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INTRODUCTION
Chronic Kidney Disease-Mineral and Bone Disease (CKD-MBD) is one of the most common complications of patients undergoing hemodialysis, and is defined, among other criteria, as the involvement of specific changes in biochemical markers such as calcium, phosphorus, parathyroid hormone (PTH) and vitamin D1,2,3. Changes in these mineral and bone parameters, associated with nutritional changes in patients on hemodialysis, are often associated with relevant adverse outcomes, mainly due to vascular calcification4,5. The development of models to predict these combinations and optimize the management of these patients may play a crucial role in this context1.
Body composition is commonly modified in individuals with Chronic Kidney Disease (CKD), characterized by the coexistence of obesity and muscle loss, which may even occur concomitantly6. The laboratory components of CKD-MBD have a significant influence on the nutritional status of these individuals4,5,6,7. In the context of hemodialysis, body composition may also reveal different aspects of the patient, such as food intake, hormonal levels, physiological aspects of the CKD stage, as well as the impact of the renal replacement therapy (RRT) modality8. Therefore, understanding the relationship between body components and the specific characteristics of these patients represents a significant challenge.
Changes in the levels of phosphorus, PTH, calcium and vitamin D play very relevant roles in the scenario and development of CKD-MBD. All of them, with their own physiological functions, will contribute differently to the metabolism of these individuals9. Some of the implications include the induction of the conversion of white adipose tissue into brown adipose tissue by PTH, the association of vitamin D with bone homeostasis, calcium deposition in soft tissues, and the increase in cardiovascular mortality associated with uncontrolled phosphorus10,11,12,13.
According to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, the management of CKD-MBD, among other therapeutic strategies, should include sequential assessments of the serum levels of these elements. This is justified because mineral and bone metabolism disorders in this population have unique characteristics and may be potentially modifiable2,5,14. Nutritional assessment is also recommended by KDIGO, but there is still limited evidence on the recommended tool, assertively, with low cost and good clinical replicability2,15. Thus, we expanded the understanding of these patients by considering their correlations with clinical, anthropometric and laboratory aspects within the CKD-MBD scenario.
In face of these considerations, the objective of this study is to analyze the correlations of the laboratory components of CKD-MBD with clinical, anthropometric and laboratory factors of patients on hemodialysis.
METHODS
Place and period of study
The study was conducted from February to September 2019 in 11 hemodialysis centers in the Geater Vitoria region, Espírito Santo, Brazil.
Study population and eligibility criteria
Initially, 1,416 patients were included. The exclusion criteria were: who were on respiratory or on contact precautions, hospitalized, transferred to another hemodialysis unit and individuals with limitations in answering the questionnaire. The total was 1,047 eligible people, with a refusal rate of 2.2% (n = 23) and 234 with missing data in the medical record; the final population studied was 790 patients. The population participating in this research was composed of individuals over 18 years of age, who had a confirmed diagnosis of CKD according to the International Classification of Diseases, version 10 (ICD-10), and who had been undergoing HD in the public network, philanthropic networks, hospitals and private clinics in the region mentioned, for more than 3 months, and signed the Informed Consent Form.
Data collection
The anthropometry was carried out after the hemodialysis sessions, by trained professionals, using standardized equipment; the measurements were carried out three times and their arithmetic mean was obtained. The individuals were barefoot, in an upright position with feet together, with as little clothing as possible, arms extended along the body and staring, to measure body mass, height and other measurements15.
The anthropometric data evaluated were: body mass index (BMI) obtained by kg/m2 and classified by the WHO16; adductor pollicis muscle thickness (APMT) was measured with the aid of an adipometer (Lange®) exerting continuous pressure, compressing the adductor muscle at the apex of the angle between thumb and index finger on the dominant hand17 and subsequently classified18; corrected arm muscle area (CAMA) was obtained from the values of arm circumference and triceps skinfold15 being classified after19; tricipital skinfold (TSF) was measured on the back of the arm using an adipometer15; hand grip strength (HGS) was performed on the participant’s dominant upper limb or with the hand without an arteriovenous fistula, using the Jamar® 12-0600 dynamometer20 and classified using the cutoff point of 27 kg for men and 16 kg for women21.
