Smaller hippocampal and entorhinal cortex volumes each contribute to the prediction of conversion to Alzheimer disease. Age and cognitive variables also contribute to prediction, and the added value of hippocampal and entorhinal cortex volumes is small. Nonetheless, combining these MRI volumes with age and cognitive measures leads to high levels of predictive accuracy that may have potential clinical application.

Recent evidence indicates that sensory and motor changes may precede the cognitive symptoms of Alzheimer’s disease (AD) by several years and may signify increased risk of developing AD. Traditionally, sensory and motor dysfunctions in aging and AD have been studied separately. To ascertain the evidence supporting the relationship between age-related changes in sensory and motor systems and the development of AD and to facilitate communication between several disciplines, the National Institute on Aging held an exploratory workshop titled “Sensory and Motor Dysfunctions in Aging and Alzheimer’s Disease”. The scientific sessions of the workshop focused on age-related and neuropathological changes in the olfactory, visual, auditory, and motor systems, followed by extensive discussion and hypothesis generation related to the possible links among sensory, cognitive, and motor domains in aging and AD. Based on the data presented and discussed at this workshop, it is clear that sensory and motor regions of the CNS are affected by Alzheimer pathology and that interventions targeting amelioration of sensory-motor deficits in AD may enhance patient function as AD progresses.

Depressed mood moderately increased the risk of developing dementia, primarily Alzheimer's disease. Whether depressed mood is a very early manifestation of Alzheimer's disease, or increases susceptibility through another mechanism, remains to be determined.

Despite the availability of safe and efficacious treatments, mood disorders remain a significant health care issue for the elderly and are associated with disability, functional decline, diminished quality of life, mortality from comorbid medical conditions or suicide, demands on caregivers, and increased service utilization. Discriminatory coverage and reimbursement policies for mental health care are a challenge for the elderly, especially those with modest incomes, and for clinicians. Minorities are particularly underserved. Access to mental health care services for most elderly individuals is inadequate, and coordination of services is lacking. There is an immediate need for collaboration among patients, families, researchers, clinicians, governmental agencies, and third-party payers to improve diagnosis, treatment, and delivery of services for elderly persons with mood disorders.

Background The utility of combining early markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer’s Disease (AD) remains uncertain. Methods 148 outpatients with MCI, broadly defined, were followed at 6-month intervals. Hypothesized baseline predictors for follow-up conversion to AD (entire sample: 39/148 converters) were cognitive test performance, informant report of functional impairment, apolipoprotein E genotype, olfactory identification deficit, MRI hippocampal and entorhinal cortex volumes. Results In the 3-year follow-up patient sample (33/126 converters), five of eight hypothesized predictors were selected by backward and stepwise logistic regression: FAQ (informant report of functioning), UPSIT (olfactory identification), SRT immediate recall (verbal memory), MRI hippocampal volume, MRI entorhinal cortex volume. For 10% false positives (90% specificity), this five-predictor combination showed 85.2% sensitivity, combining age and MMSE showed 39.4% sensitivity, and combining age, MMSE, and the three clinical predictors (SRT immediate recall, FAQ, and UPSIT) showed 81.3% sensitivity. Area under ROC curve was greater for the five-predictor combination (0.948) than age plus MMSE (0.821; p =.0009), and remained high in sub-samples with MMSE ≥ 27/30 and amnestic MCI. For the entire patient sample, based on dichotomizing estimated risk at 0.5, positive likelihood ratio was 16.8 (95% CI 6.4, 44.3) and negative likelihood ratio was 0.2 (95% CI 0.1, 0.4). Conclusions The five-predictor combination strongly predicted conversion to AD and was markedly superior to combining age and MMSE. Combining only clinically administered measures also led to strong predictive accuracy. If independently replicated, the findings have potential utility for early detection of AD.