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Serum Phosphate within Reference Levels Associates with All-Cause Mortality in Non-Dialysis CKD Patients: A 5-Year Pro-spective Cohort Study
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06 September 2024
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09 September 2024
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Abstract
Cardiovascular (CV) morbidity and mortality are markedly increased in non-dialysis patients with chronic kidney disease (CKD). Thus, the precise management of CV risk factors involved in CKD is crucial to improving outcomes. Serum phosphate (Pi) and FGF-23 levels have been linked with a higher risk of CV events in CKD. However, the exact thresholds of Pi and FGF-23, at which the risk of adverse events increases, remain unknown. We evaluated the expression of intact FGF-23 (iFGF-23) and Pi in a non-dialysis CKD patient population (n=82; 42M:40F; median age 61 years) and investigated their association with CV and renal outcomes, in a 5-year follow-up period. At baseline, the median estimated glomerular filtration rate (eGFR), iFGF-23, and Pi were 45 ml/min/1.73m2 (IQ 26.6-73.1), 69.9 µg/ml (IQ 33-117) and 3.4 mg/dL (IQ 3.3-3.9), respectively. Univariate analysis showed a strong association of both iFGF-23 and Pi with age, Charlson Comorbidity Index, hypertension, and diabetes. In addition, iFGF-23 and Pi were both associated with the composite outcome (major CV and cerebrovascular events – MACCEs, hospitalizations, and all-cause mortality) during follow-up. Moreover, Pi was independently associated with all-cause mortality during follow-up. The segmentation of the population in terciles, according to Pi (<3mg/dL; 3-3.6 mg/dL; ≥3.7 mg/dL) within reference serum levels, showed a distribution of the fatality of 0%, 20% and 80% (p =0.034), respectively. Our results reinforce the association of both iFGF-23 and Pi with composite CV outcomes in non-dialysis CKD patients and further suggest that Pi, within current reference levels, may behave as an independent risk factor for mortality in this population. It is suggested that a reassessment of Pi reference levels for early therapeutic intervention in this population may be justified.
Keywords:
Subject: Medicine and Pharmacology - Urology and Nephrology
1. Introduction
Increased mortality mainly due to cardiovascular (CV) complications is a major determinant of vital prognosis in chronic kidney disease (CKD) patients [1] Thus, it is vital to uncover accurate biomarkers that enable the identification of patients at risk and putatively serve as new therapeutic targets in the prevention of these outcomes.
One of the hallmarks of CKD is mineral metabolism disturbances [2]. As a compensatory mechanism, in the early CKD stages, before the development of overt hyperphosphatemia, FGF-23 increases in response to phosphate (Pi) levels. Activation of Klotho-FGF receptor complexes in the kidney augments urinary Pi excretion. The increase of FGF-23 occurs early during CKD progression and precedes the increase in serum parathyroid hormone (PTH), the decrease of 1,25 dihydroxy-vitamin D (1,25 Vit D) as well as increase of serum Pi levels.
Hyperphosphatemia, hyperparathyroidism, and vitamin D deficiency are considered potentially modifiable risk factors for all-cause and CV mortality in CKD patients and have been also associated with an increased risk of developing end-stage renal disease [3,4].
Moreover, several studies point to FGF-23 as a central player in the pathogenesis of CV disease and associated mortality, including myocardial fibrosis, left ventricular hypertrophy (LVH) [5], heart failure and auricular fibrillation [6]. However, associations are not causality. Although hyperphosphatemia and FGF-23 are associated with increased mortality in CKD [7] it is not clear whether compensatory increases in FGF-23 secretion induce or protect against the increased CV morbidity and mortality observed in CKD. Furthermore, the precise reference values for FGF-23 and Pi, beyond which adverse effects starts to be manifested, remain unclear, and the comprehensive impact of each factor involved is not fully understood.
In this study, we aimed to examine the relationship of FGF-23 and serum Pi with other biomarkers of kidney disease and CV risk in a non-dialysis CKD population and sought to explore their role as biomarkers of adverse outcomes and mortality during a 5-year follow-up period.
