Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Special Issue: Current evidence and perspectives for hypertension management in Asia
  • Published:

An increase in calculated small dense low-density lipoprotein cholesterol predicts new onset of hypertension in a Japanese cohort

Hypertension Research volume 46, pages 2635–2645 (2023)Cite this article

Abstract

A disorder of lipid metabolism is involved in cardiovascular diseases including hypertension. A high level of small dense low-density lipoprotein cholesterol (sdLDL-C) is a strong risk factor for atherosclerotic cardiovascular disease. However, the association between sdLDL-C and hypertension has not been fully investigated. We investigated the associations between the development of hypertension during a 10-year period and levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides (TG), and LDL-C and sdLDL-C calculated by using the Sampson equations in 28,990 Japanese subjects who received annual health examinations. After exclusion of subjects with missing data, those with hypertension, and those with TG ≥ 800 mg/dL at baseline, a total of 15,177 subjects (men/women: 9374/5803, mean age: 46 years) were recruited. During the 10-year period, 2379 men (25.4%) and 724 women (12.5%) had new onset of hypertension. Multivariable Cox proportional hazard model analyses showed that levels of HDL-C, non-HDL-C, TG and sdLDL-C, but not levels of TC and LDL-C, were independent risk factors for the development of hypertension after adjustment of age, sex, family history of hypertension, systolic blood pressure, obesity, current smoking habit, alcohol drinking habit, estimated glomerular filtration rate, diagnosis of diabetes mellitus and use of lipid-lowering drugs and that the adjusted risk of sdLDL-C (per 1-standard deviation) was highest (hazard ratio [95% confidence interval: 1.09 [1.05–1.13]). The addition of sdLDL-C to traditional risk factors for hypertension significantly improved the discriminatory capability, which was better than that of other lipid fractions. In conclusion, a high level of calculated sdLDL-C predicts the development of hypertension.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Colantonio LD, Booth JN 3rd, Bress AP, Whelton PK, Shimbo D, Levitan EB, et al. 2017 ACC/AHA Blood Pressure Treatment Guideline Recommendations and Cardiovascular Risk. J Am Coll Cardiol. 2018;72:1187–97.

    PubMed  PubMed Central  Google Scholar 

  2. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens Res. 2019;42:1235–481.

    PubMed  Google Scholar 

  3. Collaboration NCDRF. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398:957–80.

  4. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139:e1046–e81.

    PubMed  Google Scholar 

  5. Osanami A, Tanaka M, Furuhashi M, Ohnishi H, Hanawa N, Yamashita T, et al. Increased LDL-cholesterol level is associated with deterioration of renal function in males. Clin Kidney J. 2022;15:1888–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. McNamara JR, Small DM, Li Z, Schaefer EJ. Differences in LDL subspecies involve alterations in lipid composition and conformational changes in apolipoprotein B. J Lipid Res. 1996;37:1924–35.

    CAS  PubMed  Google Scholar 

  7. Berneis KK, Krauss RM. Metabolic origins and clinical significance of LDL heterogeneity. J Lipid Res. 2002;43:1363–79.

    CAS  PubMed  Google Scholar 

  8. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.

    CAS  PubMed  Google Scholar 

  9. Sampson M, Ling C, Sun Q, Harb R, Ashmaig M, Warnick R, et al. A New Equation for Calculation of Low-Density Lipoprotein Cholesterol in Patients With Normolipidemia and/or Hypertriglyceridemia. JAMA Cardiol. 2020;5:540–8.

    PubMed  Google Scholar 

  10. Hirano T. Pathophysiology of Diabetic Dyslipidemia. J Atheroscler Thromb. 2018;25:771–82.

    CAS  PubMed Central  Google Scholar 

  11. Ito Y, Fujimura M, Ohta M, Hirano T. Development of a homogeneous assay for measurement of small dense LDL cholesterol. Clin Chem. 2011;57:57–65.

    CAS  PubMed  Google Scholar 

  12. Hirano T, Ito Y, Yoshino G. Measurement of small dense low-density lipoprotein particles. J Atheroscler Thromb. 2005;12:67–72.

    CAS  PubMed  Google Scholar 

  13. Hayashi T, Koba S, Ito Y, Hirano T. Method for estimating high sdLDL-C by measuring triglyceride and apolipoprotein B levels. Lipids Health Dis. 2017;16:21.

    PubMed Central  Google Scholar 

  14. Sampson M, Wolska A, Warnick R, Lucero D, Remaley AT. A New Equation Based on the Standard Lipid Panel for Calculating Small Dense Low-Density Lipoprotein-Cholesterol and Its Use as a Risk-Enhancer Test. Clin Chem. 2021;67:987–97.

