{"id":61388,"date":"2024-09-30T10:34:50","date_gmt":"2024-09-30T10:34:50","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=61388"},"modified":"2024-10-11T16:17:00","modified_gmt":"2024-10-11T16:17:00","slug":"influence-of-kidney-diseases-on-lipid-profile-in-patients-undergoing-conservative-managements-and-hemodialysis","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no3\/influence-of-kidney-diseases-on-lipid-profile-in-patients-undergoing-conservative-managements-and-hemodialysis\/","title":{"rendered":"Influence of Kidney Diseases on Lipid Profile in Patients Undergoing Conservative Managements and Hemodialysis"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

The kidneys play a crucial role in waste removal, red blood cell production, electrolyte balance, and blood pressure regulation1<\/sup>. Renal failure occurs when the kidneys fail to function properly due to various conditions such as cancer, autoimmune diseases, infections, diabetes, and toxic substances2<\/sup>. Chronic kidney disease (CKD) is a significant global health concern, impacting up to 10% of the adult population worldwide3<\/sup>. It is characterized by a persistent anomaly in kidney structure or function, often marked by a decreased glomerular filtration rate (GFR) and albuminuria1<\/sup>.<\/p>\n\n\n\n

Between 2008 and 2014, the prevalence of end-stage kidney disease (ESKD) increased significantly4<\/sup>. In Jordan, hemodialysis is the primary treatment for nearly 4,000 patients with ESKD, with significant economic costs5<\/sup>. CKD complications, including cardiovascular diseases, bone problems, anemia, dyslipidemia, and reduced quality of life, further strain the healthcare system6<\/sup>.<\/p>\n\n\n\n

Jordanians face a high risk of CKD due to factors like diabetes, heart disease, smoking, and obesity, compounded by the country’s aging population7,8<\/sup>. Early identification and management of CKD are hindered by a lack of awareness, proper training among clinicians, and an insufficient database for early detection 9,10<\/sup>.<\/p>\n\n\n\n

Screening for CKD involves lab tests, urine tests, and imaging examinations. Effective management includes controlling blood pressure and sugar levels and avoiding certain medications like NSAIDs 11<\/sup>. Dialysis remains the frontline treatment for advanced CKD, though conservative care is gaining attention for certain patient categories 12,13<\/sup>.<\/p>\n\n\n\n

Dyslipidemia, characterized by high triglycerides, low HDL-C, and elevated LDL-C, is a modifiable cardiovascular risk factor common in CKD patients. Managing dyslipidemia is crucial for reducing cardiovascular events 14,15<\/sup>. Beta-2 microglobulin (\u03b22M) testing is valuable for monitoring CKD progression and guiding treatment decisions 16<\/sup>.<\/p>\n\n\n\n

This study investigates the\nrelationship between CKD and lipid profiles in a Jordanian population,\ncomparing CKD patients undergoing conservative management or hemodialysis with healthy\ncontrols. The findings have significant implications for CKD diagnosis,\ntreatment, and cardiovascular disease prevention.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

Chemicals and Reagents<\/strong><\/p>\n\n\n\n

All reagents were stored\nappropriately, including pre-coated microplates, HRP conjugate antibodies,\nbiotinylated antibodies, and TMB substrate solutions.<\/p>\n\n\n\n

Equipment and Materials<\/strong><\/p>\n\n\n\n

Key equipment included\nBioTek fully automated analyzers and Cobas c311 analyzers. Kits for cystatin C,\nbeta-2 microglobulin (B2M), LDL, HDL, cholesterol, urea, creatinine, and\ntriglycerides were used.<\/p>\n\n\n\n

Sample Collection<\/strong><\/p>\n\n\n\n

The cross-sectional study involved 75\nparticipants from Princess Iman Hospital, divided into three groups: 25 CKD\npatients on hemodialysis, 25 on conservative management, and 25 healthy\ncontrols. Blood samples were drawn following a 12-hour fast and analyzed\nwithout delay.<\/p>\n\n\n\n

Inclusion and Exclusion Criteria<\/strong><\/p>\n\n\n\n

Participants aged 25-92\nyears with specified CKD stages or healthy controls were included. Exclusion\ncriteria encompassed incomplete data, hyperlipidemia, metabolic disorders,\nliver dysfunction, infection, and use of lipid-lowering drugs.<\/p>\n\n\n\n

Blood Sample Collection and Analysis<\/strong><\/p>\n\n\n\n

A 5 ml blood sample was\ncollected and analyzed for creatinine using the Jaffe Kinetic test and urea\nusing the urease method on a Cobas C311 analyzer.<\/p>\n\n\n\n

Biochemical Analysis of Lipid Profile<\/strong><\/p>\n\n\n\n

Lipid profiles, including\nLDL-C, HDL-C, triglycerides, and total cholesterol, were measured using the\nRoche\/Hitachi Cobas c311 system. CHOL\/HDL-C and LDL-C\/HDL-C ratios were\ncomputed.<\/p>\n\n\n\n

Cystatin C and Beta-2 Microglobulin Measurement<\/strong><\/p>\n\n\n\n

Cystatin C was measured\nusing the EasyStep Human Cys-C ELISA Kit, and beta-2 microglobulin using the\nHuman B2M ELISA Kit, both involving enzyme immunoassay techniques and optical\ndensity measurement at 450 nm.<\/p>\n\n\n\n

e-GFR Calculation<\/strong><\/p>\n\n\n\n

e-GFR was calculated using\nthe CKD-EPI equation incorporating creatinine and cystatin C levels.<\/p>\n\n\n\n

Data Analysis<\/strong><\/p>\n\n\n\n

The analysis of the data was performed using SPSS software, version 26.0. Statistical tests included the Kruskal-Wallis test, ANOVA, student t-test, Mann-Whitney U test, and Pearson correlation. Significance was set at P < 0.05.<\/em><\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

Kidney Function Tests Among the Studied Groups<\/strong><\/p>\n\n\n\n

The control group exhibited\nsignificantly lower urea levels (24.52 \u00b1 5.17 mg\/dl) compared to conservative\nmanagement and hemodialysis patients (101.64 \u00b1 43.38 mg\/dl and 107.95 \u00b1 29.54\nmg\/dl, respectively, p<0.001) (table 1). Hemodialysis patients had the\nhighest creatinine levels (8.81 \u00b1 2.07 mg\/dl), followed by conservative\nmanagement patients (3.24 \u00b1 1.75 mg\/dl, p<0.001). Cystatin C levels were markedly\nelevated in patients undergoing hemodialysis (8.22 \u00b1 2.06 mg\/l) and conservative\nmanagement patients (2.65 \u00b1 1.67 mg\/l) compared to the control group (0.51 \u00b1\n0.079 mg\/l, p <0.001). eGFR was significantly lower\nin hemodialysis patients (5.2 \u00b1 1.6 mL\/min\/1.73m\u00b2) and conservative management\npatients (27.96 \u00b1 14.48 mL\/min\/1.73m\u00b2) compared to controls (134.8 \u00b1 9.76\nmL\/min\/1.73m\u00b2, p\n<0.001). B2M levels were markedly elevated in hemodialysis\npatients (6.45 \u00b1 0.694 mg\/L) and conservative management patients (3.87 \u00b1 0.93\nmg\/L) compared to those in the control group (0.68 \u00b1 0.119 mg\/L) (p<0.001) (figure 1).<\/p>\n\n\n\n

Table 1: Kidney function test among the studied groups<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Variable <\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

Hemodialysis patients<\/strong><\/p>\n

(n=25)<\/strong><\/p>\n<\/td>\n

\n

Conservative management patients<\/strong><\/p>\n

(n=25)<\/strong><\/p>\n<\/td>\n

\n

Control<\/strong><\/p>\n

(n=25)<\/strong><\/p>\n<\/td>\n

\n

Normal range<\/strong><\/p>\n<\/td>\n

\n

p-value<\/em><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Urea (mg\/dl)<\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/p>\n<\/td>\n

\n

107.95 \u00b1 29.54<\/p>\n<\/td>\n

\n

101.64 \u00b1 43.38<\/p>\n<\/td>\n

\n

24.52 \u00b1 5.17<\/p>\n<\/td>\n

\n

10.2-49.8<\/p>\n<\/td>\n

\n

<0.001***<\/em><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/p>\n<\/td>\n

\n

64.2-201<\/p>\n<\/td>\n

\n

61-245<\/p>\n<\/td>\n

\n

14.4-36<\/p>\n<\/td>\n<\/tr>\n

\n

Creatinine (mg\/dl)<\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/p>\n<\/td>\n

\n

8.81 \u00b1 2.07<\/p>\n<\/td>\n

\n

3.24 \u00b1 1.75<\/p>\n<\/td>\n

\n

0.76 \u00b1 0.115<\/p>\n<\/td>\n

\n

Male: (0.6-1.1)<\/p>\n

Female: (0.5-0.9)<\/p>\n<\/td>\n

\n

<0.001***<\/em><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/p>\n<\/td>\n

\n

5.9-14.66<\/p>\n<\/td>\n

\n

1.6-8<\/p>\n<\/td>\n

\n

0.6-0.95<\/p>\n<\/td>\n<\/tr>\n

\n

Cystatin C (mg\/l)<\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/p>\n<\/td>\n

\n

8.22 \u00b1 2.06<\/p>\n<\/td>\n

\n

2.65 \u00b1 1.67<\/p>\n<\/td>\n

\n

0.51 \u00b1 0.079<\/p>\n<\/td>\n

\n

0.62-1.15<\/p>\n<\/td>\n

\n

<0.001***<\/em><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/p>\n<\/td>\n

\n

5.1-14<\/p>\n<\/td>\n

\n

1.1-7.5<\/p>\n<\/td>\n

\n

0.4-0.63<\/p>\n<\/td>\n<\/tr>\n

\n

eGFR (mL\/min\/1.73m2<\/sup>)<\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/p>\n<\/td>\n

\n

5.2 \u00b1 1.6<\/p>\n<\/td>\n

\n

27.96 \u00b1 14.48<\/p>\n<\/td>\n

\n

134.8 \u00b1 9.76<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

<0.001***<\/em><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/p>\n<\/td>\n

\n

3-9<\/p>\n<\/td>\n

\n

6-52<\/p>\n<\/td>\n

\n

114-153<\/p>\n<\/td>\n<\/tr>\n

\n

B2M (mg\/L)<\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/p>\n<\/td>\n

\n

6.45 \u00b1 0.694<\/p>\n<\/td>\n

\n

3.87 \u00b1 0.93<\/p>\n<\/td>\n

\n

0.68 \u00b1 0.119<\/p>\n<\/td>\n

\n

0.7-2.0<\/p>\n<\/td>\n

\n

<0.001***<\/em><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/p>\n<\/td>\n

\n

5.5-7.5<\/p>\n<\/td>\n

\n

2.8-6.9<\/p>\n<\/td>\n

\n

0.5-7.5<\/p>\n<\/td>\n<\/tr>\n

\n

Data expressed as Mean \u00b1 SD, p-value: significance between groups, ***p <0.001,<\/em> SD: standard deviation, Min: minimum, Max: maximum.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 1:<\/strong> Kidney function tests among the studied groups: <\/strong>Hemodialysis patients show the highest levels of urea<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Lipid Profile Test Among the Studied Groups<\/strong><\/p>\n\n\n\n

The lipid profile analysis among the studied groups revealed that hemodialysis patients had significantly higher triglyceride (228.16 \u00b1 84.81 mg\/dl) and LDL levels (180.84 \u00b1 29.72 mg\/dl) compared to conservatively managed patients (224.94 \u00b1 130.38 mg\/dl TG and 125.69 \u00b1 27.41 mg\/dl LDL) and control participants (136.86 \u00b1 70.63 mg\/dl TG and 30.33 \u00b1 6.06 mg\/dl LDL) (p<0.001).<\/em> Hemodialysis patients also had the highest cholesterol levels (188.53 \u00b1 28.6 mg\/dl), despite the lack of statistical significance (p=0.054).<\/em> HDL levels were highest in controls (49.97 \u00b1 10.5 mg\/dl), followed by conservatively managed patients (39.72 \u00b1 7.26 mg\/dl), and lowest in hemodialysis patients (30.95 \u00b1 5.71 mg\/dl) (p<0.001).<\/em> In hemodialysis patients, both the cholesterol\/HDL ratio (6.04 \u00b1 1.27) and the LDL\/HDL ratio (6.165 \u00b1 2.1) were significantly elevated compared to the other groups (p<0.001).<\/em> These findings underscore the significant dyslipidemia in CKD patients, especially those undergoing hemodialysis (figure 2).<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 2:<\/strong> Lipid profile tests among the studied groups: The parameters measured include triglycerides (TG)<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between demographic characteristics and kidney function test among hemodialysis patients<\/strong><\/p>\n\n\n\n

