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Objectives: To investigate whether adolescent social connections influence body mass index (BMI) trajectories into adulthood and explore whether associations are moderated by gender, ethnicity or age., Methods: Data came from 17,719 American adolescents in grades 7-12 at baseline (1994-95) from the National Longitudinal Study of Adolescent to Adult Health. Growth curve models tested associations between baseline social connections and BMI trajectories from waves II-V including interactions for gender, ethnicity and age., Results: Stronger peer connections were associated with flatter BMI trajectories. For example, BMI for those with high peer contact was 0.79 kg/m 2 lower [95% CI -1.20, -0.38] 22 years after baseline, compared to those with low contact. Stronger family connections were associated with steeper trajectories. For example, BMI for those with high family contact was 0.52 kg/m 2 higher [95% CI 0.01, 1.02] 22 years after baseline, compared to those with low contact., Discussion: Among adolescents, stronger peer connections were associated with flatter BMI trajectories and stronger family connections with steeper trajectories. Promotion of peer-based interventions could be explored as a strategy to promote healthy weight trajectories., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

Background: Polycystic ovary syndrome (PCOS) is common in women of reproductive age and is associated with obesity. Clinical guidelines recommend weight loss, but the impact on the clinical manifestations of PCOS is unclear., Purpose: To quantify the effect of weight loss interventions on clinical features of PCOS, compared with usual care., Data Sources: MEDLINE, Embase, PsycINFO, CINAHL, Cochrane, Web of Science, and trial registries were searched from inception through June 2024., Study Selection: Randomized controlled trials comparing interventions aiming to reduce weight against usual care, including lower-intensity weight loss interventions in people with PCOS. Conversations with people with PCOS informed the outcomes., Data Extraction: Pairs of independent reviewers screened studies, extracted data, and assessed risk of bias (RoB). Outcomes included glycemic control (Homeostasis Model Assessment for Insulin Resistance [HOMA-IR], fasting insulin and glucose), hormonal markers (free androgen index [FAI] and other sex hormones), menstrual frequency, hirsutism, and PCOS-related quality of life (QoL). Pooled mean differences were obtained from random-effects meta-analysis with Knapp-Hartung adjustment., Data Synthesis: Primary analyses included 29 comparisons with 1529 participants: 13, 12, and 4 comparisons were judged as high, some, or low RoB, respectively. Twelve used behavioral interventions, 9 used glucagon-like peptide-1 (GLP1) agonists, and 8 used other weight loss medications. Weight loss interventions were associated with significantly greater improvements in HOMA-IR (mean difference, -0.45 [-0.75 to -0.15]; I 2  = 24%), FAI (mean difference, -2.03 [-3.0 to -1.07]; I 2  = 48%), and menstrual frequency (mean difference, 2.64 [0.65 to 4.63]; I 2  = 43%). There was no evidence that weight loss interventions were associated with clinically or statistically significant improvements in hirsutism, QoL, or other sex hormones, which may be due to the limited power of the available data., Limitation: There was high statistical heterogeneity in the interventions, comparators, and outcomes, largely unexplained by sensitivity and subgroup analyses., Conclusion: Weight loss interventions were associated with improvements in some important features of PCOS and should be considered as a routine treatment option for people with PCOS., Primary Funding Source: National Institute for Health and Care Research School for Primary Care Research. (PROSPERO: CRD42022367488)., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-3179.

Background: No single multimorbidity measure is validated for use in NHS (National Health Service) England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version., Objective: We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR., Methods: We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data., Results: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models., Conclusions: The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT., (©Debasish Kar, Kathryn S Taylor, Mark Joy, Sudhir Venkatesan, Wilhelmine Meeraus, Sylvia Taylor, Sneha N Anand, Filipa Ferreira, Gavin Jamie, Xuejuan Fan, Simon de Lusignan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.08.2024.)

