The five viscera score (FVS) is a diagnostic scale for traditional Chinese medicine (TCM). The purposes of current study are to elucidate the characteristics of FVS obtained from middle-aged to elderly individuals and to investigate the validity of FVS using biological medical data of middle-aged and elderly individuals. Structural equation modeling (SEM) was used to conduct assessments between FVS and medical data. Eighty men and 99 women participated in this study, whose mean ages (SD) were 58 ± 7 years in both genders showing no significant difference. FVS of women was significantly higher than that of men in the spleen of the 50s (
Acupuncture therapy is currently recognized as a complementary and alternative medicine (CAM) that is performed around the world, especially in East Asia [
That is why we developed a “five viscera score (FVS)” as a diagnostic scale for TCM. Items for this scale were statistically selected from the symptoms of the five viscera (liver, heart, spleen, lungs, and kidneys) that were extracted from key TCM literature from the past 2000 years [
Structural equation modeling (SEM) was used to conduct assessments between FVS and medical data. SEM is a method that uses a path diagram to analyze the relationship between various factors that exist behind the observed data [
Subjects included 212 individuals aged 40–65, who were residents of Wakayama Prefecture in Japan, underwent specific health checkups for metabolic syndrome, wished to participate in this study, and were able to attend the study site without assistance. Specific health checkups refer to a medical examination of individuals aged 40–74 who have public health insurance coverage. This study was conducted in August 2012 for 6 days. Consent was ultimately obtained from 189 individuals (89.2%), and the investigation was conducted in 179 individuals (84.4%). Ten individuals were excluded due to incomplete test values.
This study was approved by the Genetic Analysis Research Ethics Committee at Wakayama Medical University (number 92) and by the Ethics Committee of Kansai Vocational College of Medicine (number H25- 01). The purpose of this study was explained to the subjects and only those who signed the consent form were allowed to participate. All individuals were assigned an ID and made anonymous. Test results were consolidated by a third party.
FVS is composed of viscera symptoms as defined in TCM [
Validity of the revised version of FVS (
Subscale | Item number | Item | Factor loading |
|
Mean |
Cronbach’s |
---|---|---|---|---|---|---|
Liver | 0.79 | |||||
Q1 | I have a stiff neck | 0.82 | 2.11 (0.11) | 0.11 | ||
Q2 | I have a pulled muscle in my neck | 0.81 | 2.57 (0.14) | 0.55 | ||
Q3 | I have a backache | 0.49 | 0.77 (0.06) | 1.40 | ||
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Heart | 0.83 | |||||
Q4 | I worry about many things | 0.83 | 2.40 (0.12) | 0.40 | ||
Q5 | I worry frequently | 0.76 | 1.84 (0.10) | 0.50 | ||
Q6 | I have a lot on my mind and am not able to enjoy anything | 0.57 | 1.09 (0.07) | 1.19 | ||
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Spleen | 0.79 | |||||
Q7 | I am fatigued and this is not alleviated by anything | 0.69 | 1.76 (0.09) | 0.25 | ||
Q8 | I have to lie down due to fatigue | 0.60 | 0.93 (0.06) | 0.61 | ||
Q9 | My body feels heavy | 0.55 | 1.62 (0.08) | 0.33 | ||
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Lung | 0.63 | |||||
Q10 | My stomach rumbles | 0.74 | 1.45 (0.08) | 0.70 | ||
Q11 | I feel hungry constantly | 0.58 | 1.01 (0.07) | 1.29 | ||
Q12 | I have a runny nose | 0.41 | 0.61 (0.05) | 1.30 | ||
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Kidney | 0.78 | |||||
Q13 | I am absent minded | 0.58 | 1.70 (0.09) | 0.80 | ||
Q14 | I am not energetic | 0.53 | 1.45 (0.08) | 0.98 | ||
Q15 | My memory has deteriorated | 0.49 | 1.01 (0.07) | 1.03 |
FVS: five viscera score, “
Item discrimination represents the ability of an item to differentiate the subjects; Overall Cronbach’s
Values of factor loading, item discriminability, and the mean item difficulty were used for developing original FVS.
Cronbach’s alpha coefficient was recalculated for revised version.
