Introduction
Cardiovascular disease (CVD) is one of the deadliest
diseases in the world (1,2). According to data from the World
Health Organization, ~17 million individuals succumb to CVD each
year, accounting for 31% of all mortalities worldwide, forming a
significant threat to public health (3,4).
Cardiovascular diseases not only affect the physical health of
patients but also impose a heavy burden on the social economy
(5,6). With the aging of the global
population, the number of individuals suffering from CVD continues
to increase and the burden of the disease is gradually increasing.
With the westernization of lifestyle, unhealthy eating habits and
lack of exercise, the incidence rate of CVD among young individuals
is also growing at an alarming rate (7,8).
At present, the diagnosis of CVD mainly relies on
clinical symptoms, physical examination, ECG (9,10),
imaging examination (11) and
detection of cardiovascular disease biomarkers (12,13).
However, these methods are not well-suited for short-term risk
prediction. Different types of cardiovascular diseases, such as
coronary artery disease (CAD), structural heart disease (SHD),
heart failure and various types of arrhythmias, may have highly
similar clinical manifestations and some patients may not have
obvious symptoms in the early stages, making early diagnosis a
difficult task. At present, one or more biomarkers are commonly
used to diagnose specific cardiovascular disease (14,15)
and this single diagnosis often cannot meet the needs of clinical
diagnosis. Therefore, the present study intended to construct a
diagnostic auxiliary model combining multiple biomarkers to predict
short-term cardiovascular disease risk at an early stage, support
clinical decision-making, and optimize early treatment
strategies.
Materials and methods
Research design
The present study included a retrospective cohort of
patients who underwent routine physical examinations at Beijing
Fengtai You'anmen Hospital between January 2023 and June 2024. A
total of 400 participants were included. The median age of all
participants was 50 years (range: 21-81 years). The sex
distribution was 67.5% male (270 individuals) and 32.5% female (130
individuals). Patients diagnosed with cardiovascular disease were
selected as the case group, while healthy individuals without
cardiovascular disease were selected as the control group. The
inclusion criteria were: Age ≥18 years; the case group data should
include physiological and biochemical indicators of the patient
collected within the first 6 months before the onset of the
disease. The exclusion criteria were: Women who were pregnant or
within 6 months postpartum; severe liver and kidney dysfunction;
suffering from malignant tumors and other serious systemic
diseases; and laboratory inspection data incomplete.
The diagnostic criteria for CAD were coronary
angiography showing ≥50% luminal stenosis in at least one major
coronary artery, clinical manifestations such as chest pain, chest
tightness, or precordial discomfort and electrocardiographic
findings showing ST-segment changes or T-wave abnormalities. The
diagnostic criteria for SHD include structural abnormalities of the
heart detected by echocardiography, including valvular, myocardial
and chamber abnormalities. The diagnosis of arrhythmias was based
on electrocardiographic examination results, including standard
12-lead ECG or Holter monitoring. When the electrocardiogram showed
an abnormal cardiac rhythm, an arrhythmia was diagnosed. The
manifestations included abnormal or absent P waves, irregular RR
intervals, abnormal heart rate (tachycardia or bradycardia),
prematurely occurring ectopic beats and atrioventricular conduction
block and other electrocardiographic changes.
Data collection
Baseline information was collected from the six
months prior to the onset of cardiovascular disease and from the
normal group, including age, sex, BMI, family history of
cardiovascular disease, smoking, drinking and exercise habits. The
disease types of all patients with CVD were collected and divided
into three categories: CAD, SHD and arrhythmia. Symptom
manifestations (such as pain, dyspnea, dizziness, nausea and
vomiting, fever) and electrocardiogram changes (ST segment changes,
T wave changes) were collected in patients with CVD. Biomarker
levels in patients with CVD were measured 6 months prior to onset
and in the normal control group. High-sensitivity C-reactive
protein (hs-CRP), B-type natriuretic peptide (BNP), myoglobin
(MYO), and N-terminal pro B-type natriuretic peptide (NT-proBNP)
were detected using chemiluminescence immunoassay and enzyme-based
measurement of lactate dehydrogenase (LDH), detection of cardiac
troponin T (cTnT), cardiac troponin I (cTnI), creatine kinase-MB
(CK-MB), fibrinogen (FIB), low-density lipoprotein (LDL), and
high-density lipoprotein (HDL) were measured using
immunoturbidimetry. To ensure data validity and comparability, all
biomarker measurements were performed in the clinical laboratory of
Beijing Fengtai You'anmen Hospital using the same testing methods
and equipment. All samples were collected in the morning under
fasting conditions.
