Cardiorespiratory Fitness and the Apolipoprotein E (ApoE) Genotype and Its Interaction With Cognitive Functions and Cognitive Decline in Late-Middle-Aged Older Adults: A Prospective Study
Conditions: Aging, Cognitive Change
Sponsor: National Taiwan Normal University

Cardiorespiratory Fitness and the Apolipoprotein E (ApoE) Genotype 
and Its Interaction with Cognitive Functions and Cognitive Decline  
in Late-Middle-Aged Older Adults: A Prospective Study 
 
Principal Investigator: Yu-Kai Chang, PhD 
 
NTC: NCT05652140 
Date: 2022/12/12 
 
Aim: 
This study utilized a longitudinal design to examine naturalistic changes in 
cardiorespiratory fitness (CRF) over an 18-month period as well as the interaction of 
CRF with apolipoprotein E gene (ApoE4) on a task that modulates inhibitory control 
demand in a community-dwelling sample of middle age and older adults. Specifically, 
we conducted a registered analysis using a data-driven 2  2 factorial design to 
compare changes in behavioral and neuroelectric indices of inhibitory control between 
four middle aged and older adult groups: (1) ApoE 4 carriers displaying increases in 
CRF over 18-months, (2) ApoE 4 non-carriers displaying increases in CRF over 18-
months, (3) ApoE 4 carriers displaying decreases in CRF over 18-months, and (4) 
ApoE 4 non-carriers displaying decreases in CRF over 18-months. It was hypothesized 
that adults displaying increases in CRF would show more positive changes in inhibitory 
control outcomes than older adults displaying decreases in CRF. Further, such 
differences in the outcome measures of inhibitory control between the groups displaying 
increases and decreases in CRF were hypothesized to be greater in ApoE 4 carriers. 
 
METHODS 
Design and Sample  
This prospective study examining CRF and neurocognitive function at baseline and 
18-month follow-up in 50–70-year-old adults was registered at ClinicalTrials.gov 
(NCT05652140). The actual conduct of this study was from 7/1/2018 to 5/1/2021. 
Potential participants were recruited via advertisements and personal contact form New 
Taipei City and Taipei City, Taiwan. Initially, this study included data from 159 
participants who were (1) 50–70-years-old, (2) right-hand dominant, did not report (3) 
psychiatric and neurological disorders, (4) cardiovascular disease, (5) risk of performing 
physical activities, as measured by Physical Activity Readiness Questionnaire (PAR-
Q)1, had (6) normal color vision, (7) corrected-to-normal or normal vision based on the 
20/20 standard, and a (8) Mini-Mental Status Examination (MMSE) score  252. Due to 
the substantial overlapping of the study period with the COVID-19 pandemic, a larger-
than-expected number of participants withdrew following completion of the baseline 
stage, resulting in a final sample of 83 with complete data for the registered longitudinal 
analysis.  
 
Cardiorespiratory fitness assessment 
CEF was measured via a braked cycle ergometer (Ergoselect 100/200 Ergoline 
GmbH, Germany) using the YMCA submaximal test including three or more 3-min 

consecutive cycling stages according to ACSM’s exercise testing guidelines3. 
Participants were fitted with a Polar heart rate (HR) monitor (Polar RS800CS; Polar 
Electro, Kempele, Finland) to continuously monitor their HR and provided ratings of 
perceived exertion4 every two minutes throughout the exercise testing. Participants 
were instructed to cycle at a power of 25 W (pedaling rate: 50 revolutions per 
minute [rpm]) for the initial three minutes, and HR recorded at the end of the last minute 
of the initial 3-min stage was used to determine the workload of the following stages of 
cycling. The test was terminated if two consecutive steady-state target HRs (i.e., 
between 110 beat per minute [bpm] and 85% of the age-predicted maximal HR [220  
age]) were reached. Estimated V̇ O2max (mLꞏkg−1ꞏmin−1) was then calculated using an 
established equation3. Participants whose V̇ O2max improved and deteriorated from the 
baseline to the 18-month follow-up were classified as ‘Gaining-Fitness’ and ‘Losing-
Fitness’ groups, respectively.  
 
Stroop test 
Inhibitory control was assessed via a modified Stroop test in which a 2-cm tall 
stimulus was focally presented at a time on a black background of a computer at a 
distance of ~70 cm using Neuroscan Stim2 software (Neurosoft Labs Inc., Sterling, VA, 
USA). The stimuli included three Chinese words (紅 [RED], 綠 [GREEN)], and 藍 
[BLUE]) printed in red (RGB: 254, 0, 0), green (RGB: 255, 200, 25), or blue (RGB: 0, 0, 
254) inks. Congruent (33%) (e.g., GREEN printed in green color), incongruent (33%) 
(e.g., GREEN printed in red color), and neutral (33%) (e.g., squares colored in one of 
the three colors) trials were presented in a randomized sequence using a 500ms 
stimulus presentation duration and a fixed 2000ms inter-trial interval. Following 10 
familiarization trials, participants completed five blocks of 108 trials and were instructed 
to identify the stimulus color as quickly and accurately as possible by pressing the key 
corresponding to one of the three colors. After excluding trials with response time < 
200ms or > 1200ms5, response accuracy and mean response time for congruent and 
incongruent trials were separately calculated as behavioral outcomes for the 
subsequent statistical analyses5 6. 
 
