BACKGROUND AND OBJECTIVE:Intellectual disability (ID) is increasingly recognised as a hidden driver of cancer mortality. However, evidence on prostate cancer (PC) care in this population is limited. METHODS:The study population comprised 29 554 men with an ID and 518 739 comparators from the Clinical Practice Research Datalink Aurum database, which is linked to hospital, mortality, and cancer registry data. Poisson and Cox regression analyses were used to estimate incidence rate ratios (IRRs), risk ratios (RRs), and hazard ratios (HRs) with 95% confidence intervals (CIs) for outcomes related to PC presentation, diagnosis, treatment, and survival. KEY FINDINGS AND LIMITATIONS:The ID group presented more frequently with symptoms suggestive of PC (IRR 1.35, 95% CI 1.28-1.43) but were less likely to have a prostate-specific antigen (PSA) test within 90 d (RR 0.66, 95% CI 0.63-0.70). Following detection of elevated PSA, the ID group had fewer referrals (RR 0.83, 95% CI 0.72-0.96), biopsies (RR 0.54, 95% CI 0.41-0.71), and PC diagnoses (RR 0.51, 95% CI 0.41-0.65). The ID group were also more likely to be diagnosed on the date of death (RR 5.96, 95% CI 2.70-11.77), have missing Gleason scores (RR 1.61, 95% CI 1.27-2.01), and present with de novo metastatic PC (RR 1.79, 95% CI 1.15-2.77). Among those with Gleason scores, the rate of clinically significant PC (Gleason ≥7) was comparable between the ID and control groups, while receipt of radical treatment for nonmetastatic PC was slightly lower in the ID group (RR 0.73, 95% CI 0.51-1.00). Men with an ID had twofold higher risk of death from PC following diagnosis (HR 2.11, 95% CI 1.64-2.73). CONCLUSIONS AND CLINICAL IMPLICATIONS:Men with an ID face disparities across the PC care pathway from investigation of relevant symptoms to survival after diagnosis. Targeted interventions are needed to address these inequities.

BACKGROUND:People with an intellectual disability (ID) are at increased risk of bowel cancer. However, evidence on their presenting symptoms, diagnostic pathways, treatments and survival remains limited. METHODS:A matched cohort study was conducted using linked primary care (Clinical Practice Research Datalink), hospital, cancer, and mortality records. Outcomes included symptoms associated with bowel cancer, faecal immunochemical or faecal occult blood (FIT/FOB) testing, urgent suspected cancer (USC) referral, endoscopy, surgery, systemic anticancer therapy (SACT), and bowel cancer-specific mortality. Adjusted incidence rate ratios (aIRRs), risk ratios (aRRs), and hazard ratios (aHRs) were estimated using Poisson, modified Poisson and Cox regression. RESULTS:A total of 111,034 individuals with an ID were matched to 1,964,420 comparators. ID was associated with increased risk of bowel cancer (aHR 1.30, 1.18-1.44), particularly before age 50 years (aRR 2.19, 1.68-2.85). People with an ID presented more frequently with symptoms associated with bowel cancer (aIRR 2.59, 2.53-2.65) but, following such symptoms, were less likely to undergo FIT/FOB testing (aRR 0.74, 0.67-0.83), USC referral (aRR 0.57, 0.52-0.62), endoscopy (aRR 0.45, 0.42-0.49), or receive a diagnosis within 56 days (aRR 0.52, 0.41-0.67). They were also less likely to be diagnosed via screening (aRR 0.27, 0.14-0.50) or USC referral (aRR 0.62, 0.50-0.76), and more likely to be diagnosed via emergency presentation (aRR 1.76, 1.52-2.02), on the date of death (aRR 5.08, 2.92-8.84), or with stage IV disease (aRR 1.25, 1.01-1.56). ID was associated with similar proportions receiving curative surgery for stage I-III disease (aRR 0.98, 0.79-1.19), but markedly lower proportions receiving SACT for stage IV (aRR 0.15, 0.05-0.46), and higher bowel cancer-specific mortality across all stages (aHR 2.00, 1.71-2.33). CONCLUSIONS:People with an ID experience worse outcomes across nearly all stages of the bowel cancer care pathway, including referral, investigation, treatment and survival. Earlier screening may be justified given the elevated risk in those under age 50 years.

