Though in-person delivery of the Diabetes Prevention Program (DPP) has demonstrated medical cost savings, the economic impact of digital programs is not as well understood.This study examines the impact of a digital DPP program on reducing all-cause health care costs and utilization among 2027 adult participants at 12 months.A longitudinal, observational analysis of health care claims data was conducted on a workforce population who participated in a digital diabetes prevention program. Differences in utilization and costs from the year prior to program delivery through 1 year after enrollment were calculated using medical claims data for digital DPP participants compared to a propensity matched cohort in a differences-in-differences model.P = 0.01), with US$699 of that savings coming from reduced inpatient spend (P = 0.001). Cost savings were driven by fewer hospital admissions and shorter length of stay (P < 0.001). No other significant results in cost differences were detected. There was a trend toward savings extending into the second year, but the savings did not reach statistical significance.At 1 year, the digital DPP population had a reduction in all-cause health care spend of US$1169 per participant relative to the comparison group (These results demonstrated significant short-term health care cost savings at 1 year associated with digital DPP program delivery. Type 2 diabetes (T2D) is a pervasive and costly condition in the United States, with over 30 million Americans diagnosed with T2D, and an additional 84 million at risk for developing the chronic condition.4More employers are implementing efforts to prevent diabetes to improve the health status of the workforce and curtail future health care spending. The Diabetes Prevention Program (DPP) is an evidence-based lifestyle management program proven to reduce the risk of diabetes among people with prediabetes through improved eating patterns, physical activity, and weight loss; at three years, the DPP has been shown to reduce diabetes by 58%.5The DPP has widespread dissemination across various settings, with replications of success in reducing population risk for T2D and cardiovascular disease.14Digital delivery of the DPP can increase access to the program because of the ability to reach users at any time or in any location. Eighty one percent of US adults now own a smartphone.17The economic value of traditional, group-based, in-person DPPs has been demonstrated in cost-effectiveness and health care cost saving studies, with estimates of savings in the range of US$3000+ within 3 years.® data, a comparison group of privately insured, working adults who did not participate in the digital DPP was constructed through propensity score matching. The study was approved by the employer Human Studies Review Board with a waiver of consent approved for use of retrospective data that were not originally collected for research purposes.This study was an observational analysis of health care claims data among a population of adults with private insurance through their employer. Longitudinal data of annual health care claims were used to examine differences in health care costs and utilization for participants of a digital DPP compared to matched, nonparticipating individuals across the 12 months preceding digital DPP enrollment (the baseline year) and 24 months after program enrollment. Utilizing MarketScanThe Dow Company is a global materials science innovations and solutions company headquartered in Midland, MI. Dow operates 113 manufacturing sites in 31 countries, and employs approximately 37 000 people, approximately 17 000 of whom work in the United States. The average United States employee is 44 years old, with an average company tenure of 13 years. The workforce spans several job categories and includes both manufacturing and nonmanufacturing roles.2 ; able to engage in light physical activity; and at risk for T2D as evidenced by (a) a blood-based laboratory test in the prediabetic range , or (b) diagnosis of prediabetes or previous diagnosis of gestational diabetes, or (c) elevated score on a diabetes risk screener.All US-based employees and their spouses/domestic partners  were offered the opportunity to participate in the digital DPP if they were determined to be clinically eligible and approved for coverage. Program eligibility requirements included: 18 years of age or older; body mass index (BMI) greater than or equal to 24 kg/mTo be included in analyses, participants had to be clinically eligible based on the aforementioned criteria and have continuous employment and health care coverage by the employer for the entire study period. Enrollments began in April of 2015; all eligible participants enrolled through March 2017 were included in the initial pool. Each participant’s calendar start date in the program was used as their study index date. The preceding 12 months before index date composed the baseline year. The program spanned 12 months, comprising the treatment year. Months 13 to 24 comprised the follow-up year. Data were limited to only those participants who contributed health care claims data for the entire study period of baseline through follow-up year.® is a comprehensive, proprietary collection of linked health databases of adults in the United States. Data include health care claims data including utilization, medical diagnoses, source of insurance, and actual paid costs of enrollees in commercial, private health insurance plans sponsored by more than 150 large and medium-size employers in the United States. A matched comparison group was constructed from MarketScan for benchmarking purposes. An initial pool of 37 786 people was gathered from MarketScan data. Comparators were chosen based on comparable clinical eligibility, individual demographics, initial health status, baseline health care utilization, and employer characteristics, but comparators were confirmed to have not received the digital DPP benefit or similar program during the study period. Seven employer firms were selected for similar workforce composition and per-enrollee medical and prescription drug spending from all of the employers included in the IBM Watson Health MarketScan Database within the years of 2015 to 2018. Data from digital DPP participants in the baseline year were used for creating the matches. A match ratio of 1:1 was used. In situations with more than one match for a digital DPP participant, we randomly selected one comparison for inclusion in the analytic sample for this study. Only two cases were unable to be matched to MarketScan data and were excluded; 99% of the participants were matched to a comparison subject. The resulting analyses included 2027 digital DPP participants and 2027 matched comparison nonparticipants.MarketScanThe digital DPP is a commercially available product offered by a digital health care company . The program is recognized by the CDC Diabetes Prevention Recognition Program.The lifestyle coaches are employed by the company, have a minimum of a bachelor’s degree, and undergo required CDC Lifestyle Coach training from an approved Master Trainer prior to working with participants. Coaches are responsible for monitoring and encouraging lesson completion and health behavior tracking, facilitating discussions on the group forum, and providing individualized, private guidance and support for participants. Throughout the program, participants are asked to track their weight using a cellularly connected digital scale , and track their daily meals and physical activity. Users’ weight, activity, and meal tracking data are displayed on each participant’s personal dashboard on the digital platform and shared with their coach. The program is structured along an initial 16-week intensive phase focusing on weight loss, and a subsequent 36-week phase focusing on long-term weight maintenance and sustainment of health behavior changes, for a total of 12 months of programming. Program access remains after 12 months, but no new curriculum lessons are offered; participants retain access to a health coach, the library of archived lessons, an active peer group forum, and the health behavior tracking tools.To assess the impact of the digital DPP on the biological mechanism of action to prevent progression to T2D , we calculated the rate of change in weight from baseline to 16 weeks into the digital DPP program (end of the intensive phase), at 26 weeks (treatment year mid-point), and 52 weeks (end of the full treatment year). Average lesson completion summed across the intensive phase was calculated as a proxy for program engagement.Administrative claims data were used to identify the following indicators of health care expenditure: all-cause allowed amount per year for services provided under medical coverage and filled pharmacy prescriptions ; allowed amount for inpatient hospitalization services, allowed amount for outpatient services, and allowed amount for filled pharmacy prescriptions. Amounts represent the summed eligible amounts for the services after applying pricing guidelines, but before deducting third party, copayment, coinsurance, or deductible amounts. In addition, metrics were constructed to capture the total percent of the sample with a medical or pharmacy claim in each year of analysis .The following measures of health care utilization were used: all-cause outpatient visits per year; emergency room (ER) visits per year; inpatient admissions per year and total number of days admitted per year; and days of prescription (Rx) supply filled . Visits were counted based on a combination of unique patient and service date.All-cause utilization was examined for each metric as participants were confirmed to not have a pre-existing diabetes diagnosis at study entry. To examine the