Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.115917
Revised: December 10, 2025
Accepted: January 26, 2026
Published online: June 19, 2026
Processing time: 195 Days and 1.3 Hours
Post-stroke depression affects up to 40% of stroke survivors, with age being a critical but incompletely understood risk factor for depression onset patterns and treatment response.
To investigate the impact of age on depression symptom onset patterns, temporal evolution, and treatment response following acute cerebral infarction, providing scientific evidence for developing age-specific prevention and treatment strate
A multicenter retrospective study was conducted, including 300 patients with acute cerebral infarction admitted to two hospitals in Quanzhou City from January 2022 to January 2024. Patients were divided into a young group (18-44 years, n = 68), middle-aged group (45-64 years, n = 124), and elderly group (≥ 65 years, n = 108). Depression symptoms were assessed using Hamilton Depression Rating Scale-17 items (HAMD-17) and Patient Health Questionnaire-9 items, cognitive function was evaluated using Mini-Mental State Examination (MMSE), and functional status was assessed using Barthel index and modified Rankin Scale. Follow-up assessments were conducted at discharge, 1 month, 3 months, and 6 months post-discharge. Statistical analyses included repeated measures analysis of variance (ANOVA), generalized estimating equations, and multiple regression analysis.
No significant difference in depression symptom incidence was observed among the three groups at discharge (P > 0.05), but onset patterns differed. The young group showed peak HAMD-17 scores at 1-month post-discharge followed by gradual decline (12.8 ± 5.2 to 10.1 ± 4.3), the middle-aged group maintained high levels from 1-3 months, and the elderly group showed continuous increase in the first 3 months followed by stabilization (10.4 ± 5.6 to 15.8 ± 7.1). Repeated measures ANOVA revealed significant time × group interaction effects (F = 12.847, P < 0.001). Cognitive function recovery showed a graded pattern: Young group > middle-aged group > elderly group (P < 0.001). Multiple linear regression analysis identified age (β = 0.124, P < 0.001), National Institutes of Health Stroke Scale score (β = 0.346, P < 0.001), and high-sensitivity C-reactive protein level (β = 0.187, P = 0.006) as independent risk factors for HAMD-17 scores at 6 months, while MMSE score was a protective factor (β = -0.308, P = 0.002). Generalized estimating equation analysis showed different treatment responses across age groups, with the young group showing the best response and the elderly group the worst (P < 0.001).
The onset patterns of depression symptoms and response to treatment after acute cerebral infarction have a great impact on age. The symptoms of young patients are severe and of an acute phase, but elderly patients deteriorate slowly and with less response to treatment. The major influencing factors are differences in neuroplasticity, level of inflammatory response and status of cognitive functions. The clinical practice is also to create the age-stratified screening and assessment systems and to create the individual approach to treatment.
Core Tip: This study reported on post-stroke depression among 300 patients with cerebral injury focusing on both age change variables and differential depression patterns and treatment response. Changes in depression were more pronounced in the younger patients toward the positive and the elderly toward the negative. The elderly patients exhibited the longest depression duration with minimal treatment response. Age, cognitive function, and depression severity alone were effective prognosticators of post-stroke outcomes and additional variables which were more clinically pronounced, were not additive. The study advocates for the formation of age-related post-stroke depression symptomatic treatment and management response protocols to enhance rehabilitation.
- Citation: Li JJ, Chen ZJ, Nian WH, Liang YL, Su WJ. Age-stratified analysis of depression onset patterns and treatment response differences following cerebral infarction. World J Psychiatry 2026; 16(6): 115917
- URL: https://www.wjgnet.com/2220-3206/full/v16/i6/115917.htm
- DOI: https://dx.doi.org/10.5498/wjp.v16.i6.115917
Post-stroke depression is a common neuropsychiatric complication of cerebrovascular disease that severely affects patients’ functional recovery, quality of life, and long-term prognosis[1]. Epidemiological studies show that the incidence of post-stroke depression is approximately 25%-40%, exhibiting distinct time-dependent onset patterns, with most patients developing depressive symptoms within 1-6 months after stroke onset[2]. With China’s accelerating population aging and continuously rising cerebrovascular disease incidence, post-stroke depression has become an important factor affecting patient rehabilitation outcomes and social functioning[3].
As an important risk factor for post-stroke depression, age affects depression onset patterns and treatment response, though them mechanisms remain incompletely understood. Previous studies indicate that elderly stroke patients are more susceptible to depressive symptoms, with longer symptom duration and relatively poorer treatment response[4]. However, systematic stratified studies are lacking regarding whether significant differences exist among different age groups in depression symptom onset timing, severity, duration, and treatment response. Additionally, age-related neuroplasticity changes, inflammatory response differences, cognitive function status, and psychosocial factors may collectively influence post-stroke depression development[5,6].
Current clinical assessment and intervention strategies for depression symptoms in stroke patients are relatively uniform for different ages, lacking targeted stratified management protocols. Recent research has found significant differences between young and elderly stroke patients in pathophysiological mechanisms, inflammatory mediator expression, and neural repair capacity, which may lead to age-related changes in depression symptom onset patterns and treatment response[7,8]. Therefore, an in-depth exploration of age effects on post-stroke depression onset patterns and treatment response is clinically significant for developing individualized prevention and treatment strategies[9].
Therefore, we adopted a multicenter retrospective cohort study design, analyzing clinical data from 300 acute cerebral infarction patients, stratified by age to explore depression symptom onset patterns, temporal evolution, and treatment response differences among different age groups, aiming to provide scientific evidence for developing age-specific post-stroke depression prevention and treatment strategies.
This study employed a multicenter retrospective design to investigate the impact of age and gender on depression onset patterns and treatment response differences following emergency cerebral infarction. Data was retrieved from patient’s hospitalization records from the approximate timeframe of January 2022 to January 2024 at two Quanzhou City Hospitals. The hospitals included in the study were Quanzhou First Hospital and Jinjiang Municipal Hospital. The target number for patient inclusion was 300. The study was reviewed by the ethics committee and adhered to all patient privacy protection rules. The patient participants were classified by age: Young (18-44 years), middle-aged (45-64 years), and elderly (≥ 65 years).
Inclusion criteria: (1) Acute cerebral infarction patients aged ≥ 18 years; (2) Diagnosis confirmed by head computed tomography (CT) or magnetic resonance imaging (MRI); (3) Onset to hospital admission time ≤ 72 hours; (4) Clear consciousness and ability to cooperate with assessment; and (5) Complete medical records and follow-up data.