2. Methods
2.1. Study Population
Eighty-two non-dialysis CKD patients were recruited from the outpatient clinic of the Nephrology Dept. of Unidade Local de Saúde (ULS) São João, EPE, Porto, Portugal. Patients with acute kidney injury, ongoing immunosuppression, recent hospital admission (<2 weeks), recent infections (<1 week), acute heart failure (diagnosed according to appropriate Framingham criteria), and known psychiatric disturbances were excluded from the study. The etiology of CKD was registered, and patients were distributed according to KDIGO CKD categories, using GFR estimated by CKD-EPI formula and proteinuria. [8]
2.2. Cross-Sectional Study
Anthropometric measurements, resting systolic and diastolic blood pressure, and a validated Charlson Comorbidity Index (CCI) were assessed in all patients. Blood and urine samples were collected from all participants. Renal function (eGFR), proteinuria, serum Pi, parathormone (PTH), as well as other relevant biomarkers were evaluated using standard laboratory methods. Intact FGF-23 (iFGF-23) levels were assessed by a two-site second-generation ELISA kit (Immutopics. San Clemente, CA, USA).
2.3. Prospective Study
All recruited CKD patients were prospectively followed up for a median of 58 (IQ 30-69) months, to evaluate hard renal and CV outcomes including progression of CKD and end-stage renal disease (ESRD), hospitalizations, major adverse CV and cerebral events (MACCEs), and all-cause mortality. The MACCEs included acute coronary syndrome (ACS), acute heart failure, and stroke.
Cardiovascular death was defined as mortality due to a heart-related cause (death attributable to ACS, heart failure, arrhythmia, or sudden death) or to a cerebrovascular event. Hospitalizations included non-programmed, more than 24-hour hospital admissions for medical reasons. Admissions for trauma, surgery, or other scheduled procedures were not considered. A composite CV outcome was established including MACCEs, hospitalizations, and mortality. Renal outcomes included CKD progression, defined as serum creatinine doubling or a >50% decrease in eGFR according to CKD-EPI formula or renal replacement therapy initiation (ESRD) after enrolment.
2.4. Statistical Analysis
Continuous variables were described as minimum, percentile 25, median, percentile 75, and maximum deviations, and categorical variables were presented as absolute (n) and relative frequencies (%). Differences in continuous variables were assessed by the Mann-Whitney U test, while chi-square tests were used to analyse differences in categorical variables. A correlation analysis was performed using Spearman correlation coefficients. Logistic regression models were used to visualize the relationships of both iFGF-23 and Pi, with CKD progression, hospitalizations, MACCEs, and all-cause mortality. All reported p-values were two-sided, and the significance level was set at 5%. All analyses were conducted using SPSS software (Version 26.0 for Windows, SPSS, Chicago, IL, USA).
3. Results
3.1. Cohort Characterization
As shown in Table 1, a total of 82 non-dialysis CKD patients (42M:40F) were enrolled in the study: twenty-nine patients were included in stages 1-2, 25 patients in stages 3a-3b, and 28 patients in stages 4-5. The median age was 61 years (IQ 46-69). The CKD patients in stages 1-2 were younger than patients in CKD stages 4-5 (p<0.001). The CCI showed a significant increase across the three groups (p<0.001). Diabetes and hypertension were both more prevalent in patients with CKD stages 4-5 than in patients with CKD stages 1-2 (p=0.009 and p=0.003, respectively).
As shown in Table 1, median iFGF-23 increased significantly in the three groups, in parallel with the decrease in renal function (p<0.001). In addition, serum levels of both Pi and PTH increased significantly in the three groups in parallel with the decrease in renal function (p<0.001) (Table 1). On the other hand, serum calcium levels in CKD stages 4-5 were significantly decreased in comparison with CKD stages 3 (p=0.010).
No significant differences were observed among the three groups concerning gender, BMI, systolic and diastolic blood pressure, heart rate, and the presence of CV disease at baseline. In addition, ferritin, albumin, total and HDL cholesterol, c-reactive protein, left ventricular mass and ejection fraction did not differ among the three groups at baseline (Table 1).
The CKD patients from the three groups were receiving treatment including ACE inhibitors, ARBs, Pi binders, cholecalciferol, vitamin D analogues, and statins. However, a significant difference in prescription rates was observed solely for vitamin D analogues which were more prescribed in patients with CKD stages 4-5.
