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Selected Publications
Selected Publications
Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods
Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods
Daniel H. Katz, Jeremy M. Robbins, Shuliang Deng, Usman A. Tahir, Alexander G. Bick, Akhil Pampana, Zhi Yu, Debby Ngo, Mark D. Benson, Zsu-Zsu Chen, Daniel E. Cruz, Dongxiao Shen, Yan Gao, Claude Bouchard, Mark A. Sarzynski, Adolfo Correa, Pradeep Natarajan, James G. Wilson1, Robert E. Gerszten
High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.
https://doi.org/10.1126/sciadv.abm5164
High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.
Fig. 3. Spearman correlations between Olink and Soma1.3K reagents, which measure the same protein. (A) K-means clustering of correlations into three levels of correlation. (B) Colored bars indicate number of proteins on each platform in that correlation bin that have cis pQTLs, defined as a variant-protein association with P < 1 × 10–5 within 1 Mb of the transcription start site for the cognate gene.
Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease
Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease
Daniel H. Katz, Usman A. Tahir, Alexander G. Bick, Akhil Pampana, Debby Ngo, Mark D. Benson, Zhi Yu, Jeremy M. Robbins, Zsu-Zsu Chen, Daniel E. Cruz, Shuliang Deng, Laurie Farrell, Sumita Sinha, Alec A. Schmaier, Dongxiao Shen, Yan Gao, Michael E. Hall, Adolfo Correa, Russell P. Tracy, Peter Durda, Kent D. Taylor, Yongmei Liu, W. Craig Johnson, Xiuqing Guo, Jie Yao, Yii-Der Ida Chen, Ani W. Manichaikul, Deepti Jain, Claude Bouchard, Mark A. Sarzynski, Stephen S. Rich, Jerome I. Rotter, Thomas J. Wang, James G. Wilson, Pradeep Natarajan and Robert E. Gerszten
Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants.Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics).Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P=3.27×10-30) and MMP-3 (β=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure.Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.
https://doi.org/10.1161/CIRCULATIONAHA.121.055117
Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants.Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics).Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P=3.27×10-30) and MMP-3 (β=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure.Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.
Figure 1. Chromosomal locations of 569 protein quantitative trait loci. The locations of the protein quantitative trait loci (pQTLs) are indicated on the x axis, whereas location of the gene encoding that protein is indicated on the y axis. Locations of genes associated with many proteins are indicated above the plot. Cis associations align along the identity line, whereas trans associations are off the line. SNP indicates single nucleotide polymorphism.
Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults
Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults
Daniel H. Katz, Usman A. Tahir, Debby Ngo, Mark D. Benson, Yan Gao, Xu Shi, Matthew Nayor, Michelle J. Keyes, Martin G. Larson, Michael E. Hall, Adolfo Correa, Sumita Sinha, Dongxiao Shen, Matthew Herzig, Qiong Yang, Jeremy M. Robbins, Zsu-Zsu Chen, Daniel E. Cruz, Bennet Peterson, Ramachandran S. Vasan, Thomas J. Wang, James G. Wilson, Robert E. Gerszten
Background: Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand the underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a White cohort.Methods: We measured 1305 plasma proteins using the SomaScan platform in 1772 Black participants (mean age, 56 years; 62% women) in JHS (Jackson Heart Study) with LV mass assessed by 2-dimensional echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in White participants from FHS (Framingham Heart Study; mean age, 54 years; 56% women).Results: In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04; P=2×10−8; hazard ratio, 1.48; P=0.0001). The strongest association with LV mass was novel: LKHA4 (leukotriene-A4 hydrolase; β=0.05; P=5×10−15). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) showed a novel association with incident HF (hazard ratio, 1.32; P=0.0002). While established biomarkers such as cystatin C and NT-proBNP showed consistent associations in Black and White individuals, LKHA4 and fractalkine were significantly different between the two groups.Conclusions: We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.
