Research


Insulin resistance, genetics, and translational research

Genome wide association studies can be used to find insuline resistance contributing genes, like Nat1. Nat1 deficiency causes mitochondiral disfunction and metabolic dysregulation. (Knowles, et al., JCI 2016; Chennamsetty, et al., Cell Reports 2016)

A major focus of this research has been to elucidate the genetic basis of insulin resistance (IR), which is a necessary but insufficient precursor for Type 2 Diabetes (T2D) and a major cardiovascular disease risk factor. It is estimated that 25-33% of the US population is sufficiently insulin resistant to have serious clinical consequences. Insulin sensitivity can be quantitated using reference measures like the euglycemic clamp or insulin suppression test, or estimated less precisely with surrogate measures like fasting glucose and insulin levels. The heritability of insulin resistance is 40-50%, but the genetic basis has remained largely unexplained. 

Along with Thomas Quertermous (Prof. Cardiovascular Medicine, Stanford University), we lead an international effort to uncover IR susceptibility loci through the GENESIS (GENEticS of Insulin Sensitivity) project. GENESIS is a GWAS of ~2,800 white individuals from 4 international cohorts with direct measures of IR (euglycemic clamp or insulin suppression test) plus four additional replication cohorts. We discovered a novel insulin susceptibility locus and showed that in vitro perturbations of NAT2 caused marked effects on glucose uptake, lipolysis, and adipogenesis. Furthermore, Nat1 deficient mice have elevated fasting glucose and insulin, as well as IR confirmed by glucose and insulin tolerance tests (Knowles et al., JCI2015; Chennamsetty et al. Cell Reports. 2016).    

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Insulin resistance, genetics, and translational research

Associated Publications

1.    Fathzadeh M, Li J, Rao A, Cook N, Chennamsetty I, Seldin M, Zhou X, Sangwung P, Gloudemans MJ, Keller M, Attie A, Yang J, Wabitsch M, Carcamo-Orive I, Tada Y, Lusis AJ, Shin MK, Molony CM, McLaughlin T, Reaven G, Montgomery SB, Reilly D, Quertermous T, Ingelsson E, Knowles JWFAM13A affects body fat distribution and adipocyte function. Nat Commun. 2020 Mar 19;11(1):1465. doi: 10.1038/s41467-020-15291-z. PMID: 32193374; PMCID: PMC7081215.

2.    Chennamsetty I, Coronado M, Contrepois K, Keller MP, Carcamo-Orive I, Sandin J, Fajardo G, Whittle AJ, Fathzadeh M, Snyder M, Reaven G, Attie AD, Bernstein D, Quertemous T, Knowles JWNat1 deficiency is associated with mitochondrial dysfunction and exercise intolerance in mice. Cell Rep. 2016 Oct 4;17(2):527-540. PMCID: PMC5097870

3.     Fall T, Xie W, Poon W, Yaghootkar H, Mägi R; GENESIS consortium, Knowles JW, Lyssenko V, Weedon M, Frayling TM, Ingelsson E. Using genetic variants to assess the relationship between circulating lipids and type 2 diabetesDiabetes2015 May 6. pii: db141710. PMID: 25948681.

4.    Knowles JW, Xie W, Zhang Z, Chennemsetty I, Assimes TL, Paananen J, Hansson O, Pankow J, Goodarzi MO, Carcamo-Orive I, Morris A, Chen Y-DI, Mäkinen V-P, Ganna A, Guo X, Abbasi F, Greenawalt DM, Lum P, Molony C, Lind L, Lindgren C, Raffel LJ, Tsao PS, The RISC Consortium, The ULSAM Study, The EUGENE2 Study, The GUARDIAN Consortium, The SAPPHIRe Study, Schadt EE, Rotter JI, Sinaiko A, Reaven G, Yang X, Hsiung CA, Groop L, Cordell HJ, Laakso M, Hao K, Ingelsson E, Frayling TM, Weedon MN, Walker M, Quertermous T. Identification and validation of NAT2 as an insulin sensitivity geneJCI. 2015;125(4):1739-51 PMCID 4409020.