    PubMed  PubMed Central  Google Scholar 

  15. Landray MJ, Edmunds E, Li-Saw-Hee FL, Hughes BA, Beevers DG, Kendall MJ, et al. Abnormal low-density lipoprotein subfraction profile in patients with untreated hypertension. QJM. 2002;95:165–71.

    CAS  PubMed  Google Scholar 

  16. Kim YK, Seo HS, Lee EM, Na JO, Choi CU, Lim HE, et al. Association of hypertension with small, dense low-density lipoprotein in patients without metabolic syndrome. J Hum Hypertens. 2012;26:670–6.

    CAS  PubMed  Google Scholar 

  17. Zhang HW, Jin JL, Cao YX, Liu HH, Zhang Y, Guo YL, et al. Association of small dense LDL-cholesterol with disease severity, hypertension status and clinical outcome in patients with coronary artery disease. J Hypertens. 2021;39:511–8.

    CAS  PubMed  Google Scholar 

  18. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53:982–92.

    CAS  PubMed  Google Scholar 

  19. American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2017;40:S11–S24.

    Google Scholar 

  20. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

    CAS  PubMed  Google Scholar 

  21. Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21.

    PubMed  Google Scholar 

  22. Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012;176:473–81.

    PubMed  PubMed Central  Google Scholar 

  23. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl. 2013;48:452–8.

    CAS  Google Scholar 

  24. Laaksonen DE, Niskanen L, Nyyssonen K, Lakka TA, Laukkanen JA, Salonen JT. Dyslipidaemia as a predictor of hypertension in middle-aged men. Eur Heart J. 2008;29:2561–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Higashiura Y, Furuhashi M, Tanaka M, Takahashi S, Mori K, Miyamori D, et al. Elevated Fatty Liver Index Is Independently Associated With New Onset of Hypertension During a 10-Year Period in Both Male and Female Subjects. J Am Heart Assoc. 2021;10:e021430.

    CAS  PubMed Central  Google Scholar 

  26. Mori K, Tanaka M, Hosaka I, Mikami T, Endo K, Hanawa N, et al. Metabolic dysfunction-associated fatty liver disease is associated with an increase in systolic blood pressure over time: Linear mixed-effects model analyses. Hypertens Res. 2023;46:1110–21.

    CAS  Google Scholar 

  27. Sanchez-Inigo L, Navarro-Gonzalez D, Pastrana-Delgado J, Fernandez-Montero A, Martinez JA. Association of triglycerides and new lipid markers with the incidence of hypertension in a Spanish cohort. J Hypertens. 2016;34:1257–65.

    CAS  Google Scholar 

  28. He D, Fan F, Jia J, Jiang Y, Sun P, Wu Z, et al. Lipid profiles and the risk of new-onset hypertension in a Chinese community-based cohort. Nutr Metab Cardiovasc Dis. 2021;31:911–20.

    CAS  PubMed  Google Scholar 

  29. Tomita Y, Sakata S, Arima H, Yamato I, Ibaraki A, Ohtsubo T, et al. Relationship between casual serum triglyceride levels and the development of hypertension in Japanese. J Hypertens. 2021;39:677–82.

    CAS  PubMed  Google Scholar 

  30. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 2008;149:751–60.

    PubMed  PubMed Central  Google Scholar 

  31. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–72. discussion 207-12

    PubMed  Google Scholar 

  32. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150:795–802.

    PubMed  PubMed Central  Google Scholar 

  33. Tasic T, Tadic M, Lozic M. Hypertension in Women. Front Cardiovasc Med. 2022;9:905504.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Azizi Z, Alipour P, Raparelli V, Norris CM, Pilote L. The role of sex and gender in hypertension. J Hum Hypertens. 2022. https://doi.org/10.1038/s41371-022-00789-4.

  35. Mori K, Furuhashi M, Tanaka M, Numata K, Hisasue T, Hanawa N, et al. U-shaped relationship between serum uric acid level and decline in renal function during a 10-year period in female subjects: BOREAS-CKD2. Hypertens Res. 2021;44:107–16.

    PubMed  Google Scholar 

  36. Mori K, Furuhashi M, Tanaka M, Higashiura Y, Koyama M, Hanawa N, et al. Serum uric acid level is associated with an increase in systolic blood pressure over time in female subjects: Linear mixed-effects model analyses. Hypertens Res. 2022;45:344–53.

