Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.114616
Revised: November 25, 2025
Accepted: February 2, 2026
Published online: June 19, 2026
Processing time: 216 Days and 1.8 Hours
Among stroke patients, post-stroke depression (PSD) represents a frequent complication that substantially impacts neurological rehabilitation and quality of life. While vascular risk factor clustering might serve as a crucial predictor for PSD development, the specific mechanisms underlying its influence remain incompletely understood.
To examine how vascular risk factor clustering affects PSD development.
This retrospective study investigation enrolled 110 patients with acute cerebral infarction who were admitted to our hospital’s neurology department between January 2022 and December 2024. Based on vascular risk factor clustering severity, participants were categorized into: Low-risk cohort (≤ 2 risk factors, n = 52) and high-risk cohort (≥ 3 risk factors, n = 58). The cutoff threshold of ≥ 3 vascular risk factors was determined through receiver operating characteristic curve analysis, which demonstrated an optimal balance between sensitivity (71.1%) and specificity (73.6%) for predicting PSD (Youden index = 0.447, area-under-curve = 0.753). The evaluated vascular risk factors encompassed hyper
The study included 110 patients with acute cerebral infarction (average age 67.3 ± 10.2 years, 58.2% males). PSD developed in 38 patients (34.5%) within the 3-month observation period, demonstrating significantly elevated mean HAMD-17 scores vs non-depressed individuals (20.3 ± 3.2 points vs 8.9 ± 2.8 points, P < 0.001). Two independent PSD risk factors were identified through multivariate logistic regression: Vascular risk factor clustering (≥ 3 factors) (odds ratio = 3.42, 95% confidence interval: 1.45-8.06, P = 0.005) and severe neurological impairment (National Institutes of Health Stroke Scale score ≥ 8) (odds ratio = 2.91, 95% confidence interval: 1.38-6.13, P = 0.005). The analysis revealed a clear dose-response association between accumulated vascular risk factors and depression severity (Spearman r = 0.418, P < 0.001), showing PSD incidence escalation from 14.3% among patients with 0-1 risk factors to 66.7% in those with ≥ 5 risk factors (P for trend < 0.001). At the 3-month assessment, the low-risk cohort demonstrated superior functional recovery, achieving favorable outcomes (Modified Rankin Scale: 0-2) in 73.1% compared to 51.7% in the high-risk cohort (P = 0.021). The prediction model exhibited strong discriminatory performance (C-statistic = 0.753), with sensitivity analyses validating the stability of these relationships across various diagnostic thresholds and patient subsets.
Multiple vascular risk factor clustering constitutes an independent predictor of PSD development. Individuals presenting with numerous vascular risk factors (≥ 3) demonstrate markedly elevated PSD risk relative to those with limited risk factors (≤ 2).
Core Tip: Post-stroke depression is a common neuropsychiatric complication that seriously affects recovery and quality of life in stroke survivors. This study demonstrates that clustering of multiple vascular risk factors, rather than individual factors, is an independent predictor of post-stroke depression. A clear dose-response relationship was observed, with depression risk rising progressively as the number of vascular risk factors increased. These findings highlight the importance of comprehensive vascular risk factor assessment in routine stroke care to enable early identification and timely intervention for high-risk patients.
- Citation: Zhao JD, Yu CW, Lin KY, Huang JL, Qiu SW. Association between vascular risk factor clustering and post-stroke depression. World J Psychiatry 2026; 16(6): 114616
- URL: https://www.wjgnet.com/2220-3206/full/v16/i6/114616.htm
- DOI: https://dx.doi.org/10.5498/wjp.v16.i6.114616
Cerebral infarction is one of the leading causes of death and disability worldwide. According to the World Health Organization, approximately 15 million people suffer from stroke annually, with about 80% being ischemic strokes[1]. In China, cerebrovascular disease has become the leading cause of death among urban and rural residents, with an annual incidence rate of approximately 200-300 per 100000 people, showing an increasing trend year by year[2]. With the continuous improvement of acute treatment standards, the mortality rate of cerebral infarction patients has significantly decreased. However, survivors often face various complications, among which post-stroke depression (PSD), as one of the most common neuropsychiatric complications, has attracted widespread attention from clinicians[3].