rate of progression to diabetes, we calculated the percent of each sample with a principal diagnosis of T2D during each year of the study period. We also pooled utilization and cost metrics where the principal diagnosis related to the service was T2D, hypertension (HTN), coronary artery disease (CAD), or congestive heart failure (CHF). We included annual claims from 12 months prior to index date until 24 months after the index date. Program costs for the digital DPP were not included in health care expenditures and were billed directly to the employer.Y = a + β1Digital DPP + β2Year1 + β3β1Digital DPP × Year1 + β4Year2 + β5β1Digital DPP × Year2 + β6X. Y is the outcome for each enrollee; digital DPP is an indicator for whether an individual participated in the digital DPP program; Year1 is an indicator for whether the observation is in the first post-period year and Year2 is an indicator for whether the observation is in the second post-period year; X is a vector of the baseline covariates for age group, male gender, relationship to employer, health plan type, geographic region, rural indicator, comorbidity index, and psychiatric diagnostic group. Effects are measured by the interactions of these dummies with the parameters β3 and β5. Utilization outcomes were estimated with a negative binomial distribution and a log link. Cost outcomes were estimated with a gamma distribution and a log link. Patient diagnosis incidence outcomes were estimated with a binomial distribution and a logit link. Analyses were performed using WPS analytical software.A pre-post analysis was conducted to examine the percent of initial weight lost by the digital DPP participants from baseline to Week 16, 26, and 52. An average was calculated of the programmatic lessons completed in the first 16 weeks. In the analytic sample of health care claims data for the digital DPP participants and the matched controls, difference-in-differences regression equations were performed on patient-year level data to identify the effects of the digital DPP on the outcome variables. Regression models were used to determine confidence intervals and statistical significance of effects using the following parameters: Table 2On average, digital DPP participants completed 10.6 lessons (SD=5.8) out of 16 during the intensive phase of the program. Sixty three percent of the full population completed at least nine lessons.Among the digital DPP participants, the average weight loss at the end of the intensive phase of the program was 4.3% (SD=5.2%); with 35% of participants achieving at least a 5% weight loss. Weight loss persisted at 4.3% at Week 26, the mid-point of the active treatment year and 2 months into the maintenance phase of the program. This weight loss is similar to that seen in other nationally recognized DPP programs.P < .05). The savings were primarily from less expenditures for inpatient care (−US$699), with outpatient care (−US$308), and pharmacy (−US$46) contributing to the cost reductions. Pharmacy costs in Year 1 were not significantly different. Average diabetes prevention program cost per participant was US$571, resulting in average net health care expenditure savings of US$598 per participant in Year 1. A trend in reduced expenditures was also seen in Year 2 (−US$630), though not to the same magnitude as the first year and was not statistically significant (P = .21). There was a trend of a higher percentage of adults from the matched comparison group with a medical or pharmacy claim in Year 1 relative to the digital DPP group (P = .07), with more subjects with a medical claim as the main differentiator (P = .04).As seen in P = .03) and Year 2 (23% more) compared to the digital DPP participants . In Year 2, the comparison group had greater expenditures for pharmacy prescriptions coded to T2D, HTN, CAD, or CHF compared to the DPP group (P = .05). The frequency of ER or inpatient admissions coded to these conditions was extremely small for both years.Consistent with the patterns in expenditures, there was a significantly reduced amount of health care utilization in inpatient care for the digital DPP group in Year 1. Both the average total number of inpatient admissions per person and the length of stay per person were significantly reduced among the digital DPP participants relative to their matched controls. The digital DPP group also had a smaller total percent of the population with medical and pharmacy claims per year compared to the matched group. The comparison group had more outpatient visits coded to a principal diagnosis of T2D, HTN, CAD, or CHF across both Year 1 .While the progression to T2D diagnosis was small across the study period (less than 3% per year combined across groups), there was a reduced rate of progression in the digital DPP group (−22%) compared to the matched control in Year 1, but the differences were not statistically significant . More frequent inpatient hospitalizations and longer inpatient stays resulted in greater total spending among the untreated group. Though the magnitude was small, the untreated group also had a greater number of subjects with any kind of medical claim in the first year, more outpatient expenditures/utilization related to T2D or cardiovascular disease relative to the DPP participants in both years and more pharmacy care expenditures/utilization related to T2D or cardiovascular disease relative to the DPP participants in the second year.While significant savings were evident within the first year, the Year 2 savings did not reach statistical significance, though there was a trend toward the intervention group having fewer expenditures in Year 2. The lack of significance in Year 2 could indicate that the greatest benefit is obtained during the most intensive treatment year, whereas in maintenance years, the magnitude or volume of economic impact is smaller. Longer term follow-up and examination of accumulated economic benefit over several years would help better capture the full impact of the program and durability of savings.Two years (1 treatment year and 1 year of follow-up) appeared a bit premature to see substantive progression to diabetes in a population of this size. While we did see 22% fewer cases among the DPP group, the combined incidence across the study groups was small during the study timeframe. Most research in the DPP space examines progression to diabetes beyond a 2-year time frame,We could not identify economic analyses of diabetes prevention programs that focus on digital delivery, worksite populations, or derive data from actual health insurance claims; therefore, there are limited data to compare these findings relative to the field.The study was an observational study, and thus the participants self-selected into participating in the program. This potential selection bias may have contributed to the positive health gains seen in the treated population. However, this was a real-world case study of a preventive health service implemented under standard delivery conditions. Qualified, eligible members are typically targeted to participate without restriction in services that may benefit them. Additionally, the rigorous matching process accounted for a multitude of personal, socioeconomic, and environmental variables, and the high precision in matching is reassurance that the study cohorts had minimal differences at the outset.Another limitation was that we observed participants for only 24 months. This is a short and ambitious time frame to find significant variation in health status and progression to a chronic condition among a population that was generally healthy and only at risk for chronic conditions and may account for the small rate of incidence in chronic disease. Additionally, our methodology of classifying utilization based on principle diagnostic code associated with the visit may have resulted in an under-estimate of incident cases of chronic disease; diabetes may have been detected in visits, but if it was not the initiating factor for the visit, it would have gone undetected in our analysis. Longer term follow-up of study participants and inclusion of subsequent cohorts would be useful for tracking the rate of diabetes progression. Longer follow-up would also be instructive for evaluating the impact of diabetes prevention on longer-term cost outcomes, as economic simulation models suggest that greater savings may be expected up to 10 years out from a treatment phase.29Another limitation worth noting is that our analysis plan called for estimating adjusted outcomes using generalized linear models. Specifically, cost outcomes were estimated with a gamma distribution and a log link. The impact estimate generated from that model  is not linear or additive in nature. Instead, the estimate is interpreted on a multiplicative scale and statistical testing was applied to that scale. To further test the statistical significance of the cost results, researchers may wish to apply analyses that test the marginal effects of the intervention, such as differences in adjusted costs between treatment and comparison groups, using bootstrapping methods.Economic analyses are often explored after clinically meaningful findings have been seen; as a result, these investigations are not intentionally planned with sufficient statistical power.Our results demonstrate that net positive economic benefits can been seen from digital DPP within a 1-year time horizon. With broadened coverage and uptake by employers and health plans, the population health benefit of digital DPPs will continue to grow. Given the scalability and reduction in access barriers, including digital methods to expand reach and access of preventive services makes practical sense, and the results of this study suggest it will also make economic sense.


