Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.

OBJECTIVES: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers. DESIGN: Validity study. METHODS: Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ±20 min/day, moderate-physical activity: ±15, vigorous-physical activity: ±2.5, light-physical activity: ±30). RESULTS: The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, -3.5, interquartile range [-15.8, 12.2]; moderate-physical activity, -6.0 [-17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: -0.9 [-3.1, 0.5]; light-physical activity, -1.3 [-28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %. CONCLUSIONS: One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation.

Self-Supervised Machine Learning to Characterise Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort. METHODS: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. 39 individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. RESULTS: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5%, versus 65-231%). Our data indicate an inverse dose-response association, where taking 6,430-8,277 daily steps was associated with 37% [25-48%] and 28% [20-35%] lower risk of fatal CVD and all-cause mortality up to seven years later, compared to those taking fewer steps each day. CONCLUSIONS: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines.

Cephalopod Sex Determination and its Ancient Evolutionary Origin Revealed by Chromosome-level Assembly of the California Two-Spot Octopus.

Sex chromosomes are critical elements of sexual reproduction in many animal and plant taxa, however they show incredible diversity and rapid turnover even within clades. Here, using a chromosome-level assembly generated with long read sequencing, we report the first evidence for genetic sex determination in cephalopods. We have uncovered a sex chromosome in California two-spot octopus (Octopus bimaculoides) in which males/females show ZZ/ZO karyotypes respectively. We show that the octopus Z chromosome is an evolutionary outlier with respect to divergence and repetitive element content as compared to other chromosomes and that it is present in all coleoid cephalopods that we have examined. Our results suggest that the cephalopod Z chromosome originated between 455 and 248 million years ago and has been conserved to the present, making it the among the oldest conserved animal sex chromosomes known.

Retromer-dependent lysosomal stress in Parkinson's disease.

While causative mutations in complex disorders are rare, they can be used to extract a biological pathway whose pathogenicity can generalize to common forms of the disease. Here we begin by relying on the biological consequences of mutations in LRRK2 and VPS35, genetic causes of autosomal-dominant Parkinson's disease, to hypothesize that 'Retromer-dependent lysosomal stress' represents a pathway that can generalize to idiopathic Parkinson's disease. Next, we outline a series of studies that can test this hypothesis, including the development of biomarkers of pathway dysfunction. If validated, the hypothesis can suggest a unified mechanism of disease and might inform future diagnostic and therapeutic investigations. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

BACKGROUND: Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). PURPOSE: To investigate a convolutional neural network-based SR (CNN-SR) approach for simultaneous frequency-and-phase correction (FPC) of single-voxel Meshcher-Garwood point-resolved spectroscopy (MEGA-PRESS) MRS data. STUDY TYPE: Retrospective. SUBJECTS: Forty thousand simulated MEGA-PRESS datasets generated from FID Appliance (FID-A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA-PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. FIELD STRENGTH/SEQUENCE: 3T, MEGA-PRESS. ASSESSMENT: Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were -20 to 20 Hz and -90° to 90° and were uniformly distributed for the simulation dataset at different signal-to-noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0-5 Hz; 0-20°), medium offsets (5-10 Hz; 20-45°), and large offsets (10-20 Hz; 45-90°). STATISTICAL TESTS: Two-tailed paired t-tests for model performances in the simulation and in vivo datasets were used and a P-value <0.05 was considered statistically significant. RESULTS: CNN-SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN-SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, -0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied. DATA CONCLUSION: The proposed CNN-SR method is an efficient and accurate approach for simultaneous FPC of single-voxel MEGA-PRESS MRS data. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Incidence of Anterior Knee Pain and Functional Difficulty in a Prospectively Recruited Cohort Following Total Knee Arthroplasty With Selective Patellar Resurfacing.