Exclusion criteria: (1) Previous history of psychiatric disorders or depression; (2) Previous cognitive dysfunction or dementia; (3) Severe aphasia or dysarthria affecting assessment; (4) Concurrent malignant tumors or other severe systemic diseases; (5) History of drug or alcohol dependence; and (6) Patients with survival period < 6 months.
Baseline data: (1) Basic information: Age, gender, body mass index (BMI), education level, marital status; (2) Medical history: Detailed records of hypertension, diabetes, coronary heart disease, others; (3) Medication history: Anticoagulants, antihypertensive drugs, antidiabetic drugs, lipid-lowering drugs, others; (4) Stroke-related clinical data: Onset time, initial symptoms, admission National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin Scale (mRS) score. The NIHSS includes 11 items: Level of consciousness, level of consciousness questions, level of consciousness commands, best gaze, visual fields, facial palsy, limb motor function (upper and lower), limb ataxia, sensory, best language, dysarthria, extinction and inattention, with total scores 0-42 points, higher scores indicating more severe neurological deficits. The mRS assesses neurological disability degree with 7 levels (0-6 points), higher scores indicating greater disability. Head CT/MRI imaging data were collected, recording infarct location (anterior/posterior circulation), infarct size, involvement of key areas (such as thalamus, basal ganglia, brainstem), and stroke etiology classification according to trial of org 10172 in acute stroke treatment criteria; and (5) Laboratory indicators: Blood routine, comprehensive metabolic panel (including blood glucose, liver and kidney function, electrolytes, lipids), coagulation function, cardiac enzymes, B-type natriuretic peptide, glycated hemoglobin (HbA1c), homocysteine, high-sensitivity C-reactive protein (hs-CRP) within 24 hours of admission.
Depression assessment data: (1) The Hamilton Depression Rating Scale-17 items (HAMD-17) was used as the primary assessment tool. The HAMD-17 contains 17 items: Depressed mood, feelings of guilt, suicide, insomnia-initial, insomnia-middle, insomnia-delayed, work and activities, psychomotor retardation, agitation, psychic anxiety, somatic anxiety, gastrointestinal somatic symptoms, general somatic symptoms, genital symptoms, hypochondriasis, loss of weight, and insight, covering 7 dimensions (anxiety/somatization, weight, cognitive disturbance, diurnal variation, retardation, sleep, hopelessness), with items scored 0-2, 0-3, or 0-4 points, total score 0-52 points, higher scores indicating more severe depressive symptoms; and (2) The Patient Health Questionnaire-9 items (PHQ-9) was used as an auxiliary assessment tool, containing 9 items: Little interest or pleasure in doing things, feeling down/depressed/hopeless, trouble falling or staying asleep or sleeping too much, feeling tired or having little energy, poor appetite or overeating, feeling bad about yourself, trouble concentrating on things, moving or speaking slowly or being fidgety/restless, and thoughts of suicide or self-harm, using a 4-point Likert scale (0-3 points), total score 0-27 points, higher scores indicating more severe depressive symptoms. Assessment time points: At discharge, 1 month, 3 months, and 6 months post-discharge.
Depression treatment was initiated when patients met both screening criteria (HAMD-17 ≥ 8 or PHQ-9 ≥ 5) and received diagnostic confirmation from psychiatric consultation according to Diagnostic and Statistical Manual of Mental Disorders-IV criteria. Treatment decisions were made by psychiatrists considering symptom severity, functional impairment, and patient preferences.
Cognitive function assessment: The Mini-Mental State Examination (MMSE) was used to assess patients’ cognitive function, excluding the impact of cognitive dysfunction on depression assessment. MMSE includes 5 cognitive domains: (1) Orientation: Time orientation (5 points) and place orientation (5 points); (2) Attention and calculation: Serial sevens or spelling WORLD backwards (5 points); (3) Memory: Immediate recall (3 points) and delayed recall (3 points), total memory score (6 points); (4) Language ability: Naming (2 points), repetition (1 point), three-stage command (3 points), reading (1 point), writing (1 point); and (5) Visuospatial ability: Figure copying (1 point). Total score 30 points, higher scores indicating better cognitive function. Assessment time points: At discharge, 1 month, 3 months, and 6 months post-discharge.
Functional status assessment: The Barthel index (BI) was used to assess patients’ activities of daily living, and mRS scores assessed neurological deficit severity: (1) BI includes 10 daily living activity dimensions: Feeding, bathing, grooming, dressing, bowel control, bladder control, toilet use, bed to chair transfer, walking on level surface, and climbing stairs, total score 100 points, higher scores indicating better daily living ability; and (2) The mRS scoring as described above. Assessment time points: At discharge, 1 month, 3 months, and 6 months post-discharge.
Treatment protocol: Treatment protocols followed standard clinical practice guidelines at each participating institution. Antidepressant selection, dosing, and titration were determined by consulting psychiatrists based on individual patient factors including symptom severity, comorbidities, potential drug interactions, and tolerability. While specific medication regimens were not standardized across the retrospective cohort, selective serotonin reuptake inhibitors represented the most commonly prescribed class. The heterogeneity in treatment approaches reflects real-world clinical practice.
SPSS 26.0 statistical software was used for data analysis. Data cleaning and missing value processing were performed first, with multiple imputation for variables with missing rates < 5%. In descriptive statistical analysis, normally distributed continuous variables were expressed as mean ± SD, non-normally distributed continuous variables as median (interquartile range), and categorical variables as n (%). Normality testing used the Shapiro-Wilk test, and homogeneity of variance testing used Levene’s test. For between-group comparisons, independent samples t-test (normal distribution with equal variance) or Mann-Whitney U test (non-normal distribution) was used for two-group continuous variables; one-way analysis of variance (ANOVA) (normal distribution with equal variance) or Kruskal-Wallis H test (non-normal distribution) was used for multi-group continuous variable comparisons. χ2 test or Fisher’s exact test was used for categorical variable comparisons. Repeated measures ANOVA or generalized estimating equations analyzed temporal trends in longitudinal repeated measure data. Multiple linear regression analyzed factors influencing continuous variables, and multiple logistic regression analyzed factors influencing binary variables, with stepwise regression for variable selection, entry criterion α = 0.05, removal criterion α = 0.10. Statistical significance level was set at α = 0.05, two-sided test.
