Monocyte-to-lymphocyte ratio is a promising biomarker in patients initially receiving hemodialysis

Aim: Inflammation is very common among dialysis patients and can lead to an increase in morbidity and mortality. Monocyte-to-lymphocyte ratio (MLR) can serve as a reliable predictor of long-term survival in hemodialysis patients. However, few studies have addressed the role of MLR in patients initially receiving hemodialysis (within 3 months). In this study, we aimed to examine the association between MLR and the risk of cardiovascular and all-cause mortality in patients initially receiving hemodialysis.

Methods: In this study, a total of 216 patients newly receiving hemodialysis for at least 3 months were recruited. The associations between MLR and cardiovascular diseases (CVD) and all-cause mortality were assessed by multivariable Cox models.

Results: A total of 216 patients were included (mean age 57.65 ± 15.68 years, 42.13% male patients). Patients were divided into the low MLR group (<0.49) and the high MLR group (≥0.49). The levels of neutrophil and serum iron and the number of deaths were significantly higher in the high MLR group (P < 0.05). Spearman’s analysis showed that MLR was positively correlated with BUN (R = 0.210, P = 0.002), WBC (R = 0.178, P = 0.009), and neutrophil (R = 0.237, P < 0.001). Kaplan–Meier analysis showed that patients in the low MLR group present longer survival (64.08 ± 2.30 vs. 51.07 ± 3.12 months, P < 0.001). Multivariate Cox regression analysis showed that age, diabetes, and MLR (all P < 0.05) were factors significantly associated with a higher risk of CVD and all-cause mortality.

Conclusions: Our results showed that high MLR values are an independent risk factor for CVD and all-cause mortality in patients initially receiving hemodialysis, especially in the elderly and those with a history of diabetes.

Introduction

Chronic kidney disease (CKD) is a global public health problem due to its escalating prevalence and the rising number of people receiving dialysis. Hemodialysis is the most common modality of kidney replacement therapy, with a worldwide prevalence of more than 85% (1). Dialysis patients have poor clinical outcomes, and the 5-year survival rate after the initiation of maintenance dialysis is approximately 40% (2). Cardiovascular diseases (CVDs) and non-cardiovascular factors, especially infection-related complications, are the main causes of the increased premature mortality rate observed in the hemodialysis population, driven by immune dysfunction and chronic inflammatory states. After initiation of maintenance dialysis, CVDs account for approximately 50% of the total mortality rate, while infections account for 20% (3).

Dialysis is bidirectionally negatively correlated with persistent inflammation. A variety of risk factors in hemodialysis patients can lead to persistent inflammatory responses, such as reduced clearance of inflammatory factors, oxidative stress, uremia, malnutrition, dyslipidemia, and infections (4). Sustained inflammatory responses in hemodialysis patients contribute to a spectrum of complications through mechanisms involving immune dysregulation, oxidative stress, and multi-organ damage, like CVD, anemia, malnutrition, infections, and bone mineral disorders (5). Therefore, the monitoring of inflammation-related markers holds significant clinical value for assessing prognosis and conducting early intervention in hemodialysis patients.

Traditional inflammatory markers, including IL-1β, IL-6, IFN-γ, CRP, and TNF-α, have disadvantages such as high cost, complex technological process, and poor disease specificity, making them difficult to be widely applied in clinical practice (6). The ratios of different blood cell components, including monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and platelet-to-albumin ratio (PAR), are inexpensive and easy to obtain clinically and have been proven to be novel prognostic indicators for CKD. Previous studies have reported that MLR was a better predictor of CVD and all-cause mortality in hemodialysis and peritoneal dialysis patients (7). Because the mortality rate of patients after the initiation of maintenance dialysis is relatively high, this study aimed to examine the association between MLR and the risk of CVD and all-cause mortality in patients initially receiving hemodialysis.

