Title:Social Markers of Mild Cognitive Impairment: Proportion of Word Counts in Free Conversational Speech
Volume: 12
Issue: 6
Author(s): Hiroko H. Dodge, Nora Mattek, Mattie Gregor, Molly Bowman, Adriana Seelye, Oscar Ybarra, Meysam Asgari and Jeffrey A. Kaye
Affiliation:
关键词:
生物学标志
摘要: Background: Detecting early signs of Alzheimer’s disease (AD) and mild cognitive impairment
(MCI) during the pre-symptomatic phase is becoming increasingly important for costeffective
clinical trials and also for deriving maximum benefit from currently available treatment
strategies. However, distinguishing early signs of MCI from normal cognitive aging is difficult. Biomarkers have been
extensively examined as early indicators of the pathological process for AD, but assessing these biomarkers is expensive
and challenging to apply widely among pre-symptomatic community dwelling older adults. Here we propose assessment
of social markers, which could provide an alternative or complementary and ecologically valid strategy for
identifying the pre-symptomatic phase leading to MCI and AD. Methods: The data came from a larger randomized
controlled clinical trial (RCT), where we examined whether daily conversational interactions using remote video telecommunications
software could improve cognitive functions of older adult participants. We assessed the proportion of
words generated by participants out of total words produced by both participants and staff interviewers using transcribed
conversations during the intervention trial as an indicator of how two people (participants and interviewers) interact
with each other in one-on-one conversations. We examined whether the proportion differed between those with
intact cognition and MCI, using first, generalized estimating equations with the proportion as outcome, and second, logistic
regression models with cognitive status as outcome in order to estimate the area under ROC curve (ROC AUC).
Results: Compared to those with normal cognitive function, MCI participants generated a greater proportion of words
out of the total number of words during the timed conversation sessions (p=0.01). This difference remained after controlling
for participant age, gender, interviewer and time of assessment (p=0.03). The logistic regression models
showed the ROC AUC of identifying MCI (vs. normals) was 0.71 (95% Confidence Interval: 0.54 – 0.89) when average
proportion of word counts spoken by subjects was included univariately into the model. Conclusion: An ecologically
valid social marker such as the proportion of spoken words produced during spontaneous conversations may be
sensitive to transitions from normal cognition to MCI.