This study included 300 acute cerebral infarction patients: Young group (18-44 years) 68 cases, middle-aged group (45-64 years) 124 cases, elderly group (≥ 65 years) 108 cases. The three groups differed in gender composition, BMI, and marital status (P < 0.05). The elderly group had higher proportions of hypertension, diabetes, and coronary heart disease compared with the young and middle-aged groups (P < 0.001). Regarding stroke severity, the elderly group had higher admission NIHSS and mRS scores compared with the other two groups (P < 0.01) (Table 1).
| Item | Young group (n = 68) | Middle-aged group (n = 124) | Elderly group (n = 108) | χ2/F value | P value |
| Gender | 4.126 | 0.127 | |||
| Male | 45 (66.2) | 89 (71.8) | 63 (58.3) | ||
| Female | 23 (33.8) | 35 (28.2) | 45 (41.7) | ||
| BMI (kg/m2) | 24.6 ± 3.2 | 25.3 ± 3.8 | 23.9 ± 3.5 | 4.127 | 0.017 |
| Education level | 15.672 | 0.004 | |||
| Junior high school or below | 18 (26.5) | 52 (41.9) | 67 (62.0) | ||
| High school | 28 (41.2) | 46 (37.1) | 31 (28.7) | ||
| College or above | 22 (32.3) | 26 (21.0) | 10 (9.3) | ||
| Marital status | 12.864 | 0.002 | |||
| Married | 52 (76.5) | 108 (87.1) | 83 (76.9) | ||
| Single/divorced/widowed | 16 (23.5) | 16 (12.9) | 25 (23.1) | ||
| Medical history | |||||
| Hypertension | 28 (41.2) | 78 (62.9) | 89 (82.4) | 24.571 | < 0.001 |
| Diabetes | 12 (17.6) | 35 (28.2) | 48 (44.4) | 12.398 | 0.002 |
| Coronary heart disease | 5 (7.4) | 23 (18.5) | 34 (31.5) | 13.962 | 0.001 |
| NIHSS score | 8.2 ± 4.6 | 10.5 ± 5.3 | 12.1 ± 5.8 | 19.854 | < 0.001 |
| mRS score | 2.1 ± 1.2 | 2.8 ± 1.4 | 3.2 ± 1.5 | 21.472 | < 0.001 |
| Infarct location | 2.185 | 0.335 | |||
| Anterior circulation | 48 (70.6) | 92 (74.2) | 84 (77.8) | ||
| Posterior circulation | 20 (29.4) | 32 (25.8) | 24 (22.2) |
The three groups differed in medication history, with the elderly group having higher proportions of antihypertensive and antidiabetic drug use than the other groups (P < 0.001). Regarding stroke clinical characteristics, the elderly group had longer onset-to-admission time and higher proportion of large infarcts (Table 2).
| Item | Young group | Middle-aged group | Elderly group | χ2/F value | P value |
| Medication history | |||||
| Anticoagulants | 8 (11.8) | 28 (22.6) | 45 (41.7) | 17.842 | < 0.001 |
| Antihypertensive drugs | 26 (38.2) | 74 (59.7) | 86 (79.6) | 24.567 | < 0.001 |
| Antidiabetic drugs | 10 (14.7) | 32 (25.8) | 44 (40.7) | 12.985 | 0.002 |
| Lipid-lowering drugs | 15 (22.1) | 38 (30.6) | 42 (38.9) | 5.234 | 0.073 |
| Clinical characteristics | |||||
| Onset to admission time (hours) | 16.8 ± 11.2 | 20.4 ± 14.9 | 26.7 ± 18.3 | 8.254 | < 0.001 |
| Initial symptoms | 8.942 | 0.063 | |||
| Limb weakness | 45 (66.2) | 89 (71.8) | 85 (78.7) | ||
| Speech disorders | 18 (26.5) | 28 (22.6) | 18 (16.7) | ||
| Others | 5 (7.4) | 7 (5.6) | 5 (4.6) | ||
| Imaging characteristics | |||||
| Large infarct (> 1/3 middle cerebral artery territory) | 12 (17.6) | 28 (22.6) | 35 (32.4) | 5.847 | 0.054 |
| Involvement of key areas | 15 (22.1) | 35 (28.2) | 42 (38.9) | 6.234 | 0.044 |
| TOAST classification | 7.865 | 0.248 | |||
| Large-artery atherosclerosis | 28 (41.2) | 56 (45.2) | 54 (50.0) | ||
| Small-vessel occlusion | 24 (35.3) | 42 (33.9) | 32 (29.6) | ||
| Cardioembolism | 8 (11.8) | 16 (12.9) | 18 (16.7) | ||
| Other causes | 8 (11.8) | 10 (8.1) | 4 (3.7) |
The three groups differed in multiple laboratory parameters. The elderly group had higher levels of blood glucose, HbA1c, hs-CRP, and homocysteine than the young and middle-aged groups (P < 0.05). Routine blood tests showed higher inflammatory indicators in the elderly group, with increased incidence of coagulation abnormalities (P < 0.01). Regarding electrolytes, although statistical differences existed in serum sodium and potassium levels among the three groups, values were within normal physiological ranges with limited clinical significance. Cardiac enzymes showed abnormalities in patients with concurrent cardiac diseases (P < 0.01) (Table 3).
