SARS-CoV-2 infection causes a wide spectrum of disease severity. Identifying the immunological characteristics of severe disease and the risk factors for their development are important in the management of COVID-19. This study aimed to identify and rank clinical and immunological features associated with progression to severe COVID-19 in order to investigate an immunological signature of severe disease. One hundred and eight patients with positive SARS-CoV-2 PCR were recruited. Routine clinical and laboratory markers were measured, as well as myeloid and lymphoid whole-blood immunophenotyping and measurement of the pro-inflammatory cytokines IL-6 and soluble CD25. All analysis was carried out in a routine hospital diagnostic laboratory. Univariate analysis demonstrated that severe disease was most strongly associated with elevated CRP and IL-6, loss of DLA-DR expression on monocytes and CD10 expression on neutrophils. Unbiased machine learning demonstrated that these four features were strongly associated with severe disease, with an average prediction score for severe disease of 0.925. These results demonstrate that these four markers could be used to identify patients developing severe COVID-19 and allow timely delivery of therapeutics.  •Severe COVID-19 is characterised by a combination of emergency myelopoiesis and inflammation.•These changes can be rapidly identified in a diagnostic laboratory, facilitating intervention.•This disease signature was derived from a cohort of patients with a wide range of ages, frailty and COVID-19 severity. COVID-19; Immune phenotype; Neutrophil maturity; Machine learning; Biomarkers. EvenCOVID-19 appears to have a biphasic pattern of illness , 6, 7. IAn important consideration in the evaluation of the pathophysiology of COVID-19 is identifying signatures that are predictive of severe disease, as well as identifying patient characteristics that are associated with more severe disease phenotypes. Careful assessment of temporally linked clinical and laboratory parameters is required to identify such features. Utilising multivariate analytic and machine learning approaches offers an exciting way of integrating large and complex data sets to identify important predictive features , 27, 28.Recent reports have highlighted changes in myeloid and lymphoid compartments that can be associated with severe disease , 25. TheIdentification of patients at risk of severe disease and in need targeted treatment is complex and requires the availability of prompt objective laboratory indices. In order to be effective parameters identified in research studies need to be readily measurable in diagnostic laboratories so that results can be applied in prompt clinical decision making.This study aimed to assess the clinical and immunological features of COVID-19 disease and identify the characteristics of severe disease. We collected demographic data, immunophenotyping of myeloid and lymphoid populations, measurement of pro-inflammatory cytokines, and routine clinical laboratory parameters of patients with confirmed polymerase-chain reaction (PCR) positive SARS-CoV-2 infection. A machine learning model was applied to this dataset to identify a predictive disease signature of severe COVID-19.22.1One-hundred and eight participants were recruited (42/108 (38.9%) female). Baseline characteristics are shown in 2.2Analysis of routine laboratory tests revealed widespread changes in leukocyte populations with a marked coagulopathy and increased markers of cell turnover and inflammation . The degIn order to further delineate the panlymphopenia seen on the full blood count, detailed immunophenotyping of lymphoid cells was conducted . This coDetailed immunophenotyping of myeloid populations was also carried out, given the profound neutrophilia noted on initial investigations . Marked Between-group differences in those with severe COVID-19 in and those with mild or moderate disease were assessed. Univariate analysis of the 71 variables in our clinical and laboratory dataset identified a series of 15 factors associated with severe COVID-19 that remained statistically significant when corrected for false discovery rate A. These 2.3Having identified strong associations between specific individual immunological features and severity, multivariate analysis was used to establish the relative importance of the clinical and immunological parameters. A logistic regression model with elastic net penalty was applied to the data. The model demonstrated a high sensitivity and specificity for identification of severe disease. The area under the receiving operating characteristic (AUROC) curve was 0.93 on both training and test sets, indicating excellent model predictive performance and absence of overfitting B. These + monocytes. The next most important features were higher CRP and IL-6 levels, and a lower percentage of CD10+ neutrophils (−CD16+) and intermediate (CD14+CD16+) monocytes and lower numbers of NK cells  when considered in interaction with all 71 variables. Normalised weight values associated with the top features of the model are shown in trophils A. Striki5 levels D.+ monocytes and CD10+ neutrophils. Levels of HLA-DR+ monocytes and CD10+ neutrophils correlated positively with each other. There was a positive correlation between IL-6 and CRP with AST and sCD25. Proportional changes in non-classical and intermediate monocytes were also strongly associated with each other.Potential relationships between each variable identified in the machine learning analysis were also investigated using a clustered Spearman correlation matrix E. Unsurp3COVID-19 has presented an enormous challenge to human health and society. Pre-morbid risk factors for the development of severe COVID-19 include older age, male gender, obesity, and co-morbidities cardiovascular disease, respiratory disease, diabetes, cancer and immunodeficiency are well recognised . HoweverThe data presented here on 108 patients with SARS-CoV-2 infection confirms experimental and research laboratory work on the characteristics of severe infection , 36, 37.Our cohort is broadly reflective of those reported elsewhere, with a median age of 61, more males than females requiring admission and a mortality rate of 9%, which was higher for men , 39, 40.Another strength of this study is the use of a whole blood flow protocol, with minimal handling, no PBMC isolation step and analysis within 4 h of blood draw. This minimized artefactual alterations in myeloid populations. Patients presented to hospital and had their bloods drawn a median of 2 days prior to peak COVID-19 as defined by peak oxygen requirement, giving us insight into the immune mechanisms leading to severe respiratory distress. In addition, all of our laboratory parameters were generated within a diagnostic laboratory. This means that results were generated in a clinically-relevant timeframe.Severe disease in this cohort is characterised by elevated inflammatory markers and a shift towards emergency myelopoiesis. Those with severe illness demonstrate increases in the inflammatory cytokine IL-6, CRP and soluble CD25. This hypercytokinaemia was reported in some of the very early work from China, and confirmed with later studies . A stron+ monocytes. Increasing data have emerged showing reduced monocyte HLA-DR expression is associated with critical illness in COVID-19 [+ monocytes and reduced HLA-DR expression are appear to be an early predictor of poor outcome in other infectious states [The machine learning model demonstrated evidence of inflammation and immune perturbation early in progression to severe COVID-19, with the emergence of a quartet of markers strongly associated with severe disease. These markers are seen before the onset of peak clinical illness in this cohort and were predictive of impending deterioration. The strongest single feature associated with severe disease on multivariate analysis was a reduced proportion of HLA-DRCOVID-19 , 53, 54.s states , 51, 52.s states . The red+ neutrophils, another marker of emergency myelopoiesis. CD10 is a cell membrane metalloprotein that serves as a marker of neutrophil maturity [et al. [The second member of the quartet identified by this study as being strongly associated with severe disease is a proportional reduction in CD10maturity . Reducedmaturity , 58. Los [et al. , 55 This [et al. , 60. The [et al. .The final two members of the quartet characterising severe disease are the acute phase reactants IL-6 and CRP. Elevated levels of IL-6 (and downstream protein CRP) are well-described as signs of poor outcome in COVID-19 , 64, 65.Surprisingly, only a single categorical clinical variable, higher clinical frailty scores, emerged on multivariate analyses as associated with severity. The clinical frailty score captures biological, rather than chronological age and is a predictor of mortality in a wide-range of conditions , 68. RemThis is a single centre study in a largely white Irish cohort of predominantly hospitalised patients. Validation of these findings in other centres and with larger samples earlier in the course of disease is desirable.Understanding the mechanisms causing some individuals with SARS-COV-2 infection to progress to severe disease is required to inform therapeutic design. Through robust analysis of extensive demographic, clinical and immunological parameters captured early in disease trajectory, these parameters are ranked and adjusted for high-dimensional interactions and report a signature of severity which is characterised by inflammation (elevated IL-6 and CRP) and emergency myelopoiesis (reduced neutrophil maturity and monocyte HLA-DR expression). Our machine learning model allowed us to effectively integrate temporally linked clinical and laboratory data collected prior to peak severity to generate risk factors predictive of deterioration . Other rThis study builds on prior work describing severe COVID-19 infection, demonstrating that these features of emergency myelopoiesis develop prior to the point of peak illness and are of central importance in progression to severe COVID-19. Patients with mild disease who did not require hospitalisation are included; these patients represent an understudied cohort. Furthermore, these approaches can be delivered in an accredited clinical laboratory as routine tests. This is of particular importance given the need for early identification of individuals who may benefit from therapeutic interventions . This woLiam Townsend: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.Adam H Dyer, Sultan Imangaliyev, Arthur White and Rachel M McLoughlin: Analyzed and interpreted the data; Wrote the paper.Aifric Naughton: Performed the experiments; Wrote the paper.Jean Dunne, Nollaig M Bourke, Cliona O'Farrelly, Niall Conlon and Clíona Ní Cheallaigh: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Rachel Kiersey and Dean Holden: Performed the experiments; Wrote the paper.Aoife Mooney, Deirdre Leavy, Katie Ridge. Jamie Sugrue: Performed the experiments.Mubarak Aldoseri, Jo Hannah Kelliher, Martina Hennessy, Declan Byrne, Catriona Doyle, Ruth O'Riordan and Anne-Marie McLaughlin: Contributed reagents, materials, analysis tools or data.Paul Browne: Conceived and designed the experiments.Christopher L Bacon and Ciaran Bannan: Conceived and designed the experiments; Wrote the paper.Ignacio Martin-Loeches: Contributed reagents, materials, analysis tools or data; Wrote the paper.Colm Bergin: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.the Irish Clinical Academic Training (ICAT) Programme, the Wellcome Trust and the Health Research Board (Grant Number 203930/B/16/Z), the Health Service Executive, National Doctors Training and Planning and the Health and Social Care, Research and Development Division, Northern Ireland.L. Townsend was supported by the a Science Foundation Ireland (SFI) Investigator Award (15/IA/3041).R. McLoughlin was supported by SFI grant (Grant Code 20/SPP/3685).N. Conlon and C. Cheallaigh were supported by an Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Myotonic dystrophy (DM) is a rare, inherited disorder with multi-systemic effects that impact the skeletal muscles, eyes, heart, skin and gastrointestinal, endocrine, respiratory, and central nervous systems. DM is divided into two subtypes: DM1 can