While ambient air pollution has been associated with COVID-19 outcomes, the role of early-life exposure in childhood COVID-19 infection and sequelae remains unexplored.

To assess the associations between early-life exposure to ambient air pollutants during and childhood COVID-19 infection and sequelae.

This cross-sectional retrospective cohort study surveyed families with children aged 3-6 years in families across nine Chinese cities between December 2019 and May 2023. The primary outcomes were doctor-diagnosed childhood COVID-19 infection and sequelae. Individual exposure to PM2.5, PM2.5-10, PM10, SO2, NO2, CO, O3, and temperature were estimated.

Among 20,012 children from 60,036 participants, 5.81 % were diagnosed with COVID-19 infection, and 1.72 % had sequelae. Prenatal CO exposure was associated with higher infection risk (OR: 1.33; 95 % CI: 1.05-1.69 per IQR increase). SO2 exposure during the first trimester (OR: 3.02; 95 % CI: 1.20-7.61), second trimester (OR: 4.00; 95 % CI: 1.56-10.27) and third trimester (OR: 3.84; 95 % CI: 1.69-8.76) of pregnancy and the first year of life (OR: 8.43; 95 % CI: 1.80-39.48) was strongly associated with sequelae. Pre-existing allergies and coarser particulate matter (PM2.5-10 and PM10) amplified these associations. High relative humidity significantly increased the effect of exposure to NO2 during four-six months before pregnancy and the second trimester of pregnancy, as well as O3 exposure during the first year on childhood COVID-19 infection.

Early-life exposure to air pollutants and interactions with allergic conditions and coarser particles influence childhood COVID-19 risks.

Angiotensin-converting enzyme 2 (ACE2) was discovered 25 years ago as a negative regulator of the renin-angiotensin system, opposing the effects of angiotensin II. Beyond its well-demonstrated roles in cardiovascular regulation and COVID-19 pathology, ACE2 is involved in a plethora of physiopathological processes. In this review, we summarize the latest discoveries on the role of ACE2 in glucose homeostasis and regulation of metabolism. In the endocrine pancreas, ACE2 is expressed at low levels in β-cells, but loss of its expression inhibits glucose-stimulated insulin secretion and impairs glucose tolerance. Conversely, overexpression of ACE2 improved glycemia, suggesting that recombinant ACE2 might be a future therapy for diabetes. In the skeletal muscle of ACE2-deficient mice a progressive triglyceride accumulation was observed, whereas in diabetic kidney the initial increase in ACE2 is followed by a chronic reduction of expression in kidney tubules and impairment of glucose metabolism. At the intestinal level dysregulation of the enzyme alters the amino acid absorption and intestinal microbiome, whereas at the hepatic level ACE2 protects against diabetic fatty liver disease. Not least, ACE2 is upregulated in adipocytes in response to nutritional stimuli, and administration of recombinant ACE2 decreased body weight and increased thermogenesis. In addition to tissue-specific regulation of ACE2 function, the enzyme undergoes complex cellular posttranslational modifications that are changed during diabetes evolution, with at least proteolytic cleavage and ubiquitination leading to modifications in ACE2 activity. Detailed characterization of ACE2 in a cellular and tissue-specific manner holds promise for improving therapeutic outcomes in diabetes and metabolic disorders.

SARS-CoV-2 infections in children lead to symptoms from mild respiratory illness to severe postacute sequelae of COVID-19, including multisystem inflammatory syndrome in Children (MIS-C). We conducted a metabolic profiling of 147 children's serum samples, including acute COVID-19 patients, MIS-C patients, and healthy controls. Using nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry, we measured 1101 metabolites. The results revealed distinct metabolic profiles in acute COVID-19 and MIS-C patients, with significant alterations in lipid classes. Both conditions exhibited an elevated Apo-B100/Apo-A1 ratio and increased serum inflammatory markers. MIS-C patients showed unique disruptions, including increased triglycerides and altered lipoprotein composition. Despite milder clinical respiratory symptoms, children's metabolic disturbances mirrored those seen in severe adult COVID-19 patients, indicating a shared inflammatory response to SARS-CoV-2. This suggests potential long-term health impacts, underscoring the need for continued research into the metabolic consequences of COVID-19 in children.