Abstract Psoriasis and generalized pustular psoriasis (GPP) are associated with significant morbidity. Understanding mortality patterns, and underlying causes of death, is paramount for enhancing management and outcomes in these patients. We aimed to investigate excess mortality and cause-specific deaths in patients with psoriasis and GPP compared with the general population. This was a retrospective cohort study using data from the Clinical Practice Research Datalink, an anonymized primary care electronic health records database from general practices, linked with hospital and mortality records. Patients with a first diagnosis of psoriasis (n = 343 583) or GPP (n = 834) between 1 January 1998 and 31 December 2022 were matched with comparators without these conditions (1 : 5 in the psoriasis cohort, 1 : 10 in the GPP cohort). Flexible parametric survival models were used to estimate adjusted hazard ratios (aHRs) for all-cause and cause-specific mortality. Abridged life tables were generated to estimate life expectancy among cases and comparators. Crude mortality rates were 13.7 per 1000 person-years [95% confidence interval (CI) 13.5–13.8] in patients with psoriasis and 56.4 per 1000 person-years (95% CI 50.0–63.6) in patients with GPP. Compared with matched comparators, psoriasis and GPP were associated with increased all-cause mortality: aHR 1.18 (95% CI 1.16–1.19) and aHR 3.29 (95% CI 2.81–3.84), respectively. The main underlying causes of death in patients with psoriasis were respiratory diseases, circulatory diseases, neoplasms, digestive diseases, sepsis and urinary diseases. In GPP, the main causes were respiratory diseases, neoplasms, circulatory diseases, sepsis and digestive diseases. Diagnoses of psoriasis and GPP at any age were associated with poorer life expectancy compared with the general population; however, years of life lost declined with older age. At age 40 years, people diagnosed with psoriasis or GPP were predicted to have on average 2 years and 19 years, respectively, of potential life lost. Excess mortality and shortened life expectancy remain in patients with psoriasis and GPP. Our findings warrant further attention to the prevention and management of comorbidities.

Background Human behaviours have been classified in domains such as health, occupation and sustainability. We aimed to develop a broadly applicable behavioural framework to facilitate integrating evidence across domains. Methods The Human Behaviour Ontology (HBO), a part of the Behaviour Change Intervention Ontology (BCIO), was developed by: (1) specifying its scope, (2) identifying candidate classes from existing classifications, (3) refining it by annotating behaviours in relevant literature, (4) a stakeholder review with behavioural and ontology experts, (5) testing the inter-rater reliability of its use in annotating research reports, (6) refining classes and their relations, (7) reviewing its coverage of behaviours in theories and (8) publishing its computer-readable version. Results The initial ontology contained 128 classes (Steps 1–4), achieving an inter-rater reliability of 0.63 for familiar researchers and 0.74 after minor adjustments (to the ontology and guidance) for unfamiliar researchers. Following Steps 6–7, the published ontology included 230 classes, with six upper-level behavioural classes: human behaviour, individual human behaviour, individual human behaviour pattern, individual human behaviour change, population behaviour and population behaviour pattern. ‘Individual human behaviour’ was defined as “a bodily process of a human that involves co-ordinated contraction of striated muscles controlled by the brain”, with its 159 subclasses organised across high-level classes relating to: experiences (e.g., playing); expression (e.g., laughing); reflectiveness; harm (e.g., self-injury behaviour); harm prevention; coping; domestic activities; goals; habits; health (e.g., undergoing vaccination); life-function (e.g., breathing behaviour); interactions with materials (e.g., consumption); bodily care (e.g., washing); position (e.g., postural behaviour); social environments (e.g., communication); and behavioural substitution. Additional classes needed for characterising behaviours (e.g., frequency and duration), their attributes and behavioural abstinence were included. Relations were defined for timings, locations, participants, mental processes, functions, goals and outcomes. Conclusions The HBO provides an extensive and detailed framework for describing human behaviours.

