www.aging-us.com AGING 2021, Vol. 13, No. 19 Research Paper Circulating CD5L is associated with cardiovascular events and all- cause mortality in individuals with chronic kidney disease Esmeralda Castelblanco1,2,7, Maria R. Sarrias3, Àngels Betriu4, Berta Soldevila5,15, Maria Barranco- Altirriba1,6, Josep Franch-Nadal7,8,9,15, Jose M. Valdivielso4, Marcelino Bermudez-Lopez4, Per- Henrik Groop10,11,12,13, Elvira Fernández4, Núria Alonso5,8,15, Didac Mauricio1,7,14,15 1 Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau and Sant Pau Biomedical Research Institute (IIB Sant Pau), Barcelona 08041, Spain 2 Department of Internal Medicine, Endocrinology, Metabolism and Lipid Research Division, Washington University School of Medicine, St Louis, MO 63110, USA 3 Institute for Health Science Research Germans Trias i Pujol (IGTP) and Center for Biomedical Research on Liver and Digestive Diseases (CIBEREHD), Madrid 28029, Spain 4 Vascular and Renal Translational Research Group, Biomedical Research Institute of Lleida, (IRBLleida), Lleida 25198, Spain 5 Department of Endocrinology and Nutrition, Health Sciences Research Institute and University Hospital Germans Trias i Pujol, Badalona 08916, Spain 6 B2SLab, Department of Systems Engineering, Automation and Industrial Informatics, Polytechnic University of Catalonia, Barcelona 08034, Spain 7 DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona 08006, Spain 8 Department of Medicine, Barcelona Autonomous University (UAB), Bellaterra 08193, Spain 9 Primary Health Care Center Raval Sud, Gerència d’Atenció Primaria, Institut Català de la Salut, Barcelona 08001, Spain 10 Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki 00290, Finland 11 Abdominal Center Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki 00280, Finland 12 Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki 00100, Finland 13 Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia 14 Faculty of Medicine, University of Vic – Central University of Catalonia (UVic/UCC), Vic 08500, Spain 15 Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Barcelona 08907, Spain Correspondence to: Núria Alonso, Didac Mauricio; email: nalonso.germanstrias@gencat.cat; didacmauricio@gmail.com, https://orcid.org/0000-0002-2868-0250 Keywords: CD5L, sCD36, chronic kidney disease, cardiovascular events, mortality Received: April 26, 2021 Accepted: September 29, 2021 Published: October 10, 2021 Copyright: © 2021 Castelblanco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT This study assessed the association of CD5L and soluble CD36 (sCD36) with the risk of a cardiovascular event (CVE), including CV death and all-cause mortality in CKD. We evaluated the association of CD5L and sCD36 with a predefined composite CV endpoint (unstable angina, myocardial infarction, transient ischemic attack, cerebrovascular accident, congestive heart failure, arrhythmia, peripheral arterial disease [PAD] or amputation by PAD, aortic aneurysm, or death from CV causes) and all-cause mortality using Cox proportional hazards www.aging-us.com 22690 AGING regression, adjusted for CV risk factors. The analysis included 1,516 participants free from pre-existing CV disease followed up for 4 years. The median age was 62 years, 38.8% were female, and 26.8% had diabetes. There were 98 (6.5%) CVEs and 72 (4.8%) deaths, of which 26 (36.1%) were of CV origin. Higher baseline CD5L concentration was associated with increased risk of CVE (HR, 95% CI, 1.17, 1.0–1.36), and all-cause mortality (1.22, 1.01–1.48) after adjusting for age, sex, diabetes, systolic blood pressure, dyslipidemia, waist circumference, smoking, and CKD stage. sCD36 showed no association with adverse CV outcomes or mortality. Our study showed for the first time that higher concentrations of CD5L are associated with future CVE and all- cause mortality in individuals with CKD. INTRODUCTION Both scavenger receptors, CD5L and CD36, are essential molecules related to inflammatory responses Chronic kidney disease (CKD) is a well-known and atherosclerosis mediated by macrophages; while independent risk factor for premature cardiovascular CD36 oxLDL endocytosis prompts foam cell formation, disease (CVD) and death [1]. In individuals with CD5L facilitates CD36-mediated oxLDL uptake [16]. CKD, cardiovascular (CV) events and mortality Additionally, in adipose tissue, macrophage-derived increase progressively with declining renal function CD5L taken up by adipocytes through CD36-mediated and/or increasing albuminuria [1], with a 50% risk of endocytosis, stimulates lipolysis. In turn, the lipolytic CV mortality even before reaching end-stage renal response stimulates adipocyte inflammation favoring disease [2]. the induction of metabolic disorders predisposing to severe CVD [17]. Based on these cellular functions, CV events and mortality are only partially explained by CD36 has been proposed as a biomarker of CVD [18], the traditional risk factors of diabetes, dyslipidemia, although the soluble form in plasma (sCD36) as a CVD hypertension, obesity, smoking, and gender. predictive factor is a controversial issue. Indeed, some Furthermore, more recently described biomarkers have studies have reported that high levels of sCD36 not improved the prediction of CV events in represent a strong biomarker of CVD in individuals individuals with renal impairment [3–7]. As a result, with diabetes and of CV mortality in people with CKD current risk prediction algorithms may underestimate [19, 20], while others did not find any significant the CV risk in adults with CKD [8]. Therefore, the association with CV risk [21, 22]. Given these identification of novel and more efficient biomarkers contradictory results and since the issue has not been for early CV risk prediction is essential to be