Isabel Martín-Fuentes

Background: Coronary artery disease (CAD) increases the risk of cognitive impairment, dementia, and brain structural alterations. This heart-brain connection suggests that exercise-induced cardiovascular responses may influence brain regions vulnerable to Alzheimer's disease (AD), the most common cause of dementia. This study aimed to examine the effects of 12-weeks of either high-intensity interval training (HIIT) plus resistance training (RT) or HIIT alone on AD brain signatures in individuals with CAD, to explore moderating factors, and to assess associations between changes in AD brain signatures and cognition and physical fitness. Methods: This secondary analysis of a single-site, three-arm, single-blinded randomized controlled trial included 105 individuals with CAD (50-75 years; 21% female) randomly allocated to HIIT+RT, HIIT, or usual care (UC) groups. T1- and diffusion-weighted magnetic resonance imaging were acquired before and after the 12-week intervention. Primary outcomes were thickness/volume and gray matter mean diffusivity (GMMD) signatures, derived from seven cortical regions and the hippocampus. Moderators included age, sex, education, and baseline AD brain signatures. Results: For the thickness/volume signature, no between-group differences in changes were observed between HIIT+RT and UC (+0.13 standardized mean difference [SMD]; 95% CI, -0.07 to 0.33) or between HIIT and UC (-0.1 SMD; 95% CI, -0.3 to 0.1); however, a small but significant between-group difference in change was found between HIIT+RT and HIIT, in favor of HIIT+RT (+0.23 SMD; 95% CI, 0.03 to 0.42). For the GMMD signature, no significant between-group differences in changes were found between HIIT+RT and UC (+0.08 SMD; 95% CI, -0.18 to 0.34), HIIT and UC (+0.1 SMD; 95% CI, -0.16 to 0.36), or HIIT+RT and HIIT (-0.02 SMD; 95% CI, -0.28 to 0.23). No moderation effects were identified, and no associations were observed between changes in AD brain signatures and cognition or physical fitness. Conclusion: A 12-week HIIT+RT intervention was more effective than HIIT alone in increasing the thickness/volume signature in individuals with CAD, yet no differences were observed compared to UC and the interventions did not affect the GMMD signature. These findings suggest that the intra-session inclusion of RT with HIIT may enhance AD-related brain macrostructure in individuals with CAD more than just HIIT training.

Aims: Patients with coronary artery disease (CAD) are at risk of accelerated cognitive decline. We aimed to explore how different cardiorespiratory fitness (CRF) indicators associate with brain structure and cognitive function in these patients. Methods and results: We studied 105 stable CAD patients (62.1 ± 6.6 years, 21% female), using baseline data from the randomized controlled Heart–Brain trial. Time-to-exhaustion (TTE), peak oxygen uptake (VO2peak), ventilatory anaerobic threshold (VAT), oxygen uptake efficiency slope (OUES), peak O2-pulse, and 60-s heart rate recovery (HRrec) were determined from cardiopulmonary exercise tests. From magnetic resonance imaging, we extracted brain volumes [total-, grey-, and white matter volumes (TBV, GMV, and WMV)] and hippocampal volume (HV), and calculated the difference between estimated and chronological brain age (brainPAD). Episodic memory, processing speed, working memory, executive function/attentional control, and general cognition were evaluated. TTE, VO2peak, and OUES were positively associated with TBV (βstd range 0.15–0.19, P < 0.05) and HV (βstd range 0.23–0.36, P < 0.05). Higher OUES, HRrec, and O2-pulse were associated with lower brainPAD (βstd −0.23 to −0.32, P < 0.05). Higher VAT was associated with better working memory (βstd = 0.26, P = 0.023), and higher OUES with better executive function/attentional control (βstd = 0.20, P = 0.021). Hippocampal atrophy was more prevalent in lower vs. middle/upper VO2peak tertile (P = 0.001). Conclusion: In CAD patients, both maximal and submaximal CRF indicators were associated with larger brain volumes, with stronger region-specific association with HV, and younger physiological appearance of the brain, while associations with cognitive functions were fewer and weaker. Our findings support CRF as a biomarker of structural brain health in CAD patients.

Introduction: Alzheimer's disease (AD) is a leading cause of disability worldwide. Alterations in cerebral blood flow (CBF) and AD blood biomarkers are fundamental at early stages of AD. Exercise shows promise in delaying physiological changes, but its mechanisms for enhancing brain health remain unclear. flADex aims to examine the acute effects of different exercise types on CBF and blood biomarkers in older adults. This protocol describes the methodology and rationale of flADex. Methods: flADex is a counterbalanced crossover trial in 20 older adults, aged 68–83 years old, with negative brain amyloid status (< 12 centiloid) who are APOEε4 noncarriers. Participants will complete a 30‐min session of each condition in a randomized order: (i) moderate‐intensity aerobic exercise (60%–70% age‐predicted maximal heart rate), (ii) moderate‐intensity resistance exercise (rating of perceived exertion: 4–6 points out of 10), and (iii) resting condition. Changes in CBF are the primary outcome and will be assessed by magnetic resonance imaging using pseudo‐continuous arterial spin labeling at pre‐ and at 3 timepoints post‐condition (starting at 20, 27, 34 min). Secondary outcomes are biomarkers of AD pathology and neurodegeneration (Aβ42, Aβ40, p‐tau217, p‐tau181, BD‐tau, GFAP, NfL) and growth factors (BDNF, IGF‐1), measured through blood samples collected at pre‐ and post‐condition (at 3, 50, 70 min). Moreover, cognitive outcomes and mood status will be measured pre‐ and post‐condition. Conclusion: flADex will highlight the acute effects of different exercise types on CBF and biomarkers before beta‐amyloid accumulation. Acute effects on CBF dynamics and blood biomarkers are expected to be greater with aerobic than resistance exercise when compared to resting. CBF is expected to vary by brain region, and biomarkers to fluctuate dynamically postexercise. This will provide critical insights into exercise's impact on vascular and molecular pathways associated with AD pathology and potential recommendations for standardized blood sampling to enhance diagnostic accuracy.

