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de Klerk JA, Beulens JWJ, Bijkerk R, van Zonneveld AJ, Elders PJM, 't Hart LM, Slieker R. Circulating small non-coding RNAs are associated with the insulin-resistant and obesity-related type 2 diabetes clusters. Diabetes Obes Metab 2024. [PMID: 38984379 DOI: 10.1111/dom.15786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/18/2024] [Accepted: 06/26/2024] [Indexed: 07/11/2024]
Abstract
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.
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Affiliation(s)
- Juliette A de Klerk
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Joline W J Beulens
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
| | - Roel Bijkerk
- Department of Internal Medicine, Division of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Anton Jan van Zonneveld
- Department of Internal Medicine, Division of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Petra J M Elders
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
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Campos JDO, Oliveira TLPSDA, Vitalis O, Pereira JG, Nogueira IDCR, Santos GCJ, Chikh K, Leandro CG, da Costa-Silva JH, Pirola L. Association between Childhood Overweight and Altered Concentrations of Circulating Amino Acids. Nutrients 2024; 16:1843. [PMID: 38931197 PMCID: PMC11206240 DOI: 10.3390/nu16121843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
(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.
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Affiliation(s)
- Jéssica de Oliveira Campos
- Laboratory of Nutrition, Physical Activity and Phenotypic Plasticity, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (J.d.O.C.); (T.L.P.S.d.A.O.); (J.G.P.); (C.G.L.); (J.H.d.C.-S.)
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
- INSERM Unit 1060, CarMeN Laboratory, Lyon Civil Hospitals, Claude Bernard Lyon1 University, 69310 Pierre Bénite, France; (O.V.); (K.C.)
| | - Tafnes Laís Pereira Santos de Almeida Oliveira
- Laboratory of Nutrition, Physical Activity and Phenotypic Plasticity, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (J.d.O.C.); (T.L.P.S.d.A.O.); (J.G.P.); (C.G.L.); (J.H.d.C.-S.)
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - Oriane Vitalis
- INSERM Unit 1060, CarMeN Laboratory, Lyon Civil Hospitals, Claude Bernard Lyon1 University, 69310 Pierre Bénite, France; (O.V.); (K.C.)
| | - Jéssica Gonzaga Pereira
- Laboratory of Nutrition, Physical Activity and Phenotypic Plasticity, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (J.d.O.C.); (T.L.P.S.d.A.O.); (J.G.P.); (C.G.L.); (J.H.d.C.-S.)
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - Isabella da Costa Ribeiro Nogueira
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - Gabriela Carvalho Jurema Santos
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - Karim Chikh
- INSERM Unit 1060, CarMeN Laboratory, Lyon Civil Hospitals, Claude Bernard Lyon1 University, 69310 Pierre Bénite, France; (O.V.); (K.C.)
| | - Carol Gois Leandro
- Laboratory of Nutrition, Physical Activity and Phenotypic Plasticity, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (J.d.O.C.); (T.L.P.S.d.A.O.); (J.G.P.); (C.G.L.); (J.H.d.C.-S.)
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - João Henrique da Costa-Silva
- Laboratory of Nutrition, Physical Activity and Phenotypic Plasticity, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (J.d.O.C.); (T.L.P.S.d.A.O.); (J.G.P.); (C.G.L.); (J.H.d.C.-S.)
- Laboratory of Physical Evaluation and Signal Processing, Academic Center of Vitória, Universidade Federal de Pernambuco, UFPE, Vitória de Santo Antão 55608-680, PE, Brazil; (I.d.C.R.N.); (G.C.J.S.)
| | - Luciano Pirola
- INSERM Unit 1060, CarMeN Laboratory, Lyon Civil Hospitals, Claude Bernard Lyon1 University, 69310 Pierre Bénite, France; (O.V.); (K.C.)
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3
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Liao J, Goodrich JA, Chen W, Qiu C, Chen JC, Costello E, Alderete TL, Chatzi L, Gilliland F, Chen Z. Cardiometabolic profiles and proteomics associated with obesity phenotypes in a longitudinal cohort of young adults. Sci Rep 2024; 14:7384. [PMID: 38548792 PMCID: PMC10978904 DOI: 10.1038/s41598-024-57751-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/21/2024] [Indexed: 04/01/2024] Open
Abstract
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.
