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The Association Between Midlife Lipid Levels and Late-Life Brain Amyloid Deposition

Authors: Erin E. Bennett, MPHa, Kan Z. Gianattasio, MPPa, Timothy M. Hughes, PhDb, Thomas H. Mosley, PhDc, Dean F. Wong, MD PhDd, Rebecca F. Gottesman, MD PhDd, Melinda C. Power, ScDa

a) The George Washington University Milken Institute School of Public HealthDepartment of Epidemiology950 New Hampshire Ave. NWWashington, DC 20052

b) Wake Forest School of MedicineMedical Center Blvd. Winston-Salem, NC 27157

c) University of Mississippi Medical Center2500 N. State St. Jackson, MS 39216

d) Johns Hopkins University School of Medicine733 N. BroadwayBaltimore, MD 21205

Corresponding Author: Erin Bennett950 New Hampshire Ave. NWWashington DC, 20024Email: erinbennett@gwu.eduPhone: 202-994-1332

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Abstract

Purpose: Elevated LDL-c and total cholesterol in midlife and declines in LDL-c and total

cholesterol from midlife to late-life are associated with incident dementia, though it is unclear

whether brain amyloid deposition mediates this relationship. This research assesses the

association between midlife blood lipid levels and midlife to late-life change in lipid levels with

brain amyloid deposition.

Principal Results: The 325 included participants had a mean age of 52.3 years at baseline, and

were 57% female and 41% black. Midlife total cholesterol, LDL-c, HDL-c, and triglycerides

were not significantly associated with late-life amyloid burden after adjustment for covariates.

Similarly, associations between midlife to late-life changes in lipids and late-life amyloid

deposition were consistently null.

Conclusions: Midlife lipid levels and midlife to late-life change in lipid levels were not

significantly associated with late-life amyloid deposition. Lipids may contribute to dementia risk

independent of brain amyloid deposition.

Key Words: Alzheimer’s disease, amyloid, lipids, epidemiology

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1. Introduction

Quantifying amyloid plaque deposition with florbetapir PET scans provides an objective

measure of Alzheimer’s-related pathologic burden and allows investigators to study associations

with vascular risk factors. Because amyloid accumulation begins 10 to 20 years before the onset

of clinical symptoms,(Jack et al., 2010) vascular risk factor status in midlife is likely more

relevant for studies of amyloid burden than late-life status. In support of this, midlife diabetes,

hypertension, and BMI have previously been linked with higher amyloid burden,(Gottesman et

al., 2017b; Nagga et al., 2018) while studies of late-life vascular risk factors have yielded mixed

results.(Choi et al., 2016; Marchant et al., 2012; Rabin et al., 2018; Vemuri et al., 2015)

One vascular risk factor of particular interest is blood lipid levels. Cross-sectional studies of late-

life lipid levels and amyloid deposition have been generally null,(Hughes et al., 2014a; Toledo et

al., 2012) which may be unsurprising given the importance of midlife vascular risk factor status.

Higher total cholesterol in midlife and declining total cholesterol from midlife to late life are

associated with increased dementia risk, though mechanisms for these relationships are not

entirely understood(Anstey et al., 2008; Vemuri et al., 2015) One study reported a non-

significant association between midlife blood lipids and late-life PET amyloid 20 years later after

adjustment for important covariates.(Nagga et al., 2018) However, the lack of racial diversity

among the participants and the small sample size used for analyses of midlife lipids and PET

amyloid merit verification of their findings, particularly among nonwhite participants.

Additionally, we expand prior analyses in the ARIC PET sample to evaluate whether lipid

subfractions or change in lipid levels from midlife to late-life are associated with increased

amyloid burden.

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Thus, our goal was to describe the relationship between midlife lipid levels and change in lipid

levels from mid- to late-life and brain amyloid deposition in the community-based ARIC-PET

Amyloid Imaging Study. We further explored these associations in participants with mild

cognitive impairment (MCI), and considered potential effect modification by late-life lipid-

lowering medication use, APOE E4 status, or race.