3.1.1. Cross-Sectional Study: Associations of iFGF-23 and Pi with CV-Related Clinical Parameters at Baseline
As shown in Table 2, CV risk factors, including hypertension and diabetes, were significantly associated with iFGF-23 (p=0.005 and p<0.001, respectively) and Pi (p=0.027 and p=0.009, respectively) levels at baseline. In addition, iFGF-23 levels were also associated with the presence of CV disease at baseline (p=0.02). (Table 2).
When we examined the associations of both iFGF-23 and Pi, with renal and CV biomarkers we found that iFGF-23 and Pi were both positively correlated with age (r=0.460, p<0.001; r=0.292, p=0.008), CCI (r=0.595, p<0.001; r=0.417, p<0.001), PTH (r=0.595, p<0.001; r=0.506, p<0.001), triglycerides (r=0.248, p=0.028; r=0.224, p=0.049), uric acid (r=0.316, p=0.004; r=0.265, p=0.018), and BNP (r=0.312, p=0.042; r=0.313, p=0.041) (Table 3).
On the other hand, iFGF-23 and Pi were both negatively correlated with eGFR (r=-0.789, p<0.001; r=-0.621, p<0.0019), hemoglobin (r=-0,413, p <0.001; r=-0.538, p<0.001) and with serum albumin levels (r=-0.382, p <0.001; r=-0.286, p<0.010) (Table 3).
3.1.2. Prospective Study: Phosphate, within Reference Levels, Is independently Associated with All-Cause Mortality in a 5-Year Follow-Up
The non-dialysis CKD population was prospectively followed up for a median of 58 (IQ 30-69) months. During this period, 4 patients suffered a MACCE (2 patients an acute myocardial infarction and 2 patients a cerebrovascular event); 19 patients experienced renal function deterioration, of which 10 started dialysis; 19 patients were hospitalized for medical reasons and 5 patients died, 2 of them by MACCE. Twenty-three patients reached the combined cardiovascular outcome (MACCEs, hospitalization, and all-cause mortality) (Table 4).
We then explored the association of iFGF-23 and Pi with renal and CV outcomes, hospitalizations, and all-cause mortality, in univariate analysis. iFGF-23 was significantly associated with the composite CV outcome (52.27 vs. 78.84, p = 0.037), but not with CKD progression (52 vs 80.42, p = 0.067), hospitalizations (43 vs 22, p=0.120), all-cause mortality (55.41 vs 97.79, p=0.247) or MACCEs (54 vs. 132, p = 0.120) (Table 4). On the other hand, serum Pi was significantly associated with both the composite CV outcome (3.25 vs 3.6, p=0.045) and all-cause mortality (3.3 vs 4.0, p=0.020), in univariate analysis (Table 5). When we carried out a stepwise regression analysis, we found that serum Pi, but not iFGF23, proved to be an independent predictor of death, regardless of age, baseline CV disease, hypertension, diabetes, dyslipidemia, iFGF-23 levels, iPTH and BNP (Table 6). We then divided the population in terciles according to serum Pi levels (<3mg/dL; 3-3.6 mg/dL; ≥3.7 mg/dL) and observed a distribution of the fatality of 0%, 20%, and 80%, respectively in the three groups (p=0.034). Detailed analysis showed that minimum-maximum Pi levels, within the subgroup of patients who had a fatality ranged from 3.0 to 4.4mg/dL.
4. Discussion
In the present study we examined, in a non-dialysis CKD population under outpatient nephrological care, with renal function spanning all five CKD stages, the associations between the serum levels of iFGF-23 and Pi with CV disease biomarkers and explored prospectively their role as predictors of adverse events and mortality in a 5-year follow-up period. In unadjusted analysis, we found that both iFGF-23 and Pi levels were associated with baseline CV risk factors as well as with a composite CV outcome during follow-up. However, in adjusted analysis, we found that serum Pi levels but not iFGF-23 were associated with mortality among CKD patients, independent of other confounding factors except GFR. Segmentation of the population in terciles according to serum Pi levels at baseline showed a significant increase in mortality during follow-up among patients with serum Pi levels within the high-”normal” range but not in patients with serum Pi levels below “normal” targets. Our findings agree well with other observations that reported the independent association between elevated serum Pi levels within published targets (as opposed to below) and the risk of mortality in the general population (NHANES), as well as in non-dialysis CKD patients followed up either in a nephrology clinic [9] or in primary care [10] and further support the need to re-examine contemporary guidelines for serum Pi targets in the non-dialysis CKD population.
