
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312351
MD-D-24-02857
00052
10.1097/MD.0000000000039747
3
3500
Research Article
Observational Study
Discussion on the relationship between the distribution characteristics of TCM syndrome types and related objective indicators in hepatolenticular degeneration
Zhang Shuning MMed a
Cao Shijian MD b
Chen Yonghua MD b
Zhang Bo BM b
https://orcid.org/0009-0003-9333-4164
Yang Ji MSE b*
a Department of Brain Diseases, Geriatric Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Center for Xin’an Medicine and Modernization of Traditional Chinese Medicine of IHM, Anhui University of Chinese Medicine, Hefei, China
b Department of Brain Diseases, Geriatric Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.
* Correspondence: Ji Yang, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, China (e-mail: yangji@azyfy.com).
20 9 2024
20 9 2024
103 38 e3974718 3 2024
23 8 2024
28 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Hepatolenticular degeneration is a rare treatable autosomal recessive inherited copper metabolism disorder with a diverse clinical phenotype and an exceptionally complex pathogenesis. Early definitive phenotypic diagnosis and targeted treatment are major challenges worldwide. In this study, we strictly followed the “National Standards of the People’s Republic of China - Terminology of Traditional Chinese Medicine Clinical Diagnosis and Treatment (Syndrome Part),” “Chinese Medicine Nomenclature,” and the clinical investigation-determined traditional Chinese medicine syndrome differentiation standards at Anhui University of Chinese Medicine to select 6 of the most common traditional Chinese medicine syndrome differentiations. This study retrospectively analyzed 107 patients admitted between 2019 and 2023 with Wilson’s disease based on real-world data. After testing for normal distribution and homogeneity of variance, corresponding analysis of variance was selected, followed by post hoc multiple comparisons. Of the selected 25 objective influencing factors, 22 exhibited normal distribution, while red blood cells, hemoglobin, and type IV collagen did not pass the homogeneity of variance test. After analysis of variance, the factors ceruloplasmin (CP) and copper oxidase (SCO) showed significant differences among patients with different traditional Chinese medicine syndromes (P < .05), with partial η2 for CP being 0.13 > 0.06 and for SCO being 0.143 > 0.14. Post hoc multiple comparison results indicated significant differences in CP and SCO among patients with certain traditional Chinese medicine syndromes (P < .05). There were significant differences in the factors CP and SCO among patients with different traditional Chinese medicine syndromes. Significant differences were observed in the copper blue protein factor between damp-heat syndrome and liver and kidney deficiency syndrome, liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome, liver and kidney deficiency syndrome and phlegm heat and wind syndrome, as well as liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05). Significant differences were also found in the SCO factor between damp-heat syndrome and liver and kidney deficiency syndrome, liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome, liver and kidney deficiency syndrome and phlegm heat and wind syndrome, and liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05).

analysis of variance
hepatolenticular degeneration
post hoc multiple comparisons
real world
traditional Chinese medicine syndrome classification
OPEN-ACCESSTRUE
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pmc1. Introduction

Hepatolenticular degeneration (HLD), also known as Wilson disease (WD),[1] is a rare treatable autosomal recessive inherited copper metabolism disorder.[2] It is characterized by abnormal copper deposition in organs such as the liver, brain, kidneys, and corneas, leading to organ damage and a variety of clinical manifestations. Common symptoms include acute (or chronic) hepatitis, cirrhosis, splenomegaly, ascites, renal damage, as well as neurological and psychiatric symptoms such as limb tremors, dystonia, muscle rigidity, speech difficulties, and behavioral abnormalities. The disease may also present with skin lesions, joint symptoms, with cirrhosis, extrapyramidal symptoms, and Kayser–Fleischer rings being the most common.

In 1912, the renowned British neurologist Samuel Alexander Kinnier Wilson conducted a detailed study on postmortem materials from a group of adolescents with “pseudo-sclerosis” and identified the pathological features as cirrhosis with concomitant brain striatal degeneration. He named the condition “progressive lenticular degeneration with cirrhosis” in his work “Progressive lenticular degeneration: a familial nervous disease associated with cirrhosis.” Subsequent research revealed no fundamental differences between pseudo-sclerosis and progressive hepatic lenticular degeneration, indicating that they belong to the same disease category, eventually leading to the name HLD.[3] HLD can occur worldwide across all age groups,[4] with the most common onset between 5 and 35 years, and reports suggest onset as early as 8 months.[5] The global incidence of HLD is approximately 1 in 30,000, with a carrier rate of the pathogenic recessive gene at 1 in 907.[6,7] Treatment primarily focuses on inducing urinary copper excretion and inhibiting copper absorption in the digestive tract.[8–12]

In traditional Chinese medicine (TCM), the clinical manifestations of HLD are diverse, commonly presenting symptoms such as limb tremors, muscle spasms, distension and fullness in the abdomen, jaundice, and yellowing of the eyes and skin, which can be categorized under the TCM concepts of “distension and fullness,” “jaundice,” “abdominal distention,” “tremor syndrome,” and “spasm syndrome.” Based on the theory in the Yellow Emperor’s Inner Classic[13] that “all dizziness and vertigo are related to the liver,” liver wind internal stirring has been considered by many scholars as the fundamental pathogenesis of HLD. In clinical practice, the method of soothing the liver and calming the wind is often used, with commonly prescribed heavy and astringent shellfish-based medications. However, due to the high copper content in TCM, symptoms may worsen after use, indicating that the condition should not be solely attributed to “liver wind internal stirring.” In summary, insufficiency of the liver and kidneys, copper toxicity accumulation are the etiology of this disease, with damp-heat internal accumulation and phlegm-blood stasis as the main pathogenesis. The primary pathogenesis varies in different stages of the disease, and clinical differentiation should be emphasized.[14–16]

Various scholars have proposed different pathogenic mechanisms for HLD, including the accumulation of damp heat, congenital deficiency leading to impaired liver bile excretion,[17] and the accumulation of copper leading to the formation of phlegm and blood stasis. Overall, insufficiency of the liver and kidneys, copper accumulation, internal damp-heat, and phlegm-blood stasis are considered the main pathogenic factors of the disease. Treatment in Western medicine often relies on medications, particularly copper-chelating agents, along with hepatoprotective and antioxidant therapies. Surgery is common in end-stage chronic liver disease in HLD patients,[18] while gene therapy may offer a fundamental cure for the disease.[19] The integration of traditional Chinese and Western medicine in the treatment of HLD has shown significant efficacy,[20,21] with a focus on improving early diagnosis and tailored treatment based on different TCM syndromes.[22]

This study aims to analyze the relationship between different TCM syndromes and relevant examination indicators in 107 real-world HLD patients. By examining the correlations between these factors and different TCM syndromes in the real world, the study seeks to understand the impact of various factors on TCM syndromes. The findings will contribute to the development of combined traditional Chinese and Western medicine treatments for HLD, improve early diagnosis rates, provide evidence-based early treatment for patients with different TCM syndromes, and offer guidelines for preventive measures for the general population.

