Human metabolism is a complex interplay of genetics, cohabitation, diet, health, and environmental factors. Metabolomics, the comprehensive analysis of metabolites in biological samples, offers a powerful tool to understand this complexity. Saliva, a readily accessible biofluid, contains a rich array of metabolites, proteins, and xenobiotics, making it an ideal sample for metabolomic studies. Previous research has shown that metabolomics can identify biomarkers for diseases and stress. This study focuses on the inter- and intra-family relationships of the salivary metabolome and its associations with biomeasures relevant to ETS exposure (cotinine), antioxidant potential (uric acid), inflammation (C-reactive protein; CRP), metabolic regulation (adiponectin), and heavy metals (chromium, copper, lithium, manganese, and zinc). Family members share similar oral health, living environments, and diets, leading to hypotheses about increased similarity in their oral microbiomes and metabolomes. While previous studies have explored interfamilial metabolic similarity using targeted methods, a large-scale untargeted approach is needed to capture the full extent of metabolic relationships and potentially discover novel biomarkers of oral health. This study aims to address this gap by performing a comprehensive analysis of the salivary metabolome in a large cohort of children and their caregivers, examining the relationships within and between families and exploring associations with relevant biomeasures.

Existing literature highlights the utility of metabolomics in disease and stress biomarker discovery, particularly using saliva as a minimally invasive sample type. Studies have established the close relationship between family members' oral health attributes, living environments, and diets. The oral microbiome’s similarity within families has been extensively documented. Similarly, some studies have demonstrated the greater similarity in metabolomes between family members compared to unrelated individuals. However, much of this research has focused on well-established biomarkers, neglecting the potential for untargeted metabolomics to reveal a more comprehensive picture of familial metabolic relationships and uncover novel biomarkers of oral health. The current study builds on this foundation by employing untargeted metabolomics to analyze a large cohort of children and their caregivers, providing a more complete picture of salivary metabolomic similarity within families and potential links to relevant health indicators.

This study enrolled 1425 participants (719 children and 706 caregivers) from the Family Life Project. Saliva samples were collected and analyzed for metabolites using untargeted LC-MS metabolomics. The study measured various salivary biomeasures: adiponectin, C-reactive protein (CRP), cotinine, and uric acid using commercially available kits and a spectrophotometer. Salivary metal concentrations (chromium, copper, lithium, manganese, and zinc) were obtained from a previous study on the same cohort. Metabolome data processing involved removing internal control standards, filtering ions based on relative peak heights, normalization, and the use of community ecology tools for data analysis. Diversity was measured using Bray-Curtis and Shannon indices. Nonmetric multidimensional scaling (NMDS), linear mixed-effects models, and distance-based redundancy analysis (db-RDA) were used to explore associations between the salivary metabolome and various factors such as family dyad, sex, smoking status, and biomeasures. Clustering analysis using partitioning around medoids (PAM) was performed to identify metabotypes. Spearman correlations were used to assess the association between specific ions and salivary biomeasures.

The study identified 2046 ions in saliva samples. Most of the variation in salivary metabolomes (62%) was explained by family dyad, indicating a strong influence of the home environment on metabolism. Children and caregivers showed significantly different metabolomes (P<0.001), with 95 identified metabolites differing between groups (Padj <0.05). PAM clustering revealed two overlapping metabotypes based on the metabolomes of paired caregiver-child dyads. Beta diversity differed significantly between clusters (P<0.001). Analysis of the children's metabolomes revealed significant associations with adiponectin, CRP, and uric acid, with uric acid explaining the most variance. While ETS exposure did not significantly alter children's overall metabolomes, it did affect caregivers' metabolomes (P<0.001). Several metabolites, including nicotine, phenylethanolamine, phenylacetaldehyde, choline, creatine, N8-acetylspermidine, and histamine, were correlated with salivary cotinine. Salivary metal concentrations were associated with altered children's metabolomes (P<0.05 for each), with chromium, manganese, and copper showing significant contributions to this variation. Many metals were anticorrelated with free dipeptides and positively correlated with free amino acids, suggesting interactions with protein metabolism.

The findings strongly support the idea that the family environment has a profound impact on the salivary metabolome. The high degree of similarity in metabolomes between caregivers and children emphasizes the shared influence of the home environment on metabolic profiles. The identification of distinct metabotypes suggests the existence of subgroups with shared metabolic characteristics, though the underlying factors driving these clusters remain to be fully elucidated. The associations between specific metabolites and biomeasures of inflammation, antioxidant potential, ETS exposure, and heavy metals provide further insight into the interplay between environmental factors and human metabolism. The results highlight the potential of saliva metabolomics to identify biomarkers for various health conditions and the influence of the family environment on overall health.

This large-scale study provides novel insights into the salivary metabolome of families, demonstrating a strong influence of the family environment and the existence of distinct metabotypes. The associations identified between the metabolome and various biomeasures highlight the potential of saliva metabolomics for biomarker discovery and understanding the impact of environmental exposures on human health. Future research should focus on targeted investigations of specific metabolic pathways, integrating multi-omic approaches to better understand the complex interplay between the oral microbiome and metabolome within families.

The study primarily relied on correlative analysis, limiting the ability to establish causal relationships. The use of a single chromatography method might have biased the results, potentially overrepresenting certain metabolite classes. The relatively small number of named metabolites compared to unidentified ions limits the interpretability of a significant portion of the data. The study population's specific demographics and geographic location may limit the generalizability of the findings.