Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-27T06:41:09.311Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic and Environmental Influences on Blood Pressure and Serum Lipids Across Age-Groups

Published online by Cambridge University Press:  31 August 2023

Ke Miao Open the ORCID record for Ke Miao [Opens in a new window] ,
Yutong Wang ,
Weihua Cao ,
Jun Lv ,
Canqing Yu ,
Tao Huang ,
Dianjianyi Sun ,
Chunxiao Liao ,
Yuanjie Pang  and
Runhua Hu
...Show all authors
Ke Miao
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Yutong Wang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Weihua Cao
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Jun Lv
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Canqing Yu
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Tao Huang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Dianjianyi Sun
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Chunxiao Liao
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Yuanjie Pang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Runhua Hu
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Zengchang Pang
Affiliation:
Qingdao Center for Disease Control and Prevention, Qingdao, China
Min Yu
Affiliation:
Zhejiang Center for Disease Control and Prevention, Hangzhou, China
Hua Wang
Affiliation:
Jiangsu Center for Disease Control and Prevention, Nanjing, China
Xianping Wu
Affiliation:
Sichuan Center for Disease Control and Prevention, Chengdu, China
Yu Liu
Affiliation:
Heilongjiang Center for Disease Control and Prevention, Harbin, China
Wenjing Gao*
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
Liming Li*
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
*
Corresponding author: Wenjing Gao; Email: pkuepigwj@126.com; Liming Li; Email: lmlee@vip.163.com
Corresponding author: Wenjing Gao; Email: pkuepigwj@126.com; Liming Li; Email: lmlee@vip.163.com
Get access

Abstract

Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37–0.59) and moderate unique environmental variance (0.30–0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from −0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

Keywords

Heritabilityblood pressureserum lipidsgenetictwin study
Type
Article
Information
Twin Research and Human Genetics , Volume 26 , Issue 3 , June 2023 , pp. 223 - 230
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Society for Twin Studies

