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Low HDL cholesterol as a predictor of chronic kidney disease progression: a cross-classification approach and matched cohort analysis

Abstract

Emerging epidemiological evidence indicates that low serum high-density lipoprotein cholesterol (HDL-C) levels are associated with the risk of progression of chronic kidney disease (CKD). However, the differences in the influence of serum HDL-C levels on CKD progression in different subcohorts have rarely been examined in detail in previous studies. The aim of this study was to investigate the significance of low serum HDL-C levels as a predictor of disease progression in CKD patients according to sub-analyses using a cross-classified subcohort. We reviewed data obtained from 120 CKD patients. Prognostic factors for renal outcome were identified by the multivariate Cox proportional hazards method. Kaplan–Meier analysis was performed to assess disease progression, which was defined as a > 30% decline in the glomerular filtration rate (GFR), or end-stage renal disease. The mean age of the included participants was 58.3 ± 13.6 years. The subjects were divided into two groups (low HDL-C vs. high HDL-C). The median follow-up period was 112.8 months. The kidney survival rate in the low HDL-C group was significantly lower than that in the high HDL-C group (P < 0.0001). However, the age-stratified analysis showed no difference between the two groups in the cohort of patients ≥ 70 years old. Multivariate Cox regression analyses showed a significant association between low HDL-C [hazard ratio (HR) 4.80, P = 0.009] and a ≥ 30% eGFR decline or ESRD. This association was more evident in the cohort of patients < 70 years old (HR 4.96, P = 0.0165), especially the female subcohort (HR 13.86, P = 0.0033). Multivariate analysis showed a significant correlation between visceral fat area and serum HDL-C levels among both male (P = 0.0017) and female (P = 0.0449) patients. In a propensity score-matched cohort (patients < 70 years old), the kidney survival rate of CKD patients was significantly lower in the low HDL-C group than in the high HDL-C group (P = 0.0364). A low serum HDL-C level is a significant predictor of CKD progression, especially in female patients with CKD under 70 years of age. This finding is of importance to clinicians when determining the expected prognosis of CKD in patients.

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References

  1. Mikolasevic I, Zutelija M, Mavrinac V, Orlic L (2017) Dyslipidemia in patients with chronic kidney disease: etiology and management. Int J Nephrol Renovasc Dis 10:35–45

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cases A, Coll E (2005) Dyslipidemia and the progression of renal disease in chronic renal failure patients. Kidney Int Suppl 99:S87–S93

    Article  CAS  Google Scholar 

  3. Muntner P, Coresh J, Smith JC, Eckfeldt J, Klag MJ (2000) Plasma lipids and risk of developing renal dysfunction: the atherosclerosis risk in communities study. Kidney Int 58:293–301

    Article  CAS  PubMed  Google Scholar 

  4. Schaeffner ES, Kurth T, Curhan GC, Glynn RJ, Rexrode KM, Baigent C, Buring JE, Gaziano JM (2003) Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol 14:2084–2091

    CAS  PubMed  Google Scholar 

  5. Morton J, Zoungas S, Li Q, Patel AA, Chalmers J, Woodward M, Celermajer DS, Beulens JW, Stolk RP, Glasziou P, Ng MK, Group AC (2012) Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study. Diabetes Care 35:2201–2206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D (2004) Predictors of new-onset kidney disease in a community-based population. JAMA 291(7):844–850

    Article  CAS  PubMed  Google Scholar 

  7. Kronenberg F (2018) HDL in CKD—the devil is in the detail. J Am Soc Nephrol 29:1356–1371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kronenberg F (2016) High-density lipoprotein cholesterol on a roller coaster: where will the ride end? Kidney Int 89:747–749