Neuroelectric assessment 
Electroencephalographic (EEG) data were recorded from a 32-electrode Quik-Cap 
Neo Net (Compummedics Neuroscan Inc., Charlotte, NC, USA). Data were referenced 
to the averaged mastoids (M1/M2) using the AFz electrode as the ground, digitalized at 
a 1000Hz sampling rate, amplified 500 times with a DC to 200Hz filter, and a 60Hz 
notch filter using a SynAmps2 amplifier. Bipolar electrodes located above and below the 
left orbit and lateral to the external canthus of each eye were used to monitor 
electrooculogram. Offline data processing was performed in MATLAB (R2022a, 
Mathworks Inc.) using the EEGLAB (V8.3)7 and ERPLAB (V8.0)8 plugins. Following a 
band-pass filter (IIR Butterworth, 0.1 – 20Hz), bad channels were detected and 
interpolated using the automatic channel rejection function and the spherical 
interpolation algorithms in the EEGLAB. Eye-related artifacts were detected and 
removed by the Step-like Artifacts function of the ERPLAB. EEG epochs were 
construed from -200 – 1000ms time-locked to the stimulus onset and baseline-corrected 
using the pre-stimulus period (-200 – 0ms). Epochs displayed step-like artifacts (moving 

window width = 200ms, window step = 10ms) or signals that exceeded  100V were 
rejected. After visually inspecting the accepted epochs, the mean amplitude P3 
components were derived using 400 – 600ms post-stimulus windows, respectively. The 
Regions of interest was created to investigate parietally (P3/Pz/P4) centered P3 
components. 
 
Genetic data collection and processing 
Licensed medical technologists collected 2-ml Ethylenediaminetetraacetic acid 
venous blood samples from the participants. DNA extraction and genotyping were 
conducted at the Union Clinical Laboratory, Taipei. ApoE genotyping was identified vis 
sequencing rs429358 (ApoE C112R) and rs7412 (ApoE R158C) for three ApoE 
alleles(s) (2, 3 and 4) using the real-time polymerase chain reaction. The ApoE4 
Carrier group was defined as participants who possessed 3/4 or 4/4, while the 
ApoE4 Non-Carrier group included those who possessed 2/2, 2/3, 2/4, or ApoE 
3/3.  
 
Procedure 
Participants were invited to visit the laboratory on two separate days that were 18-
month apart. Prior to each laboratory visit, participants were instructed to avoid vigorous 
exercise for at least 24 hours and consuming alcohol or caffeine at least 6 hours. 
Baseline Visit: Participants completed the informed consent, demographic 
questionnaires, Digit Span Forward and Backward test11, MMSE, PAR-Q, and the 
computerized Stroop test with simultaneous neuroelectric assessments in a sound-
attenuated and dimly lit room, followed by the cardiorespiratory fitness assessment. 18-
Month Follow-up Visit: Participants returned to the lab to complete the same protocol at 
18 ± 3.32 months after the baseline visit along with a separate visit to the medical clinic 
to identify ApoE genotypes.  
 
Statistical Analysis Plan: 
Analyses were performed using SPSS v.26 (SPSS Inv., Chicago, IL) with a family-
wise alpha threshold set at p = .05. A two-way ANOVA, with ApoE4 Carriage (Carrier 
vs. Non-carrier) and changes in CRF (Fitness Group) (Gaining-Fitness vs. Losing-
Fitness) as between-subjects factors and Gender as a covariate, was conducted to 
analyze the demographic and physical outcomes. A similar three-way ANOVA including 
Congruency (Congruent vs. Incongruent) as an additional within-subjects factor, was 
used to analyze the baseline cognitive outcomes (response time, response accuracy, 
and P3 amplitude). A similar three-way ANOVA was conducted to analyze the 
difference scores for the cognitive outcomes (response time, response accuracy, and 
P3 amplitude). Greenhouse Geisser and Bonferroni corrections were used when the 
sphericity assumption was violated and multiple comparisons were required. Effect 
sizes were reported using partial eta squared (p2) and Cohen’s d12. The baseline data 
were presented as means and standard deviations. The change in fitness and 
neurocognitive outcomes were presented as mean and standard error.  
 
 

Reference: 
1. American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and 
Prescription. 11th ed. Philadelphia: Lippincott Williams and Wilkins 2022. 
2. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for 
grading the cognitive state of patients for the clinician. J Psychiatr Res 
1975;12(3):189–98. doi: 10.1016/0022-3956(75)90026-6 
3. American College of Sports Medicine. ACSM’s guidelines of exercise testing and 
prescription. 9 ed: Lippincott Williams & Wilkins 2014:1-456. 
4. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 
1982;14:377–81. 
5. Wu CH, Nien JT, Lin CY, et al. Cardiorespiratory fitness is associated with sustained 
neurocognitive function during a prolonged inhibitory control task in young adults: 
An ERP study. Psychophysiology 2022:e14086. 
6. Rey-Mermet A. Finding an interaction between Stroop congruency and flanker 
congruency requires a large congruency effect: A within-trial combination of 
conflict tasks. Atten Percept Psychophys 2020;82(5):2271-301. doi: 
10.3758/s13414-019-01914-1 [published Online First: 2020/01/25] 
7. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial 
EEG dynamics including independent component analysis. Journal of 
neuroscience methods 2004;134(1):9-21. 
8. Lopez-Calderon J, Luck SJ. ERPLAB: an open-source toolbox for the analysis of 
event-related potentials. Frontiers in human neuroscience 2014;8:213. 
9. Natale E, Marzi CA, Girelli M, et al. ERP and fMRI correlates of endogenous and 
exogenous focusing of visual-spatial attention. Eur J Neurosci 2006;23(9):2511-
21. doi: 10.1111/j.1460-9568.2006.04756.x 
10. Näätänen R. Attention and brain function. Hillsdale, NJ, US: Lawrence Erlbaum 
Associates, Inc 1992. 
11. Wechsler D. WAIS-III, Wechsler Adult Intelligence Scale: Administration and scoring 
manual. San Antonio, TX: Psychological Corporation 1997. 
12. Cohen J. Statistical power analysis for the behavioral sciences: Academic press 
2013.