Background and Objectives: Aging is associated with both gait impairments and cognitive decline; however, the relationship between specific gait variability parameters, gray matter volume (GMV), and cognitive function remains poorly understood. This study aims to examine the associations between gait variability parameters (derived from stride length, step length, step time, and gait velocity) and GMV and its associations with cognitive function in cognitively normal older adults. Research Design and Methods: Eighty-seven older adults (48 female) aged 65–80 from the AGUEDA trial participated in this cross-sectional analysis. The Optogait system was used to record gait parameters. T1-weighted brain images were acquired magnetic resonance imaging scanner, and GMV was calculated by whole-brain voxel-based morphometric analysis using SPM12. Cognitive function was calculated from different cognitive tests. Results: Greater stride length variability was associated with lower GMV (p < .001) in clusters located in the supramarginal gyrus (t = 4.014, k = 179, β = -0.494) and hippocampus (t = 3.670, k = 334, β = -0.394), whereas greater step length variability was linked to lower GMV in the parahippocampal gyrus (t = 3.624, k = 76, β = -0.410). However, greater step time variability was associated with greater GMV in the supplementary motor area (t = 4.117, k = 274, β = 0.449). Gait velocity variability did not show any association with GMV. Furthermore, greater GMV in the supramarginal gyrus was associated with better working memory (β = 0.252, p = .008); greater GMV in the hippocampus was associated with better attentional/inhibitory control (β = 0.275, p = .010); and greater GMV in the parahippocampal gyrus was associated with better EF (β = 0.212, p = .035), attentional/inhibitory control (β = 0.241, p = .019), and working memory (β = 0.233, p = .027). Discussion and Implications: These results suggest that gait variability could be an indicator of neurocognitive decline in older adults. Understanding these associations is essential for early dementia detection and sheds light on the complex interplay between physical function, brain health, and cognitive function during aging.

INTRODUCTION: Alzheimer’s disease (AD) is a leading cause of disability worldwide. Alterations in cerebral blood flow (CBF) and AD blood biomarkers are fundamental at early stages of AD. Exercise shows promise in delaying physiological changes, but its mechanisms for enhancing brain health remains unclear. FlADex aims to examine the acute effects of different exercise types on CBF and blood biomarkers in older adults. This protocol describes the methodology and rationale of flADex. METHODS: FlADex is a counterbalanced crossover trial that will include 20 older adults aged 68 to 83 years old with negative brain amyloid status (WO2020193500A1 and patent applications # 2450702-2, #63/693,956, # 63/679,361, and 63/672,952 .

The aim of the study is to investigate the association of cardiorespiratory fitness (CRF) and muscular strength indicators with gray matter volume (GMV) and to study whether fitness‐related regions of GMV are associated to executive function (EF) in cognitively normal older adults. Ninety‐one cognitively normal older adults (71.69 ± 3.91 years; 57.14% females) participated in this study from the AGUEDA trial. CRF was measured by a 2‐km walking test and a 6‐min walking test. Muscular strength was measured by handgrip, biceps curl, squats, and isokinetic strength tests. T1‐weigthed images were obtained through a magnetic resonance scan. GMV was determined by voxel‐based morphometric analysis. Standardized EF tests were performed. CRF did not show any positive association with GMV. Handgrip strength was positively associated with GMV (p < 0.001) in nine regions (β from 0.6 to 0.8 and k from 106 to 1927) and knee extension strength in three regions (β from 0.4 to 0.5 and k from 76 to 2776). Squats strength was negatively associated with GMV (p < 0.001) in two regions (β = −0.3, k = 1102 and k = 152) and the 2‐km walking test in one region (β = −0.4, k = 99). Only handgrip strength‐related GMV was associated with cognitive flexibility (p = 0.039, β = 0.215) and spatial working memory (p < 0.03, β 0.247–0.317), but not with EF score (p > 0.05). Muscular strength, but no CRF, may be positively related to GMV in cortical and subcortical regions, with implications for specific cognitive domains rather than the overall EF score. Specifically, handgrip strength was the indicator most associated with higher GMV, while squats strength and CRF were negatively related to GMV.ClinicalTrials.gov identifier: NCT05186090.