5.    Dimas AS*, Lagou V*, Barker A*, Knowles JW*, Magi R, Hivert MF, Benazzo A,…(>10 authors)… Collins FS, Mohlke KL, Tuomilehto J, Quertemous T, Lind L, Hansen T, Pedersen O, Walker M, Pfeiffer AF, Spranger J, Stumvoll M, Meigs JB, Wareham NJ, Kuusisto J, Laakso M, Langenberg C, Dupuis J, Watanabe RM, Florez JC, Ingelsson E, McCarthy MI, Prokopenko I. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes. 2013; PMCID: 4030103, * co-first author

6.     Knowles JW, Assimes TL, Tsao PS, Natali A, Mari A, Quertermous T, Reaven GM, Abbasi F. Measurement of insulin-mediated glucose uptake: Direct comparison of the modified insulin suppression test and the euglycemic, hyperinsulinemic clampMetabolism. 2013;62:548-553. PMCID: 3925367

7.    Xie W, Wood AR, Lyssenko V, Weedon MN, Knowles JW, Alkayyali S, Assimes TL, Quertermous T, Abbasi F, Paananen J, Haring H, Hansen T, Pedersen O, Smith U, Laakso M, Dekker JM, Nolan JJ, Groop L, Ferrannini E, Adam KP, Gall WE, Frayling TM, Walker M. Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetesDiabetes. 2013;62:2141-2150. PMCID: 3661655

8.      Ingelsson E, Langenberg C, Hivert MF, Prokopenko I, Lyssenko V, Dupuis J, Magi R, Sharp S, Jackson AU, Assimes TL, Shrader P, Knowles JW, Zethelius B, Abbasi FA, Bergman RN, Bergmann A, Berne C, Boehnke M, Bonnycastle LL, Bornstein SR…(> 20 authors)… Williams GH, Lind L, Barroso I, Quertermous T, Walker M, Wareham NJ, Meigs JB, McCarthy MI, Groop L, Watanabe RM, Florez JC. Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humansDiabetes. 2010;59:1266-1275. PMCID: 2857908


Defining the mechanisms of insulin sensitivity using iPSC model systems

We have been able to shed light on the classification of many T2D variants as either IR or pancreatic beta cell loci by using the GWAS findings from the GENESIS consortium, along with orthogonal data derived from multiple glycemic measurements, (Dimas et al., 2013; Ingelsson et al., 2010). Along with co-investigators Drs. Erik Ingelsson and Thomas Quertermous, we are working on efforts to map the causal molecular mechanisms for IR-related T2D GWAS loci. We are using both informatics approaches and high throughput genetic studies (i.e. RNASeq) with in vitro and in vivo model systems to first define the causal genes at these associated loci, and then to define their causal molecular mechanism. As part of this work, we have discovered a novel insulin susceptibility locus (NAT2), replicated that finding in additional cohorts, and showed that in vitro perturbations of this gene cause marked effects on glucose uptake, lipolysis, and adipogenesis (Knowles et al., 2015.). We have gone on to show that perturbations in NAT2 and the mouse ortholog Nat1 cause profound changes in mitochondrial function, energy balance, and metabolism and exercise capacity (Chennamsetty et al., 2016).    

For the last several years, we have led an NIH-funded U01 effort to use iPSC technology to study insulin resistance. We created induced pluripotent stem cell (iPSC) lines on ~200 individuals to use as model systems for the study of IR and other conditions. These iPSC lines are being differentiated into tissues relevant to the pathology of insulin resistance (adipose, skeletal muscle, and endothelial cells). By studying the functional and transcriptional differences between cells derived from insulin resistant and insulin sensitive individuals under basal and insulin-stimulated conditions, we have begun to map the key regulatory networks for IR and correlate this with the underlying genetic background of the individuals. The iPSC lines and resultant genetic data have been deposited at WiCell and dbGaP, respectively, as a resource for the greater scientific community (Carcamo-Orive ICell Stem Cell, 2016).

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We are using induced pluripotent stem cells to model genes associated with insulin sensitivity in vitro.Gene targets are deterined by combining large data analysis of human genetics, and their contribution to disease is assessed with technques like RNA-seq, eQTL, and ASE.

Associated Publications

1.     Carcamo-Orive I, Henrion MYR, Zhu K, Beckmann ND, Cundiff P, Moein S, et al. (2020) Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness. PLoS Comput Biol 16(12): e1008491. https://doi.org/10.1371/journal.pcbi.1008491  

2.     Marc Jan Bonder, Craig Smail, Michael J. Gloudemans, Laure Frésard, David Jakubosky, Matteo D’Antonio, Xin Li, Nicole M. Ferraro, Ivan Carcamo-Orive, Bogdan Mirauta, Daniel D. Seaton, Na Cai, Danilo Horta, YoSon Park, HipSci Consortium, iPSCORE Consortium, GENESiPS Consortium, PhLiPS Consortium, Erin N. Smith, Kelly A. Frazer, Stephen B. Montgomery, Oliver Stegle. “Systematic assessment of regulatory effects on human disease variants in pluripotent cells. bioRxiv doi: https://doi.org/10.1101/784967

