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CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2024 | Vol 7 | Issue 2 

 
 
 
 
 

    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

 
 

Abstract— An inadequate supply of blood by the heart to fulfil the metabolic needs of the body is a hallmark 

of chronic heart failure (CHF). The combination of Western and traditional Chinese medicine has been shown 

to be an effective treatment for congestive heart failure. A promising platform for providing biomarkers for 

disease subtypes has emerged in the field of metabolomics in recent years.  

The purpose of this study was to establish diagnostic biomarkers for two subtypes of congestive heart failure 

syndrome by combining nuclear magnetic resonance plasma metabolomics with traditional Chinese medicine 

diagnosis and examining 38 patients. 

To analyse the contributing NMR signals, we used Y-scrambling statistical validation, which yielded high 

reliability. Then, we ran orthogonal partial least square discriminant analysis on the processed spectra.  

 

Patients with yin deficit and yang deficiency were distinguished by their plasma metabolic patterns, according 

to the results. Lactate, glycoprotein, and lipoprotein levels were higher in the yin-deficiency group, whereas 

glucose, valine, and proline levels were lower. Lactate, glycoprotein, and pyruvic acid levels were greater in 

the yang-deficiency group, although glucose and lipoprotein levels were lower.Two TCM symptoms that may 

serve as biomarkers for congestive heart failure are abnormalities in energy utilisation and disturbances in 

fatty acid and amino acid metabolism, among other metabolic pathways and metabolites.  

This research concludes that metabolic markers for subgroups of CHF syndrome may be revealed by 

integrating metabolomics with traditional Chinese medical diagnosis. Potentially relevant plasma 

metabolites for different subtypes of CHF could increase our knowledge of the underlying processes 

= 

 
 

 

 

Plasma metabolomics combined with 

personalized diagnosis guided by Chinese 

medicine reveals subtypes of chronic heart 

failure 

Bangze Fu a, Chan Chen c, Liangtao Luo d, Dong Deng a, 
Huihui Zhao a,*, Wei Wang a,** 

 
a Beijing University of Chinese Medicine, Beijing 100029, China 
b China-Japan Friendship Hospital, Beijing 100029, China 
c Hangzhou Xiaoshan TCM Hospital, Zhejiang 311201, China 
d Capital Medical University, Beijing 100069, China 

 

 

 

 

Introduction 

 

The progressive clinical illness known as chronic 

heart failure (CHF) occurs when the heart is 

unable to pump blood effectively enough to fulfil 

the body's metabolic needs. It stands as the last 

common pathway among the many causes of 

cardiac disease.1 Patients with congestive heart 



failure still have a high death rate, even if the 

survival rate following the beginning of CHF has 

significantly improved due to the increased use of 

pharmaceutical therapies. Better strategies for the 

prevention and treatment of CHF are needed, 

since the incidence and prevalence of the 

condition are predicted to rise even more with the 

ageing population.  

Personalised medicine has replaced normal 

protocol-based illness management as the primary 

emphasis of Western life sciences, thanks to 

developments in bioinformatics and healthcare. 

For thousands of years, traditional Chinese 

medicine (TCM) has been successfully restoring 

the human system's self-regulatory abilities via its 

individualised health approach and methodical 

diagnostic methods. Echocardiographic 

measurements, the 6-minute walking distance test, 

and patients' quality of life are all improved when 

CHF patients receive treatment that combines 

traditional Chinese medicine (TCM) with Western 

medicine. This treatment improves heart function 

and decreases associated clinical symptoms, such 

as expiratory dyspnea and chronic fatigue.2 

'Syndrome type' refers to systemic dysfunctions, 

which are also given more weight by TCM 

doctors.3 It is more than just a collection of 

symptoms; it's a functional state brought about by 

responses to or interactions with pathogenic 

causes and changes in the environment.4 

Traditional Chinese Medicine (TCM) "syndrome 

type" boils down to a human system imbalance 

that causes changes in the concentration and 

relative proportions of metabolomics biomarkers 

as well as disruptions in biological metabolism 

networks. 

