EXTENDED MATERIALS AND METHODS

Participants

Participants in this study were recruited from WRAP (Sager et al., 2005) to participate in one or

more amyloid imaging sub-studies. The WRAP study consists of over 1,500 participants (mean

age=53.6 years, SD=6.6, at first WRAP cognitive assessment) and is designed to identify

biological and lifestyle risk factors associated with development of subsequent AD in a cohort

enriched for AD-risk factors [1-3]. The methods for determining parental family history (FH) have

been described previously [3, 4]. WRAP study participants come in for follow-up testing

approximately four years post their initial neuropsychological testing and every two years

thereafter. WRAP participants also may be recruited at WRAP study visits or by mail to

participate in linked studies such as the one described here in order to gather additional

biomarkers such as neuroimaging and CSF.

For this analysis, subjects were included if they had one or more amyloid and MRI scans, and

CSF collected at the time of the first amyloid scan. Additional inclusion criteria included being

between 58-75 years at their first WRAP visit, scores within the normal range at baseline

neuropsychological assessment, a Clinical Dementia Rating score of 0 or 0.5, and good general

health. Exclusion criteria included significant neurologic disease; psychiatric disorders; MRI

evidence of infection, infarction, or other focal lesions; and untreated hypertension. These

criteria resulted in a sample of 104 participants (n=69 female). Seventy-eight participants (n=53

female) had both the baseline PiB scan and a second PiB scan occurring on average two years

after the baseline scan. The first visit at which amyloid scans, Magnetic Resonance Imaging

(MRI) scans, and CSF were obtained is referred to as baseline or visit 1; the second imaging

visit is referred to as follow-up or visit 2.

MRI acquisition and processing

All participants were scanned on a GE 3.0 Tesla MR750 (Waukesha, WI) using an 8-channel

head coil. T1-weighted, T2-weighted and FLAIR anatomical scans were acquired as described

previously [5, 6]. T2-weighted and FLAIR anatomical scans were reviewed by a neuroradiologist

(H.A.R.) for exclusionary abnormalities. The T1-weighted volume was segmented into tissue

classes using the segmentation tool in SPM12 (www.fil.ion.ucl.ac.uk/spm ) .

Amyloid imaging, processing, and quantification

N=104 subjects underwent [C-11]PiB PET scanning at baseline and N=78 additionally

underwent a second PiB scan approximately two years later. Detailed methods for [C-11] PiB

radiochemical synthesis, PiB PET scanning, and distribution volume ratio (DVR) map

generation have been described previously [5] using the cerebellum as a reference region of

negligible binding. PiB DVR maps were used as the amyloid dependent variable for all voxel-

wise analyses.

A composite measurement of global amyloid derived from eight bilateral ROIs (angular gyrus,

anterior cingulate gyrus, posterior cingulate gyrus, frontal medial orbital gyrus, precuneus,

supramarginal gyrus, middle temporal gyrus, and superior temporal gyrus) was calculated for

visit 1 and visit 2 as described previously [7], and is henceforth referred to as PiB burden.

Lumbar puncture and CSF quantification

CSF was collected via lumbar puncture (LP) in the morning after a 12-hour fast using a Sprotte

25- or 24-gauge spinal needle at L3/4 or L4/5 using gentle extraction into polypropylene

syringes. CSF (22 mL) was then combined, gently mixed, and centrifuged at 2000g for 10

minutes. Supernatants were frozen in 0.5 mL aliquots in polypropylene tubes and stored at

−80°C. CSF collection and processing methods were identical across studies.

CSF Aβ42, t-tau, and p-tau181 were quantified with sandwich ELISAs (INNOTEST β-amyloid1-42,

hTAU-Ag and Phospho-Tau[181P], respectively; Fujirebio Europe, Ghent, Belgium). For the

Aβ42/Aβ40 ratio, CSF levels of Aβ42 and Aβ40 (a less amyloidogenic Aβ fragment as compared to

Aβ42) were quantified by electrochemiluminescence (ECL) using an Aβ triplex assay (MSD

Human Aβ peptide Ultra-Sensitive Kit, Meso Scale Discovery, Gaithersburg, MD). MCP-1 levels

in CSF were measured using the Meso Scale Discovery technique (MSD Human MCP-1; Meso

Scale Discovery, Gaithersburg, MD, USA). CSF levels of YKL-40 were determined using a

sandwich enzyme-linked immunosorbent assay (ELISA) (R&D Systems, Minneapolis, Minn.,

USA). CSF NFL was measured with a sandwich ELISA method as described by the

manufacturer (NF-light ELISA kit, UmanDiagnostics AB, Umeå, Sweden). All measurements

were performed in one round of analyses using one batch of reagents by board-certified

laboratory technicians who were blind to the clinical characteristics of participants. Intra-assay

coefficients of variation were below 10%.

