NEUROFILAMENT LIGHT CHAIN (NFL) AND GENERAL COGNITIVE ABILITY IN ADULTS APPROACHING MIDLIFE
INNOVATION IN AGING(2022)
Univ Calif Riverside | Univ Colorado Boulder
Abstract
Abstract Neurofilament light chain (NfL) is a biomarker indexing axonal integrity where small NfL variations may be associated with cognitive performance in early adulthood and high values associated with neurodegenerative disorders such as Alzheimer’s disease. In the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife1) individuals were tested at 28–49 years (M=33.1, SD=4.9). Quanterix Simoa assays of plasma NfL (pNfL) were measured in duplicate, and we included values for 1159 individuals where 1098 had available general cognitive ability scores and sociodemographic covariates. Unadjusted NfL values were consistent with other studies of early-mid adulthood (M = 5.9, SD = 3.1, range = 1.14 – 40.1 pg/mL) and 6% showed values outside expected normal reference limits (>10 pg/mL). After adjusting for technical covariates and skew, higher natural log-transformed pNfL was associated with age (r = 0.27) and female sex (r = 0.07). Moreover, adjusting for sociodemographic covariates, higher pNfL was associated with lower general cognitive ability (GCA) (r = -.06), where associations were more pronounced above the mean pNfL value (r = -.08). Multi-level regression analyses suggested that GCA-NfL associations were modified by age, whereby the worse performance was observed at higher ages and pNfL values (p <= 0.03), accounting for sibling relatedness and sociodemographic covariates. We observed small negative associations of higher plasma NfL and lower cognitive performance, where associations may become magnified with increasing age in early- to mid-adulthood.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper