Abstract
Background
Disrupted feeding timing may alter microbiota profiles and contribute to metabolic disturbances. This study aimed to investigate the impact of maternal chrononutrition during pregnancy on maternal gut and placental microbiota.
Methods
This study was conducted between April 2022 and February 2023 at Erciyes University Faculty of Medicine Hospitals and Kayseri Private Dünyam Hospital in Kayseri, Türkiye, among healthy pregnant women with predominantly daytime feeding (pDT, n = 10) or predominantly nighttime feeding (pNT, n = 10). A questionnaire was administered, and three-day food consumption diaries were recorded at both 20–26 and 32–36 weeks of gestation. Fecal samples were collected at 32–36 weeks of gestation, and placental samples were collected at birth and stored at -80 °C until analysis.
Results
Although total daily energy intake at both gestational periods (20–26 and 32–36 weeks) was similar between the groups, gestational weight gain was greater in the pNT group [16.30 (5.25) kg] than in the pDT group [11.90 (3.41) kg] (p = 0.039). Compared with the pDT group, the Firmicutes: Bacteroidetes ratio was higher in the maternal gut microbiota of the pNT group. Furthermore, Bacilli, Lactobacillales, Lactobacillaceae, and Lactobacillus were significantly more abundant in the pDT group than in the pNT group (p < 0.05). The intestinal and placental microbiota of the pDT and pNT groups had similar alpha and beta diversity.
Conclusions
Our findings suggest that predominantly daytime or nighttime feeding during pregnancy may influence the composition of maternal gut and placental microbiota. These microbiome alterations may have potential implications for maternal and infant health; however, larger longitudinal studies are needed to clarify their long-term relevance, including possible links with fetal programming.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12884-026-09584-2.
Keywords: Pregnancy, Chrononutrition, Maternal nutrition, Microbiota, Placenta, Fetal programming
Introduction
The Developmental Origins of Health and Disease (DOHaD) hypothesis states that the structure and physiology of the developing organism can adapt to adverse environmental conditions, thereby predisposing individuals to various pathological conditions in later life [1]. Studies have shown that maternal diet composition and total energy intake during pregnancy are associated with offspring health and disease risk [2, 3]. Incorporating the gut microbiota into the DOHaD framework may provide a better understanding of the relationship between maternal nutrition and infant health and disease [1]. Maternal nutrition during pregnancy and lactation may influence the composition and relative abundance of the maternal microbiome, thereby altering the pool of bacteria that may be transmitted to the infant during gestation and early life. Microbial transmission across the maternal–fetal interface in the placenta has been proposed as a potential mechanism; however, further studies are needed to fully validate this model [4].
Evidence is beginning to accumulate on the role of diet composition in maternal and infant health, highlighting the importance not only of what is consumed but also of when it is consumed [5]. Circadian rhythm refers to endogenous biochemical, physiological, and behavioral rhythms that follow an approximately 24-hour cycle and are synchronized with environmental cues, particularly the light–dark cycle [6]. While light–dark cycles strongly influence the central circadian rhythm, eating time, eating duration, diet composition, and host circadian status may influence intestinal circadian rhythms [6]. Predominantly nighttime feeding in pregnancy has been associated with poorer diet quality, higher dietary glycemic load and fat intake, and lower fruit and vegetable consumption [7], which may have implications for maternal and infant health. In addition, animal studies have shown that disrupted feeding timing may alter microbiota profiles and contribute to metabolic disturbances [8]. However, to our knowledge, no human study has examined the relationship between maternal chrononutrition during pregnancy and maternal gut and placental microbiota. Therefore, this study aimed to investigate the impact of maternal chrononutrition during pregnancy on maternal gut and placental microbiota.
Methods
Participants
This prospective observational study involved 37 pregnant women admitted to the obstetrics and gynecology outpatient clinics at Erciyes University Faculty of Medicine Hospitals and Kayseri Private Dünyam Hospital. Eligible participants were informed about the objectives and procedures of the study, and written informed consent was obtained from all participants prior to enrollment. Inclusion criteria were age 18 years and older, singleton pregnancy, pre-pregnancy BMI < 25 kg/m², gestational age of 20–26 weeks, elective cesarean section, delivery at term, not smoking or drinking alcohol, and absence of pregnancy-related complications (preeclampsia, gestational diabetes, anemia, osteomalacia, hypertension, etc.). Vaginal deliveries and emergency cesarean sections were excluded because the mode of delivery is a major determinant of neonatal microbial colonization and may influence maternal and placental microbiota composition. Restricting the study population to uncomplicated elective cesarean deliveries was intended to minimize potential confounding and ensure a more homogeneous study population.
