Search

BackgroundPrecision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D).MethodsWe conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that could improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination on internal validation, with lower performance on external validation.ConclusionsDespite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.Plain Language SummaryPatients with T2D are at high risk for CVD but predicting who will experience a cardiac event is challenging. Current risk tools and prognostic factors, such as laboratory tests, may not accurately predict risk in different patient populations. There is a need for personalized risk prediction tools to identify patients more accurately so that CVD prevention can be targeted to those who need it most. This study examined novel biomarkers, genetic markers, and risk scores on the prediction of CVD in individuals with T2D. We found that four laboratory markers and a genetic risk score for CHD had high predictive utility beyond traditional CVD risk factors and that risk scores had modest predictive utility when tested in diverse populations, but more studies are needed to determine their usefulness in clinical practice. The highest strength of evidence was observed for NT-proBNP, a laboratory test currently used to monitor patients with heart failure but not currently used in clinical practice for the purpose of CVD prediction in T2D.

BackgroundWhile the incidence of newly diagnosed early-onset colorectal cancer has been increasing, age-related disparity of survival outcome and treatment-related adverse events in patients with metastatic CRC (mCRC) has been inadequately studied with inconclusive findings. In this study, we aimed to evaluate such age-related disparity in this patient population.MethodsWe used individual patient data from three clinical trials (Study 1:NCT00272051, NCT 00305188 and Study 2:NCT00364013) in Project Data Sphere. All patients were diagnosed with mCRC and received first-line 5-fluorouracil and oxaliplatin. Clinical and genomic data of 763 patients with mCRC from Moffitt Cancer Center were used to assess genomic alterations and serve as an external and real-world validation cohort to evaluate overall survival (OS) disparity. Survival outcomes and treatment-related adverse events were estimated and compared in patients among three age groups: 65 years.ResultsAmong 1223 patients from previous clinical trials, 179 (14.6%) were younger than 50 years. These patients had significantly shorter progression-free survival (PFS) (HR=1.46; 95%CI=1.22–1.76;ppp=0.019), severe abdominal pain (8.4% vs 3.4% vs 3.5%,p=0.018), severe anemia (6.1% vs 1.0% vs 1.5%,pp=0.047), but significantly lower incidence of fatigue, severe diarrhea, severe fatigue, and severe neutropenia. The p=0.012), mucositis (3.6 vs 5.1 vs 5.7 weeks,p=0.051), and neutropenia (8.0 vs 9.4 vs 8.4 weeks,p=0.043), and shorter duration of mucositis (0.6 vs 0.9 vs 1.0 weeks,p=0.006). In the CTNNB1mutation (6.6% vs 3.1% vs 2.3%,p=0.047),ERBB2amplification (5.1% vs 0.6% vs 2.3%,p=0.005), andCREBBPmutation (3.1% vs 0.9% vs 0.5%,p=0.050), but lower prevalence ofBRAFmutation (7.7% vs 8.5% vs 16.7%,p=0.002).ConclusionsPatients with early-onset mCRC had worse survival outcome and unique adverse-event patterns, which could be partially attributed to distinct genomic profiles. Our findings might improve an individualized approach to chemotherapy, counseling, and management of treatment-related adverse events in this patient population.

Exocytosis in excitable cells is essential for their physiological functions. Although the exocytotic machinery controlling cellular secretion has been well investigated, the function of the vesicular cargo, i.e. secretory granular content remains obscure. Here we combine dSTORM imaging and single-domain insulin antibody, to dissect the in situ structure of insulin granule cores (IGCs) at nano level. We demonstrate that the size and shape of the IGCs can be regulated by the juxta-granular molecules Nucleobindin-2 and Enolase-1, that further contribute to the stimulated insulin secretion. IGCs located at the plasma membrane are larger than those in the cytosol. The IGCs size is decreased by ∼20% after glucose stimulation due to the release of the peripheral part of IGCs through incomplete granule fusion. Importantly, the reduction of the IGCs size is also observed in non-stimulatory pancreatic β-cells from diabetic db/db mice, Akita (Ins2+/-) mice and human Type-2 diabetic donors, in accordance with impaired secretion. These findings overall highlight the structure of exocytotic insulin cores as a novel modality amenable to targeting in the stimulated exocytosis in β-cells with impaired insulin secretion.

