Protocol for the San Diego Nathan Shock Center Clinical Cohort: a new resource for studies of human aging.

INTRODUCTION: While it is well recognised that aging is a heterogeneous process, our understanding of the determinants of biological aging and its heterogeneity remains unclear. The San Diego Nathan Shock Center (SD-NSC) Clinical Cohort aims to establish a resource of biospecimens and extensive donor clinical data such as physical, cognitive and sensory function to support other studies that aim to explore the heterogeneity of normal human aging and its biological underpinnings. METHODS AND ANALYSIS: The SD-NSC Clinical Cohort is composed of 80 individuals across the adult human lifespan. Strict inclusion and exclusion criteria are implemented to minimise extrinsic factors that may impede the study of normal aging. Across three visits, participants undergo extensive phenotyping for collection of physical performance, body composition, cognitive function, sensory ability, mental health and haematological data. During these visits, we also collected biospecimens including plasma, platelets, peripheral blood mononuclear cells and fibroblasts for banking and future studies on aging. ETHICS AND DISSEMINATION: Ethics approval from the UC San Diego School of Medicine Institutional Review Board (IRB #201 141 SHOCK Center Clinical Cohort, PI: Molina) was obtained on 11 November 2020. Written informed consent is obtained from all participants after objectives and procedures of the study have been fully explained. Congruent with the goal of establishing a core resource, biological samples and clinical data are made available to the research community through the SD-NSC.

Fatness but Not Fitness Linked to BrainAge: Longitudinal Changes in Brain Aging during an Exercise Intervention.

PURPOSE: Fitness, physical activity, body composition, and sleep have all been proposed to explain differences in brain health. We hypothesized that an exercise intervention would result in improved fitness and body composition and would be associated with improved structural brain health. METHODS: In a randomized controlled trial, we studied 485 older adults who engaged in an exercise intervention ( n = 225) or a nonexercise comparison condition ( n = 260). Using magnetic resonance imaging, we estimated the physiological age of the brain (BrainAge) and derived a predicted age difference compared with chronological age (brain-predicted age difference (BrainPAD)). Aerobic capacity, physical activity, sleep, and body composition were assessed and their impact on BrainPAD explored. RESULTS: There were no significant differences between experimental groups for any variable at any time point. The intervention group gained fitness, improved body composition, and increased total sleep time but did not have significant changes in BrainPAD. Analyses of changes in BrainPAD independent of group assignment indicated significant associations with changes in body fat percentage ( r (479) = 0.154, P = 0.001), and visceral adipose tissue (VAT) ( r (478) = 0.141, P = 0.002), but not fitness ( r (406) = -0.075, P = 0.129), sleep ( r (467) range, -0.017 to 0.063; P range, 0.171 to 0.710), or physical activity ( r (471) = -0.035, P = 0.444). With linear regression, changes in body fat percentage and VAT significantly predicted changes in BrainPAD ( ß = 0.948, P = 0.003) with 1-kg change in VAT predicting 0.948 yr of change in BrainPAD. CONCLUSIONS: In cognitively normal older adults, exercise did not appear to impact BrainPAD, although it was effective in improving fitness and body composition. Changes in body composition, but not fitness, physical activity, or sleep impacted BrainPAD. These findings suggest that focus on weight control, particularly reduction of central obesity, could be an interventional target to promote healthier brains.

Elevated Pure Tone Thresholds Are Associated with Altered Microstructure in Cortical Areas Related to Auditory Processing and Attentional Allocation.

BACKGROUND: Hearing loss is associated with cognitive decline and increased risk for Alzheimer's disease, but the basis of this association is not understood. OBJECTIVE: To determine whether hearing impairment is associated with advanced brain aging or altered microstructure in areas involved with auditory and cognitive processing. METHODS: 130 participants, (mean 76.4±7.3 years; 65% women) of the Rancho Bernardo Study of Healthy Aging had a screening audiogram in 2003-2005 and brain magnetic resonance imaging in 2014-2016. Hearing ability was defined as the average pure tone threshold (PTA) at 500, 1000, 2000, and 4000 Hz in the better-hearing ear. Brain-predicted age difference (Brain-pad) was calculated as the difference between brain-predicted age based on a validated structural imaging biomarker of brain age, and chronological age. Regional diffusion metrics in temporal and frontal cortex regions were obtained from diffusion-weighted MRIs. Linear regression analyses adjusted for age, gender, education, and health-related measures. RESULTS: PTAs were not associated with brain-PAD (ß= 0.09; 95% CI: -0.084 to 0.243; p = 0.34). PTAs were associated with reduced restricted diffusion and increased free water diffusion primarily in right hemisphere temporal and frontal areas (restricted diffusion: ßs = -0.21 to -0.30; 95% CIs from -0.48 to -0.02; ps < 0.03; free water: ßs = 0.18 to 0.26; 95% CIs 0.01 to 0.438; ps < 0.04). CONCLUSIONS: Hearing impairment is not associated with advanced brain aging but is associated with differences in brain regions involved with auditory processing and attentional control. It is thus possible that increased dementia risk associated with hearing impairment arises, in part, from compensatory brain changes that may decrease resilience.