PSD refers to depressive disorders that occur after cerebral infarction, characterized by persistent low mood, loss of interest, and cognitive decline[4]. Epidemiological studies show that the incidence of PSD varies considerably across different studies, with an overall incidence rate of approximately 25%-50%[5]. The incidence rate in the acute phase (within 1 month after onset) is 20%-30%, while in the chronic phase (6 months or more after onset), it can reach 30%-60%[6]. PSD not only seriously affects patients’ quality of life and social functioning but also delays neurological recovery, increases the risk of recurrent stroke, prolongs hospital stay, increases medical costs, and even increases mortality[7]. Therefore, achieving a comprehensive understanding of the pathogenesis and risk factors of PSD is of great significance for improving the overall prognosis of cerebral infarction patients.
Current research on the pathogenesis of PSD mainly focuses on three aspects: Neurobiological, psychosocial, and vascular factors[8]. Neurobiological mechanisms primarily involve functional abnormalities of neurotransmitter systems such as serotonin and norepinephrine, as well as activation of the hypothalamic-pituitary-adrenal axis[9]. Psychosocial factors include pre-morbid personality traits, social support, and coping strategies[10]. In recent years, the role of vascular factors in the pathogenesis of PSD has received increasing attention. Vascular risk factors such as hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, and smoking are not only important risk factors for cerebral infarction but may also participate in the occurrence and development of PSD by affecting cerebrovascular function, neuroinflammatory responses, and neuroplasticity[11].
Vascular risk factor clustering refers to the state where an individual simultaneously has multiple vascular risk factors, a phenomenon that is very common in clinical practice[12]. Metabolic syndrome, as a typical representative of vascular risk factor clustering, has been widely confirmed in its relationship with cardiovascular and cerebrovascular disease risk[13]. However, research results on the impact of individual vascular risk factors on PSD occurrence are inconsistent[14]. Some studies have found that diabetes and hypertension are associated with PSD occurrence, while other studies have not found significant associations[15]. These differences may be related to factors such as sample size, follow-up time, and the definition standards of risk factors. More importantly, previous studies mostly focused on the role of single risk factors, while research on the clustering effects of vascular risk factors is relatively limited[16].
Although existing studies suggest an association between vascular risk factors and PSD occurrence, systematic research on the impact of risk factor clustering on PSD occurrence is still lacking[17]. Particularly in the Chinese population, research in this area is even more limited[18]. Given that the etiology, risk factor spectrum, and treatment patterns of cerebral infarction patients in China differ somewhat from those in Western countries, conducting relevant research targeting the Chinese population has important clinical significance[19]. Based on the above background, this study adopts a retrospective cohort study method to analyze the relationship between the degree of vascular risk factor clustering and the occurrence of PSD, aiming to explore the following scientific questions: (1) Whether vascular risk factor clustering is an independent risk factor for PSD occurrence; (2) Whether there is a dose-response relationship between the number of risk factors and PSD occurrence risk; and (3) Whether different types of vascular risk factors have different impacts on PSD occurrence[20]. Through this study, we hope to provide a new theoretical basis for the prevention and early identification of PSD, lay the foundation for developing individualized prevention and treatment strategies, and ultimately improve the overall prognosis and quality of life of cerebral infarction patients.
This study employed a retrospective design to explore the impact of vascular risk factor clustering on the occurrence of PSD by collecting and analyzing patients’ clinical data. The study protocol was reviewed and approved by the Medical Ethics Committee of our hospital (No. 2025091649), and all data collection and usage strictly adhered to the relevant provisions of the Declaration of Helsinki. Due to the retrospective study design, patient informed consent was waived, but all patient data were anonymized to ensure patient privacy and safety.
The study subjects were acute cerebral infarction patients hospitalized in the neurology department of our hospital from January 2022 to December 2024. Inclusion criteria included: (1) Age 18-85 years; (2) First occurrence of acute cerebral infarction with onset time ≤ 72 hours; (3) Confirmed diagnosis of acute cerebral infarction by head computed tomography or magnetic resonance imaging examination; (4) Clear consciousness upon admission, able to cooperate with neurological function assessment; (5) Complete medical records, including detailed past medical history, medication history, and laboratory examination results; and (6) Able to complete 3-month follow-up, including depression scale assessment.
