Infant Gut Microbiota and Childhood Blood Pressure: Prospective Associations and the Modifying Role of Breastfeeding
Abstract
Background
Germ‐free mice experiments indicate that human gut microbiota influence blood pressure (BP), but no studies have prospectively examined if infant gut microbiota affects their future childhood BP. We aim to investigate prospective associations of infant gut microbiota diversity and composition with childhood BP, examining effect measure modification by breastfeeding and mediation by a child's body mass index.
Methods and Results
In the Copenhagen Prospective Studies on Asthma in Childhood 2010 cohort, we measured infant gut microbiota (16S rRNA V4) at 1 week, 1 month, and 1 year and child BP at 3 and 6 years. We assessed α diversity‐BP, β diversity‐BP, and microbe abundances‐BP associations using linear regression, permutational multivariate analysis of variance, and beta‐binomial count regression, respectively. Data from 526 children showed that α diversity and several Bifidobacterium spp. had protective associations with BP but only in children breastfed for ≥6 months. For instance, a 1‐unit increment in 1 month Shannon index was associated with 1.86 mm Hg (95% CI, 0.66–3.05) lower 6‐year systolic BP in children breastfed ≥6 months but a 0.73 (95% CI, −1.00 to 2.45) higher 6‐year systolic BP in those breastfed <6 months (P‐interaction=0.02). Greater abundance of 2 Bifidobacterium microbes at 1 week was negatively associated with 6‐year systolic BP when breastfeeding ≥6 months (P‐interaction<0.1). Further, abundance of 8 microbes at 1week or 1 month was linked to 3‐year or 6‐year BP (false discovery rate P<0.05), with 5 of them independent of a child's body mass index. Lastly, 1‐week unweighted UniFrac distance and 1‐year weighted UniFrac distance were associated with BP after adjustment (P<0.05).
Conclusions
Gut microbiota features at 1 week and 1 month of life were associated with BP at 6 years. Breastfeeding duration modified key associations including those for α diversity and Bifidobacteria.
Nonstandard Abbreviations and Acronyms
- ASV
- amplicon sequence variant
- COPSAC2010
- Copenhagen Prospective Studies on Asthma in Childhood 2010
- PERMANOVA
- permutational multivariate analysis of variance
Research Perspective
What Is New?
•
Infant gut microbiome diversity and composition are prospectively associated with child blood pressure at 3 and 6 years of age.
•
Alpha diversity and specific Bifidobacterium microbes showed protective associations with blood pressure, especially when infants are breastfed for at least 6 months. The presence of Bifidobacterium microbes in infant gut within the first month of life also plays a role in the beneficial associations of prolonged breastfeeding on childhood blood pressure.
What Question Should Be Addressed Next?
•
Future research should focus on shotgun metagenomic and metabolomic analysis to identify specific infant gut microbes, particularly Bifidobacterium species, and microbial metabolites associated with cardiovascular health in childhood.
Childhood hypertension is a pressing public health concern, with its prevalence doubling worldwide from 2000 to 2015, and now affecting 4% to 7% of the pediatric population.1 High blood pressure (BP) originates in utero, usually closely correlates with obesity, and tracks from childhood to adulthood,2, 3, 4, 5 However, early‐life factors that may affect childhood BP are understudied.6 One factor that is gaining attention is the human gut microbiota in early life, which is known for its lasting effects on health outcomes later in childhood.7, 8 Despite this, the specific role of the early‐life gut microbiota in childhood cardiovascular health, particularly its impact on BP, remains poorly understood, representing a missed opportunity for early prevention of hypertension.
Recent epidemiological studies in adults suggest cross‐sectional associations between gut microbiota and BP,9, 10, 11 although some studies have reported null findings.12 Experimental studies, however, have provided strong evidence that the human gut microbiome causally influences BP regulation13 through mechanisms including immune pathway regulation, interactions with the gut–autonomic nervous system–cardiorenal axis, and the production of metabolites like short‐chain fatty acids.13, 14, 15, 16 Investigating how early‐life gut microbiota, a highly modifiable intervention target, is associated with childhood BP could open opportunities for early prevention of hypertension and reduce the long‐term cardiovascular risks across the life span.