2. Methods :

2.1. Study Sample

The Atherosclerosis Risk in Communities (ARIC) Study cohort originally consisted of 15,792

participants aged 45-64 years from four sites in the United States: Washington County, MD;

Forsyth County, NC; Jackson, MS; and suburbs of Minneapolis, MN. Participants who had

undergone an MRI through the ARIC Neurocognitive Study, had no heavy alcohol use or renal

dysfunction, and had no prevalent dementia were invited to undergo florbetapir PET scans

between 2011 and 2013. Only participants from the Jackson, Washington County, and Forsyth

County sites were eligible for inclusion.(Gottesman et al., 2016) A total of 346 ARIC

participants underwent florbetapir PET scans. For this study, participants were also excluded for

missing visit 1 blood lipid data (n=7), missing visit 5 blood lipid data (n=1), missing visit 1 and

visit 5 covariate data (n=5), being retroactively diagnosed with having dementia at the time of

the PET scan (n=1), being neither white nor black (n=2), and for being in a small race-center

category (n=5, black participants in Forsyth County and Washington County), resulting in a total

analytic sample of 325 participants.

2.2. Blood Lipids

We considered the following as exposures of interest: total cholesterol (TC), high-density

lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and triglycerides.

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ARIC investigators collected blood samples by venipuncture after overnight fasting at visit 1

(1987-1989) and visit 5 (2011-2013). Detailed procedures for quantifying blood lipid levels are

available elsewhere; briefly, total cholesterol and triglycerides were assayed using enzymatic

methods,(NIH, 1987) and LDL-c was calculated using the Friedewald formula.(Lopez et al.,

2012) HDL was assayed using enzymatic methods, with sequential precipitation of LDL and

HDL by magnesium and dextran sulfate in visits 1 through 4(NIH, 1987), and measured using a

direct enzymatic method in visit 5.(NIH, 2012) Because HDL was measured using

fundamentally different laboratory procedures in visits 1 and 5, their values cannot be compared

quantitatively.(Parrinello et al., 2015) Therefore, we did not consider midlife to late-life change

in HDL-c as an exposure of interest.

2.3. Florbetapir PET Imaging

Elevated brain amyloid deposition at visit 5 was measured by florbetapir PET scans between

2011 and 2013. Detailed methods for PET image acquisition and processing are available

elsewhere.(Gottesman et al., 2016) Following injection with the labeling isotope and scanning,

images were assessed for errors and overall quality, then overlaid with participants’ most recent

MRI. Regions of interest (ROIs) were drawn around brain regions, and standardized uptake

value ratios (SUVRs) were quantified using the cerebellum gray matter as the reference region.

Global cortical SUVRs were calculated as a weighted average of amyloid deposition in the

orbitofrontal, prefrontal, and superior frontal cortices; the lateral temporal, parietal, and occipital

lobes; the precuneus, the anterior cingulate, and the posterior cingulate.(Gottesman et al., 2017b)

Elevated brain amyloid deposition was defined as a PET scan-derived global SUVR greater than

the median of 1.2, due to the high skew of SUVRs in the data and consistent with prior

investigations.(Gottesman et al., 2017a; Gottesman et al., 2016)

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2.4. Covariates

Age, gender, race-center, APOE e4 status, education, lipid-lowering medication use, BMI,

hypertension, and diabetes were considered as covariates. Age and BMI were modelled

continuously, and education was defined categorically as less than high school equivalent, high

school equivalent, or greater than high school equivalent. Because all black participants

considered in analyses were from Jackson, MS, and all white participants were from Forsyth

County, NC and Washington County, MD, it is impossible to separate the effects of race and

center in these analyses. Thus, race and center were combined into one variable with three levels:

white in Washington County, MD; white in Forsyth County, NC; and black in Jackson, MS.

APOE genotype was determined previously by TaqMan assay (Applied Biosystems, Foster City,

CA), and APOE e4 status was defined as the presence or absence of at least one copy of the

APOE e4 allele. Lipid-lowering medication use, hypertension, and diabetes at visits 1 and 5 were

treated as dichotomous (present/absent).

2.5. Statistical Analyses

Differences between those with and without elevated amyloid burden in late life were assessed

for statistical significance using Student’s t-tests and chi-square tests for continuous and

categorical variables, respectively.