able to fully explored in a large population of individuals with implement optimal risk-reduction strategies to improve CKD, we hypothesized that high concentrations of clinical outcomes. CD5L and sCD36 could be useful biomarkers of an increased risk of CV events and mortality in individuals CD5 molecule-like (CD5L), also known as apoptosis with CKD. inhibitor of macrophages (AIM), is a 40-kDa secreted glycoprotein that belongs to the scavenger receptor RESULTS cysteine-rich superfamily. It participates in a broad spectrum of biological mechanisms that control The study included 1,516 CKD subjects followed for a inflammatory responses involved in infections, median of 4.1 years (interquartile range [IQR], 3.7; atherosclerosis, and cancer [9, 10]. Additionally, CD5L 4.4). The characteristics of the study population are modulates other aspects of macrophage biology, such as described in Table 1. Median age was 62 years (IQR, antimicrobial responses through Toll-like receptor 51; 68), 38.9% (n = 590) of the participants were activation [10, 11]. female, and 26.8% (n = 406) had diabetes. Almost all CKD subjects had hypertension (n = 1,392; 91.8%) and CD36 is an 88-kDa transmembrane glycoprotein 69.5% (n = 1,053) had dyslipidemia. The etiology of expressed in a wide variety of cell types, with critical CKD was diverse: in 21.2% of cases it was related to a roles in macrophage metabolism, activation of vascular disease; in 15.7% to glomerular nephropathy; transforming growth factor beta (TGF-β), and uptake of and in 14.6% to diabetic kidney disease (additional oxidized low-density lipoprotein (oxLDL). This causes are in Supplementary Figure 1). Up to 240 receptor is associated with inflammation and stands at (15.8%) participants received a kidney transplant the crossroads of cardio- and cerebrovascular diseases during follow-up. For most variables, there were [12]. Additionally, CD36 also plays a role in significant differences by gender except for body mass atherosclerosis progression [13, 14], and it is associated index, hypertension, dyslipidemia, and diastolic blood with traditional CV risk factors [15]. pressure (Supplementary Table 1). Regarding the www.aging-us.com 22691 AGING Table 1. Demographic and clinical characteristics of the study subjects. CKD N 1516 Gender, female, n (%) 590 (38.92%) Age, years, median [IQR] 62 [51; 68] Diabetes mellitus, n (%) 406 (26.8%) Body mass index, kg/m , mean (SD) 2 28.7 (5.4) Waist circumference, cm, mean (SD) 98.9 (12.9) Active smoker, n (%) 292 (19.3%) Hypertension, n (%) 1392 (91.8%) Dyslipidemia, n (%) 1053 (69.5%) Systolic blood pressure, mmHg, mean (SD) 142.8 (21.1) Diastolic blood pressure, mmHg, mean (SD) 81.6 (11.3) Creatinine, mg/dl, median [IQR] 2.02 [1.52; 2.89] Albumin/creatinine ratio, mg/g, median [IQR] 103.5 [12.5; 455.5] eGFR, mL/min/1,73 m , median [IQR] 2 32.4 [21.9; 45.4] CKD stage 3, n (%) 669 (44.1%) CKD stage 4–5, n (%) 539 (35.6%) Dialysis, n (%) 308 (20.3%) Aspartate transaminase, U/L, median [IQR] 19 [16; 24] Alanine transaminase, U/L, median [IQR] 19 [14; 27] Total cholesterol, mg/dL, median [IQR] 177.2 [153; 205] HDL cholesterol, mg/dL, median [IQR] 47 [39; 58] LDL cholesterol, mg/dL, median [IQR] 101 [79; 122] Triglycerides, mg/dL, median [IQR] 123 [92; 175] Glucose, mg/dL, median [IQR] 98 [88; 113] HbA1c, %, median [IQR] 5.9 [5.3; 6.7] Hematocrit, %, median [IQR] 38.83 (5.09) Hemoglobin, g/dL, mean (SD) 12.9 (1.73) CD5L, ng/mL, median [IQR] 2276 [1812; 2876] sCD36, ng/mL, median [IQR] 1.0 [0.05; 6.75] Abbreviations: CKD: chronic kidney disease; eGFR: estimated glomerular filtration rate; HDL: high-density lipoprotein; IQR: interquartile range; LDL: low-density lipoprotein; SD: standard deviation. potential biomarkers, there were no significant 33.7] vs. 27.5 kg/m2 [IQR, 24.5; 30.9], waist differences in the median CD5L concentration by circumference (103 cm [IQR, 94.0; 111] vs. 96.0 cm gender (females: 2,230 ng/mL [IQR, 1,798; 2,888]; [IQR, 89.0; 105]), and systolic blood pressure (146 males: 2,295 ng/mL [IQR, 1,818; 2,875]). In contrast, mmHg [IQR, 132; 162] vs. 139 mmHg [IQR, 127; 154]), sCD36 was significantly higher in females than in and more often had hypertension (98.3% vs. 89.5%) and males (1.20 ng/mL [IQR, 0.05; 8.86] vs. 0.85 ng/mL dyslipidemia (82.3% vs. 64.8%) (Supplementary Table [IQR, 0.05; 5.37]). 2). As for the biomarkers, the median CD5L concentrations were significantly higher among the The participants with diabetes were older (65.0 years participants with diabetes (2,421.2 ng/mL; IQR, 1,899; [IQR, 58.0; 70.0] vs. 61.0 years [IQR, 50.0; 67.0]), had a 3,105) than in those without diabetes (2,206.11ng/mL; higher median body mass index (30.0 kg/m2 [IQR, 26.5; IQR, 1,782; 2,802) (Supplementary Table 2). www.aging-us.com 22692 AGING Conversely, the sCD36 concentrations were not 1.17; 95% confidence interval [CI], 1.0–1.36) after significantly different between participants with or adjusting for CV risk factors such as age, sex, diabetes, without diabetes (1.31 ng/mL [IQR, 0.05; 7.57] vs. 0.83 waist circumference, smoking, systolic blood pressure, ng/mL [IQR, 0.05; 5.93]) (Supplementary Table 2). dyslipidemia, and CKD stage (Table 2). Other independent predictors of CV event risk were older age, diabetes, Incidence of cardiovascular events smoking habit, and dialysis therapy (Table 2). The results of the model for CD5L including estimated glomerular A total of 98 (6.5%) CV events were registered during filtration rate (eGFR) are shown in Supplementary Table 4. the follow-up, yielding a CV event rate of 1.55 per 1,000 In contrast, sCD36 did not predict the occurrence of CV person-years (specific causes are described in events, although in this case the independently associated Supplementary Figure 2). Compared to