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Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.
Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, Pooley KA, Dennis J, Michailidou K, Turman C, Soucy P, Lemaçon A, Lush M, Tyrer JP, Ghoussaini M, Moradi Marjaneh M, Jiang X, Agata S, Aittomäki K, Alonso MR, Andrulis IL, Anton-Culver H, Antonenkova NN, Arason A, Arndt V, Aronson KJ, Arun BK, Auber B, Auer PL, Azzollini J, Balmaña J, Barkardottir RB, Barrowdale D, Beeghly-Fadiel A, Benitez J, Bermisheva M, Białkowska K, Blanco AM, Blomqvist C, Blot W, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Borg A, Bosse K, Brauch H, Brenner H, Briceno I, Brock IW, Brooks-Wilson A, Brüning T, Burwinkel B, Buys SS, Cai Q, Caldés T, Caligo MA, Camp NJ, Campbell I, Canzian F, Carroll JS, Carter BD, Castelao JE, Chiquette J, Christiansen H, Chung WK, Claes KBM, Clarke CL, GEMO Study Collaborators, EMBRACE Collaborators, Collée JM, Cornelissen S, Couch FJ, Cox A, Cross SS, Cybulski C, Czene K, Daly MB, de la Hoya M, Devilee P, Diez O, Ding YC, Dite GS, Domchek SM, Dörk T, Dos-Santos-Silva I, Droit A, Dubois S, Dumont M, Duran M, Durcan L, Dwek M, Eccles DM, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Floris G, Flyger H, Foretova L, Foulkes WD, Friedman E, Fritschi L, Frost D, Gabrielson M, Gago-Dominguez M, Gambino G, Ganz PA, Gapstur SM, Garber J, García-Sáenz JA, Gaudet MM, Georgoulias V, Giles GG, Glendon G, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Tibiletti MG, Greene MH, Grip M, Gronwald J, Grundy A, Guénel P, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hartikainen JM, Hartman M, He W, Healey CS, Heemskerk-Gerritsen BAM, Heyworth J, Hillemanns P, Hogervorst FBL, Hollestelle A, Hooning MJ, Hopper JL, Howell A, Huang G, Hulick PJ, Imyanitov EN, KConFab Investigators, HEBON Investigators, ABCTB Investigators, Isaacs C, Iwasaki M, Jager A, Jakimovska M, Jakubowska A, James PA, Janavicius R, Jankowitz RC, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kang D, Kapoor PM, Karlan BY, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Kirk J, Kitahara CM, Ko YD, Konstantopoulou I, Kosma VM, Koutros S, Kubelka-Sabit K, Kwong A, Kyriacou K, Laitman Y, Lambrechts D, Lee E, Leslie G, Lester J, Lesueur F, Lindblom A, Lo WY, Long J, Lophatananon A, Loud JT, Lubiński J, MacInnis RJ, Maishman T, Makalic E, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Matsuo K, Maurer T, Mavroudis D, Mayes R, McGuffog L, McLean C, Mebirouk N, Meindl A, Miller A, Miller N, Montagna M, Moreno F, Muir K, Mulligan AM, Muñoz-Garzon VM, Muranen TA, Narod SA, Nassir R, Nathanson KL, Neuhausen SL, Nevanlinna H, Neven P, Nielsen FC, Nikitina-Zake L, Norman A, Offit K, Olah E, Olopade OI, Olsson H, Orr N, Osorio A, Pankratz VS, Papp J, Park SK, Park-Simon TW, Parsons MT, Paul J, Pedersen IS, Peissel B, Peshkin B, Peterlongo P, Peto J, Plaseska-Karanfilska D, Prajzendanc K, Prentice R, Presneau N, Prokofyeva D, Pujana MA, Pylkäs K, Radice P, Ramus SJ, Rantala J, Rau-Murthy R, Rennert G, Risch HA, Robson M, Romero A, Rossing M, Saloustros E, Sánchez-Herrero E, Sandler DP, Santamariña M, Saunders C, Sawyer EJ, Scheuner MT, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schöttker B, Schürmann P, Scott C, Scott RJ, Senter L, Seynaeve CM, Shah