An Empirical Approach to Derive Water T1 from Multiparametric MR Images Using an Automated Pipeline and Comparison With Liver Stiffness.

BACKGROUND: Water T1 of the liver has been shown to be promising in discriminating the progressive forms of fatty liver diseases, inflammation, and fibrosis, yet proper correction for iron and lipid is required. PURPOSE: To examine the feasibility of an empirical approach for iron and lipid correction when measuring imaging-based T1 and to validate this approach by spectroscopy on in vivo data. STUDY TYPE: Retrospective. POPULATION: Next to mixed lipid-iron phantoms, individuals with different hepatic lipid content were investigated, including people with type 1 diabetes (N = 15, %female = 15.6, age = 43.5 ± 14.0), or type 2 diabetes mellitus (N = 21, %female = 28.9, age = 59.8 ± 9.7) and healthy volunteers (N = 9, %female = 11.1, age = 58.0 ± 8.1). FIELD STRENGTH/SEQUENCES: 3 T, balanced steady-state free precession MOdified Look-Locker Inversion recovery (MOLLI), multi- and dual-echo gradient echo Dixon, gradient echo magnetic resonance elastography (MRE). ASSESSMENT: T1 values were measured in phantoms to determine the respective correction factors. The correction was tested in vivo and validated by proton magnetic resonance spectroscopy (1 H-MRS). The quantification of liver T1 based on automatic segmentation was compared to the T1 values based on manual segmentation. The association of T1 with MRE-derived liver stiffness was evaluated. STATISTICAL TESTS: Bland-Altman plots and intraclass correlation coefficients (ICCs) were used for MOLLI vs. 1 H-MRS agreement and to compare liver T1 values from automatic vs. manual segmentation. Pearson's r correlation coefficients for T1 with hepatic lipids and liver stiffness were determined. A P-value of 0.05 was considered statistically significant. RESULTS: MOLLI T1 values after correction were found in better agreement with the 1 H-MRS-derived water T1 (ICC = 0.60 [0.37; 0.76]) in comparison with the uncorrected T1 values (ICC = 0.18 [-0.09; 0.44]). Automatic quantification yielded similar liver T1 values (ICC = 0.9995 [0.9991; 0.9997]) as with manual segmentation. A significant correlation of T1 with liver stiffness (r = 0.43 [0.11; 0.67]) was found. A marked and significant reduction in the correlation strength of T1 with liver stiffness (r = 0.05 [-0.28; 0.38], P = 0.77) was found after correction for hepatic lipid content. DATA CONCLUSION: Imaging-based correction factors enable accurate estimation of water T1 in vivo. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta-regression analysis of 120 randomized controlled trials.

AIMS: To assess the potential for precision medicine in type 2 diabetes by quantifying the variability of body weight as response to pharmacological treatment and to identify predictors which could explain this variability. METHODS: We used randomized clinical trials (RCTs) comparing glucose-lowering drugs (including but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo from four recent systematic reviews. RCTs reporting on body weight after treatment to allow for calculation of its logarithmic standard deviation (log[SD], i.e., treatment response heterogeneity) in verum (i.e., treatment) and placebo groups were included. Meta-regression analyses were performed with respect to variability of body weight after treatment and potential predictors. RESULTS: A total of 120 RCTs with a total of 43 663 participants were analysed. A slightly larger treatment response heterogeneity was shown in the verum groups, with a median log(SD) of 2.83 compared to 2.79 from placebo. After full adjustment in the meta-regression model, the difference in body weight log(SD) was -0.026 (95% confidence interval -0.044; 0.008), with greater variability in the placebo groups. Scatterplots did not show any slope divergence (i.e., interaction) between clinical predictors and the respective treatment (verum or placebo). CONCLUSIONS: We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs for body weight reduction in type 2 diabetes. The precision medicine approach may thus be of limited value in this setting.

Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

A parametric additive hazard model for time-to-event analysis.

BACKGROUND: In recent years, the use of non- and semi-parametric models which estimate hazard ratios for analysing time-to-event outcomes is continuously criticized in terms of interpretation, technical implementation, and flexibility. Hazard ratios in particular are critically discussed for their misleading interpretation as relative risks and their non-collapsibility. Additive hazard models do not have these drawbacks but are rarely used because they assume a non- or semi-parametric additive hazard which renders computation and interpretation complicated. METHODS: As a remedy, we propose a new parametric additive hazard model that allows results to be reported on the original time rather than on the hazard scale. Being an essentially parametric model, survival, hazard and probability density functions are directly available. Parameter estimation is straightforward by maximizing the log-likelihood function. RESULTS: Applying the model to different parametric distributions in a simulation study and in an exemplary application using data from a study investigating medical care to lung cancer patients, we show that the approach works well in practice. CONCLUSIONS: Our proposed parametric additive hazard model can serve as a powerful tool to analyze time-to-event outcomes due to its simple interpretation, flexibility and facilitated parameter estimation.

Mechanosensing by ß1 integrin induces angiocrine signals for liver growth and survival.

Angiocrine signals derived from endothelial cells are an important component of intercellular communication and have a key role in organ growth, regeneration and disease1-4. These signals have been identified and studied in multiple organs, including the liver, pancreas, lung, heart, bone, bone marrow, central nervous system, retina and some cancers1-4. Here we use the developing liver as a model organ to study angiocrine signals5,6, and show that the growth rate of the liver correlates both spatially and temporally with blood perfusion to this organ. By manipulating blood flow through the liver vasculature, we demonstrate that vessel perfusion activates ß1 integrin and vascular endothelial growth factor receptor 3 (VEGFR3). Notably, both ß1 integrin and VEGFR3 are strictly required for normal production of hepatocyte growth factor, survival of hepatocytes and liver growth. Ex vivo perfusion of adult mouse liver and in vitro mechanical stretching of human hepatic endothelial cells illustrate that mechanotransduction alone is sufficient to turn on angiocrine signals. When the endothelial cells are mechanically stretched, angiocrine signals trigger in vitro proliferation and survival of primary human hepatocytes. Our findings uncover a signalling pathway in vascular endothelial cells that translates blood perfusion and mechanotransduction into organ growth and maintenance.

A nonparametric proportional risk model to assess a treatment effect in time-to-event data.

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality.

How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials.

AIMS/HYPOTHESIS: There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes. METHODS: We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors. RESULTS: The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values. CONCLUSIONS/INTERPRETATION: The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs. FUNDING: The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. DATA AVAILABILITY: Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .

Risk phenotypes of diabetes and association with COVID-19 severity and death: an update of a living systematic review and meta-analysis.

