Skip to main content

Advertisement

SpringerLink
Log in

Racial Disparities and Cardiometabolic Risk: New Horizons of Intervention and Prevention

  • Macrovascular Complications in Diabetes (R SHAH, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Cardiometabolic diseases are a leading cause of morbidity and mortality in the USA and disproportionately impact racial and ethnic minorities. Multiple factors contribute to this disparity including genetic and socioeconomic factors, the latter of which contributes to disparities both through systemic barriers such as healthcare access and by directly impacting metabolism through epigenetics and environment-related alterations in the gut microbiome. This review will discuss advances in medicine that can be used to identify, prognosticate, and treat cardiometabolic diseases, and how these may be used to address existing disparities.

Recent Findings

There is growing research aimed at identifying novel cardiometabolic disease targets and expanding the use of existing pharmacotherapies based on comorbidities. Advances in metabolomics and genomics can give insight into an individual’s unique biochemical profile, providing the means for earlier identification of disease and specific treatment targets. Moreover, developments in telehealth and related medical device technologies can expand access to underserved minority populations and improve control of chronic conditions such as diabetes and hypertension.

Summary

Precision medicine may be integral to bridging the racial gap in cardiometabolic disease outcomes. Developments in genomics, metabolomics, wearable medical devices, and telehealth can result in personalized treatments for patients that account for the socioeconomic and genetic factors that contribute to poor health outcomes in minorities. As research in this field rapidly progresses, special efforts must be made to ensure inclusion of racial and ethnic minority populations in clinical research and equal access to all treatment modalities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. National Center for Health, S., Health, United States, in Health, United States, 2018. 2019, National Center for Health Statistics (US): Hyattsville (MD).

  2. Virani, S.S., et al., Heart Disease and Stroke Statistics—2021 Update. Circulation, 2021. 143(8): p. e254-e743. This is a very comprehensive review of cardiovascular disease statistics, including cardiometabolic and lifestyle risk factors.

  3. Singh, G.K., et al., Widening Socioeconomic and Racial Disparities in Cardiovascular Disease Mortality in the United States, 1969-2013. Int J MCH AIDS, 2015. 3(2): p. 106-18. In this paper, researchers used population data to estimate cardiovascular disease disparities over time and found significantly increasing disparities, caused in part by improvements in mortality for high socioecnomic and majority populations.

  4. Mazimba, S. and P.N. Peterson, JAHA spotlight on racial and ethnic disparities in cardiovascular disease. J Am Heart Assoc, 2021. 10(17): p. e023650.

  5. Mody P, et al. Most important articles on cardiovascular disease among racial and ethnic minorities. Circ Cardiovasc Qual Outcomes. 2012;5(4):e33-41.

    PubMed  Google Scholar 

  6. Graham G. Disparities in cardiovascular disease risk in the United States. Curr Cardiol Rev. 2015;11(3):238–45.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wang Y, Beydoun MA. The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28.

    CAS  PubMed  Google Scholar 

  8. Hales, C.M., et al., Prevalence of obesity and severe obesity among adults: United States, 2017–2018., in NCHS Data Brief. 2020, National Center for Health Statistics: Hyattsville, MD.

  9. Sarwar N, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.

    CAS  PubMed  Google Scholar 

  10. Prevention, C.f.D.C.a., National Diabetes Statistics report, 2020. 2020, Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services: Atlanta, GA.

  11. Paschos P, Paletas K. Non alcoholic fatty liver disease and metabolic syndrome. Hippokratia. 2009;13(1):9–19.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Chalasani N, et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67(1):328–57.

    PubMed  Google Scholar 

  13. Rich NE, et al. Racial and ethnic disparities in nonalcoholic fatty liver disease prevalence, severity, and outcomes in the United States: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2018;16(2):198-210.e2.

    PubMed  Google Scholar 

  14. Bonacini, M., et al., Racial differences in prevalence and severity of non-alcoholic fatty liver disease. http://www.wjgnet.com/, 2021.

