) 27 (51.9%) University 21 (40.4%) 16 (30.8%) MedDiet score 7.0 (5.2-9.0) 6.5 (5-7.7) Legend: MD, Mediterranean Diet arm; CT, control arm; BMI, body mass index; NIH classification, National Institutes of Health classification of body mass index. Continuous variables are reported as median (50 th percentile) and interquartile range (IQR, 25 th and 75 th percentiles). Discrete variables are reported as the number and proportion of subjects with the characteristic of interest. Table 2 – Incidence of overweight or obesity at 24 months in the offspring. Per-protocol analysis Intention to treat analysis- worst-case scenario * MD event rate n/N 3/47 3/(47+5) Proportion (95%CI) 0.06 (0.13 to 0.17 † ) 0.06 (0.01 to 0.16 † ) CT event rate (n/N) n/N 15/50 (15+2)/52 Proportion (95%CI) 0.30 (0.18 to 0.45 † ) 0.33 (0.20 to 0.47 † ) Absolute risk difference (MD-CT) -0.24 (-0.38 to -0.08 †† ) p = 0.003 ††† -0.27 (-0.41 to -0.12 †† ) p < 0.001 ††† Number needed to treat 4 (2 to 12) 3 (2 to 8) Legend: MD, Mediterranean Diet arm; CT, control arm; * Positive outcome assigned to children missed at follow-up in the CT arm (n = 2) and negative outcome assigned to those missed at follow-up in the MD arm (n = 5). † Exact (Clopper-Pearson) 95%CI †† 95%CI calculated using Newcombe 10 method ††† p-value obtained from Pearson’s Chi-square as per study design †††† Confidence intervals from Bender’s formula Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Published Journal Publication published 18 Sep, 2024 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 04 Jun, 2024 Review # 1 received at journal 21 May, 2024 Review # 2 received at journal 11 Apr, 2024 Reviewer # 2 agreed at journal 09 Apr, 2024 Reviewer # 1 agreed at journal 05 Apr, 2024 Reviewers invited by journal 24 Mar, 2024 Submission checks completed at journal 08 Mar, 2024 Editor assigned by journal 07 Mar, 2024 First submitted to journal 07 Mar, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4026361","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283176918,"identity":"96a05c65-cc84-420e-9bb8-f6bdefbf0e80","order_by":0,"name":"Roberto Berni 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1","display":"","copyAsset":false,"role":"figure","size":20293,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the PREMEDI randomized controlled trial.\u003c/p\u003e","description":"","filename":"CoppolaPREMEDItrialFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4026361/v1/54707be96521172113b4ea1b.png"},{"id":53583058,"identity":"cc1d1c91-6461-48c5-88ed-2c63e3e1653c","added_by":"auto","created_at":"2024-03-27 17:44:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27966,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of the MedDiet score at the 1\u003csup\u003est\u003c/sup\u003e, 2\u003csup\u003end\u003c/sup\u003e a 3\u003csup\u003erd\u003c/sup\u003e trimester of pregnancy.\u003c/p\u003e\n\u003cp\u003eValues are means and 95%CI obtained by random effects linear regression.\u003c/p\u003e","description":"","filename":"CoppolaPREMEDItrialFigure2.JPG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4026361/v1/e0606378410eba2cc4460207.jpg"},{"id":53583061,"identity":"5e1521b0-3925-49cd-a071-dae94ad1692e","added_by":"auto","created_at":"2024-03-27 17:44:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101803,"visible":true,"origin":"","legend":"\u003cp\u003eEpigenome-wide analysis between MD and CT arms.\u003c/p\u003e\n\u003cp\u003ePanels A, B, and C represent the Principal Component Analysis for MD (n = 11) and CT (n = 11) arms. The plots are shown considering the DNA methylation levels at CpG sites (A), genes (B) and promoters (C). Panels D, E, F represent the Hierarchical Cluster for MD (n = 11) and CT arms (n = 11). Heatmaps show the methylation profiles at selected sites/regions with the highest variance across all samples.\u003c/p\u003e","description":"","filename":"CoppolaPREMEDItrialFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4026361/v1/a26a6410c3957c8bf37541d5.jpg"},{"id":53583059,"identity":"0b7d67a4-3a39-454e-91fc-468b44b7b535","added_by":"auto","created_at":"2024-03-27 17:44:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84097,"visible":true,"origin":"","legend":"\u003cp\u003eAverage methylation and DNA methylation at each analyzed CpG site at Leptin gene promoter.\u003c/p\u003e\n\u003cp\u003eThe numbers of CpG sites are referred to the transcriptional start site (TSS). Values are means and standard errors. Between-group comparisons were performed using the student’s t-test. Legend: *** = p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"CoppolaPREMEDItrialFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4026361/v1/49d6cc9fa8f09ffafceaad5e.png"},{"id":65431703,"identity":"44004ebb-9e23-49a5-9c98-4c4b57cb8174","added_by":"auto","created_at":"2024-09-27 11:58:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":796389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4026361/v1/dc3ffaf3-9255-47a2-af65-51cbd244b25b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"\u003cp\u003eEffects of Mediterranean Diet During Pregnancy on the Onset of Overweight or Obesity in the Offspring: A Randomized Trial\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe current epidemic of pediatric obesity is a major public health issue, advocating the need of preventive strategies to limit the associated burden of disease (1). The first stages of life represent a window of opportunity to prevent the occurrence of overweight or obesity and to achieve long-term health outcomes (2). Nutritional exposures during this critical period of life are potentially modifiable factors that can influence the future disease susceptibility (3). Maternal diet during pregnancy has been linked to offspring overweight/obesity risk and could represent a potential target of intervention for disease prevention (4).