BACKGROUND: The influence of patellar resurfacing on anterior knee pain, stair climbing, and functional activity outcomes following total knee arthroplasty (TKA) are incompletely understood. This study examined the influence of patellar resurfacing on patient-reported outcome measures (PROMs) relating to anterior knee pain and function. METHODS: The Knee Injury and Osteoarthritis Outcome Score of Joint Replacement (KOOS, JR.) patient PROMs were collected preoperatively and at 12 months follow-up for 950 TKAs performed over 5 years. Indications for patellar resurfacing included Grade IV patello-femoral (PFJ) changes or mechanical PFJ findings during patellar trialing. Patellar resurfacing was performed on 393 (41%) of the 950 TKAs performed. Multivariable binomial logistic regressions were performed, using KOOS, JR. questions assessing pain during stair climbing, standing upright, and function during rising from sitting as surrogates for anterior knee pain. Independent regression models were performed for each of the targeted KOOS, JR. questions, with adjustment for age at surgery, sex, and baseline pain and function. RESULTS: No association was observed between 12-month postoperative anterior knee pain or function with patellar resurfacing (P = .17, .97). Patients who had moderate or greater preoperative pain on stairs had an increased likelihood of postoperative pain and functional difficulty (odds ratio 2.3, P = .013), while males were 42% less likely to report postoperative anterior knee pain (odds ratio 0.58, P = .002). CONCLUSION: Selective patellar resurfacing based on PFJ degeneration and mechanical PFJ symptoms produces similar improvement in PROMs for resurfaced and unresurfaced knees.

What Role Does Patello-Femoral Joint Degeneration Have on Pain and Function After Unicompartmental Knee Arthroplasty? A Prospective Observational Cohort Study.

BACKGROUND: Influences on anterior knee pain, stair climbing limitations, and function such as rising from sitting are poorly understood in unicompartmental knee arthroplasty (UKA). Original indications for UKA excluded patients who had patello-femoral disease, while more recent studies have expanded the indications to include these patients. This study examined the influence of the patello-femoral joint degeneration on patient-reported outcome measures relating to anterior knee pain and function after UKA. METHODS: Between October 2017 and August 2021, Knee Injury and Osteoarthritis Outcome Score of Joint Replacement (KOOS, JR) patient-reported outcome measures were collected preoperatively and at 12 months of follow-up for 678 medial UKAs. Patello-femoral joint status was visually graded intraoperatively. Radiographic or intraoperative medial patellar facet and trochlear patello-femoral arthritis and preoperative anterior knee pain were not considered contraindications for UKA, while grade IV lateral patello-femoral arthritis was considered a contraindication for UKA. Multivariable ordinal logistic regressions were performed, using the KOOS, JR questionnaire assessing pain during stair climbing, standing upright, and function during rising from sitting. Independent regressions were performed for each targeted KOOS, JR question, with adjustments for age at surgery, sex, and baseline pain and function scores. RESULTS: No association was observed between 12-month postoperative anterior knee pain (P = .575) and function (P = .854) with patellar osteoarthritis grading after UKA. When comparing fixed and mobile-bearing UKA designs, no association was observed between bearing type and pain (P = .663) or functional outcomes (P = .758). CONCLUSION: Pain and function improved significantly following medial UKA and was independent of medial patellar and trochlear degenerative status.

Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty.

PURPOSE: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. METHODS: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. RESULTS: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. CONCLUSIONS: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery.

Dose-response of accelerometer-measured physical activity, step count, and cancer risk in the UK Biobank: a prospective cohort analysis.

