Association of mean platelet volume/lymphocyte ratio with inflammation in non-dialysis patients with chronic kidney disease stages 1-4: A retrospective study

The association of plasma level of phenylalanine with inflammatory markers, insulin resistance, and atherosclerotic indexes in patients with phenylketonuria

Objectives

Patients with phenylketonuria (PKU) are at increasing risk of metabolic disorders and atherosclerosis. This case-control study aimed to compare the values of atherosclerosis risk factors, including insulin resistance (IR), dyslipidemia, mean of platelet volume (MPV), and systemic inflammation in adult PKU patients.

Methods

Fifty patients with PKU were categorized into two groups: well-controlled (WC) (plasma Phe < 600 µmol/L) and poorly controlled (PC) (Phe > 600 µmol/L). Twenty-five age -, gender -, and BMI-matched healthy individuals were enrolled as the control group. Serum insulin, fasting blood sugar (FBS), and lipids were measured. The systemic inflammatory index (SII) and systemic inflammatory response index (SIRI) were calculated using the counts of neutrophils, lymphocytes, monocytes, and platelets.

Results

Both PKU groups had lower serum cholesterol, low-density lipoprotein, and high-density lipoprotein than the healthy subjects (p < 0.001). No significant difference was observed in IR between the PKU patients and the control group. MPV was significantly higher in the patients with PKU compared to the healthy controls. The levels of SII and SIRI were substantially lower in the WC group compared to the healthy control group and the PC group.

Conclusions

All in all, a higher level of dyslipidemia and MPV were observed in patients with PKU compared to healthy individuals. SII and SIRI were significantly lowered in the WC group compared to the PC group and healthy individuals, suggesting the role of adherence to a restricted diet in reducing the risk of systemic inflammation in patients with PKU.

Association of white blood cell count to mean platelet volume ratio with type 2 diabetic peripheral neuropathy in a Chinese population: a cross-sectional study

BACKGROUND

The white blood cell count to mean platelet volume ratio (WMR) is considered a promising inflammatory marker, and its recognition is increasing. Inflammation is closely related to metabolic diseases such as diabetes and its complications. However, there are currently no reports on the correlation between WMR and type 2 diabetic peripheral neuropathy (DPN). This study aims to explore the correlation between WMR and DPN in type 2 diabetes patients. By understanding this association, we hope to provide a theoretical basis for preventing DPN through the improvement of inflammatory responses.

METHODS

This was a cross-sectional study involving 2515 patients with T2DM. Logistic regression analysis was conducted to assess the associations between WMR and DPN. Finally, the receiver operating characteristic curve (ROC curve) was employed to evaluate the predictive efficacy of WMR for DPN.

RESULTS

Patients in higher WMR quartiles exhibited increased presence of DPN. Additionally, WMR remained significantly associated with a higher odds ratio (OR) of DPN (OR 4.777, 95% confidence interval [CI] 1.296-17.610, P < 0.05) after multivariate adjustment. Moreover, receiver operating characteristic curve analysis indicated that the optimal cutoff value for WMR in predicting DPN presence was 0.5395 (sensitivity: 65.40%; specificity: 41.80%; and area under the curve [AUC]: 0.540).

CONCLUSIONS

In patients with T2DM, WMR was significantly increased in DPN and independently associated with an increased risk of DPN presence in Chinese patients. This suggests that WMR may serve as a useful and reliable biomarker of DPN, highlighting the importance of paying more attention to T2DM patients with high WMR to further prevent and reduce the development of DPN and related unfavorable health outcomes.

Association of systemic immune-inflammation index and systemic inflammation response index with chronic kidney disease: observational study of 40,937 adults

BACKGROUND

Chronic kidney disease (CKD) is linked to immunity and inflammation. Systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are novel measures for gauging an individual's systemic inflammatory activity. We aim to investigate the potential associations between them.

METHODS

This study encompassed a cohort of 40,937 adults from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. SII and SIRI were log2-transformed before conducting regression analysis, considering that these inflammatory markers were right skewed distributed. Weighted logistic regression models assessed the association of log2-SII and log2-SIRI levels with CKD prevalence. Weighted Cox regression models were utilized to estimate the risk of death. Subgroup analyses were performed to further clarify the effects of other covariates on the associations. Sensitivity analyses were performed to assess the robustness of our results.