Statistical analysis
The present study used R software (version 4.4.1;
https://www.R-project.org/) for
statistical analysis and graphical visualization. The Shapiro-Wilk
test was used for normality assessment, P>0.05 was considered
approximately normally distribution, whereas P<0.05 was
considered non-normally distribution. Continuous data were
summarized by median (minimum, maximum) and inter-group comparisons
are performed using unpaired tests, Specifically, an
independent-samples unpaired t-test or a nonparametric test
(Mann-Whitney U test, Kruskal-Wallis test) was used, as
appropriate. Categorical variables are expressed in frequency
(percentage) and intergroup comparisons performed using the
chi-square test or Fisher's exact test. Multiple logistic
regression analysis and random forest screening were used to
identify significant factors for CVD. The random forest used the
out-of-bag error (OOB Error) method to select the optimal
parameters (16), The OOB Error
method uses the characteristics of random forests, During training,
each tree uses only a subset of the dataset, while the remaining
samples serve as ‘out of bag’ samples. It already has a similar
cross validation function, so there is no need to split the data
into training and test sets. The factors that were significant in
the multiple logistic regression and had a higher Gini scores in
the random forest were selected as diagnostic factors for CVD.
Since these two indicators differ in scale and range, Z-score
standardization was first applied to both the regression
coefficients and the Gini index to eliminate scale differences and
ensure comparability. A diagnostic model based on the coefficients
of these significant diagnostic factors was constructed.
The specific formula was as follows:
Diagnostic model =
Coef[1]xFactor[1] +
Coef[2]xFactor[2] +
Coef[3]xFactor[3]
+……Coef[n]xFactor[n]
Where Coef was the coefficients of the multivariable
Logistic regression and the Gini index from random forest are
standardized using Z-score and then summed.
ROC curve analysis was performed to evaluate the
ability of the diagnostic models to distinguish between the normal
population and patients with CVD, as well as between different CVD
subtypes.
An independent external validation cohort (n=120)
was obtained from a different time period than the training cohort,
with patients enrolled between July 2024 and December 2024.
A post hoc power analysis for the ROC analysis was
performed using the pROC package (version 1.19.0.1) in R (version
4.4.1). The pROC package is available at https://cran.r-project.org/package=pROC.
Results
Demographic characteristics of
patients with CVD and the normal population
The median BMI was 25.4 (range: 19.5-30.8). A total
of 25.25% (101 individuals) had a family history of cardiovascular
disease. The proportion of patients with CVD who had a family
history of CVD was significantly higher than that in the healthy
control group (31.5 vs. 19%, P value 0.0057). A total of 45.75%
(183 individuals) were mild or non-smokers (0-9 cigarettes/day),
22.75% (91 individuals) were moderate smokers (10-19
cigarettes/day), and 31.5% (126 individuals) were heavy smokers
(≥20 cigarettes/day). A total of 49.5% (198 individuals) consumed
light or no alcohol (≤1 time/week), 32% (128 individuals)
moderately consumed alcohol (2-3 times/week), and 18.5% (74
individuals) heavily consumed alcohol (≥4 times/week). In terms of
exercise habits, 65.5% (262 individuals) hardly exercised (≤1
time/week), 25.5% (102 individuals) engaged in moderate intensity
exercise (2-3 times/week), and 9% (36 individuals) engaged in
vigorous-intense exercise (≥4 times/week; Table I).
 | Table IDemographic characteristics of
patients with cardiovascular disease and healthy controls. |
Table I
Demographic characteristics of
patients with cardiovascular disease and healthy controls.