participants factors were also older age, diabetes, active smoking, and without a CV event, those who did suffer a CV event dialysis therapy (Supplementary Table 5). The results of were more frequently diabetic (43.9% vs. 25.6%), on the model for sCD36 including eGFR are presented in dialysis therapy (29.6% vs. 19.7%), active smokers Supplementary Table 6. (29.6% vs. 18.5%), had a higher waist circumference (102 cm [IQR, 93;110] vs. 98.0 cm [IQR, 90; 107]), and Factors associated with all-cause mortality lower HDL cholesterol concentrations (43.0 mg/dL [IQR, 33.5; 51.2] vs. 47.0 mg/dL [IQR, 39.0; 58.5]) Cox proportional hazards models showed that CD5L (Supplementary Table 3). However, gender had no effect was an independent predictor of all-cause mortality (HR, on the proportion of incident CV events (7.0% in males 1.22; 95% CI, 1.01–1.48) after adjusting for CV risk vs. 5.9% in females) or on the CV event rate, which was factors such as age, sex, diabetes, waist circumference, 1.69 per 1,000 person-years in men and 1.32 per 1000 smoking, systolic blood pressure, dyslipidemia, and person-years in females. Moreover, participants with CKD stage (Table 3). Other significant independent diabetes had a higher rate of CV events (2.61 per 1,000 predictors of death were older age, current smoking, person-years) than those without diabetes (1.17 per diabetes and dialysis stage (Table 3). The results of the 1,000 person-years). Finally, participants who had a CV model for CD5L including eGFR are shown in event exhibited higher CD5L concentrations than those Supplementary Table 7. In contrast, sCD36 did not who did not (2,571 ng/mL [IQR, 1,910; 3,314] and predict all-cause mortality (Supplementary Table 8), but 2,245 ng/mL [IQR, 1,806; 2,847], respectively; p = older age, current smoking, waist circumference, CKD 0.004). Conversely, the sCD36 concentrations were not stage 4–5 and dialysis therapy were independent different between the groups (1.31 ng/mL [IQR, 0.05; predictors. The results of the model for sCD36 including 6.66] vs. 0.96 ng/mL [IQR, 0.05; 6.79]). eGFR are shown in Supplementary Table 9. All-cause mortality The goodness of fit over adjusted models A total of 72 deaths (6.5%) were registered during We also evaluated whether the adjusted model including follow-up with a rate of 1.11 per 1,000 person-years. CD5L and the traditional CV risk factors improved the Among them, 26 (36.1%) were CV deaths with a rate of prediction of CV events and mortality compared to the 0.4 per 1,000 person-years. CD5L and sCD36 model without CD5L. The absolute log-likelihood value concentrations were higher in those who died (n = 72), showed that inclusion of CD5L in the model slightly compared to the participants alive at the end of the improved the predictive ability compared to the model follow-up period (CD5L: 2,409 [1,876; 3,546] vs. 2,255 without CD5L (−614.3 vs. −616.0, p = 0.065). On the [1,811; 2,857] ng/m, and CD36: 3.19 [0.17; 9.66] vs. other hand, the absolute log-likelihood value showed 0.94 [0.05; 6.64] ng/mL). All-cause mortality was not that the model, including the interaction between CD5L significantly higher in males than in females (5.62% vs. and diabetes also improved the all-cause mortality model 3.39%), with an all-cause mortality rate in men of 1.32 (−424.7 vs. −426.5, p = 0.061). per 1,000 person-years and 0.79 per 1,000 person-years in women. In participants with diabetes, the proportion Competing risks approach of deaths was higher than in those without diabetes (7.39% vs. 3.78%), the rate of all-cause mortality being Of the overall population of 1,516 studied subjects, 46 1.76 vs. 0.88 per 1,000 person-years, respectively. experienced non-CV deaths and 98 CV events, of which 26 were CV-deaths. Considering all-cause mortality as a Factors associated with cardiovascular disease competing risk, the CV event rate was 1.55 (1.25–1.88) per 1,000 person-years. When considering a CV event Cox proportional hazards models revealed that CD5L was as a competing risk, the all-cause mortality rate was an independent predictor of CV events (hazard ratio [HR], 0.69 (0.5–0.92) per 1,000 person-years. www.aging-us.com 22693 AGING Table 2. Cardiovascular risk prediction cox regression model for CD5L. Predictors HR, 95% CI p-value Diabetes 1.74, 1.1–2.73 0.017 CD5L 1.17, 1.0–1.36 0.045 Age 1.32, 1.01–1.74 0.045 Gender, female 0.86, 0.54–1.36 0.512 Active smoker 2.06, 1.27–3.34 0.003 Dyslipidemia 1.04, 0.64–1.69 0.887 Waist circumference 1.17, 0.94–1.45 0.167 Systolic blood pressure 1.13, 0.92–1.39 0.257 CKD 4–5 * 1.33, 0.82–2.14 0.245 Dialysis* 2.18, 1.20–3.95 0.010 * CKD stage 3 was the reference to assess dialysis and CKD stage 4–5. Abbreviations: CI: confidence interval; CKD: chronic kidney disease; HR: hazard ratio. Table 3. All-cause mortality cox regression model for CD5L. HR, 95% CI p-value Diabetes 1.59, 0.92–2.75 0.099 CD5L 1.22, 1.01–1.48 0.043 Age 2.23, 1.50–3.32 <0.001 Gender, female 0.65, 0.36–1.15 0.136 Active smoker 1.99, 1.11–3.56 0.020 Systolic blood pressure 1.09, 0.85–1.40 0.490 Dyslipidemia 0.82, 0.47–1.41 0.467 Waist circumference 1.33, 1.02–1.73 0.034 CKD 4–5 * 1.92, 1.06–3.47 0.032 Dialysis* 4.12, 2.07–8.22 <0.001 CD5L::Diabetes 0.69, 0.45–1.07 0.098 * CKD stages 4–5 and dialysis taking CKD stage 3 as reference. Abbreviations: CI: confidence interval; CKD: chronic kidney disease; HR: hazard ratio. The Cox model revealed that the independent predictive CKD. In contrast, sCD36 was not associated with a risk variables for CV events were CD5L (HR, 1.19; 95% CI, of CVD or death. This is, to the best of our knowledge, 1.01–1.41), diabetes (HR, 1.72; 95% CI, 1.09–2.71), the first time that CD5L has been shown to be age (HR, 1.29; 95% CI, 0.99–1.68), smoking (HR, 2.03; associated and could be a