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. 2020 Mar 14;13(6):1017–1024. doi: 10.1093/ckj/sfaa014

Association of a single nucleotide polymorphism combination pattern of the Klotho gene with non-cardiovascular death in patients with chronic kidney disease

Serafi Cambray 1, Marcelino Bermudez-Lopez 1, Milica Bozic 1, Jose M Valdivielso 1,✉; the NEFRONA investigators1,a
PMCID: PMC7769551  PMID: 33391745

Abstract

Background

Chronic kidney disease (CKD) is associated with an elevated risk of all-cause mortality, with cardiovascular death being extensively investigated. However, non-cardiovascular mortality represents the biggest percentage, showing an evident increase in recent years. Klotho is a gene highly expressed in the kidney, with a clear influence on lifespan. Low levels of Klotho have been linked to CKD progression and adverse outcomes. Single nucleotide polymorphisms (SNPs) of the Klotho gene have been associated with several diseases, but studies investigating the association of Klotho SNPs with non-cardiovascular death in CKD populations are lacking.

Methods

The main aim of this study was to assess whether 11 Klotho SNPs were associated with non-cardiovascular death in a subpopulation of the National Observatory of Atherosclerosis in Nephrology (NEFRONA) study (n = 2185 CKD patients).

Results

After 48 months of follow-up, 62 cardiovascular deaths and 108 non-cardiovascular deaths were recorded. We identified a high non-cardiovascular death risk combination of SNPs corresponding to individuals carrying the most frequent allele (G) at rs562020, the rare allele (C) at rs2283368 and homozygotes for the rare allele (G) at rs2320762 (rs562020 GG/AG + rs2283368 CC/CT + rs2320762 GG). Among the patients with the three SNPs genotyped (n = 1016), 75 (7.4%) showed this combination. Furthermore, 95 (9.3%) patients showed a low-risk combination carrying all the opposite genotypes (rs562020 AA + rs2283368 TT + rs2320762 GT/TT). All the other combinations [n = 846 (83.3%)] were considered as normal risk. Using competing risk regression analysis, we confirmed that the proposed combinations are independently associated with a higher {hazard ratio [HR] 3.28 [confidence interval (CI) 1.51–7.12]} and lower [HR 6 × 10−6 (95% CI 3.3 × 10−7–1.1 × 10−5)] risk of suffering a non-cardiovascular death in the CKD population of the NEFRONA cohort compared with patients with the normal-risk combination.

Conclusions

Determination of three SNPs of the Klotho gene could help in the prediction of non-cardiovascular death in CKD.

Keywords: chronic kidney disease, Klotho, non-cardiovascular death, polymorphisms

INTRODUCTION

Chronic kidney disease (CKD) is a worldwide health problem, the incidence and prevalence of which for 2017 were estimated at 19.7 × 106 and 697 × 106, respectively [1]. It is predicted that CKD will become the fifth leading cause of years of life lost by 2040, showing a 100% increase compared with 2016 rates [2]. It has been widely documented that CKD patients show an increased risk of presenting with cardiovascular disease [3, 4], which could explain the high death rates [5, 6]. Consequently, most of the genetic studies aimed at identifying new single nucleotide polymorphisms (SNPs) associated with an increased risk of death in CKD patients are focused on cardiovascular risk [7–10]. Nevertheless, despite an evident increase in non-cardiovascular mortality in CKD patients [5, 11], no studies have focused on polymorphisms related to this type of death.

Klotho, a co-receptor of fibroblast growth factor 23 (FGF23), is a single-pass encoded protein that has 1012 amino acids and is subject to cleavage to produce soluble Klotho (sKlotho) [12]. In humans, this protein is expressed in a variety of tissues, showing particularly strong expression in kidney tubules [13]. Klotho gained relevance when it was described as an anti-ageing protein, as Klotho knockout mice showed a phenotype resembling human ageing, including shorter lifespan [14]. Furthermore, the main anti-ageing effects of Klotho were attributable to the protein produced in the kidney [15]. Currently there are many studies investigating Klotho effects on ageing in both humans and mice and many clinical trials involving the analysis of circulating sKlotho levels are being performed [16]. It has been demonstrated that the levels of both Klotho messenger RNA and protein, are decreased in CKD [17]. Furthermore, low levels of sKlotho have been linked to CKD progression [18–20] and adverse outcomes [19, 20]. However, some studies did not find a relationship between sKlotho levels and adverse outcomes in CKD patients [21, 22] after considering FGF23 levels. Indeed, Klotho effects do not have to be strictly limited to FGF23 signalling. Hu et al. showed that FGF23 was not indispensable for Klotho-induced phosphaturia [23] and that Klotho has the capacity to interfere with insulin, Wnt and transforming growth factor-β1 pathways in an FGF23-independent manner [24]. Altogether, it is clear that Klotho affects multiple age-related pathologies in FGF23-dependent and independent manners [25] and that premature death is present in the majority of patients with CKD, while the main causes are not restricted to greater cardiovascular disease incidence [4].

The Klotho gene (KL) is located on chromosome XIII and is composed of five exons that expand through >50 000 nucleotides [26]. According to the National Centre for Biotechnology Information, Klotho contains >11 000 SNPs [27]. Most of the genetic studies on Klotho have focused on the KL-VS polymorphisms located in exon 2 (name due to the F352V change of rs9536314 and C370S from rs9527025), which are related to human longevity [28], cognition [29], neuroprotection [30] and occult coronary artery disease [31]. The rs1207568 (G395A) SNP located at the promoter region has also been widely studied and has been associated with coronary artery disease [32], metabolic syndrome [33], kidney stones [34], mortality in dialysis [35] and immunoglobulin A nephropathy [36]. Other Klotho SNPs that emerged from genome-wide association studies have been related to diabetic nephropathy [37], type 2 diabetes [38, 39], atheromatous progression [40], onset of stroke [41], chronic haemodialysis mortality [42] and the incidence of CKD [43].

Considering the abovementioned data, we decided to assess how 11 Klotho SNPs were related to all-cause death, cardiovascular death and non-cardiovascular death in the National Observatory of Atherosclerosis in Nephrology (NEFRONA) study [44].