3.     Jakubosky, D., D'Antonio, M., Bonder, M. J., Smail, C., Donovan, M., Young Greenwald, W. W., Matsui, H., i2QTL Consortium, D'Antonio-Chronowska, A., Stegle, O., Smith, E. N., Montgomery, S. B., DeBoever, C., & Frazer, K. A. (2020, June). Properties of structural variants and short tandem repeats associated with gene expression and complex traits. Nature communications11(1), 2927. https://doi.org/10.1038/s41467-020-16482-4

4.    Jakubosky, D., Smith, E. N., D'Antonio, M., Jan Bonder, M., Young Greenwald, W. W., D'Antonio-Chronowska, A., Matsui, H., i2QTL Consortium, Stegle, O., Montgomery, S. B., DeBoever, C., & Frazer, K. A. (2020, June). Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats. Nature communications11(1), 2928. https://doi.org/10.1038/s41467-020-16481-5

5.    Kanchan K, Iyer K, Yanek LR, Carcamo-Orive I, Taub MA, Malley C, Baldwin K, Becker LC, Broeckel U, Cheng L, Cowan C, D’Antonio M, Frazer KA, Quertermous T, Mostoslavsky G, Murphy G, Rabinovitch M, Rader DJ, Steinberg MH, Topol E, Yang W, Knowles JW, Jaquish CE, Ruczinski I, Mathias RA. Genomic integrity of human induced pluripotent stem cells across nine studies in NHLBI NextGen program. Stem Cell Research. 2020 July. https://doi.org/10.1016/j.scr.2020.101803

6.    Shahbazi, M., Cundiff, P., Zhou, W., Lee, P., Patel, A., D’Souza, S.L., Abbasi, F., Quertermous, T., Knowles, J.W. The role of insulin as a key regulator of seeding, proliferation, and mRNA transcription of human pluripotent stem cells. Stem Cell Res Ther 10, 228 (2019). https://doi.org/10.1186/s13287-019-1319-5

7.    Carcamo-Orive I, Huang NF, Quertermous T, Knowles JWInduced Pluripotent Stem Cell-Derived Endothelial Cells in Insulin Resistance and Metabolic SyndromeArterioscler Thromb Vasc Biol. 2017 Nov;37(11):2038-2042. doi: 10.1161/ATVBAHA.117.309291. Epub 2017 Jul 20. Review. PMID 28729365

8.    Carcamo-Orive I, Hoffman GE, Cundiff P, Beckmann ND, D'Souza SL, Knowles JW, Patel A, Papatsenko D, Abbasi F, Reaven GM, Whalen S, Lee P, Shahbazi M, Henrion MY, Zhu K, Wang S, Roussos P, Schadt EE, Pandey G, Chang R, Quertermous T, Lemischka I. Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell Stem Cell. 2017 Apr 6;20(4):518-532.e9. doi: 10.1016/j.stem.2016.11.005. Epub 2016 Dec 22. PMID: 28017796, PMCID: PMC5384872


CRISPR screens for cardiometabolic traits and NAFLD

NAFLD is a disease of the liver that progresses towards cirrhosis and cardiovascular disease if left untreated.

Hepatocytes are liver cells, which we are studying to determine their role in NAFLD using CRISPR genetic screens. They are stained here with LipidTox Red to stain lipids.

We have identified a large number of loci associated with insulin resistance and related traits using human genetics. By use of pooled genetic screens based on CRISPR, which enable hundreds to thousands of programmed perturbations per experiment, we can prioritize the most likely candidate genes within these loci using unbiased experimental approaches.

In addition to studying loci associated to insulin resistance, we are also following up on loci connected to non-alcoholic fatty liver disease (NAFLD). NAFLD prevalence is expected to increase with the ever-growing diabetes and obesity epidemic, since it is strongly correlated with these diseases, and its genetic component is still poorly understood. We are currently leading projects aiming to further characterize the genetic contribution to NAFLD. Putative loci are studied in large-scale CRISPRi screens in human hepatocytes, with the ultimate goal of identifying novel genes on the pathogenic pathway of NAFLD.