potential predictor of CVD 95% CI, 1.25–3.28) and dialysis (HR, 2.12; 95% CI, and all-cause mortality in individuals with CKD. 1.17–3.84). Figure 1 shows that the probability of a CV event was higher with high CD5L concentrations but CD5L as a biomarker similar for all-cause mortality according to the Cox model adjusted with competitive risks. Our findings are in line with studies showing that CD5L might be detrimental in metabolic disorders and DISCUSSION atherosclerosis, including diabetes and CV events [17, 23]. This could be explained by the fact that CD5L is In this multicenter cohort, we found that high involved in the pathogenesis of several inflammatory circulating CD5L concentrations were associated with processes as well as in immune homeostasis [23, 24]. CV events and all-cause mortality in individuals with Macrophage-derived CD5L enters into the adipocytes www.aging-us.com 22694 AGING via CD36-mediated endocytosis; once inside the cell, it fibrillation [30]. Those results also support the associates with fatty acid synthase (FAS) and catalyzes hypothesis that CD5L could be useful for predicting the synthesis of saturated fatty acids [25]. Saturated CVD. Indeed, recent studies reported that high fatty acids activate Toll-like receptor (TLR) 4 and concentrations of CD5L strongly predict 30-day induce a response tightly associated with obesity- mortality risk in individuals with bacterial pneumonia induced inflammation [26]. Thus, CD5L acts as a key [31] and 28-day mortality in adults and pediatric factor in the initiation of obesity-associated chronic individuals with sepsis [32]. Additionally, a proteomic inflammation leading to insulin resistance [17, 27], study found that CD5L is an independent predictor of which results in the progression of atherosclerosis and acute heart rejection, which is a surrogate marker of contributes to future CV events [17]. Indeed, CD5L is mortality after transplantation [33]. Our study adds to highly expressed in foam macrophages harvested from the literature that CD5L is associated and an atherosclerotic plaques, which supports macrophage independent predictor of mortality in individuals with survival through inhibition of apoptosis and their advanced CKD, i.e., CKD stage 4–5 and dialysis consequent accumulation, in turn causing inflammatory therapy. responses within the lesion that eventually leads to disease progression [23]. sCD36 as a biomarker Previous studies in mice have shown that deletion of In our study, sCD36 concentrations did not predict CD5L reduces the accumulation of pro-inflammatory CVD or mortality. The usability of sCD36 as a M1 macrophages in myocardial tissue [28]. Moreover, biomarker of CVD is controversial. In one study in the depletion of CD5L shows cardiac effects, such as children with and without hypercholesterolemia, a high decreased systolic dysfunction, a decreased incidence of plasma sCD36 concentration was negatively associated cardiac rupture, and reduction of the infarct size during with CV risk factors (high body mass index, body the acute phase after myocardial infarction, in turn weight, waist and hip circumference, systolic blood resulting in improved survival rates [28, 29]. These pressure, and HOMA-IR), suggesting a possible findings led to the authors concluding that the inhibition protective effect of sCD36 [22]. Another study also of CD5L could prevent CVD in chronic inflammation reported possible protective effects of high sCD36 and could attenuate the functional impairment after concentrations on metabolic syndrome components in myocardial infarction [28, 29]. In humans, higher CD5L individuals with coronary artery disease [34]. The same concentrations on epicardial fat secretome were found study also observed that higher sCD36 concentrations in male subjects with heart failure who developed atrial were associated with a lower risk of left ventricular Figure 1. Probability of a cardiovascular event or all-cause mortality. Cox models adjusted for competitive risks according to CD5L levels. The continuous line is the median (1) and the dashed line is the median plus one standard deviation (0). www.aging-us.com 22695 AGING hypertrophy, although it was identified as a potential had a CV event, and tended to be higher in males and risk factor of impaired left ventricular diastolic function. those with all-cause mortality. In contrast, sCD36 has been reported as an independent risk factor for coronary artery stenosis in elderly Limitations individuals with coronary heart disease [35]. Moreover, a study conducted in subjects with moderate to Our study has several strengths and limitations worth advanced CKD followed for about 5 years found that mentioning. The main strength is that it is a large sCD36 concentrations were an independent predictor of multicenter study from diverse geographic regions in total mortality risk [36]. Finally, a study in individuals Spain. Moreover, the participants had a variety of with CKD stage 5 also found that higher sCD36 kidney disease etiologies, making our results concentrations were associated with increased 3-year generalizable to the CKD population at large. And no mortality, although the association was only significant changes were reported in the monitoring protocols of after correcting for age and gender, but not after the subjects during the follow-up period. Additionally, additional correction for diabetes and CVD [20]. all biomarker measurements were performed in the same laboratory to ensure consistency across the whole CD36 represents the primary fatty acid uptake system in cohort. Finally, we included a set of well-recognized the kidneys and appears to play a central role in CKD and well-defined variables to correct for confounding development and progression [37]. CD36 