Weight correlated significantly with B2M among hemodialysis patients (r=-0.477, p=0.016)<\/em> (Table 2).<\/p>\n\n\n\n

Table 2: Correlation between demographic characteristics and kidney function test among hemodialysis patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

Urea (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Creatinine (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cystatin C (mg\/l)<\/strong><\/p>\n<\/td>\n

\n

Beta-2 macroglobulin (mg\/L)<\/strong><\/p>\n<\/td>\n

\n

eGFR (mL\/min\/1.73m2<\/sup>)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Sex<\/p>\n

 <\/p>\n<\/td>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.084<\/p>\n<\/td>\n

\n

0.187<\/p>\n<\/td>\n

\n

0.180<\/p>\n<\/td>\n

\n

0.135<\/p>\n<\/td>\n

\n

0.064<\/p>\n<\/td>\n<\/tr>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.691<\/p>\n<\/td>\n

\n

0.370<\/p>\n<\/td>\n

\n

0.390<\/p>\n<\/td>\n

\n

0.519<\/p>\n<\/td>\n

\n

0.763<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.238<\/p>\n<\/td>\n

\n

0.152<\/p>\n<\/td>\n

\n

0.162<\/p>\n<\/td>\n

\n

0.236<\/p>\n<\/td>\n

\n

0.151<\/p>\n<\/td>\n<\/tr>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.252<\/p>\n<\/td>\n

\n

0.470<\/p>\n<\/td>\n

\n

0.439<\/p>\n<\/td>\n

\n

0.256<\/p>\n<\/td>\n

\n

0.470<\/p>\n<\/td>\n<\/tr>\n

\n

Weight<\/p>\n<\/td>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.240<\/p>\n<\/td>\n

\n

0.233<\/p>\n<\/td>\n

\n

0.209<\/p>\n<\/td>\n

\n

0.016<\/p>\n<\/td>\n

\n

0.020<\/p>\n<\/td>\n<\/tr>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.247<\/p>\n<\/td>\n

\n

0.263<\/p>\n<\/td>\n

\n

0.315<\/p>\n<\/td>\n

\n

-0.477*<\/sup><\/p>\n<\/td>\n

\n

0.925<\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between demographic data and lipid profile tests among hemodialysis patients<\/strong><\/p>\n\n\n\n

A\nsignificant negative correlation was observed between age and the LDL\/HDL ratio\namong hemodialysis patients (r=-0.41, p=0.042) (Table 3).<\/p>\n\n\n\n

Table 3: Correlation between demographic data and lipid profile tests among hemodialysis patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

TG<\/strong><\/p>\n<\/td>\n

\n

Cholesterol (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

LDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

HDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cholesterol \/HDL ratio<\/strong><\/p>\n<\/td>\n

\n

LDL\/HDL ratio<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Sex<\/p>\n

 <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.127<\/p>\n<\/td>\n

\n

0.110<\/p>\n<\/td>\n

\n

0.169<\/p>\n<\/td>\n

\n

-0.002<\/p>\n<\/td>\n

\n

0.054<\/p>\n<\/td>\n

\n

0.139<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.545<\/p>\n<\/td>\n

\n

0.600<\/p>\n<\/td>\n

\n

0.419<\/p>\n<\/td>\n

\n

0.993<\/p>\n<\/td>\n

\n

0.797<\/p>\n<\/td>\n

\n

0.506<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.120<\/p>\n<\/td>\n

\n

-0.171<\/p>\n<\/td>\n

\n

-0.153<\/p>\n<\/td>\n

\n

0.313<\/p>\n<\/td>\n

\n

-0.132<\/p>\n<\/td>\n

\n

-0.410*<\/sup><\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.567<\/p>\n<\/td>\n

\n

0.415<\/p>\n<\/td>\n

\n

0.464<\/p>\n<\/td>\n

\n

0.128<\/p>\n<\/td>\n

\n

0.530<\/p>\n<\/td>\n

\n

0.042<\/p>\n<\/td>\n<\/tr>\n

\n

Weight<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.088<\/p>\n<\/td>\n

\n

-0.147<\/p>\n<\/td>\n

\n

0.072<\/p>\n<\/td>\n

\n

0.192<\/p>\n<\/td>\n

\n

-0.114<\/p>\n<\/td>\n

\n

-0.170<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.677<\/p>\n<\/td>\n

\n

0.483<\/p>\n<\/td>\n

\n

0.732<\/p>\n<\/td>\n

\n

0.358<\/p>\n<\/td>\n

\n

0.586<\/p>\n<\/td>\n

\n

0.416<\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between demographic data and lipid profile tests among conservatively managed patients<\/strong><\/p>\n\n\n\n

Among conservatively managed\npatients, there was a significant positive correlation between age and both the\nLDL\/HDL ratio and the Cholesterol\/HDL ratio (r=0.465, p=0.019)<\/em>, (r=0.479*,\np=0.015)<\/em> respectively. Additionally, a significant positive correlation\nbetween weight and cholesterol was observed among conservatively managed\npatients (r=0.424, p=0.034)<\/em>.<\/p>\n\n\n\n

Table 4: Correlation between demographic data and lipid profile tests among conservative managed patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

TG<\/strong><\/p>\n<\/td>\n

\n

Cholesterol (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

LDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

HDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cholesterol \/HDL ratio<\/strong><\/p>\n<\/td>\n