We are currently working with two different strategies for phenotypic readouts on the pools of genome-engineered cells. In one project, we apply single-cell RNA sequencing using the recently updated CROP-seq protocol as the readout axer for CRISPRi pooled gene perturbation. This pooled screening approach allows us to evaluate many genes in parallel in a relevant biological context. Another approach is based on reporter gene expression changes following pooled sgRNA delivery for CRISPR-mediated perturbations.This single cell RNA sequencing method helps us dissect out the mechanisms underlying changes in insulin resistance and NAFLD.

CRISPR screens for cardiometabolic traits and NAFLD

Associated Publications

1.    Coming soon!

 


Cardiovascular disease: Genetics and personalized medicine

Knowing a patient's genetic risk score may help them to prevent disease.

A major focus has been the translation of genetic findings to the clinic. We have completed recruitment for a randomized clinical trial (“A Pilot Randomized Trial of Personal Genomics for Preventive Cardiology”, ClinicalTrial.gov identifier NCT01406808) with a goal of determining whether patient outcomes are improved by providing patients information about their GWAS-verified coronary disease susceptibility variants. We are actively involved in efforts to understand the promise and limitations of GWAS-genotyping and whole genome sequencing efforts for clinical care.

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Cardiovascular disease: Genetics and personalized medicine

Associated Publications

1.     Knowles JW, Ashley EA. Cardiovascular disease: The rise of the genetic risk scorePLoS Med. 2018 Mar 30;15(3):e1002546. doi: 10.1371/journal.pmed.1002546. eCollection 2018 Mar. PMID: 29601582

2.     Knowles JW, Zarafshar S, Pavlovic A, Goldstein BA, Tsai S, Li J, McConnell MV, Absher D, Ashley EA, Kiernan M, Ioannidis JPA, Assimes TL. Impact of a Genetic Risk Score for Coronary Artery Disease on Reducing Cardiovascular Risk: A Pilot Randomized Controlled StudyFront Cardiovasc Med. 2017 Aug 14;4:53. doi: 10.3389/fcvm.2017.00053. eCollection 2017 PMID: 28856136

3.    Goldstein BA, Knowles JW, Salfati E, Ioannidis JP, Assimes TL.  Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example.  Front Genet. 2014 Aug 1; 5:254.

4.    Knowles JW, Assimes TL, Kiernan M, Pavlovic A, Goldstein BA, Yank V, McConnell MV, Absher D, Bustamante C, Ashley EA, Ioannidis JP. Randomized trial of personal genomics for preventive cardiology: Design and challenges. Circulation. Cardiovascular genetics. 2012;5:368-376. PMCID: 3394683

5.    Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, Dudley JT, Ormond KE, Pavlovic A, Morgan AA, Pushkarev D, Neff NF, Hudgins L, Gong L, Hodges LM, Berlin DS, Thorn CF, Sangkuhl K, Hebert JM, Woon M, Sagreiya H, Whaley R, Knowles JW, Chou MF, Thakuria JV, Rosenbaum AM, Zaranek AW, Church GM, Greely HT, Quake SR, Altman RB. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525-1535. PMCID: 2937184


Clinical research for precision medicine in insulin resistance and cardiovascular disease

A patient's plasma glucose measures over time can indicate their levels of insulin sensitivity.

Statin drugs induce elevated fasting glucose levels in pre-diabetic patients.   

Our lab is interested in the study of insulin resistance and related sequelae including type 2 diabetes. Building on the groundbreaking work of Dr. Gerald Reaven, we conduct clinical research utilizing gold standard assessments as well as surrogate measures of insulin sensitivity.

We have demonstrated that insulin sensitivity differs between race/ethnic groups, which may have implications for clinical care.

We have also examined the relationship of insulin resistance and related traits with several important cardiovascular conditions. We have demonstrated the relationship of simple markers of insulin resistance with coronary artery calcification.

Working with Dr. James Priest, we determined that the risk of congenital heart disease is influenced by gestational hyperglycemia.

We are also studying the relationship of statin drugs and type 2 diabetes. Statins increase the risk of diabetes, but the mechanism is unclear. We are gaining insight into this phenomenon with several approaches, including the analysis of clinical trial data and undertaking a trial where we measure insulin sensitivity and insulin production pre- and post- statin (funded by the Doris Duke Foundation).