2. Materials and methods

2.1. Research object

This study retrospectively analyzed 107 patients with HLD admitted to the encephalopathy department of the geriatric center at the First Affiliated Hospital of Anhui University of Chinese Medicine between January 2019 and December 2023. Among the subjects, there were 65 males with an average age of 25.8 years, and 42 females with an average age of 27.9 years (ages were taken from the first page of the patients’ medical records).

2.2. Diagnosis and TCM syndrome classification standards

The diagnosis of HLD conforms to the diagnostic criteria in the “Chinese Guideline for Diagnosis and Treatment of Hepatolenticular Degeneration 2021”[23]; the differentiation of TCM syndromes refers to the “National Standard of the People’s Republic of China - Clinical Terminology of Traditional Chinese Medicine (Syndromes)”[24] and the “Nomenclature of Traditional Chinese Medicine”[25] issued by the Committee for the Examination and Approval of Nomenclature of TCM, combined with the preliminary clinical investigation of brain diseases by Anhui University of Chinese Medicine to formulate the TCM syndrome differentiation criteria.[26]

2.3. Inclusion criteria

Meet the diagnostic criteria for HLD; able to cooperate with clinical examinations and objective indicator tests; no restrictions on age or gender; patient’s informed consent; complete relevant medical records upon admission.

2.4. Exclusion criteria

Patients who do not meet the diagnostic criteria for HLD; patients with severe mental disorders who cannot cooperate; and patients with incomplete clinical data.

2.5. Rejection criteria

Patients who discharge themselves during treatment or have incomplete data; patients who cannot continue treatment due to sudden illness.

2.6. Dialectical classification

In this study, the TCM dialectical classification of 107 patients refers to the “Chinese Guideline for Diagnosis and Treatment of Hepatolenticular Degeneration 2021,” the “National Standard of the People’s Republic of China - Clinical Terminology of Traditional Chinese Medicine (Syndromes),” the “Nomenclature of Traditional Chinese Medicine,” and combines the preliminary clinical investigation of brain diseases by Anhui University of Chinese Medicine to formulate TCM syndrome differentiation criteria. Two (associate) chief TCM physicians diagnosed the TCM syndrome differentiation, and their opinions were consistent. The clinical research design, measurement, and evaluation method were used to verify the TCM syndrome differentiation. To ensure the accuracy of the results, the final classification result for a patient was based on the consensus of 3 opinions. If 1 opinion differed, another experienced chief physician was consulted for judgment. This study selected 6 types of TCM syndrome differentiation, including liver and kidney deficiency syndrome, spleen and kidney yang deficiency syndrome, liver and kidney yin deficiency syndrome (based on the TCM syndrome reference standards and considering the limited number of cases, liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome combine to form liver and kidney yin deficiency syndrome were combined into liver and kidney yin deficiency syndrome), damp-heat syndrome, phlegm heat and wind syndrome, syndrome of phlegm and blood stasis.

2.7. Influencing factors

In this study, we collected and analyzed the following data from 107 patients: TCM syndrome type; Hematological indicators: including white blood cells (WBCs), red blood cells (RBCs), hemoglobin (HGB), and platelets (PLT); Liver function indicators: including total bilirubin (TBIL), direct bilirubin (DBIL), in DBIL (IBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (AlB), globulin (GlB); Lipid indicators: triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL); Renal function indicators: blood urea nitrogen (BUN), creatinine (CR); copper biochemical indicators including serum copper, ceruloplasmin (CP), copper oxidase (SCO), 24-hour urine copper; liver fibrosis indicators including hyaluronic acid (HA), laminin (LN), type IV collagen (CIV), type III procollagen N-terminal peptide (PIIINP); Ultrasonic examination: portal vein diameter.

The above blood indicators were collected by fasting venous blood sampling after admission, and ultrasonic examinations were performed in the early morning on an empty stomach after admission. After excluding missing data, this study included the following objective detection indicators: WBC, RBC, HGB, PLT, TBIL, DBIL, IBIL, ALT, AST, TP, AlB, GlB, TG, TC, LDL, HDL, BUN, CR, CP, SCO, 24-hour urine copper, HA, LN, CIV, PIIINP. Based on these factors, we explored and analyzed the relationship between different TCM syndrome types and related indicators.

2.8. Statistical analysis method

All data were statistically processed using SPSS 26.0 software. Parametric data required normal distribution and homogeneity of variance tests. For parametric data from random samples that met the criteria of normal distribution and homogeneity of variance, 1-way analysis of variance was used. For data with inhomogeneous variance, Welch analysis of variance was used. For nonnormally distributed parametric data, nonparametric tests were used. A P value of <.05 indicated a statistically significant difference between the 2 groups. After analysis of variance, post hoc multiple comparisons were conducted based on the obtained data. The overall research process is shown in Figure 1.

Figure 1. Study flowchart.

3. Results

3.1. Distribution of patient syndrome types

The TCM syndrome types of 107 hospitalized patients with HLD were statistically analyzed, and the results are shown in Figure 2. Damp-heat syndrome accounted for the highest proportion at 77 cases (71.96%), followed by syndrome of phlegm and blood stasis in 18 cases (16.82%), liver and kidney deficiency syndrome in 5 cases (4.67%), and liver and kidney yin deficiency syndrome in 3 cases. There were 3 cases (2.8%) of phlegm heat and wind syndrome, and 1 case of spleen and kidney yang deficiency syndrome (0.93%).