The exclusion criteria were as follows: prebiotic or probiotic use; use of antibiotics, psychotropic drugs, chemotherapy, steroid treatment, cytokines, or immunosuppressive agents; sleep disorders; night shift work; travel to countries in a different time zone within the last month; fetal malformations or anomalies; chronic, metabolic, or gastrointestinal diseases, including diabetes, cardiovascular disease, autoimmune disease, and inflammatory bowel disease; HIV, hepatitis C, or other infections; immunosuppressive disease; history of cancer other than squamous or basal cell carcinoma of the skin treated by local excision; suspicion or treatment of toxic shock syndrome; and major gastrointestinal surgery other than cholecystectomy or appendectomy in the last 5 years. We also excluded participants whose babies were born prematurely, had low birth weight, or were diagnosed with neonatal sepsis, pneumonia, heart disease, congenital metabolic diseases, other chronic or congenital diseases, or nerve, muscle, and bone diseases. Two participants withdrew from the study, and 15 were not included in the data analysis for various reasons, resulting in 20 participants being assessed for primary outcomes (Fig. 1).
Fig. 1.
Participant flow diagram. pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding
The sample size of this study was determined based on similar microbiome studies in the literature [9, 10]. While larger cohorts are often preferred in microbiome research, previous studies have demonstrated that meaningful insights can be obtained with comparable sample sizes. Additionally, strict inclusion and exclusion criteria were applied to minimize variability and enhance the reliability of the findings.
Study design
The study began with eligible pregnant women completing a questionnaire and recording their food intake for three days (Supplementary material). Based on the distribution of energy intake across mealtimes, participants were classified as having predominantly daytime feeding (pDT) or predominantly nighttime feeding (pNT). None of the participants were engaged in night-shift work, as night-shift workers were excluded from the study; therefore, the pNT classification reflected relatively higher nighttime energy intake during pregnancy rather than a chronic shift-work-related lifestyle pattern. Studies investigating the microbiota during pregnancy typically collect fecal samples between 30 and 36 weeks of gestation [11, 12]. Therefore, in the present study, fecal samples were collected from participants’ homes at 32–36 weeks of gestation, transported to the laboratory, frozen at -80 °C within 1 h, and stored until analysis. Placental samples were collected by the physician at birth, frozen at -80 °C within 15 min, and stored until analysis.
Records of food consumption
At both gestational periods (20–26 and 32–36 weeks), three-day food consumption diaries, including two weekdays and one weekend day, were recorded and checked using the multiple-pass method [13]. Energy and nutrient intakes were analyzed using BeBiS 8.2 software, and dietary requirements were evaluated according to the Türkiye Dietary Guidelines [14].
Evaluation of mealtime
Sunlight has been reported to serve as an important environmental cue for the human circadian clock [8]. In Kayseri (37–38° N, 34–36° E), sunrise and sunset occur at 06:42 and 18:50 on March 20, 2022 (vernal equinox), 05:13 and 20:06 on June 20, 2022 (summer solstice), 06:22 and 18:35 on September 22, 2022 (autumnal equinox), and 07:53 and 17:48 on December 22, 2022 (winter solstice), respectively. Because of the large seasonal variation in daylight hours, two average daytime and nighttime intervals were defined based on the equinoxes. From the vernal equinox to the autumnal equinox, the daytime interval was defined as 06:00–19:30 and the nighttime interval as 19:31–05:59. From the autumnal equinox to the vernal equinox, the daytime interval was defined as 07:30–18:00 and the nighttime interval as 18:01–07:29 [15].
Participants were classified according to the distribution of their energy intake across daytime and nighttime intervals using three-day dietary records collected during pregnancy. Participants whose nighttime energy intake was below the median nighttime energy intake of all participants were classified as the pDT group, whereas those whose nighttime energy intake was above the median were classified as the pNT group [15].
Confounding factors
Various factors, including physical activity, sleep quality, nighttime eating behavior, and depression, may affect circadian rhythm and microbiota composition [16]. Therefore, physical activity levels were evaluated using the Pregnancy Physical Activity Questionnaire [17], sleep quality using the Pittsburgh Sleep Quality Index [18], night eating syndrome using the Night Eating Questionnaire [19], and depression risk using the Edinburgh Postpartum Depression Scale [20].
Analysis of fecal and placenta microbiota
Fecal and placental samples were analyzed by a specialized service provider. Genomic DNA was extracted from fecal and placental samples using the DiaRex® Stool Genomic DNA Extraction Kit (Cat. No. SD-0323, Ankara, Türkiye) according to the manufacturer’s protocol, with minor adaptations for placental tissue [21, 22]. Briefly, 25–50 mg of each sample was lysed using stool lysis buffer, glass beads, and zirconium beads, followed by homogenization, proteinase K digestion, lysis, ethanol precipitation, column-based purification, and elution.
The V3–V4 regions of the 16 S rRNA gene were amplified, and libraries were prepared using the Nextera XT DNA Library Preparation Kit and indices (Illumina). Sequencing was performed on the Illumina MiSeq 2 × 300 bp platform. Because microbial profiling was based on 16 S rRNA gene amplicon sequencing targeting bacterial DNA, no host DNA depletion kit was used. Potential contamination was monitored during laboratory processing using negative extraction controls and no-template PCR controls.