https://doi.org/10.1161/CIRCGEN.120.003191
Background: Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand the underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a White cohort.Methods: We measured 1305 plasma proteins using the SomaScan platform in 1772 Black participants (mean age, 56 years; 62% women) in JHS (Jackson Heart Study) with LV mass assessed by 2-dimensional echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in White participants from FHS (Framingham Heart Study; mean age, 54 years; 56% women).Results: In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04; P=2×10−8; hazard ratio, 1.48; P=0.0001). The strongest association with LV mass was novel: LKHA4 (leukotriene-A4 hydrolase; β=0.05; P=5×10−15). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) showed a novel association with incident HF (hazard ratio, 1.32; P=0.0002). While established biomarkers such as cystatin C and NT-proBNP showed consistent associations in Black and White individuals, LKHA4 and fractalkine were significantly different between the two groups.Conclusions: We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.
Figure 2. Volcano plot of proteins associated with left ventricular mass in the Jackson Heart Study. β-Estimates and their P are shown for 1305 proteins evaluated by multivariable adjusted linear models predicting ln(left ventricular mass).
Mining a GWAS of Severe Covid-19
Mining a GWAS of Severe Covid-19
Daniel H. Katz, James G. Wilson, Robert E. Gerszten
The Severe Covid-19 GWAS Group recently identified variants in two loci that increase the risk of respiratory failure in patients with Covid-19. To determine the mechanisms through which these loci might confer risk, we combined genetic data and measurements of 1305 circulating proteins (with the use of SOMAScan) from 4856 Black and White participants in three studies: the Jackson Heart Study, the Framingham Heart Study, and the Malmö Diet and Cancer Study.
Strikingly, the specific Covid-19 risk variant rs657152-A (at the ABO locus) was strongly associated with increased levels of the CD209 antigen (also known as dendritic cell [DC]–specific-intracellular adhesion molecule 3 [ICAM-3]–grabbing nonintegrin [DC-SIGN] (see Table S1 in the Supplementary Appendix, available with the full text of this letter at NEJM.org). This dendritic cell-surface protein has been found to facilitate infection by SARS-CoV-1 and other viruses and can be shed into the bloodstream. Cohort differences emerged. In the Jackson Heart Study, we found associations between rs657152-A and multiple inflammatory proteins, whereas in the Framingham and Malmö studies, we found that rs657152-A was associated with levels of adhesion proteins. We also observed that variants at the 3p21.31 locus were associated with levels of CXCL16, an inflammatory chemokine implicated in alveolitis and atherogenesis (see Table S2). These analyses suggest potential mechanisms for genetic risk in patients with Covid-19, including proteins involved in viral binding, endothelial function, and atherothrombosis.
https://www.nejm.org/doi/full/10.1056/NEJMc2025747
The Severe Covid-19 GWAS Group recently identified variants in two loci that increase the risk of respiratory failure in patients with Covid-19. To determine the mechanisms through which these loci might confer risk, we combined genetic data and measurements of 1305 circulating proteins (with the use of SOMAScan) from 4856 Black and White participants in three studies: the Jackson Heart Study, the Framingham Heart Study, and the Malmö Diet and Cancer Study.
Strikingly, the specific Covid-19 risk variant rs657152-A (at the ABO locus) was strongly associated with increased levels of the CD209 antigen (also known as dendritic cell [DC]–specific-intracellular adhesion molecule 3 [ICAM-3]–grabbing nonintegrin [DC-SIGN] (see Table S1 in the Supplementary Appendix, available with the full text of this letter at NEJM.org). This dendritic cell-surface protein has been found to facilitate infection by SARS-CoV-1 and other viruses and can be shed into the bloodstream. Cohort differences emerged. In the Jackson Heart Study, we found associations between rs657152-A and multiple inflammatory proteins, whereas in the Framingham and Malmö studies, we found that rs657152-A was associated with levels of adhesion proteins. We also observed that variants at the 3p21.31 locus were associated with levels of CXCL16, an inflammatory chemokine implicated in alveolitis and atherogenesis (see Table S2). These analyses suggest potential mechanisms for genetic risk in patients with Covid-19, including proteins involved in viral binding, endothelial function, and atherothrombosis.