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Benyamin, B., Sørensen, T. I. A., Schousboe, K., Fenger, M., Visscher, P. M., & Kyvik, K. O. (2007). Are there common genetic and environmental factors behind the endophenotypes associated with the metabolic syndrome? Diabetologia, 50, 1880–1888. https://doi.org/10.1007/s00125-007-0758-1 CrossRefGoogle ScholarPubMed
Bønaa, K. H., & Thelle, D. S. (1991). Association between blood pressure and serum lipids in a population. The Tromsø Study. Circulation, 83, 1305–1314. https://doi.org/10.1161/01.cir.83.4.1305 CrossRefGoogle Scholar
CARDIoGRAMplusC4D Consortium; Deloukas, P., Kanoni, S., Willenborg, C., Farrall, M., Assimes, T. L., Thompson, J. R., Ingelsson, E., Saleheen, D., Erdmann, J., Goldstein, B. A., Stirrups, K., König, I. R., Cazier, J. B., Johansson, A., Hall, A. S., Lee, J. Y., Willer, C. J., Chambers, J. C., Esko, T., … Samani, N. J. (2013). Large-scale association analysis identifies new risk loci for coronary artery disease. Nature Genetics, 45, 25–33. https://doi.org/10.1038/ng.2480 CrossRefGoogle ScholarPubMed
Deshmukh, M., Lee, H. W., McFarlane, S. I., & Whaley-Connell, A. (2008). Antihypertensive medications and their effects on lipid metabolism. Current Diabetes Reports, 8, 214–220. https://doi.org/10.1007/s11892-008-0037-7 CrossRefGoogle ScholarPubMed
Duan, H., Pang, Z., Zhang, D., Li, S., Kruse, T. A., Kyvik, K. O., Christensen, K., & Tan, Q. (2011). Genetic and environmental dissections of sub-phenotypes of metabolic syndrome in the Chinese population: A twin-based heritability study. Obesity Facts, 4, 99–104. https://doi.org/10.1159/000327735 CrossRefGoogle ScholarPubMed
Gao, W., Cao, W., Lv, J., Yu, C., Wu, T., Wang, S., Meng, L., Wang, D., Wang, Z., Pang, Z., Yu, M., Wang, H., Wu, X., Dong, Z., Wu, F., Jiang, G., Wang, X., Liu, Y., Deng, J., … Li, L. (2019). The Chinese National Twin Registry: A ‘gold mine’ for scientific research. Journal of Internal Medicine, 286, 299–308. https://doi.org/10.1111/joim.12926 CrossRefGoogle Scholar
GBD 2019 Risk Factors Collaborators. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 396, 1223–1249. https://doi.org/10.1016/s0140-6736(20)30752-2 CrossRefGoogle Scholar
Handa, K., Tanaka, H., Shindo, M., Kono, S., Sasaki, J., & Arakawa, K. (1990). Relationship of cigarette smoking to blood pressure and serum lipids. Atherosclerosis, 84, 189–193. https://doi.org/10.1016/0021-9150(90)90090-6 CrossRefGoogle ScholarPubMed
Hart, E. C., & Charkoudian, N. (2014). Sympathetic neural regulation of blood pressure: influences of sex and aging. Physiology (Bethesda), 29, 8–15. https://doi.org/10.1152/physiol.00031.2013 Google ScholarPubMed
Heller, D. A., de Faire, U., Pedersen, N. L., Dahlén, G., & McClearn, G. E. (1993). Genetic and environmental influences on serum lipid levels in twins. New England Journal of Medicine, 328, 1150–1156. https://doi.org/10.1056/nejm199304223281603 CrossRefGoogle ScholarPubMed
Hurtubise, J., McLellan, K., Durr, K., Onasanya, O., Nwabuko, D., & Ndisang, J. F. (2016). The different facets of dyslipidemia and hypertension in atherosclerosis. Current Atherosclerosis Reports, 18, 82. https://doi.org/10.1007/s11883-016-0632-z CrossRefGoogle ScholarPubMed
Jermendy, G., Horváth, T., Littvay, L., Steinbach, R., Jermendy, A. L., Tárnoki, A. D., Tárnoki, D. L., Métneki, J., & Osztovits, J. (2011). Effect of genetic and environmental influences on cardiometabolic risk factors: A twin study. Cardiovascular Diabetology, 10, 96. https://doi.org/10.1186/1475-2840-10-96 CrossRefGoogle ScholarPubMed