An outbreak of coronavirus disease (COVID-19) occurred on the Diamond Princess cruise ship making an international journey, which led to quarantine of the ship at Yokohama Port, Japan. A suspected COVID-19 case was defined as a passenger or crew member who developed a fever or respiratory symptoms, and a confirmed COVID-19 case had laboratory-confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Between 3 and 9 February 2020, 490 individuals were tested for SARS-CoV-2 and 172 were positive : 64–75; males: 45%) and 20 crew . Other than the Hong Kong-related index case, symptom onset for the earliest confirmed case was 22 January, 2 days after the cruise ship left port. Attack rates among passengers were similar across the decks, while beverage  and food service staff  were most affected. Attack rates tended to increase with age. A comprehensive outbreak response was implemented, including surveillance, provision of essential medical care, food and medicine delivery, isolation, infection prevention and control, sampling and disembarkation. On 7 January 2020, Chinese health officials announced detection of a case with novel coronavirus disease COVID-19) in Wuhan, China 9 in Wuha. Now, alThe reason for the high speed of COVID-19 expansion is still under debate, but transmission from asymptomatic cases or pre-symptomatic is suggested . For othHere, we report our preliminary findings from the investigation of a COVID-19 outbreak on an international cruise ship, the Diamond Princess, at Yokohama Port, Japan up to 9 February 2020. The findings from this descriptive study may facilitate increased understanding of COVID-19 and could contribute to public health responses on how to address cruise-related COVID-19 outbreak challenges.A case of COVID-19 related to the internationally-operated Diamond Princess cruise ship was reported to the Ministry of Health, Labour and Welfare (MHLW), Japan on 1 February by health officials in Hong Kong . Japan wThe cruise ship had 2,645 passengers and 1,068 crew members onboard from more than 50 countries. There were eight decks for passenger cabins and eight decks for crew cabins. The ship contained a seven-bed medical clinic staffed by doctors and nurses. After the cruise ship left Yokohama Port, Japan on 20* January, it visited Hong Kong on 25 January, Vietnam on 27 January, Taiwan on 31 January and Okinawa, Japan on 1 February, returning to Yokohama Port on 3 February.We defined a confirmed case as a passenger or crew member with positive RT-PCR test result for SARS-CoV-2 (Box). We defined a suspected case as a passenger or a crew member who had a fever ≥ 37.5 °C or respiratory symptoms during the investigation period or identified during the review of the ship’s medical clinic records. An asymptomatic case was defined as a person without symptoms at the time of investigation. We defined a close contact as someone who joined the Kagoshima tour with the index case, who shared a cabin with a confirmed case or who shared the same cabin with a suspected case.BoxConfirmed case: Passengers or crew who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by RT-PCRSuspected case: Passengers or crew who had a fever  ≥ 37.5 °C or respiratory symptomsClose contact:- Passengers or crew who joined a tour of Kagoshima, Japan- Passengers or crew who shared the same cabin with a confirmed casea- Passengers or crew who shared the same cabin with a suspected casea The third definition was only used until 6 February 2020, when the operation was strained by increasing number of suspected and confirmed cases.Starting on 3 February, quarantine officers took oropharyngeal samples from suspected cases and close contacts. Samples were tested for SARS-CoV-2 using a RT-PCR test according to the national standard . TestingWe collected all suspected and confirmed cases’ general information, clinical symptoms, RT-PCR test results and contact information from the multiple sources described in We described the epidemiology of the suspected cases and confirmed cases. We calculated the test positivity rate among suspected cases according to the date of the sample. The attack rate was calculated based on the passenger and crew data provided by the cruise ship. We used Excel to conduct all the analysis.This report was exempt from the requirement for institutional ethics review since it was a public health investigation approved by the Japanese Infectious Disease Law and Quarantine Law.A total of 490 samples, representing 490 individuals, were tested for SARS-COV-2 between 3 and 9 February. Of these, 358 (73%) were suspected cases, 86 (18%) were close contacts, two were individuals tested before emergency disembarkation for medical reasons, three were family members of these individuals and the remainder  were individuals without information about symptoms and contacts. Of the total 490 samples tested, 172 (35%) were positive. Among the 172 confirmed cases, 144 (84%) met the suspected case definition before testing while 19 (11%) shared a cabin with a confirmed case . Among tThe earliest date of symptom onset among the confirmed cases onboard was 22 January . The numTest positivity by testing date among suspected cases was 24% (36/148) from 4 to 6 February and increased to 69% (52/75) on 8 February for passengers whereas it was one of eight on 4 to 6 February and nine of 11 on 9 February for crew members .The attack rate was highest among passengers 20 to 29 years of age, but this is based on small numbers. The attack rates by age group among passengers were lowest in the 40 to 49 years group and increased with age thereafter . Attack After notification of the case from Hong Kong, the MHLW began a quarantine on the cruise ship at Yokohama Port on 3 February. The Emergency Operating Centre (EOC) was set up on 5 February, and on this date, all passengers were also isolated in their cabins and internal air re-circulation was stopped to reduce the possible risk of airborne transmission. Crew members continued to work, following guidance from the ship’s standard health precautions, and were isolated when individual crew members were considered to be infectious after assessment by the cruise ship’s medical staff.On 7 February, a temporary on-site fever clinic was