Mitochondrial signatures of infant mesenchymal stem cells predict child adiposity: The Healthy Start Study

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Gyllenhammer, Madeline Rose Keleher, Cheyret Wood, Ivana V. Yang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9476945/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Importance : Although obesity risk is multifactorial, identification of molecular pathways at birth may reveal early-life susceptibility, guiding prevention and intervention efforts. Objective : We measured transcriptional and DNA methylation profiles of umbilical cord-derived mesenchymal stem cells (MSCs), which are progenitors for body composition ( e.g ., adipose, muscle), and tested associations with childhood adiposity over the first 4-6yr of life. Design : Among 140 mother/child dyads enrolled in The Healthy Start Cohort Study, MSCs were isolated at birth and analyzed for their transcriptomic (RNAseq) and DNA methylation profile (Illumina EPIC). We measured newborn (24-72hrs after birth, n=134), infant (4-6mo, n=128), and early childhood (4-6yr, n=81) adiposity (%fat mass [%FM]) with air displacement plethysmography. A parallel in vitro adiposity phenotype was modeled as triglyceride accumulation during MSC adipogenesis . Setting : Prenatal obstetrics clinics at the University of Colorado Hospital in 2010–2014. Follow-up of women and children is ongoing. Participants : Singleton infants born to healthy women across the BMI spectrum. Exposure : Newborn MSC transcriptome. Main Outcome : Infant/childhood adiposity (%FM). Results : 302 MSC transcripts were associated with %FM at birth, infancy and early childhood ( p =5, p =296 and p =1), respectively [false discovery rate, FDR<0.05]). Geneset Enrichment Analysis of transcriptome data revealed 670 pathways associated with %FM (FDR<0.05, p =3 at birth, p =267 infancy, p =400 early childhood). Gene sets involved in extracellular matrix organization, were positively associated, while mitochondrial fatty acid beta-oxidation, the citric acid (TCA) cycle, cell cycle and chromosome/telomere maintenance were negatively associated with adiposity at 4-6mo and 4-6yr (FDR<0.05). Mitochondrial complex I pathways downregulation was associated with higher in vivo adiposity at all three timepoints (FDR<0.05), and with in vitro adiposity (p<0.05). We examined shared core enrichment genes between infant and early childhood pathways ( p= 664), as input for targeted methylation analysis. Of these shared genes, DNA methylation in four CpGs (FDR<0.2) and one noteworthy gene region, HDAC4 (FDR=0.055), associated with %FM in infancy and early childhood, respectively. Conclusions and Relevance : Newborn MSC transcriptomic and methylation features, particularly within mitochondrial pathways, were associated with adiposity through early childhood. These findings suggest that early-life mitochondrial signatures may predict biological susceptibility to greater fat mass accretion. Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Points Question : Do transcriptomic and epigenomic features from newborn mesenchymal stem cells (MSCs) prospectively associate with child adiposity? Findings : Human umbilical cord-derived MSCs are progenitors for tissues determining body composition ( e.g ., adipose, muscle). In a longitudinal cohort study of 140 mother/child dyads, the transcription and methylation profiles of MSCs were longitudinally associated with childhood adiposity over the first 4-6yr of life. Pathway analysis demonstrated that downregulation of mitochondrial pathways is associated with elevated %FM from birth to early childhood. Meaning : MSC mitochondrial transcriptomic and DNA methylation signatures present at birth are longitudinal predictors of fat mass accretion. INTRODUCTION Over recent decades, overweight and obesity have risen sharply across all racial, ethnic and socioeconomic groups, impacting 1 in 5 children and 2 in 5 adults. 1 , 2 This is largely attributed to dramatic shifts in environmental exposures and health behaviors, often referred to as an “obesogenic environment.” 3 Despite near-universal exposure to these conditions, individuals vary considerably in their susceptibility to obesity and related co-morbidities, likely shaped by complex interplay of genetic, developmental, and behavioral factors. 4 – 6 Understanding inter-individual vulnerability is critical, as it offers a window into targeted prevention and personalized treatment approaches. Many studies have investigated the molecular mechanisms of obesity risk heterogeneity. However, most human studies examined pathways in the context of established obesity, limiting causal inference. 7 Cells and tissues collected at birth are particularly useful for investigating molecular mechanisms contributing to the development of obesity before the onset of excess adiposity. Prior studies relied on placental or cord-blood cells, 6,8–18 which provide important but indirect insight into metabolic tissue development and function. In contrast, our prior findings, 19–23 along with others, 24,25 have established human infant umbilical cord–derived mesenchymal stem cells (MSCs) as a model for investigating multiple metabolic precursors underlying obesity risk (reviewed here 26 ). MSCs are progenitors for mesodermal tissues, including adipose and skeletal muscle, and thus are particularly relevant for investigating metabolic pathways underlying obesity susceptibility. Previously, we focused on hypothesis-driven MSC features, demonstrating adipogenic drivers such as zinc finger protein (Zfp)423 and peroxisome proliferator-activated receptor (PPAR)γ are influenced by fetal exposures (e.g., maternal obesity). 19 , 27 Furthermore, baseline differences in MSC lipid accumulation and handling prospectively predicted adiposity through early childhood. 21 , 22 These sub-phenotyping approaches offer potential for screening predefined pathways underlying obesity, but remain challenging to investigate in vivo prior to obesity development. In the present study, we expand our cohort and apply an unbiased, transcriptome-wide approach to test the hypothesis that MSC transcriptomic and epigenomic features present at birth associate with longitudinal child adiposity. We tested prospective association of the newborn MSC transcriptome in 140 children from birth through 4–6 years of age. Transcriptome results then informed a targeted DNA methylation analysis. To complement in vivo adiposity measures, we additionally leveraged an in vitro MSC adiposity phenotype, lipid accumulation during adipogenesis, as proof of concept that MSC transcriptomic signatures align with intrinsic fat accretion. With this parallel framework and an unbiased transcriptomic approach, we identified known and novel pathways and metabolic signatures present at birth that may underlie susceptibility to later adiposity. METHODS Population We collected MSCs from a convenience sample of 165 infants born to mothers participating in the longitudinal Healthy Start Study (Clinical Trials.gov, NCT02273297) 19 as part of the ancillary Healthy Start BabyBUMP Project, as described previously. 28 Briefly, eligible participants were ≥ 16 years old, pregnant with a singleton carry, and ≤ 23 weeks gestation. Exclusions included prior diabetes, preterm birth, or serious psychiatric illness. The Colorado Multiple Institutional Review Board approved the study, and all participants provided informed consent. The described procedures were conducted in accordance with the Declaration of Helsinki. Of the 165 children with MSCs collected at birth, 142 had MSCs that were viable for culture to collect transcriptomic data. Of these participants, 140 have matching adiposity data available for at least 1 timepoint and are included in the current report: birth (n = 134), infancy (4-6mo; n = 128) and early childhood (4–6 year; n = 82). Of the 82 participants with available data in early childhood, one was excluded for non-biologically plausible adiposity (< 1% fat mass), resulting in a sample size of 81 participants. Of these children, MSC methylation data was available in a smaller subset (n = 55 at 4-6mo, and n = 27 at 4–6 year), and in vitro MSC adiposity was available in n = 116. Figure 1 shows the study workflow. Maternal and offspring phenotyping and body composition measurement Healthy Start Study maternal phenotyping has been published elsewhere. 28 Offspring birth weight, sex, and gestational age at birth were obtained from medical records. Child weight, length, age at scan and body composition (adiposity = percent fat mass [%FM], percent fat-free mass [%FFM]; whole-body air plethysmography [PEA POD and BOD POD; COSMED, Inc.]) were measured at each postnatal visit (birth [24-72hrs after birth], infancy [4-6mo], and early childhood [4-6yr]). Mesenchymal stem cell collection The MSC culture and isolation procedures have been previously described. 19 We cultured MSCs from fresh umbilical cord tissue explants, tested for purity based on established markers 29 and conducted analyses on cells within passages 3–5. RNA sequencing and DNA methylation Undifferentiated MSC pellets were flash frozen and stored at -80C; DNA and RNA were isolated using the Qiagen AllPrep kit. We performed RNA-seq in two batches, both using Nugen mRNA kit for library preparation, the first on Illumina HiSeq4000 with 1x150bp reads and the second with 2x150bp flow cell runs on Illumina NovaSEQ 6000, with 40 million reads/sample with batch correction. We measured DNA methylation via bisulfite conversion on the Illumina EPICv1 Array. Details available in Supplementary Methods . In vitro MSC adiposity phenotype We modeled in vitro MSC adiposity as previously described, 21 via adipocyte triglyceride (TG) accumulation after 21 days of adipogenesis (referred to as MSC-TG). Statistical analyses We conducted statistical analyses using R version 4.1.3. For outcomes with non-normal distributions, we applied a log 2 transformation. Due to inconsistencies in Y-linked gene expression, offspring sex was determined from RNA-Seq expression (see Supplement). Untargeted transcriptomic analyses We quantified gene counts using Ensembl annotation for GrCh 38 (version 86, accessed 08/23/2017), filtering those with < 10 average reads/sample, leaving 15,970 genes. We tested association of these genes and %FM with gene expression as the outcome, in order to account for the distribution of RNA-Seq counts using a negative binomial model, adjusting for offspring sex and age at measurement. For newborn scans, we used gestational age. For models where infant sex or age was significantly associated with FM% (P < 0.05), we performed sensitivity analyses testing association of adjusted %FM (%FM minus random effect age or sex) with transcript data to verify these covariates were no longer significant. We applied the Benjamini-Hochberg false discovery rate (FDR) correction. 