Find the Needle in the Haystack, Then Find It Again: Replication and Validation in the 'Omics Era

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Omics-based biomarkers as useful tools in metabolic dysfunction-associated steatotic liver disease clinical practice: How far are we?

Unmet needs exist in metabolic dysfunction-associated steatotic liver disease (MASLD) risk stratification. Our ability to identify patients with MASLD with advanced fibrosis and at higher risk for adverse outcomes is still limited. Incorporating novel biomarkers could represent a meaningful improvement to current risk predictors. With this aim, omics technologies have revolutionized the process of MASLD biomarker discovery over the past decades. While the research in this field is thriving, much of the publication has been haphazard, often using single-omics data and specimen sets of convenience, with many identified candidate biomarkers but lacking clinical validation and utility. If we incorporate these biomarkers to direct patients' management, it should be considered that the roadmap for translating a newly discovered omics-based signature to an actual, analytically valid test useful in MASLD clinical practice is rigorous and, therefore, not easily accomplished. This article presents an overview of this area's current state, the conceivable opportunities and challenges of omics-based laboratory diagnostics, and a roadmap for improving MASLD biomarker research.

How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide

Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.

Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson's disease

BACKGROUND

Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.

OBJECTIVE

Identify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics.

METHODS

We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.

RESULTS

LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.

CONCLUSIONS

Our results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

Background

Anthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined.

Objectives

This study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data.

Methods

For discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children's Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10-6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively.

Results

Median age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10-6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated.

Conclusions

In our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: A scoping review

Health effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations. This scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research. Ninety-eight human studies (2004-2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median = 159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both latter populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers in targeted studies, were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring. In conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting.

Pooled analysis of frontal lobe transcriptomic data identifies key mitophagy gene changes in Alzheimer's disease brain

Background

The growing prevalence of Alzheimer's disease (AD) is becoming a global health challenge without effective treatments. Defective mitochondrial function and mitophagy have recently been suggested as etiological factors in AD, in association with abnormalities in components of the autophagic machinery like lysosomes and phagosomes. Several large transcriptomic studies have been performed on different brain regions from AD and healthy patients, and their data represent a vast source of important information that can be utilized to understand this condition. However, large integration analyses of these publicly available data, such as AD RNA-Seq data, are still missing. In addition, large-scale focused analysis on mitophagy, which seems to be relevant for the aetiology of the disease, has not yet been performed.

Methods

In this study, publicly available raw RNA-Seq data generated from healthy control and sporadic AD post-mortem human samples of the brain frontal lobe were collected and integrated. Sex-specific differential expression analysis was performed on the combined data set after batch effect correction. From the resulting set of differentially expressed genes, candidate mitophagy-related genes were identified based on their known functional roles in mitophagy, the lysosome, or the phagosome, followed by Protein-Protein Interaction (PPI) and microRNA-mRNA network analysis. The expression changes of candidate genes were further validated in human skin fibroblast and induced pluripotent stem cells (iPSCs)-derived cortical neurons from AD patients and matching healthy controls.

Results

From a large dataset (AD: 589; control: 246) based on three different datasets (i.e., ROSMAP, MSBB, & GSE110731), we identified 299 candidate mitophagy-related differentially expressed genes (DEG) in sporadic AD patients (male: 195, female: 188). Among these, the AAA ATPase VCP, the GTPase ARF1, the autophagic vesicle forming protein GABARAPL1 and the cytoskeleton protein actin beta ACTB were selected based on network degrees and existing literature. Changes in their expression were further validated in AD-relevant human in vitro models, which confirmed their down-regulation in AD conditions.

Conclusion

Through the joint analysis of multiple publicly available data sets, we identify four differentially expressed key mitophagy-related genes potentially relevant for the pathogenesis of sporadic AD. Changes in expression of these four genes were validated using two AD-relevant human in vitro models, primary human fibroblasts and iPSC-derived neurons. Our results provide foundation for further investigation of these genes as potential biomarkers or disease-modifying pharmacological targets.