present from early childhood through adulthood and also has a congenital form (cDM) while DM2 typically manifests during mid-adulthood. Both forms are progressive with no approved treatments, and unmet need for disease-modifying therapies remains high. This study interrogated health insurance claims data to explore the clinical experience, healthcare resource utilization (HCRU), and all-cause costs for DM.A total of 8541 patients with DM and 242 patients with cDM and their matched controls were selected from a database of over 200 million claimants. HCRU and all-cause costs, including pharmacy, outpatient, and inpatient services, were analyzed across four years in 12-month follow-up periods. Mean all-cause costs per DM patient were high in each of the four periods  and showed a steady increase from 13 to 23 months on, while the control group mean costs declined from $9671 in the first 12 months after the index event, to approach the US population average ($5193) over time. For cDM, the highest mean costs were in the first 12-months , and remained high  across all subsequent periods, while control mean costs approached $0. For DM and cDM, HCRU was higher compared to controls across all study periods and all-cause healthcare costs were mostly driven by inpatient and outpatient encounters. Analysis of all diagnosis codes over the study period (comorbidities) demonstrated an elevated comorbidity profile consistent with the clinical profile of DM.This study is among the first to utilize claims data to increase understanding of the clinical experience and health economic outcomes associated with DM. The markedly elevated HCRU patterns and comorbidity profile presented here add to the broad body of scientific and clinical knowledge on DM. These insights can inform clinical care and support the development of disease modifying and/or symptom-targeting therapies that address the multi-systemic, progressive nature of DM.The online version contains supplementary material available at 10.1186/s13023-022-02241-9. Myotonic dystrophy (DM) is a complex, multi-systemic neuromuscular disease that is characterized by delayed relaxation of skeletal muscles (myotonia) and progressive muscle weakness and degeneration (muscular dystrophy) –4. It isWhile past research has demonstrated a high disease burden for patients with DM, the healthcare resource utilization (HCRU) and healthcare costs for this understudied population are relatively unexplored , 18, 19.–70% of US-based physician activity, and approximately 85% of dispensed prescriptions for all pharmacies in the US. Similar methods have been used to characterize the HCRU and costs associated with medical management of spinal muscular atrophy (SMA), ankylosing spondylitis, and Duchenne muscular dystrophy (DMD) , physician office visits , ER visits , and a remarkably increased number of outpatient visits . A greater proportion also had ≥ 1 hospitalization .Similarly, patients with cDM compared with matched control patients had significantly increased mean prescription fills [15.45 (17.03) vs 7.30 (7.20); The pattern of significant difference in HCRU between DM and non-DM and cDM and non-cDM cohorts is consistent across all four follow-up periods. Additional HCRU in each of the above-listed categories is shown for DM and cDM across all study time periods in Additional file Using claims data derived from the IQVIA New Data Warehouse, this study found that the total all-cause costs and HCRU per patient per year were considerably higher for patients with DM and cDM compared with matched controls. This longitudinal, retrospective, real-world study generated a wealth of data, both in size and number of variables. It is among the first to specifically examine HCRU and total all-cause costs in DM in detail and make efforts to elucidate the experience of those congenitally affected (cDM) , 24. By The mean all-cause costs per patient per year averaged $15,852 for the DM cohort compared to $6688 for the non-DM cohort. This elevated cost is consistent with the findings of Larkindale and colleagues, who found that direct medical costs per patient with DM per year was $17,451 . For comOverall costs for the DM cohort are markedly elevated compared to controls across all four study time periods. The relatively high costs for both the DM and non-DM cohorts in the first follow-up period (< 12 months) can be attributed, at least in part, to the increased likelihood that the incident case is associated with an acute, cost-incurring medical event. In subsequent periods however, a trajectory of increasing costs for the DM cohort that reflects the chronic and progressive nature of DM is apparent, while costs for the control group steadily approach the US national average over time.Total all-cause costs for the cDM cohort compared to their control group follow a similar pattern, in that there is an increase in costs in both the cDM and control group in the < 12 months follow up period; however, for cDM initial costs are extraordinarily high, reflecting the severe nature of cDM and the intensive clinical care interventions required to treat these neonates during this early phase of their lives. In subsequent time periods, the all-cause costs for the cDM cohort decrease, but remain elevated relative to the non-cDM cohort (which decline and approach zero), reflecting the considerable ongoing needs associated with this most severe form of myotonic dystrophy.Results from this study revealed the following drivers of HRCU among patients with DM or cDM: outpatient, physician office, and ER visits; inpatient hospitalizations; and increased use of prescription medications. For example, the mean number of outpatient visits was 26.78 for the DM cohort vs. 12.23 for controls and the percentage of patients with at least one hospitalization was 2.9% vs. 1.9% in the first 12 months Table  and for or ICD-10 code) represent a conservative estimate of true incidence/prevalence. For the purposes of analysis, ICD-9 codes were not combined with corresponding/equivalent ICD-10 codes to avoid double counting codes for cases that crossed the code transition date, which would result in an overestimation. While the relative frequency (compared to controls) reported for any given ICD-9 or ICD-10 code is reliable, the absolute frequency is likely an underestimate.In this study, numerous comorbidities were identified at varying frequencies and levels of significant difference compared to controls that illustrate the disease burden in DM. The detailed comorbidity findings are not presented here but the data tables are available for further analysis upon request. The most impactful comorbidities (defined above) varied with DM type and also highlighted underappreciated symptoms, which not only can dramatically affect quality of life, but can interfere with functions important in work and/or academic performance . It should also be noted that the comorbidities identified in this study negatively affected the patient to a degree that prompted the patient to present to a medical facility for care or came to the attention of the treating clinician. During the first follow-up year, GI system symptoms 14.4%), cardiac dysrhythmias 12.6%), and organic sleep disorders (11.7%) were the most prevalent comorbidities among patients in the DM cohort . Table S1. Baseline Demographic Characteristics (post-match). Table S2. DM Cohort Most Impactful Comorbidities. Table S3. cDM Cohort Most Impactful Comorbidities. Table S4. Total HCRU & All-cause Costs for DM Cohort over 12-, 13-23, 24-35, 36-47 & 48+ Month Follow-Up Periods. Table S5. Total HCRU & All-cause Costs for cDM Cohort over 12-, 13-23, 24-35, 36-47 & 48+ Month Follow-Up Periods.


Therapeutic strategies for patients with locally advanced rectal cancer (LARC) who are achieving a pathological complete response (pCR) after neoadjuvant radio-chemotherapy (neoCRT) are being increasingly investigated. Recent trials challenge the current standard therapy of total mesorectal excision (TME). For some patients, the treatment strategy of “watch-and-wait” seems a preferable procedure. The key factor in determining individual treatment strategies following neoCRT is the precise evaluation of the tumor response. Contrast-enhanced computer tomography (ceCT) has proven its ability to discriminate benign and malign lesions in multiple cancers. In this study, we retrospectively analyzed the ceCT based density of LARC in 30 patients, undergoing neoCRT followed by TME. We compared the tumors´ pre- and post-neoCRT density and correlated the results to the amount of residual vital tumor cells in the resected tissue. Overall, the density decreased after neoCRT, with the highest decrease in patients achieving pCR. Densitometry demonstrated a specificity of 88% and sensitivity of 68% in predicting pCR. Thus, we claim that ceCT based densitometry is a useful tool in identifying patients with LARC who may benefit from a “watch-and-wait” strategy and suggest further prospective studies. Colorectal cancer (CRC) is a common malignancy worldwide, of which 30% of cases develop in the rectum , 2. For For patients with absent tumor mass after neoCRT in multiple diagnostic examinations, a “watch-and-wait” strategy, instead of TME, as an individual treatment approach is being increasingly discussed –8. Thus,The precise evaluation of tumor responses represents a key factor in determining individual treatment strategies following CRT. Differentiation of post-treatment fibrosis, edema, and residual tumor after CRT in LARC-imaging is one major challenge in implementing rectal preservation strategies.Currently, different ultrasound techniques, magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET) are widely used for restaging, however, there are still significant limitations for each approach. MRI improves preoperative staging accuracy but has limited sensitivity and specificity to predict pCR , 12. ThuIn this study, we focused on CT-densitometry based on Hounsfield units (HU) as assessed by X-ray attenuation. By now, CT-densitometry has repeatedly been reported as an effective imaging technique to differentiate benign from malignant lesions in different cancer types –17. HereIn this study, we analyzed HU changes in pre- and post-neoCRT CT-scans in a concordant region of interest (ROI) in rectal tumor areas. In patients with LARC, we were able to demonstrate significant correlations with pCR. To our knowledge, this is the first study showing ceCT-densitometry to predict rectal tumor responses following neoCRT.Based on ICD codes, patients with LARC treated in our institution between 06/2012 and 04/2020 were identified. The diagnosis of rectal cancer had to be confirmed by histopathological examination. Patients were included if pre- and post-neoCRT contrast ceCT were available and total mesorectal excision was performed after neoCRT. Radiotherapy comprised a total dose of 50.4 Gy. Time from pre-neoCRT diagnostics to the beginning of treatment had to be < 6 weeks. Time from post-neoCRT to surgery had to be <8 weeks. The TNM stage before treatment (iTNM) was set according to routine clinical examination including magnetic resonance imaging (MRI) and endoscopic examination. The post-treatment TNM stage was defined according to the pathologic report (ypTNM). The cancer stage was finally defined according to the American Joint Committee on Cancer (AJCC) with a pathologically confirmed locally advanced rectal adenocarcinoma . A histopathological report with tumor regression grading according to Dworak and a report of the percentage of residual tumor cells had to be available. The ethical committee of the Ruhr-University Bochum approved the study (#20-7013-BR).2O)/µH2O (µ:attenuation coefficient). Size of the ROI could differ between pre- and post-neoCRT imaging, with respect to the tumor size. For large or circular tumors, medium values of multiple ROI of the tumor core were used. For very small tumors, not clearly definable in ceCT, MRI was used to identify the tumor region. In this case, the ROI for ceCT based densitometry was set according to concordant MRI images. All images were evaluated by two radiologists with JiveX PACS software .All CT scans were performed in clinical routine settings with Siemens SOMATOM Definition AS  set to 40 or 64 slices and