Bioinformatics analyses were performed using QIIME 2. Quality filtering, denoising, and chimera removal were performed using the DADA2 algorithm, and amplicon sequence variants were generated. Non-target sequences were excluded before downstream analyses. Taxonomic assignment was performed using the SILVA 138 database. Alpha diversity was assessed using the Chao1, Shannon, and Simpson indices and observed operational taxonomic units (observed OTUs), while beta diversity was calculated using Bray–Curtis, Jaccard, weighted UniFrac, and unweighted UniFrac distances. Differential abundance analyses were performed using the DESeq2 R package, and taxa differing between groups were further evaluated using linear discriminant analysis effect size (LEfSe).
Statistical analysis
The IBM SPSS (version 27.0) statistical package was used to evaluate the data. Categorical variables are presented as numbers (n) and percentages (%), and continuous variables as mean and standard deviation (SD). The normality of continuous variables was assessed using the Shapiro–Wilk test, histograms, Q-Q plots, and skewness and kurtosis values. Logarithmic transformation was applied to variables that did not meet normality assumptions where appropriate, and transformed data are presented as geometric mean and standard error.
Independent samples t-tests were used to compare continuous variables between the pDT and pNT groups, and chi-square tests were used for categorical variables. Paired samples t-tests were used to compare continuous variables measured at two gestational periods, 20–26 and 32–36 weeks, within each group, and McNemar tests were used for categorical variables. For microbiota analyses, p-values for between-group comparisons were calculated using the Kruskal–Wallis test. A 95% confidence interval was used, and statistical significance was set at p < 0.05.
Results
Participants
Table 1 presents the baseline characteristics of the participants. The mean age was 31.85 (5.37) years, the mean number of pregnancies was 3.40 (1.35), and there were no instances of stillbirth. Among the pregnant women, 70% had a high school education or higher, whereas 80% were unemployed. 45% reported using dietary supplements before pregnancy, with folic acid being the only supplement used. 35% reported receiving dietary education from a dietitian. Gestational weight gain was greater in the pNT group than in the pDT group (p = 0.039).
Table 1.
Maternal and infant characteristics of the participants
| Variables | Total (n = 20) | pDT (n = 10) | pNT (n = 10) | p |
|---|---|---|---|---|
| Maternal characteristics | ||||
| Age (years) | 31.85 (5.37) | 31.90 (6.33) | 31.80 (4.56) | 0.968a |
| Height (cm) | 162.10 (6.26) | 163.60 (7.29) | 160.60 (4.97) | 0.297a |
| Pre-pregnancy body weight (kg) | 63.35 (5.25) | 64.00 (6.07) | 62.7 (4.52) | 0.594a |
| Weight gain during pregnancy (kg) | 14.10 (4.86) | 11.90 (3.41) | 16.30 (5.25) | 0.039 a |
| Number of pregnancies | 3.40 (1.35) | 3.30 (1.56) | 3.50 (1.17) | 0.751a |
| Educational Status | 0.432b | |||
| Primary School | 2 (10.0) | 0 (0.0) | 2 (20.0) | |
| Middle School | 4 (20.0) | 2 (20.0) | 2 (20.0) | |
| High School | 7 (35.0) | 4 (40.0) | 3 (30.0) | |
| University | 6 (30.0) | 4 (40.0) | 2 (20.0) | |
| Postgraduate | 1 (5.0) | 0 (0.0) | 1 (10.0) | |
| Working | 1.000b | |||
| Yes | 4 (20.0) | 2 (20.0) | 2 (20.0) | |
| No | 16 (80.0) | 8 (80.0) | 8 (80.0) | |
| Pre-pregnancy nutritional supplement use | 0.653b | |||
| Yes | 9 (45.0) | 5 (50.0) | 4 (45.0) | |
| No | 11 (55.0) | 5 (50.0) | 6 (55.0) | |
| Type of pre-pregnancy dietary supplement | N/A | |||
| Folic acid | 9 (100.0) | 5 (55.6) | 4 (44.4) | |
| Nutrition education | 0.639b | |||
| Yes | 7 (35.0) | 3 (30.0) | 4 (40.0) | |
| No | 13 (65.0) | 7 (70.0) | 6 (60.0) | |
| Infant characteristics | ||||
| Gender | 0.350b | |||
| Boys | 13 (65.0) | 8 (80.0) | 5 (50.0) | |
| Girls | 7 (35.0) | 2 (20.0) | 5 (50.0) | |
| Gestational age (weeks) | 38.31 (0.67) | 38.26 (0.54) | 38.37 (0.79) | 0.712a |
| Birth weight (g) | 3226.9 (386.7) | 3194.5 (346.6) | 3259.4 (439.7) | 0.718a |
| Birth height (cm) | 50.2 (2.03) | 50.6 (2.01) | 49.7 (2.05) | 0.336a |
| Head circumference (cm) | 35.4 (1.46) | 35.4 (1.76) | 35.5 (1.18) | 0.941a |
Abbreviations: pDT predominantly daytime feeding, pNT predominantly nighttime feeding, N/A not applicable
Values are presented as mean (SD) for continuous variables and n (%) for categorical variables
Superscript letters indicate the statistical test used:
a Independent sample t-test
b Chi-square test
Bold values indicate statistical significance at p < 0.05
Features related to the week of gestation
Characteristics of pregnant women were collected during two gestational periods, 20–26 and 32–36 weeks, at approximately 25 and 34 weeks of gestation. Most pregnant women had good sleep quality in both gestational periods (80.0% and 85.0%, respectively), and most were not at risk of depression (80.0% and 85.0%, respectively) or night eating syndrome (95.0% and 95.0%, respectively). In addition, almost all pregnant women used dietary supplements in both gestational periods (95.0% and 100.0%, respectively), and the three most commonly used supplements were iron, multivitamins, and vitamin D. All these characteristics and physical activity levels were similar between the pDT and pNT groups, as well as between the two gestational periods (p > 0.05). The pNT group showed greater gestational weight gain than the pDT group from pre-pregnancy to 20–26 weeks (p = 0.020), with a similar but non-significant trend from 20 to 26 to 32–36 weeks (p = 0.057) (Table 2).
