medigraphic.com
ISSN 2954-3835 (Electronic)
ISSN 2683-2828 (Print)

2026, Number S1

<< Back

Cardiovasc Metab Sci 2026; 37 (S1)

Multisociety Mexican Consensus on the integration of polygenic risk in cardiovascular risk stratification: implications for precision cardiovascular medicine in Mexico

Parcero-Valdés JJ, Magaña-Serrano A, Arias-Mendoza MA, Narváez-Oriani C, Altamirano-Cardoso E, Barrios V, Alcocer Díaz-Barreiro L, Pavia-López A, Gómez-Álvarez E

Language: English
References: 41
Page: s23-s33
PDF size: 926.33 Kb.


ABSTRACT

Introduction: cardiovascular disease remains the leading cause of mortality in Mexico, accounting for approximately one in four deaths nationwide. The epidemiological transition is characterized by a high prevalence of obesity, type 2 diabetes mellitus, hypertension, and atherogenic dyslipidemia from early stages of life, resulting in prolonged cumulative cardiometabolic exposure and earlier onset of atherosclerotic events. Traditional risk models, based on phenotypic variables and strongly dependent on chronological age, tend to underestimate biological susceptibility in younger individuals. Objective: to establish a multisociety position on the clinical use of Polygenic Risk Scores (PRS) in Mexico and to define a national implementation strategy linked to real-world evidence generation through the PRS-MX Registry. Material and methods: this consensus document was developed through a structured literature review and a modified Delphi methodology involving national experts in clinical cardiology, interventional cardiology, lipidology, genetics, and public health. Results: PRS is independent of traditional risk factors, improves risk reclassification in primary prevention, and identifies individuals with greater absolute benefit from lipid-lowering therapies. A three-dimensional model integrating phenotype, anatomy, and genotype is proposed. Conclusions: selective implementation of PRS in Mexico represents a step toward precision cardiovascular medicine and should be carried out in a stepwise manner linked to national evidence generation.


REFERENCES

  1. Instituto Nacional de Estadística y Geografía. Estadísticasde mortalidad en México 2023. Aguascalientes: INEGI; 2024.

  2. Instituto Nacional de Salud Pública. Encuesta Nacionalde Salud y Nutrición 2022: Resultados nacionales.Cuernavaca: INSP; 2023.

  3. Bello-Chavolla OY, Vargas-Vázquez A, Antonio-VillaNE et al. A high incidence of metabolic syndrometraits in mexicans points at obesity-related metabolicdysfunction. Diabetes Metab Syndr Obes. 2021; 14:1073-1082. doi: 10.2147/DMSO.S266568.

  4. Petermann-Rocha F, Apolinar E, Nazar G etal. Associations of diabesity with all-cause andcardiovascular disease mortality: findings from theMexico City Prospective Study. Diabetes Obes Metab.2024; 26 (6): 2199-2208.

  5. Barquera S, Hernández-Barrera L, Oviedo-Solís C etal. Obesidad en adultos. Salud Publica Mex. 2024; 66: 414-424.

  6. SCORE2 Working Group and ESC Cardiovascular RiskCollaboration. SCORE2 risk prediction algorithms: newmodels to estimate 10-year risk of cardiovascular diseasein Europe. Eur Heart J. 2021; 42 (25): 2439-2454.

  7. Visseren FLJ, Mach F, Smulders YM et al. 2021 ESCGuidelines on cardiovascular disease prevention inclinical practice. Eur Heart J. 2021; 42 (34): 3227-3337.

  8. Ference BA, Yoo W, Alesh I et al. Effect of long-termexposure to lower LDL cholesterol on the risk ofcoronary heart disease. J Am Coll Cardiol. 2012; 60(25): 2631-2639.

  9. Ference BA, Ginsberg HN, Graham I et al. Low-densitylipoproteins cause atherosclerotic cardiovasculardisease. Eur Heart J. 2017; 38 (32): 2459-2472.

  10. Natarajan P, Young R, Stitziel NO et al. Polygenic riskscore identifies subgroup with higher risk for coronaryartery disease. Circulation. 2017; 135 (22): 2091-2101.

  11. Mega JL, Stitziel NO, Smith JG et al. Genetic risk,coronary heart disease events, and the clinical benefit ofstatin therapy. Lancet. 2015; 385 (9984): 2264-2271.

  12. Inouye M, Abraham G, Nelson CP et al. Genomic riskprediction of coronary artery disease in 480,000 adults.J Am Coll Cardiol. 2018; 72 (16): 1883-1893.

  13. Khera AV, Chaffin M, Aragam KG et al. Genomewidepolygenic scores for common diseases identifyindividuals with risk equivalent to monogenicmutations. Nat Genet. 2018; 50 (9): 1219-1224.

  14. Nikpay M, Goel A, Won HH et al. A comprehensivegenome-wide association meta-analysis of coronaryartery disease. Nat Genet. 2015; 47 (10): 1121-1130.

  15. Torkamani A, Wineinger NE, Topol EJ. The personaland clinical utility of polygenic risk scores. Nat RevGenet. 2018; 19 (9): 581-590.

  16. Piepoli MF, Hoes AW, Agewall S et al. 2016 EuropeanGuidelines on cardiovascular disease prevention inclinical practice. Eur Heart J. 2016; 37 (29): 2315-2381.

