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Lipid levels are inversely associated with infectious and all-cause mortality: international MONDO study results

2018; Elsevier BV; Volume: 59; Issue: 8 Linguagem: Inglês

10.1194/jlr.p084277

ISSN

1539-7262

Autores

George A. Kaysen, Xiaoling Ye, Jochen G. Raimann, Yuedong Wang, Alice Topping, Len A. Usvyat, Stefano Stuard, Bernard Canaud, Frank M. van der Sande, Jeroen P. Kooman, Peter Kotanko,

Tópico(s)

Lipoproteins and Cardiovascular Health

Resumo

Cardiovascular (CV) events are increased 36-fold in patients with end-stage renal disease. However, randomized controlled trials to lower LDL cholesterol (LDL-C) and serum total cholesterol (TC) have not shown significant mortality improvements. An inverse association of TC and LDL-C with all-cause and CV mortality has been observed in patients on chronic dialysis. Lipoproteins also may protect against infectious diseases. We used data from 37,250 patients in the international Monitoring Dialysis Outcomes (MONDO) database to evaluate the association between lipids and infection-related or CV mortality. The study began on the first day of lipid measurement and continued for up to 4 years. We applied Cox proportional models with time-varying covariates to study associations of LDL-C, HDL cholesterol (HDL-C), and triglycerides (TGs) with all-cause, CV, infectious, and other causes of death. Overall, 6,147 patients died (19.2% from CV, 13.2% from infection, and 67.6% from other causes). After multivariable adjustment, higher LDL-C, HDL-C, and TGs were independently associated with lower all-cause death risk. Neither LDL-C nor TGs were associated with CV death, and HDL-C was associated with lower CV risk. Higher LDL-C and HDL-C were associated with a lower risk of death from infection or other non-CV causes. LDL-C was associated with reduced all-cause and infectious, but not CV mortality, which resulted in the inverse association with all-cause mortality. Cardiovascular (CV) events are increased 36-fold in patients with end-stage renal disease. However, randomized controlled trials to lower LDL cholesterol (LDL-C) and serum total cholesterol (TC) have not shown significant mortality improvements. An inverse association of TC and LDL-C with all-cause and CV mortality has been observed in patients on chronic dialysis. Lipoproteins also may protect against infectious diseases. We used data from 37,250 patients in the international Monitoring Dialysis Outcomes (MONDO) database to evaluate the association between lipids and infection-related or CV mortality. The study began on the first day of lipid measurement and continued for up to 4 years. We applied Cox proportional models with time-varying covariates to study associations of LDL-C, HDL cholesterol (HDL-C), and triglycerides (TGs) with all-cause, CV, infectious, and other causes of death. Overall, 6,147 patients died (19.2% from CV, 13.2% from infection, and 67.6% from other causes). After multivariable adjustment, higher LDL-C, HDL-C, and TGs were independently associated with lower all-cause death risk. Neither LDL-C nor TGs were associated with CV death, and HDL-C was associated with lower CV risk. Higher LDL-C and HDL-C were associated with a lower risk of death from infection or other non-CV causes. LDL-C was associated with reduced all-cause and infectious, but not CV mortality, which resulted in the inverse association with all-cause mortality. Mortality is significantly higher among patients with advanced kidney disease compared with the general population (1.Go A.S. Chertow G.M. Fan D. McCulloch C.E. Hsu C.Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.N. Engl. J. Med. 2004; 351: 1296-1305Crossref PubMed Scopus (8984) Google Scholar). The two leading causes of death in dialysis patients are CVD and infectious diseases (2.U.S. Renal Data System. USRDS 2011 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States..National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. 2011; Google Scholar, 3.Cheng X. Nayyar S. Wang M. Li X. Sun Y. Huang W. Zhang L. Wu H. Jia Q. Liu W. et al.Mortality rates among prevalent hemodialysis patients in Beijing: a comparison with USRDS data.Nephrol. Dial. Transplant. 2013; 28: 724-732Crossref PubMed Scopus (44) Google Scholar). CVD is increased 35-fold in patients with stage 5 chronic kidney disease (CKD) (1.Go A.S. Chertow G.M. Fan D. McCulloch C.E. Hsu C.Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.N. Engl. J. Med. 2004; 351: 1296-1305Crossref PubMed Scopus (8984) Google Scholar), and CVD mortality is not reduced upon initiation of dialysis therapy. Although CVD mortality has been declining, the rate of infectious hospitalizations and deaths has been increasing (4.U.S. Renal Data System. USRDS 2013 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States..National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. 2013; Google Scholar). In observational studies, higher total cholesterol and LDL cholesterol (LDL-C) are associated with better survival in chronic hemodialysis (HD) and peritoneal dialysis patients (5.Goldwasser P. Mittman N. Antignani A. Burrell D. Michel M.A. Collier J. Avram M.M. Predictors of mortality in hemodialysis patients.J. Am. Soc. Nephrol. 1993; 3: 1613-1622Crossref PubMed Google Scholar, 6.Avram M.M. Mittman N. Bonomini L. Chattopadhyay J. Fein P. Markers for survival in dialysis: a seven-year prospective study.Am. J. Kidney Dis. 1995; 26: 209-219Abstract Full Text PDF PubMed Scopus (219) Google Scholar, 7.Lowrie E.G. Huang W.H. Lew N.L. Death risk predictors among peritoneal dialysis and hemodialysis patients: a preliminary comparison.Am. J. Kidney Dis. 1995; 26: 220-228Abstract Full Text PDF PubMed Scopus (162) Google Scholar, 8.Kilpatrick R.D. McAllister C.J. Kovesdy C.P. Derose S.F. Kopple J.D. Kalantar-Zadeh K. Association between serum lipids and survival in hemodialysis patients and impact of race.J. Am. Soc. Nephrol. 2007; 18: 293-303Crossref PubMed Scopus (195) Google Scholar, 9.Park C.H. Kang E.W. Park J.T. Han S.H. Yoo T.H. Kang S.W. Chang T.I. Association of serum lipid levels over time with survival in incident peritoneal dialysis patients.J. Clin. Lipidol. 2017; 11: 945-954.e3Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar). Although treatment of hyperlipidemia or dyslipidemia has a modest effect on CVD outcomes in patients with CKD, neither overall mortality nor CVD-related events are significantly affected by lipid-lowering therapy among HD patients (10.Wanner C. Krane V. März W. Olschewski M. Mann J.F. Ruf G. Ritz E. German Diabetes and Dialysis Study Investigators. Atorvastatin in patients with type 2 diabetes mellitus undergoing hemodialysis.N. Engl. J. Med. 2005; 353: 238-248Crossref PubMed Scopus (2206) Google Scholar, 11.Baigent C. Landray M.J. Reith C. Emberson J. Wheeler D.C. Tomson C. Wanner C. Krane V. Cass A. Craig J. et al.SHARP Investigators The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomized placebo-controlled trial.Lancet. 2011; 377: 2181-2192Abstract Full Text Full Text PDF PubMed Scopus (1886) Google Scholar). If indeed LDL-C has no clear effect on CVD outcomes and is associated with reduced risk of death, the question of the relationship between LDL-C and other outcomes may provide insight into potential mechanisms of protection. The second leading cause of death among HD patients is infection (4.U.S. Renal Data System. USRDS 2013 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States..National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. 