Background Human behaviours have been classified in areas such as health, occupation and sustainability. We aimed to develop a more broadly applicable framework for behaviours to facilitate integrating evidence across domains. Methods The Human Behaviour Ontology (HBO), a part of the Behaviour Change Intervention Ontology (BCIO), was developed by: (1) specifying the ontology’s scope, (2) identifying candidate classes from existing classifications, (3) refining the ontology by applying it to code behaviours in relevant literature, (4) conducting a stakeholder review with behavioural and ontology experts, (5) testing the inter-rater reliability of its use in annotating research reports, (6) finalising classes and adding relations between classes, and (7) publishing the ontology’s computer-readable version. Results A class labelled ‘individual human behaviour’ was defined as “A bodily process of a human that involves co-ordinated contraction of striated muscles controlled by the brain.” In Steps 1-4, the ontology’s initial version was developed, with 128 classes. The inter-rater reliability for applying this version in annotations was 0.63 for researchers familiar with it and, after minor adjustments to the ontology and annotation guidance, 0.74 for researchers unfamiliar with it. Following Steps 5-6, the ontology was published with 177 classes, including 128 individual human behaviour classes organised under upper-level classes relating to (1) experiences (e.g., playing), (2) expressive (e.g., laughing), (3) harm (e.g., self-injury behaviour), (4) health (e.g., undergoing vaccination), (5) life-function (e.g., breathing behaviour), (6) interacting with materials (e.g., consumption), (7) bodily care (e.g., washing), (8) position (e.g., walking), and (9) social environments (e.g., communication). The remaining 49 classes included: ‘individual human behaviour pattern’ for repeated behaviours, ‘population behaviour’, ‘population behaviour pattern’, behavioural attributes (e.g., impulsiveness), and abstinence from behaviour. Relations were also defined to represent timings, locations, participants, mental processes, functions, goals, and outcomes. Conclusions The HBO potentially provides a coherent framework for describing human behaviours.

Background: Digital health interventions have the potential to improve health at a large scale globally by improving access to healthcare services and health-related information, but they tend to benefit more affluent and privileged groups more than those less privileged.Methods: In this narrative review, we describe how this ‘digital health divide’ can manifest across three different levels reflecting inequalities in access, skills and benefits or outcomes (i.e. the first, second, and tertiary digital divide). We also discuss four key causes of this digital divide: (i)) digital health literacy as a fundamental determinant; (ii) other personal, social, community, and societal level determinants; (iii) how technology and intervention development contribute to; and (iv) how current research practice exacerbates the digital health divide by developing a biased evidence base. Finally, we formulate implications for research, policy, and practice.Results: Specific recommendations for research include to keep digital health interventions and measurement instruments up to date with fastpaced technological changes, and to involve diverse populations in digital intervention development and evaluation research. For policy and practice, examples of recommendations are to insist on inclusive and accessible design of health technology and to ensure support for digital health intervention enactment prioritises those most vulnerable to the digital divide.Conclusion: We conclude by highlighting the importance of addressing the digital health divide to ensure that as digital technologies' inevitable presence grows, it does not leave those who could benefit most from innovative health technology behind.

Background: Variation in DNA is known to contribute to medication response, impacting both medicine effectiveness and incidence of adverse drug reactions (ADRs). However, clinical implementation of pharmacogenomics (PGx) has been slow, and the views of the public are not well understood. Aim: To assess UK national public attitudes around pharmacogenetics. Design and Methods: The survey was co-designed with the Participant Panel at Genomics England and the data were collected by the National Centre for Social Research, using its nationally representative panel of UK adults. Multivariable logistic regression analyses were used to analyse relationships between selected survey reported variables, controlled for age and sex. Results: The survey response rate was 58%. Two thousand seven hundred and nineteen responses were obtained. Most respondents (59%) had experienced either no benefit or a side effect. Forty-five per cent of respondents reported having experienced no benefit and 46% of respondents reported having experienced a side effect, with female respondents more likely to be in both groups (P < 0.0001). Despite variability in interindividual medicine response being well understood (89%), the involvement of DNA in predicting benefit or risk of a side effect is not (understood by 52% and 48%, respectively). Eighty-nine per cent would complete a PGx test, with 91% wanting direct access to this information. Eighty-five per cent of UK adults think that the NHS should offer PGx to those regularly taking many medicines. Respondents were not more worried overall about misuse of PGx data compared with other routine medical data. Experience with prescription medication impacted on views with those who were prescribed medication almost twice as likely to want a PGx test for any reason. Conclusion: Most respondents reported experience with either a medication not working for them or ADRs. There was a high level of understanding of variable medication response but a relatively low level of awareness of the role genetics plays in that variability. Most respondents would want a PGx test, to have direct access to results, and think the NHS should offer this form of testing. Importantly, respondents were not more concerned about PGx data use than that of any other routinely generated medical data. Notably, this study highlights a relationship between individuals' experiences with prescription medications and their interest in PGx testing, underscoring the potential for personalized medicine to address public healthcare needs.

