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Age disparities in lung cancer survival in New Zealand: the role of patient and clinical factors
Background Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand. Methods We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1% females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival. Results The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater age difference in survival between middle-aged and older patients with lung cancer than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival. Conclusion Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand. Key messages What is the key question? How do patient-related and clinical factors influence age pattern in lung cancer survival? What is the bottom line? Age disparities in lung cancer survival were strongest for females and non-advanced disease. Deprivation, ethnicity, comorbidity, and emergency presentation did not influence age disparities. Why read on? Our findings reinforce the call for a better representation of older adults in clinical trials and a wider use of geriatric assessment to identify patients who will benefit treatment.
Multi-Facet Clustering Variational Autoencoders
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the colour of the background. In this paper, we introduce Multi-Facet Clustering Variational Autoencoders (MFCVAE), a novel class of variational autoencoders with a hierarchy of latent variables, each with a Mixture-of-Gaussians prior, that learns multiple clusterings simultaneously, and is trained fully unsupervised and end-to-end. MFCVAE uses a progressively-trained ladder architecture which leads to highly stable performance. We provide novel theoretical results for optimising the ELBO analytically with respect to the categorical variational posterior distribution, correcting earlier influential theoretical work. On image benchmarks, we demonstrate that our approach separates out and clusters over different aspects of the data in a disentangled manner. We also show other advantages of our model: the compositionality of its latent space and that it provides controlled generation of samples.
Cardiometabolic multimorbidity, genetic risk, and dementia: a prospective cohort study.
BackgroundIndividual cardiometabolic disorders and genetic factors are associated with an increased dementia risk; however, the relationship between dementia and cardiometabolic multimorbidity is unclear. We investigated whether cardiometabolic multimorbidity increases the risk of dementia, regardless of genetic risk, and examined for associated brain structural changes.MethodsWe examined health and genetic data from 203 038 UK Biobank participants of European ancestry, aged 60 years or older without dementia at baseline assessment (2006-10) and followed up until March 31, 2021, in England and Scotland and Feb 28, 2018, in Wales, as well as brain structural data in a nested imaging subsample of 12 236 participants. A cardiometabolic multimorbidity index comprising stroke, diabetes, and myocardial infarction (one point for each), and a polygenic risk score for dementia (with low, intermediate, and high risk groups) were calculated for each participant. The main outcome measures were incident all-cause dementia and brain structural metrics.FindingsThe dementia risk associated with high cardiometabolic multimorbidity was three times greater than that associated with high genetic risk (hazard ratio [HR] 5·55, 95% CI 3·39-9·08, p<0·0001, and 1·68, 1·53-1·84, p<0·0001, respectively). Participants with both a high genetic risk and a cardiometabolic multimorbidity index of two or greater had an increased risk of developing dementia (HR 5·74, 95% CI 4·26-7·74, p<0·0001), compared with those with a low genetic risk and no cardiometabolic conditions. Crucially, we found no interaction between cardiometabolic multimorbidity and polygenic risk (p=0·18). Cardiometabolic multimorbidity was independently associated with more extensive, widespread brain structural changes including lower hippocampal volume (F2, 12 110 = 10·70; p<0·0001) and total grey matter volume (F2, 12 236 = 55·65; p<0·0001).InterpretationCardiometabolic multimorbidity was independently associated with the risk of dementia and extensive brain imaging differences to a greater extent than was genetic risk. Targeting cardiometabolic multimorbidity might help to reduce the risk of dementia, regardless of genetic risk.FundingWellcome Trust, Alzheimer's Research UK, Alan Turing Institute/Engineering and Physical Sciences Research Council, the National Institute for Health Research Applied Research Collaboration South West Peninsula, National Health and Medical Research Council, JP Moulton Foundation, and National Institute on Aging/National Institutes of Health.
Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation.
