1. Human Biology and Medicine
  2. Neuroscience
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Family history of Alzheimer’s disease alters cognition and is modified by medical and genetic factors

  1. Joshua S Talboom
  2. Asta Håberg
  3. Matthew D De Both
  4. Marcus A Naymik
  5. Isabelle Schrauwen
  6. Candace R Lewis
  7. Stacy F Bertinelli
  8. Callie Hammersland
  9. Mason A Fritz
  10. Amanda J Myers
  11. Meredith Hay
  12. Carol A Barnes
  13. Elizabeth Glisky
  14. Lee Ryan
  15. Matthew J Huentelman  Is a corresponding author
  1. The Translational Genomics Research Institute, United States
  2. Arizona Alzheimer’s Consortium, United States
  3. Norwegian University of Science and Technology, Norway
  4. University of Miami, United States
  5. University of Arizona, United States
Research Article
Cite this article as: eLife 2019;8:e46179 doi: 10.7554/eLife.46179
9 figures, 1 table, 1 data set and 4 additional files

Figures

Figure 1 with 2 supplements
Demographics of participants.

(A) World map displaying a red dot (i.e., one dot = one IP address) at the location of a participant completing the paired-associates learning (PAL) task. (B) Line plot showing the percent of males and females from 18 to 85 years old. (C) Line plot displaying the percent of participants with 12, 14, 16, or 20 years of education for each year of age from 18 to 85 years old. (D) Line plot showing the percent of participants reporting a first-degree family history of Alzheimer’s disease (FH) for each year of age from 18 to 85 years old.

https://doi.org/10.7554/eLife.46179.002
Figure 1—figure supplement 1
Regression diagnostic plots of the general linear model (GLM) including all participants (N=59,571).

(A) A plot of the residuals versus fitted values. This plot suggests that the assumptions of linearity, equality of variances, and no outliers are met. (B) A plot of quantiles of the data versus quantiles of a normal distribution. This plot suggests that there is a violation from normality in residuals and thus the error terms; however, this violation should not cause major problems because of the large number of participants in this study. (C) A plot of the square root of the standardized residuals versus the fitted values. Like plot A., this plot suggests that the variability of the residuals does not change much over the range of the dependent variable. (D) A plot of the residuals versus leverage. This plot suggests that there are no influential cases as all case fall within Cook’s distance (i.e., red dashed line not visible on the plot).

https://doi.org/10.7554/eLife.46179.003
Figure 1—figure supplement 2
Simulated additional self-report error and the impact on the significance of the FH effect in MindCrowd.

The effect of error in self-reported FH-AD status was simulated by adding additional error to the MindCrowd cohort by re-assigning individual FH responses between 1-30%. and re-running the full statistical model. This was repeated 10,000 times for each error rate. Boxplots representing the distribution of p-values for the re-analysis under the new error model are illustrated. This demonstrates that even with 8% additional error added to the self-report FH question, we would still have identified a significant association with FH in all 10,000 cases. Even with 24% additional FH self-report error we would have still reported a significant association between FH and PAL over 50% of the time (better than by chance alone). Therefore, this simulation suggests that even with significant levels of additional error in FH self-report the association between FH and PAL performance would still have been noted by our study.

https://doi.org/10.7554/eLife.46179.004
Females demonstrate enhanced paired-associates learning (PAL) performance across the aging spectrum, and this is further enlarged beginning in the 5th decade of life.

Linear regression fit (line fill ±95% confidence interval [CI], error bars ± standard error of the mean [SEM]) of the PAL total number of correct from 18 to 85 years old. Lines were split by Sex. Women performed better than men with an amplified disparity from 50 to 70 years old (BSex = −1.82, pSex <2e-16, women n = 40572, and men n = 24381).

https://doi.org/10.7554/eLife.46179.005
Educational attainment is associated with PAL performance in a milestone-related dose response that is different between the sexes.

Linear regression fits (line fill ±95% CI, error bars ± SEM) of the PAL total number of correct from 18 to 85 years old. Lines were split by Educational Attainment level, and the figure was faceted by Sex. For women and men, there were heightened PAL scores per level of Educational Attainment (BEducation = 0.31, pEducation <2e-16, 6 years n = 282, 8 years n = 181, 10 years n = 1177, 12 years n = 5367, 14 years n = 19256, 16 years n = 22942, and 20 years n = 15752).

https://doi.org/10.7554/eLife.46179.006
A first-degree relative FH of AD is associated with lower PAL performance at ages under 65.

(A) Linear regression fits (line fill ±95% CI, error bars ± SEM) of the PAL total number of correct from 18 to 85 years old. FH led to lower PAL performance before 65 years of age (BFH = −2.39, pFH = 3.47e-06, FH +n = 14739, and FH- n = 50011). (B) Linear regression fits of the PAL total number of correct from 18 to 85 years old. Lines split by Sex and FH status. FH led to lower PAL performance, an effect that was exacerbated in men (BSex*FH = −0.79, pSex*FH = 7.97e-06, FH +Women n=11119, FH- Women n = 29332, FH +Men n=3617, and FH- Men n = 20678). The inset figure displays PSM box and whisker plots, split by sex, across each decade of life. The black bar through each box and whisker plot represents the median ATT for FH. Men had worse PAL scores when compared to women at each decade of life except for the sixth (60 s: Women[ATT-0.50 ± SD0.18], Men[ATT0.09 ± SD−0.36], women n = 40572, and men n = 24381).

https://doi.org/10.7554/eLife.46179.007
Diabetes modified the FH effect on PAL performance.

