Abstract
Background
The body of patients with chronic kidney disease (CKD)is in a state of microinflammation, which further aggravates the progression of CKD and the occurrence and development of its complications. The inflammatory state of the body is significantly increased in patients with secondary hyperparathyroidism of CKD.
Methods
Patients diagnosed with chronic kidney disease in the department of Nephrology of Henan Provincial People’s Hospital were selected as the experimental group, and 58 normal subjects were selected as the control group. Collecting patient’s Clinical date, whole blood of the experimental group and control group was collected, and hs-CRP was detected by enzyme-linked immunosorbent assay. Compare the differences in clinical indicators and serum hs-CRP between the two groups. Taking PTH and hs-CRP as dependent variables, correlation analysis was conducted to clarify the relationship between PTH and hs-CRP, and further curve fitting was carried out.
Results
The levels of hemoglobin, total protein and albumin in CKD group were significantly lower than those in control group (p < 0.001). Alkaline phosphatase, creatinine, uric acid, Cyc and blood phosphorus in CKD group were higher than those in control group, and the P values were 0.016, <0.001, <0.001, <0.001, <0.001, respectively. Blood calcium was lower than control group, p < 0.001. The levels of hs-CRP in CKD group were significantly higher than those in control group, p < 0.001. Serum PTH was positively correlated with serum hs-CRP (r = 0.299, p = 0.002) in a normalized holistic analysis of CKD patients. In order to further clarify the correlation between PTH and hs-CRP, quadratic method was used to conduct curve fitting on PTH and hs-CRP and found that R = 0.361, F = 7.795, p < 0.001. Quadratic method was used to quadratic curve fitting at PTH levels <600 pg/mL, <500 pg/mL, <400 pg/mL, <300 pg/mL and <200 pg/mL, respectively, and it was found that the corresponding R = 0.281, F = 4.158, p < 0.001. R = 0.292, F = 4.397, p < 0.001; R = 0.546, F = 18.456, p < 0.001; R = 0.415, F = 7.471, p < 0.001; R = 0.403, F = 5.220, p < 0.001. It was found that there was no correlation between PTH and hs-CRP in hemodialysis patients and peritoneal dialysis patients. In order to exclude the influence of drug use on the statistical results, after excluding the patients who took over phosphorus binder and calcium binder, curve fitting method was used to find that PTH and CRP were correlated, in peritoneal dialysis group and hemodialysis group, R = 0.953, F = 39.913, p < 0.001; R = 0.448, F = 3.391, p = 0.010 respectively.
Conclusion
CKD patients have the lowest levels of inflammatory factors within a certain range of PTH.
Keywords: Chronic kidney diseases, hs-CRP, inflammation, parathyroid hormone
Introduction
The latest survey data of Lancet showed that in 2017, chronic kidney disease (CKD) affected nearly 700 million people worldwide, accounting for about 9.1% of the global population [1]. Chronic kidney disease can cause a range of complications, including abnormal mineral bone metabolism, pathological fractures, increased anemia, cognitive decline, malnutrition and other complications [2–4], and increase the risk of cardiovascular disease death, all-cause mortality, is considered to be a rapidly growing global health burden [5].
Studies have shown that microinflammatory states are common in CKD patients, and the serum inflammatory mediators and chemokines have increased in the early stage of the disease [6,7]. Microinflammatory states are non-infectious, persistent, chronic immune inflammatory responses that release circulating pro-inflammatory cytokines. There are many factors that lead to persistent chronic inflammation in CKD patients, including increased secretion of proinflammatory cytokines, oxidative stress of the body, imbalance of intestinal flora, changes in adipose tissue metabolism, chronic and recurrent infection of the body, etc [8,9]. CRP is an important indicator of inflammation in the body. Inflammation marker CRP is involved in the occurrence of malnutrition, coronary atherosclerosis, Erythropoietin (EPO) resistance and cardiovascular adverse events in CKD patients [10–12]. Abnormal mineral bone metabolism is a common complication in patients with CKD, and PTH is definitely the focus of research on abnormal mineral bone metabolism. In recent years, people have gradually begun to pay attention to the influence of PTH on inflammation level in patients with CKD, and most studies have found that PTH level is positively correlated with CRP level [13], the level of PTH in patients with CKD is extremely high when severe abnormal mineral bone metabolism occurs. At this time, parathyroidectomy is required to reduce the secretion of PTH, reduce blood phosphorus, maintain blood calcium levels, and alleviate progressive metastatic calcification. When studying the relationship between PTH level and inflammatory factors in the course of CKD, it is easy to ignore the influence of PTH level on the level of inflammation in patients with early chronic kidney disease.
