Experimental Gerontology

Background. Maximal oxygen uptake (VO2max) is a leading marker of cardiorespiratory fitness and a strong predictor of all-cause mortality. Cardiopulmonary exercise testing (CPET) is the reference method but is resource-intensive, so consumer wearables estimate VO2max from passively collected signals; these estimates compress the fitness range, returning near-correct group averages while ranking individuals poorly. No peer-reviewed validation of a smart-ring VO2max estimate against CPET has been reported, and none in a South Asian cohort. Objective. To validate the Ultrahuman Ring AIR VO2max estimate against laboratory CPET, benchmark it against published prediction equations, and assess its generalization and construct validity. Methods. In a single-site paired ring-CPET cohort (N = 101; mean CPET peak VO2 43.3 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1}, SD 9.9), peak oxygen uptake was measured by treadmill or cycle-ergometer CPET, and the Ultrahuman Ring AIR estimate was computed from passively collected signals using a transparent ensemble based on published equations. Ensemble weights and calibration were selected on an 85-subject development set by an automated search minimizing a composite 5-fold cross-validated error criterion; the locked estimate was evaluated on a 16-subject held-out test set. The calibrated coefficients are proprietary. Agreement was quantified with mean absolute error (MAE), bias, Pearson r, regression slope and Lin's concordance correlation coefficient (CCC; bootstrap 95% CIs), and Bland-Altman limits of agreement. Separately, in 181,133 de-identified Ring users (no CPET reference), construct validity was assessed against ring-measured sleep, continuous glucose monitoring (n = 2,597), and a venous blood panel (n up to 15,203), adjusted for age, sex, and BMI, with lipoprotein(a) as a pre-specified negative control. Reporting followed TRIPOD and STARD. Results. With a self-reported fitness level provided, the estimate agreed with CPET peak VO2 at MAE 4.68 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1} (95% CI 3.93 to 5.49), Pearson r 0.79, CCC 0.79, and slope 0.71. The five published equations were worse on every metric (MAE 6.2 to 10.6, CCC 0.28 to 0.56, slope 0.32 to 0.42), each compressing the fitness range. On the held-out test set (n = 16), agreement held (r 0.84, slope 0.81, MAE essentially unchanged). Without the fitness input, full-cohort MAE was 5.16, still ahead of every published equation. At population scale, higher estimated fitness tracked a healthier profile on measurements the estimate does not use: better ring-measured sleep; higher continuous-glucose time in target range (79.6% versus 61.5%, top versus bottom decile; n = 222 and 399 of 2,597 users); and lower triglycerides, fasting glucose, and HOMA-IR (n up to 15,203 assayed per marker). These associations held after adjustment for age, sex, and BMI, whereas the pre-specified negative control lipoprotein(a) did not separate the deciles. Conclusions. The Ultrahuman Ring AIR VO2max estimate agreed with laboratory CPET substantially better than published prediction equations, held its agreement on held-out subjects, and ordered a large population along independent cardiometabolic gradients consistent with true fitness.

Objectives: We tested how multi-level socioeconomic disadvantage relates to biological aging and systemic inflammation in women and men from the population-based Canadian Longitudinal Study on Aging (CLSA). Methods: We examined cross-sectional data from 8,516 CLSA participants with baseline measures on systemic inflammatory biomarkers (C-reactive protein, interleukin-6, and tumoral necrosis factor-) and biological aging (metabolomic and six DNA methylation [DNAm] age estimates). Plasma samples underwent metabolomic profiling by Metabolon, Inc. Metabolomic age was estimated separately in males and females using sex-stratified models based on age-correlated metabolite levels. DNAm data generated using the Illumina Infinium MethylationEPIC v1.0 array were used to estimate DNAm age across six established models, including Horvath, Hannum, PhenoAge, GrimAge, GrimAge2, and DunedinPACE. We used log-transformed metabolite levels to calculate metabolomic age by sex. We linked education, income, material and social deprivation to biomarkers of systemic inflammation and biological aging stratified by sex using generalized linear models. Multivariable models were adjusted by age, major behavioral risk factors, and chronic conditions. Results: Participants were aged on average of 62.6 years of age, and approximately 50% were females. In multivariable linear adjusted models, we found that in comparison to those earning [&ge;]$100K a year, women earning less <$20K were on average 1.14 (95%CI: 0.46, 1.82) year older with respect to metabolomic age; those earning [&ge;]$20K & <$50K were on average 0.90 (95%CI: 0.26, 1.53) years older; and those earning [&ge;]$50K & <$100K were on average 0.70 (95%CI: 0.05, 1.34) years older. We did not observe this dose response among men. A similar dose-response association was observed for interleukin-6 in both men and women. Discussion: These findings suggest that socioeconomic adversity influences not only inflammatory pathways but also distinct biological aging processes, including metabolomic aging.

