Karl J. Lackner

Bio: Karl J. Lackner is an academic researcher from University of Mainz. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 71, co-authored 447 publications receiving 19728 citations. Previous affiliations of Karl J. Lackner include University Medical Center Freiburg & University of Regensburg.

The gene encoding ATP-binding cassette transporter 1 is mutated in Tangier disease

Abstract: Tangier disease (TD) is an autosomal recessive disorder of lipid metabolism. It is characterized by absence of plasma high-density lipoprotein (HDL) and deposition of cholesteryl esters in the reticulo-endothelial system with splenomegaly and enlargement of tonsils and lymph nodes. Although low HDL cholesterol is associated with an increased risk for coronary artery disease, this condition is not consistently found in TD pedigrees. Metabolic studies in TD patients have revealed a rapid catabolism of HDL and its precursors. In contrast to normal mononuclear phagocytes (MNP), MNP from TD individuals degrade internalized HDL in unusual lysosomes, indicating a defect in cellular lipid metabolism. HDL-mediated cholesterol efflux and intracellular lipid trafficking and turnover are abnormal in TD fibroblasts, which have a reduced in vitro growth rate. The TD locus has been mapped to chromosome 9q31. Here we present evidence that TD is caused by mutations in ABC1, encoding a member of the ATP-binding cassette (ABC) transporter family, located on chromosome 9q22-31. We have analysed five kindreds with TD and identified seven different mutations, including three that are expected to impair the function of the gene product. The identification of ABC1 as the TD locus has implications for the understanding of cellular HDL metabolism and reverse cholesterol transport, and its association with premature cardiovascular disease.

Sensitive Troponin I Assay in Early Diagnosis of Acute Myocardial Infarction

Abstract: BACKGROUND Cardiac troponin testing is central to the diagnosis of acute myocardial infarction. We evaluated a sensitive troponin I assay for the early diagnosis and risk stratification of myocardial infarction. METHODS In a multicenter study, we determined levels of troponin I as assessed by a sensitive assay, troponin T, and traditional myocardial necrosis markers in 1818 consecutive patients with suspected acute myocardial infarction, on admission and 3 hours and 6 hours after admission. RESULTS For samples obtained on admission, the diagnostic accuracy was highest with the sensitive troponin I assay (area under the receiver-operating-characteristic curve [AUC], 0.96), as compared with the troponin T assay (AUC, 0.85) and traditional myocardial necrosis markers. With the use of the sensitive troponin I assay (cutoff value, 0.04 ng per milliliter) on admission, the clinical sensitivity was 90.7%, and the specificity was 90.2%. The diagnostic accuracy was virtually identical in baseline and serial samples, regardless of the time of chest-pain onset. In patients presenting within 3 hours after chest-pain onset, a single sensitive troponin I assay had a negative predictive value of 84.1% and a positive predictive value of 86.7%; these findings predicted a 30% rise in the troponin I level within 6 hours. A troponin I level of more than 0.04 ng per milliliter was independently associated with an increased risk of an adverse outcome at 30 days (hazard ratio, 1.96; 95% confidence interval, 1.27 to 3.05; P=0.003). CONCLUSIONS The use of a sensitive assay for troponin I improves early diagnosis of acute myocardial infarction and risk stratification, regardless of the time of chest-pain onset.

Genetics and Beyond – The Transcriptome of Human Monocytes and Disease Susceptibility

Abstract: Background Variability of gene expression in human may link gene sequence variability and phenotypes; however, non-genetic variations, alone or in combination with genetics, may also influence expression traits and have a critical role in physiological and disease processes.

Loneliness in the general population: prevalence, determinants and relations to mental health.

Abstract: While loneliness has been regarded as a risk to mental and physical health, there is a lack of current community data covering a broad age range. This study used a large and representative German adult sample to investigate loneliness. Baseline data of the Gutenberg Health Study (GHS) collected between April 2007 and April 2012 (N = 15,010; 35–74 years), were analyzed. Recruitment for the community-based, prospective, observational cohort study was performed in equal strata for gender, residence and age decades. Measures were provided by self-report and interview. Loneliness was used as a predictor for distress (depression, generalized anxiety, and suicidal ideation) in logistic regression analyses adjusting for sociodemographic variables and mental distress. A total of 10.5% of participants reported some degree of loneliness (4.9% slight, 3.9% moderate and 1.7% severely distressed by loneliness). Loneliness declined across age groups. Loneliness was stronger in women, in participants without a partner, and in those living alone and without children. Controlling for demographic variables and other sources of distress loneliness was associated with depression (OR = 1.91), generalized anxiety (OR = 1.21) and suicidal ideation (OR = 1.35). Lonely participants also smoked more and visited physicians more frequently. The findings support the view that loneliness poses a significant health problem for a sizeable part of the population with increased risks in terms of distress (depression, anxiety), suicidal ideation, health behavior and health care utilization.

Glutathione peroxidase 1 activity and cardiovascular events in patients with coronary artery disease.

Abstract: Background Cellular antioxidant enzymes such as glutathione peroxidase 1 and superoxide dismutase have a central role in the control of reactive oxygen species. In vitro data and studies in animal models suggest that these enzymes may protect against atherosclerosis, but little is known about their relevance to human disease. Methods We conducted a prospective study among 636 patients with suspected coronary artery disease, with a median follow-up period of 4.7 years (maximum, 5.4) to assess the risk of cardiovascular events associated with base-line erythrocyte glutathione peroxidase 1 and superoxide dismutase activity. Results Glutathione peroxidase 1 activity was among the strongest univariate predictors of the risk of cardiovascular events, whereas superoxide dismutase activity had no association with risk. The risk of cardiovascular events was inversely associated with increasing quartiles of glutathione peroxidase 1 activity (P for trend <0.001); patients in the highest quartile of glutathione perox...

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.).

2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC)

Abstract: ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation : The Task Force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC).

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

Abstract: Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2010 Update A Report From the American Heart Association

Abstract: Appendix I: List of Statistical Fact Sheets. URL: http://www.americanheart.org/presenter.jhtml?identifier=2007 We wish to thank Drs Brian Eigel and Michael Wolz for their valuable comments and contributions. We would like to acknowledge Tim Anderson and Tom Schneider for their editorial contributions and Karen Modesitt for her administrative assistance. Disclosures View this table: View this table: View this table: # Summary {#article-title-2} Each year, the American Heart Association, in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on disease …