Associations between midlife TC, HDL-c, LDL-c, and triglycerides, as well as change in lipid

levels from visit 1 to visit 5 and elevated amyloid deposition (SUVR>1.2), were described using

multivariable logistic regression. For analyses of visit 1 lipids and visit 5 amyloid deposition, all

lipid levels were scaled by a factor of ten. Models were first adjusted for age, race-center,

gender, and education. Further models adjusted for APOE e4 status, visit 1 values of BMI,

diabetes, and hypertension, and visit 5 lipid-lowering medication use. For analyses involving

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change in lipids, we derived variables describing change in lipid levels from visit 1 (1987) to

visit 5 (2011-2013) by subtracting visit 5 lipid values from visit 1 lipid values, and scaling by a

factor of ten. Similarly, models were first adjusted for baseline characteristics, and further

adjusted for APOE E4 status, visit 1 and visit 5 values of BMI, diabetes, and hypertension, and

visit 5 lipid-lowering medication use. Assuming the prevalence of 50% for elevated amyloid

deposition, consistent with using the median SUVR cutoff to define amyloid burden, a sample

size of 325 would yield 85.8% power to detect a minimum odds ratio of 1.4, which is an

appropriate effect size given reported associations between midlife vascular risk factors and late

life amyloid deposition. (Gottesman et al., 2017a; Hsieh et al., 1998)

In secondary analyses, we again described the associations between midlife and mid- to late-life

change in lipid levels and late-life amyloid deposition after restricting our sample to participants

with MCI at the time of amyloid PET scan. We also conducted a number of sensitivity analyses

to further validate our findings. First, we considered using linear mixed effects models to

estimate person-specific trajectories of lipids from visit 1 to visit 5 using all available lipid data

across the five ARIC study visits. Person-specific slopes of the linear trajectory of lipids were

scaled by 25 to represent person-specific average change in lipids over 25 years of follow-up

(approximately the mean follow-up from Visit 1 to Visit 5), and then by 10 to improve the

interpretability and utility of the results. These slopes were then treated as change in lipid level

variables in logistic regression models. We also used penalized splines to confirm whether

assuming linear associations between lipids and amyloid burden was appropriate.

Finally, we assessed potential effect modification by race, visit 5 lipid-lowering medication use,

and APOE e4 status using multiplicative interaction terms between potential effect modifiers and

each lipid variable of interest in partially-adjusted logistic regression models. Analyses were

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completed with SAS, Version 9.4 and RStudio, and we report 95% confidence intervals

throughout.

2.6. Standard Protocol Approvals, Registrations, and Patient Consents

All participants in the ARIC cohort provided written informed consent, and all procedures were

approved by each study site’s institutional review board.

3. Results:

Of the 346 ARIC participants who underwent PET scans, 325 participants (94%) were included

in these analyses. Baseline characteristics of included participants are summarized in

Supplementary Table 1. Of the participants, 186 (57.2%) were female and 134 (41.2%) were

black. Most had either a high school-equivalent education (42.2%) or had education beyond high

school (41.2%). At visit 1 (1987-1989), the mean age was 52.3 years, the mean BMI was 27.5

kg/m2, 28.5% of participants were hypertensive, and 5.6% were diabetic. At visit 5, over half of

the participants were taking lipid-lowering mediations (171, 52.6%), and 84 (25.8%) had MCI.

Participants were subsequently stratified by visit 5 brain amyloid deposition (total SUVR ≤ 1.2

vs. > 1.2) to test for statistical differences in demographics and visit 1 health characteristics

(Supplementary Table 1). Those with elevated amyloid deposition at visit 5 were more likely to

be black in Jackson, MS (p=0.004), more likely to have at least one APOE e4 allele (p<0.0001),

and more likely to be female (p=0.044). Participants with elevated amyloid were also older

(p=0.013), had significantly higher BMI (p=0.0012), and had higher total cholesterol (p=0.007)

and LDL cholesterol (p=0.045) at baseline. There was no significant difference between the two

groups with respect to education.