M, Sharma P, Shen CY, Shu XO, Singer CF, Slavin TP, Smichkoska S, Southey MC, Spinelli JJ, Spurdle AB, Stone J, Stoppa-Lyonnet D, Sutter C, Swerdlow AJ, Tamimi RM, Tan YY, Tapper WJ, Taylor JA, Teixeira MR, Tengström M, Teo SH, Terry MB, Teulé A, Thomassen M, Thull DL, Tischkowitz M, Toland AE, Tollenaar RAEM, Tomlinson I, Torres D, Torres-Mejía G, Troester MA, Truong T, Tung N, Tzardi M, Ulmer HU, Vachon CM, van Asperen CJ, van der Kolk LE, van Rensburg EJ, Vega A, Viel A, Vijai J, Vogel MJ, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wildiers H, Winqvist R, Wolk A, Wu AH, Yannoukakos D, Zhang Y, Zheng W, Hunter D, Pharoah PDP, Chang-Claude J, García-Closas M, Schmidt MK, Milne RL, Kristensen VN, French JD, Edwards SL, Antoniou AC, Chenevix-Trench G, Simard J, Easton DF, Kraft P, Dunning AM
(2020) Nat Genet 52: 56-73
MeSH Terms: Bayes Theorem, Biomarkers, Tumor, Breast Neoplasms, Chromosome Mapping, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, Risk Factors
Show Abstract · Added March 3, 2020
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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13 MeSH Terms
Local, nonlinear effects of cGMP and Ca2+ reduce single photon response variability in retinal rods.
Caruso G, Gurevich VV, Klaus C, Hamm H, Makino CL, DiBenedetto E
(2019) PLoS One 14: e0225948
MeSH Terms: Algorithms, Animals, Biomarkers, Calcium, Cyclic GMP, Mice, Models, Biological, Photons, Retinal Rod Photoreceptor Cells, Rod Cell Outer Segment, Signal Transduction
Show Abstract · Added March 18, 2020
The single photon response (SPR) in vertebrate photoreceptors is inherently variable due to several stochastic events in the phototransduction cascade, the main one being the shutoff of photoactivated rhodopsin. Deactivation is driven by a random number of steps, each of random duration with final quenching occurring after a random delay. Nevertheless, variability of the SPR is relatively low, making the signal highly reliable. Several biophysical and mathematical mechanisms contributing to variability suppression have been examined by the authors. Here we investigate the contribution of local depletion of cGMP by PDE*, the non linear dependence of the photocurrent on cGMP, Ca2+ feedback by making use of a fully space resolved (FSR) mathematical model, applied to two species (mouse and salamander), by varying the cGMP diffusion rate severalfold and rod outer segment diameter by an order of magnitude, and by introducing new, more refined, and time dependent variability functionals. Globally well stirred (GWS) models, and to a lesser extent transversally well stirred models (TWS), underestimate the role of nonlinearities and local cGMP depletion in quenching the variability of the circulating current with respect to fully space resolved models (FSR). These distortions minimize the true extent to which SPR is stabilized by locality in cGMP depletion, nonlinear effects linking cGMP to current, and Ca2+ feedback arising from the physical separation of E* from the ion channels located on the outer shell, and the diffusion of these second messengers in the cytoplasm.
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11 MeSH Terms
Central EP3 (E Prostanoid 3) Receptors Mediate Salt-Sensitive Hypertension and Immune Activation.