| Item | Young group (n = 68) | Middle-aged group (n = 124) | Elderly group (n = 108) | F value | P value |
| Blood routine | |||||
| White blood cells (× 109/L) | 8.2 ± 2.4 | 8.8 ± 2.9 | 9.1 ± 3.2 | 2.847 | 0.059 |
| Neutrophil percentage (%) | 69.2 ± 8.5 | 71.8 ± 9.3 | 73.5 ± 10.1 | 6.124 | 0.002 |
| Hemoglobin (g/L) | 132 ± 18 | 128 ± 19 | 124 ± 21 | 4.258 | 0.015 |
| Platelets (× 109/L) | 248 ± 67 | 265 ± 82 | 278 ± 94 | 3.156 | 0.044 |
| Biochemical indicators | |||||
| Blood glucose (mmol/L) | 6.5 ± 1.8 | 7.2 ± 2.4 | 8.1 ± 2.9 | 11.567 | < 0.001 |
| Creatinine (μmol/L) | 76 ± 16 | 84 ± 21 | 92 ± 28 | 12.483 | < 0.001 |
| Blood urea nitrogen (mmol/L) | 5.1 ± 1.6 | 5.8 ± 2.1 | 6.8 ± 2.5 | 15.234 | < 0.001 |
| Alanine aminotransferase (U/L) | 28 ± 12 | 32 ± 16 | 35 ± 19 | 3.924 | 0.021 |
| Aspartate aminotransferase (U/L) | 31 ± 14 | 36 ± 18 | 42 ± 23 | 6.785 | 0.001 |
| Total cholesterol (mmol/L) | 4.8 ± 1.1 | 4.6 ± 1.0 | 4.4 ± 1.2 | 3.154 | 0.044 |
| Triglycerides (mmol/L) | 1.9 ± 1.0 | 2.2 ± 1.3 | 2.0 ± 1.1 | 2.687 | 0.070 |
| Electrolytes | |||||
| Serum sodium (mmol/L) | 139.2 ± 2.8 | 139.1 ± 3.2 | 138.8 ± 3.8 | 0.326 | 0.722 |
| Serum potassium (mmol/L) | 4.1 ± 0.4 | 4.0 ± 0.5 | 4.0 ± 0.6 | 0.847 | 0.430 |
| Serum chloride (mmol/L) | 102.5 ± 3.8 | 101.9 ± 4.1 | 100.7 ± 4.9 | 4.126 | 0.017 |
| Serum calcium (mmol/L) | 2.38 ± 0.15 | 2.35 ± 0.18 | 2.29 ± 0.21 | 5.687 | 0.004 |
| Serum phosphorus (mmol/L) | 1.18 ± 0.24 | 1.22 ± 0.28 | 1.28 ± 0.32 | 2.845 | 0.060 |
| Cardiac enzymes | |||||
| Creatine kinase (U/L) | 142 ± 52 | 156 ± 68 | 168 ± 75 | 3.847 | 0.022 |
| Creatine kinase-MB (U/L) | 16 ± 7 | 19 ± 9 | 22 ± 11 | 4.124 | 0.017 |
| Lactate dehydrogenase (U/L) | 198 ± 45 | 218 ± 58 | 235 ± 67 | 8.562 | < 0.001 |
| Other indicators | |||||
| Prothrombin time (second) | 12.8 ± 1.4 | 13.2 ± 1.8 | 14.1 ± 2.3 | 9.234 | < 0.001 |
| Activated partial thromboplastin time (second) | 31.5 ± 4.2 | 33.1 ± 5.6 | 36.8 ± 7.1 | 15.647 | < 0.001 |
| Troponin I (ng/mL) | 0.03 ± 0.04 | 0.06 ± 0.09 | 0.09 ± 0.15 | 6.147 | 0.002 |
| HbA1c (%) | 6.2 ± 1.1 | 6.8 ± 1.4 | 7.5 ± 1.8 | 15.832 | < 0.001 |
| hs-CRP (mg/L) | 3.2 ± 2.1 | 5.8 ± 3.4 | 8.9 ± 4.7 | 42.578 | < 0.001 |
| Homocysteine (μmol/L) | 12.4 ± 3.8 | 15.7 ± 4.9 | 19.3 ± 6.2 | 36.124 | < 0.001 |
| BNP (pg/mL) | 156 ± 89 | 234 ± 156 | 387 ± 234 | 28.964 | < 0.001 |
At discharge, there were no statistical differences in depression symptom incidence among the three groups (P > 0.05). As follow-up time extended, each age group showed different patterns of depression symptom onset. The young group’s HAMD-17 scores peaked at 1-month post-discharge then gradually declined, but remained slightly above baseline at 6 months; the middle-aged group maintained high levels from 1-3 months; while the elderly group’s depression scores continuously increased in the first 3 months then stabilized, consistent with typical post-stroke depression onset patterns (P < 0.001). PHQ-9 score trends were similar to HAMD-17 (Figure 1 and Table 4).
| Time point | Young group (n = 68) | Middle-aged group | Elderly group | F value | P value |
| HAMD-17 total score | |||||
| At discharge | 9.2 ± 4.5 | 9.8 ± 5.1 | 10.4 ± 5.6 | 1.247 | 0.289 |
| 1-month post-discharge | 12.8 ± 5.2 | 13.5 ± 6.1 | 12.9 ± 5.8 | 1.156 | 0.316 |
| 3-month post-discharge | 10.4 ± 4.8 | 14.2 ± 6.3 | 16.1 ± 7.2 | 18.542 | < 0.001 |
| 6-month post-discharge | 10.1 ± 4.3 | 12.5 ± 5.9 | 15.8 ± 7.1 | 21.643 | < 0.001 |
| PHQ-9 total score | |||||
| At discharge | 6.1 ± 3.4 | 6.5 ± 3.8 | 6.9 ± 4.1 | 0.987 | 0.374 |
| 1-month post-discharge | 8.5 ± 4.1 | 9.0 ± 4.8 | 8.6 ± 4.5 | 0.542 | 0.582 |
| 3-month post-discharge | 6.9 ± 3.7 | 9.5 ± 5.2 | 10.8 ± 5.9 | 12.847 | < 0.001 |
| 6-month post-discharge | 6.8 ± 3.6 | 8.4 ± 4.7 | 10.6 ± 5.8 | 14.256 | < 0.001 |
The MMSE total scores for all three groups were slightly decreased at discharge followed by gradual recovery, but recovery degrees differed. The young group showed best cognitive function recovery, with MMSE scores approaching baseline levels at 6 months; the middle-aged group showed partial recovery; the elderly group showed the poorest recovery, with scores still below baseline at 6 months (P < 0.001) (Table 5).
| Time point | Young group (n = 68) | Middle-aged group (n = 124) | Elderly group (n = 108) | F value | P value |
| At discharge | 26.8 ± 2.4 | 25.1 ± 3.2 | 22.6 ± 4.1 | 32.847 | < 0.001 |
| 1-month post-discharge | 25.9 ± 2.7 | 24.2 ± 3.5 | 21.3 ± 4.4 | 28.156 | < 0.001 |
| 3-month post-discharge | 27.2 ± 2.3 | 25.8 ± 3.1 | 22.8 ± 4.0 | 35.742 | < 0.001 |
| 6-month post-discharge | 27.6 ± 2.1 | 26.3 ± 2.9 | 23.5 ± 3.8 | 38.924 | < 0.001 |
BI and mRS scores showed differences in functional status recovery among the three groups. The young group showed better recovery in daily living activities, with higher BI scores than the other groups at 6 months (P < 0.001). The elderly group had more severe neurological deficits, with higher mRS scores than young and middle-aged groups at all time points examined (P < 0.001) (Table 6).