Although the association between early‐life gut microbiota and childhood BP remains unexplored, breastfeeding, a key influencer of the early‐life gut microbiota, has been associated with lower childhood BP compared with formula feeding, while null associations have also been reported.17, 18, 19, 20, 21, 22, 23 Breastfeeding protects against pathogens and selectively fosters the growth of beneficial microbes, particularly Bifidobacterium species. These species are uniquely adapted to metabolize human milk oligosaccharides, the third most abundant component in breast milk, which infants cannot digest on their own.24, 25, 26 By metabolizing human milk oligosaccharides, Bifidobacteria produce important metabolites, including short‐chain fatty acids, which may contribute to cardiovascular health.27 Given that formula typically lacks human milk oligosaccharides found in breast milk, Bifidobacteria may play a central role in the cardiovascular benefits associated with breastfeeding observed in some studies. Despite the plausibility that breastfeeding and gut microbiota, especially certain Bifidobacteria, interact to influence BP later in life, this research question has yet to be examined. Addressing this question is important as it is possible that the inconsistent findings on the association between breastfeeding and BP may be partly due to prior studies not accounting for the modifying role of gut microbiota, particularly Bifidobacterium species.
To address these research gaps, our study aims to investigate the associations between the infant gut microbiota, with a particular focus on Bifidobacterium, measured at 1 week, 1 month, and 1 year and childhood BP measured at 3 and 6 years, within a longitudinal Danish birth cohort. We additionally seek to examine interaction with breastfeeding duration and the mediating role of body mass index in observed associations. Conducting this research in infants and children who have less confounding by lifestyle, diet and medications than adults, offers the opportunity for clearer, less confounded insights into the connection between gut microbiota and BP in humans. Our study also has the potential to identify an early‐life window for microbiota‐focused interventions for high BP prevention.
METHODS
The 16S rRNA gene sequences associated with this analysis have been deposited in the Sequence Read Archive repository with the accession number PRJNA417357. Other data that support the findings of this study are available from Dr Jakob Stokholm and Dr Noel Mueller upon reasonable request. We followed the Strengthening the Reporting of Observational Studies in Epidemiology cohort reporting guidelines in reporting our study.28
Population and Study Design
Participants are from the COPSAC2010 (Copenhagen Prospective Studies on Asthma in Childhood 2010) cohort, which is a population‐based, prospective, longitudinal, mother–child cohort.29, 30 The primary aim of the COPSAC2010 cohort was to understand the origins of chronic inflammatory diseases, with a particular focus on the impact of the early life microbiome on disease development. The cohort enrolled 736 pregnant individuals between 2008 and 2010, who attended pregnancy visits in hospitals in Zealand (eastern Denmark) between 22 and 26 weeks of gestation; had no endocrine, heart, or kidney disorders; and had not taken >600 IU vitamin D daily during pregnancy. In addition, 700 of their children were enrolled in the cohort and were followed up longitudinally at 1 week; 1, 3, and 6 months; every 6 months to age 3 years; yearly to age 6 years; and at 8 and 10 years.
The study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the National Committee on Health Research Ethics (H‐B‐2008‐093) and the Danish Data Protection Agency (2015‐41‐3696). Written informed consent was obtained from the pregnant participants, as well as from both parents for the participating children.
Microbiota Sequencing
Infant fecal samples were collected at 1 week, 1 month, and 1 year of age either by research staff during scheduled clinical visits or by parents at home following detailed instructions.31, 32 Samples were mixed with 1 mL of 10% vol/vol glycerol broth and stored at −80 °C until analysis. Sequencing procedure and bioinformatics were described in detail elsewhere.31, 32 In brief, after the DNA was extracted from samples, we amplified the hypervariable V4 region of the 16S rRNA gene using the modified broad range primers 515F and 806R and then performed paired‐end sequencing on the Illumina MiSeq System. We analyzed sequencing reads using QIIME 2 v2018.2.0,33 performed quality control and inferred amplicon sequence variants (ASVs) using R package DADA2,34 and assigned taxonomy using the Silva database release 132.35, 36 We named ASVs using the lowest available taxonomy level followed by the first 4 characters of an MD5 hash of the sequence via R package digest.37
BP Measurement
Research staff measured children's BP during scheduled clinical visits at 3 and 6 years of age using an oscillator (Welch Allyn, ProBP3400) following a standard protocol.38 Before the measurement, children were seated on a chair with their arm in a spine position and rested for at least 5 minutes. The machine measured BP 3 times, with a minimum of 1 minute between each measurement. We calculated mean systolic BP (SBP) and diastolic BP (DBP) from the second and third measurements. We further calibrated BP values based on the child's sex, age, and height assessed at the time of BP measurement.