  15. Carroll, M.D. and C.D. Fryar, Total and high-density lipoprotein cholesterol in adults: United States, 2015–2018., N.C.f.H. Statistics, Editor. 2020, NCHS Data Brief: Hyattsville, MD.

  16. Havranek EP, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2015;132(9):873–98.

    PubMed  Google Scholar 

  17. Soler-Botija C, Gálvez-Montón C, Bayés-Genís A. Epigenetic biomarkers in cardiovascular diseases. Front Genet. 2019;10:950.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Phillips, M.L., Gut reaction: environmental effects on the human microbiota, in Environ Health Perspect. 2009. p. A198–205.

  19. Moyer, V.A., Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med, 2012. 157(5): p. 373–8.

  20. Grundy SM, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139(25):e1082–143.

    PubMed  Google Scholar 

  21. Ussher JR, et al. The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. J Am Coll Cardiol. 2016;68(25):2850–70.

    CAS  PubMed  Google Scholar 

  22. Sonnenburg JL, Bäckhed F. Diet-microbiota interactions as moderators of human metabolism. Nature. 2016;535(7610):56–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ganna, A., et al., Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet, 2014. 10(12): p. e1004801.

  24. Park, J.Y., et al., Alteration in metabolic signature and lipid metabolism in patients with angina pectoris and myocardial infarction. PLoS One, 2015. 10(8): p. e0135228.

  25. Tang WH, Hazen SL. The contributory role of gut microbiota in cardiovascular disease. J Clin Invest. 2014;124(10):4204–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Muoio DM, Newgard CB. Mechanisms of disease: molecular and metabolic mechanisms of insulin resistance and beta-cell failure in type 2 diabetes. Nat Rev Mol Cell Biol. 2008;9(3):193–205.

    CAS  PubMed  Google Scholar 

  27. Whelton PK, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;71(19):e127–248.

    PubMed  Google Scholar 

  28. Cheatham SW, et al. The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. J Sports Med Phys Fitness. 2018;58(4):534–48.

    PubMed  Google Scholar 

  29. Lachausse RG. My student body: effects of an internet-based prevention program to decrease obesity among college students. J Am Coll Health. 2012;60(4):324–30.

    PubMed  Google Scholar 

  30. Sorgente, A., et al., Web-based interventions for weight loss or weight loss maintenance in overweight and obese people: a systematic review of systematic reviews. J Med Internet Res, 2017. 19(6): p. e229.

  31. Napolitano MA, et al. Using Facebook and text messaging to deliver a weight loss program to college students. Obesity (Silver Spring). 2013;21(1):25–31.

    Google Scholar 

  32. Strelitz J, et al. Association of weight loss and weight loss maintenance following diabetes diagnosis by screening and incidence of cardiovascular disease and all-cause mortality: an observational analysis of the ADDITION-Europe trial. Diabetes Obes Metab. 2021;23(3):730–41.

    PubMed  Google Scholar 

  33. Sheard NF, et al. Dietary carbohydrate (amount and type) in the prevention and management of diabetes: a statement by the american diabetes association. Diabetes Care. 2004;27(9):2266–71.

    CAS  PubMed  Google Scholar 

  34. Vega-López S, et al. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Diabetes Care. 2007;30(6):1412–7.

    PubMed  Google Scholar 

  35. Vrolix R, Mensink RP. Variability of the glycemic response to single food products in healthy subjects. Contemp Clin Trials. 2010;31(1):5–11.

    CAS  PubMed  Google Scholar 

  36. Zeevi D, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079–94.

    CAS  PubMed  Google Scholar 

  37. Qin J, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55–60.

    CAS  PubMed  Google Scholar 

  38. Boursier J, et al. The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota. Hepatology. 2016;63(3):764–75.

    CAS  PubMed  Google Scholar 

  39. Zhu L, et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH. Hepatology. 2013;57(2):601–9.

    CAS  PubMed  Google Scholar 

  40. Koeth RA, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Rooks MG, Garrett WS. Gut microbiota, metabolites and host immunity. Nat Rev Immunol. 2016;16(6):341–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Gilbert JA, et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature. 2016;535(7610):94–103.