AIMS/HYPOTHESIS: To provide a systematic overview of the current body of evidence on high-risk phenotypes of diabetes associated with COVID-19 severity and death. METHODS: This is the first update of our recently published living systematic review and meta-analysis. Observational studies investigating phenotypes in individuals with diabetes and confirmed SARS-CoV-2 infection with regard to COVID-19-related death and severity were included. The literature search was conducted from inception up to 14 February 2022 in PubMed, Epistemonikos, Web of Science and the COVID-19 Research Database and updated using PubMed alert to 1 December 2022. A random-effects meta-analysis was used to calculate summary relative risks (SRRs) with 95% CIs. The risk of bias was evaluated using the Quality in Prognosis Studies (QUIPS) tool and the certainty of evidence using the GRADE approach. RESULTS: A total of 169 articles (147 new studies) based on approximately 900,000 individuals were included. We conducted 177 meta-analyses (83 on COVID-19-related death and 94 on COVID-19 severity). Certainty of evidence was strengthened for associations between male sex, older age, blood glucose level at admission, chronic insulin use, chronic metformin use (inversely) and pre-existing comorbidities (CVD, chronic kidney disease, chronic obstructive pulmonary disease) and COVID-19-related death. New evidence with moderate to high certainty emerged for the association between obesity (SRR [95% CI] 1.18 [1.04, 1.34], n=21 studies), HbA1c (53-75 mmol/mol [7-9%]: 1.18 [1.06, 1.32], n=8), chronic glucagon-like peptide-1 receptor agonist use (0.83 [0.71, 0.97], n=9), pre-existing heart failure (1.33 [1.21, 1.47], n=14), pre-existing liver disease (1.40 [1.17, 1.67], n=6), the Charlson index (per 1 unit increase: 1.33 [1.13, 1.57], n=2), high levels of C-reactive protein (per 5 mg/l increase: 1.07 [1.02, 1.12], n=10), aspartate aminotransferase level (per 5 U/l increase: 1.28 [1.06, 1.54], n=5), eGFR (per 10 ml/min per 1.73 m2 increase: 0.80 [0.71, 0.90], n=6), lactate dehydrogenase level (per 10 U/l increase: 1.03 [1.01, 1.04], n=7) and lymphocyte count (per 1×109/l increase: 0.59 [0.40, 0.86], n=6) and COVID-19-related death. Similar associations were observed between risk phenotypes of diabetes and severity of COVID-19, with some new evidence on existing COVID-19  vaccination status (0.32 [0.26, 0.38], n=3), pre-existing hypertension (1.23 [1.14, 1.33], n=49), neuropathy and cancer, and high IL-6 levels. A limitation of this study is that the included studies are observational in nature and residual or unmeasured confounding cannot be ruled out. CONCLUSIONS/INTERPRETATION: Individuals with a more severe course of diabetes and pre-existing comorbidities had a poorer prognosis of COVID-19 than individuals with a milder course of the disease. REGISTRATION: PROSPERO registration no. CRD42020193692. PREVIOUS VERSION: This is a living systematic review and meta-analysis. The previous version can be found at https://link.springer.com/article/10.1007/s00125-021-05458-8 FUNDING: The German Diabetes Center (DDZ) is funded by the German Federal Ministry of Health and the Ministry of Culture and Science of the State North Rhine-Westphalia. This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD).

Serum neurofilament light chain: a novel biomarker for early diabetic sensorimotor polyneuropathy.

AIMS/HYPOTHESIS: No established blood-based biomarker exists to monitor diabetic sensorimotor polyneuropathy (DSPN) and evaluate treatment response. The neurofilament light chain (NFL), a blood biomarker of neuroaxonal damage in several neurodegenerative diseases, represents a potential biomarker for DSPN. We hypothesised that higher serum NFL levels are associated with prevalent DSPN and nerve dysfunction in individuals recently diagnosed with diabetes. METHODS: This cross-sectional study included 423 adults with type 1 and type 2 diabetes and known diabetes duration of less than 1 year from the prospective observational German Diabetes Study cohort. NFL was measured in serum samples of fasting participants in a multiplex approach using proximity extension assay technology. DSPN was assessed by neurological examination, nerve conduction studies and quantitative sensory testing. Associations of serum NFL with DSPN (defined according to the Toronto Consensus criteria) were estimated using Poisson regression, while multivariable linear and quantile regression models were used to assess associations with nerve function measures. In exploratory analyses, other biomarkers in the multiplex panel were also analysed similarly to NFL. RESULTS: DSPN was found in 16% of the study sample. Serum NFL levels increased with age. After adjustment for age, sex, waist circumference, height, HbA1c, known diabetes duration, diabetes type, cholesterol, eGFR, hypertension, CVD, use of lipid-lowering drugs and use of non-steroidal anti-inflammatory drugs, higher serum NFL levels were associated with DSPN (RR [95% CI] per 1-normalised protein expression increase, 1.92 [1.50, 2.45], p<0.0001), slower motor (all p<0.0001) and sensory (all p≤0.03) nerve conduction velocities, lower sural sensory nerve action potential (p=0.0004) and higher thermal detection threshold to warm stimuli (p=0.023 and p=0.004 for hand and foot, respectively). There was no evidence for associations between other neurological biomarkers and DSPN or nerve function measures. CONCLUSIONS/INTERPRETATION: Our findings in individuals recently diagnosed with diabetes provide new evidence associating higher serum NFL levels with DSPN and peripheral nerve dysfunction. The present study advocates NFL as a potential biomarker for DSPN.