expression bias. One limitation of the study is that we did not levels are higher in CKD subjects with diabetic measure tissue-specific biomarker levels. It is therefore nephropathy and kidney damage [38] and are closely possible that elevations in local biomarker levels in the associated with CV risk factors such as hyperlipidemia heart or kidneys could have been clinically significant, and diabetes [12]. According to previous findings from but not detected from the plasma in our study. The our group, this role is not reflected by the circulating second limitation is that we do not have the urine form of the protein (i.e., sCD36), as we previously albumin to creatinine ratio in all patients, a well-known reported similar plasma concentrations in individuals CV/mortality risk factor in CKD. Moreover, the small with or without diabetes [15] as well as in diabetic and number of CV deaths that occurred during the follow-up nondiabetic individuals with subclinical carotid was a significant limitation when analyzing the CV atherosclerosis [39]. mortality as a single outcome. The discordant results on the association between sCD36 CONCLUSIONS concentrations and atherosclerosis could be explained by several reasons. Among them, the heterogeneity of The identification of specific biomarkers for CVD is subjects included in the different studies published so crucial for the development of improved diagnostics far, ranging from healthy subjects to those with type 2 and personalized treatment strategies. In individuals diabetes, CKD or even with recent CV events; the with CKD, circulating CD5L could improve CVD different definitions of atherosclerosis adopted by prediction and may help to identify those at higher CV researchers, i.e. carotid intima-media thickness (cIMT), risk. However, further population studies on CD5L in subclinical atherosclerotic plaques or plaques associated relation to traditional risk factors are needed to validate with a recent CV event; and the different methods used its usability as a true CVD biomarker, its predictive to determine circulating sCD36, especially as there is not validity, and to explore whether its utility is restricted to a well-characterized or standardized method to evaluate individuals with CKD. its concentration [34, 40]. METHODS The relationship between decreased kidney function and high CV morbidity and mortality has been established Design and study population in individuals with diabetes [41, 42]. Our finding confirms that individuals with diabetes have a higher This study assessed the predictive ability of CD5L and susceptibility for CV events than their counterparts sCD36 for CV events and mortality in individuals with without diabetes and, in line with other studies, CKD from the National Observatory of Atherosclerosis highlights the relevance of hypertension, smoking, and in Nephrology (NEFRONA) study [47]. To evaluate the age in this population [1, 5, 43, 44]. Additionally, and in predictive ability of CD5L and sCD36, we included agreement with previous reports, gender was not 1,516 participants enrolled in the NEFRONA cohort. identified as a risk factor for CV events or death, The NEFRONA study is a multicenter, prospective although males exhibited slightly higher proportions of observational study. The design, objective, and methods both outcomes than females [45, 46]. CD5L was of the NEFRONA study have been described in detail in significantly higher in diabetic subjects and those who a previous publication [47]. Briefly, the NEFRONA www.aging-us.com 22696 AGING study included 2,445 subjects between 18 and 75 years 340PC384 microplate reader (Molecular Devices, LLC of age, with CKD but without prior CVD recruited from Sunnyvale, USA). The protein concentration was 81 hospitals and dialysis clinics throughout Spain estimated using a four-parameter logistic curve and log- between October 2009 and June 2011 [48]. Exclusion log curve fit, respectively, based on the standards’ criteria were pregnancy, life expectancy below 12 measurements. months, any active infection, previous organ transplantation, or known CVD or carotid artery Cardiovascular events procedure. During a four-year follow-up period, all CV events, CV and non-CV deaths, and kidney Participants were followed-up for 4 years, and data on transplantations were registered. The study protocol was fatal and non-fatal CV events, death due to any cause, conducted following the Declaration of Helsinki and and kidney transplants were recorded by the referring approved by the Ethics Committee of Germans Trias i physician [45]. The following CV events were Pujol Hospital and Arnau de Vilanova Hospital. An considered, as defined by the International informed consent form was signed by all the study Classification of Diseases, Ninth Revision, Clinical participants. Modification (ICD9-CM): unstable angina, myocardial infarction, transient ischemic attack, cerebrovascular Clinical and biochemical data accident, congestive heart failure, arrhythmia, peripheral arterial disease (PAD) or amputation due to Detailed information was collected at baseline PAD, and aortic aneurysm [23]. Cardiovascular including the participant’s medical history, CV risk mortality causes included myocardial infarction, factors, and medication. The physical examination arrhythmia, congestive heart failure, stroke, abdominal included standard vital tests and anthropometric aortic aneurysm, mesenteric infarction, and sudden measures, such as height, body weight, and waist-hip death. In addition, non-CV mortality from any other ratio. Dyslipidemia was defined as a recorded clinical causes was recorded; these included deaths caused by diagnosis or the current use of lipid-lowering neoplasia, accident, infection, non-determined cause, or