Imeron400 contrast agent . CT settings were the same for all patients analyzed. Images were analyzed in the portal venous phase with 70 s delay after infusion of the contrast agent. Tube voltage was 120kV in both arterial and portal-venouse phase. For detailed imaging settings, please see the Assessment of response towards neoCRT by MRI and endosonographic ultrasound were extracted from medical reports.Percent change of density was calculated as following: (HU post-neoCRT – HU pre-neoCRT)/HU pre-neoCRT *100. Thus, the decline of density was greater in patients with more negative values. Data was analyzed and processed with Graphpad Prism 6 . The correlation was analyzed by Pearson correlation and an unpaired t-test. Welch’s t-test was applied to analyze patients with pCR and those with residual tumor cells. A value of p<0.05 was considered as a significant difference in all t-tests applied . Specificity, sensitivity, and negative- and positive predictive values were calculated by two-by-two tables.2, days 1 – 5) in the first and fifth week or capecitabine in equivalent doses, followed by TME (n=26). Four patients were treated in multicenter studies testing FOLFOX as a chemotherapy regimen parallel to radiotherapy (50.4 Gy). Mean timespan between ceCT-scans (pre- and post-neoCRT) CT scan was 15.4 weeks (95%-CI: 14.5 – 16.7) and mean timespan from post-neoCRT CT scan until surgery was 2.8 weeks (95%-CI: 2.1 – 3.4) Post-neoCRT MRIs were available for 25/30 patients, endosonographic diagnostics had been performed for 21/30 patients pre-RCT. Missing MRI were due to patients´ denial or contraindications e.g., pacemaker implantation. The initial pretreatment stages of the patients in MRI and EUS and pathological stages are listed in We identified 113 patients with LARC which had an initial ceCT scan at the time of diagnosis. All patients underwent surgery in our institution. Of those, 83 were excluded for different reasons, mostly because of missing post neoCRT CT-scans Figure 2The change of ceCT-based HU in a distinct ROI between pre- and post-neoCRT CT-scans was analyzed for each patient included. For two patients, the ROI had to be set according to visible tumor lesion in MRI, because no obvious tumor was detectable in ceCT. After neoCRT, 22 of 30 patients had a lower density of the tumor sample (73%), with the largest decrease of 72% (2 = 0.30).To analyze whether the decline of tumor density was associated with the content of residual vital tumor cells in the tumor sample, the density’s percentage of decline was correlated to vital tumor cells in the histologic sample. As depicted in Next, we compared the relative decline of ceCT based density of patients with pCR (n=8) and those with vital residual tumor cells (n=22). The cut-off for pCR was set to no vital tumor cells in the histopathologic sample. We found patients with pCR to display a greater decline of density than patients with residual tumor cells Figure 4The absolute HU values in pretreatment ceCT based densitometry were not predictive for response (p=0.616 and AUC 0.554) , of all patients included, had pCR with no residual tumor cells. The specificity of MRI to identify pCR was 50% (4 of 8). Of 9 patients achieving CR in MRI, only 4 patients had pCR  = 44%). Of 16 patients without pCR, 12 were correctly identified by MRI  = 75%). The specificity of endosonographic ultrasound to identify pCR was 60% (3 of 5). Of 4 patients achieving CR in endosonographic examination, 3 had pCR (PPV = 75%). Of 17 patients not having CR assessed to endosonographic ultrasound, 15 were correctly identified (NPV = 88%). McNemar’s test showed no significant differences between ceCT based densitometry, MRI or endosonographic ultrasound , endoscopy, and MRI. Prediction of a clear resection margin at the mesorectal fascia is one major goal of preoperative imaging. As a major obstacle, extensive fibrosis and edema impair the diagnostic accuracy of MRI after neoCRT , 23. StuAs an invasive approach to identify pCR, biopsies of the primary tumor region were performed after neoCRT. However, this approach demonstrated low sensitivity of 12.9% and a poor concordance rate of 30.4% between biopsy specimens and surgical specimens .The comparison of MRI and ceCT was not the primary focus of our study. Thus, conclusions drawn are limited due to low sample numbers. However, our results are in line with previous studies demonstrating limitations predicting pCR by MRI with a limited accuracy. Accordingly, only 4 of 8 patients who achieved pCR were correctly identified by MRI, indicating the known limitations of this approach , 13, 22.We consider our approach as a useful additional tool in post-neoCRT examinations. Combined MRI, endosonographic ultrasound, and ceCT-based densitometry could enhance the safety of individual treatment strategies to avoid TME. This may be of particular interest in patients not able to undergo MRI for different reasons . This is in line with the 2016 ESGAR recommendations, where a majority of the panel agreed that a multimodal approach is needed for disease staging after neoCRT, since MRI alone seems not suitable for accurate disease staging after neoCRT .via matched analysis of two CT-exams (pre- and post neoCRT).The discrimination between benign and malignant lesions is a major obstacle of conventional radiologic imaging, and CT-based densitometry was used in other studies to overcome this limitation. For adrenal incidentaloma, non-enhanced CT and HU of ROI can be used to discriminate benign from malignant lesions , 16. In The limitations of our study are the retrospective analysis and the small cohort size, increasing the risk of selection bias. Pre- and post-neoCRT CT-scans are, thus far, not recommended as diagnostic procedures and not performed routinely. To date, abdominal ultrasound and conventional chest imaging are suitable for disease staging. A major reason for the exclusion of most patients was the missing combination of pre- and post-neoCRT CT-scans additionally to the mandatory completion of all neoCRT cycles as well as subsequent surgery. This limits the study’s clinical validity. However, based on our preliminary results, ceCT densitometry is a promising tool to extend and enhance pre-surgery diagnostics, which encourages further research, particular in patients with no nodal involvement. Moreover, our approach is easy to perform in clinical practice, compared to radiomics. In summary, a prospective study including a larger collective is needed to validate our results.thomas.mika@rub.de.The data analyzed in this study is subject to the following licenses/restrictions: patient data. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by Ethik-Kommission der Medizinischen Fakultät der Ruhr-Universität Bochum. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MM, HH and AB collected the patient data. MM, HH, KS, MT, ME, AB and TM performed data analysis. MM, AB, RS and TM wrote the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by a grant from de.NBI (grant number: FKZ 031 A 534A [REF]), a project of the German Federal Ministry of Education and Research (BMBF).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Endpoints were simultaneously evaluated at baseline, and then reassessed at 4- and 8-week treatment visits and four weeks after treatment cessation, using validated patient-reported outcome measures. A significant reduction in cognitive fatigue perception and overall FIS-40 score  and an improvement in HRQoL ) (p < 0.05) from baseline were observed within the experimental group over time. Statistically significant differences were also shown for sleep duration at 4 weeks and habitual sleep efficiency at 8 weeks in follow-up visits from baseline within the experimental group . Overall, these findings support the use of CoQ10 plus NADH supplementation as a potentially safe therapeutic option for reducing perceived cognitive fatigue and improving the health-related quality of life in ME/CFS patients. Future interventions are needed to corroborate these clinical benefits and also explore the underlying pathomechanisms of CoQ10 and NADH administration in ME/CFS.Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, multisystem, and profoundly debilitating neuroimmune disease, probably of post-viral multifactorial etiology. Unfortunately, no accurate diagnostic or laboratory tests have been established, nor are any universally effective approved drugs currently available for its treatment. This study aimed to examine whether oral coenzyme Q10 and NADH (reduced form of nicotinamide adenine dinucleotide) co-supplementation could improve perceived fatigue, unrefreshing sleep, and health-related quality of life in ME/CFS patients. A 12-week prospective, randomized, double-blind, placebo-controlled trial was conducted in 207 patients with ME/CFS, who were randomly allocated to one of two groups to receive either 200 mg of CoQ10 and 20 mg of NADH ( Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a serious, complex, and chronic multisystem illness of unknown etiology, often triggered by a persistent viral infection . It is characterized by unexplained and persistent post-exertional fatigue, which is unrelieved by rest and made worse by physical and mental effort, along with other core symptoms such as the disruption of cognitive, immunometabolic, autonomic, and neuroendocrine pathways . It affeCoenzyme Q10 (CoQ10) and the reduced form of nicotinamide adenine dinucleotide (NADH) are key components of the electron transport chain responsible for mitochondrial ATP production, which decreases free radical generation, and, in their reduced forms, they act as powerful antioxidants. CoQ10 and NADH levels and redox status have been shown to be disturbed in ME/CFS. Strong evidence has emerged that mitochondrial dysfunction, disturbed immunometabolism, and increased oxidative stress play a pivotal role in the pathogenesis of numerous illnesses, providing a robust scientific rationale for testing potential “nutraceuticals” that target these processes .As chronic fatigue is a key diagnostic symptom for ME/CFS, it has been suggested that energy metabolism may be disrupted in a subset of patients with ME/CFS. Mitochondrial dysfunction has been considered as a possible underlying pathomechanism of the illness, based on the structural and functional changes reported vis-a-vis healthy controls . Long-chOver the last decade, a growing body of data has supported the potential clinical use of dietary CoQ10 supplementation in health and chronic illnesses ,23,24,25Previously, in a proof-of-concept study, our group found a trend towards improvement over time in fatigue perception, response to physical exercise, and biochemical parameters of oxidative stress and mitochondrial metabolism  in peripheral blood mononuclear cells after CoQ10 plus NADH administration ,27,28. TData on the potential therapeutic effects of CoQ10 and NADH co-supplementation on fatigue, pain, and sleep impairments in individuals with ME/CFS are scarce. The current study was conducted to evaluate the clinical effects of CoQ10 plus NADH supplementation on fatigue perception, nonrestorative sleep problems, and HRQoL in people with ME/CFS, due to a previous pilot study in which no significant differences were found for chronic pain and sleep quality in ME/CFS.2), and hypersensitivity to CoQ10 or NADH. Patients with missing data from the follow-up visits to baseline were considered to have dropped out.A 12-week, single-center, randomized, double-blind, placebo-controlled trial was conducted in 242 Caucasian ME/CFS patients consecutively recruited from a single outpatient tertiary referral center  from January 2018 to December 2019. n = 72) or a matching placebo (n = 72), in four capsules daily for eight weeks; all participants also received their standard therapy for the disease. The CoQ10 was used in combination with NADH, in light of reports that they may have synergistic antioxidant effects and that NADH may enhance CoQ10 absorption [Of the 242 eligible ME/CFS participants screened, 35 were excluded . The remaining 207 participants were allocated to treatment by an independent investigator not otherwise involved in the intervention, using the result of a list of random numbers generated by a computer program. The participants