Age was viewed directly in the medical record and stratified into up to 59 years and 60 years old or more. The duration of hemodialysis was asked directly to the patients and categorized in years and classified as follows: “0 to 2 years”, “3 to 5 years”, “6 to 10 years” and “more than 10 years”. The sex category was self-declared. Laboratory samples were collected as routine by the clinics, and the data collected from the individuals’ clinical records during the period studied were subsequently classified according to the literature22,23.
Statistical analysis
In the statistical analysis, for the descriptive evaluation, the variables were presented as middle of median, average, standard deviation, minimum and maximum. The normality of the variables was assessed using the Shapiro Wilk test and the Mann-Whitney test was performed to compare the medians, and the Student’s T test was used to compare the means. In order to assess the existence of a linear correlation between the independent and dependent variables, the Pearson correlation test was performed for the normal distribution, and the Spearman correlation test for the non-parametric distribution of the variables. Thus, r < 0.4 (weak correlation); r ≥ 0.4 and < 0.6 (moderate correlation); r ≥ 0.6 (strong correlation). All analyses were carried out using R software (4.2.2) for Windows. The significance level adopted was 5%.
Ethical and legal aspects of research
The research work was approved by the Research Ethics Committee (Comitê de Ética em Pesquisa - CEP) of the Federal University of Espírito Santo (Universidade Federal do Espírito Santo - UFES) with registration number 2.104.942 and CAAE 68528817.4.0000.5060 and all participants signed the informed consent form.
RESULTS
Tables 1 and 2 present the other descriptive characteristics of the anthropometric, clinical and laboratory variables grouped by sex of the studied population. As evidenced in table 1, the study consisted of 790 patients on hemodialysis (HD) with an average (SD) age of 54.23 years old (SD+14.68), 42% are female, also noteworthy is the RRT time of 5.68 years (SD+5.75) in men and 5.51 years old (SD +4.67) in women, respectively.
TABLE 1 Descriptive table of clinical and anthropometric variables grouped by sex
| Sex | Variable | Age (years) | Time on HD (years | Weight (Kg) | BMI (Kg/m2) | TSF (mm) | APMT (mm) | R-HGS (kgf) | L-HGS (kgf) | AC (cm) | CAMA (cm2) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N | 790 | 748 | 788 | 786 | 788 | 789 | 768 | 760 | 788 | 786 | |
| Female (n= 338) | Average ± SD | 53.2±15.1 | 5.6±5.7 | 64.1±14.6 | 26.1±5.5 | 22.6±9.3 | 11.6±3.9 | 14.0±6.1 | 11.9±5.6 | 29.2±5.0 | 33.4±11.8 |
| Median | 55 | 4 | 62.5 | 25.22 | 22.23 | 11.3 | 14 | 11.96 | 28.63 | 31.54 | |
| IQR | 41 - 64 | 2 - 7.7 | 53.2 - 68.7 | 22.1 - 29.9 | 18 - 29 | 9 - 14.6 | 10 - 18 | 7 - 15 | 22 - 35 | 28.5 - 33 | |
| Minimum | 20 | 0 | 37 | 14.65 | 4.3 | 2.17 | 0 | 0 | 16.66 | 8.12 | |
| Maximum | 85 | 40 | 122.9 | 44.5 | 55.3 | 24.3 | 39.6 | 28.6 | 46.3 | 79.0 | |
| Male (n=452) | Average ± SD | 54.9±14.3 | 5.5±4.6 | 71.6±14.3 | 25.4±5.9 | 15.7±8.7 | 12.8±4.4 | 24.5±10.6 | 19.9±9.7 | 28.7±4.4 | 38.9±11.5 |
| Median | 57 | 4 | 69.9 | 24.7 | 14 | 12.3 | 24 | 19.8 | 28.2 | 37.8 | |
| IQR | 45 - 65 | 2 - 8 | 61 - 85.7 | 21.8 - 27.7 | 8.7 - 20 | 10 - 15.2 | 15 - 28.7 | 13 - 25 | 23 - 35 | 34.5 - 49 | |
| Minimum | 20 | 0 | 39 | 13.6 | 1.3 | 3.3 | 0 | 0 | 19 | 2.1 | |
| Maximum | 89 | 24 | 127.3 | 94.3 | 77 | 29.3 | 104.7 | 95.3 | 80.3 | 74.4 |
N = total number; IQR = interquartile range; HD = hemodialysis; BMI = body mass index; TSF= tricipital skinfold; APMT = adductor pollicis muscle thickness; R-HGS = rigth handgrip strength; L-HGS = left handgrip strength; AC= arm circunference; CAMA = corrected arm muscle area; SD = standard deviation.
