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296 results on '"SARZYNSKI, MARK A."'

1. Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks

Author

Perry, Andrew S., Farber-Eger, Eric, Gonzales, Tomas, Tanaka, Toshiko, Robbins, Jeremy M., Murthy, Venkatesh L., Stolze, Lindsey K., Zhao, Shilin, Huang, Shi, Colangelo, Laura A., Deng, Shuliang, Hou, Lifang, Lloyd-Jones, Donald M., Walker, Keenan A., Ferrucci, Luigi, Watts, Eleanor L., Barber, Jacob L., Rao, Prashant, Mi, Michael Y., Gabriel, Kelley Pettee, Hornikel, Bjoern, Sidney, Stephen, Houstis, Nicholas, Lewis, Gregory D., Liu, Gabrielle Y., Thyagarajan, Bharat, Khan, Sadiya S., Choi, Bina, Washko, George, Kalhan, Ravi, Wareham, Nick, Bouchard, Claude, Sarzynski, Mark A., Gerszten, Robert E., Brage, Soren, Wells, Quinn S., Nayor, Matthew, and Shah, Ravi V.

Published
2024
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2. Associations of an HDL apolipoproteomic index with cardiometabolic risk factors before and after exercise training in the HERITAGE Family Study

Author

Miranda Maravi, J. Sebastian, Leszczynski, Eric C., Schwartz, Charles S., Dev, Prasun K., Barber, Jacob L., Reasons, Riley J., Pearce, Ryan W., McPhaul, Michael J., Konrad, Robert J., Robbins, Jeremy M., Gerszten, Robert E., Collier, Timothy S., Bouchard, Claude, Rohatgi, Anand, and Sarzynski, Mark A.

Published
2024
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3. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

Author

Katz, Daniel H, Tahir, Usman A, Bick, Alexander G, Pampana, Akhil, Ngo, Debby, Benson, Mark D, Yu, Zhi, Robbins, Jeremy M, Chen, Zsu-Zsu, Cruz, Daniel E, Deng, Shuliang, Farrell, Laurie, Sinha, Sumita, Schmaier, Alec A, Shen, Dongxiao, Gao, Yan, Hall, Michael E, Correa, Adolfo, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Guo, Xiuqing, Yao, Jie, Ida Chen, Yii-Der, Manichaikul, Ani W, Jain, Deepti, Bouchard, Claude, Sarzynski, Mark A, Rich, Stephen S, Rotter, Jerome I, Wang, Thomas J, Wilson, James G, Natarajan, Pradeep, Gerszten, Robert E, Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April, Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, and Chavan, Sameer

Subjects
Epidemiology, Health Sciences, Aging, Heart Disease - Coronary Heart Disease, Human Genome, Genetics, Prevention, Cardiovascular, Heart Disease, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Black People, Cardiovascular Diseases, Female, Genome-Wide Association Study, Humans, Male, Proteome, cardiovascular disease, genetics, proteomics, race and ethnicity, National Heart, Lung, and Blood Institute TOPMed (Trans-Omics for Precision Medicine) Consortium†, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundPlasma 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.MethodsProteomic 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).ResultsWe 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.ConclusionsTaken 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.

Published
2022

4. Metabolome-Wide Associations of Gestational Weight Gain in Pregnant Women with Overweight and Obesity

Author

Dai, Jin, Boghossian, Nansi S, Sarzynski, Mark A, Luo, Feng, Sun, Xiaoqian, Li, Jian, Fiehn, Oliver, Liu, Jihong, and Chen, Liwei

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Prevention, Clinical Research, Obesity, Good Health and Well Being, biomarkers, gestational weight gain, metabolomics, pregnancy, obesity, overweight, Analytical Chemistry, Biochemistry and Cell Biology, Clinical Sciences, Biochemistry and cell biology, Medical biochemistry and metabolomics, Analytical chemistry
Abstract