Materials and methods

Patients

This was a single-center retrospective study that included a total of 216 patients newly receiving hemodialysis for at least 3 months between January 2018 and December 2019 in our hospital. The inclusion criteria were as follows: adults with end-stage renal disease (ESRD) requiring first-time hemodialysis. The study excluded the following populations: 1) younger than 18 years; 2) patients with malignancy, acute kidney injury, acute heart failure, active systemic infections, autoimmune diseases, or hematological diseases; 3) patients with incomplete clinical test results; and 4) patients undergoing renal replacement therapy.

Clinical outcome

The outcome of this study was CVD and all-cause mortality. All patients were followed until death, cessation of hemodialysis, or the end of the study period (31 December 2024).

Data collection

All baseline data were collected 1–3 months following an initial 3-month period of hemodialysis treatment. For all patients, we recorded demographic data, including sex, age, history of hypertension and diabetes, and primary cause of ESKD. Clinical and biochemical data included serum creatinine (SCr), blood urea nitrogen (BUN), carbon dioxide combining power (CO₂CP), serum albumin, β2-microglobulin, uric acid, cystatin, fasting blood glucose, HbA1c, low-density lipoprotein (LDL), cholesterol, triglyceride, white blood cell (WBC), neutrophil, lymphocytes, monocyte, platelet and hemoglobin levels, serum iron, serum potassium (K), calcium (Ca), phosphorus (P), and intact parathyroid hormone (iPTH). MLR, NLR, and PLR were calculated by dividing monocytes, neutrophils, and platelets by lymphocytes, respectively. PAR was calculated as the platelet-to-albumin ratio.

Statistical analysis

All statistical analyses were performed using SPSS version 20.0 (Chicago, IL, USA). The data were presented as frequency (percentage) for categorical variables, mean ± standard deviation for normally distributed continuous variables, and median (Q1–Q3) for non-normally distributed data. Continuous variables were analyzed by the Wilcoxon rank-sum test. The correlations between MLR and clinical data were performed using Spearman’s test. The X-tile software version 3.6.1 (Yale University, New Haven, USA) was used to determine the optimal cutoff points of MLR based on the outcome (8). Through X-tile analysis of survival outcomes, MLR demonstrated a continuous prognostic relationship. The analytical cohort was randomly allocated into equally sized training and validation sets for robust evaluation. Figure 1 shows MLR divided at the optimal cutoff point, as defined by the most significant (brightest pixel) on the plot (0.49). The cutoff point highlighted by the black/white circle in the left panels is shown on a histogram of the entire cohort and on a Kaplan–Meier plot (right panels; low subset = blue, high subset = gray). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of the MLR for all-cause mortality in our patients, with AUC values interpreted as 0.5 (no discrimination) to 1.0 (perfect discrimination). The univariate and multivariate Cox proportional hazards models were used to examine the associations between clinical data and cardiovascular and all-cause mortality. A P-value <0.05 was considered statistically significant.

Figure 1

Figure 1. The optimal cutoff points of MLR. (A) The X-tile plot reveals the distribution of the population. The best MLR cutoff values are shown in the histogram (B) and Kaplan–Meier plots (C) for the entire cohort. (D) The subpopulation cutoff points of the population. The optimal cutoff point of MLR was 0.49 based on the outcome, and we divided the cases into low (n = 140 cases) and high (n = 76 cases) populations based on a cutoff point.

Results

Patient and demographic details

A total of 216 patients initially receiving hemodialysis for at least 3 months were included in the final analysis. The mean (± SD) age was 57.65 ± 15.68 years, and 42.13% were male patients. Of the 216 patients, 141 patients (65.28%) had hypertension and 89 (41.20%) had diabetes. The optimal cutoff point of MLR was 0.49 based on the outcome. Patients were divided into the low MLR group (<0.49) and the high MLR group (≥0.49). There were 140 patients in the low MLR group and 76 patients in the high MLR group. The levels of neutrophil and serum iron and the number of deaths were significantly higher in the high MLR group (P < 0.05). There was no statistically significant difference among the groups in other laboratory data. The baseline characteristics of the patients are shown in Table 1. We found that neutrophil, serum iron, PCT, and the number of deaths were higher in the high MLR group (P < 0.05).