BACKGROUND: Cancer is an age-related condition, but changes to modifiable lifestyle-related behaviours, including physical activity, could impact risk. While step count is an accessible metric of activity for older adults, its association with cancer risk remains poorly understood. We investigated the association between accelerometer-measured total activity, step count, and cancer risk. METHODS: We analysed data from a prospective UK Biobank cohort of consenting participants who wore wrist-based Axivity AX3 accelerometer devices for 7 days between June 1, 2013 and Dec 23, 2015, had valid accelerometer data, and no previous cancer diagnosis at baseline. Machine learning models estimated total physical activity (vector magnitude) and step count. The primary outcome, a composite of 13 cancers previously associated with physical activity, was obtained from national registries. Hazard ratios (HR) and were calculated using Cox proportional hazard models, with attained age as the underlying timescale and adjustment for sex, ethnicity, smoking status, alcohol consumption, education, and Townsend Deprivation Index. The impact of reallocating time between behaviours was evaluated using compositional data analyses. Dose-response associations were assessed with restricted cubic splines. FINDINGS: We analysed data from 86 556 participants, who were followed up during an average of 6·1 years (age range 43-78; 48 478 [56%] female and 38 078 [44%] male; 83 830 [97%] white). 5577 incident malignant cancers occurred among these 86 556 participants. Greater total physical activity was associated with a lower risk of physical-activity-related cancer (HR per 1 SD [+8·33 milligravity per day] 0·85, 95% CI 0·81-0·89). Reallocating 30 min/day from other activities to moderate-to-vigorous physical activity behaviour was associated with lower cancer risk (HR 0·96, 0·94-0·98), as was reallocating 1 h/day to light intensity activity (HR 0·94, 0·92-0·96), compared with the mean behaviour composition among included participants. Compared with taking 5000 steps per day, taking 10 000 daily steps was associated with a significantly lower risk of physical-activity-related cancer (HR 0·81, 0·73-0·90). INTERPRETATION: In this sample from the UK Biobank, higher total physical activity and daily step count were associated with lower risk of physical-activity-related cancers. Findings suggest additional physical activity time, irrespective of intensity, may be beneficial. Increasing low intensity activity time and increasing daily step counts could be practical public health interventions to lower cancer risk, especially for aging adults. FUNDING: National Institute of Health Oxford Cambridge Scholars Program, Wellcome Trust, Swiss Re, Health Data Research UK, and Cancer Research UK.

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.

Background: Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study. Methods: We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19-81) and tested against other open-source step counting algorithms. This model was applied to ascertain daily step counts from raw wrist-worn accelerometer data of 75,493 UK Biobank participants without a prior history of cardiovascular disease (CVD) or cancer. Cox regression was used to obtain hazard ratios and 95% confidence intervals for the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. Findings: The novel step algorithm demonstrated a mean absolute percent error of 12.5% in free-living validation, detecting 98.7% of true steps and substantially outperforming other recent wrist-worn, open-source algorithms. Our data are indicative of an inverse dose-response association, where, for example, taking 6,596 to 8,474 steps per day was associated with a 39% [24-52%] and 27% [16-36%] lower risk of fatal CVD and all-cause mortality, respectively, compared to those taking fewer steps each day. Interpretation: An accurate measure of step count was ascertained using a machine learning pipeline that demonstrates state-of-the-art accuracy in internal and external validation. The expected associations with CVD and all-cause mortality indicate excellent face validity. This algorithm can be used widely for other studies that have utilised wrist-worn accelerometers and an open-source pipeline is provided to facilitate implementation.

Dietary flavanols restore hippocampal-dependent memory in older adults with lower diet quality and lower habitual flavanol consumption.

Dietary flavanols are food constituents found in certain fruits and vegetables that have been linked to cognitive aging. Previous studies suggested that consumption of dietary flavanols might specifically be associated with the hippocampal-dependent memory component of cognitive aging and that memory benefits of a flavanol intervention might depend on habitual diet quality. Here, we tested these hypotheses in the context of a large-scale study of 3,562 older adults, who were randomly assigned to a 3-y intervention of cocoa extract (500 mg of cocoa flavanols per day) or a placebo [(COcoa Supplement and Multivitamin Outcomes Study) COSMOS-Web, NCT04582617]. Using the alternative Healthy Eating Index in all participants and a urine-based biomarker of flavanol intake in a subset of participants [n = 1,361], we show that habitual flavanol consumption and diet quality at baseline are positively and selectively correlated with hippocampal-dependent memory. While the prespecified primary end point testing for an intervention-related improvement in memory in all participants after 1 y was not statistically significant, the flavanol intervention restored memory among participants in lower tertiles of habitual diet quality or habitual flavanol consumption. Increases in the flavanol biomarker over the course of the trial were associated with improving memory. Collectively, our results allow dietary flavanols to be considered in the context of a depletion-repletion paradigm and suggest that low flavanol consumption can act as a driver of the hippocampal-dependent component of cognitive aging.