Figure 2. Distribution of TCM syndrome types. TCM = traditional Chinese medicine.

3.2. Patient’s overall condition

The overall situation of related influencing factors for patients with different TCM syndrome types is shown in Table 1.

Table 1 Patient’s overall condition.

	TCM syndrome types (number of examples)	
Liver and kidney deficiency syndrome (n = 5)	Liver and kidney yin deficiency syndrome (n = 3)	Spleen and kidney yang deficiency syndrome (n = 1)	Damp-heat syndrome (n = 77)	Phlegm heat and wind syndrome (n = 3)	Syndrome of phlegm and blood stasis (n = 18)	
Gender (male/female)	4/1	1/2	1/0	44/33	3/0	12/6	
Age (mean)	14.8	27.7	25	27.7	27.7	25.2	
WBC (mean)	5.9	5.5	4.69	4.9	3.7	5.2	
RBC (mean)	4.4	4.4	3.62	4.3	4.4	4.5	
HGB (mean)	113.0	129.7	115	129.1	139.7	130.9	
PLT (mean)	239.2	143.7	200	171.6	103	154.9	
TBIL (mean)	9.5	15.4	23	15	12.8	16.6	
DBIL (mean)	2.2	3.0	4.4	3.8	3.2	4.1	
IBIL (mean)	7.3	12.4	18.6	11.2	9.7	13.6	
ALT (mean)	34.3	25.7	28.1	46.1	33.7	45.9	
AST (mean)	27.6	27.0	54.4	36.8	27	32.5	
TP (mean)	63.6	70.0	60.5	63.6	59.9	64.4	
AlB (mean)	40.1	40.0	34.9	37.2	38	39.6	
GlB (mean)	23.6	30.1	25.6	25.6	21.9	24.8	
TG (mean)	1.4	1.3	0.8	1.1	1.6	1.0	
TC (mean)	4.1	4.9	4.5	4.1	4.5	3.8	
LDL (mean)	2.3	2.9	2.7	2.3	2.8	2.1	
HDL (mean)	1.3	1.4	1.4	1.3	0.9	1.2	
BUN (mean)	4.0	4.3	6.3	5.2	6.5	4.8	
CR (mean)	45.8	63.5	73.9	66	77	72.2	
CP (mean)	0.11	0.04	0.02	0.05	0.03	0.03	
SCO (mean)	0.11	0.03	0.02	0.04	0.02	0.03	
24 h urine copper (mean)	366.6	856.0	395.3	735.5	1702	517.9	
HA (mean)	57.5	54.3	104.3	155	82.5	137.5	
LN (mean)	99.9	160.9	154.7	125	152.9	117.3	
CIV (mean)	52.7	58.5	33.6	82.3	44.4	64.9	
PIIINP (mean)	30.7	7.2	14.6	21.6	8	22.5	
AlB = albumin, ALT = alanine transaminase, AST = aspartate aminotransferase, BUN = blood urea nitrogen, CIV = collagen type Ⅳ, CP = ceruloplasmin, CR = creatinine, DBIL = direct bilirubin, GlB = globulin, HA = hyaluronic acid, HDL = high-density lipoprotein, HGB = hemoglobin, IBIL = indirect bilirubin, LDL = low-density lipoprotein, LN = laminin, PIIINP = type III procollagen N-terminal peptide, PLT = platelets, RBC = red blood cell, SCO = copper oxidase, TBIL = total bilirubin, TC = total cholesterol, TCM = traditional Chinese medicine, TG = triglycerides, TP = total protein, WBC = white blood cell.

3.3. Normal distribution test of objective detection indicators

Because the objective detection indicators of patients are quantitative data, and there are 6 types of syndrome differentiation in patients, it is necessary to first test whether these objective detection indicators follow a normal distribution before using relevant statistical analysis methods.

3.3.1. Normal distribution test of WBC indicators

A normal distribution test was conducted on the WBC indicators of 107 patients. Using the WBC indicators as the horizontal axis and frequency as the vertical axis, a frequency distribution histogram and normal distribution curve were plotted, as shown in Figure 3. Analysis of Figure 3 indicates that the WBC indicators follow a normal distribution.

Figure 3. Normal distribution test of white blood cell indicators.

3.3.2. Normal distribution test of RBC indicators

A normal distribution test was conducted on the RBC indicators of 107 patients. Using the RBC indicators as the horizontal axis and frequency as the vertical axis, a frequency distribution histogram and normal distribution curve were plotted, as shown in Figure 4. Analysis of Figure 4 indicates that the RBC indicators follow a normal distribution.

Figure 4. Normal distribution test of red blood cell indicators.

3.3.3. HGB indicator normal distribution test

The normal distribution test was performed on the HGB index of 107 patients. With the HGB index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 5. By analyzing Figure 5, it can be concluded that the HGB index conforms to the normal distribution.

Figure 5. Hemoglobin indicator normal distribution test.

3.3.4. Platelet indicator normal distribution test

The normal distribution test was performed on the platelet indicators of 107 patients. With the platelet indicators as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 6. By analyzing Figure 6, it can be concluded that the platelet index conforms to a normal distribution.

Figure 6. Platelet indicator normal distribution test.

3.3.5. Normal distribution test of TBIL index

The normal distribution test was performed on the TBIL index of 107 patients. With the TBIL index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 7. By analyzing Figure 7, it can be concluded that the TBIL index conforms to a normal distribution.

Figure 7. Normal distribution test of total bilirubin index.

3.3.6. DBIL indicator normal distribution test

The DBIL index of 107 patients was tested for normal distribution. With the DBIL index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 8. By analyzing Figure 8, it can be concluded that the DBIL index conforms to a normal distribution.

Figure 8. Direct bilirubin indicator normal distribution test.

3.3.7. In DBIL indicator normal distribution test

The DBIL index of 107 patients was tested for normal distribution. With the DBIL index as the abscissa and frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 9. By analyzing Figure 9, it can be concluded that the DBIL index conforms to a normal distribution.

Figure 9. Indirect bilirubin indicator normal distribution test.