Table 1

Table 1. Clinical characteristics and laboratory parameters of the study population.

We also employed the ROC curve analysis to determine the diagnostic accuracy of MLR (shown in Figure 2). The results showed that MLR had predictive diagnostic value for all-cause mortality in patients undergoing hemodialysis. The diagnostic threshold was MLR >0.46 (AUC = 0.591, P = 0.039), with a sensitivity of 50.9% and a specificity of 73.6%.

Figure 2

Figure 2. The ROC curve of MLR for the prediction of all-cause mortality in patients undergoing hemodialysis.

The correlation between MLR and clinical data

We analyzed the correlation between MLR and clinical data, shown in Table 2. Spearman’s analysis showed that MLR was positively correlated with BUN (R = 0.210, P = 0.002), WBC (R = 0.178, P = 0.009), and neutrophil (R = 0.237, P < 0.001).

Table 2

Table 2. Correlation between MLR with the clinical data of the study population.

MLR and cardiovascular and all-cause mortality

A total of 110 patients (50.93%) died at the end of the study. In the low MLR group, the cumulative survival rates at 1, 3, and 5 years were 97.14%, 74.29%, and 63.57%, respectively; however, in the high MLR group, the rates were 92.11%, 55.26%, and 31.58%, respectively. Kaplan–Meier analysis showed that patients in the low MLR group present longer survival (64.08 ± 2.30 vs. 51.07 ± 3.12months, P < 0.001, shown in Figure 3).

Figure 3

Figure 3. Kaplan–Meier curves for all-cause mortality in patients undergoing hemodialysis according to MLR.

The association between baseline characteristics and all-cause mortality is shown in Table 3. Univariate analysis showed that age (P < 0.001), diabetes (P < 0.001), albumin (P = 0.031), SCr (P < 0.001), cystatin (P = 0.015), MLR (P = 0.030), K (P = 0.035), P (P = 0.034), and iPTH (P = 0.023) were prognostic factors for overall survival in the total cohort. Multivariate Cox regression analysis showed that age (HR = 1.034, 95% CI: 1.017–1.051, P < 0.001), diabetes (HR = 2.126, 95% CI: 1.282–3.202, P = 0.002), and MLR (HR = 2.743, 95% CI: 1.137–6.615, P = 0.025) were factors significantly associated with a higher risk of all-cause mortality (Table 3).

Table 3

Table 3. Univariate and multivariate analyses for all-cause mortality in the total cohort.

The association between baseline characteristics and CVD mortality is shown in Table 4. Univariate analysis showed that age (P < 0.001), diabetes (P < 0.001), SCr (P = 0.026), and MLR (P = 0.033) were prognostic factors for overall survival in the total cohort. Multivariate Cox regression analysis showed that age (HR = 1.043, 95% CI: 1.016–1.071, P = 0.002), diabetes (HR = 2.490, 95% CI: 1.224–5.067, P = 0.012), and MLR (HR = 3.911, 95% CI: 1.135–13.476, P = 0.031) were factors significantly associated with a higher risk of CVD mortality (Table 4).

Table 4

Table 4. Univariate and multivariate analyses for cardiovascular disease mortality in the total cohort.

Discussion

ESRD is strongly associated with elevated risks of morbidity, mortality, and healthcare costs, primarily driven by cardiovascular complications, infections, and multi-organ dysfunction due to progressive kidney failure (2). The most common form of kidney replacement therapy is dialysis, with hemodialysis comprising 89% of all dialysis procedures (9). The mortality rate of patients after initiation of dialysis is high, and the leading causes of mortality are CVD and infections. Microinflammation is a key factor for complications and mortality in hemodialysis (10). MLR can reflect systemic inflammation and serve as a reliable predictor of long-term survival in hemodialysis patients. Compared with traditional inflammatory indicators, MLR can be used as a cost-effective routine blood test parameter to identify high-risk hemodialysis patients (6).