    CAS  PubMed  Google Scholar 

  37. Akasaka H, Yoshida H, Takizawa H, Hanawa N, Tobisawa T, Tanaka M, et al. The impact of elevation of serum uric acid level on the natural history of glomerular filtration rate (GFR) and its sex difference. Nephrol Dial Transpl. 2014;29:1932–9.

    CAS  Google Scholar 

  38. Furuhashi M. New insights into purine metabolism in metabolic diseases: role of xanthine oxidoreductase activity. Am J Physiol Endocrinol Metab. 2020;319:E827–E34.

    CAS  PubMed  Google Scholar 

  39. Furuhashi M, Matsumoto M, Tanaka M, Moniwa N, Murase T, Nakamura T, et al. Plasma Xanthine Oxidoreductase Activity as a Novel Biomarker of Metabolic Disorders in a General Population. Circ J. 2018;82:1892–9.

    CAS  PubMed  Google Scholar 

  40. Furuhashi M, Koyama M, Matsumoto M, Murase T, Nakamura T, Higashiura Y, et al. Annual change in plasma xanthine oxidoreductase activity is associated with changes in liver enzymes and body weight. Endocr J. 2019;66:777–86.

    CAS  PubMed  Google Scholar 

  41. Furuhashi M, Matsumoto M, Murase T, Nakamura T, Higashiura Y, Koyama M, et al. Independent links between plasma xanthine oxidoreductase activity and levels of adipokines. J Diabetes Investig. 2019;10:1059–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Furuhashi M, Higashiura Y, Koyama M, Tanaka M, Murase T, Nakamura T, et al. Independent association of plasma xanthine oxidoreductase activity with hypertension in nondiabetic subjects not using medication. Hypertens Res. 2021;44:1213–20.

    CAS  Google Scholar 

  43. Furuhashi M, Mori K, Tanaka M, Maeda T, Matsumoto M, Murase T, et al. Unexpected high plasma xanthine oxidoreductase activity in female subjects with low levels of uric acid. Endocr J. 2018;65:1083–92.

    CAS  Google Scholar 

  44. Packard CJ, Demant T, Stewart JP, Bedford D, Caslake MJ, Schwertfeger G, et al. Apolipoprotein B metabolism and the distribution of VLDL and LDL subfractions. J Lipid Res. 2000;41:305–18.

    CAS  Google Scholar 

  45. Goulinet S, Chapman MJ. Plasma LDL and HDL subspecies are heterogenous in particle content of tocopherols and oxygenated and hydrocarbon carotenoids. Relevance to oxidative resistance and atherogenesis. Arterioscler Thromb Vasc Biol. 1997;17:786–96.

    CAS  Google Scholar 

  46. Chancharme L, Therond P, Nigon F, Lepage S, Couturier M, Chapman MJ. Cholesteryl ester hydroperoxide lability is a key feature of the oxidative susceptibility of small, dense LDL. Arterioscler Thromb Vasc Biol. 1999;19:810–20.

    CAS  Google Scholar 

  47. Antunes BM, Cayres SU, Lira FS, Fernandes RA. Arterial Thickness and Immunometabolism: The Mediating role of Chronic Exercise. Curr Cardiol Rev. 2016;12:47–51.

    CAS  PubMed Central  Google Scholar 

  48. Maeda S, Nakanishi S, Yoneda M, Awaya T, Yamane K, Hirano T, et al. Associations between small dense LDL, HDL subfractions (HDL2, HDL3) and risk of atherosclerosis in Japanese-Americans. J Atheroscler Thromb. 2012;19:444–52.

    CAS  PubMed  Google Scholar 

  49. Ikezaki H, Furusyo N, Yokota Y, Ai M, Asztalos BF, Murata M, et al. Small Dense Low-Density Lipoprotein Cholesterol and Carotid Intimal Medial Thickness Progression. J Atheroscler Thromb. 2020;27:1108–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Austin MA, Breslow JL, Hennekens CH, Buring JE, Willett WC, Krauss RM. Low-density lipoprotein subclass patterns and risk of myocardial infarction. JAMA 1988;260:1917–21.

    CAS  Google Scholar 

  51. Arsenault BJ, Lemieux I, Despres JP, Wareham NJ, Luben R, Kastelein JJ, et al. Cholesterol levels in small LDL particles predict the risk of coronary heart disease in the EPIC-Norfolk prospective population study. Eur Heart J. 2007;28:2770–7.