3.3.8. Normal distribution test of ALT index

A normal distribution test was performed on the ALT index of 107 patients. With the ALT index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 10. By analyzing Figure 10, it can be concluded that although it is not a standard normal distribution, it can be considered that the ALT index basically conforms to the normal distribution.

Figure 10. Normal distribution test of alanine aminotransferase index.

3.3.9. Normal distribution test of AST index

A normal distribution test was performed on the AST index of 107 patients. With the AST index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 11. From the analysis of Figure 11, it can be concluded that although it is not a standard normal distribution, it can be considered that the AST index basically conforms to the normal distribution.

Figure 11. Normal distribution test of aspartate aminotransferase index.

3.3.10. Normal distribution test of TP index

The normal distribution test was performed on the TP index of 107 patients. With the TP index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 12. By analyzing Figure 12, it can be concluded that the TP index conforms to a normal distribution.

Figure 12. Normal distribution test of total protein index.

3.3.11. AlB indicator normal distribution test

The normal distribution test was performed on the AlB indicators of 107 patients. With the AlB indicator as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 13. From the analysis of Figure 13, it can be concluded that the AlB index conforms to the normal distribution.

Figure 13. Albumin indicator normal distribution test.

3.3.12. GlB indicator normal distribution test

A normal distribution test was performed on the GlB indexes of 107 patients. With the GlB index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 14. From the analysis of Figure 14, it can be concluded that the GlB index conforms to the normal distribution.

Figure 14. Globulin indicator normal distribution test.

3.3.13. Triglyceride index normal distribution test

A normal distribution test was performed on the triglyceride indicators of 107 patients. With the triglyceride indicator as the abscissa and frequency as the ordinate, a frequency distribution histogram and a normal distribution curve were drawn. The results are shown in Figure 15. By analyzing Figure 15, it can be concluded that the triglyceride index conforms to a normal distribution.

Figure 15. Triglyceride index normal distribution test.

3.3.14. Normal distribution test of TC index

The normal distribution test was performed on the TC index of 107 patients. With the TC index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 16. By analyzing Figure 16, it can be concluded that the TC index conforms to a normal distribution.

Figure 16. Normal distribution test of total cholesterol index.

3.3.15. LDL indicator normal distribution test

The LDL index of 107 patients was tested for normal distribution. With the LDL index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 17. From the analysis of Figure 17, it can be concluded that the LDL index conforms to a normal distribution.

Figure 17. Low-density lipoprotein indicator normal distribution test.

3.3.16. HDL normal distribution test

The HDL index of 107 patients was tested for normal distribution. With the HDL index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 18. From the analysis of Figure 18, it can be concluded that the HDL index conforms to a normal distribution.

Figure 18. HDL normal distribution test. HDL = high-density lipoprotein.

3.3.17. Normal distribution test of BUN index

A normal distribution test was performed on the BUN index of 107 patients. Taking the BUN index as the abscissa and frequency as the ordinate, draw the frequency distribution histogram and normal distribution curve. The results are shown in Figure 19. From the analysis of Figure 19, it can be concluded that the BUN index conforms to the normal distribution.

Figure 19. Normal distribution test of blood urea nitrogen index.

3.3.18. CR index normal distribution test

The normal distribution test was performed on the CR index of 107 patients. Taking the CR index as the abscissa and frequency as the ordinate, draw the frequency distribution histogram and normal distribution curve. The results are shown in Figure 20. From the analysis of Figure 20, it can be concluded that the CR index conforms to the normal distribution.

Figure 20. Creatinine index normal distribution test.

3.3.19. CP indicator normal distribution test

A normal distribution test was performed on the CP index of 107 patients. With the CP index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 21. From the analysis of Figure 21, it can be concluded that although it is not a standard normal distribution, it can be considered that the CP index basically conforms to the normal distribution.

Figure 21. Ceruloplasmin indicator normal distribution test.

3.3.20. SCO indicator normal distribution test

A normal distribution test was performed on the SCO indexes of 107 patients. With the SCO index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 22. By analyzing Figure 22, it can be concluded that although it is not a standard normal distribution, it can be considered that the SCO index basically conforms to the normal distribution.

Figure 22. Normal distribution test of copper oxidase index.

3.3.21. Normal distribution test of 24 hours urine copper index

A normal distribution test was performed on the 24-hour urinary copper index of 107 patients. Taking the 24-hour urinary copper index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 23. From the analysis of Figure 23, it can be concluded that although it is not a standard normal distribution, it can be considered that the 24-hour urine copper index basically conforms to the normal distribution.

Figure 23. Normal distribution test of 24 hours urine copper index.

3.3.22. Normal distribution test of hyaluronic acid index

The normal distribution test was performed on the hyaluronic acid index of 107 patients. With the hyaluronic acid index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 24. By analyzing Figure 24, it can be concluded that although it is not a standard normal distribution, it can be considered that the hyaluronic acid index basically conforms to the normal distribution.

Figure 24. Normal distribution test of hyaluronic acid index.

3.3.23. Laminin indicator normal distribution test

The laminin index of 107 patients was tested for normal distribution. With the laminin index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 25. From the analysis of Figure 25, it can be concluded that the laminin index conforms to a normal distribution.

Figure 25. Laminin indicator normal distribution test.

3.3.24. Type IV collagen index normal distribution test

A normal distribution test was performed on the type IV collagen index of 107 patients. Using the type IV collagen index as the abscissa and the frequency as the ordinate, the frequency distribution histogram and normal distribution curve were drawn. The results are shown in Figure 26. From the analysis of Figure 26, it can be concluded that although it is not a standard normal distribution, it can be considered that the type IV collagen index basically conforms to the normal distribution.

Figure 26. Type IV collagen index normal distribution test.

3.3.25. Type III procollagen N-terminal peptide indicator normal distribution test

A normal distribution test was performed on the type III procollagen N-terminal peptide index of 107 patients. Using the type III procollagen N-terminal peptide index as the abscissa and the frequency as the ordinate, draw the frequency distribution histogram and normal distribution curve. The results are as follows as shown in Figure 27. By analyzing Figure 27, it can be concluded that the type III procollagen N-terminal peptide index conforms to a normal distribution.