    Article  CAS  PubMed  Google Scholar 

  9. Coassin S, Friedel S, Kottgen A, Lamina C, Kronenberg F (2016) Is high-density lipoprotein cholesterol causally related to kidney function? Evidence from genetic epidemiological studies. Arterioscler Thromb Vasc Biol 36:2252–2258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rahman M, Yang W, Akkina S, Alper A, Anderson AH, Appel LJ, He J, Raj DS, Schelling J, Strauss L, Teal V, Rader DJ (2014) Relation of serum lipids and lipoproteins with progression of CKD: the CRIC study. Clin J Am Soc Nephrol 9:1190–1198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bowe B, Xie Y, Xian H, Balasubramanian S, Al-Aly Z (2016) Low levels of high-density lipoprotein cholesterol increase the risk of incident kidney disease and its progression. Kidney Int 89:886–896

    Article  CAS  PubMed  Google Scholar 

  12. Upadhyay A, Earley A, Lamont JL, Haynes S, Wanner C, Balk EM (2012) Lipid-lowering therapy in persons with chronic kidney disease: a systematic review and meta-analysis. Ann Intern Med 157:251–262

    Article  PubMed  Google Scholar 

  13. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30:543–552

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lanktree MB, Theriault S, Walsh M, Pare G (2018) HDL cholesterol, LDL cholesterol, and triglycerides as risk factors for CKD: a mendelian randomization study. Am J Kidney Dis 71:166–172

    Article  CAS  PubMed  Google Scholar 

  15. Sacristan JA (2013) Patient-centered medicine and patient-oriented research: improving health outcomes for individual patients. BMC Med Inform Decis Mak 13:6

    Article  PubMed  PubMed Central  Google Scholar 

  16. Levey AS, Eckardt KU, Tsukamoto Y, Levin A, Coresh J, Rossert J, De Zeeuw D, Hostetter TH, Lameire N, Eknoyan G (2005) Definition and classification of chronic kidney disease: a position statement from kidney disease—improving global outcomes (KDIGO). Kidney Int 67:2089–2100

    Article  Google Scholar 

  17. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, Yamagata K, Tomino Y, Yokoyama H, Hishida A (2009) Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 53:982–992

    Article  CAS  Google Scholar 

  18. Ording AG, Sorensen HT (2013) Concepts of comorbidities, multiple morbidities, complications, and their clinical epidemiologic analogs. Clin Epidemiol 5:199–203

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sawara Y, Takei T, Uchida K, Tsuchiya K, Nitta K (2009) Metabolic syndrome and anthropometric factors in Japanese patients with chronic kidney disease. Heart Vessels 24:199–203

    Article  PubMed  Google Scholar 

  20. Tamei N, Ogawa T, Ishida H, Ando Y, Nitta K (2010) Relationship of high-molecular-weight adiponectin levels to visceral fat accumulation in hemodialysis patients. Intern Med 49:299–305

    Article  CAS  PubMed  Google Scholar 

  21. Ogawa T, Shimada M, Ishida H, Matsuda N, Fujiu A, Ando Y, Nitta K (2009) Relation of stiffness parameter beta to carotid arteriosclerosis and silent cerebral infarction in patients on chronic hemodialysis. Int Urol Nephrol 41:739–745

    Article  CAS  PubMed  Google Scholar 

  22. Sato M, Ogawa T, Sugimoto H, Otsuka K, Nitta K (2012) Relation of carotid intima-media thickness and silent cerebral infarction to cardiovascular events and all-cause mortality in chronic hemodialysis patients. Intern Med 51:2111–2117

    Article  PubMed  Google Scholar 

  23. de Bray JM, Baud JM, Delanoy P, Camuzat JP, Dehans V, Descamp-Le Chevoir J, Launay JR, Luizy F, Sentou Y, Cales P (1998) Reproducibility in ultrasonic characterization of carotid plaques. Cerebrovasc Dis 8:273–277

    Article  PubMed  Google Scholar 

  24. Fassett RG (2014) Current and emerging treatment options for the elderly patient with chronic kidney disease. Clin Interv Aging 9:191–199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Carrero JJ (2010) Gender differences in chronic kidney disease: underpinnings and therapeutic implications. Kidney Blood Press Res 33:383–392