Kim, Y. K., Hwang, M. Y., Kim, Y. J., Moon, S., Han, S., & Kim, B.-J. (2016). Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population. Cardiovascular Diabetology, 15, Article 20. https://doi.org/10.1186/s12933-016-0337-1 CrossRefGoogle Scholar
Lepira, F. B., M’Buyamba-Kabangu, J. R., Kayembe, K. P., & Nseka, M. N. (2005). Correlates of serum lipids and lipoproteins in Congolese patients with arterial hypertension. Cardiovascular Journal of South Africa, 16, 249–255.Google ScholarPubMed
Liao, C., Gao, W., Cao, W., Lv, J., Yu, C., Wang, S., Zhao, Q., Pang, Z., Cong, L., Wang, H., Wu, X., & Li, L. (2017). Associations between obesity indicators and blood pressure in Chinese adult twins. Twin Research and Human Genetics, 20, 28–35. https://doi.org/10.1017/thg.2016.95 CrossRefGoogle ScholarPubMed
Liao, C., Gao, W., Cao, W., Lv, J., Yu, C., Wang, S., Zhou, B., Pang, Z., Cong, L., Wang, H., Wu, X., & Li, L. (2015). Associations of body composition measurements with serum lipid, glucose and insulin profile: A Chinese twin study. PLoS One, 10, e0140595. https://doi.org/10.1371/journal.pone.0140595 CrossRefGoogle ScholarPubMed
Liu, H. H., & Li, J. J. (2015). Aging and dyslipidemia: a review of potential mechanisms. Ageing Research Reviews, 19, 43–52. https://doi.org/10.1016/j.arr.2014.12.001 CrossRefGoogle ScholarPubMed
Marlatt, K. L., Pitynski-Miller, D. R., Gavin, K. M., Moreau, K. L., Melanson, E. L., Santoro, N., & Kohrt, W. M. (2022). Body composition and cardiometabolic health across the menopause transition. Obesity (Silver Spring), 30, 14–27. https://doi.org/10.1002/oby.23289 CrossRefGoogle ScholarPubMed
McCaffery, J. M., Pogue-Geile, M. F., Debski, T. T., & Manuck, S. B. (1999). Genetic and environmental causes of covariation among blood pressure, body mass and serum lipids during young adulthood: A twin study. Journal of Hypertension, 17, 1677–1685. doi: 10.1097/00004872-199917120-00004 CrossRefGoogle ScholarPubMed
Pan, L., Yang, Z., Wu, Y., Yin, R. X., Liao, Y., Wang, J., Gao, B., & Zhang, L. (2016). The prevalence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis, 248, 2–9. https://doi.org/10.1016/j.atherosclerosis.2016.02.006 CrossRefGoogle ScholarPubMed
Panizzon, M. S., Hauger, R. L., Sailors, M., Lyons, M. J., Jacobson, K. C., Murray McKenzie, R., Rana, B., Vasilopoulos, T., Vuoksimaa, E., Xian, H., Kremen, W. S., & Franz, C. E. (2015). A new look at the genetic and environmental coherence of metabolic syndrome components. Obesity (Silver Spring), 23, 2499–2507. https://doi.org/10.1002/oby.21257 CrossRefGoogle Scholar
Province, M. A., Tishler, P., & Rao, D. C. (1989). Repeated-measures model for the investigation of temporal trends using longitudinal family studies: application to systolic blood pressure. Genetic Epidemiology, 6, 333–347. https://doi.org/10.1002/gepi.1370060204 CrossRefGoogle ScholarPubMed
Rijsdijk, F. V., & Sham, P. C. (2002). Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics, 3, 119–133. https://doi.org/10.1093/bib/3.2.119 CrossRefGoogle ScholarPubMed
Ruixing, Y., Jinzhen, W., Weixiong, L., Yuming, C., Dezhai, Y., & Shangling, P. (2009). The environmental and genetic evidence for the association of hyperlipidemia and hypertension. Journal of Hypertension, 27, 251–258. https://doi.org/10.1097/HJH.0b013e32831bc74d CrossRefGoogle ScholarPubMed
Simino, J., Kume, R., Kraja, A. T., Turner, S. T., Hanis, C. L., Sheu, W., Chen, I., Jaquish, C., Cooper, R. S., Chakravarti, A., Quertermous, T., Boerwinkle, E., Hunt, S. C., & Rao, D. C. (2014). Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: The Family Blood Pressure Program. Atherosclerosis, 235, 84–93. https://doi.org/10.1016/j.atherosclerosis.2014.04.008 CrossRefGoogle ScholarPubMed
