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Background: Cardiometabolic diseases (CMD) are major global health concerns with significant morbidity and mortality. Fatigue, a common but often overlooked symptom, has been postulated as both a potential risk factor for and a consequence of these conditions. However, the relationships between fatigue and CMD remain unclear. This study aimed to investigate the relationship between fatigue and CMD using observational and genetic approaches., Method: Observational study was conducted in the UK biobank. Genetic method was employed a bidirectional MR approach to examine the causal relationship between fatigue and CMD. Genetic variants associated with fatigue were identified through a GWAS, and summary statistics from the largest available GWAS were used to obtain variants associated with stroke, CAD, T2D, and HF. Inverse variance weighting (IVW) was conducted, with weighted median, MR-Egger, and MR-PRESSO as sensitivity analyses. Multivariable MR and mediation analysis were also employed., Results: Observational analyses indicated that individuals with fatigue had a significantly increased risk of developing stroke (HR 1.44, 95 % CI 1.27-1.63), T2D (HR 1.46, 95 % CI 1.41-1.51), CAD (HR 1.45, 95 % CI 1.4-1.5), and HF (HR 1.60, 95 % CI 1.52-1.68). Mendelian randomization analyses further supported a causal relationship. Additionally, observational and genetic analyses showed T2D was found to be associated with increased levels of fatigue. Mediation analysis identified lipid metabolites as mediators in the relationship between fatigue and CMD., Conclusion: This study highlights a bidirectional relationship between fatigue and CMD, underscoring the importance of considering fatigue in the context of cardiometabolic health., Clinical Trial Number: Not applicable., Competing Interests: Declaration of competing interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Study Objectives: The diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (CFS) is based on a constellation of symptoms which center around fatigue. However, fatigue is commonly reported in the general population by people without CFS. Does the biology underlying fatigue in patients with CFS also drive fatigue experienced by individuals without CFS?, Methods: We used UK Biobank actigraphy data to characterize differences in physical activity patterns and daily temperature rhythms between participants diagnosed with CFS compared to controls. We then tested if single nucleotide variants (SNVs) previously associated with CFS are also associated with the variation of these actigraphic CFS correlates and/or subjective fatigue symptoms in the general population., Results: Participants diagnosed with CFS (n = 295) had significantly decreased overall movement (Cohen's d = 0.220, 95% CI of -0.335 to -0.106, p-value = 2.42 × 10-15), lower activity amplitudes (Cohen's d = -0.377, 95% CI of -0.492 to -0.262, p-value = 1.74 × 10-6), and lower wrist temperature amplitudes (Cohen's d = -0.173, 95% CI of -0.288 to -0.059, p-value = .002) compared to controls (n = 63,133). Of 30 tested SNVs associated in the literature with CFS, one was associated in the control population with subjective fatigue and one with actigraphic measurements (FDR < 0.05)., Conclusions: The genetic overlap of CFS risk with actigraphy and subjective fatigue phenotypes suggests that some biological mechanisms underlying pathologic fatigue in patients with CFS also underlie fatigue symptoms at a broader population level. Therefore, understanding the biology of fatigue in general may inform our understanding of CFS pathophysiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Cancer-related fatigue (CRF) is a prominent cancer-related complication occurring in Prostate cancer (PCa) patients, profoundly affecting prognosis. The lack of diagnostic criteria and biomarkers hampers the management of CRF., Methods: The CRF-related data and PCa single-cell data were retrieved from the GEO database and clinical data was downloaded from the TCGA database. The univariate logistic/Cox regression analysis were used to construct the prediction models. The predictive value of models was analyzed using the ROC curve and Kaplan-Meier survival. The hub genes were screened by an intersection analysis of DEGs. The mice model of PCa and PCa-related fatigue were established, and fatigue-like behaviors of mice were detected. The expression of selected hub genes was validated by RT-PCR and IHC analysis., Results: The diagnosis and risk models showed great predictive value both in the training and validation dataset. Five genes (Baiap2l2, Cacng4, Sytl2, Sec31b and Ms4a1) that enriched the CXCL signaling were identified as hub genes. Among all hub genes, the MS4A1 expression is the most significant in PCa-related fatigue mice., Conclusions: We identified MS4A1 as a promising biomarker for the diagnosis of PCa-related fatigue. Our findings would lay a foundation for revealing the pathogenesis and developing therapies for PCa-related fatigue., Competing Interests: Ethics Approval And Consent To Participate: All animal procedures were performed according to the Guidelines for the Care and Use of Laboratory Animals of the Chinese Animal Welfare Committee and approved by the Institutional Animal Care and Use Committee (IACUC) of Wuhan University of Renmin Hospital. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Cancer patients are commonly affected by fatigue. Herein, we sought to examine epigenetic modifications (i.e., DNA methylation) related to fatigue in peripheral blood among patients during and after treatment for head and neck cancer (HNC). Further, we determined whether these modifications were associated with gene expression and inflammatory protein markers, which we have previously linked to fatigue in HNC. This prospective, longitudinal study enrolled eligible patients with data collected at pre-radiotherapy, end of radiotherapy, and six months and one-year post-radiotherapy. Fatigue data were reported by patients using the Multidimensional Fatigue Inventory (MFI)-20. DNA methylation (Illumina MethylationEPIC) and gene expression (Applied Biosystems Clariom S) arrays and assays for seven inflammatory markers (R&D Systems multiplex) were performed. Mixed models and enrichment analyses were applied to establish the associations. A total of 386 methylation loci were associated with fatigue among 145 patients (False Discovery Rate [FDR] < 0.05). Enrichment analyses showed the involvement of genes related to immune and inflammatory responses, insulin and lipid metabolism, neuropsychological disorders, and tumors. We further identified 16 methylation-gene expression pairs (FDR < 0.05), which were linked to immune and inflammatory responses and lipid metabolism. Ninety-one percent (351) of the 386 methylation loci were also significantly associated with inflammatory markers (e.g., interleukin 6, c-reactive protein; FDR < 0.05), which further mediated the association between methylation and fatigue (FDR < 0.05). These data suggest that epigenetic modifications associated with inflammation and immunometabolism, in conjunction with relevant gene expression and protein markers, are potential targets for treating fatigue in HNC patients. The findings also merit future prospective studies in other cancer populations as well as interventional investigations., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)