Oxidative Stress-Induced Damage to RNA and DNA and Mortality in Individuals with Psychiatric Illness.

Importance: All-cause mortality and the risk for age-related medical disease is increased in individuals with psychiatric illness, but the underlying biological mechanisms are not known. Oxidative stress on nucleic acids (DNA and RNA; NA-OXS) is a molecular driver of aging and a potential pathophysiological mechanism in a range of age-related disorders. Objective: To study the levels of markers of NA-OXS in a large cohort of community-dwelling individuals with and without psychiatric illness and to evaluate their association with prospective all-cause mortality. Design, Setting, and Participants: This cohort study used a combined cohort of participants from 2 population-based health studies: the Danish General Suburban Population Study (January 2010 to October 2013) and nondiabetic control participants from the Vejle Diabetes Biobank study (March 2007 to May 2010). Individual history of psychiatric illness was characterized using register data on psychiatric diagnoses and use of psychotropic drugs before baseline examination. Urinary markers of systemic RNA (8-oxo-7,8-dihydroguanosine [8-oxoGuo]) and DNA (8-oxo-7,8-dihydro-2'-deoxyguanosine [8-oxodG]) damage from oxidation were measured by ultraperformance liquid chromatography-tandem mass spectrometry. Cox proportional hazard regression models were applied for survival analyses, using register-based all-cause mortality updated to May 2023. The follow-up time was up to 16.0 years. Exposures: History of psychiatric illness. Main Outcomes and Measures: Mortality risk according to psychiatric illness status and 8-oxoGuo or 8-oxodG excretion level. Results: A total of 7728 individuals were included (3983 [51.5%] female; mean [SD] age, 58.6 [11.9] years), 3095 of whom (40.0%) had a history of psychiatric illness. Mean (SD) baseline 8-oxoGuo was statistically significantly higher in individuals with psychiatric illness than in those without (2.4 [1.2] nmol/mmol vs 2.2 [0.9] nmol/mmol; P < .001), whereas 8-oxodG was not. All-cause mortality was higher in the psychiatric illness group vs the no psychiatric illness group (hazard ratio [HR], 1.44; 95% CI, 1.27-1.64; P < .001) and increased sequentially with each increasing tertile of 8-oxoGuo excretion in both groups to an almost doubled risk in the psychiatric illness/high 8-oxoGuo group compared to the no psychiatric illness/low 8-oxoGuo reference group (HR, 1.99; 95% CI, 1.58-2.52; P < .001). These results persisted after adjustment for a range of potential confounders and in a sensitivity analysis stratified for sex. Conclusions and Relevance: This study establishes systemic oxidative stress-induced damage to RNA as a potential mechanism in the accelerated aging observed in psychiatric disorders and urinary 8-oxoGuo as a potentially useful marker of mortality risk in individuals with psychiatric illness.

Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease.

BACKGROUND: Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables. METHODS: A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. "Desirable analytical variation" based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated. RESULTS: Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates. CONCLUSIONS: Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis.

OBJECTIVES: To identify molecular pathways and prognostic- and diagnostic plasma-protein biomarkers for diabetic retinopathy at various stages. METHODS: This exploratory, cross-sectional proteomics study involved plasma from 68 adults, including 15 healthy controls and 53 diabetes patients for various stages of diabetic retinopathy: non-diabetic retinopathy, non-proliferative diabetic retinopathy, proliferative diabetic retinopathy and diabetic macular edema. Plasma was incubated with peptide library beads and eluted proteins were tryptic digested, analyzed by liquid chromatography-tandem mass-spectrometry followed by bioinformatics. RESULTS: In the 68 samples, 248 of the 731 identified plasma-proteins were present in all samples. Analysis of variance showed differential expression of 58 proteins across the five disease subgroups. Protein-Protein Interaction network (STRING) showed enrichment of various pathways during the diabetic stages. In addition, stage-specific driver proteins were detected for early and advanced diabetic retinopathy. Hierarchical clustering showed distinct protein profiles according to disease severity and disease type. CONCLUSIONS: Molecular pathways in the cholesterol metabolism, complement system, and coagulation cascade were enriched in patients at various stages of diabetic retinopathy. The peroxisome proliferator-activated receptor signaling pathway and systemic lupus erythematosus pathways were enriched in early diabetic retinopathy. Stage-specific proteins for early - and advanced diabetic retinopathy as determined herein could be 'key' players in driving disease development and potential 'target' proteins for future therapies. For type 1 and 2 diabetes mellitus, the proteomic profiles were especially distinct during the early disease stage. Validation studies should aim to clarify the role of the detected molecular pathways, potential biomarkers, and potential 'target' proteins for future therapies in diabetic retinopathy.

