Clinical significance of platelet-to-white blood cell ratio in patients with Wilson disease: a retrospective cohort study

Participants

This study comprised a consecutive cohort of inpatients diagnosed with WD at the First Affiliated Hospital of Guangdong Pharmaceutical University from January 1, 2016, to March 30, 2022. Exclusion criteria included liver diseases of alternative etiologies, patients with pre-existing malignancies, and cases with incomplete medical records.

Data collection

Clinical data obtained from the electronic medical record system included demographic details, smoking and alcohol consumption history, medical background, duration of WD, anti-copper therapy, urinary copper levels, presence of Kayser–Fleischer rings, neuropsychiatric symptoms, platelet and WBC counts, liver injury markers (alanine transaminase (ALT), aspartate transaminase (AST), and total bilirubin), synthetic function markers (albumin, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)), triglyceride levels, coagulation markers (international normalized ratio (INR) and prothrombin time (PT)), liver fibrosis markers (procollagen type III terminal propeptide (PIIINP), type IV collagen, hyaluronic acid, and laminin), portal vein diameter, cirrhosis status, hepatic complications (splenomegaly/splenectomy, esophagogastric varices, ascites, spontaneous bacterial peritonitis (SBP), renal impairment, portal vein thrombosis, hepatic encephalopathy, liver failure, and hepatocellular carcinoma), Child–Pugh score, and hepatic decompensation. The follow-up period for compensated patients extended from baseline to either hepatic decompensation or the study’s conclusion.

Methodology of laboratory measurements

Urinary copper concentrations were determined via ICP-MS at DAAN Clinical Laboratory Central (Guangzhou, China). Serum PIIINP, type IV collagen, hyaluronic acid, and laminin levels were assessed using an automated chemiluminescence analyzer at the same facility. Platelet and WBC counts were obtained with a blood routine testing instrument (Mindray BC-6900). Standard laboratory techniques on an auto-analyzer (BECKMAN AU5800) were employed to measure serum ALT, aspartate transaminase, total bilirubin, albumin, total cholesterol, HDL-C, LDL-C, and triglycerides. PT and INR were measured and calculated using an automated coagulation analyzer (STAGO STA-R).

Definitions

In China, the definition of WD aligns with international diagnostic criteria (Czlonkowska et al., 2018), incorporating factors such as age at onset, liver and neurological symptoms, copper concentrations in serum, liver, and urine, serum ceruloplasmin levels, the presence of Kayser–Fleischer rings, and genetic testing (Wang et al., 2023). The PWR is derived by dividing the platelet count by the WBC count, with values in the lowest third (26.3) serving as the threshold. Alcoholism is defined as a weekly alcohol intake exceeding 70 g. Diagnoses for conditions like splenomegaly/splenectomy, ascites, portal vein thrombosis, and hepatocellular carcinoma rely on a combination of physical examination, imaging, pathological assessment, and patient history. Esophagogastric varices are identified through gastroscopy or imaging. SBP is diagnosed when the neutrophil count in ascitic fluid exceeds 250/mm³ without a surgically treatable cause within the abdomen. Renal impairment is identified when blood creatinine exceeds 132.6 μmol/L (Piano et al., 2013). Hepatic encephalopathy diagnosis follows the West Haven Classification (Riordan & Williams, 1997), while liver failure is indicated by an INR greater than 1.5 in conjunction with hepatic encephalopathy (Driever et al., 2021). Hepatic decompensation is diagnosed in the presence of ascites, varices, or hepatic encephalopathy (Jachs et al., 2022). The Child–Pugh classification is determined as described previously (Pugh et al., 1973).

Statistical analyses

The sample size was calculated using the online Power and Sample Size Calculators. Statistical analysis was performed with SPSS version 22 (IBM, Armonk, NY, USA), GraphPad Prism version 8.0.2, and R version 4.3.2. Categorical data were presented as frequencies (percentages), while continuous data were reported as means ± standard deviation for normally distributed data or as medians (interquartile range) for non-normally distributed data. Group comparisons were conducted using chi-square tests, Fisher’s exact tests, unpaired and paired Student’s t-tests, and Mann–Whitney U tests, depending on the data characteristics. Correlations were assessed using Pearson or Spearman methods, based on the distribution of the data.

The study examined the associations between PWR and various liver function parameters and complications using multivariate linear and logistic regression analyses. These models controlled for variables including age, sex, BMI, smoking status, alcohol use, hypertension, diabetes, and anti-copper therapy. To evaluate the diagnostic utility of platelet count, WBC count, and PWR in hepatic decompensation, receiver operating characteristic (ROC) curves were applied to assess specificity and sensitivity. Furthermore, Kaplan–Meier survival analysis and Cox regression models, both unadjusted and adjusted for factors such as age, sex, BMI, alcohol use, smoking, hypertension, diabetes, and anti-copper therapy, were employed to determine if low PWR was an independent risk factor for hepatic decompensation in WD patients. Results from the linear and logistic regressions were presented as beta coefficients with standard errors and odds ratios (ORs) with 95% confidence intervals (CIs), while Cox regression findings were reported as hazard ratios (HRs) with 95% CIs. A P value of less than 0.05 was considered statistically significant.