Linear regression fits (line fill ±95% CI, error bars ± SEM) of the PAL total number of correct in FH- Diabetes+, FH +Diabetes-, and FH +Diabetes + participants from 18 to 65 years old. Regardless of sex, diabetes in FH +participants led to lower PAL scores (BFH*Diabetes = −0.71, pFH*Diabetes = 0.04, AD- DI- n = 47970, AD- DI +n = 2041, AD +DI n=13841, and AD +DI + n=898).

https://doi.org/10.7554/eLife.46179.008
Apolipoprotein E (APOE) ε4 alleles negatively influence PAL performance in the presence of FH.

(A) Linear regression fits (line fill ±95% CI, error bars ± SEM) of the PAL total number of correct from 18 to 65 years old. Lines were split by the Number of APOE ε4 Alleles. For women and men, there was a dose-dependent-like decrease in the PAL scores per each copy of the ε4 allele (Bε4Allele = −1.30, pε4Allele = 0.03, ε2/ε2 n = 2, ε2/ε3 n = 31, ε2/ε4 n = 46, ε3/ε3 n = 174, ε3/ε4 n = 382, and ε4/ε4 n = 35).

https://doi.org/10.7554/eLife.46179.009
Author response image 1
Author response image 2
Graphs exploring the influence of additional self-report error on the FH-AD effect.

(A) Simulated additional self-report error and the impact on the significance of the FH effect in MindCrowd. The effect of error in self-reported FH-AD status was simulated by adding additional error to the MindCrowd cohort by re-assigning individual FH responses between 1-30%. and re-running the full statistical model. This was repeated 10,000 times for each error rate. Boxplots representing the distribution of p-values for the re-analysis under the new error model are illustrated. This demonstrates that even with 8% additional error added to the self-report FH question, we would still have identified a significant association with FH in all 10,000 cases. Even with 24% additional FH self-report error we would have still reported a significant association between FH and PAL over 50% of the time (better than by chance alone). Therefore, this suggests that even with significant levels of additional error in FH self-report the association between FH and PAL performance would still have been noted by our study. (B) Permutation testing of the FH effect on PAL.The p-value describing the FH effect on PAL in MindCrowd was permuted to determine the probability of a false positive under the null hypothesis. The vertical dashed red line indicates our reported t-statistic from the manuscript and the histogram of permuted t-statistics are indicated in black bars. After one million permutations of the FH data label, not a single t-statistic arising from a permutation of the data were observed to be more extreme than our reported t-statistic (permuted p-value = <1e-6). This suggests that the odds of our finding being observed due to chance is less than one in one million.

Author response image 3

Tables

Key resources table
Reagent type (species)
or resource
DesignationSource or referenceIdentifiersAdditional
information
Gene (Apolipoprotein E)APOEPMCID: PMC6106945HGNC:HGNC:613
Sequence-based reagentAPOE PCR PrimersIntegrated DNA Technologies, Inc (IDT)F 5'-ACA-GAA-TTG-GCC-CCG-GCC-TGG-TAC-3', R 5'-TAA-GCT-TGG-CAC-GGC-TGT-CCA-AGG-A-3'0.5 μL of each 50 μM Primer
Chemical compound, drugFailSafe PCR Enzyme and 2X PreMix BuffersLucigenFSP995J
Chemical compound, drugHHA1NEBR0139S0.5 μL/20 μL of PCR product
OtherWhatman 903 Protein Saver CardVWR05-715-121
Chemical compound, drugUltrapure AgaroseThermo165005004%
Chemical compound, drugGelStar Gel StainLonza505357 μL/300 mL of gel mix
Chemical compound, drugUltra-Low Range DNA LadderInvitrogen10597012
Chemical compound, drugAmplitaq Gold Fast Master MixThermo4390941
Chemical compound, drugOragene Saliva KitDNAGenotekOGR-500
Chemical compound, drugTris-Acetate-EDTA (TAE) 50 X (20L)FisherBP1332-20
Software, algorithmRThe R FoundationVersion 3.5.1 RRID:SCR_001905
Software, algorithmR package, ggplot2Comprehensive R Archive Network (CRAN)Version 3.1.1 RRID:SCR_014601
Software, algorithmR package, emmeansComprehensive R Archive Network (CRAN)Version 1.3.0
Software, algorithmR package, ZeligComprehensive R Archive Network (CRAN)Version 5.1.6
Software, algorithmR package, MatchItComprehensive R Archive Network (CRAN)Version 3.0.2

Data availability

The data that support the findings of this study are freely available at Dryad (https://datadryad.org) doi:10.5061/dryad.2867k2m).

The following data sets were generated
  1. 1
    Dryad Digital Repository
    1. J Talboom
    2. A Håberg
    3. Both M De
    4. M Naymik
    5. I Schrauwen
    6. C Lewis
    7. S Bertinelli
    8. C Hammersland
    9. M Fritz
    10. A Myers
    11. M Hay
    12. C Barnes
    13. E Glisky
    14. L Ryan
    15. M Huentelman
    (2019)
    Data from: Family history of Alzheimer's disease alters cognition and is modified by medical and genetic factors.
    https://doi.org/10.5061/dryad.j1fd7

Additional files

Source code 1

R-based statistical analysis and plotting script.

https://doi.org/10.7554/eLife.46179.010
Supplementary file 1

Sample sizes.

Table displaying the sample size (n) for each evaluated demographic, health, and lifestyle factor.

https://doi.org/10.7554/eLife.46179.011
Supplementary file 2

Questions each participant was asked and how it was coded in R.

Table displaying each question that a participant was asked on the MindCrowd website, whether the question was asked before or after the PAL test, the question number, and how each question was coded for analysis via R (version 3.5.1).

https://doi.org/10.7554/eLife.46179.012
Transparent reporting form
https://doi.org/10.7554/eLife.46179.013

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