ABSTRACT Background: Onset of cancer is linked to psychological distress and cancer is prevalent in older adults. Yet, the association to suicide is scarcely examined. The aim of this study was to assess whether cancer diagnosed in older adults is associated with suicide incidence. Methods: All older adults (65+ years) who lived in Austria in the years 2014-2021 (n=2,175,134) were followed. Of these, 223,932 were diagnosed with a new cancer. We used non-parametric survival models with inverse-probability-treatment weights to compare risk ratios (relative risk) and risk differences (absolute risk) of older adults with and without cancer. Results: Out of 2,158 suicide deaths, 442 (20.5%; 83.7% males) occurred among older adults with a new cancer diagnosis. The incidence rate was 74 among those with a new cancer diagnosis versus 23 per 100,000 person-years among those with no new cancer. One year after being diagnosed, older adults with a new cancer had a 4 times higher relative risk of dying by suicide compared to those without. The risk was highest within the first three months after diagnosis and for cancers with a poor prognosis (disseminated disease; lung, oesophagus, stomach, liver, pancreas, and brain cancers). The absolute risk of dying by suicide within 5 years after cancer diagnosis was 0.18% versus to 0.11% among those with no new cancer. Discussion: Older adults who received a new cancer diagnosis had elevated suicide risks. Provision of support to cope with mental distress should be considered at cancer diagnosis, especially for older adults with a poor prognosis.

Background. Given uncertainty about whether later-life health at similar ages is improving over time, we examined trends across multiple health domains. Methods. We analysed data from community-dwelling adults aged 50 and older in the English Longitudinal Study of Ageing in 2004/05, 2012/13, and 2023/24 (main survey: N=8389, 8549, and 6090, respectively). Outcomes included self-rated health, limiting long-standing illness, pain, mobility limitations, cardiometabolic and chronic conditions, obesity, inflammation, mental health, quality of life and memory. Weighted pooled modified Poisson and linear regressions compared outcomes over time, overall, and by age group and education, with additional adjustment for sex and wealth. Results. Adjusted estimates showed divergent trends. Fair/poor self-rated health increased from 27% to 34%, and any pain from 37% to 47%, whereas mobility impairments declined from 58% to 52%. Self-reported high cholesterol increased from 19% to 39%, while biomarker-defined high cholesterol declined from 78% to 54%; diabetes increased on both measures. Psychiatric problems increased from 6% to 10%, quality of life declined, and memory improved. However, trends differed by age and education, particularly for limiting long-standing illness, mobility limitations, cholesterol biomarkers, and mental health, indicating that aggregate trends masked unevenly distributed changes. Conclusion. Later-life health in England has not improved uniformly. Gains in functioning, biomarkers, and cognition coexist with rising pain and poorer mental health. Trends were also socially and age patterned, producing increasingly multidimensional and socially patterned health outcomes. Multidomain health monitoring is essential for interpreting population health trends and planning healthy ageing, prevention, long-term care, and work policies.

