Birth season shapes the infant metabolome and development in Tanzania: a secondary explorative analysis of the early life interventions for childhood growth and development in Tanzania (ELICIT) trial

Domain: biology
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
Rural communities in resource-limited settings are exposed to seasonal patterns in food insecurity. The pre- and early post-natal period is a key developmental window, sensitive to nutritional availability and quality. While season of birth has been associated with differences in epigenetic, developmental and health outcomes, it is unclear if this imprints on the developing metabolome and contributes to adverse phenotypic outcomes. Here we show that urinary and plasma metabolites in Tanzanian infants follow seasonal waveforms dependent upon their month of birth, persisting up to at least 18 months of life, which were related to food insecurity, breast milk composition and rainfall. This includes developmentally relevant metabolites, including choline, trimethylamine-N-oxide, and polyunsaturated fatty acids. Cognitive measures at 18 months also followed seasonal waveforms based upon month of birth which correlated with seasonal metabolites. Additionally, variation in metabolic status modulated the effectiveness of a maternal-infant nicotinamide intervention targeting stunting. Our findings show that seasonal environmental pressures shape mother-infant dyad food insecurity with lasting ramifications for the infant metabolome and development. These findings support the need for season-dependent nutritional and lifestyle interventions targeting developmental shortfalls in these communities.

Introduction
The pre- and post-natal early life environment imprints on the infant with enduring ramifications for health and development1. Nutritional availability and quality, as well as enteric infections, are major contributors, and in many settings follow seasonal variation. Emerging evidence shows seasonal patterns in food insecurity during the pre- and post-natal periods influence the developing epigenome of the infant with implications for later life phenotypes, including cardio-metabolic health, mortality, and body composition2–4. However, it is currently unclear if these seasonal patterns impart lasting effects on the infant metabolome and on development. Previous metabolomic studies highlight a diverse array of metabolic derangements associated with early life acute and chronic undernutrition and their related outcomes such as stunting, wasting, and cognitive impairments5–8. This includes disruptions to amino acid, choline, tryptophan-nicotinamide, and gut microbial metabolism. Given the limited effectiveness of nutritional and lifestyle interventions to attenuate developmental shortfalls9–12, understanding seasonal pressures on the developing metabolic phenotype may explain differences in treatment responses. Climate change has been shown to exacerbate seasonal variations in environmental conditions and is anticipated to increase the frequency and severity of droughts and food insecurity13. As such, understanding the detrimental impact of adverse climatic conditions on early life undernutrition and its impact on development remains crucial.

To investigate this, we studied infants over the first 18 months of life in Haydom, a rural agricultural town in Tanzania. Here, communities experience seasonal patterns in food insecurity due to their unimodal crop cycle. In this setting, food insecurity decreases following the harvest of beans (March) and maize (June)14,15 but rises during the pre-harvest period (November-February) when harvest stocks become depleted, and diet diversity reduces. Enteric infections are common in this population, and certain pathogens, such as Shigella, vary seasonally, having the greatest burden in the rainy season.

These infants were enrolled in the Early Life Interventions for Childhood growth and development in Tanzania (ELICIT) randomized control trial16–18. This trial had a 2×2 factorial design including nutritional nicotinamide and antimicrobial interventions, provided individually and in combination. However, no effect on the primary outcome of 18-month length was observed. Here, we studied infants from the placebo group and explored whether the month of birth imprints on the urinary and plasma metabolomes at six, 12, and 18 months of life and whether metabolic differences related to variation in developmental outcomes, namely growth and cognition. Next, we assessed if seasonal patterns in food insecurity and maternal breast milk components (i.e., vitamins, human milk oligosaccharides (HMOs), metabolites, and lipids) contributed to these patterns.

In areas with maize-based diets, deficiency of the essential amino acid tryptophan is common and has been associated with infant stunting19. Tryptophan is key for development, being necessary for protein, muscle, and neurotransmitter production. Additionally, through its metabolism via the kynurenine pathway, it is important for the synthesis of nicotinamide adenine dinucleotide (NAD+), an essential co-enzyme for energy production and growth. We, and others, have shown that the tryptophan-niacin-NAD+ pathway is related to growth and perturbed with early life undernutrition and infections5,11. This is due to reduced precursor availability (niacin, nicotinamide, tryptophan, nicotinamide riboside) and immune-associated disruptions (induction of indoleamine 2,3-dioxygenase activating the kynurenine pathway)16,20. To prevent deficiencies in this pathway, the ELICIT study included a group of infants who received nicotinamide as a nutritional intervention (nicotinamide group). This was provided via breastfeeding mothers for the first six months of life before direct supplementation to the infant until 18 months of life. As no improvement in length-for-age z-score at 18 months was observed, we investigated whether seasonal fluctuations in the metabolomic profiles across infants explained variation in responsiveness to nicotinamide. In this work, we show that infant urinary and plasma metabolites in Tanzanian infants follow seasonal waveforms dependent upon their month of birth, persisting up to at least 18 months of life. Such metabolic variation reflected seasonal changes in food insecurity, breast milk composition and rainfall. Cognitive measures at 18 months also displayed seasonal trends based upon birth month, which correlated with seasonal metabolites. Additionally, variation in metabolic status modulated the effectiveness of a maternal-infant nicotinamide intervention targeting stunting. Understanding circannual factors shaping the developing infant system may assist in tailoring interventions to increase their effectiveness against adverse outcomes associated with childhood undernutrition.

Results
Participants
This is a secondary analysis of the ELICIT cohort16, following infants born between September 2017–18 longitudinally to 18 months of life. The ELICIT study was a randomized 2×2 factorial, double-blind, placebo-controlled trial with the aim of improving early life growth using a nicotinamide and/or antimicrobial (azithromycin, nitazoxanide) intervention. Nicotinamide was provided to breastfeeding mothers (250 mg daily tablets for six months post-delivery), followed by direct infant supplementation (100 mg sachets) from six to 18 months.

Here, data from the placebo group (n=278) and infants receiving nicotinamide (n=276, breastfeeding mothers provided 250 mg daily tablets for six months post-delivery, followed by direct infant supplementation (100 mg sachets) from six to 18 months) was studied. Urine was sampled at six months, and urine and blood were sampled at 12 and 18 months. A schematic, study design overview and analysis strategy are provided in Figs. S1, S2, and Supplementary Materials (Statistical Analysis Plan), respectively. This study included infants of various nutritional states, including stunted (length-for-age Z-score [LAZ]<−2), underweight (weight-for-age Z-score [WAZ]<−2), and normal growing infants (Supplementary Data. 1). The antimicrobial group was not considered for this analysis, as the biological samples were collected prior to the administration of a single-dose of the antimicrobials at six, 12, and 18 months and their long-term biochemical consequences cannot be excluded. Additionally, receipt of non-study antibiotics was common (54−86% infants).

Birth month influences infant metabolome
To determine whether the infant metabolome exhibits patterns dependent upon month of birth, targeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics was performed on plasma samples from infants at 12 months of life from the placebo arm of the ELICIT study (n=199). Samples were collected from infants born throughout the year (collected from September 2018 to September 2019), and a total of 628 plasma metabolites were measured. Cosinor analysis21 was used to identify molecules whose concentrations followed a seasonal waveform across the population based upon the infant's month of birth (Fig. 1A–C). A cosine wave with a sloping mesor was fitted to the data using non-linear curve fitting solved by least squares, and statistically compared to H0 of solely a sloping line model using an extra-sums-of-squares F-test. A total of 46 circulating metabolites exhibited birth month dependent variation in their concentrations at 12 months (Supplementary Data. 2). This included choline, trimethylamine-N-oxide (TMAO), eicosapentaenoic acid (EPA), eicosadienoic acid, 17 phosphatidylcholines, six ceramides, three amino acids (aspartate, glutamine, glutamate), 5-aminovaleric acid (5-AVA), γ-aminobutyric acid (GABA), methionine sulfoxide, two cholesterol esters, 3-hydroxyglutaric acid, proline betaine, sphingomyelin C26.1, three hexosylceramides, three triglycerides, and octadecenoylcarnitine. Of these, seven remained significant (choline, TMAO, EPA, methionine sulfate, cer(d18:2/14:0), TG 20:0_34:1 and hex2cer(d18:1/18:0); Figs. 1B, S3)) following adjustment for covariates including enrollment WAZ, socioeconomic status, number of months of exclusive breastfeeding, and maternal factors (weight, height, age) and following correction for multiple testing (q<0.15; n=3 for q<0.10). The birth month of infants with the greatest concentration of each metabolite is determined by the peak phase of the wave. These are provided for each metabolite in Supplementary Data. 3. For example, at 12 months, February-born infants had the highest circulating aspartate, while those born six months later in August had the lowest.

To determine if these metabolic cycles were driven by contemporary pressures at 12 months of life (i.e., time of sampling) or a legacy of the month of birth, plasma was sampled six months later at 18 months. Several plasma metabolites exhibited circannual patterns across the infants at 18 months (n=27; n=6 following FDR correction (q<0.15; n=5 for q<0.10) and adjustment for confounders; Fig. 1C, Fig. S3 and Supplementary Data. 3). Metabolites whose peak phase remained static between sampling points (±1.5 months to account for season) were considered to be influenced by time of birth (Fig. S1). For instance, choline is birth-month-related as November-December born infants persistently had the highest circulating choline at 12 and 18 months, while those born May-June had the lowest (Fig. 1C). These patterns were maintained despite variable pressures across these sampling points, including food insecurity and rainfall. Conversely, metabolites influenced by sampling time were identified as those with a ~six-month (four-eight months) phase difference between the 12- and 18-month samples and so exhibit seasonal variation within individuals (Fig. S1). Interestingly, 12 plasma metabolites exhibited seasonal patterns at both sampling points including choline, TMAO, EPA, glutamate, eicosadienoic acid, cer(d18:2/14:0), hex2cer(d18:1/18:0), TG(20:0_34:1), octadecenoylcarnitine, methionine sulfoxide, and three phosphatidylcholines (PC ae C42:2, PC ae C42:3, PC ae C40:2). Of these, seven remained significant following FDR correction in at least one time point. Here, there were five birth-month-related metabolites including choline, TMAO, EPA, hex2cer(d18:1/18:0), and cer(d18:2/14:0), and two sampling-month-dependent metabolites, TG(20:0_34:1) and PC ae C42:2 (Fig. S3). After adjustment for covariates (enrollment WAZ, socioeconomic status, number of months of exclusive breastfeeding, and maternal factors (weight, height, and age), the variation in EPA and hex2cerd(18:1/18:0) was no longer significant at 18 months.

Additionally, untargeted 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics was performed on urine samples collected from the placebo group infants at six, 12, and 18 months of life (six-month, n=278; 12-months, n=270; 18-months, n=266; Fig. 1A–C). At six months (Fig. 1E), 11 urinary metabolites were identified to exhibit variation across the infants based upon the month in which they were born. These included N-methylnicotinamide (NMND; Fig. 1F), pantothenate (vitamin B5), breast milk-related metabolites (HMOs (2’-fucosyllactose (2’-FL; Fig. 1G), 3’-fucosyllactose (3’-FL)), lactose (Fig. 1H), and D-galactose), betaine, dimethylglycine (DMG; Fig. 1I), valine (Fig. 1J), alanine, and succinate. Of these nine remained significant when and adjusted for the covariates enrollment WAZ, number of months of exclusive breastfeeding, maternal age and socioeconomic status (pantothenate, dimethylglycine, lactose, succinate, valine, 2’-FL, NMND, D-galactose, and 3’FL) and corrected for multiple testing (q<0.15; n=4 for q<0.10; Figs. 1A, S4 and Supplementary Data. 3). Interestingly the seasonality was dampened in the HMO 2’-FL when adjusted for maternal body composition (weight, height). Additionally, at 12 months, the excretion of four metabolites significantly varied by birth month (q<0.15; n=4 for q<0.10; Fig. 1B, Supplementary Data 3). The peak phases of three metabolites, valine, pantothenate and succinate, shifted by ~six months between sampling points and so were considered sampling-month-dependent.

A PCA model was constructed with the seasonal metabolites to compare infants born during different three-month windows (e.g., Sep-Nov, Dec-Feb, Mar-May, Jun-Aug) at six, 12 and 18 months. Permutational multivariate analysis of variance (PERMANOVA) confirmed significant metabolic differences between the windows (adonis p=0.001–0.04, Fig. S5). Similarly, significant pairwise PLS-DA models were obtained comparing seasonal metabolites across infants born in opposing time windows (AUC 0.67–0.88; Fig. S5; metabolite VIP scores listed in Supplementary Data. 4). For metabolites identified as exhibiting seasonal variation across the population, the mean concentrations and abundances by birth season are provided in Supplementary Data. 5.

After stratifying by sex, no plasma or urine metabolites exhibited circannual patterns in the females, while two, 17, and six metabolites were noted in the males at six, 12 and 18 months respectively after FDR and covariate adjustment (enrollment WAZ, socioeconomic status, number of months of exclusive breastfeeding, and maternal factors (weight, height, age)) (Fig. S6, Supplementary Data. 6; for q<0.10, 6M n=1; 12M n=11; 18M n=5). Here, plasma choline and TMAO and urinary D-galactose were noted to be birth-month-dependent metabolites in males, while urinary pantothenate and lactose were sampling-month dependent.

Metabolomic seasonality on cognition
To determine the developmental significance of this biochemical seasonality, we explored relationships with cognitive outcomes. Child development was evaluated at 18 months using the Malawi Development Assessment Tool (MDAT)22. This assessed gross and fine motor, language, and social skills. Cosinor analysis identified that language scores (R2=0.24) and total MDAT (R2=0.13) measured at 18 months varied across infants by birth month, where those born in January and February respectively had the greatest scores (Fig. 2A). When stratified by ‘Water and sanitation, Assets, Maternal education and household Income’ (WAMI) scores23, a measure of socioeconomic status, this pattern persisted only in those with the top 50% scores (R2=0.22). When stratified by sex, this variation was only observed in the female population (R2=0.31). Cross-correlation analysis identified seven seasonal 12-month plasma metabolites that significantly correlated with language scores six months later (Fig. 2B, Supplementary Data. 7). Those infants with the greatest language scores had the highest circulating TMAO (Fig. 2C), EPA (Fig. 2D), PC ae C42:2, TG(20:0_34:1), methionine sulfoxide, hex2cer(d18:1/18:0) and hexcer(d18:1/18:0) at 12 months.

Environment shapes seasonal metabolites
Next, we explored whether fluctuating nutritional availability during early life contributed to this metabolic seasonality. Birth anthropometry reflects environmental and nutritional pressures faced by the mother-fetus prior to and during pregnancy and relates to long-term growth outcomes24. We have previously shown that infants born in the five-month preharvest period (November-March 2017–2018) had lower enrollment weights than those born in the seven months post-harvest (April to October)18. However, enrollment WAZ and LAZ did not follow 12-month seasonal waveforms, even after stratifying by WAMI.

As rainfall contributes to harvest yield, seasonality analysis was applied to Haydom rainfall data from the prenatal year (2016–2017), birth year (2017–2018) and to the 8-year average (2010–2018) (Fig. 3A). All measures significantly fitted a seasonal waveform with peak phases (highest rainfall) in January-February, reflecting annual climatic patterns. Similarly, monthly maternal food insecurity reports across an 18-month period from birth (reported as worry; expressed as % mothers) also followed a seasonal waveform peaking in January (Fig. 3B). These food insecurity patterns occur as harvest stocks deplete and food prices rise during the rainy season (November-May)14,25. Conversely, the dry season (June-October) associates with reduced food insecurity following the harvest of beans in March and maize in May (Fig. 3B). Interestingly, rainfall in the previous year cross-correlated with reported food insecurity (S. Text. 1).

To explore the influence of maternal food insecurity at birth on these seasonal metabolites sampled at 6–18 months of life, cross-correlation analysis was performed. Phase lag represents the time difference between the month of peak food insecurity and the birth month of peak metabolite abundance (Fig. S1). Four birth-month-related metabolites (consistent pattern between sampling times) significantly correlated with food insecurity, including choline, TMAO, hex2Cer(d18:1/18:0), and the omega-3 PUFA EPA. Minimal phase lags (0–40 days) were observed between their peak abundances and peak food insecurity at birth, and this pattern persisted at 12 and 18 months for choline, hex2Cer(d18:1/18:0), and EPA (Fig. 3D, E, Supplementary Data. 8). These were all highest in infants born during peak food insecurity (November-February). Additionally, food insecurity at birth cross-correlated with several seasonal metabolites at six and 12 months with a range of phase lags (Fig. 3C, D, Supplementary Data. 8 and S. Text. 2). Several metabolites displayed the highest abundance in infants born during periods of minimal food insecurity, with a ~six-month phase lag. At six months, this included the urinary excretion of NMND, a marker of nicotinamide availability used in NAD+ synthesis; betaine and DMG. Several metabolites also aligned with contemporary food insecurity at the time of sampling (Fig. S7A–C, Supplementary Data. 9 and S. Text. 3).

Seasonal variation in breast milk
In total, 81% of infants were exclusively breast fed up to six months of life, and seasonal food insecurity patterns acted indirectly on the infant via breast milk throughout this period. We therefore explored breast milk composition seasonality at the vitamin, HMO and metabolome-lipidome level. Of those exhibiting seasonality across the population, the mean metabolite concentration per season is documented in Supplementary Data. 10.

A total of 19 B vitamin-related metabolites were measured in breast milk from 292 mothers, sampled at one- and five-months postpartum. Cosinor analysis identified four metabolites at one-month, and seven at five months that displayed seasonality based on sampling month, which persist following correction for multiple testing (Fig. 3F, Supplementary Data. 11). Of these, one metabolite exhibited seasonality at both sampling times, where biotin (vitamin B7) was consistently highest from mothers who delivered in April (sampled May at one month and September at five months), a period of increasing food availability following the bean harvest in March. Nicotinamide riboside (NR) and nicotinic acid (NAc), precursors involved in the NAD+ synthesis pathway, exhibited seasonality in breast milk. NR at one-month, and NAD+ at five months, were highest in milk from mothers delivering in April (NR sampled May, NAD+ sampled September), whilst NAc was greatest in five-month breast milk collected from mothers sampled in July.

The metabolomic-lipidomic signatures of a subset of breast milk samples (54 individuals) collected at one- and five-months postpartum were characterized alongside their HMO profiles (19 compounds). Due to low sample size, cosinor analysis could not be implemented. Samples were therefore grouped by season of sampling (Sep-Nov, Dec-Feb, Mar-May, Jun-Aug) and variation was assessed by ANOVA or Kruskal-Wallis, and post-hoc tests (FDR adjusted). Mothers sampled between September–November were observed to have the greatest 2’-FL, lacto-N-fucopentaose I (LNFP-1) and glutamine in their milk at one-month postpartum. Additionally, 2’-FL and LNFP-1 were greatest in five-month milk sampled from mothers in June-August (Supplementary Data. 12). Interestingly, no birth-month-related metabolites (choline, TMAO, EPA, hex2-cer(d18:1/18:0), and cer(d18:2/14:0)) were observed to fluctuate across the year in the breast milk, suggesting intake via breast milk does not directly drive these alterations (Fig. S8).

We next assessed the impact of the nutritional environment of the mother on the breast milk metabolome. Cross-correlation was performed between seasonal breast milk B-vitamin-related metabolites and contemporary food insecurity. (Fig. 3F, Supplementary Data. 13). Short phase lags (10–90 days) were noted between peak food insecurity and maximal breast milk biotin, and NR at one-month and flavin mononucleotide, at five months. Metabolites most abundant in milk sampled during periods of high food availability (110–200 days phase lag from peak food insecurity) included pyridoxamine at one-month; pantothenic acid (vitamin B5), Nac, NAD, pyridoxal-5-phosphate, and biotin, at five months.

To assess the impact of food insecurity on the breast milk metabolome and HMOs measured from the subset of mothers, we explored biochemical variation in the season-dependent metabolites associated with contemporary maternal food insecurity reports (No food insecurity [n=40], reported food insecurity [n=14]; Wilcoxon test q<0.05; Fig. 3G, Supplementary Data. 14). At one-month, 2’-FL and LNFP-1 were lower in milk from mothers who did not report food insecurity. At five months, only three mothers from this subset reported food insecurity; therefore, statistical analysis was not performed. Additionally, to explore transmission of these seasonal patterns to the infant, cross-correlation analysis compared five-month breast milk vitamins and six-month urinary metabolites. Significant correlations were observed with a range of phase lags (Fig. S9, Supplementary Data. 15). These included no phase lag between pantothenic acid in the five-month breast milk and the six-month urine, confirmed using time series analysis, where infants persistently in the top tertile for urinary NMND excretion at six-, 12-, and 18-months experienced a less pronounced drop in LAZ growth trajectory from zero-18 months of life (p=0.001, 95% CI: p=0.0002, 0.006; Fig. 4A).

Supplementing infants with nicotinamide
Given the positive associations between urinary NMND and postnatal growth5, the impact of nicotinamide supplementation on infant anthropometry was assessed in this setting as part of the ELICIT RCT (nicotinamide group)16,17. Mothers (n=298) received 250 mg daily nicotinamide tablets for six months post-delivery while breastfeeding, followed by direct infant supplementation (100 mg sachets) from six to 18 months. Maternal nicotinamide supplementation significantly increased breast milk NR and ablated the seasonal variation of this metabolite (Figs. 4B, C, S12A-B). Additionally, six-month NMND excretion by the infants of supplemented mothers was increased, and the seasonality was lost (Figs. 4D, E, S12C). Despite these metabolic changes, nicotinamide supplementation did not alter anthropometry between the supplemented infants and those not receiving nicotinamide17.

We next explored the influence of seasonality on the effectiveness of supplementation. December-February-born infants experienced low food insecurity from three to nine months old. These infants show a strong relationship between enrollment WAZ and 12-month LAZ (n=116; r=0.38, q<0.001; Figs. 4F, S13), indicating that when food is not limiting, the starting weight of the infant influences future growth. This observation was not significant when infants from the lower 50% socioeconomic status were compared (Fig. S13). Conversely, June-August born infants (n=55) experience high food insecurity during this same developmental window as harvest stocks deplete. Here, no relationship between enrollment WAZ and 12-month LAZ was observed, independent of socioeconomic status (Fisher’s Z-test p=0.025; Figs. 4F, S13). This indicates that when food is scarce, enrollment weight has a limited influence on future growth. However, nicotinamide supplementation in infants born during this window did restore a positive correlation between enrollment WAZ and 12-month LAZ (n=55; r=0.34, correlation q=0.046; Fisher’s Z-test p=0.044; Fig. 4F). This demonstrates that supplementation may have ameliorated the impact of limited nicotinamide intake on growth.

Importantly, the enrollment status of the infant determined the efficacy of nicotinamide supplementation. As enrollment WAZ was observed to influence future growth, infants were stratified into enrollment WAZ tertiles. A two-way ANOVA and Tukey post-hoc tests were performed to investigate how birth season influences the impact of nicotinamide supplementation on infant growth (change in LAZ from enrollment to 18 months). Considering only infants in the upper tertile for enrollment WAZ (WAZ ≥ −0.13), a significant interaction was observed between birth season and nicotinamide supplementation in association with infant growth (p=0.024, Supplementary Data. 19), which was not present in those with lower enrollment weights. Time series analysis confirmed these findings, where nicotinamide supplementation in June-August born infants (i.e., those who enter increasing food insecurity at three to nine months of age) significantly reduced the drop in LAZ over 18 months compared to matched infants born in the same period not receiving supplementation (p=0.013; supplemented n=19, not supplemented n=26; Fig. 4G). This improvement was not seen for December-February born infants (i.e., those experiencing decreasing food insecurity at three to nine months of age) with the same enrollment weights (supplemented n=35, not supplemented n=28; Fig. 4H). This highlights that the effectiveness of nutritional interventions can be influenced by the season of provision, shaped by environmental pressures and inherent metabolic states.