Patient characteristics

A total of 320 WD patients were initially enrolled in the study. Following the application of exclusion criteria, which removed one patient with hepatitis B and four with malignant tumors, 315 WD patients remained for analysis. These patients were divided into two groups: 105 with a low PWR (≤26.3) and 210 with a high PWR (>26.3). Baseline characteristics for each group were presented in Table 1. ATP7B gene analysis was performed on 19 patients, all of whom displayed mutations.

Low PWR is associated with WD-related liver dysfunction

In patients with WD, those exhibiting low PWR demonstrated significantly higher ALT and total bilirubin levels compared to those with high PWR (P < 0.05 for both). With respect to synthetic function, the low PWR group showed markedly lower albumin, total cholesterol, LDL-C, and triglyceride levels (P < 0.01 for all). Coagulation parameters were also impaired in the low PWR group, which had substantially elevated INR and PT values relative to the high PWR group (P < 0.001 for both). Concerning liver fibrosis markers, concentrations of type IV collagen and hyaluronic acid were significantly higher in the low PWR group (P < 0.001 for both). Additionally, patients with WD and low PWR exhibited notably larger portal vein diameters and a higher prevalence of cirrhosis (P < 0.01 for both, Table 2). These trends were consistent across subgroups of male, female, and untreated patients (Tables S1–S3).

After adjusting for potential confounders, multivariate linear regression revealed that patients with low PWR exhibited significantly higher levels of total bilirubin, INR, PT, PIIINP, type IV collagen, hyaluronic acid, and portal vein diameter. In contrast, these patients had notably lower concentrations of albumin, total cholesterol, LDL-C, and triglycerides compared to those with high PWR (P < 0.05 for all parameters, Table 3).

Correlation analysis further demonstrated that PWR was negatively associated with ALT, total bilirubin, INR, PT, type IV collagen, hyaluronic acid, and portal vein diameter, while it showed positive correlations with albumin, total cholesterol, LDL-C, triglycerides, and laminin in patients with WD (P < 0.05 for all, Table 4).

Low PWR is a risk factor for WD-related liver complications

Patients with WD and low PWR demonstrated a significantly greater prevalence of splenomegaly or splenectomy (66.67% vs. 50.48%, P < 0.01), esophagogastric varices (13.33% vs. 1.43%, P < 0.001), ascites (14.29% vs. 0.48%, P < 0.001), hepatic encephalopathy (4.76% vs. 0.48%, P < 0.05), liver failure (4.76% vs. 0%, P < 0.01), advanced Child–Pugh classification (26.73% vs. 2.58%, P < 0.001), and hepatic decompensation (23.81% vs. 2.38%, P < 0.001, Table 5) compared to those with high PWR. Subgroup analysis further identified that low PWR was linked to a higher incidence of WD-related liver complications in both male and female patients (Tables S4 and S5).

Logistic regression analyses revealed that low PWR independently predicted several complications in WD patients. Specifically, low PWR was associated with an increased risk of splenomegaly/splenectomy (OR = 1.93, 95% CI [1.15–3.25], P < 0.05), esophagogastric varices (OR = 9.93, 95% CI [2.73–36.10], P < 0.001), ascites (OR = 21.75, 95% CI [2.71–174.37], P < 0.01), and hepatic decompensation (OR = 9.71, 95% CI [3.47–27.15], P < 0.001; Table 6).

Figure 1 illustrated the use of ROC curves to assess the diagnostic value of platelet count, WBC count, and PWR in differentiating hepatic decompensation from compensation. The area under the curve (AUC) values were 0.85 (95% CI [0.77–0.92]) for PWR, 0.78 (95% CI [0.69–0.87]) for platelet count, and 0.62 (95% CI [0.50–0.74]) for WBC count. The DeLong test confirmed that the AUC for PWR was significantly greater than for both platelet and WBC counts (P < 0.05 for each comparison).

Baseline low PWR is an independent risk factor for predicting hepatic decompensation during follow-up

Among the 285 WD patients initially presenting with hepatic compensation, 234 completed at least one follow-up visit. Over a median follow-up duration of 34.78 months (range: 15.18–50.58), 18 patients progressed to hepatic decompensation. Kaplan–Meier analysis revealed a more rapid progression to decompensation in those with low PWR (P < 0.01). After multivariable adjustments, the Cox proportional hazards model identified low PWR as an independent predictor of hepatic decompensation, with a hazard ratio of 3.68 (95% CI [1.21–11.18], P < 0.05).

Splenectomy is an effective approach to recover PWR

Hypersplenism frequently contributes to thrombocytopenia in WD patients (Strickland, Chang & Beckner, 1972), prompting an investigation into the effect of splenectomy on PWR in this cohort. Compared to patients with splenomegaly who did not undergo splenectomy, those who underwent the procedure demonstrated significantly higher platelet counts (118.00 [80.00–168.25] vs. 282.00 [221.25–338.25], P < 0.001), WBC counts (4.55 [3.53–5.71] vs. 6.24 [4.94–8.04], P < 0.001), and PWR values (27.00 [18.56–34.46] vs. 43.70 [35.21–54.69], P < 0.001, Fig. 2A). Additionally, a subgroup of nine patients who had not initially undergone splenectomy but did so during the follow-up period showed significant increases in platelet count (95.33 ± 51.39 vs. 439.33 ± 163.96, P < 0.001), WBC count (2.88 [2.32–4.62] vs. 6.03 [4.77–7.44], P < 0.05), and PWR (25.99 ± 7.88 vs. 69.72 ± 21.52, P < 0.001, Fig. 2B) after splenectomy.