Covariates Measurement
We collected information on sociodemographic characteristics, pregnancy complications, and medication use from pregnant individuals via in‐person interviews. This included mother's age at delivery, prepregnancy body mass index (BMI), household income, preeclampsia diagnosis, and antibiotic use during pregnancy. The information on antibiotic use was further validated against data from the Danish Medical Agency's Register, which contains records of all drugs filled at the pharmacy.39 We assessed maternal dietary intake during pregnancy through a semiquantitative food frequency questionnaire administered at 24 weeks of gestation, which consisted of 360 items of food and beverages covering the past 4 weeks of dietary intake before the assessment.40 This food frequency questionnaire has been previously validated for Danish pregnant population.41, 42 We derived the maternal dietary score based on principal component analysis of maternal intake of energy, macronutrients, and micronutrients. Principal component 2 was determined as the primary component that captured the variation in these dietary information, allowing us to use it as a composite score representing the overall dietary quality of the mothers in the study. For children, we obtained information on race, sex, gestational age, birth weight, and duration of any breastfeeding within the first year of age through interviews with parents. At ages 3 and 6 years, we measured children's height with a stadiometer (Harpenden, Holtain Ltd, Crymych, Dyfed, Wales) calibrated yearly, and measured children's weight while in underwear using calibrated digital weight scales. We calculated BMI as weight (kg)/height2 (m2) and converted into age‐and‐sex‐standardized World Health Organization Z scores43 using the R package zscorer.
Statistical Analysis
We used 3 features to characterize gut microbiome: α diversity (within‐individual diversity), β diversity (between‐individual diversity), and relative abundance. For α diversity, we calculated 2 indices: number of observed ASVs (indicates richness) and Shannon index44 (indicates a combination of richness and evenness). For β diversity, we used unweighted and weighted unique fraction metric (UniFrac) distances45 (quantifies dissimilarity by also considering phylogenetic aspects of ASVs). We estimated α and β diversity indices after rarefying count data to the minimum sequencing depth (2000 for microbiota measured at 1 week, 2105 at 1 month, and 2077 at 1 year) to account for differences in library sizes across participants.
We analyzed microbiome features at each time point during infancy in relation to BP at 3 and 6 years of age separately, to identify potential critical windows of microbiome exposure during infancy that may have different associations on BP development at distinct ages. We used linear regression models to examine the associations between α diversity (ie, a continuous metric) and BP. We performed permutational multivariate analysis of variance (PERMANOVA) with 9999 permutations to examine the associations between β diversity (ie, a distance matrix) and BP.46, 47, 48 To visualize β diversity patterns, we used principal coordinate analysis plots, which project pairwise distances, including non‐Euclidean measures (eg, unweighted and weighted UniFrac distances), into a lower‐dimensional space for visualization. We used beta‐binomial count regression (“corncob”)49 to identify ASVs that were differentially abundant according to BP values. Only ASVs with a prevalence of ≥10% without rarefaction were tested for differential abundance, following recommendations from Nearing et al. (2022),50 to improve the robustness and interpretability of the results. Additionally, we also tested for differential abundance at the genus level. To denote statistical significance, we used a 2‐sided false discovery rate (FDR)‐adjusted P<0.05 using the Benjamini–Hochberg method51 for differential abundance analysis, and a 2‐sided P<0.05 otherwise.
We began with a crude model without adjustment (Model 0). We then added covariates to our model to account for potential confounding. Model 1 included adjustment for maternal diet score during pregnancy (continuous), diagnosis of preeclampsia (yes versus no), infant birth weight (kg), gestational age (weeks), and total breastfeeding duration (days) at the time of microbiome measurement. We additionally added child BMI Z score (continuous) at the time of BP measurement in Model 2 to explore its potential role in mediation of microbiome‐BP associations.
We examined the joint effects of microbiota (α diversity and Bifidobacterium ASVs) and breastfeeding duration on BP using linear regression by conducting stratified analysis as well as adding interaction terms of microbiota and breastfeeding duration. We dichotomized breastfeeding duration as < versus ≥ 6 months, as the World Health Organization recommends (ideally exclusive) breastfeeding for at least the first 6 months, and this categorization would increase the public health relevance of our findings. For the analyses in which we wanted to model the biologic interaction between Bifidobacterium species and breastmilk exposure, we examined absence/presence and abundance of Bifidobacterium ASVs in early infancy (ie, at 1 week and 1 month), and considered breastfeeding status at the time points when microbiome data were collected (ie, 1 week, 1 month), in addition to dichotomizing breastfeeding duration at 6 months. This approach allowed us to interpret the data based on whether infants were still being breastfed and thus continued to be exposed to human milk oligosaccharides at or beyond the time when their microbiome was measured. We applied the centered log‐ratio transformation to the abundance of Bifidobacterium ASVs to account for the compositionality of microbiota count data. For interaction models, we adjusted for the variables included in Model 1 other than breastfeeding duration and we used a 2‐sided P<0.1 to determine statistically significant interaction. All analyses were conducted using R version 4.2.1.
RESULTS
Participant Characteristics
Our study included 526 children with at least 1 microbiome and 1 BP measurement (Table). Their mothers had a mean age of 32.2 years (SD: 4.2) at delivery, a mean prepregnancy BMI of 24.5 kg/m2 (SD: 4.3), and a mean diet score during pregnancy of 0.008 (SD: 1.0). Preeclampsia was diagnosed in 4.6% of the mothers, and 34.4% received antibiotics during pregnancy. Furthermore, 8.4% of the households had an annual income <400 000 Danish Krone. Of the children, 95.6% were of European ancestry and 77.8% were born vaginally. They had a mean gestational age at birth of 39.9 weeks (SD: 1.6) and a mean birth weight of 3.5 kg (SD: 0.5). The mean duration of any breastfeeding was 229 days (SD: 107) by the first year of age. At age 3, children had a mean calibrated SBP, DBP, and BMI Z score of 98.4 mm Hg (SD: 6.6), 62.6 mm Hg (SD: 5.1), and 0.3 (0.8), respectively; and at age 6, 98.1 mm Hg (SD: 5.9), 62.6 mm Hg (SD: 5.0), and 0.03 (0.8), respectively.
| Overall | |
|---|---|
| Maternal characteristics | |
| Age at delivery, y, mean±SD | 32.2±4.2 |
| Household income, Danish Krone, n (%) | |
| <400 000 | 44 (8.4%) |
| ≥400 000 to ≤800 000 | 284 (54.0%) |
| ≥800 000 | 197 (37.5%) |
| Prepregnancy body mass index, kg/m2, mean±SD | 24.5±4.3 |
| Diet score during pregnancy, mean±SD | 0.008±1.0 |
| Diagnosed with preeclampsia, n (%) | 24 (4.6%) |
| Treated with antibiotics during pregnancy, n (%) | 181 (34.4%) |
| Child characteristics | |
| European ancestry, n (%) | 503 (95.6%) |
| Male sex, n (%) | 268 (51.0%) |
| Vaginally delivered, n (%) | 409 (77.8%) |
| Gestational age at birth, wks, mean±SD | 39.9±1.6 |
| Birth weight, kg, mean±SD | 3.5±0.5 |
| Duration of any breastfeeding at 1 y of age, d, mean±SD | 229±107 |
| At 3 y of age | |
| Calibrated systolic blood pressure, mm Hg, mean±SD | 98.4±6.6 |
| Calibrated diastolic blood pressure, mm Hg, mean±SD | 62.6±5.1 |
| Body mass index Z score, mean±SD | 0.3±0.8* |
| At 6 y of age | |
| Calibrated systolic blood pressure, mm Hg, mean±SD | 98.1±5.9 |
| Calibrated diastolic blood pressure, mm Hg, mean±SD | 62.6±5.0 |
| Body mass index Z score, mean±SD | 0.03±0.8† |
COPSAC2010 indicates Copenhagen Prospective Studies on Asthma in Childhood 2010.
*
N missing=26.
†
N missing=18.
