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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]