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Affiliation(s)
- Jiawen Liao
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Jesse A Goodrich
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Wu Chen
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Chenyu Qiu
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Jiawen Carmen Chen
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Elizabeth Costello
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Tanya L Alderete
- Department of Integrative Physiology, University of Colorado, Boulder, CO, USA
| | - Lida Chatzi
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Frank Gilliland
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA
| | - Zhanghua Chen
- Department of Public and Population Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90032, USA.
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Xu J, Cai X, Miao Z, Yan Y, Chen D, Yang Z, Yue L, Hu W, Zhuo L, Wang J, Xue Z, Fu Y, Xu Y, Zheng J, Guo T, Chen Y. Proteome-wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8-year prospective study. Aging Cell 2024; 23:e14035. [PMID: 37970652 PMCID: PMC10861190 DOI: 10.1111/acel.14035] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/15/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023] Open
Abstract
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.
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Affiliation(s)
- Jinjian Xu
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Xue Cai
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Zelei Miao
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Yan Yan
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Danyu Chen
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Zhen‐xiao Yang
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Liang Yue
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Wei Hu
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Laibao Zhuo
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Jia‐ting Wang
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
| | - Zhangzhi Xue
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Yuanqing Fu
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Ying Xu
- Shenzhen Bao'an Center for Chronic Diseases ControlShenzhenChina
| | - Ju‐Sheng Zheng
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Tiannan Guo
- School of Life SciencesWestlake UniversityHangzhouChina
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and BiomedicineHangzhouChina
| | - Yu‐ming Chen
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public HealthSun Yat‐sen UniversityGuangzhouChina
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5
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Shah R, Zhong J, Massier L, Tanriverdi K, Hwang SJ, Haessler J, Nayor M, Zhao S, Perry AS, Wilkins JT, Shadyab AH, Manson JE, Martin L, Levy D, Kooperberg C, Freedman JE, Rydén M, Murthy VL. Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004192. [PMID: 38323454 PMCID: PMC10940209 DOI: 10.1161/circgen.123.004192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/05/2023] [Indexed: 02/08/2024]
Abstract
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.
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Affiliation(s)
- Ravi Shah
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Jiawei Zhong
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
| | - Lucas Massier
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
| | - Kahraman Tanriverdi
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | | | - Matthew Nayor
- Sections of Preventive Medicine & Epidemiology & Cardiovascular Medicine, Dept of Medicine, Dept of Epidemiology, Boston University Schools of Medicine & Public Health, Boston, MA & Framingham Heart Study, Framingham, MA
| | | | - Andrew S. Perry
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | | | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health & Human Longevity Science, Univ of California, San Diego, La Jolla, CA
| | - JoAnn E. Manson
- Dept of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Lisa Martin
- George Washington Univ School of Medicine & Health Sciences
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | | | - Jane E. Freedman
- Vanderbilt Translational & Clinical Cardiovascular Research Center, Vanderbilt Univ, Nashville, TN
| | - Mikael Rydén
- Dept of Medicine (H7), Karolinska Institutet, Stockholm, Sweden
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Carrasco-Zanini J, Pietzner M, Wheeler E, Kerrison ND, Langenberg C, Wareham NJ. Multi-omic prediction of incident type 2 diabetes. Diabetologia 2024; 67:102-112. [PMID: 37889320 PMCID: PMC10709231 DOI: 10.1007/s00125-023-06027-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
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.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK.
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7
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Liu J, Chang X, Ding X, He X, Wang J, Wang G. Effect of dapagliflozin on proteomics and metabolomics of serum from patients with type 2 diabetes. Diabetol Metab Syndr 2023; 15:251. [PMID: 38044448 PMCID: PMC10694884 DOI: 10.1186/s13098-023-01229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/24/2023] [Indexed: 12/05/2023] Open
Abstract
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.
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Affiliation(s)
- Jia Liu
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China
| | - Xiaona Chang
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China
| | - Xiaoyu Ding
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China
| | - Xueqing He
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China
| | - Jiaxuan Wang
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China
| | - Guang Wang
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, NO. 8, Gongti South Road, Chaoyang District, 100020, Beijing, China.