In this study, we aimed to examine the association between MLR and the risk of CVD and all-cause mortality in patients initially receiving hemodialysis. We enrolled 216 patients initially receiving hemodialysis for at least 3 months, and the patients were divided into the low MLR group (<0.49) and the high MLR group (≥0.49). Our results showed that the levels of neutrophil and serum iron and the number of deaths were significantly higher in the high MLR group (P < 0.05). Spearman’s analysis showed that MLR was positively correlated with BUN (P = 0.002), WBC (P = 0.009), and neutrophil (P < 0.001). Kaplan–Meier analysis showed that patients in the low MLR group present longer survival. Multivariate Cox regression analysis showed that age, diabetes, and MLR (all P < 0.05) were factors significantly associated with a higher risk of CVD and all-cause mortality.

Inflammation, driven by multifactorial mechanisms, including uremic toxin accumulation, bioincompatible dialysis membranes, oxidative stress, metabolic dysregulation, and chronic infections, is very common in hemodialysis patients, resulting in increased morbidity and mortality (11). MLR, an easily obtainable inflammatory indicator, shows broad applications in oncology, infectious diseases, cardiovascular diseases, and nephrology. It demonstrates particular utility in prognostic stratification, therapeutic response prediction, and pathophysiological mechanism exploration (12–14). MLR can reflect systemic inflammation and chronic low-grade inflammation in hemodialysis patients, because elevated MLR indicates the activation of monocytes (pro-inflammatory cells) and the depletion of lymphocytes (anti-inflammatory cells) (15). Neutrophils and monocytes are important cells of the innate immune system. They are the most important cells responsible for increased immune response to uremic toxins and chronic contact with bio-incompatible membranes during hemodialysis treatment and can release cytokines and chemokines (10, 16). This can explain our results. In this study, we found that the level of neutrophil and the number of deaths were significantly higher in the high MLR group. Furthermore, Spearman’s analysis showed that MLR was positively correlated with BUN, WBC, and neutrophil. These data coincide with other data. Previous studies also found that neutrophil and MLR are biomarkers of inflammation in dialysis patients and were related to the prognosis of the patients (5, 15, 17, 18). Additionally, the high MLR group exhibited a pre-existing hyperinflammatory state prior to treatment, which may be associated with underlying conditions such as infections, chronic kidney disease, or metabolic disorders. Our study also found that the level of serum iron was significantly higher in the high MLR group. Previous studies found that serum iron was strongly associated with inflammation in hemodialysis patients, and iron overload could increase the risk of CVD in hemodialysis patients (19, 20). Therefore, neutrophil, serum iron, and MLR can indicate the inflammatory state in hemodialysis patients.

Our study found that MLR was an independent risk factor for cardiovascular and all-cause mortality in patients initially receiving hemodialysis. CVD is the leading cause of death in dialysis patients, especially in those who have just started hemodialysis treatment (10). Chronic low-grade inflammation plays a critical role in the pathogenesis of atherosclerosis, vascular calcification, and other etiologies of cardiovascular diseases (21). In dialysis patients, monocyte-derived inflammatory macrophages contribute to endothelial injury and atherosclerosis through adhesion to the vascular endothelium, release of pro-inflammatory mediators (e.g., IL-6, TNF-α), and induction of oxidative stress, thereby causing endothelial injury and thus being associated with atherosclerotic diseases and cardiovascular events (11). On the other hand, a low lymphocyte count (lymphopenia) is regarded as a risk factor for cardiovascular diseases and overall mortality (22, 23). This might explain why an increase in the MLR is associated with adverse clinical outcomes in hemodialysis patients. Previous studies also found that MLR can predict cardiovascular and all-cause mortality in dialysis patients (7). Wen et al. (18) found that the highest MLR tertile was significantly associated with an increase in CVD and all-cause mortality in peritoneal dialysis patients. Han et al. (17) found that the combination of MLR and other inflammatory markers can predict the CVD prognosis in maintenance hemodialysis patients. Therefore, MLR can be used as a tool for clinicians to evaluate the prognosis of hemodialysis patients.