| Characteristic | All (n=400) | Patients with
cardiovascular disease (n=200) | Healthy population
(n=200) | P-value |
|---|
| Age, years
(range) | 50 (21-81) | 54 (21-81) | 48 (21-81) | 0.0252 |
| Sex | | | | 0.3367 |
|
Male | 270 (67.5%) | 140 (70%) | 130 (65%) | |
|
Female | 130 (32.5%) | 60 (30%) | 70 (35%) | |
| BMIa | 25.4 (19.5-30.8) | 25.3 (19.6-30.8) | 25.5 (19.5-30.7) | 0.573 |
| Family history of
cardiovascular disease | | | | 0.0057 |
|
Yes | 101 (25.25%) | 63 (31.5%) | 38 (19%) | |
|
No | 299 (74.75%) | 137 (68.5%) | 162 (81%) | |
| Smoking | | | | 0.2592 |
|
Mild or
none | 183 (45.75%) | 90 (45%) | 93 (46.5%) | |
|
Moderate | 91 (22.75%) | 52 (26%) | 39 (19.5%) | |
|
Heavy | 126 (31.5%) | 58 (29%) | 68 (34%) | |
| Drinking | | | | 0.3021 |
|
Mild or
none | 198 (49.5%) | 95 (47.5%) | 103 (51.5%) | |
|
Moderate | 128 (32%) | 62 (31%) | 66 (33%) | |
|
Heavy | 74 (18.5%) | 43 (21.5%) | 31 (15.5%) | |
| Exercise
habits | | | | 0.0982 |
|
Sedentary | 262 (65.5%) | 140 (70%) | 122 (61%) | |
|
Moderate
exercise | 102 (25.5%) | 47 (23.5%) | 55 (27.5%) | |
|
Intensive
exercise | 36 (9%) | 13 (6.5%) | 23 (11.5%) | |
Differences in clinical symptoms and
physiological manifestations among patients with different types of
cardiovascular diseases
Among all patients with CVD, 56% (112 individuals)
had no or mild pain, 36% (72 individuals) had moderate pain, and 8%
(16 individuals) had severe pain. A total of 59.5% (119
individuals) of patients with CVD had mild or no respiratory
distress, 31.5% (63 individuals) had moderate respiratory distress,
and 9% (18 individuals) had severe respiratory distress. The
proportion of moderate through severe respiratory distress in SHD
patients (38.46%) was significantly higher than in other groups
(6.03% in patients with CHD, 5.17% in arrhythmia patients;
P=0.0012), with the highest proportion of mild or no respiratory
distress among patients with coronary heart disease being 62.07%.
As to dizziness, 59% of patients with CVD had mild or no dizziness
and the proportion of severe dizziness in patients with arrhythmia
was significantly higher compared with the other two groups (13.79
vs. 5.17% in patients with coronary heart disease and 3.85% in
patients with arrhythmia; P=0.0234). As to nausea and vomiting, 54%
(108 individuals) of patients with CVD had mild or no nausea and
vomiting, 37% (74 individuals) had moderate nausea and vomiting and
9% (18 individuals) had severe nausea and vomiting. A total of 87%
(174 individuals) of patients with CVD had mild or no fever and 13%
(26 individuals) had moderate fever. A total of 71.5% (143
individuals) of patients with CVD had normal ST segments, 19% (38
individuals) had ST segment elevation and 9.5% (19 individuals) had
ST segment depression. A total of 76.5% (153 individuals) of
patients with CVD had normal T waves, 20% (40 individuals) had T
wave inversion, and 3.5% (7 individuals) had T-wave spikes. The
proportion of normal T waves in patients with SHD and arrhythmia
was significantly higher than that in patients with coronary heart
disease (P=0.0020; Table II).
| Table IIDifferences in clinical symptoms and
physiological manifestations among patients with different types of
cardiovascular disease. |
Table II
Differences in clinical symptoms and
physiological manifestations among patients with different types of
cardiovascular disease.
