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Associations of the Lifestyle for Brain Health index with longitudinal cognition and brain amyloid beta in clinically unimpaired older adults: Findings from the Wisconsin Registry for Alzheimer's Prevention

Abstract Introduction Modifiable health and lifestyle factors increase risk of dementia, but whether modifiable factors, when measured in late‐midlife, impact the emergence or progression of Alzheimer's disease (AD) pathophysiologic or cognitive changes remains unresolved. Methods In initially cognitively unimpaired, late middle‐aged participants (N = 1215; baseline age, M [standard deviation] = 59.3 [6.7] years) from the Wisconsin Registry for Alzheimer's Prevention (WRAP), we investigated the influence of the Lifestyle for Brain Health (LIBRA) index, a lifestyle‐based dementia risk score, on AD‐related cognitive trajectories and amyloid beta (Aβ) plaque accumulation. Results Overall, lower baseline LIBRA, denoting healthier lifestyle and lower dementia risk, was related to better overall cognitive performance, but did not moderate apolipoprotein E ε4 or Aβ‐related longitudinal cognitive trajectories. LIBRA was not significantly associated with Aβ accumulation or estimated age of Aβ onset. Discussion In WRAP, late‐midlife LIBRA scores were related to overall cognitive performance, but not AD‐related cognitive decline or Aβ accumulation in the preclinical timeframe. Highlights The Lifestyle for Brain Health (LIBRA) index was associated with cognitive performance in late‐midlife. LIBRA did not moderate apolipoprotein E ε4 or amyloid‐related cognitive decline. LIBRA was not associated with the onset or accumulation of amyloid plaques.


BACKGROUND
The pathophysiological progression of Alzheimer's disease (AD) begins with amyloid beta (Aβ) plaque accumulation and can occur two or more decades before clinical symptom onset. [1][2][3][4][5][6] Characterizing this preclinical or pre-symptomatic phase is critical for the development of effective intervention or preventive strategies. With the long prodromal period and current lack of effective treatments, attention has shifted toward preventive strategies focusing on modifiable factors that may prevent or delay the onset of dementia. [7][8][9][10] The availability of neuroimaging and fluid AD biomarkers now provides opportunity to study associations between modifiable risk factors and AD pathophysiologic and cognitive changes in the decades preceding dementia, before irreparable brain damage and cognitive symptoms occur.
Longitudinal observational studies of preclinical AD have demonstrated that cognitively unimpaired individuals with elevated Aβ exhibit faster cognitive decline and are at an increased risk for clinical disease progression; [11][12][13][14] further, recent evidence indicates that the duration of amyloid exposure in these individuals is associated with more rapid cognitive decline and increased tau burden. 6 It remains unclear whether modifiable lifestyle factors, when measured in combination, impact the emergence or progression of AD pathophysiologic or cognitive changes. Understanding the associations between modifiable lifestyle factors and longitudinal cognition in the preclinical phase of AD is critical, as modifiable factors constitute potential targets for therapeutics and preventive strategies.
The common co-occurrence of modifiable health and lifestyle factors of dementia and the increasing focus on early intervention have stimulated the development of dementia risk scores, like the Lifestyle for Brain Health (LIBRA) index, which was developed based on a systematic literature review and Delphi consensus. 8 It is a compound score, comprised of 12 modifiable lifestyle factors that are amenable to change and thus reflects one's prevention potential for dementia. 8 Several population-and patient-based cohort studies have demonstrated that lower LIBRA scores, denoting healthier lifestyle and lower lifestyle-based dementia risk, are associated with better cognitive functioning and lower risk of mild cognitive impairment (MCI) and dementia in mid-and late-life. 15

Participants
Data were from individuals enrolled in WRAP, an ongoing longitudinal observational study that follows a cohort of >1500 asymptomatic (at study entry), late-middle aged adults. 20  Briefly, diagnoses included cognitively unimpaired, MCI, impairednot MCI, and dementia. WRAP participants with complete LIBRA (described below), genetic, and cognitive data who were cognitively unimpaired at baseline (defined as their first visit with complete LIBRA factor data) were included in the current study (n = 1215; see Figure   S1 in supporting information for detailed inclusion/exclusion criteria).
APOE ε4 was genotyped as previously described. 23  to each factor in accordance with the relative risks from published meta-analyses. 8,18,28 Table 1 provides an overview of available LIBRA factors, assigned weights, and operationalization in this dataset. imaging. Detailed methods for radioligand synthesis and PET and MRI acquisition, processing and quantification, and analysis were implemented as reported previously. 29,30 Aβ burden was assessed as a global cortical average PiB distribution volume ratio (DVR), 29 and a previously established threshold of DVR >1.19 was used to determine Aβ positivity. 31 Age of Aβ onset and Aβ duration (age at visit -estimated age Aβ+), defined as the approximate number of years that an individual has had suprathreshold Aβ positivity, were estimated using a combination of group-based trajectory modeling and Bayes' theorem. 6