The items that were ultimately removed were selected by referring to previous studies that used both CTT and IRT, as described below. Items with the lowest factor loading within each subscale according to factor analysis [
To investigate the relationship between health and FVS, which is a diagnostic scale of TCM, we used medical data including body mass index (BMI), blood pressure, and blood and urine test values. Subjects provided fasting venous blood and urine samples in the morning on the health checkup day. Test variables in the subjects’ blood and urine include triglyceride (TG) (mg/dL), low-density lipoprotein cholesterol (LDL-C) (mg/dL), high-density lipoprotein cholesterol (HDL-C) (mg/dL), glucose (GLU) (mg/dL), hemoglobin A1c (HbA1c) (%), uric acid (UA) (mg/dL), glutamic oxaloacetic transaminase (GOT, AST) (IU/L), glutamic pyruvic transaminase (GPT, ALT) (IU/L),
Amos 19, a program for SEM by IBM, was used. All linkage strengths estimated in the path diagram (path coefficient) were approximation of the standardised partial regression coefficient with a mean of 0 and variance of 1; thus the results were not affected by units. The path diagram was constructed using the multiple indicator multiple cause model (MIMIC model) with the latent variable of five viscera positioned between observed medical data and FVS items (Figure
MIMIC model of the liver by the structure equation modeling. The square of the figure is an observation variable, and the central circle is a latent variable. “e” means an error. For an example, we cast sex (male 0, female 1) and age as well as medical data into the left side adopted by exploratory model specialization across the liver which is a latent capacity variable. Subscale items of the FVS are set in the right side.
In addition, coefficient of determination (
Since there is a gender difference in FVS [
There were 80 men and 99 women participating in this study. Subject characteristics as well as medical data input into SEM are shown in Table
Biomedical characteristics of respondents.
Item | Male ( |
Female ( |
|
---|---|---|---|
|
|
||
Age (y) | |||
40–49 | 12 (15.0) | 12 (12.1) | |
50–59 | 20 (25.0) | 27 (27.3) | |
60–65 | 48 (60.0) | 60 (60.6) | |
Whole subjects mean (SD) | 58.1 (7.2) | 58.1 (6.5) | |
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Male ( |
Female ( |
|
|
Mean (SD) | Mean (SD) | ||
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Medical data | |||
BMI (kg/m2) | 23.7 (2.8) | 22.0 (4.2) | <0.001 |
TG (mg/dL) | 150.8 (105.4) | 110.0 (56.3) | 0.003 |
HDL-C (mg/dL) | 59.1 (14.5) | 68.7 (12.6) | <0.001 |
LDL-C (mg/dL) | 119.5 (31.8) | 126.6 (30.2) | 0.087 |
GLU (mg/dL) | 102.1 (18.5) | 93.1 (14.7) | <0.001 |
HbA1c (%) | 5.2 (0.6) | 5.1 (0.5) | 0.545 |
UA (mg/dL) | 6.1 (1.5) | 4.3 (1.0) | <0.001 |
GOT (IU/L) | 25.7 (13.4) | 22.3 (5.7) | 0.014 |
GPT (IU/L) | 27.1 (13.6) | 19.2 (6.5) | <0.001 |
|
60.5 (130.0) | 29.5 (81.0) | <0.001 |
Urinary protein (mg/dL) | 12.3 (11.4) | 10.4 (1.8) | 0.774 |
RBC (×104/uL) | 487.3 (40.1) | 440.7 (28.4) | <0.001 |
HCT (%) | 46.2 (3.4) | 41.2 (3.0) | <0.001 |
Systolic blood pressure (mmHg) | 129.7 (17.1) | 119.1 (15.7) | <0.001 |
Diastolic blood pressure (mmHg) | 76.3 (11.3) | 69.2 (9.3) | <0.001 |
Creatinine clearance (Ccr) (mL/min) | 83.0 (16.7) | 85.8 (15.4) | 0.325 |
Albumin/creatinine ratio (mg/gCr) | 19.3 (56.2) | 13.7 (15.5) | 0.002 |
HCRP (mg/dL) | 0.042 (0.057) | 0.042 (0.094) | 0.230 |
Body mass index (BMI), triglyceride (TG) (mg/dL), low-density lipoprotein cholesterol (LDL-C) (mg/dL), high-density lipoprotein cholesterol (HDL-C) (mg/dL), glucose (GLU) (mg/dL), hemoglobin A1c (HbA1c) (%), uric acid (UA) (mg/dL), glutamic oxaloacetic transaminase (GOT, AST) (IU/L), glutamic pyruvic transaminase (GPT, ALT) (IU/L),
Table
FVS according to sex and age class.
