Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients

Ismail Setyopranoto; Astuti Prodjohardjono; Sri Sutarni; Noor Alia Susianti; Muhammad Hardhantyo; Amelia Nur Vidyanti

doi:10.12688/f1000research.109698.1

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Research Article

Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients

[version 1; peer review: 1 approved, 1 not approved]

Ismail Setyopranoto¹, Astuti Prodjohardjono¹, Sri Sutarni¹, Noor Alia Susianti², Muhammad Hardhantyo^3,4, Amelia Nur Vidyanti ¹

Ismail Setyopranoto¹, Astuti Prodjohardjono¹, [...] Sri Sutarni¹, Noor Alia Susianti², Muhammad Hardhantyo^3,4, Amelia Nur Vidyanti ¹

PUBLISHED 06 Jul 2022

Author details Author details

¹ Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
² Neurology Research Office, Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
³ Center of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
⁴ Faculty of Health Science, Universitas Respati Yogyakarta, Yogyakarta, 55281, Indonesia

Ismail Setyopranoto
Roles: Conceptualization, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Astuti Prodjohardjono
Roles: Data Curation, Funding Acquisition, Methodology, Resources, Supervision

Sri Sutarni
Roles: Methodology, Supervision, Validation

Noor Alia Susianti
Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Writing – Review & Editing

Muhammad Hardhantyo
Roles: Formal Analysis, Methodology, Software, Validation, Writing – Review & Editing

Amelia Nur Vidyanti
Roles: Conceptualization, Formal Analysis, Methodology, Validation, Visualization, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background: Reduced level of serum BDNF in acute stroke patients is associated with poor outcomes. We aimed to identify the role of serum BDNF level as a predictor for post-stroke cognitive impairment (PSCI).
Methods: This was a prospective study. We recruited acute ischemic stroke patients in Dr. Sardjito General Hospital Yogyakarta, Indonesia followed them up for 90 days (3 months). Serum BDNF was collected at day 5 and day 30 of stroke onset and measured by ELISA. Montreal Cognitive Assessment (MoCA) was used to measure the cognitive function at 90 days of follow up. ROC curve was conducted to measure the cut-off point of the BDNF level. Factors independently associated with PSCI were analyzed by using stepwise regression.
Results: Among 89 patients recruited, 60 patients (67.41%) developed PSCI. The mean age of PSCI and non-PSCI patients was 62.7 ± 9.5 and 57.5 ± 8.7, respectively (p = 0.01). Patients with dyslipidemia were less likely to develop PSCI (OR 0.19, 95%CI 0.06–0.56, p < 0.05). In addition, patients with day 5-serum BDNF level < 23.29 ng/mL were five times more likely to develop PSCI compared with their counterparts (OR 5.02, 95%CI 1.67–15.04, p < 0.05).
Conclusions: Among acute ischemic stroke patients, those with serum BDNF <23.29 ng/mL had a higher risk of developing PSCI, while those with dyslipidemia had a lower risk of PSCI. This study suggests that BDNF could be a predictor of PSCI, allowing for earlier detection and better preventive strategies.

Keywords

serum BDNF, predictor, post-stroke cognitive impairment, acute ischemic stroke

Corresponding author: Amelia Nur Vidyanti

Competing interests: No competing interests were disclosed.

Grant information: This study was partially supported by Dr. Sardjito General Hospital Yogyakarta, Indonesia (Grant number: H.K.02.03/ XI.2/19020/2018). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2022 Setyopranoto I et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Setyopranoto I, Prodjohardjono A, Sutarni S et al. Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients [version 1; peer review: 1 approved, 1 not approved]. F1000Research 2022, 11:749 (https://doi.org/10.12688/f1000research.109698.1) First published: 06 Jul 2022, 11:749 (https://doi.org/10.12688/f1000research.109698.1) Latest published: 08 Mar 2024, 11:749 (https://doi.org/10.12688/f1000research.109698.2)

Introduction

Among the stroke survivors, post stroke cognitive impairment (PSCI) is one of the common complications after stroke. Approximately, 53.4% post stroke patients suffered from PSCI which ranged from mild to severe cognitive impairment.¹ The cognitive impairment will lead to severe disability and increase the cost of health care after stroke attacks.²^,³ Nowadays, the investigations of biomarkers are developed to predict the long-term outcome and cognitive impairments after stroke. There are several growth factors that can affect the prognosis of patients with ischemic stroke in the long term, for example VEGF (vascular endothelial growth factor), BDNF (brain-derived neurotrophic factor), G-CSF (granulocyte-colony stimulating factor), Ang1 (angiopoietin 1), and SDF-1α (stromal-derived factor-1α).⁴^–⁶

The most prevalent neurotrophin, Brain-derived Neurotrophic Factor (BDNF), is involved in neuronal survival, synaptic plasticity, angiogenesis, as well as neurite outgrowth in peripheral and central nervous systems.⁷^,⁸ Both intravenous and intraventricular BDNF infusions reduced infarct size and showed neuroprotective benefits in experimental stroke model studies. Furthermore, there is evidence that BDNF is linked to various neuropsychiatric diseases.⁹

Another experimental study showed that local administration of BDNF ameliorates the functional motor recovery in ischemic stroke models.¹⁰ In human study, reduced level of serum BDNF has been found in acute ischemic stroke patients and is associated with poor functional outcome as well as more dependency.⁶^,¹¹^–¹³ BDNF could improve brain plasticity and neuronal repair after stroke by promoting angiogenesis dan neurogenesis.¹⁴ Previous studies conducted among patients free of stroke revealed that higher BDNF was associated with better visual memory and global cognitive function.¹⁵^,¹⁶ However, none of those previous studies investigated about the role of BDNF as a biomarker for predicting the development of PSCI among acute ischemic stroke patients.

Assuming that the serum BDNF levels reflect brain levels, it seemed rational to think that measuring serum BDNF levels in the early stages of stroke would be effective for stroke outcome prediction. The present study aimed to identify the role of serum BDNF level in acute ischemic stroke patients in predicting PSCI. By understanding the important role of BDNF, the development of PSCI may be detected earlier thus early intervention can be applied leading to reduce the long-term disability, social, and economical burden caused by PSCI.

Methods

Study design and participants

This was a prospective study. The data was collected from June 2019 to May 2020. We recruited patients with acute ischemic stroke in Dr. Sardjito General Hospital Yogyakarta, Indonesia.