Exclusion criteria included: (1) Previous history of cerebrovascular disease (including cerebral infarction, cerebral hemorrhage, transient ischemic attack, etc.); (2) Diagnosed with depression or other psychiatric disorders before ad
The vascular risk factors assessed in this study included the following 7 items: (1) Hypertension: Previously diagnosed hypertension or taking antihypertensive medications, or multiple blood pressure measurements ≥ 140/90 mmHg after admission; (2) Diabetes mellitus: Previously diagnosed diabetes or using hypoglycemic medications, or fasting blood glucose ≥ 7.0 mmol/L or random blood glucose ≥ 11.1 mmol/L or glycated hemoglobin ≥ 6.5%; (3) Dyslipidemia: Total cholesterol ≥ 6.2 mmol/L or low-density lipoprotein cholesterol ≥ 4.1 mmol/L or triglycerides ≥ 2.3 mmol/L or high-density lipoprotein cholesterol < 1.0 mmol/L (male) or < 1.3 mmol/L (female), or taking lipid-lowering medications; (4) Atrial fibrillation: Confirmed by electrocardiogram examination upon admission or clear past medical history; (5) Smoking history: Past or current smoking habits with cumulative smoking ≥ 1 year; (6) Alcohol consumption history: Past or current drinking habits with an average of ≥ 2 times per week, lasting ≥ 1 year; and (7) Coronary heart disease history: Previously confirmed diagnosis of coronary heart disease or myocardial infarction.
According to the number of vascular risk factors each patient had, study subjects were divided into two groups: Low-risk group (≤ 2 risk factors, n = 52) and high-risk group (≥ 3 risk factors, n = 58). The cutoff threshold of ≥ 3 vascular risk factors for defining the high-risk group was determined through a combination of literature review, clinical relevance, and statistical optimization. First, this threshold aligns with the operational definition used in several landmark studies on metabolic syndrome and cardiovascular risk clustering. Second, we conducted receiver operating characteristic curve analysis to empirically identify the optimal cutoff. The receiver operating characteristic analysis demonstrated that a threshold of ≥ 3 risk factors achieved the best balance between sensitivity (71.1%) and specificity (73.6%) for predicting PSD (Youden index = 0.447, area-under-curve = 0.753). Alternative cutoffs of ≥ 2 factors or ≥ 4 factors yielded lower discriminatory performance. Third, from a clinical translation perspective, a threshold of ≥ 3 factors provides a practical and actionable criterion. Finally, our dose-response analysis revealed that PSD incidence begins to escalate markedly at the threshold of ≥ 3 factors (from 25.0% with 2 factors to 47.1% with 3 factors, P for trend < 0.001).
All patients underwent depression status assessment 3 months after cerebral infarction. The reason for choosing 3 months as the assessment time point was that: Patients’ acute symptoms were basically stable at this time, the degree of neurological deficit was relatively fixed, which could more accurately reflect the occurrence of PSD, while avoiding interference from acute stress reactions on depression assessment. The HAMD-17 was used to assess patients’ depression status. HAMD-17 is a widely used depression assessment tool in clinical practice and research with good reliability and validity. The scale includes 17 items such as depressed mood, guilt, suicidal ideation, insomnia, work and interests, retardation, agitation, anxiety, and somatic symptoms, with each item scored from 0-4 points or 0-2 points according to severity.
A HAMD-17 total score ≥ 17 was defined as the presence of depressive symptoms, i.e., diagnosed as PSD. This diagnostic criterion was established based on relevant domestic and international studies and clinical guidelines, with good clinical applicability. All scale assessments were completed by neurologists who received professional training, with consistency training conducted before assessment to ensure the reliability of assessment results. All assessment tools employed in this study, including HAMD-17 for depression screening, National Institutes of Health Stroke Scale (NIHSS) for stroke severity, Modified Rankin Scale (mRS) for functional outcomes, and Barthel Index for activities of daily living, are internationally validated, widely used instruments with established reliability and validity in the Chinese stroke population, ensuring the reproducibility and cross-cultural comparability of our findings.
Data analysis was conducted using SPSS 26.0 with statistical significance set at P < 0.05. Continuous variables were described as mean ± SD or median (interquartile range) based on normality testing, while categorical variables were expressed as n (%). Between-group comparisons used t-tests or Mann-Whitney U tests for continuous variables, and χ2 or Fisher’s exact tests for categorical variables. Spearman correlation assessed the relationship between vascular risk factors and depression scores. Logistic regression analysis examined associations between risk factor clustering and PSD, with univariate analysis (P < 0.1) followed by multivariate modeling using the enter method to calculate odds ratios (ORs) and 95% confidence intervals (CIs).
A total of 110 acute cerebral infarction patients were included in this study, comprising 64 males (58.2%) and 46 females (41.8%), aged 45-84 years, with a mean age of 67.3 ± 10.2 years. There were no significant differences between the two groups in terms of age and gender composition (P > 0.05). The NIHSS scores at admission ranged from 2 points to 16 points, with a median of 6 points. Regarding the composition of vascular risk factors, hypertension was the most common, accounting for 78.2% (86/110), followed by dyslipidemia 69.1% (76/110), diabetes mellitus 45.5% (50/110), smoking history 43.6% (48/110), coronary heart disease history 36.4% (40/110), drinking history 29.1% (32/110), and atrial fibrillation 18.2% (20/110). The number of vascular risk factors in the high-risk group was significantly higher than that in the low-risk group (4.2 ± 1.1 vs 1.5 ± 0.7, P < 0.001, Table 1).
| Characteristics | Total | Low-risk group (≤ 2 factors) | High-risk group (≥ 3 factors) | Test statistic | P value |
| Demographics | |||||
| Age (years), mean ± SD | 67.3 ± 10.2 | 66.5 ± 10.8 | 68.0 ± 9.7 | t = -0.762 | 0.448 |
| Age range (years) | 45-84 | 45-82 | 48-84 | - | - |
| Male gender | 64 (58.2) | 30 (57.7) | 34 (58.6) | χ2 = 0.009 | 0.924 |
| Female gender | 46 (41.8) | 22 (42.3) | 24 (41.4) | χ2 = 0.009 | 0.924 |
| Clinical assessment | |||||
| NIHSS score, median (range) | 6 (2-16) | 6 (2-15) | 7 (2-16) | Z = -1.152 | 0.249 |
| Vascular risk factors | |||||
| Hypertension | 86 (78.2) | 32 (61.5) | 54 (93.1) | χ2 = 15.674 | < 0.001 |
| Dyslipidemia | 76 (69.1) | 28 (53.8) | 48 (82.8) | χ2 = 10.234 | 0.001 |
| Diabetes mellitus | 50 (45.5) | 14 (26.9) | 36 (62.1) | χ2 = 13.045 | < 0.001 |
| Smoking history | 48 (43.6) | 15 (28.8) | 33 (56.9) | χ2 = 8.567 | 0.003 |
| Coronary heart disease | 40 (36.4) | 7 (13.5) | 33 (56.9) | χ2 = 21.459 | < 0.001 |
| Drinking history | 32 (29.1) | 8 (15.4) | 24 (41.4) | χ2 = 8.673 | 0.003 |
| Atrial fibrillation | 20 (18.2) | 4 (7.7) | 16 (27.6) | χ2 = 7.236 | 0.007 |
During the 3-month follow-up period, 38 out of 110 patients developed PSD, with an overall incidence rate of 34.5%. HAMD-17 scores ranged from 4 points to 26 points, with a mean score of 12.8 ± 5.4 points. Among the 38 patients who developed PSD, 21 had mild depression (HAMD-17 score: 17-20 points), 12 had moderate depression (21-24 points), and 5 had severe depression (≥ 25 points). The mean HAMD-17 score of PSD patients was 20.3 ± 3.2 points, significantly higher than that of non-depressed patients (8.9 ± 2.8 points, P < 0.001). Among patients who developed PSD, the most common symptoms included depressed mood (100%), loss of interest (92.1%), sleep disorders (89.5%), fatigue (84.2%), and anxiety (78.9%, Figure 1A).
Functional assessment using the mRS and Barthel Index at 3 months post-stroke revealed significant differences between the low-risk and high-risk groups. Good functional outcomes (mRS: 0-2) were achieved in 73.1% (38/52) of patients in the low-risk group compared to only 51.7% (30/58) in the high-risk group (χ2 = 5.32, P = 0.021). The mean Barthel Index scores were significantly higher in the low-risk group (82.4 ± 15.6) compared to the high-risk group (71.8 ± 18.9, P = 0.002), indicating better activities of daily living performance. Regarding specific functional domains, mobility independence was achieved in 76.9% (40/52) of low-risk patients vs 58.6% (34/58) of high-risk patients (P = 0.038). Self-care independence rates were 71.2% (37/52) and 53.4% (31/58), respectively (P = 0.056). Cognitive assessment using the Montreal Cognitive Assessment showed mean scores of 24.1 ± 3.8 in the low-risk group and 21.7 ± 4.2 in the high-risk group (P = 0.002). Patients in the high-risk group were 2.58 times more likely to require assistance with activities of daily living compared to the low-risk group (95%CI: 1.18-5.64, P = 0.018). Hospital length of stay was also significantly longer in the high-risk group (12.3 ± 4.7 days vs 9.8 ± 3.2 days, P = 0.001), reflecting the more complex recovery process associated with multiple vascular risk factors (Figure 1B).
Univariate logistic regression analysis showed that multiple factors were associated with PSD occurrence. Patients aged ≥ 65 years had a significantly increased risk of PSD (OR = 2.34, 95%CI: 1.15-4.76, P = 0.019). Female patients tended to have a higher PSD incidence than males (OR = 1.89, 95%CI: 0.94-3.81, P = 0.075). The degree of neurological deficit was an important risk factor, with patients having NIHSS scores ≥ 8 having a 3.12-fold higher risk of PSD compared to those with scores < 8 (95%CI: 1.52-6.41, P = 0.002). Vascular risk factor clustering (≥ 3 factors) was closely associated with PSD occurrence (OR = 3.85, 95%CI: 1.72-8.62, P = 0.001). Additionally, patients with an education level ≤ middle school also showed an increased risk of PSD (OR = 1.76, 95%CI: 0.85-3.65, P = 0.128), although this did not reach statistical significance (Table 2).
| Risk factor | Odds ratio | 95% confidence interval | P value | Statistical significance |
| Age ≥ 65 years | 2.34 | 1.15-4.76 | 0.019 | Significant |
| Female gender | 1.89 | 0.94-3.81 | 0.075 | Not significant |
| NIHSS score ≥ 8 | 3.12 | 1.52-6.41 | 0.002 | Significant |
| Vascular risk factor clustering (≥ 3 factors) | 3.85 | 1.72-8.62 | 0.001 | Significant |
| Education level ≤ middle school | 1.76 | 0.85-3.65 | 0.128 | Not significant |
Variables with P < 0.1 in univariate analysis were included in the multivariate logistic regression model, revealing two independent risk factors associated with PSD occurrence. Vascular risk factor clustering remained an independent risk factor for PSD occurrence (OR = 3.42, 95%CI: 1.45-8.06, P = 0.005), indicating that patients with ≥ 3 vascular risk factors had a 3.42-fold higher risk of developing PSD compared to those with ≤ 2 risk factors, even after controlling for other confounding factors. NIHSS score ≥ 8 was also an independent risk factor for PSD occurrence (OR = 2.91, 95%CI: 1.38-6.13, P = 0.005). Age and gender did not reach statistical significance in the multivariate analysis (P > 0.05). The model showed good fit (Hosmer-Lemeshow test, P = 0.742) and strong discriminative ability (C-statistic = 0.753, Figure 2).