Monitoring performance of clinical artificial intelligence: a scoping review protocol.

OBJECTIVE: The objective of this scoping review is to describe the scope and nature of research on the monitoring of clinical artificial intelligence (AI) systems. The review will identify the various methodologies used to monitor clinical AI, while also mapping the factors that influence the selection of monitoring approaches. INTRODUCTION: AI is being used in clinical decision-making at an increasing rate. While much attention has been directed toward the development and validation of AI for clinical applications, the practical implementation aspects, notably the establishment of rational monitoring/quality assurance systems, has received comparatively limited scientific interest. Given the scarcity of evidence and the heterogeneity of methodologies used in this domain, there is a compelling rationale for conducting a scoping review on this subject. INCLUSION CRITERIA: This scoping review will include any publications that describe systematic, continuous, or repeated initiatives that evaluate or predict clinical performance of AI models with direct implications for the management of patients in any segment of the health care system. METHODS: Publications will be identified through searches of the MEDLINE (Ovid), Embase (Ovid), and Scopus databases. Additionally, backward and forward citation searches, as well as a thorough investigation of gray literature, will be conducted. Title and abstract screening, full-text evaluation, and data extraction will be performed by 2 or more independent reviewers. Data will be extracted using a tool developed by the authors. The results will be presented graphically and narratively. REVIEW REGISTRATION: Open Science Framework https://osf.io/afkrn.

Associations between insulin-like growth factor binding protein-2 and insulin sensitivity, metformin, and mortality in persons with T2D.

AIMS: Serum insulin-like growth factor binding protein-2 (IGFBP-2) is low in persons with type 2 diabetes mellitus (T2D) and possibly regulated by metformin. Counter-intuitively, high IGFBP-2 associates with mortality. We investigated the association between IGFBP-2, metformin-treatment, and indices of insulin sensitivity, and assessed IGFBP-2 in relation to prior comorbidity and mortality during five-year follow-up. METHODS: The study included 859 treatment-naive and 558 metformin-treated persons enrolled in the Danish Centre for Strategic Research in T2D and followed for 4.9 (3.9-5.9) years through national health registries. All proteins were determined in serum collected at enrollment. RESULTS: Following adjustment for age, metformin-treated and treatment-naive persons has similar IGFBP-2 levels. Low IGFBP-2 level was associated with increased BMI, fasting glucose, and C-peptide. IGFBP-2 was higher in the 437 persons who had comorbidities at enrollment than in those with T2D only (343 (213;528) vs. 242 (169;378) ng/mL). During follow-up, 87 persons died, and IGFBP-2 predicted mortality with an unadjusted HR (95% CI) per doubling in IGFBP-2 concentration of 2.62 (2.04;3.37) and a HR of 2.21 (1.61;3.01) following full adjustment. CONCLUSIONS: In T2D, high IGFBP-2 associates with low glucose and insulin secretion, is unaffected by metformin treatment, and associates with risk of prior comorbidity and mortality.

Blood-Biomarkers for Glucose Metabolism in Preterm Infants.