HbA1c trajectories over 3 years in people with type 2 diabetes starting second-line glucose-lowering therapy: The prospective global DISCOVER study.

AIM: To identify distinct HbA1c trajectories in people with type 2 diabetes (T2D) starting second-line glucose-lowering therapy. MATERIALS AND METHODS: DISCOVER was a 3-year observational study of individuals with T2D beginning second-line glucose-lowering therapy. Data were collected at initiation of second-line treatment (baseline) and at 6, 12, 24 and 36 months. Latent class growth modelling was used to identify groups with distinct HbA1c trajectories. RESULTS: After exclusions, 9295 participants were assessed. Four distinct HbA1c trajectories were identified. Mean HbA1c levels decreased between baseline and 6 months in all groups; 72.4% of participants showed stable good levels of glycaemic control over the remainder of follow-up, 18.0% showed stable moderate levels of glycaemic control and 2.9% showed stable poor levels of glycaemic control. Only 6.7% of participants showed highly improved glycaemic control at month 6 and stable control over the rest of follow-up. For all groups, dual oral therapy use decreased over time, compensated for by the increasing use of other treatment regimens. Use of injectable agents increased over time in groups with moderate and poor glycaemic control. Logistic regression models suggested that participants from high-income countries were more probable to be in the stable good trajectory group. CONCLUSIONS: Most people receiving second-line glucose-lowering treatment in this global cohort achieved stable good or highly improved long-term glycaemic control. One-fifth of participants showed moderate or poor glycaemic control during follow-up. Further large-scale studies are required to characterize possible factors associated with patterns of glycaemic control to inform personalized diabetes treatment.

How hazardous are hazard ratios? An empirical investigation of individual patient data from 27 large randomized clinical trials.

The use of hazard ratios as the standard treatment effect estimators for randomized trials with time-to-event outcomes has been the subject of repeated criticisms in recent years, e.g., for its non-collapsibility or with respect to (causal) interpretation. Another important issue is the built-in selection bias, which arises when the treatment is effective and when there are unobserved or not included prognostic factors that influence time-to-event. In these cases, the hazard ratio has even been termed "hazardous" because it is estimated from groups that increasingly differ in their (unobserved or omitted) baseline characteristics, yielding biased treatment estimates. We therefore adapt the Landmarking approach to assess the effect of ignoring a gradually increasing proportion of early events on the estimated hazard ratio. We propose an extension called "Dynamic Landmarking". This approach is based on successive deletion of observations, refitting Cox models and balance checking of omitted but observed prognostic factors, to obtain a visualization that can indicate built-in selection bias. In a small proof-of-concept simulation, we show that our approach is valid under the given assumptions. We further use "Dynamic Landmarking" to assess the suspected selection bias in the individual patient data sets of 27 large randomized clinical trials (RCTs). Surprisingly, we find no empirical evidence of selection bias in these RCTs and thus conclude that the supposed bias of the hazard ratio is of little practical relevance in most cases. This is mainly due to treatment effects in RCTs being small and the patient populations being homogeneous, e.g., due to inclusion and exclusion criteria.

Impact of Left Atrial Appendage Amputation on Left Atrial Morphology and Rhythm after Off-Pump CABG.