Table 2.
Participants’ gestational period characteristics
| Variables | Gestation Weeks |
pDT (n = 10) | pNT (n = 10) | p | Total (n = 20) | p |
|---|---|---|---|---|---|---|
| Body weight (kg) | 20–26 | 70.17 (4.93) | 72.64 (4.88) | 0.277a | 71.40 (4.94) | < 0.001 c |
| 32–36 | 74.46 (6.29) | 77.39 (5.73) | 0.292a | 75.92 (6.04) | ||
| Weight gained by week of gestation (kg) | 20–26 | 6.17 (2.80) | 9.94 (3.72) | 0.020 a | 8.05 (3.74) | < 0.001 c |
| 32–36 | 10.46 (3.31) | 14.69 (5.67) | 0.057a | 12.57 (5.01) | ||
| PPAQ score† | 20–26 | 176.81 (31.63) | 172.17 (25.29) | 0.906a | 174.47 (19.75) | 0.427c |
| 32–36 | 162.37 (28.94) | 173.67 (28.78) | 0.769a | 167.93 (19.93) | ||
| Sleep quality (good) | 20–26 | 8 (80.0) | 8 (80.0) | 1.000b | 16 (80.0) | 1.000d |
| 32–36 | 9 (90.0) | 8 (80.0) | 1.000b | 17 (85.0) | ||
| Depression risk (no) | 20–26 | 8 (80.0) | 8 (80.0) | 1.000b | 16 (80.0) | 1.000d |
| 32–36 | 8 (80.0) | 9 (90.0) | 1.000b | 17 (85.0) | ||
| Night eating syndrome (no) | 20–26 | 10 (100.0) | 9 (90.0) | 1.000b | 19 (95.0) | 1.000d |
| 32–36 | 10 (100.0) | 9 (90.0) | 1.000b | 19 (95.0) | ||
| Dietary supplement use (yes) | 20–26 | 10 (100.0) | 9 (90.0) | 1.000b | 19 (95.0) | N/A |
| 32–36 | 10 (100.0) | 10 (100.0) | N/A | 20 (100.0) | ||
| Iron | 20–26 | 9 (90.0) | 7 (70.0) | 0.582b | 16 (80.0) | 1.000d |
| 32–36 | 10 (100.0) | 7 (70.0) | 0.211b | 17 (85.0) | ||
| Multivitamin | 20–26 | 6 (60.0) | 6 (60.0) | 1.000b | 12 (60.0) | 1.000d |
| 32–36 | 6 (60.0) | 7 (70.0) | 0.639b | 13 (65.0) | ||
| Vitamin D | 20–26 | 5 (50.0) | 7 (70.0) | 0.650b | 12 (60.0) | 1.000d |
| 32–36 | 4 (40.0) | 7 (70.0) | 0.370b | 11 (55.0) |
Abbreviations: pDT predominantly daytime feeding, pNT predominantly nighttime feeding, PPAQ Pregnancy Physical Activity Questionnaire
Values are presented as mean (SD) for continuous variables and n (%) for categorical variables
Superscript letters indicate the statistical test used
a Independent sample t-test
b Chi-square test;
c Paired sample t-test
d McNemar test
†Geometric mean ± standard error
Bold values indicate statistical significance at p < 0.05
Dietary consumption
During both gestational periods, there were no significant differences between the groups in daily intake of energy, carbohydrates, fats, and fiber, or in the percentage of recommended intake met (p > 0.05). However, at 32–36 weeks of gestation, protein intake and the percentage of protein requirements met were lower in the pNT group than in the pDT group (p = 0.030). Furthermore, as expected, the pNT group had higher nighttime energy intake than the pDT group in both gestational periods (p < 0.001) (Table 3; Fig. 2; Supplementary Table 1). There were no significant differences in daily intake of fatty acids, vitamins, or minerals between the groups during either gestational period (Supplementary Tables 2, 3, and 4).