The employment of antibodies as a targeted drug delivery vehicle has proven successful which is exemplified by the emergence of antibody-drug conjugates (ADCs). However, ADCs are not without their shortcomings. Improvements may be made to the ADC platform by decoupling the cytotoxic drug from the delivery vehicle and conjugating an organometallic catalyst in its place. The resulting protein-metal catalyst conjugate was designed to uncage the masked cytotoxin administered as a separate entity. Macropinocytosis of albumin by cancerous cells suggests the potential of albumin acting as the tumor-targeting delivery vehicle. Herein reported are the first preparation and demonstration of ruthenium catalysts with cyclopentadienyl and quinoline-based ligands conjugated to albumin. The effective uncaging abilities were demonstrated on allyloxy carbamate (alloc)-protected rhodamine 110 and doxorubicin, providing a promising catalytic scaffold for the advancement of selective drug delivery methods in the future.

Objectives: Despite being prioritized during initial COVID-19 vaccine rollout, vulnerable individuals at high risk of severe COVID-19 (hospitalization, intensive care unit admission, or death) remain underrepresented in vaccine effectiveness (VE) studies. The RAVEN cohort study (NCT05047822) assessed AZD1222 (ChAdOx1 nCov-19) two-dose primary series VE in vulnerable populations., Methods: Using the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub, linked to secondary care, death registration, and COVID-19 datasets in England, COVID-19 outcomes in 2021 were compared in vaccinated and unvaccinated individuals matched on age, sex, region, and multimorbidity., Results: Over 4.5 million AZD1222 recipients were matched (mean follow-up ∼5 months); 68% were ≥50 years, 57% had high multimorbidity. Overall, high VE against severe COVID-19 was demonstrated, with lower VE observed in vulnerable populations. VE against hospitalization was higher in the lowest multimorbidity quartile (91.1%; 95% CI: 90.1, 92.0) than the highest quartile (80.4%; 79.7, 81.1), and among individuals ≥65 years, higher in the 'fit' (86.2%; 84.5, 87.6) than the frailest (71.8%; 69.3, 74.2). VE against hospitalization was lowest in immunosuppressed individuals (64.6%; 60.7, 68.1)., Conclusions: Based on integrated and comprehensive UK health data, overall population-level VE with AZD1222 was high. VEs were notably lower in vulnerable groups, particularly the immunosuppressed., Competing Interests: Declaration of Competing Interest Oxford University has entered into a partnership with AstraZeneca for the development of COVID-19 vaccines. KS, LC, MO, ST, SV, TM, TNT, and WM are employees of AstraZeneca and may own stock/shares. AJP was a member of the World Health Organization’s Strategic Advisory Group of Experts on Immunization until January 2022 and remains Chair of the UK Department of Health and Social Care's Joint Committee on Vaccination and Immunisation (JCVI) but does not participate in the JCVI COVID-19 committee. AJP also reports providing advice to Shionogi on COVID-19, and funding from the National Institute for Health Research (NIHR), AstraZeneca, the Bill & Melinda Gates Foundation, Wellcome, the Medical Research Council, the Serum Institute of India, and the Coalition for Epidemic Preparedness Innovations (CEPI); and being a contributor to the intellectual property licensed by Oxford University Innovation to AstraZeneca. DAC reports consulting fees from Oxford University Consulting, Bristol Myers Squibb, and grants from UK Research and Innovation, Wellcome Trust, NIHR, Hong Kong ITC, AstraZeneca, and Glaxo Smith Kline (GSK); and patents held by Oxford University Innovation. JD is an employee at Momentum data and may own stocks/shares. FDRH acknowledges support as Director of the NIHR Applied Research Collaboration Oxford Thames Valley and Theme Lead of the NIHR Oxford University Hospitals Biomedical Research Centre. He also reports fees or expenses for speaking or consultancy from AstraZeneca, Boehringer Ingelheim, Bayer, Bristol Myers Squibb/Pfizer, and Novartis. GJ reports ownership of GSK stocks/shares and honoraria form AstraZeneca. SdeL reports advisory board membership for AstraZeneca, GSK, Sanofi, and Seqirus (fees paid to University); and receiving university research grants from AstraZeneca, GSK, Sanofi, Seqirus, and Takeda. ST reports