BackgroundThe response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. MethodsWe enrolled 143 531 study participants ([≥]18 years) throughout Sweden, who contributed 10{middle dot}6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. FindingsA prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0{middle dot}78 (95% CI 0{middle dot}74-0{middle dot}83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0{middle dot}38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0{middle dot}32, 0{middle dot}45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0{middle dot}72 (0{middle dot}64, 0{middle dot}81)). The model further correctly identified on average three out of five counties (95% CI 2{middle dot}3, 3{middle dot}7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3{middle dot}0, 4{middle dot}6) during the second wave. InterpretationThe experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges. FundingSwedish Heart-Lung Foundation (20190470, 20140776), Swedish Research Council (EXODIAB, 2009-1039; 2014-03529), European Commission (ERC-2015-CoG - 681742 NASCENT), and Swedish Foundation for Strategic Research (LUDC-IRC, 15-0067) to MG or PF. European Research Council Starting Grant (801965) to TF. NO was financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab. ATC was supported in this work through the Massachusetts Consortium on Pathogen Readiness (MassCPR). ZOE Limited provided in-kind support for all aspects of building, running and supporting the app and service to all users worldwide. Support for this study for KCL researchers was provided by the National Institute for Health Research (NIHR)-funded Biomedical Research Centre based at Guys and St Thomas (GSTT) NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare. Investigators also received support from the Wellcome Trust, Medical Research Council (MRC), British Heart Foundation (BHF), Alzheimers Society, European Union, NIHR, COVID-19 Driver Relief Fund (CDRF) and the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre (BRC) based at GSTT NHS Foundation Trust in partnership with KCL. ZOE Limited developed the app for data collection as a not-for-profit endeavour. None of the funding entities had any role in study design, data analysis, data interpretation, or the writing of this manuscript.

Early reports raised concern that use of non-steroidal anti-inflammatory drugs (NSAIDs) may increase risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19). Users of the COVID Symptom Study smartphone application reported use of aspirin and other NSAIDs between March 24 and May 8, 2020. Users were queried daily about symptoms, COVID-19 testing, and healthcare seeking behavior. Cox proportional hazards regression was used to determine the risk of COVID-19 among according to aspirin or non-aspirin NSAID users. Among 2,736,091 individuals in the U.S., U.K., and Sweden, we documented 8,966 incident reports of a positive COVID-19 test over 60,817,043 person-days of follow-up. Compared to non-users and after stratifying by age, sex, country, day of study entry, and race/ethnicity, non-aspirin NSAID use was associated with a modest risk for testing COVID-19 positive (HR 1.23 [1.09, 1.32]), but no significant association was observed among aspirin users (HR 1.13 [0.92, 1.38]). After adjustment for lifestyle factors, comorbidities and baseline symptoms, any NSAID use was not associated with risk (HR 1.02 [0.94, 1.10]). Results were similar for those seeking healthcare for COVID-19 and were not substantially different according to lifestyle and sociodemographic factors or after accounting for propensity to receive testing. Our results do not support an association of NSAID use, including aspirin, with COVID-19 infection. Previous reports of a potential association may be due to higher rates of comorbidities or use of NSAIDs to treat symptoms associated with COVID-19.One Sentence SummaryNSAID use is not associated with COVID-19 risk.

ObjectivesDietary supplements may provide nutrients of relevance to ameliorate SARS-CoV-2 infection, although scientific evidence to support a role is lacking. We investigate whether the regular use of dietary supplements can reduce the risk of testing positive for SARS-CoV-2 infection in around 1.4M users of the COVID Symptom Study App who completed a supplement use questionnaire.DesignLongitudinal app-based community survey and nested case control study.SettingSubscribers to an app that was launched to enable self-reported information related to SARS-CoV-2 infection for use in the general population in three countries.Main ExposureSelf-reported regular dietary supplement usage since the beginning of the pandemic.Main Outcome MeasuresSARS-CoV-2 infection confirmed by viral RNA polymerase chain reaction test (RT-PCR) or serology test. A secondary outcome was new-onset anosmia.ResultsIn an analysis including 327,720 UK participants, the use of probiotics, omega-3 fatty acids, multivitamins or vitamin D was associated with a lower risk of SARS-CoV-2 infection by 14%(95%CI: [8%,19%]), 12%(95%CI: [8%,16%]), 13%(95%CI: [10%,16%]) and 9%(95%CI: [6%,12%]), respectively, after adjusting for potential confounders. No effect was observed for vitamin C, zinc or garlic supplements. When analyses were stratified by sex, age and body mass index (BMI), the protective associations for probiotics, omega-3 fatty acids, multivitamins and vitamin D were observed in females across all ages and BMI groups, but were not seen in men. The same overall pattern of association was observed in both the US and Swedish cohorts. Results were further confirmed in a sub-analysis of 993,365 regular app users who were not tested for SARS-CoV-2 with cases (n= 126,556) defined as those with new onset anosmia (the strongest COVID-19 predictor).ConclusionWe observed a modest but significant association between use of probiotics, omega-3 fatty acid, multivitamin or vitamin D supplements and lower risk of testing positive for SARS-CoV-2 in women. No clear benefits for men were observed nor any effect of vitamin C, garlic or zinc for men or women. Randomised controlled trials of selected supplements would be required to confirm these observational findings before any therapeutic recommendations can be made.