We included patients with: (1) first-ever stroke with the stroke onset within 5 days; and (2) aged >18-years old. We excluded those with: (1) recurrent stroke patients; (2) brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, and dementia; and (3) depression or history of psychiatric diseases.

Demographic, clinical, and laboratory data included serum BDNF were collected at day-5 of the stroke onset during hospitalization. In total, 89 eligible patients were selected during the study period.

Data collection

Outcome variable

The outcome variable was PSCI developed 3 months after stroke onset. We assessed the cognitive function at day 5, 30, and 90 after stroke onset by using Montreal Cognitive Assessment-Indonesian version (MoCA-INA) which has been validated for Indonesian population.¹⁷ PSCI group was defined if the total score of MoCA-INA at 90 days (3 months) after stroke was < 26.¹⁸ Meanwhile, a non-PSCI group was defined for those with MoCA-INA score ≥ 26.

Demographic and clinical characteristics

Demographic factors consisted of age (> 60 and ≤ 60 years old) and sex (male or female). Clinical characteristics consisted of body mass index (BMI) (obese or non-obese), hypertension (yes or no), systolic blood pressure (SBP), diastolic blood pressure (DBP), diabetes (yes or no), HbA1C, dyslipidemia (yes or no), lipid profile (the levels of triglycerides, HDL, LDL, and total cholesterol), as well as serum BDNF level at day 5 and 30 after stroke onset.

We classified the BMI based on WHO criteria for Asian population¹⁹^,²⁰ and further categorized the subjects into obese and non-obese (consisted of those with overweight and normal weight; there were no subjects with underweight). Systolic and diastolic blood pressure were measured by using sphygmomanometer performed at admission. We collected serum BDNF level at day 5 based on prior study which revealed that BDNF was reduced at acute stroke.²¹ We also collected serum BDNF at day 30 based on another study reported that BDNF level in acute stroke would be increased steadily and reached the highest level after 30 days.²²

Measurement of serum BDNF level

We took the samples from patients’ serum on days 5 and 30 following the onset of the stroke. Between 7:00 and 9:00 a.m., a serum separator tube was filled with 5 mL venous whole blood from the patients. Patients were also told not to eat or drink anything for 10 hours before getting their blood drawn. Within 60 minutes of blood sampling, the venous whole blood was allowed to coagulate for 30 minutes at room temperature before being centrifuged at 1,000 × g for 15 minutes at 40°C to separate the serum and blood clot. The serum was extracted and stored at −20°C in a separate tube. The serum sample was transported to Prodia Laboratory Jakarta in a frozen state, sealed in a heat-insulated container with dry ice.

We measure the level of serum BDNF by an enzyme-linked immunosorbent assay (ELISA) using Human BDNF Immunoassay (Quantikine^® R&D systems, Minneapolis, USA, Cat No: DBD00). The assay's minimal detection limit was 20 pg/ml, and its intra- and inter-assay coefficients of variation were both less than 10%. A monoclonal antibody specific for human BDNF was pre-coated in a 96 well polystyrene microplate (BDNF, R&D System, RD1S) at a concentration of 100L in a buffered protein base with preservative and then lyophilized. The test samples were applied in duplicate (50 L/well). In duplicate wells, a standard curve was created using BDNF (R&D systems, RD1S) at concentrations of 0, 62.5, 125, 250, 500, 1000, 2000, and 4000 pg/mL. In each well, a standard, control, or sample was added and incubated for 2 hours before adding the BDNF conjugate.

Another hour was spent incubating the mixture. Each well was aspired and washed once, then twice more for a total of three washes. Washing buffer (400 L) was used to wash each well with a squirt bottle, multi-channel pipette, manifold dispenser, or autowasher. Each well was filled with 200 mL of substrate solution and incubated at room temperature for 30 minutes, protected from light. Each well received 50 mL of Stop Solution. We lightly tapped the plate to ensure thorough mixing if the color change did not appear uniform. Using a microplate reader tuned to 450 nm, the optical density of each well was calculated in under 30 minutes.

Statistical analysis

We used the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables. We performed a receiver operating characteristics (ROC) curve analysis to determine the cut-off point of BDNF level. Bivariate analysis was conducted to analyze the relative risk of associated factors (including BDNF) between PSCI and non-PSCI group. We further performed multivariate analysis by using stepwise regression to measure factors independently associated with PSCI. All of the analyses were performed using the SPSS software version 25.0 (IBM Co. Ltd, NY, USA). A p value of < 0.05 indicated statistical significance.

Ethical consideration

This study has received ethical approval from the Medical and Health Research Ethics Committee of the Faculty of Medicine, Universitas Gadjah Mada, Indonesia (EC No. KE/FK/0682/EC/2020). All participants were provided the information regarding the study and signed the informed consent form.

Results

A total of 89 patients were included in this study, with 55.1% being within the age group of more than 60 years old. Most of the participants were male (61.8%), with 68.5% having hypertension. Serum BDNF was examined on day 5 and day 30 as a prognostic factor of PSCI in stroke patients. The mean BDNF levels at day 5 and 30 were 24.54 ± 6.42 ng/mL and 24.86 ± 8.51, respectively. As much of 67.41% of patients suffered from post-stroke cognitive impairment (PSCI) during the study period. The baseline characteristics of the study participants were presented in Table 1.

Table 1. Baseline characteristics.