BackgroundThe Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set.ObjectiveThe objective of this study is to describe the Novartis-Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes.MethodsWe report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients' baseline age, using phase III study data (≈8000 patients).ResultsNO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%-75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment.ConclusionNO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Ensembles of neural networks achieve superior performance compared to standalone networks in terms of accuracy, uncertainty calibration and robustness to dataset shift. Deep ensembles, a state-of-the-art method for uncertainty estimation, only ensemble random initializations of a fixed architecture. Instead, we propose two methods for automatically constructing ensembles with varying architectures, which implicitly trade-off individual architectures’ strengths against the ensemble’s diversity and exploit architectural variation as a source of diversity. On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift. Our further analysis and ablation studies provide evidence of higher ensemble diversity due to architectural variation, resulting in ensembles that can outperform deep ensembles, even when having weaker average base learners. To foster reproducibility, our code is available: https://github.com/automl/nes
Chest CT and Hospital Outcomes in Patients with Omicron Compared with Delta Variant SARS-CoV-2 Infection.
Background The SARS-Cov-2 Omicron variant demonstrates rapid spread but with reduced disease severity. Studies evaluating the lung imaging findings of Omicron infection versus non-Omicron variants remain lacking. Purpose To compare Omicron and Delta variants of SARS-CoV-2 by their chest CT radiological pattern, biochemical parameters, clinical severity and hospital outcomes after adjusting for vaccination status. Materials and Methods Retrospective study of hospitalized adult patients rt-PCR positive for SARS-CoV-2 with CT pulmonary angiography performed within 7 days of admission between December 1, 2021 and January 14, 2022. Blinded radiological analysis with multiple readers including RSNA CT classification, chest CT severity score (CT-SS, range 0 least severe to 25 most severe) and CT imaging features including bronchial wall thickening. Results 106 patients (Delta n=66, Omicron n=40) were evaluated (mean age, 58 years ± 18, 58 men). In the Omicron group, 37% (15/40) of CT pulmonary angiograms were categorized as normal compared with 15% (10/66) in the Delta group (p=.016). Using a generalized linear model to control for confounding variables, including vaccination status, Omicron variant infection was associated with a CT-SS that was lower by 7.2 points compared to infection with Delta variant (β=-7.2, 95%CI: -9.9, -4.5; p
Design, recruitment, and baseline characteristics of the EMPA-KIDNEY trial.
BackgroundThe effects of the sodium-glucose co-transporter-2 inhibitor empagliflozin on renal and cardiovascular disease have not been tested in a dedicated population of people with chronic kidney disease (CKD).MethodsThe EMPA-KIDNEY trial is an international randomized, double-blind, placebo-controlled trial assessing whether empagliflozin 10 mg daily reduces risk of kidney disease progression or cardiovascular death in people with CKD. People with or without diabetes mellitus (DM) were eligible provided they had: (i) an estimated glomerular filtration rate (eGFR) ≥20, <45 mL/min/1.73m2; or (ii) an eGFR ≥ 45, <90 mL/min/1.73m2 with a urinary albumin: creatinine ratio (uACR) ≥200 mg/g. The trial design is streamlined: extra work for collaborating sites is kept to a minimum, and only essential information is collected.ResultsBetween 15 May 2019 and 16 April 2021, 6609 people from eight countries in Europe, North America and East Asia were randomized. Mean age at randomization was 63.8 (SD 13.9) years, 2192 (33%) were female, and 3570 (54%) had no prior history of DM. Mean eGFR was 37.5 (14.8) mL/min/1.73m2, including 5185 (78%) with an eGFR ConclusionsEMPA-KIDNEY will evaluate the efficacy and safety of empagliflozin in a widely generalizable population of people with CKD at risk of kidney disease progression. Results are anticipated in 2022.
Niche-specific genome degradation and convergent evolution shaping Staphylococcus aureus adaptation during severe infections.
During severe infections, Staphylococcus aureus moves from its colonising sites to blood and tissues, and is exposed to new selective pressures, thus potentially driving adaptive evolution. Previous studies have shown the key role of the agr locus in S. aureus pathoadaptation, however a more comprehensive characterisation of genetic signatures of bacterial adaptation may enable prediction of clinical outcomes and reveal new targets for treatment and prevention of these infections. Here, we measured adaptation using within-host evolution analysis of 2,590 S. aureus genomes from 396 independent episodes of infection. By capturing a comprehensive repertoire of single-nucleotide and structural genome variations, we found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. These invasive strains had up to 20-fold enrichments for genome degradation signatures and displayed significantly convergent mutations in a distinctive set of genes, linked to antibiotic response and pathogenesis. In addition to agr-mediated adaptation we identified non-canonical, genome-wide significant loci including sucA-sucB and stp1. The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform correlation of infection outcomes with adaptation signatures.