Image from: Hofmann and Pöhlmann, Cell Host Microb, 2011.
Phenomapping for novel classification of heart failure with preserved ejection fraction
Phenomapping for novel classification of heart failure with preserved ejection fraction
Sanjiv J. Shah, Daniel H. Katz, Senthil Selvaraj, Michael A. Burke, Clyde W. Yancy, Mihai Gheorghiade, Robert O. Bonow, Chiang-Ching Huang, and Rahul C. Deo
Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories.Methods and results: We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107).Conclusions: Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.
https://doi.org/10.1161/CIRCULATIONAHA.114.010637
Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories.Methods and results: We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107).Conclusions: Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.
How to Manage Temporary Mechanical Circulatory Support Devices in the Critical Care Setting: Translating Physiology to the Bedside
How to Manage Temporary Mechanical Circulatory Support Devices in the Critical Care Setting: Translating Physiology to the Bedside
Prashant Rao, Daniel Katz, Michinari Hieda, Marwa Sabe
The incidence of cardiogenic shock and the utilization of mechanical circulatory support devices are increasing in the US. In this review we discuss the pathophysiology of cardiogenic shock through basic hemodynamic and myocardial energetic principles. We also explore the commonly used platforms for temporary mechanical circulatory support, their advantages, disadvantages and practical considerations relating to implementation and management. It is through the translation of underlying physiological principles that we can attempt to maximize the clinical utility of circulatory support devices and improve outcomes in cardiogenic shock.
https://doi.org/10.1016/j.hfc.2020.03.001
The incidence of cardiogenic shock and the utilization of mechanical circulatory support devices are increasing in the US. In this review we discuss the pathophysiology of cardiogenic shock through basic hemodynamic and myocardial energetic principles. We also explore the commonly used platforms for temporary mechanical circulatory support, their advantages, disadvantages and practical considerations relating to implementation and management. It is through the translation of underlying physiological principles that we can attempt to maximize the clinical utility of circulatory support devices and improve outcomes in cardiogenic shock.
Chen ZZ, Pacheco JA, Gao Y, Deng S, Peterson B, Shi X, Zheng S, Tahir UA, Katz DH, Cruz DE, Ngo D, Benson MD, Robbins JM, Guo X, Del Rocio Sevilla Gonzalez M, Manning A, Correa A, Meigs JB, Taylor KD, Rich SS, Goodarzi MO, Rotter JI, Wilson JG, Clish CB, Gerszten RE. Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans. Diabetes. 2022 Aug 23;. doi: 10.2337/db22-0033. [Epub ahead of print] PubMed PMID: 35998269.
Tahir UA*, Katz DH*, Avila-Pachecho J, Bick AG, Pampana A, Robbins JM, Yu Z, Chen ZZ, Benson MD, Cruz DE, Ngo D, Deng S, Shi X, Zheng S, Eisman AS, Farrell L, Hall ME, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YI, Manichaikul AW, Ruberg FL, Blaner WS, Jain D, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Natarajan P, Gerszten RE. Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals. Nat Commun. 2022 Aug 22;13(1):4923. doi: 10.1038/s41467-022-32275-3. PubMed PMID: 35995766; PubMed Central PMCID: PMC9395431. *Equal contribution
Katz DH, Robbins JM, Deng S, Tahir UA, Bick AG, Pampana A, Yu Z, Ngo D, Benson MD, Chen ZZ, Cruz DE, Shen D, Gao Y, Bouchard C, Sarzynski MA, Correa A, Natarajan P, Wilson JG, Gerszten RE. Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. Sci Adv. 2022 Aug 19;8(33):eabm5164. doi: 10.1126/sciadv.abm5164. Epub 2022 Aug 19. PubMed PMID: 35984888; PubMed Central PMCID: PMC9390994.