Several mechanisms might explain the pathogenic effects of serum Pi that contribute to CV disease and death in CKD. First, Pi load is considered a major driver of vascular calcification mainly through osteoblastic transformation of vascular smooth muscle cells and calcium phosphate deposition [11,12]. Second, experimental and clinical studies provided evidence favoring the association of serum Pi with LVH and myocardial fibrosis [13], both in patients with CKD and in the general population [14]. Another mechanism that might explain the association between Pi and mortality can be related with a statin-resistance state which was attributed to increases in both intestinal cholesterol absorption and local cholesterol synthesis induced by excessive Pi load. Notably, a secondary analysis of the AURORA trial revealed that higher levels of serum Pi might impair the clinical benefit of statin treatment in dialysis patients [15].
Endothelial dysfunction is a non-traditional CV risk factor in CKD whose prevalence increases when renal function deteriorates and it has been shown in translational cell-to-animal-to-human studies that Pi loading may contribute to CV risk, by inducing deleterious effects on endothelial function, probably via disruption of the nitric oxide pathway [16]. Stevens at el., showed that sustained oral Pi loading in 19 healthy volunteers caused endothelial dysfunction which was accompanied by significant increases in serum FGF-23 and urinary Pi excretion, without significant changes in serum Pi levels [16]. The deleterious influence of Pi load on endothelial function may be reversible and assume particular relevance in the CKD population, in which the compromised renal excretion of ingested Pi is counteracted by an increase in FGF-23 levels from the earliest CKD stages. We previously reported in a group of CKD patients with a mean GFR of 49 ml/min that the restriction of Pi intake for 14 days from a baseline intake of 1100 mg/day to 700 mg/day was accompanied by a significant improvement in endothelial function, going along with non-significant reductions in FGF-23, iPTH, and Pi levels [17].
In the present study, iFGF-23 levels were increased according to the decrease of renal function and were significantly associated with CV disease and CV risk factors at baseline, thus reinforcing the role of FGF-23 as an indicator of established CV disease in CKD [18]. However, iFGF-23 levels were not independently associated with the composite CV outcome or mortality during follow-up. Many controversies exist regarding FGF-23 levels and its role as a predictor versus biomarker of CV disease [19]. Elevated FGF-23 levels are not specific to CKD and were also associated with various off-target effects on multiple organs including fractures [20], infections [21], inflammation [22], hereditary hypophosphatemic rickets [23] and even prostate cancer [24]. These associations across a wide range of disease states could reflect the pleiotropy of FGF-23 in disease causation. Although several studies indicated that FGF-23 directly induce cardiac hypertrophy and myocardial fibrosis [25] and contribute to increase the incidence of CV disease, other studies do not support a causal link between elevated FGF-23 and LVH or mortality [26]. We are learning that the causal link between FGF-23 and CV disease in CKD is much more complicated than anticipated and may depend on the combination of high FGF-23 levels with low α-Klotho and higher Pi levels [19,27].
We acknowledge some limitations of our study. First, it is a single-center study with a relatively small number of patients and outcomes; second, the results in our ethnically homogeneous population may be not generalizable to other ethnic groups; third the “snapshot” evaluation of Pi and FGF-23 levels at baseline could underestimate the association with the outcomes during follow-up.
Our study has also strengths that should be emphasized: First, its prospective nature and the significant follow-up period of nearly 5 years; second, the inclusion of patients from both genders and spanning all five CKD stages; third, the assessment of the intact biologically active isoform of FGF-23 (iFGF-23) rather than the inactive C-terminal FGF-23 fragment.
In conclusion, our results agree with the view that serum Pi levels within the high-normal range, are a risk predictor for mortality in non-dialysis CKD patients. Our results also suggest that the contribution of iFGF-23 as a biomarker to CV outcomes and mortality in CKD may not outweigh the role of serum Pi levels within the high-”normal” range. Because serum Pi is a biomarker easily affordable, our findings reinforce the need to re-examine contemporary guidelines for serum Pi reference levels in the non-dialysis CKD population. This may allow the establishment of more timely interventions, whether through dietary adjustments or pharmacological measures, aimed at enhancing overall Clinical Outcomes in the CKD Population.