\u003c/p\u003e\n\u003cp\u003eThe Mediterranean Diet (MD) is one of the healthiest dietary patterns,\u0026nbsp;providing considerable amounts of fiber, antioxidants, polyphenols, vitamins, and a balanced ratio of essential fatty acids,\u0026nbsp;which impacts beneficially the overall health and protects against the occurrence of excess weight in adults (5). Emerging evidence suggests that MD during pregnancy may protect against overweight or obesity in the offspring (6). Maternal diet could affect fetal programming by epigenetic modulation of gene expression, with long-lasting effects on human health (6-8).\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003eTo date, the evidence on the effects of MD during pregnancy for the prevention of childhood overweight/obesity are scarce and based on observational studies (9-11). The \u003cu\u003eMe\u003c/u\u003editerranean \u003cu\u003eDi\u003c/u\u003eet during \u003cu\u003ePre\u003c/u\u003egnancy (PREMEDI) trial is a randomized controlled trial (RCT) aimed at addressing such limitation.\u0026nbsp;\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eTrial design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePREMEDI was a randomized, parallel-group, controlled trial aimed\u0026nbsp;at evaluating the effects of MD during pregnancy on the prevention of overweight/obesity at 24 months in the offspring. The trial was approved by the Ethics Committee of the University of Naples \u0026ldquo;Federico II\u0026rdquo; and was performed in accordance with the Helsinki Declaration and with the relevant European and Italian privacy regulations. Written informed consent to participate in the study was obtained from the women. No financial compensation was provided. The study was registered on ClinicalTrials.gov (Identifier: NCT03337802). The study was performed at the Evangelical Hospital Villa Betania in Naples, Italy from 30/11/2017 to 31/01/2021. The study is reported following the Consolidated Standards of Reporting Trials (CONSORT) guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePregnant women in their first trimester of pregnancy were eligible for the study. Inclusion criteria were Caucasian ethnicity and age range of 20 to 35 years. We excluded women with proven infections during pregnancy, twin pregnancy, malignancies, gastrointestinal tract malformations, immunodeficiency, diabetes mellitus and other chronic diseases; chronic intestinal inflammatory diseases; gastrointestinal functional disorders; celiac disease; a history of abdominal surgery; neuropsychiatric disorders; and use of a vegan diet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe women were randomly allocated in a 1:1 ratio to the control (CT) and MD arms. The CT arm received standard obstetrical and gynecological care; the MD arm received standard obstetrical and gynecological care, plus personalized MD nutritional counseling provided by certified dietitians.\u003c/p\u003e\n\u003cp\u003eWomen enrolled in the CT group were provided standard-of-care recommendations by the study gynecologists. Such recommendations included energy intake, physical activity, optimal weight gain during pregnancy based on pre-pregnancy weight (12), and hygiene rules for food-related illnesses (13).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWomen enrolled in the MD group were provided personalized nutritional counseling by certified dietitians. Nutritional counseling was performed in three face-to-face sessions at enrollment (8\u0026ndash;13 gestational weeks), 3 months (14\u0026ndash;28 gestational weeks) and 6 months after the enrollment (29\u0026ndash;40 gestational weeks).\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003eNutritional counseling was based on the following recommendations: use of\u0026nbsp;extra virgin olive oil\u0026nbsp;as the main cooking fat (at least 4 tablespoons/day); intake of 2 servings/day of vegetables; intake of 3 servings/day of fruit, avoiding juices; intake of 3 servings/day of wholegrain cereals; intake of 3 servings/day of skimmed dairy products; intake of 3 servings/week of legumes; intake of\u0026nbsp;3 servings/week of fish; intake of 3 servings/week of nuts and seeds; intake of 2 liters/day of water; low consumption of red meat and processed meat; and avoidance of refined grains, processed baked goods, pre-sliced bread, soft drinks, fresh juices, fast foods, and precooked meals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the proportion of overweight or obesity at 24 months in the offspring in the MD vs. the CT arm, as detected by the International Obesity Task Force (IOTF) growth charts (14). The analysis of the primary outcome was prespecified as intention to treat (ITT). The secondary outcomes were maternal adherence to MD as detected by the MedDiet Score (15)\u0026nbsp;and maternal weight gain.\u0026nbsp;The tertiary outcome was the evaluation of epigenetic modulation of metabolic pathways in the offspring. Non-primary outcomes were analyzed using per-protocol analysis (PPA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size calculation\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size was calculated based on the primary outcome, i.e., the proportion of offspring with overweight or obesity at 24 months.