The effectiveness and unique contribution of neuropsychological tests and the δ latent phenotype in the differential diagnosis of dementia in the uniform data set.

Abstract

Objective Two main approaches to the interpretation of cognitive test performance have been utilized for the characterization of disease: evaluating shared variance across tests, as with measures of severity, and evaluating the unique variance across tests, as with pattern and error analysis. Both methods provide necessary information, but the unique contributions of each are rarely considered. This study compares the two approaches on their ability to differentially diagnose with accuracy, while controlling for the influence of other relevant demographic and risk variables. Method Archival data requested from the NACC provided clinical diagnostic groups that were paired to one another through a genetic matching procedure. For each diagnostic pairing, two separate logistic regression models predicting clinical diagnosis were performed and compared on their predictive ability. The shared variance approach was represented through the latent phenotype δ, which served as the lone predictor in one set of models. The unique variance approach was represented through raw score values for the 12 neuropsychological test variables comprising δ, which served as the set of predictors in the second group of models. Results Examining the unique patterns of neuropsychological test performance across a battery of tests was the superior method of differentiating between competing diagnoses, and it accounted for 16-30% of the variance in diagnostic decision making. Conclusion Implications for clinical practice are discussed, including test selection and interpretation.

Blood‐based protein predictors of dementia severity as measured by δ: Replication across biofluids and cohorts

“…Etiology‐specific cognitive variance is residual (i.e., orthogonal) to δ [20]. This suggests that attempts to define disease‐specific biomarker panels relating to etiology‐specific variance in cognitive performance will not be associated with dementia severity.…”

δ scores predict mild cognitive impairment and Alzheimer's disease conversions from nondemented states

“…John et al. [25] demonstrated that δ scores are unable to distinguish between any combination of dementing conditions. Instead, only “unique” (i.e., “domain‐specific”) variance, residual to δ, was able to make such discriminations.…”

“…Second, John et al. 's [25] finding suggests that the cognitive differences that distinguish dementing illnesses may be orthogonal to δ and therefore unrelated to dementia severity. This includes domain‐specific variance, by definition.…”

“…Gavett et al [2] found d's AUC 5 0.96 for the discrimination between dementia of any etiology versus nondemented cases (MCI and controls). John et al [25] demonstrated that d scores are unable to distinguish between any combination of dementing conditions. Instead, only "unique" (i.e., "domain-specific") variance, residual to d, was able to make such discriminations.…”

Dementia is Associated with a Syndrome of Global Neuropsychiatric Disturbance

“…δ is highly accurate in distinguishing demented cases (16), but it is entirely “agnostic” as to dementia’s etiology (17). If p can be shown to be associated specifically with δ then it would directly implicate dementia itself as the transdiagnostic determinant of global psychopathology, link p to intelligence, and constrain p ’s biology to that of g and δ.…”