Two subjects had missing data for p-tau, one of which also had missing data for t-tau.

Additionally, CSF measures were inspected for outliers by visual inspection and outliers were

removed if they were greater than three standard deviations from the mean. As a result, the

number of subjects for each model varied from 99-104 for baseline analyses and 74-78 for

longitudinal analyses (Table 1).

CSF measures were selected based on their ability to detect known pathology in AD including

amyloid, neurofibrillary tangles, neuronal damage, and inflammation. Aβ42 [8-13], p-tau [9, 14,

15], and t-tau [8, 16-18] have been widely studied in various stages of AD. Decreased CSF Aβ42

and increased CSF p-tau and t-tau are thought to reflect accumulation of amyloid into plaques,

neurofibrillary tangle pathology, and neuronal damage, respectively. More novel CSF markers

assessed in this study included NFL, MCP-1, and YKL-40. NFL is a subunit of neurofilament

protein which is important for structure and transport [9]; therefore, like t-tau, elevated NFL is

considered a marker of axonal degeneration [9, 19-21]. YKL-40 [22-24] and MCP-1[25-28] are

cytokines implicated in AD and elevated levels are considered markers of inflammation and

microglial activation [27, 29, 30].

Many studies have also demonstrated the benefits of examining CSF ratios with Aβ42 rather

than each measure in isolation [9, 17, 22, 31-40]. We examined the most commonly described

ratios of p-tau/Aβ42, t-tau/Aβ42, and Aβ42/Aβ40 as well as ratios of NFL, MCP-1, and YKL-40 to

Aβ42. Only the CSF Aβ levels for the ratio of Aβ42/Aβ40 were derived from the triplex MSD assay;

all other measures of Aβ (Aβ42 alone and as a ratio of CSF/Aβ42) were derived from the

INNOTEST assay. Because it is expected that axonal injury and inflammation increase and that

Aβ42 in the CSF declines with disease progression, like tau/Aβ42, a larger ratio is indicative of

greater Alzheimer’s pathology. In order to ensure that these ratios truly reflect cumulative

pathology, we additionally examined the inverse of Aβ42 (1/Aβ42) to mimic the effect of CSF Aβ42

in the denominator without a marker of pathology in the numerator.

Cognitive data measures and collection

At each WRAP visit, participants completed a comprehensive neuropsychological battery. In

order to investigate the relationship between CSF biomarkers and episodic memory

performance, we selected the delayed memory scores from the Rey Auditory Verbal Learning

Test (RAVLT) and the Wechsler Memory Scale-Revised (WMS-R) [21-24]. For RAVLT, which

was initiated at the first wave of WRAP neuropsychology data collection, n=1 had two time

points, n=25 had three time points, n=52 had four time points, and n=25 had five time points.

For WMS-R, which was initiated at the second wave, n=25 had two time points, n=52 had three

time points, and n=25 had four time points.

Statistical analyses

Separate models were run for each CSF measure unless otherwise stated. Covariates always

include age at LP, sex, FH, and APOE4.

For models run in Statistical Package for the Social Sciences (SPSS) 22, significance is inferred

at p<.05, adjusted for multiple comparisons with False Discovery Rate (FDR) correction [41].

Regressions were hierarchical such that the model’s first step includes only covariates and the

second step adds the individual CSF measure. R2 change is calculated as the difference

between the R2 of the first and second steps of the model. Effect sizes were calculated using

Cohen’s f2 for hierarchical multiple regression; effect sizes of 0.02, 0.15, and 0.35 are

interpreted as small, medium, and large respectively. Tolerance and variance inflation factors

were inspected for all models and are assumed to be normal if tolerance is greater than .1 and

the variance inflation factor is less than 10 [42, 43]. Results are visualized using partial

regression plots, which are scatter plots of residuals from regressing PiB burden and the CSF

variable on all other predictors.