Generation | Subscale |
|
Male |
|
Female |
|
---|---|---|---|---|---|---|
Mean (SD) | Mean (SD) | |||||
Whole subjects | Liver | 80 | 3.2 (2.8) | 99 | 4.5 (3.4) | 0.019 |
Heart | 80 | 2.9 (2.8) | 99 | 3.2 (2.3) | 0.125 | |
Spleen | 80 | 3.3 (2.7) | 99 | 4.0 (2.2) | 0.032 | |
Lung | 80 | 2.3 (1.8) | 99 | 2.1 (1.5) | 0.466 | |
Kidney | 80 | 2.7 (2.4) | 99 | 2.8 (1.8) | 0.406 | |
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40–49 | Liver | 12 | 3.4 (2.2) | 12 | 3.3 (2.5) | 0.887 |
Heart | 12 | 4.2 (4.4) | 12 | 3.1 (2.2) | 0.932 | |
Spleen | 12 | 4.2 (4.4) | 12 | 3.7 (2.1) | 0.713 | |
Lung | 12 | 3.2 (1.9) | 12 | 2.3 (1.5) | 0.291 | |
Kidney | 12 | 3.5 (3.3) | 12 | 1.8 (1.3) | 0.266 | |
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50–59 | Liver | 20 | 4.3 (2.7) | 27 | 5.9 (3.5) | 0.186 |
Heart | 20 | 3.2 (2.1) | 27 | 3.5 (1.9) | 0.499 | |
Spleen | 20 | 3.7 (1.9) | 27 | 5.1 (1.8) | 0.019 | |
Lung | 20 | 2.6 (1.5) | 27 | 2.4 (1.6) | 0.584 | |
Kidney | 20 | 3.4 (2.1) | 27 | 3.1 (1.2) | 0.583 | |
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60–65 | Liver | 48 | 2.7 (2.9) | 60 | 4.1 (3.4) | 0.030 |
Heart | 48 | 2.5 (2.5) | 60 | 3.2 (2.5) | 0.129 | |
Spleen | 48 | 3.0 (2.5) | 60 | 3.6 (2.3) | 0.174 | |
Lung | 48 | 2.0 (1.9) | 60 | 1.9 (1.5) | 0.940 | |
Kidney | 48 | 2.3 (2.1) | 60 | 2.9 (2.0) | 0.075 |
Mann-Whitney
Using SEM, a model specification search was carried out for each viscus in order to identify observed variables (medical data) on the left side that significantly influence the latent variable (viscera). Medical data that were input for each subscale of FVS created 262,144 combinations for analysis, and the most suitable model was used.
Table
The effects on viscera from biomedical factors.
Subscale | Item | Path coefficient |
|
|
GFI | AGFI | RMSEA |
---|---|---|---|---|---|---|---|
Liver | Sex* | 0.273 | <0.001 | 0.182 | 0.898 | 0.830 | 0.117 |
Age | −0.041 | 0.577 | |||||
HbA1c | −0.179 | 0.015 | |||||
Diastolic blood pressure* | 0.178 | 0.016 | |||||
HCRP | 0.157 | 0.033 | |||||
HDL-C* | −0.131 | 0.075 | |||||
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Heart | Sex | 0.085 | 0.269 | 0.026 | 0.975 | 0.924 | 0.086 |
Age | −0.136 | 0.079 | |||||
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Spleen | Sex | 0.172 | 0.012 | 0.348 | 0.811 | 0.716 | 0.174 |
Age | −0.129 | 0.059 | |||||
GLU* | −0.223 | 0.001 | |||||
GOT* | 0.312 | <0.001 | |||||
|
−0.290 | <0.001 | |||||
BMI | 0.172 | 0.012 | |||||
Diastolic blood pressure | 0.156 | 0.023 | |||||
HCRP | 0.131 | 0.054 | |||||
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Lung | Sex | −0.024 | 0.789 | 0.155 | 0.967 | 0.940 | 0.042 |
Age* | −0.252 | 0.008 | |||||
LDL-C | 0.150 | 0.101 | |||||
BMI* | 0.188 | 0.042 | |||||
HCRP* | 0.181 | 0.050 | |||||
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Kidney | Sex | −0.004 | 0.955 | 0.092 | 0.900 | 0.821 | 0.142 |
Age | 0.016 | 0.841 | |||||
HbA1c* | −0.193 | 0.016 | |||||
Ccr* | 0.153 | 0.057 | |||||
HCRP | 0.176 | 0.028 |
Gender and age were forcibly included.
*The item by which the biomedical factors’ effects on viscera are characterized.
The value of path coefficient was used for standardized estimates.