launched to address the increasing number of febrile passengers and crew members by Japanese medical staff, while the ship’s medical clinic focused on other patients, those with non-COVID-19-related symptoms, on the cruise ship. Respiratory samples were taken daily from suspected cases and were shipped to the quarantine laboratories for testing once or twice a day. The laboratory results came the following day and all confirmed cases were disembarked and transferred to designated hospitals within a day.MHLW provided medicine, surgical/N95 masks, gloves, alcohol-based hand rub, and basic knowledge of coronavirus infection and infection prevention to passengers (e.g. hand hygiene and cough etiquette). It also provided training on infection prevention and control to the crew. Quarantine personnel wore surgical masks, except during meals at the EOC on the cruise ship, with added eye protection, long-sleeve gowns and gloves when taking samples. MHLW decided to disembark all the asymptomatic passengers and crew members, and those with symptoms who tested negative after 14 days of quarantine. Because of the mass testing workload, MHLW prioritised testing populations, with individuals over 80 years of age prioritised for sampling before disembarkation.The outbreak response operation was complex and we worked closely with the captain, managers and crew of the cruise ship to make decisions for the next steps in response efforts. The primary challenge identified was food preparation and delivery to quarantined passengers. Because passengers needed to be isolated, we requested the crew to continue to work, so meals were prepared and delivered by crew members under the guidance of infection prevention and control staff. Another challenge was the language barrier which made it difficult to conduct effective isolation measures because of the delay in providing instruction; however, collaboration with crew members worked efficiently.This report details the early phase of the outbreak investigation on a cruise ship quarantined in Yokohama Port that followed the confirmation of a disembarked passenger having COVID-19. This event required a large-scale quarantine that we had not experienced before, with a large number of international passengers and crew members further adding to the public health challenge. We believe that the findings from our experience are useful to respond to a similar COVID-19 event in an international cruise ship such as that quarantined at California in March 2020  or NagasThe earliest date of symptom onset for a confirmed case was 22 January, which was only 2 days after the start of the voyage and which suggests that the case could have already been infected before the voyage. The outbreak of COVID-19 had already been reported in Wuhan by mid-January, and so it was also possible that more than one COVID-19 case might have been on board the cruise ship. It is also reasonable to infer that the index case became infectious while onboard the cruise ship. The epidemic curve of symptomatic cases showed that passengers and crew members with fever and/or respiratory illness increased immediately after the start of the cruise. There were several well-attended cocktail and wine parties during the cruise where large numbers of passengers gathered at one place for several hours. Even though passengers had no symptoms when they attended the party, possible transmission of SARS-CoV-2 from asymptomatic or pre-symptomatic cases has been reported ,17. AtteThe results suggest that the transmission dynamics among crew was different from those among passengers. Among passengers, the highest attack rate was observed in the 20 to 29 years of age group, and this might be because of frequent contacts among young people, which could be a driving force of the disease’s spread. The only interaction between crew and passengers was during crew work shifts, as crew members spent most of their time in a relatively fixed place in their crew quarters. The attack rate among food service workers was higher than among other occupations among the crew , which sPassengers and crew aged over 50 years were more likely to develop symptoms, especially individuals over 80 years of age. Because older people were at higher risk of developing severe infections , they weWe detected 24 asymptomatic cases (14%) among confirmed cases, mainly among passengers who joined a bus tour in Kagoshima. It is important to note that being asymptomatic at one point of time does not exclude the possibility of developing symptoms later on ,20. The The increasing test positivity rate among the crew was a worrying sign even though most were based on small numbers. Test positivity is used as a surrogate of incidence , and theThe strength of this study includes the availability of a classic cohort and information from all passengers and crew onboard the ship. Two main limitations of this report are sampling and reporting biases. Under the emergency response for a novel infectious disease, it was difficult to test the entire target population because of limited testing capacity. As a result, we changed our strategy to test cabin mates of positive cases only if they met the criteria of suspected cases. Also, crew had to continue their daily routine along with tasks related to the quarantine, and fever may not have been reported comprehensively. Finally, we tried to identify the incubation period by evaluating cabin mates of the confirmed cases. However, it was impossible to distinguish the source of the infection as being the cabin mate or an exposure before the isolation.We summarised the epidemiology of an early phase of a COVID-19 outbreak on a cruise ship. The outbreak started shortly after the start of the journey of the cruise ship and among passengers, it seemed to be controlled by the isolation in their cabin. The decision to continue to have crew working was a political one aiming to ensure the provision of ongoing passenger services and ship operations during quarantine at a time when there was a concurrent need to limit further transmission on board the ship. A quarantine operation on a large cruise ship requires a comprehensive outbreak response including a high-level of collaboration with crew members.