Increased parietal and occipital lobe gyrification predicts conversion to syndromal psychosis in a clinical high-risk cohort.

BACKGROUND: Local gyrification index (lGI), indicative of the degree of cortical folding is a proxy marker for early cortical neurodevelopmental abnormalities. We studied the difference in lGI between those who do and do not convert to psychosis (non-converters) in a clinical high-risk (CHR) cohort, and whether lGI predicts conversion to psychosis. METHODS: Seventy-two CHR participants with attenuated positive symptom syndrome were followed up for two years. The difference in baseline whole-brain lGI was examined on the T1-weighted MRIs between, i)CHR (N = 72) and healthy controls (N = 19), ii)Converters to psychosis (N = 24) and non-converters (N = 48), adjusting for age and sex, on Freesurfer-6.0. The significant cluster obtained in the converters versus non-converters comparison was registered as a region of interest to individual images of all 72 participants and lGI values were extracted from this region. A cox proportional hazards model was applied with these values to study whether lGI predicts conversion to psychosis. RESULTS: lGI was not different between CHR and healthy controls. lGI was increased in converters in the right-sided inferior parietal and lateral occipital areas (corrected cluster-wise-p-value = 0.009, cohen's f = 0.42) compared to non-converters, which significantly increased the risk of onset of psychosis (p = 0.029, hazard ratio = 1.471). CONCLUSIONS: Increased gyrification in the right-sided inferior parietal and lateral occipital area differentiates converters to psychosis in CHR, significantly increasing the risk of conversion to psychosis. This measure may reflect underlying traits in parts of the brain that develop earliest in-utero (parietal and occipital), conferring a heightened vulnerability to convert to syndromal psychosis subsequently.

Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria mosquito.

Species distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquito Anopheles funestus comprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants of An. funestus diversification was impeded by lack of modern genomic resources. Here, we applied deep whole-genome sequencing and analysis to test the hypothesis that these two forms are ecotypes differentially adapted to breeding in natural swamps versus irrigated rice fields. We demonstrate genome-wide differentiation despite extensive microsympatry, synchronicity, and ongoing hybridization. Demographic inference supports a split only ~1,300 y ago, closely following the massive expansion of domesticated African rice cultivation ~1,850 y ago. Regions of highest divergence, concentrated in chromosomal inversions, were under selection during lineage splitting, consistent with local adaptation. The origin of nearly all variations implicated in adaptation, including chromosomal inversions, substantially predates the ecotype split, suggesting that rapid adaptation was fueled mainly by standing genetic variation. Sharp inversion frequency differences likely facilitated adaptive divergence between ecotypes by suppressing recombination between opposing chromosomal orientations of the two ecotypes, while permitting free recombination within the structurally monomorphic rice ecotype. Our results align with growing evidence from diverse taxa that rapid ecological diversification can arise from evolutionarily old structural genetic variants that modify genetic recombination.

Dimerization of the Alzheimer's disease pathogenic receptor SORLA regulates its association with retromer.

SORL1, the gene encoding the large multidomain SORLA protein, has emerged as only the fourth gene that when mutated can by itself cause Alzheimer's disease (AD), and as a gene reliably linked to both the early- and late-onset forms of the disease. SORLA is known to interact with the endosomal trafficking regulatory complex called retromer in regulating the recycling of endosomal cargo, including the amyloid precursor protein (APP) and the glutamate receptor GluA1. Nevertheless, SORLA's precise structural-functional relationship in endosomal recycling tubules remains unknown. Here, we address these outstanding questions by relying on crystallographic and artificial-intelligence evidence to generate a structural model for how SORLA folds and fits into retromer-positive endosomal tubules, where it is found to dimerize via both SORLA's fibronectin-type-III (3Fn)- and VPS10p-domains. Moreover, we identify a SORLA fragment comprising the 3Fn-, transmembrane, and cytoplasmic domains that has the capacity to form a dimer, and to enhance retromer-dependent recycling of APP by decreasing its amyloidogenic processing. Collectively, these observations generate a model for how SORLA dimer (and possibly polymer) formation can function in stabilizing and enhancing retromer function at endosome tubules. These findings can inform investigation of the many AD-associated SORL1 variants for evidence of pathogenicity and can guide discovery of novel drugs for the disease.

