Observational Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jan 6, 2020; 8(1): 76-87
Published online Jan 6, 2020. doi: 10.12998/wjcc.v8.i1.76
Relationship between β-amyloid protein 1-42, thyroid hormone levels and the risk of cognitive impairment after ischemic stroke
Lei Mao, Xiao-Han Chen, Jian-Hua Zhuang, Peng Li, Yi-Xin Xu, Yu-Chen Zhao, Yue-Jin Ma, Bin He, You Yin
Lei Mao, Xiao-Han Chen, Jian-Hua Zhuang, Peng Li, Yi-Xin Xu, Yu-Chen Zhao, Yue-Jin Ma, Bin He, You Yin, Department of Neurology, Changzheng Hospital, the PLA Naval Medical University, Shanghai 200003, China
Lei Mao, Department of Neurology, Shanghai First People’s Hospital Baoshan Branch, Shanghai 200003, China
Author contributions: Mao L and Chen XH contributed equally to this study; Mao L, Chen XH, Zhuang JH and Yin Y designed the research; Mao L, Chen XH, Li P, Xu YX, Zhao YC, Ma YJ and He B performed the research; Zhuang JH, Li P and Xu YX contributed new analytic tools; Mao L, Chen XH, Ma YJ and He B analyzed the data; Mao L, Chen XH, Zhuang JH, Li P, Xu YX, Zhao YC, Ma YJ, He B and Yin Y wrote the paper.
Supported by Science and Technology Support Projects in Biomedicine Field of Shanghai Science and Technology Commission, No. 19441907500; Naval Medical University Military Medical Innovation, No. 2017JS07; Science and Technology Action Innovation Program by Science and Technology Commission of Shanghai, No. 17411950104.
Institutional review board statement: The study was approved by the Ethics Committee of Changzheng Hospital, the PLA Naval Medical University.
Informed consent statement: All patients gave informed consent.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE statement, and the manuscript was prepared and revised according to the STROBE statement.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: You Yin, MD, PhD, Chief Doctor, Chief Physician, Department of Neurology, Changzheng Hospital, the PLA Naval Medical University, NO. 415 Fengyang Road, Huangpu District, Shanghai 200003, China. yinyou179@163.com
Received: September 8, 2019
Peer-review started: September 8, 2019
First decision: November 12, 2019
Revised: November 24, 2019
Accepted: November 27, 2019
Article in press: November 27, 2019
Published online: January 6, 2020
Processing time: 120 Days and 23.4 Hours
Abstract
BACKGROUND

Post-stroke cognitive impairment (PSCI) is not only a common consequence of stroke but also an important factor for adverse prognosis of patients. Biochemical indicators such as blood lipids and blood pressure are affected by many factors, and the ability of evaluating the progress of patients with PSCI is insufficient. Therefore, it is necessary to find sensitive markers for predicting the progress of patients and avoiding PSCI. Recent studies have shown that β-amyloid protein 1-42 (Aβ1-42) and thyroid hormone levels are closely related to PSCI, which may be the influencing factors of PSCI, but there are few related studies.

AIM

To investigate the relationship between serum levels of Aβ and thyroid hormones in acute stage and PSCI and its predicted value.

METHODS

A total of 195 patients with acute cerebral infarction confirmed from June 2016 to January 2018 were enrolled in this study. Baseline data and serological indicators were recorded to assess cognitive function of patients. All patients were followed up for 1 year. Their cognitive functions were evaluated within 1 wk, 3 mo, 6 mo and 1 yr after stroke. At the end of follow-up, the patients were divided into PSCI and non-PSCI according to Montreal cognitive assessment score, and the relationship between biochemical indexes and the progression of PSCI was explored.

RESULTS

Compared with patients with non-PSCI, the levels of Aβ1-42, triiodothyronine (T3) and free thyroxin were lower in the patients with PSCI. Repeated measures analysis of variance showed that the overall content of Aβ1-42 and T3 in PSCI was also lower than that of the non-PSCI patients. Further analysis revealed that Aβ1-42 (r = 0.348), T3 (r = 0.273) and free thyroxin (r = 0.214) were positively correlated with disease progression (P < 0.05), suggesting that these indicators have the potential to predict disease progression and outcome. Cox regression analysis showed that Aβ1-42 and T3 were important factors of PSCI. Then stratified analysis showed that the lower the Aβ1-42 and T3, the higher risk of PSCI in patients who were aged over 70, female and illiterate.

CONCLUSION

Aβ1-42 and T3 have the ability to predict the progression of PSCI, which is expected to be applied clinically to reduce the incidence of PSCI and improve the quality of life of patients.

Keywords: Post-stroke cognitive impairment; Triiodothyronine; β-amyloid protein; Prognosis; Montreal cognitive assessment; Free thyroxin

Core tip: Post-stroke cognitive impairment (PSCI) has become a deepening public health problem. PSCI diagnosis is mostly based on clinical manifestations, neuroimaging and a series of neuropsychological tests. However, early diagnosis is difficult because the implementation of the above-mentioned scheme depends on the cooperation of patients. Therefore, it is necessary to identify early biomarkers. Our study found that β-amyloid protein 1-42 and triiodothyronine can dynamically monitor the patient’s clinical status and correlates with progression of the disease. It is suggested that β-amyloid protein 1-42 and triiodothyronine have the ability to predict the progress of PSCI and can be broadly applied.