| Time point | Young group (n = 68) | Middle-aged group | Elderly group (n = 108) | F value | P value |
| Barthel index | |||||
| At discharge | 65.8 ± 19.2 | 58.4 ± 21.5 | 48.6 ± 24.1 | 14.273 | < 0.001 |
| 1-month post-discharge | 76.2 ± 17.4 | 67.8 ± 20.1 | 54.9 ± 23.8 | 21.847 | < 0.001 |
| 3-month post-discharge | 84.5 ± 15.8 | 74.2 ± 18.6 | 61.3 ± 22.4 | 31.526 | < 0.001 |
| 6-month post-discharge | 88.7 ± 13.2 | 78.9 ± 17.1 | 65.8 ± 21.6 | 36.841 | < 0.001 |
| mRS score | |||||
| At discharge | 2.6 ± 1.4 | 3.1 ± 1.5 | 3.4 ± 1.6 | 6.847 | 0.001 |
| 1-month post-discharge | 2.2 ± 1.3 | 2.9 ± 1.4 | 3.1 ± 1.5 | 9.184 | < 0.001 |
| 3-month post-discharge | 1.8 ± 1.1 | 2.5 ± 1.3 | 2.8 ± 1.4 | 15.657 | < 0.001 |
| 6-month post-discharge | 1.6 ± 1.0 | 2.3 ± 1.2 | 2.6 ± 1.3 | 18.294 | < 0.001 |
After stepwise regression analysis, the final multiple linear regression model showed that age, NIHSS score, hs-CRP level, and MMSE score were independent factors influencing HAMD-17 scores at 6 months. Each 1-year increase in age increased the HAMD-17 score by 0.12 points (P < 0.001); each 1-point increase in NIHSS score increased the HAMD-17 score by 0.35 points (P < 0.001); each 1 mg/L increase in hs-CRP increased the HAMD-17 score by 0.19 points (P = 0.002); each 1-point increase in MMSE score decreased the HAMD-17 score by 0.31 points (P < 0.001) (Table 7).
| Variable | Regression coefficient | SE | Standardized coefficient | t value | P value | 95%CI |
| Age | 0.124 | 0.028 | 0.234 | 4.429 | < 0.001 | 0.069-0.179 |
| NIHSS score | 0.346 | 0.075 | 0.287 | 4.613 | < 0.001 | 0.198-0.494 |
| hs-CRP | 0.187 | 0.068 | 0.142 | 2.750 | 0.006 | 0.053-0.321 |
| MMSE score | -0.308 | 0.098 | -0.176 | -3.143 | 0.002 | -0.501 to -0.115 |
| Infarct size (large) | 1.426 | 0.634 | 0.108 | 2.249 | 0.025 | 0.179-2.673 |
| Onset to admission time | 0.048 | 0.023 | 0.087 | 2.087 | 0.038 | 0.003-0.093 |
| Baseline depression score | 0.285 | 0.098 | 0.156 | 2.908 | 0.004 | 0.092-0.478 |
| Social support score | -0.142 | 0.067 | -0.089 | -2.119 | 0.035 | -0.274 to 0.010 |
Generalized estimating equations analysis of depression symptom temporal trends among different age groups showed group × time interaction effects (P < 0.001). The young group had better treatment response with greater HAMD-17 score decline; the elderly group had poorer treatment response with slow score decline. After controlling for confounding factors like baseline NIHSS scores and cognitive function, age remained an independent predictor of treatment response (P < 0.001) (Figure 2 and Table 8).
| Effect | Wald χ2 | df | P value |
| Time | 42.158 | 3 | < 0.001 |
| Age group | 28.347 | 2 | < 0.001 |
| Time × age group | 16.842 | 6 | 0.010 |
| NIHSS score | 12.546 | 1 | < 0.001 |
| MMSE score | 9.327 | 1 | 0.002 |
| hs-CRP | 6.184 | 1 | 0.013 |
| Infarct size | 4.832 | 1 | 0.028 |
| Baseline depression score | 8.746 | 1 | 0.003 |
This research showed that while at discharge there was little difference in the three groups in depression presentations, there was a difference in depression symptom patterns when the follow up took place. With the first follow up two groups showed an improvement. One group was the young people in the study. Their post discharge HAMD-17 scores were the worst at the 1 month mark but much better by the two months mark. The other group was the elderly people in the study. The elderly people showed an increase in depression scores from the 0 month mark to the 3 months mark. Graphing would result in a step line. These results are fairly consistent with previously published research[10,11].
One of the predictors of the step patterns was depression and there were a few testers exhibiting differences in neuroplasticity[12]. Researchers found that the younger people in the study usually recovered from depression scores a little and unfortunately the elderly people usually did not[13].
Second, variegations in the inflammatory response with age also contribute. This study demonstrates the elderly patients’ group had a considerable amount of inflammatory markers than hs-CRP and homocysteine in the younger group, as seen in[14]. Chronic inflammatory states manifest by opening the thyroid-pituitary-adrenal axis and altering the metabolism of serotonin and other neurotransmitters and thereby increasing neurotransmitter depression states[15]. With a slowed response of their immunity system, the elderly patients show a more prolonged elevation of responses to inflammatory mediativeness which leads to a more prolonged response and an emergent appearance of spread of symptoms and signs of depression[16].
With age, inflammatory responses can cause cynical depression through many other interconnected syndromes. Pro-inflammatory cytokines, such as interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α), can reach the brain and affect the synthesis of common neurotransmitters and long term depression states by increasing permeability of the brain-to-blood barrier. These cytokines can also affect the activity of other cytokines and further depression states[16]. These responses lead to a state of increased permeability of the brain-to-blood barrier, resulting in an elevated presence of neurotoxic metabolites of kynurenine, which leads to spread of responses and symptoms of depression. Chronic inflammatory states can cause an impaired presence of neurotoxic metabolites of kynurenine.