To assess the impact of post-COVID clinics by examining the association between their early usage and downstream health care utilization.

In a case-control study spanning data from March 11, 2020 to June 1, 2023, patients with Long COVID were identified from a major health system using diagnosis codes. The Fast, Large-Scale Almost Matching Exactly algorithm was used to match patients who presented early to post-COVID clinics with patients with Long COVID who did not attend such clinics. Matching was performed on demographic characteristics, acute COVID severity, comorbidities, diagnosis date, and vaccination, to reduce confounders for the comparison of the health care utilization and mortality between cohorts.

When exactly matching on all 46 features, the algorithm yielded 2814 matched patients, of whom 692 (24.6%; 66.6% females; mean [SD] age, 48.8 [14.5] years) were seen in post-COVID clinics within the first 6 months and 2122 (75.4%; 64.1% females; mean [SD] age, 49.7 [15.2] years) who were not. The average treatment effect (95% CI) of early post-COVID clinic usage was -0.60 (-0.83 to -0.39) on inpatient visits, -0.19 (-0.26 to -0.11) on emergency department visits, 7.62 (6.96-8.56) on outpatient visits, -$3467 (-$6267 to -$754) on estimated costs, and -0.006 (-0.010 to -0.003) on mortality.

Early usage of post-COVID clinics by patients with Long COVID is associated with not only fewer downstream inpatient stays, emergency department visits, estimated costs, and reduced mortality within the first 6 months but also greater outpatient utilization. Results suggest early post-COVID clinic involvement shifts care to outpatient settings, potentially reducing costs and mortality.

Comparing the characteristics of patients with long COVID to those with other post-acute infection syndromes (PAIS) could potentially provide clues to common underlying disease processes that may affect patient recovery.

We identified records of patients who had documented SARS-CoV-2 tests in the University of Washington Medicine electronic health record (EHR) database from January 1, 2019, through January 31, 2022 (n = 139,472). Patients were classified into three groups: 1) long COVID defined by a positive SARS-CoV-2 test and a long COVID-related diagnosis code (n = 580); 2) recovered COVID defined by a positive test and no long COVID associated diagnosis codes (n = 7,437); and 3) non-COVID PAIS defined by a negative test, non-SARS-CoV-2 related PAIS diagnosis codes, and no COVID related codes (n = 106). Using multivariate logistic regression, we compared the clinical characteristics of these groups at three timeframes to address preclinical, acute and post-acute diagnoses: before index SARS-CoV-2 test, within 30 days of index test, and > 30 days after index test.

The long COVID group had a higher Charlson comorbidity index [median (IQR), 2 (0-4)] than the other two patient groups [median (IQR), 1 (0-3) and 1 (0-3)]. The long COVID and non-COVID PAIS patients were older and had greater smoking exposure than the recovered COVID group. Compared to the recovered COVID control group, the long COVID group had more health problems prior to the infection, including respiratory and metabolic as well as more severe infections and comorbidities based on the ICD codes found in the acute phase records. In the post-acute timeframe, many symptoms were more likely to be associated with long COVID than recovered patients with COVID-19 including abnormalities of heart beat [OR (95% CI), 5.31 (3.96-7.13)], cognition, perception, or emotional state symptoms [OR (95% CI), 5.14 (3.81-6.92)], malaise and fatigue [OR (95% CI), 4.20 (3.13-5.63)], and sleep disorders [OR (95% CI), 2.47, (1.79-3.43)], all p < 0.05. In contrast, the non-COVID PAIS group shared many similarities with the long COVID group across all three timeframes.

Patients diagnosed with long COVID were more similar to patients with a non-COVID-related PAIS than to recovered patients with COVID-19. This suggests risk factors for PAIS may be similar and independent of the infectious agent.