| Clinical symptoms
and physiological manifestation | Patients with
cardiovascular disease (n=200) | Coronary artery
disease (n=116) | Structural heart
disease (n=26) | Arrhythmias
(n=58) | P-value |
|---|
| Pain | | | | | 0.1875321 |
|
Mild or
none | 112 (56%) | 59 (50.86%) | 15 (57.69%) | 38 (65.52%) | |
|
Moderate | 72 (36%) | 47 (40.52%) | 7 (26.92%) | 18 (31.03%) | |
|
Heavy | 16 (8%) | 10 (8.62%) | 4 (15.38%) | 2 (3.45%) | |
| Dyspnea | | | | | 0.001220462 |
|
Mild or
none | 119 (59.5%) | 72 (62.07%) | 10 (38.46%) | 37 (63.79%) | |
|
Moderate | 63 (31.5%) | 37 (31.9%) | 8 (30.77%) | 18 (31.03%) | |
|
Heavy | 18 (9%) | 7 (6.03%) | 8 (30.77%) | 3 (5.17%) | |
| Dizziness | | | | | 0.02343554 |
|
Mild or
none | 118 (59%) | 77 (66.38%) | 11 (42.31%) | 30 (51.72%) | |
|
Moderate | 67 (33.5%) | 33 (28.45%) | 14 (53.85%) | 20 (34.48%) | |
|
Heavy | 15 (7.5%) | 6 (5.17%) | 1 (3.85%) | 8 (13.79%) | |
| Nausea and
Vomiting | | | | | 0.109 |
|
Mild or
none | 108 (54%) | 64 (55.17%) | 16 (61.54%) | 28 (48.28%) | |
|
Moderate | 74 (37%) | 46 (39.66%) | 8 (30.77%) | 20 (34.48%) | |
|
Heavy | 18 (9%) | 6 (5.17%) | 2 (7.69%) | 10 (17.24%) | |
| Fever | | | | | 0.073 |
|
Mild or
none | 174 (87%) | 106 (91.38%) | 20 (76.92%) | 48 (82.76%) | |
|
Moderate | 26 (13%) | 10 (8.62%) | 6 (23.08%) | 10 (17.24%) | |
| ST Segment
Changes | | | | | 0.1759263 |
|
Normal | 143 (71.5%) | 77 (66.38%) | 18 (69.23%) | 48 (82.76%) | |
|
ST
Elevation | 38 (19%) | 27 (23.28%) | 6 (23.08%) | 5 (8.62%) | |
|
ST
Depression | 19 (9.5%) | 12 (10.34%) | 2 (7.69%) | 5 (8.62%) | |
| T-Wave Changes | | | | | 0.002042715 |
|
Normal | 153 (76.5%) | 78 (67.24%) | 25 (96.15%) | 50 (86.21%) | |
|
Inverted
T-Wave | 40 (20%) | 34 (29.31%) | 1 (3.85%) | 5 (8.62%) | |
|
Peaked
T-Wave | 7 (3.5%) | 4 (3.45%) | 0 (0%) | 3 (5.17%) | |
Differences in levels of
cardiovascular disease biomarkers between patients with different
types of cardiovascular diseases and the normal population
The levels of hs-CRP and BNP were markedly higher in
patients with SHD than in the other three groups. The levels of
cTnT, cTnI, LDH, CK-MB, FIB, MYO and N-terminal pre-B natriuretic
peptide (NT proBNP) were markedly higher in patients with coronary
heart disease than in the other three groups. There were no
significant differences in LDL and HDL between the four groups
(Table III).
| Table IIIDifferences in cardiovascular disease
biomarkers among patients with different types of cardiovascular
disease. |
Table III
Differences in cardiovascular disease
biomarkers among patients with different types of cardiovascular
disease.