RESULTS

6986 participants with CKD were recorded, and 2818 patients died during a mean follow-up time of 100 months. After adjusting for all covariates, the highest level of log2-SII increased the CKD incidence (odds ratio [OR]: 1.47, 95% confidence intervals [CI]: 1.32-1.65, P < 0.001), as well as log2-SIRI (OR: 1.79, 95% CI 1.60-2.01, P < 0.001) when compared with the lowest level reference group. The highest level of log2-SII significantly increased all-cause mortality (hazard risk [HR]: 1.29; 95% CI 1.13-1.48, P < 0.001), cardiovascular mortality (HR: 1.61, 95% CI 1.25-2.09, P < 0.001), and hypertension mortality (HR: 1.73, 95% CI 1.23-2.42, P = 0.001) in CKD patients. Additionally, the positive associations were also found between log2-SIRI and all cause (HR: 1.54, 95% CI 1.35-1.76, P < 0.001), cardiovascular (HR: 1.90, 95% CI 1.38-2.60, P < 0.001), and hypertension mortality (HR: 2.15, 95% CI 1.56-2.94, P < 0.001). Subgroup analyses unveiled variations in these effects among different populations.

CONCLUSION

There existed a substantial association of SII and SIRI levels with CKD prevalence, as well as mortality in patients with CKD in the U.S.

POPULATION

Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier

BACKGROUND

Low-grade inflammation is a significant component of chronic kidney disease (CKD). Systemic immune inflammation index (SII), a newly defined ratio combining neutrophil, lymphocyte, and platelet counts, has not yet been evaluated in the pediatric CKD population nor in the context of CKD progression or dialysis. Thus, this study aimed to analyze the complete blood cell count (CBC)-driven parameters, including SII, in children with CKD and to assess their potential usefulness in the prediction of the need for chronic dialysis.

METHODS

A single-center, retrospective study was conducted on 27 predialysis children with CKD stages 4-5 and 39 children on chronic dialysis. The data were analyzed with the artificial intelligence tools.

RESULTS

The Random Forest Classifier (RFC) model with the input variables of neutrophil count, mean platelet volume (MPV), and SII turned out to be the best predictor of the progression of pediatric CKD into end-stage kidney disease (ESKD) requiring dialysis. Out of these variables, SII showed the largest share in the prediction of the need for renal replacement therapy.

CONCLUSIONS

Chronic inflammation plays a pivotal role in the progression of CKD into ESKD. Among CBC-driven ratios, SII seems to be the most useful predictor of the need for chronic dialysis in CKD children.

Evaluating the baseline hemoglobin, albumin, lymphocyte, and platelet (HALP) score in the United States adult population and comorbidities: an analysis of the NHANES

Introduction

As a composite immunonutritional biomarker, the Hemoglobin, Albumin, Lymphocyte, Platelet (HALP) score has shown promise in assessing a patient's overall health status by integrating several routinely collected laboratory indicators. This biomarker has been examined in many different populations of patients and disease states (i.e., cancer), but an integrated, universal rubric using standardized thresholds has not thus far been developed. Pre-existing large population-based databases represent an ideal source to examine the distribution of HALP and the influence of diverse health statuses on this score.

Methods

We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between 2017-2020, evaluating 8,245 participants across numerous demographic, socioeconomic, and health-related variables. Univariate and multivariate linear regression analyses assessed the associations between HALP scores and these factors.

Results

Our findings revealed significant associations between HALP scores and various demographic, socioeconomic, and health conditions. The median HALP score among the representative population was 49.0, with varying median scores across different groups and normal reference ranges for males and females. Multivariate regression analysis showed that anemia treatment, age over 65 years, weak/failing kidneys, and cancer were independent risk factors associated with lower HALP scores. Male participants demonstrated higher HALP scores than female participants, and age was inversely related to HALP. Moreover, HALP scores were negatively associated with the number of comorbidities.

Conclusion/discussion

This study set out to explore the HALP score from a population-based perspective, uncovering notable associations that offer vital insights into the score's clinical relevance and future applications. By determining a median HALP score of 49.0 and normal reference ranges within our diverse, representative sample, we establish a robust foundation for researchers to refine optimal HALP applications and thresholds. Considering the growing focus on personalized medicine, HALP holds promise as a prognostic tool, enabling clinicians to comprehend their patients' immunonutritional status better and deliver customized care.