Therefore, this study focuses on patients with early CKD and those who do not meet the surgical standards of Secondary hyperparathyroidism (SHPT)as research objects to clarify the relationship between PTH level and inflammatory factors in such patients, increase the understanding of the relationship between PTH and CRP, so as to guide clinical monitoring and medication.
Method
Flow chart of study subjects and screened patients
CKD Patients who visited nephrology outpatient Department of Henan Provincial People’s Hospital from January 1, 2022 to June 30, 2022 were selected as the experimental group. Inclusion criteria: 1、The age range is 18–75 years old. 2、Meets the diagnostic criteria for chronic kidney disease (at least one term is satisfied): (1) Marker of kidney injury: ①Albuminuria(urinary albumin excretion rate (UAER)≥30mg/24h or urinary albumin creatinine ratio (UACR)≥30mg/g); ②Abnormal sediment in urine; ③Renal tubule-associated lesions, These include tubular granuloid or vacuolar degeneration, tubular dilatation, severe vacuolar degeneration, and even epithelial cell shedding; ④Histological abnormality; ⑤ Radiographic findings showed structural abnormalities; ⑥History of kidney transplantation.(2) glomerular filtration rate (GFR) decline estimated GFR (eGFR)<60 ml/min/1.73m2. 3、Enrolled patients agreed to sign informed consent. 4、Refused to sign the informed consent. Exclusion criteria: 1. Infectious diseases. 2, Accompanied by malignant diseases such as tumors. 3. Surgical treatment was performed within 6 months before enrollment. 4. Reach SHPT surgery standard [14]. 5. Refuse to sign the informed consent. Fifty-eight normal person in physical examination department of Henan Provincial People’s Hospital were selected as control group. This study was approved by Ethics Committee of Henan Provincial People’s Hospital. All participants have signed informed consent forms. The screening flow chart is shown in Figure 1.
Figure 1.
Screening flow chart.
Clinical data and specimen collection
General clinical data of the subjects were collected: These include gender, age, hemoglobin, total protein, albumin, total cholesterol, triglycerides, alkaline phosphatase, serum calcium, serum phosphorus, and serum PTH. 2 mL of whole blood was taken from the patient during blood drawing in the laboratory, centrifuged at room temperature within 30 min (3000 rpm × 10min), and the serum was absorbed and divided into a 200 µl centrifuge tube, labeled, and temporarily stored at −80 °C for use.
Detection of hs-CRP
Samples were taken out which were frozen and stored at −80 °C, balanced to room temperature, centrifuged at 10000r/min for 10 min, and the serum hs-CRP (Elabscience, E-EL-H5134c, China) level was detected by enzyme-linked immunosorbent assay (ELISA) according to the instructions.
Statistical method
Statistical software SPSS23.0 was used for statistical analysis of the data. Using the Shapiro–Wilk to have normality test, The measurement data conforming to normal distribution were mean ± standard deviation (X ± S, p > 0.05), Measurement data with non-normal distribution are represented by quartiles ([M (Q1, Q3)], p < 0.05). Independent sample t test was used for comparison between the two groups conforming to normal distribution, rank sum test was used for comparison between the two groups not conforming to normal distribution, and chi-square test was used for qualitative data. Correlation analysis uses Spearman or Pearson analysis and P value <0.05 was considered statistically significant. Curve fitting was analyzed by Quadratic method.