Methods
The principle investigator of this project was Estomih, Mduma, Ph.D, Research Manager at Haydom Global Health Research Center, who led the local research effort, with a team that included local physicians, clinical research coordinators and field team members. The recruitment process began with the research team meeting with local ten-cell community leaders and educating them about the project's rationale and goals. Recruitment proceeded with local community healthcare workers, who identified to study field team members all prospective participants-mothers with known pregnancies or births. Recruitment was open to all families in the region regardless of race/ethnicity or social status. That said, the area around Haydom Lutheran Hospital is a low-resource setting, and all participants lived in poverty. The project goals focused on assessing potential interventions to reduce childhood stunting and increase cognitive development - issues clearly relevant to the local community. The results of the research have been shared locally, though the study demonstrated a lack of efficacy of the interventions tested, limiting any new benefit the community could experience.

Study design
The Early Life Interventions for Childhood growth and development in Tanzania (ELICIT) study is a randomized 2×2 factorial, double-blind, placebo-controlled trial with the aim of improving early life growth using a nicotinamide and/or antimicrobial (azithromycin, nitazoxanide) intervention. This trial was based in the Haydom Global Health Research Center at Haydom Lutheran Hospital in Haydom, Tanzania. The primary outcome from the ELICIT study was 18-month mean LAZ, followed by the secondary outcomes of 18-month WAZ, MAZ, HCZ, hospitalization, all-cause mortality, and childhood illness. This manuscript performs a secondary explorative analysis of the cohort with the focus on biochemical seasonality. The detailed study design and methods of data collection as per the Consolidated Standards of Reporting Trials (CONSORT) guidelines have been previously published15, alongside the baseline characteristics of the participants17, the primary outcomes14 and statistical analysis plan. Participants were enrolled into one of four treatment groups (1:1:1:1). This divided the individuals to receive either nicotinamide plus placebo (n=276), antimicrobial plus placebo (n=277), both nicotinamide and antimicrobial (n=267), or both placebos (n=278). In this manuscript, only infants from the placebo/placebo and nicotinamide/placebo arms of the study were considered. The seasonality analysis in this manuscript included placebo/placebo arm infants to observe seasonal patterns unaffected by the intervention. Only infants from the nicotinamide/placebo arm were included when considering the intervention to separate the individual intervention effects. The antimicrobial intervention was not considered in this manuscript as the biological samples were collected prior to the administration of the single-dose antimicrobials. Additionally, receipt of non-study antibiotics was common (54−86% infants).

Study setting
Haydom is a rural area of north-central Tanzania, where the population of ~20,000 rely primarily on subsistence agriculture, determined by the area unimodal crop cycle and rainy seasons. Here, food is available following the harvest of beans during the long rains (March-May) and of maize during the dry season (June-October). However, during the pre-harvest period (November-February), when the short rains and planting season occur, harvest stocks become depleted, and the diversity of the foods consumed is reduced. Families in this area typically do not have access to improved water sources and have a monthly income of less than 58,000 Tanzanian shillings (~US$25)14,17,43. There is very low HIV seroprevalence in mothers of childbearing age in this population (<2%), and Haydom is not a high-risk malaria site due to the elevation (1700 m)52. The predominant dietary staple for participants was maize, accompanied by beans, which are typically consumed around the time of their harvest, and eggs. Sunflower oil was commonly accessible, while meat, chicken, dried fish, bananas, avocadoes, and mangoes were available but not consumed in significant quantities due to their high cost at local markets.

Participants
Between September 5, 2017, and August 31, 2018, community health workers within the recruitment area identified pregnant women or those who had recently delivered. These women were then contacted by field team members at their homes to determine interest in the study. To be eligible to partake in the study, the following criteria were assessed: maternal age ≥18 years, child age ≤14 days, and the family’s intent to live no more than 25 km from Haydom Lutheran Hospital while the study was undertaken. Exclusion criteria for the study comprised of infant enrollment weight <1500g, multiple gestation, significant birth defect or neonatal illness, and lack of intent to breastfeed. Infants sex was determined based on self-report.

Randomization and blinding
Randomization was performed using permuted blocks (block size eight) and a reproducible seed by study author JPM, with these detailed contained at the research center. Blinding began with the intervention manufacturer, who provided both the intervention and placebo. An allocation code for the treatment and placebo was provided and kept by a non-study investigator in a sealed opaque envelope. Randomization block details were provided to field teams at enrollment to randomize participants. Blinding continued for the participants and investigators until study completion, and the primary outcome analysis was completed.

Intervention procedures
Nicotinamide versus placebo (both manufactured by VITA-gen, New York, USA) was given to the mothers as a daily 250 mg tablet for the first six months of life. Throughout this time, trained community and study health workers provided mothers with breastfeeding support and counseling surrounding the guidelines of the World Health Organization (WHO) and the Tanzanian Ministry of Health of the benefits of exclusively breastfeeding for the first six months of life. From six-18 months of life, nicotinamide was directly supplemented to the infant by mouth as a daily 100 mg sachet. This sachet contained powder that was mixed into a small volume of age-appropriate food by the mothers. To assess compliance, pill and sachet counts were undertaken at each dose distribution timepoint (every two months), and each month, mothers were asked about the frequency of ingestion.

Sample collection
The study design publication includes detailed information on sample and data collection, including the anthropometry, breastfeeding status, feeding questionnaires, childhood illness and treatments, vaccine reports, developmental assessments (including Malawi Developmental Assessment Tool [MDAT]21), breast milk, blood, and urine collection for metabolomic analysis15. Briefly, monthly home visits were performed from one to 18 months of life (±7 days). Economic and demographic information, such as the geographical area of residence (ward) and tribal affiliations, were provided by the mothers at the baseline visit. The socioeconomic status of the family was determined following a protocol previously designed from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health (MAL-ED) study based on improved water and sanitation, assets, maternal education, and household income (WAMI)22. At the following monthly visits, mothers were asked about breastfeeding status, childhood illness and treatment, and intervention compliance. Household food insecurity was also assessed at each monthly visit with the question “Since the last visit, has the mother worried that the household would not have enough food?”. Any frequency of worry in response to this question was considered. At each three-month visit, anthropometric data were obtained for the child. Length was assessed by measuring board, weight by digital scales, and MUAC and head circumference with measuring tape.

Blood samples were collected at 12 (placebo n=199; nicotinamide n=199) and 18 (placebo n=199; nicotinamide n=198) months of life via phlebotomy, transported on ice to the laboratory to be processed into plasma, and stored at −80°C for shipping to the United Kingdom for metabolomic analysis. Urine samples were collected into a sterile bag at six (placebo n=278; nicotinamide n=276), 12 (placebo n=270; nicotinamide n=267), and 18 months (placebo n=266; nicotinamide n=263), before being transported on ice to the research site and then stored at −80°C to be shipped to the United Kingdom for metabolomic analysis. Breast milk was collected at one- and five-months postpartum (placebo n=292; nicotinamide n=295) at the midpoint of a feeding session. The area around the areola was cleaned using soap and water and rinsed with deionized water prior to sampling. Approximately 8 mL was expressed by hand into a sterile container, which was placed on ice for transportation to the laboratory. Here, the samples were aliquoted whilst being shielded from light and stored at −80°C and shipped to the United States of America for metabolomic analysis.

Rainfall data was collected from the weather station in Haydom, including historic rainfall from July 2010-July 2018.

Full methodological details of the Malawi Developmental Assessment Tool (MDAT) are documented in the cognitive outcomes53 and study design publications15. Briefly, this test has been designed and validated to test gross and fine motor, language, and social development domains in a culturally relevant manner for rural or lower-resource settings in sub-Saharan areas with good reliability (94–100%)21. For Haydom specifically, the MDAT has been adapted, piloted, and validated by the MAL-ED study team54. The assessment was delivered to participants in Swahili or Iraqw (the first language of most of the participants) by three trained field team members. To ensure accuracy in translation, the assessment was translated into Swahili and then back-translated into English by a second independent interpreter. For each task, individuals were scored either one (pass) or zero (not pass). They continued to complete tasks in each subtest until they completed six tasks consecutively with a score of zero. To monitor consistency, all assessments were video recorded and 10-20% of these were watched back each week by trained study team personnel.

1H NMR spectroscopy and UPLC-MS-based metabolic profiling
Urinary metabolic profiles were measured by 1H nuclear magnetic resonance (NMR) spectroscopy. Here, urine (630 ml) was combined with 70 ml of phosphate buffer solution (pH 7.4, 100 % D2O) containing 1 mM of the internal standard, 3-trimethylsilyl-1-[2,2,3,3-2H4] propionate (TSP). Samples were vortexed to mix, spun at 10,000g, and the supernatant was transferred to 5 mm NMR tubes. All samples were measured with a 700 MHz Bruker NMR spectrometer equipped with a cryoprobe and refrigerated SampleJet autosampler maintained at 6°C (Bruker Biospin GmbH, Rheinstetten, Germany). A pooled biological QC was prepared and analyzed repeatedly throughout the run to confirm reproducibility (Fig. S16). A standard one-dimensional solvent suppression pulse sequence (relaxation delay, 90° pulse, 4-ms delay, 90° pulse, mixing time, 90° pulse, acquire free induction decay) was used to measure each sample. Each spectrum was acquired with 32 scans, 4 dummy scans, 64,000 frequency domain points and a spectral window set to 20 ppm (parts per million). All spectra were automatically phase and baseline corrected and referenced to the TSP resonance at δ 0.0 in Topspin 3.2 (Bruker Biospin GmbH, Rheinstetten, Germany). The raw spectra were digitized, aligned, and normalized in Matlab (version 2018a, MathWorks Inc.) using the Imperial Metabolic Profiling and Chemometrics Toolbox (https://github.com/csmsoftware/IMPaCTS). Redundant spectral peaks (TSP, water, and urea) were excised, and the resulting spectra were manually aligned using a recursive segment-wise peak alignment method. Due to variation in dilution across urine samples, a probabilistic quotient normalization method was used to reduce these effects.

Plasma metabolic profiles were measured on a Waters Acquity Premier ultra-performance liquid chromatography system coupled to a Xevo TQ-XS mass spectrometer using the Biocrates MxP Quant 500 kit (Biocrates Life Sciences, Innsbruck, Austria)55. This kit was also used to generate breast milk metabolic profiles using an AB Sciex 5500 QTRAP mass spectrometer. The analysis of small molecules (106 metabolites) was performed by LC-MS/MS and flow injection analysis for the lipids and hexoses (524 metabolites). This kit incorporates internal standards that are used for metabolite identification and quality control samples to assess reproducibility. This data was acquired following the manufacturer’s instructions. The complete list of metabolites included is documented in the kit specification56, and the quantification was achieved using a 7-point standard curve of internal standards. Here, 10 ml of thawed samples were added to a 96-well plate containing inserts of internal standards supplied by Biocrates and dried using nitrogen flow. Samples were incubated with 5% phenylisothiocyanate (PITC), dried, shaken following the addition of 5 mM ammonium acetate in methanol, before being transferred to separate 96-well plates diluted with either pure water or FIA solvent. Metabolites were measured in positive and negative ionization modes. The LC-MS/MS method involved the injection of 5 µl of extract into the Biocrates MxP Quant 500 kit UHPLC column at 50°C using a 5.8 min solvent gradient with 0.2% formic acid in water and 0.2% formic acid in acetonitrile. Secondly, the FIA-MS/MS method involved the injection of 20 µl of extracts. The raw data from the UPLC-MS and FIA analyses were processed using the Biocrates MetIDQTM software (version Oxygen-DB110-302305) in accordance with the manufacturer’s protocol. To minimize technical variation across the different plates, batch correction was performed using standard quality control samples (QC2) repeated across (n=4) each plate (Fig. S16).