Background Developing behaviour change interventions able to tackle major challenges such as non-communicable diseases or climate change requires effective and efficient use of scientific evidence. The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. The HBCP used smoking cessation as the first ‘proof of concept’ domain but intends to extend its methodology to other behaviours. The aims of this paper are to (i) assess the extent to which methods developed for annotating smoking cessation intervention reports were generalisable to a corpus of physical activity evidence, and (ii) describe the steps involved in developing this second HBCP corpus. Methods The development of the physical activity corpus involved: (i) reviewing the suitability of smoking cessation codes already used in the HBCP, (ii) defining the selection criteria and scope, (iii) identifying and screening records for inclusion, and (iv) annotating intervention reports using a code set of 200+ entities from the Behaviour Change Intervention Ontology. Results Stage 1 highlighted the need to modify the smoking cessation behavioural outcome codes for application to physical activity. One hundred physical activity intervention reports were reviewed, and 11 physical activity experts were consulted to inform the adapted code set. Stage 2 involved narrowing down the scope of the corpus to interventions targeting moderate-to-vigorous physical activity. In stage 3, 111 physical activity intervention reports were identified, which were then annotated in stage 4. Conclusions Smoking cessation annotation methods developed as part of the HBCP were mostly transferable to the physical activity domain. However, the codes applied to behavioural outcome variables required adaptations. This paper can help anyone interested in building a body of research to develop automated evidence synthesis methods in physical activity or for other behaviours.

Background:People with a learning disability (LD, also known as intellectual disability) face poorer health outcomes, yet the burden of cancer in this population is poorly understood. This study investigated cancer-related outcomes in people with a LD compared to the general population. Methods:A matched cohort study was conducted using linked primary care, hospital, mortality, and cancer registry data from Clinical Practice Research Datalink (CPRD) Aurum. In total, 180,911 individuals with a LD were matched with 3,405,467 controls. Outcomes included urgent suspected cancer (USC) referrals, cancer diagnoses, treatment within six months, and overall survival (OS) post-diagnosis. Findings:Individuals with a LD had fewer USC referrals within 28 days of possible cancer symptoms (adjusted risk ratio [aRR] 0.52, 95% confidence interval [CI] 0.49-0.55). LD was associated with several cancers, including sarcoma (adjusted hazard ratio [aHR] 1.98, 1.65-2.39), central nervous system (aHR 3.42, 2.99-3.90), testicular (aHR 2.06, 1.61-2.62), and uterine cancers (aHR 1.69, 1.40-2.05) as well as cancer before age 50 years (aHR 1.74, 1.63-1.86). Absolute incidence was lower in individuals with a LD compared to without (3396 [1.9%] vs 67,506 [2.0%]) due to increased all-cause mortality (aHR 3.19, 3.12-3.27). LD was associated with fewer diagnoses via USC referrals (aRR 0.81, 0.76-0.86), fewer treatments within six months (aRR 0.83, 0.80-0.85) and shorter OS (median 4.4 years, 95% CI 3.9-5.1 vs 9.1 years, 8.8-9.5; aHR 1.73, 1.65-1.83). Melanoma, breast, and prostate cancers were less common but had up to a fourfold increased risk of death after diagnosis in individuals with a LD. Interpretation:Individuals with a LD have higher cancer risk, more diagnoses outside USC pathways, fewer treatments, and poorer prognosis. Fewer diagnoses of some cancers, alongside worse outcomes, may indicate under-investigation. As premature all-cause mortality improves, cancer burden in this population may rise disproportionately. Funding:NIHR Greater Manchester Patient Safety Research Collaboration (NIHR204295).

