--- title: "Hillblom Aging Network (HAN)" id: "3840" type: "cohort" slug: "hillblom-aging-network" published_at: "2022-09-22T19:39:59+00:00" modified_at: "2024-09-18T16:42:20+00:00" url: "https://dss.niagads.org/cohorts/hillblom-aging-network/" markdown_url: "https://dss.niagads.org/cohorts/hillblom-aging-network.md" excerpt: "Participants were enrolled in the Hillblom Aging Network at the University of California, San Francisco (UCSF) Memory and Aging Center. All participants underwent comprehensive neurobehavioral evaluations and met the following inclusionary criteria at baseline: 1) clinically normal based on consensus..." taxonomy_cohort_categories: - "ADSP" taxonomy_cohort_countries: - "United States of America" --- ## Description Participants were enrolled in the Hillblom Aging Network at the University of California, San Francisco (UCSF) Memory and Aging Center. All participants underwent comprehensive neurobehavioral evaluations and met the following inclusionary criteria at baseline: 1) clinically normal based on consensus conference with a neurologist and board-certified neuropsychologist; 2) no history of neurological disorder known to impact cognition (e.g., epilepsy, stroke); and 3) functionally intact as defined by an informant-obtained CDR global score of 0 ([Morris, 1993](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R49) ). More specifically, the determination of clinically normal by consensus conference involved ruling out the presence of mild cognitive impairment, dementia, or any other neurological condition resulting in cognitive, behavioral, motor, or functional decline (e.g., Parkinson’s disease), according to widely used diagnostic criteria (e.g., [Albert et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R1) ; [Armstrong et al., 2013](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R4) ; [Gorno-Tempini et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R24) ; [Höglinger et al., 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R30) ; [McKeith et al., 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R45) ; [McKhann et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R46) ; [Postuma et al., 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R55) ; [Rascovsky et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R56) ). Three main sources of information were considered by the neurologist and neuropsychologist during the diagnostic conference. First, participants underwent a thorough evaluation with the neurologist that involved a comprehensive neurological examination, clinical interview, and review of systems. Second, neuroimaging (structural MRI) was reviewed to screen out gross brain pathology with potential to negatively impact cognition (e.g., tumor). Third, participants completed a battery of neuropsychological tests to objectively assess major domains of cognitive function, including attention, executive functioning, memory, language, and visuospatial skills. Cognitive impairment was defined by the presence of subjective cognitive decline, as reported by the participant or informant, together with objective performance on neuropsychological testing that was below expectation given the participant’s age and level of premorbid functioning ([Albert et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839841/#R1) ). In making the determination of clinically normal, emphasis was placed on ruling out any declines in the participant’s ability to perform everyday tasks due to cognitive changes. University of California San Francisco Alzheimer’s Disease Research Center under grant P30AG062422; Larry L. Hillblom Network under Grant 2014-A-004-NET; R01AG032289 (PI: JK); R01AG048234 (PI: JK) Espeland MA, Yassine H, Hayden KD, Hugenschmidt C, Bennett WL, Chao A, Neiberg R, Kahn SE, Luchsinger JA; Action for Health in Diabetes (Look AHEAD) Research Group. Sex-related differences in cognitive trajectories in older individuals with type 2 diabetes and overweight or obesity. Alzheimers Dement (N Y). 2021 Apr 9;7(1):e12160. doi: 10.1002/trc2.12160. PMID: [33860069](https://pubmed.ncbi.nlm.nih.gov/33860069/) ; PMCID: PMC8033410. Casaletto KB, Elahi FM, Staffaroni AM, Walters S, Contreras WR, Wolf A, Dubal D, Miller B, Yaffe K, Kramer JH. Cognitive aging is not created equally: differentiating unique cognitive phenotypes in “normal” adults. Neurobiol Aging. 2019 May;77:13-19. doi: 10.1016/j.neurobiolaging.2019.01.007. Epub 2019 Jan 24. PMID: 30772736; PMCID: PMC6486874. Staffaroni AM, Brown JA, Casaletto KB, Elahi FM, Deng J, Neuhaus J, Cobigo Y, Mumford PS, Walters S, Saloner R, Karydas A, Coppola G, Rosen HJ, Miller BL, Seeley WW, Kramer JH. The Longitudinal Trajectory of Default Mode Network Connectivity in Healthy Older Adults Varies As a Function of Age and Is Associated with Changes in Episodic Memory and Processing Speed. J Neurosci. 2018 Mar 14;38(11):2809-2817. doi: 10.1523/JNEUROSCI.3067-17.2018. Epub 2018 Feb 13. PMID: 29440553; PMCID: PMC5852659. Yokoyama JS, Sturm VE, Bonham LW, Klein E, Arfanakis K, Yu L, Coppola G, Kramer JH, Bennett DA, Miller BL, Dubal DB. Variation in longevity gene KLOTHO is associated with greater cortical volumes. Ann Clin Transl Neurol. 2015 Mar;2(3):215-30. doi: 10.1002/acn3.161. Epub 2015 Jan 26. PMID: 25815349; PMCID: PMC4369272. ## Related Datasets - [NG00067 – ADSP Umbrella](https://dss.niagads.org/datasets/ng00067/) This dataset includes sequencing data and harmonized phenotypes from cohorts sequenced by the Alzheimer’s Disease Sequencing Project and other AD and Related Dementia’s studies. Samples are processed using a common… [Learn more](https://dss.niagads.org/datasets/ng00067/) - [NG00196- Genotyping short tandem repeats using the ADSP R4 cohort](https://dss.niagads.org/datasets/ng00196/) A two-step pipeline was used to first identify expanded short tandem repeats (STRs) in ADSP samples using ExpansionHunter Denovo and then genotype the identified STRs, along with additional polymorphic STRs… [Learn more](https://dss.niagads.org/datasets/ng00196/) ## Related Studies - [sa000001 - Alzheimer’s Disease Sequencing Project (ADSP)](https://dss.niagads.org/studies/sa000001/) Background An initiative in response to the National Alzheimer’s Project Act (NAPA) has been working towards new biological insights and cures for Alzheimer’s Disease (AD) since its introduction by NIH… [Learn more](https://dss.niagads.org/studies/sa000001/) - [sa000087 - Genotyping short tandem repeats using ADSP R4](https://dss.niagads.org/studies/sa000087/) Variation in tandem repeats (TRs), particularly large expansions of triplet repeats (e.g., polyCAG), is known to cause a number of late-onset neurological diseases. Due to their repetitive and degenerate nature,… [Learn more](https://dss.niagads.org/studies/sa000087/) ## Related Sample Sets - [snd10031 - ADSP-FUS2 WGS](https://dss.niagads.org/sample-sets/snd10031/) The ADSP-FUS is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for late-onset Alzheimer Disease (LOAD). A concern in AD genetic studies is… [Learn more](https://dss.niagads.org/sample-sets/snd10031/) - [snd10147 - ADSP R4 Short Tandem Repeats (STRs)](https://dss.niagads.org/sample-sets/snd10147/) This dataset comprises short tandem repeat (STR) genotypes from 31,681 whole-genome sequencing (WGS) samples from the ADSP R4 data release. The STRs were genotyped using ExpansionHunter with a custom catalog… [Learn more](https://dss.niagads.org/sample-sets/snd10147/) ```