\n

LDL\/HDL ratio<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Sex<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.203<\/p>\n<\/td>\n

\n

0.163<\/p>\n<\/td>\n

\n

-0.117<\/p>\n<\/td>\n

\n

0.158<\/p>\n<\/td>\n

\n

0.007<\/p>\n<\/td>\n

\n

-0.195<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.331<\/p>\n<\/td>\n

\n

0.437<\/p>\n<\/td>\n

\n

0.577<\/p>\n<\/td>\n

\n

0.449<\/p>\n<\/td>\n

\n

0.974<\/p>\n<\/td>\n

\n

0.351<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.312<\/p>\n<\/td>\n

\n

0.222<\/p>\n<\/td>\n

\n

0.205<\/p>\n<\/td>\n

\n

-0.389<\/p>\n<\/td>\n

\n

0.479<\/p>\n<\/td>\n

\n

0.465<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.129<\/p>\n<\/td>\n

\n

0.286<\/p>\n<\/td>\n

\n

0.327<\/p>\n<\/td>\n

\n

0.055<\/p>\n<\/td>\n

\n

0.015*<\/sup><\/p>\n<\/td>\n

\n

0.019*<\/sup><\/p>\n<\/td>\n<\/tr>\n

\n

Weight<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.317<\/p>\n<\/td>\n

\n

0.424<\/p>\n<\/td>\n

\n

-0.055<\/p>\n<\/td>\n

\n

0.043<\/p>\n<\/td>\n

\n

0.255<\/p>\n<\/td>\n

\n

-0.045<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.123<\/p>\n<\/td>\n

\n

0.034*<\/sup><\/p>\n<\/td>\n

\n

0.794<\/p>\n<\/td>\n

\n

0.837<\/p>\n<\/td>\n

\n

0.219<\/p>\n<\/td>\n

\n

0.830<\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between kidney function and lipid profile tests among conservatively managed patients<\/strong><\/p>\n\n\n\n

There was a significant positive correlation between cholesterol and creatinine (r=0.55, p=0.004),<\/em> cholesterol and cystatin c (r=0.538<\/em>, p=0.006<\/em>), cholesterol and B2M (r=0.577<\/em>, p=0.003<\/em>), and a significant negative correlation between cholesterol and eGFR among conservative managed patients (Table 5). <\/p>\n\n\n\n

Table 5: Correlation between kidney function and lipid profile tests among conservative managed patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

TG<\/strong><\/p>\n<\/td>\n

\n

Cholesterol (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

LDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

HDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cholesterol \/HDL ratio<\/strong><\/p>\n<\/td>\n

\n

LDL\/HDL ratio<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

Urea (mg\/dl)<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.110<\/p>\n<\/td>\n

\n

0.146<\/p>\n<\/td>\n

\n

-0.257<\/p>\n<\/td>\n

\n

-0.189<\/p>\n<\/td>\n

\n

0.220<\/p>\n<\/td>\n

\n

-0.049<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.602<\/p>\n<\/td>\n

\n

0.487<\/p>\n<\/td>\n

\n

0.214<\/p>\n<\/td>\n

\n

0.366<\/p>\n<\/td>\n

\n

0.291<\/p>\n<\/td>\n

\n

0.816<\/p>\n<\/td>\n<\/tr>\n

\n

Creatinine (mg\/dl) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.057<\/p>\n<\/td>\n

\n

0.550<\/p>\n<\/td>\n

\n

0.036<\/p>\n<\/td>\n

\n

-0.045<\/p>\n<\/td>\n

\n

0.303<\/p>\n<\/td>\n

\n

0.028<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.788<\/p>\n<\/td>\n

\n

0.004**<\/sup><\/p>\n<\/td>\n

\n

0.863<\/p>\n<\/td>\n

\n

0.830<\/p>\n<\/td>\n

\n

0.141<\/p>\n<\/td>\n

\n

0.894<\/p>\n<\/td>\n<\/tr>\n

\n

Cystatin C (mg\/l)<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.089<\/p>\n<\/td>\n

\n

0.538<\/p>\n<\/td>\n

\n

-0.006<\/p>\n<\/td>\n

\n

-0.068<\/p>\n<\/td>\n

\n

0.315<\/p>\n<\/td>\n

\n

0.019<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.673<\/p>\n<\/td>\n

\n

0.006**<\/sup><\/p>\n<\/td>\n

\n

0.976<\/p>\n<\/td>\n

\n

0.745<\/p>\n<\/td>\n

\n

0.125<\/p>\n<\/td>\n

\n

0.928<\/p>\n<\/td>\n<\/tr>\n

\n

Beta-2 macroglobulin (mg\/L) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.188<\/p>\n<\/td>\n

\n

0.577<\/p>\n<\/td>\n

\n

-0.128<\/p>\n<\/td>\n

\n

-0.048<\/p>\n<\/td>\n

\n

0.329<\/p>\n<\/td>\n

\n

-0.085<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.367<\/p>\n<\/td>\n

\n

0.003**<\/sup><\/p>\n<\/td>\n

\n

0.541<\/p>\n<\/td>\n

\n

0.818<\/p>\n<\/td>\n

\n

0.108<\/p>\n<\/td>\n

\n

0.685<\/p>\n<\/td>\n<\/tr>\n

\n

eGFR (mL\/min\/1.73m2<\/sup>) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.125<\/p>\n<\/td>\n

\n

-0.575<\/p>\n<\/td>\n

\n

-0.129<\/p>\n<\/td>\n

\n

0.055<\/p>\n<\/td>\n

\n

-0.317<\/p>\n<\/td>\n

\n

-0.091<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.550<\/p>\n<\/td>\n

\n

0.003**<\/sup><\/p>\n<\/td>\n

\n

0.539<\/p>\n<\/td>\n

\n

0.792<\/p>\n<\/td>\n

\n

0.123<\/p>\n<\/td>\n

\n

0.665<\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between demographic data and kidney function test among the studied patients.<\/strong><\/p>\n\n\n\n

A significant negative correlation between weight and B2M was found among the studied patients (r=-0.37, p=0.008) (Table 6).<\/p>\n\n\n\n