Excessive gestational weight gain (GWG) is associated with adverse pregnancy outcomes. This metabolome-wide association study aimed to identify metabolomic markers for GWG. This longitudinal study included 39 Black and White pregnant women with a prepregnancy body mass index (BMI) of ≥ 25 kg/m2. Untargeted metabolomic profiling was performed using fasting plasma samples collected at baseline (mean: 12.1 weeks) and 32 weeks of gestation. The associations of metabolites at each time point and changes between the two time points with GWG were examined by linear and least absolute shrinkage and selection operator (LASSO) regression analyses. Pearson correlations between the identified metabolites and cardiometabolic biomarkers were examined. Of the 769 annotated metabolites, 88 metabolites at 32 weeks were individually associated with GWG, with four (phosphatidylcholine (PC) 34:4, triacylglycerol (TAG) 52:6, arachidonic acid, isoleucine) jointly associated with GWG (area under the receiver operating characteristic curve (AUC) for excessive GWG: 0.80, 95% CI: 0.67, 0.93). No correlations were observed between the 88 metabolites and insulin, C-peptide, and high-sensitivity C-reactive protein at 32 weeks. Twelve metabolites at baseline (AUC for excessive GWG: 0.80, 95% CI: 0.62, 0.99) and three metabolite changes (AUC for excessive GWG: 0.73, 95% CI: 0.44, 1.00) were jointly associated with GWG. We identified novel metabolites in the first and third trimesters associated with GWG, which may shed light on the pathophysiology of GWG.

Published
2022

5. Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals

Author

Tahir, Usman A, Katz, Daniel H, Avila-Pachecho, Julian, Bick, Alexander G, Pampana, Akhil, Robbins, Jeremy M, Yu, Zhi, Chen, Zsu-Zsu, Benson, Mark D, Cruz, Daniel E, Ngo, Debby, Deng, Shuliang, Shi, Xu, Zheng, Shuning, Eisman, Aaron S, Farrell, Laurie, Hall, Michael E, Correa, Adolfo, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Guo, Xiuqing, Yao, Jie, Chen, Yii-Der Ida, Manichaikul, Ani W, Ruberg, Frederick L, Blaner, William S, Jain, Deepti, Bouchard, Claude, Sarzynski, Mark A, Rich, Stephen S, Rotter, Jerome I, Wang, Thomas J, Wilson, James G, Clish, Clary B, Natarajan, Pradeep, and Gerszten, Robert E

Subjects
Analytical Chemistry, Biological Sciences, Biomedical and Clinical Sciences, Chemical Sciences, Genetics, Medical Biochemistry and Metabolomics, Clinical Research, Orphan Drug, Biotechnology, Human Genome, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Black People, Cardiovascular Diseases, Genome-Wide Association Study, Humans, Metabolome, Metabolomics, Tandem Mass Spectrometry, NHLBI Trans-Omics for Precision Medicine 1 Consortium
Abstract

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

Published
2022

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

Author

Benson, Mark D., Eisman, Aaron S., Tahir, Usman A., Katz, Daniel H., Deng, Shuliang, Ngo, Debby, Robbins, Jeremy M., Hofmann, Alissa, Shi, Xu, Zheng, Shuning, Keyes, Michelle, Yu, Zhi, Gao, Yan, Farrell, Laurie, Shen, Dongxiao, Chen, Zsu-Zsu, Cruz, Daniel E., Sims, Mario, Correa, Adolfo, Tracy, Russell P., Durda, Peter, Taylor, Kent D., Liu, Yongmei, Johnson, W. Craig, Guo, Xiuqing, Yao, Jie, Chen, Yii-Der Ida, Manichaikul, Ani W., Jain, Deepti, Yang, Qiong, Bouchard, Claude, Sarzynski, Mark A., Rich, Stephen S., Rotter, Jerome I., Wang, Thomas J., Wilson, James G., Clish, Clary B., Sarkar, Indra Neil, Natarajan, Pradeep, and Gerszten, Robert E.

Published
2023
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11. Plasma protein patterns as comprehensive indicators of health

Author

Williams, Stephen A, Kivimaki, Mika, Langenberg, Claudia, Hingorani, Aroon D, Casas, JP, Bouchard, Claude, Jonasson, Christian, Sarzynski, Mark A, Shipley, Martin J, Alexander, Leigh, Ash, Jessica, Bauer, Tim, Chadwick, Jessica, Datta, Gargi, DeLisle, Robert Kirk, Hagar, Yolanda, Hinterberg, Michael, Ostroff, Rachel, Weiss, Sophie, Ganz, Peter, and Wareham, Nicholas J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Nutrition, Behavioral and Social Science, Obesity, Prevention, Heart Disease, Cardiovascular, Generic health relevance, Metabolic and endocrine, Good Health and Well Being, Adipose Tissue, Blood Proteins, Body Composition, Exercise, Female, Humans, Intra-Abdominal Fat, Life Style, Liver, Male, Precision Medicine, Risk Factors, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Published
2019

13. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Author

Wang, Zhe, Emmerich, Andrew, Pillon, Nicolas J., Moore, Tim, Hemerich, Daiane, Cornelis, Marilyn C., Mazzaferro, Eugenia, Broos, Siacia, Ahluwalia, Tarunveer S., Bartz, Traci M., Bentley, Amy R., Bielak, Lawrence F., Chong, Mike, Chu, Audrey Y., Berry, Diane, Dorajoo, Rajkumar, Dueker, Nicole D., Kasbohm, Elisa, Feenstra, Bjarke, Feitosa, Mary F., Gieger, Christian, Graff, Mariaelisa, Hall, Leanne M., Haller, Toomas, Hartwig, Fernando P., Hillis, David A., Huikari, Ville, Heard-Costa, Nancy, Holzapfel, Christina, Jackson, Anne U., Johansson, Åsa, Jørgensen, Anja Moltke, Kaakinen, Marika A., Karlsson, Robert, Kerr, Kathleen F., Kim, Boram, Koolhaas, Chantal M., Kutalik, Zoltan, Lagou, Vasiliki, Lind, Penelope A., Lorentzon, Mattias, Lyytikäinen, Leo-Pekka, Mangino, Massimo, Metzendorf, Christoph, Monroe, Kristine R., Pacolet, Alexander, Pérusse, Louis, Pool, Rene, Richmond, Rebecca C., Rivera, Natalia V., Robiou-du-Pont, Sebastien, Schraut, Katharina E., Schulz, Christina-Alexandra, Stringham, Heather M., Tanaka, Toshiko, Teumer, Alexander, Turman, Constance, van der Most, Peter J., Vanmunster, Mathias, van Rooij, Frank J. A., van Vliet-Ostaptchouk, Jana V., Zhang, Xiaoshuai, Zhao, Jing-Hua, Zhao, Wei, Balkhiyarova, Zhanna, Balslev-Harder, Marie N., Baumeister, Sebastian E., Beilby, John, Blangero, John, Boomsma, Dorret I., Brage, Soren, Braund, Peter S., Brody, Jennifer A., Bruinenberg, Marcel, Ekelund, Ulf, Liu, Ching-Ti, Cole, John W., Collins, Francis S., Cupples, L. Adrienne, Esko, Tõnu, Enroth, Stefan, Faul, Jessica D., Fernandez-Rhodes, Lindsay, Fohner, Alison E., Franco, Oscar H., Galesloot, Tessel E., Gordon, Scott D., Grarup, Niels, Hartman, Catharina A., Heiss, Gerardo, Hui, Jennie, Illig, Thomas, Jago, Russell, James, Alan, Joshi, Peter K., Jung, Taeyeong, Kähönen, Mika, Kilpeläinen, Tuomas O., Koh, Woon-Puay, Kolcic, Ivana, Kraft, Peter P., Kuusisto, Johanna, Launer, Lenore J., Li, Aihua, Linneberg, Allan, Luan, Jian’an, Vidal, Pedro Marques, Medland, Sarah E., Milaneschi, Yuri, Moscati, Arden, Musk, Bill, Nelson, Christopher P., Nolte, Ilja M., Pedersen, Nancy L., Peters, Annette, Peyser, Patricia A., Power, Christine, Raitakari, Olli T., Reedik, Mägi, Reiner, Alex P., Ridker, Paul M., Rudan, Igor, Ryan, Kathy, Sarzynski, Mark A., Scott, Laura J., Scott, Robert A., Sidney, Stephen, Siggeirsdottir, Kristin, Smith, Albert V., Smith, Jennifer A., Sonestedt, Emily, Strøm, Marin, Tai, E. Shyong, Teo, Koon K., Thorand, Barbara, Tönjes, Anke, Tremblay, Angelo, Uitterlinden, Andre G., Vangipurapu, Jagadish, van Schoor, Natasja, Völker, Uwe, Willemsen, Gonneke, Williams, Kayleen, Wong, Quenna, Xu, Huichun, Young, Kristin L., Yuan, Jian Min, Zillikens, M. Carola, Zonderman, Alan B., Ameur, Adam, Bandinelli, Stefania, Bis, Joshua C., Boehnke, Michael, Bouchard, Claude, Chasman, Daniel I., Smith, George Davey, de Geus, Eco J. C., Deldicque, Louise, Dörr, Marcus, Evans, Michele K., Ferrucci, Luigi, Fornage, Myriam, Fox, Caroline, Garland, Jr, Theodore, Gudnason, Vilmundur, Gyllensten, Ulf, Hansen, Torben, Hayward, Caroline, Horta, Bernardo L., Hyppönen, Elina, Jarvelin, Marjo-Riitta, Johnson, W. Craig, Kardia, Sharon L. R., Kiemeney, Lambertus A., Laakso, Markku, Langenberg, Claudia, Lehtimäki, Terho, Marchand, Loic Le, Magnusson, Patrik K. E., Martin, Nicholas G., Melbye, Mads, Metspalu, Andres, Meyre, David, North, Kari E., Ohlsson, Claes, Oldehinkel, Albertine J., Orho-Melander, Marju, Pare, Guillaume, Park, Taesung, Pedersen, Oluf, Penninx, Brenda W. J. H., Pers, Tune H., Polasek, Ozren, Prokopenko, Inga, Rotimi, Charles N., Samani, Nilesh J., Sim, Xueling, Snieder, Harold, Sørensen, Thorkild I. A., Spector, Tim D., Timpson, Nicholas J., van Dam, Rob M., van der Velde, Nathalie, van Duijn, Cornelia M., Vollenweider, Peter, Völzke, Henry, Voortman, Trudy, Waeber, Gérard, Wareham, Nicholas J., Weir, David R., Wichmann, Heinz-Erich, Wilson, James F., Hevener, Andrea L., Krook, Anna, Zierath, Juleen R., Thomis, Martine A. I., Loos, Ruth J. F., and Hoed, Marcel den