| Cardiovascular
disease biomarker | Coronary artery
disease (n=116) | Structural heart
disease (n=26) | Arrhythmias
(n=58) | Healthy population
(n=200) | P-value |
|---|
| High-sensitivity
C-reactive protein, mg/l (range) | 2.4 (1.0-3.5) | 2.9 (1.7-3.2) | 1.7 (1.2-2.3) | 0.5 (0.3-0.8) |
4.230x10-5 |
| B-type Natriuretic
Peptide, pg/ml, (range) | 49.8
(11.8-99.8) | 69.2
(40.3-81.9) | 54.8
(22.5-81.5) | 32.8
(29.7-36.3) |
1.560x10-7 |
| Cardiac troponin T,
µg/l (range)a | 0.10
(0.02-0.15) | 0.09
(0.03-0.15) | 0.09
(0.02-0.15) | 0.07
(0.02-0.12) |
5.830x10-4 |
| Cardiac troponin I,
µg/l (range) | 0.027
(0.002-0.043) | 0.023
(0.006-0.043) | 0.023
(0.002-0.041) | 0.020
(0.001-0.033) |
3.770x10-² |
| Lactate
dehydrogenase, U/l (range) | 193.86
(117.75-254.84) | 186.55
(122.19-252.23) | 186.07
(115.27-248.75) | 174.07
(114.64-254.99) |
1.810x10-² |
| Creatine kinase-MB,
ng/ml (range)a | 3.92
(0.14-6.52) | 3.84
(0.82-6.54) | 3.18
(0.08-6.49) | 3.03
(0.02-6.50) |
3.700x10-² |
| Fibrinogen, g/l
(range) | 3.45
(2.23-4.40) | 3.39
(2.25-4.24) | 3.29
(2.24-4.39) | 3.13
(2.21-4.39) |
4.250x10-² |
| Low-density
lipoprotein, g/l (range)a | 1.13
(0.60-1.60) | 1.13
(0.60-1.54) | 1.12
(0.60-1.60) | 1.06
(0.60-1.60) |
5.650x10-¹ |
| High-density
lipoprotein, mg/dl (range) | 38.41
(22.42-57.79) | 39.41
(28.78-53.65) | 40.24
(20.75-56.96) | 42.14
(20.53-57.75) |
8.650x10-¹ |
| Myoglobin, ng/ml
(range) | 57.29
(28.78-75.93) | 57.10
(33.25-76.45) | 56.66
(28.88-75.97) | 52.86
(28.68-76.45) |
1.960x10-² |
| N-terminal pre-B
natriuretic peptide, pg/ml (range) | 172.37
(29.96-279.01) | 161.02
(35.55-276.71) | 157.09
(34.46-278.99) | 131.97
(30.61-277.71) |
1.510x10-² |
Multivariate logistic regression (MLR)
screening for significant factors of cardiovascular disease
The results showed that cTnI was a significant
predictor (P=0.000), with an odds ratio (OR) of 1.597, indicating
that the higher cTnI levels were associated with a higher the risk
of cardiovascular disease. CK-MB was also a significant predictor
(P=0.000), with an OR of 1.254, indicating that higher CK-MB levels
were associated with a higher the risk of cardiovascular disease.
MYO was a significant predictor (P=0.000) with an OR of 1.028,
indicating that the higher the level of MYO, the higher the risk of
cardiovascular disease. NT proBNP was also a significant predictor
(P=0.000), with an OR of 1.001, indicating that the higher the
level of NT proBNP levels were associated with a higher risk of
cardiovascular disease (Table
IV).
| Table IVMultivariate logistic regression to
screen cardiovascular disease biomarkers in cardiovascular disease
and normal populations. |
Table IV
Multivariate logistic regression to
screen cardiovascular disease biomarkers in cardiovascular disease
and normal populations.