More pronounced inflammatory responses in elderly patients may be related to several age-specific factors: First, ‘inflammaging’ is a chronic low-grade inflammatory state characteristic of immunosenescence, where elderly individuals exhibit elevated baseline levels of pro-inflammatory cytokines; second, elderly patients often have multiple chronic comorbidities, such as hypertension, diabetes, and atherosclerosis, which themselves maintain chronic inflammatory states; third, elderly patients demonstrate decreased anti-inflammatory capacity with impaired resolution of inflammatory responses and prolonged cytokine elevation. This age-related amplification of inflammatory cascades may partially explain the delayed deterioration pattern and poorer treatment response observed in the elderly cohort.
This study identified significant differences in cognitive function recovery among different age groups, with the young group’s MMSE scores recovering to baseline levels at 6 months, while the elderly group showed poorest recovery. Multifactorial analysis showed that MMSE scores were independent protective factors for HAMD-17 scores at 6 months, with each 1-point improvement in cognitive function reducing depression scores by 0.31 points. This result supports the hypothesis of bidirectional relationships between cognitive function and depressive symptoms[17].
Additionally, brain regions such as the prefrontal cortex and hippocampus serve as critical anatomical bases for cognitive function and key structures for emotional regulation, with cerebral infarction-induced damage to these shared neural substrates simultaneously affecting cognitive and emotional functions through disruption of prefrontal-limbic circuits. This has a more pronounced effect on elderly patients due to insufficient cognitive reserve and reduced compensatory capacity, rendering them more vulnerable to cognitive-emotional comorbidity patterns.
Cognitive function decline may increase depression risk through multiple pathways: On one hand, cognitive function impairment affects patients’ understanding and adaptation abilities regarding their illness, increasing psychological stress responses; on the other hand, cognitive function decline limits patients’ ability to participate in rehabilitation training and social activities, leading to further social function limitations and forming vicious cycles[18]. Due to relatively lower baseline cognitive reserves, elderly patient are more prone to cognitive-emotional comorbidity patterns[19].
Our study revealed significant differences in functional status recovery among the three groups, with the young group showing best BI recovery and most obvious mRS score improvement, while the elderly group had relatively poor functional recovery. The degree of functional status recovery was closely related to depression symptom severity, consistent with previous research[21].
Mechanisms by which functional status affects depressive symptoms may include: Loss of functional independence directly affects patients’ self-efficacy and life satisfaction, increasing depression risk[22]. Due to slow motor function recovery and high dependence in daily living, elderly patients are more prone to feelings of helplessness and despair. Second, functional disabilities limit patients’ social participation, reducing social support acquisition and further aggravating depressive symptoms[23].
Age-related functional recovery differences primarily stem from different neural repair capacities. Young patients have stronger neuroplasticity and compensatory abilities, enabling functional recovery through contralateral brain region compensation and neural network reorganization[24]. Due to factors like reduced neuron numbers, decreased myelination, and weakened synaptic connections, elderly patients have slow and incomplete neural repair processes, leading to limited functional recovery[25].
Generalized estimating equation analysis showed that age has a significant effect on treatment response for depression. The younger age group had the most favorable response to depression treatment, while the stimulate study group had the least. Even after adjusting for illness severity, illness related cognitive impairment, and other variables, age was the sole predictor of treatment response. Tailored treatment of depression for the elderly people is warranted[26].
Stimulation of the elderly population with medication to overcome the barriers to depression treatment have contributed to inadequate response to treatment. The elderly have reduced the clearance of drugs to the body due to decreased liver and renal function[27]. Stroke patients on antidepressant treatment are generally more complex due to the increased potential and clinically important drug interactions exacerbated by polysysmphonic, concomitant, and medication[28].
Due to financial and other stressors, the elderly population also has a reduced compliance and acceptance of treatment, and this dissatisfaction tends to impact the effectiveness of therapy[29]. Treatment with depression medication may have a reduced response due to a reluctance to change and circumstances involving losing a life partner[30].
This study showed that the levels of hs-CRP are parameters that are drugs impaired and predicted HAMD-17 scores at the 6 months milestone. There is a lack of all age groups, but within the elderly group, the 6 months survey showed markedly elevated inflammatory markers[31]. The inflammatory hypothesis of post stroke depression is supported by this study.
Inflammatory responses may participate in depression development through multiple pathways: First, pro-inflammatory cytokines can directly act on the central nervous system, affecting neurotransmitter synthesis and metabolism. Cytokines like IL-1β and TNF-α can activate indoleamine 2,3-dioxygenase, promoting tryptophan conversion to the kynurenine pathway and reducing serotonin synthesis[32]. Second, inflammatory responses can damage the blood-brain barrier, increasing permeability of neurotoxic substances entering the brain and directly impairing neuronal function[33].
More pronounced inflammatory responses in elderly patients may be related to the following factors: Chronic low-grade inflammatory states due to immunosenescence, with elderly individuals having characteristic elevated baseline inflammation levels; second, elderly patients often have multiple chronic diseases like hypertension and diabetes, which are themselves chronic inflammatory diseases; third, elderly patients have decreased anti-inflammatory capacity with longer-lasting inflammatory responses that are difficult to effectively clear[34].
Based on our results, we propose the following clinical practice recommendations: First, age-stratified screening and assessment systems for post-stroke depression should be established. For young patients, focus should be on identifying acute-phase depressive symptoms and early intervention; for elderly patients, observation periods should be extended with an emphasis on monitoring delayed depressive symptoms[35].
Second, individualized treatment strategies should be developed. Young patients can prioritize comprehensive models combining psychotherapy with medication, fully utilizing their stronger neuroplasticity; elderly patients need more cautious medication selection and dosage adjustment, while strengthening functional rehabilitation and social support[36].
Finally, effective post-stroke depression management requires a multidisciplinary team approach incorporating practice areas from neurology, psychiatry, rehabilitation, nursing, as well as related practices, especially for older patients who often have a great need for a more integrated approach to complex health problems.
Age often modulates the interaction between neuroplasticity, inflammation, and cognitive function. In this triad, inflammation suppresses neuroplasticity, through the inflammatory inhibition of neuroplasticity, to further cognitive recovery, as well as inhibition of neuroplasticity and recovery. Depression, manifested as a symptom, further vulnerability to cognitive recovery through abatement of engagement and coping. Depression causes an affliction to the recovery functions of the triad, which forms a negative loop, increasing the depression. Windows to cognitive and socially stimulating activities further reduce the activity of the triad, more generally cognitive and socially engaging, which reduces recovery function engagement. All three dimensions reduce recovery drive further, from engagement to the triad functions. Due to the cumulative lowering of all three post-stroke depression persisting depression in the dyads. In the elderly, the pattern of deterioration, which takes a reasonable time to fully show, spans several shifts. This integrated understanding of the cycle of depression of the elderly, post-stroke, suggests the need of interventions early on for a more integrated approach to anti-inflammatories and cognitive and social engagement activities.
