| 101. |
- Varsavsky, Thomas, et al.
(författare)
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Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application : a prospective, observational study
- 2021
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Ingår i: The Lancet Public Health. - : Elsevier. - 2468-2667. ; 6:1, s. 21-29
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Tidskriftsartikel (refereegranskat)abstract
- Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. Funding: Zoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation.
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| 102. |
- Wahl, Simone, et al.
(författare)
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Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity
- 2017
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Ingår i: Nature. - : NATURE PUBLISHING GROUP. - 0028-0836 .- 1476-4687. ; 541:7635, s. 81-
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Tidskriftsartikel (refereegranskat)abstract
- Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type (2) diabetes, cardiovascular disease and related metabolic and inflammatory disturbances(1,2). Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation(3-6), a key regulator of gene expression and molecular phenotype(7). Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 x 10(-7), range P = 9.2 x 10(-8) to 6.0 x 10(-46); n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 x 10(-6), range P = 5.5 x 10(-6) to 6.1 x 10(-35), n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 x 10(-54)). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
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| 103. |
- Williams, Frances M. K., et al.
(författare)
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The heritable determinants of cartilage oligomeric matrix protein
- 2006
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Ingår i: Arthritis and Rheumatism. - : Wiley. - 1529-0131 .- 0004-3591. ; 54:7, s. 2147-2151
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Cartilage oligomeric matrix protein (COMP) is a cartilage matrix macromolecule. The protein is detectable in serum and has been investigated as a biomarker of osteoarthritis (OA). An association between COMP and OA has been shown, yet the precise factors governing serum levels of COMP remain unclear. The aim of this study was to determine whether genetic factors influence serum levels of COMP. Methods. A classic twin study was conducted using COMP levels in serum obtained from healthy female twin volunteers. COMP levels were determined by an inhibition enzyme-linked immunosorbent assay method. The heritability of COMP was determined by comparing correlation among 160 monozygotic and 349 dizygotic twin pairs. Data on potential confounding factors, including age, body mass index, and the presence of OA as assessed by hand, hip, and knee radiographs, were included in the analysis. Results. Serum levels of COMP showed a correlation of 0.72 (95% confidence interval [95% CI] 0.650.80) among monozygotic twin pairs and 0.47 (95% CI 0.39-0.55) in dizygotic pairs. This equated to an estimated heritability for COMP of 40% (95% CI 20-60%). Although age and body mass index were found to be significantly associated with COMP in regression analysis, taking the effects of these factors into account did not influence the estimate of heritability. Conclusion. This study showed that heritable factors influence serum levels of the cartilage matrix biomarker COMP. Together with other published data, the results suggest that genetic factors operate at an early stage in the etiologic pathways that influence the development of radiographically discernible OA.
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| 104. |
- Wyatt, Patrick, et al.
(författare)
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Postprandial glycaemic dips predict appetite and energy intake in healthy individuals
- 2021
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Ingår i: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 3:4, s. 523-529
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Tidskriftsartikel (refereegranskat)abstract
- Understanding how to modulate appetite in humans is key to developing successful weight loss interventions. Here, we showed that postprandial glucose dips 2–3 h after a meal are a better predictor of postprandial self-reported hunger and subsequent energy intake than peak glucose at 0–2 h and glucose incremental area under the blood glucose curve at 0–2 h. We explore the links among postprandial glucose, appetite and subsequent energy intake in 1,070 participants from a UK exploratory and US validation cohort, who consumed 8,624 standardized meals followed by 71,715 ad libitum meals, using continuous glucose monitors to record postprandial glycaemia. For participants eating each of the standardized meals, the average postprandial glucose dip at 2–3 h relative to baseline level predicted an increase in hunger at 2–3 h (r = 0.16, P < 0.001), shorter time until next meal (r = −0.14, P < 0.001), greater energy intake at 3–4 h (r = 0.19, P < 0.001) and greater energy intake at 24 h (r = 0.27, P < 0.001). Results were directionally consistent in the US validation cohort. These data provide a quantitative assessment of the relevance of postprandial glycaemia in appetite and energy intake modulation.
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| 105. |
- Zheng, Hou-Feng, et al.
(författare)
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Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture
- 2015
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 526:7571, s. 112-
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Tidskriftsartikel (refereegranskat)abstract
- The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF <= 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants(1-8), as well as rare, population specific, coding variants(9). Here we identify novel non-coding genetic variants with large effects on BMD (n(total) = 53,236) and fracture (n(total) = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD8 (rs11692564(T), MAF51.6%, replication effect size510.20 s.d., P-meta = 2 x 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 x 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1cre/flox mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size +10.41 s.d., P-meta = 1 x 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
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