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Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants.
BackgroundMovement behaviours, including physical activity, sedentary behaviour, and sleep have been shown to be associated with several chronic diseases. However, they have not been objectively measured in large-scale prospective cohort studies in low-and middle-income countries. We aim to describe the patterns of device-measured movement behaviours collected in the China Kadoorie Biobank (CKB) study.MethodsDuring 2020 and 2021, a random subset of 25,087 surviving CKB individuals participated in the 3rd resurvey of the CKB. Among them, 22,511 (89.7%) agreed to wear an Axivity AX3 wrist-worn triaxial accelerometer for seven consecutive days to assess their habitual movement behaviours. We developed a machine-learning model to infer time spent in four movement behaviours [i.e. sleep, sedentary behaviour, light intensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)]. Descriptive analyses were performed for wear-time compliance and patterns of movement behaviours by different participant characteristics.ResultsData from 21,897 participants (aged 65.4 ± 9.1 years; 35.4% men) were received for demographic and wear-time analysis, with a median wear-time of 6.9 days (IQR: 6.1-7.0). Among them, 20,370 eligible participants were included in movement behavior analyses. On average, they had 31.1 mg/day (total acceleration) overall activity level, accumulated 7.7 h/day (32.3%) of sleep time, 8.8 h/day (36.6%) sedentary, 5.7 h/day (23.9%) in light physical activity, and 104.4 min/day (7.2%) in moderate-to-vigorous physical activity. There was an inverse relationship between age and overall acceleration with an observed decline of 5.4 mg/day (17.4%) per additional decade. Women showed a higher activity level than men (32.3 vs 28.8 mg/day) and there was a marked geographical disparity in the overall activity level and time allocation.ConclusionsThis is the first large-scale accelerometer data collected among Chinese adults, which provides rich and comprehensive information about device-measured movement behaviour patterns. This resource will enhance our knowledge about the potential relevance of different movement behaviours for chronic disease in Chinese adults.
Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda.
HIV incidence in eastern and southern Africa has historically been concentrated among girls and women aged 15-24 years. As new cases decline with HIV interventions, population-level infection dynamics may shift by age and gender. Here, we integrated population-based surveillance of 38,749 participants in the Rakai Community Cohort Study and longitudinal deep-sequence viral phylogenetics to assess how HIV incidence and population groups driving transmission have changed from 2003 to 2018 in Uganda. We observed 1,117 individuals in the incidence cohort and 1,978 individuals in the transmission cohort. HIV viral suppression increased more rapidly in women than men, however incidence declined more slowly in women than men. We found that age-specific transmission flows shifted: whereas HIV transmission to girls and women (aged 15-24 years) from older men declined by about one-third, transmission to women (aged 25-34 years) from men that were 0-6 years older increased by half in 2003 to 2018. Based on changes in transmission flows, we estimated that closing the gender gap in viral suppression could have reduced HIV incidence in women by half in 2018. This study suggests that HIV programmes to increase HIV suppression in men are critical to reduce incidence in women, close gender gaps in infection burden and improve men's health in Africa.
Gaussian Processes on Graphs Via Spectral Kernel Learning
We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph. The model is designed to capture various graph signal structures through a highly adaptive kernel that incorporates a flexible polynomial function in the graph spectral domain. Unlike most existing approaches, we propose to learn such a spectral kernel defined on a discrete space. In addition, this kernel has the interpretability of graph filtering achieved by a bespoke maximum likelihood learning algorithm that enforces the positivity of the spectrum. We demonstrate the interpretability of the model through synthetic experiments from which we show various ground truth spectral filters can be accurately recovered, and the adaptability translates to improved predictive performances compared to the baselines on real-world graph data of various characteristics.
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes. In particular, when the data exhibit varying levels of smoothness on the graph, a multi-scale approach is required to capture the relevant information. In this work, we propose a Gaussian process model using spectral graph wavelets, which can naturally aggregate neighbourhood information at different scales. Through maximum likelihood optimisation of the model hyperparameters, the wavelets automatically adapt to the different frequencies in the data, and as a result our model goes beyond capturing low frequency information. We achieve scalability to larger graphs by using a spectrum-adaptive polynomial approximation of the filter function, which is designed to yield a low approximation error in dense areas of the graph spectrum. Synthetic and real-world experiments demonstrate the ability of our model to infer scales accurately and produce competitive performances against state-of-the-art models in graph-based learning tasks.
Sex-Specific Body Mass Index to Optimize Low Correlation With Height and High Correlation With Fatness: A UK Biobank Study.
Body mass index (BMI: weight [kg]/height[m]2) is commonly used to measure general adiposity. However, evidence of its appropriateness for males and females remained inconsistent. This study aimed to identify the most appropriate sex-specific power value that height should be raised to in the formula and that would make it achieve height independency and body fatness dependency. We randomly assigned UK Biobank participants recruited between 2006-2010 in the UK (n=489873; mean age 56.5 years; 94.2% White) into training and testing sets (80%:20%). Using height raised to the power of -50.00 to 50.00, the optimal power value that either minimised correlation with height or maximised correlation with body fat percentage were identified using age-adjusted correlations. The optimal power values for height were 1.77 for males and 1.33 for females. The new formulas resulted in 4.5% of females and 2.4% of males being reclassified into a different BMI category, and did not show significant improvement (in area under Receiver Operating Characteristics Curve, sensitivity and specificity) in identifying individuals with excessive body fat percentage, or in risk prediction of all-cause mortality. Therefore, the conventional BMI formula is still valuable in research and disease screening for both sexes.
Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D.
Vitamin D status-a complex trait influenced by environmental and genetic factors-is tightly associated with skin colour and ancestry. Yet very few studies have investigated the genetic underpinnings of vitamin D levels across diverse ancestries, and the ones that have, relied on small sample sizes, resulting in inconclusive results. Here, we conduct genome-wide association studies (GWAS) of 25 hydroxyvitamin D (25OHD)-the main circulating form of vitamin D-in 442,435 individuals from four broad genetically-determined ancestry groups represented in the UK Biobank: European (N = 421,867), South Asian (N = 9,983), African (N = 8,306) and East Asian (N = 2,279). We identify a new genetic determinant of 25OHD (rs146759773) in individuals of African ancestry, which was not detected in previous analysis of much larger European cohorts due to low minor allele frequency. We show genome-wide significant evidence of dominance effects in 25OHD that protect against vitamin D deficiency. Given that key events in the synthesis of 25OHD occur in the skin and are affected by pigmentation levels, we conduct GWAS of 25OHD stratified by skin colour and identify new associations. Lastly, we test the interaction between skin colour and variants associated with variance in 25OHD levels and identify two loci (rs10832254 and rs1352846) whose association with 25OHD differs in individuals of distinct complexions. Collectively, our results provide new insights into the complex relationship between 25OHD and skin colour and highlight the importance of diversity in genomic studies. Despite the much larger rates of vitamin D deficiency that we and others report for ancestry groups with dark skin (e.g., South Asian), our study highlights the importance of considering ancestral background and/or skin colour when assessing the implications of low vitamin D.
Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.
Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
Maternal circulating miRNAs contribute to negative pregnancy outcomes by altering placental transcriptome and fetal vascular dynamics.
Circulating miRNAs the in blood are promising biomarkers for predicting pregnancy complications and adverse birth outcomes. Previous work identified 11 gestationally elevated maternal circulating miRNAs (HEamiRNAs) that predicted infant growth deficits following prenatal alcohol exposure and regulated epithelial-mesenchymal transition in the placenta. Here we show that a single intravascular administration of pooled murine-conserved HEamiRNAs to pregnant mice on gestational day 10 (GD10) attenuates umbilical cord blood flow during gestation, explaining the observed intrauterine growth restriction (IUGR), specifically decreased fetal weight, and morphometric indices of cranial growth. Moreover, RNAseq of the fetal portion of the placenta demonstrated that this single exposure has lasting transcriptomic changes, including upregulation of members of the Notch pathway (Dll4, Rfng, Hey1), which is a pathway important for trophoblast migration and differentiation. Weighted gene co-expression network analysis also identified chemokine signaling, which is responsible for regulating immune cell-mediated angiogenesis in the placenta, as an important predictor of fetal growth and head size. Our data suggest that HEamiRNAs perturb the expression of placental genes relevant for angiogenesis, resulting in impaired umbilical cord blood flow and subsequently, IUGR.
The epidemiology of multidrug-resistant organisms in persons diagnosed with cancer in Norway, 2008-2018: expanding surveillance using existing laboratory and register data.
Surveillance has revealed an increase of multidrug-resistant organisms (MDROs), even in low-prevalent settings such as Norway. MDROs pose a particular threat to at-risk populations, including persons with cancer. It is necessary to include such populations in future infection surveillance. By combining existing data sources, we aimed to describe the epidemiology of MDROs in persons diagnosed with cancer in Norway from 2008 to 2018. A cohort was established using data from the Cancer Registry of Norway, which was then linked to notifications of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin- and/or linezolid-resistant enterococci (V/LRE), and carbapenemase-producing Gram-negative bacilli (CP-GNB) from the Norwegian Surveillance System for Communicable Diseases, and laboratory data on third-generation cephalosporin-resistant Enterobacterales (3GCR-E) from Oslo University Hospital (OUH). We described the incidence of MDROs and resistance proportion in Enterobacterales from 6 months prior to the person's first cancer diagnosis and up to 3 years after. The cohort included 322,005 persons, of which 0.3% (878) were diagnosed with notifiable MDROs. Peak incidence rates per 100,000 person-years were 60.9 for MRSA, 97.2 for V/LRE, and 6.8 for CP-GNB. The proportion of 3GCR-E in Enterobacterales in blood or urine cultures at OUH was 6% (746/12,534). Despite overall low MDRO incidence, there was an unfavourable trend in the incidence and resistance proportion of Gram-negative bacteria. To address this, there is a need for effective infection control and surveillance. Our study demonstrated the feasibility of expanding the surveillance of MDROs and at-risk populations through the linkage of existing laboratory and register data.