Table 6: Correlation between demographic data and kidney function test among the studied patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

Urea (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Creatinine (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cystatin C (mg\/l)<\/strong><\/p>\n<\/td>\n

\n

Beta-2 macroglobulin (mg\/L)<\/strong><\/p>\n<\/td>\n

\n

eGFR (mL\/min\/1.73m2<\/sup>)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Sex<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.114<\/p>\n<\/td>\n

\n

0.173<\/p>\n<\/td>\n

\n

0.171<\/p>\n<\/td>\n

\n

0.067<\/p>\n<\/td>\n

\n

-0.046<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.430<\/p>\n<\/td>\n

\n

0.228<\/p>\n<\/td>\n

\n

0.235<\/p>\n<\/td>\n

\n

0.642<\/p>\n<\/td>\n

\n

0.750<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.094<\/p>\n<\/td>\n

\n

-0.094<\/p>\n<\/td>\n

\n

-0.092<\/p>\n<\/td>\n

\n

-0.070<\/p>\n<\/td>\n

\n

>0.001*<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.515<\/p>\n<\/td>\n

\n

0.518<\/p>\n<\/td>\n

\n

0.526<\/p>\n<\/td>\n

\n

0.629<\/p>\n<\/td>\n

\n

0.999<\/p>\n<\/td>\n<\/tr>\n

\n

Weight<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.007<\/p>\n<\/td>\n

\n

-0.157<\/p>\n<\/td>\n

\n

-0.169<\/p>\n<\/td>\n

\n

-0.370<\/p>\n<\/td>\n

\n

0.192<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.964<\/p>\n<\/td>\n

\n

0.276<\/p>\n<\/td>\n

\n

0.241<\/p>\n<\/td>\n

\n

0.008**<\/sup><\/p>\n<\/td>\n

\n

0.183<\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation between demographic data and lipid profile tests among the studied patients.<\/strong><\/p>\n\n\n\n

There was a statistically positive correlation between HDL and weight (r=0.29, p=0.041)<\/em> and another significant negative correlation between LDL\/HDL ratio and weight (r=-0.248, p=0.046)<\/em> among the studied patients (Table 7).<\/p>\n\n\n\n

Table 7: Correlation between demographic data and lipid profile tests among the studied patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

TG<\/strong><\/p>\n<\/td>\n

\n

Cholesterol (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

LDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

HDL (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cholesterol \/HDL ratio<\/strong><\/p>\n<\/td>\n

\n

LDL\/HDL ratio<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Sex<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.174<\/p>\n<\/td>\n

\n

0.160<\/p>\n<\/td>\n

\n

0.043<\/p>\n<\/td>\n

\n

0.023<\/p>\n<\/td>\n

\n

0.075<\/p>\n<\/td>\n

\n

0.060<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.228<\/p>\n<\/td>\n

\n

0.267<\/p>\n<\/td>\n

\n

0.769<\/p>\n<\/td>\n

\n

0.875<\/p>\n<\/td>\n

\n

0.605<\/p>\n<\/td>\n

\n

0.680<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.127<\/p>\n<\/td>\n

\n

0.003<\/p>\n<\/td>\n

\n

0.008<\/p>\n<\/td>\n

\n

-0.033<\/p>\n<\/td>\n

\n

0.116<\/p>\n<\/td>\n

\n

-0.124<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.380<\/p>\n<\/td>\n

\n

0.986<\/p>\n<\/td>\n

\n

0.955<\/p>\n<\/td>\n

\n

0.822<\/p>\n<\/td>\n

\n

0.422<\/p>\n<\/td>\n

\n

0.393<\/p>\n<\/td>\n<\/tr>\n

\n

Weight<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.196<\/p>\n<\/td>\n

\n

-0.009<\/p>\n<\/td>\n

\n

-0.097<\/p>\n<\/td>\n

\n

0.290<\/p>\n<\/td>\n

\n

-0.146<\/p>\n<\/td>\n

\n

-0.284<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/p>\n<\/td>\n

\n

0.172<\/p>\n<\/td>\n

\n

0.950<\/p>\n<\/td>\n

\n

0.501<\/p>\n<\/td>\n

\n

0.041*<\/sup><\/p>\n<\/td>\n

\n

0.312<\/p>\n<\/td>\n

\n

0.046*<\/sup><\/p>\n<\/td>\n<\/tr>\n

\n

*. The correlation is significant at the 0.05 level (two-tailed).<\/p>\n<\/td>\n<\/tr>\n

\n

**. The correlation is significant at the 0.01 level (two-tailed).<\/p>\n

r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Correlation Between Kidney Function and Lipid Profile Tests Among the Studied Patients<\/strong><\/p>\n\n\n\n

In the cohort\nof 50 patients with chronic kidney disease (CKD) (both hemodialysis and\nconservatively managed), significant correlations were observed between lipid\nprofile tests and kidney function markers. Cholesterol levels showed a\nsignificant positive correlation with creatinine (r= 0.414, p =0.003<\/em>),\ncystatin C (r= 0.412, p= 0.003<\/em>), and B2M (r =0.427, p =0.002<\/em>),\nand a significant negative correlation with eGFR (r= -0.498, p <0.001<\/em>).\nLDL levels were positively correlated with creatinine (r= 0.359, p= 0.01<\/em>)\nand cystatin C (r= 0.344, p= 0.015<\/em>), and negatively correlated with eGFR\n(r= -0.291, p= 0.04)<\/em>. HDL levels showed significant negative\ncorrelations with creatinine (r= -0.544, p <0.001<\/em>), cystatin C (r=\n-0.577, p <0.001<\/em>), and B2M (r= -0.519, p <0.001)<\/em>, and a\nsignificant positive correlation with eGFR (r= 0.46, p= 0.001).<\/em> The\ncholesterol\/HDL ratio had significant positive correlations with creatinine (r=0.593,\np<0.001)<\/em>, cystatin C (r= 0.605, p <0.001),<\/em> and B2M (r= 0.561,\np <0.001),<\/em> and a significant negative correlation with eGFR (r= -0.542,\np <0.001).<\/em> Similarly, the LDL\/HDL ratio showed positive correlations\nwith creatinine (r= 0.525, p <0.001),<\/em> cystatin C (r= 0.528, p <0.001<\/em>),\nand B2M (r= 0.442, p= 0.001<\/em>), and a negative correlation with eGFR (r=\n-0.419, p =0.002).<\/em> These results illustrate the intricate relationship\nbetween impaired kidney function and altered lipid metabolism in CKD patients.<\/p>\n\n\n\n