MATERIALS AND METHODS

Study population

The NEFRONA study is a prospective, multicentre cohort study to analyse the morbidity and mortality of CKD patients in Spain [45]. From 2009 to 2012, we recruited patients between 18 and 75 years old with different degrees of CKD (950 Stage 3, 807 Stages 4 and 5 and 688 on dialysis) and 559 non-CKD volunteers (eGFR >60 mL/min/1.73 m2) and followed them for 4 years, collecting data every 6 months on fatal and non-fatal cardiovascular events (CVEs), non-cardiovascular death or initiation of renal replacement therapy. Physicians responsible for patient recruitment recorded the incidence of CVEs following the International Classification of Diseases, Ninth Revision, Clinical Modification. For out-of-hospital deaths, the cause of death was established upon interviewing family members. All included volunteers signed an informed consent provided by their respective local ethics committee that previously approved the study. Volunteers with a history of CVEs, significant carotid stenosis, active infections (human immunodeficiency virus, tuberculosis), pregnancy, life expectancy <12 months and having received any organ transplant or carotid artery surgery were not allowed to participate in the NEFRONA study.

For this study, controls and non-Caucasian volunteers were excluded. A flow diagram of the experiment design is shown in Supplementary data, Figure S1.

SNP selection and genotyping

DNA was extracted from blood samples of the NEFRONA study gathered in the REDinREN Biobank [46]. The 11 Klotho SNPs selected (rs9536254, rs567170, rs577912, rs580332, rs495392, rs2320762, rs562020, rs576404, rs385564, rs9536282 and rs2283368) were included in a previous study in which SNPs related to bone mineral disorders and vascular calcification were selected [43]. Moreover, SNPs should not have been previously related to cardiovascular or non-cardiovascular death. Genotyping was performed as previously described [40]. Hardy–Weinberg equilibrium (HWE) was performed by means of a chi-squared test and genotyping quality control was done by including Coriell Institute Biorepository replicates in each plate (samples NA07348, NA07349 and NA07351).

Clinical data and biochemical variables

An itinerant team including a nurse and two technicians obtained anthropometric data, clinical data and blood samples. Family history of cardiovascular death and cardiovascular risk factors was obtained on recruitment day. Atherosclerosis assessment was performed at baseline as previously described [47, 48] and plaques were defined according to the American Society of Echocardiography and the Mannheim Carotid Intima-Media Thickness (cIMT) Consensus as cIMT lumen protrusion ≥1.5 mm [49, 50].

Statistical analysis

All statistical analyses were done using SPSS statistics software version 24.0.0.0 (IBM, Armonk, NY, USA). Quantitative variables are shown as average and standard deviation (SD), qualitative variables as n and percent and quantitative variables not following a normal distribution as median [quartile 1 (Q1)–quartile 3 (Q3)]. Comparisons between groups were done using Student’s t test, chi-squared test and Mann–Whitney U test, respectively. For comparisons between more than two groups, we used an analysis of variance (ANOVA) and multiple comparisons between groups were performed with an honestly significant difference Tukey’s test. Multivariable Fine and Gray competing risk regressions were performed for non-cardiovascular death, choosing as competing risk cardiovascular death and receiving a kidney transplant. Data are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). P-values <0.05 were considered statistically significant.

The linkage disequilibrium of selected SNPs was calculated with CubeX [51] .

RESULTS

The analysis encompassed only patients with a Caucasian genetic background and CKD [2906 (96.73% of the NEFRONA study)] and subsequently only the ones with data on at least one of the Klotho SNPs analysed [2185 (72.7% of the NEFRONA study); Supplementary data, Figure S1]. During the 48 months of follow-up, we recorded 62 cardiovascular deaths (2.8%) and 108 non-cardiovascular deaths (4.9%), for 170 total deaths in our cohort (7.7%). Table 1 shows that the variables related to both cardiovascular death and non-cardiovascular death were diabetes, advanced CKD stages and atheromatous plaque on the basal evaluation. Higher levels of intact parathyroid hormone, C-reactive protein and phosphate were also associated with both types of death; moreover, lower levels of albumin and serum calcium were related to both cardiovascular and non-cardiovascular death. Consequently, all these variables were associated with any death, which also showed an association with male sex and diastolic blood pressure. Pulse pressure, high-density lipoprotein cholesterol, glucose and pathological ankle–brachial index were associated with cardiovascular death, while hypertension, total cholesterol and low-density lipoprotein cholesterol were associated with non-cardiovascular death.

Table 1.

Epidemiological, clinical and biochemical parameters of cardiovascular, non-cardiovascular and any death of the cohort