| Term | Estimate | Standard error | Statistic | P-value | Odds ratio | Confidence
interval-lower | Confidence
interval-upper |
|---|
| Age | 0.000 | 0.000 | 0.814 | 0.416 | 1.000 | 1.000 | 1.001 |
| Family history of
cardiovascular disease | -0.001 | 0.013 | -0.042 | 0.967 | 0.999 | 0.974 | 1.026 |
| High-sensitivity
C-reactive protein | 0.002 | 0.005 | 0.456 | 0.649 | 1.002 | 0.992 | 1.013 |
| B-type natriuretic
peptide | 0.000 | 0.000 | 1.268 | 0.206 | 1.000 | 1.000 | 1.000 |
| Cardiac troponin
T | -0.004 | 0.005 | -0.933 | 0.351 | 0.996 | 0.987 | 1.005 |
| Cardiac troponin
I | 0.468 | 0.109 | 4.289 | 0.000 | 1.597 | 1.289 | 1.978 |
| Lactate
dehydrogenase | 0.000 | 0.000 | -0.221 | 0.825 | 1.000 | 1.000 | 1.000 |
| Creatine
kinase-MB | 0.226 | 0.021 | 10.839 | 0.000 | 1.254 | 1.204 | 1.306 |
| Fibrinogen | 0.002 | 0.006 | 0.323 | 0.747 | 1.002 | 0.990 | 1.013 |
| Myoglobin | 0.028 | 0.003 | 10.359 | 0.000 | 1.028 | 1.022 | 1.034 |
| N-terminal pre-B
natriuretic peptide | 0.001 | 0.000 | 13.478 | 0.000 | 1.001 | 1.001 | 1.002 |
Random forest screening for
significant factors of CVD
The random forest results showed that the Gini
indices of CK-MB, NT-proBNP, cTnI and MYO were relatively high,
indicating that these indicators were crucial for the occurrence of
cardiovascular disease (Fig.
1).
External validation
The model's generalizability was further evaluated
in the external validation cohort using ROC curves and calibration
plots. The results showed that the Area Under the Curve (AUC)
values for the MLR and random forest models were 0.675 [95% (CI):
0.578-0.771] and 0.688 (95% CI: 0.593-0.782), respectively. The
calibration plots demonstrated good agreement between the predicted
probabilities and the measured outcomes, indicating acceptable
calibration performance of the models (Fig. S1A-D).
Construction of the diagnostic
model
According to the aforementioned results, CK-MB,
NT-proBNP, cTnI, and MYO were both significant in MLR and ranked
high in random forest visualization analysis, so these four factors
were selected to construct the model. The formula of the diagnostic
model is:
Diagnostic score = 0.801 x cTnI +1.275 x CK.MB -
0.027 x NT - proBNP - 2.049 x MYO.
The performance of the model was measured using the
ROC curve. The results showed that the AUC value performance of the
model was highest at 0.719 (95% CI: 0.670-0.769), with a
sensitivity of 0.855 and a specificity of 0.510. This performance
substantially outperformed the individual biomarkers cTnI, CK-MB,
MYO and NT-proBNP (Fig. 2A-E;
Table V). To evaluate the model's
robustness, we sequentially removed each biomarker to perform a
sensitivity analysis. The results showed that after removing any
biomarker, the model's AUC decreased by 0.033-0.044 (Fig. S2A-D), and still remained at a
similar level, indicating that the model had good stability.
| Table VROC curve parameters. |
Table V
ROC curve parameters.
| | AUC | AUC-CI-lower | AUC-CI-upper | Best-threshold | Youden | Sensitivity | Specificity |
|---|
| Cardiovascular
Disease | 0.719 | 0.670 | 0.769 | 3.870 | 0.365 | 0.855 | 0.510 |
| Structural Heart
Disease | 0.771 | 0.702 | 0.840 | 4.077 | 0.495 | 0.888 | 0.607 |
| Coronary Artery
Disease | 0.715 | 0.631 | 0.798 | 4.916 | 0.341 | 0.824 | 0.517 |
| Cardiac troponin
I | 0.583 | 0.527 | 0.639 | 1.591 | 0.180 | 0.525 | 0.655 |
| Creatine
kinase-MB | 0.601 | 0.546 | 0.657 | 8.797 | 0.180 | 0.510 | 0.670 |
| Myoglobin | 0.582 | 0.527 | 0.638 | 98.445 | 0.140 | 0.710 | 0.430 |
| N-terminal pre-B
natriuretic peptide | 0.587 | 0.531 | 0.643 | 359.604 | 0.185 | 0.800 | 0.385 |
Diagnostic model for differentiating
different types of CVD
The diagnostic model can also differentiate between
arrhythmias, SHD and coronary artery disease, with AUC values of
0.771 (95% CI: 0.702-0.840) and 0.715 (95% CI: 0.631-0.798),
respectively When the diagnostic model value is between 3.870 and
4.077, it indicates arrhythmias; between 4.077 and 4.916, it
indicates SHD; and above 4.916, it indicates coronary artery
disease (Table V; Fig. 3A and B).