Table 3.
Participants’ daily energy and nutrient intakes
| Nutrients | Gestation Weeks |
pDT (n = 10) | pNT (n = 10) | p a | Total (n = 20) | p b |
|---|---|---|---|---|---|---|
| Energy (kcal) | 20–26 | 1586.50 (353.02) | 1798.62 (179.78) | 0.114 | 1692.56 (293.57) | 0.121 |
| 32–36 | 1697.41 (452.07) | 1903.52 (286.54) | 0.239 | 1800.46 (383.25) | ||
| Nighttime energy (kcal) | 20–26 | 133.10 (115.14) | 782.14 (177.92) | < 0.001 | 457.62 (363.49) | 0.014 |
| 32–36 | 245.74 (185.77) | 858.64 (156.09) | < 0.001 | 552.19 (356.01) | ||
| Carbohydrate (%) | 20–26 | 44.97 (3.68) | 46.77 (4.55) | 0.344 | 45.87 (4.13) | 0.342 |
| 32–36 | 46.34 (5.88) | 48.00 (3.64) | 0.457 | 47.17 (4.83) | ||
| Protein (%) | 20–26 | 14.80 (1.69) | 14.25 (2.37) | 0.558 | 14.52 (2.02) | 0.528 |
| 32–36 | 16.20 (2.77) | 13.80 (1.47) | 0.030 | 15.00 (2.48) | ||
| Fat (%) | 20–26 | 40.22 (3.80) | 38.96 (3.36) | 0.446 | 39.59 (3.55) | 0.66 |
| 32–36 | 37.45 (4.61) | 38.18 (3.08) | 0.681 | 37.81 (3.83) | ||
| Fiber (g)† | 20–26 | 18.61 (2.75) | 22.59 (1.48) | 0.189 | 20.50 (1.55) | 0.564 |
| 32–36 | 19.25 (3.38) | 20.35 (1.46) | 0.730 | 19.79 (1.79) |
Abbreviations: pDT predominantly daytime feeding, pNT predominantly nighttime feeding
Values are presented as mean (SD) for continuous variables
Superscript letters indicate the statistical test used
a Independent sample t-test;
b Paired sample t-test
†Geometric mean ± standard error
Bold values indicate statistical significance at p < 0.05
Fig. 2.
Percentages of participants meeting their energy and macronutrient requirements. pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding
Intestinal microbiota analyses
Fecal microbiota composition
The phylum-level relative abundances were presented descriptively. In the pDT group, the relative abundances were Firmicutes (73.53%), Bacteroidetes (17.14%), Actinobacteria (5.04%), Proteobacteria (3.03%), and Verrucomicrobia (1.24%). In the pNT group, the relative abundances were Firmicutes (73.51%), Bacteroidetes (16.83%), Actinobacteria (5.50%), Proteobacteria (3.38%), and Verrucomicrobia (0.75%) (Fig. 3a). At the genus level, Faecalibacterium was the dominant genus, with relative abundances of 16.0% in the pDT group and 18.94% in the pNT group. The relative abundances of Bacteroides (9.28% vs. 11.79%), Dialister (7.68% vs. 8.86%), Roseburia (6.78% vs. 8.86%), and Coprococcus (3.81% vs. 4.40%) were observed in the pDT and pNT groups, respectively. The relative abundances of Prevotella (9.11% vs. 5.11%), Blautia (7.40% vs. 4.87%), and Oscillospira (6.28% vs. 5.43%) were observed in the pDT and pNT groups, respectively (Fig. 3b). At the family level, the relative abundance of Prevotellaceae was 7.17% in the pDT group and 4.01% in the pNT group, whereas the relative abundance of Erysipelotrichaceae was 2.06% and 2.29%, respectively (Supplementary Fig. 1).
Fig. 3.
Fecal microbiota composition of the study groups at the phylum and genus levels. Relative abundances of the bacterial community are shown at the phylum (a) and genus (b) levels. pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding.
Alpha and beta diversity
The Simpson index (Fig. 4a), Shannon index (Fig. 4b), Chao-1 index (Supplementary Fig. 2a), and observed OTUs (Supplementary Fig. 2b) in maternal fecal samples did not differ significantly between the pDT and pNT groups (p > 0.05). Principal coordinate analysis (PCoA) based on Bray–Curtis (Fig. 4c), Jaccard (Fig. 4d), weighted UniFrac (Fig. 4e), and unweighted UniFrac (Fig. 4f) distances showed no statistically significant differences between the groups in maternal fecal samples (p > 0.05).
Fig. 4.
Alpha and beta diversity plots of fecal microbiota in the study groups. Alpha diversity was compared using the Simpson (a) and Shannon (b) indices, with no significant difference between the groups in maternal fecal samples (p > 0.05). Beta diversity was assessed using Bray–Curtis (c), Jaccard (d), weighted UniFrac (e), and unweighted UniFrac (f) distances, with no statistically significant difference between the groups (p > 0.05). pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding.