Parameters		n (%) or mean ± SD
Sex a. Male b. Female		55 (61.8%) 34 (38.2%)
Age, Mean ± SD, years		61.03 ± 9.50
Age group >60 years ≤60 years		49 (55.1%) 40 (44.95)
BMI, Mean ± SD, kg/m²		24.30 ± 3.64
BMI categories a. Obese b. Non-obese		52 (58.4%) 37 (41.6%)
Hypertension a. Yes b. No		61 (68.5%) 28 (31.5%)
Systolic blood pressure (mmHg)		164.12 ± 30.80
Diastolic blood pressure (mmHg)		92.15 ± 15.57
Diabetes a. Yes b. No		24 (27.0%) 65 (73.0%)
HbA1C (%)		7.04±2.19
Dyslipidemia a. Yes b. No		23 (25.8%) 66 (74.2%)
Triglycerides level (mg/dL)		174.36 ± 201.19
HDL level (mg/dL)		41.12 ± 8.48
LDL level (mg/dL)		137 ± 44.38
Total cholesterol level (mg/dL)		201.45 ± 70.35
BDNF at day 5 (ng/mL)		24.54 ± 6.42
BDNF at day 30 (ng/mL)		24.86 ± 8.51
MoCA-INA score at day 5		18.20 ± 6.30
MoCA-INA score at day 30		21.13 ± 5.97
MoCA-INA score at day 90		22.47 ± 5.73
PSCI a. Yes b. No		60 (67.4%) 29 (32.6%)

Factors associated with PSCI were analyzed further as shown in Table 2. We found that age, dyslipidemia, and triglyceride levels showed a significant difference between the PSCI and non-PSCI groups (p < 0.05). Older patients had a higher risk to have PSCI compared with their younger counterparts (RR: 1.41, 95% CI: 1.03–1.94, p < 0.05). Patients with dyslipidemia were less likely to have PSCI compared with those without dyslipidemia. However, the PSCI group had a lower triglycerides level than the non-PSCI group (p < 0.05). In addition, the PSCI group had a lower MoCA-INA score than the counterpart group (p < 0.001) (Table 2).

Table 2. Bivariate analysis of factors associated with PSCI.

Variables	PSCI (n = 60)	Non-PSCI (n = 29)	RR (95% CI)	p
Demographical characteristics
Sex a. Female b. Male	41 (74.5%)	14 (25.5%)	1.33 (0.95-1.87)	0.06
Age (years)	62.7 ± 9.5	57.5 ± 8.7		0.01*
Age a. >60 years b. ≤60 years	38 (77.6%) 22 (55.0%)	11 (22.4%) 18 (45.0%)	1.41 (1.03-1.94)	0.02*
Clinical examination
BMI (kg/m²)	24.1 ± 3.5	24.7 ± 3.9		0.46
Obese a. Yes b. No	35 (67.3%) 25 (67.6%)	17 (32.7%) 12 (32.4%)	0.99 (0.74-1.34)	0.98
Systolic Blood Pressure (mmHg)	162.4 ± 32.9	167.6 ± 25.9		0.46
Diastolic Blood Pressure (mmHg)	90 (60-140)	90 (60-130)		0.82
MoCA-INA Score at day 5	16 (1-28)	24 (13-29)		<0.001*
MoCA-INA Score at day 30	20 (2-28)	25 (22-30)		<0.001*
MoCA-INA Score at day 90	21.50 (2-25)	28 (26-30)		<0.001*
Comorbidities
Hypertension a. Yes b. No	38 (62.3%) 22 (78.6%)	23 (37.7%) 6 (21.4%)	0.79 (0.6-1.04)	0.13
Diabetes a. Yes b. No	15 (62.5%) 45 (69.2%)	9 (37.5%) 20 (30.8%)	0.90 (0.64-1.28)	0.55
Dyslipidemia a. Yes b. No	9 (39.1%) 51 (77.3%)	14 (60.9%) 15 (22.7%)	0.51 (0.29-0.86)	0.001*
Laboratory parameters
HbA1C (%)	5.95 (5.30-17.50)	6.30 (4.60-10.60)		0.11
Triglyceride (mg/dL)	127.50 (57-564)	150 (77-18.29)		0.04*
HDL (mg/dL)	40.85 (16-61)	41 (25-67)		0.96
LDL (mg/dL)	135.50 (21-222)	146 (67-353)		0.06
Total Cholesterol (mg/dL)	193.50 (96-285)	202 (61.80-690)		0.16
BDNF level at day 5 (pg/mL)	22.86 ± 5.97	28.01 ± 5.99		<0.001**
Categorization of BDNF level at day 5 a. <23.29 ng/mL b. >23.29 ng/mL	34 (85%) 26 (53.1%)	6 (15%) 23 (46.9%)	1.60 (1.19-2.15)	0.001*
BDNF level at day 30 (pg/mL)	24.42 ± 8.70	25.76 ± 8.19		0.488
Categorization of BDNF level at day 30 a. <28.79 ng/mL b. >28.79 ng/mL	45 (72.6%) 15 (55.6%)	17 (27.4%) 12 (44.4%)	1.31 (0.90-1.89)	0.115

* p < 0.05.

** p < 0.001.

Based on the ROC curve (Supplementary Figure 1), the cut-off points of serum BDNF level at day 5 (BDNF I) was 23.29 ng/ml and at day 30 (BDNF II) was 28.79 ng/mL. These cut-off points were then included in the bivariate analysis, and we found that the mean serum BDNF level at day 5 in PSCI group was significantly lower than non-PSCI group (p < 0.001). However, there was no difference for BDNF level at day 30 in both groups. The patients with the BDNF level <23.29 ng/mL were more likely to develop PSCI (RR: 1.60, 95% CI: 1.19–2,15, p = 0.001) (Table 2).

For investigating factors that were independently associated with PSCI, we performed a multivariate analysis with stepwise regression. After adjusting by the covariates, we found that serum BDNF level at day 5 and dyslipidemia were significantly associated with PSCI. Patients with dyslipidemia were less likely to develop PSCI compared with the counterpart group (OR 0.19, 95%CI: 0.06–0.56, p < 0.05). Nevertheless, patients with day 5-BDNF level <23.29 pg/mL were five times more likely to develop PSCI compared with their counterparts (OR 5.02, 95%CI: 1.67–15.04, p < 0.05) (Table 3).

Table 3. Multivariate analysis of factors associated with PSCI.

Variables	OR	95% CI	p
Constant	0.60	-	0.69
BDNF at day 5 (<23.29 ng/mL)	5.02	1.67-15.04	0.004*
Dyslipidemia	0.19	0.06-0.56	0.003*

* p < 0.05.

Discussion

The present study demonstrated that serum BDNF level at acute phase (day 5) and dyslipidemia were associated with PSCI in first-ever acute ischemic stroke patients. Patients with BDNF level <23.29 ng/mL had a higher risk of developing PSCI, while those with dyslipidemia had a lower risk of PSCI. To our knowledge, this is the first study that demonstrated that serum BDNF could be used as a predictor for post-stroke cognitive impairment 3 months after stroke.