Table 2 Descriptive table of laboratory variables grouped by sex
| Sex | Variable | Hb (g/dl) | P (mEq/l) | K (mEq/l) | Alb (g/dl) | Ferritin (UI/ml) | TSI (%) | Leu (/ml) | AF (UI/ml) | Ca ion mEq/l | Vith (ng/ml) | PTH (UI/ml) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | 88 | 79 | 80 | 714 | 326 | 300 | 720 | 723 | 670 | 532 | 727 | |
| Female (n=338) | Average ± SD | 9.3±2.7 | 5.1±1.1 | 5.4±0.8 | 3.7±0.4 | 233.6±197.4 | 24.1±12.2 | 6.7±2.5 | 251.8±328.6 | 4.2±0.5 | 32.8±13.1 | 762.3±566.0 |
| Median | 9 | 5 | 5 | 3.8 | 168.5 | 22 | 6.5 | 136 | 4.26 | 32 | 535 | |
| IQR | 8 – 10 | 4.7 – 6 | 5 – 6 | 3.7 – 4 | 81 – 351 | 16.5 – 27 | 5.0 – 8.0 | 93.5 – 230 | 4 – 4.75 | 23 – 42 | 362 –1027 | |
| Minimum | 4 | 2 | 3.9 | 2.6 | 10 | 6 | 2.35 | 48 | 2.3 | 7 | 204 | |
| Maximum | 20 | 8 | 7 | 7.6 | 1024 | 85 | 28.2 | 2452 | 6.14 | 70 | 3000 | |
| Female (n=338) | Average ± SD | 10.1±1.7 | 5.7±2.6 | 5.6±0.8 | 3.8±0.6 | 272.3±369.5 | 25.0±12.5 | 6.1±2.0 | 213.6±271.5 | 4.2±0.5 | 34.4±13.0 | 755.2±532.7 |
| Median | 10 | 5 | 6 | 3.9 | 162 | 22 | 5.8 | 129 | 4.3 | 34 | 560.5 | |
| IQR | 9 – 11 | 4 – 7 | 5 – 6 | 3.8 – 4 | 77.5 – 321 | 16.5 – 29 | 4 – 7 | 83 – 203 | 4 – 4.5 | 27.7 – 43 | 354.5 – 974 | |
| Minimum | 6 | 3 | 3 | 0.9 | 9 | 4 | 2 | 3.4 | 2.39 | 6.4 | 200 | |
| Maximum | 14 | 18 | 7 | 13 | 2848 | 85 | 14 | 2850 | 6.19 | 78 | 2598 |
N = individuals number; IQR= interquartile range; Hb= hemoglobin; P = phosphorus; K = potassium; Alb = albumin; TSI = transferrin saturation index; Leu = leukocytes; AF = alkaline phosphatase; Ca ion = Calcium ionic; Vit D =vitamin D; PTH = parathyroid hormone; SD = standard deviation.