Cardiovascular Death
 Non-cardiovascular death
 Any death
Variable Yes (n = 62) No (n = 2123) P-value Yes (n = 108) No (n = 2077) P-value Yes (n = 170) No (n = 2015) P- value
Age (years) 63.02 (9.39) 58.21 (12.66) 0.000 64.78 (9.65) 58.01 (12.6) 0.000 64.1 (9.5) 57.86 (12.7) 0.000
Sex (female), n (%) 19 (30.6) 805 (37.9) 0.244 34 (31.5) 790 (38) 0.171 53 (31.2) 771 (38.3) 0,067
Smoking (yes), n (%) 38 (61.3) 1213 (57.1) 0.515 70 (64.8) 1181 (56.9) 0.103 108 (63.5) 1143 (56.7) 0.085
Diabetes (yes), n (%) 32 (51.6) 523 (24.6) 0.000 38 (35.2) 517 (24.9) 0.017 70 (41.2) 485 (24.1) 0.000
Hypertension (yes), n (%) 57 (91.9) 1933 (91.1) 0.810 91 (84.3) 1899 (91.4) 0.011 148 (87.1) 1842 (91.4) 0.056
Dyslipidaemia (yes), 39 (62.9) 1426 (67.2) 0.481 64 (59.3) 1401 (67.5) 0.077 103 (60.6) 1362 (67.6) 0.062
CKD stage, n (%)
 Stage 3 16 (25.8) 851 (40.1) 0.001 26 (24.1) 841 (40.5) 0.001 42 (24.7) 825 (40.9) 0.000
 Stages 4 and 5 16 (25.8) 708 (33.3) 38 (35.2) 686 (33) 54 (31.8) 670 (33.3)
 Dialysis 30 (48.4) 564 (26.6) 44 (40.7) 550 (26.5) 74 (43.5) 520 (25.8)
Body mass index (kg/m2) 29.01 (5.89) 28.25 (5.15) 0.261 28.21 (5) 28.28 (5.18) 0.884 28.5 (5.36) 28.26 (5.16) 0.561
Systolic blood pressure (mmHg) 147.9 (25.8) 142.9 (21.8) 0.074 142.5 (24.3) 143 (21.8) 0.791 144.5 (24.9) 142.9 (21.6) 0.429
Diastolic blood pressure (mmHg) 78.7 (13.7) 81.4 (11.6) 0.138 79.27 (11.5) 81.45(11.7) 0.06 79.08 (12.35) 81.53 (11.65) 0.009
Pulse pressure (mmHg) 69.1 (20.2) 61.5 (18.2) 0.001 63.23 (18.7) 61.63 (18.36) 0.377 65.41 (19.46) 61.39 (18.2) 0.006
Haematocrit (%) 37.5 (5.5) 38.5 (5) 0.117 36.47 (5) 38.66 (5) 0.000 36.86 (5.22) 38.69 (5.02) 0.000
Albumin (g/dL) 3.83 (0.46) 4.1 (0.46) 0.000 3.93 (0.5) 4.1 (0.46) 0.001 3.9 (0.49) 4.1 (0.45) 0.000
iPTH (pg/mL), median (Q1-Q3) 166 (105–264) 120 (68–220) 0.006 157 (76.6–263) 120 (68–220) 0.048 161.8 (86–263) 118 (68–217) 0.001
C-reactive protein, median (Q1-Q3) 4 (1.4–8.1) 1.96 (0.94–4.32) 0.002 4.6 (1.68–10.2) 1.92 (0.92–4.2) 0.000 4.24 (1.66–9.7) 1.9 (0.9–4.1) 0.000
Total cholesterol (mg/dL) 173.32 (51) 178.7 (38.9) 0.29 169.9 (39.7) 179.03 (39.2) 0.02 171.15 (44) 179.2 (38.84) 0.011
High-density lipoprotein cholesterol (mg/dL) 45.07 (15.34) 49.55 (15.39) 0.032 49.9 (17.67) 49.3 (15.28) 0.713 48.15 (17) 49.53 (15.2) 0.295
Low-density lipoprotein cholesterol (mg/dL) 98.13 (36.32) 102.14 (33.3) 0.381 93.25 (32.5) 102.47 (33.4) 0.01 95.08 (34) 102.6 (33.1) 0.009
Triglycerides (mg/dL) 149.76 (79) 144.4 (81.2) 0.623 140.3 (81) 144.77 (81.21) 0.588 143.6 (80) 144.6 (81,2) 0.888
Glucose (mg/dL) 122 (52.93) 107.2 (39.15) 0.035 104.2 (41.7) 107.8 (39) 0.358 110.6 (46.6) 107.4 (39) 0.385
Calcium (mg/dL) 9.11 (0.58) 9.32 (0.59) 0.007 9.15 (07) 9.32 (0.58) 0.018 9.14 (0.66) 9.33 (0.58) 0.000
Phosphate (mg/dL) 4.62 (1.23) 4.03 (1.09) 0.000 4.19 (1.14) 4.04 (1.09) 0.167 4.35 (1.19) 4.02 (1.08) 0.000
Sodium (mEq/L) 139.67 (3.26) 140.31 (3) 0.107 139.55 (4.2) 140.33 (2.99) 0.06 139.6 (3.87) 140.35 (2.98) 0.014
Potassium (mEq/L) 5.02 (0.73) 4.8 (0.6) 0.025 4.9 (0.7) 4.8 (0.59) 0.152 4.95 (0.71) 4.8 (0.59) 0.009
Any plaque basal (yes), n (%) 60 (96.8) 1489 (70.1) 0.000 98 (88.9) 1453 (70) 0.000 156 (91.8) 1393 (69.1) 0.000
Pathology ankle–brachial index (yes), n (%) 33 (53.2) 587 (27.9) 0.000 38 (36.2) 582 (28.2) 0.079 71 (42.5) 549 (27.5) 0.000
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Values are expressed as average (SD) unless stated otherwise. Comparisons between groups performed with Student’s t test for quantitative normally distributed variables, Mann–Whitney U test for non-normally distributed quantitative variables and Chi-squared test for categorical data. Statistically significant values are in bold. iPTH: intact parathyroid hormone.

After proving that all the SNPs satisfied the HWE (Supplementary data, Table S1), we analysed the relationship of all genotypes with cardiovascular death, non-cardiovascular death and any death (Supplementary data, Table S2) and elaborated the models of dominance. Supplementary data, Table S3 shows the allele frequencies in our cohort. As shown in Table 2, under dominant assumption of the most frequent allele G of the rs562020 genotype, the carriers of the A allele in homozygosity showed a lower incidence of any death. When the dominant model for the less frequent C allele of rs2283368 was tested, we noted that TT carriers showed a lower incidence of any death and non-cardiovascular death and CT + TT carriers showed a higher incidence of both, as cardiovascular death was equal in both groups. It is obvious that the differences observed in any death under the dominant model of rs2283368 were mainly due to the marked difference in non-cardiovascular death. Finally, the dominant model for the more frequent allele T of rs2320762 showed a greater incidence of non-cardiovascular death and of any death for the GG genotype.

Table 2.

Proposed SNPs and models with effects on cardiovascular, non-cardiovascular and any death

rs562020_DOM G (n = 2185)
 rs2283368_DOM C (n = 1017)
 rs2320762_DOM T (n = 2184)
GG + AG AA P-value CC + CT TT P-value GG GT + TT P–value
Type of death n = 1944 n = 241 n = 216 n = 801 n = 318 n = 1866 Whole cohort (n =  2185)
Cardiovascular death, n (%) 59 (3) 3 (1.2) 0.114 7 (3.2) 27 (3.4) 0.925 11 (3.5) 51 (2.7) 0.471 62 (2.8)
Non-cardiovascular death, n (%) 101 (5.2) 7 (2.9) 0.122 22 (10.2) 33 (4.1) 0.000 26 (8.2) 82 (4.4) 0.004 108 (4.9)
All death, n (%) 160 (8.2) 10 (4.1) 0.026 29 (13.4) 60 (7.5) 0.006 37 (11.6) 133 (7.1) 0.006 170 (7.8)
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Comparisons between groups were performed with the chi-squared test. Statistically significant values are in bold.