The coefficients of determination of the model were 0.182 for the liver, 0.026 for the heart, 0.348 for the spleen, 0.155 for the lungs, and 0.092 for the kidneys, indicating that the spleen had the highest value. Medical data that had significant (or slightly significant) influence on viscera were gender, HbA1c, diastolic blood pressure, HCRP, and HDL-C in the liver; age in the heart; gender, age, GLU, GOT,
Furthermore, in order to elucidate items that especially affect the viscera, we selected items from the above list with high path coefficients that did not overlap other viscera (if there was an overlap, the item with a greater path coefficient was selected) and determined them to be distinctive items. This procedure was conducted to extract factors that make influences on the viscera most strongly. In other words, such items are considered to be well representative of the viscera’s characteristics. Since the path coefficients were standardized as mentioned above, they were comparable to each other. Distinctive items that influenced the viscera as well as their path coefficients were gender (0.273), diastolic blood pressure (0.178), and HDL-C (−0.131) for the liver; GLU (−0.223), GOT (0.312), and
The path coefficients from the latent variables (viscera) to the FVS items on the right side were as follows: for the liver, “I have a stiff neck” (0.857), “I have a pulled muscle in my neck” (0.892), and “I have a backache” (0.612); for the heart, “I worry about many things” (0.883), “I worry frequently” (0.942), and “I have a lot on my mind and am not able to enjoy anything” (0.716); for the spleen, “I am fatigued and this is not alleviated by anything” (0.802), “I have to lie down due to fatigue” (0.719), and “My body feels heavy” (0.870); for the lungs, “My stomach rumbles” (0.690), “I feel hungry constantly” (0.640), and “I have a runny nose” (0.271); and for the kidneys, “I am absent minded” (0.858), “I am not energetic” (0.752), and “My memory has deteriorated” (0.513). With the exception of “I have a runny nose” in the lungs, the path coefficients were greater than 0.5, indicating that FVS items are strongly influenced by latent viscera.
To test the validity of FVS in middle-aged and elderly individuals, we investigated the characteristics of FVS as well as the relationship between medical data (external criteria) and FVS using SEM with the MIMIC model. The results indicated that the characteristics showed gender differences, consistent with previous findings, and SEM showed a lot of the medical data influencing the viscera.
FVS by age group showed that women in their 50s and 60s had higher scores, and this was consistent with a previous study that reported the relationship between different genders in 594 young to middle-aged individuals [
Concerning subjective health status, the Medical Outcome Study Short-Form 36-Item Health Survey version 2 (SF-36), an evaluation scale that is used globally, and Japanese national standard values of this SF-36 have been reported [
SEM showed that many of the biomedical factors influenced viscera with the most distinctive items being gender, diastolic blood pressure, and HDL-C for the liver; GLU, GOT, and
Because the viscera in TCM are comprehensive concepts that not only possess morphological aspects, but also entail functional aspects, the influence from medical data to the viscera is restrictive. Furthermore, gender and age were both input compulsorily, thereby lowering the coefficient of determination and the goodness of fit of the model.
Most of the items in FVS are unidentified complaints. In both Western medicine and TCM, unidentified complaints are crucial to disease prevention; thus it was clinically important that there was an association between the medical data based on Western medicine with TCM. Moreover, it is noteworthy that the specific related medical data fit the descriptions of viscera characteristics in TCM textbooks as shown below [
The lung systemically circulates Qi and bodily fluids (such as saliva and tears). Stagnation leads to the development of edema. In addition, invasions by the common cold (i.e., external pathogens (wind)) are likely to occur. Hence, the lung is considered to be associated with factors related to obesity and inflammation, such as BMI or HCRP, which might reflect the spleen disorder rather than lung disorder by the TCM criteria. However, since FVS is a newly created scale as mentioned earlier, some of which might be inconsistent with the TCM literature. The kidney is supplemented by the energy created from foods and drinks. In addition, it eliminates liquids that are no longer necessary as urine. Therefore, the kidney is considered to be associated with factors related to blood sugar or renal function such as HbA1c or creatinine clearance. Because kidney and spleen dysfunction is attributed to diabetes, it is unclear why GLU and HbA1c were negatively associated with spleen and kidney as well as the discrepancy between GOT and
In a TCM study of hypertension, Wu et al. discovered two biomarkers from diagnostic standards of multiple TCM syndromes and reported that these are useful for classifying and discriminating TCM syndrome [
In a study of TCM diagnosis presented in 2012, Wang et al. reported that SEM is an effective method [
In TCM, patients are typically diagnosed in a comprehensive manner using four methods: “observation,” “auscultation and smell,” “inquiry,” and “palpation” [
We found gender differences of FVS and identified several biomedical factors that significantly influenced viscera by application of structural equation modeling. The current findings suggest that FVS can be useful for TCM diagnosis incorporated with Western medicine in middle-aged and elderly individuals.
A part of the current study was presented at the 63rd Annual Congress of the Japan Society of Acupuncture and Moxibustion.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to express their heartfelt appreciation to all those who assisted them in this study. This work was supported by JSPS KAKENHI Grant no. 25293153.