Still in relation to the variables presented in table 1, the average weight among women was 64.1kg (DP+14,6) and in men 71.6kg (DP+14,3), with a BMI of 26.1kg/m2 (SD+5.5) and 25.4kg/m2 (SD+5.9) respectively. The TSF it was 22.6mm (SD+9.3) in women and 15.7mm (SD+8.7) in men. APMT was 11.6mm (SD+3.9) in females and 12.8mm (SD+4.4) in males. A right HGS it was 14kg (SD+6.1) and 11.9kg (SD+5.6) the left in women and in men right HGS 24.5kg (SD+10.6) and left HGS 19.9kg (SD+9.7). The AC evidenced was 29.2cm (SD+5) in women and 28.7cm (SD+4.4) in men. Concluding table 1, CAMA of 33.4cm2 (SD+11.8) in females and 38.9cm2 (SD+11.5) in males.
Regarding the variables related to CKD-MBD, women had phosphorus 5.19mg/dL (SD+1.19) and men had 5.79mg/dL (SD+2.61), as well as ionic calcium 4.25mg/dL (SD+0.51) and 4.26mg/dL (SD+0.51), vitamin D 32.8mg/dL (SD+13.12) and 34.48mg/dL (SD +13.08) and PTH 762.5mg/dL (SD+566.02) and 755.24mg/ dL (SD+532.73) in that order, as these variables are the determining factors in the definition of CKD-MBD. The laboratory variables were also highlighted: hemoglobin, potassium, albumin, ferritin, transferrin saturation index, leukocytes and alkaline phosphatase, as shown in table 2.
Table 3 shows the correlations between the laboratory variables of CKD-MBD and the clinical, anthropometric and laboratory variables. In this way, the variables with the highest correlations found in the present study stand out. The longer the RRT time, the higher PTH levels (r = 0.582, p = <0.001),the other variables analyzed showed weaker correlations as shown in table 3.
Table 3 Table of correlations between CKD-MBD laboratory variables with clinical, anthropometric and laboratory variables
| Calcium ionic | Phosphorus | Vitamin D | PTH | |
|---|---|---|---|---|
| Age | r = 0,10 (IC 95% 0,02 a 0,10) (p = 0,009) * | r = -0,41 (IC 95% -0,02 a 0,43) (p = 0,036)* | r = -0,14 (IC 95% -0,03 a -0,19) (p = 0,001) * | r = -0,21 (IC 95% -0,14 a 0,28) (p < 0,001) * |
| RRT | r = 0,96 (IC 95% 0,02 a 0,16) (p = 0,015) * | r = -0,10 (IC 95% -0,03 a 0,40) (p = 0,394) | r = 0,13 (IC 95% 0,06 a 0,12) (p = 0,004)* | r = 0,58 (IC 95% 0,50 a 0,64) (p = <0,001) * |
| Weight | r = -0,06 (IC 95%-0,01 a 0,02) (p = 0,097) | r = 0,13 (IC 95%-0,20 a 0,24) (p = 0,248) | r = -0,15 (IC 95% -0,04 a -0,16) (p = <0,001)* | r = -0,00 (IC 95% -0,16 a 0,01) (p = 0,954) |
| BMI | r = -0,82 (IC 95% -0,81 a -0,99) (p = 0,046) * | r = 0,13 (IC 95% -0,26 a 0,18) (p = 0,703) | r = -0,20 (IC 95% -0,03 a -0,31) (p = <0,001)* | r = -0,00 (IC 95% -0,00 a 0,01) (p = 0,921) |
| TSF | r = -0,07 (IC 95% -0,13 a 0,02) (p = 0,073) | r = 0,13 (IC 95% -0,29 a 0,15) (p = 0,243) | r = -0,60 (IC 95% -0,43 a -0,80) (p = 0,020)* | r = -0,05 (IC 95% -0,02 a 0,12) (p = 0,094) |
| APMT | r = -0,03 (IC 95%-0,13 a 0,03) (p = 0,391) | r = 0,17 (IC 95% -0,08 a 0,35) (p = 0,140) | r = 0,60 (IC 95% 0,53 a 0,84) (p = 0,018)* | r = 0,04 (IC 95% -0,09 a 0,06) (p = 0,265) |
