Klemens Budde

Bio: Klemens Budde is an academic researcher from Charité. The author has contributed to research in topics: Transplantation & Kidney transplantation. The author has an hindex of 69, co-authored 578 publications receiving 18570 citations. Previous affiliations of Klemens Budde include Yale University & Actelion.

Angiotensin II type 1-receptor activating antibodies in renal-allograft rejection.

Abstract: Background Antibodies against HLA antigens cause refractory allograft rejection with vasculopathy in some, but not all, patients. Methods We studied 33 kidney-transplant recipients who had refractory vascular rejection. Thirteen had donor-specific anti-HLA antibodies, whereas 20 did not. Malignant hypertension was present in 16 of the patients without anti-HLA antibodies, 4 of whom had seizures. The remaining 17 patients had no malignant hypertension. We hypothesized that activating antibodies targeting the angiotensin II type 1 (AT1) receptor might be involved. Results Activating IgG antibodies targeting the AT1 receptor were detected in serum from all 16 patients with malignant hypertension and without anti-HLA antibodies, but in no other patients. These receptor-activating antibodies are subclass IgG1 and IgG3 antibodies that bind to two different epitopes on the second extracellular loop of the AT1 receptor. Tissue factor expression was increased in renal-biopsy specimens from patients with these anti...

Everolimus in patients with autosomal dominant polycystic kidney disease.

Abstract: BACKGROUND Autosomal dominant polycystic kidney disease (ADPKD) is a slowly progressive hereditary disorder that usually leads to end-stage renal disease. Although the underlying gene mutations were identified several years ago, efficacious therapy to curtail cyst growth and prevent renal failure is not available. Experimental and observational studies suggest that the mammalian target of rapamycin (mTOR) pathway plays a critical role in cyst growth. METHODS In this 2-year, double-blind trial, we randomly assigned 433 patients with ADPKD to receive either placebo or the mTOR inhibitor everolimus. The primary outcome was the change in total kidney volume, as measured on magnetic resonance imaging, at 12 and 24 months. RESULTS Total kidney volume increased between baseline and 1 year by 102 ml in the everolimus group, versus 157 ml in the placebo group (P = 0.02) and between baseline and 2 years by 230 ml and 301 ml, respectively (P = 0.06). Cyst volume increased by 76 ml in the everolimus group and 98 ml in the placebo group after 1 year (P = 0.27) and by 181 ml and 215 ml, respectively, after 2 years (P = 0.28). Parenchymal volume increased by 26 ml in the everolimus group and 62 ml in the placebo group after 1 year (P = 0.003) and by 56 ml and 93 ml, respectively, after 2 years (P = 0.11). The mean decrement in the estimated glomerular filtration rate after 24 months was 8.9 ml per minute per 1.73 m 2 of body-surface area in the everolimus group versus 7.7 ml per minute in the placebo group (P = 0.15). Drug-specific adverse events were more common in the everolimus group; the rate of infection was similar in the two groups. CONCLUSIONS Within the 2-year study period, as compared with placebo, everolimus slowed the increase in total kidney volume of patients with ADPKD but did not slow the decline in progressive renal impairment. (EudraCT number, 2006-001485-16; ClinicalTrials .gov number, NCT00414440.)

Therapeutic Drug Monitoring of Tacrolimus-Personalized Therapy : Second Consensus Report

Abstract: Ten years ago, a consensus report on the optimization of tacrolimus was published in this journal. In 2017, the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicity (IATDMCT) decided to issue an updated consensus report considering the most relevant advances in tacrolimus pharmacokinetics (PK), pharmacogenetics (PG), pharmacodynamics, and immunologic biomarkers, with the aim to provide analytical and drug-exposure recommendations to assist TDM professionals and clinicians to individualize tacrolimus TDM and treatment. The consensus is based on in-depth literature searches regarding each topic that is addressed in this document. Thirty-seven international experts in the field of TDM of tacrolimus as well as its PG and biomarkers contributed to the drafting of sections most relevant for their expertise. Whenever applicable, the quality of evidence and the strength of recommendations were graded according to a published grading guide. After iterated editing, the final version of the complete document was approved by all authors. For each category of solid organ and stem cell transplantation, the current state of PK monitoring is discussed and the specific targets of tacrolimus trough concentrations (predose sample C0) are presented for subgroups of patients along with the grading of these recommendations. In addition, tacrolimus area under the concentration-time curve determination is proposed as the best TDM option early after transplantation, at the time of immunosuppression minimization, for special populations, and specific clinical situations. For indications other than transplantation, the potentially effective tacrolimus concentrations in systemic treatment are discussed without formal grading. The importance of consistency, calibration, proficiency testing, and the requirement for standardization and need for traceability and reference materials is highlighted. The status for alternative approaches for tacrolimus TDM is presented including dried blood spots, volumetric absorptive microsampling, and the development of intracellular measurements of tacrolimus. The association between CYP3A5 genotype and tacrolimus dose requirement is consistent (Grading A I). So far, pharmacodynamic and immunologic biomarkers have not entered routine monitoring, but determination of residual nuclear factor of activated T cells-regulated gene expression supports the identification of renal transplant recipients at risk of rejection, infections, and malignancy (B II). In addition, monitoring intracellular T-cell IFN-g production can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II). Although cell-free DNA seems a promising biomarker of acute donor injury and to assess the minimally effective C0 of tacrolimus, multicenter prospective interventional studies are required to better evaluate its clinical utility in solid organ transplantation. Population PK models including CYP3A5 and CYP3A4 genotypes will be considered to guide initial tacrolimus dosing. Future studies should investigate the clinical benefit of time-to-event models to better evaluate biomarkers as predictive of personal response, the risk of rejection, and graft outcome. The Expert Committee concludes that considerable advances in the different fields of tacrolimus monitoring have been achieved during this last decade. Continued efforts should focus on the opportunities to implement in clinical routine the combination of new standardized PK approaches with PG, and valid biomarkers to further personalize tacrolimus therapy and to improve long-term outcomes for treated patients.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

Abstract: Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter. Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Chronic renal failure after transplantation of a nonrenal organ.

Abstract: Background Transplantation of nonrenal organs is often complicated by chronic renal disease with multifactorial causes. We conducted a population-based cohort analysis to evaluate the incidence of chronic renal failure, risk factors for it, and the associated hazard of death in recipients of nonrenal transplants. Methods Pretransplantation and post-transplantation clinical variables and data from a registry of patients with end-stage renal disease (ESRD) were linked in order to estimate the cumulative incidence of chronic renal failure (defined as a glomerular filtration rate of 29 ml per minute per 1.73 m2 of body-surface area or less or the development of ESRD) and the associated risk of death among 69,321 persons who received nonrenal transplants in the United States between 1990 and 2000. Results During a median follow-up of 36 months, chronic renal failure developed in 11,426 patients (16.5 percent). Of these patients, 3297 (28.9 percent) required maintenance dialysis or renal transplantation. The five-year risk of chronic renal failure varied according to the type of organ transplanted - from 6.9 percent among recipients of heart-lung transplants to 21.3 percent among recipients of intestine transplants. Multivariate analysis indicated that an increased risk of chronic renal failure was associated with increasing age (relative risk per 10-year increment, 1.36; P Conclusions The five-year risk of chronic renal failure after transplantation of a nonrenal organ ranges from 7 to 21 percent, depending on the type of organ transplanted. The occurrence of chronic renal failure among patients with a nonrenal transplant is associated with an increase by a factor of more than four in the risk of death.

Genetics of rheumatoid arthritis contributes to biology and drug discovery

Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.