ABSTRACT Background: Athletes who return to soccer after anterior cruciate ligament reconstruction (ACLR) remain at elevated risk of secondary injury despite meeting conventional discharge criteria, and neuromuscular deficits in the reconstructed limb are known to be exposed by fatigue. Objective: To determine whether match-play fatigue differentially affects muscle stiffness, countermovement jump (CMJ) force symmetry, and rate of force development (RFD) asymmetry between soccer players with a history of ACLR and uninjured teammates. Methods: A prospective, cross-sectional, matched-control study enrolled 128 competitive soccer players (64 ACLR, 6-22 months post-surgery; 64 uninjured controls) across five recruitment waves (February-June 2026). Bilateral CMJ peak vertical force, jump height, RFD, and myotonometric stiffness of the rectus femoris (RF), vastus medialis (VM), and biceps femoris (BF) were recorded immediately before and after a standardized competitive match. Fatigue was quantified from second-half heart rate (percentage of age-predicted maximum) and end-match rating of perceived exertion (RPE). Within-group pre-to-post changes were evaluated with paired t-tests, between-group differences in the magnitude of change with independent-samples t-tests, and associations between fatigue indices and asymmetry changes with Pearson correlations. Results: Match play reduced CMJ limb symmetry index (LSI) in both groups, but the decline was more than three-fold greater in the ACLR group, 92.6% (SD 5.4%) to 85.1% (SD 7.1%), than in control group, 97.3% (SD 3.9%) to 95.0% (SD 4.2%), group-by-time difference, p < 0.001, (d = 0.64). RFD asymmetry approximately doubled in the ACLR group, 10.6% (SD 4.1%) to 17.6% (SD 6.5%), compared with a smaller rise in control group, 4.6% (SD 2.4%) to 6.3% (SD 3.7%); p < 0.001, d = 0.77). Involved-limb stiffness losses in the ACLR group exceeded those of controls for the RF (-21.2 vs. -9.2 N/m, p < 0.001), VM (-17.7 vs. -6.1 N/m, p < 0.001), and BF (-13.3 vs. -6.6 N/m, p < 0.001), whereas uninvolved-limb stiffness losses did not differ between groups (all p > 0.05). Fatigue markers (heart rate, RPE) were not significantly correlated with the magnitude of individual asymmetry change (|r| [&le;] 0.18, p > 0.15). Conclusions: In competitive soccer players 6-22 months after ACLR, match-play fatigue selectively compromises stiffness and explosive force output of the reconstructed limb, widening inter-limb asymmetries beyond what is seen in uninjured teammates, even though global cardiovascular and perceptual fatigue were comparable between groups. These findings suggest that return-to-sport testing performed only in a rested state may underestimate residual neuromuscular deficits, and support fatigue-inclusive assessment protocols before athletes are cleared for unrestricted competition. Abbreviations: ACL: anterior cruciate ligament, ACLR: anterior cruciate ligament reconstruction, BF: biceps femoris, CMJ: countermovement jump, HRmax: maximum heart rate, LSI: limb symmetry index, RF: rectus femoris, RFD: rate of force development, RPE: rating of perceived exertion, RTS: return to sport, VM: vastus medialis, SD: standard deviation. Keywords: Anterior cruciate ligament reconstruction, muscle fatigue, muscle stiffness, countermovement jump, limb symmetry index, rate of force development, soccer, return to sport.

Deep learning classifiers applied to structural MRI (sMRI) have achieved high performance in detecting Alzheimer's Disease (AD), yet systematic investigation of their failure modes remains limited. In this study, we trained two deep learning architectures to classify AD from cognitively normal (CN) participants using sMRI data from the ADNI dataset, and examined whether misclassifications persist across models and training configurations. We identified a subgroup of subjects who were persistently misclassified across 100 model instances, and found that these subjects exhibited a markedly different atrophy subtype distribution compared to correctly classified AD cases, with substantial enrichment of hippocampal-sparing and minimal atrophy subtypes. To disentangle whether persistent false negatives (FN) reflect earlier disease stage or atypically presenting disease, we analyzed longitudinal follow-up scans and tested whether model predictions changed as neurodegeneration progressed. A change in prediction (from FN to true positive (TP)) was observed in only a subgroup of subjects and required intervals of up to five years, suggesting that persistent misclassification may not always be explained by disease staging alone. Although the sample size is small, these findings underscore the importance of accounting for disease heterogeneity in the development and evaluation of clinical AI models for AD detection.

Purpose This study aimed to examine the association between screen time and depressive symptoms among Chinese college students, and to investigate the mediating roles of sleep quality and emotion regulation in this relationship. Furthermore, a serial mediation model was constructed to elucidate the underlying psychological mechanisms linking screen exposure to depression. Methods A stratified cluster sampling method was employed to recruit 10,999 college students for a cross-sectional questionnaire survey. Data were collected on screen time, sleep quality, emotion regulation ability, and depressive symptoms. Descriptive statistics, correlation analyses, and regression analyses were conducted using SPSS 26.0 A serial mediation model was tested using the PROCESS macro (Model 6), and bootstrapping procedures were applied to estimate the significance of indirect effects. Results Correlation analyses indicated that screen time was significantly positively associated with depressive symptoms (r = 0.16, p < 0.01) and sleep quality (r = 0.15, p < 0.01), and significantly negatively associated with emotion regulation (r = -0.13, p < 0.01). Sleep quality was positively correlated with depressive symptoms (r = 0.31, p < 0.01), whereas emotion regulation was negatively correlated with depressive symptoms (r = -0.42, p < 0.01). Regression analyses further showed that screen time significantly positively predicted depressive symptoms ({beta} = 0.712, p < 0.001), positively predicted sleep quality ({beta} = 0.217, p < 0.001), and negatively predicted emotion regulation ({beta} = -0.085, p < 0.001). In addition, both sleep quality ({beta} = 1.318, p < 0.001) and emotion regulation ({beta} = -0.424, p < 0.001) were significant predictors of depressive symptoms. Mediation analyses demonstrated that sleep quality significantly mediated the association between screen time and depressive symptoms (95% CI [0.239, 0.332]), as did emotion regulation (95% CI [0.269, 0.416]). Moreover, a significant serial mediation effect of sleep quality and emotion regulation was observed in the relationship between screen time and depressive symptoms (95% CI [0.082, 0.117]). Conclusion Screen time is significantly associated with depressive symptoms among college students, with sleep quality and emotion regulation serving as important mediating mechanisms. Extended screen exposure may be linked to higher levels of depressive symptoms by impairing sleep quality and weakening emotion regulation capacity.