| R-HGS | r = -0,00 (IC 95%-0,06 a 0,09) (p = 0,97) | r = 0,17 (IC 95% -0,14 a 0,30) (p = 0,135) | r = 0,402 (IC 95% 0,22 a 0,58) (p = <0,001)* | r = -0,017 (IC 95% -0,02 a 0,13) (p = 0,643) |
| L-HGS | r = 0,01 (IC 95% -0,05 A 0,10) (p = 0,740) | r = 0,04 (IC 95% -0,12 A 0,32) (p = 0,710) | r = 0,12 (IC 95% 0,03 a 0,20) (p = 0,005) * | r = -0,03 (IC 95% -0,02 A 0,13) (p = 0,391) |
| AC | r = 0,01 (IC 95% -0,09 a 0,06) (p = 0,954) | r = 0,066 (IC 95% -0,52 A 0,54) (p = 0,959) | r = -0,252 (IC 95% -0,02 a -0,41) (p = 0,021)* | r = 0,024 (IC 95% -0,17 A 0,21) (p = 0,800) |
| CAMA | r = 0,00 (IC 95% -0,04 a 0,11) (p = 0,953) | r = 0,18 (IC 95% -0,15 a 0,29) (p = 0,119) | r = - 0,77 (IC 95% -0,34 a -0,84) (p = 0,024)* | r = -0,00 (IC 95% -0,00 a 0,10) (p = 0,957) |
| Hemoglobin | r = 0,32 (IC 95% 0,11 a 0,32) (p = 0,004) * | r = -0,08 (IC 95% -0,68 a 0,65) (p = 0,834) | r = 0,28 (IC 95 %0,17 a 0,34) (p = 0,031)* | r = 0,01 (IC 95% -0,29 a 0,13) (p = 0,954) |
| Potassium | r = -0,10 (IC 95% -0,43 a 0,01) (p = 0,389) | r = 0,56 (IC 95% 0,06 a 0,80) (p = 0,020)* | r = 0,26 (IC 95%0,01 a 0,49) (p = 0,054)* | r = -0,079 (IC 95% -0,026 A 0,184) (p = 0,493) |
| Albumin | r = 0,48 (IC 95% 0,39 a 0,51) (p < 0,001) * | r = -0,06 (IC 95% -0,16 a 0,28) (p = 0,587) | r = 0,20 (IC 95% 0,11 a 0,28) (p = <0,001)* | r = -0,01 (IC 95%-0,01 a 0,06) (p = 0,790) |
| Ferritin | r = 0,03 (IC 95% -0,18 a 0,10) (p = 0,635) | r = -0,22 (IC 95% -0,46 a 0,15) (p = 0,172) | r = -0,05 (IC 95% -0,18 a 0,08) (p = 0,437) | r = -0,07 (IC 95% -0,00 a 0,08) (p = 0,197) |
| TSI | r = 0,01 (IC 95% -0,15 a 0,10) (p = 0,872) | r = -0,11 (IC 95% -0,54 a 0,14) (p = 0,567) | r = -0,05 (IC 95% -0,09 a 0,19) (p = 0,910) | r = 0,034 (IC 95% -0,01 a 0,09) (p = 0,571) |
| Leukocytes | r = 0,06 (IC 95%-0,04 a 0,11) (p = 0,132) | r = 0,11 (IC 95% -0,12 a 0,34) (p = 0,331) | r = -0,03 (IC 95%-0,06 a 0,12) (p = 0,474) | r = 0,06 (IC 95% 0,01 a 0,14) (p = 0,013) * |
| AF | r = 0,07 (IC 95%-0,02 a 0,13) (p = 0,061) | r = -0,20 (IC 95% -0,32 a 0,13) (p = 0,091) | r = -0,08 (IC 95% -0,01 a -0,18) (p = 0,072) | r = 0,54 (IC 95% 0,49 a 0,59) (p <0,001) * |
| Calciumionic | - | r = -0,186 (IC 95% -0,360 a 0,097) (p = 0,019) * | r = 0,194 (IC 95% 0,110 a 0,278) (p<0,001)* | r = 0,057 (IC 95% 0,002 a 0,153) (p = 0,138) |
| Phosphorus | r = -0,186 (IC 95% -0,360 a 0,097) (p = 0,119) | - | r = -0,178 (IC 95% -0,437 a 0,108) (p = 0,220) | r = 0,169 (IC 95% -0,261 a 0,185) (p = 0,141) |
| Vitamin D | r = 0,19 (IC 95%0,11 a 0,27) (p = <0,001) * | r = -0,14 (IC 95% -0,44 a 0,11) (p = 0,330) | - | r = -0,04 (IC 95% -0,01 a 0,03) (p = 0,406) |
| PTH | r = 0,06 (IC 95% 0,00 a 0,15) (p = 0,138) | r = 0,17 (IC 95% -0,26 a 0,19) (p = 0,141) | r = -0,04 (IC 95% -0,03 a -0,14) (p= 0,228) | - |