Table 8: Correlation between kidney function and lipid profile tests among the studied patients<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Parameter<\/strong><\/p>\n<\/td>\n

\n

Urea (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Creatinine (mg\/dl)<\/strong><\/p>\n<\/td>\n

\n

Cystatin C (mg\/l)<\/strong><\/p>\n<\/td>\n

\n

B2M (mg\/L)<\/strong><\/p>\n<\/td>\n

\n

eGFR (mL\/min\/1.73m2<\/sup>)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

TG<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.091<\/p>\n<\/td>\n

\n

0.068<\/p>\n<\/td>\n

\n

0.062<\/p>\n<\/td>\n

\n

-0.045<\/p>\n<\/td>\n

\n

0.046<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.531<\/p>\n<\/td>\n

\n

0.638<\/p>\n<\/td>\n

\n

0.667<\/p>\n<\/td>\n

\n

0.757<\/p>\n<\/td>\n

\n

0.752<\/p>\n<\/td>\n<\/tr>\n

\n

Cholesterol (mg\/dl) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.082<\/p>\n<\/td>\n

\n

0.414<\/p>\n<\/td>\n

\n

0.412<\/p>\n<\/td>\n

\n

0.427<\/p>\n<\/td>\n

\n

-0.498<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.572<\/p>\n<\/td>\n

\n

0.003**<\/p>\n<\/td>\n

\n

0.003**<\/p>\n<\/td>\n

\n

0.002**<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n<\/tr>\n

\n

LDL (mg\/dl) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.207<\/p>\n<\/td>\n

\n

0.359<\/p>\n<\/td>\n

\n

0.344<\/p>\n<\/td>\n

\n

0.249<\/p>\n<\/td>\n

\n

-0.291<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.149<\/p>\n<\/td>\n

\n

0.010**<\/p>\n<\/td>\n

\n

0.015*<\/p>\n<\/td>\n

\n

0.081<\/p>\n<\/td>\n

\n

0.040*<\/p>\n<\/td>\n<\/tr>\n

\n

HDL (mg\/dl) <\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

-0.219<\/p>\n<\/td>\n

\n

-0.544<\/p>\n<\/td>\n

\n

-0.557<\/p>\n<\/td>\n

\n

-0.519<\/p>\n<\/td>\n

\n

0.460<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.127<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

0.001***<\/p>\n<\/td>\n<\/tr>\n

\n

Cholesterol \/HDL ratio<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

 <\/p>\n

0.187<\/p>\n<\/td>\n

\n

0.593<\/p>\n<\/td>\n

\n

0.605<\/p>\n<\/td>\n

\n

0.561<\/p>\n<\/td>\n

\n

-0.542<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.194<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n<\/tr>\n

\n

LDL\/HDL ratio<\/p>\n<\/td>\n

\n

r-value<\/p>\n<\/td>\n

\n

0.039<\/p>\n<\/td>\n

\n

0.525<\/p>\n<\/td>\n

\n

0.528<\/p>\n<\/td>\n

\n

0.442<\/p>\n<\/td>\n

\n

-0.419<\/p>\n<\/td>\n<\/tr>\n

\n

p-value<\/em><\/p>\n<\/td>\n

\n

0.788<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

<0.001***<\/p>\n<\/td>\n

\n

0.001***<\/p>\n<\/td>\n

\n

0.002**<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

**.  The correlation is significant at the 0.01 level (two-tailed). r-value represents the Pearson correlation, and the p-value indicates the significance between parameters. Significance levels are denoted as follows: *p < 0.05, **p < 0.01,***p < 0.001<\/em>.<\/p>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

Chronic kidney disease (CKD) significantly impacts morbidity and mortality, affecting various organ systems due to disrupted physiological pathways resulting from renal function loss17<\/sup>. This study assessed kidney function and lipid profiles in CKD patients undergoing hemodialysis, conservative management, and healthy controls, revealing significant disparities in biochemical parameters across these groups, indicating different metabolic profiles.<\/p>\n\n\n\n

Urea levels, a key marker for nitrogen metabolism and renal function, were lower in control subjects compared to both hemodialysis and conservatively managed patients. This difference is expected, as impaired renal functions typically result in the accumulation of urea in the blood. Elevated urea levels in CKD patients highlight the impact of renal dysfunction on nitrogen metabolism. Creatinine levels, a widely used indicator of kidney function, were highest in the hemodialysis group and elevated in the conservatively treated group compared to controls. These differences reflect the impact of treatment methods on kidney function and align with the general trend observed in previous studies18,19<\/sup>. Cystatin C is a more sensitive and specific marker for estimating glomerular filtration rate (GFR) compared to conventional markers like creatinine20<\/sup>. The study found higher cystatin C levels in both hemodialysis and conservatively managed patients compared to controls, underscoring its utility in assessing renal function, particularly in compromised kidney function scenarios.<\/p>\n\n\n\n

Estimated GFR (eGFR) is crucial for understanding different renal management approaches. The study observed significantly lower eGFR values in both hemodialysis and conservatively managed patients compared to controls, confirming compromised kidney filtration mechanisms in these treatment patterns. Beta-2 microglobulin (B2M) levels were considerably higher in hemodialysis and conservatively managed patients compared to controls, indicating abnormal protein balance likely due to renal dysfunction 21<\/sup>. B2M is a valuable marker for monitoring kidney health and disease progression.<\/p>\n\n\n\n

In lipid Profile Tests, TG levels were higher in hemodialysis and conservatively managed patients compared to controls. This finding aligns with previous studies showing dyslipidemia in CKD patients22<\/sup>, highlighting the need for focused lipid profile management in these populations. Cholesterol levels were highest in hemodialysis patients, followed by conservatively managed patients, with the lowest levels in the control group. This pattern suggests an impact of renal disease on cholesterol metabolism, despite the absence of statistical significance, which is likely attributed to the limited sample size. Further research is needed to explore the relationship between lipid management and kidney function.<\/p>\n\n\n\n