Author Contributions
Conceptualization, A.C., J.Q.-S. and M.P.; methodology, A.C., J.Q.-S.; software, A.C., J.Q.-S., and C.C.D.; validation, A.C., J.Q.-S. and C.C.D.; formal analysis, A.C., J.Q.-S. and C.C.D.; investigation, A.C., J.Q.-S., N.P. C.C.D. and M.P.; resources, A.C., J.Q.-S. and M.P.; data curation, A.C., J.Q.-S., N.P. and C.C.D.; writing—original draft preparation, A.C., J.Q.-S., C.C.D. and M.P.; writing—review and editing, A.C., J.Q.-S. and M.P.; visualization, A.C., J.Q.-S., N.P.; C.C.D. and M.P.; supervision, M.P. and J.Q.-S.; project administration, J.Q.-S. and M.P.; funding acquisition, M.P. and J.Q.-S. All authors have read and agreed to the published version of the manuscript.
Funding
This work was financed by FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-Operacional Programme for Competitiveness and Internationalisation (POCI), Portugal 2020, and by Portuguese funds through FCT-Fundação para a Ciência e a Tecnolo- gia/Ministério da Ciência, Tecnologia e Ensino Superior in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274), and a grant from Portuguese Society of Nephrology.
Institutional Review Board Statement
The research was approved by the Ethics Committee for Health and the Local Institutional Review Board of São João University Hospital Centre (CES 251.14) and was carried out in accordance with the Declaration of Helsinki (2008) of the World Medical Association.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Acknowledgments
This article is a result of the project NORTE-01-0145-FEDER-000012, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicting of Interests
All the authors declare that there is no conflict of interest.
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Table 1.
Characterization of the non-dialysis patient population by CKD stages (n=82).
| Stage 1-2 (n=29) |
Stage 3a-3b (n=25) |
Stage 4-5 (n=28) |
p1 | p* 1-2 vs 3a-3b | p* 1-2 vs 4-5 | p* 3a-3b vs 4-5 | |
|---|---|---|---|---|---|---|---|
| DEMOGRAFIC DATA | |||||||
| Age (years), mean±sd | 49.2 ±14.3 | 58.0 ±15.6 | 66.3 ±13.7 | <0.001 | 0.083 | <0.001 | 0.125 |
| Gender Male (n, %) | 11 (37.9) | 17 (68.0) | 14 (50.0) | 0.087 | |||
| Charlson Index score mean±sd | 1.6 ±2.1 | 4.4 ±2.9 | 6.1 ±2.4 | <0.001 | <0.001 | <0.001 | 0.037 |
| Body mass index mean±sd | 29.4 ±6.8 | 28.9 ±4.5 | 25.5 ±4.9 | 0.075 | |||
| Diabetes, n (%) | 5 (13.8) | 8 (32.0) | 14 (50.0) | 0.0133 | 0,327 | 0,009 | 0,552 |
| Hypertension n (%) | 16 (52.2 | 20 (80.0) | 26 (92.9) | 0.0033 | 0,162 | 0,003 | 0,702 |
| Systolic blood pressure, (mmHg) | 129 ±18 | 135 ±25 | 132 ±18 | 0.577 | |||
| Diastolic blood pressure (mmHg) | 74 ±13 | 77 ±13 | 76 ±15 | 0.449 | |||