Results of logistic regressions modeling the association between visit 1 lipids and odds of

elevated amyloid burden at visit 5 are presented in Supplementary Table 2. In unadjusted

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analyses, a 10 mg/dL increase in midlife total cholesterol was associated with 8% higher odds of

elevated amyloid burden at visit 5 (OR = 1.08, 95% CI = 1.02-1.15. p-value = 0.008), and a 10

mg/dL increase in midlife LDL-c was associated with 6% higher odds of elevated amyloid

burden at visit 5 (OR = 1.06, 95% CI = 1.00-1.13, p-value = 0.047). However, these associations

were attenuated and not statistically significant after adjustment for age, race-center, gender, and

education (total cholesterol: OR = 1.06, 95% CI = 1.00-1.13, p-value = 0.067; LDL-c: OR =

1.05, 95% CI = 0.99-1.13, p-value = 0.13). Results did not change after further adjustment for

APOE e4 status and potential intermediates. There was no statistically significant association

between total triglycerides or HDL-c at visit 1 and elevated amyloid burden at visit 5 in

unadjusted, partially adjusted, or fully-adjusted models. Using scatterplots and loess trend lines

in crude analyses (see Supplementary Figure 1) and penalized splines to describe the association

between lipids and amyloid burden did not provide robust evidence of a nonlinear relationship.

Results were similar in analyses restricted to participants with MCI. In unadjusted analyses, there

was a significant association between midlife total cholesterol and LDL-c with elevated amyloid

burden in late-life (total cholesterol: OR = 1.20, 95% CI = 1.03-1.40, p-value = 0.017; LDL-c:

OR = 1.16, 95% CI = 1.00-1.34, p-value = 0.047), though these associations were attenuated and

insignificant after adjustment for demographics. There was no association between midlife HDL-

c or triglycerides and late-life amyloid accumulation in participants with MCI.

Results for the association between a 10 mg/dL decrease in blood lipid levels from visit 1 to visit

5 and elevated brain amyloid deposition at visit 5 are shown in Supplementary Table 3. There

was no association between change in total cholesterol, LDL-c, or triglycerides from midlife to

late-life and subsequent amyloid burden in unadjusted or adjusted models. We found no evidence

of associations between midlife to late-life change in lipids and amyloid burden in participants

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with MCI (Supplementary Table 3), and associations between amyloid deposition and individual

lipid trajectories for each participant produced similar results (Supplementary Table 4).

Finally, there was no evidence to suggest effect modification by race, late-life lipid lowering

medication use, or APOE e4 status (all interaction p-values >0.05).

4. Discussion

This study explored the relationship between midlife blood lipid levels and mid- to late-life

change in blood lipid levels with late-life brain amyloid burden. There were significant

associations between visit 1 total cholesterol and LDL-c with subsequent amyloid deposition in

late-life in unadjusted analyses. However, these were fully attenuated after adjustment for basic

demographics, emphasizing the importance of considering these factors in future studies

evaluating the relationship between lipids and dementia. We also found no association between

change in lipids from midlife to late life and elevated amyloid burden in late life. None of these

associations were modified either by race, late-life lipid lowering medication use, or APOE e4

status. We similarly found no evidence that midlife and mid- to late-life change in lipid levels are

associated with increased amyloid deposition in participants with MCI, who may be more likely

to develop dementia. However, the small number of participants with MCI in our sample may

have made us underpowered to detect small but significant associations.

Our results are largely consistent with those of the Swedish BioFINDER Study, which

examined the association between midlife lipids and late-life brain PET amyloid deposition in

134 white participants. Investigators reported that higher midlife triglycerides were significantly

associated with abnormal amyloid burden 20 years later, while associations for midlife total

cholesterol, LDL-c, and HDL-c were null in adjusted models. However, the association between

midlife triglycerides and late-life amyloid burden was fully attenuated after adjustment for

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additional covariates including cardiovascular disease, smoking, physical activity, and lipid-

lowering medication use.(Nagga et al., 2018) Our study extends the generalizability of these

results to black individuals, corroborates the null findings with a significantly larger sample size,

and, to our knowledge, was the first to assess whether the association between steeper declines in

lipids from mid- to late-life and dementia may be attributable to elevated amyloid burden in late

life.

We also report associations similar to those in a previous ARIC-PET study, which found an

adverse, but non-significant association between midlife total cholesterol of at least 200 mg/dL

and elevated amyloid deposition in late-life.(Gottesman et al., 2017b) Analogous analyses

produced a similarly elevated, non-significant point estimate in our data. However, there was no

robust evidence of either a linear or nonlinear association between total cholesterol and amyloid

burden in spline analyses. Our consideration of non-dichotomous total cholesterol, which had

not been previously considered, suggests that the elevated point estimate from previous work is

an artifact of the cut-point used for total cholesterol, although this study may be underpowered to

detect associations confined to the extremes of serum total cholesterol. Unlike the prior analyses,

we also explored midlife lipid subfractions and triglycerides as potential predictors of PET

amyloid burden.