were randomly assigned in a double-blind fashion in a 1:1 ratio to receive either 200 mg of CoQ10 plus 20 mg of NADH , and 11 in the placebo group . Twenty-four patients were lost to follow-up . Four discontinued the study due to the presence of a concurrent process . Finally, 16 patients withdrew at their own request . The remaining 144 cases of ME/CFS  completed all the study protocol procedures and were included in the overall analysis of outcome measures see .Patients randomized to the CoQ10 plus NADH experimental group received four enteric-coated tablets daily consisting of active ingredients (50 mg of CoQ10 and 5 mg of NADH) and excipients (20 mg of phosphatidylserine and 40 mg of vitamin C). Patients randomized to placebo received supplementation comprising four enteric-coated tablets daily containing only excipients. Both experimental and placebo tablets were identical in size, color, opacity, shape, presentation, and packaging. All tablets were manufactured and donated by Vitae Health Innovation, S.L. . The study pharmacist recorded all treatments supplied on the medication-dispensing forms along with the original script.https://clinicaltrials.gov as NCT03186027 (accessed on 29 June 2017).This unicenter, randomized, double-blind, placebo-controlled cohort study design was conducted to evaluate the clinical benefits of oral CoQ10 plus NADH administration on fatigue perception, unrefreshing sleep problems, and HRQoL in ME/CFS. Clinical visits throughout the study are detailed in The prospectively defined main outcome measure was the change in self-reported fatigue scores assessed using the validated Fatigue Impact Scale (FIS-40) from baseline to the final study visit held three months later. The study was powered to detect a 3-point difference between active treatment group and placebo. The FIS-40 comprises 40 items divided into three domains that describe how perceived fatigue impacts upon cognitive (10 items), physical (10 items), and psychosocial functioning (20 items) over the previous four weeks. Each item is scored from 0 (no fatigue) to 4 (severe fatigue). The total FIS-40 score is calculated by adding together responses from the 40 questions (score range 0–160). Higher scores indicate more functional limitations due to severe fatigue .The secondary outcome measures included changes in sleep disturbances and health-related quality of life through validated self-reported PSQI and HRQoL questionnaires, respectively.Sleep disturbances were assessed through the self-administered 19-item Pittsburgh Sleep Quality Index (PSQI) questionnaire. Scores are obtained on each of seven domains of sleep quality: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep perturbations, use of sleeping medication, and daytime dysfunction. Each component is scored from 0 to 3 (0 = no sleep problems and 3 = severe sleep problems). The global PSQI score ranges from 0 to 21 points, with scores of ≥5 indicating poorer sleep quality [The 36-item Short Form Health Survey (SF-36) was used to assess health-related quality of life. The SF-36 is a broadly-based self-reported survey of health-related physical and mental functioning status in ME/CFS and other chronic conditions. It assesses functioning on eight subscales including domains of physical functioning, physical role, bodily pain, general health, social functioning, vitality, emotional role, and mental health, and two additional general subscales covering the physical and mental health domains, rated on a scale from 0 to 100. Lower scores indicate a more negative impact of an individual’s health on functioning [https://www.imim.es/ofertadeserveis/software-public/granmo/, accessed on 12 June 2018).The sample size was estimated based on data from a previous clinical trial of CoQ10 and NADH supplementation in people with ME/CFS . Allowinn = 0). All adverse events following administration of study product intake were monitored until the end of the study. No serious adverse events or suspected adverse reactions  were reported during or after the end of the study.All participants were asked to return any remaining study product after the intervention. Adherence was measured by calculating all remaining tablets. Participants who did not take the supplement on more than two non-consecutive days or consecutive days were considered non-compliant  for continuous variables and the Chi-square test for categorical variables. For all comparisons, a two-sided level of statistical significance of 0.05 was considered. All analyses were performed on the data set using all available information with intention-to-treat (ITT) criteria. An analysis of covariance (one-way ANCOVA) was performed using the changes in overall FIS-40 score from baseline to the last follow-up visit of the study as the primary outcome measure, and entering the overall FIS-40, PSQI, and HRQoL scores at baseline as continuous predictors and the enrolling investigator and treatment assigned as categorical predictors. The statistical analysis observed the ICH-E9 guidelines as well as all the rules of good clinical practice. All data were analyzed using the SAS program, version 9.4 .A descriptive statistical analysis was carried out for all study variables. Results are shown as means ± standard deviation (SD). A Kolmogorov–Smirnov test was used to determine the distribution and normality of data for both groups. Continuous variables were defined by the number of valid cases and expressed as mean ± SD. Categorical variables were described using absolute and relative frequencies of each category over the total of valid values. Differences in comparison analysis between the two groups were assessed using Student’s p = 0.005 and p = 0.010, respectively). The psychosocial domain showed a nominal improvement in the active group at the 4-week visit, though without reaching statistical significance (p = 0.053).p = 0.022). However, this improvement was not significant at the follow-up visits from baseline . FIS-40 domain scores evolved in parallel between groups over the course of the study.In the CoQ10 + NADH group, the total FIS-40 score significantly decreased at the 4-week visit from baseline (p = 0.018). Moreover, in the within-experimental group comparison analysis, we found statistically significant differences for the PSQI domains from baseline over time .p = 0.036 and p = 0.001, respectively). The bodily pain domain improved at the 4-week visit from baseline (p = 0.043). In the placebo group, a reduction in vitality was observed 4 weeks after treatment discontinuation (p = 0.042).The physical functioning showed better scores in the CoQ10 plus NADH group; statistically significant improvements were observed at both visits from baseline during treatment  compared to baseline. Likewise, our dietary nutraceutical combination may have influenced the overall FIS-40 domain score—reducing it at the 4-week visit from baseline (p = 0.022), although the difference was not significant at the end of treatment visit —and also the psychological domain, which showed a marked improvement at the 4-week visit  within the experimental group. In previous research carried out by our group and others, with nutraceutical interventions to assess mitochondrial dysfunction in ME/CFS, fatigue was the primary endpoint in eight out of nine studies [Alongside chronic fatigue, the most hallmark symptoms of ME/CFS are intolerance to physical/mental exercise , alterations in concentration/memory, nonrestorative sleep, orthostatic intolerance, and generalized chronic pain . Given t studies ,47,48,49 studies ,47,48,49 studies ,45. The In experiments on rodents, CoQ10 treatment improved cognitive deficits by modulating mitochondrial functions and was effective in protecting against cognitive impairments and neurodegeneration in experimental animal models ,51. FukuClinical trials have shown that oral replacement supplements of CoQ10 plus/or NADH significantly reduce fatigue perception and other symptoms associated with chronic diseases. Several studies in people with fibromyalgia  have repOur intragroup comparison analysis found that ME/CFS patients supplemented with CoQ10 plus NADH showed significant increases in the physical function domain of the SF-36 at both follow-up visits. This result is of interest, because our review of the scientific literature revealed improvements in health-related quality of life after CoQ10 administration in other related chronic conditions such as fibromyalgia and Gulf War Syndrome ,56, but In the broad group of neurological manifestations of ME/CFS, the component of generalized pain stands out, both in the context of ME/CFS and in the case of comorbid fibromyalgia . In our In patients with ME/CFS, nonrestorative sleep leads to a significant alteration in sleep quality . Our resPrevious studies investigating the beneficial clinical effects of CoQ10 supplementation in heart failure (HF) have been conducted over the last few years. The findings have reported an improvement in bioenergetic and functional levels and endothelial function in these HF patients ,60.Previous reports have suggested that dietary supplements such as CoQ10 or NADH are safe and well-tolerated . Our resThis study has some limitations that must be considered. Firstly, the primary endpoint, the perception of fatigue recorded through the FIS-40 questionnaire, is a subjective parameter. In future work, objective physiological and biological parameters might be used, such as the maximum oxygen consumption and lactate/glucose levels during and after performing a cardiopulmonary challenge test (2-day consecutive CPET). Secondly, there were limitations associated with the study design, research setting, and participants’ selection. The fact that all patients were Caucasian ME/CFS women and were recruited from a single tertiary referral center may have increased the proportion of seriously ill patients, and we must be cautious when generalizing these results to other populations in other healthcare settings such as primary care or the general population. Thirdly, the lack of statistical differences found between groups may have been due to the placebo effect (phosphatidylserine and vitamin C as excipients). Finally, the doses and follow-up timing of the intervention were pre-established, and so the dose–response effect could not be monitored and recorded among the study participants.However, our study also has some important strengths: (1) the analysis of the combination of CoQ10 plus NADH as a nutritional supplement in ME/CFS, with the inclusion of a substantial number of participants, (2) the improvement in the perception of cognitive fatigue and health-related quality of life achieved by the combination of CoQ10 plus NADH, (3) the homogeneous inclusion criteria through the 1994 CDC/Fukuda definition, and (4) the consideration of gender with the inclusion only of women—the recruitment of age-and sex-matched participants is essential in mitochondrial function studies, due to age-related mitochondrial function decline —and (5) the demonstration of the safety and good tolerance of the CoQ10 plus NADH administration among participants.2 as “gold standard” measure) and lactate/glucose ratio following an ergospirometry provocation test, and the assessment of information processing speed using computerized neuropsychological tests in order to verify the potential benefits of antioxidant therapy in people with ME/CFS.Multicenter trials are now required with homogeneous patient samples after carrying out the correct clinical phenotyping , and with recommendations to eat a balanced diet. We also support long-term follow-up studies including objective physiological and biological indicators, such as peak oxygen consumption (VOn = 207). Our findings suggest that, over a two-month period, this combination is potentially effective in reducing cognitive fatigue  and overall fatigue perception, thus improving HRQoL in ME/CFS. The study shows that CoQ10 and NADH can be safely co-administered to ME/CFS patients and are generally well-tolerated at the dosages indicated. A therapeutic effect was also demonstrated on sleep quality within the experimental group. Long-term RCTs in larger ME/CFS cohorts should now be performed to confirm the effectiveness of CoQ10 and NADH co-supplementation in treating the hallmark symptom of post-exertional malaise using two-day consecutive cardiopulmonary exercise testing (2-day CPET). To the best of our knowledge, this is the first study to assess the effects of oral CoQ10 plus NADH supplementation administered to a substantial number of ME/CFS patients (


