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The hypothalamus regulates homeostasis across the lifespan and is emerging as a regulator of aging. In murine models, aging-related changes in the hypothalamus, including microinflammation and gliosis, promote accelerated neurocognitive decline. We investigated relationships between hypothalamic microstructure and features of neurocognitive aging, including cortical thickness and cognition, in a cohort of community-dwelling older adults (age range 65–97 years, n=124). Hypothalamic microstructure was evaluated with two magnetic resonance imaging diffusion metrics: mean diffusivity (MD) and fractional anisotropy (FA), using a novel image processing pipeline. Hypothalamic MD was cross-sectionally positively associated with age and it was negatively associated with cortical thickness. Hypothalamic FA, independent of cortical thickness, was cross-sectionally positively associated with neurocognitive scores. An exploratory analysis of longitudinal neurocognitive performance suggested that lower hypothalamic FA may predict cognitive decline. No associations between hypothalamic MD, age, and cortical thickness were identified in a younger control cohort (age range 18–63 years, n=99). To our knowledge, this is the first study to demonstrate that hypothalamic microstructure is associated with features of neurocognitive aging in humans. • In animal models, aging-related changes in the hypothalamus drive brain aging. • The role of the hypothalamus in neurocognitive aging in humans is unknown. • We show that hypothalamic microstructure is altered with aging in older adults. • Hypothalamic microstructure is associated with cortical thickness and cognition. [ABSTRACT FROM AUTHOR]

Objectives: To describe the psychometric properties of the Picture‐based Memory Impairment Screen (PMIS) in a multidisciplinary memory disorder center serving an ethnically and educationally diverse community. Design: Cross‐sectional cohort study. Setting: Montefiore Center for Aging Brain (CAB) Participants: Individuals with cognitive complaints (N=405; average age 76±10, 66% female). Measurements: A geriatrician or neurologist administered the PMIS, and a neuropsychologist administered the Blessed Information, Memory, and Concentration (BIMC) test and determined whether participants had dementia, mild cognitive impairment (MCI), or subjective cognitive complaints (SCC). Results: Mean PMIS scores were 4.0±2.6 in participants with dementia (n=194), 6.8±1.5 in those with MCI (n= 155), and 7.0±1.8 in those with SCC (n= 56) (p<.001). PMIS scores showed similar significant linear trends when analyzed according to ethnicity, education, sex, and language. The PMIS was negatively correlated with BIMC score (p<.001). The PMIS had positive predictive value of 77%, negative predictive value of 73%, sensitivity of 68%, and specificity of 81% to detect all‐cause dementia in this population of individuals with cognitive complaints. Conclusion: The PMIS is a quick, valid screening tool to identify cognitive impairment in individuals with cognitive complaints that accounts for cultural and educational differences. [ABSTRACT FROM AUTHOR]

Aging is a complex trait characterized by a diverse spectrum of endophenotypes. By utilizing the SomaScan® proteomic platform in 1,025 participants of the LonGenity cohort (age range: 65–95, 55.7% females), we found that 754 of 4,265 proteins were associated with chronological age. Pleiotrophin (PTN; β[SE] = 0.0262 [0.0012]; p = 3.21 × 10−86), WNT1‐inducible‐signaling pathway protein 2 (WISP‐2; β[SE] = 0.0189 [0.0009]; p = 4.60 × 10−82), chordin‐like protein 1 (CRDL1; β[SE] = 0.0203[0.0010]; p = 1.45 × 10−77), transgelin (TAGL; β[SE] = 0.0215 [0.0011]; p = 9.70 × 10−71), and R‐spondin‐1(RSPO1; β[SE] = 0.0208 [0.0011]; p = 1.09 × 10−70), were the proteins most significantly associated with age. Weighted gene co‐expression network analysis identified two of nine modules (clusters of highly correlated proteins) to be significantly associated with chronological age and demonstrated that the biology of aging overlapped with complex age‐associated diseases and other age‐related traits. The correlation between proteomic age prediction based on elastic net regression and chronological age was 0.8 (p < 2.2E−16). Pathway analysis showed that inflammatory response, organismal injury and abnormalities, cell and organismal survival, and death pathways were associated with aging. The present study made novel associations between a number of proteins and aging, constructed a proteomic age model that predicted mortality, and suggested possible proteomic signatures possessed by a cohort enriched for familial exceptional longevity. [ABSTRACT FROM AUTHOR]