30 Gene Set Enrichment Analysis We performed gene set enrichment analysis (GSEA) for Gene Ontology (GO) and Reactome pathways using Fast Gene Set Enrichment Analysis using fgsea for R, 31 with transcripts ranked by log 10 p-value and direction of association. Because GSEA includes all transcripts, it enables detection of coordinated shifts across transcripts within a pathway while retaining directionality. We identified leading edge genes (core genes driving pathway enrichment, see Supplement) in significant pathways (FDR < 0.05) and evaluated overlap of 4–6 year associations with at least one other timepoint using a Fisher’s exact test. These overlapping genes were used as input for the subsequent targeted methylation analyses (p = 664 genes). Targeted methylation analyses We performed data processing and quality control as detailed previously. 32 18,529 probes mapped to the 664 leading edge genes identified by GSEA. This targeted probe list was used to examine MSC DNA methylation associations with child %FM at 4-6mo and at 4–6 year, with linear models adjusted for child sex and age. We applied FDR correction 30 to identify differentially methylated probes (DMPs). To test for differentially methylated regions (DMRs), we used Methylated CpGs Set Enrichment Analysis in R (mCSEA ver. 1.18.0). Post hoc pathway analysis with in vitro MSC adiposity phenotype To support functional interpretation, we performed post hoc analyses using leading edge genes from the six mitochondrial GO pathways that consistently showed associations with %FM. We calculated summed z-scores of leading-edge genes counts for each pathway and correlated these results with the in vitro MSC adiposity phenotype (MSC-TG). RESULTS Maternal and child characteristics are presented in Table 1 and do not differ notably from all potentially eligible participants in Healthy Start ( Supplemental Table 1 ). Race and ethnicity frequencies were similar to the U.S. childbearing population (NCHS 2019), 33 supporting the generalizability of our findings. About half of the women entered pregnancy at normal weight (n = 71, 51%), and the birth %FM is similar to previous reports; 34 however, the %FM at 4-6mo and 4–6 year was lower. 35 Table 1 Maternal and child characteristics Legend: MSC= mesenchymal stem cell; data are mean ± standard deviation (SD), unless otherwise stated. ( n = 140 ) Mean (SD) or n (%) Mother Age 28.6 (6.1) Primiparous, n 72 (51%) Pre-pregnancy BMI (kg/m 2 ) 25.0 (5.0) Women with obesity, n 19 (14%) Race and ethnicity, n Non-Hispanic White 80 (57%) Hispanic 36 (26%) Non-Hispanic Black 14 (10%) Other 10 (7%) Child Females, n 59 (42%) Gestational age at birth (wks) 39.5 (1.2) MSC time to confluence (d) 27.1 (7.8) Birth weight (g) 3272 (429) Fat mass (kg) at birth ( n = 134 ) 0.31 (0.2) Fat mass (%) at birth ( n = 134 ) 9.6 (4.1) Age at 4–6 month visit (mo) 4.8 (0.9) Weight (kg) at infancy 4-6mo visit ( n = 129 ) 6.8 (0.9) Fat mass (kg) at infancy 4-6mo visit ( n = 128 ) 1.6 (0.5) Fat mass (%) at infancy 4-6mo visit ( n = 128 ) 24.7 (5.6) Age at child visit (yrs) 4.6 (0.2) Weight (kg) at child 4–6 year visit ( n = 85 ) 17.8 (3.7) Fat mass (kg) at child 4–6 year visit ( n = 81 ) 3.5 (2.1) Fat mass (%) at child 4–6 year visit ( n = 81 ) 19.4 (7.0) MSC transcripts are associated with adiposity at each postnatal assessment We tested associations of mRNA transcripts with %FM at each postnatal assessment. 302 MSC transcripts were associated with childhood adiposity from birth through 4–6 years of age (Fig. 2 , Supplemental Tables S2a, S3–S5 ; FDR < 0.05). Specifically, there were 5 transcripts associated with %FM at birth, 296 associated at 4-6mo, and 1 gene MTND2P28, positively associated at 4–6 year (Fig. 2 , Table 2 ). Table 2 Top 5 MSC transcripts associated with child adiposity Birth % Fat Mass Ensembl Gene ID Gene Symbol Magnitude a p-value FDR ENSG00000162631 NTNG1 -1.42 7.60E-82 1.21E-77* ENSG00000169297 NR0B1 1.89 4.40E-69 3.54E-65* ENSG00000176435 CLEC14A -0.59 5.20E-09 0.00003* ENSG00000166165 CKB -1.16 9.10E-07 0.00360* ENSG00000163032 VSNL1 -0.97 1.20E-05 0.03900* Infancy (4–6 mo) % Fat Mass Ensembl Gene ID Gene Symbol Magnitude a p-value FDR ENSG00000097021 ACOT7 -0.31 6.90E-09 0.00011* ENSG00000148926 ADM -0.62 4.00E-08 0.00019* ENSG00000166920 C15orf48 -0.93 5.10E-08 0.00019* ENSG00000138356 AOX1 -0.67 5.70E-08 0.00019* ENSG00000178343 SHISA3 -1.37 5.90E-08 0.00019* Early Childhood (4–6 year) % Fat Mass Ensembl Gene ID Gene Symbol Magnitude a p-value FDR ENSG00000225630 MTND2P28 1.44 9.70E-09 0.00015* ENSG00000105664 COMP 1.01 2.90E-05 0.23000 ENSG00000173418 NAA20 -0.15 8.70E-05 0.37000 ENSG00000183853 KIRREL1 0.2 9.20E-05 0.37000 ENSG00000163257 DCAF16 0.14 2.00E-04 0.38000 a Magnitude= Expected change in gene expression for 10% increase in fat mass; FDR= Benjamini-Hochberg false discovery rate (FDR) correction; *FDR < 0.05 significance level GSEA findings show mitochondrial genes consistently associate with adiposity through 4–6 years We conducted GSEA for transcript association with %FM at the three postnatal assessments using Reactome and GO databases. A total of 670 Reactome, and 577 GO processes associated with %FM from birth to 4–6 year (FDR < 0.05; in Supplemental Tables S2b, S7–S8 ). Of these, the largest number of pathways associated with %FM occurred at the 4–6 year timepoint (Reactome = 400, GO = 345, Fig. 3 a, 3 c). Next, we searched for pathways that showed consistent relationships with %FM from birth through 4–6 year (i.e., across all three child assessments). There were 11 significant GO pathways, with 6 of these processes related to downregulation in mitochondrial function (e.g., mitochondrial respiratory chain complex I (CI), respirasome) (FDR < 0.05, Fig. 3 b, Supplemental Table S9 ) . Additionally, there were 2 Reactome pathways that associated with %FM across all three assessments, both of which related to downregulated mitochondrial function (i.e., fatty acid beta-oxidation, CI biogenesis), though the association was not statistically significant (FDR < 0.2, Supplemental Table S9 ). In addition, there were a large number of shared pathways associated with %FM 4-6mo and 4–6 year assessments (FDR < 0.05; Reactome N = 205, GO = 103; Fig. 3 a, 3 c, Supplemental Table S10 ). The enrichment analyses for the shared Reactome pathways are illustrated in Fig. 3 d; we observed that these pathways showed the same direction of effect across time points. Overall, we found upregulation of pathways involved in extracellular matrix (ECM) and collagen synthesis (e.g., ECM proteoglycans, collagen biosynthesis) and downregulation of pathways related to the mitochondria (e.g., citric acid cycle, CI biogenesis, respiratory electron transport), cell cycle, DNA stability and telomere function (e.g., chromosome and telomere maintenance, TP53 DNA damage response), and metabolic signaling (e.g., AKT signaling, PTEN Regulation, MAPK family). In addition, there were several altered pathways related to stem cell differentiation and fate (e.g., beta-catenin, WNT, RUNX2 signaling). These Reactome pathways are complemented by GO pathway analyses showing similar shared downregulation in mitochondrial oxidative phosphorylation, upregulated ECM and collagen organization and altered signaling pathways involved in pluripotent stem cell fate and regulation ( Supplemental Table S10 ). Targeted Methylation Analyses Next, we examined the leading-edge genes shared between significant infant and early childhood pathways, as input for targeted methylation analysis. From the untargeted Reactome GSEA (FDR < 0.05) there were 1,211 leading edge genes for the 4-6mo infant time point, and 1,442 leading edge genes from the 4–6 year early childhood timepoint. 664 genes were on both lists (Fig. 3 e, Supplemental Table S11 ); 18,529 probes mapped to these genes for the subsequent targeted methylation analysis. Of these shared genes, DNA methylation in four CpGs, linked to NDUFB2, GNS, EXOSC10 and TOMM7 , nominally associated with %FM in infancy (FDR < 0.2, Supplemental Tables S2c, S12-13 ). In addition, 14 DMRs nominally associated with %FM in infancy and early childhood (FDR < 0.1), including one noteworthy gene region, HDAC4 , (FDR = 0.055) (Fig. 3 f, Supplemental Tables S2c, S14-15 ). Of these DMRs, 5 promoters were significantly associated with %FM in infancy, CTSK , SCP2 , CDK7, TEX14 , and SF3B2 (FDR < 0.05), and of these, CTSK demonstrated paired nominal associations with %FM in early childhood (p < 0.05). Notably the methylation changes uncovered here (with 1 exception) were common at both the 4-6mo and 4–6 year timepoints. Mitochondrial genes are inversely associated with in vitro MSC adiposity phenotype Using the parallel in vivo-in vitro adiposity framework, we next tested whether pathways linked to child adiposity also mapped onto MSC intrinsic fat accretion upon adipogenesis. We selected six mitochondrial GO pathways, each showing consistent associations with child adiposity and matched directionally across all time points, for post hoc proof of concept functional analysis (see pathways in Table 3a ). As shown in Fig. 4 , the summed z-score of leading-edge genes from these pathways showed significant correlations with in vitro MSC adiposity (MSC-TG; p < 0.05, r = -0.19 to -0.33), reflecting the same directional pattern observed with in vivo adiposity. DISCUSSION This study identified transcriptomic and epigenomic profiles in MSCs at birth that prospectively associated with adiposity through early childhood. We observed downregulation of mitochondrial pathway genes in MSCs from children with higher adiposity across all timepoints. Moreover, the parallel in vivo-in vitro adiposity framework showed these same mitochondrial genes associated with greater fat accumulation in the in vitro MSC adiposity model, highlighting a link between mitochondrial MSC signatures and intrinsic propensity for fat accretion. Targeted methylation analyses further revealed decreased methylation of HDAC4 , a histone deacetylase that regulates mitochondrial biogenesis and function; 36 notably, HDAC inhibitors are in development for the treatment of diabetes and its complications. 36 Taken together, these findings further highlight the role of mitochondrial genes and DNA methylation patterns as early precursors of childhood adiposity. We observed fewer individual transcripts associated with adiposity at 4–6 years than 4–6 months, consistent with reduced statistical power due to the smaller sample size at the later timepoint. We therefore performed GSEA, which leverages the full, ranked distribution of tested genes to detect coordinated, modest shifts across biologically related pathways even when individual transcript associations are more subtle. 37 The shared pathways across timepoints supports biological consistency, underscoring that differences in individual transcript results likely reflect changes in power across time rather than underlying biology. A key finding is the consistent inverse association of MSC mitochondrial pathways with adiposity, including respiratory chain CI, fatty acid β-oxidation, and the mitochondrial inner-membrane. These findings parallel an in vivo study of weight-discordant monozygotic twins reporting downregulation of mitochondrial transcriptional signatures of adipose tissue from heavier versus leaner siblings; most notably reporting downregulation in OXPHOS and fatty acid β-oxidation transcriptomic pathways and CI protein levels. 