Furthermore, <\/strong>Hemodialysis patients had the highest levels of “bad” LDL cholesterol, while the control group had the highest levels of “good” HDL cholesterol. These results emphasize the need for personalized care to reduce cardiovascular risk in CKD patients23,24<\/sup>. The study also found significantly higher cholesterol\/HDL and LDL\/HDL ratios in hemodialysis patients compared to other groups, indicating higher cardiovascular risk.<\/p>\n\n\n\n

In Correlations Between Biochemical Markers and Clinical Parameters, the hemodialysis group, a significant inverse correlation between weight and B2M was observed, suggesting that patients experiencing greater weight loss exhibit higher B2M levels due to reduced dilution. This finding highlights the importance of monitoring nutritional status in hemodialysis patients25,26<\/sup>.<\/p>\n\n\n\n

In the hemodialysis group, a notable negative correlation between age and the LDL\/HDL ratio was observed, indicating higher LDL\/HDL ratios in younger patients. This result, differing from previous research27<\/sup>, suggests the need for age-specific evaluations in lipid profile management. The study did not identify a significant relationship between lipid profiles and renal function among individuals undergoing hemodialysis, which contrasts with some previous research28,29<\/sup>. This discrepancy may be due to the complex interactions between dietary habits, medication use, and other factors influencing lipid profiles in dialysis patients.<\/p>\n\n\n\n

In the conservatively managed group, older patients exhibited higher LDL\/HDL ratios, consistent with the impact of aging on lipid metabolism and cardiovascular risk30<\/sup>. Significant correlations between cholesterol levels and kidney function markers suggest a link between dyslipidemia and renal impairment in this group, supporting findings from other research 31<\/sup>.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

In conclusion,\nthis study underscores the importance of comprehensive management of lipid\nprofiles and kidney function in CKD patients to improve health outcomes.\nTargeted lipid management strategies are essential to mitigate cardiovascular\nrisks in these populations.
\nAdvancing CKD stages can alter lipid metabolism, increasing atherogenesis risk\nand leading to worse prognosis and higher mortality. Regular lipid profile\nmonitoring and management in CKD patients, even at early stages, is crucial to\nreduce cardiovascular morbidity and mortality. Multi-centric studies are\nrecommended to better understand lipid profile patterns in CKD patients.<\/p>\n\n\n\n

Acknowledgment<\/strong><\/p>\n\n\n\n

The authors are grateful to Al-Ahliyya Amman University, Jordan.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

The author(s) do not have any conflict of interest. <\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

The authors received funding for this work from Al-Ahliyya Amman University. Jordan with grant No.2023\/14-4. <\/p>\n\n\n\n

Data Availability Statement<\/strong><\/p>\n\n\n\n

This statement does not apply to this article<\/p>\n\n\n\n

Ethical Approval<\/strong><\/p>\n\n\n\n

The ethical committee approves the research, ethical approval number is IRB: AAU\/1\/3\/2023-2024.<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and therefore, informed consent was not required.<\/p>\n\n\n\n

Authors\u2019 Contribution <\/strong><\/p>\n\n\n\n

Minwer Salah: Data Collection, Methodology, Writing \u2013 Original Draft.<\/p>\n\n\n\n