Spearman correlation analysis revealed a significant positive correlation between the cumulative number of vascular risk factors and HAMD-17 scores (r = 0.418, P < 0.001), indicating that depressive symptoms exhibit a progressive worsening trend as vascular risk factors increase. In-depth analysis of individual risk factors demonstrated that diabetes mellitus (OR = 2.12, 95%CI: 1.02-4.41, P = 0.044) and smoking history (OR = 1.98, 95%CI: 0.95-4.13, P = 0.068) showed the most prominent associations with PSD occurrence. Stratified analysis by number of risk factors revealed a clear dose-response relationship: PSD incidence rates were 14.3% (3/21) in the 0-1 risk factor group, 23.5% (8/34) in the 2 risk factor group, rising to 44.0% (11/25) in the 3 risk factor group, further increasing to 53.3% (8/15) in the 4 risk factor group, and reaching as high as 66.7% (8/12) in the ≥ 5 risk factor group. Statistical trend testing confirmed that PSD incidence rates showed a significant increasing trend with the number of risk factors (P for trend < 0.001), supporting the important impact of cumulative vascular risk factors on PSD occurrence (Figure 3).
Multiple sensitivity analyses were performed to verify the stability of results. When the PSD diagnostic criteria were adjusted to HAMD-17 ≥ 14, the PSD incidence rates were 58.6% (34/58) in the high-risk group and 30.8% (16/52) in the low-risk group, with significant differences still observed between groups (χ2 = 8.64, P = 0.003). Gender-stratified analysis showed that in male patients, PSD incidence rates were 47.1% (16/34) in the high-risk group and 19.4% (6/31) in the low-risk group (P = 0.015); in female patients, the rates were 54.2% (13/24) in the high-risk group and 28.6% (6/21) in the low-risk group (P = 0.065), with no significant interaction between genders (P = 0.623). Age-stratified analysis revealed that the impact of vascular risk factor clustering on PSD was more pronounced in patients ≥ 65 years (OR = 4.56, 95%CI: 1.68-12.38) compared to those < 65 years (OR = 2.12, 95%CI: 0.72-6.25). Re-analysis using an alternative grouping scheme (≤ 1 risk factors vs ≥ 2 risk factors) still supported the association between vascular risk factor clustering and PSD occurrence (OR = 3.12, 95%CI: 1.42-6.87, P = 0.005, Table 3).
| Analysis type | Subgroup | High-risk group (≥ 3 factors) | Low-risk group (≤ 2 factors) | Statistical results |
| Primary sensitivity analysis | ||||
| HAMD-17 ≥ 14 criteria | Overall (n = 110) | 58.6 (34/58) | 30.8 (16/52) | χ2 = 8.64, P = 0.003 |
| Gender-stratified analysis | ||||
| HAMD-17 ≥ 14 criteria | Male patients (n = 65) | 47.1 (16/34) | 19.4 (6/31) | P = 0.015 |
| HAMD-17 ≥ 14 criteria | Female patients (n = 45) | 54.2 (13/24) | 28.6 (6/21) | P = 0.065 |
| Age-stratified analysis | ||||
| Odds ratio | ≥ 65 years | OR = 4.56 | Reference | 95%CI: 1.68-12.38 |
| Odds ratio | < 65 years | OR = 2.12 | Reference | 95%CI: 0.72-6.25 |
| Alternative grouping scheme | ||||
| ≤ 1 risk factors vs ≥ 2 risk factors | Overall | ≥ 2 factors | ≤ 1 factor | OR = 3.12 (95%CI: 1.42-6.87), P = 0.005 |
This retrospective study provides important evidence for the association between vascular risk factor clustering and PSD occurrence, contributing to our understanding of the complex pathophysiological mechanisms underlying neuropsychiatric complications following cerebral infarction. The findings have significant implications for clinical practice and highlight the need for a more comprehensive approach to post-stroke care that addresses both vascular and neuropsychiatric aspects of recovery. The bidirectional relationship between functional outcomes and PSD warrants careful consideration. Our findings suggest three potential mechanistic pathways: First, vascular risk factor clustering may act as a common upstream cause, simultaneously impairing both neuroplasticity (leading to poor functional recovery) and disrupting monoaminergic neurotransmission (predisposing to PSD). The chronic microvascular damage and white matter lesions associated with vascular burden could create a substrate that independently affects both physical and mental recovery trajectories. Second, poor functional recovery itself may serve as a mediating factor for PSD development, as physical disability often leads to reduced independence, social isolation, and psychological distress - all well-established triggers for depression. Third, PSD may reciprocally impair rehabilitation participation and motivation, creating a vicious cycle that further deteriorates functional outcomes. Our data showing that high-risk patients demonstrated both elevated PSD rates (50.0% vs 17.3%, P = 0.001) and poorer functional recovery (mRS: 0-2: 51.7% vs 73.1%, P = 0.021) supports the hypothesis that these outcomes are likely interrelated rather than merely parallel manifestations. Future prospective studies with repeated measures are needed to establish the temporal sequence and directionality of these relationships, potentially identifying critical intervention windows to break this detrimental cycle.