This was an exploratory, prospective, longitudinal, cohort study that aimed to establish "healthy" reference levels related to growth parameters and glucose metabolites in preterm infants. This was conducted to further investigate growth and metabolic disturbances potentially related to neonatal illness. The study sample consisted of 108 preterm infants born before 32 weeks in 2018-2019 in the Capital Region of Denmark. Repetitive blood samples were acquired at the neonatal wards, while clinical data were obtained from the regional hospital medical record system. Thirty-four "healthy" preterm infants (31%) were identified. The "ill" infants were divided into four subgroups dependent on gestational age and small for gestational age. Reference levels for the growth parameters and metabolic biomarkers glucose, albumin, and adiponectin, and two glucose control indicators, glycated albumin and fructosamine, were determined for the "healthy" and "ill" subgroups. The "ill" extremely preterm infants had increased glucose levels (mean difference 0.71 mmol/L, 95% CI 0.23; 1.18 mmol/L) and glycated albumin (corrected; %) (mean difference 0.92 mmol/L, 95% CI 0.38 mmol/L;1.47 mmol/L) compared to the "healthy" infants. In "ill" extremely preterm infants and "ill" very preterm infants born small for gestational age, levels of biomarkers containing proteins were decreased. In the "Ill" extremely preterm infants and infants born small for gestational age, postnatal growth was continuously decreased throughout the postconceptional period. The short-term glucose-control indicator, glycated albumin (corrected; %), reflected well the high glucose levels due to its correction for the depleted plasma-protein pool.

Specific prediction of mortality by oxidative stress-induced damage to RNA vs. DNA in humans.

Modifications of nucleic acids (DNA and RNA) from oxidative stress is a potential driver of aging per se and of mortality in age-associated medical disorders such as type 2 diabetes (T2D). In a human cohort, we found a strong prediction of all-cause mortality by a marker of systemic oxidation of RNA in patients with T2D (n = 2672) and in nondiabetic control subjects (n = 4079). The finding persisted after the adjustment of established modifiers of oxidative stress (including BMI, smoking, and glycated hemoglobin). In contrast, systemic levels of DNA damage from oxidation, which traditionally has been causally linked to both T2D and aging, failed to predict mortality. Strikingly, these findings were subsequently replicated in an independent general population study (n = 3649). The data demonstrate a specific importance of RNA damage from oxidation in T2D and general aging.

CRP, C-Peptide, and Risk of First-Time Cardiovascular Events and Mortality in Early Type 2 Diabetes: A Danish Cohort Study.

OBJECTIVE: We investigated the relationship between hs-CRP, a marker of low-grade inflammation, alone or in combination with C-peptide, a marker of hyperinsulinemia/insulin resistance, and risk for cardiovascular events (CVEs) and mortality in patients recently diagnosed with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: In patients with recent-onset T2D, we measured serum hs-CRP (n = 7,301) and C-peptide (n = 5,765) in the prospective Danish Centre for Strategic Research in Type 2 Diabetes cohort study. Patients with no prior CVE (n = 6,407) were followed until first myocardial infarction, stroke, coronary revascularization, or cardiovascular death, and all patients (n = 7,301) were followed for all-cause mortality. We computed adjusted hazard ratios (aHRs) by Cox regression and tested for the interaction between hs-CRP and C-peptide. RESULTS: During follow-up (median 4.8 years), high (>3 mg/L) versus low (<1 mg/L) hs-CRP was associated with increased CVE risk (aHR 1.45 [95% CI 1.07-1.96]) and with even greater risk of all-cause mortality (2.47 [1.88-3.25]). Compared with patients with low hs-CRP (≤3 mg/L) and low C-peptide (<1,470 pmol/L), those with high levels of both biomarkers had the highest CVE (1.61 [1.10-2.34]) and all-cause mortality risk (2.36 [1.73-3.21]). Among patients with high C-peptide, risk of CVEs did not differ by low or high hs-CRP, whereas risk of all-cause mortality did. CONCLUSIONS: The finding of high hs-CRP as a stronger prognostic biomarker of all-cause mortality than of CVEs may facilitate improved early detection and prevention of deadly diseases besides CVEs. Conversely, elevated C-peptide as a strong CVE biomarker supports the need to target hyperinsulinemia/insulin resistance in T2D CVE prevention.