    Article  CAS  PubMed  Google Scholar 

  26. Matsushita K, Chen J, Sang Y, Ballew SH, Shimazaki R, Fukagawa M, Imai E, Coresh J, Hishida A (2016) Risk of end-stage renal disease in Japanese patients with chronic kidney disease increases proportionately to decline in estimated glomerular filtration rate. Kidney Int 90:1109–1114

    Article  PubMed  Google Scholar 

  27. Lanktree MB, Theriault S, Walsh M, Pare G (2017) HDL cholesterol, LDL cholesterol, and triglycerides as risk factors for CKD: a Mendelian randomization study. Am J Kidney Dis 71(2):166–172

    Article  CAS  PubMed  Google Scholar 

  28. Kang HT, Kim JK, Kim JY, Linton JA, Yoon JH, Koh SB (2012) Independent association of TG/HDL-C with urinary albumin excretion in normotensive subjects in a rural Korean population. Clin Chim Acta 413:319–324

    Article  CAS  PubMed  Google Scholar 

  29. Tsuruya K, Yoshida H, Nagata M, Kitazono T, Iseki K, Iseki C, Fujimoto S, Konta T, Moriyama T, Yamagata K, Narita I, Kimura K, Kondo M, Asahi K, Kurahashi I, Ohashi Y, Watanabe T (2015) Impact of the triglycerides to high-density lipoprotein cholesterol ratio on the incidence and progression of CKD: a longitudinal study in a large Japanese population. Am J Kidney Dis 66:972–983

    Article  CAS  PubMed  Google Scholar 

  30. Streja E, Kovesdy CP, Streja DA, Moradi H, Kalantar-Zadeh K, Kashyap ML (2015) Niacin and progression of CKD. Am J Kidney Dis 65:785–798

    Article  CAS  PubMed  Google Scholar 

  31. Vaziri ND (2010) Lipotoxicity and impaired high density lipoprotein-mediated reverse cholesterol transport in chronic kidney disease. J Ren Nutr 20:S35–S43

    Article  CAS  PubMed  Google Scholar 

  32. Vaziri ND, Navab M, Fogelman AM (2010) HDL metabolism and activity in chronic kidney disease. Nat Rev Nephrol 6:287–296

    Article  CAS  PubMed  Google Scholar 

  33. Hwang YC, Fujimoto WY, Hayashi T, Kahn SE, Leonetti DL, Boyko EJ (2016) Increased visceral adipose tissue is an independent predictor for future development of atherogenic dyslipidemia. J Clin Endocrinol Metab 101:678–685

    Article  CAS  PubMed  Google Scholar 

  34. Wajchenberg BL (2000) Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 21:697–738

    Article  CAS  Google Scholar 

  35. Kataoka H, Ariyama Y, Deushi M, Osaka M, Nitta K, Yoshida M (2016) Inhibitory effect of serotonin antagonist on leukocyte–endothelial interactions in vivo and in vitro. PLoS ONE 11:e0147929

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kambham N, Markowitz GS, Valeri AM, Lin J, D’Agati VD (2001) Obesity-related glomerulopathy: an emerging epidemic. Kidney Int 59:1498–1509

    Article  CAS  PubMed  Google Scholar 

  37. Kataoka H, Ohara M, Shibui K, Sato M, Suzuki T, Amemiya N, Watanabe Y, Honda K, Mochizuki T, Nitta K (2012) Overweight and obesity accelerate the progression of IgA nephropathy: prognostic utility of a combination of BMI and histopathological parameters. Clin Exp Nephrol 16:706–712

    Article  CAS  PubMed  Google Scholar 

  38. Harvey JM, Howie AJ, Lee SJ, Newbold KM, Adu D, Michael J, Beevers DG (1992) Renal biopsy findings in hypertensive patients with proteinuria. Lancet 340:1435–1436