LEfSe analysis (LDA threshold > 2, p < 0.05) was used to identify taxa that differed significantly between the groups. Bacilli, Lactobacillales, Lactobacillaceae, and Lactobacillus were more abundant in the pDT group at the class, order, family, and genus levels, respectively (p < 0.05) (Fig. 5).
Fig. 5.
LEfSe analysis showing bacterial taxa with significantly different relative abundances between the study groups. LDA scores are shown for differentially abundant taxa. c, class; o, order; f, family; g, genus; pDT, predominantly daytime feeding
Placental microbiota analyses
Placental microbiota composition
The phylum-level relative abundances were presented descriptively. In the pDT group, the relative abundances were Proteobacteria (93.66%), Firmicutes (4.78%), Actinobacteria (1.00%), and Bacteroidetes (0.49%). In the pNT group, the relative abundances were Proteobacteria (90.78%), Firmicutes (5.10%), Bacteroidetes (2.65%), and Actinobacteria (1.01%) (Fig. 6a). At the genus level, Ralstonia was the dominant genus, with relative abundances of 88.56% in the pDT group and 84.12% in the pNT group. The relative abundances of Delftia (1.58% vs. 1.12%), Oscillospira (0.52% vs. 0.24%), and Acinetobacter (0.19% vs. 0.15%) were observed in the pDT and pNT groups, respectively. The relative abundances of Stenotrophomonas (3.66% vs. 4.50%) and Streptococcus (0.07% vs. 1.76%) were observed in the pDT and pNT groups, respectively (Fig. 6b). At the order level, the relative abundance of Pseudomonadales was 0.53% in the pDT group and 0.31% in the pNT group (Supplementary Fig. 3).
Fig. 6.
Placental microbiota composition of the study groups at phylum and genus levels. Relative abundances of the bacterial community are shown at the phylum (a) and genus (b) levels. pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding.
Alpha and beta diversity
The Simpson index (Fig. 7a), Shannon index (Fig. 7b), Chao-1 index (Supplementary Fig. 4a), and observed OTUs (Supplementary Fig. 4b) in placental samples did not differ significantly between the pDT and pNT groups (p > 0.05). Principal coordinate analysis (PCoA) based on Bray–Curtis (Fig. 7c), Jaccard (Fig. 7d), weighted UniFrac (Fig. 7e), and unweighted UniFrac (Fig. 7f) distances showed no statistically significant differences between the groups in placental samples (p > 0.05).
Fig. 7.
Alpha and beta diversity plots of placental microbiota in the study groups. Alpha diversity was compared using the Simpson (a) and Shannon (b) indices, with no significant difference between the groups in placental samples (p > 0.05). Beta diversity was assessed using Bray–Curtis (c), Jaccard (d), weighted UniFrac (e), and unweighted UniFrac (f) distances, with no statistically significant difference between the groups (p > 0.05). pDT, predominantly daytime feeding; pNT, predominantly nighttime feeding.
Characteristics of Infants
The newborns were 65% boys and 35% girls. Between the pDT and pNT groups, there was no statistically significant difference in gestational age, birth weight and length, or head circumference (p > 0.05) (Table 1). When birth weight was examined descriptively by infant sex, mean birth weight was 3279.38 (335.67) g in boys and 2855.00 (63.64) g in girls in the pDT group, and 3331.80 (532.90) g in boys and 3187.00 (371.31) g in girls in the pNT group (Supplementary Table 5).
Discussion
In recent years, researchers have underscored the significance of circadian rhythms in shaping the composition and function of the gut microbiota [23]. Nutrition is one of the environmental factors that most strongly influences peripheral circadian clocks. Similarly, diet quality and mealtimes may influence both the composition and function of gut microorganisms. Disruption of circadian rhythms has been hypothesized to contribute to functional and structural dysbiosis, thereby promoting pathological mechanisms [23]. Human studies have not yet assessed the potential impact of maternal circadian rhythm disruption on developing generations, and the available evidence is currently limited to animal models [24, 25]. Disrupted feeding timing may alter microbiota profiles and contribute to metabolic disturbances [26, 27]; however, these associations have not yet been examined in pregnant women. To our knowledge, the present study is the first to investigate the relationship between maternal chrononutrition during pregnancy and maternal gut and placental microbiota.
