Hemoglobin variability is a common problem among hemodialysis patients. We have previously demonstrated an association between Hb variability and left ventricular mass index. In this study, we investigated a possible relation between Hb variability and carotid intima-media thickness (CIMT). Twelve-month hemoglobin (Hb) values of 135 patients on maintenance hemodialysis were examined retrospectively. The range of 11–12 gr/dl was accepted as normal according to the KDOQI guidelines. Hemoglobin levels were classified as: Hb   12 gr/dl:High. According to 12-month Hb trajectory, the patients were divided into three groups: low–normal (LN), normal–high (NH) and low–high (LH). The CIMT measurements were taken on common carotid arteries bilaterally, and the average of these measurements were taken. The groups were compared in terms of CIMT measurements, demographic and laboratory features. The LN, NH and LH groups were similar in terms of age, gender, incidence of diabetes mellitus, hypertension and cardiovascular diseases. Duration of hemodialysis, hemodialysis adequacy, serum lipids and CaxP products were also similar among the groups. The mean CIMT value was 0.601 ± 0.107, 0.744 ± 0.139 and 0.604 ± 0.134 mm in the LN, LH and NH groups, respectively (p < 0.001). CIMT was significantly higher in LH than in the other two groups. In our study, when the three groups with similar risk factors for atherosclerosis were examined, we found that the LH group with the highest hemoglobin variability has the highest CIMT. This study is the first study to demonstrate that Hb variability is associated with an increase in CIMT in HD patients.

The relation between hemoglobin variability and carotid intima-media thickness in chronic hemodialysis patients.

Introduction. Cardiovascular complications are the most frequent cause of death in chronic kidney disease that happens due to both general and uremic risk factors. Recently, the medical literature has declared the carotid artery intima-media thickness to be an indicator for predicting cardiovascular diseases. Methods. This paper is an attempt to introduce an analytical cross-sectional study of 128 hemodialysis patients. The researchers collected the data by reviewing medical records, interviewing the patients, chemical analysis of the patient's serum and carotid artery Doppler ultrasound, and providing the relevant questionnaire. We performed descriptive statistics, bivariate correlation, and general linear model (GLM) analysis. And, the significance level of hypothesis tests was .05. Results. Seventy-three patients (57%) were male, and 55 (43%) were female. The mean and standard deviation of the age was 58.66 +/- 15.54 years. Nearly 42% of patients affected by diabetes, 95.3% were hypertensive and 28.1% had a history of cardiovascular disease. In the bivariate analysis, age, serum albumin, serum magnesium, hypertension, and history of cardiovascular disease showed a statistically significant relationship with carotid intima-media thickness (CIMT). In GLM, we observed a statistically significant relationship between CIMT, age and magnesium. Conclusion. Increased CIMT is observed in a considerable percentage of hemodialysis patients. Age and serum magnesium concentration demonstrate a statistically significant association with CIMT. We recommend more precise long-term longitudinal follow-up studies to investigate the relationship between biochemical risk factors and CIMT. Therefore, multivariate analysis is necessary to assess the simultaneous effects of independent variables and manage influences of confounding factors. We also recommend developing a practical guideline for periodic determination of CIMT in hemodialysis patients to implement convenient preventive or therapeutic measures.

Carotid Intima-media Thickness in Hemodialysis Patients and Related Biochemical and Clinical Factors.

BACKGROUND To investigate the increase in carotid artery intima-media thickness (IMT) and influencing factors in hemodialysis patients. METHODS 90 hemodialysis patients were divided into groups according to their dialysis duration; 30 healthy subjects were included as controls. High-frequency Doppler ultrasound was used to calculate the carotid artery IMT in, 12-, 36-, and 60-month groups; biochemical parameters were also measured. Data from different groups were compared and the associations between the risk factors and IMT were investigated. RESULTS Increased IMT was found in the patients in all groups compared with controls (p < 0.05). IMT was also significantly higher in the 36-month group than in the 12-month group (p < 0.01), while no significant difference in the IMT was found between the 60-month group and the 36-month group. A positive association was found between the carotid artery IMT and systolic pressure (SBP, r = 0.39, p < 0.01), mean arterial pressure (r = 0.291, p < 0.01), C-reactive protein (CRP, r = 0.511, p < 0.01), serum phosphate (r = 0.518, p < 0.01), and calcium-phosphorus product (r = 0.170, p < 0.01); a negative association was found between carotid artery IMT and plasma albumin (r = -0.095, p < 0.01) and prealbumin (r = -0.65, p < 0.01). Multivariate linear regression showed that high SBP (t = 2.004, p < 0.01), high serum phosphate (t = 2.531, p < 0.01), and increased CRP (t = 4.737, p < 0.01) were independent risk factors for IMT increase. CONCLUSIONS Hemodialysis increased carotid artery IMT associated with dialysis duration; high SBP, high serum phosphate, and increased CRP are independent risk factors for IMT increase, and IMT was also associated with the development of atherosclerosis.

Carotid Artery Intima-Media Thickness and Influencing Factors in Hemodialysis Patients.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yan Xu

Author Tools

Chat about Author

Papers

Carotid Artery Intima-Media Thickness and Influencing Factors in Hemodialysis Patients.