Breast milk vitamin and HMO profiles were also generated. For the vitamin profiles, internal standards were added to 100 ml of whole human milk samples, mixed, and centrifuged at 10,000 xg for 10 min at 4°C to defat the sample. A 50 ml aliquot of whey was taken, and the proteins were precipitated with methanol. After another centrifugation step, the supernatant was transferred into a new glass vial, evaporated to dryness, reconstituted, and filtered. Analysis was performed using a SCIEX Exion-AD UHPLC coupled to a SCIEX 6500+ QTRAP mass spectrometer (SCIEX, Framingham, MA, USA), using a Phenomenex NX-C18 column, 250 × 3 mm, 3 μm, and a gradient of an aqueous solution of 10 mM ammonium formate + 0.05% ammonium hydroxide (A) and acetonitrile (B) from 99% to 5% A in a 12 min run. Quantification was carried out by ratio responses using isotope-stable internal standards and a 9-point standard curve. Additionally, the HMO profiles were generated for a subset of participants (placebo n=54; nicotinamide n=48) from the breast milk samples. These were generated on a high-performance LC system with fluorescence detection. Firstly, raffinose was added to 40 μl of breast milk as an internal standard to facilitate quantification. A high-throughput solid-phase extraction was used to isolate the HMOs, involving the removal of lipids, proteins, and lactose. The resultant HMOs were fluorescently labeled for HPLC-FLD detection based upon retention times and mass spectrometry.

Variables with greater than 80% missing values (i.e., those below the limit of quantification) across the sample set were excluded. Outliers were identified using Grubbs’ test of log-transformed data and removed.

Statistical analysis and data visualization
Cosinor analysis was performed in MATLAB (version R2022b, MathWorks Inc.; Statistics and Machine Learning Toolbox; Optimization Toolbox). Analyses were applied to metabolomic datasets (UPLC-MS-based plasma and breast milk profiles, and urinary 1H NMR profiles), as well as metadata relating to the individuals (anthropometric measures at different sampling points, cognitive outcomes) and environmental variables (Haydom rainfall data and food insecurity reports) across the birth months of participants. For breast milk data, cosinor analysis was applied across sampling months. For the urinary 1H NMR data, the cosinor approach was applied to each data point in the urinary 1H NMR profiles. Metabolite peaks with data points identified to significantly fit cosine distributions were integrated to obtain the relative abundance of that metabolite. Only those metabolites whose peak integral also followed a significant cosine distribution were included in further analysis. As food insecurity was recorded from each mother monthly over their 18-month study period, the mean food insecurity scores were calculated across all mothers for each 10-day period, and any response of worry was included.

A cosine wave with a sloping mesor was fitted to the data using non-linear curve fitting solved by least squares, and statistically compared to H0 of solely a sloping line model using an extra-sums-of-squares F-test. All p-values were corrected for multiplicity of testing according to Benjamini-Hochberg. Features with a q<0.15 at at least one sampling time point were considered significant cosine fits and included in further analysis. This threshold was selected due to the large number of metabolites measured and the covariation that exists across the metabolomic dataset through shared metabolic pathways or functions. The analyses were repeated using residualized features according to a linear model including potential covariates (enrollment weight-for-age z-score, socioeconomic status, number of months of exclusive breastfeeding, and maternal factors (weight, height, age)) and stratified by sex. As the metabolomic-lipidomic and HMO profiles were only measured in a subset of samples, seasonal differences were investigated using one-way ANOVA with Tukey’s HSD post-hoc for normally distributed residuals, or Kruskal-Wallis with pairwise Wilcoxon rank-sum tests performed in R (version 4.2.1; stats package57. Features with a q<0.15 were considered significant.

PERMANOVA analysis assessed variance in the metabolomes of participants by birth season using metabolites identified as following a 12-month cosine distribution using the vegan package (version 2.6.10) in R58. This was performed on scaled data with the LOD replaced with the minimum value divided by √2. An adonis p-value was produced following 1000 permutations, where p<0.05 indicated that participants were clustered significantly more with others from the same birth season. PLS-DA models were used to confirm the classification of participants into three-month birth windows by the metabolites identified as following a 12-month cosine distribution at six, 12, and 18 months and generate VIP scores. For each model, the X matrix was composed of the concentrations of the metabolites (scaled data with the LOD replaced with the minimum value divided by √2), whilst birth season (September-November versus March-May; December-February versus June-August) was used as the predictive Y component. PLS-DA models were performed using the mixOmics (3.21) package in R59. A seven-fold cross-validation approach was used to assess the predictive capacity of each model, while permutation testing demonstrated the model's validity (1000 permutations). Area under curve (AUC) and variable importance (VIP) scores were generated for each model.

Cross-correlation analysis was also performed in MATLAB (version R2022b, MathWorks Inc.; Statistics and Machine Learning Toolbox, Bioinformatics Toolbox, Signal Processing Toolbox). Metabolite concentrations were z-scored within each metabolite, and cross-correlated with reported food insecurity to find the optimal lag (largest correlation) and associated R2 and p-value. All p-values were corrected for multiplicity of testing according to Benjamini-Hochberg, and q<0.05 were considered statistically significant correlations. A stricter threshold was used for cross-correlation analysis due to the independence of the variables tested. For the metabolomic-lipidomic and HMO breast milk subset, Wilcoxon tests were used to detect metabolites that differed by reported food insecurity (any vs no) by mothers at the time of sampling. An q<0.05 was also used to detect significance in this analysis. Analyses were performed in R using stats package57.

Short time series analysis was performed using the Short Asynchronous Time-series Analysis (santaR) package (version 1.2.3)60 in R to determine differences in growth trajectories between the intervention groups, and urinary NMND excretion tertiles. This method uses a smoothing spline functional model where six splines were selected when modeling anthropometric change over seven time points.

Other analyses were performed in R version 4.2.1 primarily using the following packages: ggplot2 version 3.4.1 for plotting61, dplyr version 1.0.10 for data manipulation62, and stats57 for statistical analyses. The exact number of samples used to calculate a statistical difference is indicated in each figure legend. Shapiro-Wilk’s tests were used to assess the normality of the data. For normally distributed data, Student’s T tests and Pearson’s correlations were used. Conversely, Spearman’s Rank correlations and Wilcoxon tests were used when analyzing skewed data. The false discovery rate was corrected using the Benjamini-Hochberg procedure to correct p-values for multiple testing. Features with a q<0.05 were considered statistically significant in the analysis unless stated previously. A Fisher’s Z transformation was applied to the correlation coefficients, and a Z-test was used to determine statistical difference. Growth data were analyzed using a Two-Way Analysis of Variance (ANOVA) with birth season and intervention group as independent variables. Adobe Illustrator was used to finalize the figures.