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information (‘entities’) in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the ‘FLAIR’ framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator’s score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms (e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper (1)).

Background The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions. Evaluation of BCTTv1 showed the need to extend it into a formal ontology, improve its labels and definitions, add BCTs and subdivide existing BCTs. We aimed to develop a Behaviour Change Technique Ontology (BCTO) that would meet these needs. Methods The BCTO was developed by: (1) collating and synthesising feedback from multiple sources; (2) extracting information from published studies and classification systems; (3) multiple iterations of reviewing and refining entities, and their labels, definitions and relationships; (4) refining the ontology via expert stakeholder review of its comprehensiveness and clarity; (5) testing whether researchers could reliably apply the ontology to identify BCTs in intervention reports; and (6) making it available online and creating a computer-readable version. Results Initially there were 282 proposed changes to BCTTv1. Following first-round review, 19 BCTs were split into two or more BCTs, 27 new BCTs were added and 26 BCTs were moved into a different group, giving 161 BCTs hierarchically organised into 12 logically defined higher-level groups in up to five hierarchical levels. Following expert stakeholder review, the refined ontology had 247 BCTs hierarchically organised into 20 higher-level groups. Independent annotations of intervention evaluation reports by researchers familiar and unfamiliar with the ontology resulted in good levels of inter-rater reliability (0.82 and 0.79, respectively). Following revision informed by this exercise, 34 BCTs were added, resulting in the first published version of the BCTO containing 281 BCTs organised into 20 higher-level groups over five hierarchical levels. Discussion The BCTO provides a standard terminology and comprehensive classification system for the content of behaviour change interventions that can be reliably used to describe interventions. The development and maintenance of an ontology is an iterative and ongoing process; no ontology is ever ‘finished’. The BCTO will continue to evolve and grow (e.g. new BCTs or improved definitions) as a result of user feedback and new available evidence.

Background Artificial intelligence (AI) has considerable potential to enhance public health. People using AI systems for public health decisions, or who are affected by such decisions, may need to understand how these systems work, or articulate how much they want decision-makers to trust the system. This public engagement project, part of the Human Behaviour-Change Project, aimed to a) explore people’s views regarding trust in, and use of, AI for public health decisions and, based on that, b) create a toolkit of resources to facilitate people critically questioning the use of an AI system. Methods Six online, public engagement workshops were conducted in England in 2021 to inform the content and design of the toolkit. Twenty-four people including members of the public, public health professionals, and researchers worked with a graphic designer to create the toolkit. Results The resulting ‘AI in Public Health Toolkit’ contains resources to enable people to evaluate AI systems and provides a roadmap for the decision process, a set of suggested questions to ask about an AI system, a guide to features of good answers and a ‘personal views tool’ prompting reflection on the answers received. Participants suggested that public health decision-makers should use the Toolkit to consult people representative of those affected by the decision to recommend whether an AI system should be used in that instance. Conclusions The ‘AI in Public Health Toolkit’ has the potential to facilitate public engagement in the use of AI in public health. The Toolkit gives those developing AI-driven systems a sense of the public’s queries regarding such systems. The resources in the Toolkit can also facilitate conversations about broader AI applications to healthcare and public services.