38 We extend these findings by demonstrating prospective associations in these same mitochondrial pathways, measured before the onset of excessive adipose tissue accumulation or overt obesity, with subsequent childhood adiposity levels. This highlights their potential role as early predictors rather than mere correlates of established obesity. In vitro studies support a causal relationship. For example, impaired CI activity increases adipocyte fat accumulation, as CI inhibition drives dose-dependent triglyceride accumulation in 3T3-L1 pre-adipocytes. 39 Our in vitro MSC adiposity phenotype reinforces this, as undifferentiated MSCs with lower mitochondrial transcript levels accumulated more lipid during adipogenesis. Interestingly, the only individual gene associated with adiposity at 4–6 y was MTND2P28 , a non-coding mitochondrial pseudogene with potential RNA-mediated regulatory mechanisms, such as methylation of both nuclear and mitochondrial DNA. 40 , 41 How MTND2P28 contributes to early adiposity remains unknown and merits future study. We also observed upregulation of ECM and collagen synthesis transcripts in association with offspring adiposity. ECM remodeling is essential for adipose tissue expansion, and ECM accumulation and fibrosis have been implicated in obesity-related metabolic dysfunction. 42 These processes may be primed in MSCs at birth, potentially influencing adipose tissue development and function in early life. Consistent with this, we identified hypomethylation of cathepsin K ( CTSK ) associated with greater adiposity at both childhood timepoints. CTSK , a cysteine protease involved in ECM remodeling, is upregulated in adipose tissue of adults with obesity 43 and promotes adipocyte differentiation. 44 Both genetic deletion 45 and pharmacologic inhibition of CTSK 46 , 47 reduce adiposity and improve glucose metabolism in animal models, highlighting CTSK as a potential therapeutic target for obesity and related metabolic disorders. Targeted methylation analysis revealed a link between DNA methylation of HDAC4 and child adiposity. Prior studies reported differences in HDAC4 methylation in children with obesity 48 , 49 and altered expression in adipose tissue and blood cells of adults with obesity. 50 Our data are the first to demonstrate a prospective relationship between HDAC4 methylation at birth. HDAC4 regulates inflammatory and metabolic processes by deacetylating transcription factors, 51 and plays a role in mitochondrial biogenesis and function. 36 Several epigenome-wide association studies (EWAS) have examined newborn DNA methylation in relation to birthweight or adiposity. 6 , 11 – 18 Most used cord blood and weight-based adiposity proxies (e.g., BMI percentile), while a few used cord tissue containing MSCs 6 or direct adiposity measures beyond birth (e.g., DEXA). 13 , 15 , 18 None have assessed transcriptomics or isolated MSCs. Across multiple studies, 6,14,15,17 including our own, 18 pathways related to cell cycle regulation, stem cell fate signaling, mitochondrial metabolism, and ECM remodeling are consistently associated with child adiposity. For example, Alfano et al. 14 reported methylation differences in ARID5B and KLF9 , transcriptional regulators of chromatin accessibility and adipocyte differentiation, and in AURKC , a key cell cycle regulator. These findings functionally overlap with our MSC transcriptomic pathways involving transcriptional and differentiation regulators ( RUNX , WNT /β-catenin, NOTCH , Hedgehog, TP53) , and cell cycle components ( AURKA and PLK1) . Methylation differences in ARID5B also appeared in two other EWAS studies, 11,12 while in our study MSC ARID5B expression showed a trend-level relationship with adiposity across all child time points (p < 0.1). Despite cell heterogeneity and limited recurrence of specific methylation sites (reviewed here 52 – 54 ), these studies suggest that early-life molecular features, captured via methylation or transcriptomics, converge on shared pathways that may influence adiposity trajectories. Limitations to our study include modest sample size at later timepoints, particularly for methylation data. Additionally, these analyses were performed in young children of predominantly European ancestry, with relatively low adiposity and obesity rates, limiting generalizability. Replication in older, more diverse children with broader adiposity and geographic variation is needed. Our study strengths include a well-characterized longitudinal cohort with precision adiposity measures, integration of transcriptomic and epigenomic data, and rigorous statistical design and analyses. Unlike most studies estimating adiposity by BMI, which poorly detects excess body fat in early childhood, 55,56 we measured body composition using air displacement plethysmography, improving interpretability of our molecular associations. This study is the first to interrogate unbiased transcriptomic profiles of newborn MSCs associated with repeated adiposity measures from birth through 6 years of age. By using purified progenitor cells of adipose and muscle lineages rather than mixed-cell samples, we captured molecular signatures present at birth that precede tissue development and obesity onset. This approach revealed mitochondrial and ECM-related pathways as early molecular determinants of adiposity, supported by functional evidence linking MSC transcriptomic profiles to in vitro fat accumulation. These findings position mitochondrial and ECM pathways as potential early biomarkers or therapeutic targets and underscore MSCs as a valuable model for studying developmental origins of obesity. Declarations Funding: This study is supported by NIH R01DK117168 to KEB, the American Diabetes Association (1-18-ITCS-016 to KEB) and the Environmental Influences on Child Health Outcomes (ECHO) Program (NIH 1UG3OD023248 to DD), and the preparation of this manuscript was supported by NIH R00 HD097302 to LEG. The Healthy Start BabyBUMP Project is supported by grants from the American Heart Association (predoctoral fellowship 14PRE18230008) and by the parent Healthy Start Study (R01 DK076648 to DD), and the Colorado Clinical and Translational Sciences Institute (UL1 TR001082) for maternal visits and collection of birth measures. The funders had no influence on the results of the study. Clinical Trial Registration: Clinical trial reg. no. NCT02273297. Disclosures: The authors declared no conflict of interest. Data Statement : The data that support the findings of this study are available from the corresponding author, [KB], upon reasonable request. Author Contributions: KEB conceived this project and designed the experiments. CW, IVY and KK designed the statistical approach and performed the primary transcriptomic and epigenomic analyses. LEG performed supplementary analyses. KEB and MRK performed the experiments. KEB and LEG created the figures. LEG interpreted the results and drafted the manuscript. DD conceptualized and implemented the parent Healthy Start study. All authors edited the manuscript and approved the final version of the manuscript. KEB is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. References Fryar CD, Carroll MD, Afful J. 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Gyllenhammer LE, Zaegel V, Duensing AM et al. Lipidomics of infant mesenchymal stem cells associate with the maternal milieu and child adiposity. JCI Insight , 2024; 9(19). Erickson ML, Patinkin ZW, Duensing AM et al. Maternal metabolic health drives mesenchymal stem cell metabolism and infant fat mass at birth. JCI Insight, 2021. Jevtovic F, Zheng D, Houmard JA, et al. Effects of Maternal Exercise Modes on Glucose and Lipid Metabolism in Offspring Stem Cells. J Clin Endocrinol Metab. 2023;108(7):e360–70. Jevtovic F, Zheng D, Houmard JA, et al. Myogenically differentiated mesenchymal stem cell insulin sensitivity is associated with infant adiposity at 1 and 6 months of age. Obes (Silver Spring). 2023;31(9):2349–58. Gyllenhammer LE, Boyle KE. New Frontiers: Umbilical Cord Mesenchymal Stem Cells Uncover Developmental Roots and Biological Underpinnings of Obesity Susceptibility. Curr Obes Rep. 2025;14(1):10. Keleher MR, Shubhangi S, Brown A, et al. Adipocyte hypertrophy in mesenchymal stem cells from infants of mothers with obesity. Obes (Silver Spring). 2023;31(8):2090–102. Starling AP, Brinton JT, Glueck DH, et al. Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start study. Am J Clin Nutr. 2015;101(2):302–9. Dominici M, Le Blanc K, Mueller I, et al. Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. Cytotherapy. 2006;8(4):315–7. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 2018;57(1):289–300. Korotkevich G, Sukhov V, Budin N et al. Fast gene set Enrich analysis bioRxiv, 2021:060012. Casagrande SS, Aviles-Santa ML, Sotres-Alvarez D et al. Association between gestational diabetes and 6-year incident diabetes: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). BMJ Open Diabetes Res Care, 2022; 10(6). Statistics NCfH. Natality public-use file and CD-ROM. Hyattsville, MD: National Center for Health Statistics. [cited Accessed 2021; Available from: https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm Barbour LA, Hernandez TL, Reynolds RM, et al. Striking differences in estimates of infant adiposity by new and old DXA software, PEAPOD and skin-folds at 2 weeks and 1 year of life. Pediatr Obes. 2016;11(4):264–71. Andres A, Hull HR, Shankar K, et al. Longitudinal body composition of children born to mothers with normal weight, overweight, and obesity. Obes (Silver Spring). 2015;23(6):1252–8. Yang J, He J, Ismail M, et al. HDAC inhibition induces autophagy and mitochondrial biogenesis to maintain mitochondrial homeostasis during cardiac ischemia/reperfusion injury. J Mol Cell Cardiol. 2019;130:36–48. Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. Heinonen S, Muniandy M, Buzkova J, et al. Mitochondria-related transcriptional signature is downregulated in adipocytes in obesity: a study of young healthy MZ twins. Diabetologia. 2017;60(1):169–81. Vankoningsloo S, Piens M, Lecocq C, et al. Mitochondrial dysfunction induces triglyceride accumulation in 3T3-L1 cells: role of fatty acid beta-oxidation and glucose. J Lipid Res. 2005;46(6):1133–49. Pink RC, Wicks K, Caley DP, et al. Pseudogenes: pseudo-functional or key regulators in health and disease? RNA. 2011;17(5):792–8. Walker BR, Moraes CT. Nuclear-Mitochondrial Interactions Biomolecules, 2022; 12(3). Ruiz-Ojeda FJ, Mendez-Gutierrez A, Aguilera CM, Plaza-Diaz J. Extracellular Matrix Remodeling of Adipose Tissue in Obesity and Metabolic Diseases. Int J Mol Sci, 2019; 20(19). Chiellini C, Costa M, Novelli SE, et al. Identification of cathepsin K as a novel marker of adiposity in white adipose tissue. J Cell Physiol. 