Results
Comparison of general clinical data between subjects and normal control group
A total of 107 patients with CKD were included in this study, including 66 males and 41 females. There were 58 normal control groups, including 31 males and 27 females. There was no significant difference in age and gender composition between the CKD group and the control group, with p values of 0.130 and 0.406, respectively. The levels of hemoglobin, total protein and albumin in the CKD group were significantly lower than those in the control group, and the differences were statistically significant, with all p values <0.001. There was no significant difference in total cholesterol level between the CKD group and the control group (p = 0.418); the triglyceride level in CKD patients was higher than that in control group (p = 0.001). Alkaline phosphatase, creatinine, uric acid, Cyc and blood phosphorus in CKD group were significantly higher than those in control group, with p values of 0.016, <0.001, <0.001, <0.001, <0.001, respectively; blood calcium was lower than control group, p < 0.001. The level of PTH was significantly higher than those in normal reference range, CKD’s hs-CRP levels were significantly higher than those in the control group (p < 0.001). According to the classification of hemodialysis peritoneal dialysis patients and non-dialysis patients, the general clinical data were listed. Among the 37 hemodialysis patients, 33 were accompanied by hypertension, 20 were accompanied by diabetes, 9 were accompanied by coronary heart disease, 1 was accompanied by myocardial infarction, and 6 were accompanied by stroke. Among the 16 patients with peritoneal dialysis, 14 had hypertension, 2 had diabetes, 1 had coronary heart disease, and 1 had stroke. Among the 54 non-dialysis patients, 46 had hypertension, 8 had diabetes, 3 had coronary heart disease, and 4 had stroke; see Tables 1 and 2 for details.
Table 1.
Comparison of general clinical data.
| Parameters | CKD group (107) | Control group/normal reference range | t/Z/χ2 value | p |
|---|---|---|---|---|
| Age (years) | 56 (48,64) | 52.28 ± 9.72 | −1.514 | 0.130 |
| Sex ratio (male/female) | 66/41 | 31/27 | 0.819 | 0.406 |
| Hb (g/L) | 99.24 ± 21.36 | 138.26 ± 15.28 | −12.248 | <0.001 |
| TP (g/L) | 61.71 ± 9.06 | 70.82 ± 9.38 | −6.109 | <0.001 |
| ALB (g/L) | 35.85 ± 5.73 | 44.31 ± 4.62 | −9.692 | <0.001 |
| CHOL (mmol/L) | 4.12 (3.08,4.96) | 4.12 ± 0.64 | −0.810 | 0.418 |
| TG (mmol/L) | 1.56 (1.12,2.10) | 1.22 ± 0.44 | −3.463 | 0.001 |
| ALP (U/L) | 75.70 (59.00,96.70) | 68.13 ± 18.02 | −2.413 | 0.016 |
| Cr (µmol/L) | 569.00 (376.00,847.00) | 65.31 ± 12.41 | −10.520 | <0.001 |
| UA (µmol/L) | 409.32 ± 128.96 | 298.67 ± 76.95 | 5.934 | <0.001 |
| Cyc (mg/L) | 4.14 ± 1.59 | 0.83 ± 0.13 | 8.045 | <0.001 |
| P (mmol/L) | 1.68 (1.38,2.08) | 1.12 ± 0.19 | −5.835 | <0.001 |
| Ca (mmol/L) | 2.14 (2.01,2.25) | 2.33 ± 0.07 | −7.919 | <0.001 |
| PTH (pg/mL) | 184.00 (124.7,317.5) | 12.00–88.00 | – | – |
| hs-CRP (pg/mL) | 273.35 (128.52,407.08) | 32.39 (22.88, 45.30) | −8.444 | <0.001 |
Hb Hemoglobin; TP total protein; ALB albumin; CHOL cholesterol; TG triglyceride; ALP alkaline phosphatase; Cr creatinine; UA uric acid; cyc: cystatin C; P: serum phosphorus; Ca: serum calcium; PTH: parathyroid hormone; hs-CRP: hypersensitive C-reactive protein.
Values denote mean ± standard deviation, median (interquartile interval).
Table 2.
General clinical data between patients without dialysis and patients with different dialysis modes.