The psychological correlates of distinct neural states occurring during wakeful rest.
When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants' experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain's response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.
The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial.
BackgroundPhysical activity (PA) may positively stimulate the brain, cognition and mental health during adolescence, a period of dynamic neurobiological development. High-intensity interval training (HIIT) or vigorous PA interventions are time-efficient, scalable and can be easily implemented in existing school curricula, yet their effects on cognitive, academic and mental health outcomes are unclear. The primary aim of the Fit to Study trial was to investigate whether a pragmatic and scalable HIIT-style VPA intervention delivered during school physical education (PE) could improve attainment in maths. The primary outcome has previously been reported and was null. Here, we report the effect of the intervention on prespecified secondary outcomes, including cardiorespiratory fitness, cognitive performance, and mental health in young adolescents.MethodsThe Fit to Study cluster randomised controlled trial included Year 8 pupils (n = 18,261, aged 12-13) from 104 secondary state schools in South/Mid-England. Schools were randomised into an intervention condition (n = 52), in which PE teachers delivered an additional 10 min of VPA per PE lesson for one academic year (2017-2018), or into a "PE as usual" control condition. Secondary outcomes included assessments of cardiorespiratory fitness (20-m shuttle run), cognitive performance (executive functions, relational memory and processing speed) and mental health (Strength and Difficulties Questionnaire and self-esteem measures). The primary intention-to-treat (ITT) analysis used linear models and structural equation models with cluster-robust standard errors to test for intervention effects. A complier-average causal effect (CACE) was estimated using a two-stage least squares procedure.ResultsThe HIIT-style VPA intervention did not significantly improve cardiorespiratory fitness, cognitive performance (executive functions, relational memory or processed speed), or mental health (all p > 0.05). Subgroup analyses showed no significant moderation of intervention effects by sex, socioeconomic status or baseline fitness levels. Changes in cardiorespiratory fitness were not significantly related to changes in cognitive or mental health outcomes. The trial was marked by high drop-out and low intervention compliance. Findings from the CACE analysis were in line with those from the ITT analysis.ConclusionThe one-academic year HIIT-style VPA intervention delivered during regular school PE did not significantly improve fitness, cognitive performance or mental health, but these findings should be interpreted with caution given low implementation fidelity and high drop-out. Well-controlled, large-scale, school-based trials that examine the effectiveness of HIIT-style interventions to enhance cognitive and mental health outcomes are warranted.Trial registrationISRCTN registry, 15,730,512 . Trial protocol and analysis plan for primary outcome prospectively registered on 30th March 2017. ClinicalTrials.gov , NCT03286725 . Secondary measures (focus of current manuscript) retrospectively registered on 18 September 2017.
Mendelian imputation of parental genotypes improves estimates of direct genetic effects.
Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.
Impact of Reduced Sampling Rate on Accelerometer-based Physical Activity Monitoring and Machine Learning Activity Classification
Purpose Lowering the sampling rate of accelerometer devices can dramatically increase study monitoring periods through longer battery life, however the validity of its output is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behaviour types. Methods Healthy adults wore two Axivity AX3 accelerometers on the dominant wrist and two on the hip for 24 hours. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate-to-vigorous activity, and behavioural activities were calculated using standard methods. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results 54 participants wore both hip and wrist monitors, with 45 of the participants contributing >20 hours of wear time at the hip and 51 contributing >20 hours of wear time at the wrist. Strong correlation was observed between 25 Hz and 100 Hz sampling rates in overall activity measurement (r = 0.962 to 0.991), yet consistently lower overall acceleration was observed in data collected at 25 Hz (12.3% to 12.8%). Excellent agreement between sampling rates was observed in all machine learning classified activities (r = 0.850 to 0.952). Wrist-worn vector magnitude measured at 25 Hz (Acc 25 ) can be compared to 100 Hz (Acc 100 ) data using the transformation, Acc 100 = 1.038*Acc 25 + 3.310. Conclusions 25 Hz and 100 Hz accelerometer data are highly correlated with predictable differences which can be accounted for in inter-study comparisons. Sampling rate should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.