Re-examination of statistical relationships between dietary fats and other risk factors, and cardiovascular disease, based on two crucial datasets

Abstract

Cardiovascular disease (CVD) is the major cause of death in many regions around the world, and several of its risk factors might be linked to diets. To improve public health and the understanding of this topic, we look at the recent Minnesota Coronary Experiment (MCE) analysis that used t-test and Cox model to evaluate CVD risks. However, these parametric methods might suffer from three problems: small sample size, right-censored bias, and lack of long-term evidence. To overcome the first of these challenges, we utilize a nonparametric permutation test to examine the relationship between dietary fats and serum total cholesterol. To address the second problem, we use a resampling-based rank test to examine whether the serum total cholesterol level affects CVD deaths. For the third issue, we use some extra-Framingham Heart Study (FHS) data with an A/B test to look for meta-relationship between diets, risk factors, and CVD risks. We show that, firstly, the link between low saturated fat diets and reduction in serum total cholesterol is strong. Secondly, reducing serum total cholesterol does not robustly have an impact on CVD hazards in the diet group. Lastly, the A/B test result suggests a more complicated relationship regarding abnormal diastolic blood pressure ranges caused by diets and how these might affect the associative link between the cholesterol level and heart disease risks. This study not only helps us to deeply analyze the MCE data but also, in combination with the long-term FHS data, reveals possible complex relationships behind diets, risk factors, and heart disease.

Keywords

A/B test / bioinformatics / cardiovascular disease / nonparametric statistics

References

[1] Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, Ahmed M., Aksut B., Alam T., Alam K., et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70:1–25.

[2] Executive Committee on Diet Heart Diseases N. H. I. U. The national diet-heart study. 1968; at the website of the University of Minnesota.

[3] Frantz ID, Jr, Keys A. R01 he 0986-03 research grant application: Effect of a dietary change on human cardiovascular disease “the Minnesota coronary survey”; 1967.

[4] Ramsden CE, Zamora D, Majchrzak-Hong S, Faurot KR, Broste SK, Frantz RP, et al. Re-evaluation of the traditional diet-heart hypothesis: analysis of recovered data from Minnesota coronary experiment (1968-73). BMJ. 2016;353:i1246.

[5] Broste SK. Lifetable analysis of the Minnesota coronary survey. University of Minnesota; 1981.

[6] Frantz ID, Jr, Dawson EA, Ashman PL, Gatewood LC, Bartsch GE, Kuba K, et al. Test of effect of lipid lowering by diet on cardiovascular risk. The minnesota coronary survey. Arteriosclerosis. 1989;9(1):129–35.

[7] Beinortas T, Mahtani KR, Nunan D. Revisiting the diet-heart hypothesis: Critical appraisal of the Minnesota coronary experiment. BMJ. 2017;357:j2108.

[8] Song H, Chen L, Cui Y, Li Q, Wang Q, Fan J, et al. Denoising of mr and ct images using cascaded multi-supervision convolutional neural networks with progressive training. Neurocomputing. 2022;469:354–65.

[9] Xiao M, Liu G, Xie J, Dai Z, Wei Z, Ren Z, et al. 2019nCoVAS: developing the web service for epidemic transmission prediction, genome analysis, and psychological stress assessment for 2019-nCoV. IEEE ACM Trans Comput Biol Bioinf. 2021;18(4):1250–61.

[10] Xiao M, Yang X, Yu J, Zhang L. Cgidla: Developing the web server for cpg island related density and laups (lineage-associated underrepresented permutations) study. IEEE ACM Trans Comput Biol Bioinf. 2020;17(6):2148–54.

[11] Zhang L, Liu G, Kong M, Li T, Wu D, Zhou X, et al. Revealing dynamic regulations and the related key proteins of myelomainitiating cells by integrating experimental data into a systems biological model. Bioinformatics. 2021;37(11):1554–61.

[12] Zhang L, Xiao M, Zhou J, Yu J. Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA). Bioinformatics. 2018;34(21):3624–30.

[13] Lai X, Zhou J, Wessely A, Heppt M, Maier A, Berking C, et al. A disease network-based deep learning approach for characterizing melanoma. Int J Cancer. 2022;150(6):1029–44.

[14] Xia Y, Yang C, Hu N, Yang Z, He X, Li T, et al. Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model. BMC Genom. 2017;18(S1):950.

[15] You Y, Ru X, Lei W, Li T, Xiao M, Zheng H, et al. Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for glioblastoma multiforme. BMC Bioinf. 2020;21(S13):383.

[16] Ramsden CE, Zamora D, Leelarthaepin B, Majchrzak-Hong SF, Faurot KR, Suchindran CM, et al. Use of dietary linoleic acid for secondary prevention of coronary heart disease and death: evaluation of recovered data from the sydney diet heart study and updated meta-analysis. BMJ Br Med J (Clin Res Ed). 2013;346(feb04 3):e8707.

[17] Guan J, Li J, Liang S, Li R, Li X, Shi X, et al. Neuroseg: automated cell detection and segmentation for in vivo two-photon ca²⁺ imaging data. Brain Struct Funct. 2018;223(1):519–33.

[18] Zhang L, Dai Z, Yu J, Xiao M. Cpg-island-based annotation and analysis of human housekeeping genes. Briefings Bioinf. 2021;22(1):515–25.

[19] Ditzhaus M, Janssen A. Bootstrap and permutation rank tests for proportional hazards under right censoring. Lifetime Data Anal. 2020;26(3):493–517.

[20] Zhang L, Zhang L, Guo Y, Xiao M, Feng L, Yang C, et al. Mcdb: a comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction. Acta Pharm Sin B. 2021;11(10):3092–104.

[21] Zhang L, Zhao J, Bi H, Yang X, Zhang Z, Su Y, et al. Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication. Hortic Res. 2021;8(1):62.

[22] Zhang L, Zheng C, Li T, Xing L, Zeng H, Li T, et al. Building up a robust risk mathematical platform to predict colorectal cancer. Complexity. 2017;2017:1–14.

[23] Ross SM. Chapter 15 — simulation, bootstrap statistical methods, and permutation tests. In: Introduction to probability and statistics for engineers and scientists. 4th ed. Boston: Academic Press; 2009. p. 613–40.

[24] Hooper L, Martin N, Jimoh OF, Kirk C, Foster E, Abdelhamid AS. Reduction in saturated fat intake for cardiovascular disease. Cochrane Database Syst Rev. 2020;8:CD011737.

[25] Dawber T. The framingham study: the epidemiology of atherosclerotic disease; 2013.

[26] Mahmood SS, Levy D, Vasan RS, Wang TJ. The framingham heart study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383(9921):999–1008.

RIGHTS & PERMISSIONS

2024 The Authors. Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.