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8
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Benson MD, Eisman AS, Tahir UA, Katz DH, Deng S, Ngo D, Robbins JM, Hofmann A, Shi X, Zheng S, Keyes M, Yu Z, Gao Y, Farrell L, Shen D, Chen ZZ, Cruz DE, Sims M, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Jain D, Yang Q, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Sarkar IN, Natarajan P, Gerszten RE. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma. Cell Metab 2023; 35:1646-1660.e3. [PMID: 37582364 PMCID: PMC11118091 DOI: 10.1016/j.cmet.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/12/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
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.
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Affiliation(s)
- Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alissa Hofmann
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yan Gao
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mario Sims
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, Columbia, SC, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine Harvard Medical School, Boston, MA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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9
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Yin X, Li J, Bose D, Okamoto J, Kwon A, Jackson AU, Silva LF, Oravilahti A, Stringham HM, Ripatti S, Daly M, Palotie A, Scott LJ, Burant CF, Fauman EB, Wen X, Boehnke M, Laakso M, Morrison J. Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.26.23291721. [PMID: 37425837 PMCID: PMC10327254 DOI: 10.1101/2023.06.26.23291721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
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.
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10
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Liu B, Wu W, Cui L, Zheng X, Li N, Zhang X, Duan G. A novel co-target of ACY1 governing plasma membrane translocation of SphK1 contributes to inflammatory and neuropathic pain. iScience 2023; 26:106989. [PMID: 37378314 PMCID: PMC10291574 DOI: 10.1016/j.isci.2023.106989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/31/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
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.
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Affiliation(s)
- Baowen Liu
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
- Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenyao Wu
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
- Department of Anesthesiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - LingLing Cui
- Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Anesthesiology, Wuhan third Hospital/Tongren Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Xuemei Zheng
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Ningbo Li
- Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xianwei Zhang
- Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guangyou Duan
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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11
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Abstract
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.
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Affiliation(s)
- Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
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12
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Cronjé HT, Mi MY, Austin TR, Biggs ML, Siscovick DS, Lemaitre RN, Psaty BM, Tracy RP, Djoussé L, Kizer JR, Ix JH, Rao P, Robbins JM, Barber JL, Sarzynski MA, Clish CB, Bouchard C, Mukamal KJ, Gerszten RE, Jensen MK. Plasma Proteomic Risk Markers of Incident Type 2 Diabetes Reflect Physiologically Distinct Components of Glucose-Insulin Homeostasis. Diabetes 2023; 72:666-673. [PMID: 36749929 PMCID: PMC10130486 DOI: 10.2337/db22-0628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/02/2023] [Indexed: 02/09/2023]
Abstract
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.
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Affiliation(s)
- Héléne T. Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Michael Y. Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Mary L. Biggs
- Department of Biostatistics, University of Washington, Seattle, WA
| | | | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, Health Systems and Population Health, University of Washington, Seattle, WA
| | - Russell P. Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT
| | - Luc Djoussé
- Division of Aging, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Jorge R. Kizer
- Cardiology Section San Francisco Veterans Affairs Health Care System, San Francisco, CA
- Department of Medicine, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Joachim H. Ix
- Division of Nephrology-Hypertension, University of California, San Diego, La Jolla, CA
| | - Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jeremy M. Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jacob L. Barber
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Mark A. Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | | | | | - Kenneth J. Mukamal
- Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Majken K. Jensen
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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13
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Mohanraj L, Wolf H, Silvey S, Liu J, Toor A, Swift-Scanlan T. DNA Methylation Changes in Autologous Hematopoietic Stem Cell Transplant Patients. Biol Res Nurs 2023; 25:310-325. [PMID: 36321693 PMCID: PMC10236442 DOI: 10.1177/10998004221135628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
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.