An integral part of systematical biology, 

metabolomics allows for dynamic in vivo and in 

vitro investigations of healthy tissues and organs 

utilising non-invasive methods in settings that are 

very close to their natural habitat.5 Thus, it is 

possible to get a better understanding of the 

biochemical alterations linked to illness 

development via metabolomics detection and 

analysis of biological materials. Early illness 

detection and the development of predictive 

diagnostic systems may be possible with the 

discovery of metabolic biomarkers linked to 

certain diseases. The most frequent cardiovascular 

illness seen in clinical practice, heart failure, 

reportedly benefits greatly from metab-olomics.6 

In recent years, metabolomics has also shown 

great promise in investigations of TCM. The 

promise of metabolomics in assessing disease 

state and TCM-guided personalised treatment has 

been highlighted by multiple studies7,8 that 

combined metabolomics methods with TCM 

syndrome types; these studies showed fingerprints 

of metabolic changes that characterise diseases 

diagnosed by Western medicine.  

 

Nuclear magnetic resonance (NMR) spectroscopy, 

which offers the benefits of high resolution and 

sensitivity, has been extensively used in 

metabolomics research and is one of the most 

popular platforms for metabolomics analysis. The 

metabolomics pathways and processes underlying 

CHF may be better understood with the use of 

NMR while investigating TCM symptoms and 

treatments.  

This study used nuclear magnetic resonance 

(NMR) spectroscopy to investigate the following 

in 38 individuals with congestive heart failure 

(CHF):  

 
(1) potential metabolic biomarkers contributing to 

discriminate TCM syndrome types (yang-

deficiency vs. non-yang-deficiency, and yin-

deficiency vs. non- yin-deficiency); and 

(2) similarities and differences in TCM syndrome-

related biomarker patterns. We hypothesize that 

combining TCM diagnosis with metabolomics 

could provide quantitative biological evidence 

for TCM diagnosis by identifying CHF subtypes 

with related plasma meta- bolic patterns. 

 

Materials and methods 
 
Participants and study design 

 
The study was designed as an explorative study 

without intervention. Patients with a history of 

coronary heart disease that met the CHF diagnostic 

criteria in accordance with the Guidelines for the 

Diagnosis and Management of Chronic Heart Failure 

established by the Chinese Society of Cardiology of the 

Chinese Medical Association in 2007 were 

enrolled. Eligibility criteria were age ≥45years and 

left ventricular ejection fraction <50%. All patients 
were in New York Heart Association (NYHA) classes 
IIeIV. All pa- 

tients  underwent  our  standardized  recruitment  and 

management. They were diagnosed by two experienced 

doctors independently to reduce subjective factors. A 

pre- study screening involved a physical exam that 

included echocardiography and clinical laboratory tests 

and was performed immediately. Patients with end-

stage renal or liver disease, ongoing infection and long-

term immuno- suppressive therapy were excluded. 

Thirty-eight patients attending the Heart Diseases 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2024 | Vol 7 | Issue 2 

 
 
 
 
 

    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

 

 

 

 

 

 

 

 

15/23 

17/21 

142.6  4.3 

3.9  0.6 

7.1  2.1 

101  32 

128.7  20.1 

20/18 

14/24 

19/19 

 14/24 

 20/18 

Range 

 

25.6e35.5 

 

 

 

Mean  SD 

61.4  5.13 

28.8  2.4 

21/17 

3 

54.3  8.2 

22/16 

 

Age/years 

BMI/kg m—2 
Sex M:F 

Median NYHA class 

Mean ejection fraction 

Etiology ischemic: 

non-ischemic 

Hypertensive: non- 

hypertensive 

Hyperlipidemia: non- 

hyperlipidemia 

DM: non DM 

Smoker: non-smoker 

Na+ 

K+ 
Urea 

Creatinine 

Hemoglobin 

Beta-blokers Y:N 

ACE inhibitors Y:N 

Diuretics Y:N 

Table 1 Demographic details of participants. 

Clinic at Beijing University of Chinese Medicine Affiliated 

Hospital from January 2013 to September 2014 were 

finally enrolled in the study. Samples of venous blood 

were collected, and the detailed clinical data are 

shown in Table 1. 

The study was approved by the Ethical Committee at 

the Affiliated Hospital of Beijing University of Chinese 

Medi- cine, and written informed consent was acquired 

from all participants recruited. 