Magnetic Resonance Imaging (MRI) derived brain age varies substantially between individuals, but it remains unclear whether early deviations from normal brain ageing precede future cognitive decline and whether they provide predictive value beyond conventional MRI measures. Here, we investigated whether MRI-derived brain age gap estimation (BrainAGE) identifies early structural brain ageing differences among cognitively normal individuals who later develop mild cognitive impairment (MCI) or dementia. We analysed longitudinal structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and replicated the main findings in the population-based Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE). Individuals who later converted to MCI or dementia had higher BrainAGE values several years before diagnosis and, in ADNI, showed steeper longitudinal increases than stable individuals. Elevated BrainAGE values were also associated with increased risk of future conversion to MCI in cognitively healthy individuals and faster subsequent memory decline. Cross-sectional differences and the association between BrainAGE and risk of future conversion were replicated in OSTPRE. Importantly, adding BrainAGE to models including demographic, APOE4, cognitive, and MRI-derived measures consistently improved prediction of future cognitive outcomes, with the greatest benefit observed for individuals who converted after longer follow-up. These findings show that structural brain ageing begins to diverge years before the onset of MCI. BrainAGE captures this early divergence, providing complementary information beyond conventional structural MRI measures that may improve the early identification of cognitively normal individuals at increased risk of future cognitive decline when integrated with other biomarkers.

Background: Plasma phosphorylated tau-217 (pTau-217) measurements for use in Alzheimer disease (AD) identification require thresholds to define positivity and there exist different approaches to operationally defining the boundary. We compared amyloid {beta} (AB) PET-anchored and distribution-based positivity cut-off values and explored how these mapped onto latent biomarker states. Methods: We analysed plasma pTau-217 measured in the Bio-Hermes-001 cohort (N = 990) using an immunoassay (Lilly) and mass spectrometry assay (University of Gothenburg). Gaussian mixture models were used to identify latent classes and thresholds were derived in two ways: achieving 90% specificity for AB PET positivity and exceeding the mean + 2SDs of the lowest latent class. We explore classes in reference to AB PET status and clinical diagnosis, as well as agreement between approaches using Cohen kappa for both assays. Results: In both assays, three latent biomarker classes were identified with monotonic increases in AD clinical diagnosis and AB PET positivity. PET-anchored thresholds showed lower specificity but higher sensitivity to amyloid positivity than distribution-based thresholds. Overall agreement between the approaches was acceptable (k = 0.678 for Lilly and 0.575 for University of Gothenburg), with disagreement concentrated in the intermediate latent class. Classes with the lowest and highest pTau-217 concentrations were classified consistently using both thresholds Discussion: The two thresholding approaches yielded similar classifications at both the negative and positive tail of the observed biomarker distribution, but classify intermediate concentrations differently. The boundary definition influenced pTau-217 positivity more than the analytical platform itself. Thresholding approaches may capture different pTau-217 biomarker states, therefore such methodological decisions should be grounded in the context of the intended application.

Background: Accelerated biological aging can be assessed with DNA methylation (DNAm)- based epigenetic clocks. Research suggests that greater DNAm is associated with faster cognitive decline and risk of Alzheimer disease (AD) and other dementias. However, most studies have relied on single-time-point measurements of clocks, rather than evaluating dynamic changes over time. We examined the association between 15-year epigenetic aging trajectories and brain health outcomes in midlife. Methods: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with [&ge;]3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within &plusmn1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, A{beta}42/A{beta}40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*{varepsilon}4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes). Results: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower A{beta}42/A{beta}40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group. Conclusion: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.