Statistical analyses
Statistical analyses were performed with R version 4.0.5 (The R Foundation). Kruskal-Wallis and chi-squared tests were used to examine baseline differences in demographics and LIBRA factors among three LIBRA risk groups based on LIBRA tertiles (low risk: LIBRA scores between -4.2 and 0; moderate risk: LIBRA scores between 0.1 and 2.0; High risk: LIBRA scores between 2.1 and 8.1; Table 2).
To examine whether baseline LIBRA moderates the negative associations of APOE ε4 or Aβ duration with cognitive decline, we modeled longitudinal standardized PACC-3 trajectories using linear mixed effects (LME) models (nlme package, R). Models included age, age 2 , sex, baseline Wide Range Achievement Test 3rd Edition (WRAT3) reading score, and practice fixed effects (age and WRAT3 centered using baseline means) as well as subject-specific random intercepts and agerelated slopes (unstructured covariance). 6 The first set of LME models (N = 1215) included a three-way interaction among baseline LIBRA, APOE ε4 carriage, and age to determine whether lifestyle and genetic risk increase the likelihood of cognitive decline beyond their separate effects (i.e., synergistic effect). A second set of LME models (PiB subset, N = 285) examined the combined influence of baseline LIBRA and Aβ duration on longitudinal PACC-3, and included a three-way interaction among baseline LIBRA, Aβ duration, and age. For each set, we followed a backward stepwise approach, removing highest order non-significant interactions sequentially. Two-sided P-values <.05 were considered statistically significant, and Akaike information criteria (AIC) statistics were used to determine the model of best fit. Secondary analyses repeated each LME model set using time-varying LIBRA (i.e., LIBRA at each cognitive assessment) to assess whether concurrent LIBRA moderates the negative associations of APOE ε4 carriage or Aβ duration on PACC-3 decline.
Associations of baseline LIBRA with Aβ burden and estimated age of Aβ onset were investigated using linear regressions that

Modifiable factor Definition Weight a
Low/moderate alcohol use Self-reported frequency of alcohol use, where low-to-moderate consumption was defined as two drinks or less in a day for men and one drink or less per day for women, including (non-drinkers).

−1.0
Coronary artery disease Coronary artery disease was based on physician review of self-reported medical history. Coronary artery disease included history of recurrent chest pain with exercise (angina pectoris), history of heart attack (myocardial infarction), or history of cardiac interventions (e.g., angioplasty, stenting, or coronary bypass surgery).

+1.0
Physical inactivity Self-reported physical activity on CHAMPS questionnaire, where physical inactivity was defined as fewer than 150 minutes of moderate exercise per week.

+1.1
Renal dysfunction The Chronic Kidney Disease Epidemiology Collaboration equation was used to estimate GFR from serum creatinine and other clinical parameters, and renal dysfunction was defined as GFR < 60 mL/min/1.73m 2 .

Diabetes
Diabetes was determined from self-reported use of anti-diabetic medication or, if no self-report, fasting blood glucose ≥126 mg/dL.

+1.3
High cholesterol Hypercholesterolemia was defined as self-reported use of anti-cholinergic medication or, if no self-report, total serum cholesterol ≥240 mg/dL.

Smoking
Smoking was defined from self-reported history of smoking at least once in the past month. +1.5 Obesity BMI ≥30 kg/m 2 calculated from physical examination at the study visit. +1.6

Hypertension
Hypertension was defined as self-report of use of anti-hypertensive medication or, if no self-report, mean systolic blood pressure ≥130 mmHg or mean diastolic blood pressure ≥80 mmHg.

Depression
The Center for Epidemiologic Studies-Depression Scale was used to measure depressive symptoms. Participants were considered depressed if their sum score ≥16.

Participants
Baseline sample characteristics and frequency of LIBRA factors are presented overall and by baseline LIBRA risk groups (

LIBRA and longitudinal cognitive decline
Of primary interest was whether baseline LIBRA moderates the neg-  Table S2 in supporting information). In the final reduced model in the PiB subset (Table 3, Figure 1B), baseline LIBRA was associated with better PACC-3 performance (P < .001) and both the linear and quadratic interactions between Aβ duration and age indicated significantly faster rates of PACC-3 decline among those who were Aβ positive longer.
Results were similar when investigating the associations of concurrent LIBRA with APOE ε4 carriage and Aβ duration on PACC-3 decline (Tables S3-S4 in supporting information).