Taking into account data from individual genetic models, we postulated a high non-cardiovascular death genotype combination. This combination corresponds to individuals carrying high-risk allele G of the rs562020 SNP, the high-risk allele C of the rs2283368 SNP and the high-risk genotype GG of the rs2320762 SNP (rs562020 GG/AG + rs2283368 CC/CT + rs2320762 GG); the three SNPs were not in linkage disequilibrium (Supplementary data, Table S3). In our population, 75 (7.3%) patients showed this genotype. Furthermore, we also postulated that carriers of the alternative genotypes would show fewer non-cardiovascular deaths (rs562020 AA + rs2283368 TT + rs2320762 GT/TT), with 95 (9.3%) patients showing this low-risk combination of genotypes. All the other combinations [846 (83.2%)] were considered as normal risk. It should be noted that all three SNPs could be correctly genotyped in 1016 patients (271 CKD3, 223 CKD 4–5 and 522 on dialysis), 46.4% of the initial study cohort. To test if our hypothesis was certain, the cohort was divided according to the three proposed risk combinations of genotypes and bivariate analysis was performed. As shown in Table 3, the three groups only showed significant differences in non-cardiovascular death and any death incidence, and the only parameters that were close to reaching significance were the presence of basal atheroma plaques and glucose levels.

Table 3.

Epidemiological, clinical, biochemical parameters and cause of death according to the three proposed genotypes

Variable High-risk phenotype (n = 75) All others (n = 846) Low-risk phenotype (n = 95) P-value
rs562020 GG/AG + rs2320762 GG + rs2283368 CC/CT rs562020 AA + rs2320762 GT/TT + rs2283368 TT
Age (years) 57.43 (12.32) 56.98 (13.28) 57.45 (11.91) 0.957
Sex (female), n (%) 30 (40) 325 (38.4) 41 (43.2) 0.656
Smoking (yes), n (%) 47 (62.7) 484 (57.2) 46 (48.4) 0.147
Diabetes (yes), n (%) 17 (22.7) 191 (22.6) 22 (23.2) 0.992
Hypertension (yes), n (%) 63 (84) 762 (90.1) 88 (92.6) 0.160
Dyslipidaemia (yes), n (%) 46 (61.3) 543 (64.2) 68 (71.6) 0.296
 Stage 3 20 (26.7) 233 (27.5) 18 (18.9) 0.429
 Stages 4 and 5 14 (18.7) 185 (21.9) 24 (25.3)
 Dialysis 41 (54.7) 428 (50.6) 53 (55.8)
Months in dialysis 26.49 (25.96) 27.59 (33.96) 28.02 (28.18) 0.961
Type of dialysis (HD), n (%) 28 (68.3) 276 (64.5) 33 (62.3) 0.830
Body mass index (kg/m2) 27.45 (5.29) 27.77 (5.29) 27.95 (5.64) 0.751
Systolic blood pressure (mmHg) 140.4 (19.62) 142.3 (23.43) 140.05 (20.94) 0.719
Diastolic blood pressure (mmHg) 82.07(10.25) 80.84 (12.75) 82.09 (12.09) 0.717
Pulse pressure (mmHg) 58.36 (15.7) 61.52 (19.1) 58 (17.76) 0.308
Haematocrit (%) 38.27 (4.58) 37.59 (5) 37.68 (3.9) 0.538
Albumin (g/dL) 3.95 (0.5) 4.02 (0.44) 4.06 (0.4) 0.165
iPTH (pg/mL) 238.97 (232.18) 211.71 (204.13) 211.5 (182.86) 0.590
C-reactive protein 4.53 (6.4) 5 (9.35) 3 (4.11) 0.214
Total cholesterol (mg/dL) 175.16 (44.94) 173.26 (39.83) 174.46 (39.49) 0.931
High-density lipoprotein cholesterol (mg/dL) 49.19(18) 49.09 (16.1) 48.65 (12.93) 0.968
Low-density lipoprotein cholesterol (mg/dL) 95.96 (40.5) 97.74 (33.6) 98.07 (33.47) 0.898
Triglycerides (mg/dL) 157.63 (107.56) 139.26 (73.77) 148 (71) 0.162
Glucose (mg/dL) 101.46 (32.65) 105.68 (41.52) 113.04 (49.18) 0.083
Calcium (mg/dL) 9.19 (0.75) 9.23 (0.62) 9.21 (0.57) 0.9
Phosphate (mg/dL) 4.2 (1.34) 4.34 (1.26) 4.47 (1.32) 0.250
Sodium (mEq/L) 139.43 (3.27) 139.9 (3.29) 139.72 (2.92) 0.516
Potassium (mEq/L) 4.93 (0.65) 4.83 (0.65) 4.85 (0.62) 0.496
Any plaque basal (yes), n (%) 47 (62.7) 598 (70.7) 73 (76.8) 0.131
Pathological ankle–brachial index (yes), n (%) 19 (25.7) 256 (30.6) 36 (38.3) 0.184
Cardiovascular death (yes), n (%) 3 (4) 30 (3.5) 1 (1.1) 0.417
Non-cardiovascular death (yes), n (%) 10 (13.3) 43 (5.1) 2 (2.1) 0.003
Any death (yes), n (%) 13 (17.3) 73 (8.6) 3 (3.2) 0.005
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Values are presented asaverage and SD unless stated otherwise. Comparisons between groups performed with ANOVA for quantitative variables and chi-squared test for categorical data. Statistically significant values are in bold. iPTH: intact parathyroid hormone.

Finally, in order to determine the importance of the proposed genotypes in predicting non-cardiovascular death in our cohort, we performed a competing risk regression analysis adjusting for all the variables in the NEFRONA study that could contribute to non-cardiovascular death. As shown in Table 4, after adjusting for all the possible confounding variables, the proposed combinations of genotypes still showed a significant association with non-cardiovascular death.

Table 4.

Coefficient of competing risk (n = 704)

Variable HR (95% CI) P-value
Age 1.046 (0.994–1.101) 0.09
Sex (women) 1.109 (0.474–2.598) 0.810
Smoking (yes) 1.544 (0.587–4.056) 0.380
Diabetes (yes) 1.209 (0.460–3.175) 0.700
Hypertension (yes) 0.709 (0.286–1.757) 0.460
Dyslipidaemia (yes) 0.495 (0.243–1.010) 0.054
CKD Stages 4 and 5 1.275 (0.365–4.453) 0.700
Dialysis 2.22 (0.744–6.630) 0.150
Diastolic blood pressure, mmHg 0.991 (0.960–1.023) 0.550
Pulse pressure, mmHg 0.990 (0.968–1.011) 0.350
Haematocrit (%) 0.925 (0.845–1.012) 0.085
C-reactive protein 1.015 (0.993–1.037) 0.150
Total cholesterol, mg/dL 1.009 (0.993–1.025) 0.270
Low-density lipoprotein cholesterol, mg/dL 0.993 (0.976–1.011) 0.440
Any plaque basal 2.574 (0.631–10.489) 0.190
Sodium, mEq/L 0.957 (0.833–1.100) 0.530
Potassium, mEq/L 1.410 (0.814–2.440) 0.220
Klotho high-risk genotype 3.286 (1.516–7.129) 0.003
Klotho low-risk genotype 6 × 10−6 (3.3 × 10−7–1.1 × 10−5) 0.000
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Statistically significant values are in bold.

