Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk

Circulating small non-coding RNAs are associated with the insulin-resistant and obesity-related type 2 diabetes clusters

AIM

To uncover differences in small non-coding RNAs (sncRNAs) in individuals with type 2 diabetes (T2D) categorized into five clusters based on individual characteristics, which may aid in the identification of those prone to rapid progression.

MATERIALS AND METHODS

In the Hoorn Diabetes Care System (DCS) cohort, participants were clustered by age, body mass index (BMI), and glycated haemoglobin, C-peptide and high-density lipoprotein (HDL) cholesterol levels, yielding severe insulin-deficient diabetes, severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes, and mild diabetes with high HDL cholesterol clusters (n = 412). Utilizing plasma sncRNA-sequencing, we identified distinct cluster-specific sncRNAs. Validation was performed in a smaller DCS Hoorn dataset (n = 138). To elucidate their potential functions, we examined tissue expression, identified potential targets or (co-)regulated proteins, conducted gene set enrichment analyses on the targets through Reactome, and examined tissue expression of the (co-)regulated proteins.

RESULTS

The insulin-resistant cluster exhibited aberrant expression of 10 sncRNAs, while the high BMI cluster featured eight differentially expressed sncRNAs. Multiple (co-)regulated proteins were identified for sncRNAs associated with both clusters. Proteins associated with both clusters showed enrichment for metabolism. Proteins that specifically and only associated with the SIRD cluster showed enrichment for immune-related signalling. Furthermore, MOD cluster-specific associated proteins showed enrichment for the complement system.

CONCLUSIONS

Our research showed differential sncRNA levels among type 2 diabetes clusters. This may reflect and could deepen our understanding of molecular mechanisms, in development, progression, and risk factors for each cluster.

Association between Childhood Overweight and Altered Concentrations of Circulating Amino Acids

(1) Background: Dysregulated serum amino acids (AA) are known to be associated with obesity and risk of Type 2 Diabetes (T2D) in adults, and recent studies support the same notion in the pubertal age. It is, however, unknown whether childhood overweight may already display alterations of circulating AA. (2) Methods: We used liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)-targeted metabolomics to determine plasma concentrations of AA and AA-related molecules in 36 children aged 7-12 years with normal weight or overweight. Clinical and anthropometric parameters were measured. (3) Results: Overweight in children is associated with an altered AA profile, with increased branched-chain amino acids (BCAA) and decreased glycine levels, with no clinically manifested metabolic conditions. Moreover, z-BMI was positively and negatively correlated with BCAA and glycine levels, respectively, even after adjustment for age and gender. We also found a correlation between the AA profile and clinical parameters such as lipids profile and glycemia. (4) Conclusions: A pattern of low glycine, and increased BCAA is correlated to z-BMI, total cholesterol, and triglycerides in overweight but otherwise healthy children. Our data suggest that, in childhood overweight, AA disturbances may precede other clinical parameters, thus providing an early indicator for the later development of metabolic disease.

Cardiometabolic profiles and proteomics associated with obesity phenotypes in a longitudinal cohort of young adults

To assess cardiometabolic profiles and proteomics to identify biomarkers associated with the metabolically healthy and unhealthy obesity. Young adults (N = 156) enrolled were classified as not having obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) based on NCEP ATP-III criteria. Plasma proteomics at study entry were measured using Olink Cardiometabolic Explore panel. Linear regression was used to assess associations between proteomics and obesity groups as well as cardiometabolic traits of glucose, insulin, and lipid profiles at baseline and follow-up visits. Enriched biological pathways were further identified based on the significant proteomic features. Among the baseline 95 (61%) and 61 (39%) participants classified as not having obesity and having obesity (8 MHO and 53 MUHO), respectively. Eighty of the participants were followed-up with an average 4.6 years. Forty-one proteins were associated with obesity (FDR < 0.05), 29 of which had strong associations with insulin-related traits and lipid profiles (FDR < 0.05). Inflammation, immunomodulation, extracellular matrix remodeling and endoplasmic reticulum lumen functions were enriched by 40 proteins. In this study population, obesity and MHO were associated with insulin resistance and dysregulated lipid profiles. The underlying mechanism included elevated inflammation and deteriorated extracellular matrix remodeling function.