OBJECTIVES: Left atrial appendage (LAA) amputation concomitant to coronary artery bypass grafting (CABG) has become an increasingly performed technique in patients with atrial fibrillation (AF) or with sinus rhythm and a CHA2DS2-VASc score ≥2. However, LAA amputation has come under suspicion to cause postoperative atrial fibrillation (POAF) due to left atrial (LA) dilation. This study aims to assess this assumption in patients undergoing CABG in off-pump technique with and without amputation of the LAA. METHODS: Patients who underwent isolated CABG in off-pump technique without history of AF were retrospectively examined. Cohorts were divided according to the concomitant execution of LAA amputation. LA volume was measured by transthoracic echocardiography and rhythm was analyzed by electrocardiography, medication protocol, and visit documentation. Propensity score (PS) matching was performed based on 20 preoperative risk variables to correct for selection bias. RESULTS: A total of 1,522 patients were enrolled, with 1,267 in the control group and 255 in the LAA amputation group. Occurrence of POAF was compared in 243 PS-matched patient pairs. Neither the unmatched cohort (odds ratio [OR] 0.82; 95% confidence interval or CI [0.61; 1.11], p = 0.19) nor the PS-matched cohort (OR 0.94; 95% CI [0.62; 1.41], p = 0.75) showed significant differences in POAF occurrence. Subgroup analysis of sex, use of ß-blockers, pulmonary disease, ejection fraction, and CHA2DS2-VASc-Score also showed no tendencies. LA volume did not change significantly (p = 0.18, 95% CI [-0.29; 1.51]). CONCLUSION: Surgical amputation of the LAA concomitant to CABG did not lead to LA dilation and has no significant impact on the occurrence of POAF.

Incidence of newly diagnosed diabetes after Covid-19.

AIMS/HYPOTHESIS: The aim of this work was to investigate diabetes incidence after infection with coronavirus disease-2019 (Covid-19). Individuals with acute upper respiratory tract infections (AURI), which are frequently caused by viruses, were selected as a non-exposed control group. METHODS: We performed a retrospective cohort analysis of the Disease Analyzer, which comprises a representative panel of 1171 physicians' practices throughout Germany (March 2020 to January 2021: 8.8 million patients). Newly diagnosed diabetes was defined based on ICD-10 codes (type 2 diabetes: E11; other forms of diabetes: E12-E14) during follow-up until July 2021 (median for Covid-19, 119 days; median for AURI 161 days). Propensity score matching (1:1) for sex, age, health insurance, index month for Covid-19/AURI and comorbidity (obesity, hypertension, hyperlipidaemia, myocardial infarction, stroke) was performed. Individuals using corticosteroids within 30 days after the index dates were excluded. Poisson regression models were fitted to obtain incidence rate ratios (IRRs) for diabetes. RESULTS: There were 35,865 individuals with documented Covid-19 in the study period. After propensity score matching, demographic and clinical characteristics were similar in 35,865 AURI controls (mean age 43 years; 46% female). Individuals with Covid-19 showed an increased type 2 diabetes incidence compared with AURI (15.8 vs 12.3 per 1000 person-years). Using marginal models to account for correlation of observations within matched pairs, an IRR for type 2 diabetes of 1.28 (95% CI 1.05, 1.57) was estimated. The IRR was not increased for other forms of diabetes. CONCLUSIONS/INTERPRETATION: Covid-19 confers an increased risk for type 2 diabetes. If confirmed, these results support the active monitoring of glucose dysregulation after recovery from mild forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Socio-economic inequalities in glycaemic control in recently diagnosed adults with type 1 and type 2 diabetes.