DISCUSSION

This study identifies a combination of SNPs of three Klotho polymorphisms that are associated with a higher or lower risk of suffering non-cardiovascular death in a CKD population. Patients with CKD show an increased risk of death, and traditionally research has focused on the high rates of cardiovascular death presented by this population [3, 52]. However, CKD patients also show an increased risk of non-cardiovascular death [5, 11, 53], and less attention has been paid to this group of patients.

The main causes of non-cardiovascular death among CKD patients are infectious diseases and cancer. CKD patients show increased risk of infection in both early CKD stages [54] and in dialysis [55], and it has been recently shown that mice with reduced Klotho levels have lower survival rates after induced sepsis [56]. Unfortunately, the low numbers of deaths due to infection in the high- and low-risk groups (two and one, respectively) preclude further studies in this direction. Klotho also shows antitumor properties [57], and its levels are reduced in certain tumours, such as pancreatic [58] and breast [59], while high levels are considered indicate a more favorable prognosis in many cancer types, such as hepatocellular [60] and oesophageal [61] carcinomas. In our cohort, deaths attributable to cancer were three in the high-risk group and zero in the low-risk group, but again, the numbers are too low to perform further analyses. Despite this, we cannot discard the possible role of the described combination of genotypes in infections and/or cancer deaths in CKD patients.

The three polymorphisms identified in this work were previously analysed in a cohort of CKD patients on haemodialysis [42] and none of them was related to any type of death. Differences between our study and the study by Friedman et al. [42] could be attributed to the 11% non-Caucasian genetic background in their work, the different composition of the cohort (the Friedman et al. study was exclusively haemodialysis patients) and/or the fact that they did not test combinations of different polymorphisms. Nevertheless, it is worth mentioning that in our population the rs577912 SNP that Friedman et al. related to any death in their study is close to reaching a significant association in our study (P = 0.062; Supplementary data, Table S2). The rs562020 SNP has been related to better cognitive function in people 92–100 years old and, in the same work, rs2283368 was significantly associated with cognitive decline >7 years [62]. In the Framingham cohort, the relationship between rs562020 and the presence of valvar or vascular calcification was analysed and no relationship was found [63], data that agree with our results showing no relationship between the polymorphism and cardiovascular death.

This work has certain limitations. The first limitation is that we did not analyse Klotho plasma levels, so we cannot correlate the proposed genotypes with protein levels. The second limitation is that we do not have a validation cohort to reassess the importance of the proposed combination of SNPs. Despite evident limitations, our work has certain strengths that we would like to highlight. First is that we focused on Klotho SNPs that have not been extensively studied, so we pave the way for further studies using the proposed genotypes in various diseases in which Klotho could be involved. Second is that the NEFRONA study is very comprehensive, allowing us to correct for the effect of the proposed combination of genotypes by multiple possible confounding factors. Third is that our cohort is relatively big, with a prospective design and a relatively long follow-up time. Fourth is that we have considered a combination of three SNPs to check the relationship with all-cause death, an approach that could help to unravel synergistic effects of different polymorphisms that are missed by individual SNP analysis.

In summary, we have identified a combination of SNPs of the Klotho gene independently associated with higher and lower risks for non-cardiovascular death in CKD. Validation of these findings could be useful in the stratification of non-cardiovascular death risk in CKD patients.

SUPPLEMENTARY DATA

Supplementary data are available at ckj online.

Supplementary Material

sfaa014_Supplementary_Data
Click here for additional data file. (42.5KB, docx)