Published
2022
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19. Abstract 13308: Complementary Investigations of Plasma Proteomics Identify Novel Protein Associations With Incident Diabetes and Glucose Metabolism

Author

Mi, Michael Y, Cronje, Helene T, Austin, Thomas R, Biggs, Mary L, Siscovick, David S, Lemaitre, Rozenn N, Psaty, Bruce M, Tracy, Russell P, Djousse, Luc, Kizer, Jorge R, IX, Joachim H, Sotoodehnia, Nona, Rao, Prashant, Robbins, Jeremy M, Barber, Jacob L, Sarzynski, Mark A, Clish, Clary, Bouchard, Claude, Mukamal, Ken J, Jensen, Majken K, and Gerszten, Robert E

Published
2022
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21. Human plasma proteomic profiles indicative of cardiorespiratory fitness

Author

Robbins, Jeremy M., Peterson, Bennet, Schranner, Daniela, Tahir, Usman A., Rienmüller, Theresa, Deng, Shuliang, Keyes, Michelle J., Katz, Daniel H., Beltran, Pierre M. Jean, Barber, Jacob L., Baumgartner, Christian, Carr, Steven A., Ghosh, Sujoy, Shen, Changyu, Jennings, Lori L., Ross, Robert, Sarzynski, Mark A., Bouchard, Claude, and Gerszten, Robert E.

Published
2021
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23. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights into Cardiovascular Disease

Author

Katz, Daniel H., Tahir, Usman A., Bick, Alexander G., Pampana, Akhil, Ngo, Debby, Benson, Mark D., Yu, Zhi, Robbins, Jeremy M., Chen, Zsu-Zsu, Cruz, Daniel E., Deng, Shuliang, Farrell, Laurie, Sinha, Sumita, Schmaier, Alec A., Shen, Dongxiao, Gao, Yan, Hall, Michael E., Correa, Adolfo, Tracy, Russell P., Durda, Peter, Taylor, Kent D., Liu, Yongmei, Johnson, W. Craig, Guo, Xiuqing, Yao, Jie, Ida Chen, Yii-Der, Manichaikul, Ani W., Jain, Deepti, Bouchard, Claude, Sarzynski, Mark A., Rich, Stephen S., Rotter, Jerome I., Wang, Thomas J., Wilson, James G., Natarajan, Pradeep, and Gerszten, Robert E.

Published
2021
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24. The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers

Author

SARZYNSKI, MARK A., RICE, TREVA K., DESPRÉS, JEAN-PIERRE, PÉRUSSE, LOUIS, TREMBLAY, ANGELO, STANFORTH, PHILIP R., TCHERNOF, ANDRÉ, BARBER, JACOB L., FALCIANI, FRANCESCO, CLISH, CLARY, ROBBINS, JEREMY M., GHOSH, SUJOY, GERSZTEN, ROBERT E., LEON, ARTHUR S., SKINNER, JAMES S., RAO, D. C., and BOUCHARD, CLAUDE

Published
2022
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28. Left ventricle function and post-transcriptional events with exercise training in pigs.