\u0026nbsp;Based on prior observational studies, we expected an incidence of overweight or obesity at 24 months of 25%. To detect an absolute difference of 20% in the proportion of overweight or obesity at 24 months at an alpha level of 0.05 and with a power of 80%, 49 mother-child pairs per group are necessary (Pearson Chi-square test). Assuming a dropout rate of up to 5% as in our previous studies, we enrolled 52 mother-child pairs per group, for a total of 104 pairs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRandomization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA central randomization was used to allocate women in a 1:1 ratio into treatment arms. The randomization list was generated by applying the\u0026nbsp;\u003cem\u003eralloc\u003c/em\u003e command with block sizes of 2 in Stata version 14.2 (Stata Corporation, College Station, TX, USA) (16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAllocation concealment\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The treatments were consecutively numbered according to the randomization list, which was known only to the study coordinator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlinding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlinding of the patients and of the outcome assessors was not possible because of the nature of the intervention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Monitoring and Data Management\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Study monitoring was performed by an independent clinical trial monitor and included on-site visits and telephone interviews with the investigators. The monitor reviewed the clinical forms for completeness, clarity, and consistency and instructed the researchers to make any needed corrections or additions. The clinical researchers entered data in a case report form. Such data was anonymized and entered into an electronic database by the same researcher. The database underwent data cleaning according to standard procedures and was locked before statistical analysis, performed by a statistician.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eContinuous variables were reported as median (50\u003csup\u003eth\u003c/sup\u003e percentile) and interquartile range (IQR, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles). Discrete variables were reported as the number and proportion of subjects with the characteristic of interest. The main outcome was the proportion of children who were overweight or obese at 24 months. The analysis of the main outcome was performed using the prespecified Pearson\u0026rsquo;s Chi-square test (see sample size calculation). The 95% confidence intervals of the risk difference between the experimental and control arms were calculated using Newcombe 10 method (17) and those of the number needed to treat using Bender\u0026rsquo;s method (18). The other outcomes, i.e., maternal adherence to treatment was analyzed using random-effect liner regression with MedDiet Score (continuous, score) or weight (continuous, kg) as response variable and treatment (discrete: 0 = CT; 1 = MD), trimester (discrete: 0 = 1\u003csup\u003est\u003c/sup\u003e trimester; 1 = 2\u003csup\u003end\u003c/sup\u003e trimester; 2 = 3\u003csup\u003erd\u003c/sup\u003e trimester), and a treatmentXtime (discreteXdiscrete) interaction as predictors (19); maternal weight gain at the end of pregnancy, and the epigenetic modulation of metabolic pathways in the offspring were analyzed performing an unpaired t-test to compare the groups. Statistical analysis was performed using Stata 18.0 (Stata Corporation, College Station, TX, USA) and GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The following data were collected at the enrollment: anamnestic and clinical features, personal and anthropometric data, socio-demographic factors, gestational age, allergies, number of cohabitants, pets, sports activities, use of drugs, smoking exposure, education level, family and living conditions.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Furthermore, a validated 14-item questionnaire on MD-adherence (MedDiet Score) was administered by a dietitian and the results were recorded (15). Each item assigned a score of 0 or 1, with a total score ranging from 0 to 14; MD adequate adherence was determined if the MedDiet score \u0026ge; 9. The intake of drugs, dietary supplements, pre-, pro-, and symbiotics were recorded in the same chart. Then, follow-up visits at 8\u0026ndash;13, 14\u0026ndash;28 and 29\u0026ndash;40 gestational weeks were scheduled. During these visits, we performed a full physical examination, anamnestic and clinical data collection, MedDiet Score assessment.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;At delivery, a cord blood sample of at least 10 ml was collected and the neonatal clinical features were evaluated.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;After delivery, for all babies a follow-up visit was planned every 3 months for the first 12 months of life and then every 6 months until the age of 2 years. At each visit, we performed complete anamnestic and clinical evaluation, body growth assessment, occurrence of allergic disorders, occurrence of other conditions, number of times of antibiotics use.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The diagnosis of overweight or obesity in the offspring at 24 months of age was made using the International Obesity Task Force (IOTF) body mass index (BMI) cut-offs (14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA isolation from cord blood, methylome analyses and ultra-deep DNA methylation at leptin gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCord blood (5ml) was collected from all births at the time of delivery by trained nursing staff in the EDTA tubes. Genomic DNA was extracted using the DNA Extraction Kit (GE Healthcare, Uppsala, Sweden) following the manufacturer\u0026apos;s protocol.