    CAS  PubMed  Google Scholar 

  43. Zhu W, et al. Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell. 2016;165(1):111–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Tilg H, et al. The intestinal microbiota fuelling metabolic inflammation. Nat Rev Immunol. 2020;20(1):40–54.

    CAS  PubMed  Google Scholar 

  45. Rodbard D. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther. 2017;19(S3):S25-s37.

    PubMed  Google Scholar 

  46. Eiland L, Thangavelu T, Drincic A. Has technology improved diabetes management in relation to age, gender, and ethnicity? Curr Diab Rep. 2019;19(11):111.

    PubMed  Google Scholar 

  47. Honigberg MC, et al. Use of glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes and cardiovascular disease: a review. JAMA Cardiology. 2020;5(10):1182–90.

    PubMed  PubMed Central  Google Scholar 

  48. Del Olmo-Garcia MI, Merino-Torres JF. GLP-1 receptor agonists and cardiovascular disease in patients with type 2 diabetes. J Diabetes Res. 2018;2018:4020492–4020492.

    PubMed  PubMed Central  Google Scholar 

  49. Wilding JPH, et al. Once-weekly semaglutide in adults with overweight or obesity. N Engl J Med. 2021;384(11):989–1002.

    CAS  PubMed  Google Scholar 

  50. Mann JFE, et al. Liraglutide and renal outcomes in type 2 diabetes. N Engl J Med. 2017;377(9):839–48.

    CAS  PubMed  Google Scholar 

  51. Wadden TA, et al. Effect of subcutaneous semaglutide vs placebo as an adjunct to intensive behavioral therapy on body weight in adults with overweight or obesity: the STEP 3 Randomized Clinical Trial. JAMA. 2021;325(14):1403–13.

    CAS  PubMed  Google Scholar 

  52. Rubino D, et al. Effect of continued weekly subcutaneous semaglutide vs placebo on weight loss maintenance in adults with overweight or obesity: the STEP 4 Randomized Clinical Trial. JAMA. 2021;325(14):1414–25.

    CAS  PubMed  Google Scholar 

  53. McGuire DK, et al. Association of SGLT2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes: a meta-analysis. JAMA Cardiology. 2021;6(2):148–58.

    PubMed  Google Scholar 

  54. Perkovic V, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295–306.

    CAS  PubMed  Google Scholar 

  55. Zannad F, et al. SGLT2 inhibitors in patients with heart failure with reduced ejection fraction: a meta-analysis of the EMPEROR-Reduced and DAPA-HF trials. Lancet. 2020;396(10254):819–29.

    PubMed  Google Scholar 

  56. Yancy CW, et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137–61.

    PubMed  Google Scholar 

  57. Cooper, L.B., et al., Use of mineralocorticoid receptor antagonists in patients with heart failure and comorbid diabetes mellitus or chronic kidney disease. Journal of the American Heart Association, 2017. 6(12): p. e006540.

  58. Bakris GL, et al. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. N Engl J Med. 2020;383(23):2219–29.

    CAS  PubMed  Google Scholar 

  59. Pitt, B., et al., Cardiovascular events with finerenone in kidney disease and type 2 diabetes. N Engl J Med, 2021.

  60. Pitt B, et al. Safety and tolerability of the novel non-steroidal mineralocorticoid receptor antagonist BAY 94–8862 in patients with chronic heart failure and mild or moderate chronic kidney disease: a randomized, double-blind trial. Eur Heart J. 2013;34(31):2453–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Libby P, Hansson GK. From focal lipid storage to systemic inflammation: JACC review topic of the week. J Am Coll Cardiol. 2019;74(12):1594–607.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Ross R. Atherosclerosis–an inflammatory disease. N Engl J Med. 1999;340(2):115–26.

    CAS  PubMed  Google Scholar 

  63. Cinoku, I.I., C.P. Mavragani, and H.M. Moutsopoulos, Atherosclerosis: beyond the lipid storage hypothesis. The role of autoimmunity. Eur J Clin Invest, 2020. 50(2): p. e13195.