    Article  CAS  PubMed  Google Scholar 

  39. Sun X, Xiao Y, Li PM, Ma XY, Sun XJ, Lv WS, Wu YL, Liu P, Wang YG (2018) Association of serum high-density lipoprotein cholesterol with microalbuminuria in type 2 diabetes patients. Lipids Health Dis 17:229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tu ST, Chang SJ, Chen JF, Tien KJ, Hsiao JY, Chen HC, Hsieh MC (2010) Prevention of diabetic nephropathy by tight target control in an asian population with type 2 diabetes mellitus: a 4-year prospective analysis. Arch Intern Med 170:155–161

    Article  CAS  PubMed  Google Scholar 

  41. Baldi S, Frascerra S, Ferrannini E, Natali A (2007) LDL resistance to oxidation: effects of lipid phenotype, autologous HDL and alanine. Clin Chim Acta 379:95–100

    Article  CAS  PubMed  Google Scholar 

  42. Morgantini C, Natali A, Boldrini B, Imaizumi S, Navab M, Fogelman AM, Ferrannini E, Reddy ST (2011) Anti-inflammatory and antioxidant properties of HDLs are impaired in type 2 diabetes. Diabetes 60:2617–2623

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhang J, Zhang Y, Liu H, Bai H, Wang Y, Jiang C, Fan P (2015) Antioxidant properties of high-density lipoproteins are impaired in women with polycystic ovary syndrome. Fertil Steril 103:1346–1354

    Article  CAS  PubMed  Google Scholar 

  44. Stevenson JC, Crook D, Godsland IF (1993) Influence of age and menopause on serum lipids and lipoproteins in healthy women. Atherosclerosis 98:83–90

    Article  CAS  PubMed  Google Scholar 

  45. Rannevik G, Jeppsson S, Johnell O, Bjerre B, Laurell-Borulf Y, Svanberg L (1995) A longitudinal study of the perimenopausal transition: altered profiles of steroid and pituitary hormones, SHBG and bone mineral density. Maturitas 21:103–113

    Article  CAS  PubMed  Google Scholar 

  46. Boden WE, Probstfield JL, Anderson T, Chaitman BR, Desvignes-Nickens P, Koprowicz K, McBride R, Teo K, Weintraub W (2011) Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med 365:2255–2267

    Article  CAS  PubMed  Google Scholar 

  47. Hayward RA, Kent DM, Vijan S, Hofer TP (2006) Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med Res Methodol 6:18

    Article  PubMed  PubMed Central  Google Scholar 

  48. van Dijk PC, Zwinderman AH, Dekker FW, Schon S, Stel VS, Finne P, Jager KJ (2007) Effect of general population mortality on the north–south mortality gradient in patients on replacement therapy in Europe. Kidney Int 71:53–59

    Article  PubMed  Google Scholar 

  49. Pilote L, Dasgupta K, Guru V, Humphries KH, McGrath J, Norris C, Rabi D, Tremblay J, Alamian A, Barnett T, Cox J, Ghali WA, Grace S, Hamet P, Ho T, Kirkland S, Lambert M, Libersan D, O’Loughlin J, Paradis G, Petrovich M, Tagalakis V (2007) A comprehensive view of sex-specific issues related to cardiovascular disease. CMAJ 176:S1–S44

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We express our gratitude to Dr. Takahiro Mochizuki(†Deceased 25 June 2017) for his advice on this work and for his contribution to medical care and medical research in Japan. We want to specially thank Dr. Yukako Sawara, Mrs. Naomi Iwasa, and Mrs. Rie Yoshida for contributing to this article by collecting clinical data.

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Correspondence to Hiroshi Kataoka.

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Kawachi, K., Kataoka, H., Manabe, S. et al. Low HDL cholesterol as a predictor of chronic kidney disease progression: a cross-classification approach and matched cohort analysis. Heart Vessels 34, 1440–1455 (2019). https://doi.org/10.1007/s00380-019-01375-4

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  • DOI: https://doi.org/10.1007/s00380-019-01375-4

Keywords

  • Chronic kidney disease (CKD)
  • High-density lipoprotein cholesterol (HDL-C)
  • Propensity score matching
  • Cross-classification approach
  • Female