Figure 27. Type III procollagen N-terminal peptide indicator normal distribution test.

3.4. Test for homogeneity of variances

As the influencing factors used all passed the normal distribution test, it was necessary to conduct a test for homogeneity of variance, with the results shown in Table 2. Since there was only 1 case of spleen and kidney yang deficiency syndrome, its standard deviation was null. Among the different TCM syndrome samples, there were no significant differences (P > .05) observed for WBCs, platelets, alanine transaminase, AST, TBIL, DBIL, in DBIL, TP, AlB, GlB, BUN, CR, TC, TG, LDL, HDL, CP, SCO, 24-hour urine copper, hyaluronic acid, laminin, and type III procollagen N-terminal peptide, meeting the prerequisite of homogeneity of variance for analysis of variance. Therefore, 1-way analysis of variance can be used to study the differences. However, for RBCs, HGB, and type IV collagen, which showed significance (P < .05) among the TCM syndrome samples, and did not exhibit homogeneity of variance, with noticeable data fluctuations inconsistency, Welch analysis of variance can be used for differential studies.

Table 2 Homogeneity of variance test results.

	TCM syndrome types (standard deviation)	F	P	
Liver and kidney deficiency syndrome (n = 5)	Liver and kidney yin deficiency syndrome (n = 3)	Spleen and kidney yang deficiency syndrome (n = 1)	Damp-heat syndrome (n = 77)	Phlegm heat and wind syndrome (n = 3)	Syndrome of phlegm and blood stasis (n = 18)	
WBC	2.17	1.2	Null	1.63	1	2.11	1.464	.208	
RBC	1.72	0.14	Null	0.61	0.53	0.48	3.804	.003	
HGB	36.43	4.51	Null	17.11	19.63	15.5	2.397	.042	
PLT	76.48	57.18	Null	85.73	13.86	74.49	1.264	.286	
ALT	22.96	18.58	Null	57.87	21.36	40.91	0.552	.737	
AST	13.14	9.54	Null	31.76	12.12	20.86	0.523	.759	
TBIL	8.08	3.76	Null	6.81	3.58	6.92	0.858	.512	
DBIL	1.23	1.05	Null	2.08	0.4	2.93	0.956	.449	
IBIL	7.09	2.72	Null	5.51	3.18	7.67	1.168	.33	
TP	4.73	7.16	Null	4.9	5.25	3.71	1.208	.311	
AlB	3.31	3.26	Null	6.59	1.73	3.35	1.193	.318	
GlB	2.24	4.3	Null	4.51	3.52	2.86	1.619	.162	
BUN	0.49	0.97	Null	1.45	1.35	1.3	1.403	.23	
CR	28.4	18.1	Null	23.43	1.96	31.4	1.108	.361	
TC	1.26	0.41	Null	0.92	1.2	1.03	0.96	.446	
TG	1.18	0.65	Null	0.5	0.71	0.53	1.917	.098	
LDL	0.87	0.23	Null	0.65	0.87	0.68	0.976	.436	
HDL	0.35	0.19	Null	0.38	0.05	0.29	1.782	.123	
CP	0.05	0.02	Null	0.04	0	0.04	1.828	.114	
SCO	0.07	0.01	Null	0.04	0	0.03	2.224	.058	
24 h urine copper	271.75	704.19	Null	807.4	1477.66	336.92	1.871	.106	
CIV	12.92	31.96	Null	55.3	1.45	22.75	2.608	.029	
HA	20.24	25.1	Null	157.38	62.72	84.99	2.248	.055	
LN	54.42	75.83	Null	50.86	69.25	49.16	0.677	.642	
PIIINP	23.35	1.92	Null	15.03	2.17	16.05	2.301	.052	
Bold indicates statistical significance (P < .05).

AlB = albumin, ALT = alanine transaminase, AST = aspartate aminotransferase, BUN = blood urea nitrogen, CIV = collagen type Ⅳ, CP = ceruloplasmin, CR = creatinine, DBIL = direct bilirubin, GlB = globulin, HA = hyaluronic acid, HDL = high-density lipoprotein, HGB = hemoglobin, IBIL = indirect bilirubin, LDL = low-density lipoprotein, LN = laminin, PIIINP = type III procollagen N-terminal peptide, PLT = platelets, RBC = red blood cell, SCO = copper oxidase, TBIL = total bilirubin, TC = total cholesterol, TCM = traditional Chinese medicine, TG = triglycerides, TP = total protein, WBC = white blood cell.

3.5. Variance analysis

According to the homogeneity of variance test results, a variance analysis was conducted on the quantitative data of WBC, RBC, HGB, PLT, TBIL, DBIL, IBIL, ALT, AST, TP, Alb, Glb, TG, TC, LDL, HDL, BUN, CR, CP, SCO, 24-hour urine copper, HA, LN, CIV, and PIIINP. The results are shown in Table 3, where P > .05 indicates no statistical significance, and P < .05 indicates statistical significance. For the influencing factors showing significant differences, further analysis of the difference magnitude was conducted using effect size, with the results presented in Table 4.

Table 3 ANOVA summary table.