Other studies exploring the cross-sectional relationship between lipids and amyloid deposition

have reported mixed results. One found an association between lower HDL cholesterol and

higher LDL cholesterol with late-life amyloid(Reed et al., 2014) and another suggested a positive

association between late-life triglycerides and amyloid(Choi et al., 2016). A recent study also

investigated LDL particle size, and found that specifically large LDL particles were positively

associated with elevated amyloid.(Lee et al., 2019) Otherwise, investigators have reported

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consistently null associations.(Hughes et al., 2014b) For example, the Ginkgo Evaluation of

Memory Study (2002-2008) reported no significant difference in mean serum lipid levels

between those with and without elevated amyloid burden.(Hughes et al., 2014b) Reasons for

these discrepancies remain unclear.

Our findings are somewhat surprising given the epidemiological literature suggesting an

association between midlife dyslipidemia and risk of dementia.(Anstey et al., 2008; Vemuri et

al., 2015) Interestingly, prior work in the ARIC cohort reported an association between midlife

dyslipidemia and cognitive decline,(Power et al., 2018b) but another study in the ARIC cohort

found no significant association between midlife lipids and incident dementia, although

significant associations with other midlife vascular risk factors were noted.(Gottesman et al.,

2017a) One potential explanation is that lipid particle size is more closely associated with

amyloid deposition than overall lipid levels. Large LDL-c levels have been associated with

greater amyloid accumulation in both cross-sectional(Lee et al., 2019) and longitudinal

analyses(Nagga et al., 2018). These findings support a potential mechanism by which LDL

particles enriched with ApoE compete with beta-amyloid to bind with LDL receptors, hindering

amyloid clearance and resulting in higher levels of large LDL in plasma(Lee et al., 2019). It is

also possible that an effect of dyslipidemia and amyloid is limited to those with extremely high

LDL-c, such as those with familial hyperlipidemia, and we are underpowered to detect it given

the small number of persons with extremely high lipids in our sample. We encourage future

studies to confirm or refute these possibilities. Alternately, it is possible that lipids are related to

another pathological process that leads to cognitive change independent of the characteristic

amyloid deposition associated with Alzheimer’s disease dementia.

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Another hallmark of Alzheimer’s disease, in addition to amyloid plaque formation, is the

presence of neurofibrillary tangles (NFTs) in the brain.(Hardy and Selkoe, 2002) If lipids are

involved in the formation of NFTs, we may only see an association with amyloid after

neurofibrillary tangle formation has occurred. Relatedly, the Swedish BioFINDER study saw

more robust associations between lipids and a combined measure for both amyloid-beta 1-42

protein relative to hyperphosphorylated tau, which is a precursor of NFTs, than between lipids

and PET-measured amyloid alone.(Nagga et al., 2018) Alternately, lipids may contribute to

dementia pathogenesis or severity through impacts on cerebrovascular health, specifically by

increasing risk of stroke and ischemic brain injury. Blood lipids likely contribute to the

development of overt and subclinical cerebrovascular disease, which appear to contribute to

dementia independent of pathologic pathways involving amyloid.(Power et al., 2018a) Autopsy

studies have identified microvascular degeneration, lacunes, and infarcts in the brains of

Alzheimer’s patients, and many of these pathologies have been linked to lipids. For example, one

study found that higher blood triglycerides were associated with white matter hyperintensity

volume and frequency of lacunes,(Schilling et al., 2014) while another found an association

between HDL-cholesterol serum levels and deep cerebral microbleeds. Future studies should

investigate whether lipids may increase dementia risk by contributing to NFT formation or

cerebrovascular disease.

We also did not see an association between change in blood lipids and amyloid burden. It is

possible that prior reports of an association between decline in total cholesterol from midlife to

late-life and subsequent risk of dementia(Mielke et al., 2010) were due to reverse causality,

where dementia causes general apathy and changes in appetite that subsequently lead to a decline

in blood lipid levels. Future studies should verify these findings.