Coronavirus disease 2019 (COVID-19) is known to cause a severe acute respiratory syndrome with increased morbidity and mortality due to multiorgan involvement. COVID-19 is associated with an increased risk of venous thromboembolism (VTE), ranging from asymptomatic to potentially fatal presentations. Predictors of VTE in COVID-19 are not fully defined, and the role of anticoagulation in these patients is debatable. Here we discuss two cases of COVID-19, who initially presented with mild COVID-19 symptoms and later with potentially fatal VTE within 30 days of initial presentation. The first case is of a 42-year-old gentleman with a history of sarcoidosis and a recent diagnosis of COVID-19 pneumonia who was in isolation at home and presented with syncope and worsening shortness of breath. He was hemodynamically unstable and resuscitated with fluid management in the emergency department. The chest angiogram imaging studies showed massive pulmonary embolism with right heart strain, which was confirmed with bedside point-of-care ultrasound. The patient deteriorated clinically and received an intravenous tissue plasminogen activator in the emergency. He was discharged home under stable condition on oral anticoagulation. The second patient is a 63-year-old gentleman with chronic obstructive pulmonary disease, obesity, sleep apnea, and a recent diagnosis of COVID-19 pneumonia, which was complicated with an ischemic stroke, who presented with worsening complaints of shortness of breath and palpitation. The chest angiogram imaging showed bilateral pulmonary embolism. An echocardiogram showed mild right heart strain. The lower extremity duplex ultrasound showed bilateral deep vein thrombosis. The patient underwent catheter-directed thrombolysis and discharged on oral anticoagulation. There is a need to develop stronger predictors to provide thromboprophylaxis in COVID-19 pneumonia to prevent life-threatening VTE. Coronavirus disease 2019 (COVID-19) is known to cause severe acute respiratory syndrome and is accountable for the pandemic leading to approximately 111 million cases and two million deaths worldwide as of February 2021 . PredictCase 12 of 68 mmHg and an elevated alveolar-arterial gradient of 47.5 mmHg (expected: 14.5 mmHg). Chest examination revealed decreased breath sounds bilaterally without wheezing or crackles. The rest of the examination was unremarkable. The patient was stabilized with fluid resuscitation and oxygen therapy in the ED. A 42-year-old Caucasian male with a medical history significant for sarcoidosis, well-controlled essential hypertension, and a recent diagnosis of COVID-19 pneumonia (10 days before presentation), who was in home isolation, presented to the emergency department (ED) after a syncopal episode and shortness of breath. On physical examination, the patient was hemodynamically unstable with a heart rate of 150 beats per minute, blood pressure of 56/24 mmHg, and respiratory rate of 51 breaths/minute. His room air oxygen saturation was 96%, and arterial blood gas revealed hypoxemia with a pOLaboratories workup on presentation is shown in Table Case 2A 63-year-old man with obstructive sleep apnea, chronic obstructive pulmonary disease on 2 L home oxygen, morbid obesity, heart failure, and recent diagnosis COVID-19 pneumonia (two weeks before presentation) complicated with an ischemic stroke presented with sudden onset shortness of breath, palpitation, and fatigue. On examination, heart rate was 112 beats per minute, blood pressure was 114/79 mm Hg, respiratory rate was 18 breaths/minute, and oxygen saturation was 96% on 2 L of oxygen. Respiratory and cardiovascular examinations were unremarkable. Neurological examination was significant for decreased motor strength to 3/5 bilaterally due to deconditioning and history of recent right basilar ischemic stroke.Laboratories workup on presentation is shown in Table In both the presented cases, the common thing is the D-dimer elevation, which put them at a higher risk of a PE. These are just two different PE presentations in patients with COVID-19 which tell us that we need to have a high clinical suspicion of a thrombotic event in a patient diagnosed with COVID. Timeline-wise, we do not know when a person is more at risk of developing a prothrombotic state. We suggest considering oral anticoagulation prophylaxis in the outpatient setting for patients with COVID symptoms with high baseline D-dimer levels to reduce life-threatening thromboembolism incidences. The retrospective study by Yin et al. showed that COVID-19 patients with high D-dimer levels on anticoagulation prophylaxis had a lower 28-day mortality compared to patients not on anticoagulation prophylaxis . In a stCOVID-19 is associated with an increased risk of VTE. These patients must be screened and treated to prevent life-threatening massive PE or other complications of VTE.


King County communities experiencing high rates of COVID-19 face a disproportionate cumulative burden of environmental and social inequities. Cumulative environmental health impacts should therefore systematically be considered when assessing for risk of exposure to and health complications resulting from COVID-19.Few studies have assessed how the intersection of social determinants of health and environmental hazards contributes to racial disparities in COVID-19. The aim of our study was to compare COVID-19 disparities in testing and positivity to cumulative environmental health impacts, and to assess how unique social and environmental determinants of health relate to COVID-19 positivity in Seattle, King County, WA, at the census tract level. Publicly available data (n = 397 census tracts) were obtained from Public Health–Seattle & King County, 2018 ACS 5-year estimates, and the Washington Tracking Network. COVID-19 testing and positive case rates as of July 12, 2020, were mapped and compared to Washington State Environmental Health Disparities (EHD) Map cumulative impact rankings. We calculated odds ratios from a series of univariable and multivariable logistic regression analyses using cumulative impact rankings, and community-level socioeconomic, health, and environmental factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. We found a remarkable overlap between Washington EHD cumulative impact rankings and COVID-19 positivity in King County. Census tracts with ≥ 10 % COVID-19 positivity had significantly lower COVID-19 testing rates and higher proportions of people of color and faced a combination of low socioeconomic status–related outcomes, poor community health outcomes, and significantly higher concentrations of fine particulate matter (PMThe online version contains supplementary material available at 10.1007/s40615-021-01063-y. Racial disparities in COVID-19 have been widely reported in the USA, where communities of color experience a disproportionate burden of infection and mortality , few stuCommunity-level analysis, which relies on data aggregated by geographic boundaries such as census tracts, allows for the consideration of social and environmental determinants of health that may be contributing to COVID-19 disparities independently of individual-level attributes. The mechanisms by which community characteristics lead to racial health disparities are complex; however, structural and place-based factors such as systemic racism, residential racial segregation, concentrated poverty, access to healthcare, and proximity to environmental hazards are widely associated with adverse health outcomes –9. OngoiStudies that have assessed racial disparities in COVID-19 at the community level have focused on social determinants of health like economic stability, education, housing conditions, and access to testing –19. One 2.5) and nitrogen dioxide (NO2) experience increased rates of COVID-19 [2.5 exposures and US county–level COVID-19 mortality rates after accounting for area-level confounders [Yet community-level research on COVID-19 disparities has rarely accounted for exposure to air pollution. Social inequities including concentrated poverty , higher COVID-19 –25. BecaCOVID-19 , these ffounders .Cumulative environmental health impact analysis is a method for evaluating community susceptibility to environmental health hazards that considers both social and environmental determinants of health. The method assigns composite cumulative impact rankings based on pollution burden and population vulnerability across geographic bounds. These rankings indicate the potential a community has for combined and interactive exposure to environmental hazards and socioeconomic stressors that contribute to environmental health disparities . PreviouIn Seattle, King County, WA, the site of the USA’s first COVID-19 outbreak, local agencies led by Public Health–Seattle & King County have been proactive in mobilizing increased testing facilities in highly positive communities and high-volume vaccination programs and sites. Nevertheless, data indicate that communities of color in King County remain disproportionately impacted by the epidemic , 31. CumThe goal of this study is to compare census tract–level COVID-19 disparities in testing and positivity to cumulative environmental health impacts in Seattle, King County, WA. We then aim to assess how unique social and environmental determinants of health relate to COVID-19 positivity at the census tract level. We hypothesize that in King County, communities facing the greatest cumulative impacts are disproportionately burdened by COVID-19 and in need of upscaled prevention resources.On July 12, 2020, during the summer peak of infection in King County, we obtained the cumulative number and rate of COVID-19 tests, positive cases, hospitalizations, and deaths recorded per King County census tract from the Public Health–Seattle & King County daily COVID-19 summary dashboard . Public Environmental exposures Environmental effects Sensitive populations Socioeconomic factors We downloaded census tract–level cumulative impact rankings, environmental exposures rankings, environmental effects rankings, sensitive populations rankings, and socioeconomic factors rankings from the Washington Environmental Health Disparities (EHD) Map . The EHDWashington State census tracts are assigned a relative, decile score for each of the 19 indicators based on rank-order of the raw values, as detailed by Min et al. . Census The composite, relative rankings allow for assessment of the cumulative impact of multiple indicators across communities. Communities with a cumulative impact ranking of 9 or 10 are considered highly impacted by existing environmental health disparities and thus at risk for adverse health outcomes. Because Washington EHD cumulative impact rankings were developed through a community-engaged process that explored upstream exposures and vulnerability factors that impact environmental health disparities broadly, they can serve as a good preliminary indicator of community vulnerability to COVID-19.As Washington EHD cumulative impact rankings are not intended to diagnose specific community health issues , additioWe used the 2014–2018 American Community Survey (ACS) 5-year estimates for census tract–level data on race (ACS Table: DP05) and percentage living below 185% federal poverty level (FPL) ; ACS TaTo account for COVID-19 testing access, we recorded the locations of testing facilities on July 5, 2020, from King County’s Open Access Testing Locations Tool. To account for facility-based outbreaks, on July 24, 2020, we recorded the locations of long-term care facilities with 5 or more deaths due to COVID-19 from the Public Health–Seattle & King County Long Term Care Data Dashboard.Our assessment of cumulative impacts and COVID-19 was conducted in multiple stages. We first mapped rates of COVID-19 tests and positive cases per 1000 census tract residents and COVID-19 positivity as of July 12, 2020, according to quintiles and conducted side-by-side visual comparisons with the distribution of Washington EHD cumulative impact