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Affiliation(s)
- Lathika Mohanraj
- Department of Adult Health and Nursing
Systems, VCU School of Nursing, Richmond, VA, USA
| | - Hope Wolf
- Department of Human and Molecular Genetics, VCU School of Medicine, Richmond, VA, USA
| | - Scott Silvey
- Department of Biostatistics, VCU School of Medicine, Richmond, VA, USA
| | - Jinze Liu
- Department of Biostatistics, VCU School of Medicine, Richmond, VA, USA
| | - Amir Toor
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, USA
| | - Theresa Swift-Scanlan
- Endowed Professor and Director,
Biobehavioral Research Lab, VCU School of Nursing, Richmond, VA, USA
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14
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Chen ZZ, Gao Y, Keyes MJ, Deng S, Mi M, Farrell LA, Shen D, Tahir UA, Cruz DE, Ngo D, Benson MD, Robbins JM, Correa A, Wilson JG, Gerszten RE. Protein Markers of Diabetes Discovered in an African American Cohort. Diabetes 2023; 72:532-543. [PMID: 36630488 PMCID: PMC10033249 DOI: 10.2337/db22-0710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/05/2023] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard School of Medicine, Boston, MA
| | - Yan Gao
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS
| | - Michelle J. Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Mi
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Laurie A. Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Usman A. Tahir
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Daniel E. Cruz
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Debby Ngo
- Harvard School of Medicine, Boston, MA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Mark D. Benson
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jeremy M. Robbins
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Adolfo Correa
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS
| | - James G. Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert E. Gerszten
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Broad Institute of MIT and Harvard, Boston, MA
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15
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Rooney MR, Chen J, Echouffo-Tcheugui JB, Walker KA, Schlosser P, Surapaneni A, Tang O, Chen J, Ballantyne CM, Boerwinkle E, Ndumele CE, Demmer RT, Pankow JS, Lutsey PL, Wagenknecht LE, Liang Y, Sim X, van Dam R, Tai ES, Grams ME, Selvin E, Coresh J. Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care 2023; 46:733-741. [PMID: 36706097 PMCID: PMC10090896 DOI: 10.2337/dc22-1830] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/29/2022] [Indexed: 01/28/2023]
Abstract
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.
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Affiliation(s)
- Mary R. Rooney
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Justin B. Echouffo-Tcheugui
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University, Baltimore, MD
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Aditya Surapaneni
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY
| | - Olive Tang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jinyu Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Science, University of Texas Health Science Center, Houston, TX
| | | | - Ryan T. Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Lynne E. Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Yujian Liang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rob van Dam
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC
| | - E. Shyong Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Morgan E. Grams
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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16
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He S, Lim GE. The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes. Adv Biol (Weinh) 2023; 7:e2200151. [PMID: 36398493 DOI: 10.1002/adbi.202200151] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/04/2022] [Indexed: 11/19/2022]
Abstract
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.
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Affiliation(s)
- Siyi He
- Department of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Edouard Montpetit Blvd, Montreal, Québec, H3T 1J4, Canada.,Cardiometabolic Axis, Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), 900 rue St Denis, Montreal, Québec, H2X 0A9, Canada
| | - Gareth E Lim
- Department of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Edouard Montpetit Blvd, Montreal, Québec, H3T 1J4, Canada.,Cardiometabolic Axis, Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), 900 rue St Denis, Montreal, Québec, H2X 0A9, Canada
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17
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Sargsyan A, Doridot L, Hannou SA, Tong W, Srinivasan H, Ivison R, Monn R, Kou HH, Haldeman JM, Arlotto M, White PJ, Grimsrud PA, Astapova I, Tsai LT, Herman MA. HGFAC is a ChREBP-regulated hepatokine that enhances glucose and lipid homeostasis. JCI Insight 2023; 8:e153740. [PMID: 36413406 PMCID: PMC9870088 DOI: 10.1172/jci.insight.153740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/17/2022] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Ashot Sargsyan
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Ludivine Doridot
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts, USA
| | - Sarah A. Hannou
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Wenxin Tong
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Harini Srinivasan
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Rachael Ivison
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts, USA
| | - Ruby Monn
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Henry H. Kou
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Jonathan M. Haldeman
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Michelle Arlotto
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
| | - Phillip J. White
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, and
- Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, USA
| | - Paul A. Grimsrud
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, and
| | - Inna Astapova
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, and
| | - Linus T. Tsai
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mark A. Herman
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, and
- Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, USA
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18
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Zhu Y, Yang X, Zu Y. Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure. Front Cell Dev Biol 2022; 10:1089915. [PMID: 36544902 PMCID: PMC9760806 DOI: 10.3389/fcell.2022.1089915] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 11/23/2022] [Indexed: 12/08/2022] Open
Abstract
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.