Austin TR, McHugh CP, Brody JA, Bis JC, Sitlani CM, Bartz TM, Biggs ML, Bansal N, Buzkova P, Carr SA, deFilippi CR, Elkind MSV, Fink HA, Floyd JS, Fohner AE, Gerszten RE, Heckbert SR, Katz DH, Kizer JR, Lemaitre RN, Longstreth WT, McKnight B, Mei H, Mukamal KJ, Newman AB, Ngo D, Odden MC, Vasan RS, Shojaie A, Simon N, Smith GD, Davies NM, Siscovick DS, Sotoodehnia N, Tracy RP, Wiggins KL, Zheng J, Psaty BM. Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing. Eur J Epidemiol. 2022 Jul;37(7):755-765. doi: 10.1007/s10654-022-00888-z. Epub 2022 Jul 5. PubMed PMID: 35790642; PubMed Central PMCID: PMC9255954.
Katz DH, Thompson AD. Proteomics in Heart Failure: From Benchtop to Bedside. J Card Fail. 2022 Apr;28(4):601-603. doi: 10.1016/j.cardfail.2021.12.003. Epub 2021 Dec 18. PubMed PMID: 34933100.
Katz DH*, Tahir UA*, Bick AG, Pampana A, Ngo D, Benson MD, Yu Z, Robbins JM, Chen ZZ, Cruz DE, Deng S, Farrell L, Sinha S, Schmaier AA, Shen D, Gao Y, Hall ME, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Ida Chen YD, Manichaikul AW, Jain D, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Natarajan P, Gerszten RE. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease. Circulation. 2022 Feb;145(5):357-370. doi: 10.1161/CIRCULATIONAHA.121.055117. Epub 2021 Nov 24. PubMed PMID: 34814699; PubMed Central PMCID: PMC9158509. *Equal contribution
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. 2022 Feb 1;7(2):184-194. doi: 10.1001/jamacardio.2021.4925. PubMed PMID: 34851361; PubMed Central PMCID: PMC8637385.
Katz DH*, Tahir UA*, Ngo D, Benson MD, Gao Y, Shi X, Nayor M, Keyes MJ, Larson MG, Hall ME, Correa A, Sinha S, Shen D, Herzig M, Yang Q, Robbins JM, Chen ZZ, Cruz DE, Peterson B, Vasan RS, Wang TJ, Wilson JG, Gerszten RE. Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults. Circ Genom Precis Med. 2021 Jun;14(3):e003191. doi: 10.1161/CIRCGEN.120.003191. Epub 2021 May 21. PubMed PMID: 34019435; NIHMSID:NIHMS1703704. *Equal contribution
Robbins JM, Peterson B, Schranner D, Tahir UA, Rienmüller T, Deng S, Keyes MJ, Katz DH, Beltran PMJ, Barber JL, Baumgartner C, Carr SA, Ghosh S, Shen C, Jennings LL, Ross R, Sarzynski MA, Bouchard C, Gerszten RE. Human plasma proteomic profiles indicative of cardiorespiratory fitness. Nat Metab. 2021 Jun;3(6):786-797. doi: 10.1038/s42255-021-00400-z. Epub 2021 May 27. PubMed PMID: 34045743.
Rebholz CM, Gao Y, Talegawkar S, Tucker KL, Colantonio LD, Muntner P, Ngo D, Chen ZZ, Cruz D, Katz DH, Tahir UA, Clish C, Gerszten RE, Wilson JG. Metabolomic Markers of Southern Dietary Patterns in the Jackson Heart Study. Mol Nutr Food Res. 2021 Apr;65(8):e2000796. doi: 10.1002/mnfr.202000796. Epub 2021 Mar 11. PubMed PMID: 33629508.