| Parameters | Hemodialysis patient (37) | Peritoneal dialysis patient (16) | Non-dialysis patient (54) |
|---|---|---|---|
| Age (years) | 56.08 ± 9.69 | 50.50 ± 13.47 | 55.72 ± 13.46 |
| Sex ratio (male/female) | 21/16 | 12/4 | 33/21 |
| Hb (g/L) | 106.67 ± 20.13 | 100.81 ± 19.29 | 93.72 ± 21.49 |
| TP (g/L) | 66.83 ± 8.65 | 57.41 ± 5.24 | 59.47 ± 8.76 |
| ALB (g/L) | 39.66 ± 5.14 | 32.69 ± 3.38 | 34.17 ± 5.35 |
| CHOL (mmol/L) | 3.38 ± 0.93 | 3.96 ± 1.66 | 4.30 ± 1.34 |
| TG (mmol/L) | 1.89 ± 1.20 | 1.44 (0.82, 1.81) | 1.52 (1.15, 2.12) |
| ALP (U/L) | 89.17 ± 57.79 | 66.75 (56.73, 99.33) | 77 (59.30, 94.08) |
| Creatinine (µmol/L) | 781.92 ± 295.71 | 851.25 ± 450.19 | 427.50 (267.00, 620.75) |
| Uric acid (µmol/L) | 337.84 ± 114.32 | 374 (302, 432.75) | 468.48 ± 112.80 |
| Cystatin C (mg/L) | 6.09 ± 1.33 | 5.57 ± 1.06 | 3.34 ± 1.11 |
| P (mmol/L) | 2.00 ± 0.85 | 1.88 ± 0.47 | 1.53 (1.33, 1.94) |
| Ca (mmol/L) | 2.19 ± 0.17 | 2.04 ± 0.22 | 2.09 ± 0.20 |
| PTH (pg/mL) | 239.59 ± 149.62 | 236.65 (185.35, 353.58) | 164.50 (108.25, 335.53) |
| hs-CRP | 264.10 ± 170.86 | 308.13 ± 275.20 | 248.85 |
| Complication hypertension (%) | 89.19 | 87.50 | 85.19 |
| Complication diabetes mellitus (%) | 54.05 | 12.50 | 14.82 |
| Complication coronary heart disease (%) | 24.32 | 6.25 | 5.56 |
| Complication myocardial infarction (%) | 2.70 | 0 | 0 |
| Complicated cerebral infarction (%) | 16.216 | 6.25 | 7.40 |
| Take P-binder (%) | 0 | 6.25 | 0 |
| Take calcifiers (%) | 13.5 | 18.75 | 0 |
Values denote mean ± standard deviation, median (interquartile interval).
Correlation analysis of PTH, hs-CRP and general clinical data in experimental group
Serum PTH in CKD patients was positively correlated with TP (r = 0.239, p = 0.017), ALB (r = 0.258, p = 0.010), ALP (r = 0.319, p = 0.001), Cr (r = 0.234, p = 0.019), P (r = 0.266, p = 0.008). It was negatively correlated with Ca (r = –0.197, p = 0.049). hs-CRP was positively correlated with ALP in CKD patients (r = 0.216, p = 0.031); for details, see Tables 3 and 4,.
Table 3.
Correlation analysis with PTH as dependent variable.
| Dependent variable | Independent variable | r value | p value |
|---|---|---|---|
| PTH | Hb (g/L) | −0.073 | 0.474 |
| TP (g/L) | 0.239 | 0.017 | |
| ALB (g/L) | 0.258 | 0.010 | |
| CHOL (mmol/L) | 00.103 | 0.390 | |
| TG (mmol/L) | 0.134 | 0.279 | |
| ALP (U/L) | 0.319 | 0.001 | |
| Cr (µmol/L) | 0.234 | 0.019 | |
| UA (µmol/L) | 0.148 | 0.142 | |
| Cyc (mg/L) | 0.182 | 0.171 | |
| P (mmol/L) | 0.266 | 0.008 | |
| Ca (mmol/L) | −0.197 | 0.049 |
Table 4.
Correlation analysis with hs-CRP as dependent variable.