It's possible that cerebrovascular-related brain lesions indicated by white matter hyperintensities

on T2-weighted MRI could affect results. A histogram of total lesion volume (TLV) as measured

using the SPSS Lesion Segmentation Tool [44] revealed one outlier. This outlier was not

exceptional in regard to other relevant variables. To further rule out a cerebrovascular

component, baseline and longitudinal regression analyses in SPSS were performed first, with

the entire sample, second, excluding this outlier and third, controlling for TLV in the model. As

results did not differ with the exception of minor fluctuations in p-values, all following models and

results reflect analyses with the full sample without TLV as a covariate.

Because sample sizes varied across analyses, we additionally performed baseline and

longitudinal regression analyses in SPSS in the exact same sample to rule out the possibility of

sampling issues. This resulted in N=93 subjects with no missing data for any biomarkers for

baseline analyses and N=70 for longitudinal analyses. Comparison of results with the maximal

and stable sample sizes is presented in Supplementary Table 1. While there were a few

fluctuations, they did not change our main conclusions. Given that each analysis is treated

separately (and interpretation of results includes correction for multiple comparisons), that larger

sample sizes provide more reliable results with greater precision and power, and that results did

not change substantially with the smaller sample size, results are presented from the maximal

sample sizes in the main manuscript.

Concurrent CSF measures and amyloid

PiB burden

PiB burden at visit 1 was entered as the dependent variable in a multiple regression model in

SPSS 22, and individual CSF measures were entered as the independent variable of interest,

along with covariates.

To minimize effects of multicollinearity among CSF measures, a separate model was run for

each CSF analyte or ratio, adjusting for covariates. However, AD is a multifaceted disease and

ratios are only able to capture two pathological features. Therefore, we also examined a

comprehensive regression model with all CSF biomarkers with the exception of p-tau because it

was highly correlated with t-tau (spearman’s rho = .881). Using baseline PiB burden as the

outcome, we performed hierarchical regression with z-scores of CSF t-tau, MCP-1, YKL-40,

NFL, and Aβ42 in addition to the standard covariates to determine the additional predictive power

of each biomarker to the overall model. For each of these CSF measures, a model would

include all covariates and four of the five CSF variables as the first step and then the fifth CSF

measures would be added as the second step. Standardized rather than unstandardized beta-

coefficients are reported for easier comparison of contributions of each variable to the model.

Regional β-amyloid

A multiple regression framework in Statistical Parametric Mapping (SPM) 12

(http://www.fil.ion.ucl.ac.uk/spm/) was used to assess relationships between CSF markers

associated with AD pathology and amyloid in the brain. Baseline PiB DVR images were entered

as the dependent variable and individual CSF measures were input as the independent variable

of interest in addition to the standard covariates. Analyses were restricted to the cerebral gray

matter using a template-based mask. Significance was inferred at the voxel peak-level when

α<0.05 with multiple comparisons by family wise error (FWE) correction and a cluster extent

>100 voxels.

Baseline CSF measures and longitudinal amyloid

While there are many ways to approach longitudinal data analysis with two time points, we

chose a standard approach of regressing a follow-up variable on the baseline variable and

covariates [45, 46]. This method statistically controls for variance in the baseline state, enabling

the interpretation of variables affecting follow-up state independent of baseline.

PiB burden

PiB burden at visit 2 was entered as the dependent variable in a multiple regression model in

SPSS 22. In addition to the standard set of covariates, we also controlled for PiB burden at visit

1 and then looked at the effect of the CSF analyte. By controlling for amyloid load at visit 1 and

looking at amyloid load at visit 2 as the outcome, the coefficient on an individual CSF measure

is interpreted as the effect of that CSF measure on longitudinal amyloid load, controlling for

baseline amyloid load. Amyloid scans occurred about 2 years apart (mean=25.47 months;

SD=2.37 months; range=21-33 months); because the study was designed to keep this interval

uniform, and variation was relatively symmetrical, it was not included as a covariate in the

regression models.

We repeated the hierarchical regression model, which included the five CSF measures (t-tau,

MCP-1, YKL-40, NFL, and Aβ42) simultaneously, for longitudinal PiB burden. Each model

included the standard covariates as well as baseline PiB burden, and four of the five CSF

variables as the first step; then the fifth CSF measure was added as the second step.