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Affiliation(s)
- Yihao Zhu
- International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, China,Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, China
| | - Xiaojing Yang
- International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, China,Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, China
| | - Yao Zu
- International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, China,Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, China,Marine Biomedical Science and Technology Innovation Platform of Lin-gang Special Area, Shanghai, China,*Correspondence: Yao Zu,
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19
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Belkadi A, Thareja G, Abbaszadeh F, Badii R, Fauman E, Albagha OM, Suhre K. Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population. CELL GENOMICS 2022; 3:100218. [PMID: 36777185 PMCID: PMC9903797 DOI: 10.1016/j.xgen.2022.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 07/16/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Aziz Belkadi
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | | | - Omar M.E. Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar,Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA,Corresponding author
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20
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Wen D, Zheng Z, Surapaneni A, Yu B, Zhou L, Zhou W, Xie D, Shou H, Avila-Pacheco J, Kalim S, He J, Hsu CY, Parsa A, Rao P, Sondheimer J, Townsend R, Waikar SS, Rebholz CM, Denburg MR, Kimmel PL, Vasan RS, Clish CB, Coresh J, Feldman HI, Grams ME, Rhee EP. Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study. JCI Insight 2022; 7:e161696. [PMID: 36048534 PMCID: PMC9714776 DOI: 10.1172/jci.insight.161696] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
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).
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Affiliation(s)
- Donghai Wen
- Nephrology Division and
- Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, Texas, USA
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wen Zhou
- Nephrology Division and
- Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Dawei Xie
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Chi-Yuan Hsu
- Division of Nephrology, University of California San Francisco School of Medicine, San Francisco, California, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Afshin Parsa
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, Maryland, USA
| | - Panduranga Rao
- Division of Nephrology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - James Sondheimer
- Division of Nephrology and Hypertension, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Raymond Townsend
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sushrut S. Waikar
- Section of Nephrology, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts, USA
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michelle R. Denburg
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Pediatric Nephrology, Children’s Hospital of Philadelphia, and
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, Maryland, USA
| | - Ramachandran S. Vasan
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
- Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Medicine, New York University, New York, New York, USA
| | - Eugene P. Rhee
- Nephrology Division and
- Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
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21
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Bomba L, Walter K, Guo Q, Surendran P, Kundu K, Nongmaithem S, Karim MA, Stewart ID, Langenberg C, Danesh J, Di Angelantonio E, Roberts DJ, Ouwehand WH, Dunham I, Butterworth AS, Soranzo N. Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites. Am J Hum Genet 2022; 109:1038-1054. [PMID: 35568032 PMCID: PMC9247822 DOI: 10.1016/j.ajhg.2022.04.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/13/2022] [Indexed: 12/11/2022] Open
Abstract
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.
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Affiliation(s)
- Lorenzo Bomba
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Klaudia Walter
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Qi Guo
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | - Kousik Kundu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | - Suraj Nongmaithem
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Mohd Anisul Karim
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK; Computational Medicine, Berlin Institute of Health at Charité - Utniversitätsmedizin Berlin, Berlin 10117, Germany
| | - John Danesh
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; Human Technopole, Palazzo Italia, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
| | - David J Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; NHS Blood and Transplant-Oxford Centre, Level 2, John Radcliffe Hospital, Oxford OX3 9BQ, UK; Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9BQ, UK
| | - Willem H Ouwehand
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK
| | | | - Ian Dunham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge CB2 0AW, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB2 0QQ, UK; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK; Human Technopole, Palazzo Italia, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy.
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22
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Gou W, Yue L, Tang XY, Wu YY, Cai X, Shuai M, Miao Z, Fu Y, Chen H, Jiang Z, Wang J, Tian Y, Xiao C, Xiang N, Wu Z, Chen YM, Guo T, Zheng JS. Circulating Proteome and Progression of Type 2 Diabetes. J Clin Endocrinol Metab 2022; 107:1616-1625. [PMID: 35184183 DOI: 10.1210/clinem/dgac098] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Indexed: 02/13/2023]
Abstract
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.