| Dependent variable | Independent variable | r value | p value |
|---|---|---|---|
| hs-CRP | Hb (g/L) | −0.111 | 0.275 |
| TP (g/L) | 0.068 | 0.502 | |
| ALB (g/L) | 0.078 | 0.439 | |
| CHOL (mmol/L) | 0.042 | 0.726 | |
| TG (mmol/L) | 0.179 | 0.148 | |
| ALP (U/L) | 0.216 | 0.031 | |
| Cr (µmol/L) | 0.002 | 0.986 | |
| UA (µmol/L) | −0.073 | 0.471 | |
| Cyc (mg/L) | −0.137 | 0.306 | |
| P (mmol/L) | 0.047 | 0.643 | |
| Ca (mmol/L) | 0.027 | 0.794 |
Correlation analysis between PTH and hs-CRP
Spearman analysis was performed in all patients with CKD showed a positive correlation between serum PTH and serum hs-CRP (r = 0.299, p = 0.002). In order to further clarify the correlation between PTH and hs-CRP, Quadratic method was used to conduct curve fitting on PTH and hs-CRP and found that R = 0.361, F = 7.795, p < 0.001. Quadratic method was used to quadratic curve fitting at PTH levels <600 pg/mL (100 patients), <500 pg/mL (97 patients), <400 pg/mL (90 patients), <300 pg/mL (75 patients) and <200 pg/mL (57 patients), respectively, and it was found that the corresponding R = 0.281, F = 4.158, p < 0.001. R = 0.292, F = 4.397, p < 0.001; R = 0.546, F = 18.456, p < 0.001; R = 0.415, F = 7.471, p < 0.001; R = 0.403, F = 5.220, p = 0.001, see Figure 2 for details. The classification of CKD patients according to whether dialysis and dialysis method found that there was no correlation between PTH and hs-CRP in 37 hemodialysis patients (R = 0.291, F = 1.570, p = 0.113), no correlation between PTH and hs-CRP in 16 peritoneal dialysis patients (R = 0.447, F = 1.210, p = 0.193) by curve fitting statistical method, The correlation between PTH and CRP was found by curve fitting statistical method in non-dialysis patients (R = 0.442, F = 6.193, p < 0.001). In order to exclude the influence of drug use on the statistical results, after excluding the patients who took over phosphorus binder and calcium binder, curve fitting method was used to find that PTH and CRP were correlated, in peritoneal dialysis(12 patients) group and hemodialysis group(30 patients), R = 0.953, F = 39.913, p < 0.001; R = 0.448, F = 3.391, p = 0.010 respectively. Quadratic method was used to quadratic curve fitting at PTH levels <600pg/mL (90 patients), <500pg/mL (88 patients), <400 pg/mL(84 patients), <300pg/mL (74 patients) and <200pg/mL (56 patients), respectively, and it was found that the corresponding R = 0.363, F = 6.610, p < 0.001; R = 0.363, F = 6.464, p < 0.001; R = 0.562, F = 18.094, p < 0.001; R = 0.412, F = 7.264, p < 0.001; R = 0.398, F = 4.996, p = 0.001; see Figure 3 for details.
Figure 2.
Curve fitting of PTH and hs-CRP in all enrolled patients (A). Curve fitting of PTH and hs-CRP when PTH <600 pg/mL (B). Curve fitting of PTH and hs-CRP when PTH <500 pg/mL (C). Curve fitting of PTH and hs-CRP when PTH <400 pg/mL (D). Curve fitting of PTH and hs-CRP when PTH <300 pg/mL (E). Curve fitting of PTH and hs-CRP when PTH <200 pg/mL (F).
Figure 3.
Curve fitting of PTH and hs-CRP in peritoneal dialysis patients who were not use calcium–phosphorus binders (A). Curve fitting of PTH and hs-CRP in hemodialysis patient who were not use calcium–phosphorus binders (B). Curve fitting of PTH and hs-CRP in patients who did not use calcium–phosphorus binders with PTH <600 pg/mL (C). Curve fitting of PTH and hs-CRP in patients who did not use calcium–phosphorus binders with PTH <500 pg/mL (D). Curve fitting of PTH and hs-CRP in patients who did not use calcium–phosphorus binders with PTH <400 pg/mL (E). Curve fitting of PTH and hs-CRP in patients who did not use calcium–phosphorus binders with PTH <300 pg/mL (F). Curve fitting of PTH and hs-CRP in patients who did not use calcium–phosphorus binders with PTH <2200 pg/mL (F).
Discussion
After hypertension and diabetes, CKD has become a major chronic disease in the world, which attracts more and more attention [15]. Inflammation plays an important role in the course of almost all benign and malignant tumors, acute and chronic diseases. Patients with CKD may have chronic inflammation, which further aggravates CKD [16]. In patients with SHPT, the level of inflammation was found to be significantly higher than the normal reference range, and there was previous evidence that the level of inflammatory factors was gradually increased with the increase of PTH level, and the two were independently correlated [13]. This study found that in the early stage of CKD, when SHPT patients did not meet the surgical indications, the level of PTH was not simply positively correlated with the level of inflammatory factors, but within a certain range of PTH level, the level of inflammatory factors was the lowest.