RRT = renal replacement therapy; BMI = Body mass index; TSF = tricipital skinfold; APMT = adductor pollicis muscle thickness; R-HGS = right handgrip strength; L-HGS = left handgrip strength; AC= arm circunference; CAMA = corrected arm muscle area; TSI = transferrin saturation index; AF= alkaline phosphatase; PTH = parathyroid hormone; SD standard deviation. *correlation with statistical significance.
Regarding phosphorus levels, the younger the patient’s age, the higher laboratory phosphorus levels (r = -0.413, p = 0.036). Potassium values (r = 0.556, p = 0.020) showed a moderate positive correlation in relation to phosphorus. The other variables showed weak correlations.
Regarding vitamin D concentrations, a positive correlation was observed in the variables: APMT (r = 0.602, p = 0.018) and R-HGS (r = 0.402, p = <0.001). Negative correlation with TSF (r = -0.600, p = 0.020) and CAMA (r = - 0.769, p = 0.024), therefore, the higher the values of these anthropometric markers, the lower the vitamin D levels. Likewise, the other variables showed weak correlations.
In general, patients who had higher ionic calcium levels had longer RRT time, due to the strong positive correlation (r= 0.961, p = 0.015). Conversely, the lower the ionic calcium levels, the higher the BMI (r = -0.82, p = 0.046), with a strong negative correlation.Age, weight, TSF, APMT, right HGS, left HGS, AC, CAMA, hemoglobin, potassium, albumin, ferritin, transferrin saturation index, leukocytes, alkaline phosphatase, phosphorus, vitamin D and PTH, showed weaker correlations in relation to ionic calcium, according to table 3.
DISCUSSION
The main findings of this study showed significant correlations between the laboratory variables involved in the diagnosis of CKD-MBD and the clinical, anthropometric and laboratory characteristics of individuals undergoing hemodialysis. In summary, the study revealed that laboratory PTH and serum calcium levels positively correlate with the duration of renal replacement therapy. A negative correlation was observed between calcium levels and BMI values of individuals on hemodialysis. Furthermore, laboratory values for phosphorus showed a positive correlation with potassium and a negative correlation with the age of individuals. In conclusion, vitamin D concentration levels indicated a strong positive correlation with APMT and a moderate correlation with R-HGS, and a strong negative correlation with TSF and CAMA.
The results of this study with a population on hemodialysis within the national territory indicate that serum PTH levels increase progressively over the time the individual is undergoing this modality of renal replacement therapy, as evidenced by a moderately significant positive correlation. This association is consistent with the evidence found in the literature in general1,24,25. The increase in PTH may result in consequences such as increased bone remodeling in an erroneous and disordered way, as well as cardiac changes, which in general are evidenced mainly in people with longer periods of time on hemodialysis25.