2003;195(2):309–21. Han J, Luo T, Gu Y, et al. Cathepsin K regulates adipocyte differentiation: possible involvement of type I collagen degradation. Endocr J. 2009;56(1):55–63. Funicello M, Novelli M, Ragni M, et al. Cathepsin K null mice show reduced adiposity during the rapid accumulation of fat stores. PLoS ONE. 2007;2(8):e683. Yang M, Sun J, Zhang T, et al. Deficiency and inhibition of cathepsin K reduce body weight gain and increase glucose metabolism in mice. Arterioscler Thromb Vasc Biol. 2008;28(12):2202–8. Han J, Wei L, Xu W, et al. CTSK inhibitor exert its anti-obesity effects through regulating adipocyte differentiation in high-fat diet induced obese mice. Endocr J. 2015;62(4):309–17. Fradin D, Boelle PY, Belot MP, et al. Genome-Wide Methylation Analysis Identifies Specific Epigenetic Marks In Severely Obese Children. Sci Rep. 2017;7:46311. Li Y, Zhou Y, Zhu L, et al. Genome-wide analysis reveals that altered methylation in specific CpG loci is associated with childhood obesity. J Cell Biochem. 2018;119(9):7490–7. Abu-Farha M, Tiss A, Abubaker J, et al. Proteomics analysis of human obesity reveals the epigenetic factor HDAC4 as a potential target for obesity. PLoS ONE. 2013;8(9):e75342. Kang H, Park YK, Lee JY, Bae M. Roles of Histone Deacetylase 4 in the Inflammatory and Metabolic Processes. Diabetes Metab J. 2024;48(3):340–53. Vehmeijer FOL, Kupers LK, Sharp GC, et al. DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies. Genome Med. 2020;12(1):105. Lima RS, de Assis Silva Gomes J, Moreira PR. An overview about DNA methylation in childhood obesity: Characteristics of the studies and main findings. J Cell Biochem. 2020;121(5–6):3042–57. Alfano R, Robinson O, Handakas E, et al. Perspectives and challenges of epigenetic determinants of childhood obesity: A systematic review. Obes Rev. 2022;23(Suppl 1):e13389. Javed A, Jumean M, Murad MH, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10(3):234–44. Vanderwall C, Randall Clark R, Eickhoff J, Carrel AL. BMI is a poor predictor of adiposity in young overweight and obese children. BMC Pediatr. 2017;17(1):135. Additional Declarations No competing interests reported. Supplementary Files GyllenhammerMSCRNASeqSupplementaryMethodsMarch27.docx GyllenhammerMSCRNAseqandchildadipositySupplementalTablesMarch2026.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 20 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9476945","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635472257,"identity":"c1e70823-920e-419d-98d9-b26d65cffd59","order_by":0,"name":"Lauren E. Gyllenhammer","email":"","orcid":"","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"E.","lastName":"Gyllenhammer","suffix":""},{"id":635472258,"identity":"c0a2dbfe-957e-4bb5-a2f9-b80755e4dc39","order_by":1,"name":"Madeline Rose Keleher","email":"","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":false,"prefix":"","firstName":"Madeline","middleName":"Rose","lastName":"Keleher","suffix":""},{"id":635472259,"identity":"cb1fecd2-5def-4c6e-974f-e51b42274416","order_by":2,"name":"Cheyret Wood","email":"","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":false,"prefix":"","firstName":"Cheyret","middleName":"","lastName":"Wood","suffix":""},{"id":635472260,"identity":"635a1707-80a0-450b-a7a8-ff054349f788","order_by":3,"name":"Ivana V. Yang","email":"","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":false,"prefix":"","firstName":"Ivana","middleName":"V.","lastName":"Yang","suffix":""},{"id":635472261,"identity":"817e9750-1322-4174-8e0d-2f45a608c2e9","order_by":4,"name":"Jacob E. Friedman","email":"","orcid":"","institution":"University of Oklahoma Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"E.","lastName":"Friedman","suffix":""},{"id":635472263,"identity":"c4bdef81-e2fb-4740-8b6f-1fca72a2ec9e","order_by":5,"name":"Thomas Jansson","email":"","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Jansson","suffix":""},{"id":635472265,"identity":"01f5b774-2de4-484c-b640-aa781e94486c","order_by":6,"name":"Dana Dabelea","email":"","orcid":"","institution":"The Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center","correspondingAuthor":false,"prefix":"","firstName":"Dana","middleName":"","lastName":"Dabelea","suffix":""},{"id":635472266,"identity":"7e667302-8e29-466e-a631-0826e923cd30","order_by":7,"name":"Katerina Kechris","email":"","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":false,"prefix":"","firstName":"Katerina","middleName":"","lastName":"Kechris","suffix":""},{"id":635472267,"identity":"e890d160-6fb7-41b4-b072-0544d01f2cf9","order_by":8,"name":"Kristen E. Boyle","email":"data:image/png;base64,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","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":true,"prefix":"","firstName":"Kristen","middleName":"E.","lastName":"Boyle","suffix":""}],"badges":[],"createdAt":"2026-04-21 00:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9476945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9476945/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108977651,"identity":"d5248f99-080d-45e0-98ca-180268df28ea","added_by":"auto","created_at":"2026-05-11 11:32:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Workflow.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend: \u003c/strong\u003eThis diagram summarizes the multi‑stage dataset used to examine how the transcriptional and epigenetic features of umbilical cord–derived mesenchymal stem cells (MSC) related to early-life adiposity. MSCs were collected from 165 Healthy Start participants, yielding 140 viable undifferentiated MSC transcriptomes (15,970 genes). Adiposity measures were available for 134 newborns (birth, 24–72 hours), 128 infants (4–6 months), and 81 children (4–6 years). DNA methylation (CpG) data were available for a subset of participants (n=55 at 4–6 months; n=27 at 4–6 years). Gene set enrichment analyses identified pathways associated with adiposity that overlapped across time points (false discovery rate \u0026lt;0.05), with 664 leading‑edge genes mapping to 18,529 CpG sites. A proof‑of‑concept adipogenesis model (n=116 MSC samples) was used to test whether adiposity‑linked gene sets in undifferentiated MSCs also related to fat accretion during in‑vitro differentiation. Together, the workflow demonstrates convergence between \u003cem\u003ein‑vivo\u003c/em\u003e adiposity measures and \u003cem\u003ein‑vitro\u003c/em\u003e MSC adipogenesis.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/d88480654cfa881e375e5042.jpeg"},{"id":108957339,"identity":"8e89dd7d-f1f8-4c59-94cc-bb2faf3bf8ab","added_by":"auto","created_at":"2026-05-11 08:18:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMSC transcriptomic analysis of genes associated with longitudinal child adiposity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: The associations between newborn mesenchymal stem cell (MSC) mRNA transcripts with percent fat mass (%FM) at birth (a) infancy (4-6 mo) (b) and early childhood (4-6 yr) (c). Significant transcripts with positive association with %FM are highlighted red, significant negative associations are highlighted blue (FDR\u0026lt;0.05). Magnitude represents the expected change in gene expression for 10% increase in fat mass.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/cfbbcd26feb44dff4c32b6fb.jpeg"},{"id":108957342,"identity":"10e6a001-2f3a-4d92-8991-c41d400955ed","added_by":"auto","created_at":"2026-05-11 08:18:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":626892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMSC GSEA pathways and DNA methylation associate with adiposity through 4-6 years\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: We conducted gene set enrichment analysis (GSEA) for transcript association with percent fat mass (%FM) at the three postnatal assessments and interrogated overlapping pathways using Gene Ontology (GO) (a) and Reactome databases (c) FDR\u0026lt;0.05). There were 11 GO processes that significantly related to %FM at all three timepoints (b), and 205 Reactome pathways shared between %FM at infancy (4-6mo) and early childhood (4-6yr) (d; FDR\u0026lt;0.05). Next, we examined the overlap of leading-edge genes shared between significant infant and early childhood pathways (e) as input for targeted methylation analysis, and examined the overlap of the top differentially methylated DNA regions (DMR) with %FM at infancy (4-6mo) and early childhood (4-6yr) (f; all have FDR\u0026lt;0.05 for at least one timepoint, except \u003cem\u003eHDAC4\u003c/em\u003e FDR=0.055).\u003c/p\u003e\n\u003cp\u003e*NES= normalized enrichment score, which reflects the strength and direction of coordinated enrichment of predefined gene or genomic feature sets across the ranked transcript or probe list.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/30212faefedbf287bf9e6070.png"},{"id":108957341,"identity":"7bfabec8-acca-455a-9338-537fee763184","added_by":"auto","created_at":"2026-05-11 08:18:32","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":670524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMitochondrial genes are inversely associated with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e MSC adiposity phenotype\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: Six mitochondrial GO pathways showing consistent directional associations with child adiposity were selected and the summed z-score of leading-edge genes was calculated for each. Correlations between MSC mitochondrial z-score and \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity phenotype (MSC-TG), measured as triglyceride accumulation after 21 days of adipogenic differentiation, are shown (a-f) (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/399973013647890307678d07.jpeg"},{"id":108979923,"identity":"74ac1b58-9fd9-44e0-84f2-47f92019d56f","added_by":"auto","created_at":"2026-05-11 12:02:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2331994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/77ffbb0e-caa8-4ff0-a31d-d7ba06ef7117.pdf"},{"id":108978077,"identity":"85ba955d-b0ea-46b7-b524-cdfa64fcc9ad","added_by":"auto","created_at":"2026-05-11 11:33:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43465,"visible":true,"origin":"","legend":"","description":"","filename":"GyllenhammerMSCRNASeqSupplementaryMethodsMarch27.docx","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/b6f0a6d4a92cdb96e03d6815.docx"},{"id":108957337,"identity":"5a7cd93d-4c6e-4da2-8aaf-19f303aa1c49","added_by":"auto","created_at":"2026-05-11 08:18:32","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9462913,"visible":true,"origin":"","legend":"","description":"","filename":"GyllenhammerMSCRNAseqandchildadipositySupplementalTablesMarch2026.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9476945/v1/3971cb98e40115fe27105a5b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitochondrial signatures of infant mesenchymal stem cells predict child adiposity: The Healthy Start Study","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e: Do transcriptomic and epigenomic features from newborn mesenchymal stem cells (MSCs) prospectively associate with child adiposity?