Author

Samani, Stephanie L., Barlow, Shayne C., Freeburg, Lisa A., Jones, Traci L., Poole, Marlee, Sarzynski, Mark A., Zile, Michael R., Shazly, Tarek, and Spinale, Francis G.

Subjects
EXERCISE therapy, SPECKLE tracking echocardiography, ECHOCARDIOGRAPHY, CARDIOVASCULAR fitness, SWINE, ISOMETRIC exercise
Abstract

Background: Standardized exercise protocols have been shown to improve overall cardiovascular fitness, but direct effects on left ventricular (LV) function, particularly diastolic function and relation to post-transcriptional molecular pathways (microRNAs (miRs)) are poorly understood. This project tested the central hypothesis that adaptive LV remodeling resulting from a large animal exercise training protocol, would be directly associated with specific miRs responsible for regulating pathways relevant to LV myocardial stiffness and geometry. Methods and results: Pigs (n = 9; 25 Kg) underwent a 4 week exercise training protocol (10 degrees elevation, 2.5 mph, 10 min, 5 days/week) whereby LV chamber stiffness (KC) and regional myocardial stiffness (rKm) were measured by Doppler/speckle tracking echocardiography. Age and weight matched non-exercise pigs (n = 6) served as controls. LV KC fell by approximately 50% and rKm by 30% following exercise (both p < 0.05). Using an 84 miR array, 34 (40%) miRs changed with exercise, whereby 8 of the changed miRs (miR-19a, miR-22, miR-30e, miR-99a, miR-142, miR-144, miR-199a, and miR-497) were correlated to the change in KC (r ≥ 0.5 p < 0.05) and mapped to matrix and calcium handling processes. Additionally, miR-22 and miR-30e decreased with exercise and mapped to a localized inflammatory process, the inflammasome (NLRP-3, whereby a 2-fold decrease in NLRP-3 mRNA occurred with exercise (p < 0.05). Conclusion: Chronic exercise reduced LV chamber and myocardial stiffness and was correlated to miRs that map to myocardial relaxation processes as well as local inflammatory pathways. These unique findings set the stage for utilization of myocardial miR profiling to identify underlying mechanisms by which exercise causes changes in LV myocardial structure and function. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Omics-driven investigation of the biology underlying intrinsic submaximal working capacity and its trainability.

Author

Hota, Monalisa, Barber, Jacob L., Ruiz-Ramie, Jonathan J., Schwartz, Charles S., Lam, Do Thuy Uyen Ha, Rao, Prashant, Mi, Michael Y., Katz, Daniel H., Robbins, Jeremy M., Clish, Clary B., Gerszten, Robert E., Sarzynski, Mark A., Ghosh, Sujoy, and Bouchard, Claude

Subjects
HEART beat, EXERCISE intensity, AEROBIC capacity, BIOLOGY, ERYTHROCYTES, CARDIOPULMONARY fitness
Abstract

Submaximal exercise capacity is an indicator of cardiorespiratory fitness with clinical and public health implications. Submaximal exercise capacity and its response to exercise programs are characterized by heritability levels of about 40%. Using physical working capacity (power output) at a heart rate of 150 beats/min (PWC150) as an indicator of submaximal exercise capacity in subjects of the HERITAGE Family Study, we have undertaken multi-omics and in silico explorations of the underlying biology of PWC150 and its response to 20 wk of endurance training. Our goal was to illuminate the biological processes and identify panels of genes associated with human variability in intrinsic PWC150 (iPWC150) and its trainability (dPWC150). Our bioinformatics approach was based on a combination of genome-wide association, skeletal muscle gene expression, and plasma proteomics and metabolomics experiments. Genes, proteins, and metabolites showing significant associations with iPWC150 or dPWC150 were further queried for the enrichment of biological pathways. We compared genotype-phenotype associations of emerging candidate genes with reported functional consequences of gene knockouts in mouse models. We investigated the associations between DNA variants and multiple muscle and cardiovascular phenotypes measured in HERITAGE subjects. Two panels of prioritized genes of biological relevance to iPWC150 (13 genes) and dPWC150 (6 genes) were identified, supporting the hypothesis that genes and pathways associated with iPWC150 are different from those underlying dPWC150. Finally, the functions of these genes and pathways suggested that human variation in submaximal exercise capacity is mainly driven by skeletal muscle morphology and metabolism and red blood cell oxygen-carrying capacity. NEW & NOTEWORTHY Multi-omics and in silico explorations of the genes and underlying biology of submaximal exercise capacity and its response to 20 wk of endurance training were undertaken. Prioritized genes were identified: 13 genes for variation in submaximal exercise capacity in the sedentary state and 5 genes for the response level to endurance training, with no overlap between them. Genes and pathways associated with submaximal exercise capacity in the sedentary state are different from those underlying trainability. [ABSTRACT FROM AUTHOR]