Inclusion and ethics
The study, and transfer of data and materials, were approved and was closely followed by all local research regulatory agencies, including the Tanzanian National Institute for Medical Research (NIMR, reference number HQ/R.8a/Vol.IX/2424), the University of Virginia Health Sciences Research Institutional Review Board (HSR-IRB, reference #19465), and Tanzanian Food and Drug Administration (reference number TFDA0017/CTR/0005/02). The study also received ethical approval from the University of Southampton Research Ethics Committee (reference number 61337.A1). Study oversight was provided by FHI360 (North Carolina, United States of America). This study was registered in ClinicalTrials.gov (NCT03268902) prior to the start of enrollment. Mothers gave written informed consent for themselves and their infant to participate in any intervention arm either during pregnancy or at the time of enrollment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "While the idea of the study is of interest, there are several issues that have not been properly accounted for.\nThe clinical cohort is not very clearly described and while the description is lengthy it is not easy to find information of the\nnumber of samples for each sample type, and there is no clinical data stated for the infants or they mothers, nor how many\nparticipants there were for each season, breastfeeding at the different timepoints etc.. Overall, the sample size in placebo\npart of the study was quite small, so the number of subjects for each month must have been relatively low. A table\nsummarising this would be good to include. Supplementary files were not available, making a detailed investigation difficult.\nThe statistical analyses are not properly described and there is not sufficient description of the possible con-founding factors\nand how these were taken into consideration in the statistical analyses. Only enrolment weight and length of breastfeeding\nare mentioned, but other factors, such as the weight at sampling, infant sex, dietary factors, maternal factors such as BMI and\nage are very relevant in relation to metabolic profiles. There is no data on how the breastfeeding impacted the results. There\nis no quality control of the metabolomics data or description of the reliability of the metabolite identification.\nThe authors state that the birth month showed significant association with specific metabolites. However, the results could\nbe potentially explained by the month the samples were collected, and the food-intake/availability at that time. This is directly\nlinked with the birth month, and thus the food intake on the sampling point. There was a short comment that there was a one\nsampling-month related metabolite. This is not sufficient, and a more comprehensive analysis/description should be added.\nHow was the situation with the urine metabolome? There should be more discussion of this on the manuscript. This is the\nmajor issue on this study.\nRelated to urine samples, the results showed several metabolites stated to be linked with breast milk. How many infants\nwere still breastfed at the two later sampling points?\nThere is some data given from the breast milk samples, however, the duration of the breastfeeding, and its impact on\nmetabolic profiles is not described and as the sampling time of the breast milk did not fully match with the sampling of blood\nand urine, there are several possible issues that should be better explored in the study. Also the BM samples were grouped\nby birth month while it would be much more logic to group them by season of sample collection. Maternal diet and food\navailability is likely to have a major impact of the BM composition.\nThe figure texts lack sufficient description; none of the figures explains what are the panels A, B, C etc.", "reviewer_2": "Overall Comments:\nThis study sought to characterize the effects of birth month on the infant metabolome through the first 18 months of life in\nTanzanian infants. Other recent work in the field has shown that birth month/season leaves epigenetic imprints on infants\nand that seasonal weather changes can alter intervention effectiveness, food availability, and pathogen exposure. The\nauthors used a multidisciplinary approach that compared MS and NMR identification and quantification of urinary and\nplasma metabolites with breastmilk metabolites, infant anthropometry, and infant language scores, while also incorporating\nrainfall data, knowledge of local growing seasons, and survey data about perceived food insecurity during the birth month\nand at the time of sampling. The authors show evidence that birth season has long-lasting effects on important aspects of the\ninfant metabolome; that these changes seem to correlate with season-related changes in food insecurity in the first six\nmonths of life; and that a subset of metabolites are correlated with differences in language development at 12 and 18\nmonths. Importantly, the authors explore the efficacy of a nicotinamide intervention to alter birth-month-related deficits in this\ncrucial vitamin and find that supplementation makes a significant corrective impact on WAZ at enrollment to LAZ at 12\nmonths in the group most impacted by birth-season-associated food insecurity. Overall, this paper provides valuable insights\ninto birth-season-related effects on infant undernutrition, which can be applied by those planning future interventions in low\nand middle income countries to maximize intervention effectiveness. I offer the following as suggestions to improve the\nmanuscript.\nMajor Comments:\nIntroduction: In the first paragraph, it would be valuable to briefly mention other potential sources of birth month impacts on\ninfant health and development including pathogen exposure/enteric dysfunction responses to rainfall, before getting into why\nand how this study chooses to look at the impacts of food insecurity. Though covered in the discussion, these additional\nsources are important enough to be considered throughout the paper. It would also underscore the importance of this study\nto briefly mention the potential impacts of climate change on seasonal variation in rainfall and food insecurity. Likewise, the\nsecond paragraph should include a discussion of the seasonal patterns in pathogen infection in Haydom. In Line 61, it\nwould be helpful to include an overview of the arms in the trial and the impact of the interventions on the primary and/or\nsecondary outcomes of the trial. This would improve the rationale or set up of the third paragraph describing effect measure\nmodification.\nPage 2, Lines 87-90: Please provide an overview of the ELICIT trial arms. Include details on the trial set up (i.e., a 2×2\nfactorial design, administration of antimicrobials (azithromycin and nitazoxanide, randomised together) and nicotinamide).\nProvide a rationale for why this secondary analysis only used a subset of two arms. Also, provide the total N from these two\narms.\nPage 6, Lines 204-205: It is unclear what this hypothesized relationship is based on - perhaps missing reference or\nreference to supplementary data - “These were all highest in infants born during peak food insecurity (November-February)\nand may be associated with maternal-infant wasting around the time of delivery.”\nFigure 3: Panel labels effective here; would be nice to see these added to other figures. For Panel F though, the subtitle\nfeels misleading since numbers represent phase lag in days rather than any seasonal variation – consider changing the\nlabel to “Peak phase season variation in breastmilk vitamins”\nFigure 4: Similarity in color between upper and lower tertile and the other key (control and nicotinamide) makes it difficult to\ndistinguish them. Would also suggest moving control and nicotinamide key somewhere more central so it resembles the\nother legends or making it larger/more obvious. Would recommend rearranging panels so that axes and deeper similarities\nalign more. Ex: Panels A,G,H would be easier to understand in a row; Panels B/C, D/E would make more sense stacked\natop one another.\nDiscussion: A thorough discussion of birth month effect modification by different factors (by sex, SES, enrolment WAZ etc. as\nmentioned in results pg 4 line 150-153) is warranted here. Since these differential effects have major implications for\nintervention planning, they are important to interpret and discuss.\nPage 12, Lines 433-435: This could be expanded to include more specifics, especially regarding effects of rainfall on\nenteropathogen exposure and potentially EED. This underscores the importance of targeted interventions as children in the\ncrucial developmental phase that encompasses the first six months of life may be facing the dual challenges of increasing\nfood insecurity and increased exposure to environmental pathogens during the rainy season.\nMinor Comments:\nPage 1, Line 27-28: “contributions of this to these adverse phenotypic outcomes” feels awkward due to unnecessary\nrepetition of “this”. Consider changing to “it is unclear if this imprints on the developing metabolome and contributes to\nadverse phenotypic outcomes”\nPage 1, Line 30: consider changing “persisting up to 18 months of life” to “persisting up to at least 18 months of life” to clarify\nthat this finding is due to length of study.\nPage 2, Line 48: consider deleting “its influence”.\nPage 2, Line 72-73: should reword “the tryptophan-niacin-NAD+ pathway is perturbed with early life undernutrition and is\nrelated to growth” to “the tryptophan-niacin-NAD+ pathway is related to growth and perturbed with early life undernutrition” to\nsimplify and clarify importance.\nPage 13, Lines 474-488: Figure 3 caption missing extensive list of acronyms and abbreviations.", "reviewer_3": "This is an interesting and well-conducted study, showing that seasonal availability of food affects the infant metabolome in a\nlow-income context, with potential implications for health and development.\nI request the authors address the following comments, which I hope can strengthen the paper:\nThe study does not state any clear hypotheses and there are no sample size calculations. Could the authors clarify what\naspects of the analysis plan were defined a priori, including e.g. decisions on seasonal bins, length of phase lag and\nstratification analyses? The obvious concern here is the risk of ‘false positives’, given the multiple outcomes from\nmetabolomics conducted on plasma and urine samples. The authors attempt to address this using false discovery rate\ncorrections. I am not qualified to comment in detail on the statistics, but do request the authors justify the decision to apply\nFDR corrections at p<0.25 for some analyses as reported at lines 454 and 484, rather than the ‘typical’ p<0.05.\nThe decision to look only at the placebo/placebo arm and nicotinamide/placebo arms of the study (line 543) requires\njustification.\nFull details on sample collection and analysis would be good, bearing in mind that sample handling is so important for\nmetabolomics. The level of detail is insufficient for reproducibility.\nThe authors should discuss why seasonal relationships in plasma and urine metabolite concentrations are seen only in\nmales but not in females (line 151).\nLooking at the seasonal patterns in metabolite concentrations (Figure 1) – the charts are plotted by month, from September\nto August. For several metabolites including choline, the concentrations in September and August are substantially different\nwith no overlap in the 95% CI, yet these are adjacent months. How can this be explained? To me, this points towards other,\nnon-seasonal drivers, possibly in combination with large sampling or measurement error when sample sizes are subset by\nmonth?\nThe study assumes a pathway from season to food insecurity to infant metabolome. There are, however, other potential\npathways between season and infant metabolome that merit consideration. For example, moisture and temperature can\naffect cereal grain metabolism post-harvest. The aspartate data stand out to me, given associations between climate\nconditions during the growing of cereals and the free asparagine concentration in the resultant crop, e.g. Curtis et al., Journal\nof Agricultural and Food Chemistry 2009 57:1013-1021 DOI: 10.1021/jf8031292\nAlso, there may be seasonal variation in the geographic source of food items, e.g. more home-produced food in months after\nharvest, and more purchased food (likely grown further from the home) in the ‘lean’ season. Asparagine (and potentially\nother) amino acid concentrations, and other chemical entities including mineral micronutrients, are likely to vary depending\non the location of production, including due to soil factors (e.g. Gashu et al., Nature. 2021;594:71-6), hence there will be\nseasonal variability in nutrient intakes independent of experience of food insecurity, and potentially important spatial\nvariability in associations between season of birth, nutrient intakes and the infant metabolome. At line 436, the authors\nsuggest that birth month acts as a proxy for these combination of seasonal drivers – however, these diverse pathways could\nlead to very different relationships between season and metabolite concentrations in other geographic settings.\nThe authors examined seasonal associations between metabolites and cognitive outcomes. At line 377, the authors suggest\nthe observed associations demonstrate developmental relevance of metabolic seasonality. I think this statement is not\nsufficiently evidenced by the findings of this exploratory analysis. There are many pathways between season of birth and\ncognitive development, and many potential confounders for the association between metabolite concentrations and\ncognitive test scores. For example, labour patterns are likely to be seasonal which may affect breastfeeding patterns, and\nevidence from other settings including the UK shows associations between season of birth and cognitive development\noutcomes with hypothesised pathways including seasonal variability in maternal vitamin D status and opportunities for\nsocial interactions. And, of course, seasonal variability in consumption of food groups including fruits and vegetables could\ninfluence the infant metabolome and cognitive development without a direct interaction between the latter two. The authors\ndo note limitations at line 431, but the inherent limitations of the study design are given insufficient weight and I suggest the\nauthors use more cautious language in suggesting a direct relationship between metabolites and cognitive outcomes.\nAccording to Figure 3A, reported food insecurity in the lean season was greater in 2017 than 2018 and probably 2019. Was\n2017 a particularly bad year? The rainfall data suggest two false starts to the rains and a late, intense and short rainy period.\nCould the authors comment on this, and any potential implications for the wider external validity of the seasonal associations\nobserved?