Greater variability in lipid measurements associated with cardiovascular disease and mortality: A 10‐year diabetes cohort study
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Aim: To examine the associations between variability in lipids and the risk of cardiovascular disease (CVD) and mortality in patients with type 2 diabetes based on low‐density lipoprotein‐cholesterol (LDL‐C), the total cholesterol (TC) to high‐density lipoprotein‐cholesterol (HDL‐C) ratio and triglycerides (TG). Materials and methods: A retrospective cohort study included 125 047 primary care patients with type 2 diabetes aged 45‐84 years without CVD during 2008‐2012. The variability of LDL‐C, TC to HDL‐C and TG was determined using the standard deviation of variables in a mixed effects model to minimize regression dilution bias. The associations between variability in lipids and CVD and mortality risk were assessed by Cox regression. Subgroup analyses based on patients’ baseline characteristics were also conducted. Results: A total of 19 913 CVD events and 15 329 mortalities were recorded after a median follow‐up period of 77.5 months (0.8 million person‐years), suggesting a positive linear relationship between variability in lipids and the risk of CVD and mortality. Each unit increase in the variability of LDL‐C (mmol/L), the TC to HDL‐C ratio and TG (mmol/L) was associated with a 27% (HR: 1.27 [95% CI: 1.20‐1.34]), 31% (HR:1.31 [95% CI: 1.25‐1.38]) and 9% (HR: 1.09 [95% CI: 1.04‐1.15]) increase in the risk of composite endpoint of CVD and mortality, respectively. Age‐specific effects were also found when comparing LDL‐C variability, with patients aged 45‐54 years (HR: 1.70 [95% CI: 1.42‐2.02]) exhibiting a 53% increased risk for the composite endpoints than those aged 75‐84 years (HR: 1.11 [95% CI: 1.01‐1.23]). Similar age effects were observed for both the TC to HDL‐C ratio and TG variability. Significant associations remained consistent among most of the subgroups. Conclusions: Variability in respective lipids are significant factors in predicting CVD and mortality in primary care patients with type 2 diabetes, with the strongest effects related to LDL‐C and the TC to HDL‐C ratio and most significant in the younger age group of patients aged 45‐54 years. Further study is warranted to confirm these findings.
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For T2DM patients, glycemic or lipid values and their variability can significantly predict all-cause mortality and the occurrence of complications, including micro- and macrovascular complications.
“For T2DM patients, glycemic or lipid values and their variability can significantly predict all-cause mortality and the occurrence of complications, including micro- and macrovascular complications [7, 8].”
From The Effect of Berberine on Metabolic Profiles in Type 2 Diabetic Patients: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials (2021) · cites this paper as [7]
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Cites
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742 · see citing works
Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972) · Clinical Chemistry · doi:10.1093/clinchem/18.6.499 · Cited by 32,423 · see citing works
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works
Antihyperlipidemic and Biochemical Activities of Mcy Protein in Streptozotocin Induced Diabetic Rats
Marella, S., Maddirela, D. R., Badri, K. R., Kumar, M. V. J., & Chippada, A. R. (2015) · Cellular Physiology and Biochemistry · doi:10.1159/000373954 · Cited by 5,938 · see citing works
2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2019
(2018) · Diabetes Care · doi:10.2337/dc19-s002 · Cited by 2,738 · see citing works
Hippisley-Cox, J., Coupland, C., & Brindle, P. (2017) · BMJ · doi:10.1136/bmj.j2099 · Cited by 1,703 · see citing works
Chronic Kidney Disease and Mortality Risk
Tonelli, M. A., Wiebe, N., Culleton, B. F., House, A. A., Rabbat, C., Fok, M. N., McAlister, F. A., & Garg, A. X. (2006) · Journal of the American Society of Nephrology · doi:10.1681/asn.2005101085 · Cited by 1,655 · see citing works
Rapsomaniki, E., Timmis, A., George, J., Pujades-Rodriguez, M., Shah, A. D., Denaxas, S., White, I. R., Caulfield, M. J., Deanfield, J. E., Smeeth, L., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60685-1 · Cited by 1,643 · see citing works
Association of cardiometabolic multimorbidity with mortality
Di Angelantonio, E., Kaptoge, S., Wormser, D., Willeit, P., Butterworth, A. S., Bansal, N., O’Keeffe, L. M., Gao, P., Wood, A. M., Burgess, S., et al. (2015) · JAMA · doi:10.1001/jama.2015.7008 · Cited by 1,155 · see citing works
Multiple Imputation for Nonresponse in Surveys.