Proteome-wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8-year prospective study

The role of circulatory proteomics in osteoporosis is unclear. Proteome-wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography-tandem mass spectrometry (LC-MS/MS) at baseline, and the 2nd, and 3rd follow-ups (7704 person-tests) in the prospective Chinese cohorts with 9.8 follow-up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA) at follow-ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)-related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed-effects model (LMM). Meta-analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta-analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD-year increase in KDM-Proage was associated with higher risk of LS-OP (hazard ratio [HR], 1.25; 95% CI, 1.14-1.36, p = 4.96 × 10-06 ), and FN-OP (HR, 1.13; 95% CI, 1.02-1.23, p = 9.71 × 10-03 ). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.

Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults

BACKGROUND

The circulating proteome may encode early pathways of diabetes susceptibility in young adults for surveillance and intervention. Here, we define proteomic correlates of tissue phenotypes and diabetes in young adults.

METHODS

We used penalized models and principal components analysis to generate parsimonious proteomic signatures of diabetes susceptibility based on phenotypes and on diabetes diagnosis across 184 proteins in >2000 young adults in the CARDIA (Coronary Artery Risk Development in Young Adults study; mean age, 32 years; 44% women; 43% Black; mean body mass index, 25.6±4.9 kg/m2), with validation against diabetes in >1800 individuals in the FHS (Framingham Heart Study) and WHI (Women's Health Initiative).

RESULTS

In 184 proteins in >2000 young adults in CARDIA, we identified 2 proteotypes of diabetes susceptibility-a proinflammatory fat proteotype (visceral fat, liver fat, inflammatory biomarkers) and a muscularity proteotype (muscle mass), linked to diabetes in CARDIA and WHI/FHS. These proteotypes specified broad mechanisms of early diabetes pathogenesis, including transorgan communication, hepatic and skeletal muscle stress responses, vascular inflammation and hemostasis, fibrosis, and renal injury. Using human adipose tissue single cell/nuclear RNA-seq, we demonstrate expression at transcriptional level for implicated proteins across adipocytes and nonadipocyte cell types (eg, fibroadipogenic precursors, immune and vascular cells). Using functional assays in human adipose tissue, we demonstrate the association of expression of genes encoding these implicated proteins with adipose tissue metabolism, inflammation, and insulin resistance.

CONCLUSIONS

A multifaceted discovery effort uniting proteomics, underlying clinical susceptibility phenotypes, and tissue expression patterns may uncover potentially novel functional biomarkers of early diabetes susceptibility in young adults for future mechanistic evaluation.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Effect of dapagliflozin on proteomics and metabolomics of serum from patients with type 2 diabetes

BACKGROUND

Sodium-glucose co-transporter 2 (SGLT2) inhibitors reduced the risk of cardiovascular and renal outcomes in patients with type 2 diabetes (T2D), but the underlying mechanism has not been well elucidated. The circulating levels of proteins and metabolites reflect the overall state of the human body. This study aimed to evaluate the effect of dapagliflozin on the proteome and metabolome in patients with newly diagnosed T2D.

METHODS

A total of 57 newly diagnosed T2D patients were enrolled, and received 12 weeks of dapagliflozin treatment (10 mg/d, AstraZeneca). Serum proteome and metabolome were investigated at the baseline and after dapagliflozin treatment.

RESULTS

Dapagliflozin significantly decreased HbA1c, BMI, and HOMA-IR in T2D patients (all p < 0.01). Multivariate models indicated clear separations of proteomics and metabolomics data between the baseline and after dapagliflozin treatment. A total of 38 differentially abundant proteins including 23 increased and 15 decreased proteins, and 35 differentially abundant metabolites including 17 increased and 18 decreased metabolites, were identified. In addition to influencing glucose metabolism (glycolysis/gluconeogenesis and pentose phosphate pathway), dapagliflozin significantly increased sex hormone-binding globulin, transferrin receptor protein 1, disintegrin, and metalloprotease-like decysin-1 and apolipoprotein A-IV levels, and decreased complement C3, fibronectin, afamin, attractin, xanthine, and uric acid levels.