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: Human umbilical cord-derived MSCs are progenitors for tissues determining body composition (\u003cem\u003ee.g\u003c/em\u003e., adipose, muscle). In a longitudinal cohort study of 140 mother/child dyads, the transcription and methylation profiles of MSCs were longitudinally associated with childhood adiposity over the first 4-6yr of life. Pathway analysis demonstrated that downregulation of mitochondrial pathways is associated with elevated %FM from birth to early childhood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeaning\u003c/strong\u003e: MSC mitochondrial transcriptomic and DNA methylation signatures present at birth are longitudinal predictors of fat mass accretion.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eOver recent decades, overweight and obesity have risen sharply across all racial, ethnic and socioeconomic groups, impacting 1 in 5 children and 2 in 5 adults.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This is largely attributed to dramatic shifts in environmental exposures and health behaviors, often referred to as an \u0026ldquo;obesogenic environment.\u0026rdquo;\u003csup\u003e3\u003c/sup\u003e Despite near-universal exposure to these conditions, individuals vary considerably in their susceptibility to obesity and related co-morbidities, likely shaped by complex interplay of genetic, developmental, and behavioral factors.\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Understanding inter-individual vulnerability is critical, as it offers a window into targeted prevention and personalized treatment approaches.\u003c/p\u003e \u003cp\u003eMany studies have investigated the molecular mechanisms of obesity risk heterogeneity. However, most human studies examined pathways in the context of established obesity, limiting causal inference.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Cells and tissues collected at birth are particularly useful for investigating molecular mechanisms contributing to the development of obesity before the onset of excess adiposity. Prior studies relied on placental or cord-blood cells,\u003csup\u003e6,8\u0026ndash;18\u003c/sup\u003e which provide important but indirect insight into metabolic tissue development and function. In contrast, our prior findings,\u003csup\u003e19\u0026ndash;23\u003c/sup\u003e along with others,\u003csup\u003e24,25\u003c/sup\u003e have established human infant umbilical cord\u0026ndash;derived mesenchymal stem cells (MSCs) as a model for investigating multiple metabolic precursors underlying obesity risk (reviewed here\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e). MSCs are progenitors for mesodermal tissues, including adipose and skeletal muscle, and thus are particularly relevant for investigating metabolic pathways underlying obesity susceptibility. Previously, we focused on hypothesis-driven MSC features, demonstrating adipogenic drivers such as zinc finger protein (Zfp)423 and peroxisome proliferator-activated receptor (PPAR)γ are influenced by fetal exposures (e.g., maternal obesity).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Furthermore, baseline differences in MSC lipid accumulation and handling prospectively predicted adiposity through early childhood.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These sub-phenotyping approaches offer potential for screening predefined pathways underlying obesity, but remain challenging to investigate \u003cem\u003ein vivo\u003c/em\u003e prior to obesity development.\u003c/p\u003e \u003cp\u003eIn the present study, we expand our cohort and apply an unbiased, transcriptome-wide approach to test the hypothesis that MSC transcriptomic and epigenomic features present at birth associate with longitudinal child adiposity. We tested prospective association of the newborn MSC transcriptome in 140 children from birth through 4\u0026ndash;6 years of age. Transcriptome results then informed a targeted DNA methylation analysis. To complement \u003cem\u003ein vivo\u003c/em\u003e adiposity measures, we additionally leveraged an \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity phenotype, lipid accumulation during adipogenesis, as proof of concept that MSC transcriptomic signatures align with intrinsic fat accretion. With this parallel framework and an unbiased transcriptomic approach, we identified known and novel pathways and metabolic signatures present at birth that may underlie susceptibility to later adiposity.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation\u003c/h2\u003e \u003cp\u003eWe collected MSCs from a convenience sample of 165 infants born to mothers participating in the longitudinal Healthy Start Study (Clinical Trials.gov, NCT02273297)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e as part of the ancillary Healthy Start BabyBUMP Project, as described previously.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Briefly, eligible participants were \u0026ge;\u0026thinsp;16 years old, pregnant with a singleton carry, and \u0026le;\u0026thinsp;23 weeks gestation. Exclusions included prior diabetes, preterm birth, or serious psychiatric illness. The Colorado Multiple Institutional Review Board approved the study, and all participants provided informed consent. The described procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eOf the 165 children with MSCs collected at birth, 142 had MSCs that were viable for culture to collect transcriptomic data. Of these participants, 140 have matching adiposity data available for at least 1 timepoint and are included in the current report: birth (n\u0026thinsp;=\u0026thinsp;134), infancy (4-6mo; n\u0026thinsp;=\u0026thinsp;128) and early childhood (4\u0026ndash;6\u0026nbsp;year; n\u0026thinsp;=\u0026thinsp;82). Of the 82 participants with available data in early childhood, one was excluded for non-biologically plausible adiposity (\u0026lt;\u0026thinsp;1% fat mass), resulting in a sample size of 81 participants. Of these children, MSC methylation data was available in a smaller subset (n\u0026thinsp;=\u0026thinsp;55 at 4-6mo, and n\u0026thinsp;=\u0026thinsp;27 at 4\u0026ndash;6\u0026nbsp;year), and \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity was available in n\u0026thinsp;=\u0026thinsp;116. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the study workflow.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaternal and offspring phenotyping and body composition measurement\u003c/h3\u003e\n\u003cp\u003eHealthy Start Study maternal phenotyping has been published elsewhere.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Offspring birth weight, sex, and gestational age at birth were obtained from medical records. Child weight, length, age at scan and body composition (adiposity\u0026thinsp;=\u0026thinsp;percent fat mass [%FM], percent fat-free mass [%FFM]; whole-body air plethysmography [PEA POD and BOD POD; COSMED, Inc.]) were measured at each postnatal visit (birth [24-72hrs after birth], infancy [4-6mo], and early childhood [4-6yr]).\u003c/p\u003e\n\u003ch3\u003eMesenchymal stem cell collection\u003c/h3\u003e\n\u003cp\u003eThe MSC culture and isolation procedures have been previously described.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e We cultured MSCs from fresh umbilical cord tissue explants, tested for purity based on established markers\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and conducted analyses on cells within passages 3\u0026ndash;5.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing and DNA methylation\u003c/h3\u003e\n\u003cp\u003eUndifferentiated MSC pellets were flash frozen and stored at -80C; DNA and RNA were isolated using the Qiagen AllPrep kit. We performed RNA-seq in two batches, both using Nugen mRNA kit for library preparation, the first on Illumina HiSeq4000 with 1x150bp reads and the second with 2x150bp flow cell runs on Illumina NovaSEQ 6000, with 40\u0026nbsp;million reads/sample with batch correction. We measured DNA methylation via bisulfite conversion on the Illumina EPICv1 Array. Details available in \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003eMSC adiposity phenotype\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe modeled \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity as previously described,\u003csup\u003e21\u003c/sup\u003e via adipocyte triglyceride (TG) accumulation after 21 days of adipogenesis (referred to as MSC-TG).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eWe conducted statistical analyses using R version 4.1.3. For outcomes with non-normal distributions, we applied a log\u003csub\u003e2\u003c/sub\u003e transformation. Due to inconsistencies in Y-linked gene expression, offspring sex was determined from RNA-Seq expression (see Supplement).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUntargeted transcriptomic analyses\u003c/h2\u003e \u003cp\u003eWe quantified gene counts using Ensembl annotation for GrCh 38 (version 86, accessed 08/23/2017), filtering those with \u0026lt;\u0026thinsp;10 average reads/sample, leaving 15,970 genes. We tested association of these genes and %FM with gene expression as the outcome, in order to account for the distribution of RNA-Seq counts using a negative binomial model, adjusting for offspring sex and age at measurement. For newborn scans, we used gestational age. For models where infant sex or age was significantly associated with FM% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we performed sensitivity analyses testing association of adjusted %FM (%FM minus random effect age or sex) with transcript data to verify these covariates were no longer significant. We applied the Benjamini-Hochberg false discovery rate (FDR) correction.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene Set Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eWe performed gene set enrichment analysis (GSEA) for Gene Ontology (GO) and Reactome pathways using Fast Gene Set Enrichment Analysis using fgsea for R,\u003csup\u003e31\u003c/sup\u003e with transcripts ranked by log\u003csub\u003e10\u003c/sub\u003e p-value and direction of association. Because GSEA includes all transcripts, it enables detection of coordinated shifts across transcripts within a pathway while retaining directionality. We identified leading edge genes (core genes driving pathway enrichment, see Supplement) in significant pathways (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and evaluated overlap of 4\u0026ndash;6\u0026nbsp;year associations with at least one other timepoint using a Fisher\u0026rsquo;s exact test. These overlapping genes were used as input for the subsequent targeted methylation analyses (p\u0026thinsp;=\u0026thinsp;664 genes).\u003c/p\u003e\n\u003ch3\u003eTargeted methylation analyses\u003c/h3\u003e\n\u003cp\u003eWe performed data processing and quality control as detailed previously.