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Clinical research for precision medicine in insulin resistance and cardiovascular disease

Associated Publications

1.  Viraj Raygor1, Fahim Abbasi1, Laura C Lazzeroni2,3, Sun Kim1,4, Erik Ingelsson1,4,5, Gerald M Reaven4,5* Joshua W Knowles1,4,5. Impact of race/ethnicity on insulin resistance and hypertriglyceridaemia. Diabetes and Vascular Disease Research.2019 Mar 1. doi: 10.1177/1479164118813890. PMCID: PMC6713231

2.      Helle EIT, Biegley P, Knowles JW, Leader JB, Pendergrass S, Yang W, Reaven GR, Shaw GM, Ritchie M, Priest JR. J Pediatr. First Trimester Plasma Glucose Values in Women without Diabetes are Associated with Risk for Congenital Heart Disease in Offspring2017 Dec 13. Pii: S0022-3476(17)31457-9. Doi: 10.1016/j.jpeds.2017.10.046.

3.     Reaven GM, Knowles JW, Leonard D, Barlow CE, Willis BL, Haskell WL, Maron DJ. Relationship between simple markers of insulin resistance and coronary artery calcificationJ Clin Lipidol. 2017 Jun 6: pii: S1933-2874(17)30339-2. doi: 10.1016/j.jacl.2017.05.013.   

4.     Ingelsson E, Knowles JW. Levering Human Genetics to Understand the Relation of LDL Cholesterol with Type 2 DiabetesClinical Chemistry. 17 May 2017. doi: 10.1373/clinchem.2016.268565.

5.     Kohli P, Knowles JW*, Sarraju A, Waters DD, Reaven G. Metabolic Markers to Predict Incident Diabetes Mellitus in Statin-Treated Patients (From the Treating to New Targets (TNT) and the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) Trials)Amer. J. of Cardiology, 2016 Nov 1;118(9):1275-1281. PMID: 27614854

6.    Abbasi F, Kohli P, Reaven GM, Knowles JWHypertriglyceridemia: A simple approach to identify insulin resistance and enhanced cardio-metabolic risk in patients with prediabetesDiabetes Res Clin Pract. 2016Oct;120:156-61. doi: 10.1016/j.diabres.2016.07.024. PMID 27565692.

7.     Priest JR, Yang W, Reaven G, Knowles JW, Shaw GM. Maternal Midpregnancy GlucoseLevels and Risk of Congenital Heart Disease in Offspring. JAMA Pediatr. 2015 Dec 1;169(12):1112-6. doi: 10.1001/jamapediatrics.2015.2831.PMID: 26457543.

8. Knowles JW, Assimes TL, Tsao PS, Natali A, Mari A, Quertermous T, Reaven GM, Abbasi F. Measurement of insulin-mediated glucose uptake: Direct comparison of the modified insulin suppression test and the euglycemic, hyperinsulinemic clamp. Metabolism. 2013;62:548-553. PMCID: 3925367


Familial Hypercholesterolemia, lipids, and machine learning

Familial Hypercholsterolemia (FH) is a hereditary condition caused by a variety of genetic mutations that lead to significantly elevated LDL cholesterol (LDL-C levels), contributing to a 20-fold increased lifetime risk for cardiovascular disease. Despite its high prevalence in the United States (1 in 200 individuals), it is estimated that less than 10% of FH patients are formally diagnosed. However, with proper family-based screening, diagnosis, and treatment, morbidity and mortality can be greatly ameliorated. 

Working as the Chief Research Advisor for the FH Foundationa patient-founded, non-profit organization for individuals with FH, we have lead and directed multiple efforts to improve the diagnosis and treatment of FH patients nationally and internationally, including: developing a national patient registry for FH; participating in an AHA Scientific Statement on FH; writing the GeneReview of FH; writing an application to the Centers for Medicare & Medicaid Services to get specific ICD 10 codes FH (approved); and advocating for access to guideline based-therapies in high risk patients and for access to genetic testing.

Familial hypercholestrolemia can be diagnosed through genetic testing, and through pattern obserations on family trees.

The Familial Hypercholesterolemia Global Summit, 2019

                                          

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Familial Hypercholesterolemia, lipids, and machine learning

Associated Publications

1.    Banda JM, Sarraju A, Abbasi F, Parizo J, Pariani M, Ison H, Briskin E, Hannah Wand H, Dubois S, Jung K, Myers SA, Rader DJ, Leader JB, Murray MF, Myers KD, Wilemon K, Shah NH, Knowles JW. Finding missed cases of familial hypercholesterolemia in health systems using machine learningnpj Digital Medicine 2 , 1–8. 2019 April doi.org/10.1038/s41746-019-0101-5

