Letter to the Editor Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Jun 15, 2024; 15(6): 1381-1383
Published online Jun 15, 2024. doi: 10.4239/wjd.v15.i6.1381
Age-specific heterogeneity of genetic susceptibility to cardiovascular disease might have opposite outcomes depending on the presence of prediabetes
Chaeyoung Lee, Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, South Korea
ORCID number: Chaeyoung Lee (0000-0002-2940-1778).
Author contributions: Lee C conceived ideas and constructs and wrote this article as the sole author.
Supported by National Research Foundation of Korea, No. 2018R1A2B6004867.
Conflict-of-interest statement: The author declares that there is no conflict of interest regarding the publication of this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chaeyoung Lee, PhD, Professor, Department of Bioinformatics and Life Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, South Korea. clee@ssu.ac.kr
Received: February 27, 2024
Revised: April 21, 2024
Accepted: April 29, 2024
Published online: June 15, 2024

Abstract

Examining age-specific heterogeneity of susceptibility to cardiovascular disease is also essential in individuals without prediabetes to determine its relative size and direction compared to those with prediabetes. Of particular interest, age-specific heterogeneity in genetic susceptibility may exhibit opposite directions depending on the presence or absence of prediabetes.

Key Words: Age-specific difference, Cardiovascular disease, Genetic heterogeneity by age, Genetic susceptibility, Prediabetes

Core Tip: When prediabetes is present, younger individuals may demonstrate a heightened genetic susceptibility to cardiovascular disease (CVD) compared to their older counterparts. Conversely, in the absence of prediabetes, older individuals might harbor a greater genetic predisposition to CVD than their younger counterparts. This hypothetical scenario warrants confirmation through future studies, addressing specific issues highlighted in this article.



TO THE EDITOR

The paper titled ‘Age-specific differences in the association between prediabetes and cardiovascular diseases (CVDs) in China: A national cross-sectional study’ by Xie et al[1] holds significance for readers of the World Journal of Diabetes with an interest in CVD etiology, offering valuable insights and findings in this discipline. The authors propose age-specific susceptibility to CVD in the presence of prediabetes, revealing a CVD prevalence of 0.29% for young individuals (20-40 years old) and 2.85% for older individuals. Additionally, they predict a 10-year CVD risk of 3.84% (67/1746) and 18.50% (934/5049), respectively. These age-specific heterogeneity findings are valuable for preventing CVD in the Chinese population and for developing accurate estimates using more refined age intervals. Nevertheless, there are noteworthy aspects that warrant further consideration.

Intuitively, it is essential to predict CVD risks for young and older individuals without prediabetes. This is crucial not only for the risk estimates themselves but also for understanding their differences, as demonstrated similarly with prediabetes in the study by Xie et al[1]. Furthermore, comparing such age-specific heterogeneity in normal individuals to that in individuals with prediabetes is particularly noteworthy. A relative difference can be calculated as the ratio of age-specific difference in individuals with prediabetes to that in normal individuals. This relative difference, alongside the absolute difference discussed in the article, may be of great concern to geneticists and individuals with prediabetes in the Chinese population.

Xie et al[1] have undertaken additional efforts to elucidate the heterogeneity by age. Their intriguing results reveal that the 10-year CVD risk associated with prediabetes in the younger age group is primarily influenced by family history. Conversely, in the elderly, the risk is significantly influenced by region and residential area. These findings suggest that, with prediabetes, younger individuals might possess a larger genetic susceptibility to CVD than their older counterparts. In contrast, without prediabetes, it is suspected that older individuals might exhibit a greater genetic susceptibility to CVD than younger individuals. This speculation aligns with a previous study conducted in our laboratory, indicating that the elderly population tends to display higher heritability for complex human traits[2]. However, it should not be overlooked that there are notable differences between this heritability study[2] and the study of Xie et al[1]. For instance, the heritability study was conducted using a Korean population and did not analyze susceptibility to CVD, although critical risk factors such as body mass index[3-5], low-density lipoprotein cholesterol[6-8], and pulse pressure[9-11] were considered. If a positive genetic correlation between susceptibilities to CVD and prediabetes exists, the higher heritability in the elderly aligns with study of Xie et al[1], where older individuals with prediabetes show a higher prevalence of family history of CVD (21.80%) compared to younger individuals (15.82%). However, confirming the larger genetic susceptibility to CVD in young individuals with prediabetes should be a priority. This requires utilizing the same study design and, of course, involving the Chinese population. In this regard, there is an urgent need for a future study on CVD risks extended to the young and elderly normal populations, as mentioned in the preceding paragraph.

I would like to replace the last point with the following question: Does age-specific susceptibility to CVD in the presence of prediabetes exist for both genders? If so, are there differences in size or direction by gender? These questions also apply to studies in normal populations proposed in the current article. Emphasizing these questions is crucial, given that CVD risk factors generally differ between men and women[12-15]. They can be addressed by analytical models that efficiently explain the heterogeneity by gender.

I believe that such a study dissecting the causal factors for CVD in normal individuals and comparing them to those in individuals with prediabetes would be valuable not only for the normal individuals but also for individuals with prediabetes. Estimating heritability in a mixed model framework[16-18] is also warranted to comprehend the age-specific heterogeneity of genetic susceptibility to CVD and to confirm potential opposite directions depending on the presence or absence of prediabetes. This analysis additionally provides reasonable risk prediction scores for the CVD genetic susceptibility of individuals[19]. The underlying genetic etiology of CVD will further help to understand its pathophysiological mechanisms linked to prediabetes and accelerate the era of precision cardiology[20,21].