ownership of GSK stocks/shares. UH is an employee at Oxford University and reports membership on a Janssen advisory committee, funding from Seqirus for podcasts, and research funding from Seqirus and AstraZeneca. WH resorts partial salary funding from AstraZeneca-sponsored research grants. AD, AF, DK, KST, MF, MJ, RB, RW, SNA, and XF declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Aims: In people with heart failure (HF), a high body mass index (BMI) has been linked with better outcomes ('obesity paradox'), but there is limited evidence in community populations across long-term follow-up. We aimed to examine the association between BMI and long-term survival in patients with HF in a large primary care cohort., Methods: We included patients with incident HF aged ≥45 years from the Clinical Practice Research Datalink (2000-2017). We used Kaplan-Meier curves, Cox regression and penalised spline methods to assess the association of pre-diagnostic BMI, based on WHO classification, with all-cause mortality., Results: There were 47 531 participants with HF (median age 78.0 years (IQR 70-84), 45.8% female, 79.0% white ethnicity, median BMI 27.1 (IQR 23.9-31.0)) and 25 013 (52.6%) died during follow-up. Compared with healthy weight, people with overweight (HR 0.78, 95% CI 0.75 to 0.81, risk difference (RD) -4.1%), obesity class I (HR 0.76, 95% CI 0.73 to 0.80, RD -4.5%) and class II (HR 0.76, 95% CI 0.71 to 0.81, RD -4.5%) were at decreased risk of death, whereas people with underweight were at increased risk (HR 1.59, 95% CI 1.45 to 1.75, RD 11.2%). In those underweight, this risk was greater among men than women (p value for interaction=0.02). Class III obesity was associated with increased risk of all-cause mortality compared with overweight (HR 1.23, 95% CI 1.17 to 1.29)., Conclusion: The U-shaped relationship between BMI and long-term all-cause mortality suggests a personalised approach to identifying optimal weight may be needed for patients with HF in primary care. Underweight people have the poorest prognosis and should be recognised as high-risk., Competing Interests: Competing interests: CJT reports personal fees and non-financial support from Roche outside the submitted work. All other authors report no conflicts of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Purpose: The present work investigated dietary changes amongst individuals living with and beyond cancer (LWBC) from before to during the pandemic. To identify those at greatest risk of unhealthy changes, it was further examined whether patterns varied by sociodemographic, health-related, and COVID-19-related characteristics., Methods: This longitudinal cohort study analysed data from 716 individuals LWBC participating in the Advancing Survivorship Cancer Outcomes Trial (ASCOT). Using data provided before and during the pandemic, changes in fruit and vegetable, snack, and alcohol intake were tested using mixed-effect regression models., Results: Fruit and vegetable (95%CI: - 0.30; - 0.04) and alcohol consumption (95%CI: - 1.25; - 0.31) decreased, whilst snacking increased (95%CI: 0.19; 0.53). Women and individuals with limited social contact were more likely to reduce fruit and vegetable intake during the pandemic. Women and individuals with poorer sleep quality, limited social contact, and shielding requirements and without higher education were more likely to increase snacking during the pandemic. Individuals with poorer sleep quality, poorer mental health, and regular social contact were more likely to decrease alcohol consumption during the pandemic., Conclusions: Findings suggest decreased intake for fruit, vegetable, and alcohol consumption and increased snack intake in response to the pandemic amongst individuals LWBC. These changes appear to differ across various characteristics, suggesting the pandemic has not equally impacted everyone in this population. Findings highlight the need for targeted post-COVID strategies to support individuals LWBC most adversely affected by the pandemic, including women and socially isolated individuals. This encourages resources to be prioritised amongst these groups to prevent further negative impact of the pandemic. Whilst the findings are statistically significant, practically they appear less important. This is necessary to acknowledge when considering interventions and next steps., (© 2023. The Author(s).)

