Diet in Food Insecurity: A Mediator of Metabolic Health?

Objective: Food insecurity (FI) is associated with poor metabolic health. It is assumed that energy intake and diet quality underlie this association. We tested the hypothesis that dietary factors (quantity and quality) mediate the association of FI with excess weight, waist circumference and glycemic control [glycohemoglobin (A1C)]. Methods: A mediation analysis was performed on data from the National Health And Nutrition Examination Survey using FI as an independent variable; body mass index (BMI), waist circumference, and A1C as metabolic outcome variables and total energy intake, macronutrients, and diet quality measured by the Healthy Eating Index-2015 (HEI-2015) as potential mediators. Results: Despite a greater prevalence of obesity in participants experiencing FI, daily reported energy intake was similar in food-secure and -insecure subjects. In adjusted analyses of the overall cohort, none of the examined dietary factors mediated associations between FI and metabolic outcomes. In race-stratified analyses, total sugar consumption was a partial mediator of BMI in non-Hispanic Whites, while diet quality measures (HEI-2015 total score and added sugar subscore) were partial mediators of waist circumference and BMI, respectively, for those in the "other" ethnic group. Conclusion: Dietary factors are not the main factors underlying the association of FI with metabolic health. Future studies should investigate whether other social determinants of health commonly present in the context of FI play a role in this association.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Krüppel-like factor 4 in transcriptional control of the three unique isoforms of Agouti-related peptide in mice.

Agouti-related peptide (AgRP/Agrp) within the hypothalamic arcuate nucleus (ARC) contributes to the control of energy balance, and dysregulated Agrp may contribute to metabolic adaptation during prolonged obesity. In mice, three isoforms of Agrp are encoded via distinct first exons. Agrp-A (ENSMUST00000005849.11) contributed 95% of total Agrp in mouse ARC, whereas Agrp-B (ENSMUST00000194654.2) dominated in placenta (73%). Conditional deletion of Klf4 from Agrp-expressing cells (Klf4Agrp-KO mice) reduced Agrp mRNA and increased energy expenditure but had no effects on food intake or the relative abundance of Agrp isoforms in the ARC. Chronic high-fat diet feeding masked these effects of Klf4 deletion, highlighting the context-dependent contribution of KLF4 to Agrp control. In the GT1-7 mouse hypothalamic cell culture model, which expresses all three isoforms of Agrp (including Agrp-C, ENSMUST00000194091.6), inhibition of extracellular signal-regulated kinase (ERK) simultaneously increased KLF4 binding to the Agrp promoter and stimulated Agrp expression. In addition, siRNA-mediated knockdown of Klf4 reduced expression of Agrp. We conclude that the expression of individual isoforms of Agrp in the mouse is dependent upon cell type and that KLF4 directly promotes the transcription of Agrp via a mechanism that is superseded during obesity.NEW & NOTEWORTHY In mice, three distinct isoforms of Agouti-related peptide are encoded via distinct first exons. In the arcuate nucleus of the hypothalamus, Krüppel-like factor 4 stimulates transcription of the dominant isoform in lean mice, but this mechanism is altered during diet-induced obesity.

Mitochondrial-targeted antioxidant attenuates preeclampsia-like phenotypes induced by syncytiotrophoblast-specific Gαq signaling.

Syncytiotrophoblast stress is theorized to drive development of preeclampsia, but its molecular causes and consequences remain largely undefined. Multiple hormones implicated in preeclampsia signal via the Gαq cascade, leading to the hypothesis that excess Gαq signaling within the syncytiotrophoblast may contribute. First, we present data supporting increased Gαq signaling and antioxidant responses within villous and syncytiotrophoblast samples of human preeclamptic placenta. Second, Gαq was activated in mouse placenta using Cre-lox and DREADD methodologies. Syncytiotrophoblast-restricted Gαq activation caused hypertension, kidney damage, proteinuria, elevated circulating proinflammatory factors, decreased placental vascularization, diminished spiral artery diameter, and augmented responses to mitochondrial-derived superoxide. Administration of the mitochondrial-targeted antioxidant Mitoquinone attenuated maternal proteinuria, lowered circulating inflammatory and anti-angiogenic mediators, and maintained placental vascularization. These data demonstrate a causal relationship between syncytiotrophoblast stress and the development of preeclampsia and identify elevated Gαq signaling and mitochondrial reactive oxygen species as a cause of this stress.