Biomarker analysis allows for the detection and prediction of disease as well as health monitoring. The use of interstitial fluid (ISF) as a matrix for biomarkers has recently gained interest. This study aimed to compare levels of inflammatory markers in ISF from suction blister fluid (SBF) and plasma.Plasma and SBF were collected from 18 healthy individuals. Samples were analyzed for 92 inflammation-related protein biomarkers by Proximity Extension Assay (PEA). Protein profiles in the two matrices were compared using traditional and multivariate statistics.Out of 92 targeted proteins, 70 were successfully quantified in both plasma and SBF. Overall, plasma and SBF displayed distinct protein profiles with up to 40-fold difference in abundance of specific proteins. The levels of 25 proteins were significantly correlated between plasma and SBF and several of these were recognized as potential markers to monitor health using ISF.Skin ISF and plasma have unique protein profiles but many inflammatory markers are proportionally related between the matrices at the individual level. ISF is a promising biofluid for the monitoring of biomarkers in clinical studies and routine analyses. Blood is the gold standard matrix for the analysis of most biomarkers in humans, i.e., for the monitoring of molecules that relate to health and for the detection or prediction of disease. Recently, interstitial fluid (ISF) has been proposed as an alternative or complementing matrix to blood for these purposes . ISF surTwo areas for the use of biomarkers in ISF have been proposed: I) as an alternative to blood to monitor systemic responses ; and II)Proteomics studies have identified proteins that are unique to ISF, unique to plasma, or found in both compartments –7. SimilInflammation is characterized by the release of a large repertoire of molecules at the inflamed tissue and also into systemic circulation. Blood interleukins and cytokines are used as diagnostic biomarkers for inflammation caused by allergic reactions , autoimmHere, we analyzed the levels of 92 inflammatory markers in ISF from suction blisters and in plasma using semi-quantitative proteomics by Proximity Extension Assay (PEA). In comparison to mass spectrometry, which has been the primary method for analyzing the proteomes of ISF and plasma, PEA offers superior analytical sensitivity that allows the quantification of low abundant proteins, thereby capturing information on mediators with important signaling roles. Our results show that ISF and plasma have unique protein profiles but that many inflammatory markers are proportionally related between the matrices on an individual subject level.2EDTA tubes . The tubes were kept at room temperature for 15 min and then centrifuged at 4°C and 2000 × g for 15 min. The plasma was aliquoted into cryovials and store at -80°C until analysis.EDTA plasma and suction blister fluid (SBF) samples were obtained from 18 healthy human volunteers. The cohort included 13 females and 5 males with a mean age of 50.3 years (min/max 29.0/64.3 years). The same set of samples has previously been used to analyze lipidomic profiles in SBF and plasma . All samMicroneedle devices that cause minimal tissue damage and pain have been developed and are promising tools for harvesting ISF . These dPEA protein quantification was performed by Olink Proteomics  . The PEAThe lower limit of quantification (LOD) for the method was set to a value of 3SD above the mean of the background estimated from negative controls included in the assay. Values below LOD have a high risk to not be in the linear part of the calibration curve and should therefore be interpreted with caution. Proteins where less than 75% of the samples were above LOD were excluded from the analysis. The average intra-assay coefficient of variation (CV) for all analyzed proteins was 4%. 70% of the quantified proteins had intra-assay CV of <5%, 15 proteins of 5–10% CV, 2 proteins of 10–15% CV, and 5 proteins CV of >15%. Protein abundance is reported in normalized protein eXpression (NPX), an arbitrary unit in Log2 scale. A high NPX value indicates a high protein concentration although NPX values between different proteins are not comparable.Multivariate data analyses were performed using SIMCA 15  and univariate statistical analyses were performed using IBM SPSS Statistics, version 26 .t-test followed by the Benjamini-Hochberg procedure to control for the false discovery rate. Linear correlations in protein abundances between plasma and SBF were measured using Pearson’s correlation coefficient.Protein enrichment was tested by comparing protein NPX values between plasma and SBF using Student’s 2X, the cumulative fraction of X variation in the model (goodness of fit in X); R2Y, the cumulative fraction of Y variation in the model (goodness of fit in Y); Q2, an estimate of the predictive ability of the model calculated by internal seven-fold cross-validation of the data.For multivariate modeling, data were unit variance scaled. Model performances are described using RPrincipal component analysis (PCA) is an unsupervised multivariate modeling method that can be used to extract overall trends and identify outliers in a dataset. In PCA, the dimensionality of the data is reduced so that it can be visualized in a few principal components where the most valuable information of the variables is retained. This allows for the identification of clustering of samples, for example, based on matrix type as in this study, and to find underlying variables that drive this separation.2 indicates good predictive ability), permutation test, and ANOVA of the cross-validated residuals (CV-ANOVA)  is a supervised modeling approach  aimed toV-ANOVA) . In the Of the 92 targeted proteins, 75 (82%) and 76 (83%) in plasma and SBF, respectively, could be quantified above LOD Table S1Next, fold change in protein levels between plasma and SBF was investigated. For this and further analyses, only the 70 proteins that were detectable above the LOD in both plasma and SBF were considered. Thus, proteins unique to plasma or SBF were not included in the following analyses. The difference in protein levels between the two matrices is visualized in the volcano-plot in Fig 1B. Highlighted are proteins with an NPX difference of >2 between plasma and SBF. Since NPX is in the Log2 scale, this represents proteins that show more than a fourfold abundance difference between the matrices. Of the proteins in plasma, 24 were significantly enriched, and in SBF 38 proteins were significantly enriched Table S32X=0.69, and Q2 = 0.62). The score plot and loading plot can be compared to find relationships between observations and variables. Plasma and SBF samples were separated in the first component and in most, but not all cases the second component separated the study subjects.Protein profiles were further investigated using principal component analysis (PCA). The PCA score plot shows correlations between observations . The ability of the OPLS-DA model to discriminate plasma from SBF samples was validated by a permutation test in which the sample class was randomly permuted 999 times , samples were separated based on group classification (plasma or SBF). In this supervised model, the between-group variation is captured in one component that is orthogonal to other variations (within-group). 99 times Table S3To further explore the relationship between inflammatory markers in plasma and SBF, the correlation in protein abundance between plasma and SBF was investigated. Out of the 70 proteins, 25 showed significant correlations between plasma and SBF at the p<0.05 level Table S1Circulating biomarkers are routinely used in clinical and laboratory settings to study inflammatory responses. Here, we studied the relationship between inflammation-related markers in plasma and SBF in a cohort of healthy subjects. Our main finding is that many of the analyzed inflammatory markers show correlated abundance in plasma and SBF.Of the 92 proteins targeted in the PEA analysis, six were quantifiable only in SBF: C-C motif chemokine ligand 7/monocyte chemotactic protein 3 (MCP-3), interleukin 20 receptor subunit alpha (IL20RA), interleukin 22 receptor subunit alpha 1 (IL22RA1), interleukin 6 family cytokine/Leukemia inhibitory factor (LIF), IL33 (interleukin 33), and interleukin 1 alpha (IL-1α). IL33 has been shown to accumulate in inflamed skin in a mouse model of poison ivy-induced allergic dermatitis and contribute to itching . IL33 anThe low proportion of proteins only detected in SBF in this study is consistent with results from Tran et al. who reported that less than 1% out of over 3000 detected proteins were unique to ISF compared with serum and plasma . On the We found that a large proportion of the quantified proteins were more abundant in SBF than in plasma. This is in contrast to the overall protein concentration which is about five times higher in plasma , 5, but Leishmania infection , PEA offers superior accuracy and sensitivity in the quantification of low abundant proteins.In conclusion, many proteins relating to inflammation show correlated levels in plasma and SBF in healthy subjects and are potential targets for monitoring systemic conditions. Other inflammatory mediators show matrix dependent abundance profiles and may represent targets in the monitoring of local tissue-specific responses.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Regionala etikprövningsnämnden in Göteborg (no 118-16). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AN and AH developed the concept and design of the study. AN, US, and KC collected and prepared samples. AN and US conducted data acquisition and analysis. AN, US, KC, and AH interpreted the data and performed statistical analyses. US and AN wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by VINNOVA Swelife and Medtech4Health (2018-00245).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.