CONCLUSIONS

The circulating proteome and metabolome in newly diagnosed T2D patients were significantly changed after dapagliflozin treatment. These changes in proteins and metabolites might be associated with the beneficial effect of dapagliflozin on cardiovascular and renal outcomes.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health

Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Nevertheless, their causal effects on human diseases have not been evaluated comprehensively. We performed two-sample Mendelian randomization to systematically infer the causal effects of 1,099 plasma metabolites measured in 6,136 Finnish men from the METSIM study on risk of 2,099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen. We identified evidence for 282 causal effects of 70 metabolites on 183 disease endpoints (FDR<1%). We found 25 metabolites with potential causal effects across multiple disease domains, including ascorbic acid 2-sulfate affecting 26 disease endpoints in 12 disease domains. Our study suggests that N-acetyl-2-aminooctanoate and glycocholenate sulfate affect risk of atrial fibrillation through two distinct metabolic pathways and that N-methylpipecolate may mediate the causal effect of N6, N6-dimethyllysine on anxious personality disorder. This study highlights the broad causal impact of plasma metabolites and widespread metabolic connections across diseases.

A novel co-target of ACY1 governing plasma membrane translocation of SphK1 contributes to inflammatory and neuropathic pain

Previous studies validate that inhibiting sodium channel 1.8 (Nav1.8) effectively relieves inflammatory and neuropathic pain. However, Nav1.8 blockers have cardiac side effects in addition to analgesic effects. Here, we constructed a spinal differential protein expression profile using Nav1.8 knockout mice to screen common downstream proteins of Nav1.8 in inflammatory and neuropathic pain. We found that aminoacylase 1 (ACY1) expression was increased in wild-type mice compared to Nav1.8 knockout mice in both pain models. Moreover, spinal ACY1 overexpression induced mechanical allodynia in naive mice, while ACY1 suppression alleviated inflammatory and neuropathic pain. Further, ACY1 could interact with sphingosine kinase 1 and promote its membrane translocation, resulting in sphingosine-1-phosphate upregulation and the activation of glutamatergic neurons and astrocytes. In conclusion, ACY1 acts as a common downstream effector protein of Nav1.8 in inflammatory and neuropathic pain and could be a new and precise therapeutic target for chronic pain.

Molecular Biomarkers for Cardiometabolic Disease: Risk Assessment in Young Individuals

Cardiometabolic diseases, including cardiovascular disease and diabetes, are major causes of morbidity and mortality worldwide. Despite progress in prevention and treatment, recent trends show a stalling in the reduction of cardiovascular disease morbidity and mortality, paralleled by increasing rates of cardiometabolic disease risk factors in young adults, underscoring the importance of risk assessments in this population. This review highlights the evidence for molecular biomarkers for early risk assessment in young individuals. We examine the utility of traditional biomarkers in young individuals and discuss novel, nontraditional biomarkers specific to pathways contributing to early cardiometabolic disease risk. Additionally, we explore emerging omic technologies and analytical approaches that could enhance risk assessment for cardiometabolic disease.

Plasma Proteomic Risk Markers of Incident Type 2 Diabetes Reflect Physiologically Distinct Components of Glucose-Insulin Homeostasis