What Happens In Utero Does Not Stay In Utero: a Review of Evidence for Prenatal Epigenetic Programming by Per- and Polyfluoroalkyl Substances (PFAS) in Infants, Children, and Adolescents

PURPOSE OF REVIEW

Review human literature on the relationship between prenatal exposure to per- and polyfluoroalkyl substances (PFAS) and epigenetic modifications in infants, children, and adolescents < 18 years of age.

RECENT FINDINGS

Eleven studies were identified, with study populations located in the U.S., Taiwan, Japan, and the Kingdom of Denmark. Many studies (n = 5) were cross-sectional, with PFAS exposure and epigenetic outcomes measured in the same tissue collected at delivery via cord blood or dried newborn blood spots. The other six studies were prospective, with prenatal PFAS measured on maternal blood during pregnancy and DNA methylation (DNAm) assessed in cord blood and childhood peripheral leukocytes (n = 1 study). Epigenetic marks of interest included global DNAm measures (LINE-1, Alu, and an ELISA-based method), candidate genes (IFG2, H19, and MEST), and epigenome-wide DNA methylation via array-based methods (Infinium 450 K and EPIC). Two studies using array-based methods employed discovery and validation paradigms, in which a small subset of loci (n = 6 and n = 4) were replicated in the discovery population. One site (TNXB) was a hit in two independent studies. Collectively, loci associated with PFAS were in regions involved in growth and development, lipid metabolism, and nutrient metabolism. There is moderate human evidence supporting associations of prenatal PFAS exposure on DNAm at birth, with one study suggesting sustained effects into childhood. Future studies are warranted to link PFAS-associated DNAm to health outcomes, as well as to investigate the role of other epigenetic marks such as hydroxymethylation, miRNA expression, and histone modifications.

Costs and Benefits of Popular P-Value Correction Methods in Three Models of Quantitative Omic Experiments

The multiple hypothesis testing problem is inherent in large-scale quantitative "omic" experiments such as mass spectrometry-based proteomics. Yet, tools for comparing the costs and benefits of different p-value correction methods under different experimental conditions are lacking. We performed thousands of simulations of omic experiments under a range of experimental conditions and applied correction using the Benjamini-Hochberg (BH), Bonferroni, and permutation-based false discovery proportion (FDP) estimation methods. The tremendous false discovery rate (FDR) benefit of correction was confirmed in a range of different contexts. No correction method can guarantee a low FDP in a single experiment, but the probability of a high FDP is small when a high number and proportion of corrected p-values are significant. On average, correction decreased sensitivity, but the sensitivity costs of BH and permutation were generally modest compared to the FDR benefits. In a given experiment, observed sensitivity was always maintained or decreased by BH and Bonferroni, whereas it was often increased by permutation. Overall, permutation had better FDR and sensitivity than BH. We show how increasing sample size, decreasing variability, or increasing effect size can enable the detection of all true changes while still correcting p-values, and we present basic guidelines for omic experimental design. Analysis of an experimental proteomic data set with defined changes corroborated these trends. We developed an R Shiny web application for further exploration and visualization of these models, which we call the Simulator of P-value Multiple Hypothesis Correction (SIMPLYCORRECT) and a high-performance R package, permFDP, for easy use of the permutation-based FDP estimation method.

Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress

Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.