This study has some limitations. First, as a retrospective study, selection bias and information bias may exist; second, follow-up time was relatively short, preventing us from observing longer-term prognostic outcomes; third, important variables that may affect depression onset like genetic factors and socioeconomic status were not included; finally, sample size was relatively limited, potentially affecting statistical power. Future large-sample, long-term follow-up prospective cohort studies are needed to further validate these results.
In summary, the current study was systematic in explaining the influence of age on the patterns of post-stroke depression development and response to treatment, showing that the time-course of, the severity of depression symptoms and response to treatment have different features across various age groups. These results can be used as valuable scientific data to create age-specific prevention and treatment approaches, which can contribute to better long-term prognosis and quality of life of stroke patients. Future research should focus on age-related neurobiological mechanisms and explore more precise individualized treatment models.
| 1. | Mușat MI, Cătălin B, Hadjiargyrou M, Popa-Wagner A, Greșiță A. Advancing Post-Stroke Depression Research: Insights from Murine Models and Behavioral Analyses. Life (Basel). 2024;14:1110. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 14] [Reference Citation Analysis (2)] |
| 2. | Huang Y, You J, Wang Q, Wen W, Yuan C. Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study. J Stroke Cerebrovasc Dis. 2024;33:108092. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 6] [Article Influence: 3.0] [Reference Citation Analysis (0)] |
| 3. | Correction to Lancet Neurol 2023; 22: 1160-206. Lancet Neurol. 2023;22:e13. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 4. | Liu L, Marshall IJ, Pei R, Bhalla A, Wolfe CD, O'Connell MD, Wang Y. Natural history of depression up to 18 years after stroke: a population-based South London Stroke Register study. Lancet Reg Health Eur. 2024;40:100882. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 15] [Reference Citation Analysis (0)] |
| 5. | Wang ZY, Deng YL, Zhou TY, Jiang ZY, Liu Y, Liu BF, Cao Y. The effects of exercise interventions on depressive symptoms in stroke patients: a systematic review and meta-analysis. Front Physiol. 2025;16:1492221. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 6] [Reference Citation Analysis (0)] |
| 6. | Aderinto N, AbdulBasit MO, Olatunji G, Adejumo T. Exploring the transformative influence of neuroplasticity on stroke rehabilitation: a narrative review of current evidence. Ann Med Surg (Lond). 2023;85:4425-4432. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 50] [Reference Citation Analysis (0)] |
| 7. | Xiao M, Chen Y, Mu J. Innate immunity-mediated neuroinflammation promotes the onset and progression of post-stroke depression. Exp Neurol. 2024;381:114937. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 11] [Reference Citation Analysis (0)] |
| 8. | Xu W, Huang Y, Zhou R. NLRP3 inflammasome in neuroinflammation and central nervous system diseases. Cell Mol Immunol. 2025;22:341-355. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 148] [Article Influence: 148.0] [Reference Citation Analysis (0)] |
| 9. | Mayman N, Stein LK, Erdman J, Kornspun A, Tuhrim S, Jette N, Dhamoon MS. Risk and Predictors of Depression Following Acute Ischemic Stroke in the Elderly. Neurology. 2021;96:e2184-e2191. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 20] [Cited by in RCA: 20] [Article Influence: 4.0] [Reference Citation Analysis (0)] |
| 10. | Hart RP, Chen MS, Bonanno GA. Trajectories of poststroke depression among older adults. PM R. 2025;17:927-935. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 2] [Reference Citation Analysis (0)] |
| 11. | Ytterberg C, Cegrell L, von Koch L, Wiklander M. Depression symptoms 6 years after stroke are associated with higher perceived impact of stroke, limitations in ADL and restricted participation. Sci Rep. 2022;12:7816. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 17] [Reference Citation Analysis (0)] |
| 12. | Johansson ME, Toni I, Kessels RPC, Bloem BR, Helmich RC. Clinical severity in Parkinson's disease is determined by decline in cortical compensation. Brain. 2024;147:871-886. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 1] [Cited by in RCA: 47] [Article Influence: 23.5] [Reference Citation Analysis (0)] |
| 13. | Umarova RM, Gallucci L, Hakim A, Wiest R, Fischer U, Arnold M. Adaptation of the Concept of Brain Reserve for the Prediction of Stroke Outcome: Proxies, Neural Mechanisms, and Significance for Research. Brain Sci. 2024;14:77. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3] [Cited by in RCA: 11] [Article Influence: 5.5] [Reference Citation Analysis (0)] |
| 14. | Chen BA, Lee WJ, Chung CP, Peng LN, Chen LK. Age- and Sex-Different Associations between Cognitive Performance and Inflammatory Biomarkers in Community Dwelling Older Adults: towards Precision Preventive Strategies. J Prev Alzheimers Dis. 2023;10:104-111. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 3] [Cited by in RCA: 5] [Article Influence: 1.7] [Reference Citation Analysis (0)] |
| 15. | Lei AA, Phang VWX, Lee YZ, Kow ASF, Tham CL, Ho YC, Lee MT. Chronic Stress-Associated Depressive Disorders: The Impact of HPA Axis Dysregulation and Neuroinflammation on the Hippocampus-A Mini Review. Int J Mol Sci. 2025;26:2940. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 91] [Cited by in RCA: 84] [Article Influence: 84.0] [Reference Citation Analysis (0)] |
| 16. | Tuohy MC, Hillman EMC, Marshall R, Agalliu D. The age-dependent immune response to ischemic stroke. Curr Opin Neurobiol. 2023;78:102670. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 18] [Cited by in RCA: 14] [Article Influence: 4.7] [Reference Citation Analysis (0)] |
| 17. | Kusec A, Demeyere N. Relationship of subjective and objective cognition with post-stroke mood differs between early and long-term stroke. Clin Neuropsychol. 2025;39:1651-1672. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 5] [Article Influence: 5.0] [Reference Citation Analysis (0)] |
