RESUMEN
Loss of function mutations in STAT3 (STAT3-LOF; autosomal dominant hyper-IgE (Job's) syndrome) are associated with a variety of musculoskeletal manifestations, including scoliosis, osteoporosis, and minimal trauma fractures. This retrospective magnetic resonance (MR) imaging study sought to characterize an unusual pattern of cervical spine degeneration among a cohort of STAT3-LOF patients. Cervical spine MR images of the STAT3-LOF cohort (n = 38) were assessed for a variety of degenerative changes and compared to age-matched groups of controls (n = 42) without known immune or musculoskeletal abnormalities. A unique pattern of premature cervical spine degeneration was identified among the STAT3-LOF cohort which included straightening and eventual reversal of the normal cervical lordosis, mainly due to multi-level spondylolisthesis, as well as early development of spinal canal narrowing, cord compression, and myelomalacia. Cervical spine degeneration in the STAT3-LOF cohort was significantly worse than controls in both the 30-45 and 45 + age groups. Moderate to severe degenerative changes were present after age 30, and markedly worsened over time in several cases. Bone mineral density (BMD) had a moderate negative correlation with cervical degeneration severity and a strong negative correlation with age among STAT3-LOF participants. Cervical degeneration in STAT3-LOF appears to be progressive and could result in cord compromise if left unaddressed. Focused history and physical examination for signs of neurologic compromise as well as periodic MR imaging are thus recommended for the evaluation of premature cervical spine degeneration in STAT3-LOF patients after age 30 so that timely surgical interventions may be considered to prevent spinal cord damage and permanent neurological deficits.
Asunto(s)
Vértebras Cervicales/patología , Mutación con Pérdida de Función/genética , Factor de Transcripción STAT3/genética , Enfermedades de la Columna Vertebral/diagnóstico , Enfermedades de la Columna Vertebral/etiología , Adolescente , Adulto , Densidad Ósea , Femenino , Estudios de Asociación Genética , Predisposición Genética a la Enfermedad , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Escoliosis/diagnóstico , Escoliosis/etiología , Índice de Severidad de la Enfermedad , Adulto JovenAsunto(s)
Tracto Gastrointestinal/patología , Síndrome de Job/complicaciones , Síndrome de Job/genética , Mutación con Pérdida de Función , Factor de Transcripción STAT3/deficiencia , Perforación Espontánea/diagnóstico , Perforación Espontánea/etiología , Adulto , Alelos , Estudios de Asociación Genética , Predisposición Genética a la Enfermedad , Humanos , Perforación Intestinal , Síndrome de Job/diagnóstico , MasculinoRESUMEN
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.
RESUMEN
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.