A Review of Major Danish Biobanks: Advantages and Possibilities of Health Research in Denmark.

Biobank research may lead to an improved understanding of disease etiology and advance personalized medicine. Denmark (population ~5.9 million) provides a unique setting for population-based health research. The country is a rich source of biobanks and the universal, tax-funded healthcare system delivers routinely collected data to numerous registries and databases. By virtue of the civil registration number (assigned uniquely to all Danish citizens), biological specimens stored in biobanks can be combined with clinical and demographic data from these population-based health registries and databases. In this review, we aim to provide an understanding of advantages and possibilities of biobank research in Denmark. As knowledge about the Danish setting is needed to grasp the full potential, we first introduce the Danish healthcare system, the Civil Registration System, the population-based registries, and the interface with biobanks. We then describe the biobank infrastructures, comprising the Danish National Biobank Initiative, the Bio- and Genome Bank Denmark, and the Danish National Genome Center. Further, we briefly provide an overview of fourteen selected biobanks, including: The Danish Newborn Screening Biobank; The Danish National Birth Cohort; The Danish Twin Registry Biobank; Diet, Cancer and Health; Diet, Cancer and Health - Next generations; Danish Centre for Strategic Research in Type 2 Diabetes; Vejle Diabetes Biobank; The Copenhagen Hospital Biobank; The Copenhagen City Heart Study; The Copenhagen General Population Study; The Danish Cancer Biobank; The Danish Rheumatological Biobank; The Danish Blood Donor Study; and The Danish Pathology Databank. Last, we inform on practical aspects, such as data access, and discuss future implications.

Sensitivity and performance of three novel quantitative assays of SARS-CoV-2 nucleoprotein in blood.

To assess if SARS-CoV-2 (COVID-19) systemic disease can be determined by available nucleoprotein assays, we compared the performance of three commercial SARS-CoV-2 nucleoprotein (N) assays in plasma. A total of 272 plasma samples collected in the period November-December 2021 were analyzed by the methods Simoa SARS CoV-2 N Protein Advantage Kit [Quanterix Simoa], Solsten SARS-CoV-2 Antigen enzyme immunosorbent assay (ELISA) [Solsten ELISA], and Elecsys SARS-CoV-2 Antigen electrochemiluminescence immunoassay [Elecsys ECLIA]. Additionally, a dilution series of inactivated virus culture was analyzed by the three assays. The SARS CoV-2 PCR-status was not known for the patients. Linear correlation in the pairwise correlation between assays as well as linearity of dilution series of inactivated virus culture was estimated by Spearman score. Sensitivity and specificity were estimated by pairwise comparison. The three assays showed poor agreement on patient samples with regards to concentration. Performance on virus culture was excellent but with different level of detection (LOD). Positive vs negative results show comparable sensitivity and specificity of Quanterix Simoa and Solsten ELISA, with a higher LOD in Elecsys ECLIA and thus lower sensitivity and high specificity. N by all tested assays can be used as a marker for systemic COVID-19 disease.

POCT urine dipstick versus central laboratory analyses: Diagnostic performance and logistics in the medical emergency department.