Xiao L, Itani HA, do Carmo LS, Carver LS, Breyer RM, Harrison DG
(2019) Hypertension 74: 1507-1515
MeSH Terms: Adaptive Immunity, Analysis of Variance, Animals, Biomarkers, Biopsy, Needle, Brain, Dinoprostone, Disease Models, Animal, Female, Flow Cytometry, Hypertension, Immunohistochemistry, Male, Mice, Mice, Inbred C57BL, NG-Nitroarginine Methyl Ester, Random Allocation, Real-Time Polymerase Chain Reaction, Receptors, Prostaglandin E, EP3 Subtype, Sodium, Dietary
Show Abstract · Added December 3, 2019
We recently identified a pathway underlying immune activation in hypertension. Proteins oxidatively modified by reactive isoLG (isolevuglandin) accumulate in dendritic cells (DCs). PGE (Prostaglandin E2) has been implicated in the inflammation associated with hypertension. We hypothesized that PGE via its EP (E prostanoid) 3 receptor contributes to DC activation in hypertension. EP3 mice and wild-type littermates were exposed to sequential hypertensive stimuli involving an initial 2-week exposure to the nitric oxide synthase inhibitor N-nitro-L-arginine methyl ester hydrochloride in drinking water, followed by a 2-week washout period, and a subsequent 4% high-salt diet for 3 weeks. In wild-type mice, this protocol increased systolic pressure from 123±2 to 148±8 mm Hg (<0.05). This was associated with marked renal inflammation and a striking accumulation of isoLG adducts in splenic DCs. However, the increases in blood pressure, renal T-cell infiltration, and DC isoLG formation were completely prevented in EP3 mice. Similar protective effects were also observed in wild-type mice that received intracerebroventricular injection of a lentiviral vector encoding shRNA targeting the EP3 receptor. Further, in vitro experiments indicated that PGE also acts directly on DCs via its EP1 receptors to stimulate intracellular isoLG formation. Together, these findings provide new insight into how EP receptors in both the central nervous system and peripherally on DCs promote inflammation in salt-induced hypertension.
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Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.
Schmidt AF, Holmes MV, Preiss D, Swerdlow DI, Denaxas S, Fatemifar G, Faraway R, Finan C, Valentine D, Fairhurst-Hunter Z, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh K, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Lill CM, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott RA, Luan J, Bobak M, Malyutina S, Pająk A, Kubinova R, Tamosiunas A, Pikhart H, Grarup N, Pedersen O, Hansen T, Linneberg A, Jess T, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Lester KH, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Scott R, Schofield P, O'Donnell M, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Lifelines Cohort authors, Christen T, Mook-Kanamori DO, ICBP Consortium, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Dörr M, Lerch MM, Völker U, Völzke H, Ward J, Pell JP, Meade T, Christophersen IE, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Roussel R, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Hopewell JC, Seshadri S, METASTROKE Consortium of the ISGC, Dale C, Costa RPE, Ridker PM, Chasman DI, Reiner AP, Ritchie MD, Lange LA, Cornish AJ, Dobbins SE, Hemminki K, Kinnersley B, Sanson M, Labreche K, Simon M, Bondy M, Law P, Speedy H, Allan J, Li N, Went M, Weinhold N, Morgan G, Sonneveld P, Nilsson B, Goldschmidt H, Sud A, Engert A, Hansson M, Hemingway H, Asselbergs FW, Patel RS, Keating BJ, Sattar N, Houlston R, Casas JP, Hingorani AD
(2019) BMC Cardiovasc Disord 19: 240
MeSH Terms: Anticholesteremic Agents, Biomarkers, Brain Ischemia, Cholesterol, LDL, Down-Regulation, Dyslipidemias, Genome-Wide Association Study, Humans, Myocardial Infarction, Polymorphism, Single Nucleotide, Proprotein Convertase 9, Randomized Controlled Trials as Topic, Risk Assessment, Risk Factors, Serine Proteinase Inhibitors, Stroke, Treatment Outcome
Show Abstract · Added March 24, 2020
BACKGROUND - We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.
METHODS - Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration.
RESULTS - The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable.
CONCLUSIONS - Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.
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17 MeSH Terms
Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program.
Chen ZZ, Liu J, Morningstar J, Heckman-Stoddard BM, Lee CG, Dagogo-Jack S, Ferguson JF, Hamman RF, Knowler WC, Mather KJ, Perreault L, Florez JC, Wang TJ, Clish C, Temprosa M, Gerszten RE, Diabetes Prevention Program Research Group
(2019) Diabetes 68: 2337-2349
MeSH Terms: Adult, Aged, Biomarkers, Cytosine, Diabetes Mellitus, Type 2, Female, Humans, Incidence, Life Style, Male, Metabolome, Middle Aged, Risk Factors
Show Abstract · Added March 3, 2020
Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.