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There were limitations to this study that merit discussion. Because all black participants in our

sample came from one study center (Jackson, MS), it is impossible to disentangle the effects of

place and race on reported associations. Additionally, the measurement of circulating lipoprotein

may have relevance to the central nervous system due to the near complete separation of brain

and circulating lipoproteins by the blood brain barrier.(Saeed et al., 2014)

This study also had several strengths. Importantly, this was the first study to consider the

relationship between midlife blood lipid levels and late-life elevated amyloid burden in a biracial

cohort and the first study to consider a relationship between changes in blood lipids from midlife

to late-life and subsequent amyloid burden. Our sample size was larger than those of previous

studies considering the association between lipids and brain amyloid, enhancing the precision of

our reported estimates. Finally, the length of follow-up between baseline and florbetapir PET

scans was between 24 and 26 years, allowing us to identify relevant potential risk factors.

5. Conclusions

Our study found no association between either midlife blood lipid levels or change in lipid levels

from midlife to late-life and elevated amyloid burden in late-life. Because lipids have

consistently been associated with dementia risk, lipids may contribute to dementia risk through a

non-amyloid pathological process. Future research should consider other mechanisms by which

lipids may increase dementia risk.

6. Acknowledgements

The authors thank the staff and participants of the ARIC study for their important contributions.

7. Study Funding

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The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal

funds from the National Heart, Lung, and Blood Institute, National Institutes of Health,

Department of Health and Human Services, under Contract nos. (HHSN268201700001I,

HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I).

Neurocognitive data is collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899,

2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD), and with

previous brain MRI examinations funded by R01-HL70825 from the NHLBI. The ARIC-PET

study is funded by the National Institute on Aging (R01AG040282). Neither NHLBI nor NIA

had any role in design and conduct of the study; management, analysis, and interpretation of the

data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for

publication. Avid Radiopharmaceuticals provided the isotope (florbetapir) for the study, but had

no role in study design or interpretation.

8. Conflicts of Interest

Erin E. Bennett: Reports no conflicts of interest

Kan Z. Gianattasio: Reports no conflicts of interest

Timothy M. Hughes: Reports no conflicts of interest

Thomas H. Mosley: Reports no conflicts of interest

Dean F. Wong: Reports no conflicts of interest

Rebecca F. Gottesman: Associate Editor of Neurology

Melinda C. Power: Reports no conflicts of interest

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Supplementary Table 1: Characteristics of Eligible Atherosclerosis Risk in Communities ARIC-PET Participants at Baseline, Visit 1

(1987-1989)

Overall (N=325) Total SUVR>1.2 (N=166) Total SUVR≤1.2 (N=159) p-value

a

Female, No. (%) 186 (57.2) 104 (62.7) 82 (51.6) 0.044

Race-center, No. (%): 0.0004

Black in Jackson, MS 134 (41.2) 86 (51.8) 48 (30.2)

White in Forsyth County, NC 69 (21.2) 29 (17.5) 40 (25.2)

White in Washington County, MD 122 (37.5) 51 (30.7) 71 (44.7)

APOE e4 genotype, No. (%) <.0001

No e4 alleles 226 (69.5) 99 (59.6) 127 (79.9)

One or two e4 allele 99 (30.5) 67 (40.4) 32 (20.1)

Education, No. (%): 0.27

Less than high school 54 (16.6) 33 (19.9) 21 (13.2)

High school or equivalent 137 (42.2) 67 (40.4) 70 (44)

Greater than high school 134 (41.2) 66 (39.8) 68 (42.8)

Age, y, mean (SD) 52.3 (5.2) 53.0 (5) 51.5 (5.4) 0.013

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BMI, kg/m2, mean (SD) 27.5 (4.4) 28.2 (4.5) 26.7 (4.1) 0.0012