Published
2023
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33. Author Correction: Human plasma proteomic profiles indicative of cardiorespiratory fitness

Author

Robbins, Jeremy M., Peterson, Bennet, Schranner, Daniela, Tahir, Usman A., Rienmüller, Theresa, Deng, Shuliang, Keyes, Michelle J., Katz, Daniel H., Beltran, Pierre M. Jean, Barber, Jacob L., Baumgartner, Christian, Carr, Steven A., Ghosh, Sujoy, Shen, Changyu, Jennings, Lori L., Ross, Robert, Sarzynski, Mark A., Bouchard, Claude, and Gerszten, Robert E.

Published
2021
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45. Effect of individual characteristics and aerobic training on the %HRR-%V̇O2R relationship.

Author

Ferri Marini, Carlo, Sisti, Davide, Skinner, James S., Sarzynski, Mark A., Bouchard, Claude, Amatori, Stefano, Rocchi, Marco B. L., Piccoli, Giovanni, Stocchi, Vilberto, Federici, Ario, and Lucertini, Francesco

Subjects
EXERCISE tests, STATISTICS, AEROBIC exercises, OXYGEN consumption, MULTIPLE regression analysis, EXERCISE physiology, REGRESSION analysis, T-test (Statistics), EXERCISE intensity, HEART beat, DESCRIPTIVE statistics, RESEARCH funding, DATA analysis
Abstract

This study aimed to assess if, during incremental exercise, considering individual characteristics can make the relationship between the percentages of heart rate (HRR) and oxygen uptake (V̇O2R) reserve either 1:1 or more accurate. Cycle ergometer data of the maximal incremental exercise tests performed by 450 healthy and sedentary participants (17-66 years) of the HERITAGE Family Study, grouped for sex, ethnicity, age, body fat, resting HR, and V̇O2max, were used to calculate the individual linear regressions between %HRR and %V̇O2R. The mean slope and intercept of the individual linear regressions of each subgroup were compared with 1 and 0 (identity line), respectively, using Hotelling tests followed by post-hoc one-sample t-tests. Two multiple linear regressions were also performed, using either the slopes or intercepts of the individual linear regressions as dependent variables and sex, age, resting HR, and V̇O2max as independent variables. The mean %HRR-%V̇O2R relationships of all subgroups differed from the identity line. Moreover, individual linear regression intercepts (8.9 ± 16.0) and slopes (0.971 ± 0.190) changed (p < 0.001) after 20 weeks of aerobic training (13.1 ± 11.1 and 0.891 ± 0.122). The multiple linear regressions could explain only 3.8% and 1.3% of the variance in the intercepts and slopes, whose variability remained high (standard error of estimate of 15.8 and 0.189). In conclusion, the %HRR-%V̇O2R relationship differs from the identity line regardless of individual characteristics and their difference increased after aerobic training. Moreover, due to the high interindividual variability, using a single equation for the whole population seems not suitable for representing the %HRR-%V̇O2R relationship of a given subject, even when several individual characteristics are considered. Highlights The association between %HRR and %V̇O2R is not 1:1 even when individuals are grouped by age, sex, ethnicity, body composition, HRrest, and V̇O2max. Using several subject characteristics to identify the individual's %HRR-%V̇O2R relationship does not meaningfully increase its prediction accuracy or reduce the interindividual variability of %HRR-%V̇O2R relationships Using a single equation for the whole population is not suitable for representing the relationship of a given subject; hence, individual relationships should be preferred when prescribing the intensity of aerobic exercise. The individual %HRR-%V̇O2R relationship should be periodically assessed due to the potential training induced changes in the relationship. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Inverse association between apolipoprotein C-II and cardiovascular mortality: role of lipoprotein lipase activity modulation.