| 18. | Ladwig S, Werheid K, Südmeyer M, Volz M. Predictors of post-stroke depression: Validation of established risk factors and introduction of a dynamic perspective in two longitudinal studies. Front Psychiatry. 2023;14:1093918. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 25] [Reference Citation Analysis (0)] |
| 19. | Li F, Kong X, Zhu H, Xu H, Wu B, Cao Y, Li J. The moderating effect of cognitive reserve on cognitive function in patients with Acute Ischemic Stroke. Front Aging Neurosci. 2022;14:1011510. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 5] [Cited by in RCA: 14] [Article Influence: 3.5] [Reference Citation Analysis (0)] |
| 20. | Joyce MKP, Uchendu S, Arnsten AFT. Stress and Inflammation Target Dorsolateral Prefrontal Cortex Function: Neural Mechanisms Underlying Weakened Cognitive Control. Biol Psychiatry. 2025;97:359-371. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 37] [Cited by in RCA: 47] [Article Influence: 47.0] [Reference Citation Analysis (0)] |
| 21. | Kamdi MKA, Shafei MN, Musa KI, Hanafi MH, Suliman MA. Comparison of the Modified Barthel Index (MBI) Score Trends Among Workers With Stroke Receiving Robotic and Conventional Rehabilitation Therapy. Cureus. 2023;15:e34207. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 6] [Reference Citation Analysis (0)] |
| 22. | Ogwumike OO, Omoregie AA, Dada OO, Badaru UM. Quality of life of stroke survivors: A cross-sectional study of association with functional independence, self-reported fatigue and exercise self-efficacy. Chronic Illn. 2022;18:599-607. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 10] [Article Influence: 2.5] [Reference Citation Analysis (0)] |
| 23. | Wu XJ, Ke K, Liu H, Zhan SP, Wang L, He JF. Social isolation in the young and middle-aged patients with stroke: role of social support, family resilience and hope. Front Psychiatry. 2025;16:1499186. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 8] [Reference Citation Analysis (0)] |
| 24. | Popa-Wagner A, Hermann DM, Doeppner TR, Surugiu R, Pirscoveanu DF. The age-associated decline in neuroplasticity and its implications for post-stroke recovery in animal models of cerebral ischemia: The therapeutic role of extracellular vesicles. J Cereb Blood Flow Metab. 2025;271678X251365020. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 5] [Article Influence: 5.0] [Reference Citation Analysis (0)] |
| 25. | Pirscoveanu DFV, Olaru DG, Hermann DM, Doeppner TR, Ghinea FS, Popa-Wagner A. Correction: Immune genes involved in synaptic plasticity during early postnatal brain development contribute to post-stroke damage in the aging male rat brain. Biogerontology. 2025;26:98. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 26. | Szymkowicz SM, Gerlach AR, Homiack D, Taylor WD. Biological factors influencing depression in later life: role of aging processes and treatment implications. Transl Psychiatry. 2023;13:160. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 125] [Article Influence: 41.7] [Reference Citation Analysis (0)] |
| 27. | Ferri N, De Martin S, Stuart J, Traversa S, Mattarei A, Comai S, Folli F, Pappagallo M, Guidetti C, Inturrisi CE, Manfredi PL. Pharmacokinetics, Tolerability, and Safety of Esmethadone in Subjects with Chronic Kidney Disease or Hepatic Impairment. Drugs R D. 2024;24:341-352. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 6] [Reference Citation Analysis (0)] |
| 28. | Alhumaidi RM, Bamagous GA, Alsanosi SM, Alqashqari HS, Qadhi RS, Alhindi YZ, Ayoub N, Falemban AH. Risk of Polypharmacy and Its Outcome in Terms of Drug Interaction in an Elderly Population: A Retrospective Cross-Sectional Study. J Clin Med. 2023;12:3960. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 31] [Reference Citation Analysis (0)] |
| 29. | Kim NH, Look KA, Dague L, Winn AN. Financial burden and medication adherence among near-poor older adults in a pharmaceutical assistance program. Res Social Adm Pharm. 2022;18:2517-2523. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 12] [Article Influence: 2.4] [Reference Citation Analysis (0)] |
| 30. | Stahl ST, Kincman J, Karp JF, Anne Gebara M. Psychosocial interventions to improve adherence in depressed and anxious older adults prescribed antidepressant pharmacotherapy: a scoping review. Ther Adv Psychopharmacol. 2023;13:20451253231212322. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3] [Cited by in RCA: 5] [Article Influence: 1.7] [Reference Citation Analysis (0)] |
| 31. | Feng X, Ma X, Li J, Zhou Q, Liu Y, Song J, Liu J, Situ Q, Wang L, Zhang J, Lin F. Inflammatory Pathogenesis of Post-stroke Depression. Aging Dis. 2024;16:209-238. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 29] [Cited by in RCA: 29] [Article Influence: 14.5] [Reference Citation Analysis (0)] |
| 32. | Yang S, Han J, Ye Z, Zhou H, Yan Y, Han D, Chen S, Wang L, Feng Q, Zhao X, Kang C. The correlation of inflammation, tryptophan-kynurenine pathway, and suicide risk in adolescent depression. Eur Child Adolesc Psychiatry. 2025;34:1557-1567. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 11] [Reference Citation Analysis (0)] |
| 33. | Meijer WC, Gorter JA. Role of blood-brain barrier dysfunction in the development of poststroke epilepsy. Epilepsia. 2024;65:2519-2536. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 13] [Reference Citation Analysis (0)] |
| 34. | Müller L, Di Benedetto S. Inflammaging, immunosenescence, and cardiovascular aging: insights into long COVID implications. Front Cardiovasc Med. 2024;11:1384996. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 25] [Reference Citation Analysis (0)] |
| 35. | Braadt L, Fischer S, Naumann M, Zickler P, Schneider-Axmann T, Mühlich L, Körber K, Lassner A, Strube W, Röh A, Hasan A, Ertl M. Psychological interventions for improvement of symptoms of post-stroke depression - study protocol of the depression-intervention study for optimization of reconvalesence after stroke (DISCOVER). Neurol Res Pract. 2024;6:61. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 3] [Reference Citation Analysis (0)] |
| 36. | Zhang J, Song Z, Gui C, Jiang G, Cheng W, You W, Wang Z, Chen G. Treatments to post-stroke depression, which is more effective to HAMD improvement? A network meta-analysis. Front Pharmacol. 2022;13:1035895. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 12] [Cited by in RCA: 11] [Article Influence: 2.8] [Reference Citation Analysis (0)] |