Objective-Increased levels of C-peptide, a cleavage product of proinsulin, circulate in patients with insulin resistance and early type 2 diabetes, a high-risk population for the development of a diffuse and extensive pattern of arteriosclerosis. This study tested the hypothesis that C-peptide might participate in atherogenesis in these patients. Method and Results-We demonstrate significantly higher intimal C-peptide deposition in thoracic artery specimens from young diabetic subjects compared with matched nondiabetic controls as determined by immunohistochemical staining. C-peptide colocalized with monocytes/macrophages in the arterial intima of artery specimen from diabetic subjects. In vitro, C-peptide stimulated monocyte chemotaxis in a concentration-dependent manner with a maximal 2.3Ϯ0.4-fold increase at 1 nmol/L C-peptide. Pertussis toxin, wortmannin, and LY294002 inhibited C-peptide-induced monocyte chemotaxis, suggesting the involvement of pertussis toxin-sensitive G-proteins as well as a phosphoinositide 3-kinase (PI3K)-dependent mechanism. In addition, C-peptide treatment activated PI3K in human monocytes, as demonstrated by PI3K activity assays. Conclusion-C-peptide accumulated in the vessel wall in early atherogenesis in diabetic subjects and may promote monocyte migration into developing lesions. These data support the hypothesis that C-peptide may play an active role in atherogenesis in diabetic patients and suggest a new mechanism for accelerated arterial disease in diabetes.

Aims Patients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality. Methods and results We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable Poisson regression models were fitted to assess the association between different types of pre-existing heart disease and in-hospital mortality. A total of 16 511 patients with COVID-19 were included (21.1% aged 66–75 years; 40.2% female) and 31.5% had a history of heart disease. Patients with heart disease were older, predominantly male, and often had other comorbid conditions when compared with those without. Mortality was higher in patients with cardiac disease (29.7%; n = 1545 vs. 15.9%; n = 1797). However, following multivariable adjustment, this difference was not significant [adjusted risk ratio (aRR) 1.08, 95% confidence interval (CI) 1.02–1.15; P = 0.12 (corrected for multiple testing)]. Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for heart failure (aRR 1.19, 95% CI 1.10–1.30; P < 0.018) particularly for severe (New York Heart Association class III/IV) heart failure (aRR 1.41, 95% CI 1.20–1.64; P < 0.018). None of the other heart disease subtypes, including ischaemic heart disease, remained significant after multivariable adjustment. Serious cardiac complications were diagnosed in <1% of patients. Conclusion Considerable heterogeneity exists in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with heart failure are at greatest risk of death when hospitalized with COVID-19. Serious cardiac complications are rare during hospitalization.

Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

The impact of preexisting neurodegenerative diseases on superimposed SARS-CoV-2 infections remains controversial. Here we examined the course and outcome of SARS-CoV-2 infections in patients affected by Parkinson's disease (PD) or dementia compared to matched controls without neurodegenerative diseases in the LEOSS (Lean European Open Survey on SARS-CoV-2-infected patients) cohort, a large-scale prospective multicenter cohort study. 1 The LEOSS scientific data set comprises anonymous data after data quality control, including plausibility checks. Collected data include demographic information, standardized clinical classification of the SARS-CoV-2 severity (hospitalization and discharge), administered medical care (eg, intensive care unit [ICU] stay, and ventilation), preexisting and concomitant signs and symptoms, medication, laboratory parameters, and mortality. The patient sample age is grouped in decades. Our study population comprised n = 4310 SARS-CoV-2infected patients (59% men). Forty of them had PD (median decade: 76-85 years, 63% men); 290 had dementia (median decade: 76-85 years, 50% men) (Supplementary Tables S1 and S2). Dementia was classified into Alzheimer's disease (22.1%), vascular dementia (13.3%), other dementia (12.4%), and unknown/missing value (52.1%). More than 95% of the patients were from tertiary referral centers in Germany between March 2020 and November 2020. Using a systematic sampling strategy, we extracted 15 controls randomly from the study population for each PD patient (1:15) and 2 randomly selected controls for each dementia patient (1:2). Any potentially confounding effects resulting from variability in age and sex were fully adjusted for by the matching procedure. To avoid bias, we handled patients and controls the same way according to standard epidemiological principles.

Aims Patients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality. Method and results We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable modified Poisson regression models were fitted to assess the association between different types of pre-existent heart disease and in-hospital mortality. 10,481 patients with COVID-19 were included (22.4% aged 66-75 years; 38.7% female) of which 30.5% had a history of cardiac disease. Patients with heart disease were older, predominantly male and more likely to have other comorbid conditions when compared to those without. COVID-19 symptoms at presentation did not differ between these groups. Mortality was higher in patients with cardiac disease (30.3%; n=968 versus 15.7%; n=1143). However, following multivariable adjustment this difference was not significant (adjusted risk ratio (aRR) 1.06 [95% CI 0.98-1.15, p-value 0.13]). Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for NYHA III/IV heart failure (aRR 1.43 [95% CI 1.22-1.68, p-value <0.001]) and atrial fibrillation (aRR 1.14 [95% CI 1.04-1.24, p-value 0.01]). None of the other heart disease subtypes, including ischemic heart disease, remained significant after multivariable adjustment. Conclusion There is considerable heterogeneity in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with severe heart failure are at greatest risk of death when hospitalized with COVID-19.

Purpose Reported antibiotic use in coronavirus disease 2019 (COVID-19) is far higher than the actual rate of reported bacterial co- and superinfection. A better understanding of antibiotic therapy in COVID-19 is necessary. Methods 6457 SARS-CoV-2-infected cases, documented from March 18, 2020, until February 16, 2021, in the LEOSS cohort were analyzed. As primary endpoint, the correlation between any antibiotic treatment and all-cause mortality/progression to the next more advanced phase of disease was calculated for adult patients in the complicated phase of disease and procalcitonin (PCT) ≤ 0.5 ng/ml. The analysis took the confounders gender, age, and comorbidities into account. Results Three thousand, six hundred twenty-seven cases matched all inclusion criteria for analyses. For the primary endpoint, antibiotic treatment was not correlated with lower all-cause mortality or progression to the next more advanced (critical) phase (n = 996) (both p > 0.05). For the secondary endpoints, patients in the uncomplicated phase (n = 1195), regardless of PCT level, had no lower all-cause mortality and did not progress less to the next more advanced (complicated) phase when treated with antibiotics (p > 0.05). Patients in the complicated phase with PCT > 0.5 ng/ml and antibiotic treatment (n = 286) had a significantly increased all-cause mortality (p = 0.029) but no significantly different probability of progression to the critical phase (p > 0.05). Conclusion In this cohort, antibiotics in SARS-CoV-2-infected patients were not associated with positive effects on all-cause mortality or disease progression. Additional studies are needed. Advice of local antibiotic stewardship- (ABS-) teams and local educational campaigns should be sought to improve rational antibiotic use in COVID-19 patients.

Purpose We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free. Methods COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled ≥ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan–Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free. Results Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49–59 years compared to < 49 years (aHR 0.70, 95% CI 0.56–0.87), female sex (aHR 0.78, 95% CI 0.65–0.93), lower educational level (aHR 0.77, 95% CI 0.64–0.93), living with a partner (aHR 0.81, 95% CI 0.66–0.99), low resilience (aHR 0.65, 95% CI 0.47–0.90), steroid treatment (aHR 0.22, 95% CI 0.05–0.90) and no medication (aHR 0.74, 95% CI 0.62–0.89) during acute infection. Conclusion In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant’s characteristics that are difficult to modify.

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