TY - JOUR
T1 - Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia
AU - van der Veere, Pieter J
AU - Hoogland, Jeroen
AU - Visser, Leonie N C
AU - Van Harten, Argonde C
AU - Rhodius-Meester, Hanneke F
AU - Sikkes, Sietske A M
AU - Venkatraghavan, Vikram
AU - Barkhof, Frederik
AU - Teunissen, Charlotte E
AU - van de Giessen, Elsmarieke
AU - Berkhof, Johannes
AU - Van Der Flier, Wiesje M
N1 - Publisher Copyright:
Copyright © 2024 The Author(s)
PY - 2024/8/13
Y1 - 2024/8/13
N2 - Background and Objectives Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. Methods From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE e4 dose, CSF β-amyloid (Aβ) 1–42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). Results In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1–4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1–42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1–42, age, APOE e4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1–42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4–6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. Discussion We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.
AB - Background and Objectives Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. Methods From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE e4 dose, CSF β-amyloid (Aβ) 1–42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). Results In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1–4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1–42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1–42, age, APOE e4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1–42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4–6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. Discussion We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.
KW - Aged
KW - Amyloid beta-Peptides/cerebrospinal fluid
KW - Biomarkers/cerebrospinal fluid
KW - Brain/diagnostic imaging
KW - Cognitive Dysfunction/cerebrospinal fluid
KW - Cohort Studies
KW - Dementia/diagnostic imaging
KW - Disease Progression
KW - Female
KW - Humans
KW - Magnetic Resonance Imaging
KW - Male
KW - Mental Status and Dementia Tests
KW - Middle Aged
KW - Peptide Fragments/cerebrospinal fluid
KW - Positron-Emission Tomography
KW - tau Proteins/cerebrospinal fluid
U2 - 10.1212/WNL.0000000000209605
DO - 10.1212/WNL.0000000000209605
M3 - Article
C2 - 38986053
SN - 0028-3878
VL - 103
JO - Neurology
JF - Neurology
IS - 3
M1 - e209605
ER -