	TCM syndrome types (standard deviation)	F	P	
Liver and kidney deficiency syndrome (n = 5)	Liver and kidney yin deficiency syndrome (n = 3)	Spleen and kidney yang deficiency syndrome (n = 1)	Damp-heat syndrome (n = 77)	Phlegm heat and wind syndrome (n = 3)	Syndrome of phlegm and blood stasis (n = 18)	
WBC	2.17	1.2	Null	1.63	1	2.11	0.834	.528	
RBC	1.72	0.14	Null	0.61	0.53	0.48	0.524	.757	
HGB	36.43	4.51	Null	17.11	19.63	15.5	1.172	.328	
PLT	76.48	57.18	Null	85.73	13.86	74.49	1.32	.261	
ALT	22.96	18.58	Null	57.87	21.36	40.91	0.171	.973	
AST	13.14	9.54	Null	31.76	12.12	20.86	0.331	.893	
TBIL	8.08	3.76	Null	6.81	3.58	6.92	1.21	.31	
DBIL	1.23	1.05	Null	2.08	0.4	2.93	0.771	.573	
IBIL	7.09	2.72	Null	5.51	3.18	7.67	1.374	.241	
TP	4.73	7.16	Null	4.9	5.25	3.71	1.595	.168	
AlB	3.31	3.26	Null	6.59	1.73	3.35	0.748	.59	
GlB	2.24	4.3	Null	4.51	3.52	2.86	1.464	.208	
BUN	0.49	0.97	Null	1.45	1.35	1.3	1.852	.109	
CR	28.4	18.1	Null	23.43	1.96	31.4	1.016	.412	
TC	1.26	0.41	Null	0.92	1.2	1.03	0.985	.431	
TG	1.18	0.65	Null	0.5	0.71	0.53	1.123	.353	
LDL	0.87	0.23	Null	0.65	0.87	0.68	1.085	.373	
HDL	0.35	0.19	Null	0.38	0.05	0.29	0.834	.529	
CP	0.05	0.02	Null	0.04	0	0.04	3.03	.014	
SCO	0.07	0.01	Null	0.04	0	0.03	3.374	.007	
24 h urine copper	271.75	704.19	Null	807.4	1477.66	336.92	1.562	.178	
CIV	12.92	31.96	Null	55.3	1.45	22.75	1.137	.346	
HA	20.24	25.1	Null	157.38	62.72	84.99	0.844	.522	
LN	54.42	75.83	Null	50.86	69.25	49.16	0.841	.523	
PIIINP	23.35	1.92	Null	15.03	2.17	16.05	1.404	.229	
Bold indicates statistical significance (P < .05).

AlB = albumin, ALT = alanine transaminase, AST = aspartate aminotransferase, ANOVA = analysis of variance, BUN = blood urea nitrogen, CIV = collagen type Ⅳ, CP = ceruloplasmin, CR = creatinine, DBIL = direct bilirubin, GlB = globulin, HA = hyaluronic acid, HDL = high-density lipoprotein, HGB = hemoglobin, IBIL = indirect bilirubin, LDL = low-density lipoprotein, LN = laminin, PIIINP = type III procollagen N-terminal peptide, PLT = platelets, RBC = red blood cell, SCO = copper oxidase, TBIL = total bilirubin, TC = total cholesterol, TCM = traditional Chinese medicine, TG = triglycerides, TP = total protein, WBC = white blood cell.

Table 4 In-depth analysis-effect size indicator.

	SSB	SST	Partial η2	Cohen f	
CP	0.025	0.188	0.13	0.387	
SCO	0.029	0.201	0.143	0.409	
CP = ceruloplasmin, SCO = copper oxidase, SSB = sum of squares between groups, SST = total sum of squares.

3.6. Post hoc multiple comparisons

After the analysis of variance, it was found that patients with different TCM syndromes showed significance (P < .05) for both CP and SCO. Therefore, the LSD method was used for post hoc multiple comparison analysis to further explore the differences between pairwise groups. The results are presented in Table 5 and Figure 27, where P > .05 indicates no statistical significance, and P < .05 indicates statistical significance. The letter annotation method results are shown in Table 6. If a significance level of 0.05 is used, lowercase letters (abcd, etc) are used for annotation. If a significance level of 0.01 is used, uppercase letters (ABCD, etc) are used for annotation. If 2 items share the same letter, such as “a” and “ab,” it indicates that there is no significance at the .05 level. Conversely, if 2 items have completely different letters, such as “a” and “b,” it indicates a significant difference at the .05 level.

Table 5 Post hoc multiple comparison results.

	(I)name	(J)name	(I)mean	(J)mean	Difference value (I-J)	P	Cohen d value	
CP	DHS	LKDS	0.054	0.105	−0.052	.007	−1.28	
DHS	LKYDS	0.054	0.04	0.014	.549	0.354	
DHS	PHWS	0.054	0.026	0.028	.242	0.693	
DHS	SPBS	0.054	0.033	0.02	.055	0.508	
LKDS	LKYDS	0.105	0.04	0.066	.028	1.633	
LKDS	PHWS	0.105	0.026	0.079	.008	1.973	
LKDS	SPBS	0.105	0.033	0.072	.001	1.788	
LKYDS	PHWS	0.04	0.026	0.014	.678	0.34	
LKYDS	SPBS	0.04	0.033	0.006	.805	0.155	
PHWS	SPBS	0.026	0.033	−0.007	.767	−0.185	
SCO	DHS	LKDS	0.038	0.108	−0.07	.000	−1.697	
DHS	LKYDS	0.038	0.025	0.013	.589	0.319	
DHS	PHWS	0.038	0.017	0.021	.393	0.505	
DHS	SPBS	0.038	0.027	0.011	.311	0.267	
LKDS	LKYDS	0.108	0.025	0.083	.007	2.016	
LKDS	PHWS	0.108	0.017	0.091	.003	2.202	
LKDS	SPBS	0.108	0.027	0.081	0	1.964	
LKYDS	PHWS	0.025	0.017	0.008	.821	0.185	
LKYDS	SPBS	0.025	0.027	−0.002	.933	−0.052	
PHWS	SPBS	0.017	0.027	−0.01	.704	−0.238	
Bold indicates statistical significance (P < .05).

CP = ceruloplasmin, DHS = damp-heat syndrome, LKDS = liver and kidney deficiency syndrome, LKYDS = liver and kidney yin deficiency syndrome, PHWS = phlegm heat and wind syndrome, SCO = copper oxidase, SPBS = syndrome of phlegm and blood stasis.

Table 6 Letter notation results.

Analysis item	TCM syndrome types	Mean	Letter annotation (0.05 level)	Letter annotation (0.01 level)	
CP	Damp-heat syndrome	0.05	b	B	
Liver and kidney deficiency syndrome	0.11	a	A	
Liver and kidney yin deficiency syndrome	0.04	b	B	
Phlegm heat and wind syndrome	0.03	b	B	
Spleen and kidney yang deficiency syndrome	0.01	b	B	
Syndrome of phlegm and blood stasis	0.03	b	B	
SCO	Damp-heat syndrome	0.04	b	B	
Liver and kidney deficiency syndrome	0.11	a	A	
Liver and kidney yin deficiency syndrome	0.02	b	B	
Phlegm heat and wind syndrome	0.02	b	B	
Spleen and kidney yang deficiency syndrome	0.02	b	B	
Syndrome of phlegm and blood stasis	0.03	b	B	
CP= ceruloplasmin, SCO= copper oxidase, TCM = traditional Chinese medicine.