Social epidemiology of Fitbit daily steps in early adolescence.

BACKGROUND: Sociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US. METHODS: We analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d-1 over the course of 21 days. RESULTS: Participants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d-1. In mixed-effects models, 1543.6 more steps d-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d-1), >$200,000 versus <$25,000 household income (1003.3 more steps d-1), and having married/partnered parents versus unmarried/unpartnered parents (326.3 more steps d-1). We found effect modification by household income for Black adolescents and by sex for Asian adolescents. CONCLUSIONS: Given sociodemographic differences in adolescent steps d-1, physical activity guidelines should focus on key populations and adopt strategies optimized for adolescents from diverse backgrounds. IMPACT: Sociodemographic disparities in physical activity have been documented but mostly rely on self-reported data, which can be limited by reporting and prevarication bias. In this demographically diverse sample of 10-14-year-old early adolescents in the U.S., we found notable and nuanced sociodemographic disparities in Fitbit steps per day. More daily steps were recorded for male versus female sex, Black versus White race, heterosexual versus sexual minority, >$100,000 versus <$25,000 household income, and having married/partnered versus unmarried/unpartnered parents. We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Adolescent sedentary behavior and body composition in early adulthood: results from a cohort study.

BACKGROUND: This study investigates the cross-sectional and prospective associations between accelerometer-measured sedentary behavior and body composition from adolescence to early adulthood. METHODS: Data from the Santiago Longitudinal Study were analyzed (n = 212). Sedentary time was measured at age 16 years, and body composition (body mass index [BMI], waist circumference, waist-to-height ratio [WHtR], fat mass percentage, and lean mass percentage) was examined at both age 16 and 23 years. Adjusted linear regression models estimated associations between sedentary time, sedentary bout duration, and body composition, overall and by sex. RESULTS: In all analyses, mean sedentary bout duration was not associated with body composition. In cross-sectional analyses, more sedentary time during adolescence was significantly associated with lower BMI, waist circumference, WHtR, fat mass percentage, and higher lean mass percentage (p < 0.05). One standard deviation increase in daily sedentary time was prospectively associated with lower body mass index (ß = -1.22 kg/m2, 95% CI: -2.02, -0.42), waist circumference (ß = -2.39 cm, 95% CI: -4.03, -0.75), and WHtR (ß = -0.014, 95% CI: -0.024, -0.004). Sedentary time at 16 years was not associated with changes in body composition from 16 to 23 years. CONCLUSIONS: Sedentary behavior in adolescence is not adversely associated with body composition profiles in early adulthood. IMPACT: Little is known about the effect of device-measured sedentary behavior on body composition during the transition from adolescence to early adulthood. Among participants in the Santiago Longitudinal Study, more accelerometer-measured sedentary time during adolescence was associated with lower BMI, waist circumference, and waist-to-height ratio in early adulthood though point estimates were generally small in magnitude. Sedentary behavior in adolescence was not detrimentally associated with healthy body composition profiles in early adulthood. Public health interventions aimed at reducing obesity rates could consider other behaviors, such as physical activity and healthy diet, instead of sitting time.

Associations of Lean Mass, Muscular Strength, and Physical Function with Trabecular Bone Score in Older Adults.

