Risk Factors and Multidimensional Assessment of Long Coronavirus Disease Fatigue: A Nested Case-Control Study.

BACKGROUND: Fatigue is the most prevalent and debilitating long-COVID (coronavirus disease) symptom; however, risk factors and pathophysiology of this condition remain unknown. We assessed risk factors for long-COVID fatigue and explored its possible pathophysiology. METHODS: This was a nested case-control study in a COVID recovery clinic. Individuals with (cases) and without (controls) significant fatigue were included. We performed a multidimensional assessment evaluating various parameters, including pulmonary function tests and cardiopulmonary exercise testing, and implemented multivariable logistic regression to assess risk factors for significant long-COVID fatigue. RESULTS: A total of 141 individuals were included. The mean age was 47 (SD: 13) years; 115 (82%) were recovering from mild coronavirus disease 2019 (COVID-19). Mean time for evaluation was 8 months following COVID-19. Sixty-six (47%) individuals were classified with significant long-COVID fatigue. They had a significantly higher number of children, lower proportion of hypothyroidism, higher proportion of sore throat during acute illness, higher proportions of long-COVID symptoms, and of physical limitation in daily activities. Individuals with long-COVID fatigue also had poorer sleep quality and higher degree of depression. They had significantly lower heart rate [153.52 (22.64) vs 163.52 (18.53); P = .038] and oxygen consumption per kilogram [27.69 (7.52) vs 30.71 (7.52); P = .036] at peak exercise. The 2 independent risk factors for fatigue identified in multivariable analysis were peak exercise heart rate (OR: .79 per 10 beats/minute; 95% CI: .65-.96; P = .019) and long-COVID memory impairment (OR: 3.76; 95% CI: 1.57-9.01; P = .003). CONCLUSIONS: Long-COVID fatigue may be related to autonomic dysfunction, impaired cognition, and decreased mood. This may suggest a limbic-vagal pathophysiology. CLINICAL TRIALS REGISTRATION: NCT04851561.

Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.Significance: These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. Cancer Res; 78(20); 6001-10. ©2018 AACR.