Our finding corroborates a prior cross-sectional study that reported that stroke survivors with cognitive impairment had a reduced level of serum BDNF and that the cognitive performance score was positively correlated with BDNF level.²³ However, this study measured the level of BDNF during the post-acute phase with a median duration of illness was 10 months after stroke onset. Due to the nature of the cross-sectional study, this prior study could not suggest that BDNF can serve as a predictor for PSCI.

There are some proposed mechanisms for explaining why BDNF is associated with cognitive function in post-stroke patients. First, BDNF could increase angiogenesis, neurogenesis, and promote brain repair.²⁴ In animal model of aging stroke, BDNF mediated facilitation of reversal learning/cognitive flexibility by inducing angiogenesis and neurogenesis which further improved the functional recovery of cognitive after stroke.²⁴ Second, BDNF plays important roles in regulation and maintenance of synaptic plasticity. Synaptic plasticity is essential for maintaining normal cognition and attenuating brain’s resilience to recover from ischemic injury.¹⁴ BDNF involves in regulating the long-term potentiation (LTP) and long-term depression (LTD),²⁵ as well as mediates learning and memory process in post-stroke rehabilitation.²⁶^–²⁸

In the present study, we also found that patients with dyslipidemia had lower risk of developing PSCI. This finding is in accordance to prior study which reported that patients with dyslipidemia had a decreased risk of cognitive decline after stroke.²⁹ The Framingham Heart Study also found similar result that high cholesterol level was associated with better cognitive function.³⁰ However, contrary to the present study, a longitudinal study conducted in China found that increased total cholesterol and LDL were associated with cognitive impairment.³¹ The underlying mechanism of how dyslipidemia was associated with cognitive decline remains elusive. The interplay between dyslipidemia and cognitive function is very complex and may involve the distribution of fat mass and is influenced by age.²⁰

The present study shows evidence that serum BDNF in acute stroke and dyslipidemia are associated with PSCI. This finding may help clinicians and researchers to predict PSCI earlier thus could prevent the worsening of cognitive function in post-stroke patients. Nevertheless, this study has some limitations. First, the sample size was small due to the participants only came from a tertiary hospital in Indonesia. Hence, the findings may not be generalized for the whole population. Second, the follow-up period was only 90 days (3 months) after stroke. Follow-up for a longer period of time will be beneficial to explore the potential role of BDNF as a solid biomarker. Moreover, we did not explore the molecular mechanisms of how reduced serum BDNF could impair cognitive function. More knowledge on this issue particularly related to the signaling pathway may be helpful in clinical and research settings.

Conclusions

Acute ischemic stroke patients with lower serum BDNF (< 23.29 ng/mL) had a higher risk of developing PSCI, while those with dyslipidemia had a lower risk of PSCI. This study suggests that BDNF could be a predictor of PSCI, allowing for earlier detection and better preventive strategies.

Data availability statement

Underlying data is available on Zenodo: https://doi.org/10.5281/zenodo.6038470.

Author contributions

IS: conceptualization, methodology, validation, visualization, writing-original draft, writing-review & editing; AP: data curation, methodology, resources, supervision, funding acquisition; SS: methodology, validation, supervision; NAS: data curation, formal analysis, investigation, project administration, writing-review & editing; MH: methodology, formal analysis, software, validation, writing-review & editing; ANV: conceptualization, methodology, formal analysis, validation, visualization, writing-review & editing.

Acknowledgements

The authors would like to thank all the patients who participated in this study and all data collectors for their genuine cooperation.

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31. Ma C, Yin Z, Zhu P, et al.: Blood cholesterol in late-life and cognitive decline: a longitudinal study of the Chinese elderly. Mol. Neurodegener. 2017; 12: 1–9.

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Version 2

VERSION 2 PUBLISHED 06 Jul 2022

Author details Author details

Ismail Setyopranoto
Roles: Conceptualization, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Astuti Prodjohardjono
Roles: Data Curation, Funding Acquisition, Methodology, Resources, Supervision

Sri Sutarni
Roles: Methodology, Supervision, Validation

Noor Alia Susianti
Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Writing – Review & Editing

Muhammad Hardhantyo
Roles: Formal Analysis, Methodology, Software, Validation, Writing – Review & Editing

Amelia Nur Vidyanti
Roles: Conceptualization, Formal Analysis, Methodology, Validation, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This study was partially supported by Dr. Sardjito General Hospital Yogyakarta, Indonesia (Grant number: H.K.02.03/ XI.2/19020/2018). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

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Revised

Published: 08 Mar 2024, 11:749

https://doi.org/10.12688/f1000research.109698.2

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Published: 06 Jul 2022, 11:749

https://doi.org/10.12688/f1000research.109698.1

© 2022 Setyopranoto I et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Setyopranoto I, Prodjohardjono A, Sutarni S et al. Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients [version 1; peer review: 1 approved, 1 not approved]. F1000Research 2022, 11:749 (https://doi.org/10.12688/f1000research.109698.1)

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Reviewer Report 20 Jul 2023

Shubham Misra, Yale University School of Medicine, New Haven, Connecticut, USA

Not Approved

https://doi.org/10.5256/f1000research.121236.r176320

The authors determine the role of serum BDNF as a biomarker of post-stroke cognitive impairment. Though an interesting topic, there are several inherent limitations in the article including small sample size, late biomarker measurement, less duration of follow up, that ... Continue reading

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.
BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.
The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.
Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.
Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?
Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.
Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.
Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.
Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?
Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.
The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.
Results - Mention all the variables that were entered into the multivariable analysis.
Results - Mention if there was any loss to follow-up in the study.
How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Neurology, stroke, biomarker research.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

CITE

Report a concern

Author Response 13 Apr 2024

Amelia Nur Vidyanti, Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia

13 Apr 2024

Author Response
My comments include:
1. Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.
Response: We have now provided the full form of BDNF, ... Continue reading
My comments include:

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.

Response: We have now provided the full form of BDNF, ELISA, and ROC in the abstract. Thank you for your suggestion.

BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.

Response: Based on prior studies, the clinical staging of stroke (Cramer, 2008; Rehme et al., 2012; Zhao et al., 2014) is generally accepted as follows: the first 2 weeks are defined as the acute stage; 3–11 weeks post-stroke is termed the subacute stage in which most changes occur; 12–24 weeks post-stroke is the early chronic stage; and more than 24 weeks post-stroke is the chronic stage. BDNF in the present study was measured at day 5 after stroke onset in which it was still in the acute stage of stroke.