Pickering emulsions stabilized by TEMPO-oxidized chitin nanocrystals (T-ChNCs) were developed for quercetin delivery. T-ChNCs were synthesized by TEMPO oxidation chitin and systematically characterized in terms of their physicochemical properties. T-ChNCs were rod-like with a length of 279.7 ± 11.5 nm and zeta potential around -56.1 ± 1.6 mV. The Pickering emulsions were analyzed through an optical microscope and CLSM. The results showed that the emulsion had a small droplet size (972.9 ± 86.0 to 1322.3 ± 447.7 nm), a high absolute zeta potential value (-48.2 ± 0.8 to -52.9 ± 1.9 mV) and a high encapsulation efficiency (quercetin: 79.6%). The emulsion stability was measured at different levels of T-ChNCs and pH values. The droplet size and zeta potential decreased with longer storage periods. The emulsions formed by T-ChNCs retarded the release of quercetin at half rate of that of the quercetin ethanol solution. These findings indicated that T-ChNCs are a promising candidate for effectively stabilizing Pickering emulsions and controlling release of quercetin.

Background: Patients with ESKD treated with hemodialysis in the United States have persistently higher rates of nonadherence compared with patients in other developed countries. Nonadherence is associated with an increased risk of death and higher medical expenditure. There is an urgent need to address it with feasible, effective interventions as the prevalence of patients on hemodialysis in the United States continues to grow. However, published adherence interventions demonstrate limited long-term efficacy., Methods: We conducted a synthesis of qualitative studies on adherence to hemodialysis treatment, medications, and fluid and dietary restrictions to identify gaps in published adherence interventions, searching PubMed, CINAHL, PsychInfo, Embase, and Web of Science databases. We analyzed qualitative data with a priori codes derived from the World Health Organization's adherence framework and subsequent codes from thematic analysis., Results: We screened 1775 articles and extracted qualitative data from 12. The qualitative data revealed 20 factors unique to hemodialysis across the World Health Organization's five dimensions of adherence. In addition, two overarching themes emerged from the data: (1) adherence in the context of patients' whole lives and (2) dialysis treatment as a double-edged sword. Patient-level factors reflected in the qualitative data extended beyond knowledge about hemodialysis treatment or motivation to adhere to treatment. Patients described a profound grieving process over the loss of their "old self" that impacted adherence. They also navigated complex challenges that could be exacerbated by social determinants of health as they balanced treatment, life tasks, and social roles., Conclusions: This review adds to the growing evidence that one-size-fits-all approaches to improving adherence among patients on hemodialysis are inadequate. Adherence may improve when routine care incorporates patient context and provides ongoing support to patients and families as they navigate the logistical, physical, and psychological hardships of living with dialysis. New research is urgently needed to guide a change in course., (Copyright © 2022 by the American Society of Nephrology.)

The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality, cases and hospitalisations help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we combine the forecasts to extract the wisdom of the crowd. We use a dataset that has been made publicly available from the COVID-19 Forecast Hub. A notable feature of the dataset is that the availability of forecasts from participating teams varies greatly across the 40 weeks in our study. We evaluate the accuracy of combining methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods. In addition, we propose several new weighted combining methods. Our results show that, although the median was very useful for the early weeks of the pandemic, the simple average was preferable thereafter, and that, as a history of forecast accuracy accumulates, the best results can be produced by a weighted combining method that uses weights that are inversely proportional to the historical accuracy of the individual forecasting teams., (© 2021 Elsevier B.V. All rights reserved.)

Background: Natriuretic peptide (NP) testing is recommended for patients presenting to primary care with symptoms of chronic heart failure (HF) to prioritise referral for diagnosis., Aim: To report NP test performance at European Society of Cardiology (ESC) and National Institute for Health and Care Excellence (NICE) guideline referral thresholds., Design and Setting: Diagnostic accuracy study using linked primary and secondary care data (2004 to 2018)., Method: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of NP testing for HF diagnosis was assessed., Results: In total, 229 580 patients had an NP test and 21 102 (9.2%) were diagnosed with HF within 6 months. The ESC NT-proBNP threshold ≥125 pg/mL had a sensitivity of 94.6% (95% confidence interval [CI] = 94.2 to 95.0) and specificity of 50.0% (95% CI = 49.7 to 50.3), compared with sensitivity of 81.7% (95% CI = 81.0 to 82.3) and specificity of 80.3% (95% CI = 80.0 to 80.5) for the NICE NT-proBNP ≥400 pg/mL threshold. PPVs for an NT-proBNP test were 16.4% (95% CI = 16.1 to 16.6) and 30.0% (95% CI = 29.6 to 30.5) for ESC and NICE thresholds, respectively. For both guidelines, nearly all patients with an NT-proBNP level below the threshold did not have HF (NPV: ESC 98.9%, 95% CI = 98.8 to 99.0 and NICE 97.7%, 95% CI = 97.6 to 97.8)., Conclusion: At the higher NICE chronic HF guideline NP thresholds, one in five cases are initially missed in primary care but the lower ESC thresholds require more diagnostic assessments. NP is a reliable 'rule-out' test at both cut-points. The optimal NP threshold will depend on the priorities and capacity of the healthcare system., (© The Authors.)