rankings.We then evaluated how Washington EHD cumulative impact rankings and individual census tract–level factors related to COVID-19 disparities by dividing census tracts into two groups: high positivity  and referent (census tracts with less than 10% positivity). The World Health Organization (WHO) uses 10% positivity as their testing capacity target. If more than 10% of test results are positive in a community, active cases are likely to be missed and case numbers will continue rising . To asseUsing our second exposure set , we summarized independent community-level indicators in high positivity and referent census tracts . We calculated odds ratios with a series of univariable logistic regression using independent community-level factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. To account for multiple comparisons, a Bonferroni correction was applied, and the significance threshold was set at P <0.01. We conducted additional multivariable logistic regression analyses testing associations between the outcome variable and SES, community health, and environmental factors, separately. Because of potential collinearity among the independent variables, Pearson correlations were used to identify highly correlated variables (> 0.70). Since we were interested in how each community-level characteristic related to COVID-19 positivity, we chose not to combine SES, health, and environmental variables in the same model to avoid having highly correlated variables in the same model. The significance threshold for all adjusted odds ratios (AOR) was set at P < 0.05. We mapped the locations of no-insurance-required testing facilities in relation to ≥ 10 % positive census tracts in order to assess proximity to free testing. Finally, we undertook a sensitivity analysis of the effect of restricting analyses to census tracts with no facility-based outbreaks, and of using other internationally recognized COVID-19 positivity thresholds . All statistical analysis was completed using R version 4.0.2 . Maps were created with the leaflet 2.0.3 package.In Seattle and King County, WA, 397 census tracts have an average of 5449 residents each , accounting for 2.16 million residents in total. As of July 12, 2020, the average cumulative COVID-19 testing rate across census tracts was 94.8 tests per 1000 residents (SD = 38.3). The average cumulative positive case rate was 5.3 cases per 1000 residents (SD = 3.6) and overall county positivity was 5.6%. Census tract positive case rates, positivity, and testing rates as of July 12 and cumulative impact rankings from Washington’s EHD mapping tool are displayed in Fig. As of July 12, 2020, 64 of King County’s 397 census tracts had high positivity (≥ 10%). Residents of these 64 tracts accounted for 16.0%  of King County’s population. In high positivity tracts, 34.1% of residents were living under 185% FPL, compared to 19.4% in all King County. People of color represented 58.1% of the population, compared to 39.6% in all King County. Ten of the 64 tracts were the site of facility-based outbreaks  and the percentage of the population living near heavy traffic roadways  were significantly higher in census tracts with high positivity than in referent tracts. Concentrations of ozone, nitrogen oxides (NOx), and toxic releases from facilities did not vary significantly .Unique social and environmental determinants of health are described according to COVID-19 positivity group in Table 2.5 significantly increased the likelihood of high COVID-19 positivity after adjustment for other environmental exposures and population density . NOx concentration was significantly negatively associated with high positivity . Neither ozone, toxic waste, nor the percentage of a census tract population living near heavy traffic roadways had a strong influence on COVID-19 positivity when adjusted for PM2.5, NOx, and population density.Before assessing the association between groups of SES factors, community health factors, environmental factors, and COVID-19 positivity, Pearson correlations were computed between tract-level variables of the same group. English and education level were found to be highly correlated (0.796), as were BMI and diabetes-related mortality (0.70); thus, only the more upstream determinants (education level and BMI) were kept in the SES and community health multivariable models. The SES logistic regression model estimated associations between high COVID-19 positivity, area SES factors, and the percentage of people of color in a census tract Table . AdjusteThough high positivity census tracts were home to nearly half of identified facility-based outbreaks (10/23), sensitivity analysis excluding all census tracts where facility-based outbreaks had occurred affected only one overall result: COVID-19 mortality rates no longer differed significantly between positivity groups during univariable regression analysis . Further sensitivity analyses verified the appropriateness of the 10% positivity threshold, as other cut offs resulted in less meaningful comparisons between positivity groups .This study was the first to apply the Washington Environmental Health Disparities mapping tool in the context of the COVID-19 outbreak in Seattle, King County, WA. We found remarkable overlap between the Washington EHD Map and COVID-19 positivity in King County. Geospatial mapping and univariable logistic regression showed that census tracts with the highest cumulative impact rankings were facing a disproportionate burden of COVID-19 infection. These same communities were experiencing lower testing rates, indicating that highly impacted King County communities are in need of upscaled COVID-19 prevention resources. Of the subcategories of inequities contributing to Washington EHD cumulative impacts, multivariable logistic regression showed that SES and race were the greatest contributors to community-level COVID-19 disparities, highlighting a need for long-term structural interventions that address systemic racism and socioeconomic inequities in addition to short-term disease prevention and control solutions.While Washington EHD cumulative impact rankings proved a rapid and effective tool for identifying community vulnerability to COVID-19, further analysis showed the value of tailoring cumulative impacts to the current epidemic. By assessing how unique community-level factors related to COVID-19 positivity, our study provides evidence of significant social and environmental inequities in communities facing high rates of COVID-19. Low socioeconomic status, existing health disparities, and poor air quality intersect resulting in that the communities most exposed to COVID-19 are the least equipped to combat its financial and health consequences. Logistic regression models point to where resources can be redirected to halt this cycle. For example, socioeconomic and health factors like crowded living conditions and BMI contribute to COVID-19 positivity independently of other racial, SES, and health disparities.Our findings that highly COVID-19-positive census tracts had significantly higher proportions of Hispanic, Black, and low-income residents are consistent with other community-level studies , 17. MulResidents in highly positive census tracts faced the added burden of higher BMI, higher rates of cardiovascular and diabetes-related mortality, shorter overall life expectancies, and, consequently, higher rates of COVID-19 hospitalizations. Multivariable logistic regression that accounted for multiple health inequities showed that BMI was the strongest underlying mediator on the pathway between race, SES, air pollution, and COVID-19. This is coherent with obesity (BMI > 30), which disproportionately impacts communities of color , being bSimilar to the results from New York City , we foun2.5 was positively associated with high community positivity, whereas NOx concentration was negatively associated. Though most studies that have assessed links between NOx and COVID-19 incidence have found a significant positive correlation [2 and rates of COVID-19 infection [x emissions lead to increased ozone concentration which may negatively impact respiratory health and thus COVID susceptibility, is possible in King County . However, our study did not identify a significant association between ozone and high COVID-19 positivity after adjustment for NOx, PM2.5, traffic density, and toxic waste emissions. Our finding that highly positive census tracts faced significantly higher levels of PM2.5 even after adjustment for population density adds to the growing body of international evidence that a correlation exists between PM2.5 and COVID-19 [2.5 has been widely associated with respiratory conditions like asthma, bronchitis, pneumonia, and chronic obstructive pulmonary disease (COPD), all of which increase the likelihood of severe COVID-19 [2.5 varied by only 0.87 μg/m3 between King County positivity groups, a nationwide study of more than 3000 US counties found that an increase of only 1 μg/m3 in PM2.5 was associated with an 8% increase in the COVID-19 death rate [2.5 on the region’s COVID-19 outbreak [2.5.To our knowledge, this is the first study to assess the relationship between COVID-19 and environmental exposures in King County. Using a logistic regression model that adjusted for potential environmental confounders and population density, we found that PMrelation , in Milanfection . Their eCOVID-19 –25. PartCOVID-19  argue thCOVID-19 . Though ath rate . The magoutbreak . DispropIn addition to social, health-related, and environmental drivers of community COVID-19 positivity, other underlying factors may be contributing to a high proportion of tests coming back positive. Lower testing rates brought on by limited access to and/or use of testing may have contributed to high positivity virally, by allowing cases to circulate undetected in the community, but also mathematically, by decreasing the size of the denominator. Odds ratios from univariable regression analysis suggest a larger relative importance of positive case rates in driving high positivity, emphasizing heightened vulnerability to community spread. Nevertheless, a tendency for symptomatic people who are more likely to be positive for COVID-19 to seek testing, and the fact that testing is done on a voluntary basis, may be contributing to positive case rates and population characteristics that are not entirely representative of the underlying community.Given our findings that highly COVID-19-positive communities face cumulative risks to environmental and social inequities, community-level investments for mitigating cumulative environmental health impacts are of critical importance. Community-based participatory research (CBPR), for example, could be a powerful tool for identifying tailored intervention strategies and policy change to reduce COVID-19 disparities in communities facing disproportionate cumulative impacts. Our study points to a need for increased understanding of how identified social inequities are contributing to racial disparities in COVID-19; of how to improve social and healthcare services and COVID-19 prevention and control resources for vulnerable communities; and of the mechanisms by which environmental exposures are exacerbating COVID-19 disparities in King County. CPBR partnerships, by engaging researchers and community stakeholders as equal partners in the research process, would help prioritize these research questions, translate findings into policy, and invest in highly impacted communities by helping residents to acquire new skills and become community leaders .2.5 filtration materials in the home and workplace, as recommended by Huang et al. [To address underlying socioeconomic inequities contributing to COVID-19 disparities, research suggests that accessible high-quality education programs, housing quality and mobility programs, income supplements, and employment interventions for specific vulnerable groups could all be effective interventions for reducing health disparities . To addrg et al. , may be In the introduction, we argued that a cumulative impact approach can provide a novel framework for more equitable allocation of resources. Indeed, we found that COVID-19 risk shared a great deal of spatial overlap with other environmental risks which was neither obvious