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e 18,529 probes mapped to the 664 leading edge genes identified by GSEA. This targeted probe list was used to examine MSC DNA methylation associations with child %FM at 4-6mo and at 4\u0026ndash;6\u0026nbsp;year, with linear models adjusted for child sex and age. We applied FDR correction\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e to identify differentially methylated probes (DMPs). To test for differentially methylated regions (DMRs), we used Methylated CpGs Set Enrichment Analysis in R (mCSEA ver. 1.18.0).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePost hoc pathway analysis with in vitro MSC adiposity phenotype\u003c/h2\u003e \u003cp\u003eTo support functional interpretation, we performed post hoc analyses using leading edge genes from the six mitochondrial GO pathways that consistently showed associations with %FM. We calculated summed z-scores of leading-edge genes counts for each pathway and correlated these results with the \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity phenotype (MSC-TG).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eMaternal and child characteristics are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and do not differ notably from all potentially eligible participants in Healthy Start (\u003cstrong\u003eSupplemental Table\u0026nbsp;1\u003c/strong\u003e). Race and ethnicity frequencies were similar to the U.S. childbearing population (NCHS 2019),\u003csup\u003e33\u003c/sup\u003e supporting the generalizability of our findings. About half of the women entered pregnancy at normal weight (n\u0026thinsp;=\u0026thinsp;71, 51%), and the birth %FM is similar to previous reports;\u003csup\u003e34\u003c/sup\u003e however, the %FM at 4-6mo and 4\u0026ndash;6 year was lower.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal and child characteristics\u0026nbsp;\u003c/strong\u003eLegend: MSC= mesenchymal stem cell; data are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), unless otherwise stated.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;140\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMean (SD) or n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMother\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e28.6 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrimiparous, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e25.0 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWomen with obesity, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e19 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRace and ethnicity, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e80 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e36 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemales, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e59 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGestational age at birth (wks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e39.5 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMSC time to confluence (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27.1 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBirth weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3272 (429)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (kg) at birth (\u003cem\u003en\u0026thinsp;=\u0026thinsp;134\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.31 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (%) at birth (\u003cem\u003en\u0026thinsp;=\u0026thinsp;134\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9.6 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge at 4\u0026ndash;6 month visit (mo)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWeight (kg) at infancy 4-6mo visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;129\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (kg) at infancy 4-6mo visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;128\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (%) at infancy 4-6mo visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;128\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24.7 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge at child visit (yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.6 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWeight (kg) at child 4\u0026ndash;6\u0026nbsp;year visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;85\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17.8 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (kg) at child 4\u0026ndash;6\u0026nbsp;year visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;81\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.5 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFat mass (%) at child 4\u0026ndash;6\u0026nbsp;year visit (\u003cem\u003en\u0026thinsp;=\u0026thinsp;81\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e19.4 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMSC transcripts are associated with adiposity at each postnatal assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe tested associations of mRNA transcripts with %FM at each postnatal assessment. 302 MSC transcripts were associated with childhood adiposity from birth through 4\u0026ndash;6 years of age (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cstrong\u003eSupplemental Tables S2a, S3\u0026ndash;S5\u003c/strong\u003e; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, there were 5 transcripts associated with %FM at birth, 296 associated at 4-6mo, and 1 gene MTND2P28, positively associated at 4\u0026ndash;6 year (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 5 MSC transcripts associated with child adiposity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eBirth % Fat Mass\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEnsembl Gene ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGene Symbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMagnitude\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000162631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eNTNG1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7.60E-82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.21E-77*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000169297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eNR0B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.40E-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.54E-65*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000176435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eCLEC14A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000166165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eCKB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.10E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00360*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000163032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eVSNL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.20E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.03900*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfancy (4\u0026ndash;6 mo) % Fat Mass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnsembl Gene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMagnitude\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000097021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eACOT7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.90E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000148926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eADM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.00E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000166920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eC15orf48\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.10E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000138356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eAOX1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.70E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000178343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eSHISA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.90E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly Childhood (4\u0026ndash;6\u0026nbsp;year) % Fat Mass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnsembl Gene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMagnitude\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000225630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eMTND2P28\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.70E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.00015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000105664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eCOMP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.23000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000173418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eNAA20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.70E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.37000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000183853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eKIRREL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.20E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.37000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eENSG00000163257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eDCAF16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.00E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.38000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003eMagnitude= Expected change in gene expression for 10% increase in fat mass; FDR= Benjamini-Hochberg false discovery rate (FDR) correction; *FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance level\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGSEA findings show mitochondrial genes consistently associate with adiposity through 4\u0026ndash;6 years\u003c/h2\u003e\n \u003cp\u003eWe conducted GSEA for transcript association with %FM at the three postnatal assessments using Reactome and GO databases. A total of 670 Reactome, and 577 GO processes associated with %FM from birth to 4\u0026ndash;6\u0026nbsp;year (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; in \u003cstrong\u003eSupplemental Tables S2b, S7\u0026ndash;S8\u003c/strong\u003e). Of these, the largest number of pathways associated with %FM occurred at the 4\u0026ndash;6\u0026nbsp;year timepoint (Reactome\u0026thinsp;=\u0026thinsp;400, GO\u0026thinsp;=\u0026thinsp;345, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\n \u003cp\u003eNext, we searched for pathways that showed consistent relationships with %FM from birth through 4\u0026ndash;6\u0026nbsp;year (i.e., across all three child assessments). There were 11 significant GO pathways, with 6 of these processes related to downregulation in mitochondrial function (e.g., mitochondrial respiratory chain complex I (CI), respirasome) (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cstrong\u003eSupplemental