Standardized rather than unstandardized beta-coefficients are reported for easier comparison of

contributions of each variable to the model.

Regional β-amyloid

To gain spatial resolution on findings from the above analyses, regressions using Biological

Parametric Mapping (BPM), an SPM5 toolbox for multimodal image analysis based on a voxel-

wise use of the general linear model [47], were performed on CSF measures that were

associated with longitudinal PiB burden. The PiB DVR scan from the second visit was the

dependent variable and the PiB DVR scan from the first visit was used as an imaging covariate

in addition to five non-imaging covariates: age, sex, APOE4, FH, and CSF biomarker level.

Because visit 1 amyloid burden was such a strong predictor of visit 2 amyloid burden, we chose

a more moderate threshold for the resultant longitudinal statistical maps of α<0.001

(uncorrected) together with a cluster extent >250 voxels; however, our primary inference was

still at the peak voxel where significance was again evaluated at α(FWE)<0.05.

CSF measures and cognitive decline

Linear mixed effects regression was used to model the effect of the CSF biomarkers that

predicted longitudinal Aβ accumulation on longitudinal cognitive decline, measured by tests of

delayed recall. First, unconditional means models adjusting for random effects were examined

using unstructured covariance structure. Next, conditional models were run which included

significant random effects plus fixed effects of sex, APOE4, FH, interval between first cognitive

evaluation and lumbar puncture (months), literacy (Time 1 WRAT III Reading scores), CSF

biomarker level, time (age at each visit), and the interaction of time x CSF measure (slope).

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TABLE LEGENDS

Supplementary Table 1. Comparison of results with the maximal sample size for each CSF

measure and with the exact same sample size, which includes only participants with no missing

CSF data. Std. Beta = standardized beta coefficient. Aβ=beta-amyloid. P-tau=phosphorylated-

tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte chemoattractant protein

1. YKL-40=chitinase-3-like protein. Changes in significant findings (from p<.05 to p>.05 or vise

versa) between models with the two sample sizes are bolded and italicized.

Supplementary Table 2. Baseline SPSS regression results. CSF=cerebrospinal fluid.

FWE=family wise error correction. BPM=Biological Parametric Mapping. CI=confidence interval.

*R2 change is calculated based on adding each CSF variable to the base model with only

covariates. †Significant at p(FDR)<.05; ‡Significant at p<.05. Cohen’s f2 measures effect size for

hierarchical multiple regression; §Small effect size (0.02 to 0.15); ¶Medium effect size (0.15 to

0.35); #Large effect size (>0.35). Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau.

NFL=neurofilament light protein. MCP-1=monocyte chemoattractant protein 1. YKL-

40=chitinase-3-like protein.

Supplementary Table 3. Descriptions of significant peaks for CSF and baseline PiB analyses.

CSF=cerebrospinal fluid. MNI= Montreal Neurological Institute standard brain. FWE=family wise

error correction. PiB=Pittsburgh compound B amyloid imaging. Aβ=beta-amyloid. P-

tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte

chemoattractant protein 1. YKL-40=chitinase-3-like protein.

Supplementary Table 4. Longitudinal SPSS regression results. CSF=cerebrospinal fluid.

FWE=family wise error correction. BPM=Biological Parametric Mapping. CI=confidence interval.

*R2 change is calculated based on adding each CSF variable to the base model with only

covariates. †Significant at p(FDR)<.05; ‡Significant at p<.05. Cohen’s f2 measures effect size for

hierarchical multiple regression; §Small effect size (0.02 to 0.15). Aβ=beta-amyloid. P-

tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte

chemoattractant protein 1. YKL-40=chitinase-3-like protein.

Supplementary Table 5. Descriptions of significant peaks for longitudinal voxel-wise (BPM)

analyses. BPM=Biological Parametric Mapping. CSF=cerebrospinal fluid. MNI= Montreal

Neurological Institute standard brain. FWE=family wise error correction. PiB=Pittsburgh

compound B amyloid imaging. Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau.

NFL=neurofilament light protein. MCP-1=monocyte chemoattractant protein 1.

Supplementary Table 6. Affect of CSF ratios beyond 1/Aβ42. Aβ=beta-amyloid. P-

tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte

chemoattractant protein 1. *Significant R2 change at p<.05.

Supplementary Table 1. Comparison of SPSS regression results with maximal vs. stable

sample sizes

CSF

biomarker

Baseline PiB Longitudinal PiB

N=99-104 N=93 N=74-78 N=70

Std.