Why Hippocampal Glutamate Levels Are Elevated in Schizophrenia.

This article discusses why glutamate levels are abnormally elevated in the hippocampus of patients with schizophrenia and related disorders.

Anatomical biology guides a search for nutrients for the aging brain.

Considerable evidence has established the importance of specific nutrients that have been found vital for the developing brain. We hypothesize that in a similar manner there should be nutrients vital to the aging brain and that based on aging's distinct pathophysiology they should be different than those essential to development. Specific brain networks that govern cognition are particularly vulnerable to the aging process, resulting in what is referred to as 'cognitive aging'. Common late-life disorders, however, such as Alzheimer's disease also target these same brain networks. Studies have disambiguated cognitive aging from late-life disease by isolating regions and biological pathways within each network differentially linked to one or the other. This anatomical biology anchors a framework to identify nutrients and/or dietary bioactives relevant to cognitive aging whose utility is illustrated via a decades-long research program into how dietary bioactive flavanols benefit the brain. As we are living longer in cognitively more demanding lives, the framework's ultimate goal is to generate specific dietary recommendations that will fortify our mind for its golden years.

Amount and intensity of physical activity and risk of incident cancer in the UK Biobank.

Importance: The influence of total daily and light intensity activity on cancer risk remains unclear, as most existing knowledge is drawn from studies relying on self-reported leisure-time activities of moderate-vigorous intensity. Objective: To investigate associations between total daily activity, including step counts, and activity intensity on incident cancer risk. Design Setting and Participants: Prospective analysis of cancer-free UK Biobank participants who wore accelerometers for 7-days (between 2013-2015), followed for cancer incidence through national registries (mean follow-up 5.8 years (SD=1.3)). Exposures: Time-series machine learning models derived daily total activity (average acceleration), behaviour time, step counts, and peak 30-minute cadence from wrist-based accelerometer data. Main Outcomes and Measures: A composite cancer outcome of 13 cancers previously associated with low physical activity (bladder, breast, colon, endometrial, oesophageal adenocarcinoma, gastric cardia, head and neck, kidney, liver, lung, myeloid leukaemia, myeloma, and rectum) based on previous studies of self-reported activity. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI), adjusted for age, sex, ethnicity, smoking, alcohol, education, Townsend Deprivation Index, and reproductive factors. Associations of reducing sedentary time in favour of increased light and moderate-vigorous activity were examined using compositional data analyses. Results: Among 86 556 participants (mean age 62.0 years (SD=7.9) at accelerometer assessment), 2 669 cancers occurred. Higher total physical activity was associated with a lower overall cancer risk (HR1SD=0.85, [95%CI 0.81-0.89]). On average, reallocating one hour/day from sedentary behaviour to moderate-vigorous physical activity was associated with a lower risk (HR=0.92, [0.89-0.95]), as was reallocating one hour/day to light-intensity physical activity (HR=0.94, [0.92-0.96]). Compared to individuals taking 5 000 daily steps, those who took 9 000 steps had an 18% lower risk of physical-activity-related cancer (HR=0.82, [0.74-0.90]). We found no significant association with peak 30-minute cadence after adjusting for total steps. Conclusion and Relevance: Higher total daily physical activity and less sedentary time, in favour of both light and moderate-vigorous intensity activity, were associated with a lower risk of certain cancers. For less active adults, increasing step counts by 4 000 daily steps may be a practical public health intervention for lowering the risk of some cancers.