© 2019 by the American Diabetes Association.
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13 MeSH Terms
Fetal exposure to maternal inflammation interrupts murine intestinal development and increases susceptibility to neonatal intestinal injury.
Elgin TG, Fricke EM, Gong H, Reese J, Mills DA, Kalantera KM, Underwood MA, McElroy SJ
(2019) Dis Model Mech 12:
MeSH Terms: Animals, Animals, Newborn, Biomarkers, Cecum, Cytokines, Disease Susceptibility, Female, Fetus, Goblet Cells, Inflammation, Intestine, Small, Lipopolysaccharides, Mice, Inbred C57BL, Microbiota, Paneth Cells, Pregnancy
Show Abstract · Added July 28, 2020
Fetal exposure to chorioamnionitis can impact the outcomes of the developing fetus both at the time of birth and in the subsequent neonatal period. Infants exposed to chorioamnionitis have a higher incidence of gastrointestinal (GI) pathology, including necrotizing enterocolitis (NEC); however, the mechanism remains undefined. To simulate the fetal exposure to maternal inflammation (FEMI) induced by chorioamnionitis, pregnant mice (C57BL/6J, , or ) were injected intraperitoneally on embryonic day (E)15.5 with lipopolysaccharide (LPS; 100 µg/kg body weight). Pups were delivered at term, and reared to postnatal day (P)0, P7, P14, P28 or P56. Serum and intestinal tissue samples were collected to quantify growth, inflammatory markers, histological intestinal injury, and goblet and Paneth cells. To determine whether FEMI increased subsequent susceptibility to intestinal injury, a secondary dose of LPS (100 µg/kg body weight) was given on P5, prior to tissue harvesting on P7. FEMI had no effect on growth of the offspring or their small intestine. FEMI significantly decreased both goblet and Paneth cell numbers while simultaneously increasing serum levels of IL-1β, IL-10, KC/GRO (CXCL1 and CXCL2), TNF and IL-6. These alterations were IL-6 dependent and, importantly, increased susceptibility to LPS-induced intestinal injury later in life. Our data show that FEMI impairs normal intestinal development by decreasing components of innate immunity and simultaneously increasing markers of inflammation. These changes increase susceptibility to intestinal injury later in life and provide novel mechanistic data to potentially explain why preterm infants exposed to chorioamnionitis prior to birth have a higher incidence of NEC and other GI disorders.
© 2019. Published by The Company of Biologists Ltd.
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MeSH Terms
Sex differences in anthracycline-induced cardiotoxicity: the benefits of estrogens.
Cadeddu Dessalvi C, Pepe A, Penna C, Gimelli A, Madonna R, Mele D, Monte I, Novo G, Nugara C, Zito C, Moslehi JJ, de Boer RA, Lyon AR, Tocchetti CG, Mercuro G
(2019) Heart Fail Rev 24: 915-925
MeSH Terms: Anthracyclines, Biomarkers, Cardiotonic Agents, Cardiotoxicity, Echocardiography, Female, Gonadal Steroid Hormones, Heart, Heart Failure, Humans, Magnetic Resonance Spectroscopy, Male, Mitochondria, Nuclear Medicine, Oxidative Stress, Prognosis, Reperfusion Injury, Risk Factors, Sex Characteristics
Show Abstract · Added November 12, 2019
Anthracyclines are the cornerstone for many oncologic treatments, but their cardiotoxicity has been recognized for several decades. Female subjects, especially before puberty and adolescence, or after menopause, seem to be more at increased risk, with the prognostic impact of this sex issue being less consistent compared to other cardiovascular risk factors. Several studies imply that sex differences could depend on the lack of the protective effect of sex hormones against the anthracycline-initiated damage in cardiac cells, or on differential mitochondria-related oxidative gene expression. This is also reflected by the results obtained with different diagnostic methods, such as cardiovascular biomarkers and imaging techniques (echocardiography, magnetic resonance, and nuclear medicine) in the diagnosis and monitoring of cardiotoxicity, confirming that sex differences exist. The same is true about protective strategies from anthracycline cardiotoxicity. Indeed, first studied to withstand oxidative damage in response to ischemia/reperfusion (I/R) injury, cardioprotection has different outcomes in men and women. A number of studies assessed the differences in I/R response between male and female hearts, with oxidative stress and apoptosis being shared mechanisms between the I/R and anthracyclines heart damage. Sex hormones can modulate these mechanisms, thus confirming their importance in the pathophysiology in cardioprotection not only from the ischemia/reperfusion damage, but also from anthracyclines, fueling further cardio-oncologic research on the topic.