ACKNOWLEDGEMENTS

The author would like to thank the editor, anonymous reviewers, company editor-in-chief for their helpful comments on an earlier version of this article.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Endocrinology and metabolism

Country of origin: South Korea

Peer-review report’s classification

Scientific Quality: Grade B

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade A

P-Reviewer: Chauhan S, United States S-Editor: Lin C L-Editor: A P-Editor: Yuan YY

References
1.  Xie S, Yu LP, Chen F, Wang Y, Deng RF, Zhang XL, Zhang B. Age-specific differences in the association between prediabetes and cardiovascular diseases in China: A national cross-sectional study. World J Diabetes. 2024;15:240-250.  [PubMed]  [DOI]  [Cited in This Article: ]
2.  Lee D, Lee C. Age- and gender-dependent heterogeneous proportion of variation explained by SNPs in quantitative traits reflecting human health. Age (Dordr). 2015;37:19.  [PubMed]  [DOI]  [Cited in This Article: ]
3.  Bjerregaard LG, Adelborg K, Baker JL. Change in body mass index from childhood onwards and risk of adult cardiovascular disease(). Trends Cardiovasc Med. 2020;30:39-45.  [PubMed]  [DOI]  [Cited in This Article: ]
4.  Samson R, Ennezat PV, Le Jemtel TH, Oparil S. Cardiovascular Disease Risk Reduction and Body Mass Index. Curr Hypertens Rep. 2022;24:535-546.  [PubMed]  [DOI]  [Cited in This Article: ]
5.  Kibret KT, Strugnell C, Backholer K, Peeters A, Tegegne TK, Nichols M. Life-course trajectories of body mass index and cardiovascular disease risks and health outcomes in adulthood: Systematic review and meta-analysis. Obes Rev. 2024;25:e13695.  [PubMed]  [DOI]  [Cited in This Article: ]
6.  Packard C, Chapman MJ, Sibartie M, Laufs U, Masana L. Intensive low-density lipoprotein cholesterol lowering in cardiovascular disease prevention: opportunities and challenges. Heart. 2021;107:1369-1375.  [PubMed]  [DOI]  [Cited in This Article: ]
7.  Duarte Lau F, Giugliano RP. Lipoprotein(a) and its Significance in Cardiovascular Disease: A Review. JAMA Cardiol. 2022;7:760-769.  [PubMed]  [DOI]  [Cited in This Article: ]
8.  Wilkinson MJ, Lepor NE, Michos ED. Evolving Management of Low-Density Lipoprotein Cholesterol: A Personalized Approach to Preventing Atherosclerotic Cardiovascular Disease Across the Risk Continuum. J Am Heart Assoc. 2023;12:e028892.  [PubMed]  [DOI]  [Cited in This Article: ]
9.  Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, McManus RJ. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098.  [PubMed]  [DOI]  [Cited in This Article: ]
10.  Niiranen TJ, Kalesan B, Mitchell GF, Vasan RS. Relative Contributions of Pulse Pressure and Arterial Stiffness to Cardiovascular Disease. Hypertension. 2019;73:712-717.  [PubMed]  [DOI]  [Cited in This Article: ]
11.  Lloyd-Jones DM. Cumulative Blood Pressure Measurement for Cardiovascular Disease Prediction: Promise and Pitfalls. J Am Coll Cardiol. 2022;80:1156-1158.  [PubMed]  [DOI]  [Cited in This Article: ]
12.  Winham SJ, de Andrade M, Miller VM. Genetics of cardiovascular disease: Importance of sex and ethnicity. Atherosclerosis. 2015;241:219-228.  [PubMed]  [DOI]  [Cited in This Article: ]
13.  Singh GM, Becquart N, Cruz M, Acevedo A, Mozaffarian D, Naumova EN. Spatiotemporal and Demographic Trends and Disparities in Cardiovascular Disease Among Older Adults in the United States Based on 181 Million Hospitalization Records. J Am Heart Assoc. 2019;8:e012727.  [PubMed]  [DOI]  [Cited in This Article: ]
14.  Ribeiro Rosa K, Fruschein Annichino R, de Azevedo E Souza Munhoz M, Gomes Machado E, Marchi E, Castano-Betancourt MC. Role of central obesity on pain onset and its association with cardiovascular disease: a retrospective study of a hospital cohort of patients with osteoarthritis. BMJ Open. 2022;12:e066453.  [PubMed]  [DOI]  [Cited in This Article: ]
15.  González-Del-Hoyo M, Rossello X, Peral V, Pocock S, Van de Werf F, Chin CT, Danchin N, Lee SW, Medina J, Huo Y, Bueno H. Impact of standard modifiable cardiovascular risk factors on 2-year all-cause mortality: Insights from an international cohort of 23,489 patients with acute coronary syndrome. Am Heart J. 2023;264:20-30.  [PubMed]  [DOI]  [Cited in This Article: ]
16.  Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348-354.  [PubMed]  [DOI]  [Cited in This Article: ]
17.  Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42:355-360.  [PubMed]  [DOI]  [Cited in This Article: ]
18.  Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76-82.  [PubMed]  [DOI]  [Cited in This Article: ]
19.  Lee C. Best Linear Unbiased Prediction of Individual Polygenic Susceptibility to Sporadic Vascular Dementia. J Alzheimers Dis. 2016;53:1115-1119.  [PubMed]  [DOI]  [Cited in This Article: ]
20.  Sethi Y, Patel N, Kaka N, Kaiwan O, Kar J, Moinuddin A, Goel A, Chopra H, Cavalu S. Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review. J Clin Med. 2023;12.  [PubMed]  [DOI]  [Cited in This Article: ]
21.  Bamba H, Singh G, John J, Inban P, Prajjwal P, Alhussain H, Marsool MDM. Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A systematic review. Curr Probl Cardiol. 2024;49:102470.  [PubMed]  [DOI]  [Cited in This Article: ]