Hypertension b, No. (%) 92 (28.5) 55 (33.3) 37 (23.4) 0.048

Diabetes c, No. (%) 18 (5.6) 10 (6) 8 (5.1) 0.71

Lipid-lowering medication use, No. (%) 4 (1.3) 1 (0.6) 3 (1.9) 0.31

Total cholesterol, mean (SD), mg/dL 208.6 (39) 214.3 (39.4) 202.6 (37.9) 0.007

LDL-c, mean (SD), mg/dL 132.1 (36.4) 136.0 (37.8) 127.9 (34.4) 0.045

HDL-c, mean (SD), mg/dL 49.2 (10.6) 50.3 (10.7) 48.1 (10.3) 0.058

Total triglycerides, mean (SD), mg/dL 115.0 (60.7) 115.6 (56.5) 114.3 (65) 0.84

Change in total cholesterol d, mean (SD),

mg/dL

27.7 (50.1) 27.6 (52.1) 27.9 (48.0) 0.95

Change in HDL-c, mean (SD), mg/dL -2.2 (9.9) -3.2 (10.1) -1.2 (9.6) 0.06

Change in LDL-c, mean (SD), mg/dL 27.0 (45.1) 27.1 (47.2) 27.0 (43.0) 0.99

Change in total triglycerides, mean (SD), mg/dL -7.0 (55.2) -5.7 (50.3) -8.5 (60.1) 0.65

Abbreviations: SUVR = standardized uptake value ratio, BMI = body mass index, LDL-c = low-density lipoprotein, HDL-c = high-density lipoprotein. a) P-value for null hypothesis of no statistical difference between frequencies or values between two groups: SUVR>1.2 and SUVR<1.2. A p-value <0.05 is considered statistically significant. b)Hypertension defined as a systolic blood pressure above 140 mm/Hg, diastolic blood pressure above 90 mm/Hg, or current use of hypertension medication. c)Diabetes defined as a fasting blood glucose concentration of 126 mg/dL. d) A positive value for all change in lipid variables represents a decrease in lipid levels from Visit 1 to Visit 5.

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Supplementary Table 2: Odds Ratios for the Association Between a 10 mg/dL increase in Visit 1 Blood Lipid Levels and Visit 5

Amyloid Burden a

Unadjusted, OR

(95% CI)

Model 1b, OR

(95% CI)

Model 2c, OR

(95% CI)

Model 3d, OR

(95% CI)

All participants (N=325)

Total cholesterol, 10 mg/dL 1.08 (1.02, 1.15) 1.06 (1.00, 1.13) 1.05 (0.98, 1.12) 1.06 (0.98, 1.13)

LDL-c, 10 mg/dL 1.06 (1.00, 1.13) 1.05 (0.99, 1.13) 1.04 (0.97, 1.11) 1.04 (0.97, 1.12)

HDL-c, 10 mg/dL 1.15 (1.00, 1.32) 1.01 (0.85, 1.19) 1.02 (0.86, 1.21) 1.10 (0.92, 1.33)

Total triglycerides, 10 mg/dL 1.00 (0.97, 1.04) 1.03 (0.99, 1.07) 1.03 (0.99, 1.07) 1.01 (0.97, 1.06)

Participants with MCI (N=84)

Total cholesterol, 10 mg/dL 1.20 (1.03, 1.40) 1.15 (0.96, 1.37) 1.14 (0.94, 1.39) 1.14 (0.90, 1.44)

LDL-c, 10 mg/dL 1.16 (1.00, 1.34) 1.15 (0.96, 1.37) 1.14 (0.94, 1.38) 1.12 (0.90, 1.39)

HDL-c, 10 mg/dL 1.38 (0.98, 1.93) 1.10 (0.70, 1.73) 1.12 (0.68, 1.85) 1.76 (0.93, 3.31)

Total triglycerides, 10 mg/dL 0.96 (0.88, 1.04) 0.98 (0.90, 1.08) 0.98 (0.89, 1.08) 0.88 (0.77, 1.01)

Abbreviations: OR = odds ratio, SUVR = standardized uptake value ratio, 95% CI = confidence interval, LDL-c = low-density lipoprotein, HDL-c = high-density lipoproteinAll analyses performed using multivariable logistic regressiona)Amyloid burden defined as a global cortical SUVR>1.2 for global brain amyloid deposition. b)Model 1 adjusted for age, gender, race-center, and education. c)Model 2 adjusted for all covariates in Model 1, plus APOE e4 status.

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d)Model 3 adjusted for all covariates in Model 2 in addition to visit 1 BMI, diabetes, and hypertension, and visit 5 lipid-lowering medication use.