Ngo D, Benson MD, Long JZ, Chen ZZ, Wang R, Nath AK, Keyes MJ, Shen D, Sinha S, Kuhn E, Morningstar JE, Shi X, Peterson BD, Chan C, Katz DH, Tahir UA, Farrell LA, Melander O, Mosley JD, Carr SA, Vasan RS, Larson MG, Smith JG, Wang TJ, Yang Q, Gerszten RE. Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI Insight. 2021 Mar 8;6(5). doi: 10.1172/jci.insight.144392. PubMed PMID: 33591955.
Tahir UA*, Katz DH*, Zhao T, Ngo D, Cruz DE, Robbins JM, Chen ZZ, Peterson B, Benson MD, Shi X, Dailey L, Andersson C, Vasan RS, Gao Y, Shen C, Correa A, Hall ME, Wang TJ, Clish CB, Wilson JG, Gerszten RE. Metabolomic Profiles and Heart Failure Risk in Black Adults: Insights From the Jackson Heart Study. Circ Heart Fail. 2021 Jan;14(1):e007275. doi: 10.1161/CIRCHEARTFAILURE.120.007275. Epub 2021 Jan 19. PubMed PMID: 33464957. *Equal contribution
Katz DH, Wilson JG, Gerszten RE. Mining a GWAS of Severe Covid-19. N Engl J Med. 2020 Dec 24;383(26):2589. doi: 10.1056/NEJMc2025747. Epub 2020 Nov 24. PubMed PMID: 33289972.
Ngo D, Wen D, Gao Y, Keyes MJ, Drury ER, Katz DH, Benson MD, Sinha S, Shen D, Farrell LA, Peterson BD, Friedman DJ, Elmariah S, Young BA, Smith JG, Yang Q, Vasan RS, Larson MG, Correa A, Humphreys BD, Wang TJ, Pollak MR, Wilson JG, Gerszten RE, Rhee EP. Circulating testican-2 is a podocyte-derived marker of kidney health. Proc Natl Acad Sci U S A. 2020 Oct 6;117(40):25026-25035. doi: 10.1073/pnas.2009606117. Epub 2020 Sep 21. PubMed PMID: 32958645.
Rao P, Katz D, Hieda M, Sabe M. How to Manage Temporary Mechanical Circulatory Support Devices in the Critical Care Setting: Translating Physiology to the Bedside. Heart Fail Clin. 2020 Jul;16(3):283-293. doi: 10.1016/j.hfc.2020.03.001. Review. PubMed PMID: 32503752.
Katz D, Gavin MC. Stable Ischemic Heart Disease. Ann Intern Med. 2019 Aug 6;171(3):ITC17-ITC32. doi: 10.7326/AITC201908060. PubMed PMID: 31382288.
Katz DH, Gerszten RE. A Role for Branched-Chain Amino Acids in the Pathophysiology of Diabetes: Using Data to Guide Discovery. Clin Chem. 2018 Aug;64(8):1250-1251. doi: 10.1373/clinchem.2017.273516. Epub 2018 Mar 5. Review. PubMed PMID: 29506975.
Katz DH, Deo RC, Aguilar FG, Selvaraj S, Martinez EE, Beussink-Nelson L, Kim KA, Peng J, Irvin MR, Tiwari H, Rao DC, Arnett DK, Shah SJ. Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction. J Cardiovasc Transl Res. 2017 Jun;10(3):275-284. doi: 10.1007/s12265-017-9739-z. Epub 2017 Mar 3. PubMed PMID: 28258421.
Aguilar FG, Selvaraj S, Martinez EE, Katz DH, Beussink L, Kim KY, Ping J, Rasmussen-Torvik L, Goyal A, Sha J, Irvin MR, Arnett DK, Shah SJ. Archeological Echocardiography: Digitization and Speckle Tracking Analysis of Archival Echocardiograms in the HyperGEN Study. Echocardiography. 2016 Mar;33(3):386-97. doi: 10.1111/echo.13095. Epub 2015 Nov 3. PubMed PMID: 26525308; PubMed Central PMCID: PMC4775325.