Determination of Metabolomics Profiling in BPA-Induced Impaired Metabolism

Exposure to bisphenol A (BPA) is unavoidable and it has far-reaching negative effects on living systems. This study aimed to explore the toxic effects of BPA in an experimental animal model through a metabolomics approach that is useful in measuring small molecule perturbations. Beside this, we also examined the ameliorative effects of resveratrol (RSV) against BPA-induced disturbances in experimental mice. This study was conducted for 28 days, and the results showed that BPA indeed induced an impairment in amino acid metabolism, taking place in the mitochondria by significantly (p < 0.05) decreasing the levels of certain amino acids, i.e., taurine, threonine, asparagine, leucine, norleucine, and glutamic acid in the mice plasma. However, the administration of RSV did prove effective against the BPA-induced intoxication and significantly (p < 0.05) restored the level of free amino acids. Lipid metabolites, L-carnitine, sphinganine, phytosphingosine, and lysophosphatidylcholine were also determined in the mice serum. A significant (p < 0.05) decline in glutathione peroxidase (GPx), superoxide dismutase (SOD,) glutathione, and catalase levels and an elevation in malondialdehyde level in the BPA group confirmed the generation of oxidative stress and lipid peroxidation in experimental mice exposed to BPA. The expression of Carnitine palmitoyltransferase I (CPT-I), carnitine palmitoyltransferase II (CPT-II), lecithin−cholesterol acyltransferase (LCAT), carnitine O-octanoyltransferase (CROT), carnitine-acylcarnitine translocase (CACT), and 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR) genes was significantly upregulated in the liver tissue homogenates of experimental mice exposed to BPA, although RSV regulated the expression of these genes when compared with BPA treated experimental mice. CPT-I, CPT-II, and CACT genes are located in the mitochondria and are involved in the metabolism and transportation of carnitine. Hence, this study confirms that BPA exposure induced oxidative stress, upregulated gene expression, and impaired lipid and amino acid metabolism in experimental mice.

Advancing artificial intelligence-assisted pre-screening for fragile X syndrome

BACKGROUND

Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis.

METHODS

The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988-2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification).

RESULTS

52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798.

CONCLUSIONS

This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients' health outcomes.

Versatile Functional Energy Metabolism Platform Working From Research to Patient: An Integrated View of Cell Bioenergetics

Mitochondrial dysfunctions that were not discovered during preclinical and clinical testing have been responsible for at least restriction of use as far as withdrawal of many drugs. To solve mitochondrial machinery complexity, integrative methodologies combining different data, coupled or not to mathematic modelling into systems biology, could represent a strategic way but are still very hard to implement. These technologies should be accurate and precise to avoid accumulation of errors that can lead to misinterpretations, and then alter prediction efficiency. To address such issue, we have developed a versatile functional energy metabolism platform that can measure quantitatively, in parallel, with a very high precision and accuracy, a high number of biological parameters like substrates or enzyme cascade activities in essential metabolism units (glycolysis, respiratory chain ATP production, oxidative stress...) Its versatility (our platform works on either cell lines or small animals and human samples) allows cell metabolism pathways fine tuning comparison from preclinical to clinical studies. Applied here to OXPHOS and/or oxidative stress as an example, it allows discriminating compounds with acute toxic effects but, most importantly, those inducing low noise chronic ones.

Associations between indicators of socioeconomic position and DNA methylation: a scoping review

BACKGROUND

Socioeconomic position (SEP) is a major determinant of health across the life course. Yet, little is known about the biological mechanisms explaining this relationship. One possibility widely pursued in the scientific literature is that SEP becomes biologically embedded through epigenetic processes such as DNA methylation (DNAm), wherein the socioeconomic environment causes no alteration in the DNA sequence but modifies gene activity in ways that shape health.

METHODS

To understand the evidence supporting a potential SEP-DNAm link, we performed a scoping review of published empirical findings on the association between SEP assessed from prenatal development to adulthood and DNAm measured across the life course, with an emphasis on exploring how the developmental timing, duration, and type of SEP exposure influenced DNAm.

RESULTS

Across the 37 identified studies, we found that: (1) SEP-related DNAm signatures varied across the timing, duration, and type of SEP indicator; (2) however, longitudinal studies examining repeated SEP and DNAm measures are generally lacking; and (3) prior studies are conceptually and methodologically diverse, limiting the interpretability of findings across studies with respect to these three SEP features.

