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Associations Between Insulin-Like Growth Factor-1 and Resting-State Functional Connectivity in Cognitively Unimpaired Midlife Adults.
Li, Tianqi; Pappas, Colleen; Klinedinst, Brandon; Pollpeter, Amy; Larsen, Brittany; Hoth, Nathan; Anton, Faith; Wang, Qian; Willette, Auriel A.
Affiliation
  • Li T; Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
  • Pappas C; Genetics and Genomics Interdepartmental Graduate Program, Iowa State University, Ames, IA, USA.
  • Klinedinst B; Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
  • Pollpeter A; Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
  • Larsen B; Neuroscience Interdepartmental Graduate Program Interdepartmental Graduate Program, Iowa State University, Ames, IA, USA.
  • Hoth N; Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
  • Anton F; Bioinformatics and Computational Biology Interdepartmental Graduate Program, Iowa State University, Ames, IA, USA.
  • Wang Q; Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
  • Willette AA; Neuroscience Interdepartmental Graduate Program Interdepartmental Graduate Program, Iowa State University, Ames, IA, USA.
J Alzheimers Dis ; 94(s1): S309-S318, 2023.
Article in En | MEDLINE | ID: mdl-36710671
BACKGROUND: Insulin-like growth factor (IGF)-1 plays an important role in Alzheimer's disease (AD) pathogenesis and increases disease risk. However, prior research examining IGF-1 levels and brain neural network activity is mixed. OBJECTIVE: The present study investigated the relationship between IGF-1 levels and 21 neural networks, as measured by functional magnetic resonance imaging (fMRI) in 13,235 UK Biobank participants. METHODS: Linear mixed models were used to regress IGF-1 against the intrinsic functional connectivity (i.e., degree of network activity) for each neural network. Interactions between IGF-1 and AD risk factors such as Apolipoprotein E4 (APOE4) genotype, sex, AD family history, and age were also tested. RESULTS: Higher IGF-1 was associated with more network activity in the right Executive Function neural network. IGF-1 interactions with APOE4 or sex implicated motor, primary/extrastriate visual, and executive function related neural networks. Neural network activity trends with increasing IGF-1 were different in different age groups. Higher IGF-1 levels relate to much more network activity in the Sensorimotor Network and Cerebellum Network in early-life participants (40-52 years old), compared with mid-life (52-59 years old) and late-life (59-70 years old) participants. CONCLUSION: These findings suggest that sex and APOE4 genotype may modify the relationship between IGF-1 and brain network activities related to visual, motor, and cognitive processing. Additionally, IGF-1 may have an age-dependent effect on neural network connectivity.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Alzheimer Disease Type of study: Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: J Alzheimers Dis Journal subject: GERIATRIA / NEUROLOGIA Year: 2023 Document type: Article Affiliation country: Estados Unidos Country of publication: Países Bajos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Alzheimer Disease Type of study: Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: J Alzheimers Dis Journal subject: GERIATRIA / NEUROLOGIA Year: 2023 Document type: Article Affiliation country: Estados Unidos Country of publication: Países Bajos