Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension

doi:10.21203/rs.3.rs-5223664/v1

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Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension

https://doi.org/10.21203/rs.3.rs-5223664/v1

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Hypertension combined with hyperhomocysteinemia significantly raises the risk of ischemic stroke. Our study aimed to develop and validate a biomarker-based prediction model for ischemic stroke in H-type hypertension patients. We retrospectively included 3,305 patients in the development cohort, and externally validated in 103 patients from another cohort. Logistic regression, LASSO regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis (DCA) respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. There were 1,415 and 42 patients with ischemic stroke in the development and validation cohorts. The final model included 8 predictors: age, antihypertensive therapy, biomarkers (serum magnesium, serum potassium, proteinuria and hypersensitive C-reactive protein), and comorbidities (atrial fibrillation and hyperlipidemia). The optimal model, named A₂BC ischemic stroke model, showed good discrimination and calibration ability for ischemic stroke with AUC of 0.91 and 0.87 in the internal and external validation cohorts. The A₂BC ischemic stroke model had satisfactory predictive performances to assist clinicians in accurately identifying the risk of ischemic stroke for patients with H-type hypertension.

Health sciences/Diseases/Cardiovascular diseases

Biological sciences/Biochemistry

H-type hypertension

ischemic stroke

predicted model

Due to its high incidence, recurrence, mortality, and disability rates, stroke continues to be the second leading cause of death worldwide, according to the Global Burden of Disease Study (GBD) 2019¹. Intravenous thrombolysis and endovascular thrombectomy are the main treatments for ischemic stroke at the moment². A limited therapeutic window presents a significant challenge for nations with insufficient or unbalanced medical resources³. Therefore, the best way to lessen the burden of stroke is early prevention.

More than half of stroke patients worldwide are attributed to hypertension, making it one of the most significant modifiable risk factors for stroke⁴. Additionally, there is a causal relationship between homocysteine (Hcy) concentration and stroke⁵. The prevalence of hyperhomocysteinemia (HHcy) is about 3 / 4 of the hypertension population in China. H-type hypertension is defined as hypertension combined with elevated Hcy level⁶. Surprisingly, vascular damage is worsened by a synergistic effect between Hcy and hypertension⁷. The above suggests that H-type hypertension patients should be focused on monitoring the risk of ischemic stroke.

The Framingham stroke risk profile (FSP) and CHA2DS2-VASc score are widely used to assess the risk of stroke in general population and and nonvalvular atrial fibrillation (AF) patients^8,9. However, there are few validated tools available for assessing the risk of ischemic stroke in patients with H-type hypertension. Clinicians typically manage high-risk population in the cardiovascular field using a combination of demographics characteristics and medical history, along with some laboratory indicators. By employing this strategy, our study aims to screen out high-risk groups for ischemic stroke, develop and validate a high-performance prediction model for ischemic stroke in patients with H-type hypertension, and facilitate further risk stratification management by clinicians for patients.

Baseline characteristics

According to the inclusion and exclusion criteria, among the 11,631 patients diagnosed with H-type hypertension at Beijing Anzhen Hospital from January 2022 and December 2023, 4,632 suffered an ischemic stroke. A total of 3,305 had medical records in same hospital between January 2018 and December 2021, and 2,340 were assigned to the training set and 965 to the testing set (Supplementary Fig. 1). Another 103 H-type hypertension patients, including 61 patients without ischemic stroke and 42 patients with ischemic stroke, were enrolled as an external validation cohort from the China-Japan Friendship Hospital (Supplementary Fig. 2). Detailed information about the characteristics of patients in the total cohort, training, and internal validation sets are shown in Table 1 and Supplementary Table 1, respectively. As shown in Table 1, patients with ischemic stroke were older with higher SBP and had a higher proportion of smokers and a history of cardiovascular disease (all P < 0.05) as compared to non-stroke patients.

Table 1

Baseline clinical and biochemical characteristics of all patients
Variable	Total (n = 3408)	non-Stroke (n = 1951)	Ischemic stroke (n = 1457)	P value
Age, years, median (IQR)	56 (42–66)	46 (37–58)	65 (57–74)	< 0.001
Male, %	2435 (71.4%)	1382 (70.8%)	1053 (72.3%)	0.358
BMI, median (IQR)	26.26 (24.00-28.98)	26.84 (24.38–29.59)	25.61 (23.44–28.04)	< 0.001
SBP, mmHg, median (IQR)	142 (130–155)	140 (130–152)	145 (132–159)	< 0.001
DBP, mmHg, median (IQR)	86 (78–97)	90 (80–100)	82 (74–92)	< 0.001
Smoke, %	1605 (47.1%)	882 (45.2%)	723 (49.6%)	0.011
Drink, %	1482 (43.5%)	873 (44.7%)	609 (41.8%)	0.086
Diabetes mellitus, %	1043 (30.6%)	405 (20.8%)	638 (43.8%)	< 0.001
Hyperlipidemia, %	2796 (82.0%)	1413 (72.4%)	1383 (94.9%)	< 0.001
Coronary heart disease, %	729 (21.4%)	315 (16.1%)	414 (28.4%)	< 0.001
Atrial fibrillation, %	178 (5.2%)	16 (0.8%)	162 (11.1%)	< 0.001
Antihypertensive drugs, %	2287 (67.1%)	1366 (70.0%)	921 (63.2%)	< 0.001
Antiplatelet drugs, %	218 (6.4%)	123 (6.3%)	95 (6.5%)	0.799
Family history of cerebral infarction, %	216 (6.3%)	118 (6.0%)	98 (6.7%)	0.422
hs-CRP, mg/L, median (IQR)	1.34 (0.65–3.18)	1.17 (0.60–2.65)	1.72 (0.78–4.83)	< 0.001
Na, mmol/L, median (IQR)	140.6 (139.0-142.0)	140.4 (138.9-141.7)	140.9 (139.2-142.4)	< 0.001
K, mmol/L, median (IQR)	4.05 (3.80–4.27)	4.13 (3.92–4.33)	3.90 (3.68–4.15)	< 0.001
Mg, mmol/L, median (IQR)	0.91 (0.86–0.95)	0.92 (0.87–0.96)	0.89 (0.84–0.94)	< 0.001
TBil, µmol/L, median (IQR)	13.00 (10.05–16.82)	13.20 (10.52-17.00)	12.51 (9.40–16.60)	< 0.001
DBil, µmol/L, median (IQR)	4.20 (3.05–5.57)	4.21 (3.06–5.47)	4.20 (3.03–5.71)	0.202
Urinary protein, %				< 0.001
-	2831 (83.1%)	1804 (92.5%)	1027 (70.5%)
+	425 (12.5%)	95 (4.9%)	330 (22.6%)
++	108 (3.3%)	38 (1.9%)	70 (5.0%)
+++	40 (1.2%)	13 (0.7%)	27 (1.9%)
Hcy, µmol/L, %				< 0.001
< 15	2092 (61.4%)	1260 (64.6%)	832 (57.1%)
15–30	1051 (30.8%)	541 (27.7%)	510 (35.0%)
> 30	265 (7.8%)	150 (7.7%)	115 (7.9%)
Carotid artery stenosis, %	282 (8.3%)	144 (7.4%)	138 (9.5%)	0.028
IQR, interquartile range; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-CRP, hypersensitive C-reactive protein; Na, Sodium; K, Potassium; Mg, magnesium; TBil, total bilirubin; DBil, direct bilirubin; Hcy, homocysteine.

Predictor selections

There were 16 variables with P < 0.05 by univariate logistic regression (Supplementary Table 2). After stepwise regression, 13 variables were ultimately retained, namely, age, antihypertensive therapy, hyperlipidemia, atrial fibrillation (AF), diabetes mellitus (DM), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy and proteinuria. In best subset selection regression, when the model included eight variables, the BIC of the model reached its minimum. These eight variables were age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria, respectively (Fig. 1A and B). In LASSO regression, 17 variables were selected with a lambda that is within 1 standard error (SE), namely age, gender, antihypertensive therapy, antiplatelet therapy, hyperlipidemia, AF, DM, coronary artery disease (CAD), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy, proteinuria and carotid artery stenosis (Fig. 1C and D). Eventually, eight variables were included to develop models: age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria (Fig. 2).

Model development and validation

Eight variables were entered into a multivariable logistic regression model, linear kernel SVM model, random forest model, and XGBoost model, respectively. Four models yielded the AUC of 0.905 (95% CI: 0.887–0.924), 0.896 (95% CI: 0.876–0.915), 0.893 (95% CI: 0.872–0.914), 0.909 (95% CI: 0.890–0.927) for the risk of ischemic stroke (Fig. 3 and Table 2). The difference of AUC between logistic regression model and XGBoost model was not significant (DeLong test, P = 0.406). Based on the maximal Youden’s index, the threshold of four models were 55%, 46%, 37%, and 43% in order. The XGBoost model had the highest sensitivity, 0.825, with a specificity of 0.860.