Kershaw, C. D., & Rubin, D. B. (1989) · Biometrics · doi:10.2307/2532070 · Cited by 392 · see citing works
Hedeker, D., Mermelstein, R. J., & Demirtaş, H. (2007) · Biometrics · doi:10.1111/j.1541-0420.2007.00924.x · Cited by 333 · see citing works
Serban, M.-C., Colantonio, L. D., Manthripragada, A. D., Monda, K. L., Bittner, V. A., Banach, M., Chen, L., Huang, L., Dent, R., Kent, S. T., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.12.036 · Cited by 318 · see citing works
Improved estimates of floating absolute risk
Plummer, M. (2003) · Statistics in Medicine · doi:10.1002/sim.1485 · Cited by 267 · see citing works
Kim, M. K., Han, K., Kim, H., Park, Y., Kwon, H., Yoon, K., & Lee, S. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx585 · Cited by 264 · see citing works
Cardiovascular outcomes associated with use of clarithromycin: population based study
Wong, A. Y. S., Root, A. A., Douglas, I., Chui, C. S. L., Chan, E. W., Ghebremichael‐Weldeselassie, Y., Siu, C., Smeeth, L. L., & Wong, I. C. K. (2016) · BMJ · doi:10.1136/bmj.h6926 · Cited by 237 · see citing works
Self-citationChan, E. W., Lau, W. C. Y., Leung, W. K., Mok, M. T. C., He, Y., Tong, T. S. M., & Wong, I. C. K. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.05.002 · Cited by 227 · see citing works
Self-citationVisit-to-Visit Low-Density Lipoprotein Cholesterol Variability and Risk of Cardiovascular Outcomes
Bangalore, S., Breazna, A., DeMicco, D. A., Wun, C., & Messerli, F. H. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.02.017 · Cited by 218 · see citing works
Statin non-adherence and residual cardiovascular risk: There is need for substantial improvement
Banach, M., Stulc, T., Dent, R., & Toth, P. P. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.09.075 · Cited by 197 · see citing works
Ceriello, A., Cosmo, S. D., Rossi, M. C., Lucisano, G., Genovese, S., Pontremoli, R., Fioretto, P., Giorda, C. B., Pacilli, A., Viazzi, F. C., et al. (2017) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12976 · Cited by 100 · see citing works
Clark, D., Nicholls, S. J., John, J. S., Elshazly, M. B., Kapadia, S. R., Tuzcu, E. M., Nissen, S. E., & Puri, R. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy209 · Cited by 93 · see citing works
Variations in lipid levels according to menstrual cycle phase: clinical implications
Mumford, S. L., Dasharathy, S. S., & Pollack, A. Z. (2011) · Clinical Lipidology · doi:10.2217/clp.11.9 · Cited by 90 · see citing works
Boey, E., Gay, G. M. W., Poh, K. K., Yeo, T., Tan, H. C., & Lee, C. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.10.110 · Cited by 76 · see citing works
Low Lipid Levels and High Variability are Associated With the Risk of New‐Onset Atrial Fibrillation
Lee, H., Lee, S., Choi, E., Han, K., & Oh, S. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012771 · Cited by 76 · see citing works
Bangalore, S., Fayyad, R. S., Messerli, F. H., Laskey, R. E., DeMicco, D. A., Kastelein, J. J. P., & Waters, D. D. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.10.037 · Cited by 75 · see citing works
Variability in Total Cholesterol Is Associated With the Risk of End-Stage Renal Disease
Kim, M. K., Han, K., Koh, E. S., et al. (2017) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.117.309803 · Cited by 69 · see citing works
Visit-to-visit variability of lipid measurements as predictors of cardiovascular events
Waters, D. D., Bangalore, S., Fayyad, R. S., DeMicco, D. A., Laskey, R. E., Melamed, S., & Barter, P. J. (2017) · Journal of clinical lipidology · doi:10.1016/j.jacl.2017.12.003 · Cited by 68 · see citing works
Lee, E. Y., Yang, Y., Kim, H., Cho, J., Yoon, K., Chung, W. S., Lee, S., & Chang, K. (2018) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2018.10.012 · Cited by 66 · see citing works
A Clinical Practice Guideline to Guide a System Approach to Diabetes Care in Hong Kong
Lau, I. T. (2017) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2017.41.2.81 · Cited by 64 · see citing works
Atrial Fibrillation and Non-cardiovascular Diseases: A Systematic Review
Ferreira, C., Providencia, R. A., Ferreira, M. J., & Gonçalves, L. M. M. (2015) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20150142 · Cited by 52 · see citing works