In a cohort involving 107,299 people, it was observed that younger individuals undergoing hemodialysis had higher phosphorus levels1. These findings were corroborated by the present study, which identified this negative correlation between serum phosphorus levels and the age of people, which may be attributed to a lower phosphate intake among older patients, as well as the tendency to decrease of bone remodeling with advancing age26. Still in relation to serum phosphorus levels, this showed a moderate positive correlation with potassium levels. This phenomenon may be explained by the excessive consumption of foods rich in phosphorus, calcium, sodium and potassium in the diet, portraying contemporary eating patterns that may be understood as barriers to complying with the dietary recommendations established in the literature in this population27,28,29.
The vitamin D which at adequate levels in the body is referenced in the literature as triggering favorable effects on muscles, showed in the present study a positive correlation with APMT and R-HGS30,31. A particularly important tool as a determinant of muscle strength is HGS, mainly because it is not directly influenced by hydration status, but there is scarce data in the CKD population, especially in individuals undergoing chronic hemodialysis31,32. Regarding R-HGS, previous studies demonstrated an association between serum vitamin D level and HGS in patients undergoing hemodialysis, independently of nutritional status30. Another tool also suggested in the literature as a marker for nutritional status is APMT, which may predict handgrip strength in individuals undergoing hemodialysis32,33. In this same scenario, a strongly positive correlation was also identified between vitamin D and APMT.
Still regarding vitamin D and this study findings, a strong negative correlation was evidenced with TSF and CAMA. Obesity has been reported as a common nutritional disorder in the hemodialysis population34. The inverse association between serum vitamin D levels and obesity has been widely documented in several studies, using various anthropometric tools. One of the hypotheses for this association is the sequestration of vitamin D by adipose tissue35,36. There are few studies analyzing vitamin D levels and obesity in a CKD population undergoing hemodialysis, but this is consistent with our findings in patients with higher TSF and CAMA37.
Patients on hemodialysis frequently develop arterial calcifications, which are generally associated with excess of body calcium content. The use of dialysate solutions with calcium concentrations above 2.5mEq/l may be a contributing factor to a positive calcium balance in these people, which results in this condition38. This study identified a strong positive association between serum ionic calcium levels and the duration of renal replacement therapy. The literature also reinforces that maintaining a balance in serum calcium levels is very complex in the dialysis population and studies reinforce the multiple interaction between calcium consumption, vitamin D supplementation and calcium content in the dialysate38,39.
Regarding the strongly negative correlation found in the study between higher BMI and lower ionic calcium levels, we may only speculate that obese people on hemodialysis have been associated with higher concentrations of PTH, perhaps due to their own nutritional status37.
Some of the variables analyzed showed weak correlations with the laboratory components of CKD- MBD, as shown in table 3. This weak relationship suggests a low or even non-existent linear association between the variables, which can be interpreted in different ways in the literature. One possible explanation would be the interference of factors not considered in the results, indicating the need for further in-depth analysis to better understand these interactions. In addition, other methodological approaches may be required to explore possible non-linear or weak relationships.
Understanding the correlations between laboratory elements of CKD-MBD and anthropometric, clinical and laboratory conditions in this study emphasizes the unprecedented nature of the research, reinforced by the scenario of the possibility of early and assertive intervention in this population. As it is a cross-sectional observational study, there are limitations; therefore, causality may not be inferred directly. The study was carried out in a geographic area that covers the metropolitan region of a large capital in the Southeast region and may be expanded to other geographic areas. Besides, the population is similar to other populations, therefore, the findings may be extrapolated.
Our analyses strengthen the recommendations of the NFK-KDOQI guidelines, regarding the normalization of these parameters in the management of CKD-MBD, mainly due to the impact of this approach on the survival of patients on hemodialysis.
CONCLUSION
We concluded that the correlations between the laboratory components of CKD-MBD and the clinical, anthropometric and laboratory characteristics studied were, in many aspects, strongly representative correlations, showing that in clinical practice many aspects found may contribute to a better understanding of the individual profile of this specific population.