To estimate the life expectancy gains that could be realised from increases in Queenslanders physical activity (PA) levels. Design Lifetable analysis Setting, Participants We modelled the 2025 Queensland population aged [&ge;]40 years. Modelled scenarios We applied two approaches. In the first, we estimated life expectancy differences between device-measured PA quartiles, with quartile1 representing the least active and quartile 4 the most active. In the second, we compared observed device-measured PA levels in Queensland with scenarios in which all individuals moved to either [&ge;]12,000 steps/day or [&le;]2,000 steps/day. We converted the steps per day by age group and PA quartile into equivalent daily minutes of moderate-intensity walking at 4.8 km/h. Additional scenarios were explored in sensitivity analyses. Main outcomes Changes in life expectancy, and total life-years gained over the lifetime of the modelled population. Benefits were also translated into minutes of life gained per additional hour walked. Results If all Queenslanders aged [&ge;]40 years were as active as the most active quartile, life expectancy at birth could be 88.3 years, an increase of 4.8 years above the life expectancy at observed activity levels. The life expectancy differences between individuals in the least active quartile and the most active quartile was 9.7 years. Achieving the activity level of the most active quartile would require individuals in the lowest activity quartile to undertake an additional 85.9 minutes/day of moderate-intensity walking, with each extra hour of PA associated with an average gain of approximately 3 hours (177 minutes) of life. In step-based modelling, life expectancy in the most active scenario (all achieving [&ge;]12,000 steps/day) was higher by {approx}7.1 years compared with the least active scenario (all at [&le;]2,000 steps/day). Conclusions Increasing PA could yield meaningful gains in life expectancy for Queenslanders, with the largest gains seen in least active individuals. Our findings strengthen the case for prioritising investment in PA -promoting programs and environments.

Introduction: The Diabetes Prevention Program Outcomes Study (DPPOS) is an established cohort of aging persons with pre-diabetes and type 2 diabetes with 25 years of median follow-up. In 2022 DPPOS added Alzheimer's disease (AD), and AD related dementias (ADRD) phenotyping using the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDSv3), which included a standardized neurological examination across 25 clinical sites, administered by clinical staff and interpreted centrally by clinicians. Methods: A DPPOS video-based asynchronous neurological examination (DPPOS-VANE) was developed iteratively through consensus from research clinicians and staff feedback to harmonize with UDSv3 to identify common neurological diagnoses aside from dementia including diabetic cranial neuropathies, stroke and parkinsonism. DPPOS-VANE was designed to be conducted without direct participant contact by the examiner, reproducible, and independent of clinical skills of PCs. An iPad camera recorded the video exam, comprised of assessments of extraocular and facial movements, visual fields, speech, gross motor strength, pronator drift, praxis and parkinsonism. A 10-minute training video demonstrated the examination step-by-step with scripts and instructions in English and Spanish. Site-specific performance review, feedback, and staff certification preceded central reading of video recordings by physicians. After two years of implementation, 1286 DPPOS-VANEs led to 1284 examination reviews. Of these, 1204 (93%) were completed by having the examiner follow the standard script. Overall, 1237 examinations (96%) were delivered as planned, 41 (3%) had minor errors but were still usable, and 6 (0.4%) had major deviations in exam technique; two additional recorded evaluations were not usable as recorded videos were inaccessible due to technical errors. Each examination was completed within 10-15 minutes. Each site on average completed 51.4 examinations (range 14-92). Discussion: Engaging 55 research staff across 25 sites and 3 physician-reviewers, this study is the first to demonstrate feasibility of a VANE as an efficient neurological examination model enabled by commonly used devices. Such a multisite standardized VANE represents a novel paradigm for large epidemiological studies.

INTRODUCTION: Brain age gap (BAG) is the difference between a person's chronological age and the age predicted from the structural appearance of their brain on MRI. A higher BAG indicates an older-appearing brain and provides a global marker of structural brain aging across the Alzheimer's disease continuum. Prior studies suggest that females may show greater Alzheimer's disease-related pathology or faster late-stage neurodegeneration than males. We tested whether sex was associated with baseline BAG or longitudinal BAG change after accounting for APOE {epsilon}4 genetic risk, amyloid positivity, cognitive severity, and disease stage. METHODS: We developed a domain-adaptive deep learning model to estimate BAG from T1-weighted MRIs, training it on 26,512 neurologically healthy UK Biobank data and fine-tuning it on 2,974 amyloid-negative cognitively normal samples from Mayo Clinic Study of Aging and OASIS-3 cohorts. We applied the model to ADNI and used hierarchical mixed-effects models to test whether sex was associated with BAG trajectories after adjusting for Alzheimer's disease risk factors. RESULTS: After adjustment for Alzheimer's disease risk factors, there was no baseline sex differences in BAG. Longitudinally, females showed greater BAG acceleration than males, but this effect was moderated by APOE {epsilon}4 status. APOE {epsilon}4 accelerated brain aging in a dose-dependent manner, independent of amyloid burden. DISCUSSION: Sex differences in BAG across the AD continuum were largely explained by APOE {epsilon}4-related acceleration rather than by an independent effect of sex alone. These findings suggest that females may be more vulnerable to APOE {epsilon}4-associated structural brain aging over time.