INTRODUCTION/BACKGROUND: Trabecular bone score (TBS) is an indirect measurement of bone quality and microarchitecture determined from dual-energy X-ray absorptiometry (DXA) imaging of the lumbar spine. TBS predicts fracture risk independent of bone mass/density, suggesting this assessment of bone quality adds value to the understanding of patients' bone health. While lean mass and muscular strength have been associated with higher bone density and lower fracture risk among older adults, the literature is limited regarding the relationship of lean mass and strength with TBS. The purpose of this study was to determine associations of DXA-determined total body and trunk lean mass, maximal muscular strength, and gait speed as a measure of physical function, with TBS in 141 older adults (65-84 yr, 72.5 +/- 5.1 yr, 74% women). METHODOLOGY: Assessments included lumbar spine (L1-L4) bone density and total body and trunk lean mass by DXA, lower body (leg press) and upper body (seated row) strength by one repetition maximum tests, hand grip strength, and usual gait speed. TBS was derived from the lumbar spine DXA scan. Multivariable linear regression determined the contribution of proposed predictors to TBS. RESULTS: After adjusting for age, sex, and lumbar spine bone density, upper body strength significantly predicted TBS (unadjusted/adjusted R2= 0.16/ 0.11, ß coefficient =0.378, p=0.005), while total body lean mass index showed a trend in the expected direction (ß coefficient =0.243, p=0.053). Gait speed and grip strength were not associated with TBS (p>0.05). CONCLUSION: Maximum strength of primarily back muscles measured as the seated row appears important to bone quality as measured by TBS, independent of bone density. Additional research on exercise training targeting back strength is needed to determine its clinical utility in preventing vertebral fractures among older adults.

BrainAge of patients with severe late-life depression referred for electroconvulsive therapy.

BACKGROUND: Severe depression is associated with accelerated brain aging. BrainAge gap, the difference between predicted and observed BrainAge, was investigated in patients with late-life depression (LLD). We aimed to examine BrainAge gap in LLD and its associations with clinical characteristics indexing LLD chronicity, current severity, prior to electroconvulsive therapy (ECT) and ECT outcome. METHODS: Data was analyzed from the Mood Disorders in Elderly treated with Electroconvulsive Therapy (MODECT) study. A previously established BrainAge algorithm (BrainAge R by James Cole, (https://github.com/james-cole/brainageR)) was applied to pre-ECT T1-weighted structural MRI-scans of 42 patients who underwent ECT. RESULTS: A BrainAge gap of 1.8 years (SD = 5.5) was observed, Cohen's d = 0.3. No significant associations between BrainAge gap, number of previous episodes, current episode duration, age of onset, depression severity, psychotic symptoms or ECT outcome were observed. LIMITATIONS: Limited sample size. CONCLUSIONS: Our initial findings suggest an older BrainAge than chronological age in patients with severe LLD referred for ECT, however with high degree of variability and direction of the gap. No associations were found with clinical measures. Larger samples are needed to better understand brain aging and to evaluate the usability of BrainAge gap as potential biomarker of prognosis an treatment-response in LLD. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02667353.

Neuroimaging-Derived Predicted Brain Age and Alcohol Use Among Community-Dwelling Older Adults.

OBJECTIVES: Observational studies have suggested that moderate alcohol use is associated with reduced risk of dementia. However, the nature of this association is not understood. We investigated whether light to moderate alcohol use may be associated with slower brain aging, among a cohort of older community-dwelling adults using a biomarker of brain age based on structural neuroimaging measures. DESIGN: Cross-sectional observational study. PARTICIPANTS: Well-characterized members of a longitudinal cohort study who underwent neuroimaging. We categorized the 163 participants (mean age 76.7 ± 7.7, 60% women) into current nondrinkers, light drinkers (1-7 drinks/week) moderate drinkers (>7-14 drinks/week), or heavier drinkers (>14 drinks/week). MEASUREMENTS: We calculated brain-predicted age using structural MRIs processed with the BrainAgeR program, and calculated the difference between brain-predicted age and chronological age (brain-predicted age difference, or brain-PAD). We used analysis of variance to determine if brain-PAD differed across alcohol groups, controlling for potential confounders. RESULTS: Brain-PAD differed across alcohol groups (F[3, 150] = 4.02; p = 0.009) with heavier drinkers showing older brain-PAD than light drinkers (by about 6 years). Brain-PAD did not differ across light, moderate, and nondrinkers. Similar results were obtained after adjusting for potentially mediating health-related measures, and after excluding individuals with a history of heavier drinking. DISCUSSION: Among this sample of healthy older adults, consumption of more than 14 drinks/week was associated with a biomarker of advanced brain aging. Light and moderate drinking was not associated with slower brain aging relative to non-drinking.

Association of Physical Activity and Screen Time With Body Mass Index Among US Adolescents.