Conditions that disrupt circadian rhythms, such as jet lag and night shift work, have been associated with changes in the gut microbiota in previous studies [26, 28]. When individuals working day shifts switched to night shifts, the relative abundance of Firmicutes increased, whereas that of Bacteroidetes decreased [9]. Similarly, the present study found that the Firmicutes: Bacteroidetes ratio was higher in the pNT group than in the pDT group. Although the functional significance of the Firmicutes: Bacteroidetes ratio remains unclear, it is considered an indicator of structural changes in the gut microbiota [29]. Some studies have reported an association between an increased Firmicutes: Bacteroidetes ratio and human obesity [16, 30]. Therefore, the higher relative abundance of Firmicutes and lower relative abundance of Bacteroidetes observed in the gut microbiota of the pNT group may be associated with greater gestational weight gain. Although there was no difference between the pDT and pNT groups in pre-pregnancy body weight or total daily energy intake during either trimester, gestational weight gain was higher in the pNT group than in the pDT group. This finding highlights the potential importance of meal timing and the distribution of energy intake throughout the day for gestational weight gain, and suggests that differences in the relative abundance of Firmicutes and Bacteroidetes may partly contribute to this association. Furthermore, the lower protein intake observed in the pNT group during the third trimester compared with the pDT group may also have influenced this result.
Physical or biological stress can affect the gut microbiota. In this context, reports indicate an increase in the abundance of certain fecal bacteria, including Firmicutes and Actinobacteria [31]. Studies have shown that circadian rhythm disruption, which is a type of biological stress [31], can alter the gut microbiota, affecting not only Firmicutes and Bacteroidetes but also other bacterial taxa [26]. Previous studies have reported that individuals working night shifts exhibit a higher relative abundance of Prevotellaceae compared with those working day shifts [9, 26]. However, unlike previous studies, the present study found that the relative abundance of Prevotellaceae was higher in the pDT group than in the pNT group. On the other hand, Mortas et al. (2020) showed that the relative abundances of Erysipelotrichaceae and Coprococcus increased during night shifts compared with day shifts [9]. Similarly, in the present study, the relative abundances of Erysipelotrichaceae and Coprococcus were higher in the pNT group than in the pDT group. Studies have demonstrated that an increased abundance of Erysipelotrichaceae can alter liver and lipid metabolism in both humans and animals [10, 32, 33]. Despite its association with a healthy gut due to its butyrate-producing genus [34], Coprococcus has also been linked to neuroinflammation [35]. Given these reported associations, the higher relative abundances of Erysipelotrichaceae and Coprococcus observed in the pNT group may have potential implications for maternal and infant health. However, as the present study did not include long-term follow-up data, these findings should be interpreted with caution. Further longitudinal studies are needed to determine whether meal timing-related alterations in maternal gut microbiota have sustained metabolic effects or may be involved in fetal programming-related pathways.
The present study found significantly higher relative abundances of Bacilli, Lactobacillales, Lactobacillaceae, and Lactobacillus in the pDT group than in the pNT group based on LEfSe analysis, suggesting that these taxa may be associated with predominantly daytime feeding. Lactobacillus is an important genus involved in maintaining intestinal mucosal integrity [36]. Qi et al. (2019) showed that Lactobacilli can promote intestinal mucosal development by producing L-ornithine from arginine. L-ornithine increases the aryl hydrocarbon receptor ligand L-kynurenine, which is produced through tryptophan metabolism in gut epithelial cells, thereby increasing RORγt+, IL-22+, and ILC3 cells. These intestinal immune cells may influence mucosal cell proliferation and mucin production and help regulate intestinal mucosal homeostasis. Given the role of intestinal mucosal homeostasis in diseases such as colitis and metabolism-related disorders, these findings may provide insight into potential preventive or therapeutic approaches [36].
Previous animal and human studies have found that disrupted circadian rhythms or different chronotypes were not associated with statistically significant differences in alpha and beta diversity [37, 38]. Similarly, the present study observed differences at different taxonomic levels but found no statistically significant differences in alpha or beta diversity between the pDT and pNT groups. Maternal diet can influence the gut microbiota during pregnancy to varying degrees. Certain macronutrients are associated with different bacterial compositions and relative abundances and may positively or negatively modulate gut microbiota diversity [39]. For example, Röytiö et al. (2017) showed that alpha diversity decreased as dietary cholesterol, total fat, and saturated fat intake increased in pregnant women [40]. Given the potential influence of dietary components on the gut microbiota during pregnancy, the similar nutrient intake observed between the pDT and pNT groups may partly explain the lack of differences in alpha and beta diversity in the present study.
In the past, it was thought that the intrauterine environment was sterile and that the newborn’s first contact with the microbiota occurred during birth. However, both culture- and PCR-based techniques have revealed the presence of bacteria in the fetal membranes of healthy term pregnancies [41], cord blood [42], and amniotic fluid [43], indicating the formation of microbiota in the intrauterine environment. Aagaard et al. (2014) showed that the placenta contains a low-biomass but metabolically rich microbiome. The placental microbiome is mainly composed of non-pathogenic commensal bacteria from the phyla Firmicutes, Tenericutes, Proteobacteria, Bacteroidetes, and Fusobacteria [44]. Similarly, this study found that Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria constituted most of the placental microbiota in pregnant women. Furthermore, compared with the pDT group, the pNT group had a relatively lower Proteobacteria abundance but a relatively higher Firmicutes abundance. Although no previous study has examined how chrononutrition affects the placental microbiota, Bassols et al. (2016) found that, in pregnant women with gestational diabetes, Firmicutes increased while Proteobacteria decreased compared with healthy pregnant women [45]. Maternal obesity and excessive gestational weight gain may lead to placental microbiota dysbiosis [46], and this finding in the present study may be partly attributable to the greater gestational weight gain observed in the pNT group. However, this mechanism needs to be elucidated.