Background: Bidirectional next-day associations between sleep disturbances and affective symptoms have been shown in previous research, yet the consecutive day effects between these factors remains poorly understood. Methods: We analysed longitudinal ecological momentary assessment (EMA) data obtained from a subsample of young persons in the Brazilian High-Risk Cohort (BHRC) study collected in 2020-2021. Participants reported sleep quality each morning and rated affective symptoms relating to mood, anxiety, and energy four times daily for 28 days. We selected 88 individuals (17.83{+/-}1.74 years, 56 [63.6%] female gender) with at least one instance where individuals were observed three-days in a row. Within-person bidirectional next-day effects between sleep quality and affective symptoms were estimated using mixed-effects regression analysis adjusting. We then applied g-estimation approaches to estimate the effect that lagged sleep quality and consecutive improvements in sleep quality had on affective symptoms. Results: Sleep quality and affective symptoms had bidirectional next-day effects, with sleep quality tending to have greater influence on affective symptoms than the reverse. Improved lagged sleep quality had positive effects on affective symptoms incrementally above the prior night's sleep quality. Also, improvement of sleep quality across consecutive days had incremental and approximately equal effects on affective symptoms. Conclusions: Sleep quality and affective symptoms exhibit a feedback loop, whereby poor sleep quality influences affective symptoms over consecutive days. Breaking these feedback loops, by improving sleep quality across several consecutive nights should improve affective symptoms. This supports interventions that target sustained improvement in sleep and possibly circadian regulation to improve affective symptoms.

INTRODUCTION: The Diabetes Prevention Program (DPP) was a randomized clinical trial designed to prevent type 2 diabetes (T2D) in adults with prediabetes. The DPP Outcomes Study (DPPOS) is the 30-year follow-up of this cohort, focusing on T2D, prediabetes, and related complications. Cognitive assessments began in 2009 and expanded in 2022 to examine cognitive impairment, including Alzheimer's disease (AD) and AD related dementias (ADRD), in the surviving cohort. To support these aims, the National Alzheimer's Coordinating Center Uniform Data Set version 3 (NACC-UDSv3), the standardized framework used by Alzheimer's Disease Research Centers, was implemented in DPPOS in 2022 to enable data sharing with NACC. These forms were complemented by cognitive tests administered in DPPOS. We aimed to integrate the NACC-UDSv3 into the existing longitudinal DPPOS framework while maintaining fidelity to its structure and developing automated reports to streamline cognitive outcomes adjudication. METHODS: Items from the 16 NACC-UDSv3 data forms were compared with those already collected within DPPOS to integrate overlapping similar items, add missing NACC-UDSv3 items, and create a dataset harmonized with NACC-UDSv3. Forms were adapted for electronic data capture (EDC) using the MIDAS (Multimodal Integrated Data Acquisition System, George Washington University). Automated reports integrated current and prior neuropsychological scores to support adjudications. In the first wave of the DPPOS-AD/ADRD study, 1561 cognitive adjudications were successfully completed using the harmonized DPPOS and NACC-UDSv3 data implemented into MIDAS. DISCUSSION: The DPPOS-AD/ADRD project demonstrated that NACC-UDSv3 can be successfully integrated into a long-standing longitudinal cohort not originally designed for AD/ADRD research. The harmonization, electronic capture, and automated adjudication processes may provide a practical framework for other cohorts seeking to incorporate NACC-UDSv3 to align with national AD/ADRD research standards.