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Affiliation(s)
- Wanglong Gou
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Liang Yue
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xin-Yi Tang
- The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yan-Yan Wu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health; Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xue Cai
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Menglei Shuai
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Zelei Miao
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hao Chen
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
- Westlake Omics (Hangzhou) Biotechnology Co., Hangzhou, China
| | - Zengliang Jiang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jiali Wang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Yunyi Tian
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Congmei Xiao
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Nan Xiang
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zhen Wu
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, China
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health; Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Tiannan Guo
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Ju-Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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23
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Gadd DA, Hillary RF, McCartney DL, Zaghlool SB, Stevenson AJ, Cheng Y, Fawns-Ritchie C, Nangle C, Campbell A, Flaig R, Harris SE, Walker RM, Shi L, Tucker-Drob EM, Gieger C, Peters A, Waldenberger M, Graumann J, McRae AF, Deary IJ, Porteous DJ, Hayward C, Visscher PM, Cox SR, Evans KL, McIntosh AM, Suhre K, Marioni RE. Epigenetic scores for the circulating proteome as tools for disease prediction. eLife 2022; 11:e71802. [PMID: 35023833 PMCID: PMC8880990 DOI: 10.7554/elife.71802] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education CityDohaQatar
- Computer Engineering Department, Virginia TechBlacksburgUnited States
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Chloe Fawns-Ritchie
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
| | - Cliff Nangle
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Robin Flaig
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Sarah E Harris
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - Rosie M Walker
- Centre for Clinical Brain Sciences, Chancellor’s Building, University of EdinburghEdinburghUnited Kingdom
| | - Liu Shi
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at AustinAustinUnited States
- Population Research Center, The University of Texas at AustinAustinUnited States
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
- German Center for Diabetes Research (DZD)NeuherbergGermany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
- German Center for Diabetes Research (DZD)NeuherbergGermany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart AllianceMunichGermany
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff InstituteBad NauheimGermany
- German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute of Heart and Lung ResearchBad NauheimGermany
| | - Allan F McRae
- Institute for Molecular Bioscience, University of QueenslandBrisbaneAustralia
| | - Ian J Deary
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of QueenslandBrisbaneAustralia
| | - Simon R Cox
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
- Lothian Birth Cohorts, University of EdinburghEdinburghUnited Kingdom
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh HospitalEdinburghUnited Kingdom
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education CityDohaQatar
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
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24
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Cruz DE, Tahir UA, Hu J, Ngo D, Chen ZZ, Robbins JM, Katz D, Balasubramanian R, Peterson B, Deng S, Benson MD, Shi X, Dailey L, Gao Y, Correa A, Wang TJ, Clish CB, Rexrode KM, Wilson JG, Gerszten RE. Metabolomic Analysis of Coronary Heart Disease in an African American Cohort From the Jackson Heart Study. JAMA Cardiol 2021; 7:184-194. [PMID: 34851361 DOI: 10.1001/jamacardio.2021.4925] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Jie Hu
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Daniel Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Raji Balasubramanian
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Bennet Peterson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Lucas Dailey
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Yan Gao
- Department of Medicine, University of Mississippi Medical Center, Jackson
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson
| | - Thomas J Wang
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Kathryn M Rexrode
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts.,Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
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25
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Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, Marten J, Zahid S, Chaffin M, Liu Y, Abraham G, Ouwehand WH, Roberts DJ, Watkins NA, Drew BG, Calkin AC, Di Angelantonio E, Soranzo N, Burgess S, Chapman M, Kathiresan S, Khera AV, Danesh J, Butterworth AS, Inouye M. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat Metab 2021; 3:1476-1483. [PMID: 34750571 PMCID: PMC8574944 DOI: 10.1038/s42255-021-00478-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/14/2021] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Matthew Arnold
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Shu Mei Teo
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Sol Lim
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Petar Scepanovic
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Marten
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sohail Zahid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark Chaffin
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yingying Liu
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Willem H Ouwehand
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - David J Roberts
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford, UK
| | - Nicholas A Watkins
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
| | - Brian G Drew
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Anna C Calkin
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Centre for Health Data Science, Human Technopole, Milan, Italy
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Michael Chapman
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | | | - Amit V Khera
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia.
- The Alan Turing Institute, London, UK.
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