  17. Mach F, Baigent C, Catapano AL et al. 2019 ESC/EASGuidelines for the management of dyslipidaemias: lipidmodification to reduce cardiovascular risk. Eur HeartJ. 2020; 41 (1): 111-188.

  18. Grundy SM, Stone NJ, Bailey AL et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management ofblood cholesterol: a report of the American Collegeof Cardiology/American Heart Association Task Forceon clinical practice guidelines. Circulation. 2019; 139(25): e1082-e1143.

  19. Budoff MJ, Young R, Burke G et al. Ten-year associationof coronary artery calcium with atheroscleroticcardiovascular disease (ASCVD) events: the multiethnicstudy of atherosclerosis (MESA). Eur Heart J. 2018; 39 (25): 2401-2408.

  20. Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE.Coronary calcium score and cardiovascular risk. J AmColl Cardiol. 2018; 72 (4): 434-447.

  21. Mach F, Baigent C, Catapano AL et al. 2020 ESC/EASdyslipidaemia focused update. Eur Heart J. 2020; 41(1): 111-188.

  22. Iribarren C, Lu M, Elosua R et al. Interplay betweenfamily history and polygenic risk for coronary heartdisease. Am J Prev Cardiol. 2026; 26: 101497. doi:10.1016/j.ajpc.2026.101497.

  23. Mosley JD, Gupta DK, Tan J et al. Predictive accuracyof a polygenic risk score compared with a clinical riskscore for incident coronary heart disease. JAMA. 2020;76 (18): 2117-2127.

  24. Iribarren C, Lu M, Elosua R et al. Polygenic risk andincident coronary heart disease in a large multiethniccohort. Am J Prev Cardiol. 2024; 18: 100661.

  25. Lloyd-Jones DM, Braun LT, Ndumele CE et al. Use ofrisk assessment tools to guide decision-making in theprimary prevention of atherosclerotic cardiovasculardisease: a special report from the American HeartAssociation and American College of Cardiology. JAm Coll Cardiol. 2019; 73 (24): 3153-3167. doi:10.1016/j.jacc.2018.11.005.

  26. Sabatine MS, Giugliano RP, Keech AC et al. Evolocumaband clinical outcomes in patients with cardiovasculardisease. N Engl J Med. 2017; 376 (18): 1713-1722.

  27. Schwartz GG, Steg PG, Szarek M et al. Alirocumaband cardiovascular outcomes after acute coronarysyndrome. N Engl J Med. 2018; 379 (22): 2097-2107.

  28. Gaziano TA, Young CR, Fitzmaurice G et al. Laboratorybasedversus non-laboratory-based cardiovascular riskassessment. Circulation. 2008; 117 (5): 541-548.

  29. Kim JJ, Patel MR, Blaha MJ et al. Coronary arterycalcium scoring in primary prevention. JAMA Cardiol.2021; 6 (10): 1165-1173.

  30. Sniderman AD, Tsimikas S, Fazio S. The severehypercholesterolemia phenotype: clinical diagnosis,management, and emerging therapies. J Am CollCardiol. 2014; 63 (19): 1935-1947.

  31. Bolli A, Di Domenico P, Pastorino R et al. Risk ofcoronary artery disease conferred by LDL cholesteroldepends on polygenic background. Circulation. 2021;143: 1452-1454.

  32. Iribarren C, Lu M, Elosua R et al. Joint considerationof LDL-C and polygenic risk. JACC Adv. 2025; 4 (11):102228.

  33. Harper C, Misra A, Patel AP et al. Polygenic risk scoresenhance LDL cholesterol-based risk stratification. AmJ Prev Cardiol. 2026; 26: 101487.

  34. Moreno-Estrada A, Gignoux CR, Fernández-López JC etal. Human genetics. The genetics of Mexico recapitulatesNative American substructure and affects biomedicaltraits. Science. 2014; 344 (6189): 1280-1285.

  35. Martin AR, Kanai M, Kamatani Y et al. Clinical use ofcurrent polygenic risk scores may exacerbate healthdisparities. Nat Genet. 2019; 51 (4): 584-591.

  36. Duncan L, Shen H, Gelaye B et al. Analysis of polygenicrisk score usage in diverse populations. Nat Commun.2019; 10 (1): 3328.

  37. Peterson RE, Kuchenbaecker K, Walters RK et al.Genome-wide association studies in ancestrally diversepopulations: opportunities, methods, pitfalls, andrecommendations. Cell. 2019; 179 (3): 589-603.

  38. Khera AV, Emdin CA, Drake I et al. Genetic risk,adherence to a healthy lifestyle, and coronary disease.Circulation. 2016; 134 (20): 1570-1580.

  39. Hollands GJ, French DP, Griffin SJ et al. The impactof communicating genetic risks of disease on riskreducinghealth behaviour: systematic review withmeta-analysis. BMJ. 2016; 352: i1102.

  40. Iribarren C, Lu M, Gulati M et al. Interplay betweenlifestyle factors and polygenic risk. Int J CardiolCardiovasc Risk Prev. 2024; 23: 200350.

  41. Nilsen P. Making sense of implementation theories,models and frameworks. Implement Sci. 2015; 10: 53.




2020     |     www.medigraphic.com