4. Discussion

The distribution of TCM syndrome types in acute cerebral infarction follows a certain pattern. In descending order of distribution ratio, the syndrome types were wind phlegm obstruction syndrome, wind yang disturbance syndrome, blood stasis syndrome, phlegm stasis blocking collateral syndrome, yin-deficiency wind syndrome, phlegm obstruction syndrome, phlegm heat fu empirical, and phlegm qi stagnation syndrome.

Existing research suggests that there is a correlation between different TCM syndrome types in patients with HLD and related diagnostic indicators. Furthermore, various influencing factors also have an impact on these TCM syndrome types. Therefore, it is necessary to further explore the specific correlations and influencing factors.[15,27]

Between January 2019 and December 2023, a retrospective analysis was conducted on 107 patients admitted to the Department of Neurology at the Geriatric Disease Center of the First Affiliated Hospital of Anhui University of Chinese Medicine due to WD. All selected patients strictly met the inclusion, exclusion, and elimination criteria in the literature, making the obtained results reliable.

After obtaining the influencing factors of patients: WBC, RBC, HGB, PLT, TBIL, DBIL, IBIL, ALT, AST, TP, AlB, GlB, TG, TC, LDL, HDL, BUN, CR, CP, SCO, 24 hours urine copper, HA, LN, CIV, PIIINP, as they are quantitative data, it is necessary to assess whether they follow a normal distribution before choosing the corresponding statistical methods. Therefore, a normal distribution test was conducted on these influencing factors, and it was found that they all followed a normal distribution based on frequency distribution histograms and normal distribution curves.

After the normal distribution test, homogeneity of variance testing was performed. According to Table 2, the influencing factors WBC, PLT, TBIL, DBIL, IBIL, ALT, AST, TP, AlB, GlB, TG, TC, LDL, HDL, BUN, CR, CP, SCO, 24-hour urine copper, HA, LN, PIIINP passed the homogeneity of variance test (P > .05). The influencing factors RBC, HGB, and CIV did not pass the homogeneity of variance test (P < .05). Therefore, single-factor analysis of variance and Welch analysis of variance were respectively chosen for analysis.

As shown in Table 3, patients with different TCM syndromes did not show statistical significance (P > .05) in the influencing factors WBC, RBC, HGB, PLT, TBIL, DBIL, IBIL, ALT, AST, TP, AlB, GlB, TG, TC, LDL, HDL, BUN, CR, 24 hours urine copper, HA, LN, CIV, PIIINP. This indicates that patients with different syndromes exhibited consistency in these influencing factors and did not show differences. However, patients with different syndromes showed statistical significance (P < .05) in the influencing factors CP and SCO, indicating differences in these factors among different syndromes. This is further supported by Table 4. Partial η2 represents the effect size (magnitude of difference), with larger values indicating greater differences. When using partial η2 to represent effect size, the critical points for small, medium, and large effects are 0.01, 0.06, and 0.14, respectively. The partial η2 for CP is 0.13 > 0.06, indicating a medium effect size for the impact of different syndromes on CP. The partial η2 for SCO is 0.143 > 0.14, indicating a large effect size for the impact of different syndromes on SCO.

After the analysis of variance, patients with different TCM syndromes showed significant differences in the CP and SCO indicators (P < .05). Therefore, post hoc multiple comparison analysis was conducted using the LSD method.

From Figure 27, it can be observed that for the CP indicator, patients with liver and kidney deficiency syndrome had the highest mean value, followed by damp-heat syndrome, liver and kidney yin deficiency syndrome, syndrome of phlegm and blood stasis, phlegm heat and wind syndrome, and spleen and kidney yang deficiency syndrome. For the SCO indicator, patients with liver and kidney deficiency syndrome had the highest mean value, followed by damp-heat syndrome, syndrome of phlegm and blood stasis, liver and kidney yin deficiency syndrome, spleen and kidney yang deficiency syndrome, and phlegm heat and wind syndrome. Analyzing Tables 5 and 6 in conjunction with Figure 27, Figure 28, it can be concluded that there is a significant difference in the CP factor between damp-heat syndrome and liver and kidney deficiency syndrome; liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome; liver and kidney deficiency syndrome and phlegm heat and wind syndrome; and liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05). The SCO factor exhibits significant differences between damp-heat syndrome and liver and kidney deficiency syndrome; liver and kidney deficiency syndrome and liver and kidney yin deficiency syndrome; liver and kidney deficiency syndrome and phlegm heat and wind syndrome; and liver and kidney deficiency syndrome and syndrome of phlegm and blood stasis (P < .05).

Figure 28. Post hoc multiple comparison comparison chart of CP and SCO. CP = ceruloplasmin, SCO = copper oxidase.

All the case information included in this study was sourced from the Inpatient Department of the Neurology Department at the Geriatric Disease Center of Anhui Provincial Hospital of TCM. Although efforts were made to be as random and objective as possible, there may still be clinical data bias and selection bias to some extent. In future work, we will aim to expand the sample size and selection range as much as possible. In this study, patients were rigorously classified by experts according to the standards mentioned earlier, and the 6 selected TCM syndromes, while not completely representative of the actual patient conditions, cover the majority of manifestations of WD patients. In the future, through multicenter and large-sample prospective studies, we will further explore the relationship between different syndromes of WD patients and relevant objective indicators.

Author contributions

Conceptualization: Shuning Zhang, Shijian Cao.

Data curation: Shuning Zhang.

Investigation: Shuning Zhang.

Methodology: Shuning Zhang.

Project administration: Shuning Zhang.

Resources: Shuning Zhang.

Software: Shuning Zhang, Yonghua Chen, Ji Yang.

Supervision: Shuning Zhang.

Writing—original draft: Shuning Zhang.

Writing—review & editing: Shuning Zhang, Shijian Cao, Bo Zhang, Ji Yang.