Snieder, H., van Doornen, L. J., & Boomsma, D. I. (1999). Dissecting the genetic architecture of lipids, lipoproteins, and apolipoproteins: lessons from twin studies. Arteriosclerosis, Thrombosis, and Vascular Biology, 19, 2826–2834. https://doi.org/10.1161/01.atv.19.12.2826 CrossRefGoogle ScholarPubMed
Tang, N., Ma, J., Tao, R., Chen, Z., Yang, Y., He, Q., Lv, Y., Lan, Z., & Zhou, J. (2022). The effects of the interaction between BMI and dyslipidemia on hypertension in adults. Scientific Reports, 12, 927. https://doi.org/10.1038/s41598-022-04968-8 CrossRefGoogle ScholarPubMed
Tobin, M. D., Sheehan, N. A., Scurrah, K. J., & Burton, P. R. (2005). Adjusting for treatment effects in studies of quantitative traits: Antihypertensive therapy and systolic blood pressure. Statistics in Medicine, 24, 2911–2935. https://doi.org/10.1002/sim.2165 CrossRefGoogle ScholarPubMed
Wakabayashi, I. (2009). Influence of body weight on the relationships of alcohol drinking with blood pressure and serum lipids in women. Preventive Medicine, 49, 374–379. https://doi.org/10.1016/j.ypmed.2009.07.015 CrossRefGoogle ScholarPubMed
Wang, B., Gao, W., Yu, C., Cao, W., Lv, J., Wang, S., Pang, Z., Cong, L., Wang, H., Wu, X., & Li, L. (2015). Determination of zygosity in adult Chinese twins using the 450k methylation array versus questionnaire data. PLoS One, 10, e0123992. https://doi.org/10.1371/journal.pone.0123992 CrossRefGoogle ScholarPubMed
Webb, T. R., Erdmann, J., Stirrups, K. E., Stitziel, N. O., Masca, N. G., Jansen, H., Kanoni, S., Nelson, C. P., Ferrario, P. G., König, I. R., Eicher, J. D., Johnson, A. D., Hamby, S. E., Betsholtz, C., Ruusalepp, A., Franzén, O., Schadt, E. E., Björkegren, J. L., Weeke, P. E., … Kathiresan, S., Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. (2017). Systematic evaluation of pleiotropy identifies 6 further loci associated with coronary artery disease. Journal of the American College of Cardiology, 69, 823–836. https://doi.org/10.1016/j.jacc.2016.11.056 CrossRefGoogle ScholarPubMed
Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich, M. L., Mora, S., Beckmann, J. S., Bragg-Gresham, J. L., Chang, H. Y., Demirkan, A., Den Hertog, H. M., Do, R., Donnelly, L. A., Ehret, G. B., Esko, T., … Abecasis, G. R.; Global Lipids Genetics Consortium. (2013). Discovery and refinement of loci associated with lipid levels. Nature Genetics, 45, 1274–1283. https://doi.org/10.1038/ng.2797 Google ScholarPubMed
Yao, C., Chen, B. H., Joehanes, R., Otlu, B., Zhang, X., Liu, C., Huan, T., Tastan, O., Cupples, L. A., Meigs, J. B., Fox, C. S., Freedman, J. E., Courchesne, P., O’Donnell, C. J., Munson, P. J., Keles, S., & Levy, D. (2015). Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes. Circulation, 131, 536–549. https://doi.org/10.1161/circulationaha.114.010696 CrossRefGoogle ScholarPubMed
Zhang, S., Liu, X., Yu, Y., Hong, X., Christoffel, K. K., Wang, B., Tsai, H. J., Li, Z., Liu, X., Tang, G., Xing, H., Brickman, W. J., Zimmerman, D., Xu, X., & Wang, X. (2009). Genetic and environmental contributions to phenotypic components of metabolic syndrome: a population-based twin study. Obesity (Silver Spring), 17, 1581–1587. https://doi.org/10.1038/oby.2009.125 CrossRefGoogle ScholarPubMed
Zhang, X., Sun, Z., Zheng, L., Li, J., Liu, S., Xu, C., Li, J., Zhao, F., Hu, D., & Sun, Y. (2007). Prevalence of dyslipidemia and associated factors among the hypertensive rural chinese population. Archives of Medical Research, 38, 432–439. https://doi.org/10.1016/j.arcmed.2006.12.005 CrossRefGoogle ScholarPubMed
Supplementary material: File

Miao et al. supplementary material

Tables S1-S9 and Figure S1

Download Miao et al. supplementary material(File)
File 333 KB