Dementia risk in a diverse population: A single-region nested case-control study in the East End of London

Background: Most evidence about dementia risk comes from relatively affluent people of White European ancestry. We aimed to determine the association between ethnicity, area level socioeconomic deprivation and dementia risk, and the extent to which variation in risk might be attributable to known modifiable clinical risk factors and health behaviours. Methods: In this nested case-control study, we analysed data from primary care medical records of a population of 1,016,277 from four inner East London boroughs, United Kingdom, collected between 2009 and 2018. The outcome measures were odds ratios for dementia according to ethnicity and deprivation, before and after the addition of major modifiable risk factors for dementia; and weighted population attributable risk for comparison between individual risk factors. Findings: We identified 4137 dementia cases and 15,754 matched controls (mean age for cases and controls were 80·7 years, (SD 8·7); 81·3 years, (SD 8·9) respectively, range 27–103). Black and South Asian ethnicity were both associated with increased risk of dementia relative to White (odds ratios [95% CI]: Black 1·43 [1·31–1·56]; South Asian 1.17 [1·06–1·29]). Area-level deprivation was independently associated with an increased risk of dementia in a dose-dependent manner. Black and South Asian ethnicity were both associated with a younger age at dementia diagnosis (odds ratios [95%CI]: 0·70 [0·61–0·80] and 0·55 [0·47–0·65], respectively). Population attributable risk was higher for ethnicity (9·7%) and deprivation (11·7%) than for any established modifiable risk factor in this population. Interpretation: Ethnicity and area-level deprivation are independently associated with dementia risk in East London. This effect may not be attributable to the effect of known risk factors

Significance of lobular intraepithelial neoplasia at margins of breast conservation specimens: a report of 38 cases and literature review

<p>Abstract</p> <p>Background</p> <p>Presence of lobular intraepithelial neoplasia (LIN) is not routinely reported as part of margin assessment in breast conservation therapy (BCT) as in ductal carcinoma in situ (DCIS). With new emerging evidence of LIN as possible precursor lesion, the hypothesis is that LIN at the margin may increase the risk of local recurrence with BCT. The aim is to determine whether there is an increase incidence of recurrence when LIN is found at surgical margins on BCT.</p> <p>Methods</p> <p>We retrospectively reviewed a total of 1,334 BCT at a single institution in a 10 year period. Inclusion criteria are positive margin with LIN from primary BCT containing invasive and/or in situ carcinoma with comparison to the negative control group who had similar diseases with negative margin for LIN.</p> <p>Results</p> <p>We identified 38 cases (2.8%) with LIN either lobular carcinoma in situ/atypical lobular hyperplasia (LCIS/ALH) at a margin on initial BCT with 36% recurrence rate. Of the 38 cases: 5 (13%) were lost to follow-up, 12 (32%) had no further procedures performed and 21 (55%) had re-excision. Out of 21 patients who had re-excisions, 12 (57%) had residual invasive carcinoma or DCIS, three (14%) had pleomorphic LCIS and 4 (19%) showed residual classic type LCIS. 71% had significant residual disease (local recurrence) and 29% had no residual disease. A negative control group consisted of 38 cases. We found two patients with bone or brain metastasis and one local recurrence. Clinical follow up periods range from 1 to 109 months.</p> <p>Conclusions</p> <p>LIN found at a margin on BCT showed a significant recurrent ipsilateral disease. Our study supports the view that LIN seen at the margin may play a role in recurrence.</p

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com

Abiotic ammonium formation in the presence of Ni-Fe metals and alloys and its implications for the Hadean nitrogen cycle

Experiments with dinitrogen-, nitrite-, nitrate-containing solutions were conducted without headspace in Ti reactors (200°C), borosilicate septum bottles (70°C) and HDPE tubes (22°C) in the presence of Fe and Ni metal, awaruite (Ni80Fe20) and tetrataenite (Ni50Fe50). In general, metals used in this investigation were more reactive than alloys toward all investigated nitrogen species. Nitrite and nitrate were converted to ammonium more rapidly than dinitrogen, and the reduction process had a strong temperature dependence. We concluded from our experimental observations that Hadean submarine hydrothermal systems could have supplied significant quantities of ammonium for reactions that are generally associated with prebiotic synthesis, especially in localized environments. Several natural meteorites (octahedrites) were found to contain up to 22 ppm Ntot. While the oxidation state of N in the octahedrites was not determined, XPS analysis of metals and alloys used in the study shows that N is likely present as nitride (N3-). This observation may have implications toward the Hadean environment, since, terrestrial (e.g., oceanic) ammonium production may have been supplemented by reduced nitrogen delivered by metal-rich meteorites. This notion is based on the fact that nitrogen dissolves into metallic melts

The shared genetic architecture of modifiable risk for Alzheimer's disease: a genomic structural equation modelling study.

Targeting modifiable risk factors may help to prevent Alzheimer's disease (AD), but the pathways by which these risk factors influence AD risk remain incompletely understood. We identified genome-wide association studies for AD and its major modifiable risk factors. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling. We identified complex networks of genetic overlap among AD risk factors, but AD itself was largely genetically distinct. The data were best explained by a bi-factor model, incorporating a Common Factor for AD risk, and 3 orthogonal sub-clusters of risk factors. Taken together, our findings suggest that there is extensive shared genetic architecture between AD modifiable risk factors, but this is largely independent of AD genetic pathways. Extensive genetic pleiotropy between risk factors may influence AD indirectly by decreasing cognitive reserve or increasing risk of multimorbidity, leading to poorer brain health. Further work to understand the biology reflected by this communality may provide novel mechanistic insights that could help to prioritise targets for dementia prevention