ATA sonographic patterns are associated with distinct clinical features and pathological outcomes, and effectively stratify the cancer risk in ITNs. Thus, the ATA sonographic patterns should be used not only to set the size threshold for biopsy, but also to personalize management after the biopsy.

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Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.

Context Cushing disease (CD) is a life-threating disease, with increased mortality in comparison with the general population. Objective This study aimed to evaluate standardized mortality ratios (SMRs) in CD patients. We also analyzed independent risk factors related to increased mortality. Design We conducted a longitudinal cohort study in a third level specialty center, from 1979 to 2018 in patients with CD. Results From 1375 cases with pathology diagnosis of pituitary adenoma, 191 cases have the confirmed diagnosis of CD (14%). 172 patients completed follow-up, with a mean age at diagnosis of 33±11 years, female predominance (n=154, 89.5%), majority of them microadenoma (n=136, 79%), and a median follow-up of 7.5 years (2.4-15). Eighteen patients (10.5%) died; 8 of them (44%) with active CD, 8 (44%) under remission, and 2 (11%) under disease control. Estimated all-cause SMR=3.1, 95%CI 1.9-4.8, p <0.001. Cardiovascular disease was the main cause of death (SMR=4.2, 1.5-9.3, p=0.01). Multivariate Cox regression models adjusted for potential cofounders showed that diabetes (HR=5.2, IC95% 1.8-15.4, p=0.002), high cortisol levels after 1600 h at diagnosis (3.4, 2.3-7.0, p=0.02), and active CD (7.5, 3.8-17.3, p=0.003), significantly increased the risk of mortality. Conclusions Main cause of CD mortality was cardiovascular disease. Main risk factors for mortality were uncontrolled diabetes, persistently high cortisol levels after 1600 h at diagnosis, and active disease at last follow-up.