The statistical power of the ROC curves for both the
overall cardiovascular disease diagnosis and the discrimination of
disease subtypes was >0.9. This indicated that the present study
had sufficient statistical power to demonstrate that the observed
AUC was significantly greater than 0.5 (random classification).
Discussion
Although CAD, SHD and arrhythmias differ in their
pathophysiological mechanisms, they all essentially belong to the
broader category of CVD. Therefore, the present study hypothesized
that these conditions may share certain overlapping changes in
biomarkers. Some studies support this view, showing that myocardial
injury biomarkers such as cTnI and NT-proBNP are closely associated
with CAD (17,18) and are also related to SHD and
arrhythmias (19,20). This suggests that these biomarkers
may reflect common pathological processes shared across different
CVD, such as subclinical myocardial injury, cardiac stress, or
early hemodynamic alterations. Therefore, changes in these
indicators may serve as cross-disease biological signals for
cardiovascular risk assessment or preliminary screening.
The present study identified CK-MB, NT-proBNP, cTnI
and MYO as essential diagnostic biomarkers for cardiovascular
disease. CK-MB is an enzyme primarily found in myocardial tissue
(21). When myocardial damage
occurs, CK-MB is released into the bloodstream, resulting in
elevated CK-MB levels. Therefore, CK-MB serves as an important
biomarker for diagnosing myocardial injury (22). In patients with CAD, CK-MB is
commonly used to evaluate the degree of myocardial ischemia or
injury. Following stent implantation, CK-MB can also be used to
monitor treatment response (23).
In patients with arrhythmia, since there is no significant
myocardial damage, the clinical utility of CK-MB is relatively
limited. For patients with SHD, CK-MB is commonly used to evaluate
myocardial injury, monitor treatment response to percutaneous
coronary intervention or stent placement and assess complications
such as worsening heart failure or myocardial ischemia.
NT-proBNP is a precursor of BNP, primarily secreted
by ventricular myocytes to assess cardiac function (24). In coronary artery disease (CAD),
elevated NT-proBNP may indicate left ventricular dysfunction,
myocardial ischemia, or decreased efficacy of drug therapy. In SHD,
increased NT-proBNP is frequently associated with elevated
ventricular wall stress, myocardial fibrosis, or poor treatment
outcomes, implying the need for treatment optimization (25). In arrhythmias, NT-proBNP elevation
may be associated with left atrial dilation and atrial remodeling.
Additionally, NT-proBNP levels can help assess treatment efficacy,
significant reductions following antiarrhythmic drug therapy or
catheter ablation suggest successful intervention.
cTnI is one of the most sensitive and specific
biomarkers for detecting myocardial injury and is a key component
of the cardiac troponin complex (26,27),
cTnI is also valuable for detecting CAD, particularly in patients
with acute coronary syndrome, where its levels rise markedly
(28). In SHD, elevated cTnI may
indicate progressive myocardial damage and an increased risk of
heart failure. Additionally, cTnI serves an essential function in
arrhythmia diagnosis. Severe arrhythmias, such as atrial
fibrillation or ventricular arrhythmias, can lead to myocardial
hypoxia, often accompanied by ischemia or mild myocardial injury,
resulting in increased cTnI levels. This makes cTnI a valuable
marker for assessing the severity and impact of arrhythmias on
cardiac health.
MYO is an oxygen-binding protein found in skeletal
muscle and myocardium, primarily responsible for storing and
transporting oxygen to support cellular respiration during hypoxia
or intense exercise. MYO is commonly used as a biomarker for acute
myocardial infarction and skeletal muscle injury (29). When myocardial cells are damaged,
MYO is rapidly released into the bloodstream. Due to its early
release and quick clearance, it functions as an early indicator of
myocardial injury. However, its low specificity necessitates the
use of additional biomarkers to ensure accurate diagnosis.