\u003c/p\u003e\n\u003cp\u003eMethylome analyses were performed by using Epic Array Illumina 850k. Bioinformatic analyses were performed on IDAT files by applying RnBeads R-based scripts (20, 21). As a first step, the quality score was determined. According to sample annotations, batch effects and phenotype covariates were identified. DNA methylation distributions and intergroup as well as intragroup variability in methylation profiles were analyzed. Differential methylation between groups of samples was calculated. Differentially methylated CpG sites, promoters and CpG island were calculated among single samples and between groups by Mann Whitney tests. According to the dissimilarities in terms of DNA methylation at each of the 850k CpG sites a Principal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComponent Analysis (PCA) was performed and PCA plots were generated.\u003c/p\u003e\n\u003cp\u003eTo analyze DNA methylation at the Leptin gene, we generated an amplicon library for sequencing as previously described (22). Briefly, genomic DNA was submitted to bisulfite treatment and a double amplification strategy was adopted. The first PCR step was performed using bisulfite-specific Leptin primers with Hot Start Taq (Qiagen) and with the following temperature conditions: 95\u0026deg;C for 15 min; 36 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 53\u0026deg;C for 40 s, and elongation at 72\u0026deg;C for 1 min; 72\u0026deg;C for 10 min. The second PCR protocol was performed to add multiplexing indices to the first amplicons (forward and reverse \u0026ldquo;Nextera XT\u0026rdquo; primers, Illumina, San Diego, CA, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Master Mix KAPA Uracil plus (Roche, Basel, Switzerland) was used for the second amplification and the PCRs were performed with the following temperature conditions: 95\u0026deg;C for 3 min; 12 cycles of denaturation at 98 \u0026deg;C for 20 s, annealing at 55\u0026deg;C for 30 s, and elongation at 72 \u0026deg;C for 50 s; 72\u0026deg;C for 5 min. Both PCR steps were followed by purification using magnetic Beads (Beckman-Coulter, Brea, CA, USA) according to the manufacturer\u0026rsquo;s instructions. All amplicons were quantified using Qubit\u0026reg; 2.0 Fluorometer. An equimolar amplicon library was generated and then diluted to a final concentration of 8 pM. Phix control library (Illumina) [10% (v/v)] was added to increase diversity of base calling during sequencing. The library was subjected to sequencing using V2-nano reagent kits on the Illumina MiSeq system (Illumina).\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 110 women were assessed for eligibility. Five\u0026nbsp;women were excluded because they did not meet the inclusion criteria, and one woman because she declined to participate. The remaining 104 women were randomized in a 1:1 ratio into the MD (n = 52) or CT (n = 52) arm. Five women in the MD arm and 2 women in the CT arm were lost to follow-up (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe women’s baseline features are given in \u003cstrong\u003eTable 1\u003c/strong\u003e. Women enrolled in the two study groups were comparable for all the demographic and anamnestic features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncidence of overweight or obesity at 24 months\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The incidence of overweight or obesity in the MD and CT arms is reported in\u0026nbsp;\u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAt per-protocol (PPA) analysis, the absolute risk difference for overweight or obesity in MD vs. CT was -0.24 (95%CI -0.38 to -0.08), corresponding to an NNT of 4 (95%CI 2 to 12).\u0026nbsp;\u0026nbsp;At intention to treat analysis assuming a \u003cem\u003eworst-case scenario\u003c/em\u003e, i.e., with MD children lost to follow-up in the MD group (n = 5) assigned a negative outcome and\u0026nbsp;with CT children lost to follow-up (n = 2) assigned a positive outcome, the absolute risk difference for MD vs. CT was -0.27 (95%CI -0.41 to -0.12), corresponding to an NNT of 3 (95%CI 2 to 8).\u003c/p\u003e\n\u003cp\u003eThe main outcome is separated into its components, i.e., overweight or obesity (PPA). No child (0%) of the mothers enrolled in the MD arm had obesity as compared to 4 (8%) of those born from mothers in the CT group, with corresponding figures of 3 (6%) vs. 11 (22%) for overweight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChange of the MedDiet score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel A of \u003cstrong\u003eFigure 2\u003c/strong\u003e plots the changes of the MedDiet score in the MD and CT arms at the 1\u003csup\u003est\u003c/sup\u003e, 2\u003csup\u003end\u003c/sup\u003e, and 3\u003csup\u003erd\u003c/sup\u003e trimester of pregnancy (PPA).\u003c/p\u003e\n\u003cp\u003eThere was a clear increase of the MedDiet Score during the trial in the MD compared to the CT arm. Panel B of Figure 2 shows that the mean (95%CI) increase of the MedDiet score was 0.7 (-0.1 to 1.6, Bonferroni corrected p-value =\u0026nbsp;0.08) at the 1\u003csup\u003est\u003c/sup\u003e, 3.0 (2.2 to 3.8, Bonferroni corrected p-value \u0026lt; 0.0001) at the 2\u003csup\u003end\u003c/sup\u003e and 4.1 (3.2 to 4.9, Bonferroni corrected p-value \u0026lt; 0.0001) at the 3\u003csup\u003erd\u003c/sup\u003e trimester of pregnancy. The mean (95%CI) of the MedDiet score in the MD arm was ≥ 9, meaning excellent MD-adherence, already starting from the 2\u003csup\u003end\u003c/sup\u003e trimester.