medication [47]. Biochemical parameters were unknown death. obtained from a routine fasting blood test, and the glomerular filtration rate (eGFR) was estimated using Statistical analysis the Modification of Diet in Renal Disease Study formula (MDRD-4) [49]. The criteria for diabetes Categorical variables are presented as frequencies and were: a previous diagnosis of diabetes recorded in the percentages, while the numerical ones are expressed as individual's medical history, a fasting plasma glucose the mean with standard deviation or the median with ≥126 mg/dl or glycated hemoglobin (HbA1c) ≥6.5% the first and last quartiles (the interquartile range (48 mmol/mol) determined by laboratory testing, or a [IQR]). current prescription of any anti-diabetic drug [50]. We used Cox regression models to estimate raw and Determination of CD5L and sCD36 adjusted hazard ratios (HRs) of CV events in relation to CD5L and sCD36. Potential confounders considered for Plasma concentrations of human CD5L and sCD36 adjustment in the multivariable models were: diabetes, were measured using commercially available kits: age, sex, smoking, body mass index, abdominal CircuLex human AIM/CD5L/Sp ELISA (Medical and circumference, systolic blood pressure, dyslipidemia, Biological Laboratories, Nagova, Aichi-ken, Japan) and CKD stage and eGFR. Fine and Gray modeling was sCD36 ELISA (Nordic BioSite, Täby, Sweden); the used to estimate risk prediction whilst illustrating the detection limits were 0.754 ng/mL and 1.95 ng/mL, effect of competing risk with no CV death and kidney respectively. Experiments were done in duplicate, with transplantation. Cause-specific HRs were reported from appropriate dilutions according to the manufacturer's the Cox model, as were subhazard ratio and cumulative instructions. Briefly, samples were incubated in incidence function from the Fine and Gray regressions. microtiter wells coated with antibodies for either protein We tested the proportional hazard assumption for 2 hours. After incubation and washing, a graphically and analytically with the test of biotinylated antibody conjugated with streptavidin proportional-hazards assumption. We used a likelihood peroxidase was added to the wells for 1 hour. After a ratio test to assess if the model with CD5L improved the second incubation and washing step, the substrate goodness of fit over CV events and death. Confidence tetramethylbenzidine was added to the wells at room intervals at the 95% level were calculated whenever temperature for 20–30 minutes, followed by the possible. All analyses were conducted with the free addition of sulfuric acid to stop the enzymatic reaction. software environment for statistical computing R The absorbance was read at 450 nm using a SpectraMax version 3.5.3 (2019-03-11) for Windows. www.aging-us.com 22697 AGING Abbreviations cardiovascular events, and hospitalization. N Engl J Med. 2004; 351:1296–305. CKD: chronic kidney disease; CV: cardiovascular; https://doi.org/10.1056/NEJMoa041031 CVD: cardiovascular disease; sCD36: soluble cluster of PMID:15385656 differentiation 36; CD5L: CD5 molecule-like. 2. Vaziri ND, Norris K. Lipid disorders and their relevance to outcomes in chronic kidney disease. AUTHOR CONTRIBUTIONS Blood Purif. 2011; 31:189–96. https://doi.org/10.1159/000321845 DM, NA and MRS, contributed to the study design, PMID:21228589 conducting the study, EC contributed to conducting the 3. Cambray S, Galimudi RK, Bozic M, Bermúdez-López study, data analysis, and writing of the manuscript. AB, M, Rodríguez I, Valdivielso JM. The rs1126616 Single BS, JMV, MBL, and MBA contributed to the data Nucleotide Polymorphism of the Osteopontin Gene Is collection and conducting the study. JFN, PHG, and EF, Independently Associated with Cardiovascular Events contributed to data interpretation and discussion. DM, in a Chronic Kidney Disease Cohort. J Clin Med. 2019; NA, and PHG contributed to writing of the manuscript. 8:592. All authors critically reviewed the manuscript and https://doi.org/10.3390/jcm8050592 approved the final version for publication. PMID:31036794 ACKNOWLEDGMENTS 4. Bozic M, Méndez-Barbero N, Gutiérrez-Muñoz C, Betriu A, Egido J, Fernández E, Martín-Ventura JL, We are grateful to Nuria Villalmanzo for her valuable Valdivielso JM, Blanco-Colio LM, and Investigators assistance in conducting the laboratory techniques. The from the NEFRONA study. Combination of biomarkers authors also thank statistical programmers Cristian Tebe of vascular calcification and sTWEAK to predict and Pau Satorra for their help. Also, we want to cardiovascular events in chronic kidney disease. particularly acknowledge the participants, the NEFRONA Atherosclerosis. 2018; 270:13–20. team (Teresa Vidal, Eva Castro, Virtudes María, Teresa https://doi.org/10.1016/j.atherosclerosis.2018.01.011 Molí, Meritxell Soria) and the Biobank of RedInRen for PMID:29407881 their collaboration. The authors acknowledge Amanda 5. Afghahi H, Svensson MK, Pirouzifard M, Eliasson B, Prowse (Lochside Medical Communications Ltd.) and Svensson AM. Blood pressure level and risk of major Mònica Gratacòs for support in editing the paper. cardiovascular events and all-cause of mortality in patients with type 2 diabetes and renal impairment: CONFLICTS OF INTEREST an observational study from the Swedish National Diabetes Register. Diabetologia. 