| Heart rate (bpm) | 70 ±14 | 66 ±12 | 73 ±13 | 0.232 | |||
| Cardiovascular disease, n (%) | 4 (13.8) | 9 (36.0) | 10 (35.7) | 0.104 | |||
| CKD RELATED PARAMETERS | |||||||
| eGFR CKD-EPI (ml/min/1,73m2) mean±sd | 97.7 ±24.1 | 44.5 ±7.9 | 19.9 ±7.0 | <0.001 | <0.001 | <0.001 | <0.001 |
| iFGF-23, median (IQR) | 28,3 (14,1-54,6) |
67,8 (46,2-91,5) |
145,9 (98,1-219,9) |
<0,001 | <0,001 | <0,001 | <0,001 |
| Calcium (mg/dL) mean±sd | 4.6 ±0.2 | 4.8 ±0.3 | 4.5 ±0.3 | 0.013 | 0.222 | 0.604 | 0.010 |
| Phosphate (mg/dL) mean±sd | 3.0 ± 0.4 | 3.3 ±0.5 | 3.9 ±0.6 | <0.001 | 0.118 | <0.001 | <0.001 |
| Parathormone (pg/mL) mean±sd | 53.1±24.6 | 75.5±39.3 | 183.6 ±158.0 | <0.001 | >0.999 | <0.001 | 0.001 |
| Protein/creatinine ratio (mg/g), median (IQR) | 221.0 (84.0-841.6) |
402.0 (152.0-1035.4) |
981.4 (453.0-2966.0) |
0.0042 | 0.336 | 0.006 | 0.123 |
| CARDIOVASCULAR RELATED PARAMETERS | |||||||
| Hemoglobin | 13.4 (12.3-15.20) | 13.2 (12.5-14.8) | 11.1 (10.6-12.3) | <0.001 | >0.999 | <0.001 | <0.001 |
| Ferritin | 144 (51-324) | 104 (67-183) | 172 (75-256) | 0.633 | |||
| Sedimentation velocity mean±sd | 26±22 | 23 ±16 | 54 ±32 | 0.005 | >0.999 | 0.012 | 0.014 |
| Albumin (g/dL) mean±sd | 40.5 ±4.1 | 39.9 ±8.6 | 37.9 ±4.5 | 0,221 | |||
| Total Cholesterol (mg/dL) mean±sd | 182 ±44 | 179 ±42 | 178 ±47 | 0.940 | |||
| HDL Cholesterol (mg/dL) mean±sd | 53 ±16 | 54 ±15 | 47 ±15 | 0.240 | |||
| Triglycerides (mg/dL) mean±sd | 129 ±98 | 111 ±39 | 174±85 | 0.018 | >0.999 | 0.129 | 0.020 |
| Acid uric mean±sd | 5.7 ±2.2 | 7.1 ±1.5 | 7.7 ±1.9 | <0.001 | 0.033 | <0.001 | 0.777 |
| C reactive protein (mg/L) median (IQR) | 2.0 (1.0-4.3) |
3.0 (1.6-8.1) |
2.8 (1.1-8.4) |
0.5012 | |||
| BNP (pg/mL) median (IQR) | 30.2 (18.3-70.1) |
74.8 (67.0-105.0) |
74.5 (45.4-327.8) |
0.0302 | 0.189 | 0.036 | >0.999 |
| Left Ventricular Mass (g) mean±sd | 269.5 ±163.5 | 175.0 ±72.6 | 189.5 ±61.2 | 0.333 | |||
| Ejection Fraction (%) mean±sd | 60 ±9 | 62 ±8 | 61 ±8 | 0.921 | |||
| THERAPEUTICS | |||||||
| ACE inhibitors, n (%) | 12 (41.4) | 9 (36.0) | 7 (25.0) | 0.416 | |||
| ARBs, n (%) | 15 (51.7) | 14 (56.0) | 20 (71.4) | 0.091 | |||
| Phosphate binders, n (%) | 1 (3.4) | 1 (4.0) | 2 (7.1) | 0.787 | |||
| Cholecalciferol, n (%) | 7 (24.1) | 4 (16.0) | 11 (39.3) | 0.149 | |||
| Vitamine D analogues, n (%) | 1 (3.4) | 5 (20.0) | 13 (46.4) | <0.001 | 0.235 | <0.001 | 0.240 |
| Statins, n (%) | 14 (48.3) | 12 (48.0) | 20 (71.4) | 0.131 | |||
1T teste for independent sample; 2Mann-Whitney test; 3Chi-Square test; * Bonferroni test.
Table 2.
Associations between iFGF-23 and phosphate levels with patients’ demographic characteristics and comorbidities (*Mann-Whitney test and ‡Spearman correlation (significance to p-value <0.05).
Table 2.