We used TCM to, investigate general syndromes and 

classified them into two study groups: (1) yin-deficiency 

vs. non-yin-deficiency (Group 1); and (2) yang-

deficiency VS non-yang-deficiency (Group 2). According 

to Clinical Terminology of Traditional Chinese Medical 

Diagnosis and TreatmentdSyndromes,9 yin-deficiency is 

described as low fever, night sweats, afternoon 

zygomaticus red, dysphoria with feverish sensation of 

the chest palms and soles, dry mouth and throat, red 

tongue with little coating and thready rapid pulse. And 

yang-deficiency is a cluster of symptoms including an 

aversion to cold, dispirited feelings and lack of 

motivation, diarrhea before dawn, shortness of breath, 

frequent urination, edema, and lia- bility to catch cold. 

Using these criteria, 15 patients were assessed as being 

in the yin-deficiency group and 7 pa- tients as being in 

the yang-deficiency group, others of 38 patients were 

diagnosed as being in combined syndromes of CHF. 

 

 

 Sample collection and preparation 

 
Clinical parameters included gender, age, ejection 

frac- tion, creatinine, electrolyte, urea, B-type 

natriuretic pep- tide, platelet count, hemoglobin, 

fasting blood glucose, triglyceride levels and total 

cholesterol. Complications including diabetes, 

hypertension or dyslipidemia, and drug- taking 

information of patients were noted at inclusion. 

Venous blood of 38 CHF patients at the Heart 

Failure Clinic were also collected in 5 mL Vacutainer 

tubes with chelating agent ethylene diamine 

tetraacetic acid (EDTA) 

and centrifuged at 3000 rpm for 10 min. The blood 
sample was then separated into equal aliquotsand 

stored at —80◦C until analysis.10 

For NMR analysis, plasma samples were thawed at 

room temperature. After being centrifuged at 13 

000 rpm for 10 min, 200 mL samples of supernatant 

were removed. D2O (400 mL) was added and the 

mixture was centrifuged again. Following 

centrifugation, 550 mL of supernatant was trans- 

ferred to a 5-mm diameter specific NMR tube for NMR 

analysis. The reaction was performed using a Varian 

VNMRS 600 MHz NMR spectrometer (Varian Medical 

Systems, Inc., 

Palo Alto, CA, USA) at 25◦C. 

 
1
H-NMR spectroscopy 

 
The spectra were acquired by Carr—Purcell—

Meiboom—Gill (CPMG) sequence D-[—90◦-(t-180◦-t)n-
ACQ] and Longitudi- nal Eddy-Delay (LED) sequence. 
Both small molecular me- 

tabolites and lipid components in the plasma were 

observed respectively. The free induction decays were 

transferred into 64 K data points with a spectral width 

of 8000 Hz and 

64 scans, then zero-filled to double size and 

multiplied before Fourier transformation, which was 

applied with an exponential window function to 

produce a 0.5 Hz broad- ening line. We identified 

plasma metabolites by comparison with chemical 

shifts, which is detailed in a previous report.11 

 

Spectral and statistical analysis 

 
Spectra were manually phased, baseline corrected and 

normalized. Each spectrum was referenced using 

internal lactate CH3 resonance at d1.33 by Mest-

ReNova7.1.0 soft- ware (Mestrelab Research, A 

Corun˜a, Spain). Signals from d0.5 to d9.0 for each 

sample were automatically binned with a 0.005 ppm 

width. Water and EDTA metal complex regions were 

excluded.12 

Prior to multivariate data analysis, statistical 
analyzes were performed on the data using SIMCA-



P+12 software (Umetrics, Umea, Sweden) as variables 
and then mean- centered and pareto-scaled. 

To analyze the NMR data holistically and 

discriminate CHF patients with different TCM 

syndrome types and con- trols, we applied both 

principal component analysis and orthogonal partial 

least-squares discriminant analysis (OPLS-DA).13,14 

Score and loading plots were calculated to 

demonstrate discriminatory metabolites for each 

group. Each point in a score plot pointed to the 

projection of a NMR spectrum (patient sample) on 

the predictive (horizontal axis) and orthogonal 

components of the model (vertical axis). On the 

loading plot, positive signals represented those plasma 

metabolites revealed increased concentrations in CHF 

pa- tients diagnosed with yin-deficiency or yang-

deficiency syndrome. Accordingly, a negative signal 

corresponded to those down-regulated plasma 

metabolites.15 

The key metabolites resulting in discrimination were 

also analyzed by peak integration. And independent sam- 

ples t-test were used to identify main differences in 

selected signals. 