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Supplementary Figure 1: Scatterplot and Loess Trend Line Between Visit 1 Blood Lipid Levels and Visit 5 Amyloid Burden

Abbreviations: LDL-c = low-density lipoprotein, HDL-c = high-density lipoprotein, SUVR = standardized uptake value ratio

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Supplementary Table 3: Odds Ratios for the Association Between 10 mg/dL Higher V1 – V5 Blood Lipid Levels and Visit 5 Amyloid

Burden a

Unadjusted, OR

(95% CI)

Model 1b, OR

(95% CI)

Model 2c, OR

(95% CI)

Model 3d, OR

(95% CI)

All participants (N=325)

V1-V5 10 mg/dL higher total cholesterol 1.00 (0.96, 1.04) 0.99 (0.95, 1.04) 1.00 (0.95, 1.05) 0.99 (0.92, 1.05)

V1-V5 10 mg/dL higher LDL-c 1.00 (0.95, 1.05) 1.00 (0.95, 1.06) 1.00 (0.95, 1.06) 0.99 (0.92, 1.07)

V1-V5 10 mg/dL higher triglycerides 1.01 (0.97, 1.05) 1.02 (0.97, 1.06) 1.02 (0.98, 1.07) 1.01 (0.96, 1.06)

Participants with MCI (N=84)

V1-V5 10 mg/dL higher total cholesterol 1.03 (0.94, 1.13) 0.98 (0.88, 1.09) 1.03 (0.91, 1.18) 0.98 (0.92, 1.05)

V1-V5 10 mg/dL higher LDL-c 1.04 (0.94, 1.15) 1.00 (0.89, 1.13) 1.07 (0.92, 1.25) 0.99 (0.92, 1.07)

V1-V5 10 mg/dL higher triglycerides 0.98 (0.90, 1.08) 0.98 (0.89, 1.08) 0.98 (0.88, 1.10) 1.01 (0.96, 1.06)

Abbreviations: OR = odds ratio, SUVR = standardized uptake value ratio, 95% CI = confidence interval, LDL-c = low-density lipoprotein, HDL-c = high-density lipoprotein

All analyses performed using multivariable logistic regressionInterpretation: an odds ratio of 1.02 would suggest that a 10-unit greater decrease in lipids from Visit 1 to Visit 5 is associated with a 2% higher odds of elevated brain amyloid at visit 5. a)Amyloid burden defined as a global cortical SUVR>1.2 for global brain amyloid deposition.b)Model 1 adjusted for age, gender, race-center, and education. c)Model 2 adjusted for covariates in Model 1, plus APOE e4 status. d)Model 3 adjusted for all covariates in Model 2 in addition to visit 1 and visit 5 BMI, diabetes, and hypertension, and visit 5 lipid-lowering medication use.

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Supplementary Table 4: Odds Ratios for the Association Between Individual Lipid Trajectories from Visit 1 to Visit 5 and Visit 5

Amyloid Burden a

Unadjusted OR, 95% CI Model 1b, 95%

CI

Model 2c, 95%

CI

All participants (N=325)

V1-V5 10 mg/dL higher total

cholesterol

1.01 (0.95, 1.07) 1.01 (0.95, 1.08) 0.99 (0.90, 1.10)

V1-V5 10 mg/dL higher LDL-c 1.02 (0.95, 1.09) 1.02 (0.95, 1.10) 1.02 (0.91, 1.14)

V1-V5 10 mg/dL higher triglycerides 1.00 (0.90, 1.11) 1.04 (0.92, 1.18) 1.04 (0.89, 1.20)

Abbreviations: OR = odds ratio, SUVR = standardized uptake value ratio, 95% CI = confidence interval, LDL-c = low-density lipoprotein, HDL-c = high-density lipoproteinAll analyses performed using multivariable logistic regressionInterpretation: an odds ratio of 1.01 means that a 10 mg/dL increase in an individual’s average change in lipids over 25 years is associated with 1% higher odds of elevated amyloid burden. a)Amyloid burden defined as a global cortical SUVR>1.2.b)Model 1 adjusted for age, gender, race/center, APOE e4 status, and education. c)Model 2 adjusted for all covariates in model 1, visit 5 lipid-lowering medication use, and visit 1- visit 5 BMI, diabetes, and hypertension.

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