Background: Amyloid beta (Aβ) is a protein associated with dementia‐related pathologies such Alzheimer’s Disease (AD). The accumulation of Aβ in the brain leads to neurodegeneration, cognitive decline, and motor impairment. Those factors might appear years before the onset of apparent symptoms. There is evidence for an association between gait performance and Aβ levels. Previous studies showed that gait variability has been associated with amyloid in older adults with mild dementia. Moreover, reduced gait velocity, has been associated with greater brain Aβ accumulation in adults with spontaneous memory complaints. However, the nature of this relationship needs further investigation. Thus, the aim of this study was to examine the associations between gait velocity and gait velocity variability, and brain amyloid burden in cognitively normal older adults. Our hypothesis was that gait parameters might differently predict amyloid‐β levels.Method: Eighty‐six cognitively normal older adults participated in this study. The Optogait system (Microgate,Italy) was used to assess gait parameters. Participants walked back and forth across a 5‐m long track made up of two bars set 1‐meter apart at their maximum walking pace for 5 minutes. The mean and coefficient of variation (CV) of gait velocity (m/s) were calculated as gait parameters. All participants underwent T1‐weighted MRI scans and [18F] Florbetaben amyloid‐PET (positron emission tomography) scans. PET images were quantified using the Centiloid (CL) method with a 12 CL cutoff for amyloid positivity. The associations between gait parameters and amyloid burden were assessed by individual linear regressions unadjusted and adjusted by age, sex, and education as covariates.Result: Table 1 shows descriptive characteristics of the participants (22% Aβ+; 19 participants). Results showed a positive trend for an association between gait variability and amyloid burden (unadjusted: β = 0.21, p = 0.05; adjusted: β = 0.20, p = 0.08). There was no association between gait velocity and amyloid burden (unadjusted: β = ‐0.08, p = 0.42; adjusted: β = ‐0.08, p = 0.54).Conclusion: Greater gait velocity variability, but not gait velocity, was marginally associated with higher amyloid burden. This result suggests that gait velocity variability might be an important marker of amyloid burden in cognitively normal older adults.

Background: Amyloid‐beta (Aβ) plaques are considered the initiating pathological hallmark of Alzheimer’s disease (AD). While animal studies suggest that physical exercise may have the potential to clear Aβ, there is limited research on humans, particularly in relation to resistance exercise. The aim of this study was to investigate the effect of a 24‐week resistance exercise program on brain Aβ accumulation in cognitively normal older adults.Method: The study included 79 cognitively normal older adults with valid baseline and follow‐up data who were randomly assigned to either a 24‐weeks resistance exercise program (n = 42) or a wait‐list control group (n = 37) as part of the AGUEDA trial (NCT05186090) (Table 1). Participants underwent T1‐weighted Magnetic resonance imaging and [¹⁸F]Florbetaben amyloid‐PET (positron emission tomography) scans at baseline and after a 24‐weeks follow‐up period. Amyloid burden was quantified using the Centiloid (CL) method. Changes in amyloid burden between the groups were analyzed using analysis of covariance on an intention‐to‐treat basis including baseline CL values as covariateResult: At baseline, the CL for the entire sample was 7.27 ± 25.81, and the prevalence of amyloid positivity using a 12 CL cutoff was 21.5% (n = 17, 10 women), with no significant differences between exercise and control group (p = 0.120 and 0.399, respectively). The estimated marginal mean change after 6 months was 3.51 CL for the exercise group and 1.78 CL for the control group, with no significant differences between the groups (CL group difference: 1.73 ± 1.43 SE; z‐score = 0.032), 95% confidence interval (CI): ‐1.13 to 4.59, p = 0.233).Conclusion: The 24‐weeks resistance exercise program did not alter global amyloid brain burden in cognitively normal older adults. Further research with longer exercise interventions and different a manipulation of exercise parameters is needed to elucidate the role of exercise on brain amyloid burden in cognitively normal older adults.

Foot stiffness is a modulator of sprint performance. However, studies that analysed foot angular velocities using inertial measuring units (IMU) for different events within the sprint contact time phase are scarce. The aim of this study was to investigate the relationship between angular foot step-by-step kinematics and sprint performance during a 50-metre sprint in experienced male and female sprinters. Foot kinematics were measured using IMU devices integrated with a 3-axis gyroscope and a laser gun. The main findings were that men performed faster sprints (6.11 ± 0.35 s vs. 6.77 ± 0.24 s), but the maximal angular foot kinematics were the same between sexes. Maximal angular velocities increased until strides 6–7, where they stabilized. Time from touchdown to maximal dorsiflexion velocity did not change between strides, whereas time from maximal dorsiflexion velocity to toe off decreased until stride 6. Plantarflexion velocities, especially in toe off, showed the greatest associations with sprint times, whereas maximal dorsiflexion velocity presented no association with sprint times. The time from dorsiflexion velocity to toe off from stride 7 onwards determined the sprint performance and was shorter for faster sprinters. The analysis of these variables provides essential information to athletes and coaches that may help to enhance the quality and efficiency of the sprint cycle by giving detailed information on each single stride of the sprint.