\nI am not convinced that you would deliver any common public health interventions differently on the basis of this study, when\nit is already well-known that the hungry season brings food security and nutrition-related challenges. Perhaps the authors\ncan explain which aspects of common interventions or which particular interventions might be adapted or tailored for\nseasonality, where this does not already occur? To my knowledge, supplementation with choline or nicotinamide are not\ncommon public health interventions. However, the study could be used to inform the design of relevant future studies\nincluding monitoring and evaluation studies, including in terms of sampling timings.\nThe inclusion and ethics section lacks detail. Please could the authors provide\ninformation on the ethical review committees and relevant application reference numbers? Also, please clarify whether the current study, i.e. analysis of infant\nmetabolites, was included in the main ELICIT study protocol as reviewed and approved by the relevant ethics committees, or\nwhether the current study operated under separate protocols and approvals? This section should also specify information on\ndata and material transfer agreements.\nThe Supplementary Tables were not available in the reviewer materials and I am not able to comment on the adequacy of\nthe authors’ data sharing plans.\nMinor comments\nLine 214. This should be seasonal variation in, not via, breastmilk\nFigure S1. There is a typo in section C, i.e. ‘individuals’"}, "author_response": "1. The clinical cohort is not very clearly described and while the description is lengthy it\nis not easy to find information of the number of samples for each sample type, and\nthere is no clinical data stated for the infants or they mothers, nor how many\nparticipants there were for each season, breastfeeding at the different timepoints\netc.. Overall, the sample size in placebo part of the study was quite small, so the\nnumber of subjects for each month must have been relatively low. A table\nsummarising this would be good to include.\n Sadly, it appears that the supplementary tables were not available to the reviewers.\nThis seems to be confirmed by the reviewer “Supplementary files were not available,\nmaking a detailed investigation difficult”. Supplementary table 1 details participant\ncharacteristics and numbers split into birth seasons. Following this suggestion, we\nhave now added sample numbers per birth season to this table. As can be seen from\nthe table, sample numbers were sufficient for the described analyses given that we\ndetect interesting seasonal changes with several replicated over multiple time points.\nFurthermore, by adjusting for multiple testing we have limited the risk of false\npositives.\nSupplementary Table ST1: Baseline characteristics of the cohort by intervention arm and birth season.\nPlacebo Arm Nicotinamide Arm\nBirth Season Sep-Nov Dec-Feb Mar-May Jun-Aug Sep-Nov Dec-Feb Mar-May Jun-Aug\nTotal individuals 43 116 83 55 42 114 87 55\nSamples (n)\nPlasma\n12 month 22 81 61 35 22 74 68 35\n18 month 22 80 61 36 22 74 68 34\nUrine\n6 month 39 112 75 52 40 105 80 51\n12 month 39 104 70 48 39 101 77 50\n18 month 36 105 71 44 38 102 79 47\nBreastmilk B vitamins\n1 month 39 115 82 52 41 112 86 55\n5 month 41 116 82 53 40 108 85 53\nBreastmilk metabolome and HMOs\n1 month 4 14 24 12 3 6 29 10\n5 month 4 14 24 12 3 6 29 10\nSocidodemographics\nFemales 18 (41.86) 63 (54.31) 40 (48.19) 19 (34.55) 23 (54.76) 53 (46.49) 39 (44.83) 30 (54.55)\nFirstborn child 36 (83.72) 92 (79.31) 63 (75.9) 44 (80) 33 (78.57) 103 (90.35) 66 (75.86) 48 (87.27)\nNumber of children 4 ± 2.71 3.97 ± 2.69 3.8 ± 2.44 3.83 ± 2.53 4.02 ± 2.22 4.14 ± 2.29 3.8 ± 2.51 3.89 ± 2.16\nHospital birth 20 (46.51) 62 (53.45) 55 (66.27) 30 (54.55) 20 (47.62) 50 (43.86) 47 (54.02) 28 (50.91)\nMaternal age 27.26 ± 7.25 27.81 ± 7.23 27.37 ± 7.05 28.36 ± 6.97 27.71 ± 6.12 27.39 ± 5.66 27.76 ± 6.58 28.11 ± 6.13\nMothers with >= 7 years education 32 (74.42) 93 (80.17) 68 (81.93) 43 (78.18) 29 (69.05) 79 (69.3) 67 (77.01) 43 (78.18)\nMonthly income (/1,000 Tanzanian shillings) 50.66 ± 77.88 46.9 ± 43.48 52.78 ± 69.52 54.81 ± 85.46 60.08 ± 62.72 48.12 ± 40.44 40.12 ± 46.63 52.73 ± 49.09\nRisk factors\nExclusive breastfeeding in the first month 42 (97.67) 116 (100) 83 (100) 54 (98.18) 41 (97.62) 113 (99.12) 86 (98.85) 55 (100)\nRoutine treatment of drinking water 4 (9.3) 19 (16.38) 14 (16.87) 4 (7.27) 6 (14.29) 19 (16.67) 5 (5.75) 6 (10.91)\nDrinking water > 10 minutes from home 32 (74.42) 98 (84.48) 69 (83.13) 43 (78.18) 35 (83.33) 100 (87.72) 64 (73.56) 44 (80)\nCrowding 9 (20.93) 17 (14.66) 19 (22.89) 9 (16.36) 10 (23.81) 18 (15.79) 17 (19.54) 15 (27.27)\nWater and sanitation, Assets, Maternal education, and household\n0.30, 0.14 0.33, 0.18 0.31, 0.14 0.36, 0.18 0.33, 0.16 0.30, 0.17 0.30, 0.16 0.30, 0.16\nIncome index score (median, interquartile range)\nHousehold assets\nMattress 25 (58.14) 69 (59.48) 50 (60.24) 39 (70.91) 20 (47.62) 56 (49.12) 45 (51.72) 37 (67.27)\nTable 18 (41.86) 56 (48.28) 38 (45.78) 32 (58.18) 19 (45.24) 37 (32.46) 28 (32.18) 27 (49.09)\nBench 39 (90.7) 102 (87.93) 73 (87.95) 49 (89.09) 37 (88.1) 94 (82.46) 65 (74.71) 46 (83.64)\nSeparate kitchen 27 (62.79) 67 (57.76) 59 (71.08) 38 (69.09) 28 (66.67) 66 (57.89) 53 (60.92) 37 (67.27)\nRefrigerator 0 (0) 0 (0) 1 (1.2) 1 (1.82) 0 (0) 0 (0) 0 (0) 0 (0)\nTelevision 3 (6.98) 5 (4.31) 5 (6.02) 6 (10.91) 7 (16.67) 9 (7.89) 4 (4.6) 4 (7.27)\nMobile phone 32 (74.42) 94 (81.03) 71 (85.54) 47 (85.45) 36 (85.71) 84 (73.68) 73 (83.91) 49 (89.09)\nFamily bank account 3 (6.98) 9 (7.76) 6 (7.23) 3 (5.45) 2 (4.76) 3 (2.63) 1 (1.15) 3 (5.45)\nAnthropometry\nEnrollment weight-for-age z-score -0.76 ± 1 -0.72 ± 0.93 -0.51 ± 0.98 -0.28 ± 0.93 -0.65 ± 0.97 -0.78 ± 1.08 -0.55 ± 0.9 -0.4 ± 1.02\nEnrollment length-for-age z-score -0.58 ± 1.14 -0.66 ± 0.96 -0.85 ± 1.00 -0.84 ± 0.87 -0.59 ± 0.98 -0.73 ± 1.06 -0.85 ± 1.02 -0.84 ± 1.14\nEnrollment head-circumference-for-age z-score 0.03 ± 1.18 -0.12 ± 0.96 -0.06 ± 1.09 0 ± 0.92 0.22 ± 1.14 -0.06 ± 1.03 0.06 ± 0.89 0.26 ± 0.91\nMean ± SD is shown for continuous variables and the number (percentage) for dichotomous variables unless otherwise stated.\n2.\na. The statistical analyses are not properly described\n All the statistical analyses performed in this work are clearly described in the\nmethods section (Page 20-21; line 775-843). Following this suggestion, we have now\nadded a sentence to main results section to clearly explain the cosinor analysis and\nimprove interpretation (Page 3; line 118-120).\n “Cosinor analysis (20) was used to identify molecules whose concentrations followed\na seasonal waveform across the population based upon the infant’s month of birth\n(Fig.1A-C). A cosine wave with a sloping mesor was fitted to the data using non-\nlinear curve fitting solved by least squares, and statistically compared to H0 of solely\na sloping line model using and extra-sums-of-squares F-test.”\nb. there is not sufficient description of the possible con-founding factors and how\nthese were taken into consideration in the statistical analyses. Only enrolment\nweight and length of breastfeeding are mentioned, but other factors, such as\nthe weight at sampling, infant sex, dietary factors, maternal factors such as\nBMI and age are very relevant in relation to metabolic profiles. There is no\ndata on how the breastfeeding impacted the results.\n In this work, cosinor analysis was performed on both unadjusted and adjusted\nmetabolomes. Previously, the analysis included enrolment weight as a covariate to\nadjust for prenatal exposures, and length of breastfeeding to account for differences\nin dietary factors. Additionally, to account for infant sex, the analysis was performed\nin a sex stratified manner.\n We thank the reviewer for their suggestion, and we have now additionally included\nmaternal height, weight, and age as covariates in the analysis.\no Page 2 line 126-130. “Of these, seven remained significant (choline, TMAO, EPA, methionine sulfate, cer(d18:2/14:0), TG 20:0_34:1 and\nhex2cer(d18:1/18:0); Fig.1A, Table.S2)) following correction for multiple\ntesting (q < 0.15) and adjustment for covariates including enrolment weight-\nfor-age z-score, socioeconomic status, number of months of exclusive\nbreastfeeding, and maternal factors (weight, height, age).”\no Page 4 line 164-169 “Of these nine remained significant when corrected for\nmultiple testing (q < 0.15) and adjusted for the covariates enrolment WAZ,\nnumber of months of exclusive breastfeeding, maternal age and\nsocioeconomic status (pantothenate, dimethylglycine, lactose, succinate,\nvaline, 2’-FL, NMND, D-galactose, and 3’FL) (Fig.1A; Table.S3). Interestingly\nthe seasonality was dampened in the HMO 2’-FL when adjusted for maternal\nbody composition (weight, height).”\no Page 20 line 792-795 “The analyses were repeated using residualized\nfeatures according to a linear model including potential covariates (enrolment\nweight-for-age z-score, socioeconomic status, number of months of exclusive\nbreastfeeding, and maternal factors (weight, height, age)).”\nFor the other pressures mentioned by the reviewer,\n Cosinor analysis was performed on the cohort as a whole and stratified by\ninfant sex. Page 4 line 181-185 \" After stratifying by sex, no plasma or urine\nmetabolites exhibited circannual patterns in the females, while two, 17, and\nsix metabolites were noted in the males at six, 12 and 18 months respectively\nafter FDR and covariate adjustment (enrolment weight-for-age z-score,\nsocioeconomic status, number of months of exclusive breastfeeding, and\nmaternal factors (weight, height, age)). (Fig.S4; Table.S6).” and displayed in\nsupplementary figure S4. Following this suggestion, we have also added a\nsection in the discussion related to the sex differences to emphasise the\nimportance of this variable.\nFig.S4. The male infant metabolome is more sensitive to environmental\npressures. Plasma and urine metabolites that follow significant cosine\ndistributions based upon birth month in males only at A) six months (n = 149),\nB) 12 months, C) 18 months (n = 141; q < 0.15) following adjustment for\nenrolment weight-for-age z-score, socioeconomic status, number of months\nof exclusive breastfeeding, and maternal factors (weight, height, age).\nColored by birth season of greatest abundance.\no Page 12 line 450-454 “Interestingly, sex differences were also\nobserved where the male metabolome appears more sensitive to\nseasonal perturbations than the female. This aligns with literature\nreporting more distinct metabolic differences in adult males following\nperiods of early life undernutrition compared to community controls\n(40,41), and a greater adverse response of the male fetus to stress\nduring pregnancy and post-natal life (42,43).”\n Regarding weight at sampling, we have performed cosinor analysis on LAZ,\nWAZ, MAZ (muac-for-age Z-score) and HAZ (head circumference-for-age Z-\nscore) and identify no seasonal effect on these measures. The findings of\nthese analyses have now been included in a table, as well as their reference\nin the text. As no seasonal effect was observed we did not adjust for this in\nour models.\no Page 8, line 306-310. “As with enrolment anthropometry, no significant\nseasonal waveforms were detected across anthropometric measures\n(LAZ, WAZ, HCZ [head circumference-for-age Z-scores], and MAZ\n[mid-upper arm-for-age circumference (MUAC) Z-scores]) by birth\nmonth from three-18 months (Table.S17).”\n Regarding additional measures related to diet, we performed cosinor analysis\non food insecurity and identified a seasonal effect. To explore the impact of\nthis on seasonal metabolic variation, cross correlation analysis was\nperformed and indicated a relationship between seasonal metabolic variation\nand seasonal food insecurity at birth and at the time of sampling.\n Nevertheless, we acknowledge that additional seasonal pressures remain\nunaccounted for in our data such as infections and have highlighted these as\na limitation in the discussion. See also response to Reviewer 2 comment 11.\n3. There is no quality control of the metabolomics data or description of the reliability of\nthe metabolite identification.\n The plasma metabolomes were measured by LC-MS using commercially available\ntargeted metabolomic kits (Biocrates MxP Quant 500). This was performed using\nstandard manufacturers protocols. The kit includes standards for the compounds to\nensure reliability of metabolite identification and quantification and incorporates a\nquality control sample interspersed throughout the analytical run to ensure reliability\nand reproducibility. This was used for batch correction to minimise technical variation\nacross the different plates. A sentence has been added to the methods section of the\nmanuscript describing this approach to improve clarity.\n Page 19 line 736-737. “This kit incorporates internal standards for metabolite\nidentification and quality control samples to assess reproducibility. This data was\nacquired following the manufacturer’s instructions. The complete list of\nmetabolites included is documented in the kit specification (47) and the\nquantification was achieved using a 7-point standard curve of internal standards”.\n Page 19 line 748-750. “To minimize technical variation across the different plates,\nbatch correction was performed using standard quality control samples included\nin each plate.”\n For the NMR analysis, a pooled sample was prepared from all study samples\nforming a biological QC. This was analysed repeatedly throughout the run to\nconfirm reproducibility. A sentence has been added to the methods to reflect this\nPage 19 line 716-717: “A pooled biological QC was prepared and analyzed\nrepeatedly throughout the run to confirm reproducibility.”.\n4. The authors state that the birth month showed significant association with specific\nmetabolites. However, the results could be potentially explained by the month the\nsamples were collected, and the food-intake/availability at that time. This is directly\nlinked with the birth month, and thus the food intake on the sampling point. There\nwas a short comment that there was a one sampling-month related metabolite. This\nis not sufficient, and a more comprehensive analysis/description should be added.\nHow was the situation with the urine metabolome? There should be more discussion\nof this on the manuscript. This is the major issue on this study.\n We agree with the reviewer that this is the major focus of the manuscript.\n We identified several metabolites that exhibited cosinor variation in urine and plasma\nsamples collected at 6, 12 and 18 months. To identify those that were influenced by\nthe birth month of the infant, we identified metabolites whose peak concentration\nremained static in the same infants when sampled six months apart. For example,\nplasma choline as shown in Figure 1 is highest in infants born November-January at\n12 months and remains highest in infants born in November-January sampled 6\nmonths later at 18 months. Therefore, this cannot be “potentially explained by the\nmonth the samples were collected” and why we defined these metabolites as birth\nmonth related. Similarly, there are metabolites whose peak concentration shifts by 6\nmonths when sampled 6 months later. We define these as sampling month related\nmetabolites. For example, the triglyceride (20:0_34:1) is most abundant in plasma\nfrom March-May born infants sampled at 12 months, but most abundant in plasma\nfrom September-November infants sampled at 18 months.