α Diversity, BP, and Modification Effects of Breastfeeding
Overall, we found no associations between α diversity and BP at any time points. However, breastfeeding duration significantly modified many of the associations between α diversity and BP, with positive associations observed for infants who were breastfed for less than 6 months and negative associations for infants who were breastfed beyond 6 months (Figure 1). Specifically, statistically significant interaction by breastfeeding duration was observed for the associations between observed ASVs at 1 week with SBP and DBP at 6 years (P for interaction=0.049 and 0.009, respectively), and observed ASVs at 1 month with DBP at 3 years (P for interaction=0.018), SBP at 6 years (P for interaction=0.032), and DBP (P for interaction=0.042) at 6 years. As for Shannon index, we observed statistically significant interaction by breastfeeding duration for its association at 1 month with DBP at 3 years (P for interaction=0.065) and SBP at 6 years (P for interaction=0.026). For example, for each 1‐unit increment in Shannon index at 1 month, infants breastfed >6 months had a 1.86 mm Hg (95% CI, 0.66–3.05) lower SBP at 6 years, whereas infants breastfed <6 months had a 0.73 mm Hg (95% CI, −1.00 to 2.45) higher SBP at 6 years (P for interaction=0.026; Figure 1). Other associations between α diversity and BP that were not modified by breastfeeding duration were not statistically significant, with or without the inclusion of the child's BMI Z score in the model (results not shown).
Figure 1. Prospective associations between infant gut microbiome α diversity indices and child BP at 3 and 6 years, overall and by breastfeeding duration.
The effect sizes and 95% CIs are interpreted as the changes in BP per 1‐unit increment in α diversity indices. Models were adjusted for maternal diet score during pregnancy (continuous), diagnosis of preeclampsia (yes vs no), infant birth weight (kg), gestational age (wks), and breastfeeding duration (d) at the time of microbiome measurement (for overall estimates only). P values shown on figure indicated statistical significance for interaction. ASVs indicates amplicon sequencing variants; BP, blood pressure; DBP, diastolic blood pressure; and SBP, systolic blood pressure.
β Diversity and BP
Regarding β diversity, the unweighted UniFrac distance at 1 week and weighted UniFrac distance at 1 year were significantly associated with BP after adjustment (Figure 2). Specifically, 0.64% (F=2.05, P=0.009, Figure S1) and 0.62% (F=2.00, P=0.012, Figure S1) of the variance in the UniFrac distances at 1 week was explained by SBP and DBP at 3 years, respectively. In addition, 0.61% (F=2.00, P=0.047, Figure S1) of the variance in the weighted UniFrac distance at 1 year was explained by SBP at 3 years. Inclusion of the child's BMI Z score in the model did not alter these findings (results not shown).
Figure 2. Proportions of variance in infant gut microbiome β diversity matrices explained by child BP.
Variances explained are corresponded to R2 values in % scale estimated using permutational multivariate analysis of variance with 9999 permutations. Models were adjusted for maternal diet score during pregnancy (continuous), diagnosis of preeclampsia (yes vs no), infant birth weight (kg), gestational age (wks), and breastfeeding duration (d) at the time of microbiome measurement. Star symbols denote statistically significant results (P<0.05). BP indicates blood pressure; DBP, diastolic blood pressure; SBP, systolic blood pressure; and UniFrac, unique fraction metric.
Microbiota Abundance and BP
Eight ASVs at 1 week or 1 month were differentially abundant (FDR‐P<0.05) according to BP at 3 or 6 years (Figure 3, Figure S2). Of the 8 ASVs associated with BP at 3 or 6 years, a total of 5 ASVs showed significant associations independently of child's BMI. Higher abundance of Actinomyces‐cdeb at 1 week was associated with lower SBP (FDR‐P>0.05) and lower DBP (FDR‐P<0.01) at 6 years. Higher abundance of Helicobacter pylori‐45cd at 1 week was associated with higher SBP (FDR‐P<0.05) and DBP (FDR‐P<0.01) at 3 years. Higher abundance of Staphylococcus‐d2a5 at 1 month was associated with higher SBP and DBP at 3 years (both FDR‐P<0.001), but this association was negative with DBP at 6 years (FDR‐P<0.01). Higher abundance of Clostridium ss 1‐25e4 at 1 month was associated with lower BP at 3 and 6 years (FDR‐P<0.05 with DBP at 3 years). Higher abundance of Enterobacteriaceae‐81ff at 1 week was associated with lower SBP (FDR‐P>0.05) and DBP (FDR‐P<0.05) at 6 years.
Figure 3. Infant gut microbial ASVs that are differentially abundant according to child BP levels.