Reports of “Long-COVID”, are rising but little is known about prevalence, risk factors, or whether it is possible to predict a protracted course early in the disease. We analysed data from 4182 incident cases of COVID-19 who logged their symptoms prospectively in the COVID Symptom Study app. 558 (13.3%) had symptoms lasting >=28 days, 189 (4.5%) for >=8 weeks and 95 (2.3%) for >=12 weeks. Long-COVID was characterised by symptoms of fatigue, headache, dyspnoea and anosmia and was more likely with increasing age, BMI and female sex. Experiencing more than five symptoms during the first week of illness was associated with Long-COVID, OR=3.53 [2.76;4.50]. A simple model to distinguish between short and long-COVID at 7 days, which gained a ROC-AUC of 76%, was replicated in an independent sample of 2472 antibody positive individuals. This model could be used to identify individuals for clinical trials to reduce long-term symptoms and target education and rehabilitation services.

BackgroundFrom the beginning of COVID-19 pandemic, pregnant women have been considered at greater risk of severe morbidity and mortality. However, data on hospitalized pregnant women show that the symptom profile and risk factors for severe disease are similar to those among women who are not pregnant, although preterm birth, Cesarean delivery, and stillbirth may be more frequent and vertical transmission is possible. Limited data are available for the cohort of pregnant women that gave rise to these hospitalized cases, hindering our ability to quantify risk of COVID-19 sequelae for pregnant women in the community.ObjectiveTo test the hypothesis that pregnant women in community differ in their COVID-19 symptoms profile and disease severity compared to non-pregnant women. This was assessed in two community-based cohorts of women aged 18-44 years in the United Kingdom, Sweden and the United States of America.Study designThis observational study used prospectively collected longitudinal (smartphone application interface) and cross-sectional (web-based survey) data. Participants in the discovery cohort were drawn from 400,750 UK, Sweden and US women (79 pregnant who tested positive) who self-reported symptoms and events longitudinally via their smartphone, and a replication cohort drawn from 1,344,966 USA women (162 pregnant who tested positive) cross-sectional self-reports samples from the social media active user base. The study compared frequencies of symptoms and events, including self-reported SARS-CoV-2 testing and differences between pregnant and non-pregnant women who were hospitalized and those who recovered in the community. Multivariable regression was used to investigate disease severity and comorbidity effects.ResultsPregnant and non-pregnant women positive for SARS-CoV-2 infection drawn from these community cohorts were not different with respect to COVID-19-related severity. Pregnant women were more likely to have received SARS-CoV-2 testing than non-pregnant, despite reporting fewer clinical symptoms. Pre-existing lung disease was most closely associated with the severity of symptoms in pregnant hospitalized women. Heart and kidney diseases and diabetes were additional factors of increased risk. The most frequent symptoms among all non-hospitalized women were anosmia [63% in pregnant, 92% in non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant women who were hospitalized. Gastrointestinal symptoms, including nausea and vomiting, were different among pregnant and non-pregnant women who developed severe outcomes.ConclusionsAlthough pregnancy is widely considered a risk factor for SARS-CoV-2 infection and outcomes, and was associated with higher propensity for testing, the profile of symptom characteristics and severity in our community-based cohorts were comparable to those observed among non-pregnant women, except for the gastrointestinal symptoms. Consistent with observations in non-pregnant populations, comorbidities such as lung disease and diabetes were associated with an increased risk of more severe SARS-CoV-2 infection during pregnancy. Pregnant women with pre-existing conditions require careful monitoring for the evolution of their symptoms during SARS-CoV-2 infection.