Epidemiology of Sarcoidosis in U.S. Veterans from 2003 to 2019

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Characteristics of Patients with Sarcoidosis with Emphasis on Acute vs. Chronic Forms-A Single Center Experience

Sarcoidosis is a granulomatous disease of unknown etiology that can affect almost any organ. Although the acute form can have spontaneous regression, a certain number of patients can have a chronic form, which leads to an increase in mortality and a decrease in the quality of life. Considering that the risk factors are still unknown, we wanted to compare the characteristics of patients with acute and chronic forms of sarcoidosis in Serbia in order to determine significant differences between them with hopes of contributing to everyday clinical practice. A total of 2380 patients treated in our clinic were enrolled in this study. They were separated into the following two groups: 1126 patients with acute form and 1254 patients with chronic form. They were further compared by gender, smoking status, radiological status, exposition, biomarkers for sarcoidosis, organ involvement, and other comorbidities; the distribution of patients according to regions of Serbia was also noted. Statistical significance was found in radiological findings (p < 0.001), biomarkers (calcium in 24 h urine p < 0.001; chitotriosidase p = 0.001), and the affliction of organs (p < 0.001). The differences noted in this paper could help improve our understanding of this disease.

A Review of Machine Learning Algorithms for Biomedical Applications

As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.

Epidemiology of Sarcoidosis

Sarcoidosis is a systemic, granulomatous disease with variable presentation earning it the term "the great mimicker." The current epidemiology confirms that the disease occurs worldwide, affecting both sexes, and all races, ethnicities, and ages. To date, no causal exposure or agent has been identified. The organ systems most frequently affected by sarcoidosis are also those with greatest exposure to the natural world suggesting environmental and lifestyle contributions to the disease. These include particulate matter, microorganisms, nicotine, and obesity. In this article, we review the epidemiology of sarcoidosis and discuss these non-genetic risk factors in the hope of providing important insight into sarcoidosis and stimulating future research.

Sarcoidosis and its relation to other immune-mediated diseases: Epidemiological insights

Several epidemiological studies show a co-occurrence of sarcoidosis with other immune-mediated diseases (IMD). There are many similarities between sarcoidosis and IMDs in their geographical distribution and risk factors. Understanding these similarities and identifying the differences can help us to better understand sarcoidosis and put it into context with other IMDs. In this review, we present the current knowledge about the overlap between sarcoidosis and other IMDs derived from epidemiological studies. Epidemiologic methods utilize study design and statistical analysis to describe the patterns in data and, ideally, identify causal relationships between an exposure and a health outcome. We discuss how study design and analysis may affect the interpretation of epidemiological studies on this topic and highlight some theories that attempt to explain the relation between sarcoidosis and other IMDs.

Sarcoidosis, Mycobacterium paratuberculosis and Noncaseating Granulomas: Who Moved My Cheese

Clinical and histological similarities between sarcoidosis and tuberculosis have driven repeated investigations looking for a mycobacterial cause of sarcoidosis. Over 50 years ago, "anonymous mycobacteria" were suggested to have a role in the etiology of sarcoidosis. Both tuberculosis and sarcoidosis have a predilection for lung involvement, though each can be found in any area of the body. A key histopathologic feature of both sarcoidosis and tuberculosis is the granuloma-while the tuberculous caseating granuloma has an area of caseous necrosis with a cheesy consistency; the non-caseating granuloma of sarcoidosis does not have this feature. This article reviews and reiterates the complicity of the infectious agent, Mycobacterium avium subsp. paratuberculosis (MAP) as a cause of sarcoidosis. MAP is involved in a parallel story as the putative cause of Crohn's disease, another disease featuring noncaseating granulomas. MAP is a zoonotic agent infecting ruminant animals and is found in dairy products and in environmental contamination of water and air. Despite increasing evidence tying MAP to several human diseases, there is a continued resistance to embracing its pleiotropic roles. "Who Moved My Cheese" is a simple yet powerful book that explores the ways in which individuals react to change. Extending the metaphor, the "non-cheesy" granuloma of sarcoidosis actually contains the difficult-to-detect "cheese", MAP; MAP did not move, it was there all along.