Beta

p-

value

Std.

Beta

p-

value

Std.

Beta

p-

value

Std.

Beta

p-

value

Aβ42 -.444 .000 -.425 .000 -.068 .147 -.115 .019

Aβ42 /Aβ40 -.607 .000 -.580 .000 -.126 .017 -.173 .001

T-tau .263 .005 .159 .110 .034 .402 .030 .508

P-tau .193 .040 .122 .226 .012 .773 .010 .823

NFL .116 .255 -.050 .656 .025 .561 -.013 .799

MCP-1 -.019 .842 -.040 .696 -.027 .501 .016 .727

YKL-40 -.096 .311 -.153 .128 -.048 .237 -.053 .248

1/Aβ42 .521 .000 .567 .000 .085 .078 .143 .007

T-tau/Aβ42 .594 .000 .591 .000 .145 .004 .159 .003

P-tau/Aβ42 .569 .000 .582 .000 .138 .006 .140 .010

NFL/Aβ42 .574 .000 .532 .000 .137 .011 .100 .079

MCP-1/Aβ42 .397 .000 .412 .000 .097 .033 .111 .026

YKL-40/Aβ42 .385 .000 .347 .001 .035 .467 .042 .404

Comparison of results with the maximal sample size for each CSF measure and with the exact

same sample size, which includes only participants with no missing CSF data. Std. Beta =

standardized beta coefficient. Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau.

NFL=neurofilament light protein. MCP-1=monocyte chemoattractant protein 1. YKL-

40=chitinase-3-like protein. Changes in significant findings (from p<.05 to p>.05 or vise versa)

between models with the two sample sizes are bolded and italicized.

Supplementary Table 2. Baseline SPSS regression results

CSF variable N

Unstandardized

Beta

Coefficient

T Sig. 95% C.I. R2 R2 change* Cohen’s f2

Aβ42 103 -0.000333 -4.997 .000† -.000466; -.000201 .333 .172 0.258¶

Aβ42 /Aβ40 104 -5.046 -7.603 .000† -6.363; -3.729 .470 .313 0.591#

T-tau 103 0.000359 2.907 .005† 0.000114; 0.000605 .224 .068 0.088§

P-tau 102 .002 2.083 .040‡ 0.000102; 0.004228 .204 .036 0.045§

NFL 102 0.000097 1.144 .255 -0.000071;

0.000265

.170 .011 0.013

MCP-1 104 -0.000022 -.200 .842 -0.000241;

0.000197

.158 0.000345 0.001

YKL-40 102 -3.6079E-7 -1.019 .311 -0.000001; 3.417E-7 .137 .009 0.011

1/Aβ42 101 180.946 6.136 .000† 122.401; 239.490 .401 .238 0.397#

T-tau/Aβ42 100 .440 7.447 .000† .323; .557 .466 .315 0.588#

P-tau/Aβ42 99 3.353 .482 .000† 2.397; 4.310 .458 .282 0.520#

NFL/Aβ42 99 .256 5.630 .000† .166; .347 .378 .212 0.341¶

MCP-1/Aβ42 101 .186 4.234 .000† .099; .274 .304 .131 0.190¶

YKL-40/Aβ42 99 .001 3.886 .000† 0.000461; 0.001424 .287 .116 0.163¶

CSF=cerebrospinal fluid. FWE=family wise error correction. BPM=Biological Parametric

Mapping. CI=confidence interval. *R2 change is calculated based on adding each CSF variable

to the base model with only covariates. †Significant at p(FDR)<.05; ‡Significant at p<.05.

Cohen’s f2 measures effect size for hierarchical multiple regression; §Small effect size (0.02 to

0.15); ¶Medium effect size (0.15 to 0.35); #Large effect size (>0.35). Aβ=beta-amyloid. P-

tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte

chemoattractant protein 1. YKL-40=chitinase-3-like protein.