The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.

Response: We thank the reviewer for the valuable comments and suggestions. The reason why we measured serum BDNF level at day 5 after stroke index is based on prior study.
“Previous study showed that decrease BDNF level after 4 days from stroke index is associated with poor outcome in acute ischemic stroke patients. The cell death is completed in 5 days after stroke, and BDNF will lead to neuroplasticity during this time. Subsequently, lower serum BDNF will impair the neuroregeneration and may lead to poor outcome in acute ischemic stroke patients (Clarkson et al., 2011; Tuwar et al., 2023).”

Regarding PSCI identification, we agree that longer follow-up is needed because cognitive functioning after stroke may have been fluctuated due to the change of cerebral hemodynamics. However, prior studies have demonstrated that the definition of PSCI is any severity of cognitive impairment, regardless of cause, noted after an overt stroke, and it occurs from 3-6 months after stroke (Sundar & Adwani, 2010; Garcia et al., 2015; He at al., 2023; Husseini et al., 2023). Therefore, in the present study we identified PSCI at 90 days (3 months) after stroke. We have elaborated on this issue in the method section of the revised paper.

Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.

Response: Thank you for your suggestions. We have now mentioned these details in the study design and participants’ section.
“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?

Response: We have now provided some details in the inclusion and exclusion criteria as below:

“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.

Response: We used Kolmogorov-Smirnov test to determine the normality of numerical data, the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables.
Thank you for your suggestions. We have now provided this information in the revised paper.

Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.

Response: TRIPOD guidelines is attached in the resubmission along with the revised manuscript.

Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.

Response: We have provided the information regarding OR in the statistical analysis and mentioned it in the result and discussion section.

Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?

Response: We calculated the cut-off value by using Youden Index. We have now added this information in the Statistical analysis section.

Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.

Response: We have now provided the multivariate analysis with some prediction models in the revised paper such as stated in the statistical analysis section and the result section (Table 3). Thank you for your valuable suggestion.

The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.

Response: We have now provided the sample size calculation in the revised paper as below:
“We estimated the minimum sample size and found that it might be acceptable to represent the entire population. The sample size was calculated using the following for two population proportions (one-sided test) (Lwanga & Lemeshow, 1991):

Equation 1

n= sample size
P1= proportion of PSCI with lower serum BDNF
P2 = proportion of PSCI with higher serum BDNF
Based on previous study, the proportion of PSCI with lower serum BDNF is 88,7% (Prodjohardjono, 2020) and the proportion of PSCI with higher serum BDNF is 65,5% (Havlovska et al., 2023). With = 1.96 and = 0.84, the minimum sample size is as follows:

Equation 2

n = 40 participants for each representing group
With the 10% of dropout rate, the minimum sample size is 88 participants. Initially, we collected 99 participants. However, 10 participants were loss to follow up due to some reasons. In the end, there were only 89 participants who completed the study.”

Results - Mention all the variables that were entered into the multivariable analysis.

Response: We have reperformed the analysis and provided the multivariable analysis with 3 models in which all the variables were entered (Table 3). Thank you for your valuable suggestion.

Results - Mention if there was any loss to follow-up in the study.

Response: We have added this information in the sample size section accordingly.

How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Response: We have now provided the information regarding the follow up in the study design section. Thank you for your valuable suggestions.
“Demographic, clinical, and laboratory data were collected at day 5 of the stroke onset during hospitalization. Serum BDNF was collected at day 5 of the stroke onset during hospitalization and at day 30 during the home visit. Cognitive functioning was measured at day 5, 30, and 90. The follow-up was carried out by the study team through home visits.”
My comments include:

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.

Response: We have now provided the full form of BDNF, ELISA, and ROC in the abstract. Thank you for your suggestion.

BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.

Response: Based on prior studies, the clinical staging of stroke (Cramer, 2008; Rehme et al., 2012; Zhao et al., 2014) is generally accepted as follows: the first 2 weeks are defined as the acute stage; 3–11 weeks post-stroke is termed the subacute stage in which most changes occur; 12–24 weeks post-stroke is the early chronic stage; and more than 24 weeks post-stroke is the chronic stage. BDNF in the present study was measured at day 5 after stroke onset in which it was still in the acute stage of stroke.

The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.

Response: We thank the reviewer for the valuable comments and suggestions. The reason why we measured serum BDNF level at day 5 after stroke index is based on prior study.
“Previous study showed that decrease BDNF level after 4 days from stroke index is associated with poor outcome in acute ischemic stroke patients. The cell death is completed in 5 days after stroke, and BDNF will lead to neuroplasticity during this time. Subsequently, lower serum BDNF will impair the neuroregeneration and may lead to poor outcome in acute ischemic stroke patients (Clarkson et al., 2011; Tuwar et al., 2023).”

Regarding PSCI identification, we agree that longer follow-up is needed because cognitive functioning after stroke may have been fluctuated due to the change of cerebral hemodynamics. However, prior studies have demonstrated that the definition of PSCI is any severity of cognitive impairment, regardless of cause, noted after an overt stroke, and it occurs from 3-6 months after stroke (Sundar & Adwani, 2010; Garcia et al., 2015; He at al., 2023; Husseini et al., 2023). Therefore, in the present study we identified PSCI at 90 days (3 months) after stroke. We have elaborated on this issue in the method section of the revised paper.

Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.

Response: Thank you for your suggestions. We have now mentioned these details in the study design and participants’ section.
“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?

Response: We have now provided some details in the inclusion and exclusion criteria as below:

“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.

Response: We used Kolmogorov-Smirnov test to determine the normality of numerical data, the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables.
Thank you for your suggestions. We have now provided this information in the revised paper.

Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.

Response: TRIPOD guidelines is attached in the resubmission along with the revised manuscript.

Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.

Response: We have provided the information regarding OR in the statistical analysis and mentioned it in the result and discussion section.

Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?

Response: We calculated the cut-off value by using Youden Index. We have now added this information in the Statistical analysis section.

Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.

Response: We have now provided the multivariate analysis with some prediction models in the revised paper such as stated in the statistical analysis section and the result section (Table 3). Thank you for your valuable suggestion.

The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.