\n The sampling month and birth month related metabolites are indicated in figure 1A-C\nwith open and closed samples.\nFig.1: Systemic and urinary metabolome variation at 6, 12 and 18 months is\ninfluenced by the month of birth. A-C) Bar chart of 6-, 12- and 18-month urinary, and\n12- and 18-month plasma metabolites that follow a seasonal waveform based upon\nmonth of birth, from cosinor analysis (plasma n = 199; urine 6-month n = 278; 12-\nmonth n = 270; 18-month n = 266; p < 0.05 & q < 0.15 at ≥ one sampling point).\nColor indicates birth season of peak abundance. Solid circle represents birth-month-\ndependent metabolites (±1.5-month difference between birth month of peak\nabundance at two sampling points), unfilled circle indicates sampling-month-\ndependent (> four-month difference). D) Mean plasma choline concentration by birth\nmonth at 12 and 18 months (bold line; colored by sampling point; n = 199) following\ncosine distribution (dashed line) with 95% CI overlaid (shaded). E) Median 1H NMR\nsix-month urinary metabolic spectral profile (n = 278). Metabolites identified to\nsignificantly fit cosine waveforms based upon birth month are colored by birth season\nof peak abundance. Urinary metabolite relative abundance obtained by integrating\narea under spectral regions. F-J) Examples of urinary metabolites at six months that\nfollow a cosine distribution (dashed line) by birth month. Mean relative abundance\nwith 95% CI overlaid (shaded). EPA, eicosapentaenoic acid; TMAO, trimethylamine\nN-oxide; 2-PY, N-methyl-2-pyridone-5-carboxamide; NMND, N-methyl nicotinamide;\n2’-FL, 2’-fucosyllactose; 3’FL, 3-fucosyllactose; DMG, dimethylglycine.\n Moreover, we explain this process in supplementary figure 1C.\n In the revised manuscript, the contribution of sampling month to metabolic patterns\nhas been enhanced to make the following points:\no Page 3 line 134-136 “To determine if these metabolic cycles were driven by\ncontemporary pressures at 12 months of life (i.e., time of sampling) or a\nlegacy of the month of birth, plasma was sampled six months later at 18\nmonths”\no Page 3 line 138-146 “Metabolites whose peak phase remained static between\nsampling points (± 1.5-months to account for season) were considered to be\ninfluenced by time of birth (Fig.S1). For instance, choline is birth-month-\nrelated as November-December born infants persistently had the highest\ncirculating choline at 12 and 18 months, while those born May-June had the\nlowest (Fig.1C). These patterns were maintained despite variable pressures\nacross these sampling points, including food insecurity, and rainfall.\nConversely, those metabolites influenced by sampling time were identified as\nthose with ~six-month (four-eight months) phase difference between the 12-\nand 18-month samples and so exhibit seasonal variation within individuals\n(Fig.S1).”\no In relation to the urinary metabolome Page 4 line 170-171 “The peak phases\nof three metabolites, valine, pantothenate and succinate, shifted by ~six\nmonths between sampling points and so were considered sampling month\ndependent.”.\no In figure 1A-C, the circles above the bar chart also denote the metabolites\n(plasma and urinary) that were observed to follow seasonal patterns across\nthe infant at multiple time points and hence if they were ‘birth month related’\n(no shift) or ‘sampling month related’ (~6 month shift).\n5. Related to urine samples, the results showed several metabolites stated to be linked\nwith breast milk. How many infants were still breastfed at the two later sampling\npoints?\n The breastmilk related metabolites identified in the urinary samples were only\nobserved to follow seasonal variation across the infants in the six-month samples.\nMothers were encouraged to breastfeed exclusively for the first six months and as\nmentioned on page 7 line 247 “81% of infants were exclusively breastfed up to six\nmonths of life”. Seasonal variation across the infants in the breastmilk related\nmetabolites were not observed in the urine at the latter two sampling points.\n6. There is some data given from the breast milk samples, however, the duration of the\nbreastfeeding, and its impact on metabolic profiles is not described and as the\nsampling time of the breast milk did not fully match with the sampling of blood\nand urine, there are several possible issues that should be better explored in the study.\n In this cohort, 81% of infants were exclusively breastfed up to six months of life (page\n7, line 247). We do not have data on breastfeeding duration after this time point.\n We aimed to explore the impact of seasonal variation in the breastmilk on the\nseasonality observed across the metabolomes of the infants. To do this, we\nperformed cross-correlation analysis between the five-month breastmilk samples and\nthe six-month urinary samples. We agree with the reviewer the potential limitations of\ncomparing breastmilk/infant samples at non-matching time points, therefore we only\nperformed such analysis at the timepoints closest in sampling (e.g, 5 – 6 months).\no Page 8 line 287-293 “Additionally, to explore transmission of these seasonal\npatterns to the infant, cross-correlation analysis compared five-month\nbreastmilk vitamins and six-month urinary metabolites. Significant correlations\nwere observed with a range of phase lags (Fig.S8; Table.S15). These\nincluded no phase lag between pantothenic acid in the five-month breastmilk\nand the six-month urine, with the greatest abundance of this vitamin noted in\nFebruary/March-born infants. This demonstrates the imprinting of seasonal\npatterns in breastmilk composition on the metabolic signatures of the infant.”\n7. Also the BM samples were grouped by birth month while it would be much more logic\nto group them by season of sample collection.\n We thank the reviewer for this comment, we have updated the manuscript\naccordingly to improve the readability of the manuscript.\no Page 7 line 252-261 “Cosinor analysis identified four metabolites at one-\nmonth, and seven at five months that displayed seasonality based on\nsampling month, which persist following correction for multiple testing (Fig.3F;\nTable.S11). Of these, one metabolite exhibited seasonality at both sampling\ntimes, with biotin (vitamin B7) consistently highest in milk from mothers who\ndelivered in April (sampled May at one month and September at five months),\na period of increasing food availability following the bean harvest in March.\nNicotinamide riboside (NR) and nicotinic acid (NAc), precursors involved in\nthe NAD+ synthesis pathway, exhibited seasonality in breastmilk. NR at one-\nmonth, and NAD+ at five months were highest in milk from mothers delivering\nin April (NR sampled May, NAD+ sampled September), whilst NAc was\ngreatest in five-month breastmilk collected from mothers sampled in July.”\no Page 7 line 265-271 “Samples were therefore grouped by season of birth\nsampling (Sep-Nov, Dec-Feb, Mar-May, Jun-Aug) and variation was assessed\nby ANOVA or Kruskal-Wallis, and post-hoc tests (FDR adjusted). Mothers\nsampled between September – November were observed to have the\ngreatest 2’-FL, lacto-N-fucopentaose I (LNFP-1) and glutamine in their milk at\none-month postpartum. Additionally, 2’-FL and LNFP-1 were greatest in five-\nmonth milk sampled from mothers in June-August (Table.S12).”\no Page 14 line 531-533 “F) Heatmap of 1- and 5-month breastmilk metabolites\ndisplaying seasonality based on sampling month from cosinor analysis (n =\n292; q < 0.15).”\no Page 20 line 779 “For breastmilk data, cosinor analysis was applied across\nsampling months.”\n8. Maternal diet and food availability is likely to have a major impact of the BM\ncomposition.\n We agree that maternal diet and food availability are highly influential on BM\ncomposition. To explore this, we chose to perform cross-correlation analysis between\nthe relative abundances of the B vitamins in the breastmilk across the\nmothers/infants and contemporary food insecurity.\no Page 7 line 274-281 “We next assessed the impact of the nutritional\nenvironment of the mother on the breastmilk metabolome. Cross correlation\nwas performed between seasonal breastmilk B-vitamin-related metabolites\nand contemporary food insecurity. (Fig.3F; Table.S13). Short phase lags (10-\n90 days) were noted between peak food insecurity and maximal breastmilk\nbiotin, and NR at one-month and flavin mononucleotide, at five months.\nMetabolites most abundant in milk sampled during periods of high food\navailability (110-200 days phase lag from peak food insecurity) included\npyridoxamine at one-month; pantothenic acid (vitamin B5), Nac, NAD,\npyridoxal-5-phosphate, and biotin, at five months”.\n As the whole breastmilk metabolome and HMOs were only measured in a subset of\nmothers/infants there was insufficient numbers to perform cross correlation analysis.\nTherefore, to explore this question we used wilcoxon tests to compare metabolite\nabundances between mothers who did versus did not report food insecurity at the\ntime of sampling.\no Page 7 line 282-287 “To assess the impact of food insecurity on the\nbreastmilk metabolome and HMOs measured from the subset of mothers, we\nexplored biochemical variation in the season-dependent metabolites\nassociated with contemporary maternal food insecurity reports (No food\ninsecurity [n = 40], reported food insecurity [n = 14]; Wilcoxon test q < 0.05;\nFig.3G; Table.S14). At one-month, 2’-FL and LNFP-1 were lower in milk from\nmothers who did not report food insecurity. At five months, only three mothers\nfrom this subset reported food insecurity, therefore statistical analysis was not\nperformed.”\n9. The figure texts lack sufficient description; none of the figures explains what are the\npanels A, B, C etc.\n We believe this issue relates to the requested format of the journal where the figure\nand its title is placed at the appropriate location within the manuscript and the full\nfigure legend/description is included at the end. All the requested information is\npresent see page 14-15 line 495-554."}, {"round": 2, "reviewer_comments": {"reviewer_1": "While the manuscript has improved, there are now several parts on the new data that is making the interpretation more\nconfusing, and it is still not fully clear that the birth month is the driving factor is many of the observations listed here. The\nmain concerns are that of the seven metabolites that after adjustment are linked with birth month (choline, TMAO, EPA,\nmethionine sulfate, cer(d18:2/14:0), TG 20:0_34:1 and hex2cer(d18:1/18:0) most of them are also linked with seasonal\nvariation (choline, TMAO, EPA, cer(d18:2/14:0), TG 20:0_34:1 and hex2cer(d18:1/18:0. This is not clearly discussed in the\nmanuscript. Which one is the main driving force, the birth month or the season? In addition, the associations seem to be\nrather weak, as FDR correction of 0.15 was used, normally one would use FDR= 0.05 or in some case, FDR of 0.1. How\nwould the results look if more stringent FDR would be used? Overall, it would be good to have a similar plot that is shown in\nFig1D for choline for all metabolites that were listed significant, showing also the error range of measurements, both related\nto the birth month and for month of sampling.\nThe circannual patterns were adjusted with sex, and after that, no significant patterns were observed in female infants. Why\nwas not the birth month related analyses stratified by sex?\nUrine metabolomics, why only the impact of birth months has been investigated, and the impact of sampling month has not\nbeen done in a similar manner than was done for plasma metabolomics?\nPCA model shown in Fig S3, is the sampling month considered in the model? How the model would look if the grouping\nwould be done according to the sampling month, rather than birth month?\nFigure 1 E-J are not explained in the text. Fig 1 E is not very informative.\nLine 109-110: The authors have added information that large part of the cohort has been getting antibiotics. Use of\nantibiotics can have a significant impact on metabolome, was this considered in the analyses? Is the use of antibiotics\nrelated to season?\nThere is still no information of the quality control. Just stating that the analyses were using standard manufacturers protocols\ndoes not ensure robust results, neither does the use of quality control samples. The LC-MS is known to be prone to\nanalytical variability, even when standard protocols are being applied. As the QC samples have been used, it should be\nstraightforward to give the data on the QC analyses. Statement that a batch correction was used, for quite a low number of\nanalyses, raises concerns of the quality of the analyses.", "reviewer_2": "Thank you for comprehensively addressing my comments. A few additional comments below.\nMinor Comments:\nLine 98-100: “Here, data from the placebo group (n = 278) and infants receiving nicotinamide (n = 276, breastfeeding\nmothers provided 250 mg daily tablets for six months post-delivery, followed by direct infant supplementation (100 mg\nsachets) from six to 18 months) was studied.”\nThe layered parenthetical is obscure and difficult to understand. The additional details about how the nicotinamide was\nintroduced are important and deserve their own sentence.\nLine 56-57: “As climate change is a major contributor to increasing food insecurity, understanding and preventing its\ndetrimental impact on early life undernutrition remains crucial.”\nThis sentence would benefit from a more thorough transition, clarifying how seasonality is related to anticipated changes in\nclimate, especially regarding droughts.", "reviewer_3": "Thank you for considering and addressing my comments. I am happy with the changes made."}, "author_response": "Response to reviewers\nWe are delighted that Reviewers 2 and 3 appreciated our comprehensive responses to\ntheir remarks and are now satisfied with the manuscript. We have made the modest\nchanges requested to the manuscript (latest modifications highlighted in the manuscript\nin green; responses to first review highlighted in red).\nReviewer #2:\nThank you for comprehensively addressing my comments. A few additional comments\nbelow.\nMinor Comments:\nLine 98-100: “Here, data from the placebo group (n = 278) and infants receiving\nnicotinamide (n = 276, breastfeeding mothers provided 250 mg daily tablets for six months\npost-delivery, followed by direct infant supplementation (100 mg sachets) from six to 18\nmonths) was studied.”\nThe layered parenthetical is obscure and difficult to understand. The additional details\nabout how the nicotinamide was introduced are important and deserve their own\nsentence.\nThis has been amended to read:\nPage 3, Line 98-100: “Nicotinamide was provided to breastfeeding mothers (250 mg daily\ntablets for six months post-delivery) followed by direct infant supplementation (100 mg\nsachets) from six to 18 months. Here, data from the placebo group (n = 278) and infants\nreceiving nicotinamide (n = 276) was studied.”