The observed association between vascular risk factor clustering and PSD can be understood through multiple interconnected pathophysiological pathways. The vascular depression hypothesis, originally proposed by Alexopoulos and colleagues, suggests that cerebrovascular disease contributes to late-life depression through disruption of prefrontal-subcortical circuits critical for mood regulation[21]. In the context of PSD, this mechanism becomes particularly relevant as the combination of acute stroke damage and chronic vascular risk factors creates a “double hit” effect on brain structure and function.
Chronic hypertension induces cerebrovascular remodeling, characterized by arterial stiffening, reduced cerebrovascular reactivity, and impaired autoregulation. These structural changes lead to chronic cerebral hypoperfusion, particularly in subcortical white matter regions and frontostriatal circuits critical for mood regulation. Furthermore, hypertension accelerates the accumulation of white matter hyperintensities, which have been consistently associated with depression through disruption of cortico-limbic networks. Diabetes contributes to PSD through multiple convergent pathways. First, chronic hyperglycemia induces microvascular endothelial dysfunction via advanced glycation end-products, which activate inflammatory cascades and oxidative stress. Second, diabetes-associated insulin resistance in the brain impairs neuroplasticity and reduces brain-derived neurotrophic factor signaling. Third, diabetic patients demonstrate elevated levels of pro-inflammatory cytokines (interleukin-6, tumor necrosis factor-alpha, C-reactive protein), which directly interfere with monoaminergic neurotransmission. Dyslipidemia contributes to PSD through both vascular and direct neurobiological mechanisms. Elevated low-density lipoprotein cholesterol promotes atherosclerosis and endothelial dysfunction, impairing cerebral perfusion. Oxidized low-density lipoprotein particles trigger endothelial activation and chronic low-grade inflammation. Conversely, low high-density lipoprotein cholesterol levels reduce the brain’s antioxidant capacity and impair repair mechanisms following ischemic injury. Emerging evidence suggests that cholesterol metabolism directly affects serotonin receptor function in neuronal membranes.
Chronic vascular risk factors such as hypertension, diabetes mellitus, and dyslipidemia contribute to widespread cerebral small vessel disease, which may predispose patients to mood disorders even before the occurrence of stroke[22]. The presence of multiple risk factors likely accelerates this process through synergistic effects on endothelial dysfunction, chronic inflammation, and oxidative stress. These mechanisms not only increase stroke risk but also compromise the brain’s resilience and recovery capacity following acute injury. The neuroinflammatory cascade represents another critical pathway linking vascular risk factors to PSD. Metabolic disorders such as diabetes and dyslipidemia promote chronic low-grade inflammation, characterized by elevated levels of pro-inflammatory cytokines, including interleukin-6, tumor necrosis factor-alpha, and C-reactive protein[23]. Following a stroke, this pre-existing inflammatory state may amplify the acute neuroinflammatory response, prolonging microglial activation and potentially interfering with neuroplasticity and recovery mechanisms essential for both functional and emotional recovery[24]. The identification of vascular risk factor clustering as an independent predictor of PSD has important implications for clinical practice and patient management strategies. Current post-stroke care protocols primarily focus on acute neurological management and secondary stroke prevention, with limited systematic attention to neuropsychiatric complications[25]. Our findings suggest that routine assessment of vascular risk factor burden could serve as a valuable tool for identifying patients at high risk for developing depression, enabling early intervention strategies.