To obtain a more objective statistical estimation, we 

performed ‘Y-scrambling’ validation and calculated R2 

(correlation coefficients) and Q2 (prediction properties) 

values to evaluate our OPLS-DA models.16 

 

Results 
 
Clinical characteristics of participants 

 
Detailed clinical data characteristics and plasma samples 

acquired from all CHF patients with different TCM syn- 

drome types were collected. The groups showed no dif- 

ferences in any demographic characteristic, such as 

gender, age or body mass index. 

 

Metabolomics analysis of plasma samples of CHF 

with TCM syndromes 

 

As metabolomics has the advantage of being able to iden- 

tify metabolomics biomarker profiles and reveal relation- 

ships among TCM syndrome subtypes, metabolic profiling 

coupled with multivariate analysis was applied in this 

study.Based on metabolic profiling, CHF patients with yin- 

deficiency or yang-deficiency and controls were able to 

be easily distinguished in principal component analysis 

score plots. The first two principal components were 

selected, which described 65.7% of the total variance of 

the plasma metabolome. 

OPLS-DA is a newly developed data analysis method 

combining orthogonal signal correction and partial 

least squares, and has been widely used in clinical 

studies.10,17 Here, we also performed an OPLS-DA 

pattern recognition model with one predictive 

component and four orthogonal components to further 

identify plasma metabolites that differed in 

concentrations in CHF patients with different TCM 

syndrome types. OPLS-DA score plots revealed that 

yin-deficiency patients were statistically 

distinguishable from controls (R2Y Z 0.608 Q2 Z 

0.327). The former index shows the explanative ability 

of the syndrome classifica- tion, and the latter is the 

result of seven-fold cross-vali- dation, and suggests 

that the OPLS-DA models were robust.18 The patterns 

of Group 1 and 2 are clearly distinct from that of the 

control group along the t[1]-axis direction of the first 

principle component, without any crossover or overlap 

(Figs. 1 and 2). This separating trend clearly in- 

dicates that metabolic profiling varied according to 

different TCM syndromes. 

Further analysis of loading plots illustrated 

correspond- ing changes of metabolites of high 

variable importance, which accounted for 

metabolomics fingerprint changes and discrimination 

in score plots. Nine plasma metabolites of yin-

deficiency patients and control groups could be deter- 

mined in the 600 MHz one-dimensional CPMG and LED1 

H- NMR spectra, ranked by the largest variable 

importance,19 to be significantly altered 

metabolites of CHF patients 

 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2024 | Vol 7 | Issue 2 

 
 
 
 
 

    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

Fig. 1 OPLS-DA score plots of Group 1. Black box: OPLS-DA score plots displaying discrimination between CHF yang-deficiency 

patients. Red circle: OPLS-DA score plots displaying discrimination between CHF yang-deficiency controls. 



 

 
 

Fig. 2 OPLS-DA score plots of Group 2. Black box: OPLS-DA score plots displaying discrimination between CHF yang-deficiency 

patients. Red circle: OPLS-DA score plots displaying discrimination between CHF yang-deficiency controls. 

 

with yin-deficiency syndrome (i.e. potential biomarkers) (Figs. 3, 4 and Table 2A). CHF patients with yang-deficiency 

syndrome were examined as above. Nine metabolites were positively identified as potential biomarkers from variable 

importance values (Figs. 5, 6 and Table 2B).Statistical validation 

 
We performed ‘Y-scrambling’ statistical validation to cor- rect chance correlation and evaluate the OPLS-DA model. The Y-

variable of case and control group were randomly 

 

 

Fig. 3 OPLS-DA loadings plots of key metabolites by CPMG sequence of Group 1. OPLS-DA loadings plots demonstrating 

discrimination of key metabolite levels between CHF yin-deficiency patients and controls. 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2024 | Vol 7 | Issue 2 

 
 
 
 
 

    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

 

 

Fig. 4 OPLS-DA loadings plots of key metabolites by 

LED sequence of Group 1. OPLS-DA loadings plots 

demonstrating discrimi- nation of key metabolite levels 

between CHF yin-deficiency patients and controls. 