Supplementary Table 3. Descriptions of significant peaks for CSF and baseline PiB analyses

CSF measure Anatomical RegionMNI

coordinate

# of voxels in

clusterT-statistic p-value (FWE)

Aβ42 Right precuneus 4 -74 42 11651 6.37 0.000

Right supramarginal gyrus 36 -48 52 1004 5.09 0.005

Left middle temporal gyrus -54 -18 -6 190 4.90 0.009

Left insular cortex -44 4 6 121 4.87 0.011

Left supramarginal gyrus -52 -48 36 145 4.62 0.025

Aβ42/Aβ40 Left middle frontal gyrus 42 34 22 71994 8.69 0.000

Left fusiform gyrus -22 -44 -12 198 5.44 0.001

Right fusiform gyrus 30 -42 -18 126 5.01 0.007

1/Aβ42 Right precuneus 4 -64 52 33320 7.47 0.000

Left inferior frontal gyrus -36 240-6 994 5.55 0.000

T-tau/Aβ42 Left supramarginal gyrus -58 -42 26 59106 9.13 0.000

P-tau/Aβ42 Left supramarginal gyrus -58 -42 26 41424 6.98 0.000

Left middle cingulate gyrus -2 -26 44 7669 6.49 0.000

NFL/Aβ42 Right middle temporal gyrus 66 -48 10 5791 6.83 0.000

Left middle temporal gyrus -58 -50 6 7404 5.96 0.000

Left middle cingulate gyrus -4 -22 44 9495 5.68 0.000

Right inferior frontal gyrus 38 22 -16 187 5.15 0.001

Left inferior parietal lobule -40 -46 52 111 4.85 0.004

Left middle frontal gyrus -30 38 -14 194 4.70 0.008

Superior frontal gyrus 24 56 10 341 4.58 0.012

MCP1/Aβ42 Left anterior cingulate gyrus -2 26 26 2482 6.00 0.000

Right precuneus 4 -66 52 1804 5.52 0.001

Right putamen 18 6 -12 298 5.13 0.004

Right superior parietal lobule 34 -50 52 112 5.08 0.005

YKL40/Aβ42 Left precuneus -16 -78 40 1264 5.27 0.003

Left superior frontal gyrus -22 42 40 114 5.15 0.004

Left middle frontal gyrus -30 38 -16 149 5.13 0.005

CSF=cerebrospinal fluid. MNI= Montreal Neurological Institute standard brain. FWE=family wise

error correction. PiB=Pittsburgh compound Bamyloid imaging. Aβ=beta-amyloid. P-

tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein. MCP-1=monocyte

chemoattractant protein 1. YKL-40=chitinase-3-like protein.

Supplementary Table 4. Longitudinal SPSS regression results

CSF variable N

Unstandardized

Beta

Coefficient

T Sig. 95% C.I. R2 R2 change* Cohen’s f2

Aβ42 78 -0.000057 -1.467 .147 -0.000134; 0.000020 .898 .003 0.029§

Aβ42/Aβ40 78 -1.335 -2.454 .017‡ -2.220; -.230 .903 .008 0.082§

T-tau 77 0.000053 .844 .402 -0.000072; 0.000177 .899 .001 0.010

P-tau 76 0.000161 .290 .773 -.001; .001 .898 0.00012 0.000

NFL 77 0.000024 .585 .561 -0.000057; 0.000105 .896 .001 0.010

MCP-1 78 -0.000035 -.677 .501 -0.000138; 0.000068 .895 .001 0.000

YKL-40 76 -1.9371E-7 -1.192 .237 -5.1788E-7; 1.3047E-

7

.897 .002 0.019

1/Aβ42 76 33.959 1.792 .078 -3.849; 71.768 .897 .005 0.039§

T-tau/Aβ42 75 .121 2.947 .004† .039; .203 .898 .013 0.127§

P-tau/Aβ42 75 1.042 2.865 .006† .316; 1.767 .909 .011 0.121§

NFL/Aβ42 76 .072 2.607 .011† .017; .127 .905 .009 0.105§

MCP-1/Aβ42 76 .051 2.179 .033‡ .004; .099 .905 .007 0.074§

YKL-40/Aβ42 74 0.000103 .732 .467 -0.000177; 0.000383 .893 .001 0.009

CSF=cerebrospinal fluid. FWE=family wise error correction. BPM=Biological Parametric

Mapping. CI=confidence interval. *R2 change is calculated based on adding each CSF variable

to the base model with only covariates. †Significant at p(FDR)<.05; ‡Significant at p<.05.

Cohen’s f2 measures effect size for hierarchical multiple regression; §Small effect size (0.02 to

0.15). Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light

protein. MCP-1=monocyte chemoattractant protein 1. YKL-40=chitinase-3-like protein.