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePregnancy weight gain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median weight gain during pregnancy (PPA) was similar in both groups. At the end of pregnancy, both groups gained a median of 11.00 kg, 95%CI 8.00 to 12.00 into the MD arm, and 95%CI 9.00 to 13.00 into the CT arm (p=0.09).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide DNA methylation analyses in women following MD during pregnancy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether specific DNA methylation signatures are associated with MD during pregnancy, we analyzed the genome-wide methylome using the Infinium EPIC array, which evaluates the methylation status of 850 000 probes. To this aim, we randomly selected 11 women from the MD arm and 11 from the CT arm. Given the high amount of data produced by genome-wide methylation analysis, we compared the DNA methylation of MD to CT women by performing three layers of bioinformatic analyses. First, we compared DNA methylation of MD arm and CT women considering all the quality filtered probes of the methylome; second, we focused only on CpG sites, genes, and promoters with the highest between-group difference; lastly, we went in depth by analyzing differentially methylated genes containing at least 5 differentially methylated CpGs. To these aims, we analyzed IDAT files from the methylome array by using R-based RnBeads scripts (18, 19). After quality filtering, 686 509 probes were retained and subjected to the subsequent analyses. First, we evaluated whether MD women differed from CT women at the epigenome-wide level. To this aim, we performed PCA by clustering samples according to the rate of methylation at single CpG sites, genes, and promoters levels (\u003cstrong\u003eFigure 3A, 3B and 3C\u003c/strong\u003e, respectively). In all analyzed regions and CpG sites, the analysis of whole methylome profiles gave similar results in the MD and CT women. This may be partially explained by the high inter-individual variability of women belonging to each group and by the small number of women (n = 22) considered in the present study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then performed hierarchical clustering based on the methylation levels at sites/regions with the highest variance across all samples. By this way, we selected CpGs, promoters and genes that may be more discriminant between MD group and CT group. Also in this case, we did not observe differences between the two groups (\u003cstrong\u003eFigure 3D, 3E, 3F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then went in depth by analyzing CpG sites with at least 5% different methylation between MD and CT women. Among the filtered 686 509 CpG sites, we found 86 020 CpG sites hypomethylated in the MD arm and 77 471 CpG sites hypermethylated in the MD arm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequently, we associated each identified CpG site to genes. We identified 9 860 hypomethylated genes and 8744 hypermethylated genes in the MD arm. We submitted the lists of genes to the DAVID Bioinformatics tool and searched for the genes that have been associated to obesity through the GAD Gene-Disease Association tool (23). We found 282 hypomethylated genes in the MD arm annotated as “Body Mass Index” and 240 hypermethylated genes in the same group annotated as associated “Body Weight”. Among these selected genes, we retained only those with at least 10 CpG sites differentially methylated between the two arms \u003cstrong\u003e(\u003c/strong\u003e\u003cem\u003edata not shown\u003c/em\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted DNA methylation at the leptin gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the leptin gene is the principal gene associated with obesity, we analyzed DNA methylation at the promoter region of Leptin gene in MD and CT women with a high-resolution approach through amplicon-bisulphite sequencing. We analyzed a region of 317 base pairs comprising 28 CpG sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that MD women had the levels of DNA methylation at the leptin gene were significantly higher than in women of CT arm (mean [SD], 30.4% [1.02] \u003cem\u003evs.\u003c/em\u003e 16.9% [2.99], MD \u003cem\u003evs.\u003c/em\u003e CT arm, p\u0026lt;0.0001) (\u003cstrong\u003eFigure 4A\u003c/strong\u003e).\u0026nbsp;\u0026nbsp;Moreover, the higher methylation levels in MD arm characterized all the analyzed CpG sites (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). The observed methylation increases at the leptin promoter may indicate that the expression level of this obesity-related gene may be lower in women following MD and thus, this may be considered a protective event likely induced by the MD.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe current epidemic of pediatric obesity necessitates the implementation of effective preventive measures (24). Nutrition is a major modifiable factor that could influence the development of disease throughout the lifespan (25). Nevertheless, the effects of nutritional exposures during the prenatal period are likely to be the deepest, resulting in long-term phenotypic changes in the offspring (7, 26). Thus, targeting the maternal diet during pregnancy could be a feasible strategy for the prevention of overweight and obesity later in the life. MD is considered a healthy dietary pattern and has been associated with a lower risk of non-communicable diseases, including obesity (27). In particular, MD during pregnancy has been proposed as a potential dietary strategy to prevent overweight or obesity in children (28).