The effect sizes are interpreted as the change in BP per 1‐unit increment in logit transformed ASV abundance estimated using beta‐binomial count regression. Models were adjusted for maternal diet score during pregnancy (continuous), diagnosis of preeclampsia (yes vs no), infant birth weight (kg), gestational age (wks), and breastfeeding duration (d) at the time of microbiome measurement. Symbols denoted results with false discovery rate adjusted P<0.05 using the Benjamini–Hochberg method. Sample sizes for the analysis of microbiome at 1 wk, 1 mo, and 1 y with BP at 3 y were 315, 346, and 354, respectively; sample sizes for the analysis with BP at 6 y were 405, 444, and 454, respectively. See Table S1 for the full taxonomy of the ASVs shown in this figure. ASV indicates amplicon sequence variant; BP, blood pressure; DBP, diastolic blood pressure; FDR, false discovery rate; and SBP, systolic blood pressure.
Moreover, 3 ASVs were associated with BP only before BMI adjustment. Higher abundance of Parabacteroides‐a68a at 1 week was associated with higher SBP (FDR‐P>0.05) and higher DBP (FDR‐P<0.05) at 6 years. Higher abundance of Ruminococcus gnavus‐daa9 at 1 week was associated with higher SBP (FDR‐P<0.05) and DBP (FDR‐P<0.01) at 3 years. Higher abundance of Blautia‐7105 at 1 month was associated with lower SBP (FDR‐P<0.01) and lower DBP (FDR‐P>0.05) at 3 years. Abundance of ASVs at 1 year was not significantly associated with BP at any time point. Table S1 presented the full taxonomy of the ASVs described in the Results section. The mean count and relative abundance of the differentially abundant ASVs were presented in Table S2. The interpretations of the findings at the genus level (Figure S3) were consistent with the findings at the ASV level.
Bifidobacterium ASVs and Breastfeeding Interaction
Although no Bifidobacterium ASVs were associated with BP in our differential abundance analyses, some Bifidobacterium ASVs (in particular Bifidobacterium‐a976 and Bifidobacterium‐78ef in early infancy, ie, at 1 week and 1 month) interacted with breastfeeding duration to influence SBP at 3 (Figure 4A1,A2) and 6 years (Figure 4B1,B2). Higher abundances of these Bifidobacterium ASVs in early infancy showed a protective association against higher SBP when breastfeeding duration lasted ≥1 week for SBP at 3 years and when breastfeeding duration lasted ≥6 months for SBP at 6 years. Concurrently, the beneficial effects of prolonged breastfeeding on SBP were more evident in the presence of these Bifidobacterium ASVs in early infancy (Figure 4). For example, when Bifidobacterium‐a976 was present at 1 week, the difference in predicted SBP at 3 years comparing breastfed >1 week and ≤1 week was larger and indicative of a protective effect as compared to when Bifidobacterium‐a976 was absent at 1 week (P for interaction=0.026; Figure 4A1).
Figure 4. Interaction effects of Bifidobacterium ASVs and breastfeeding status/duration on SBP at 3 (A1, A2) and 6 (B1, B2) years.
Estimates are based on linear regression models with the interaction terms of Bifidobacterium ASVs abundance and absence/presence with breastfeeding status/duration. Models were adjusted for maternal diet score during pregnancy (continuous), diagnosis of preeclampsia (yes vs no), infant birth weight (kg), and gestational age (wks). See Table S1 for the full taxonomy of the ASVs shown in this figure. ASV indicates amplicon sequence variant; CLR, centered log‐ratio transformation; and SBP, systolic blood pressure.
DISCUSSION
Our study unveils the complex interplay between infant gut microbiota and breastfeeding duration with respect to their impacts on childhood BP at 3 and 6 years of age, using data from a population‐based cohort of Danish children. We found that α diversity and certain Bifidobacterium ASVs had protective associations with BP, with suggestive benefits emerging only when infants were breastfed for at least 6 months. We also observed that the presence of certain Bifidobacterium ASVs in infant gut within the first month of life was critical for the beneficial associations of prolonged breastfeeding on childhood BP. Furthermore, although abundance of 5 ASVs at 1 week or 1 month was associated with BP independently of BMI, adjustment for BMI reduced the associations of 3 ASVs that were signficant before adjustment for BMI. Our findings suggest a potential significance of early‐life gut microbiota on cardiovascular health in early childhood and emphasizes that breastfeeding duration and BMI were key factors influencing this association.