Author

Silbernagel, Günther, Chen, Yan Q, Rief, Martin, Kleber, Marcus E, Hoffmann, Michael M, Stojakovic, Tatjana, Stang, Andreas, Sarzynski, Mark A, Bouchard, Claude, März, Winfried, Qian, Yue-Wei, Scharnagl, Hubert, and Konrad, Robert J

Subjects
LIPOPROTEIN lipase, APOLIPOPROTEIN C, CARRIER proteins, HIGH density lipoproteins, LIPASES
Abstract

Aims Apolipoprotein C-II (ApoC-II) is thought to activate lipoprotein lipase (LPL) and is therefore a possible target for treating hypertriglyceridemia. Its relationship with cardiovascular risk has not been investigated in large-scale epidemiologic studies, particularly allowing for apolipoprotein C-III (ApoC-III), an LPL antagonist. Furthermore, the exact mechanism of ApoC-II–mediated LPL activation is unclear. Methods and results ApoC-II was measured in 3141 LURIC participants of which 590 died from cardiovascular diseases during a median (inter-quartile range) follow-up of 9.9 (8.7–10.7) years. Apolipoprotein C-II–mediated activation of the glycosylphosphatidylinositol high-density lipoprotein binding protein 1 (GPIHBP1)–LPL complex was studied using enzymatic activity assays with fluorometric lipase and very low-density lipoprotein (VLDL) substrates. The mean ApoC-II concentration was 4.5 (2.4) mg/dL. The relationship of ApoC-II quintiles with cardiovascular mortality exhibited a trend toward an inverse J-shape, with the highest risk in the first (lowest) quintile and lowest risk in the middle quintile. Compared with the first quintile, all other quintiles were associated with decreased cardiovascular mortality after multivariate adjustments including ApoC-III as a covariate (all P < 0.05). In experiments using fluorometric substrate-based lipase assays, there was a bell-shaped relationship for the effect of ApoC-II on GPIHBP1–LPL activity when exogenous ApoC-II was added. In ApoC-II-containing VLDL substrate-based lipase assays, GPIHBP1–LPL enzymatic activity was almost completely blocked by a neutralizing anti-ApoC-II antibody. Conclusion The present epidemiologic data suggest that increasing low circulating ApoC-II levels may reduce cardiovascular risk. This conclusion is supported by the observation that optimal ApoC-II concentrations are required for maximal GPIHBP1–LPL enzymatic activity. [ABSTRACT FROM AUTHOR]

Published
2023
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50. The Association of Cardiorespiratory Fitness and Ideal Cardiovascular Health in the Aerobics Center Longitudinal Study.

Author

Ross, Leanna M., Barber, Jacob L., McLain, Alexander C., Weaver, R. Glenn, Xuemei Sui, Blair, Steven N., and Sarzynski, Mark A.

Subjects
CARDIOPULMONARY system, PHYSICAL activity, EPIDEMIOLOGY, PUBLIC health, CONFIDENCE intervals
Abstract

Background: This study examined the cross-sectional and longitudinal associations of cardiorespiratory fitness (CRF) and ideal cardiovascular health (CVH). Methods: CRF and the 7 CVH components were measured in 11,590 (8865 males; 2725 females) adults at baseline and in 2532 (2160 males; 372 females) adults with at least one follow-up examination from the Aerobics Center Longitudinal Study. Ideal CVH score was calculated as a composite of 7 measures, each scored 0 to 2. CVH groups were based on participant point score: ≤7 (poor), 8 to 11 (intermediate), and 12 to 14 (ideal). Analyses included general linear, logistic regression, and linear mixed models. Results: At baseline, participants in the high CRF category had 21% and 45% higher mean CVH scores than those in the moderate and poor CRF categories (P < .001). The adjusted odds (95% confidence interval) of being in the poor CVH group at baseline were 4.9 (4.4-5.4) and 16.9 (14.3-19.9) times greater for individuals with moderate and low CRF, respectively, compared with those with high CRF (P < .001). Longitudinal analysis found that for every 1-minute increase in treadmill time, CVH score increased by 0.23 units (P < .001) independent of age, sex, exam number, and exam year. Conclusions: Higher CRF is associated with better CVH profiles, and improving CRF over time is independently associated with greater improvements in CVH. [ABSTRACT FROM AUTHOR]

Published
2019
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