ACKNOWLEDGEMENTS

The authors would like to thank the NEFRONA team (Eva Castro, Virtudes María, Teresa Molí, Teresa Vidal and Meritxell Soria) and the Biobank of RedInRen for their invaluable support. The NEFRONA study is funded by a research grant from AbbVie and the Spanish government RETIC (RD16/0009/0011) and FIS PI16/01354. The NEFRONA study investigator group is composed by the following: Aladrén Regidor, Ma José. Hospital Comarcal Ernest Lluch (Calatayud); Almirall, Jaume; Ponz, Esther. Corporació Parc Taulí (Barcelona); Arteaga Coloma, Jesús. Hospital de Navarra (Pamplona); Bajo Rubio, Ma Auxiliadora; Díaz, Raquel Raquel Hospital La Paz (Madrid); Belart Rodríguez, Montserrat. Sistemes Renals (Lleida); Gascón, Antonio, Hospital Obispo Polanco (Teruel); Bover Sanjuan, Jordi. Fundació Puigvert. IIB Sant Pau (Barcelona); Bronsoms Artero, Josep. Clínica Girona (Girona); Cabezuelo Romero, Juan B; Muray Cases, Salomé. Hospital Reina Sofía (Murcia); Calviño Varela, Jesús. Hospital Universitario Lugus Augusti (Lugo); Caro Acevedo, Pilar. Clínica Ruber (Madrid); Carreras Bassa, Jordi. Diaverum Baix Llobregat (Barcelona); Cases Amenós, Aleix; Massó Jiménez, Elisabet. Hospital Clínic (Barcelona); Moreno López, Rosario. Hospital de la Defensa (Zaragoza); Cigarrán Guldris, Secundino; López Prieto, Saray. Hospital Da Costa (Lugo); Comas Mongay, Lourdes. Hospital General de Vic (Barcelona); Comerma, Isabel. Hospital General de Manresa (Barcelona); Compte Jové, Ma Teresa, Hospital Santa Creu Jesús (Tarragona); Cuberes Izquierdo, Marta. Hospital Reina Sofía (Navarra); de Álvaro, Fernando; Hevia Ojanguren, Covadonga. Hospital Infanta Sofía (Madrid); de Arriba de la Fuente, Gabriel. Hospital Universitario Guadalajara (Guadalajara); del Pino y Pino, Ma Dolores. Complejo Hospitalario Universitario Torrecardenas (Almería); Diaz-Tejeiro Izquierdo, Rafael; Ahijado Hormigos, Francisco Hospital Virgen de la Salud (Toledo); Dotori, Marta. USP Marbella (Málaga); Duarte, Verónica. Hospital de Terrassa (Barcelona); Estupiñan Torres, Sara. Hospital Universitario Canarias (Santa Cruz de Tenerife); Fernández Reyes, Ma José. Hospital de Segovia (Segovia); Fernández Rodríguez, Ma Loreto. Hospital Príncipe de Asturias (Madrid); Fernández, Guillermina. Clínica Santa Isabel (Sevilla); Galán Serrano, Antonio. Hospital General Universitario de Valencia (Valencia); García Cantón, Cesar. Hospital Universitario Insular de Gran Canaria (Las Palmas); García Herrera, Antonio L. Hospital Universitario Puerto Real (Cádiz); García Mena, Mercedes. Hospital San Juan de Dios (Zaragoza); Gil Sacaluga, Luis; Aguilar, Maria. Hospital Virgen del Rocío (Sevilla); Górriz, José Luis. Hospital Universitario Doctor Peset (Valencia); Huarte Loza, Emma. Hospital San Pedro (Logroño); Lerma, José Luis. Hospital Universitario Salamanca (Salamanca); Liebana Cañada, Antonio. Hospital de Jaén (Jaén); Marín Álvarez, Jesús Pedro. Hospital San Pedro de Alcántara (Cáceres); Martín Alemany, Nàdia. Hospital Jose p Trueta (Girona); Martín García, Jesús. Hospital Nuestra Señora de Sonsoles (Ávila); Martínez Castelao, Alberto. Hospital Universitari de Bellvitge (Barcelona); Martínez Villaescusa, María. Complejo Hospitalario Universitario de Albacete (Albacete); Martínez, Isabel. Hospital Galdakao (Bilbao); Moina Eguren, Iñigo. Hospital Basurto (Bilbao); Moreno Los Huertos, Silvia. Hospital Santa Bárbara (Soria); Mouzo Mirco, Ricardo. Hospital El Bierzo, Ponferrada (León); Munar Vila, Antonia. Hospital Universitari Son Espases (Palma de Mallorca); Muñoz Díaz, Ana Beatriz. Hospital Virgen del Consuelo (Valencia); Navarro González, Juan F. Hospital Universitario Nuestra Señora de Candelaria (Santa Cruz de Tenerife); Nieto, Javier; Carreño, Agustín. Hospital General Universitario de Ciudad Real (Ciudad Real); Novoa Fernández, Enrique. Complexo Hospitalario de Ourense (Ourense); Ortiz, Alberto; Fernandez, Beatriz. IIS-Fundación Jiménez Díaz (Madrid); Paraíso, Vicente. Hospital Universitario del Henares (Madrid); Pérez Fontán, Miguel. Complejo Hospitalario Universitario A Coruña (A Coruña); Peris Domingo, Ana. Hospital Francesc de Borja (Valencia); Piñera Haces, Celestino. Hospital Universitario Marqués de Valdecilla (Santander); Prados Garrido, Ma Dolores. Hospital Universitario San Cecilio (Granada); Prieto Velasco, Mario. Hospital de León (León); Puig Marí, Carmina. Hospital d’Igualada (Barcelona); Rivera Gorrín, Maite. Hospital Universitario Ramón y Cajal (Madrid); Rubio, Esther. Hospital Puerta del Hierro (Madrid); Ruiz, Pilar. Hospital Sant Joan Despí Moisès Broggi (Barcelona); Salgueira Lazo, Mercedes; Martínez Puerto, Ana Isabel. Hospital Virgen Macarena (Sevilla); Sánchez Tomero, José Antonio. Hospital Universitario de la Princesa (Madrid); Sánchez, José Emilio. Hospital Universitario Central de Asturias (Oviedo); Sans Lorman, Ramon. Hospital de Figueres (Girona); Saracho, Ramon. Hospital de Santiago (Vitoria); Sarrias, Maria; Serón, Daniel. Hospital Universitari Vall d’Hebron (Barcelona); Soler, María José; Barrios, Clara. Hospital del Mar (Barcelona); Sousa, Fernando. Hospital Rio Carrión (Palencia); Toran, Daniel. Hospital General de Jerez (Cadiz); Tornero Molina, Fernando. Hospital de Sureste (Arganda del Rey); Usón Carrasco, José Javier. Hospital Virgen de la Luz (Cuenca); Valera Cortes, Ildefonso. Hospital Virgen de la Victoria (Málaga); Vilaprinyo del Perugia, Ma Merce. Institut Catala d’Urologia i Nefrologia (Barcelona); Virto Ruiz, Rafael C. Hospital San Jorge (Huesca); Vicente Pallarés Carratalá Clinica MEDEFIS (Vila-real. Castellón), Carlos Santos Altozano CS Azuqueca de Henares (Guadalajara); Miguel Artigao Ródenas CS Zona III (Albacete); Inés Gil Gil Área Básica Sanitaria de Arán. CAP Viella (Lleida); Francisco Adan Gil CS Alfaro (La Rioja); Emilio García Criado Centro de Salud del Carpio (Córdoba.); Rafael Durá Belinchón CS Godella (Valencia); Jose Ma Fernández Toro CS Zona Centro (Cáceres); Juan Antonio Divisón Garrote Centro de Salud de Casas Ibáñez. Consultorio de Fuentealbilla (Albacete).

FUNDING

This study was funded by the intramural programme of the IRBLleida, the Instituto de Salud Carlos III (RETIC RD16/0009, PI15/00960 and PI16/01354) and FEDER funds.

AUTHORS’ CONTRIBUTIONS

V.J.M. and C.S. were involved in the study concept and design. Data acquisition was done by B.M. and data interpretation by V.J.M., C.S. and B.L.M. C.S. and V.J.M. were involved in the statistical analysis. Drafting of the manuscript was done by C.S. and V.J.M. All authors participated in critical revision of the manuscript for important intellectual content. Study supervision was done by V.J.M. The NEFRONA investigators collected baseline and prospective follow-up data for the cohort.

CONFLICT OF INTEREST STATEMENT

None declared.