Importance: The Physical Activity Guidelines Advisory Committee Scientific Report identified important research gaps to inform future guidance for adolescents, including limited evidence on the importance of sedentary behaviors (screen time) and their interactions with physical activity for adolescent health outcomes, including overweight and obesity. Objective: To identify the independent associations of physical activity and screen time categories, and the interactions between physical activity and screen time categories, with body mass index (BMI) and overweight and obesity in adolescents. Design, Setting, and Participants: This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) Study collected from September 10, 2018, to September 29, 2020. Data were analyzed from July 8 to December 20, 2022. A total of 5797 adolescents aged 10 to 14 years from 21 racially and ethnically diverse study sites across the US were included in the analysis. Exposures: Categories of total step count per day (with 1000 to 6000 steps per day indicating low, >6000 to 12 000 steps per day indicating medium, and >12 000 steps per day indicating high), as measured by a wearable digital device (Fitbit), and categories of self-reported screen time hours per day (with 0 to 4 hours per day indicating low, >4 to 8 hours per day indicating medium, and >8 hours per day indicating high). Main Outcomes and Measures: Participant BMI was calculated as weight in kilograms divided by height in meters squared and converted into sex- and age-specific percentiles in accordance with the Centers for Disease Control and Prevention growth curves and definitions. Individuals were classified as having overweight or obesity if their BMI was in the 85th percentile or higher for sex and age. Results: Among 5797 adolescents included in the analytic sample, 50.4% were male, 61.0% were White, 35.0% had overweight or obesity, and the mean (SD) age was 12.0 (0.6) years. Mean (SD) reported screen time use was 6.5 (5.4) hours per day, and mean (SD) overall step count was 9246.6 (3111.3) steps per day. In models including both screen time and step count, medium (risk ratio [RR], 1.24; 95% CI, 1.12-1.37) and high (RR, 1.29; 95% CI, 1.16-1.44) screen time categories were associated with higher overweight or obesity risk compared with the low screen time category. Medium (RR, 1.19; 95% CI, 1.06-1.35) and low (RR, 1.30; 95% CI, 1.11-1.51) step count categories were associated with higher overweight or obesity risk compared with the high step count category. Evidence of effect modification between screen time and step count was observed for BMI percentile. For instance, among adolescents with low screen use, medium step count was associated with a 1.55 higher BMI percentile, and low step count was associated with a 7.48 higher BMI percentile. However, among those with high screen use, step count categories did not significantly change the association with higher BMI percentile (low step count: 8.79 higher BMI percentile; medium step count: 8.76 higher BMI percentile; high step count: 8.26 higher BMI percentile). Conclusions and Relevance: In this cross-sectional study, a combination of low screen time and high step count was associated with lower BMI percentile in adolescents. These results suggest that high step count may not offset higher overweight or obesity risk for adolescents with high screen time, and low screen time may not offset higher overweight or obesity risk for adolescents with low step count. These findings addressed several research gaps identified by the Physical Activity Guidelines Advisory Committee Scientific Report and may be used to inform future screen time and physical activity guidance for adolescents.

Greater accelerometer-measured physical activity is associated with better cognition and cerebrovascular health in older adults.

OBJECTIVES: Physical activity (PA) may help maintain brain structure and function in aging. Since the intensity of PA needed to effect cognition and cerebrovascular health remains unknown, we examined associations between PA and cognition, regional white matter hyperintensities (WMH), and regional cerebral blood flow (CBF) in older adults. METHOD: Forty-three older adults without cognitive impairment underwent magnetic resonance imaging (MRI) and comprehensive neuropsychological assessment. Waist-worn accelerometers objectively measured PA for approximately one week. RESULTS: Higher time spent in moderate to vigorous PA (MVPA) was uniquely associated with better memory and executive functioning after adjusting for all light PA. Higher MVPA was also uniquely associated with lower frontal WMH volume although the finding was no longer significant after additionally adjusting for age and accelerometer wear time. MVPA was not associated with CBF. Higher time spent in all light PA was uniquely associated with higher CBF but not with cognitive performance or WMH volume. CONCLUSIONS: Engaging in PA may be beneficial for cerebrovascular health, and MVPA in particular may help preserve memory and executive function in otherwise cognitively healthy older adults. There may be differential effects of engaging in lighter PA and MVPA on MRI markers of cerebrovascular health although this needs to be confirmed in future studies with larger samples. Future randomized controlled trials that increase PA are needed to elucidate cause-effect associations between PA and cerebrovascular health.