High-throughput proteomics allows researchers to simultaneously explore the roles of thousands of biomarkers in the pathophysiology of diabetes. We conducted proteomic association studies of incident type 2 diabetes and physiologic responses to an intravenous glucose tolerance test (IVGTT) to identify novel protein contributors to glucose homeostasis and diabetes risk. We tested 4,776 SomaScan proteins measured in relation to 18-year incident diabetes risk in participants from the Cardiovascular Health Study (N = 2,631) and IVGTT-derived measures in participants from the HERITAGE Family Study (N = 752). We characterize 51 proteins that were associated with longitudinal diabetes risk, using their respective 39, 9, and 8 concurrent associations with insulin sensitivity index (SI), acute insulin response to glucose (AIRG), and glucose effectiveness (SG). Twelve of the 51 diabetes associations appear to be novel, including β-glucuronidase, which was associated with increased diabetes risk and lower SG, suggesting an alternative pathway to insulin for glucose disposal; and plexin-B2, which also was associated with increased diabetes risk, but with lower AIRG, and not with SI, indicating a mechanism related instead to pancreatic dysfunction. Other novel protein associations included alcohol dehydrogenase-1C, fructose-bisphosphate aldolase-B, sorbitol dehydrogenase with elevated type 2 diabetes risk, and a leucine-rich repeat containing protein-15 and myocilin with decreased risk.

ARTICLE HIGHLIGHTS

Plasma proteins are associated with the risk of incident diabetes in older adults independent of various demographic, lifestyle, and biochemical risk factors. These same proteins are associated with subtle differences in measures of glucose homeostasis earlier in life. Proteins that are associated with lower insulin sensitivity in individuals without diabetes tend to be associated with appropriate compensatory mechanisms, such as a stronger acute insulin response or higher glucose effectiveness. Proteins that are associated with future diabetes risk, but not with insulin insensitivity, tend to be associated with lower glucose effectiveness and/or impaired acute insulin response.

DNA Methylation Changes in Autologous Hematopoietic Stem Cell Transplant Patients

BACKGROUND

Blood cancers may be potentially cured with hematopoietic stem cell transplantation (HCT); however, standard pre-assessments for transplant eligibility do not capture all contributing factors for transplant outcomes. Epigenetic biomarkers predict outcomes in various diseases. This pilot study aims to explore epigenetic changes (epigenetic age and differentially methylated genes) in patients before and after autologous HCT, that can serve as potential biomarkers to better predict HCT outcomes.

METHODS

This study used a prospective longitudinal study design to compare genome wide DNA methylation changes in 36 autologous HCT eligible patients recruited from the Cellular Immunotherapies and Transplant clinic at a designated National Cancer Center.

RESULTS

Genome-wide DNA methylation, measured by the Illumina Infinium Human Methylation 850K BeadChip, showed a significant difference in DNA methylation patterns post-HCT compared to pre-HCT. Compared to baseline levels of DNA methylation pre-HCT, 3358 CpG sites were hypo-methylated and 3687 were hyper-methylated. Identified differentially methylated positions overlapped with genes involved in hematopoiesis, blood cancers, inflammation and immune responses. Enrichment analyses showed significant alterations in biological processes such as immune response and cell structure organization, however no significant pathways were noted. Though participants had an advanced epigenetic age compared to chronologic age before and after HCT, both epigenetic age and accelerated age decreased post-HCT.

CONCLUSION

Epigenetic changes, both in epigenetic age and differentially methylated genes were observed in autologous HCT recipients, and should be explored as biomarkers to predict transplant outcomes after autologous HCT in larger, longitudinal studies.

Protein Markers of Diabetes Discovered in an African American Cohort

Proteomics has been used to study type 2 diabetes, but the majority of available data are from White participants. Here, we extend prior work by analyzing a large cohort of self-identified African Americans in the Jackson Heart Study (n = 1,313). We found 325 proteins associated with incident diabetes after adjusting for age, sex, and sample batch (false discovery rate q < 0.05) measured using a single-stranded DNA aptamer affinity-based method on fasting plasma samples. A subset was independent of established markers of diabetes development pathways, such as adiposity, glycemia, and/or insulin resistance, suggesting potential novel biological processes associated with disease development. Thirty-six associations remained significant after additional adjustments for BMI, fasting plasma glucose, cholesterol levels, hypertension, statin use, and renal function. Twelve associations, including the top associations of complement factor H, formimidoyltransferase cyclodeaminase, serine/threonine-protein kinase 17B, and high-mobility group protein B1, were replicated in a meta-analysis of two self-identified White cohorts-the Framingham Heart Study and the Malmö Diet and Cancer Study-supporting the generalizability of these biomarkers. A selection of these diabetes-associated proteins also improved risk prediction. Thus, we uncovered both novel and broadly generalizable associations by studying a diverse population, providing a more complete understanding of the diabetes-associated proteome.

Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study

OBJECTIVE

The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis.

RESEARCH DESIGN AND METHODS

In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments.

RESULTS

There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk.

CONCLUSIONS

We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

HGFAC is a ChREBP-regulated hepatokine that enhances glucose and lipid homeostasis

Carbohydrate response element-binding protein (ChREBP) is a carbohydrate-sensing transcription factor that regulates both adaptive and maladaptive genomic responses in coordination of systemic fuel homeostasis. Genetic variants in the ChREBP locus associate with diverse metabolic traits in humans, including circulating lipids. To identify novel ChREBP-regulated hepatokines that contribute to its systemic metabolic effects, we integrated ChREBP ChIP-Seq analysis in mouse liver with human genetic and genomic data for lipid traits and identified hepatocyte growth factor activator (HGFAC) as a promising ChREBP-regulated candidate in mice and humans. HGFAC is a protease that activates the pleiotropic hormone hepatocyte growth factor. We demonstrate that HGFAC-KO mice had phenotypes concordant with putative loss-of-function variants in human HGFAC. Moreover, in gain- and loss-of-function genetic mouse models, we demonstrate that HGFAC enhanced lipid and glucose homeostasis, which may be mediated in part through actions to activate hepatic PPARγ activity. Together, our studies show that ChREBP mediated an adaptive response to overnutrition via activation of HGFAC in the liver to preserve glucose and lipid homeostasis.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population

Natural human knockouts of genes associated with desirable outcomes, such as PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.

Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study

BACKGROUNDMetabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into disease pathogenesis.METHODSWe examined the association between blood metabolites and CKD progression, defined as the subsequent development of end-stage renal disease (ESRD) or estimated glomerular filtrate rate (eGFR) halving, in 1,773 participants of the Chronic Renal Insufficiency Cohort (CRIC) study, 962 participants of the African-American Study of Kidney Disease and Hypertension (AASK), and 5,305 participants of the Atherosclerosis Risk in Communities (ARIC) study.RESULTSIn CRIC, more than half of the measured metabolites were associated with CKD progression in minimally adjusted Cox proportional hazards models, but the number and strength of associations were markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten metabolites were significantly associated with CKD progression in fully adjusted models in CRIC; 3 of these metabolites were also significant in fully adjusted models in AASK and ARIC, highlighting potential markers of glomerular filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), and azotemia (homocitrulline). Our findings also highlight N-acetylserine as a potential marker of kidney tubular function, with significant associations with CKD progression observed in CRIC and ARIC.CONCLUSIONOur findings demonstrate the application of metabolomics to identify potential biomarkers and causal pathways in CKD progression.FUNDINGThis study was supported by the NIH (U01 DK106981, U01 DK106982, U01 DK085689, R01 DK108803, and R01 DK124399).

Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites

Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.

Circulating Proteome and Progression of Type 2 Diabetes

CONTEXT

Circulating proteomes may provide intervention targets for type 2 diabetes (T2D).

OBJECTIVE

We aimed to identify proteomic biomarkers associated with incident T2D and assess its joint effect with dietary or lifestyle factors on the T2D risk.

METHODS

We established 2 nested case-control studies for incident T2D: discovery cohort (median 6.5 years of follow-up, 285 case-control pairs) and validation cohort (median 2.8 years of follow-up, 38 case-control pairs). We integrated untargeted mass spectrometry-based proteomics and interpretable machine learning to identify T2D-related proteomic biomarkers. We constructed a protein risk score (PRS) with the identified proteomic biomarkers and used a generalized estimating equation to evaluate PRS-T2D relationship with repeated profiled proteome. We evaluated association of PRS with trajectory of glycemic traits in another non-T2D cohort (n = 376). Multiplicative interactions of dietary or lifestyle factors with PRS were evaluated using logistic regression.