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19 MeSH Terms
Assessing cardiac safety in oncology drug development.
Seltzer JH, Gintant G, Amiri-Kordestani L, Singer J, Koplowitz LP, Moslehi JJ, Barac A, Yu AF
(2019) Am Heart J 214: 125-133
MeSH Terms: Antineoplastic Agents, Antineoplastic Agents, Immunological, Biomarkers, Cardiologists, Cardiovascular Diseases, Cell Line, Tumor, Clinical Trials as Topic, Data Collection, Drug Development, Drug Screening Assays, Antitumor, Heart, Humans, Immunotherapy, Medical Oncology, Research Design, Trastuzumab
Added November 12, 2019
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16 MeSH Terms
Plasma apoM and S1P levels are inversely associated with mortality in African Americans with type 2 diabetes mellitus.
Liu M, Frej C, Langefeld CD, Divers J, Bowden DW, Carr JJ, Gebre AK, Xu J, Larsson B, Dahlbäck B, Freedman BI, Parks JS
(2019) J Lipid Res 60: 1425-1431
MeSH Terms: African Americans, Apolipoproteins M, Biomarkers, Diabetes Mellitus, Type 2, Disease-Free Survival, Female, Humans, Lysophospholipids, Male, Middle Aged, Sphingosine, Survival Rate
Show Abstract · Added January 10, 2020
apoM is a minor HDL apolipoprotein and carrier for sphingosine-1-phosphate (S1P). HDL apoM and S1P concentrations are inversely associated with atherosclerosis progression in rodents. We evaluated associations between plasma concentrations of S1P, plasma concentrations of apoM, and HDL apoM levels with prevalent subclinical atherosclerosis and mortality in the African American-Diabetes Heart Study participants (N = 545). Associations between plasma S1P, plasma apoM, and HDL apoM with subclinical atherosclerosis and mortality were assessed using multivariate parametric, nonparametric, and Cox proportional hazards models. At baseline, participants' median (25th percentile, 75th percentile) age was 55 (49, 62) years old and their coronary artery calcium (CAC) mass score was 26.5 (0.0, 346.5). Plasma S1P, plasma apoM, and HDL apoM were not associated with CAC. After 64 (57.6, 70.3) months of follow-up, 81 deaths were recorded. Higher concentrations of plasma S1P [odds ratio (OR) = 0.14, = 0.01] and plasma apoM (OR = 0.10, = 0.02), but not HDL apoM ( = 0.89), were associated with lower mortality after adjusting for age, sex, statin use, CAC, kidney function, and albuminuria. We conclude that plasma S1P and apoM concentrations are inversely and independently associated with mortality, but not CAC, in African Americans with type 2 diabetes after accounting for conventional risk factors.
Copyright © 2019 Liu et al.
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12 MeSH Terms
Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics.
Wells QS, Gupta DK, Smith JG, Collins SP, Storrow AB, Ferguson J, Smith ML, Pulley JM, Collier S, Wang X, Roden DM, Gerszten RE, Wang TJ
(2019) J Am Coll Cardiol 73: 2195-2205
MeSH Terms: Academic Medical Centers, Acceleration, Aged, Automation, Biological Specimen Banks, Biomarkers, Cohort Studies, Electronic Health Records, Female, Heart Failure, Humans, Male, Middle Aged, Proportional Hazards Models, Prospective Studies, Proteomics, Reproducibility of Results, Risk Assessment, Sensitivity and Specificity, Thrombospondins
Show Abstract · Added March 3, 2020
BACKGROUND - Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition.
OBJECTIVES - The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics.
METHODS - Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts.
RESULTS - In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both).
CONCLUSIONS - A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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20 MeSH Terms