REFERENCES

  • 1. James SL, Abate D, Abate KH  et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet  2018; 392: 1789–1858 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Foreman KJ, Marquez N, Dolgert A  et al.  Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet  2018; 392: 2052–2090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Sarnak MJ, Levey AS, Schoolwerth AC  et al.  Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation.  2003; 108: 2154–2169 [DOI] [PubMed] [Google Scholar]
  • 4. Thompson S, James M, Wiebe N  et al.  Cause of death in patients with reduced kidney function. J Am Soc Nephrol  2015; 26: 2504–2511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. de Jager DJ, Grootendorst DC, Jager KJ  et al.  Cardiovascular and noncardiovascular mortality among patients starting dialysis. JAMA  2009; 302: 1782–1789 [DOI] [PubMed] [Google Scholar]
  • 6. Raymond NT, Zehnder D, Smith SC  et al.  Elevated relative mortality risk with mild-to-moderate chronic kidney disease decreases with age. Nephrol Dial Transplant  2007; 22: 3214–3220 [DOI] [PubMed] [Google Scholar]
  • 7. Brancaccio D, Biondi ML, Gallieni M  et al.  Matrix GLA protein gene polymorphisms: clinical correlates and cardiovascular mortality in chronic kidney disease patients. Am J Nephrol  2005; 25: 548–552 [DOI] [PubMed] [Google Scholar]
  • 8. Rodrigo E, Pich S, Subirana I  et al.  A clinical-genetic approach to assessing cardiovascular risk in patients with CKD. Clin Kidney J  2017; 10: 672–678 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Spoto B, Mattace-Raso F, Sijbrands E  et al.  Association of IL-6 and a functional polymorphism in the IL-6 gene with cardiovascular events in patients with CKD. Clin J Am Soc Nephrol  2015; 10: 232–240 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. O’Seaghdha CM, Tin A, Yang Q  et al.  Association of a cystatin C gene variant with cystatin C levels, CKD, and risk of incident cardiovascular disease and mortality. Am J Kidney Dis  2014; 63: 16–22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. de Jager DJ, Vervloet MG, Dekker FW.  Noncardiovascular mortality in CKD: an epidemiological perspective. Nat Rev Nephrol  2014; 10: 208–214 [DOI] [PubMed] [Google Scholar]
  • 12. Xu Y, Sun Z.  Molecular basis of Klotho: from gene to function in aging. Endocr Rev  2015; 36: 174–193 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Lim K, Groen A, Molostvov G  et al.  α-Klotho expression in human tissues. J Clin Endocrinol Metab  2015; 100: E1308–E1318 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Kuro-O M, Matsumura Y, Aizawa H  et al.  Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature  1997; 390: 45–51 [DOI] [PubMed] [Google Scholar]
  • 15. Lindberg K, Amin R, Moe OW  et al.  The kidney is the principal organ mediating klotho effects. J Am Soc Nephrol  2014; 25: 2169–2175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Cheikhi A, Barchowsky A, Sahu A  et al.  Klotho: an elephant in aging research. J Gerontol A Biol Sci Med Sci  2019; 74: 1031–1042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Hu MC, Kuro-O M, Moe OW.  Klotho and chronic kidney disease. Contrib Nephrol  2013; 180: 47–63 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Drew DA, Katz R, Kritchevsky S  et al.  Association between soluble klotho and change in kidney function: the health aging and body composition study. J Am Soc Nephrol  2017; 28: 1859–1866 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Kim HR, Nam BY, Kim DW  et al.  Circulating α-klotho levels in CKD and relationship to progression. Am J Kidney Dis  2013; 61: 899–909 [DOI] [PubMed] [Google Scholar]
  • 20. Liu QF, Ye JM, Yu LX  et al.  Plasma s-Klotho is related to kidney function and predicts adverse renal outcomes in patients with advanced chronic kidney disease. J Investig Med  2018; 66: 669–675 [DOI] [PubMed] [Google Scholar]
  • 21. Nowak A, Friedrich B, Artunc F  et al.  Prognostic value and link to atrial fibrillation of soluble Klotho and FGF23 in hemodialysis patients. PLoS One  2014; 9: e100688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Seiler S, Rogacev KS, Roth HJ  et al.  Associations of FGF-23 and sKlotho with cardiovascular outcomes among patients with CKD stages 24. Clin J Am Soc Nephrol  2014; 9: 1049–1058 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Hu MC, Shi M, Zhang J  et al.  Klotho: a novel phosphaturic substance acting as an autocrine enzyme in the renal proximal tubule. FASEB J  2010; 24: 3438–3450 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Hu MC, Shiizaki K, Kuro-O M  et al.  Fibroblast growth factor 23 and Klotho: physiology and pathophysiology of an endocrine network of mineral metabolism. Annu Rev Physiol  2013; 75: 503–533 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Kuro-O M.  The Klotho proteins in health and disease. Nat Rev Nephrol  2019; 15: 27–44 [DOI] [PubMed] [Google Scholar]
  • 26. Matsumura Y, Aizawa H, Shiraki-Iida T  et al.  Identification of the human klotho gene and its two transcripts encoding membrane and secreted klotho protein. Biochem Biophys Res Commun  1998; 242: 626–630 [DOI] [PubMed] [Google Scholar]
  • 27.National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=9365&chooseRs=all (25 May 2019, date last accessed)
  • 28. Arking DE, Krebsova A, Macek M  et al.  Association of human aging with a functional variant of klotho. Proc Natl Acad Sci USA  2002; 99: 856–861 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Dubal DB, Yokoyama JS, Zhu L  et al.  Life extension factor klotho enhances cognition. Cell Rep  2014; 7: 1065–1076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Erickson CM, Schultz SA, Oh JM  et al.  KLOTHO heterozygosity attenuates APOE4-related amyloid burden in preclinical AD. Neurology  2019; 92: e1878–e1889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Arking DE, Becker DM, Yanek LR  et al.  KLOTHO allele status and the risk of early-onset occult coronary artery disease. Am J Hum Genet  2003; 72: 1154–1161 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Jo SH, Kim SG, Choi YJ  et al.  KLOTHO gene polymorphism is associated with coronary artery stenosis but not with coronary calcification in a Korean population. Int Heart J  2009; 50: 23–32 [DOI] [PubMed] [Google Scholar]
  • 33. Luo L, Hao Q, Dong B  et al.  The Klotho gene G-395A polymorphism and metabolic syndrome in very elderly people. BMC Geriatr  2016; 16: 46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Telci D, Dogan AU, Ozbek E  et al.  KLOTHO gene polymorphism of G395A is associated with kidney stones. Am J Nephrol  2011; 33: 337–343 [DOI] [PubMed] [Google Scholar]