Table S9\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e. Additionally, there were 2 Reactome pathways that associated with %FM across all three assessments, both of which related to downregulated mitochondrial function (i.e., fatty acid beta-oxidation, CI biogenesis), though the association was not statistically significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.2, \u003cstrong\u003eSupplemental Table S9\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eIn addition, there were a large number of shared pathways associated with %FM 4-6mo and 4\u0026ndash;6\u0026nbsp;year assessments (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Reactome N\u0026thinsp;=\u0026thinsp;205, GO\u0026thinsp;=\u0026thinsp;103; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, \u003cstrong\u003eSupplemental Table S10\u003c/strong\u003e). The enrichment analyses for the shared Reactome pathways are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ed; we observed that these pathways showed the same direction of effect across time points. Overall, we found upregulation of pathways involved in extracellular matrix (ECM) and collagen synthesis (e.g., ECM proteoglycans, collagen biosynthesis) and downregulation of pathways related to the mitochondria (e.g., citric acid cycle, CI biogenesis, respiratory electron transport), cell cycle, DNA stability and telomere function (e.g., chromosome and telomere maintenance, TP53 DNA damage response), and metabolic signaling (e.g., AKT signaling, PTEN Regulation, MAPK family). In addition, there were several altered pathways related to stem cell differentiation and fate (e.g., beta-catenin, WNT, RUNX2 signaling). These Reactome pathways are complemented by GO pathway analyses showing similar shared downregulation in mitochondrial oxidative phosphorylation, upregulated ECM and collagen organization and altered signaling pathways involved in pluripotent stem cell fate and regulation (\u003cstrong\u003eSupplemental Table S10\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eTargeted Methylation Analyses\u003c/h2\u003e\n \u003cp\u003eNext, we examined the leading-edge genes shared between significant infant and early childhood pathways, as input for targeted methylation analysis. From the untargeted Reactome GSEA (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) there were 1,211 leading edge genes for the 4-6mo infant time point, and 1,442 leading edge genes from the 4\u0026ndash;6\u0026nbsp;year early childhood timepoint. 664 genes were on both lists (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, \u003cstrong\u003eSupplemental Table S11\u003c/strong\u003e); 18,529 probes mapped to these genes for the subsequent targeted methylation analysis. Of these shared genes, DNA methylation in four CpGs, linked to \u003cem\u003eNDUFB2, GNS, EXOSC10\u003c/em\u003e and \u003cem\u003eTOMM7\u003c/em\u003e, nominally associated with %FM in infancy (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.2, \u003cstrong\u003eSupplemental Tables S2c, S12-13\u003c/strong\u003e). In addition, 14 DMRs nominally associated with %FM in infancy and early childhood (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1), including one noteworthy gene region, \u003cem\u003eHDAC4\u003c/em\u003e, (FDR\u0026thinsp;=\u0026thinsp;0.055) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, \u003cstrong\u003eSupplemental Tables S2c, S14-15\u003c/strong\u003e). Of these DMRs, 5 promoters were significantly associated with %FM in infancy, \u003cem\u003eCTSK\u003c/em\u003e, \u003cem\u003eSCP2\u003c/em\u003e, \u003cem\u003eCDK7, TEX14\u003c/em\u003e, and \u003cem\u003eSF3B2\u003c/em\u003e (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and of these, \u003cem\u003eCTSK\u003c/em\u003e demonstrated paired nominal associations with %FM in early childhood (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably the methylation changes uncovered here (with 1 exception) were common at both the 4-6mo and 4\u0026ndash;6\u0026nbsp;year timepoints.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMitochondrial genes are inversely associated with\u003c/strong\u003e \u003cstrong\u003ein vitro\u003c/strong\u003e \u003cstrong\u003eMSC adiposity phenotype\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUsing the parallel \u003cem\u003ein vivo-in vitro\u003c/em\u003e adiposity framework, we next tested whether pathways linked to child adiposity also mapped onto MSC intrinsic fat accretion upon adipogenesis. We selected six mitochondrial GO pathways, each showing consistent associations with child adiposity and matched directionally across all time points, for post hoc proof of concept functional analysis (see pathways in \u003cstrong\u003eTable\u0026nbsp;3a\u003c/strong\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the summed z-score of leading-edge genes from these pathways showed significant correlations with \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity (MSC-TG; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r = -0.19 to -0.33), reflecting the same directional pattern observed with \u003cem\u003ein vivo\u003c/em\u003e adiposity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study identified transcriptomic and epigenomic profiles in MSCs at birth that prospectively associated with adiposity through early childhood. We observed downregulation of mitochondrial pathway genes in MSCs from children with higher adiposity across all timepoints. Moreover, the parallel \u003cem\u003ein vivo-in vitro\u003c/em\u003e adiposity framework showed these same mitochondrial genes associated with greater fat accumulation in the \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity model, highlighting a link between mitochondrial MSC signatures and intrinsic propensity for fat accretion. Targeted methylation analyses further revealed decreased methylation of \u003cem\u003eHDAC4\u003c/em\u003e, a histone deacetylase that regulates mitochondrial biogenesis and function;\u003csup\u003e36\u003c/sup\u003e notably, HDAC inhibitors are in development for the treatment of diabetes and its complications.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Taken together, these findings further highlight the role of mitochondrial genes and DNA methylation patterns as early precursors of childhood adiposity.\u003c/p\u003e \u003cp\u003eWe observed fewer individual transcripts associated with adiposity at 4\u0026ndash;6 years than 4\u0026ndash;6 months, consistent with reduced statistical power due to the smaller sample size at the later timepoint. We therefore performed GSEA, which leverages the full, ranked distribution of tested genes to detect coordinated, modest shifts across biologically related pathways even when individual transcript associations are more subtle.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e The shared pathways across timepoints supports biological consistency, underscoring that differences in individual transcript results likely reflect changes in power across time rather than underlying biology.\u003c/p\u003e \u003cp\u003eA key finding is the consistent inverse association of MSC mitochondrial pathways with adiposity, including respiratory chain CI, fatty acid β-oxidation, and the mitochondrial inner-membrane. These findings parallel an \u003cem\u003ein vivo\u003c/em\u003e study of weight-discordant monozygotic twins reporting downregulation of mitochondrial transcriptional signatures of adipose tissue from heavier versus leaner siblings; most notably reporting downregulation in OXPHOS and fatty acid β-oxidation transcriptomic pathways and CI protein levels.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e We extend these findings by demonstrating prospective associations in these same mitochondrial pathways, measured before the onset of excessive adipose tissue accumulation or overt obesity, with subsequent childhood adiposity levels. This highlights their potential role as early predictors rather than mere correlates of established obesity. \u003cem\u003eIn vitro\u003c/em\u003e studies support a causal relationship. For example, impaired CI activity increases adipocyte fat accumulation, as CI inhibition drives dose-dependent triglyceride accumulation in 3T3-L1 pre-adipocytes.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Our \u003cem\u003ein vitro\u003c/em\u003e MSC adiposity phenotype reinforces this, as undifferentiated MSCs with lower mitochondrial transcript levels accumulated more lipid during adipogenesis. Interestingly, the only individual gene associated with adiposity at 4\u0026ndash;6 y was \u003cem\u003eMTND2P28\u003c/em\u003e, a non-coding mitochondrial pseudogene with potential RNA-mediated regulatory mechanisms, such as methylation of both nuclear and mitochondrial DNA.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e How \u003cem\u003eMTND2P28\u003c/em\u003e contributes to early adiposity remains unknown and merits future study.\u003c/p\u003e \u003cp\u003eWe also observed upregulation of ECM and collagen synthesis transcripts in association with offspring adiposity. ECM remodeling is essential for adipose tissue expansion, and ECM accumulation and fibrosis have been implicated in obesity-related metabolic dysfunction.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e These processes may be primed in MSCs at birth, potentially influencing adipose tissue development and function in early life. Consistent with this, we identified hypomethylation of cathepsin K (\u003cem\u003eCTSK\u003c/em\u003e) associated with greater adiposity at both childhood timepoints. \u003cem\u003eCTSK\u003c/em\u003e, a cysteine protease involved in ECM remodeling, is upregulated in adipose tissue of adults with obesity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and promotes adipocyte differentiation.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Both genetic deletion\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and pharmacologic inhibition of CTSK\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e reduce adiposity and improve glucose metabolism in animal models, highlighting \u003cem\u003eCTSK\u003c/em\u003e as a potential therapeutic target for obesity and related metabolic disorders.\u003c/p\u003e \u003cp\u003eTargeted methylation analysis revealed a link between DNA methylation of \u003cem\u003eHDAC4\u003c/em\u003e and child adiposity. Prior studies reported differences in \u003cem\u003eHDAC4\u003c/em\u003e methylation in children with obesity\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and altered expression in adipose tissue and blood cells of adults with obesity.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Our data are the first to demonstrate a prospective relationship between \u003cem\u003eHDAC4\u003c/em\u003e methylation at birth. \u003cem\u003eHDAC4\u003c/em\u003e regulates inflammatory and metabolic processes by deacetylating transcription factors,\u003csup\u003e51\u003c/sup\u003e and plays a role in mitochondrial biogenesis and function.