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Background: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. Methods: The IARC‐ARCAGE European case–control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74–0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61–0.64). Conclusion: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi‐omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high‐throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch‐based accelerometry). [ABSTRACT FROM AUTHOR]

Aim: To investigate whether continuous HbA1c levels and HbA1c‐polygenic risk scores (HbA1c‐PRS) are significantly associated with worse brain health independent of type 2 diabetes (T2D) diagnosis (vs. not), by examining brain structure and cognitive test score phenotypes. Methods: Using UK Biobank data (n = 39 283), we tested whether HbA1c levels and/or HbA1c‐PRS were associated with cognitive test scores and brain imaging phenotypes. We adjusted for confounders of age, sex, Townsend deprivation score, level of education, genotyping chip, eight genetic principal components, smoking, alcohol intake frequency, cholesterol medication, body mass index, T2D and apolipoprotein (APOE) e4 dosage. Results: We found an association between higher HbA1c levels and poorer performance on symbol digit substitution scores (standardized beta [β] = −0.022, P =.001) in the fully adjusted model. We also found an association between higher HbA1c levels and worse brain MRI phenotypes of grey matter (GM; fully‐adjusted β = −0.026, P <.001), whole brain volume (β = −0.072, P =.0113) and a general factor of frontal lobe GM (β = −0.022, P <.001) in partially and fully adjusted models. HbA1c‐PRS were significantly associated with GM volume in the fully adjusted model (β = −0.010, P =.0113); however, when adjusted for HbA1c levels, the association was not significant. Conclusions: Our findings suggest that measured HbA1c is associated with poorer cognitive health, and that HbA1c‐PRS do not add significant information to this. [ABSTRACT FROM AUTHOR]

Pre-imputation quality control, imputation and post-imputation cleaning were conducted by UK Biobank (described in the UK Biobank release documentation).[10] Locus rs10305492 was directly genotyped [as per all single nucleotide polymorphisms (SNPs) reported herein]. Blood samples were collected at the baseline assessment and DNA extracted, genotyped and quality controlled by the central UK Biobank team.[10] In 2018, UK Biobank released genetic data for 487 409 individuals, genotyped using the Affymetrix UK BiLEVE Axiom or the Affymetrix UK Biobank Axiom arrays (Santa Clara, CA, USA) containing over 95% common content. DATA AVAILABILITY STATEMENT The UK Biobank resource is available to bona fide researchers for health-related research in the public interest. Testing for association between exonic glucagon-like peptide 1 receptor mutation with physical and brain health traits in UK Biobank. [Extracted from the article]

Background: Pathways explaining racial/ethnic disparities in dementia risk are under‐evaluated. Method: We examine those disparities and their related pathways among UK biobank study respondents (50‐74y, N = 323,483; 3.6% non‐White minorities) using a series of Cox proportional hazards and generalized structural equations models (GSEM). Result: After ≤15 years, 5,491 all‐cause dementia cases were diagnosed. Minority status increased dementia risk by 24% (HR = 1.24, 95% CI: 1.07‐1.45, P = 0.005), an association attenuated by socio‐economic status (SES), (HR = 1.12, 95% CI: 0.96‐1.31). Total race‐dementia effect was mediated through both SES and Life's essential 8 lifestyle sub‐score (LE8LIFESTYLE), combining diet, smoking, physical activity, and sleep factors. SES was inversely related to dementia risk (HR = 0.69, 95% CI: 0.67, 0.72, P<0.001). Pathways explaining excess dementia risk among minorities included 'RACE_ETHN(‐)→SES(‐)→◊ DEMENTIA', 'RACE_ETHN(‐)→ SES(‐)→ COGN(+)→ DEMENTIA' and 'RACE_ETHN(‐)→SES(+)→ LE8LIFESTYLE(‐)→'DEMENTIA', with SES reflecting higher socio‐economic status, COGN reflecting poorer cognitive performance and LE8LIFESTYLE reflecting better cardiovascular health, specifically lifestyle factors, with higher scores. RACE_ETHN is the Non‐White minority vs. White contrast. Conclusion: Pending future interventions, lifestyle factors including diet, smoking, physical activity and sleep are crucial for reducing racial and socio‐economic disparities in dementia. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to determine risk of being SARS‐CoV‐2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. Methods: We used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS‐CoV‐2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without. Results: Of 401,268 participants in total, 13,331 tested positive for SARS‐CoV‐2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS‐CoV‐2 positive (risk ratio‐RR 1.35; 95% confidence interval‐CI 1.24–1.47) and severe infection (RR 1.30; 95% CI 1.04–1.59), compared to those without. The excess risk of being tested positive for SARS‐CoV‐2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes. Conclusion: The totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate whether the association between a validated genetic profile risk score for BMI (GPRS-BMI) (based on 93 single-nucleotide polymorphisms) and phenotypic obesity (BMI) was modified by the combined categories of physical activity (PA) and sedentary behaviors in a large population-based study.Methods: This study included cross-sectional baseline data from 338,216 white European adult men and women aged 37 to 73 years. Interaction effects of GPRS-BMI with the combined categories of PA and sedentary behaviors on BMI were investigated.Results: There was a significant interaction between GPRS-BMI and the combined categories of objectively measured PA and total sedentary behavior (P[interaction]   =  3.5 × 10-6 ); among physically inactive and highly sedentary individuals, BMI was higher by 0.60 kg/m2 per 1-SD increase in GPRS-obesity (P  =  8.9 × 10-50 ), whereas the relevant BMI difference was 38% lower among physically active individuals and those with low sedentary time (β: 0.37 kg/m2 ; P  =  2.3 × 10-51 ). A similar pattern was observed for the combined categories of objective PA and TV viewing (inactive/high TV viewing β: 0.60 vs. active/low TV viewing β: 0.40 kg/m2 ; P[interaction]   =  2.9 × 10-6 ).Conclusions: This study provides evidence that combined categories of PA and sedentary behaviors modify the extent to which genetic predisposition to obesity results in higher BMI. [ABSTRACT FROM AUTHOR]

Background: The extent to which multiple cardiovascular and metabolic co‐morbidity and genetic factors combine or interact to increase dementia risk is unknown. Method: We analysed 435 105 participants from the UK Biobank prospective cohort of European ancestry, aged 40 to 72 years, without dementia at baseline (2006‐2010), followed until 2021. A cardiometabolic multimorbidity index with a point given for co‐morbid stroke, diabetes and heart disease (myocardial infarction, atrial fibrillation or heart failure) was calculated for each participant along with a polygenic risk score for dementia with low (lowest quintile), intermediate (quintiles 2 to 4) and high (highest quintile) risk categories. The primary outcome was incident all‐cause dementia during follow‐up. Result: Participants with both a high genetic risk and a cardiometabolic multimorbidity index of two or greater had an increased risk of developing dementia, hazard ratio of 5.01(95% confidence interval 3.35 ‐ 7.49), compared to those with a low genetic risk and no cardiometabolic conditions. The risk of developing dementia increased monotonically with increasing cardiometabolic morbidity. Crucially, there was no interaction between cardiometabolic multimorbidity and polygenic risk (P = 0.11). Conclusion: Cardiometabolic multimorbidity was independently associated with higher dementia risk, compared with genetic dementia risk. Addressing cardiometabolic multimorbidity may help to reduce dementia risk regardless of genetic risk. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]