AimsHistorically, atherosclerotic cardiovascular disease (ASCVD) risk profile mitigation has had a predominant focus on low density lipoprotein cholesterol (LDL-C). In this narrative review we explore the residual ASCVD risk profile beyond LDL-C with a focus on hypertriglyceridaemia, recent clinical trials of therapeutics targeting hypertriglyceridaemia and novel modalities addressing other residual ASCVD risk factors.FindingsHypertriglyceridaemia remains a significant ASCVD risk despite low LDL-C in statin or proprotein convertase subtilisin/kexin type 9 inhibitor-treated patients. Large population-based observational studies have consistently demonstrated an association between hypertriglyceridaemia with ASCVD. This relationship is complicated by the co-existence of low high-density lipoprotein cholesterol. Despite significantly improving atherogenic dyslipidaemia, the most recent clinical trial outcome has cast doubt on the utility of pharmacologically lowering triglyceride concentrations using fibrates. On the other hand, purified eicosapentaenoic acid (EPA), but not in combination with docosahexaenoic acid (DHA), has produced favourable ASCVD outcomes. The outcome of these trials suggests alternate pathways involved in ASCVD risk modulation. Several other pharmacotherapies have been proposed to address other ASCVD risk factors targeting inflammation, thrombotic and metabolic factors.ImplicationsHypertriglyceridaemia poses a significant residual ASCVD risk in patients already on LDL-C lowering therapy. Results from pharmacologically lowering triglyceride are conflicting. The role of fibrates and combination of EPA and DHA is under question but there is now convincing evidence of ASCVD risk reduction with pure EPA in a subgroup of patients with hypertriglyceridaemia. Clinical guidelines should be updated in line with recent clinical trials evidence. Novel agents targeting non-conventional ASCVD risks need further evaluation.

International Journal of DermatologyEarly View Letter to the Editor Prevalence of anxiety and depression symptoms among patients with psoriasis in Kabul, Afghanistan Ahmad Khalid Aalemi, Corresponding Author Ahmad Khalid Aalemi [email protected] orcid.org/0000-0003-0111-634X Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorAlison K. Wright, Alison K. Wright orcid.org/0000-0002-8418-8332 Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorKhatera Habib, Khatera Habib Department of Dermatology, Maiwand Teaching Hospital, Kabul University of Medical Sciences, Kabul, AfghanistanSearch for more papers by this authorNahid Raofi, Nahid Raofi Department of Dermatology, Maiwand Teaching Hospital, Kabul University of Medical Sciences, Kabul, AfghanistanSearch for more papers by this authorDarren M. Ashcroft, Darren M. Ashcroft orcid.org/0000-0002-2958-915X Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorChristopher E. M. Griffiths, Christopher E. M. Griffiths orcid.org/0000-0001-5371-4427 Centre for Dermatology Research, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, UK Department of Dermatology, King's College Hospital, King's College London, London, UKSearch for more papers by this author Ahmad Khalid Aalemi, Corresponding Author Ahmad Khalid Aalemi [email protected] orcid.org/0000-0003-0111-634X Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorAlison K. Wright, Alison K. Wright orcid.org/0000-0002-8418-8332 Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorKhatera Habib, Khatera Habib Department of Dermatology, Maiwand Teaching Hospital, Kabul University of Medical Sciences, Kabul, AfghanistanSearch for more papers by this authorNahid Raofi, Nahid Raofi Department of Dermatology, Maiwand Teaching Hospital, Kabul University of Medical Sciences, Kabul, AfghanistanSearch for more papers by this authorDarren M. Ashcroft, Darren M. Ashcroft orcid.org/0000-0002-2958-915X Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorChristopher E. M. Griffiths, Christopher E. M. Griffiths orcid.org/0000-0001-5371-4427 Centre for Dermatology Research, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, UK Department of Dermatology, King's College Hospital, King's College London, London, UKSearch for more papers by this author First published: 23 June 2024 https://doi.org/10.1111/ijd.17343 Conflict of interest: CEMG has received research grants and/or honoraria from Abbvie, Almirall, Anaptysbio, Artax, Boehringer-Ingelheim, Boots UK Ltd, Bristol Meyers Squibb, Evelo Biosciences, GSK, Inmagene, Janssen, LEO Foundation, Lilly, ONO Pharmaceuticals, Novartis, Pfizer, and UCB. DMA has received research grant funding from the Leo Foundation, UCB, Amgen, Almirall, Janssen, Boehringer–Ingelheim, and Bristol Myers Squibb. Funding source: None. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References 1Parisi R, Iskandar IYK, Kontopantelis E, Augustin M, Griffiths CEM, Ashcroft DM. National, regional, and worldwide epidemiology of psoriasis: systematic analysis and modelling study. BMJ. 2020; 369:m1590. 10.1136/bmj.m1590 PubMedGoogle Scholar 2Picardi A, Abeni D. Stressful life events and skin diseases: disentangling evidence from myth. 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Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be ‘objective’ measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.