Klein DA, Katz DH, Beussink-Nelson L, Sanchez CL, Strzelczyk TA, Shah SJ. Association of Chronic Kidney Disease With Chronotropic Incompetence in Heart Failure With Preserved Ejection Fraction. Am J Cardiol. 2015 Oct 1;116(7):1093-100. doi: 10.1016/j.amjcard.2015.06.038. Epub 2015 Jul 21. PubMed PMID: 26260398; PubMed Central PMCID: PMC4567946.
Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, Bonow RO, Huang CC, Deo RC. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015 Jan 20;131(3):269-79. doi: 10.1161/CIRCULATIONAHA.114.010637. Epub 2014 Nov 14. PubMed PMID: 25398313; PubMed Central PMCID: PMC4302027.
Katz DH, Burns JA, Aguilar FG, Beussink L, Shah SJ. Albuminuria is independently associated with cardiac remodeling, abnormal right and left ventricular function, and worse outcomes in heart failure with preserved ejection fraction. JACC Heart Fail. 2014 Dec;2(6):586-96. doi: 10.1016/j.jchf.2014.05.016. Epub 2014 Oct 1. PubMed PMID: 25282032; PubMed Central PMCID: PMC4256131.
Katz DH, Intwala SS, Stone NJ. Addressing statin adverse effects in the clinic: the 5 Ms. J Cardiovasc Pharmacol Ther.2014 Nov;19(6):533-42. doi: 10.1177/1074248414529622. Epub 2014 Apr 25. Review. PubMed PMID: 24770611.
Shah SJ, Katz DH, Deo RC. Phenotypic spectrum of heart failure with preserved ejection fraction. Heart Fail Clin. 2014 Jul;10(3):407-18. doi: 10.1016/j.hfc.2014.04.008. Epub 2014 May 22. Review. PubMed PMID: 24975905; PubMed Central PMCID: PMC4076705.
Stone NJ, Intwala S, Katz D. Response by Neil J. Stone, Sunny Intwala, and Dan Katz. J Am Geriatr Soc. 2014 May;62(5):947-8. doi: 10.1111/jgs.12788_3. Epub 2014 May 6. PubMed PMID: 24801252.
Stone NJ, Intwala S, Katz D. Statins in very elderly adults (debate). J Am Geriatr Soc. 2014 May;62(5):943-5. doi: 10.1111/jgs.12788_1. Epub 2014 May 6. Review. PubMed PMID: 24800984.
Burke MA, Katz DH, Beussink L, Selvaraj S, Gupta DK, Fox J, Chakrabarti S, Sauer AJ, Rich JD, Freed BH, Shah SJ.Prognostic importance of pathophysiologic markers in patients with heart failure and preserved ejection fraction. Circ Heart Fail. 2014 Mar 1;7(2):288-99. doi: 10.1161/CIRCHEARTFAILURE.113.000854. Epub 2013 Dec 23. PubMed PMID: 24365774; PubMed Central PMCID: PMC5947992.
Katz DH, Selvaraj S, Aguilar FG, Martinez EE, Beussink L, Kim KY, Peng J, Sha J, Irvin MR, Eckfeldt JH, Turner ST, Freedman BI, Arnett DK, Shah SJ. Association of low-grade albuminuria with adverse cardiac mechanics: findings from the hypertension genetic epidemiology network (HyperGEN) study. Circulation. 2014 Jan 7;129(1):42-50. doi: 10.1161/CIRCULATIONAHA.113.003429. Epub 2013 Sep 27. PubMed PMID: 24077169; PubMed Central PMCID: PMC3888488.
Katz DH, Beussink L, Sauer AJ, Freed BH, Burke MA, Shah SJ. Prevalence, clinical characteristics, and outcomes associated with eccentric versus concentric left ventricular hypertrophy in heart failure with preserved ejection fraction.Am J Cardiol. 2013 Oct 15;112(8):1158-64. doi: 10.1016/j.amjcard.2013.05.061. Epub 2013 Jun 28. PubMed PMID: 23810323; PubMed Central PMCID: PMC3788852.
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