nor quantified before our analysis. From an equity standpoint, policies and resources directed towards COVID-19 prevention and control should acknowledge and address these overlapping risks. COVID-19 prevention and control resources could be used to improve indoor air quality; environmental policies and resources could be used to improve infectious disease preparedness and mitigation.Our assessment of cumulative impacts in highly positive census tracts was limited by several factors. Because American Indian, Alaska Native, Native Hawaiian, and other Pacific Islander communities make up only 1.3% of King County’s total population, community-level analysis provided limited understanding of how COVID-19 and underlying disparities were affecting these populations specifically. Though analyzing cumulative COVID-19 rates allowed us to assess long-term disparities, lack of data on daily/weekly testing and case rates prevented us from assessing the role of temporal dynamics of the epidemic on COVID-19 outcomes. Data on respiratory health outcomes like COPD and asthma were not publicly available at the census tract level and associations between respiratory health and COVID-19 positivity could not be examined. Finally, it is important to note the potential limitations of using census tracts as units for evaluating community-level drivers of disease burden. Analyzing the residential locations of people testing positive for COVID-19 and seeking testing, while the finest resolution analysis possible with publicly available data, may not account for the spatial extent of individuals’ activities relating to COVID-19 risk . Unfortunately, data on essential jobs were unavailable at the census tract level and we could not account for geographic spillover of work-related risk as part of our analysis.This study highlights the extent to which communities with high COVID-19 positivity in King County face cumulative risks to environmental and social inequities. This suggests that cumulative environmental health impacts should be systematically considered when assessing risk of exposure to and health complications resulting from COVID-19. Our findings also indicate that applying existing environmental health mapping tools to the COVID-19 response and at the outset of future epidemics could be a rapid and effective way to identify the potential for, and prevent, health disparities in vulnerable communities by guiding prioritization of community interventions and investments to protect disproportionately exposed populations.Our findings underline the continued need for disease prevention and control resources in the southwestern region of King County. Government and academic institutions should work closely with community-based organizations like cultural centers, faith-based, and community organizations on strategies for improving preventive services access and use and decreasing positivity rates in minoritized communities. These strategies should focus on the unique strengths in these communities that can help to combat and prevent COVID-19. From a structural standpoint, long-term interventions focusing on economic empowerment, community health programs, and clean air initiatives are important considerations for addressing root causes of community vulnerability to COVID-19. Future research is needed on how respiratory health outcomes and risk of exposure through essential jobs fit into the COVID-19 cumulative impacts framework, and on how the COVID-19 pandemic is affecting underrepresented populations like American Indians, Alaska Natives, Native Hawaiians, and other Pacific Islanders. With widespread COVID-19 vaccination programs underway, it is critical that cumulative impacts assessment be effectively applied to prioritize those most in need of protection.Supplementary Figure 1(JPG 542 kb)


Spouses offer a primary source of support and may provide critical assistance for behavior change. A diet-exercise intervention previously found efficacious in improving cancer survivors’ lifestyle behaviors was adapted to utilize a couples-based approach. The aims were to test the feasibility of this couples-based (CB) intervention and compare its efficacy to the same program delivered to the survivor-only (SO). Twenty-two survivor-spouse couples completed baseline assessments and were randomized to the CB or SO interventions. The study surpassed feasibility benchmarks; 91% of survivors and 86% of spouses completed a 6-month follow-up. Survivors and spouses attended 94% and 91% of sessions, respectively. The SO survivors showed significant improvements on the 30-s chair stand and arm curl tests, weight, and fruit and vegetable (F and V) consumption. The CB survivors showed significant improvements on the 6-min walk and 2-min step tests, body weight, and fat and F and V consumption. Improvement in the 30-s chair stand and arm curl tests was significantly better for SO survivors. The SO spouses showed no significant changes in outcome measures, but the CB spouses showed significant improvements in moderate-to-strenuous physical activity, weight, and fat and F and V consumption. Weight loss was significantly greater in CB spouses compared to SO spouses. Findings demonstrate feasibility, warranting further investigation of CB approaches to promote lifestyle change among cancer survivors and spouses. Roughly half a century ago, the nation’s War on Cancer was launched and has resulted in major increases in survival through improvements in early detection and treatment . There aResearch in cancer survivors has shown that interventions promoting a healthy weight, a healthy diet, and increased physical activity improve quality of life (QOL), physical functioning and overall health status, and reduce the risk of chronic disease ,9,10 andResearch is ongoing to explore how best to deliver lifestyle interventions for cancer survivors ,18,19. SA recent scoping review by Ellis et al.  calls foThe aims of the present study were to conduct a pilot trial to test the feasibility of a CB multi-behavior change program  and to compare its efficacy to the same program delivered to the survivor only (SO) in 22 survivors of breast, prostate, and colorectal cancer and their spousal partners, both of whom were identified as having poor health behaviors. The hypothesis was that survivors randomized to the CB format compared to the SO format would show favorable changes in physical activity, physical performance, body weight status, body composition, and diet. Likewise, and additionally, spouses would show greater changes in outcomes with the CB format compared to the SO format.This study employs a 2-arm, single-blinded, randomized controlled trial (RCT) that evaluated a 6-month diet and exercise intervention delivered in either a CB or SO format. All participants completed assessments at baseline and 6 months (post-intervention).Eligibility for the survivor included: (1) Diagnosis of loco-regional breast cancer (Stages 0-IIIA), prostate cancer (Stages I-II), or colorectal cancer (Stages I-II); (2) Completion of primary cancer treatment and at least 3 months from surgery; (3) No history of other cancers (excluding non-melanoma skin cancer); (4) <150 min of moderate-to-vigorous intense physical activity (PA) per week; (5) Fruit and vegetable (F and V) intake <7 servings/day for women or <9 servings/day for men; (6) Age 18 years or older; (7) Able to read and speak English; (8) Living within the Houston area (Harris or a contiguous county); (9) No pre-existing medical conditions that precluded adherence to an unsupervised PA program or high fruit and vegetable (F and V) diet ; (10) HaEligibility for the spouse included criteria 4–12 listed above. Exclusion criteria for survivor or spouse included using a walker or wheelchair/scooter, being pregnant, or reporting any conditions that are listed on the Physical Activity Readiness Questionnaire (PAR-Q) .This research was approved by The University of Texas MD Anderson Institutional Review Board. Participants who participated in another MD Anderson approved protocol and who indicated they would like to be contacted for future lifestyle trials were recruited for this study. First, potential participants were contacted and verified for eligibility following verbal consent for screening, including cancer diagnosis and treatment status, and having lived with a spouse or significant other for at least 1 year. Next, they were screened for current physical activity using the Godin Leisure Time Exercise Questionnaire (GLTEQ) and F and V intake using the 2009 Texas Behavioral Risk Factor Surveillance System (BRFSS) dietary questions, as well as whether there were any pre-existing medical conditions that precluded their participation using the PAR-Q. Survivors endorsing any item on the PAR-Q were required to have a medical release from their physician to clear them for participation in the study. Following permission to contact their spouses, an identical process was then used to solicit spousal interest, gain verbal consent, and screen for eligibility. For this study, both survivors and their spouses had to be eligible and provide written consent for participation. For all survivors and/or spouses not interested in participating, information regarding reasons for refusal was collected.After completing baseline assessments, participants were assigned to 1 of the 2 study conditions using a form of adaptive allocation referred to as minimization . The fol2, a healthy weight loss goal of 1–2 lbs. per week  was encouraged. To adapt to the 6-month timeline, the RENEW intervention materials were modified such that they could be delivered within the study period and included materials directed toward the spouse. For survivors randomized to the survivor-only arm, the materials were similar; however, there was no reference to working with a spouse on behavior change efforts. Survivors (and their spouses if randomized to the CB arm) were provided with a tailored workbook and 3 tailored print newsletters over the 6-month study period. All print materials provided motivational messages tailored on stage of readiness [®; T-Factor 2006© Guide to the Fat Content of Foods; portion plate; pedometer; web camera; headset; and log books to track their exercise and diet behaviors. In addition to the intrapersonal  cognitive-behavioral strategies oriented toward one’s own behavior change, participants in the CB intervention learned interpersonal cognitive-behavioral skills, including communal coping, joint problem-solving, and healthy communication. They also received the counseling sessions together as a couple.The diet and exercise intervention, based on Social Cognitive Theory, was a tailored correspondence and web-based counseling regimen initially developed and proven efficacious for breast, prostate, and colorectal cancer survivors by Demark Wahnefried and colleagues in Reach out to EnhaNcE Wellness (RENEW) . The beheadiness  that acceadiness . Skills All participants (survivors and spouses) were assessed in-person at MD Anderson at baseline and at a 6-month follow-up. Assessment personnel were blinded to the participant’s study condition. Accrual, attrition, and patient satisfaction served as the primary endpoints of this feasibility study, and other outcomes, such as physical activity, physical performance, weight status, body composition, and dietary intake were also assessed. Each survivor and spouse who completed the baseline and 6-month assessments received compensation in the form of $25 gift cards: one following each completed assessment (up to $100 per couple). Participants also received relevant assessment results at the end of the study. If one member of the survivor-spouse dyad dropped out, the remaining member of the couple could continue.To address the study aims, feasibility, and exploratory outcome measures were collected:Feasibility measures included accrual, attrition, participant views on intervention acceptability, and the monitoring of adverse events. For recruitment, the number of participants contacted about the study, who were eligible and who consented to participate, were tracked. Retention was calculated as the percentage of participants assessed at baseline who completed the 6-month assessments. Drop outs were tracked by study condition. Session