Our study also found that age and diabetes were factors significantly associated with a higher risk of cardiovascular and all-cause mortality in patients initially receiving hemodialysis. In elderly patients, the function of T cells declines and the immune response weakens (24). When combined with diabetes or hemodialysis, the inflammatory response increases, and the risk of infection significantly increases (25). In patients with diabetes, hyperglycemia can further lead to the accumulation of pro-inflammatory substances, the activation of monocytes, and the release of inflammatory mediators, thereby causing infections and a decline in immune function (26). Qiu et al. (27) found that in diabetes patients with CKD in the intensive care unit, high MLR was significantly associated with increased risk of 90-day all-cause mortality. Therefore, age and diabetes also have an impact on the poor prognosis of hemodialysis patients.

There are still several limitations in our study. First, this is a single-center retrospective study; the number of patients and events is limited, and there may be inherent biases. Second, other traditional inflammatory markers, such as CRP, interleukin, and TNF, were not included in our study because they are expensive and not routinely measured in hemodialysis patients. Third, we did not explore the mechanisms and the inflammatory pathway in hemodialysis patients. Further studies should be carried out to explore these results.

Conclusions

In conclusion, our results revealed that high MLR values are an independent risk factor for cardiovascular and all-cause mortality in hemodialysis patients, especially in the elderly and those with a history of diabetes. MLR is a straightforward and inexpensive indicator to reflect systemic inflammation status. Our findings suggested that MLR can be used as a tool for clinicians to evaluate the prognosis of hemodialysis patients. Further research should focus on the inflammatory pathway to reduce inflammation and improve the prognosis of hemodialysis patients.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: The datasets are available from the corresponding author on reasonable request.

Ethics statement

The studies involving humans were approved by the Ethical Committee of Zhongshan People’s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from the patients’ previous blood test results. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements. Ethics number: 2025-085.

Author contributions

AX: Writing – original draft, Data curation. AT: Software, Writing – original draft. MY: Writing – original draft. YX: Supervision, Writing – review & editing. JL: Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by grants from the Zhongshan Science and Technology Bureau Project (Nos. 2023B1025 and 2023B1026).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declare that no Generative AI was used in the creation of this manuscript.

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References

1. Thurlow JS, Joshi M, Yan G, Norris KC, Agodoa LY, Yuan CM, et al. Global epidemiology of end-stage kidney disease and disparities in kidney replacement therapy. Am J Nephrol. (2021) 52:98–107. doi: 10.1159/000514550

PubMed Abstract | Crossref Full Text | Google Scholar

2. Flythe JE and Watnick S. Dialysis for chronic kidney failure: A review. JAMA. (2024) 332:1559–73. doi: 10.1001/jama.2024.16338

PubMed Abstract | Crossref Full Text | Google Scholar

3. Carrero JJ, de Jager DJ, Verduijn M, Ravani P, De Meester J, Heaf JG, et al. Cardiovascular and noncardiovascular mortality among men and women starting dialysis. Clin J Am Soc Nephrol: CJASN. (2011) 6:1722–30. doi: 10.2215/cjn.11331210

PubMed Abstract | Crossref Full Text | Google Scholar

4. Kadatane SP, Satariano M, Massey M, Mongan K, and Raina R. The role of inflammation in CKD. Cells. (2023) 12:1581. doi: 10.3390/cells12121581

PubMed Abstract | Crossref Full Text | Google Scholar

5. Liao J, Wei D, Sun C, Yang Y, Wei Y, and Liu X. Prognostic value of the combination of neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio and plat elet-to-lymphocyte ratio on mortality in patients on maintenance hemodialysis. BMC Nephrol. (2022) 23:393. doi: 10.1186/s12882-022-03020-1