Prior research has observed increased risks for numerous chronic conditions among individuals with Long COVID. Chronic conditions have been associated with employment limitations and increased economic hardships. Data from the Behavioral Risk Factor Surveillance System (BRFSS) present an opportunity to examine changes by employment status in the prevalence of a range of chronic conditions between 2019 (pre-pandemic) and, in 2022, by self-reported COVID-19 or Long COVID.

We assessed the prevalence of chronic conditions in 2022 by employment status and self-reported COVID-19 and Long COVID history using data from BRFSS for adults of prime working age (25-54 years) who were employed for wages, self-employed, unemployed less than 1 year, unemployed 1 year or more, or unable to work. For each chronic condition (coronary heart disease and myocardial infarction [combined], stroke, ever and current asthma, chronic obstructive pulmonary disease, kidney disease, diabetes, and arthritis), we generated adjusted prevalence ratios (aPRs) comparing 2022 prevalence by COVID-19/Long COVID category to prevalences among respondents in that employment status before the pandemic (2019).

The prevalence of both asthma and diabetes increased significantly between 2019 and 2022 among respondents in all included employment categories and COVID-19/Long COVID histories combined. Among employed respondents with Long COVID in 2022, aPRs using 2019 prevalence figures for all employed respondents as a baseline for comparison had statistically significant elevations for every chronic condition assessed.

The increased prevalence of a range of chronic conditions between 2019 and 2022 among adults with Long COVID may present a burden for individuals, the workplace, the healthcare system, and the economy. Additional research in a longitudinal context could better quantify these associations. Efforts to prevent, identify, and treat Long COVID can reduce this burden.

Being widespread across the globe, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) keeps evolving and generating new variants and continuously poses threat to public health, especially to the population with chronic comorbidities. Diabetes mellitus is one of high-risk factors for severe outcome of coronavirus disease 2019 (COVID-19). Establishment of animal models that parallel the clinical and pathological features of COVID-19 complicated with diabetes is thus highly essential. Here, in this study, we constructed leptin receptor gene knockout hamsters with the phenotype of diabetes mellitus (db/db), and revealed that the diabetic hamsters were more susceptible to SARS-CoV-2 and its variants than wild-type hamsters. SARS-CoV-2 and its variants induced a stronger immune cytokine response in the lungs of diabetic hamsters than in wild-type hamsters. Comparative histopathology analyses also showed that infection of SARS-CoV-2 and the variants caused more severe lung tissue injury in diabetic hamsters, and may induce serious complications such as diabetic kidney disease and cardiac lesions. Our findings demonstrated that despite the decreased respiratory pathogenicity, the SARS-CoV-2 variants were still capable of impairing other organs such as kidney and heart in diabetic hamsters, suggesting that the risk of evolving SARS-CoV-2 variants to diabetic patients should never be neglected. This hamster model may help better understand the pathogenesis mechanism of severe COVID-19 in patients with diabetes. It will also aid in development and testing of effective therapeutics and prophylactic treatments against SARS-CoV-2 variants among these high-risk populations.

The 2019 coronavirus disease (COVID-19) pandemic has changed the characteristics of many diseases. It remains unclear whether idiopathic inflammatory myopathies (IIMs) exhibit distinct phenotypes in the context of COVID-19. This retrospective study analyzed patients with IIMs admitted to West China Hospital from January 2022 to December 2023. Among them, 171 had a history of COVID-19 (prior COVID-19 [PC]), while 121 did not (no prior COVID-19 [NPC]). Medical histories, lab tests, and echocardiography data were compared. The PC group exhibited a greater incidence of cardiac damage, including a greater proportion of cardiac injury (P = 0.016), clinical diagnosis of myocarditis (P = 0.02), palpitation (P = 0.031), and Myositis Activity Assessment Visual Analog Scale/Myositis Intention-to-Treat Activity Index cardiovascular involvement scores (all P < 0.001), and elevated levels of myoglobin (P = 0.03), creatinine kinase MB (P = 0.015), cardiac troponin T (P = 0.011), N-terminal pro-B-type natriuretic peptide (P = 0.028), lactate dehydrogenase (P = 0.033), and hydroxybutyrate dehydrogenase (P = 0.019). Echocardiographic analysis revealed a greater diameter of the left atrium (P = 0.040), left ventricle (P = 0.013), greater end-diastolic dimension (P = 0.042), and greater end-diastolic volume (P = 0.036) in the PC group than in the NPC group. Transcriptional data analysis based on public databases indicated that various mechanisms, including collagen matrix proliferation, calcium ion pathway regulation, oxidative stress, cell proliferation, and inflammatory molecules, collectively contribute to the pathogenesis of myocardial damage in patients with IIMs and COVID-19. The study serves as a crucial reminder for clinicians to remain vigilant regarding the enduring cardiovascular consequences associated with IIMs after COVID-19.