Table 2

Predict performances of four models on the testing set
	AUC (95%CI)	Sensitivity	Specificity	Accuracy	PPV	NPV
Logistic model	0.905 (0.887–0.924)^#	0.745	0.905	0.833	0.860	0.816
SVM model	0.896 (0.876–0.915)^*	0.778	0.851	0.819	0.806	0.828
Random forest model	0.893 (0.872–0.914)	0.820	0.840	0.831	0.803	0.854
XGBoost model	0.909 (0.890–0.927)	0.825	0.860	0.845	0.825	0.860
^#: There was no significant difference in AUC between the logistic model and the XGBoost model by Delong test; ^*: There was no significant difference in AUC between the SVM model and the random forest model by Delong test; AUC: area under curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; SVM: support vector machine.

Calibration plots were used to assess the calibration of models. As shown in Fig. 4, four models had a good calibration. Among them, the predicted odds of the outcome of the logistic regression model and XGBoost model were close to the actual probability (Fig. 4A and D). Four models resulted in a high net benefit, especially the logistic regression model and XGBoost model (Fig. 5). Conclusively, the logistic regression model and XGBoost model exhibited excellent discrimination and calibration performance. Considering the visualization and scalability of the prediction model, we ultimately chose the logical regression model as the optimal model. The weight coefficients of eight variables in the logistic regression model was shown in Supplementary Fig. 3. Serum magnesium, serum potassium, AF, and hyperlipidemia have a higher weight in the optimal model.

In the external cohort, the logistic regression model achieved an AUC of 0.872 (95% CI: 0.805–0.939) showing good discrimination capacity (Supplementary Fig. 4 and Supplementary Table 3). The logistic regression model also was well-calibrated and had a high net benefit in the external cohort (Supplementary Fig. 5 and Supplementary Fig. 6).

Model Visualization

The eight variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia) were fitted a logistic regression model to predict the risk of ischemic stroke in H-type hypertension patients was termed the A₂BC ischemic stroke model and presented as a nomogram (Fig. 6). The variables were listed separately, and the cumulative score is matched to a risk score.

Based on two independent retrospective cohorts with a large sample size, our study developed and internally and externally validated a model to predict the risk of ischemic stroke. This model included 8 variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia), which termed the A₂BC ischemic stroke model. The A₂BC ischemic stroke model showed great discrimination and calibration for the risk of ischemic stroke, with similar findings when externally validated.

At present, the most effective treatment of acute ischemic stroke (AIS) is reperfusion therapy in therapeutic time window, including intravenous thrombolysis (IVT) and endovascular therapy (EVT), but about 3/4 patients present over 4.5 hours after stroke onset or with an unknown time of onset¹⁰. Besides, there are numerous contraindications associated with IVT that must be carefully considered¹¹. The rates of IVT and EVT were 5·64% and 1·45% between 2019 and 2020 in China¹². Recurrent ischemic stroke is another challenge even with improved secondary prevention, recurrence rates of ischemic stroke seem unchanged over time¹³. Because of above all, primary prevention of high-risk population may be another effective way to improve the burden of ischemic stroke. However, there is an unmet need for accurate and validated models for estimating risk of ischemic stroke.

Some guidelines propose FSP as a reliable tool for 10-year stroke risk estimates¹⁴. Despite its widespread application, the validity of the FSP has not been sufficiently studied in populations with different age range or ethnicity. A prospective study showed that FSP overestimates the risk of stroke in Chinese¹⁵. In the same way, both the CV risk calculator and Stroke Riskometer need to be validated and adapted in the Chinese population^16,17. Also, although most risk factors have an independent effect on ischemic stroke, interactions may exist between these factors when considering predicting overall risk. A combined analysis of hypertension and Hcy showed they act additively to increase the risk of stroke²². Therefore, it is necessary to establish a prediction model for ischemic stroke specific to the H-type hypertension subset. One of the purposes of risk assessment is to guide an appropriate primary prevention program. Additional folic acid significantly reduces the risk of first stroke in hypertension patients, compared with antihypertensive therapy alone¹⁸.

Serum magnesium, an inorganic ion, is given the most weight in our model, and serum potassium is also significant. Magnesium and potassium are crucial trace elements for organisms, as we all know. Magnesium helps to prevent ischemic stroke. Through various mechanisms, it lowers blood pressure more effectively than potassium¹⁹. Inflammation, endothelial dysfunction, and platelet dysfunction have all been linked to low magnesium levels²⁰. Stroke risk was 2.5 times higher for diuretic users with low serum potassium than for those with high serum potassium²¹. When compared to adults receiving antihypertensive therapy, hypokalemia is independently associated with an increased risk of ischemic stroke and is unrelated to diuretics.