PubMed Abstract | Crossref Full Text | Google Scholar

6. Yang Y, Xu Y, Lu P, Zhou H, Yang M, and Xiang L. The prognostic value of monocyte-to-lymphocyte ratio in peritoneal dialysis patients. Eur J Med Res. (2023) 28:152. doi: 10.1186/s40001-023-01073-y

PubMed Abstract | Crossref Full Text | Google Scholar

7. Xiang F, Chen R, Cao X, Shen B, Liu Z, Tan X, et al. Monocyte/lymphocyte ratio as a better predictor of cardiovascular and all-cause mortality in hemodialysis patients: A prospective cohort study. Hemodialysis international. Int Symp Home Hemodialysis. (2018) 22:82–92. doi: 10.1111/hdi.12549

PubMed Abstract | Crossref Full Text | Google Scholar

8. Camp RL, Dolled-Filhart M, and Rimm. DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. (2004) 10:7252–9. doi: 10.1158/1078-0432.Ccr-04-0713

PubMed Abstract | Crossref Full Text | Google Scholar

9. Pecoits-Filho R, Okpechi IG, Donner JA, Harris DCH, Aljubori HM, Bello AK, et al. Capturing and monitoring global differences in untreated and treated end-stage kidney disease, kidney replacement therapy modality, and outcomes. Kidney Int Supplements. (2020) 10:e3–9. doi: 10.1016/j.kisu.2019.11.001

PubMed Abstract | Crossref Full Text | Google Scholar

10. Wang Y and Gao. Inflammation L. and cardiovascular disease associated with hemodialysis for end-stage renal disease. Front Pharmacol. (2022) 13:800950. doi: 10.3389/fphar.2022.800950

PubMed Abstract | Crossref Full Text | Google Scholar

11. Campo S, Lacquaniti A, Trombetta D, Smeriglio A, and Monardo P. Immune system dysfunction and inflammation in hemodialysis patients: two sides of the same coin. J Clin Med. (2022) 11:3759. doi: 10.3390/jcm11133759

PubMed Abstract | Crossref Full Text | Google Scholar

12. Leng J, Wu F, and Zhang L. Prognostic significance of pretreatment neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or monocyte-to-lymphocyte ratio in endometrial neoplasms: A systematic review and meta-analysis. Front Oncol. (2022) 12:734948. doi: 10.3389/fonc.2022.734948

PubMed Abstract | Crossref Full Text | Google Scholar

13. Liu W, Weng S, Cao C, Yi Y, Wu Y, and Peng D. Association between monocyte-lymphocyte ratio and all-cause and cardiovascular mortality in patients with chronic kidney diseases: A data analysis from national health and nutrition examination survey (NHANES) 2003-2010. Renal Fail. (2024) 46:2352126. doi: 10.1080/0886022x.2024.2352126

PubMed Abstract | Crossref Full Text | Google Scholar

14. Tudurachi BS, Anghel L, Tudurachi A, Sascău RA, and Stătescu C. Assessment of inflammatory hematological ratios (NLR, PLR, MLR, LMR and monocyte/HDL-cholesterol ratio) in acute myocardial infarction and particularities in young patients. Int J Mol Sci. (2023) 24:14378. doi: 10.3390/ijms241814378

PubMed Abstract | Crossref Full Text | Google Scholar

15. Muto R, Kato S, Lindholm B, Qureshi AR, Ishimoto T, Kosugi T, et al. Increased monocyte/lymphocyte ratio as risk marker for cardiovascular events and infectious disease hospitalization in dialysis patients. Blood Purifica. (2022) 51:747–55. doi: 10.1159/000519289

PubMed Abstract | Crossref Full Text | Google Scholar

16. Losappio V, Franzin R, Infante B, Godeas G, Gesualdo L, Fersini A, et al. Molecular mechanisms of premature aging in hemodialysis: the complex interplay between innate and adaptive immune dysfunction. Int J Mol Sci. (2020) 21:3422. doi: 10.3390/ijms21103422