Response: We have now provided the sample size calculation in the revised paper as below:
“We estimated the minimum sample size and found that it might be acceptable to represent the entire population. The sample size was calculated using the following for two population proportions (one-sided test) (Lwanga & Lemeshow, 1991):

Equation 1

n= sample size
P1= proportion of PSCI with lower serum BDNF
P2 = proportion of PSCI with higher serum BDNF
Based on previous study, the proportion of PSCI with lower serum BDNF is 88,7% (Prodjohardjono, 2020) and the proportion of PSCI with higher serum BDNF is 65,5% (Havlovska et al., 2023). With = 1.96 and = 0.84, the minimum sample size is as follows:

Equation 2

n = 40 participants for each representing group
With the 10% of dropout rate, the minimum sample size is 88 participants. Initially, we collected 99 participants. However, 10 participants were loss to follow up due to some reasons. In the end, there were only 89 participants who completed the study.”

Results - Mention all the variables that were entered into the multivariable analysis.

Response: We have reperformed the analysis and provided the multivariable analysis with 3 models in which all the variables were entered (Table 3). Thank you for your valuable suggestion.

Results - Mention if there was any loss to follow-up in the study.

Response: We have added this information in the sample size section accordingly.

How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Response: We have now provided the information regarding the follow up in the study design section. Thank you for your valuable suggestions.
“Demographic, clinical, and laboratory data were collected at day 5 of the stroke onset during hospitalization. Serum BDNF was collected at day 5 of the stroke onset during hospitalization and at day 30 during the home visit. Cognitive functioning was measured at day 5, 30, and 90. The follow-up was carried out by the study team through home visits.”
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 13 Apr 2024

Amelia Nur Vidyanti, Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia

13 Apr 2024

Author Response
My comments include:
1. Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.
Response: We have now provided the full form of BDNF, ... Continue reading
My comments include:

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.

Response: We have now provided the full form of BDNF, ELISA, and ROC in the abstract. Thank you for your suggestion.

BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.

Response: Based on prior studies, the clinical staging of stroke (Cramer, 2008; Rehme et al., 2012; Zhao et al., 2014) is generally accepted as follows: the first 2 weeks are defined as the acute stage; 3–11 weeks post-stroke is termed the subacute stage in which most changes occur; 12–24 weeks post-stroke is the early chronic stage; and more than 24 weeks post-stroke is the chronic stage. BDNF in the present study was measured at day 5 after stroke onset in which it was still in the acute stage of stroke.

The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.

Response: We thank the reviewer for the valuable comments and suggestions. The reason why we measured serum BDNF level at day 5 after stroke index is based on prior study.
“Previous study showed that decrease BDNF level after 4 days from stroke index is associated with poor outcome in acute ischemic stroke patients. The cell death is completed in 5 days after stroke, and BDNF will lead to neuroplasticity during this time. Subsequently, lower serum BDNF will impair the neuroregeneration and may lead to poor outcome in acute ischemic stroke patients (Clarkson et al., 2011; Tuwar et al., 2023).”

Regarding PSCI identification, we agree that longer follow-up is needed because cognitive functioning after stroke may have been fluctuated due to the change of cerebral hemodynamics. However, prior studies have demonstrated that the definition of PSCI is any severity of cognitive impairment, regardless of cause, noted after an overt stroke, and it occurs from 3-6 months after stroke (Sundar & Adwani, 2010; Garcia et al., 2015; He at al., 2023; Husseini et al., 2023). Therefore, in the present study we identified PSCI at 90 days (3 months) after stroke. We have elaborated on this issue in the method section of the revised paper.

Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.

Response: Thank you for your suggestions. We have now mentioned these details in the study design and participants’ section.
“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?

Response: We have now provided some details in the inclusion and exclusion criteria as below:

“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.

Response: We used Kolmogorov-Smirnov test to determine the normality of numerical data, the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables.
Thank you for your suggestions. We have now provided this information in the revised paper.

Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.

Response: TRIPOD guidelines is attached in the resubmission along with the revised manuscript.

Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.

Response: We have provided the information regarding OR in the statistical analysis and mentioned it in the result and discussion section.

Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?

Response: We calculated the cut-off value by using Youden Index. We have now added this information in the Statistical analysis section.

Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.

Response: We have now provided the multivariate analysis with some prediction models in the revised paper such as stated in the statistical analysis section and the result section (Table 3). Thank you for your valuable suggestion.

The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.

Response: We have now provided the sample size calculation in the revised paper as below:
“We estimated the minimum sample size and found that it might be acceptable to represent the entire population. The sample size was calculated using the following for two population proportions (one-sided test) (Lwanga & Lemeshow, 1991):

Equation 1

n= sample size
P1= proportion of PSCI with lower serum BDNF
P2 = proportion of PSCI with higher serum BDNF
Based on previous study, the proportion of PSCI with lower serum BDNF is 88,7% (Prodjohardjono, 2020) and the proportion of PSCI with higher serum BDNF is 65,5% (Havlovska et al., 2023). With = 1.96 and = 0.84, the minimum sample size is as follows:

Equation 2

n = 40 participants for each representing group
With the 10% of dropout rate, the minimum sample size is 88 participants. Initially, we collected 99 participants. However, 10 participants were loss to follow up due to some reasons. In the end, there were only 89 participants who completed the study.”

Results - Mention all the variables that were entered into the multivariable analysis.

Response: We have reperformed the analysis and provided the multivariable analysis with 3 models in which all the variables were entered (Table 3). Thank you for your valuable suggestion.

Results - Mention if there was any loss to follow-up in the study.

Response: We have added this information in the sample size section accordingly.

How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Response: We have now provided the information regarding the follow up in the study design section. Thank you for your valuable suggestions.
“Demographic, clinical, and laboratory data were collected at day 5 of the stroke onset during hospitalization. Serum BDNF was collected at day 5 of the stroke onset during hospitalization and at day 30 during the home visit. Cognitive functioning was measured at day 5, 30, and 90. The follow-up was carried out by the study team through home visits.”
My comments include:

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.

Response: We have now provided the full form of BDNF, ELISA, and ROC in the abstract. Thank you for your suggestion.

BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.

Response: Based on prior studies, the clinical staging of stroke (Cramer, 2008; Rehme et al., 2012; Zhao et al., 2014) is generally accepted as follows: the first 2 weeks are defined as the acute stage; 3–11 weeks post-stroke is termed the subacute stage in which most changes occur; 12–24 weeks post-stroke is the early chronic stage; and more than 24 weeks post-stroke is the chronic stage. BDNF in the present study was measured at day 5 after stroke onset in which it was still in the acute stage of stroke.