Associations between iFGF-23 and phosphate levels with patients’ demographic characteristics and comorbidities (*Mann-Whitney test and ‡Spearman correlation (significance to p-value <0.05).
| Circulating intact FGF-23 | Serum Phosphate | |||||
|---|---|---|---|---|---|---|
| Median | p | r | Median | p | r | |
| Gender (Female/Male) | (59.9/72.5) | 0,492* | (3.40/3.35) | 0,599* | ||
| Age (years) | 0,460 (<0.001)‡ | 0.292 (0,008) ‡ | ||||
| Hypertension (Yes/No) | 79.63 | 0,005* | 3.40 | 0,027* | ||
|
Diabetes (Yes/No) |
115.43 | <0,001* | 3.65 | 0,009* | ||
|
Dyslipidemia (Yes/No) |
78.32 | 0,075* | 3.40 | 0,358* | ||
|
Baseline CV disease (Yes/No) |
92.00 | 0,02* | 3.50 | 0,137* | ||
|
Baseline CbV disease (Yes/No) |
121.48 | 0,146* | 3.40 | 0,601* | ||
| Charlson Comorbidity Index | 0,595 (<0.001)‡ | 0.417 (<0.001)‡ | ||||
CV—cardiovascular disease; CbV—cerebrovascular disease.
Table 3.
Correlation between iFGF-23 and serum phosphate levels, with clinical variables in non-dialysis CKD patients (n=82). Spearman correlation (significance to p-value <0.05).
Table 3.
Correlation between iFGF-23 and serum phosphate levels, with clinical variables in non-dialysis CKD patients (n=82). Spearman correlation (significance to p-value <0.05).
| Circulating intact FGF-23 | Serum Phosphate |
|
|---|---|---|
| CKD RELATED PARAMETERS | ||
| eGFR CKD-EPI (ml/min/1,73m2) | -0,789 (<0.001) | -0.621 (<0.001) |
| Creatinine (mg/dL) | 0,756(<0.001) | 0.632 (<0.001) |
| Urea (mg/dL) | 0,713 (<0.001) | 0.668 (<0.001) |
| Protein/creatinine ratio (mg/g) | 0,297 (0.010) | 0.376 (<0.001) |
| Calcium (mg/dL) | -0,292 (0.008) | -0.125 (0.267) |
| Phosphate (mg/dL) | 0,558 (<0.001) | -- |
| Intact FGF-23 (mg/mL) | -- | 0,558 (<0.001) |
| Parathormone (pg/mL) | 0,595 (<0.001) | 0.506 (<0.001) |
| 25-OH-Vitamin D (ng/mL) | 0,142 (0,260) | 0.024 (0.852) |
|
CARDIOVASCULAR RELATED PARAMETERS |
||
| Hemoglobin (g/dL) | -0,413 (<0.001) | -0.538 (<0.001) |
| Ferritin (µg/L) | 0.105 (0.396) | -0.003 (0.984) |
| Albumin (g/dL) | -0,382 (<0.001) | -0,286 (<0.010) |
| Total Cholesterol (mg/dL) | -0,245 (0.023) | -0,217 (0.056) |
| HDL Cholesterol (mg/dL) | -0,262 (0.020) | -0.087 (0.448) |
| Triglycerides (mg/dL) | 0,248 (0.028) | 0.224 (0.049) |
| Uric Acid (mg/dL) | 0,316 (0,004) | 0.265 (0.018) |
| C reactive protein (mg/L) | 0,125 (0,362) | -0.041 (0.768) |
| BNP (pg/mL) | 0,312 (0,042) | 0.313 (0.041) |
| Left Ventricular Hypertrophy | -0,152 (0,674) | 0.128 (0.724) |
| Ejection Fraction | 0,073 (0,760) | 0.69 .772) |
eGFR: estimated glomerular filtration rate; BNP: B-type natriuretic peptide.
Table 4.
Cardiovascular and renal outcomes upon follow-up [mean 58months (IQ 30-69)] in the studied population.
Table 4.
Cardiovascular and renal outcomes upon follow-up [mean 58months (IQ 30-69)] in the studied population.
| Outcome (n = 65) | (n, %) |
|---|---|
| MACCEs | 4 (6,2) |
| Acute myocardial infarction | 1 (1,5) |
| Stroke | 1 (1,5) |
| Composite outcome on CKD progression*1 | 19 (29,2) |
| Progression to ESRD | 10 (15,4) |
| Hospitalizations | 22 (33,8) |
| All-cause mortality | 5 (8) |
| CV mortality | 2 (3) |
| Composite cardiovascular outcome * | 23 (35,4) |
MACCEs: major adverse cardiovascular and cerebrovascular events; ESRD: End Stage Renal Disease; CV: cardiovascular. *Composite cardiovascular outcome: MACCEs, hospitalizations for medical reasons and all-cause mortality.