RESULTS

Seven proteins (SHBG, CAND1, APOF, SELL, MIA3, CFH, IGHV1-2) were retained as the proteomic biomarkers for incident T2D. PRS (per SD change) was positively associated with incident T2D across 2 cohorts, with an odds ratio 1.29 (95% CI, 1.08-1.54) and 1.84 (1.19-2.84), respectively. Participants with a higher PRS had a higher probability showing unfavored glycemic trait trajectory in the non-T2D cohort. Red meat intake and PRS showed a multiplicative interaction on T2D risk in the discovery (P = 0.003) and validation cohort (P = 0.017).

CONCLUSION

This study identified proteomic biomarkers for incident T2D among the Chinese populations. The higher intake of red meat may synergistically interact with the proteomic biomarkers to exaggerate the T2D risk.

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Metabolomic Analysis of Coronary Heart Disease in an African American Cohort From the Jackson Heart Study

Importance

African American individuals have disproportionate rates of coronary heart disease (CHD) but lower levels of coronary artery calcium (CAC), a marker of subclinical CHD, than non-Hispanic White individuals. African American individuals may have distinct metabolite profiles associated with incident CHD risk compared with non-Hispanic White individuals, and examination of these differences could highlight important processes that differ between them.

Objectives

To identify novel biomarkers of incident CHD and CAC among African American individuals and to replicate incident CHD findings in a multiethnic cohort.

Design, Setting, and Participants

This analysis targeted plasma metabolomic profiling of 2346 participants in the Jackson Heart Study (JHS), a prospective population-based cohort study that included 5306 African American participants who were examined at baseline (2000-2004) and 2 follow-up visits. Replication of CHD-associated metabolites was sought among 1588 multiethnic participants from the Women's Health Initiative (WHI), a prospective population-based multiethnic cohort study of 161 808 postmenopausal women who were examined at baseline (1991-1995) and ongoing follow-up visits. Regression analyses were performed for each metabolite to examine the associations with incident CHD and CAC scores. Data were collected from the WHI between 1994 and 2009 and from the JHS between 2000 and 2015. All data were analyzed from November 2020 to August 2021.

Exposures

Plasma metabolites.

Main Outcomes and Measures

Incident CHD was defined as definite or probable myocardial infarction or definite fatal CHD in both the JHS and WHI cohorts. In the JHS cohort, silent myocardial infarction between examinations (as determined by electrocardiography) and coronary revascularization were included in the incident CHD analysis. Coronary artery calcium was measured using a 16-channel computed tomographic system and reported as an Agatston score.

Results

Among 2346 African American individuals in the JHS cohort, the mean (SD) age was 56 (13) years, and 1468 individuals (62.6%) were female. Among 1588 postmenopausal women in the WHI cohort, the mean (SD) age was 67 (7) years; 217 individuals (13.7%) self-identified as African American, 1219 (76.8%) as non-Hispanic White, and 152 (9.6%) as other races or ethnicities. In the fully adjusted model including 1876 individuals, 46 of 303 targeted metabolites were associated with incident CHD (false discovery rate q <0.100). Data for 32 of the 46 metabolites were available in the WHI cohort, and 13 incident CHD-associated metabolites from the JHS cohort were replicated in the WHI cohort. A total of 1439 participants from the JHS cohort with available CAC scores received metabolomic profiling. Nine metabolites were associated with CAC scores. Minimal overlap was found between the results from the incident CHD and CAC analyses, with only 3 metabolites shared between the 2 analyses.

Conclusions and Relevance

This cohort study identified metabolites that were associated with incident CHD among African American individuals, including 13 incident CHD-associated metabolites that were replicated in a multiethnic population and 9 novel metabolites that included N-acylamides, leucine, and lipid species. These findings may help to elucidate common and distinct metabolic processes that may be associated with CHD among individuals with different self-identified race.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.