The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.

Response: We thank the reviewer for the valuable comments and suggestions. The reason why we measured serum BDNF level at day 5 after stroke index is based on prior study.
“Previous study showed that decrease BDNF level after 4 days from stroke index is associated with poor outcome in acute ischemic stroke patients. The cell death is completed in 5 days after stroke, and BDNF will lead to neuroplasticity during this time. Subsequently, lower serum BDNF will impair the neuroregeneration and may lead to poor outcome in acute ischemic stroke patients (Clarkson et al., 2011; Tuwar et al., 2023).”

Regarding PSCI identification, we agree that longer follow-up is needed because cognitive functioning after stroke may have been fluctuated due to the change of cerebral hemodynamics. However, prior studies have demonstrated that the definition of PSCI is any severity of cognitive impairment, regardless of cause, noted after an overt stroke, and it occurs from 3-6 months after stroke (Sundar & Adwani, 2010; Garcia et al., 2015; He at al., 2023; Husseini et al., 2023). Therefore, in the present study we identified PSCI at 90 days (3 months) after stroke. We have elaborated on this issue in the method section of the revised paper.

Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.

Response: Thank you for your suggestions. We have now mentioned these details in the study design and participants’ section.
“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?

Response: We have now provided some details in the inclusion and exclusion criteria as below:

“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.

Response: We used Kolmogorov-Smirnov test to determine the normality of numerical data, the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables.
Thank you for your suggestions. We have now provided this information in the revised paper.

Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.

Response: TRIPOD guidelines is attached in the resubmission along with the revised manuscript.

Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.

Response: We have provided the information regarding OR in the statistical analysis and mentioned it in the result and discussion section.

Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?

Response: We calculated the cut-off value by using Youden Index. We have now added this information in the Statistical analysis section.

Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.

Response: We have now provided the multivariate analysis with some prediction models in the revised paper such as stated in the statistical analysis section and the result section (Table 3). Thank you for your valuable suggestion.

The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.

Response: We have now provided the sample size calculation in the revised paper as below:
“We estimated the minimum sample size and found that it might be acceptable to represent the entire population. The sample size was calculated using the following for two population proportions (one-sided test) (Lwanga & Lemeshow, 1991):

Equation 1

n= sample size
P1= proportion of PSCI with lower serum BDNF
P2 = proportion of PSCI with higher serum BDNF
Based on previous study, the proportion of PSCI with lower serum BDNF is 88,7% (Prodjohardjono, 2020) and the proportion of PSCI with higher serum BDNF is 65,5% (Havlovska et al., 2023). With = 1.96 and = 0.84, the minimum sample size is as follows:

Equation 2

n = 40 participants for each representing group
With the 10% of dropout rate, the minimum sample size is 88 participants. Initially, we collected 99 participants. However, 10 participants were loss to follow up due to some reasons. In the end, there were only 89 participants who completed the study.”

Results - Mention all the variables that were entered into the multivariable analysis.

Response: We have reperformed the analysis and provided the multivariable analysis with 3 models in which all the variables were entered (Table 3). Thank you for your valuable suggestion.

Results - Mention if there was any loss to follow-up in the study.

Response: We have added this information in the sample size section accordingly.

How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Response: We have now provided the information regarding the follow up in the study design section. Thank you for your valuable suggestions.
“Demographic, clinical, and laboratory data were collected at day 5 of the stroke onset during hospitalization. Serum BDNF was collected at day 5 of the stroke onset during hospitalization and at day 30 during the home visit. Cognitive functioning was measured at day 5, 30, and 90. The follow-up was carried out by the study team through home visits.”
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 10 Aug 2022

Syahrul Syahrul, Department of Neurology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

Approved

https://doi.org/10.5256/f1000research.121236.r146499

This research article is presented according to accurate scientific research methods with appropriate and reliable literature sources. The research design is also in accordance with the research methodology and carried out with good technical method. The research methodology with statistical analysis that is available is quite good and allows for replication by other researchers. Interpretation Statistical analysis is carried out accordingly so that the implementation of the research results can be carried out. All data sources underlying the results are available to ensure full reproducibility. The conclusions in this article are supported by the results of the study.

Low BDNF serum in patients with acute ischemic stroke as a predictor of impaired cognitive function must be confirmed by neurobehaviour examination and neuroimaging (MRI) examination of the head. The diagnosis of cognitive dysfunction in patients with acute ischemic stroke must be carried out comprehensively by taking into account the risk factors for stroke and other organ system disorders.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Stroke (Neurology)

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Version 2 (revision) 08 Mar 24
Version 1 06 Jul 22	read	read

Syahrul Syahrul, Universitas Syiah Kuala, Banda Aceh, Indonesia
Shubham Misra, Yale University School of Medicine, New Haven, USA

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20 Jul 2023 | for Version 1

Shubham Misra, Yale University School of Medicine, New Haven, Connecticut, USA

12 Views Cite this report Responses(1)

Not Approved

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.
BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.
The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.
Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.
Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?
Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.
Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.
Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.
Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?
Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.
The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.
Results - Mention all the variables that were entered into the multivariable analysis.
Results - Mention if there was any loss to follow-up in the study.
How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Neurology, stroke, biomarker research.

Respond to this report

Responses (1)

Author Response

13 Apr 2024

Amelia Nur Vidyanti, Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia

My comments include:

Abstract - Provide full form of BDNF, ELISA, ROC when used for the first time.

Response: We have now provided the full form of BDNF, ELISA, and ROC in the abstract. Thank you for your suggestion.

BDNF level is being measured at day 5 in ischemic stroke patients. These are not acute patients. Please modify your terminology as it does not reflect acute stroke.

Response: Based on prior studies, the clinical staging of stroke (Cramer, 2008; Rehme et al., 2012; Zhao et al., 2014) is generally accepted as follows: the first 2 weeks are defined as the acute stage; 3–11 weeks post-stroke is termed the subacute stage in which most changes occur; 12–24 weeks post-stroke is the early chronic stage; and more than 24 weeks post-stroke is the chronic stage. BDNF in the present study was measured at day 5 after stroke onset in which it was still in the acute stage of stroke.