  64. Dregan A, et al. Chronic inflammatory disorders and risk of type 2 diabetes mellitus, coronary heart disease, and stroke: a population-based cohort study. Circulation. 2014;130(10):837–44.

    CAS  PubMed  Google Scholar 

  65. Murphy AJ, Febbraio MA. Immune-based therapies in cardiovascular and metabolic diseases: past, present and future. Nat Rev Immunol. 2021;21(10):669–79.

    CAS  PubMed  Google Scholar 

  66. Shaposhnik Z, et al. The synergistic inhibition of atherogenesis in apoE-/- mice between pravastatin and the sPLA2 inhibitor varespladib (A-002). J Lipid Res. 2009;50(4):623–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Nicholls SJ, et al. Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 Randomized Clinical Trial. JAMA. 2014;311(3):252–62.

    CAS  PubMed  Google Scholar 

  68. Wilensky RL, et al. Inhibition of lipoprotein-associated phospholipase A2 reduces complex coronary atherosclerotic plaque development. Nat Med. 2008;14(10):1059–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. White HD, et al. Darapladib for preventing ischemic events in stable coronary heart disease. N Engl J Med. 2014;370(18):1702–11.

    CAS  PubMed  Google Scholar 

  70. Ridker PM, et al. Low-dose methotrexate for the prevention of atherosclerotic events. N Engl J Med. 2019;380(8):752–62.

    CAS  PubMed  Google Scholar 

  71. Chung ES, et al. Randomized, double-blind, placebo-controlled, pilot trial of infliximab, a chimeric monoclonal antibody to tumor necrosis factor-alpha, in patients with moderate-to-severe heart failure: results of the anti-TNF Therapy Against Congestive Heart Failure (ATTACH) trial. Circulation. 2003;107(25):3133–40.

    CAS  PubMed  Google Scholar 

  72. Esser N, Paquot N, Scheen AJ. Anti-inflammatory agents to treat or prevent type 2 diabetes, metabolic syndrome and cardiovascular disease. Expert Opin Investig Drugs. 2015;24(3):283–307.

    CAS  PubMed  Google Scholar 

  73. Bernstein LE, et al. Effects of etanercept in patients with the metabolic syndrome. Arch Intern Med. 2006;166(8):902–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Tardif JC, et al. Effects of succinobucol (AGI-1067) after an acute coronary syndrome: a randomised, double-blind, placebo-controlled trial. Lancet. 2008;371(9626):1761–8.

    CAS  PubMed  Google Scholar 

  75. Ridker PM, et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377(12):1119–31.

    CAS  PubMed  Google Scholar 

  76. Levine GN, et al. 2016 ACC/AHA guideline focused update on duration of dual antiplatelet therapy in patients with coronary artery disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2016;68(10):1082–115.

    PubMed  Google Scholar 

  77. Mega JL, et al. Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis. JAMA. 2010;304(16):1821–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Klein MD, et al. Clinical utility of <i>CYP2C19</i> genotyping to guide antiplatelet therapy in patients with an acute coronary syndrome or undergoing percutaneous coronary intervention. Arterioscler Thromb Vasc Biol. 2019;39(4):647–52.

    CAS  PubMed  Google Scholar 

  79. Tada H, et al. Rare and deleterious mutations in ABCG5/ABCG8 genes contribute to mimicking and worsening of familial hypercholesterolemia phenotype. Circ J. 2019;83(9):1917–24.

    CAS  PubMed  Google Scholar 

  80. Tada H, et al. Sitosterolemia, hypercholesterolemia, and coronary artery disease. J Atheroscler Thromb. 2018;25(9):783–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Tada H, et al. Beneficial effect of ezetimibe-atorvastatin combination therapy in patients with a mutation in ABCG5 or ABCG8 gene. Lipids Health Dis. 2020;19(1):3.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Ogura M. PCSK9 inhibition in the management of familial hypercholesterolemia. J Cardiol. 2018;71(1):1–7.