2015; 58:1203–11. The authors declare that they have no conflicts of https://doi.org/10.1007/s00125-015-3548-1 interest. A patent protecting a method to measure CD5L PMID:25773403 levels in human samples was submitted to the European 6. Gluba-Brzózka A, Michalska-Kasiczak M, Franczyk- Patent Office by MRS. Skóra B, Nocuń M, Banach M, Rysz J. Markers of increased cardiovascular risk in patients with chronic FUNDING kidney disease. Lipids Health Dis. 2014; 13:135. https://doi.org/10.1186/1476-511X-13-135 This research was supported by grants from the PMID:25145866 European Foundation for the Study of Diabetes (2014- EFSD-00914) Düsseldorf, Germany; the European 7. Sun J, Axelsson J, Machowska A, Heimbürger O, Regional Development Fund; and the Carlos III Bárány P, Lindholm B, Lindström K, Stenvinkel P, National Institute of Health (PI14/1772) Madrid, Spain. Qureshi AR. 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Valdivielso JM, Betriu A, Martinez-Alonso M, Arroyo D, Bermudez-Lopez M, Fernandez E, and NEFRONA investigators. Factors predicting cardiovascular events in chronic kidney disease patients. Role of www.aging-us.com 22701 AGING SUPPLEMENTARY MATERIALS Supplementary Figures Supplementary Figure 1. Etiology of chronic kidney disease in the study group. www.aging-us.com 22702 AGING Supplementary Figure 2. Type of cardiovascular events in the study group. www.aging-us.com 22703 AGING Supplementary Tables Supplementary Table 1. Clinical characteristics of the study group by gender. Male Female p. overall N, (%) 926 (61.08) 590 (38.92) Diabetes, n (%) 265 (28.6) 141 (23.9) 0.050 Body mass index, kg/m , median [IQR] 2 28.1 [25.3; 31.2] 27.8 [24.3; 32.5] 0.975 Waist circumference, cm, median [IQR] 100 [93.0; 108] 95.0 [86.0; 105] <0.001 Active smoker, n (%) 218 (23.5) 74 (12.5) <0.001 Hypertension, n (%) 854 (92.2) 538 (91.2) 0.533 Dyslipidemia, n (%) 659 (71.2) 394 (66.8) 0.080 SBP, mmHg, median [IQR] 142 [130; 157] 139 [126; 156] 0.004 DBP, mmHg, median [IQR] 82.0 [75.0; 89.0] 80.0 [73.0; 88.0] 0.095 Age, years, median [IQR] 62.0 [53.0; 69.0] 61.0 [50.0; 68.0] 0.062 Creatinine, mg/dL, median [IQR] 2.11 [1.60; 3.00] 1.90 [1.40; 2.60] <0.001 eGFR, mL/min/1.73 m , median [IQR] 2 34.3 [22.9; 46.9] 29.6 [20.2; 41.3] <0.001 CKD stage, n (%) <0.001 CKD-3 447 (48.3) 222 (37.6) CKD4-5 306 (33.0) 233 (39.5) Dialysis 173 (18.7) 135 (22.9) Albumin/creatinine ratio, mg/g, median [IQR] 135 [14.2; 509] 72.8 [10.3; 372] 0.039 Total cholesterol, mg/dL, median [IQR] 171 [148; 197] 188 [165; 214] <0.001 HDL cholesterol, mg/dL, median [IQR] 44.0 [36.0; 52.0] 53.0 [45.0; 66.0] <0.001 LDL cholesterol, mg/dL, median [IQR] 98.0 [76.0; 118] 106 [83.5; 126] <0.001 Triglycerides, mg/dL, median [IQR] 124 [94.2; 179] 121 [89.0; 167] 0.018 Glucose, mg/dL, median [IQR] 101 [90.0; 116] 94.0 [85.0; 109] <0.001 HbA1c, %, median [IQR] 5.90 [5.38; 6.60] 5.90 [5.30; 7.10] 0.545 Hematocrit, median [IQR] 39.9 [36.0; 43.5] 37.0 [34.7; 39.9] <0.001 Hemoglobin, g/dL, median [IQR] 13.2 [12.0; 14.6] 12.2 [11.4; 13.2] <0.001 CD5L, ng/mL, median [IQR] 2295 [1818; 2875] 2230 [1798; 2888] 0.397 sCD36, ng/mL, median [IQR] 0.85 [0.05; 5.37] 1.20 [0.05; 8.86] 0.028 Cardiovascular event, n (%) 65 (7.02) 33 (5.59) 0.320 Kidney transplant, n (%) 138 (14.9) 102 (17.3) 0.243 Death 0.093 Alive at the end of follow-up, n (%) 874 (94.4) 570 (96.6) CV death, n (%) 17 (1.84) 9 (1.53) Non-CV death, n (%) 35 (3.78) 11 (1.86) Abbreviations: CDK: chronic kidney disease; CV: cardiovascular; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin; HDL: high-density lipoprotein; IQR: interquartile range; LDL: low-density lipoprotein; SBP: systolic blood pressure. www.aging-us.com 22704 AGING Supplementary Table 2. Clinical characteristics of the study group by the presence of diabetes. No diabetes Diabetes p. overall N, (%) 1110 (73.22%) 406 (26.78%) Gender, female, n (%) 449 (40.5%) 141 (34.7%) 0.050 Body mass index, kg/m , median [IQR] 2 27.5 [24.5; 30.9] 30.0 [26.5; 33.7] <0.001 Waist circumference, cm, median [IQR] 96.0 [89.0; 105] 103 [94.0; 111] <0.001 Active smoker, n (%) 216 (19.5%) 76 (18.7%) 0.803 Hypertension, n (%) 993 (89.5%) 399 (98.3%) <0.001 Dyslipidemia, n (%) 719 (64.8%) 334 (82.3%) <0.001 SBP, mmHg, median [IQR] 139 [127; 154] 146 [132; 162] <0.001 DBP, mmHg, median [IQR] 82.0 [75.0; 89.0] 79.5 [72.0; 86.0] <0.001 Age, years, median [IQR] 61.0 [50.0; 67.0] 65.0 [58.0; 70.0] <0.001 Creatinine, mg/dL, median [IQR] 2.08 [1.51; 2.90] 1.95 [1.54; 2.77] 0.337 eGFR, mL/min/1.73 m , median [IQR] 2 31.6 [21.4; 45.3] 34.3 [22.5; 45.7] 0.306 CKD stage, n (%) <0.001 CKD-3 468 (42.2%) 201 (49.5%) CKD4-5 381 (34.3%) 158 (38.9%) Dialysis 261 (23.5%) 47 (11.6%) Albumin/creatinine ratio, mg/g, median [IQR] 83.5 [10.3; 381] 178 [25.4; 687] 0.001 Total cholesterol, mg/dL, median [IQR] 180 [156; 208] 171 [144; 197] <0.001 HDL cholesterol, mg/dL, median [IQR] 48.0 [40.0; 59.0] 44.0 [36.0; 54.0] <0.001 LDL cholesterol, mg/dL, median [IQR] 105 [83.9; 125] 90.0 [71.6; 111] <0.001 Triglycerides, mg/dL, median [IQR] 118 [89.8; 164] 140 [101; 205] <0.001 Glucose, mg/dL, median [IQR] 94.0 [86.0; 103] 134 [108; 169] <0.001 HbA1c, %, median [IQR] 5.50 [5.10; 5.80] 6.90 [6.20; 8.00] <0.001 Hematocrit, median [IQR] 38.8 [35.5; 42.3] 37.7 [34.8; 41.1] 0.002 Hemoglobin, g/dL, median [IQR] 12.8 [11.8; 14.2] 12.5 [11.5; 13.8] 0.005 CD5L, ng/mL, median [IQR] 2206 [1782; 2802] 2421 [1899; 3105] <0.001 sCD36, ng/mL, median [IQR] 0.83 [0.05; 5.93] 1.31 [0.05; 7.57] 0.311 Cardiovascular event, n (%) 55 (4.95%) 43 (10.6%) <0.001 Kidney transplant, n (%) 200 (18.0%) 40 (9.85%) <0.001 Death 0.013 Alive at the end of follow-up, n (%) 1068 (96.2) 376 (92.6%) CV death, n (%) 16 (1.44%) 10 (2.46%) Non-CV death, n (%) 26 (2.34%) 20 (4.93%) Abbreviations: CDK: chronic kidney disease; CV: cardiovascular; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin; HDL: high-density lipoprotein; IQR: interquartile range; LDL: low-density lipoprotein; SBP: systolic blood pressure. www.aging-us.com 22705 AGING Supplementary Table 3. Clinical characteristics of the study group by the presence of a cardiovascular event during follow-up. NoCVE CVE p. overall N, (%) 1418 (93.54%) 98 (6.46%) Diabetes, n (%) 363 (25.6%) 