In this study, the levels of alkaline phosphatase, creatinine, uric acid and cystatin C in patients with CKD were significantly higher than those in the normal group, which was consistent with the general clinical characteristics of patients with CKD. Compared with the normal control group, the nutritional status of patients with CKD was poor and renal anemia was prone to occur, which was consistent with the results of previous studies [17]. Most CKD patients are accompanied by hypertension, diabetes, coronary heart disease, myocardial infarction, stroke and other diseases in the past. The majority of patients with high blood pressure. Hypertension and CKD are closely related pathophysiological states, so persistent hypertension can lead to deterioration of kidney function, and a progressive decline in kidney function can in turn lead to deterioration of blood pressure control [18]. In our general data, hemoglobin was significantly lower in CKD patients than in normal controls. The main causes of renal anemia are reduced EPO production in the kidney and a reduction in iron, the raw material for the synthesis of hemoglobin. Iron deficiency is caused by hidden blood loss, infection, inflammation, surgery, venipuncture, and the preservation of blood by dialysis and dialysis devices [19]. Hypoxia inducible factor (HIF-1) is believed to play an important role in the cellular perception of hypoxia. In recent years, researchers have found that under normal oxygen conditions, HIF-α is hydroxylated by prolyl hydroxylase domain proteins, and then undergoes proteasome degradation. Under hypoxic conditions, HIF-α does not degrade, translocations to the nucleus, binds to HIF-β, activates hypoxic response elements, initiates gene transcription of erythropoietin, and leads to increased production of erythropoietin [20]. Roxadustat is a novel oral HIF prolyl hydroxylase inhibitor (PHI) for the treatment of renal anemia. Roxadustat therapy effectively increases and maintains hemoglobin levels and decreases ferritin and CRP [21]. Abnormal protein metabolism is easy to occur in the process of CKD, mainly due to the body’s insufficient intake, increased needs or extra loss of nutrients, which leads to the decline of protein and energy reserves in the body and the failure to meet the body’s metabolic needs, thus resulting in a state of nutritional deficiency [22]. In this study, we also found that the total protein and albumin levels of CKD patients were significantly reduced. In addition, in this study, the blood phosphorus level increased and the blood calcium level decreased in CKD patients, mainly because the renal failure patients had blocked phosphorus excretion, weakened calcium reabsorption and weakened intestinal calcium absorption capacity. SHPT caused bone salt release and further increased blood phosphorus. Both animal models of CKD and clinical studies of patients with CKD have shown obvious calcium and phosphorus metabolism disorders [23,24]. Therefore, in addition to improving renal function, more attention should be paid to nutritional support therapy to protect patients with CKD.
Inflammation is associated with the progression of CKD and its complications. Multiple studies have shown chronic inflammation in patients with CKD [25,26]. Inflammation and oxidative stress can cause mesangial dilation and tubule fibrosis [27]. The study showed that the expression of NFκB, a marker of oxidative stress in the glomeruli of CKD mice was increased [28]. Moreover, IL-1β, IL-6, or IL-12 are involved in the progression of diabetic nephropathy [27]. In this study, it was also found that the hs-CRP level in CKD group was significantly higher than that in normal control group. Epidemiological and genetic studies have linked inflammation to the progression of CKD and its cardiovascular complications [29]. Persistent low-grade inflammation and premature aging are key markers of uremia and lead to impaired health, reduced quality of life and increased mortality in patients with CKD [30,31]. In a prospective study of 3,875 patients with stage CKD2-4, baseline data analysis showed that higher creatinine and lower glomerular filtration rate were associated with higher levels of inflammatory cytokines IL-6, TNF-α, and hs-CRP [32]. It is well known that CKD and arterial stiffness are associated with increased cardiovascular morbidity and mortality. A study by Eliot’s team [33] included nearly 4000 patients with CKD for the detection of five inflammatory markers and the determination of arterial stiffness. IL-6, hs-CRP and complex inflammation scores were observed to be significantly correlated with at least two indicators (pulse wave propagation rate, enhancement index, central pulse pressure, peripheral pulse pressure amplification) to assess arterial stiffness, demonstrating that inflammation is involved in the progression of CKD as well as its complications. Therefore, we also need to pay attention to inflammation in CKD patients, and it is particularly important to explore the mechanism causing chronic inflammation in CKD patients in the future.