The main innovation of the present study consisted
in the development of a diagnostic model intended as an auxiliary
tool to help identify CVD at an early stage and assist in
differentiating between CAD, SHD and arrhythmia. Health examination
data from 6 months prior to diagnosis to was used build the model,
enabling short-term prediction of cardiovascular disease risk and
illustrating its potential as an auxiliary tool for timely clinical
intervention. Unlike previous studies, which typically focused on a
single disease type, few have extensively addressed multiple
cardiovascular conditions within an integrated framework. This
model may have clinical utility, as it could assist physicians at
the early diagnostic stage by providing additional information to
differentiate cardiovascular conditions. However, the present model
integrated multiple cardiovascular diseases and no previously
reported diagnostic models that differentiated between CAD, SHD and
arrhythmias was found. Therefore, a direct comparison of AUC values
with existing studies was not feasible.
The present study also had several limitations.
First, this was a retrospective study, which may have introduced
selection bias into data. Second, the sample size was relatively
small and the data were collected at a single center. In addition,
CVD was simply categorized into three major groups, possibly
overlooking some complex disease subtypes. In the future,
larger-scale, multicenter prospective randomized controlled studies
are needed, and these classifications should be further refined to
improve the precision and usability of the model. The diagnostic
model shows relatively limited specificity but high sensitivity,
showing a good ability to identify true patients. Therefore, the
model may be more suitable as an auxiliary tool for screening or
early detection rather than as a standalone method for clinical
confirmation. The cardiac biomarkers included in the present study
are all indicators reported in previous studies. Therefore, the
novelty of the present study consisted mainly in the combined
modeling of multiple biomarkers. rather than the discovery of novel
biomarkers. Due to differences in prediction horizon and study
objectives, the model developed in the present study was not
directly compared with existing long-term cardiovascular risk
prediction models. Future studies with longer follow-up periods
might be conducted to allow comparisons with established risk
scoring systems. Although all biomarkers were collected within six
months prior to diagnosis, the exact sampling times varied, which
may have introduced some heterogeneity. Future studies may benefit
from more precisely defining the sampling time intervals.
In conclusion, CK-MB, NT-proBNP, cTnI and MYO play
important roles in the diagnosis of cardiovascular diseases. The
diagnostic model constructed based on this can not only effectively
diagnose cardiovascular disease as well as accurately distinguish
coronary artery disease, SHD and arrhythmia. It has potential
clinical application value in increasing diagnostic accuracy and
individualized therapy.
Supplementary Material
Performance evaluation in the external
validation set (A) ROC curve of MLR in the external validation set.
(B) ROC curve of RF in the external validation set. (C) Calibration
curve of MLR. (D) Calibration curve of RF. MLR, multivariate
logistic regression; RF, random forest.
Sensitivity analysis (A) Excluding
cTnI (B) Excluding CK-MB (C) Excluding MYO and (D) Excluding
NT-proBNP. cTnI, cardiac troponin I; CK-MB, creatine kinase-MB;
MYO, myoglobin; NT-proBNP, N-terminal pre-B natriuretic
peptide.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
HZ was responsible for conceptualization, collection
of raw data and preprocessing of the raw data, methodology,
software and writing the original draft. JW and QL were responsible
for formal analysis, project administration, the preparation of
figures, writing, reviewing and editing. TZ and XC were responsible
for selecting study participants according to the inclusion and
exclusion criteria and completing the collection of clinical
samples, supervision, the external validation of the diagnostic
model and verification of the results, writing, reviewing and
editing. HZ and JW confirm the authenticity of all the raw data.
All authors read and approved the final manuscript.
Ethics approval and consent to
participate
The present study was approved by the Ethics
Committee of the Beijing Fengtai You'anmen Hospital (approval
number: LL-8701) The present study was conducted in strict
accordance with the Declaration of Helsinki and relevant ethical
guidelines. The research content involved in this research meets
the requirements of medical ethics and academic morality of our
hospital, and the research content is reasonable, the risks are
controllable, and there are no violations. The relevant research
performed was in line with the safe, standardized and true
scientific research guiding principles, and in line with the
requirements of the clinical research ethics code. The requirement
for informed consent was waived by the Ethics Committee due to the
retrospective nature of the study.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
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