OBJECTIVES: The aim of this study was to evaluate the logistics and diagnostic performances of dipstick analyses compared to their counterpart central laboratory analyses for detection of bacteriuria, proteinuria, hyperglycemia, ketosis and hematuria. DESIGN AND METHODS: Urine dipstick results, urine culture results, flow cytometric cell counts, U-albumin-to-creatinine ratio, P-glucose and P-beta-hydroxybutyrate were retrospectively reviewed in a cohort of consecutive patients admitted to the medical emergency departments of two Danish hospitals. Sensitivity, specificity and predictive values of traditional dipstick analysis were estimated and dipstick was compared to flow cytometry for detection of significant bacteriuria using logistic regression. Turn-around-time for central laboratory analyses were assessed. RESULTS: For each comparison, 1,997 patients or more were included. Traditional dipstick analyses for proteinuria, bacteriuria and ketosis reached sensitivities of up to 90%, while sensitivity for hyperglycemia was 59%. Flow cytometry outperformed traditional dipstick analysis for detection of bacteriuria with a difference in the area under the ROC-curve of 0.07. Turn-around-times for 95% delivery of central laboratory analysis results ranged from approximately 1½ to 2 h. CONCLUSIONS: For the detection of bacteriuria and albuminuria, central laboratory analyses reach better performance than dipstick analysis while achieving acceptable turn-around-times and are thus viable alternatives to dipstick analysis. For detection of ketosis and hyperglycemia, dipstick analysis does not perform adequately, but as very short turn-around-time is often required, these conditions may be best diagnosed by point-of-care blood test rather than dipstick or central laboratory analyses. Dipstick hemoglobin analysis, flow cytometry and microscopic evaluation may serve each their distinct purposes, and thus are relevant in the emergency department.

14-fold increased prevalence of rare glucokinase gene variant carriers in unselected Danish patients with newly diagnosed type 2 diabetes.

AIMS: Rare variants in the glucokinase gene (GCK) cause Maturity-Onset Diabetes of the Young (MODY2/GCK-MODY). We investigated the prevalence of GCK variants, phenotypic characteristics, micro- and macrovascular disease at baseline and follow-up, and treatment among individuals with and without pathogenic GCK variants. METHODS: This is a cross-sectional study in a population-based cohort of 5,433 individuals without diabetes (Inter99 cohort) and in 2,855 patients with a new clinical diagnosis of type 2 diabetes (DD2 cohort) with sequencing of GCK. Phenotypic characteristics, presence of micro- and macrovascular disease and treatment information were available for patients in the DD2 cohort at baseline and after an average follow-up of 7.4 years. RESULTS: Twenty-two carriers of potentially deleterious GCK variants were found among patients with type 2 diabetes compared to three among 5,433 nondiabetic individuals [OR = 14.1 (95 % CI 4.2; 47.0), p = 8.9*10-6]. Patients with type 2 diabetes carrying GCK variants had significantly lower waist circumference, hip circumference and BMI, compared to non-carriers. Three GCK variant carriers with diabetes had microvascular complications during follow-up. CONCLUSIONS: Approximately 0.8% of Danish patients with newly diagnosed type 2 diabetes carry non-synonymous variants in GCK and resemble patients with GCK-MODY. Glucose-lowering treatment cessation should be considered in this subset of diabetes patients.

Erroneous data: The Achilles' heel of AI and personalized medicine.

This paper reviews dilemmas and implications of erroneous data for clinical implementation of AI. It is well-known that if erroneous and biased data are used to train AI, there is a risk of systematic error. However, even perfectly trained AI applications can produce faulty outputs if fed with erroneous inputs. To counter such problems, we suggest 3 steps: (1) AI should focus on data of the highest quality, in essence paraclinical data and digital images, (2) patients should be granted simple access to the input data that feed the AI, and granted a right to request changes to erroneous data, and (3) automated high-throughput methods for error-correction should be implemented in domains with faulty data when possible. Also, we conclude that erroneous data is a reality even for highly reputable Danish data sources, and thus, legal framework for the correction of errors is universally needed.

Systemic DNA and RNA damage from oxidation after serotonergic treatment of unipolar depression.

