Flexible parametrization of graph-theoretical features from individual-specific networks for prediction.

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study.

BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. METHODS: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. RESULTS: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. CONCLUSIONS: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Early progression of chronic histologic lesions in kidney transplant biopsies is not associated with HLA histocompatibility.

BACKGROUND: Early progression of chronic histologic lesions in kidney allografts represents the main finding in graft attrition. The objective of this retrospective cohort study was to elucidate whether HLA histocompatibility is associated with progression of chronic histologic lesions in the first year post-transplant. Established associations of de novo donor-specific antibody (dnDSA) formation with HLA mismatch and microvascular inflammation (MVI) were calculated to allow for comparability with other study cohorts. METHODS: We included 117 adult kidney transplant recipients, transplanted between 2016 and 2020 from predominantly deceased donors, who had surveillance biopsies at three and twelve months. Histologic lesion scores were assessed according to the Banff classification. HLA mismatch scores (i.e. eplet, predicted indirectly recognizable HLA-epitopes algorithm (PIRCHE-II), HLA epitope mismatch algorithm (HLA-EMMA), HLA whole antigen A/B/DR) were calculated for all transplant pairs. Formation of dnDSAs was quantified by single antigen beads. RESULTS: More than one third of patients exhibited a progression of chronic lesion scores by at least one Banff grade in tubular atrophy (ct), interstitial fibrosis (ci), arteriolar hyalinosis (ah) and inflammation in the area of interstitial fibrosis and tubular atrophy (i-IFTA) from the three to the twelve-month biopsy. Multivariable proportional odds logistic regression models revealed no association of HLA mismatch scores with progression of histologic lesions, except for ah and especially HLA-EMMA DRB1 (OR = 1.10, 95%-CI: 1.03-1.18). Furthermore, the established associations of dnDSA formation with HLA mismatch and MVI (OR = 5.31, 95-% CI: 1.19-22.57) could be confirmed in our cohort. CONCLUSIONS: These data support the association of HLA mismatch and alloimmune response, while suggesting that other factors contribute to early progression of chronic histologic lesions.

Individual-specific networks for prediction modelling - A scoping review of methods.

BACKGROUND: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. METHODS: We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase. RESULTS: Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. CONCLUSION: The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease.

Importance: Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. Objective: To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. Design, Setting, and Participants: This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023. Main Outcomes and Measures: Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. Results: Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. Conclusions and Relevance: In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.

Effects of Reduced-Dose Anti-Human T-Lymphocyte Globulin on Overall and Donor-Specific T-Cell Repertoire Reconstitution in Sensitized Kidney Transplant Recipients.

Background: Pre-sensitized kidney transplant recipients have a higher risk for rejection following kidney transplantation and therefore receive lymphodepletional induction therapy with anti-human T-lymphocyte globulin (ATLG) whereas non-sensitized patients are induced in many centers with basiliximab. The time course of lymphocyte reconstitution with regard to the overall and donor-reactive T-cell receptor (TCR) specificity remains elusive. Methods/Design: Five kidney transplant recipients receiving a 1.5-mg/kg ATLG induction therapy over 7 days and five patients with 2 × 20 mg basiliximab induction therapy were longitudinally monitored. Peripheral mononuclear cells were sampled pre-transplant and within 1, 3, and 12 months after transplantation, and their overall and donor-reactive TCRs were determined by next-generation sequencing of the TCR beta CDR3 region. Overall TCR repertoire diversity, turnover, and donor specificity were assessed at all timepoints. Results: We observed an increase in the donor-reactive TCR repertoire after transplantation in patients, independent of lymphocyte counts or induction therapy. Donor-reactive CD4 T-cell frequency in the ATLG group increased from 1.14% + -0.63 to 2.03% + -1.09 and from 0.93% + -0.63 to 1.82% + -1.17 in the basiliximab group in the first month. Diversity measurements of the entire T-cell repertoire and repertoire turnover showed no statistical difference between the two induction therapies. The difference in mean clonality between groups was 0.03 and 0.07 pre-transplant in the CD4 and CD8 fractions, respectively, and was not different over time (CD4: F(1.45, 11.6) = 0.64 p = 0.496; CD8: F(3, 24) = 0.60 p = 0.620). The mean difference in R20, a metric for immune dominance, between groups was -0.006 in CD4 and 0.001 in CD8 T-cells and not statistically different between the groups and subsequent timepoints (CD4: F(3, 24) = 0.85 p = 0.479; CD8: F(1.19, 9.52) = 0.79 p = 0.418). Conclusion: Reduced-dose ATLG induction therapy led to an initial lymphodepletion followed by an increase in the percentage of donor-reactive T-cells after transplantation similar to basiliximab induction therapy. Furthermore, reduced-dose ATLG did not change the overall TCR repertoire in terms of a narrowed or skewed TCR repertoire after immune reconstitution, comparable to non-depletional induction therapy.

Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution.

Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims.

A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol.

BACKGROUND: Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy. METHODS: This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application. DISCUSSION: Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression.

Prospective Tracking of Donor-Reactive T-Cell Clones in the Circulation and Rejecting Human Kidney Allografts.

Background: Antigen recognition of allo-peptides and HLA molecules leads to the activation of donor-reactive T-cells following transplantation, potentially causing T-cell-mediated rejection (TCMR). Sequencing of the T-cell receptor (TCR) repertoire can be used to track the donor-reactive repertoire in blood and tissue of patients after kidney transplantation. Methods/Design: In this prospective cohort study, 117 non-sensitized kidney transplant recipients with anti-CD25 induction were included. Peripheral mononuclear cells (PBMCs) were sampled pre-transplant and at the time of protocol or indication biopsies together with graft tissue. Next-generation sequencing (NGS) of the CDR3 region of the TCRbeta chain was performed after donor stimulation in mixed lymphocyte reactions to define the donor-reactive TCR repertoire. Blood and tissue of six patients experiencing a TCMR and six patients without rejection on protocol biopsies were interrogated for these TCRs. To elucidate common features of T-cell clonotypes, a network analysis of the TCR repertoires was performed. Results: After transplantation, the frequency of circulating donor-reactive CD4 T-cells increased significantly from 0.86 ± 0.40% to 2.06 ± 0.40% of all CD4 cells (p < 0.001, mean dif.: -1.197, CI: -1.802, -0.593). The number of circulating donor-reactive CD4 clonotypes increased from 0.72 ± 0.33% to 1.89 ± 0.33% (p < 0.001, mean dif.: -1.168, CI: -1.724, -0.612). No difference in the percentage of donor-reactive T-cells in the circulation at transplant biopsy was found between subjects experiencing a TCMR and the control group [p = 0.64 (CD4+), p = 0.52 (CD8+)]. Graft-infiltrating T-cells showed an up to six-fold increase of donor-reactive T-cell clonotypes compared to the blood at the same time (3.7 vs. 0.6% and 2.4 vs. 1.5%), but the infiltrating TCR repertoire was not reflected by the composition of the circulating TCR repertoire despite some overlap. Network analysis showed a distinct segregation of the donor-reactive repertoire with higher modularity than the overall TCR repertoire in the blood. These findings indicate an unchoreographed process of diverse T-cell clones directed against numerous non-self antigens found in the allograft. Conclusion: Donor-reactive T-cells are enriched in the kidney allograft during a TCMR episode, and dominant tissue clones are also found in the blood. Trial Registration: Clinicaltrials.gov: NCT: 03422224 (https://clinicaltrials.gov/ct2/show/NCT03422224).

Routine preoperative aortic computed tomography angiography is associated with reduced risk of stroke in coronary artery bypass grafting: a propensity-matched analysis.

OBJECTIVES: The aim of this study was to determine stroke rates in patients who did or did not undergo routine computed tomography angiography (CTA) aortic imaging before isolated coronary artery bypass grafting (CABG). METHODS: We conducted a retrospective analysis of a prospectively maintained single-centre registry. Between 2009 and 2016, a total of 2320 consecutive patients who underwent isolated CABG at our institution were identified. Propensity score matching was used to create a paired cohort of patients with similar baseline characteristics who did (CTA cohort) or did not (non-CTA cohort) undergo preoperative aortic CTA. The primary end point of the analysis was in-hospital stroke. RESULTS: In 435 propensity score-matched pairs, stroke occurred in 4 patients (0.92%) in the CTA cohort and in 14 patients (3.22%) in the non-CTA cohort (P = 0.017). Routine preoperative aortic CTA was associated with a significantly reduced risk of in-hospital stroke [relative risk 0.29, 95% confidence interval (CI) 0.09-0.86; P = 0.026; absolute risk reduction 2.3%, 95% CI 0.4-4.2; P = 0.017; number needed to treat = 44, 95% CI 24-242]. CONCLUSIONS: A preoperative screening for atheromatous aortic disease using CTA is associated with reduced risk of stroke after CABG. The routine use of preoperative aortic CTA could be applied so that surgical manipulation of the ascending aorta can be selectively reduced or avoided in patients with atheromatous aortic disease.