The Power of the Heterogeneous Stock Rat Founder Strains in Modeling Metabolic Disease.

Metabolic diseases are a host of complex conditions, including obesity, diabetes mellitus, and metabolic syndrome. Endocrine control systems (eg, adrenals, thyroid, gonads) are causally linked to metabolic health outcomes. N/NIH Heterogeneous Stock (HS) rats are a genetically heterogeneous outbred population developed for genetic studies of complex traits. Genetic mapping studies in adult HS rats identified loci associated with cardiometabolic risks, such as glucose intolerance, insulin resistance, and increased body mass index. This study determined underappreciated metabolic health traits and the associated endocrine glands within available substrains of the HS rat founders. We hypothesize that the genetic diversity of the HS rat founder strains causes a range of endocrine health conditions contributing to the diversity of cardiometabolic disease risks. ACI/EurMcwi, BN/NHsdMcwi, BUF/MnaMcwi, F344/StmMcwi, M520/NRrrcMcwi, and WKY/NCrl rats of both sexes were studied from birth until 13 weeks of age. Birth weight was recorded, body weight was measured weekly, metabolic characteristics were assessed, and blood and tissues were collected. Our data show wide variation in endocrine traits and metabolic health states in ACI, BN, BUF, F344, M520, and WKY rat strains. This is the first report to compare birth weight, resting metabolic rate, endocrine gland weight, hypothalamic-pituitary-thyroid axis hormones, and brown adipose tissue weight in these rat strains. Importantly, this work unveils new potential for the HS rat population to model early life adversity and adrenal and thyroid pathophysiology. The HS population likely inherited risk alleles for these strain-specific traits, making the HS rat a powerful model to investigate interventions on endocrine and metabolic health.

Cell-specific transcriptome changes in the hypothalamic arcuate nucleus in a mouse deoxycorticosterone acetate-salt model of hypertension.

A common preclinical model of hypertension characterized by low circulating renin is the "deoxycorticosterone acetate (DOCA)-salt" model, which influences blood pressure and metabolism through mechanisms involving the angiotensin II type 1 receptor (AT1R) in the brain. More specifically, AT1R within Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) has been implicated in selected effects of DOCA-salt. In addition, microglia have been implicated in the cerebrovascular effects of DOCA-salt and angiotensin II. To characterize DOCA-salt effects upon the transcriptomes of individual cell types within the ARC, we used single-nucleus RNA sequencing (snRNAseq) to examine this region from male C57BL/6J mice that underwent sham or DOCA-salt treatment. Thirty-two unique primary cell type clusters were identified. Sub-clustering of neuropeptide-related clusters resulted in identification of three distinct AgRP subclusters. DOCA-salt treatment caused subtype-specific changes in gene expression patterns associated with AT1R and G protein signaling, neurotransmitter uptake, synapse functions, and hormone secretion. In addition, two primary cell type clusters were identified as resting versus activated microglia, and multiple distinct subtypes of activated microglia were suggested by sub-cluster analysis. While DOCA-salt had no overall effect on total microglial density within the ARC, DOCA-salt appeared to cause a redistribution of the relative abundance of activated microglia subtypes. These data provide novel insights into cell-specific molecular changes occurring within the ARC during DOCA-salt treatment, and prompt increased investigation of the physiological and pathophysiological significance of distinct subtypes of neuronal and glial cell types.

The Rat Genome Database: Genetic, Genomic, and Phenotypic Data Across Multiple Species.