The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection. The severity of the disease varies from asymptomatic to lethal with a case mortality rate in the 20 most affected countries ranging between 1 and 15.3% . Severe disease shows manifestations of both acute respiratory distress syndrome (ARDS) and cytokine release syndrome (CRS)3. In pediatric patients, a blood vessel inflammatory pathology similar to Kawasaki disease is sometimes present4. All of these presentations have common elements of abnormality of inflammatory responses and manifestations of vascular defects such as thrombosis, which may be causally related9.COVID-19 emerged as a global pandemic through the first half of 202010. Some of the identified comorbidities that lead to severe disease are Cardiovascular Disease, Diabetes, Hepatitis, Lung Disease, and Kidney Disease17.Since the emergence and global transmission of the SARS-CoV-2 virus, many studies have reported that patients with certain underlying medical conditions have especially severe responses to the coronavirus infection18. We hypothesize that by exploring the underlying genetic basis of comorbidities associated with severe disease, we can identify putative host genes and pathways that are responsible for or contribute to the severity. Identification of these genes and pathways can serve as a gateway for further investigation into understanding how the host responds to the virus and for potential therapeutic strategies to interfere with a severe outcome.Understanding what makes some patients suffer from severe COVID-19 is an ongoing puzzle that is being investigated from both the virus and host perspectives23. In particular, the laboratory mouse has been extensively utilized as an animal model to study these conditions24. As a result, mouse strains carrying mutations in shared genes or genes in shared pathways, and engineered to be capable of being infected by the virus, can present useful starting points for investigating the biological basis of disease severity25.We interrogated gene sets that are associated with the five previously mentioned underlying comorbidities to determine gene products that are shared among them. We identified several pathways and phenotypes in common, including those that are associated with severe COVID-19 pathology. All of the comorbid diseases have been and continue to be actively studied, now in the additional context of response to SARS-CoV-2 infectionWe report here on investigations of the host genetics and genomics of a set of comorbidity conditions. We include data identifying the shared pathways and cellular mechanisms associated with these diseases and correlate these data with recent studies of the genetic basis of COVID-19 to identify elements that are shared among comorbidities and the host response to the disease. Our results suggest specific directions of future study to understand the genetic foundation of severe COVID-19.www.geneweaver.org)26. Genes associated with Cardiovascular Disease, Diabetes, Hepatitis, Lung Disease were derived from gene sets associated with MeSH terms that relate to these comorbidities. The gene set for Kidney Disease was derived from the union of genes associated with Proteinuria Hematuria, Elevated Serum Creatinine, Increased Blood Urea Nitrogen and Decreased Glomerular Filtration Rate in the Human Phenotype Ontology (HPO)27. The genes in the MeSH and HPO gene sets and associated metadata (indicating their association with COVID-19 and citations supporting the association) were incorporated into GW and used for analysis. The comorbidity-related gene sets are shown in Table All gene sets used in our analysis are publicly available from the GeneWeaver resource (29 and captured gene sets available from these studies into the GeneWeaver environment. Details are shown in Table To create gene sets that are directly related to SARS-CoV-2 infection (COVID-19), we identified several relevant reportsTo identify genes that were shared by all five comorbidities or four out of five comorbidities, we used the GeneWeaver ‘Combine GeneSets’ tool. To visualize the intersection of comorbidity gene sets graphically, we used the GeneWeaver ‘HiSim graph’ tool. To create a hierarchical view of interleukin pathways and the genes that are shared among comorbidities we used the HiSim graph tool at the GeneWeaver resource with homology excluded.30 and (2) we conducted a pathway enrichment analysis using the Reactome Knowledgebase resource31.To evaluate the shared biology of the common genes, we interrogated the genes that were shared among comorbidities in two ways: (1) we conducted a phenotype enrichment analysis using the VLAD enrichment toolwww.informatics.jax.org). The Mouse Genome Database (MGD) group captures phenotypic data using the Mammalian Phenotype Ontology (MP), a computable ontological structure, that can be queried and used for phenotypic enrichment analysis32. MGD also integrates these murine data into the context of human disease data based on orthology and gene expression32. This integrated resource allowed us to exploit the associations of mouse genes and their phenotypes for enrichment purposes using VLAD, and gives us an entry into identification of potential mouse models for future study33.To investigate details of the phenotypes associated with the shared genes, we took advantage of the mammalian phenotype data available from the Mouse Genome Informatics site 34 using the Alliance release 3.1 stringent mouse-human orthology set. If a human gene symbol matched more than one mouse marker, that gene was not included in the analysis. This resulted in the following fourteen human genes being excluded from the analysis: AGTR1, CCL2, CFH, CYP2D6, CYP3A4, GSTM1, GSTP1, HAMP, HLA-B, HLA-DRB1, IFNA1, MMP1, SERPINA1, and TIMP2. We did not identify mouse orthologs for two human genes . Excluding these 16 from the initial 123 genes that were shared among four of five comorbidities, left us with 107 remaining mouse orthologs which we used in the VLAD analysis. All but one of the 107 mouse genes  had annotations to MP. The VLAD phenotype analysis was run on 20 August 2020 using annotation data from 20 August 2020, ontology data from 9 July 2020 and default parameters.Mouse orthologs for the shared human genes were identified using data available from the Alliance of Genome Resources (Alliance) (https://reactome.org/PathwayBrowser/#TOOL=AT) based on Reactome version 72. The analysis was performed on 18 May 2020. Results were downloaded using the ‘Pathway Analysis Results’ and ‘Analysis Report’ functionality at Reactome.For pathway enrichment, we submitted the gene lists to the Reactome ‘Analyze Gene List’ enrichment tool (2 fold change >  = 2) genes in host transcriptional response to SARS-CoV-2 in three cell cultures: human A549 lung alveolar cells (102 genes), Calu3 human lung adenocarcinoma epithelial cells (333 genes), normal human bronchial epithelium (NHBE) cells (14 genes); genes that are up-regulated in post-mortem lung samples from COVID-19-positive patients relative to biopsied healthy lung tissue from uninfected individuals (586 genes)28; immune-response and angiogenesis-related genes that are up-regulated in lungs from patients who died from COVID-19 (114 genes)5; genes that are overexpressed in severe compared to mild cases of COVID-19 (4 genes)29. We corrected for any symbols that were out of date and again used the Reactome Pathway analysis tool. The analysis was performed on 10 August 2020.Pathway enrichment analysis was also performed for six COVID-19-related