  • 35. Ko GJ, Lee YM, Lee EA  et al.  The association of Klotho gene polymorphism with the mortality of patients on maintenance dialysis. Clin Nephrol  2013; 80: 263–269 [DOI] [PubMed] [Google Scholar]
  • 36. Ko GJ, Lee EA, Jeon US  et al.  The association of Klotho polymorphism with disease progression and mortality in IgA nephropathy. Kidney Blood Press Res  2012; 36: 191–199 [DOI] [PubMed] [Google Scholar]
  • 37. Iyengar SK, Sedor JR, Freedman BI  et al. ; Family Investigation of Nephropathy and Diabetes. Genome-wide association and trans-ethnic meta-analysis for advanced diabetic kidney disease: Family Investigation of Nephropathy and Diabetes (FIND). PLoS Genet  2015; 11: e1005352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Zhao W, Rasheed A, Tikkanen E  et al.  Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat Genet  2017; 49: 1450–1457 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Xue A, Wu Y, Zhu Z  et al.  Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun  2018; 9: 2941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Valdivielso JM, Bozic M, Galimudi RK  et al.  Association of the rs495392 Klotho polymorphism with atheromatosis progression in patients with chronic kidney disease. Nephrol Dial Transplant  2018; 34: 2079–2088 [DOI] [PubMed] [Google Scholar]
  • 41. Yokoyama S, Oguro R, Yamamoto K  et al.  A klotho gene single nucleotide polymorphism is associated with the onset of stroke and plasma klotho concentration. Aging  2018; 11: 104–114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Friedman DJ, Afkarian M, Tamez H  et al.  Klotho variants and chronic hemodialysis mortality. J Bone Miner Res  2009; 24: 1847–1855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Valls J, Cambray S, Pérez-Guallar C  et al.  Association of candidate gene polymorphisms with chronic kidney disease: results of a case-control analysis in the nefrona cohort. Front Genet  2019; 10: 118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Arroyo D, Betriu A, Martinez-Alonso M  et al.  Observational multicenter study to evaluate the prevalence and prognosis of subclinical atheromatosis in a Spanish chronic kidney disease cohort: baseline data from the NEFRONA study. BMC Nephrol  2014; 15: 168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Junyent M, Martínez M, Borràs M  et al.  Predicting cardiovascular disease morbidity and mortality in chronic kidney disease in Spain. The rationale and design of NEFRONA: a prospective, multicenter, observational cohort study. BMC Nephrol  2010; 11: 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Calleros-Basilio L, Cortés MA, García-Jerez A  et al.  Quality assurance of samples and processes in the Spanish Renal Research Network (REDinREN) Biobank. Biopreserv Biobank  2016; 14: 499–510 [DOI] [PubMed] [Google Scholar]
  • 47. Gracia M, Betriu À, Martínez-Alonso M  et al.  Predictors of subclinical atheromatosis progression over 2 years in patients with different stages of CKD. Clin J Am Soc Nephrol  2016; 11: 287–296 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Betriu A, Martinez-Alonso M, Arcidiacono MV  et al. ; on behalf of the investigators from the NEFRONA study. Prevalence of subclinical atheromatosis and associated risk factors in chronic kidney disease: the NEFRONA study. Nephrol Dial Transplant  2014; 29: 1415–1422 [DOI] [PubMed] [Google Scholar]
  • 49. Stein JH, Korcarz CE, Hurst RT  et al. ; American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr  2008; 21: 93–111; quiz 189–190. [DOI] [PubMed] [Google Scholar]
  • 50. Touboul PJ, Hennerici MG, Meairs S  et al.  Mannheim carotid intima-media thickness and plaque consensus (2004–2006–2011). An update on behalf of the advisory board of the 3rd, 4th and 5th watching the risk symposia, at the 13th, 15th and 20th European Stroke Conferences, Mannheim, Germany, 2004, Brussels, Belgium, 2006, and Hamburg, Germany, 2011. Cerebrovasc Dis  2012; 34: 290–296 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Gaunt TR, Rodríguez S, Day IN.  Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX'. BMC Bioinformatics  2007; 8: 428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR  et al.  Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet  2013; 382: 339–352 [DOI] [PubMed] [Google Scholar]
  • 53. Runesson B, Qureshi AR, Xu H  et al.  Causes of death across categories of estimated glomerular filtration rate: the Stockholm CREAtinine Measurements (SCREAM) project. PLoS One  2019; 14: e0209440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Dalrymple LS, Katz R, Kestenbaum B  et al.  The risk of infection-related hospitalization with decreased kidney function. Am J Kidney Dis  2012; 59: 356–363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Berman SJ, Johnson EW, Nakatsu C  et al.  Burden of infection in patients with end-stage renal disease requiring long-term dialysis. Clin Infect Dis  2004; 39: 1747–1753 [DOI] [PubMed] [Google Scholar]
  • 56. Jorge LB, Coelho FO, Sanches TR  et al.  Klotho deficiency aggravates sepsis-related multiple organ dysfunction. Am J Physiol Renal Physiol  2019; 316: F438–F448 [DOI] [PubMed] [Google Scholar]
  • 57. Rubinek T, Wolf I.  The role of alpha-klotho as a universal tumor suppressor. Vitam Horm  2016; 101: 197–214 [DOI] [PubMed] [Google Scholar]
  • 58. Abramovitz L, Rubinek T, Ligumsky H  et al.  KL1 internal repeat mediates klotho tumor suppressor activities and inhibits bFGF and IGF-I signaling in pancreatic cancer. Clin Cancer Res  2011; 17: 4254–4266 [DOI] [PubMed] [Google Scholar]
  • 59. Rubinek T, Shulman M, Israeli S  et al.  Epigenetic silencing of the tumor suppressor klotho in human breast cancer. Breast Cancer Res Treat  2012; 133: 649–657 [DOI] [PubMed] [Google Scholar]
  • 60. Xie B, Zhou J, Yuan L  et al.  Epigenetic silencing of Klotho expression correlates with poor prognosis of human hepatocellular carcinoma. Hum Pathol  2013; 44: 795–801 [DOI] [PubMed] [Google Scholar]
  • 61. Tang X, Fan Z, Wang Y  et al.  Expression of klotho and β-catenin in esophageal squamous cell carcinoma, and their clinicopathological and prognostic significance. Dis Esophagus  2016; 29: 207–214 [DOI] [PubMed] [Google Scholar]
  • 62. Mengel-From J, Soerensen M, Nygaard M  et al.  Genetic vriants in KLOTHO associate with cognitive function in the oldest old group. J Gerotol A 2016; 71: 1151–1159 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Tangri N, Alam A, Wooten EC  et al.  Lack of association of Klotho gene variants with valvular and vascular calcification in Caucasians: a candidate gene study of the Framingham Offspring Cohort. Nephrol Dial Transplant  2011; 26: 3998–4002 [DOI] [PMC free article] [PubMed] [Google Scholar]

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