Previous studies have indicated that antidepressants that inhibit the serotonin transporter reduces oxidative stress. DNA and RNA damage from oxidation is involved in aging and a range of age-related pathophysiological processes. Here, we studied the urinary excretion of markers of DNA and RNA damage from oxidation, 8-oxodG and 8-oxoGuo, respectively, in the NeuroPharm cohort of 100 drug-free patients with unipolar depression and in 856 non-psychiatric community controls. Patients were subsequently treated for 8 weeks with escitalopram in flexible doses of 5-20 mg; seven of these switched to duloxetine by week 4, as allowed by the protocol. At week 8, 82 patients were followed up clinically and with measurements of 8-oxodG/8-oxoGuo. Contextual data were collected in patients, including markers of cortisol excretion and low-grade inflammation. The intervention was associated with a substantial reduction in both 8-oxodG/8-oxoGuo excretion (25% and 10%, respectively). The change was not significantly correlated to measures of clinical improvement. Both markers were strongly and negatively correlated to cortisol, as measured by the area under the curve for the full-day salivary cortisol excretion. Surprisingly, patients had similar levels of 8-oxodG excretion and lower levels of 8-oxoGuo excretion at baseline compared to the controls. We conclude that intervention with serotonin reuptake inhibitors in unipolar depression is associated with a reduction in systemic DNA and RNA damage from oxidation. To our knowledge, this to date the largest intervention study to characterize this phenomenon, and the first to include a marker of RNA oxidation.

Type 2 diabetes classification: a data-driven cluster study of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort.

INTRODUCTION: A Swedish data-driven cluster study identified four distinct type 2 diabetes (T2D) clusters, based on age at diagnosis, body mass index (BMI), hemoglobin A1c (HbA1c) level, and homeostatic model assessment 2 (HOMA2) estimates of insulin resistance and beta-cell function. A Danish study proposed three T2D phenotypes (insulinopenic, hyperinsulinemic, and classical) based on HOMA2 measures only. We examined these two new T2D classifications using the Danish Centre for Strategic Research in Type 2 Diabetes cohort. RESEARCH DESIGN AND METHODS: In 3529 individuals, we first performed a k-means cluster analysis with a forced k-value of four to replicate the Swedish clusters: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild age-related (MARD), and mild obesity-related (MOD) diabetes. Next, we did an analysis open to alternative k-values (ie, data determined the optimal number of clusters). Finally, we compared the data-driven clusters with the three Danish phenotypes. RESULTS: Compared with the Swedish findings, the replicated Danish SIDD cluster included patients with lower mean HbA1c (86 mmol/mol vs 101 mmol/mol), and the Danish MOD cluster patients were less obese (mean BMI 32 kg/m2 vs 36 kg/m2). Our data-driven alternative k-value analysis suggested the optimal number of T2D clusters in our data to be three, rather than four. When comparing the four replicated Swedish clusters with the three proposed Danish phenotypes, 81%, 79%, and 69% of the SIDD, MOD, and MARD patients, respectively, fitted the classical T2D phenotype, whereas 70% of SIRD patients fitted the hyperinsulinemic phenotype. Among the three alternative data-driven clusters, 60% of patients in the most insulin-resistant cluster constituted 76% of patients with a hyperinsulinemic phenotype. CONCLUSION: Different HOMA2-based approaches did not classify patients with T2D in a consistent manner. The T2D classes characterized by high insulin resistance/hyperinsulinemia appeared most distinct.

Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes.

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 case subjects and 279,507 control subjects from 7 European-ancestry cohorts, including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five variants had minor allele frequency of <5% and were each associated with more than a doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19; P = 1 × 10-16) and a stronger effect in men than in women (for interaction, P = 7 × 10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL cholesterol and a 20% increase in triglycerides; colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared with GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests.