2013; Google Scholar). Epidemiologically, lipoprotein levels have been associated with protection from infectious events (12.Iribarren C. Jacobs Jr D.R. Sidney S. Claxton A.J. Feingold K.R. Cohort study of serum total cholesterol and in-hospital incidence of infectious diseases.Epidemiol. Infect. 1998; 121: 335-347Crossref PubMed Scopus (70) Google Scholar, 13.Feingold K.R. Grunfeld C. Lipids: a key player in the battle between the host and microorganisms.J. Lipid Res. 2012; 53: 2487-2489Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar). HDL cholesterol (HDL-C) plays a role in the transfer of lipotoxins, endotoxin, and lipopolysaccharide (LPS) to the liver, where it is degraded (14.Level J.H. Marquart J.A. Abraham P.R. van den Ende A.E. Molhuizen H.O. van Deventer S.J. Meijers J.C. Lipopolysaccharide is transferred from high-density to low-density lipoproteins by lipopolysaccharide-binding protein and phospholipid transfer protein.Infect. Immun. 2005; 73: 2321-2326Crossref PubMed Scopus (73) Google Scholar, 15.Berbée J.F. Havekes L.M. Rensen P.C. Apolipoproteins modulate the inflammatory response to lipopolysaccharide.J. Endotoxin Res. 2005; 11: 97-103Crossref PubMed Scopus (112) Google Scholar, 16.Read T.E. Harris H.W. Grunfeld C. Feingold K.R. Kane J.P. Rapp J.H. The protective effect of serum lipoproteins against bacterial lipopolysaccharide.Eur. Heart J. 1993; 14: 125-129PubMed Google Scholar). HDL-C contains LPS binding protein (LBP) that participates in this transfer (17.Wurfel M.M. Kunitake S.T. Lichenstein H. Kane J.P. Wright S.D. Lipopolysaccharide (LPS)-binding protein is carried on lipoproteins and acts as a cofactor in the neutralization of LPS.J. Exp. Med. 1994; 180: 1025-1035Crossref PubMed Scopus (373) Google Scholar). Inflammation even in the absence of clinical infection is strongly associated with all-cause and CVD mortality in patients with end-stage renal disease (18.Yeun J.Y. Levine R.A. Mantadilok V. Kaysen G.A. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients.Am. J. Kidney Dis. 2000; 35: 469-476Abstract Full Text Full Text PDF PubMed Scopus (794) Google Scholar). HDL has been shown to be protective against polymicrobial sepsis in mice (19.Guo L. Ai J. Zheng Z. Howatt D.A. Daugherty A. Huang B. Li X.A. High density lipoprotein protects against polymicrobe-induced sepsis in mice.J. Biol. Chem. 2013; 288: 17947-17953Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar). LDL-C has been shown to be inversely associated with infectious outcomes in patient populations with normal renal function (12.Iribarren C. Jacobs Jr D.R. Sidney S. Claxton A.J. Feingold K.R. Cohort study of serum total cholesterol and in-hospital incidence of infectious diseases.Epidemiol. Infect. 1998; 121: 335-347Crossref PubMed Scopus (70) Google Scholar) and likely plays a key role in host defense against both bacterial and some viral pathogens (13.Feingold K.R. Grunfeld C. Lipids: a key player in the battle between the host and microorganisms.J. Lipid Res. 2012; 53: 2487-2489Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar). Interventional studies in animal models strongly suggest that lipoproteins may potentially play an important role in the innate immune response (20.Han R. Plasma lipoproteins are important components of the immune system.Microbiol. Immunol. 