 

permutated first and the statistical model was rebuilt. In 

addition, we recorded and analyzed trends of the predic- 

tive power and goodness of fit at each step. Two hundred 

rounds of reshuffling showed that the separation model 

was reliable, and that its high predictability was not 

affected by random or over-fitting of the data, as both 

permutated R2 and Q2 values were markedly lower than 

the corresponding original values (Fig. 7A, B). Even though 

this study may not include all possible confounding factors 

in the patients, our validation through randomization of 

the Y-variable suggests that these variations should not be 

key attributors for discrimination between case and 

control groups, or affect the predictability of our model. 

Discussion 

 
CHF is clinically associated with high mortality and 

morbidity, decreased quality of life and substantial 

burden on health care systems. Despite advances in 

drug treatment strategies for CHF, the number of 

deaths resulting from this condition continues to rise.20 

TCM pays particular attention to the integrity and 

holism of the human body and its inter- relationship 



with nature. TCM also adheres to basic princi- ple of 

treatment based on differentiation of symptoms and 

signs, treats the same disease by different methods 

and different diseases by the same method, and 

advocates individualized treatment, which vividly 

reflects the essence 

 
 

 
 
 
 
 
 
 
 
 
 

 

Table 2A Key metabolites differentiating CHF yin-deficiency patients and controls. 

No Metabolite (Chemical shift) YIP NYIP P-value VIP 

1 Valine 1.04 0.2526  0.0628 0.3031  0.0507 0.007 1.76 

2 VLDL/LDL 1.26, 1.3, 1.34 1.6539  0.0885 1.5791  0.0996 0.010 2.59 

3 Lactate 1.33, 4.12 1.4396  0.4708 1.1235  0.2363 0.017 3.99 

4 Alanine 1.48 0.2748  0.0873 0.4077  0.1251 0.000 2.11 

5 Proline 3.33 0.0353  0.0283 0.0511  0.0226 0.029 3.93 

6 Glucose 3.47; 3.72, 4.64, 5.23 4.1008  0.4777 4.7331  0.6542 0.007 3.68 

7 Glycoprotein (NeAc) 2.02 0.6093  0.0223 0.4289  0.0696 0.000 2.54 

8 Carnitine 2.44 0.0725  0.0138 0.0904  0.0351 0.001 2.39 

Abbreviations: YIP, yin-deficiency patients; NYIP, non-yin deficiency patients; LDL, low-density lipoprotein; VLDL, very low-

density li- poprotein; HDL, high-density lipoprotein; Values expressed as the mean(SD) (range); P values were calculated 

from the Independent- samples T Test; Variable importance in the projection (VIP) was acquired from the OPLS-DA model. 

 

 
 
 



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    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

 

 

Fig. 5 OPLS-DA loadings plots of key metabolites by CPMG sequence of Group 2. OPLS-DA loadings plots demonstrating 

discrimination of key metabolite levels between CHF yin-deficiency patients and controls. 

TCM treatment.21 Treatment based on syndrome differ- entiation is at the core of TCM therapy for CHF. 

In terms of the perspective of TCM, CHF may occur in all differentiation types, including yang deficiency, blood sta- sis and 

yin-deficiency, to name a few. Some Chinese herbs have been demonstrated to be safe and effective in the 

management of CHF in both animal models and in humans.22,23 Modern biological research has now begun integrating 

various research technologies and methods to tackle difficult biological problems at bio-molecular levels, which is 

exemplified by studies in the new scientific field of metabolomics. It is important that potential correlations 

 



 

Fig. 6 OPLS-DA loadings plots of key metabolites by LED sequence of Group 2. OPLS-DA loadings plots demonstrating discrimi- 

nation of key metabolite levels between CHF yin-deficiency patients and controls. 

 

 

 

 

Table 2B Key metabolites differentiating CHF yang-deficiency patients and controls. 

