Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10⁻⁵). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer’s Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer’s Disease Center [NIA ADC], and Alzheimer’s Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62–4.24, p = 1.0 × 10⁻²²). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10⁻²⁶) and longitudinal progression from normal aging to AD (NIA ADC, Cochran–Armitage trend test, p = 1.5 × 10⁻¹⁰), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10⁻⁶, and Consortium to Establish a Registry for Alzheimer’s Disease score for neuritic plaques, p = 6.8 × 10⁻⁶) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10⁻⁶, and hippocampus, p = 7.9 × 10⁻⁵). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

Cross-sectional estimates of age-related changes in brain structure and function were compared with 6-y longitudinal estimates. The results indicated increased sensitivity of the longitudinal approach as well as qualitative differences. Critically, the cross-sectional analyses were suggestive of age-related frontal overrecruitment, whereas the longitudinal analyses revealed frontal underrecruitment with advancing age. The cross-sectional observation of overrecruitment reflected a select elderly sample. However, when followed over time, this sample showed reduced frontal recruitment. These findings dispute inferences of true age changes on the basis of age differences, hence challenging some contemporary models of neurocognitive aging, and demonstrate age-related decline in frontal brain volume as well as functional response.

By integrating behavioral measures and imaging data, previous investigations have explored the relationship between biological markers of aging and cognitive functions. Evidence from functional and structural neuroimaging has revealed that hippocampal volume and activation patterns in the medial temporal lobe (MTL) may predict cognitive performance in old age. Most past demonstrations of age-related differences in brain structure–function were based on cross-sectional comparisons. Here, the relationship between 6-year intraindividual change in functional magnetic resonance imaging (fMRI) signal and change in memory performance over 2 decades was examined. Correlations between intraindividual change in fMRI signal during episodic encoding and change in memory performance measured outside of scanning were used as an estimate for relating brain–behavior changes. The results revealed a positive relationship between activation change in the hippocampus (HC) and change in memory performance, reflecting reduced hippocampal activation in participants with declining performance. Using a similar analytic approach as for the functional data, we found that individuals with declining performance had reduced HC volume compared with individuals with intact performance. These observations provide a strong link between cognitive change in older adults and MTL structure and function and thus provide insights into brain correlates of individual variability in aging trajectories.

Schizophrenia is a severe, chronic psychiatric disorder that ranks among the leading causes of disability worldwide.¹ Although the diagnosis of schizophrenia is based on the presence of positive and negative symptoms, cognitive dysfunction is regarded as a core component of the disorder.^2-4 Compared with healthy individuals, patients with schizophrenia display widespread cognitive impairments including deficits in learning, memory, processing speed, attention, and executive functioning.^3,5,6 Cognitive dysfunction often precedes the onset of psychosis by several years^7,8 and is an important factor associated with functional outcomes in schizophrenia.^2,9 Despite this fact, current treatment strategies largely fail to ameliorate the cognitive deficits in patients with schizophrenia.^2,4,10 To develop more efficient treatment strategies for patients with schizophrenia, a better understanding of the pathogenesis underlying these cognitive deficits is needed.