\u003c/p\u003e\n\u003cp\u003ePrevious prospective and retrospective observational studies yielded conflicting results about the effects of MD in pregnancy for the prevention of overweight or obesity in the offspring. A study of 1827 mother-child pairs of the Spanish “Infancia y Medio Ambiente” cohort reported no association between MD during pregnancy and childhood overweight and obesity. However, there was an inverse association between adherence to MD and waist circumference, a surrogate measure of abdominal obesity (29). On the contrary, a study analyzing 997 mother-child pairs from “Project Viva” in Massachusetts (USA) and one of 569 pairs from the “Rhea study” in Crete (Greece) showed that maternal MD in pregnancy was associated to lower BMI standard deviation scores in children aged 4–10 years (9). The “NEST” prospective cohort study, including 929 mother-child pairs, reported that higher adherence to MD during pregnancy was associated with lower body size at birth and that such an effect was maintained to ages 3 to 5 and 6 to 8 years in the offspring (30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, in a cross-sectional study\u0026nbsp;aimed at evaluating the impact of dietary counseling promoting adherence to MD in obese pregnant women, it was shown that adhering to the MD resulted in reduced gestational weight gain, newborn birth weight, fat mass, and cord leptin level (10).\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe lack of RCTs aimed at evaluating the effects of MD nutritional counseling during pregnancy on overweight or obesity in the offspring is a major limitation to understanding the role of MD in obesity prevention. The PREMEDI trial was designed to evaluate the effects of MD nutritional counseling during pregnancy on the occurrence of overweight or obesity at 24 months in the offspring. We found that the adherence to MD at baseline was low in all pregnant women. Our findings agree with previous studies, showing that MD adherence during pregnancy is generally poor (31). However, our findings suggest that personalized nutritional counseling could be effective in improving MD adherence during pregnancy. Nutritional counseling appears to be central to improving MD adherence, as reported by studies performed in adults (32).\u003c/p\u003e\n\u003cp\u003eEven if PREMEDI was the first RCT to evaluate the effects of MD nutritional counseling during pregnancy on the incidence of overweight/obesity in the offspring, it is not without limitations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the nature of the treatment, it was not possible to blind the participants and the researchers to allocation groups. Other limitations of our study were the relatively small sample size, the small number of samples available to perform the genome-wide DNA methylation analysis, and the lack of evaluation of more objective biomarkers of MD adherence, such as plasma or urinary levels of selected nutrients.\u003c/p\u003e\n\u003cp\u003eAmong the mechanisms by which MD in pregnancy may affect fetal programming and exert long-term protective effects on the offspring are epigenetic mechanisms (6). Epigenetic modifications include DNA methylation, histone modification, non-coding RNA modification and\u0026nbsp;hereditable\u0026nbsp;changes in gene expression without changes in DNA sequencing (33). Maternal diet leads to specific epigenetic signatures that may potentially predispose to the development of late-life obesity (34). Adherence to MD pattern has been associated with increased maternal gut microbial diversity, promoting the abundance of beneficial metabolites and bacteria able to modulate epigenetic mechanisms (35). The high intake of plant foods, such as whole grains, legumes, vegetables, and fruits, is the key component driving this association (36). Once produced, these microbial metabolites, e.g., butyrate, folate and biotin, may epigenetically regulate the energy homeostasis and the substrates’ metabolism, potentially providing an anti-obesity effect (37, 38).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, the proportion of overweight or obesity was lower in children of mothers enrolled in the MD arm. This effect was associated with a higher DNA methylation rate of the leptin gene in cord blood mononuclear cells, suggesting reduced gene expression. Leptin is a growth hormone that regulates appetite, metabolism, and body fat distribution; its synthesis occurs in the placenta and its cord blood levels are associated with newborn anthropometry and body fat (39, 40). Our results are in line with those of another study reporting low levels of leptin in the cord blood of women following MD during pregnancy (10).\u003c/p\u003e\n\u003cp\u003eLastly, to the best of our knowledge, there are no published data on the global epigenetic effects of MD on the offspring. In fact, most studies reported the epigenetic effects of isolated dietary ingredients typical of the MD pattern (6). Methylome data obtained from PREMEDI suggest that MD could modulate epigenetic mechanisms involved in the gene’s expression relevant to overweight or obesity.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, nutritional counseling aimed at promoting MD adherence during pregnancy may protect the offspring against overweight or obesity at the age of 24 months. This effect could be mediated, at least in part, by an epigenetic modulation of leptin expression. Our findings support the role of MD during pregnancy as a safe, effective, and potentially cost-saving strategy against the pediatric obesity pandemic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank women and families for their participation in this study. We thank all physicians, nurses, technicians, and all the staff members for the big support during the study. The data underlying this article will be shared on reasonable request to the corresponding author.\u0026nbsp;The study was supported by the Department of Translational Medical Science of the University of Naples Federico II (Naples, Italy), which received funding from the National Recovery and Resilience Plan, European Union-Next-Generation EU (On Foods-Research and Innovation Network on Food and Nutrition Sustainability, Safety and Security-Working on Foods, code PE0000003) and from the Italian Ministry of Health-Health Operational Plan Trajectory 5-Line of action “Creation of an action program for the fight against malnutrition in all its forms and for the dissemination of the principles of the diet Mediterranean” (Mediterranean Diet for Human Health Lab, “MeDiHealthLab”, code T5-AN-07).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.B.C., S.C., L.P., and R.N. study design;\u0026nbsp;\u0026nbsp;S.C., L.C., A.A., M.N., F.M., A.P., and R.B.C. data acquisition;\u0026nbsp;G.B. statistical analysis of clinical outcomes;\u0026nbsp;L.P., D.C., M.C., and L.C. laboratory analysis and statistical analysis of laboratory outcomes;\u0026nbsp;S.C., L.P., G.B., M.C., and R.B.C. drafting of manuscript;\u0026nbsp;\u0026nbsp;All authors critical revision and approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Sodium butyrate epigenetically modulates high-fat diet-induced skeletal muscle mitochondrial adaptation, obesity and insulin resistance through nucleosome positioning. Br J Pharmacol. 2015;172:2782\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y. Epigenetic Mechanisms Link Maternal Diets and Gut Microbiome to Obesity in the Offspring. Front Genet. 2018;9:342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoggard, N, Haggarty, P, Thomas, L, Lea, RG. Leptin expression in placental and fetal tissues: does leptin have a functional role? Biochem Soc Trans. 2001;29:57\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonnelly, JM, Lindsay, KL, Walsh, JM, Horan, M, Molloy, EJ, McAuliffe, FM. Fetal metabolic influences of neonatal anthropometry and adiposity. BMC Pediatr. 2015;15:175.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u0026ndash; Baseline features of the MD and CT arms.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMD arm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCT arm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (28;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (29;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy\u0026nbsp;weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.0 (56.0-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.0 (54.0-72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy height (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65 (1.62-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62 (1.60-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.6 (20.6;25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.0 (20.0;27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy BMI-NIH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;27 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedDiet score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0 (5.2-9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 (5-7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eMD, Mediterranean Diet arm; CT, control arm;\u0026nbsp;BMI, body mass index; NIH classification, National Institutes of Health classification of body mass index.\u0026nbsp;Continuous variables are reported as median (50\u003csup\u003eth\u003c/sup\u003e percentile) and interquartile range (IQR, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles). Discrete variables are reported as the number and proportion of subjects with the characteristic of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash;\u0026nbsp;\u003c/strong\u003eIncidence of overweight or obesity at 24 months in the offspring.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePer-protocol analysis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntention to treat analysis-\u003c/p\u003e\n \u003cp\u003eworst-case scenario\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMD event rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;n/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/(47+5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Proportion (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06 (0.13 to 0.17 \u003csup\u003e\u0026dagger;\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06 (0.01 to 0.16 \u003csup\u003e\u0026dagger;\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCT event rate (n/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;n/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15/50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(15+2)/52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Proportion (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30 (0.18 to 0.45 \u003csup\u003e\u0026dagger;\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33 (0.20 to 0.47 \u003csup\u003e\u0026dagger;\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsolute risk difference\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(MD-CT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.24 (-0.38 to -0.08 \u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003ep = 0.003 \u003csup\u003e\u0026dagger;\u0026dagger;\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.27 (-0.41 to -0.12 \u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.001 \u003csup\u003e\u0026dagger;\u0026dagger;\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber needed to treat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2 to 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (2 to 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e\u003c/sup\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eMD, Mediterranean Diet arm; CT, control arm; \u003csup\u003e*\u003c/sup\u003ePositive outcome assigned to children missed at follow-up in the CT arm (n = 2) and negative outcome assigned to those missed at follow-up in the MD arm (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Exact (Clopper-Pearson) 95%CI\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e 95%CI calculated using Newcombe 10 method\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u0026dagger;\u0026dagger;\u003c/sup\u003e p-value obtained from Pearson\u0026rsquo;s Chi-square as per study design\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u0026dagger;\u0026dagger;\u0026dagger;\u003c/sup\u003e Confidence intervals from Bender\u0026rsquo;s formula\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Randomized controlled trial, Mediterranean Diet, Pregnancy, Children, Pediatric Obesity","lastPublishedDoi":"10.21203/rs.3.rs-4026361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4026361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaternal diet during pregnancy could represent a potential target for pediatric overweight/obesity prevention. Mediterranean Diet (MD) is one of the healthiest dietary models exerting protective effects against excess weight. To date, the evidence on the MD-effects during pregnancy for the prevention of childhood overweight/obesity are scarce and based on observational studies. The \u003cu\u003eMe\u003c/u\u003editerranean \u003cu\u003eDi\u003c/u\u003eet during \u003cu\u003ePre\u003c/u\u003egnancy (PREMEDI) trial has been designed to evaluate the efficacy of a nutritional counseling aimed at promoting MD-adherence during pregnancy on the occurrence of overweight or obesity at 24 months in the offspring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PREMEDI was a randomized-controlled, parallel groups, prospective trial. 104 women in their first trimester of pregnancy were randomly assigned to standard obstetrical and gynecological care alone (CT group, n=52) or plus a nutritional counseling promoting MD (MD group, n=52). 5 women in the MD arm and 2 women in the CT arm were lost to follow-up. Women enrolled in the MD group were provided 3 session of nutritional counseling (one session for trimester). The primary outcome was the proportion of overweight or obesity at 24 months. Other outcomes included maternal MD-adherence, maternal weight gain, and epigenetic modulation of genes involved in metabolic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA lower proportion of overweight or obesity was observed at 24 months in children of MD-arm mothers compared to those in the CT arm (6% \u003cem\u003evs.\u003c/em\u003e 33%, absolute risk difference=-27%, 95%CI -41% to -12%, intention to treat analysis, p\u0026lt;0.001; number needed to treat 3, 95%CI 2 to 8). This effect was associated with a higher DNA methylation rate of the leptin gene in cord blood (30.4% [1.02 SD] \u003cem\u003evs.\u003c/em\u003e 16.9% [2.99 SD], MD vs. CT arm, p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD during pregnancy is an effective strategy to prevent pediatric overweight/obesity at 24 months. This effect could be mediated, at least in part, by an epigenetic modulation of leptin expression.\u003c/p\u003e","manuscriptTitle":"Effects of Mediterranean Diet During Pregnancy on the Onset of Overweight or Obesity in the Offspring: A Randomized Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:44:51","doi":"10.21203/rs.3.rs-4026361/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-06-04T07:49:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-21T15:44:06+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-04-11T18:15:12+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-04-09T13:44:32+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-04-05T07:53:16+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-03-24T08:51:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-08T11:37:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-07T14:32:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2024-03-07T14:32:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15623276-721e-49b0-a189-91a4212689cd","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29940603,"name":"Health sciences/Diseases/Nutrition disorders/Obesity"},{"id":29940604,"name":"Biological sciences/Physiology/Metabolism/Fat metabolism"}],"tags":[],"updatedAt":"2024-09-27T10:44:15+00:00","versionOfRecord":{"articleIdentity":"rs-4026361","link":"https://doi.org/10.1038/s41366-024-01626-z","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2024-09-18 04:00:00","publishedOnDateReadable":"September 18th, 2024"},"versionCreatedAt":"2024-03-27 17:44:51","video":"","vorDoi":"10.1038/s41366-024-01626-z","vorDoiUrl":"https://doi.org/10.1038/s41366-024-01626-z","workflowStages":[]},"version":"v1","identity":"rs-4026361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4026361","identity":"rs-4026361","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}