BACKGROUND AND AIMS: It is unclear whether socio-economic status (SES) is associated with glycaemic control in people with recently diagnosed diabetes. The aim was to investigate whether SES is related to haemoglobin A1c (HbA1c) during the first year after diagnosis in people with type 1 and type 2 diabetes and if metabolic, quality of care or mental factors may explain the association. METHODS: In the German Diabetes Study, people with type 1 (n = 274, median age 36 [25th; 75th percentile: 28; 48] years) and type 2 diabetes (n = 424, 54 [47; 60] years) underwent detailed metabolic characterisation within the first year after diagnosis. SES was documented using a standardised questionnaire. Associations between SES and HbA1c were assessed using multivariable linear regression and restricted cubic spline regression analyses. Additional covariables were patient characteristics, laboratory measurements, health behaviour, quality of care and depression variables. Models were separately fitted for diabetes type, SES and its dimensions (income, education, occupation). RESULTS: Higher SES score was associated with lower HbA1c (-0.7 mmol/mol per unit increase in SES, 95% CI: -1.1; -0.2 mmol/mol [-0.1%, 95% CI: -0.1; 0.0%]) in people with type 1 diabetes. Included covariates did not attenuate this association. In people with type 2 diabetes, effect estimates were close to zero indicating no relevant difference. CONCLUSION: Socio-economic inequalities in HbA1c already exist during the first year after diagnosis in people with type 1 diabetes. The absence of association between glycaemic control and SES in type 2 diabetes could be due to the lower complexity of diabetes therapy compared to type 1 diabetes.

Performance of several types of beta-binomial models in comparison to standard approaches for meta-analyses with very few studies.

BACKGROUND: Meta-analyses are used to summarise the results of several studies on a specific research question. Standard methods for meta-analyses, namely inverse variance random effects models, have unfavourable properties if only very few (2 - 4) studies are available. Therefore, alternative meta-analytic methods are needed. In the case of binary data, the "common-rho" beta-binomial model has shown good results in situations with sparse data or few studies. The major concern of this model is that it ignores the fact that each treatment arm is paired with a respective control arm from the same study. Thus, the randomisation to a study arm of a specific study is disrespected, which may lead to compromised estimates of the treatment effect. Therefore, we extended this model to a version that respects randomisation. The aim of this simulation study was to compare the "common-rho" beta-binomial model and several other beta-binomial models with standard meta-analyses models, including generalised linear mixed models and several inverse variance random effects models. METHODS: We conducted a simulation study comparing beta-binomial models and various standard meta-analysis methods. The design of the simulation aimed to consider meta-analytic situations occurring in practice. RESULTS: No method performed well in scenarios with only 2 studies in the random effects scenario. In this situation, a fixed effect model or a qualitative summary of the study results may be preferable. In scenarios with 3 or 4 studies, most methods satisfied the nominal coverage probability. The "common-rho" beta-binomial model showed the highest power under the alternative hypothesis. The beta-binomial model respecting randomisation did not improve performance. CONCLUSION: The "common-rho" beta-binomial appears to be a good option for meta-analyses of very few studies. As residual concerns about the consequences of disrespecting randomisation may still exist, we recommend a sensitivity analysis with a standard meta-analysis method that respects randomisation.

Three-variate trajectories of metabolic control, body mass index, and insulin dose: Heterogeneous response to initiation of pump therapy in youth with type 1 diabetes.