The apnea-hypopnea index (AHI), the conventional metric of obstructive sleep apnea (OSA) severity, is typically studied using scalar summaries of sleep architecture, such as the total time spent in each sleep stage. Although clinically interpretable, these summaries fail to capture the temporal organization of overnight sleep-stage sequences and may obscure stage-specific associations with OSA severity. Modeling the complete sleep-stage trajectory provides substantially richer temporal information; however, because total sleep duration varies across individuals, sleep-stage trajectories are observed over subject-specific domains, limiting the applicability of conventional functional regression methods that assume a common observation interval. We therefore applied Variable-Domain Functional Regression (VDFR) to overnight polysomnographic data from the APPLES study (n= 1,103), treating the epoch-by-epoch sleep-stage sequence as a continuous, variable-length functional predictor of AHI. We compared three levels of sleep-stage granularity: five stages (Wakefulness, N1, N2, N3, REM), three stages (Wakefulness, Non-REM, REM), and binary staging (Wakefulness vs. Sleep). Functional sleep-stage terms were significant across all staging granularities and model structures (all p-values [&le;]0.001). Wake, N1, and N2 were positively associated with AHI, whereas N3 and REM were negatively associated, with REM exhibiting the strongest association. These effects were attenuated under coarser staging representations, highlighting the importance of preserving fine-grained sleep architecture. To our knowledge, this is the first application of VDFR to overnight polysomnographic data in OSA, showing that accommodating subject-specific sleep durations enables the identification of stage-specific temporal associations with AHI severity that are attenuated or obscured by coarser staging and conventional scalar analyses.

Introduction Adolescent idiopathic scoliosis (AIS) requires frequent x-rays for management, exposing young patients to cumulative radiation risks. While radiation-sparing imaging modalities exist, access across the National Health Service (NHS) remains uneven and information given to patients is variable. This qualitative study investigated the systemic, geographic, and interpersonal dynamics of AIS imaging in England. Design This qualitative study employed in-depth semi-structured interviews with healthcare professionals (HCPs) from NHS paediatric spinal centres, patients aged 13 to 25 years old with AIS and parents/carers of young people with AIS. Setting England. Participants A total of 22 HCPs from 13/24 NHS paediatric spinal centres in England, 19 10-25 years with AIS and 11 parents/carers. Results Conventional x-ray remains the main imaging modality. Significant geographic inequality exists. The most commonly available radiation-sparing imaging modality available is the EOS system, which uses slot-scanning technology, is available at 7 centres in England, primarily in London imaging networks. Acquisition of EOS systems is currently driven by local charitable funding rather than a centralised strategy, with high capital and installation costs cited as primary barriers. Inconsistent knowledge of imaging within primary care and a lack of specialist expertise in local secondary care services led to diagnostic redundancy, gatekeeping, and low value inconsistent imaging. These systemic delays frequently closed the window for conservative treatments like bracing. A professional balancing act exists between the duty to inform and the desire to minimise patient anxiety. HCPs often use selective communication regarding radiation risks. Conversely, families demonstrate high relational trust with HCPs and low baseline knowledge of cumulative exposure, often viewing frequent imaging as a reassuring marker of clinical progress. In centres with EOS systems, clinicians felt empowered to lead proactive, transparent risk discussions. In standard X-ray settings, dialogue remains reactive and infrequent, leading to a reliance on implied rather than truly informed consent. Conclusions AIS imaging in England is variable. Geographic location dictates access to low-dose radiation technology and the quality of informed consent. Systemic inefficiencies and fragmented referral pathways contribute to diagnostic redundancy and delayed specialist care. National standardisation of clinical pathways, information provision and a centralised strategy for low-dose technology procurement are essential to eliminate structural inequalities and ensure equitable, transparent care for all patients.