The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and disease. The Rat Genome Database (RGD, https://rgd.mcw.edu) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes, cellular components, and chemical interactions for genes, quantitative trait loci, and strains. We present an overview of the database followed by specific examples that can be used to gain experience in employing RGD to explore the wealth of functional data available for the rat and other species. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Navigating the Rat Genome Database (RGD) home page Basic Protocol 2: Using the RGD search functions Basic Protocol 3: Searching for quantitative trait loci Basic Protocol 4: Using the RGD genome browser (JBrowse) to find phenotypic annotations Basic Protocol 5: Using OntoMate to find gene-disease data Basic Protocol 6: Using MOET to find gene-ontology enrichment Basic Protocol 7: Using OLGA to generate gene lists for analysis Basic Protocol 8: Using the GA tool to analyze ontology annotations for genes Basic Protocol 9: Using the RGD InterViewer tool to find protein interaction data Basic Protocol 10: Using the RGD Variant Visualizer tool to find genetic variant data Basic Protocol 11: Using the RGD Disease Portals to find disease, phenotype, and other information Basic Protocol 12: Using the RGD Phenotypes & Models Portal to find qualitative and quantitative phenotype data and other rat strain-related information Basic Protocol 13: Using the RGD Pathway Portal to find disease and phenotype data via molecular pathways.

Genetic background in the rat affects endocrine and metabolic outcomes of bisphenol F exposure.

Environmental bisphenol compounds like bisphenol F (BPF) are endocrine-disrupting chemicals (EDCs) affecting adipose and classical endocrine systems. Genetic factors that influence EDC exposure outcomes are poorly understood and are unaccounted variables that may contribute to the large range of reported outcomes in the human population. We previously demonstrated that BPF exposure increased body growth and adiposity in male N/NIH heterogeneous stock (HS) rats, a genetically heterogeneous outbred population. We hypothesize that the founder strains of the HS rat exhibit EDC effects that were strain- and sex-dependent. Weanling littermate pairs of male and female ACI, BN, BUF, F344, M520, and WKY rats randomly received either vehicle (0.1% EtOH) or 1.125 mg BPF/l in 0.1% EtOH for 10 weeks in drinking water. Body weight and fluid intake were measured weekly, metabolic parameters were assessed, and blood and tissues were collected. BPF increased thyroid weight in ACI males, thymus and kidney weight in BUF females, adrenal weight in WKY males, and possibly increased pituitary weight in BN males. BUF females also developed a disruption in activity and metabolic rate with BPF exposure. These sex- and strain-specific exposure outcomes illustrate that HS rat founders possess diverse bisphenol-exposure risk alleles and suggest that BPF exposure may intensify inherent organ system dysfunction existing in the HS rat founders. We propose that the HS rat will be an invaluable model for dissecting gene EDC interactions on health.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared to its predecessor. Gene annotations are now more complete, significantly improving the mapping precision of genomic, transcriptomic, and proteomics data sets. We jointly analyzed 163 short-read whole genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ~20.0 million sequence variations, of which 18.7 thousand are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Rare disease research resources at the Rat Genome Database.

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource.

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal.

The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD's COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

Body Composition and Metabolic Changes in a Lyon Hypertensive Congenic Rat and Identification of Ercc6l2 as a Positional Candidate Gene.

Central obesity is genetically complex, and its exponential increase in the last decades have made it a critical public health issue. The Lyon Hypertensive (LH) rat is a well-characterized hypertensive model that also exhibits spontaneous and profound differences in body weight and adiposity, relative to its metabolically healthy control, the Lyon Normotensive (LN) rat. The mechanisms underlying the body weight differences between these strains are not well-understood, thus a congenic model (LH17LNa) was developed where a portion of the proximal arm of LN chromosome 17 is introgressed on the LH genomic background to assess the contribution of LN alleles on obesity features. Male and female LH17LNa rats were studied, but male congenics did not significantly differ from LH in this study. Female LH17LNa rats exhibited decreases in total body growth, as well as major alterations to their body composition and adiposity. The LH17LNa female rats also showed decreases in metabolic rate, and a reduction in food intake. The increased adiposity in the female LH17LNa rats was specific to abdominal white adipose tissue, and this phenomenon was further explained by significant hypertrophy in those adipocytes, with no evidence of adipocyte hyperplasia. Sequencing of the parental strains identified a novel frameshift mutation in the candidate gene Ercc6l2, which is involved in transcription-coupled DNA repair, and is implicated in premature aging. The discovery of the significance of Ercc6l2 in the context of female-specific adipocyte biology could represent a novel role of DNA repair failure syndromes in obesity pathogenesis.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses.