OBJECTIVE: Continuous subcutaneous insulin infusion (CSII) in youths with type 1 diabetes (T1D) is often associated with lower HbA1c, lower total daily insulin dose (TDD), and lower body mass index (BMI) compared with multiple daily injections (MDI). Individual responses to CSII are diverse. The aim was to identify unique three-variate patterns of HbA1c, BMI standard deviation score (SDS), and TDD after switching to CSII. METHODS: Five thousand one hundred and thirty-three youths (≤20 years; 48% boys; median age at pump start 12.5 years) with T1D duration ≥3 years at CSII initiation were selected from the multicenter DPV registry. We applied group-based multitrajectory modeling to identify groups of individuals following similar trajectories. Measurements were aggregated quarterly during a 3-year follow-up period. Trajectory variables were changes of HbA1c, BMI-SDS, and TDD from baseline (delta = quarterly aggregated values at each time point [i] minus the respective baseline value). RESULTS: Four groups of diverging Delta-HbA1c, Delta-BMI-SDS, and Delta-TDD patterns were identified. All showed improvements in HbA1c during the first 3 months. Group 1 (12%) was characterized by modest HbA1c increase thereafter, TDD reduction, and stable BMI-SDS. In Group 2 (39%), increasing HbA1c, decreasing BMI-SDS, and stable TDD were found. By contrast, sustainably improved HbA1c, increasing BMI-SDS, and stable TDD were observed in Group 3 (32%). Group 4 (17%) was characterized by increasing levels for HbA1c, BMI-SDS, and TDD. Between-group differences in baseline HbA1c, BMI-SDS, TDD as well as in sex ratio, age at diabetes onset and at pump start were observed. CONCLUSIONS: Definite trajectories of glycemic control, BMI, and TDD over 3 years after CSII initiation were identified in youths with T1D allowing a more personalized treatment recommendation.

Statistical Analysis in the German National Cohort (NAKO) - Specific Aspects and General Recommendations.

The German National Cohort (NAKO) is an ongoing, prospective multicenter cohort study, which started recruitment in 2014 and includes more than 205,000 women and men aged 19-74 years. The study data will be available to the global research community for analyses. Although the ultimate decision about the analytic methods will be made by the respective investigator, in this paper we provide the basis for a harmonized approach to the statistical analyses in the NAKO. We discuss specific aspects of the study (e.g., data collection, weighting to account for the sampling design), but also give general recommendations which may apply to other large cohort studies as well.

Risk phenotypes of diabetes and association with COVID-19 severity and death: a living systematic review and meta-analysis.

AIMS/HYPOTHESIS: Diabetes has been identified as a risk factor for poor prognosis of coronavirus disease-2019 (COVID-19). The aim of this study is to identify high-risk phenotypes of diabetes associated with COVID-19 severity and death. METHODS: This is the first edition of a living systematic review and meta-analysis on observational studies investigating phenotypes in individuals with diabetes and COVID-19-related death and severity. Four different databases were searched up to 10 October 2020. We used a random effects meta-analysis to calculate summary relative risks (SRR) with 95% CI. The certainty of evidence was evaluated by the GRADE tool. RESULTS: A total of 22 articles, including 17,687 individuals, met our inclusion criteria. For COVID-19-related death among individuals with diabetes and COVID-19, there was high to moderate certainty of evidence for associations (SRR [95% CI]) between male sex (1.28 [1.02, 1.61], n = 10 studies), older age (>65 years: 3.49 [1.82, 6.69], n = 6 studies), pre-existing comorbidities (cardiovascular disease: 1.56 [1.09, 2.24], n = 8 studies; chronic kidney disease: 1.93 [1.28, 2.90], n = 6 studies; chronic obstructive pulmonary disease: 1.40 [1.21, 1.62], n = 5 studies), diabetes treatment (insulin use: 1.75 [1.01, 3.03], n = 5 studies; metformin use: 0.50 [0.28, 0.90], n = 4 studies) and blood glucose at admission (≥11 mmol/l: 8.60 [2.25, 32.83], n = 2 studies). Similar, but generally weaker and less precise associations were observed between risk phenotypes of diabetes and severity of COVID-19. CONCLUSIONS/INTERPRETATION: Individuals with a more severe course of diabetes have a poorer prognosis of COVID-19 compared with individuals with a milder course of disease. To further strengthen the evidence, more studies on this topic that account for potential confounders are warranted. REGISTRATION: PROSPERO registration ID CRD42020193692.

Impact of Immunosuppressive Agents on Clinical Manifestations and Outcome of Staphylococcus aureus Bloodstream Infection: A Propensity Score-Matched Analysis in 2 Large, Prospectively Evaluated Cohorts.