2.    Iacocca MA, Chora JR, Carrié A, Freiberger T, Leigh SE, Defesche JC, Kurtz CL, DiStefano MT, Santos RD, Humphries SE, Mata P, Jannes CE, Hooper AJ, Wilemon KA, Benlian P, O'Connor R, Garcia J, Wand H, Tichy L, Sijbrands EJ, Hegele RA, Bourbon M, Knowles JWClinGen FH Variant Curation Expert PanelHum Mutat. 2018 Nov;39(11):1631-1640. doi: 10.1002/humu.23634. PMID:30311388

3.    Sturm AC, Knowles JW*, Gidding SS, Ahmad ZS, Ahmed CD, Ballantyne CM, Baum SJ, Bourbon M, Carrié A, Cuchel M, de Ferranti SD, Defesche JC, Freiberger T, Hershberger RE, Hovingh GK, Karayan L, Kastelein JJP, Kindt I, Lane SR, Leigh SE, Linton MF, Mata P, Neal WA, Nordestgaard BG, Santos RD, Harada-Shiba M, Sijbrands EJ, Stitziel NO, Yamashita S, Wilemon KA, Ledbetter DH, Rader DJ; Convened by the Familial Hypercholesterolemia Foundation. Clinical Genetic Testing for Familial Hypercholesterolemia: JACC Scientific Expert PanelJ Am Coll Cardiol. 2018Aug 7;72(6):662-680. doi: 10.1016/j.jacc.2018.05.044. PMID: 30071997

4.      Rodriguez F, Knowles JW, Maron DJ, Virani SS, Heidenreich PA. Frequency of Statin Use in Patients With Low-Density Lipoprotein Cholesterol ≥190 mg/dl from the Veterans Affairs Health SystemAm J Cardiol. 2018 Jun 7. pii: S0002-9149(18)31180-9. doi: 10.1016/j.amjcard.2018.05.008. PMID: 30055758

5.      Knowles JW, Rader DJ, Khoury MJ. Cascade Screening for Familial Hypercholesterolemia and the Use of Genetic TestingJAMA. 2017 Jul 25;318(4):381-382. PMID: 28742895

6.    Knowles JW, Howard WB, Karayan L, Baum SJ, Wilemon KA, Ballantyne CM, Myers KD. Access to Non-Statin Lipid Lowering Therapies in Patients at High-Risk of Atherosclerotic Cardiovascular DiseaseCirculation. 2017 Apr 26. pii: CIRCULATIONAHA.117.027705. doi: 10.1161/CIRCULATIONAHA.117.027705.

7.      Rodriguez, F, Maron, DJ, Knowles JW, Virani, SS, & Heidenreich, PA. Association Between Intensity of Statin Therapy and Mortality in Patients With Atherosclerotic Cardiovascular DiseaseJAMA Cardiology. 2016 Nov 9. doi: 10.1001/jamacardio.2016.4052.

8.    deGoma EM, Ahmad ZS, O'Brien EC, Kindt I, Shrader P, Newman CB, Pokharel Y, Baum SJ, Hemphill LC, Hudgins LC, Ahmed CD, Gidding SS, Duffy D, Neal W, Wilemon K, Roe MT, Rader DJ, Ballantyne CM, Linton MF, Duell PB, Shapiro MD, Moriarty PM, Knowles JWTreatment Gaps in Adults With Heterozygous Familial Hypercholesterolemia in the United States: Data From the CASCADE-FH Registry. Circ Cardiovasc Genet. 2016 Jun;9(3):240-9. Epub 2016 Mar 24. PMCID: PMC5315030

9.      Gidding SS, Ann Champagne M, de Ferranti SD, Defesche J, Ito MK, Knowles JW, McCrindle B, Raal F, Rader D, Santos RD, Lopes-Virella M, Watts GF, Wierzbicki AS The Agenda for Familial Hypercholesterolemia: A Scientific Statement From the American Heart Association. American Heart Association.  Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young,  Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, and Council on Lifestyle and Cardiometabolic Health. Circulation. 2015 Dec;132(22):2167-92. doi: 10.1161. Epub 2015 Oct 28. PMID: 26510694.

10.    Knowles JW, O’Brien EC, Greendale K, Wilemon K. Genest J, Sperling LS, Neal WA, Rader DJ, Khoury MJ., Reducing the Burden of Disease and Death from Familial Hypercholesterolemia: A Call to Action. Am Heart J. 2014 Dec; 168(6):807-11. PMCID: PMC4683103

11.     Youngblom E., Knowles JWFamilial Hypercholesterolemia. 2014 Jan 2. In: Pagon RA, Adam MP, Bird TD, et al., editors. GeneReviews™ [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2014. http://www.ncbi.nlm.nih.gov/books/NBK174884