Our findings extend beyond just establishing a prospective link between gut microbiota and BP in children by elucidating a potential role of breastfeeding in this association. Although breast milk is widely recognized for its benefits in infant growth and development,52 its association with BP has shown both null21, 22, 23 and protective17, 18, 19, 20 findings in previous epidemiological studies. These discrepancies may arise from varying methods of quantifying breastfeeding, categorizing it based on exclusivity, duration, or simply as any versus none. Our findings do align with a recent study by Miliku et al. (2021),19 which identified the early days of life, including during the hospital stay, as the critical window for breastfeeding's beneficial effects on SBP at 3 years. Our study further expands on this by demonstrating how breastfeeding and the presence and abundance of Bifidobacterium within the first month of age showed interactive associations on BP at 3 years.
The interaction between breastfeeding and certain Bifidobacterium species with respect to their influence on blood pressure is biologically plausible as breastfeeding provides human milk oligosaccharides that promote the growth of specific Bifidobacterium species in the infant gut.24 Our findings underscore the importance of considering the influence of Bifidobacteria in studies examining the association between breastfeeding and BP. The mixed findings of previous studies could partly stem from overlooking this critical factor. On the other hand, in the absence of sufficient human milk oligosaccharides in infants, Bifidobacterium may metabolize alternative sources, such as the glycans in the mucin layer of the intestines. This shift in metabolic activity could lead to increased intestinal permeability,53 a condition that may facilitate the entry of proinflammatory lipopolysaccharides from the gut to into the blood stream, triggering inflammation‐associated rises in BP. This hypothesis needs to be examined further to better understand the complex interaction between Bifidobacteria and breastfeeding in early life, and to identify how this interaction is connected with childhood BP outcomes.
Although Miliku et al. (2021)19 reported that the association between breastfeeding in infancy and SBP at 3 years was not influenced by breastfeeding exclusivity or duration, our study adds a new dimension to this understanding. We revealed that breastfeeding for the first 6 months and up to 1 year seems key for modulating the associations of gut microbiota α diversity and certain Bifidobacterium species with BP from ages 3 to 6 years. This is in line with the World Health Organization's recommendations for (ideally exclusive) breastfeeding for the first 6 months and continued breastfeeding throughout the first year. Yet, adherence to these guidelines varies globally due to factors such as misinformation, aggressive formula marketing, inadequate maternity leave policies, and a lack of support in workplaces.52 Our findings thus call for public health policies and support systems to facilitate prolonged breastfeeding in order to grasp this critical early‐life stage for optimal gut microbiota development and long‐term health benefits.
Another novel contribution of our study is the identification of a prospective association between higher H. pylori abundance in infancy and higher BP in childhood. This extends existing knowledge primarily based on adult populations. A recent systematic review and meta‐analysis of 55 studies with 198 750 adults found that individuals with H. pylori had a 32% (95% CI, 1,15–1.52) higher odds of hypertension, 1.86 mm Hg (95% CI, 1.21–2.50) higher SBP, and 1.12 mm Hg (95% CI, 0.81–1.43) higher DBP than those without H. pylori.54 This pathogenetic bacterium colonizes human stomach and triggers persistent, low‐grade inflammation both locally and systemically, contributing to the development of gastrointestinal and cardiometabolic diseases.55, 56 It is most commonly acquired in early childhood from the environment or family members.57 In children, H. pylori prevalence ranges from 1.2% to 12.2% in developed countries, with even higher prevalence in developing countries.58 The detection of H. pylori in infant gut as early as within the first month of life and its adverse effects on childhood BP, as observed in our study, pinpoints the necessity for increased awareness of H. pylori colonization in infants and also prompts further research into its short‐ and long‐term health impacts.