No Metabolite (Chemical shift) YADP NYADP P-Value VIP 

1 HDL 0.82 0.1447  0.0439 0.1979  0.0468 0.0180 2.51 

2 Pyruvic acid 0.94 0.0967  0.0181 0.0667  0.0106 0.000 1.45 

3 VLDL/LDL 1.26, 1.3, 1.34 1.5539  0.0885 1.8791  0.0996 0.010 2.50 

4 Lactate 1.33, 4.12 1.4396  0.4708 1.1235  0.2363 0.017 3.70 

5 Alanine 1.48 0.4748  0.0873 0.2077  0.1251 0.000 2.03 

6 Glutamate 2.15, 2.52 0.0829  0.0169 0.0586  0.0189 0.000 1.95 

7 Glucose 3.47; 3.72, 4.64, 5.23 4.1008  0.4777 4.5371  0.6542 0.007 3.01 

8 Glycoprotein (NeAc) 2.02 0.6093  0.0223 0.4289  0.0696 0.000 2.38 

Abbreviations: YAP, yang-deficiency patients; NYAP, non-yang deficiency patients; LDL, low-density lipoprotein; VLDL, very low-density 

lipoprotein; HDL, high-density lipoprotein. Values are expressed as mean (SD) (range). P-values were calculated from independent 

samples t-tests. Variable importance in the projection (VIP) was acquired from the OPLS-DA model. 

 

ln order to find new ways to treat CHF by 

studying the correlation between TCM symptom 

type and metabolites.  

The purpose of this exploratory investigation was 

to identify metabolic markers of traditional 

Chinese medicine (TCM) syndromes in 

congestive heart failure (CHF) and to develop 

novel diagnostic techniques in patients with yin-

deficiency and yang-deficiency condition. Our 

method, which included plasma metabolomics in 

addition to TCM syndrome type identification, 

demonstrated efficacy across all experimental 

groups. 

We identified distinguishable metabolites that 

could differentiate CHF patients with each TCM 

syndrome from controls in this study. These 

metabolites include energy metabolites (glucose, 

lactate and glycoprotein), lipid/protein complexes 

(high-density lipoprotein, low-density lipoprotein, 

very low-density lipoprotein) and amino acids 

(alanine, glutamate, valine, glycine, proline and 

carnitine). These findings provide support for the 

hypothesis that this metabolomics approach may 

contribute to our knowledge of TCM symptoms 

associated with CHF.  

 

This research has some limitations, as do many 

novel diagnostic techniques. To begin, metabolic 

profiles might be impacted by various 

confounding variables. Additionally, this strategy 

has to be validated by research with bigger 

cohorts. Secondly, it is challenging to ascribe a 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2024 | Vol 7 | Issue 2 

 
 
 
 
 

    ISSN : 2693 6356 

2024 | Vol 7 | Issue 2 

 

 
 

metabolic fingerprint to certain metabolic 

activities since plasma samples reflect metabolic 

processes in several organs.24 Nevertheless, CHF 

patients exhibiting certain TCM symptoms, which 

may serve as indicators of illness, have changed 

metabolite levels to a certain extent. This matter 

needs more mechanical research.  

Levels of glucose, valine, proline, alanine, and 

carnitine were lower in yin-deficiency patients 

compared to non-deficiency patients, while levels 

of lactate, glycoprotein, and LDL/VLDL were 

higher in yin-deficiency patients.  

Patients with congestive heart failure (CHF) who 

also have yin-deficiency often have abnormalities 

in energy metabolism, as shown by elevated 

lactate and hypoglycemia.18 Glycoproteins have 

an impact on cellular immunity and metabolic 

energy supply in humans, and they are intricately 

linked to the pathology and physiology of cell 

proliferation.25 The most notable difference 

between the yin-deficiency patients and the 

control groups was the observation of elevated 

levels of LDL and VLDL in the CHF patients with 

yin-deficiency. The significance of 

apolipoproteins in lipid metabolism suggests that 

this metabolomics profile may be linked to 

lipolysis, a pathway for energy utilisation that 

functions as a backup.  

Patients with coronary atherosclerotic disease who 

also have yin-deficiency syndrome may have 

elevated proline levels, according to certain 

reports.26 Alanine and valine are two examples of 

the well-known non-essential and essential amino 

acids that are found at low plasma levels in 

individuals with CHF who have yin-deficiency 

syndrome. This condition causes a progressive 

disruption of the body's internal homeostasis. This 

finding is in agreement with the metabolomics 

study conducted by Yan et al. in rats with qi-

deficiency and yin-deficiency syndromes that 

demonstrated an association between energy 

metabolism and oxidative stress response, as well 

as an increase in inositol and a decrease in valine, 

glycine, and serine.27 Furthermore, there was a 

significant decrease in carnitine, a crucial 

molecule in fat metabolism and energy 

production, in these individuals. Research has 

shown that L-carnitine may enhance the 

absorption of free fatty acids, allowing glucose to 

be used as an oxidative fuel in some scenarios.28, 

29 Inadequate carnitine levels disrupt 

mitochondrial oxidation, which in turn causes 

metabolic imbalances and cardiac problems. 