This study systematically analyzed the clinical features, laboratory test results, and echocardiographic findings of patients with IIMs, comparing those with and without a history of COVID-19 infection. The analysis revealed significant alterations in the clinical manifestations of IIM patients after COVID-19, particularly in relation to cardiac involvement. Our findings highlight the crucial importance for clinicians to maintain vigilance concerning the potential long-term cardiovascular sequelae associated with IIMs in post-COVID-19 patients.

AbstractCOVID-19 may primarily cause respiratory symptoms but can lead to long-term effects known as long COVID. COVID-19-induced diabetes mellitus was reported in many patients which shares characteristics of types 1 and 2 (T1DM and T2DM). This study aims to identify and analyze the reported cases of new onset diabetes post-COVID-19 infection. Several databases were used to conduct a comprehensive literature search to target studies reporting cases of T1DM or T2DM post-COVID-19 infection. Screening, data extraction, and cross checking were performed by two independent reviewers. Only 43 studies met our inclusion criteria. Our results revealed that the overall prevalence of new onset diabetes post-COVID-19 was 1.37% with higher prevalence for T2DM (0.84%) as compared to T1DM (0.017%) while the type of diabetes was not reported in 0.51% of the cases. Several risk factors for developing diabetes post-COVID-19 infection were identified including the type of SARS-CoV-2 variant, age, comorbidities and the vaccination status. The direct viral attack of the pancreatic beta cells as well as inflammation and the anti-inflammatory corticosteroids were proposed as possible mechanisms of the COVID-19 induced diabetes. A multidisciplinary approach involving endocrinologists, primary care physicians, and infectious disease specialists should be implemented in the management of post-COVID patients to address both the acute and long-term complications, including metabolic changes and risk of diabetes.

Studies of new-onset diabetes as a post-acute sequela of SARS-CoV-2 infection are difficult to generalize to all socio-demographic subgroups.

To study the risk of new-onset diabetes after SARS-CoV-2 infection in a socio-demographically diverse sample.

Retrospective cohort study of electronic health record (EHR) data available from the OneFlorida + clinical research network within the National Patient-Centered Clinical Research Network (PCORnet).

Persons aged 18 or older were included as part of an Exposed cohort (positive SARS-CoV-2 test or COVID-19 diagnosis between 1 March 2020 and 29 January 2022; n = 43,906), a contemporary unexposed cohort (negative SARS-CoV-2 test; n = 162,683), or an age-sex matched historical control cohort (index visits between 2 Mar 2018 and 30 Jan 2020; n = 40,957).

The primary outcome was new-onset type 2 diabetes ≥ 30 days after index visit. Hazard ratios and cases per 1000 person-years of new-onset diabetes were studied using target trial approaches for observational data. Associations were reported by sex, race/ethnicity, age, and hospitalization status subgroups.

The sample was 62% female, 21.4% non-Hispanic Black, and 21.4% Hispanic; mean age was 51.8 (SD, 18.9) years. Relative to historical controls (cases, 28.2 [26.0-30.5]), the unexposed (HR, 1.28 [95% CI, 1.18-1.39]; excess cases, [5.1-10.3]), and exposed cohorts (HR, 1.64 [95% CI, 1.50-1.80]; excess cases, 17.3 [13.7-20.8]) had higher risk of new-onset T2DM. Relative to the unexposed cohort, the exposed cohort had a higher risk (HR, 1.28 [1.19-1.37]); excess cases, 9.5 [6.4-12.7]). Findings were similar across subgroups.