In children, high BP and obesity are closely connected from a pathophysiological standpoint, much like in adults.59 Our study suggest that children's BMI may mediate a part of the positive associations observed for Parabacteroides and R. gnavus ASVs with BP and the negative association of Blautia ASV with BP as the estimates and significance decreased after BMI adjustment. Parabacteroides are gram‐negative bacteria with proinflammatory endotoxins known as lipopolysaccharides. They have been found to be more abundant in adults with hypertension60, 61 but less abundant in those with obesity.61 R. gnavus is also an inflammation‐promoting opportunistic pathogen and was linked to hypertension in female adults11 and greater percentage of body fat in adults.62 Conversely, Blautia is generally considered to have probiotic functions and was negatively correlated with BP and BMI in both adults63, 64 and children,65 although some studies have also reported opposite associations.10, 66, 67 Along with these mixed findings in adults, our study emphasizes the importance of continuing investigation into the links of these 3 microbes with BP in children, particularly BMI's potential mediation role. Additionally, as different species within the same genus can have varying health impacts, studies employing more detailed microbiota identification, such as metagenomic sequencing, will be more effective than 16S rRNA sequencing in identifying specific species and strains that affect BP through obesity‐related mechanisms.
Our study's key strength lies in its repeated measures of the gut microbiota throughout infancy and the longitudinal tracking of BP during early childhood in the COPSAC2010 cohort. This approach enables us to address limitations of previous cross‐sectional, epidemiological studies by prospectively assessing the effects of early‐life gut microbiota on childhood BP, thereby contributing to the understanding of their potential causal links in humans. Analyzing gut microbiome associations with childhood BP separately for each time point revealed timing‐specific findings, both in terms of microbiome measures and BP outcomes. This approach aligns with the dynamic changes occurring in the infant gut microbiome and the rapid physiological development of child BP during early childhood, which may indicate biologically meaningful exposure windows. As such, our study, being among the first to examine prospective associations between gut microbiome and BP in a pediatric population, offers more granular findings that could guide hypothesis generation for future longitudinal studies on this research question. Multiomic approaches, including combining microbiome with metabolomics, transcriptomics, and epigenetics, in prospective birth cohorts will enhance our understanding of how the early‐life gut microbiota interacts with human hosts and shapes long‐term health outcomes. The high retention rate in the COPSAC2010 cohort further increases the validity of our findings by reducing the potential selection bias.
Nonetheless, our reliance on 16S rRNA gene sequencing limits our ability to specify exact microbial species and strains, which may affect the comparability of our findings across studies. Additionally, we used the SILVA release 132 database for taxonomic classification, which, although appropriate at the time of data processing, has since been updated with newer releases containing expanded rRNA sequences and refined taxonomic classifications. Future studies could benefit from analysis with more current databases to ensure the most up‐to‐date taxonomic identification. We also acknowledge the limitation of conducting our analyses at the ASV level, as some argue that it may introduce artificial separation of closely related bacterial genomes.68 However, ASV‐level analysis offers significant benefits including granular taxonomic resolution, increased precision and accuracy, and enhanced comparability.69, 70 Nevertheless, we performed genus‐level differential abundance analyses, which yielded consistent interpretations with ASV‐level results. Future research incorporating metagenomics could complement the insights gained from 16S rRNA gene sequencing by providing species‐level identification. Other limitations include the lack of diversity within the COPSAC2010 cohort, which consists predominantly of children of European ancestry from higher socioeconomic backgrounds. This may limit the generalizability of our findings to populations of different ethnicities and socioeconomic status with distinct microbial profiles due to factors such as different diet and lifestyles. Lastly, due to the observational nature of this study, we acknowledge the potential for residual and unmeasured confounding that might influence the observed associations. Future studies are encouraged to incorporate detailed information on factors such as antibiotic use during pregnancy, delivery, and the postnatal period to investigate their role in the association between the infant gut microbiome and child BP.
CONCLUSIONS
Our study provides pioneering evidence linking infant gut microbiota with BP at 3 and 6 years of age, highlighting early life as a critical period for microbiota‐focused interventions aimed at preventing hypertension. This finding holds significance for public health, given the established pattern of BP tracking from childhood into adulthood and its long‐term health implications. Our study also reinforces the importance of promoting breastfeeding through infancy, not only for optimal gut microbiota development but also for better cardiovascular health across the life course. Future research should focus on metagenomic analysis to identify specific infant gut microbes, particularly Bifidobacterium species, and metabolomics to identify microbial metabolites associated with cardiovascular health in childhood.
Sources of Funding
Noel T. Mueller was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K01HL141589 and R01HL166473.
Footnotes
This article was sent to Yen‐Hung Lin, MD, PhD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at Supplemental Material
For Sources of Funding and Disclosures, see page 10.
Supplemental Material
Tables S1–S2
Figures S1–S3
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© 2025 The Author(s). Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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Received: 1 July 2024
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Published online: 27 February 2025
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