PubMed Abstract | Crossref Full Text | Google Scholar

17. Han XX, Zhang HY, Kong JW, Liu YX, Zhang KR, and Ren. WY. Inflammatory index is a promising biomarker for maintenance hemodialysis patients with cardiovascular disease. Eur J Med Res. (2024) 29:544. doi: 10.1186/s40001-024-02117-7

PubMed Abstract | Crossref Full Text | Google Scholar

18. Wen Y, Zhan X, Wang N, Peng F, Feng X, and Wu. X. Monocyte/lymphocyte ratio and cardiovascular disease mortality in peritoneal dialysis patients. Mediators inflamm. (2020) 2020:9852507. doi: 10.1155/2020/9852507

PubMed Abstract | Crossref Full Text | Google Scholar

19. Kletzmayr J and Hörl WH. Iron overload and cardiovascular complications in dialysis patients. Nephrol Dialysis Transplant. (2002) 17 Suppl 2:25–9. doi: 10.1093/ndt/17.suppl_2.25

PubMed Abstract | Crossref Full Text | Google Scholar

20. Rasić-Milutinović Z, Perunicić G, Pljesa S, Gluvić Z, Ilić M, and Stokić. E. The effect of nutritional status, body composition, inflammation and serum iron on the developement of insulin resistance among patients on long-term hemodialysis. Med Pregled. (2007) 60:33–8.

PubMed Abstract | Google Scholar

21. Dai L, Golembiewska E, Lindholm B, and Stenvinkel P. End-Stage Renal Disease, Inflammation and cardiovascular outcomes. Contrib to Nephrol. (2017) 191:32–43. doi: 10.1159/000479254

PubMed Abstract | Crossref Full Text | Google Scholar

22. Núñez J, Miñana G, Bodí V, Núñez E, Sanchis J, Husser O, et al. Low lymphocyte count and cardiovascular diseases. Curr Med Chem. (2011) 18:3226–33. doi: 10.2174/092986711796391633

PubMed Abstract | Crossref Full Text | Google Scholar

23. Zidar DA, Al-Kindi SG, Liu Y, Krieger NI, Perzynski AT, Osnard M, et al. Association of lymphopenia with risk of mortality among adults in the US general population. JAMA Netw Open. (2019) 2:e1916526. doi: 10.1001/jamanetworkopen.2019.16526

PubMed Abstract | Crossref Full Text | Google Scholar

24. Wong GCL, Strickland MC, and Larbi. A. Changes in T cell homeostasis and vaccine responses in old age. Interdiscip Topics Gerontol Geriatrics. (2020) 43:36–55. doi: 10.1159/000504487

PubMed Abstract | Crossref Full Text | Google Scholar

25. Marrocos MSM, Teixeira AA, Quinto BM, Canzian MEF, Manfredi S, and Batista. MC. Diabetes acts on mortality in hemodialysis patients predicted by asymmetric dimethylarginine and inflammation. Nefrologia. (2022) 42:177–85. doi: 10.1016/j.nefroe.2022.05.008

PubMed Abstract | Crossref Full Text | Google Scholar

26. Guo W, Song Y, Sun Y, Du H, Cai Y, You Q, et al. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011-2018. Front Endocrinol. (2022) 13:1071465. doi: 10.3389/fendo.2022.1071465

PubMed Abstract | Crossref Full Text | Google Scholar

27. Qiu C, Liu S, Li X, Li W, Hu G, and Liu. F. Prognostic value of monocyte-to-lymphocyte ratio for 90-day all-cause mortality in type 2 diabetes mellitus patients with chronic kidney disease. Sci Rep. (2023) 13:13136. doi: 10.1038/s41598-023-40429-6

PubMed Abstract | Crossref Full Text | Google Scholar