Adverse childhood experiences (ACEs) are linked to poorer mental health outcomes, and growing evidence implicates biological and genetic pathways from early adversity to psychopathology. However, little is known about the relationship of ACEs and their underlying biological and genetic mechanisms with older people's mental health responses to the COVID-19 pandemic. We tested the associations of ACEs, hair cortisol, C-reactive protein (CRP), and polygenic scores (PGS) with depression, anxiety, and loneliness among older adults during the COVID-19 pandemic, accounting for the potential interplay of ACEs with biological and genetic risk markers. Data were drawn from the English Longitudinal Study of Ageing, a prospective cohort study of older adults living in England. Retrospective information on ACEs were collected in 2006/7, while CRP and hair cortisol were measured at wave 6 (2012/13). Psychological distress was assessed before the pandemic (2018-19) and at two COVID-19 assessments in 2020 (June-July and November-December). Associations were tested on 2050 participants using linear/logistic regression models adjusted for pre-pandemic outcome measures and mixed-effect models to assess changes before and during the pandemic. The results showed that ACEs were associated with higher levels of depression (OR = 2.55[95%CI:1.81,3.59]) anxiety (OR = 1.84[95%CI:1.13,3.01]), and loneliness (b = 0.28[95%CI:0.14,0.42]) during the pandemic. Hair cortisol was related to an increased risk of depression (OR = 1.15[95%CI:1.04,1.26]), and CRP was associated with greater loneliness scores (b = 0.16[95%CI:0.03,0.30]). The relationship between cortisol and psychological distress was larger among participants with ACEs (e.g., OR depression  = 1.07[95%CI:1.00,1.14]). Further, individuals with high CRP experienced greater increases in feelings of loneliness from before to during the pandemic, compared to those with lower CRP levels (interaction effect=0.23; 95%CI:0.1-0.37). Individuals with 2+ ACEs experienced greater increases in depressive symptoms compared to those with none (interaction effect=2.09; 95%CI:1.1-3.98). Higher levels of hair cortisol were also related to worse changes in depressive symptoms across timepoints (interaction effect=1.84;95%CI:1.41-2.41). These results highlight the lasting impact of biosocial vulnerabilities on older adults' mental health responses to new environmental stressors. They also implicate biological mechanisms in the pathophysiology of later-life psychological distress., (© 2022. The Author(s).)

Background: A combined forecast from multiple models is typically more accurate than an individual forecast, but there are few examples of studies of combining in infectious disease forecasting. We investigated the accuracy of different ways of combining interval forecasts of weekly incident and cumulative coronavirus disease-2019 (COVID-19) mortality., Methods: We considered weekly interval forecasts, for 1- to 4-week prediction horizons, with out-of-sample periods of approximately 18 months ending on 8 January 2022, for multiple locations in the United States, using data from the COVID-19 Forecast Hub. Our comparison involved simple and more complex combining methods, including methods that involve trimming outliers or performance-based weights. Prediction accuracy was evaluated using interval scores, weighted interval scores, skill scores, ranks, and reliability diagrams., Results: The weighted inverse score and median combining methods performed best for forecasts of incident deaths. Overall, the leading inverse score method was 12% better than the mean benchmark method in forecasting the 95% interval and, considering all interval forecasts, the median was 7% better than the mean. Overall, the median was the most accurate method for forecasts of cumulative deaths. Compared to the mean, the median's accuracy was 65% better in forecasting the 95% interval, and 43% better considering all interval forecasts. For all combining methods except the median, combining forecasts from only compartmental models produced better forecasts than combining forecasts from all models., Conclusions: Combining forecasts can improve the contribution of probabilistic forecasting to health policy decision making during epidemics. The relative performance of combining methods depends on the extent of outliers and the type of models in the combination. The median combination has the advantage of being robust to outlying forecasts. Our results support the Hub's use of the median and we recommend further investigation into the use of weighted methods., Competing Interests: The authors have declared that no competing interests exist.