2010; 54: 246-253Crossref PubMed Scopus (50) Google Scholar, 21.Ravnskov U. High cholesterol may protect against infections and atherosclerosis.QJM. 2003; 96: 927-934Crossref PubMed Scopus (77) Google Scholar, 22.Khovidhunkit W. Kim M.S. Memon R.A. Shigenaga J.K. Moser A.H. Feingold K.R. Grunfeld C. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host.J. Lipid Res. 2004; 45: 1169-1196Abstract Full Text Full Text PDF PubMed Scopus (1079) Google Scholar) and protect against sepsis in experimental animal models (19.Guo L. Ai J. Zheng Z. Howatt D.A. Daugherty A. Huang B. Li X.A. High density lipoprotein protects against polymicrobe-induced sepsis in mice.J. Biol. Chem. 2013; 288: 17947-17953Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar). Additionally, infection per se may alter the structure and function of lipoproteins (22.Khovidhunkit W. Kim M.S. Memon R.A. Shigenaga J.K. Moser A.H. Feingold K.R. Grunfeld C. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host.J. Lipid Res. 2004; 45: 1169-1196Abstract Full Text Full Text PDF PubMed Scopus (1079) Google Scholar, 23.Kitchens R.L. Thompson P.A. Munford R.S. O'Keefe G.E. Acute inflammation and infection maintain circulating phospholipid levels and enhance lipopolysaccharide binding to plasma lipoproteins.J. Lipid Res. 2003; 44: 2339-2348Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar). Thus, it is plausible that the observed association of high total cholesterol and LDL-C levels with survival (5.Goldwasser P. Mittman N. Antignani A. Burrell D. Michel M.A. Collier J. Avram M.M. Predictors of mortality in hemodialysis patients.J. Am. Soc. Nephrol. 1993; 3: 1613-1622Crossref PubMed Google Scholar, 6.Avram M.M. Mittman N. Bonomini L. Chattopadhyay J. Fein P. Markers for survival in dialysis: a seven-year prospective study.Am. J. Kidney Dis. 1995; 26: 209-219Abstract Full Text PDF PubMed Scopus (219) Google Scholar, 7.Lowrie E.G. Huang W.H. Lew N.L. Death risk predictors among peritoneal dialysis and hemodialysis patients: a preliminary comparison.Am. J. Kidney Dis. 1995; 26: 220-228Abstract Full Text PDF PubMed Scopus (162) Google Scholar) and the lack of an effect of lipid-lowering therapies on all-cause mortality in interventional studies (11.Baigent C. Landray M.J. Reith C. Emberson J. Wheeler D.C. Tomson C. Wanner C. Krane V. Cass A. Craig J. et al.SHARP Investigators The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomized placebo-controlled trial.Lancet. 2011; 377: 2181-2192Abstract Full Text Full Text PDF PubMed Scopus (1886) Google Scholar) is based at least in part upon a protective effect of lipoprotein classes on infectious outcomes. Some lipoproteins also behave as acute-phase proteins in response to inflammation (24.Kaysen G.A. Dalrymple L.S. Grimes B. Chertow G.M. Kornak J. Johansen K. Changes in serum inflammatory markers are associated with changes in apolipoprotein A1 but not B after the initiation of dialysis.Nephrol. Dial. Transplant. 2014; 29: 430-437Crossref PubMed Scopus (8) Google Scholar, 25.Liao K.P. Playford M.P. Frits M. Coblyn J.S. Iannaccone C. Weinblatt M.E. Shadick N.S. Mehta N.N. The association between reduction in inflammation and changes in lipoprotein levels and HDL cholesterol efflux capacity in rheumatoid arthritis.J. Am. Heart Assoc. 2015; 4: e001588Crossref PubMed Google Scholar, 26.Esteve E. Ricart W. Fernández-Real J.M. Dyslipidemia and inflammation: an evolutionary conserved mechanism.Clin. Nutr. 2005; 24: 16-31Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar), so that it is important to distinguish between lipoprotein levels prior to an infectious event as opposed to their levels during infection, as well as to control for variables associated with inflammation to try to separate cause from effect. We conducted a retrospective cohort study to analysis the outcomes in an international cohort of in-center HD patients from the Monitoring Dialysis Outcomes (MONDO) database (27.von Gersdorff G.D. Usvyat L. Marcelli D. Grassmann A. Marelli C. Etter M. Kooman J.P. Power A. Toffelmire T. Haviv Y.S. et al.Monitoring dialysis outcomes across the world—the MONDO Global Database Consortium.Blood Purif. 