Identifying dynamic changes in biomarkers and clinical profiles is essential for understanding the progression of Alzheimer's disease (AD). The relevant studies have primarily relied on patients with autosomal dominant AD; however, relevant studies in sporadic AD are poorly understood. Here, we analyzed longitudinal data from 665 participants (mean follow-up 4.90 ± 2.83 years). By aligning normal cognition (CN) baseline with a clinical diagnosis of mild cognitive impairment (MCI) or AD, we studied the progression of AD using a linear mixed model to estimate the clinical and biomarker changes from stable CN to MCI to AD. The results showed that the trajectory of hippocampal volume and fluorodeoxyglucose (FDG) was consistent with the clinical measures in that they did not follow a hypothetical sigmoid curve but rather showed a slow change in the initial stage and accelerated changes in the later stage from MCI conversion to AD. Dramatic hippocampal atrophy and the ADAS13 increase were, respectively, 2.5 and 1 years earlier than the MCI onset. Besides, cognitively normal people with elevated and normal amyloid showed no significant differences in clinical measures, hippocampal volume, or FDG. These results reveal that pre-MCI to pre-AD may be a better time window for future clinical trial design.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/cercor/bhab017 | DOI Listing |
Kaohsiung J Med Sci
January 2025
Department of Psychiatry, School of Medicine, Kaohsiung Medical University Kaohsiung, Taiwan.
Attention-deficit/hyperactivity disorder (ADHD) is a common psychiatric condition among children and adolescents, often associated with a high risk of psychiatric comorbidities. Currently, ADHD diagnosis relies exclusively on clinical presentation and patient history, underscoring the need for clinically relevant, reliable, and objective biomarkers. Such biomarkers may enable earlier diagnosis and lead to improved treatment outcomes.
View Article and Find Full Text PDFPostgrad Med J
January 2025
Department of Pediatric Metabolic Diseases, University of Health Sciences, Ankara Etlik City Hospital, Ankara 06170, Turkey.
Metabolism is the name given to all of the chemical reactions in the cell involving thousands of proteins, including enzymes, receptors, and transporters. Inborn errors of metabolism (IEM) are caused by defects in the production and breakdown of proteins, fats, and carbohydrates. Micro ribonucleic acids (miRNAs) are short non-coding RNA molecules, ⁓19-25 nucleotides long, hairpin-shaped, produced from DNA.
View Article and Find Full Text PDFExpert Rev Proteomics
January 2025
Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
Introduction: Identifying early risks of developing Alzheimer's disease (AD) is a major challenge as the number of patients with AD steadily increases and requires innovative solutions. Current molecular diagnostic modalities, such as cerebrospinal fluid (CSF) testing and positron emission tomography (PET) imaging, exhibit limitations in their applicability for large-scale screening. In recent years, there has been a marked shift toward the development of blood plasma-based diagnostic tests, which offer a more accessible and clinically viable alternative for widespread use.
View Article and Find Full Text PDFHum Mol Genet
January 2025
Department of Reproductive Medicine, The First Affiliated Hospital of Henan University of CM, No. 19, Renmin Road, Jinshui District, Zhengzhou City, Henan Province, China.
This study systematically explores the oncogenic role of the long non-coding RNA (lncRNA) LINC00115 in endometrial cancer (EC) and reveals its unique mechanism in promoting proliferation, invasion, and metastasis via the JAK/STAT signaling pathway. LINC00115 is significantly upregulated in EC tissues and closely associated with advanced TNM staging and lymph node metastasis. Functional assays showed that knockdown of LINC00115 suppressed EC cell proliferation, invasion, and metastasis, while overexpression enhanced these malignant behaviors.
View Article and Find Full Text PDFInt J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!