Table 5.
Association between FGF-23 and phosphate with mortality and renal and cardiovascular outcomes during follow-up in the studied population.
Table 5.
Association between FGF-23 and phosphate with mortality and renal and cardiovascular outcomes during follow-up in the studied population.
| Circulating intact FGF-23 | Serum Phosphate | ||||
|---|---|---|---|---|---|
| n | Median | p+ | Median | p+ | |
| CKD Progression (n=19) | 19 | 80.42 | 0,067 | 3.50 | 0,077 |
| All-cause mortality (n=5) | 5 | 97.79 | 0,247 | 4.40 | 0,020 |
| Hospitalizations (n=22) | 22 | 78.58 | 0,095 | 3.60 | 0,100 |
| MACCES (n=4) | 4 | 132.53 | 0,120 | 3.70 | 0,203 |
| Composite cardiovascular Outcome (n=23) | 78.84 | 0,037 | 3.25 | 0,045 |
*Composite cardiovascular outcome: MACCEs, hospitalizations for medical reasons and all-cause mortality.
Table 6.
Association between mortality and phosphate, adjusted to renal function, age Charlson comorbidity index, cardiovascular disease, hypertension, diabetes, dyslipidemia, iFGF-23, iPTH hemoglobin, PCR and BNP (logistic regression).
Table 6.
Association between mortality and phosphate, adjusted to renal function, age Charlson comorbidity index, cardiovascular disease, hypertension, diabetes, dyslipidemia, iFGF-23, iPTH hemoglobin, PCR and BNP (logistic regression).
| Mortality | |||
|---|---|---|---|
| OR | IC 95% | p | |
| Model 1 | |||
| PHOSPHATE | 6,137 | 0,809-46,573 | 0,079 |
| Renal function | 0,994 | 0,953-1,038 | 0,796 |
| Model 2 | |||
| PHOSPHATE | 6,244 | 1,067-36,543 | 0,042 |
| Age | 1,048 | 0,967-1,136 | 0,253 |
| Model 3 | |||
| PHOSPHATE | 5,948 | 0,981-36,062 | 0,052 |
| Charlson comorbidity index | 1,436 | 0,992-2,080 | 0,055 |
| Model 4 | |||
| PHOSPHATE | 7,577 | 1,0403-40,909 | 0,019 |
| Cardiovascular disease | 1,906 | 0,250-14,545 | 0,534 |
| Model 5 | |||
| PHOSPHATE | 10,943 | 1,727-69,363 | 0,011 |
| Hypertension | 0,170 | 0,016-1,818 | 0,143 |
| Model 6 | |||
| PHOSPHATE | 7,188 | 1,375-37,563 | 0,019 |
| Diabetes | 1,174 | 0,158-8,733 | 0,876 |
| Model 7 | |||
| PHOSPHATE | 8,361 | 1,390-50,295 | 0,020 |
| Dyslipidemia | 0,587 | 0,069-4,981 | 0,0625 |
| Model 8 | |||
| PHOSPHATE | 5,383 | 1,026-33,220 | 0,047 |
| iFGF-23 | 1,002 | 0,998-1,005 | 0,384 |
| Model 9 | |||
| PHOSPHATE | 15.024 | 1.033-218.459 | 0.047 |
| iPTH | 0.997 | 0.989-1.005 | 0.471 |
| Model 10 | |||
| PHOSPHATE | 7,167 | 1,051-48,855 | 0,044 |
| Hemoglobin | 0,994 | 0,560-1,764 | 0,982 |
| Model 11 | |||
| PHOSPHATE | 3,605 | 0,442-29,428 | 0,231 |
| PCR | 1,030 | 0,986-1,077 | 0,185 |
| Model 12 | |||
| PHOSPHATE | 6,015 | 1,042-34,729 | 0,045 |
| BNP | 1,003 | 0,996-1,010 | 0,397 |
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