    PubMed  Google Scholar 

  83. FDA approves first treatment for weight management for people with certain rare genetic conditions. 2020.

  84. Akbari, P., et al., Sequencing of 640,000 exomes identifies <i>GPR75</i> variants associated with protection from obesity. Science, 2021. 373(6550): p. eabf8683.

  85. Hodatsu A, et al. Compound heterozygosity deteriorates phenotypes of hypertrophic cardiomyopathy with founder MYBPC3 mutation: evidence from patients and zebrafish models. Am J Physiol Heart Circ Physiol. 2014;307(11):H1594–604.

    CAS  PubMed  Google Scholar 

  86. Khera AV, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Tada H, Kawashiri MA, Yamagishi M. Comprehensive genotyping in dyslipidemia: mendelian dyslipidemias caused by rare variants and Mendelian randomization studies using common variants. J Hum Genet. 2017;62(4):453–8.

    CAS  PubMed  Google Scholar 

  88. Byrd AS, Toth AT, Stanford FC. Racial disparities in obesity treatment. Curr Obes Rep. 2018;7(2):130–8.

    PubMed  PubMed Central  Google Scholar 

  89. Committee, P.E. Bariatric surgery procedures. 2021 [cited 2021 November 21].

  90. McCarty TR, Thompson CC. The current state of bariatric endoscopy. Dig Endosc. 2021;33(3):321–34.

    PubMed  Google Scholar 

  91. Coakley M, et al. Dialogues on diversifying clinical trials: successful strategies for engaging women and minorities in clinical trials. J Womens Health (Larchmt). 2012;21(7):713–6.

    Google Scholar 

  92. Sirugo G, Williams SM, Tishkoff SA. The Missing Diversity in Human Genetic Studies. Cell. 2019;177(1):26–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Bentley AR, Callier S, Rotimi CN. Diversity and inclusion in genomic research: why the uneven progress? J Community Genet. 2017;8(4):255–66.

    PubMed  PubMed Central  Google Scholar 

  94. Kahn, J.M., et al., Strategies to improve diversity, equity, and inclusion in clinical trials. Cancer, 2021.

  95. Clark, L.T., et al., Increasing Diversity in Clinical Trials: Overcoming Critical Barriers. Current Problems in Cardiology, 2019. 44(5): p. 148-172. In this paper, the authors investigated barriers to participation in clinical trials in minority communities both through literatire review and direct interviews with stakeholers. They identified critical themes in barriers and proposed solutions for how to increase minority participation in clinical research.

Download references

Funding

National Institutes of Health NIDDK P30 DK040561 (FCS) and L30 DK118710 (FCS).

Author information

Authors and Affiliations

  1. Massachusetts General Hospital Department of Medicine, 55 Fruit Street, Boston, MA, 02114, USA

    Eftitan Y. Akam, Akua A. Nuako & Afkera K. Daniel

  2. Massachusetts General Hospital Department of Pediatrics, 55 Fruit Street, Boston, MA, 02114, USA

    Eftitan Y. Akam & Afkera K. Daniel

  3. Harvard Medical School, Boston, MA, 02115, USA

    Eftitan Y. Akam, Akua A. Nuako, Afkera K. Daniel & Fatima Cody Stanford

  4. Massachusetts General Hospital, MGH Weight Center, Department of Medicine-Division of Endocrinology-Neuroendocrine, Department of Pediatrics-Division of Endocrinology, Nutrition Obesity Research Center at Harvard (NORCH), 50 Staniford Street, Boston, MA, 02114, USA

    Fatima Cody Stanford

Authors
  1. Eftitan Y. Akam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akua A. Nuako
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Afkera K. Daniel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fatima Cody Stanford
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eftitan Y. Akam.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Macrovascular Complications in Diabetes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akam, E.Y., Nuako, A.A., Daniel, A.K. et al. Racial Disparities and Cardiometabolic Risk: New Horizons of Intervention and Prevention. Curr Diab Rep 22, 129–136 (2022). https://doi.org/10.1007/s11892-022-01451-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11892-022-01451-6

Keywords

Search

Navigation