Dyslipidemia is an independent risk factor for stroke²². The risk of an ischemic stroke can be decreased by lowering atherogenic lipoproteins^23,24. New lipid-lowering medications have made it possible to lower LDL-C to extremely low levels, but doing so will raise the risk of hemorrhagic stroke²⁵. A significant risk factor for stroke is AF. A thrombus from the left atrial (LA) cavity, particularly the left atrial appendage (LAA), is primarily responsible for ischemic stroke associated with AF²⁶. Plasma Hcy levels were found to be associated with LA/LAA thrombus and could be used to predict the risk of LA/LAA thrombus in non-valvular AF patients with low CHA2DS2-VASc scores²⁷.

Numerous studies have demonstrated that hypertension can cause cerebrovascular diseases through a variety of mechanisms, including adapting automatic regulation of cerebral blood flow (CBF) to hypertension²⁸, endothelial dysfunction, reduction of nitric oxide (NO)²⁹, elevated levels of angiotensin II (Ang II) leading to cerebral artery hypertrophy and inward remodeling³⁰. Fortunately, the negative effects of hypertension can be offset by a variety of antihypertensive medications. Using long-lasting dihydropyridine-Ca²⁺ channel blocker attributes to the normalization of autoregulation of CBF³¹. Similarly, other types of antihypertensive drugs also have this effect³². Additionally, combining antihypertensive medications in suboptimal doses can establish tolerance and effectively treat the remodeling of cerebral arteries brought on by hypertension³³.

Proteinuria, the other factor in our model, is a common sign of renal damage and has a particularly strong association with stroke^34,35. Researchers have proposed the term "cerebro-renal interaction" because kidney disease and cerebrovascular disease are closely related³⁶. The above can be explained by the idea that increased urinary protein excretion rate may be connected to significant vascular damage³⁷. According to epidemiological studies, people over 65 account for the majority of stroke cases, and the risk rises with age^38,39. Variety in circulation factors in the systemic environment, cellular senescence, and hypertension during human aging can all increase the risk of stroke⁴⁰.

The A₂BC ischemic stroke model consists of 8 general variables, which are simple to collect in clinical practice, there is no need to take into account specialized examination equipment and technical personnel, allowing community hospitals to conduct rapid screening and significantly saving medical resources. Our study had some limitations as well. First, certain H-type hypertension-specific risk factors for ischemic stroke, like MTHFR polymorphism, have not been studied. However, not many community hospitals in China offer MTHFR polymorphism detection services. Second, since our research was a retrospective study and the prediction model created by the machine learning algorithm was just a reflection of mathematical logic, there was no causal relationship. Therefore, even though we have demonstrated the model's good performance on an external validation cohort, more clinical data and prospective queues were required to improve the model's performance in specific clinical application scenarios. Finally, we excluded secondary hypertension in patients with H-type hypertension, as the causes of secondary hypertension are diverse, and the predictive factors are complex. Therefore, our model cannot be used for secondary hypertension patients with elevated Hcy levels.

Study population

This retrospective cohort study consecutively included inpatients diagnosed with H-type hypertension, whether or not they suffered first ischemic stroke, at Beijing Anzhen Hospital, Capital Medical University from January 2022 to December 2023. Patients with secondary hypertension or a history of ischemic stroke would be excluded. These patients would also be excluded if they lack data in Beijing Anzhen Hospital from January 2018 to December 2021. Meanwhile, we extracted an external validation cohort from the China-Japan Friendship Hospital between January 2023 and June 2023.

Patients with hypertension were diagnosed according to the International Society of Hypertension recommendations⁴¹. Systolic blood pressure (SBP) in the office or clinic was ≥ 140 mmHg and/or diastolic blood pressure (DBP) was ≥ 90 mmHg following repeated examinations were considered as hypertension. In addition, the guideline also suggested that blood pressure < 140 / 90 mmHg in patients with a history of hypertension and currently using antihypertensive drugs were still diagnosed as hypertension. Patients with essential hypertension were identified when secondary hypertension was excluded. Hypertension patients, together with serum Hcy concentrations ≥ 10 µmol/L, were identified as H-type hypertension⁴². Ischemic stroke was confirmed via computed tomography (CT) or brain magnetic resonance imaging (MRI) combined with clinical symptoms and signs. Our study was conducted according to the Declaration of Helsinki and was approved by the hospital’s ethical review board (Beijing Anzhen Hospital, Capital Medical University, Beijing, China). The need to obtain informed consent was waived by the hospital’s ethical review board.

Additional Information

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contribution

Study concept and design: Hui Yuan, Ke Chen. Data collection: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Data analysis and interpretation: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Drafting of the manuscript: Ke Chen, Hui Yuan. Critical revision of the manuscript: all authors. Final approval: all authors.

Acknowledgement

This work was supported by the National Key Research and Development Program (2022YFC2009600) (2022YFC2009602).

Data Availability

Some or all data sets generated and/or analyzed during the present study are not publicly available but are available from the corresponding author upon reasonable request.

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Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension

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