These metabolic processes include carbs, proteins, 

and lipids; in patients with congestive heart failure 

and yin-deficiency syndrome, they are suggestive 

of a complex metabolic disease.  

Secondly, when comparing yang-deficiency 

patients to non-yang-deficiency patients, the 

former had lower glucose, LDL/VLDL, and HDL 

levels and higher lactate, glycoprotein, pyruvic 

acid, alanine, and glutamate levels. According to 

traditional Chinese medicine (TCM), yang-

deficiency manifests in the latter stages of many 

illnesses and is characterised by symptoms of 

persistent weakness, hypofunction, 

hypometabolism, and degenerative changes.30 

Patients with congestive heart failure (CHF) with 

yang-deficiency had metabolic abnormalities that 

were highly indicative of carbohydrate and energy 

metabolism disorders, including decreased 

glucose metabolism and increased lactate, alanine, 

and pyruvate. An increase in hepatic 

gluconeogenesis to produce more pyruvate, a 

substrate for glucose, may be indicated by a shift 

in pyruvic acid, suggesting that endogenous 

glucose synthesis may be augmented.31  

The yang-deficiency syndrome is often seen in 

individuals with stage III and IV CHF, which is in 

line with our results. There was also an increase in 

glycoprotein levels.  

 

 



 

Fig. 7  (A) Statistical validation of the OPLS-DA model of 

Group 1. A permutation test performed with 200 random 

permutations in a PLSDA model showing R2 (green 

triangles) and Q2 (blue boxes) values from permuted 

analysis (bottom left) as significantly lower than 

corresponding original values (top right). (B) Statistical 

validation of the OPLS-DA model of Group 2. A 

permutation test performed with 200 random 

permutations in a PLSDA model showing R2 (green 

triangles) and Q2 (blue boxes) values from permuted 

analysis (bottom left) as significantly lower than 

corresponding original values (top right). 

indicate immune defects in patients,32 while generally 

lower lipoprotein levels, including LDL/VLDL and HDL, 

suggest insufficient absorption and utilization of 

protein during these phases. Higher excretion levels of 

measured metabolites (glutamate and alanine) in Group 

2 partici- pants could indicate further more potential 

disturbances of renal function, resulting in these patients 

missing metab- olites necessary for carbohydrate and 

energy metabolism.33 A study in China investigated 

urinary metabolites of yang-deficiency syndrome in 

patients with chronic kidney disease and reported that 

key distinguishing metabolites differing between yang-

deficiency syndrome patients and the control group 

included alanine, diethylamine and pro- line.34 As 

essential substances in cellular activities, such 

deficiencies will affect energy supply in all aspects of the 

human body. These alterations are likely important 

contributing factors to the altered metabolite profiling 

of 

CHF patients with yang-deficiency syndrome. 
This study is an early phase investigation examing TCM 

syndrome types of CHF based on a small number of study 

participants. Importantly, this study has demonstrated 

that two TCM syndromes were able to be distinguished 

based on their plasma metabolic patterns. While the 

findings of this study are very promising, further research 

using larger cohort is required to confirm and validate the 

reliability of individualized treatment of CHF based on 

TCM subtypes. 

 

Conclusion 

The present investigation sought metabolic 

subgroups in CHF by combining NMR plasma 

metabolomics grounded in biology with TCM 

personalised diagnostics. By analysing metabolic 

patterns in plasma, researchers were able to 

distinguish between two kinds of TCM syndrome 

associated with CHF. The decreased levels of 

sugars, proteins, lipids, and amino acids in Group 

2 as compared to Group 1 suggest that these 

individuals have more disruptions in energy and 

carbohydrate metabolism as well as renal 

function. Plasma metabolites have the potential to 



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2024 | Vol 7 | Issue 2 

 

 
 

provide light on metabolomics pathways and 

prognosis, which might enhance personalised 

therapies for CHF and aid in the detection of 

subtypes of the disease. Validation of the TCM 

subgroups discovered in this research and 

assessment of intervention responses to 

prospective metabolic therapies or medications 

need further investigations.  

 

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