BACKGROUND: Staphylococcus aureus bloodstream infection (SAB) is a common, life-threatening infection. The impact of immunosuppressive agents on the outcome of patients with SAB is incompletely understood. METHODS: Data from 2 large prospective, international, multicenter cohort studies (Invasive Staphylococcus aureus Infections Cohort [INSTINCT] and International Staphylococcus aureus Collaboration [ISAC]) between 2006 and 2015 were analyzed. Patients receiving immunosuppressive agents were identified and a 1:1 propensity score-matched analysis was performed to adjust for baseline characteristics of patients. Overall survival and time to SAB-related late complications (SAB relapse, infective endocarditis, osteomyelitis, or other deep-seated manifestations) were analyzed by Cox regression and competing risk analyses, respectively. This approach was then repeated for specific immunosuppressive agents (corticosteroid monotherapy and immunosuppressive agents other than steroids [IMOTS]). RESULTS: Of 3188 analyzed patients, 309 were receiving immunosuppressive treatment according to our definitions and were matched to 309 nonimmunosuppressed patients. After propensity score matching, baseline characteristics were well balanced. In the Cox regression analysis, we observed no significant difference in survival between the 2 groups (death during follow-up: 105/309 [33.9%] immunosuppressed vs 94/309 [30.4%] nonimmunosuppressed; hazard ratio [HR], 1.20 [95% confidence interval {CI}, .84-1.71]). Competing risk analysis showed a cause-specific HR of 1.81 (95% CI, .85-3.87) for SAB-related late complications in patients receiving immunosuppressive agents. The cause-specific HR was higher in patients taking IMOTS (3.69 [95% CI, 1.41-9.68]). CONCLUSIONS: Immunosuppressive agents were not associated with an overall higher mortality. The risk for SAB-related late complications in patients receiving specific immunosuppressive agents such as IMOTS warrants further investigations.

Associations between haemoglobin A1c and mortality rate in the KORA S4 and the Heinz Nixdorf Recall population-based cohort studies.

BACKGROUND: There is limited knowledge about mortality risk in persons with increased haemoglobin A1c (HbA1c ) levels below the diabetes threshold. Moreover, little is known about how associations between increased HbA1c and mortality depend on the length of follow-up. Therefore, we studied associations between HbA1c and mortality over long-term follow-up in persons with and without known diabetes. METHODS: We used data from two German population-based cohort studies: KORA S4 Study (Southern Germany, n = 1458, baseline visits in 1999 to 2001, baseline age 55 to 74 years, mortality follow-up 16.8 years) and Heinz Nixdorf Recall (HNR) Study (Ruhr area, n = 4613, baseline visits in 2000 to 2003, baseline age 45 to 75 years, mortality follow-up 17.8 years). Adjusted log-linear models were fitted to estimate relative risks (RRs) with 95% confidence intervals (CI). RESULTS: In both cohorts, participants with HbA1c 39 to 41 mmol/mol (5.7%-5.9%) and HbA1c 42 to 46 mmol/mol (6.0% to 6.4%) did not have a larger overall mortality risk than participants with HbA1c < 39 mmol/mol (5.7%): the corresponding adjusted RRs were 1.00 (95% CI: 0.83-1.21) and 1.01 (0.80-1.27) in KORA and 0.99 (0.82-1.21) and 0.83 (0.65-1.07) in the HNR Study. For the pooled cohorts, the RR for HbA1c 39 to 46 mmol/mol (5.7%-6.4%) was 0.96 (0.85-1.07). Associations between newly detected diabetes (HbA1c ≥ 6.5%) and mortality were weak after 4 and 8 years of follow-up, but were stronger after 12 years of follow-up, whereas associations between previously known diabetes (baseline) and mortality decreased. CONCLUSIONS: HbA1c -defined pre-diabetes is not associated with overall mortality. For newly detected and previously known diabetes, mortality risks vary with length of follow-up.