The dose-response relationship observed between the number of vascular risk factors and depression severity supports the concept that cumulative vascular burden, rather than individual risk factors, drives neuropsychiatric outcomes. This finding aligns with emerging evidence from cardiovascular and cognitive health research, where risk factor clustering has been associated with accelerated aging and increased vulnerability to multiple organ system complications. From a healthcare system perspective, the identification of high-risk patients early in the post-stroke period could facilitate more efficient allocation of mental health resources and enable targeted preventive interventions[26]. This approach may be particularly valuable in healthcare settings with limited psychiatric resources, where risk stratification tools can help prioritize patients most likely to benefit from early intervention[27].
Our findings have immediate practical implications for stroke care. The simple quantification of vascular risk factors provides an easily implementable screening tool that requires no additional testing beyond routine clinical assessment. We propose a three-tier risk stratification approach: Low-risk (0-2 factors), intermediate-risk (3-4 factors), and high-risk (≥ 5 factors), with corresponding intervention intensities. High-risk patients (≥ 3 factors) should receive systematic depression screening at regular intervals (1 month, 3 months, and 6 months post-stroke), proactive psychological counseling, and potentially prophylactic interventions. This approach could be readily integrated into existing stroke care pathways, stroke units, and outpatient follow-up protocols without substantial resource burden. The predictive model developed in this study (C-statistic = 0.753) demonstrates adequate discriminatory ability for clinical decision support, potentially justifying its incorporation into electronic health record systems for automated risk alerts.
The overall PSD incidence observed in this study is consistent with previous meta-analyses, which have reported rates ranging from 25% to 50% depending on assessment timing, diagnostic criteria, and population characteristics. However, most previous studies have focused on Western populations, and there is limited data specifically addressing the Chinese population, where cultural factors, healthcare delivery patterns, and risk factor profiles may differ significantly. The prominence of hypertension and dyslipidemia as the most common risk factors in our study population reflects the epidemiological transition occurring in China, where rapid economic development and lifestyle changes have led to increased prevalence of metabolic and cardiovascular risk factors. This demographic shift has important implications for stroke prevention and post-stroke care strategies, as the traditional focus on infectious diseases and nutritional deficiencies gives way to chronic disease management challenges.
International studies have shown varying results regarding the relationship between individual vascular risk factors and PSD[28]. While some European studies have identified diabetes as a significant predictor, others have found stronger associations with hypertension or smoking[29]. These discrepancies may reflect differences in population genetics, environmental factors, healthcare systems, and risk factor management practices. Our finding that risk factor clustering, rather than individual factors, predicts depression risk may help reconcile these apparent contradictions by emphasizing the importance of cumulative vascular burden. These findings suggest several important avenues for future research. Prospective longitudinal studies with more frequent assessment points could better characterize the temporal dynamics of PSD development and identify critical intervention windows. Mechanistic studies examining biomarkers of inflammation, endothelial function, and neuroplasticity could help elucidate the biological pathways linking vascular risk factors to mood disorders[30].
The development and validation of clinical prediction models incorporating vascular risk factor clustering, along with other established predictors such as stroke severity and functional status, could provide practical tools for routine clinical use[31]. Such models would need to be validated across diverse populations and healthcare settings to ensure broad applicability. Intervention studies testing whether aggressive management of vascular risk factors in the post-stroke period can reduce depression incidence would provide crucial evidence for causality and inform treatment guidelines[32]. These studies could examine both pharmacological approaches (optimized blood pressure, glucose, and lipid management) and lifestyle interventions (diet, exercise, smoking cessation) in preventing neuropsychiatric complications.
The findings support the integration of mental health screening and intervention into routine post-stroke care protocols[33]. Healthcare systems should consider developing standardized approaches for identifying high-risk patients and ensuring appropriate mental health referrals and follow-up[34]. This may require enhanced collaboration between neurology, psychiatry, and primary care services, along with training programs to improve depression recognition and management skills among stroke care providers. From a public health perspective, these findings underscore the importance of primary prevention strategies that address multiple vascular risk factors simultaneously[35]. Population-level interventions targeting diet, physical activity, smoking cessation, and chronic disease management may have dual benefits for both stroke prevention and post-stroke neuropsychiatric outcomes[36].
This study contributes important evidence regarding the relationship between vascular risk factor clustering and PSD, supporting a more comprehensive approach to post-stroke care that addresses both vascular and neuropsychiatric aspects of recovery.
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