gene sets shown in Table To test our hypothesis that comorbidities associated with COVID-19 severity have common underlying molecular bases, we chose five comorbidities that have been reported in the literature as closely associated with poorer disease outcome: Kidney Disease, Liver Disease, Diabetes, Lung Disease and Cardiovascular Disease. We searched the GeneWeaver Data repository for gene sets associated with these comorbidities and identified gene sets from MeSH and HPO that we used in our analyses . For genes shared among all five comorbidities, VLAD enrichment analysis identified 762 significantly enriched (p =  < 0.05) mammalian phenotypes   with 3232 terms included in the enrichment analysis . VLAD analysis showed that the major areas of the ontology with the most highly significant enrichment were, as in the analysis for the eight genes shared by all the comorbidities, in inflammatory response and infection, leukocyte biology and blood vessel morphology. Abnormal blood coagulation was no longer in the most highly significant group of phenotypes, but was significantly enriched (p = 1.52 × 10e-11).Similar to our results for the eight genes shared among all five comorbidities, the mouse orthologs of the 123 genes shared in four out of five comorbidities showed many genes associated with each of the most significant phenotypes Table .Table 5S35.In addition to our phenotype analysis, we were also interested in investigating whether the genes shared among comorbidities were enriched for specific biological pathways. To answer this question, we used our human gene sets and the Reactome Knowledgebase in an enrichment analysis for biological pathways. Reactome is a manually curated resource that captures information about reactions, their relationships and the genes and chemicals that play a role in those reactionsAPOA1 and APOE, are shared among several plasma lipoprotein assembly, remodeling and clearance pathways. Three genes, APOA1, F2 and F5, are found in the hemostasis pathway, all are included in platelet activation and the latter two in blood clotting. Five of the eight genes shared among the comorbidities were involved in immune system pathways: B2M, HMOX1, CTLA4, STAT3 and F2. Of these five genes, three are included in cytokine signaling: B2M, HMOX1 and STAT3. Other informative pathways showed that APOA1, APOE, F2 are in GPCR downstream signaling, and F5, APOA1, APOE are in vesicle-mediated transport.We interrogated the Reactome Knowledgebase using the eight genes that were shared among all five comorbidities and identified 103 pathways/subpathways that were significantly enriched . Reactome pathways are organized into a hierarchical format where grouping pathways are subcategorized into more specific pathways which in turn are eventually represented by individual reactions. Reactome captures information about not only the genes and molecules that act in a pathway but also those that are acted upon, thus casting a wide net for genes that are included in an analysis. The 25 most significantly enriched pathways grouped under several parent pathways are shown in Fig. We repeated the pathway enrichment analysis with the 123 genes that were shared by four out of five comorbidities. We identified 172 pathways that were significantly enriched . These results supported and confirmed the results we obtained with the eight genes that were shared among all five comorbidities. Although with lower significance, enriched pathways include ‘common pathway of fibrin clot formation’ , ‘platelet degranulation’  and ‘plasma lipoprotein assembly remodeling and clearance’ . Immune signaling pathways and particularly interleukin signaling pathways were frequent in our enrichment results . Phenotype analysis also revealed ‘abnormal blood coagulation’ and ‘decreased platelet aggregation’ (p = 5.81 × 10e−8 and p = 2.64 × 10e−2 respectively) in common with the pathway analysis. Our results from the analyses of the 123 genes conserved in four out of five comorbidities were also consistent. The pathway analysis revealed that the 25 most significant pathways were pathways related to the immune system, in particular there was concordance with the results from the eight genes, identifying pathways related to inflammatory responses, interferon and interleukin signaling. In total, the results of all of our analyses show that five comorbidities associated with severe COVID-19 share common physiological aspects including cytokine signaling, blood clotting, and plasma lipoprotein biochemistry.STAT3 which is found in nine of the eleven significantly enriched interleukin signaling pathways. IL12B is shared among four signaling pathways (data not shown).To further investigate whether there is a common molecular basis for the interleukin signaling pathway enrichment we examined the Reactome enrichment results using the 123 genes conserved in four out of five comorbidities for interleukin signaling pathways . We created gene sets of the shared genes that were in Reactome interleukin pathways that are significantly enriched (FDR < 0.05). We used the HiSim graph tool in the GeneWeaver resource to create a graphical view of the genes that are found in the sets. The gene conserved among the largest number of sets is 29. GS398329 represents 114 genes associated with angiogenesis or inflammation that were upregulated in COVID-19 postmortem samples5. Two gene sets, GS398539 and GS398534, of 119 and 333 genes respectively represent genes that are upregulated in two distinct lung adenocarcinoma epithelial cells infected with SARS-CoV-2; GS398533 with 14 genes represents genes that are upregulated in normal human bronchial epithelium cells infected with SARS-CoV-2; GS398334 represents 587 genes upregulated in post-mortem COVID-19 samples28. We ran Reactome pathway enrichment analysis on each of these sets and determined the enriched pathways that were shared with those identified in the comorbidity analyses . Unsurprisingly, GS398329 and GS398287, which were preselected for genes involved in the immune response, were enriched for immune response pathways. GS398329, preselected to be associated with angiogenesis, also showed significant enrichment for the comorbidity pathways associated with platelet biology. All of the data sets showed significant enrichment for signaling mediated by interleukin-4, -10 and -13  and autoimmune response42. Hmox1tm1Mlee/Hmox1tm1Mleemice are another example with cardiovascular, immune and liver system phenotypes .The results of our phenotype analysis using mouse orthologs of shared human genes shows that phenotypic enrichment is consistent with the pathway enrichment using the human genes and is also consistent with pathologies associated with severe COVID-19: blood coagulation, inflammation and cardiovascular pathologies43. As our understanding of the disease has progressed, a number of comorbidities associated with the disease have been identified that lead to greater severity. The goal of our work is to identify underlying genetic factors that might explain the mechanism of why certain comorbidities lead to more severe disease. To this end, we studied genetic features of five comorbidities that are associated with severe COVID-19: Cardiovascular Disease, Diabetes, Hepatitis, Lung Disease, and Kidney Disease. We identified common genes that were associated with each of the comorbidities and the pathways and phenotypes with which they are associated. We compared the results of the comorbidity analysis with genes that were directly associated with SARS-CoV-2 and showed that