When PTH level is in a certain range, the level of inflammatory factors is the lowest. In this study, with PTH as the dependent variable, correlation analysis was conducted in CKD patients and it was found that PTH was positively correlated with total protein, albumin, alkaline phosphatase, creatinine, blood calcium and blood phosphorus. In addition, we also found that PTH was positively correlated with hs-CRP, but the correlation was not strong. An animal study by Jorge B’s group [34] found that levels of PTH inflammatory cytokines were significantly higher in rats with severe secondary hyperparathyroidism than in other groups. However, a 2016 study showed no correlation between PTH level and inflammatory cytokines in patients with end-stage renal disease, suggesting that the inflammation level in patients with CKD may be caused by other factors other than SHPT [35]. In conclusion, the relationship between PTH level and inflammatory factors is not completely clear. In combination with the increased PTH level in the patients with CKD included in this study, and most of the patients did not enter the dialysis stage, nor did they meet the surgical standards for SHPT, it is speculated that there is no single rigorous positive correlation between PTH level and CRP considering the complex pathogenesis of patients with CKD. The curves of PTH and hs-CRP were boldly fitted, and it was finally found that the level of PTH was 100–300pg/ml, and the inflammation level was lower than that of other PTH levels. In order to further obtain the perfect curve, we fitted PTH <600 pg/mL, PTH <500 pg/mL, PTH <400 pg/mL, PTH <300 pg/mL and PTH <200 pg/mL respectively. The results were significant, and the best fit was found when PTH <400 pg/mL. KDIGO guidelines as early as 2003 recommended maintaining PTH levels in the 150–300 pg/mL range, rather than lower PTH levels being better. A 2010 study of nearly 800 HD patients followed for five years found that low PTH could lead to protein energy expenditure and inflammation, and patients with serum PTH of 100 to 150 pg/ml lived longer than other groups [36]. In our study, we found that there was no relationship between PTH and CRP in hemodialysis and peritoneal dialysis patients. Since the patients with end-stage kidney disease mostly have calcium and phosphorus metabolism disorders, and the patients take more drugs to regulate calcium and phosphorus metabolism, we excluded the patients taking the above drugs, and there was a significant correlation between PTH and CRP. Compared with the aforementioned correlation between PTH and CRP at different stages, the correlation between the two was more significant after excluding drug use. In general, the relationship between PTH level and body inflammation in patients with CKD needs to be studied in groups, so as to better guide the clinical management of chronic kidney disease abnormal mineral bone metabolism disease (CKD–MBD).
Previous studies have shown a positive correlation between PTH level and hs-CRP in normal population and patients with asymptomatic primary hyperparathyroidism [37,38]. In this study, the low level of PTH and the corresponding CRP level in CKD patients showed a downward trend, but even the low level of PTH was higher than that of normal people, that is, the relationship between PTH level and CRP was not simply positive. Combined with the disorder of the internal environment in patients with CKD, other factors may interfere with the expression of CRP level. The specific mechanism of regulating CRP level in patients with CKD needs to be further verified by molecular basic experimental studies.
In short, there are multiple mechanisms in the body of patients with CKD that cause increased levels of inflammation, which further aggravate the progression of CKD and its complications. Therefore, controlling the inflammation level in CKD is the key. This study found that the inflammation level in CKD patients was lowest when the PTH level was in a certain concentration range. Given the small number of subjects included in this study and the single-center data, it is still necessary to further expand the sample size to increase the persuasiveness of this conclusion.
Acknowledgments
We thank Henan Provincial People’s Hospital for supporting this study and all the participants involved in this study.
Funding Statement
This study was funded by Medical Science and Technology Project of Henan Province (No. LHGJ20190615); Breakthroughs in science and technology in Henan Province (No. 232102311110); Provincial major science and technology project (No. 201300310700).
Ethics statement
This study was approved by the Medical Ethics Committee of Henan Provincial People’s Hospital (NO.2020.206).
Authors contributions
Qin Xu, Shasha Wang, Qiuyue Xiong and Ning Wang were responsible for data collection; Qin Xu, Shasha Wang, Yifeng Hu and Lei Yan were responsible for statistical analysis of data; Qin Xu was responsible for paper drafting; Fengmin Shao participated in the study design and modification of the paper content, the final review of the paper content and scientific research fund support; Huixia Cao assisted in clinical data collection and scientific research fund support. All authors agree to take responsibility for all aspects of their work. All authors listed meet the criteria for authorship in the ICMJE guidelines.
Disclosure statement
There are no conflicts of interest among the authors and all participating authors have no relevant affiliation or financial involvement with any organization or entity.
Data availability of statement
The data that support the findings of this study are available from the corresponding author, [Fengmin Shao], upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, [Fengmin Shao], upon reasonable request.