2013; 36: 165-172Crossref PubMed Scopus (17) Google Scholar) to study the relationship between lipid levels during the prior 4-year period and all-cause mortality, death attributed to infectious diseases, and death attributed to cardiovascular (CV), and in order to establish whether any protective effects against infectious death by specific lipoprotein classes offset potential injurious effects on CVD mortality. We also investigated the relationship between each of the lipoprotein classes and noninfectious and non-CV mortality (other causes of mortality) to determine whether the entire effect of specific lipoprotein classes were the result of CV-related mortality, infection-related mortality, or other causes. The MONDO initiative is an international retrospective cohort study that comprised all the chronic HD patients from 41 countries of Fresenius Medical Care (FMC) Europe, Middle East, and Africa; FMC South America; FMC Asia Pacific; and United States–Renal Research Institute (RRI); FMC Latin America; Maastricht University Hospital and University of Einthoven in the Netherlands; Hadassah Medical Center in Israel; Imperial College in the United Kingdom; National Heart Institute of Mexico City in Mexico; Nephro Solution and Kuratorium für Dialyze und Nierentransplantation in Germany; and Pontifical Catholic University of Parana in Brazil (27.von Gersdorff G.D. Usvyat L. Marcelli D. Grassmann A. Marelli C. Etter M. Kooman J.P. Power A. Toffelmire T. Haviv Y.S. et al.Monitoring dialysis outcomes across the world—the MONDO Global Database Consortium.Blood Purif. 2013; 36: 165-172Crossref PubMed Scopus (17) Google Scholar). Corresponding data providers were responsible for the primary data collection and safeguarding the usage of patient data in accordance with local data protection laws and privacy. Patients were stratified into regions per the United Nations geographical scheme (28.United Nations Statistics Division.Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings. 2018; Google Scholar). All the identifiable variables were removed before data were transferred to FMC-RRI (New York, NY). Multiple levels of internal data validation controls were applied before the data were integrated into a master database. All the research conducted by the MONDO Initiative complied with national and international ethical, compliance, and legal standards. The study was approved by the Western Institutional Review Board (ES-16-005). All the adult patients who survived more than 90 days on HD and with at least one complete lipid panel [LDL-C, HDL-C, and triglycerides (TGs)] measured between January 1, 2000 and December 31, 2012 within the MONDO master database were included. Therefore, the final study population consisted of 37,250 patients from Eastern, Southern, and Western Europe, as well as West Asia and the RRI (Fig. 1A). Patients were included at the first available lipoprotein measurement and were followed for up to 4 years until an event (death, censored, loss-to-follow-up, or recovered from renal failure) occurred (Fig. 1B). The primary exposures of interest were time-varying serum lipid levels of calculated LDL-C, HDL-C, and TG. Given the fact that albumin, creatinine, C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR), BMI, age, and dialysis vintage (time after initial start