Autonomic dysfunction is a common sequela of mild traumatic brain injury (mTBI). Physical activity progression is an integral component of mTBI rehabilitation, particularly in addressing autonomic dysfunction. However, clinicians often rely on point-in-time evaluation of orthostatic and exercise intolerance to guide activity recommendations. Commercially available wearable devices (e.g., Fitbits) provide an opportunity to evaluate heart rate response to activity in a real-world setting. Previous work has used physiologic (heart rate) and activity (step count) data to identify subgroups of adults with stroke that may be used to guide activity recommendations. This method may be useful to subgroup youth post-mTBI to identify those who have abnormal physiologic responses to activity. We aimed to identify subgroups using heart rate and step count data in adolescents presenting for specialty care after diagnosed mTBI. Eighty participants aged 13-18 within six months of mTBI diagnosis were recruited to wear a Fitbit Sense 2. Data from seven days and two nights collected within fourteen days of enrollment were included. A group-based steps per minute (SPM) threshold (25th percentile; 10 SPM) and individualized heart rate threshold (20% heart rate reserve (HRR)) were used to classify each minute of active daytime data into one of four quadrants: SPM>10 & HRR>20% (QI), SPM<10 & HRR>20% (QII), SPM<10 & HRR<20% (QIII), and SPM>10 & HRR<20% (QIV). We used percentage of minutes in each quadrant, mean steps per day, percentage of minutes with zero steps, mean SPM in QI, and resting heart rate in a k-means clustering algorithm to identify subgroups. We evaluated subgroup differences by clustering variables using Kruskal-Wallis tests. Sixty-one participants were included. Three subgroups emerged: Sedentary (n=12), Active (n=23), and Atypically Elevated Heart Rate (AEHR; n=26). Subgroups varied significantly on all clustering variables (p<0.01). The Active subgroup took a high number of steps per day, had lower sedentary time, and had the highest activity intensity (mean SPM in QI). The Sedentary subgroup took fewer steps per day compared to the Active subgroup, had high sedentary time, and showed the highest resting heart rate. The AEHR subgroup took fewer steps per day compared to the Active subgroup and had high sedentary time. The AEHR subgroup also spent a higher percentage of time with an atypically high heart rate response to low levels of activity compared to the other subgroups. Our findings suggest that data from wearable devices can identify subgroups of adolescents with mTBI with distinct physiologic/physical activity profiles, which may ultimately be used to inform personalized activity prescriptions. Future work should aim to understand how the identified subgroups relate to longitudinal outcomes.

Background: Electronic health record (EHR)-linked biorepositories provide opportunities to advance epidemiological research in Alzheimer's disease (AD) and related dementias. Objective: Evaluate the extraction, curation, and associative validity of Mini Mental State Examination (MMSE) scores from the VA EHR for participants in the VA Million Veteran Program (MVP). Methods: The sample (N = 49,555; 7.4% women) included a multiethnic cohort (European [68.3%], African [20.4%], Hispanic [9.0%]) with EHR-extracted MMSE scores; 30.7% were apolipoprotein E (APOE) {epsilon}4 carriers, and 25.8% had multiple scores. Linear regressions examined cross-sectional associations between {epsilon}4 dosage (0, 1, 2) and first and lowest MMSE scores. MMSE scores were also evaluated against MVP dementia diagnostic algorithms in participants aged [&ge;]65 years. Results: Among participants of European ancestry, there was a significant {epsilon}4 dose-response relationship (ps < .001) with MMSE scores. Homozygote carriers scored lower than heterozygote carriers (Mdiff: first = -0.5; lowest = -0.9), who scored lower than non-carriers (Mdiff: first = -0.4; lowest = -0.6). Among Veterans of African and Hispanic ancestry, no dose-response relationship was observed, although {epsilon}4 carriers had lower scores than non-carriers (ps [&le;] .04). MMSE scores corresponded strongly with dementia case/control status across phenotypes: mild impairment on the MMSE was strongly associated with AD (odds ratio [OR] = 11.48), with more severe MMSE impairment showing stronger associations (moderate OR = 17.95; severe OR = 27.83). Conclusion: This study demonstrated MMSE scores can be systematically extracted and curated from the VA EHR. Findings offer a scalable framework for future studies on risk stratification, highlighting the potential for harnessing MVP to explore genetic and clinical factors contributing to cognitive and dementia outcomes in diverse samples.

Premature ovarian insufficiency (POI) significantly impacts quality of life, yet the immediate cognitive landscape and lived experience of younger women remain under-researched. In 125 young women (aged 19-48; 66 with idiopathic POI, 59 age-matched controls), we examined self-reported cognitive distress and symptom burden within the POI cohort and compared objective global and domain-specific cognitive performance between groups. Objective accuracy scores were derived from six online tasks (Cognitron) and combined into a robust global measure. Within the POI cohort, there were significant differences in symptom burden domains ({chi}(3) = 61.90, p<0.001), with psychological and sexual symptoms reported at a significantly higher intensity than physical and vasomotor symptoms (all p<0.001). Furthermore, the standardised magnitude of perceived cognitive distress (56.20%) was significantly greater than that of overall symptom burden (42.00%, p<0.001). Case-control comparisons revealed no significant differences in global cognitive performance (p=0.615), yet the POI cohort performed significantly less accurate than controls in verbal analogical reasoning (-0.86 SD, 95% CI: -1.52, -0.20, p = 0.011). The findings highlight an urgent need for comprehensive emotional and psychosexual support in POI care. Additionally, the presence of high cognitive distress alongside localised objective deficits demonstrates that cognitive health monitoring must be proactive in early adulthood, especially given their established long-term risks for later-life cognitive decline and dementia.