Abbreviations:

AlB albumin

ALT alanine transaminase

AST aspartate aminotransferase

BUN blood urea nitrogen

CIV collagen type IV

CP ceruloplasmin

CR creatinine

DBIL direct bilirubin

GlB globulin

HA hyaluronic acid

HDL high-density lipoprotein

HGB hemoglobin

HLD hepatolenticular degeneration

IBIL indirect bilirubin

LDL low-density lipoprotein

LN laminin

PIIINP type III procollagen N-terminal peptide

PLT platelets

RBC red blood cell

SCO copper oxidase

TBIL total bilirubin

TC total cholesterol

TG triglycerides

TP total protein

WBC white blood cell

WD Wilson disease

This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, number: 2023AH-32, and Following the Helsinki Declaration Guidelines

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Zhang S, Cao S, Chen Y, Zhang B, Yang J. Discussion on the relationship between the distribution characteristics of TCM syndrome types and related objective indicators in hepatolenticular degeneration. Medicine 2024;103:38(e39747).
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References

[1] Członkowska A Litwin T Dusek P . Wilson disease. Nat Rev Dis Primers. 2018;4 :21.30190489
[2] Nishimuta M Masui K Yamamoto T . Copper deposition in oligodendroglial cells in an autopsied case of hepatolenticular degeneration. Neuropathology. 2018;38 :321–8.29468756
[3] Homburger F Kozol HL . Hepatolenticular degeneration. J Am Med Assoc. 1946;130 :6–14.21006177
[4] Nagappa M Sinha S Saini JS . Non-Wilsonian hepatolenticular degeneration: clinical and MRI observations in four families from south India. J Clin Neurosci. 2016;27 :91–4.26765764
[5] Yang K Deng Z Wang J . Clinical analysis of hepatolenticular degeneration in 38 children. J Clin Pediatr. 2017;12 :733–6.
[6] Wei D Chun M Qing W . Analysis and classification of tremor characteristics of hepatolenticular degeneration. International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence 7. China: Springer International Publishing, 2020:1276–85.
[7] Xiao QQ Wang ZX Fan JG . Factors influencing the clinical phenotype of hepatolenticular degeneration. Zhonghua Gan Zang Bing Za Zhi. 2023;31 :207–11.37137840
[8] Yi LP Zhang W Wu Z . Present status of diagnosis and treatment of hepatolenticular degeneration. Zhonghua Gan Zang Bing Za Zhi. 2019;27 :161–5.30929331
[9] Wang N Wu H Zhou A . Gandou decoction decreases copper levels and alleviates hepatic injury in copper-laden hepatolenticular degeneration model rats. Front Pharmacol. 2020;11 :582390.33746737
[10] Fang F . Long-term treatment and follow-up management of children with hepatolenticular degeneration. J Clin Hepatol. 2017;12 :1936–8.
[11] Shen Y Guo B Wang L . Significance of amylase monitoring in peritoneal drainage fluid after splenectomy: a clinical analysis of splenectomy in 167 patients with hepatolenticular degeneration. Am Surg. 2020;86 :334–40.32391757
[12] Dong Y Wu ZY . Challenges and suggestions for precise diagnosis and treatment of Wilson’s disease. World J Pediatr. 2021;17 :561–5.34714531
[13] Ke SX . The principles of health, illness and treatment—the key concepts from “The Yellow Emperor’s Classic of Internal Medicine”. J Ayurveda Integr Med. 2023;14 :100637.36460575
[14] SiMin Z WangFeng C Lu Z . Latest advances in the treatment of hepatolenticular degeneration. J Clin Hepatobiliary Dis. 2020;36 :218–21.
[15] Yang X Wang T Tang Y Shao Y Gao Y Wu P . Treatment of liver fibrosis in hepatolenticular degeneration with traditional Chinese medicine: systematic review of meta-analysis, network pharmacology and molecular dynamics simulation. Front Med. 2023;10 :1193132.
[16] Yin F Zhou A . Protective mechanism of Gandou decoction in a copper-laden hepatolenticular degeneration model: in vitro pharmacology and cell metabolomics. Front Pharmacol. 2022;13 :848897.35401189
[17] Pronicki M . Wilson disease–liver pathology. Handbook Clin Neurol. 2017;142 :71–5.
[18] Fan JG Li Y Hao X . Effectiveness and economic evaluation of polyene phosphatidyl choline in patients with liver diseases based on real-world research. Front Pharmacol. 2022;13 :806787.35330831
[19] Ovchinnikov AV Shprakh VV . Hepatolenticular degeneration: diagnostic difficulties (practical experience). Acta Biomedica Scientifica. 2016;1 :198–201.
[20] Cheng M Wu H Wu H Liu X Zhou A . Metabolic profiling of copper-laden hepatolenticular degeneration model rats and the interventional effects of Gandou decoction using UPLC-Q-TOF/MS. J Pharm Biomed Anal. 2019;164 :187–95.30390561
[21] Li WJ Chen C You ZF Yang R-M Wang X-P . Current drug managements of Wilson’s disease: from west to east. Curr Neuropharmacol. 2016;14 :322–5.26639459
[22] Vyalova NV Doloka DS Proskokova TN . Hepatolenticular degeneration with hidden pathology of liver: case report. Ann Clin Exp Neurol. 2017;11 :72–5.
[23] Neurogenetics Group of the Neurology Branch of the Chinese Medical Association. Chinese guidelines for the diagnosis and treatment of hepatolenticular degeneration 2021. Chin J Neurol. 2021;54 :310–9.
[24] State Bureau of Technical Supervision. Syndrome Part of Clinical Diagnosis and Treatment Terminology of Traditional Chinese Medicine. China: Beijing China Standard Press, 1997:2.
[25] Traditional Chinese Medicine Terminology Approval Committee. Terminology of Traditional Chinese Medicine. China: Beijing Science Press, 2005:58–108.
[26] Wenming Y Yuanyuan B Bo Z . Diagnosis and treatment plan for hepatolenticular degeneration. Clin J Tradit Chin Med. 2012;24 :1130.
[27] Qian N Wei T Yang W . Discussion on medication rules of TCM in the treatment of hepatolenticular degeneration based on data mining. Chinese Journal of Information on Traditional Chinese Medicine. 2021;28 :29–36.