    CAS  PubMed  Google Scholar 

  52. El Harchaoui K, van der Steeg WA, Stroes ES, Kuivenhoven JA, Otvos JD, Wareham NJ, et al. Value of low-density lipoprotein particle number and size as predictors of coronary artery disease in apparently healthy men and women: the EPIC-Norfolk Prospective Population Study. J Am Coll Cardiol. 2007;49:547–53.

    CAS  Google Scholar 

  53. Tsai MY, Steffen BT, Guan W, McClelland RL, Warnick R, McConnell J, et al. New automated assay of small dense low-density lipoprotein cholesterol identifies risk of coronary heart disease: the Multi-ethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol. 2014;34:196–201.

    CAS  PubMed  Google Scholar 

  54. Higashioka M, Sakata S, Honda T, Hata J, Yoshida D, Hirakawa Y, et al. Small Dense Low-Density Lipoprotein Cholesterol and the Risk of Coronary Heart Disease in a Japanese Community. J Atheroscler Thromb. 2020;27:669–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Takiwaki M, Tomoda F, Koike T, Taki T, Inoue H, Kigawa M, et al. Increased levels of small dense low-density lipoprotein cholesterol associated with hemorheological abnormalities in untreated, early-stage essential hypertensives. Hypertens Res. 2014;37:1008–13.

    CAS  PubMed  Google Scholar 

  56. Beevers G, Lip GY, O'Brien E. ABC of hypertension: The pathophysiology of hypertension. BMJ 2001;322:912–6.

    CAS  PubMed Central  Google Scholar 

  57. Safar ME. Arterial stiffness as a risk factor for clinical hypertension. Nat Rev Cardiol. 2018;15:97–105.

    PubMed  Google Scholar 

  58. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111–88.

    PubMed  Google Scholar 

  59. Jia G, Sowers JR. Hypertension in Diabetes: An Update of Basic Mechanisms and Clinical Disease. Hypertension 2021;78:1197–205.

    CAS  Google Scholar 

  60. Taskinen MR. Insulin resistance and lipoprotein metabolism. Curr Opin Lipido. 1995;6:153–60.

    CAS  Google Scholar 

  61. Hirano T, Ito Y. Accuracy of Small Dense Low-density Lipoprotein-cholesterol Concentration Estimated via Sampson’s Equation in Healthy Subjects and Patients with Diabetes. J Atheroscler Thromb. 2022. https://doi.org/10.5551/jat.63865.

  62. Jain A, Puri R, Nair DR. South Asians: why are they at a higher risk for cardiovascular disease? Curr Opin Cardiol. 2017;32:430–6.

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Keita Numata and Takashi Hisasue for data management.

Funding

MT and MF were supported by grants from Japan Society for the Promotion of Science (20K08913, 22K08313, 23K07993).

Author information

Authors and Affiliations

  1. Department of Cardiovascular, Renal and Metabolic Medicine, Sapporo Medical University School of Medicine, Sapporo, Japan

    Marenao Tanaka, Tatsuya Sato, Keisuke Endo, Masafumi Inyaku, Hirofumi Ohnishi & Masato Furuhashi

  2. Tanaka Medical Clinic, Yoichi, Japan

    Marenao Tanaka

  3. Department of Cellular Physiology and Signal Transduction, Sapporo Medical University School of Medicine, Sapporo, Japan

    Tatsuya Sato

  4. Department of Immunology and Microbiology, National Defense Medical College, Tokorozawa, Japan

    Kazuma Mori

  5. Department of Cardiovascular Surgery, Sapporo Medical University School of Medicine, Sapporo, Japan

    Itaru Hosaka & Takuma Mikami

  6. Department of Neurosurgery, Sapporo Medical University School of Medicine, Sapporo, Japan

    Yukinori Akiyama

  7. Department of Public Health, Sapporo Medical University School of Medicine, Sapporo, Japan

    Hirofumi Ohnishi

  8. Department of Health Checkup and Promotion, Keijinkai Maruyama Clinic, Sapporo, Japan

    Nagisa Hanawa

Authors
  1. Marenao Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tatsuya Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keisuke Endo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masafumi Inyaku
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kazuma Mori
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Itaru Hosaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takuma Mikami
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yukinori Akiyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hirofumi Ohnishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nagisa Hanawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Masato Furuhashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masato Furuhashi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tanaka, M., Sato, T., Endo, K. et al. An increase in calculated small dense low-density lipoprotein cholesterol predicts new onset of hypertension in a Japanese cohort. Hypertens Res 46, 2635–2645 (2023). https://doi.org/10.1038/s41440-023-01392-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41440-023-01392-x

Keywords

This article is cited by

Search

Advanced search

Quick links