Research square · 2026
Limited incremental prognostic value of dyadic self- and informant-reported everyday cognition for incident MCI or Alzheimer's disease dementia in ADNI: an exploratory prognostic modelling study.
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Cheng Wan Kiu Winkey
Аннотация
UNLABELLED: Background Subjective cognitive decline (SCD) measures, including self- and informant-reported concern, are candidate prognostic markers for Alzheimer's disease (AD), but their incremental value over conventional biomarkers is uncertain. We evaluated, in an internally cross-validated ADNI sample, whether a dyadic mean and self-informant gap of baseline Everyday Cognition (ECog) total scores improve prediction of first recorded clinical diagnosis of MCI or AD dementia beyond the prespecified conventional predictors.
METHODS: We included 1,762 cognitively normal at-risk ADNI participants (events = 288: 261 MCI, 27 AD dementia). Elastic-net Cox models (M2 conventional-predictor, M3 conventional-predictor + dyadic mean, M4 conventional-predictor + mean + gap) were fit using nested cross-validation with fold-wise imputation and standardization.
RESULTS: M2 C-index 0.682, M3 0.692, M4 0.687. Adding the dyadic mean yielded an estimated ΔC of 0.010 (95% CI -0.003 to 0.024), which was attenuated to ΔC = 0.002 (95% CI -0.013 to 0.018) after including landmark-year protocol-era candidate variables. The gap did not improve discrimination beyond the dyadic mean. In an exploratory biomarker-naive gatekeeping comparison, adding dyadic ECog to a clinical baseline of age, sex, education, and MMSE raised the C-index from 0.635 to 0.678 (ΔC = 0.044, 95% CI 0.023 to 0.064), nearly matching the biomarker-rich model (ΔC = 0.004, 95% CI -0.026 to 0.033 for M2 versus the gatekeeping model).
CONCLUSIONS: In this internally validated ADNI cohort, a baseline dyadic ECog mean provided at most a small and statistically uncertain increment in prognostic discrimination beyond conventional predictors (ΔC = 0.010, 95% CI -0.003 to 0.024), which was largely attenuated after including protocol-era and report-availability candidate variables (ΔC = 0.002). The self-informant gap provided no additional discrimination. Dyadic ECog may be better positioned as a low-cost gatekeeper in biomarker-naive settings than as an add-on to biomarker-rich models; neither role is established without external validation.
METHODS: We included 1,762 cognitively normal at-risk ADNI participants (events = 288: 261 MCI, 27 AD dementia). Elastic-net Cox models (M2 conventional-predictor, M3 conventional-predictor + dyadic mean, M4 conventional-predictor + mean + gap) were fit using nested cross-validation with fold-wise imputation and standardization.
RESULTS: M2 C-index 0.682, M3 0.692, M4 0.687. Adding the dyadic mean yielded an estimated ΔC of 0.010 (95% CI -0.003 to 0.024), which was attenuated to ΔC = 0.002 (95% CI -0.013 to 0.018) after including landmark-year protocol-era candidate variables. The gap did not improve discrimination beyond the dyadic mean. In an exploratory biomarker-naive gatekeeping comparison, adding dyadic ECog to a clinical baseline of age, sex, education, and MMSE raised the C-index from 0.635 to 0.678 (ΔC = 0.044, 95% CI 0.023 to 0.064), nearly matching the biomarker-rich model (ΔC = 0.004, 95% CI -0.026 to 0.033 for M2 versus the gatekeeping model).
CONCLUSIONS: In this internally validated ADNI cohort, a baseline dyadic ECog mean provided at most a small and statistically uncertain increment in prognostic discrimination beyond conventional predictors (ΔC = 0.010, 95% CI -0.003 to 0.024), which was largely attenuated after including protocol-era and report-availability candidate variables (ΔC = 0.002). The self-informant gap provided no additional discrimination. Dyadic ECog may be better positioned as a low-cost gatekeeper in biomarker-naive settings than as an add-on to biomarker-rich models; neither role is established without external validation.