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

Methods for the Comprehensive in vivo Analysis of Energy Flux, Fluid Homeostasis, Blood Pressure, and Ventilatory Function in Rodents.

Cardiovascular disease represents the leading cause of death in the United States, and metabolic diseases such as obesity represent the primary impediment to improving cardiovascular health. Rodent (mouse and rat) models are widely used to model cardiometabolic disease, and as a result, there is increasing interest in the development of accurate and precise methodologies with sufficiently high resolution to dissect mechanisms controlling cardiometabolic physiology in these small organisms. Further, there is great utility in the development of centralized core facilities furnished with high-throughput equipment configurations and staffed with professional content experts to guide investigators and ensure the rigor and reproducibility of experimental endeavors. Here, we outline the array of specialized equipment and approaches that are employed within the Comprehensive Rodent Metabolic Phenotyping Core (CRMPC) and our collaborating laboratories within the Departments of Physiology, Pediatrics, Microbiology & Immunology, and Biomedical Engineering at the Medical College of Wisconsin (MCW), for the detailed mechanistic dissection of cardiometabolic function in mice and rats. We highlight selected methods for the analysis of body composition and fluid compartmentalization, electrolyte accumulation and flux, energy accumulation and flux, physical activity, ingestive behaviors, ventilatory function, blood pressure, heart rate, autonomic function, and assessment and manipulation of the gut microbiota. Further, we include discussion of the advantages and disadvantages of these approaches for their use with rodent models, and considerations for experimental designs using these methods.

Cardiometabolic effects of DOCA-salt in male C57BL/6J mice are variably dependent on sodium and nonsodium components of diet.

Hypertension characterized by low circulating renin activity accounts for roughly 25%-30% of primary hypertension in humans and can be modeled experimentally via deoxycorticosterone acetate (DOCA)-salt treatment. In this model, phenotypes develop in progressive phases, although the timelines and relative contributions of various mechanisms to phenotype development can be distinct between laboratories. To explore interactions among environmental influences such as diet formulation and dietary sodium (Na) content on phenotype development in the DOCA-salt paradigm, we examined an array of cardiometabolic endpoints in young adult male C57BL/6J mice during sham or DOCA-salt treatments when mice were maintained on several common, commercially available laboratory rodent "chow" diets including PicoLab 5L0D (0.39% Na), Envigo 7913 (0.31% Na), Envigo 2920x (0.15% Na), or a customized version of Envigo 2920x (0.4% Na). Energy balance (weight gain, food intake, digestive efficiency, and energy efficiency), fluid and electrolyte homeostasis (fluid intake, Na intake, fecal Na content, hydration, and fluid compartmentalization), renal functions (urine production rate, glomerular filtration rate, urine Na excretion, renal expression of renin, vasopressin receptors, aquaporin-2 and relationships among markers of vasopressin release, aquaporin-2 shedding, and urine osmolality), and blood pressure, all exhibited changes that were subject to interactions between diet and DOCA-salt. Interestingly, some of these phenotypes, including blood pressure and hydration, were dependent on nonsodium dietary components, as Na-matched diets resulted in distinct phenotype development. These findings provide a broad and robust illustration of an environment × treatment interaction that impacts the use and interpretation of a common rodent model of low-renin hypertension.

PhenoGeneRanker: Gene and Phenotype Prioritization Using Multiplex Heterogeneous Networks.

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally related genes tend to be close to each other in the biological networks. Recently, we introduced PhenoGeneRanker, a network-propagation algorithm for multiplex heterogeneous networks. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p values of gene and phenotype ranks using random stratified sampling of seeds of genes and phenotypes based on their connectivity degree in the network. In this study, we introduce the PhenoGeneRanker Bioconductor package and its application to multi-omics rat genome datasets to rank hypertension disease-related genes and strains. We showed that PhenoGeneRanker performed better to rank hypertension disease-related genes using multiplex gene networks than aggregated gene networks. We also showed that PhenoGeneRanker performed better to rank hypertension disease-related strains using multiplex phenotype network than single or aggregated phenotype networks. We performed a rigorous hyperparameter analysis and, finally showed that Gene Ontology (GO) enrichment of statistically significant top-ranked genes resulted in hypertension disease-related GO terms.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome.

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.