attendance was monitored to measure exposure. Intervention acceptability was assessed by asking participants, “Would you recommend this program to other cancer survivors?” at the 6-month follow-up. Possible responses included “Yes,” “Maybe,” and “No.” Responses were compiled across study conditions.Exploratory outcome measures included physical activity, physical performance, body composition, weight, and diet.Physical activity was assessed with the 3-item modified version of the Godin Leisure Time Exercise Questionnaire to ascertain self-reported moderate and strenuous leisure time exercise . To provPhysical performance was measured using a variety of tests that assessed endurance, strength, and agility. The 6-min walk test and 2-min step test were used as measures of aerobic function. The 6-minute walk test has been validated in older adults by comparing it to a treadmill walking test measuring the time to get to 85% of an age-adjusted maximum heart rate . The 2-mDiet was assessed with the Automated Self-administered 24-h Dietary Recall (ASA24) to document the participant’s food intake for a total of 24 h. Two interviews were obtained, 1 for a weekday and 1 for a weekend day . The fooSpouse exploratory outcome measures included the same measures of physical activity, physical performance, diet, and weight. These were assessed at the same time points as survivors.Finally, demographic/medical questions for survivors were collected at baseline and included age, sex, race/ethnicity, education level, employment status, cancer type/stage, and treatment types. Demographic data for spouses included age, sex, and race/ethnicity.Summary statistics were calculated for demographic and clinical characteristics for the study population by study condition.Feasibility was determined by 3 criteria: (1) The completion of accrual within 1 year; (2) An attrition rate of 20% or less; and (3) No occurrence of serious adverse events that are directly attributable to the intervention.p < 0.05.For the exploratory outcome measures, we calculated the means and standard deviations. Prior to computing the sum for moderate-to-strenuous physical activity, moderate and strenuous physical activity variables were each truncated at 420 min per week. Paired t-tests or Wilcoxon signed-rank tests were used to assess within group differences between the 6-month and baseline measurements. We also calculated the difference between the 6-month measurement and baseline and compared it between groups using a 2-sample t-test or Mann-Whitney U test. To estimate the effect of the study group assignment (CB arm relative to SO arm) on changes in exploratory outcome variables, multivariable linear regression models were fit for each outcome. The covariates included in multivariable linear regression models were selected based on the univariates analysis with p-values > 0.05). As published previously [eviously , survivop-values > 0.05).The average age of spouses was similar to that of survivors. The majority of spouses were male and white. The average BMI of spouses also was in the overweight range, though higher than survivors. There were no significant differences in demographic characteristics between spouses randomized to the SO and CB conditions  enrolled and completed baseline assessments . One couNine-of-ten survivors in the SO group (90.0%) completed the 6-month follow-up measures, and 11-of-12 survivors in the CB group (92.7%) completed the 6-month follow-up measures. Thus, the attrition rate among survivors was 9.1%. Eight-of-ten spouses in the SO condition (80%) and 11-of-12 spouses in the CB condition (92.7%) completed the 6-month follow-up measures, resulting in a 13.6% attrition rate among spouses. The overall attrition rate, regardless of survivor status or study condition, was 11.4%.Survivors in the SO condition attended an average of 97% of sessions, and survivors in the CB condition attended an average of 92% of sessions. Combining both groups, survivors attended 94% of sessions with no significant differences between study conditions. Spouses (CB condition only) attended an average of 91% of sessions.In terms of intervention acceptability, 12 survivors (6 CB and 6 SO) and 5 spouses (4 CB and 1 SO) completed the follow-up question asking whether they would recommend the program to other cancer survivors. Among CB survivors, 5 (83%) responded, “Yes,” and 1 (17%) responded, “No.” Among SO survivors, 4 (67%) responded, “Yes,” and 2 (33%) responded “Maybe.” Among spouses, all 4 spouses from the CB group (100%) responded, “Yes” and the 1 spouse from the SO group responded “Maybe.”There were no intervention-related adverse events.p-values indicating the significance of change within the study arm and the significance of the difference in change between the study arms is also presented.There were no significant changes in either self-reported or accelerometer measures or physical activity between study time points among survivors randomized to the SO vs. CB conditions, and there were no significant differences in physical activity change between these groups.Despite no differences in physical activity, there were significant changes in physical performance from baseline to the 6-month follow-up. Survivors randomized to the CB condition showed significant improvement in both the 6-min walk test and the 2-min step test at 6 months, whereas survivors randomized to the SO condition showed no significant change in these measures. No significant between-arm differences were detected for either of these measures. Survivors randomized to the SO condition showed significant improvement in the 30-s sit-to-stand test and in the arm curl test, whereas survivors in the CB condition showed no significant change in either of these tests. Improvements in these tests were significantly better for the SO vs. the CB arms.Survivors in both the SO and CB arms demonstrated significant weight loss over the 6-month period with no between-arm differences in weight loss noted.The SO arm survivors reported significantly higher F&V consumption at 6 months compared to baseline, as did CB arm survivors. Survivors in the CB arm also had significant decreases in saturated fat consumption. There were no significant between-arm differences in change scores for any of the dietary variables.Spouses randomized to the CB condition reported significantly higher strenuous + moderate physical activity at 6 months compared to baseline, but there was no significant change in this variable for spouses in the SO arm. There were no significant changes in either arm in accelerometer-measured physical activity. There were no significant differences between arms in the amount of change in either self-reported or accelerometer-measured physical activity.There were no significant changes in physical performance measures from baseline to 6 months among spouses randomized to either study condition, and there were no significant differences in physical performance changes between groups. Spouses randomized to CB condition demonstrated significant weight loss at 6 months relative to baseline. Spouses randomized to the SO condition showed no significant change in weight, and there was no significant difference in weight change between groups.Spouses randomized to the CB condition showed significantly reduced consumption of total fat and saturated fat and significantly increased consumption of F&Vs at 6 months relative to baseline. In contrast, spouses randomized to the SO condition showed no significant changes in total fat, saturated fat, or F&V consumption from baseline to 6 months. There were no significant differences in 6-month changes in dietary variables between spouses randomized to SO vs. CB study conditions.p = 0.04) and arm curls  among survivors. Among spouses, randomization to the CB condition showed a significant, negative association with change in vigorous physical activity .Few studies have examined the feasibility or outcomes of spouse-based interventions to improve lifestyle behaviors among cancer survivors . This stAs hypothesized, the intervention was indeed feasible, with survivors and spouses surpassing metrics for retention and intervention session attendance. The enrollment rate for this study was 12.7%, which is typical of interventions targeting diet and physical activity among cancer survivor-partner dyads ,45, partCancer survivors and their partners tend to struggle to consume healthy diets and engage in sufficient exercise,  so estabIn addition to intervention feasibility, our study provides some evidence of intervention benefits for both cancer survivors and their partners. Survivors in both the CB and SO groups improved health behaviors and related outcomes with between-group comparisons demonstrating few differences. However, spouses in the CB intervention demonstrated significant improvements in health behaviors and related outcomes, while those examined as part of the SO group  demonstrated none. Though samples were small, these findings suggest that CB interventions may help enhance delivery to some cancer survivors and may provide an important opportunity for behavior change among spouses.Recently published studies involving lifestyle interventions for dyads featuring cancer survivors and their caregivers or family members have demonstrated mixed results. Kamen et al. found no significant improvements in physical activity (steps per day) among cancer survivors or caregivers (95% of whom were spouses/partners) following a 6-week exercise intervention, and there was no significant difference between participants who were randomized to engage with their caregivers and those randomized to individual intervention . In contThis study has important strengths and limitations. Strengths include a strong, randomized study designed to compare intervention feasibility and primary and secondary outcomes between study groups. Primary outcomes included valid measures of diet, and both objective and self-reported physical activity, and secondary outcomes included valid and objective measures of physical performance, body composition, and anthropometrics. Intervention adherence and retention were very strong and similar between study groups, helping to limit concerns about intervention fidelity, attrition bias, or missing data. The enrollment rate of 12.7%, though on par with dyadic lifestyle interventions for cancer survivors, suggests that those who actually participated may have been particularly motivated to engage in a lifestyle intervention with their spouses. Future efforts to enroll cancer survivors in dyadic lifestyle interventions may benefit from directly involving clinicians in recruitment efforts. Though the randomized design helps ensure that there were no systematic differences in motivation between groups, the overall findings of the study may not generalize to the broader population of cancer survivors, many of whom may be less motivated to make healthy lifestyle changes. Generalizability may also be limited by the relatively homogeneous, sociodemographic profile of study participants, as most participants were non-Hispanic white and well-educated, and had an opposite sex partner/spouse. Future research should recruit more diverse samples; in particular the needs of couples who are not heterosexual or are from different racial/ethnic groups needs focused study. The study had a number of secondary outcomes, multiple testing, and potentially spurious significant differences, which is another important limitation. Finally, as the primary study purpose was to examine the feasibility of the lifestyle intervention, the lack of statistical power to detect differences between groups in outcome measures was a primary limitation. The promising feasibility metrics in adherence and retention we observed in this study, coupled with plans to enhance recruitment strategies to enroll a larger and potentially more generalizable population, lend promise to a large, impactful RCT examining differences in outcomes by intervention delivery strategies.Few studies have incorporated spouses in behavioral interventions for cancer survivors, despite the importance of relationships between cancer survivors and their loved ones in survivorship and the potential to broaden the impact of positive behavior change . Cancer