The main limitation of this study is the late measurement of biomarker at day 5 and small length of follow up. This is not clinically useful. An earlier detection is warranted. Why were BDNF levels measured at day 5 and not sooner, for e.g., within 24 hours? An earlier collection of blood sample in acute stroke patients would facilitate earlier detection of PSCI. Also, a longer follow-up is warranted to accurately identify cognitive impairment in stroke patients. Please elaborate on this in your discussion section.

Response: We thank the reviewer for the valuable comments and suggestions. The reason why we measured serum BDNF level at day 5 after stroke index is based on prior study.
“Previous study showed that decrease BDNF level after 4 days from stroke index is associated with poor outcome in acute ischemic stroke patients. The cell death is completed in 5 days after stroke, and BDNF will lead to neuroplasticity during this time. Subsequently, lower serum BDNF will impair the neuroregeneration and may lead to poor outcome in acute ischemic stroke patients (Clarkson et al., 2011; Tuwar et al., 2023).”

Regarding PSCI identification, we agree that longer follow-up is needed because cognitive functioning after stroke may have been fluctuated due to the change of cerebral hemodynamics. However, prior studies have demonstrated that the definition of PSCI is any severity of cognitive impairment, regardless of cause, noted after an overt stroke, and it occurs from 3-6 months after stroke (Sundar & Adwani, 2010; Garcia et al., 2015; He at al., 2023; Husseini et al., 2023). Therefore, in the present study we identified PSCI at 90 days (3 months) after stroke. We have elaborated on this issue in the method section of the revised paper.

Methods: How was stroke diagnosis made in this study? Was it clinical or supplemented with neuroimaging? What was the definition of stroke. Please mention these details in the study design and participants section.

Response: Thank you for your suggestions. We have now mentioned these details in the study design and participants’ section.
“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Methods - More details regarding the inclusion and exclusion criteria is required. The provided information is insufficient. Did the authors include both stroke subtypes? What about pregnant females?

Response: We have now provided some details in the inclusion and exclusion criteria as below:

“We included patients with: (1) first-ever acute ischemic stroke with <5 days after stroke onset, which is diagnosed based on clinical examination and head CT scan immediately after patients’ initial assessment in the emergency room; and (2) Age >18-years old. We excluded those with: (1) Recurrent stroke patients; (2) Brain tumor, brain trauma, encephalitis, Parkinson’s disease, epilepsy, pregnant females, and dementia; and (3) Depression or history of psychiatric diseases.”

Statistical analysis - Did the authors check the normality of the biomarker levels and other variables used in this study? If yes, then please mention the normality test used.

Response: We used Kolmogorov-Smirnov test to determine the normality of numerical data, the independent t-test, Mann-Whitney, and Chi-square test for analyzing the statistical differences between variables.
Thank you for your suggestions. We have now provided this information in the revised paper.

Statistical analysis - The authors should refer to the TRIPOD guidelines for reporting the results of a prognostic biomarker study and should report their results in accordance with those guidelines.

Response: TRIPOD guidelines is attached in the resubmission along with the revised manuscript.

Statistical analysis - Please mention the measure of effect (Odds ratio or Risk ratio) that the authors used to synthesize their results.

Response: We have provided the information regarding OR in the statistical analysis and mentioned it in the result and discussion section.

Statistical analysis - How did the authors calculate the cut-off value using the ROC curve? Was it manually done or using any index, for e.g., Youden Index?

Response: We calculated the cut-off value by using Youden Index. We have now added this information in the Statistical analysis section.

Statistical analysis - The authors should make two multivariable prediction models. One including biomarker and other predictors and the other without the biomarker. This will help identify if the addition of biomarker to the prediction model improved the predictive performance in predicting cognitive impairment after stroke.

Response: We have now provided the multivariate analysis with some prediction models in the revised paper such as stated in the statistical analysis section and the result section (Table 3). Thank you for your valuable suggestion.

The sample size of this study is too small to derive any meaningful conclusion. How did the authors derive this sample size? There is no sample size calculation provided in the manuscript. Please provide one.

Response: We have now provided the sample size calculation in the revised paper as below:
“We estimated the minimum sample size and found that it might be acceptable to represent the entire population. The sample size was calculated using the following for two population proportions (one-sided test) (Lwanga & Lemeshow, 1991):

Equation 1

n= sample size
P1= proportion of PSCI with lower serum BDNF
P2 = proportion of PSCI with higher serum BDNF
Based on previous study, the proportion of PSCI with lower serum BDNF is 88,7% (Prodjohardjono, 2020) and the proportion of PSCI with higher serum BDNF is 65,5% (Havlovska et al., 2023). With

= 1.96 and

= 0.84, the minimum sample size is as follows:

Equation 2

n = 40 participants for each representing group
With the 10% of dropout rate, the minimum sample size is 88 participants. Initially, we collected 99 participants. However, 10 participants were loss to follow up due to some reasons. In the end, there were only 89 participants who completed the study.”

Results - Mention all the variables that were entered into the multivariable analysis.

Response: We have reperformed the analysis and provided the multivariable analysis with 3 models in which all the variables were entered (Table 3). Thank you for your valuable suggestion.

Results - Mention if there was any loss to follow-up in the study.

Response: We have added this information in the sample size section accordingly.

How was the follow-up conducted and by whom? Was it telephonic or face-to-face? There is no information regarding that.

Response: We have now provided the information regarding the follow up in the study design section. Thank you for your valuable suggestions.
“Demographic, clinical, and laboratory data were collected at day 5 of the stroke onset during hospitalization. Serum BDNF was collected at day 5 of the stroke onset during hospitalization and at day 30 during the home visit. Cognitive functioning was measured at day 5, 30, and 90. The follow-up was carried out by the study team through home visits.”

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

18 Views

10 Aug 2022 | for Version 1

Syahrul Syahrul, Department of Neurology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

18 Views Cite this report Responses(0)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Stroke (Neurology)

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (0)

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Lower serum BDNF as a predictor of post-stroke cognitive impairment in acute ischemic stroke patients

Abstract

Keywords

Introduction

Methods

Study design and participants

Data collection

Statistical analysis

Ethical consideration

Results

Table 1. Baseline characteristics.

Table 2. Bivariate analysis of factors associated with PSCI.

Table 3. Multivariate analysis of factors associated with PSCI.

Discussion

Conclusions

Data availability statement

Author contributions

Acknowledgements

References

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