43 (43.9%) <0.001 Gender, female, n (%) 557 (39.3%) 33 (33.7%) 0.320 Body mass index, kg/m , median [IQR] 2 28.0 [24.9; 31.7] 29.3 [25.7; 32.4] 0.117 Waist circumference, cm, median [IQR] 98.0 [90.0; 107] 102 [93.0; 110] 0.009 Active smoker, n (%) 263 (18.5%) 29 (29.6%) 0.011 Hypertension, n (%) 1298 (91.5%) 94 (95.9%) 0.180 Dyslipidemia, n (%) 980 (69.1%) 73 (74.5%) 0.315 SBP, mmHg, median [IQR] 140 [128; 156] 146 [131; 163] 0.125 DBP, mmHg, median [IQR] 81.0 [74.0; 89.0] 83.0 [72.0; 89.8] 0.844 Age, years, median [IQR] 62.0 [51.0; 68.0] 64.0 [54.0; 69.8] 0.074 Creatinine, mg/dL, median [IQR] 2.00 [1.51; 2.87] 2.13 [1.70; 3.14] 0.093 eGFR, mL/min/1.73 m , median [IQR] 2 32.5 [21.9; 45.7] 30.1 [19.6; 39.1] 0.089 CKD stage, n (%) 0.046 CKD-3 634 (44.7%) 35 (35.7%) CKD4-5 505 (35.6%) 34 (34.7%) Dialysis 279 (19.7%) 29 (29.6%) Albumin/creatinine ratio, mg/g, median [IQR] 96.0 [11.9; 448] 212 [91.1; 1000] 0.003 Total cholesterol, mg/dL, median [IQR] 178 [154; 205] 169 [139; 206] 0.185 HDL cholesterol, mg/dL, median [IQR] 47.0 [39.0; 58.5] 43.0 [33.5; 51.2] <0.001 LDL cholesterol, mg/dL, median [IQR] 101 [80.4; 122] 93.5 [67.3; 117] 0.054 Triglycerides, mg/dL, median [IQR] 122 [91.0; 174] 135 [104; 184] 0.088 Glucose, mg/dL, median [IQR] 98.0 [87.0; 112] 103 [91.0; 152] 0.002 HbA1c, %, median [IQR] 5.80 [5.30; 6.70] 6.80 [6.00; 8.00] <0.001 Hematocrit, median [IQR] 38.6 [35.3; 42.0] 37.8 [34.0; 41.8] 0.090 Hemoglobin, g/dL, median [IQR] 12.8 [11.8; 14.1] 12.4 [11.3; 14.0] 0.066 CD5L, ng/mL, median [IQR] 2245 [1806; 2847] 2571 [1910; 3314] 0.004 sCD36, ng/mL, median [IQR] 0.96 [0.05; 6.79] 1.31 [0.05; 6.66] 0.347 Kidney transplant, n (%) 229 (16.1%) 11 (11.2%) 0.251 Death <0.001 Alive at the end of follow-up, n (%) 1374 (96.9%) 70 (71.4%) CV death, n (%) 0 (0.00%) 26 (26.5%) Non-CV death, n (%) 44 (3.10%) 2 (2.04%) Abbreviations: CDK: chronic kidney disease; CV: cardiovascular; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin; HDL: high-density lipoprotein; IQR: interquartile range; LDL: low-density lipoprotein; SBP: systolic blood pressure. www.aging-us.com 22706 AGING Supplementary Table 4. Cardiovascular risk prediction cox regression model for CD5L. Predictors HR, 95% CI p-value Diabetes 1.56, 0.94– 2.59 0.083 CD5L 1.23, 1.02–1.48 0.030 Age 1.26, 0.91–1.73 0.161 Gender, female 0.86, 0.51–1.46 0.570 Active smoker 2.50, 1.47–4.26 0.001 Dyslipidemia 1.64, 0.85–3.16 0.138 Waist circumference 1.24, 0.97–1.58 0.091 Systolic blood pressure 1.24, 0.98–1.58 0.073 eGFR (MDRD4) 0.76, 0.59–0.98 0.037 Abbreviations: CI: confidence interval; eGFR: estimated glomerular filtration rate; HR: hazard ratio; MDRD: Modification of Diet in Renal Disease. Supplementary Table 5. Cardiovascular risk prediction cox regression model for sCD36. Predictors HR, 95% CI p Diabetes 1.83 1.17–2.87 0.009 sCD36 1.03 0.83–1.29 0.772 Age 1.36 1.04–1.79 0.025 Gender, female 0.88 0.55–1.39 0.574 Smoker 2.08 1.28–3.38 0.003 Dyslipidemia 1.03 0.63–1.68 0.912 Waist circumference 1.17 0.94–1.46 0.148 Systolic blood pressure 1.12 0.91–1.39 0.272 CKD4–5 * 1.37 0.85–2.20 0.198 Dialysis* 2.45 1.37–4.38 0.003 * CKD stages 4–5 and dialysis taking CKD stage 3 as reference. Abbreviations: CI: confidence interval; HR: hazard ratio; CKD: chronic kidney disease. Supplementary Table 6. Cardiovascular risk prediction cox regression model for sCD36. Predictors HR, 95% CI p Diabetes 1.69, 1.03–2.77 0.039 sCD36 1.07, 0.83–1.38 0.586 Age 1.29, 0.94–1.78 0.113 Gender, female 0.89, 0.53–1.51 0.5671 Smoker 2.54, 1.49–4.35 0.001 Dyslipidemia 1.63, 0.85–3.13 0.145 www.aging-us.com 22707 AGING Waist circumference 1.26, 0.98–1.61 0.070 Systolic blood pressure 1.25, 0.98–1.59 0.069 eGFR (MDRD4) 0.75, 0.58–0.96 0.025 Abbreviations: CI: confidence interval; eGFR: estimated glomerular filtration rate; HR: hazard ratio; MDRD: Modification of Diet in Renal Disease. Supplementary Table 7. All-cause mortality cox regression model for CD5L. HR, 95% CI p-value Diabetes 1.71, 0.92–3.16 0.088 CD5L 1.25, 0.89–1.75 0.202 CD5L::Diabetes 0.73, 0.43–1.27 0.268 Age 0.45, 0.21–0.95 0.002 Gender, female 0.63, 0.36–1.13 0.037 Active smoker 2.17, 1.11–4.24 0.023 Dyslipidemia 1.38, 0.63– 3.02 0.421 Waist circumference 1.42, 1.03–1.96 0.031 Systolic blood pressure 1.11, 0.82–1.50 0.498 eGFR (MDRD4) 0.63, 0.46–0.88 0.007 Abbreviations: CKD: chronic kidney disease; CI: confidence interval; eGFR: estimated glomerular filtration rate; HR: hazard ratio; MDRD: Modification of Diet in Renal Disease. *CKD stage 3 was the reference to assess dialysis and CKD stage 4–5. Supplementary Table 8. All-cause mortality cox regression model for CD36. Predictors HR 95% CI p Diabetes 1.50 0.87–2.58 0.143 CD36 1.12 0.89–1.41 0.320 Age 2.28 1.53–3.39 <0.001 Gender, female 0.62 0.35–1.10 0.103 Smoker 2.07 1.15–3.72 0.015 Systolic blood pressure 1.08 0.84–1.38 0.562 Dyslipidemia 0.83 0.48–1.45 0.517 Waist circumference 1.31 1.01–1.70 0.046 CKD 4–5 * 1.96 1.09–3.55 0.026 Dialysis* 4.40 2.26–8.58 <0.001 Abbreviations: CKD: chronic kidney disease; confidence interval; HR: hazard ratio. *CKD stages 4–5 and dialysis taking CKD stage 3 as reference. www.aging-us.com 22708 AGING Supplementary Table 9. All-cause mortality cox regression model for CD36. Predictors HR 95% CI p Diabetes 1.70, 0.92–3.14 0.091 CD36 1.18, 0.89–1.55 0.245 Age 2.27, 1.38–3.74 0.001 Gender, female 0.43, 0.20–0.91 0.027 Smoker 2.21, 1.13–4.32 0.021 Dyslipidemia 1.35, 0.62–2.95 0.447 Waist circumference 1.41, 1.02–1.94 0.036 Systolic blood pressure 1.09, 0.81–1.47 0.578 eGFR (MDRD4) 0.63, 0.46–0.87 0.006 Abbreviations: CI: confidence interval; eGFR: estimated glomerular filtration rate; HR: hazard ratio; MDRD: Modification of Diet in Renal Disease. *CKD stages 4–5 and dialysis taking CKD stage 3 as reference. www.aging-us.com 22709 AGING