Additionally, the relative abundance of the Pseudomonadales order and the Acinetobacter genus was lower in the pNT group than in the pDT group. A low abundance of placental Acinetobacter has been associated with a more unfavorable metabolic and inflammatory phenotype, including higher maternal glucose levels and lower placental expression of anti-inflammatory mediators such as IL-10 and TIMP3 [45]. These alterations may reflect impaired immunological tolerance and a less favorable intrauterine environment. Even in pregnancies without clinically apparent complications, such microbiota-related alterations may have potential implications for infant health later in life. However, further research is needed to determine how circadian rhythm-based nutrition influences the placental microbiota and whether these alterations are involved in fetal programming-related pathways.
Chrononutrition may be associated with metabolic disturbances that contribute to unfavorable pregnancy outcomes and infant health [8]. However, previous studies have reported no association between predominantly nighttime maternal feeding and birth weight or gestational age [47, 48]. Similarly, the present study found no differences in gestational age, birth weight, birth length, or head circumference between infants in the pDT and pNT groups. This finding may be explained by the strict inclusion and exclusion criteria applied in the present study, as factors that could affect intrauterine development were excluded, and only infants born at term with normal birth weight were included. Therefore, further research is needed to clarify the potential impact of chrononutrition on infant health.
This study has some limitations. First, examining the gut microbiota only in the third trimester may restrict the generalizability of the findings to the entire pregnancy period. Second, the relatively small sample size may limit the broader applicability of the results. However, the strict inclusion and exclusion criteria aimed to minimize variability and enhance the reliability of the findings. Future studies with larger cohorts are needed to confirm these results in more diverse populations. Finally, we were unable to assess the long-term metabolic effects of differences in the microbiota, as participants were not followed after delivery. To gain a deeper understanding of how maternal chrononutrition may influence maternal and infant metabolism through the microbiota, future research should incorporate longitudinal follow-up and examine microbiota composition across all three trimesters, as well as in the placenta, breast milk, and infant gut after birth. Despite these limitations, to our knowledge, this study provides the first preliminary evidence on the relationship between maternal chrononutrition during pregnancy and both intestinal and placental microbiota.
Conclusion
In conclusion, the findings of the present study suggest that predominantly daytime or nighttime feeding during pregnancy may be associated with differences in the composition of both intestinal and placental microbiota. The present study also found that predominant nighttime energy consumption was associated with greater gestational weight gain, even though total daily energy intake did not exceed daily requirements. These differences in intestinal and placental microbiota may have potential implications for maternal and infant health. However, given the small sample size and single-center design, the findings should be interpreted with caution. Further large-scale longitudinal studies are needed to confirm these associations and to investigate their long-term relevance. A better understanding of the relationships among meal timing, maternal gut and placental microbiota, and pregnancy outcomes may help inform future nutritional recommendations during pregnancy.
Supplementary Information
Acknowledgements
We thank Zuleyha Guzel and Aynur Simur, who are medical doctors in gynecology and obstetrics at Dunyam Hospital, for their help with data collection, as well as Samet Ece and Ahmet Varis for their technical assistance. We are also grateful to all the participants who took part in this study.
Abbreviations
- DOHaD
the Developmental Origins of Health and Disease
- LefSe
Linear discriminant analysis effect size
- observed OTUs
observed operational taxonomic units
- PcoA
Principal coordinate analysis
- pDT
predominantly daytime feeding
- pNT
predominantly nighttime feeding
Authors’ contributions
E.K. designed the study protocol, conducted the research, prepared the manuscript, and had primary responsibility for the final content. Z.C.A. designed the study protocol, conducted the statistical analysis, and contributed to manuscript writing. F.O. conducted the research and reviewed the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the Erciyes University Scientific Research Projects Coordination Unit with project numbers of TDK-2022-11922.
Data availability
Availability of data and materials Sequencing reads and metadata were deposited as entire raw data in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA BioProject ID: PRJNA1244373). The datasets generated and/or analyzed during the current study are available in the NCBI sequence read archive (SRA), available at: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1244373?reviewer=ltcljapjvgup6c3rvobgmv0g9.
Declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving participants were approved on March 23, 2022, by the Erciyes University Clinical Research Ethics Committee with decision number 2022/244. Written informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
Availability of data and materials Sequencing reads and metadata were deposited as entire raw data in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA BioProject ID: PRJNA1244373). The datasets generated and/or analyzed during the current study are available in the NCBI sequence read archive (SRA), available at: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1244373?reviewer=ltcljapjvgup6c3rvobgmv0g9.