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Several epigenome-wide association studies (EWAS) have examined newborn DNA methylation in relation to birthweight or adiposity.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Most used cord blood and weight-based adiposity proxies (e.g., BMI percentile), while a few used cord tissue containing MSCs\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e or direct adiposity measures beyond birth (e.g., DEXA).\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e None have assessed transcriptomics or isolated MSCs. Across multiple studies, \u003csup\u003e6,14,15,17\u003c/sup\u003e including our own,\u003csup\u003e18\u003c/sup\u003e pathways related to cell cycle regulation, stem cell fate signaling, mitochondrial metabolism, and ECM remodeling are consistently associated with child adiposity. For example, Alfano et al.\u003csup\u003e14\u003c/sup\u003e reported methylation differences in \u003cem\u003eARID5B\u003c/em\u003e and \u003cem\u003eKLF9\u003c/em\u003e, transcriptional regulators of chromatin accessibility and adipocyte differentiation, and in \u003cem\u003eAURKC\u003c/em\u003e, a key cell cycle regulator. These findings functionally overlap with our MSC transcriptomic pathways involving transcriptional and differentiation regulators (\u003cem\u003eRUNX\u003c/em\u003e, \u003cem\u003eWNT\u003c/em\u003e/β-catenin, \u003cem\u003eNOTCH\u003c/em\u003e, Hedgehog, \u003cem\u003eTP53)\u003c/em\u003e, and cell cycle components (\u003cem\u003eAURKA\u003c/em\u003e and \u003cem\u003ePLK1)\u003c/em\u003e. Methylation differences in \u003cem\u003eARID5B\u003c/em\u003e also appeared in two other EWAS studies,\u003csup\u003e11,12\u003c/sup\u003e while in our study MSC \u003cem\u003eARID5B\u003c/em\u003e expression showed a trend-level relationship with adiposity across all child time points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Despite cell heterogeneity and limited recurrence of specific methylation sites (reviewed here\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e), these studies suggest that early-life molecular features, captured via methylation or transcriptomics, converge on shared pathways that may influence adiposity trajectories.\u003c/p\u003e \u003cp\u003eLimitations to our study include modest sample size at later timepoints, particularly for methylation data. Additionally, these analyses were performed in young children of predominantly European ancestry, with relatively low adiposity and obesity rates, limiting generalizability. Replication in older, more diverse children with broader adiposity and geographic variation is needed. Our study strengths include a well-characterized longitudinal cohort with precision adiposity measures, integration of transcriptomic and epigenomic data, and rigorous statistical design and analyses. Unlike most studies estimating adiposity by BMI, which poorly detects excess body fat in early childhood,\u003csup\u003e55,56\u003c/sup\u003e we measured body composition using air displacement plethysmography, improving interpretability of our molecular associations.\u003c/p\u003e \u003cp\u003eThis study is the first to interrogate unbiased transcriptomic profiles of newborn MSCs associated with repeated adiposity measures from birth through 6 years of age. By using purified progenitor cells of adipose and muscle lineages rather than mixed-cell samples, we captured molecular signatures present at birth that precede tissue development and obesity onset. This approach revealed mitochondrial and ECM-related pathways as early molecular determinants of adiposity, supported by functional evidence linking MSC transcriptomic profiles to \u003cem\u003ein vitro\u003c/em\u003e fat accumulation. These findings position mitochondrial and ECM pathways as potential early biomarkers or therapeutic targets and underscore MSCs as a valuable model for studying developmental origins of obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study is supported by NIH R01DK117168 to KEB, the American Diabetes Association (1-18-ITCS-016 to KEB) and the Environmental Influences on Child Health Outcomes (ECHO) Program (NIH 1UG3OD023248 to DD), and the preparation of this manuscript was supported by NIH R00 HD097302 to LEG. The Healthy Start BabyBUMP Project is supported by grants from the American Heart Association (predoctoral fellowship 14PRE18230008) and by the parent Healthy Start Study (R01 DK076648 to DD), and the Colorado Clinical and Translational Sciences Institute (UL1 TR001082) for maternal visits and collection of birth measures. The funders had no influence on the results of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration:\u0026nbsp;\u003c/strong\u003eClinical trial reg. no.\u0026nbsp;NCT02273297.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e The authors declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Statement\u003c/strong\u003e: The data that support the findings of this study are available from the corresponding author, [KB], upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e KEB conceived this project and designed the experiments. CW, IVY and KK designed the statistical approach and performed the primary transcriptomic and epigenomic analyses. LEG performed supplementary analyses. KEB and MRK performed the experiments. KEB and LEG created the figures. LEG interpreted the results and drafted the manuscript. DD conceptualized and implemented the parent Healthy Start study. All authors edited the manuscript and approved the final version of the manuscript. KEB is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFryar CD, Carroll MD, Afful J. Prevalence of overweight, obesity, and severe obesity among adults aged 20 and over: United States, 1960\u0026ndash;1962 through 2017\u0026ndash;2018. NCHS Health E-Stats, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFryar CD, Carroll MD, Afful J. Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2\u0026ndash;19 years: United States, 1963\u0026ndash;1965 through 2017\u0026ndash;2018. 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Obes Rev. 2022;23(Suppl 1):e13389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaved A, Jumean M, Murad MH, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10(3):234\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderwall C, Randall Clark R, Eickhoff J, Carrel AL. BMI is a poor predictor of adiposity in young overweight and obese children. BMC Pediatr. 2017;17(1):135.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9476945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9476945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eImportance\u003c/strong\u003e: Although obesity risk is multifactorial, identification of molecular pathways at birth may reveal early-life susceptibility, guiding prevention and intervention efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: We measured transcriptional and DNA methylation profiles of umbilical cord-derived mesenchymal stem cells (MSCs), which are progenitors for body composition (\u003cem\u003ee.g\u003c/em\u003e., adipose, muscle), and tested associations with childhood adiposity over the first 4-6yr of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e: Among 140 mother/child dyads enrolled in The Healthy Start Cohort Study, MSCs were isolated at birth and analyzed for their transcriptomic (RNAseq) and DNA methylation profile (Illumina EPIC). We measured newborn (24-72hrs after birth, n=134), infant (4-6mo, n=128), and early childhood (4-6yr, n=81) adiposity (%fat mass [%FM]) with air displacement plethysmography. A parallel \u003cem\u003ein vitro\u003c/em\u003e adiposity phenotype was modeled as triglyceride accumulation during MSC adipogenesis\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e: Prenatal obstetrics clinics at the University of Colorado Hospital in 2010–2014. Follow-up of women and children is ongoing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e: Singleton infants born to healthy women across the BMI spectrum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e: Newborn MSC transcriptome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Outcome\u003c/strong\u003e: Infant/childhood adiposity (%FM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 302 MSC transcripts were associated with %FM at birth, infancy and early childhood (\u003cem\u003ep\u003c/em\u003e=5, \u003cem\u003ep\u003c/em\u003e=296 and \u003cem\u003ep\u003c/em\u003e=1), respectively [false discovery rate, FDR\u0026lt;0.05]). Geneset Enrichment Analysis of transcriptome data revealed 670 pathways associated with %FM (FDR\u0026lt;0.05, \u003cem\u003ep\u003c/em\u003e=3 at birth, \u003cem\u003ep\u003c/em\u003e=267 infancy, \u003cem\u003ep\u003c/em\u003e=400 early childhood). Gene sets involved in extracellular matrix organization, were positively\u003cem\u003e \u003c/em\u003eassociated, while mitochondrial fatty acid beta-oxidation, the citric acid (TCA) cycle, cell cycle and chromosome/telomere maintenance were negatively associated with adiposity at 4-6mo and 4-6yr (FDR\u0026lt;0.05). Mitochondrial complex I pathways downregulation was associated with higher \u003cem\u003ein vivo\u003c/em\u003e adiposity at all three timepoints (FDR\u0026lt;0.05), and with \u003cem\u003ein vitro\u003c/em\u003e adiposity (p\u0026lt;0.05). We examined shared core enrichment genes between infant and early childhood pathways (\u003cem\u003ep=\u003c/em\u003e664), as input for targeted methylation analysis. Of these shared genes, DNA methylation in four CpGs (FDR\u0026lt;0.2) and one noteworthy gene region, \u003cem\u003eHDAC4 \u003c/em\u003e(FDR=0.055), associated with %FM in infancy and early childhood, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and Relevance\u003c/strong\u003e: Newborn MSC transcriptomic and methylation features, particularly within mitochondrial pathways, were associated with adiposity through early childhood. These findings suggest that early-life mitochondrial signatures may predict biological susceptibility to greater fat mass accretion.\u003c/p\u003e","manuscriptTitle":"Mitochondrial signatures of infant mesenchymal stem cells predict child adiposity: The Healthy Start Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 08:18:23","doi":"10.21203/rs.3.rs-9476945/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-29T13:57:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T08:17:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T08:04:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-04-21T00:02:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"071ade63-0778-4fd7-8014-5dd231d3352d","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"6","date":"2026-04-29T13:57:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:18:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 08:18:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9476945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9476945","identity":"rs-9476945","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}