CONCLUSIONS

Given the complex relationship between SEP and DNAm across the lifespan, these findings underscore the importance of analyzing SEP features, including timing, duration, and type. To guide future research, we highlight additional research gaps and propose four recommendations to further unravel the relationship between SEP and DNAm.

A metabolome and microbiome wide association study of healthy eating index points to the mechanisms linking dietary pattern and metabolic status

BACKGROUND

Healthy eating index (HEI), a measure of diet quality, associates with metabolic health outcomes; however, the molecular basis is unclear. We conducted a multi-omic study to examine whether HEI associates with the circulatory and gut metabolome and investigated the gut microbiome-HEI interaction on circulating and gut metabolites.

METHODS

Through a cross-sectional study, we evaluated diet quality in healthy individuals [the ABO Glycoproteomics in Platelets and Endothelial Cells (ABO) Study, n = 73], metabolites (measured at Metabolon Inc.) in plasma (n = 800) and gut (n = 767) and the gut microbiome at enterotype and microbial taxa (n = 296) levels. Pathway analysis was conducted using Metaboanalyst 4.0. We performed multi-variable linear regression to explore both the HEI-metabolites and HEI-microbiome associations and how metabolites were affected by the HEI-microbiome interaction. In the Fish oils and Adipose Inflammation Reduction (FAIR) Study (n = 25), analyses on HEI and plasma metabolites were replicated. Estimates of findings from both studies were pooled in random-effects meta-analysis.

RESULTS

The HEI-2015 was associated with 74 plasma and 73 gut metabolites (mostly lipids) and with 47 metabolites in the meta-analysis of the ABO and FAIR Studies. Compared to Enterotype-1 participants, those with Enterotype-2 had higher diet quality (p = 0.01). We also identified 9 microbial genera associated with HEI, and 35 plasma and 40 gut metabolites linked to the HEI-gut microbiome interaction. Pathways involved in the metabolism of polar lipids, amino acids and caffeine strongly associated with diet quality. However, the HEI-microbiome interaction not only influenced the pathways involved in the metabolism of branch-chain amino acids, it also affected upstream pathways including nucleotide metabolism and amino acids biosynthesis.

CONCLUSIONS

Our multi-omic analysis demonstrated that changes in metabolism, measured by either circulatory/gut metabolites or metabolic pathways, are influenced by not only diet quality but also gut microbiome alterations shaped by the quality of diet consumed. Future work is needed to explore the causality in the interplay between HEI and gut-microbiome composition in metabolism.

Metabolite Profiles of the Relationship between Body Mass Index (BMI) Milestones and Metabolic Risk during Early Adolescence

Early growth is associated with future metabolic risk; however, little is known of the underlying biological pathways. In this prospective study of 249 boys and 227 girls, we sought to identify sex-specific metabolite profiles that mark the relationship between age and magnitude of the infancy body mass index (BMI) peak, and the childhood BMI rebound with a metabolic syndrome z-score (MetS z-score) during early adolescence (median age 12.8 years). Thirteen consensus metabolite networks were generated between male and female adolescents using weighted correlation network analysis. In girls, none of the networks were related to BMI milestones after false discovery rate (FDR) correction at 5%. In boys, age and/or magnitude of BMI at rebound were associated with three metabolite eigenvector (ME) networks comprising androgen hormones (ME7), lysophospholipids (ME8), and diacylglycerols (ME11) after FDR correction. These networks were also associated with MetS z-score in boys after accounting for age and race/ethnicity: ME7 (1.43 [95% CI: 0.52, 2.34] units higher MetS z-score per 1 unit of ME7), ME8 (-1.01 [95% CI: -1.96, -0.07]), and ME11 (2.88 [95% CI: 2.06, 3.70]). These findings suggest that alterations in sex steroid hormone and lipid metabolism are involved in the relationship of early growth with future metabolic risk in males.