of renal replacement therapy) also measured routinely in majority of the study cohort, each of the parameters of interest was treated as a time-dependent parameter while performing the analyses. The primary outcomes of interest were time to all-cause and infectious-based hospitalizations that resulted in mortality (infection-related mortalities; see supplemental data). Secondary outcomes of interest were CV-based hospitalizations that resulted in mortality (CV-related mortality; see supplemental data) and other mortalities that were not infection-related and not CV-related causes. CV-based hospitalizations were chosen to report arterial injury similar to outcomes chosen in the SHARP trial (11.Baigent C. Landray M.J. Reith C. Emberson J. Wheeler D.C. Tomson C. Wanner C. Krane V. Cass A. Craig J. et al.SHARP Investigators The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomized placebo-controlled trial.Lancet. 2011; 377: 2181-2192Abstract Full Text Full Text PDF PubMed Scopus (1886) Google Scholar). We considered died due to infection-related or CV-related if the primary International Classification of Diseases (ICD), 9th revision, Clinical Modification (for RRI's patients) or ICD, 10th revision (for Europe and West Asia's patients) diagnosis code was related to infection-related or CV-related hospitalizations. The corresponding documented free-text descriptions were applied to further validate the cause-specific mortalities. Continuous variables were reported as means (± SD) or median (interquartile range) depending on the distribution of the data. To test the significant differences of the continuous variables, Kruskal-Wallis, one-way ANOVA, or ANOVA were performed. Categorical variables were presented as proportions and test via chi-square tests. Cox proportional hazard regression models with time-varying covariates (29.Fisher L.D. Lin D.Y. Time dependent covariates in the Cox proportional-hazards regression model.Annu. Rev. Public Health. 1999; 20: 145-157Crossref PubMed Scopus (613) Google Scholar) were performed to examine the univariate associations between each of the lipoproteins (LDL-C, HDL-C, and TGs per mmol/l) and all-cause mortality, infection-related mortality, and CV-related mortality as well as other mortalities in separate models. Up to 48 time-varying values of each lipoprotein per participant using lipoprotein values measured at different time points were included in each analysis. For patients with more than one lipid measurement during the baseline period, the previous value was replaced by the upcoming value while conducting the analyses. To account for changes in serum lipid levels over time and assess the associations between lipid levels and each of the cause-specific deaths, the following six models with incremental levels of adjustments were conducted: 1) model 1: each serum lipoprotein (noted as continuous value) with age, dialysis vintage, gender, vascular access, and BMI, as well as the geographical region that the patients came from; 2) model 2: model 1 plus diabetic status; 3) model 3: model 2 plus serum creatinine levels; 4) model 4: model 3 plus serum albumin; 5) model 5: model 3 plus serum CRP; and 6) model 6: model 3 plus serum albumin, creatinine, CRP, and NLR levels. We defined model 6 as the preferable model, which contained nutritional and inflammation markers, because we hypothesized that the inverse association between HDL-C, LDL-C, and infection-related mortality should remain while accounting for the effect of nutritional status and inflammation. Analyses were performed with SAS (Version 9.4; Cary, NC) and R (Version 3.1.3) (30.R Core Team. R: a language and environment for statistical computing..R Foundation for Statistical Computing, Vienna, Austria. 