OBJECTIVES: To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). METHODS: An analytical profile consisting of 25 predefined routine laboratory blood tests was introduced to general practitioners (GPs) to be used for patients with non-specific symptoms, as an additional tool to identify individuals at increased risk of cancer. Consecutive analytical profiles ordered by GPs from November 29th 2011 until March 1st 2020 were included. AI and LR analysis were performed on data from 6,592 analytical profiles for their ability to detect cancer. Cohort I for model development included 5,224 analytical profiles ordered by GP's from November 29th 2011 until the December 31st 2018, while 1,368 analytical profiles included from January 1st 2019 until March 1st 2020 constituted the "out of time" validation test Cohort II. The main outcome measure was a cancer diagnosis within 90 days. RESULTS: The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the "out of time" validation test Cohort II, the obtained results were comparable. CONCLUSIONS: The AI risk score may be a valuable tool in the clinical decision-making. The score should be further validated to determine its applicability in other populations.

Quantifying SARS-CoV-2 nucleocapsid antigen in oropharyngeal swabs using single molecule array technology.

This study aimed to develop a highly sensitive SARS-CoV-2 nucleocapsid antigen assay using the single molecule array (Simoa) technology and compare it with real time RT-PCR as used in routine clinical practice with the ambition to achieve a comparative technical and clinical sensitivity. Samples were available from 148 SARS-CoV-2 real time RT-PCR positive and 73 SARS-CoV-2 real time RT-PCR negative oropharyngeal swabs. For determination of technical sensitivity SARS-CoV-2 virus culture material was used. The samples were treated with lysis buffer and analyzed using both an in-house and a pre-commercial SARS-CoV-2 nucleocapsid antigen assay on Simoa. Both nucleocapsid antigen assays have a technical sensitivity corresponding to around 100 SARS-CoV-2 RNA molecules/mL. Using a cut-off at 0.1 pg/mL the pre-commercial SARS-CoV-2 nucleocapsid antigen assay had a sensitivity of 96% (95% CI 91.4-98.5%) and specificity of 100% (95% CI 95.1-100%). In comparison the in-house nucleocapsid antigen assay had sensitivity of 95% (95% CI 89.3-98.1%) and a specificity of 100% (95% CI 95.1-100%) using a cut-off at 0.01 pg/mL. The two SARS-CoV-2 nucleocapsid antigen assays correlated with r = 0.91 (P < 0.0001). The in-house and the pre-commercial SARS-CoV-2 nucleocapsid antigen assay demonstrated technical and clinical sensitivity comparable to real-time RT-PCR methods for identifying SARS-CoV-2 infected patients and thus can be used clinically as well as serve as a reference method for antigen Point of Care Testing.

Daily monitoring of viral load measured as SARS-CoV-2 antigen and RNA in blood, IL-6, CRP and complement C3d predicts outcome in patients hospitalized with COVID-19.

OBJECTIVES: We hypothesized that the amount of antigen produced in the body during a COVID-19 infection might differ between patients, and that maximum concentrations would predict the degree of both inflammation and outcome for patients. METHODS: Eighty-four hospitalized and SARS-CoV-2 PCR swab-positive patients, were followed with blood sampling every day until discharge or death. A total of 444 serial EDTA plasma samples were analyzed for a range of biomarkers: SARS-CoV-2 nuclear antigen and RNA concentration, complement activation as well as several inflammatory markers, and KL-6 as a lung marker. The patients were divided into outcome groups depending on need of respiratory support and death/survival. RESULTS: Circulating SARS-CoV-2 nuclear antigen levels were above the detection limit in blood in 65 out of 84 COVID-19 PCR swab-positive patients on day one of hospitalization, as was viral RNA in plasma in 30 out of 84. In all patients, complete antigen clearance was observed within 24 days. There were definite statistically significant differences between the groups depending on their biomarkers, showing that the concentrations of virus RNA and antigen were correlated to the inflammatory biomarker levels, respiratory treatment and death. CONCLUSIONS: Viral antigen is cleared in parallel with the virus RNA levels. The levels of antigens and SARS-CoV-2 RNA in the blood correlates with the level of IL-6, inflammation, respiratory failure and death. We propose that the antigens levels together with RNA in blood can be used to predict the severity of disease, outcome, and the clearance of the virus from the body.