F I G U R E 1
Associations of APOE ε4, Aβ duration, and LIBRA with longitudinal cognition. For visualization purposes, the modelled longitudinal z-PACC-3 trajectories from the final reduced model (Table 3)

LIBRA and Aβ burden
A secondary goal was to examine whether baseline LIBRA was associated with Aβ plaque accumulation. After adjusting for age at the most recent PiB visit, APOE ε4 carriage, and time between baseline  Table 2). In WRAP, we did not observe a clear association between baseline LIBRA and Aβ burden; analyses failed to show an association between markers of early Aβ development and lifestyle in late-midlife.

DISCUSSION
These findings are consistent with several studies among cognitively unimpaired individuals that did not find a correlation between vascular health and Aβ burden. [38][39][40] For example, the largest study to date of preclinical AD involving cognitively healthy people with Aβ imaging showed that elevated brain Aβ was associated with APOE ε4 carriage but not with multiple self-reported lifestyle risk factors encompassed by the LIBRA score (e.g., physical activity, body mass index, alcohol intake, smoking). 37 Our results, showing a lack of an association between LIBRA and brain Aβ among cognitively normal late-middle aged adults, in combination with a recent cross-sectional population-based study showing an association of LIBRA with non-AD specific measures of brain health (e.g., white matter hyperintensities and brain atrophy) 16 suggest that lifestyle, as indexed by LIBRA, may play an important role in general brain health, but may not influence early AD-related brain changes.
These results are best understood in the context of the study sample. With the relatively long period of follow-up and enrichment for AD risk, WRAP is uniquely powered to detect AD-related changes in latemidlife during the pre-symptomatic timeframe. Notably, in the current study, <5% of individuals progressed to clinical levels of impairment.
Thus, studies with a greater proportion of individuals progressing to MCI or dementia will be needed to fully elucidate the moderating role of lifestyle on AD-related clinical progression in the symptomatic timeframe. Additionally, it is worth considering that given the preclinical timeframe (≈60 years of age at baseline) and limited sample with elevated Aβ, we may have been underpowered to detect an interaction between LIBRA and Aβ-related cognitive decline. As Aβ accumulates over years to decades, it will be important to continue to examine the associations between midlife lifestyle, Aβ accumulation, and cognition over longer periods of follow-up.
The comprehensive study visits in WRAP made it possible to operationalize LIBRA factors based on clinical data from physical examination in combination with self-reported medical history. While data on most LIBRA factors were available, the absence of diet, which is a protective LIBRA factor, could have weakened the overall LIBRA score.
Also, it remains unknown whether or the extent to which pharmacological treatment of LIBRA factors may have influenced our findings.
Additionally, participation requires serial cognitive and health assessments, which may have resulted in a sample consisting of individuals with better overall health and lifestyle, and consequently, those with a higher range of lifestyle-related dementia risk may be underrepresented in the study sample. Finally, it is worth noting that WRAP reflects the demographics (e.g., primarily White) and health characteristics (e.g., rates of obesity, hypertension, diabetes) of the Wisconsin population, which reduces the generalizability of our findings. 41 Larger, more representative samples will be necessary to understand the impact of lifestyle on AD-related cognitive changes in late-midlife.

CONCLUSIONS
In this late-middle aged AD risk-enriched sample, lower LIBRA scores, indicating a brain-healthy lifestyle, were strongly associated with better overall cognitive performance when accounting for the negative influence of APOE ε4 and Aβ duration on preclinical cognitive decline.
There was no evidence that LIBRA in late-midlife was related to Aβ accumulation or moderated APOE ε4 or Aβ-related cognitive decline.
Lifestyle, as indexed by LIBRA, may not be related to pathophysiologic or cognitive changes in the early, preclinical stages of the AD continuum; whether lifestyle contributes to AD progression later in the disease trajectory warrants further study. Research NIH UL1TR002375 (Cody). We extend our deepest thanks to the WRAP participants and staff for their invaluable contributions to the study.

CONFLICTS OF INTEREST
Dr. Johnson has participated on an advisory panel for and received an equipment grant from Roche Diagnostics, and he has received support (sponsoring of an observational study and provision of precursor for tau imaging) from Cerveau Technologies. No other disclosures were reported. Author disclosures are available in the supporting information.