2015; Google Scholar). Clinical and laboratory data were directly imported electronically in all the European, West Asian, and all North America-RRI clinics. All the lipoproteins were measured routinely from monthly to half-yearly in Europe and West Asia clinics, measured annually in FMC-RRI clinics. Serum albumin was measured by the bromocresol green method in most of the studied clinics. In Portugal, bromocresol purple method was used. Both methods were calibrated to international standard European Reference Materials DA 470k/International Federation of Clinical Chemistry and Laboratory Medicine. Serum creatinine was measured routinely by the Jaffe method in all FMC Europe and RRI clinics. Serum CRP level was measured with conventional assay in all the studied clinics. NLR were measured routinely monthly in all the regions. All the clinical and laboratory parameters that were measured by different methods were calibrated to US standards before any analyses were conducted (31.Usvyat L.A. Haviv Y.S. Etter M. Kooman J. Marcelli D. Marelli C. Power A. Toffelmire T. Wang Y. Kotanko P. The MONitoring Dialysis Outcomes (MONDO) initiative.Blood Purif. 2013; 35: 37-48Crossref PubMed Scopus (31) Google Scholar). In total, 37,250 HD patients were included in this multinational cohort, with median age of 64 years; 59% male; 36% entered the study cohort as incident patients; median dialysis vintage (time on dialysis) was 3.04 years at the beginning of the study; median follow-up time (time from first lipid measurement to event) was 3.4 years; 6,246 (16.77%) from Eastern Europe, 18,358 (49.28%) from Southern Europe; 1,058 (2.84%) from Western Europe; 10,699 (28.72%) from West Asia; and 888 (2.38%) from United States-RRI. Patient characteristics stratified by cause-specific death are presented in Table 1. The flow chart of the study cohort and study design is presented in Fig. 1A. Out of 37,250 patients included in the study, 6,147 died due to all causes, with the rate of 163 per 1,000 patient years; 1,183 (19.24%) died due to CV, 883 (13.21%) died due to infections, 4,024 died due to any other causes that are not CV and not infections, and 57 (0.9%) had missing information of cause of death (Fig. 1A).TABLE 1Baseline characteristics of the study cohortParametersAll PatientsDied from All ReasonsDied from CVDied from All InfectionsDied from Others (Not Infections and Not CV)Patients Who SurvivedNo. of patients37,2506,1471,1838124,15231,103Age (years), Median (Q1, Q3)64 (52, 74)72 (63, 79)71 (62, 78)72 (63, 79)73 (63, 79)62 (51, 73)BMI25.71 ± 6.6224.27 ± 5.4824.74 ± 5.0624.59 ± 5.5424.06 ± 5.5826.72 ± 5.57Diabetics (%)15.5718.5017.9521.4318.0815.00Gender: male (%)58.6059.7762.5557.6459.3958.37Catheter (%)44.6847.3750.0447.0446.6844.14Vintage (years), Median (Q1, Q3)3.04 (0.13, 4.15)2.63 (0.11, 3.78)2.73 (0.11, 4.00)2.51 (0.10, 3.41)2.64 (0.11, 3.74)3.12 (0.13, 4.24)Serum albumin (g/dl)3.85 ± 0.423.68 ± 0.493.75 ± 0.453.62 ± 0.523.67 ± 0.493.89 ± 0.41Serum creatinine ([mg/dl)7.74 ± 2.356.77 ± 2.096.96 ± 2.076.74 ± 2.086.72 ± 2.097.94 ± 0.36CRP (mg/dl), Median (Q1, Q3)8.73 (1.16, 10.09)12.64 (1.83, 16.00)11.29 (1.53, 15.07)14.76 (2.66, 19.34)12.61 (1.82, 15.72)7.96 (1.08, 1.24)NLR3.22 ± 2.473.75 ± 3.003.52 ± 2.054.00 ± 3.403.77 ± 3.113.12 ± 2.34LDL-C (mmol/l)All regions2.45 ± 0.872.39 ± 0.882.46 ± 0.922.30 ± 0.912.39 ± 0.872.47 ± 0.87Eastern Europe (n = 6,246)2.64 ± 1.022.54 ± 0.972.57 ± 1.032.51 ± 1.002.53 ± 0.952.66 ± 1.03Southern Europe (n = 18,358)2.27 ± 0.782.21 ± 0.812.20 ± 0.832.18 ± 0.832.22 ± 0.812.29 ± 0.77Western Europe (n = 1,058)2.31 ± 0.932.31 ± 0.932.20 ± 0.832.05 ± 1.632.35 ± 0.922.31 ± 0.93West Asia (n = 10,699)2.71 ± 0.832.70 ± 0.872.84 ± 0.852.65 ± 1.032.67 ± 0.852.71 ± 0.83United States (n = 888)2.03 ± 0.822.08 ± 0.811.96 ± 0.872.15 ± 0.712.07 ± 0.822.02 ± 0.82HDL-C (mmol/l)All regions1.05 ± 0.341.03 ± 0.341.01 ± 0.321.07 ± 0.341.03 ± 0.341.06 ± 0.34Eastern Europe (n = 6,246)1.06 ± 0.361.00 ± 0.391.06 ± 0.391.04 ± 0.380.98 ± 0.381.07 ± 0.36Southern Europe (n = 18,358)1.14 ± 0.341.11 ± 0.331.09 ± 0.341.14 ± 0.331.11 ± 0.331.15 ± 0.34Western Europe (n = 1,058)1.90 ± 0.411.12 ± 0.351.14 ± 0.411.32 ± 0.191.11 ± 0.341.20 ± 0.42West Asia (n = 10,699)0.89 ± 0.260.88 ± 0.270.87 ± 0.240.84 ± 0.250.89 ± 0.270.90 ± 0.25United States (n = 888)1.09 ± 0.371.10 ± 0.381.00 ± 0.311.06 ± 0.301.11 ± 0.401.09 ± 0.36TG (mmol/l)All regions1.86 ± 1.021.72 ± 0.951.80 ± 1.011.71 ± 0.951.69 ± 0.931.89 ± 1.02Eastern Europe (n = 6,246)1.92 ± 1.131.87 ± 1.101.81 ± 1.131.89 ± 0.941.89 ± 1.111.93 ± 1.14Southern Europe (n = 18,358)1.74 ± 0.881.57 ± 0.781.63 ± 0.821.59 ± 0.781.55 ± 0.771.77 ± 0.90Western Europe (n = 1,058)1.63 ± 0.901.42 ± 0.681.50 ± 0.781.17 ± 0.711.41 ± 0.661.65 ± 0.91West Asia (n = 10,699)2.07 ± 1.131.93 ± 1.092.06 ± 1.181.98 ± 1.281.88 ± 1.042.10 ± 1.14United States (n = 888)1.84 ± 1.071.78 ± 1.091.71 ± 0.752.04 ± 1.341.73 ± 1.041.86 ± 1.07Data are presented as mean ± SD for normal distributed variables, or median for not normal distributed variables (25th and 75th percentile). All the categorical variables were reported at the time of first lipid measurement. Contentious variables were presented as the average value during the study period. Open table in a new tab Data are presented as mean ± SD for normal distributed variables, or median for not normal distributed variables (25th and 75th percentile). All the categorical variables were reported at the time of first lipid measurement. Contentious variables were presented as the average value during the study period. By univariate analyses, LDL-C [hazard ratio (HR): 0.85, 95% CI: 0.82–0.87], HDL-C (HR: 0.64, 95% CI: 0.59–0.79), and TG (HR: 0.77, 95% CI: 0.75–0.79) levels were positively associated with survival in HD patients (Table 2). By multivariate analysis, higher LDL-C (HR: 0.82, 95% CI: 0.79–0.85), HDL-C (HR: 0.42, 95% CI: 0.38–0.47), and TG (HR: 0.86, 95% CI: 0.84–0.89) concentration were significantly associated with lower all-cause mortality after adjustment for demographics and diabetic status, as well as following adjustment for nutritional marker, noted as serum creatinine (Fig. 2, model 3). Higher LDL-C, HDL-C, and TGs remain significantly associated with lower all-cause mortality while adding serum albumin (Fig. 2, model 4) or CRP (Fig. 2, model 5) into the model. In the fully adjusted model (Fig. 2, model 6) LDL-C (HR: 0.87, 95% CI: 0.84–0.90), HDL-C (HR: 60, 95% CI: 0.55–0.66), and TGs (HR: 0.93, 95% CI: 0.90–0.96) (Fig. 2, model 6) remained associated with lower mortality, suggesting that the effects of the lipoproteins were not mediated by nutritional or inflammatory status.TABLE 2Association between time-varying serum lipid levels and outcomes without adjustmentsAll-Cause MortalityCV-Related MortalityInfection-Related MortalityOther Mortality (Not Infection and CV Related)Lipid ParametersHR (95% CI)PHR (95% CI)PHR (95% CI)PHR (95% CI)PLDL (mmol/l)0.87 (0.84–0.89)<0.00010.97 (0.92–1.03)0.320.78 (0.72–0.84)<0.00010.84 (0.82–0.87)<0.0001HDL (mmol/l)0.68 (0.64–0.74)<0.00010.61 (0.52–0.73)<0.00010.89 (0.74–1.07)0.220.70 (0.65–0.76)<0.0001TG (mmol/l)0.79 (0.77–0.82)<0.00010.90 (0.85–0.95)0.00010.77 (0.71–0.83)<0.00010.77 (0.74–0.79)<0.0001 Open table in a new

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