Belal Almajali: Visualization, Supervision, Project Administration.<\/p>\n\n\n\n

Saad Alfawaeir: Visualization, Supervision, Project Administration.<\/p>\n\n\n\n

Khaled A. Ahmed: Funding Acquisition, Resources, Supervision.<\/p>\n\n\n\n

Osama alsayed: Analysis, Writing \u2013 Review & Editing.<\/p>\n\n\n\n

Hamid Ali Nagi Al-Jamal: Conceptualization, Editing<\/p>\n\n\n\n

References <\/strong><\/p>\n\n\n\n

  1. Pasha F., Vatani K., Mosalanejad S., Nemati M.A.H., Omidi H. Prevalence of urinary abnormality, electrolytes disorders and renal insufficiency in COVID-19 patients in affiliated hospitals of Islamic Azad University, Tehran Medical Sciences, 2019-2022. Med Sci Jour Islam Azad Univ-Tehran Med Branch. 2023;33(3):295-304.
    CrossRef<\/a><\/li>
  2. Ammirati A.L. Chronic kidney disease. Rev Assoc Med Bras. 2020;66
    CrossRef<\/a><\/li>
  3. Colvin R.B., Chang A. Diagnostic pathology: kidney diseases. Elsevier Health Sci. 2019.<\/li>
  4. Kovesdy C.P. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl.<\/em> 2022;12(1):7-11.
     CrossRef <\/a><\/li>
  5. Al-Shdaifat E., Manaf M.R. The economic burden of hemodialysis in Jordan. Indian J Med Sci.<\/em> 2013;(67)5\/6:103.
    CrossRef <\/a><\/li>
  6. Edwards N.C., Price A.M., Steeds R.P., Ferro C.J., Townend J.N. Management of heart failure in patients with kidney disease\u2014Updates from the 2021 ESC guidelines. Nephrol Dial Transplant<\/em>. 2023;38(8):1798-1806.
     CrossRef <\/a><\/li>
  7. Akl K., Said R. Need for a Jordanian National Registry of chronic renal disease. Jordan Med J.<\/em> 2010;44(4):437-441.<\/li>
  8. Zoccali C., Mallamaci F. The cardiovascular\u2013renal link and the health burden of kidney failure. Oxford Univ Press US<\/em>. 2023;44:1167-1169.
     CrossRef <\/a><\/li>
  9. Khalil A.A., Abed M.A., Ahmad M., Mansour A.H. Under\u2010diagnosed chronic kidney disease in Jordanian adults: prevalence and correlates. J Ren Care<\/em>. 2018;44(1):12-18.
     CrossRef <\/a><\/li>
  10. Tonkin-Crine S., Santer M., Leydon G.M., Murtagh F.E., Farrington K., Caskey F., Rayner H., Roderick P. GPs\u2019 views on managing advanced chronic kidney disease in primary care: a qualitative study. Br J Gen Pract<\/em>. 2015;65(636).
     CrossRef <\/a><\/li>
  11. Carney E.F. The impact of chronic kidney disease on global health. Nat Rev Nephrol<\/em>. 2020;16(5):251-251.
     CrossRef <\/a><\/li>
  12. Jankowski J., Floege J., Fliser D., B\u00f6hm M., Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation<\/em>. 2021;143(11):1157-1172.
     CrossRef <\/a><\/li>
  13. Bakris G.L., Agarwal R., Anker S.D., Pitt B., Ruilope L.M., Rossing P., Kolkhof P., Nowack C., Schloemer P., Joseph A. Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. N Engl J Med<\/em>. 2020;383(23):2219-2229.
     CrossRef <\/a><\/li>
  14. Mart\u00ednez-Hern\u00e1ndez S.L., Mu\u00f1oz-Ortega M.H., \u00c1vila-Blanco M.E., Medina-Piza\u00f1o M.Y., Ventura-Ju\u00e1rez J. Novel Approaches in Chronic Renal Failure without Renal Replacement Therapy: A Review. Biomedicines<\/em>. 2023;11(10):2828.
     CrossRef <\/a><\/li>
  15. Thobani A., Jacobson T.A. Dyslipidemia in patients with kidney disease. Cardiol Clin<\/em>. 2021;39(3):353-363.
     CrossRef <\/a><\/li>
  16. Gilbert S.F., Weiner D.E. National Kidney Foundation Primer on Kidney Diseases, E-Book. Elsevier Health Sci<\/em>. 2022.<\/li>
  17. Saini M., Vamne A., Kumar V., Chandel M. The study of pattern of lipid profile in chronic kidney disease patients on conservative management and hemodialysis: A comparative study. Cureus<\/em>. 2022;14(1).
    CrossRef <\/a><\/li>
  18. Ebert N., Bevc S., B\u00f6kenkamp A., Gaillard F., Hornum M., Jager K.J., Mariat C., Eriksen B.O., Palsson R., Rule A.D. Assessment of kidney function: clinical indications for measured GFR. Clin Kidney J<\/em>. 2021;14(8):1861-1870.
     CrossRef <\/a><\/li>
  19. Narimani R., Esmaeili M., Rasta S.H., Khosroshahi H.T., Mobed A. Trend in creatinine determining methods: Conventional methods to molecular\u2010based methods. Anal Sci Adv<\/em>. 2021;2(5-6):308-325.
     CrossRef <\/a><\/li>
  20. Benoit S.W., Ciccia E.A., Devarajan P. Cystatin C as a biomarker of chronic kidney disease: latest developments. Expert Rev Mol Diagn<\/em>. 2020;20(10):1019-1026.
    CrossRef <\/a><\/li>
  21. Zheng Z., Geng J., Jiang Y., Zhang M., Yang R., Ge G., Xu H., Zhang X. Kidney diseases. Clin Mol Diagn<\/em>. 2021;553-582.
     CrossRef <\/a><\/li>
  22. Theofilis P., Vordoni A., Koukoulaki M., Vlachopanos G., Kalaitzidis R.G. Dyslipidemia in chronic kidney disease: contemporary concepts and future therapeutic perspectives. Am J Nephrol<\/em>. 2021;52(9):693-701.
     CrossRef <\/a><\/li>
  23. Florens N., Calzada C., Lemoine S., Boulet M.M., Guillot N., Barba C., Roux J., Delolme F., Page A., Poux J.M. CKD increases carbonylation of HDL and is associated with impaired antiaggregant properties. J Am Soc Nephrol<\/em>. 2020;31(7):1462-1477.
     CrossRef <\/a><\/li>
  24. Kapoor S., Lal A., Mukhiya G.K. Study of HbA1c (Glycated Hemoglobin) Levels and Lipid Profile in Patients Undergoing Hemodialysis. Shineeks Publ<\/em>. 2023.<\/li>
  25. Koh E.S., Lee K., Kim S.H., Kim Y.O., Jin D.C., Song H.C., Choi E.J., Kim Y.L., Kim Y.S., Kang S.W. Serum \u03b22-microglobulin predicts mortality in peritoneal dialysis patients: a prospective cohort study. Am J Nephrol<\/em>. 2015;42(2):91-98.
    CrossRef <\/a><\/li>
  26. Okuno S., Ishimura E., Kohno K., Fujino-Katoh Y., Maeno Y., Yamakawa T., Inaba M., Nishizawa Y. Serum \u03b22-microglobulin level is a significant predictor of mortality in maintenance haemodialysis patients. Nephrol Dial Transplant<\/em>. 2009;24(2):571-577.
     CrossRef <\/a><\/li>
  27. Niepolski L., Drzewiecka H., Warcho\u0142 W. Circulating vascular endothelial growth factor receptor 2 levels and their association with lipid abnormalities in patients on hemodialysis. Biomed Rep<\/em>. 2021;14(4):1-9.
     CrossRef <\/a><\/li>
  28. Mondal E., Khan M., Hossain M., Moshwan M., Saha R., Das S., Moniruzzaman M. The pattern of lipid profile in patients with chronic kidney disease. Mymensingh Med J<\/em>. 2021;30(1):48-55.<\/li>
  29. Wang X., Chen H., Shao X., Xiong C., Hong G., Chen J., Li X., You X., Gao P., Chen Y. Association of lipid parameters with the risk of chronic kidney disease: a longitudinal study based on populations in Southern China. Diabetes Metab Syndr Obes<\/em>. 2020;663-670.
    CrossRef <\/a><\/li>
  30. Partridge L., Deelen J., Slagboom P.E. Facing up to the global challenges of ageing. Nature<\/em>. 2018;561(7721):45-56.
     CrossRef <\/a><\/li>
  31. Weiner D.E., Sarnak M.J. Managing dyslipidemia in chronic kidney disease. J Gen Intern Med<\/em>. 2004;19(10):1045-1052.
     CrossRef <\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

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