Characterizing mild cognitive impairment to predict incident dementia in adults with bipolar disorder: What should the benchmark be?

Abstract

Objective: Although mild cognitive impairment (MCI) is generally considered a risk state for dementia, its prevalence and association with dementia are impacted by the number of tests and cut-points used to assess cognition and define “impairment,” and sources of norms. Here, we investigate how these methodological variations impact estimates of incident dementia in adults with bipolar disorder (BD), a vulnerable population with pre-existing cognitive deficits and increased dementia risk. Method: Neuropsychological data from 148 adults with BD and 13,610 healthy controls (HC) were drawn from the National Alzheimer’s Coordinating Center. BD participants’ scores were standardized against published norms and again using regression-based norms generated from HC within the same catchment area as individual BD patients (“site-specific norms”), varying the number of within-domain tests (one vs. two) and the cut-points (−1 vs. −1.5 SD) used to operationalize MCI. Results: Site-specific norms were more sensitive to incident dementia (88.6%–94.3%) than published norms (74.3%–88.6%), but only when using a “single test” definition of impairment. Specificity (22.1%–74.3%), accuracy (37.8%–68.9%), and positive predictive values (26.1%–38.3%) were overall poor. Applying a “single test” definition of impairment resulted in better negative predictive values using site-specific (92.3%–93.3%) than published norms (83.6%–86.2%), and a substantial increase in relative risk of incident dementia relative to published norms. Conclusions: Neuropsychologists should define “impairment” as scores below −1.0 or −1.5 SD on at least two within-domain measures when using published norms to interpret cognitive performance in adults with BD.

Keywords:

• bipolar disorder
• adults
• normative data
• mild cognitive impairment
• dementia

Acknowledgements

We are grateful to Reha Sandhu for her assistance in preparing the data for this manuscript. The NACC database is funded by National Institute on Aging/National Institutes of Health (NIA/NIH) Grant U01 AG016976. NACC data are contributed by the NIA funded Alzheimer’s Disease Centers (ADC): P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI Marie-Francoise Chesselet, MD, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), and P50 AG047270 (PI Stephen Strittmatter, MD, PhD). In addition, this work was supported by NIA grants awarded to D.A. Bennett #P30AG10161 and #R01AG17917.

Disclosure statement

The authors have no conflicts to report.

Additional information

Funding

This work was supported by a Canada Research Chair to Dr. Callahan.