they shared common pathways involved in the immune response and platelet biology. Our results are encouraging in that these areas of physiology have also been correlated with severe disease. Here we discuss our results in the context of COVID-19 severity.COVID-19 is a global health concern. The disease is complex and varies in severity from asymptomatic to lethal44 and is a downstream signaling player in the IL-6 pathway through the IL6ST protein46. IL-12 is produced in response to infection and signals through the JAK-STAT pathway, including STAT3, to induce the proliferation of NK cells and T cells. These cells in turn trigger cytokine signaling including interferon gamma47.Our analysis of genes shared among both comorbidities and SARS-CoV-2 infection identified several interleukin signaling pathways that were enriched in both categories. Interleukin-4/-13 and interleukin-10 signaling shared enrichment among the largest number of gene sets we examined, and interleukin-6, interleukin-12 and interleukin-2 shared enrichment between at least one comorbidity set and a set of genes upregulated in patients who died from COVID-19. STAT3 positively regulates the transcription of IL-6, which controls inflammation48. An interesting aspect of this proposal is its action through STAT3, one of the genes that we also found conserved in the comorbidities we studied. Targeting the JAK-STAT pathway has been proposed as a therapeutic approach to COVID-192. Our results show that STAT3 is conserved in all five comorbidities we analyzed. This supports the hypothesis that a promising therapeutic strategy to combat severe COVID-19 compounded by preexisting comorbidities may be to target common JAK-STAT pathways.One mechanism proposed for the severity of COVID-19 is the coincidence of severe acute respiratory distress triggered by a cytokine related syndrome triggered by the angiotensin signaling pathway49. Abnormal clotting has been observed in severe COVID-19 patients and it has been suggested as a complication that leads to more severe disease51. Magro et al. reported that the abnormal clotting observed in severe COVID-19 patients correlated with activation of the complement pathway52. Our results show that HMOX1, APOA1, APOE and two members of the coagulation cascade, F2 and F5, are shared among all five comorbidities we examined. In mice, Hmox1 deficiency leads to coagulation defects and results in arterial damage due to oxidative stress53. APOA1 is released during platelet degranulation as part of the platelet secretory granule54 (Reactome:R-HSA-482770). APOA1 levels have also been shown to be significantly decreased in severe COVID-19 patients55. APOE is a gene that encodes a lipid binding protein involved in cholesterol metabolism56. Preliminary evidence suggests that the e4 allele of APOE may lead to a higher risk of deep vein thrombosis and the same allele also predicts severe COVID-1958. F2 and F5 are both involved in the formation of a fibrin clot (Reactome:R-HSA-140877). The complement pathway and coagulation cascade have been shown to interact, tying together inflammation and hemostasis59. Additionally in a proteomic study of proteins differentially expressed in the serum of 28 severe COVID-19 patients compared with non-severe COVID-19 patients, Shen et al. report that 50 of 93 differentially regulated proteins fall into three categories one of which is platelet degranulation55. These results suggest that one of the factors contributing to severe disease in patients with any of the five comorbidities may be due to an underlying genetic mechanism that acts through the hemostatic pathway.In addition to immune signaling pathways, we also saw shared enrichment for pathways involved in platelet biology. Platelets are the cells that are responsible for blood clottingCtla4tm1Shr/Ctla4tm1Shr and Hmox1tm1Mlee/Hmox1tm1Mlee homozygous mutations display multiple phenotypes consistent with severe COVID-19 pathology. Mice engineered for mutations in these genes crossed with mice engineered to carry the human ACE-2 SARS-CoV-2 viral receptor, ICR-Tg(Ace2-ACE2)1Cqin/J, would be a starting point to explore the underlying genetic variants related to comorbidities that interact with viral infection60. Humanized mice, like the Apoetm3(APOE*4)Mae strain, which carries the human E3 variant mentioned above as being implicated in both severe disease and an underlying thrombosis pathology, could be used in conjunction with ICR-Tg(Ace2-ACE2)1Cqin/J to study the effects of the human variant on viral infection. A comprehensive resource for using the mouse as a model system for COVID-19 research is maintained by the Mouse Genome Informatics Group [http://www.informatics.jax.org/mgihome/other/coronavirus.shtml].Our results show that genes that are shared among five comorbidities associated with severe COVID-19 identify pathways that are consistent with the pathologies associated with the disease. In our analysis we excluded mouse orthologs that did not correlate 1:1 with human genes to avoid potential skewing of the enrichment analysis by having multiple paralogs over-represented. Despite this, and the exclusion of several potentially important immune system genes such as some histocompatibility genes, our results show that analysis using mouse orthologs of the shared genes also identifies phenotypes that are consistent with disease pathology. The laboratory mouse provides a tractable system to study the effects of genetic foundations of the comorbidities and severe disease. As mentioned above mice carrying 62. Our studies provide a gateway to understand how host genetics interacts with and influences the consequences of viral infection. Note that this work will be limited by the sample types used in the original experimental analyses: Our analysis of samples isolated from specific tissues or cell types will be limited to pathways that are relevant to those tissues. For example, samples from blood may identify pathways active in blood cell types but miss pathways that are significant in cardiac or lung tissue. Our knowledge about COVID-19 continues to grow at a rapid rate and future work will entail the examination of additional comorbidities, more specific comorbidities, a wider survey of genes beyond our initial seed set from MeSH and HPO. As more expression data become available from a larger number of samples, we may be able to better discern which pathways are common to all tissues and which are affected only in some tissues but not others. As we learn more about correlations between individual comorbidities and disease pathologies, we may be able to identify specific pathway/comorbidity combinations that can be used to inform us about treatment decisions. Our work also provides an entry point into an experimental system using the laboratory mouse to manipulate host genetics and to study its subsequent effect on the pathology of viral infection.In this study we have used a bioinformatics approach to interrogate genes associated with five COVID-19 comorbidities that correlate with severe disease. Using genes that have been annotated to these comorbidities in the MeSH or HPO resource we have shown that genes are shared among the comorbidities and that shared genes are enriched for pathways that could be the genetic basis for the pathologies observed with severe COVID-19, specifically our results suggest that the interrelated pathways of hemostasis and inflammation may be key players in understanding the severity of comorbidities with COVID-19Supplementary Information 1.Supplementary Information 2.