\nLine 56-57: “As climate change is a major contributor to increasing food insecurity,\nunderstanding and preventing its detrimental impact on early life undernutrition remains\ncrucial.”\nThis sentence would benefit from a more thorough transition, clarifying how seasonality\nis related to anticipated changes in climate, especially regarding droughts.\nWe have now added the following sentence to clarify this point.\nPage 2, Line 56-59: “Climate change has been shown to exacerbate seasonal variations\nin environmental conditions and is anticipated to increase the frequency and severity of\ndroughts and food insecurity (13). As such, understanding the detrimental impact of\nadverse climatic conditions on early life undernutrition and its impact on development\nremains crucial.”\nReviewer #3:\nThank you for considering and addressing my comments. I am happy with the changes\nmade.\nReviewer #1:\nIn contrast to the other reviewers, Reviewer 1 still had queries about the work and our\nresponses to earlier comments. Many of these can be answered by information already\ncontained within the manuscript and its supplementary information. We have addressed\nthe comments for Reviewer 1 below and signposted information within the manuscript\nthat should clarify areas of misunderstanding.\nWhile the manuscript has improved, there are now several parts on the new data that is\nmaking the interpretation more confusing, and it is still not fully clear that the birth month\nis the driving factor is many of the observations listed here. The main concerns are that\nof the seven metabolites that after adjustment are linked with birth month (choline, TMAO,\nEPA, methionine sulfate, cer(d18:2/14:0), TG 20:0_34:1 and hex2cer(d18:1/18:0) most of\nthem are also linked with seasonal variation (choline, TMAO, EPA, cer(d18:2/14:0), TG\n20:0_34:1 and hex2cer(d18:1/18:0. This is not clearly discussed in the manuscript.\nWhich one is the main driving force, the birth month or the season?\n This is the major focus of the manuscript, and our approach has been designed to\nspecifically answer this question (i.e. distinguish between birth month and\nsampling month-related metabolites).\n We previously included a schematic to explain the approach in the supplementary\ninformation (Supplementary Figure S1).\n This is clearly explained in the manuscript and in our ‘Response to Reviewers’\ndocument.\nIn addition, the associations seem to be rather weak, as FDR correction of 0.15 was used,\nnormally one would use FDR= 0.05 or in some case, FDR of 0.1.\n We disagree with the reviewer on this point. The metabolites being discussed are\nthose whose plasma/urine abundance fits a cosine wave across >200 infants\ndepending on the month in which they were born and/or month they were sampled.\n An FDR correction (0.15) was then applied to the significance of these cosine fits\n(across >500 molecules), further reducing the risk of false positives.\n Several metabolites exhibit seasonal patterns at multiple time points, reinforcing\nthe biological relevance and validity of our findings.\n As such, this is not a weak association.\n We are happy to see that Reviewer 3 agrees with this approach.\n Correcting for multiple testing is especially difficult in metabolomics analysis\nbecause of the wealth of metabolites measured. Many of these molecules are not\ntruly independent and can covary due to their shared metabolic pathways,\nfunctions and complex network of interactions. As such, conservative threshold\nvalues (FDR = 0.05) are overly strict and can result in a high number of false\nnegatives.\n The FDR threshold of 0.15 is a good compromise to avoid false positives while\nlimiting the amount of false negatives. This has been employed in many\nmetabolomics studies previously, e.g.\no DOI: 10.1038/s41598-018-35372-w\no DOI: 10.1002/ijc.32218\no DOI: 10.1038/s41398-022-01856-7\no DOI: 10.3390/ijms24119736.\nHow would the results look if more stringent FDR would be used?\n As expected, at a stricter FDR, some of these findings are lost. For example, TG\n(20:0_34:1) following adjustment for enrolment WAZ, number of months of\nexclusive breastfeeding, socioeconomic status and maternal factors is significant\nat 18 months (p = 0.0005; q = 0.06) at a stricter FDR, but not at 12 months (p =\n0.001; q = 0.11). Using a more stringent FDR would introduce a greater number of\nfalse negatives and potentially mask biologically important results.\n Supplementary Tables 2 and 3 shows the results for the plasma and urinary\nmetabolomes with their q values so that readers have all the information requested\nby the reviewer.\n From here, 28 plasma metabolites are significant at 0.25, 11 at 0.15, 7 at 0.10 and\n5 at 0.05. Following adjustment for covariates, 23 plasma metabolites are\nsignificant at 0.25, 10 at 0.15, 7 at 0.1 and 5 at 0.05. Additionally, 17 urinary\nmetabolites are significant at 0.25, 17 at 0.15, 15 at 0.1, and nine at 0.05. After\ncovariate adjustment, 14 urinary metabolites are significant at 0.25, nine at 0.15,\nsix at 0.1 and four at 0.05.\nOverall, it would be good to have a similar plot that is shown in Fig1D for choline for all\nmetabolites that were listed significant, showing also the error range of measurements,\nboth related to the birth month and for month of sampling.\n This has been shown in Figure 1E-J for several urinary metabolites.\n We have added these plots for the other plasma metabolites in Supplementary\nFigure S3 and urinary metabolites in Supplementary Figure S4.\nFig.S3.\nVariation in plasma metabolome at 12 and 18 months is influenced by the month of birth\nA-L) Mean plasma metabolite concentrations by birth month at 12 (grey) and 18 (green) months\n(bold line; n = 199) following cosine distribution (dashed line) with 95% CI overlaid (shaded).\nMetabolites plotted at time point observed to follow a seasonal waveform based upon month of\nbirth, from cosinor analysis (plasma n = 199; p < 0.05 & q < 0.15 at ≥ one sampling point).\nFig.S4.\nVariation in urinary metabolome at 6, 12 and 18 months is influenced by the month of birth\nA-P) Mean urinary metabolite relative abundances by birth month at six (pink), 12 (grey) and 18\n(green) months (bold line) following cosine distribution (dashed line) with 95% CI overlaid\n(shaded). Metabolites plotted at time point observed to follow a seasonal waveform based upon\nmonth of birth, from cosinor analysis (6-month n = 278; 12-month n = 270; 18-month n = 266; p <\n0.05 & q < 0.15 at ≥ one sampling point).\nThe circannual patterns were adjusted with sex, and after that, no significant patterns\nwere observed in female infants. Why was not the birth month related analyses stratified\nby sex?\n This was performed (see Fig S4).\n We have added the following sentence to the main text:\nPage 4, line 188-190: “Here, plasma choline and TMAO and urinary D-galactose were\nnoted to be birth month dependent metabolites in males while urinary pantothenate and\nlactose were sampling month dependent”.\nUrine metabolomics, why only the impact of birth months has been investigated, and the\nimpact of sampling month has not been done in a similar manner than was done for\nplasma metabolomics?\n The same analysis was performed in the urine and plasma samples which is\nclearly displayed in Figure 1A-C and explained in the manuscript.\nPCA model shown in Fig S3, is the sampling month considered in the model? How the\nmodel would look if the grouping would be done according to the sampling month, rather\nthan birth month?\n This will not alter the model.\n Here, the PCA model was built using metabolites measured at one time point (e.g.\n6, 12, or 18 months) as the descriptor matrix. PCA is an unsupervised approach\nbased on variance and does not consider birth month or sampling month.\n In the resulting scores plot from the PCA model (shown in FigS3), the observations\n(infants) are coloured according to the infant’s birth month (e.g. September-\nNovember vs March-May).\n If the observations are coloured according to sampling month, the scores plot will\nlook the same because the September-November born infants will have one colour\n(as sampled March-May) while March-May born infants will have a different colour\nas sampled (September-November).\nFigure 1 E-J are not explained in the text. Fig 1 E is not very informative.\n For clarity we have directed the reader to each individual panel in Fig1 when the\nrelevant urinary result is stated in the manuscript text (see page 4, lines 164-177).\n However, we disagree with the comment that Fig 1E is not very informative. This\nis an 1H NMR spectrum where peaks that follow a circannual rhythm are\nhighlighted and coloured by the birth month at which they are highest in the urine.\nThis captures a large amount of information in a single figure.\nLine 109-110: The authors have added information that large part of the cohort has been\ngetting antibiotics. Use of antibiotics can have a significant impact on metabolome, was\nthis considered in the analyses? Is the use of antibiotics related to season?\n We have limited information regarding any exposure to antibiotics (Yes, No) in the\ntime prior to sampling. This included up to 86% of the population and intake did\nnot differ by birth month.\n We have added this information to the main text: Page 3, lines 114-115:\n“Additionally, receipt of non-study antibiotics was common (54 - 86% infants) and\nnot season-dependent.”\nThere is still no information of the quality control. Just stating that the analyses were using\nstandard manufacturers protocols does not ensure robust results, neither does the use of\nquality control samples. The LC-MS is known to be prone to analytical variability, even\nwhen standard protocols are being applied. As the QC samples have been used, it should\nbe straightforward to give the data on the QC analyses. Statement that a batch correction\nwas used, for quite a low number of analyses, raises concerns of the quality of the\nanalyses.\n For the LCMS analysis a commercially available kit was used as well as the\naccompanying software for data processing, which performed the batch correction\n(the reviewer may understand this as normalization) steps as part of the standard\nanalytical pipeline. The supplier is committed to quality\n(https://biocrates.com/quality-policy/).\n Batch correction is standard in LCMS-based metabolomic profiling especially\nlarge-scale studies. In this analytical run, plasma samples from all four study\ngroups at 12 months and 18 months were analysed (N = 1,558). This would not be\nconsidered a low number of analyses in the metabolomics community.\n As requested, the scores plots from the PCA models built on the plasma and\nurinary metabolites have been added to the supplementary information (Figure\nS16).\no The study samples are coloured by batch, highlighting no batch-dependent\nvariation.\no The QC samples are coloured in black. From the scores plot the\nreproducibility of the QC samples is clear.\n Collectively, this illustrates the quality, reproducibility and validity of our analysis.\nFig.S16.\nQuality control sample and batch distribution of plasma and urinary metabolomes\nA) PCA scores plot of plasma metabolites colored by batch number. Median QC per batch colored\nblack. B) PCA scores plot of urinary 1H NMR spectra colored by batch number. Pooled biological\nQC per batch colored in black."}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors present a comprehensive analysis of how birth season shapes the infant metabolome and developmental\noutcomes in a large Tanzanian cohort. This revision has addressed the major concerns raised previously and substantially\nstrengthened the manuscript in response to Reviewer 1’s comments. The distinction between birth-month and sampling-\nmonth dependent metabolites is presented with schematics and supplementary figures. Additional analyses and\nexplanations (stratification by sex, inclusion of antibiotic exposure, QC plots, and FDR thresholds) directly address the\nearlier points.\nReviewer 1 rightly noted that FDR thresholds of 0.05 or 0.01 are more conventional and stringent. The use of an FDR\nthreshold of 0.15 is more lenient, but given the exploratory nature of the study, the consistent seasonal patterns observed for\nseveral metabolites (e.g., choline, TMAO, EPA, ceramides) across multiple time points, and the transparent reporting of\nstricter cut-offs in the supplement, this approach is acceptable.\nThe manuscript is much stronger, though a few minor refinements would improve clarity:\n• Emphasize that “birth month” functions as a proxy for seasonal exposures rather than a causal factor.\n• Make explicit in the main text where stricter FDR thresholds still support the robustness of key findings.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Response to reviewers\nWe are delighted that the reviewers are satisfied with the manuscript. We have made the\nmodest changes requested to the manuscript.\nReviewer #4\nThe authors present a comprehensive analysis of how birth season shapes the infant\nmetabolome and developmental outcomes in a large Tanzanian cohort. This revision has\naddressed the major concerns raised previously and substantially strengthened the\nmanuscript in response to Reviewer 1’s comments. The distinction between birth-month and\nsampling-month dependent metabolites is presented with schematics and supplementary\nfigures. Additional analyses and explanations (stratification by sex, inclusion of antibiotic\nexposure, QC plots, and FDR thresholds) directly address the earlier points.\nReviewer 1 rightly noted that FDR thresholds of 0.05 or 0.01 are more conventional and\nstringent. The use of an FDR threshold of 0.15 is more lenient, but given the exploratory\nnature of the study, the consistent seasonal patterns observed for several metabolites (e.g.,\ncholine, TMAO, EPA, ceramides) across multiple time points, and the transparent reporting\nof stricter cut-offs in the supplement, this approach is acceptable.\nThe manuscript is much stronger, though a few minor refinements would improve clarity:\n1. Emphasize that “birth month” functions as a proxy for seasonal exposures rather\nthan a causal factor.\n A sentence has been included in the discussion to emphasize that birth month is\na proxy for the range of season exposures. Other examples of seasonal\npressures are listed in the limitations section of the discussion.\n Line 473-474 “Birth month represents a proxy for this combination of complex\nand interacting pressures.”\n2. Make explicit in the main text where stricter FDR thresholds still support the\nrobustness of key findings\n Throughout the main text, the number of associations that remain following a\nstricter FDR threshold have been included. The exact p and q values are also\nincluded in the Supplementary Tables both including covariates and raw.\n Line 145 “n = 3 for q < 0.10”\n Line 152 “n = 5 for q < 0.10”\n Line 183 “n = 4 for q < 0.10”\n Line 186 “n = 4 for q < 0.10”\n Line 201 “for q < 0.10, 6M n = 1; 12M n = 11; 18M n = 5”"}]

Decision letter