Wan, E., Fung, C., Yu, E., Fong, D., Chen, J., & Lam, C. (2016) · Diabetes Care · doi:10.2337/dc16-1617 · Cited by 49 · see citing works
Self-citationBarrett, J., Huille, R., Parker, R., Yano, Y., & Griswold, M. (2018) · Statistics in Medicine · doi:10.1002/sim.8074 · Cited by 47 · see citing works
Self-citationBarrett, J., Huille, R., Parker, R., Yano, Y., & Griswold, M. E. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.34938 · Cited by 33 · see citing works
Self-citationWan, E., Fong, D., Fung, C., Yu, E., Chin, W. Y., Chan, A. K. L., & Lam, C. (2018) · Diabetes, obesity and metabolism · doi:10.1111/dom.13066 · Cited by 31 · see citing works
Self-citationRoh, E., Chung, H. S., Lee, J. S., Kim, J. A., Lee, Y., Hong, S., Kim, N. H., Yoo, H. J., Seo, J. A., Kim, S. G., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215687 · Cited by 27 · see citing works
Comparison of various lipid parameters in association of target organ damage: a cohort study
Chi, C., Teliewubai, J., Lu, Y., Fan, X., Yu, S., Xiong, J., Zhou, Y., Ji, H., Zhang, Y., & Xu, Y. (2018) · Lipids in Health and Disease · doi:10.1186/s12944-018-0800-y · Cited by 24 · see citing works
Biomarker variability and cardiovascular disease residual risk
Simpson, W. G. (2019) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000627 · Cited by 23 · see citing works
Hsu, W., Lai, C.-W., Chen, S., Chiou, H.-Y. C., Hsiao, P., Shin, S., & Lee, M. (2019) · Endocrine Practice · doi:10.4158/ep-2019-0002 · Cited by 17 · see citing works
Research Progress of Hypertriglyceridemia and Coronary Heart Disease
Ji, Y., & Bai, C. (2018) · Heart and Mind · doi:10.4103/hm.hm_2_19 · Cited by 13 · see citing works
Variability in atherogenic lipoproteins and coronary artery disease progression
Alfonso, F., Rivero, F., & Sánchez‐Madrid, F. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy348 · Cited by 9 · see citing works
Gu, J. F. (2019) · The Anatolian Journal of Cardiology · doi:10.14744/anatoljcardiol.2019.26428 · Cited by 7 · see citing works
Yan, Y.-Q., Huang, Y., Zhou, D., et al. (2019) · Kidney & Blood Pressure Research · doi:10.1159/000501367 · Cited by 4 · see citing works
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Guo, J., Chen, H., Zhang, X., Lou, W., Zhang, P., Qiu, Y., Zhang, C., Wang, Y., & Liu, W. J. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/2074610 · Cited by 110 · see citing works
Self-citationLee, S., Zhou, J., Wong, W. T., Liu, T., Wu, W. K. K., Wong, I. C. K., Zhang, Q., & Tse, G. (2021) · BMC Endocrine Disorders · doi:10.1186/s12902-021-00751-4 · Cited by 97 · see citing works
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- Authors
- Wan, E. Y. F., Yu, E. Y. T., Chin, W. Y., Barrett, J. K., Mok, A. H. Y., Lau, C. S. T., Wang, Y., Wong, I. C. K., Chan, E. W. Y., & Lam, C. L. K.
- Affiliations
- University of Hong Kong (HK)
- University of Cambridge (GB)
- UCL School of Pharmacy (GB)
- Corresponding author
- Wan, Eric Y. F.
- ORCID
- Venue
- Diabetes, Obesity and Metabolism
- Publisher
- Wiley
- Published
- 2020-06-24
- Volume
- 22
- Issue
- 10
- Pages
- 1777-1788
- ISSN
- 1462-8902 (ISSN-L)
- 1463-1326
- PMC
- PMC7540339
- OpenAlex
- W3031452311
- Cited by
- 52
- References
- 41
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 17 · 2022: 11 · 2021: 6 · 2020: 2
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diabetes Obesity and Metabolism (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Bristol-Myers Squibb
- Pfizer
- Hospital Authority
- Food and Health Bureau (14151181)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- lipid variability, type 2 diabetes, cardiovascular risk, mortality, LDL cholesterol, TC/HDL-C ratio, triglycerides, age-specific effects, regression dilution bias, primary care patients, cardiovascular risk prediction, cardiovascular risk stratification, cardiovascular risk education
- MeSH terms
- Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Lipids, Cholesterol, HDL, Middle Aged, Retrospective Studies, Risk Factors, Triglycerides, Cohort Studies
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