Supplementary Table 5. Descriptions of significant peaks for longitudinal voxel-wise (BPM)

analyses

CSF measure

(contrast)Anatomical Region

MNI

coordinates

# of voxels

in clusterT-statistic p-value (FWE)

Aβ42/Aβ40 Left supramarginal gyrus -50 -52 22 3576 6.87 0.000

Left middle frontal gyrus -20 42 30 3526 4.61 0.049

Ttau/Aβ42 Left superior frontal gyrus -20 44 28 1327 4.98 0.010

Left angular gyrus -42 -58 24 618 4.90 0.014

Ptau/Aβ42 Left superior frontal gyrus -18 44 30 5496 5.24 0.024

Right precuneus 2 -80 34 1718 5.14 0.033

Right superior temporal gyrus 50 -18 2 1768 4.84 0.084

NFL/Aβ42 Precentral gyrus -48 -6 14 513 5.15 0.031

MCP1/Aβ42 Left insula -44 -10 18 2150 5.99 0.002

Right precuneus 6 -56 30 2654 5.12 0.035

BPM=Biological Parametric Mapping. CSF=cerebrospinal fluid. MNI= Montreal Neurological

Institute standard brain. FWE=family wise error correction. PiB=Pittsburgh compound B amyloid

imaging. Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light

protein. MCP-1=monocyte chemoattractant protein 1.

Supplementary Table 6. Affect of CSF ratios beyond 1/Aβ42

CSF model R2 change 1/Aβ42 CSF/Aβ42

Step 1

to step 2

Step 2

to step 3

Standardized

β

p-

value

Standardized

β

p-value

t-tau/Aβ42 .011* .008* .168 .001 .104 .021

p-tau/Aβ42 .010* .005* .156 .002 .084 .049

NFL/Aβ42 .005 .005 .119 .022 .091 .059

MCP-1/Aβ42 .009* .000 .117 .015 .006 .870

Aβ=beta-amyloid. P-tau=phosphorylated-tau. T-tau=total tau. NFL=neurofilament light protein.

MCP-1=monocyte chemoattractant protein 1.

*Significant R2 change at p<.05

SUPPLEMENTARY ANALYSIS

Statistical analysis

To further probe the added value of the different ratios, we performed a supplementary analysis

to examine the effect of the CSF/Aβ42 ratios above and beyond 1/Aβ42. We ran simple

regressions with CSF/Aβ42 as the independent variable and 1/Aβ42 as the only predictor, and

saved the unstandardized residual. This residual, therefore, is interpreted as the effect of

CSF/Aβ42 beyond what is explained by 1/Aβ42. Next, we ran hierarchical regressions with the

same base model (step 1) as described in the statistical methods for longitudinal PiB burden.

The second step of the regression was to add 1/Aβ42, and the saved residual was added in the

third step. The R2 change from the 2nd to the 3rd step as well as the unstandardized β coefficients

in the full model for 1/Aβ42 and the residual give an indication of additional variance explained by

CSF/Aβ42 not accounted for by 1/Aβ42. We did this for t-tau/Aβ42, p-tau/Aβ42, NFL/Aβ42, and MCP-

1/Aβ42.

Results

After accounting for the effect of 1/Aβ42, the standardized beta coefficient for the residuals of

both t-tau/Aβ42 and p-tau/Aβ42 were still significant at p<.05, as was the R2 change from step 2 to

step 3 in the hierarchical models. Furthermore, the R2 change was of a similar magnitude for

1/Aβ42 as tau/Aβ42. In contrast, NFL/Aβ42 and MCP-1/Aβ42 did not account for additional variance

after 1/Aβ42 was included in the model, as indicated by non-significant R2 changes and

standardized beta coefficients. However, the standardized beta coefficient for NFL/Aβ42 was

trending at p=.059.

Discussion

This supplemental analysis provides compelling evidence that p-tau/Aβ42and t-tau/Aβ42 provide

unique variance above and beyond 1/Aβ42. However, it does not rule out NFL/Aβ42 or MCP-1/

Aβ42 as important biomarkers. Instead, it suggests that the effect is primarily driven by Aβ42 in

the denominator. NFL/Aβ42 or MCP-1/Aβ42 should be investigated further with different AD-

relevant outcomes and at more advances ages and stages of preclinical AD.