A unified metric of human immune health.

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.

Multiomics integration of 22 immune-mediated monogenic diseases reveals an emergent axis of human immune health.

Monogenic diseases are often studied in isolation due to their rarity. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with age- and sex-matched healthy controls. Despite clearly detectable disease-specific and "pan-disease" signatures, individuals possess stable personal immune states over time. Temporally stable differences among subjects tend to dominate over differences attributable to disease conditions or medication use. Unsupervised principal variation analysis of personal immune states and machine learning classification distinguishing between healthy controls and patients converge to a metric of immune health (IHM). The IHM discriminates healthy from multiple polygenic autoimmune and inflammatory disease states in independent cohorts, marks healthy aging, and is a pre-vaccination predictor of antibody responses to influenza vaccination in the elderly. We identified easy-to-measure circulating protein biomarker surrogates of the IHM that capture immune health variations beyond age. Our work provides a conceptual framework and biomarkers for defining and measuring human immune health.

Design of an algorithm for the diagnostic approach of patients with joint pain.

BACKGROUND: Rheumatic diseases are a reason for frequent consultation with primary care doctors. Unfortunately, there is a high percentage of misdiagnosis. OBJECTIVE: To design an algorithm to be used by primary care physicians to improve the diagnostic approach of the patient with joint pain, and thus improve the diagnostic capacity in four rheumatic diseases. METHODS: Based on the information obtained from a literature review, we identified the main symptoms, signs, and paraclinical tests related to the diagnosis of rheumatoid arthritis, spondyloarthritis with peripheral involvement, systemic lupus erythematosus with joint involvement, and osteoarthritis. We conducted 3 consultations with a group of expert rheumatologists, using the Delphi technique, to design a diagnostic algorithm that has as a starting point "joint pain" as a common symptom for the four diseases. RESULTS: Thirty-nine rheumatologists from 18 countries of Ibero-America participated in the Delphi exercise. In the first consultation, we presented 94 items to the experts (35 symptoms, 31 signs, and 28 paraclinical tests) candidates to be part of the algorithm; 74 items (25 symptoms, 27 signs, and 22 paraclinical tests) were chosen. In the second consultation, the decision nodes of the algorithm were chosen, and in the third, its final structure was defined. The Delphi exercise lasted 8 months; 100% of the experts participated in the three consultations. CONCLUSION: We present an algorithm designed through an international consensus of experts, in which Delphi methodology was used, to support primary care physicians in the clinical approach to patients with joint pain. Key Points • We developed an algorithm with the participation of rheumatologists from 18 countries of Ibero-America, which gives a global vision of the clinical context of the patient with joint pain. • We integrated four rheumatic diseases into one tool with one common symptom: joint pain. It is a novel tool, as it is the first algorithm that will support the primary care physician in the consideration of four different rheumatic diseases. • It will improve the correct diagnosis and reduce the number of paraclinical tests requested by primary care physicians, in the management of patients with joint pain. This point was verified in a recently published study in the journal Rheumatology International (reference number 31).

Haemophagocytic lymphohistiocytosis presenting as liver failure following Epstein-Barr and prior hepatitis A infections.

Haemophagocytic lymphohistiocytosis (HLH) is associated with high mortality even after prompt diagnosis. We present a young man with HLH triggered by two common viral diseases, infectious mononucleosis and hepatitis A. This patient presented with fever, rapidly progressive liver failure, anasarca and cholestasis, followed by anaemia and neutropenia. His carbohydrate antigen 19-9 reached over 9000 U/mL. Initial bone marrow and liver biopsies did not show histological features of malignancy or HLH. The patient was finally diagnosed and treated almost 1 year after the initial symptoms started, and had an excellent response with etoposide and dexamethasone. This case is unusual because it was triggered following mononucleosis in a patient with positive total antibodies against hepatitis A, with rapidly developing liver failure, and also because the patient's response was excellent despite the delay in treatment. It underscores the importance of suspecting HLH when severe systemic illness develops after a viral infection, even in the absence of clear histological features.