The pandemic period was associated with increased T2DM cases across all socio-demographic subgroups; the greatest risk was observed among individuals exposed to SARS-CoV-2.

Posttransplant diabetes mellitus (PTDM) is an important complication for solid organ transplant recipients (SOTRs). COVID-19 has been associated with an increased risk of incident diabetes in the general population. However, the association between COVID-19 and new-onset PTDM has not been explored.

Using the National COVID Cohort Collaborative Enclave, we conducted a cohort study of adults without diabetes receiving a solid organ transplant (heart, lung, kidney, or liver) in the United States between April 1, 2020, and March 31, 2023, with and without a first diagnosis of COVID-19 (COVID + versus COVID - ) within 180 d of SOT. We propensity score matched a single COVID + SOTR with a COVID - SOTR who was diabetes free at the same point posttransplant. Within this matched cohort, we used multivariable Cox proportional hazards models to examine the adjusted risk of PTDM associated with COVID + .

Among 1342 COVID + SOTRs matched to 1342 COVID - SOTRs, the crude rate of newly diagnosed PTDM in the 2 y post-COVID was 17% in those with versus 13% in those without COVID-19 ( P = 0.007). COVID-19 was significantly associated with new PTDM (adjusted hazard ratio, 1.37; 95% confidence interval, 1.12-1.68 at 2 y).

Similar to other viral infections, COVID-19 is associated with an increased risk of PTDM in SOTRs.

Long COVID is a multisystemic, fluctuating condition inducing a high burden on affected people. Despite the existence of some guidelines, its management remains complicated. We aimed to demonstrate that symptoms after a COVID-19 infection evolve following different trajectories from the initial infection until 24 months after, to identify the determinants of these trajectories, and the quality of life of people in these trajectories.

Study participants from the Predi-COVID cohort were digitally followed from their acute SARS-CoV-2 infection until a maximum of 24 months. Data from 10 common symptoms collected at study inclusion, and months 12, 15, and 24 awere used to create a total symptom score. Impact of symptoms on quality of life was assessed at month 24 using standardized questionnaires and ad-hoc questions. Latent classes mixed models were used to identify total score symptom trajectories and individual symptoms trajectories.

We included 555 participants with at least 2 different time points available during follow-up (Baseline and at least one of the M12, M15 or M24 questionnaires). We identified 2 total symptom score trajectories: T1 "Mild symptoms, fast resolution" (N = 376; 67.7%), and T2 "Elevated and persisting symptoms" (N = 179; 32.3%). The main determinants of being in T2 were: older age (OR = 1.86; p = 0.003), to be a woman (OR = 1.81; p = 0.001)), elevated BMI (OR = 3.97; p < 0.001), and the presence of multi comorbidities (OR = 2.67; p = 0.005). Symptoms impacted the quality of life more in T2 than in T1 at 24 months (high fatigue level: 64.8% vs 19.5%, altered respiratory quality of life: 42.6% vs 4.6%, anxiety: 24.1% vs 4.6%, stress: 57.4% vs 35.6%, and bad sleep: 75.9% vs 51.1%).

A third of our study population was in the T2 "Elevated and persisting symptoms" trajectory, presenting high symptom frequencies up to 24 months after initial infection, with a significant impact on quality of life. This work underlined the urgent need to better identify individuals most vulnerable to long-term complications to develop tailored interventions for them.

Clinicaltrials.gov NCT04380987 (date of registration: 2020-05-07).

Long COVID syndrome has had a major impact on million patients' lives worldwide. The cardiovascular system is an important aspect of this multifaceted disease that may manifest in many ways. We have hereby performed a narrative review in order to identify the extent of the cardiovascular manifestations of the Long COVID syndrome.

An in-depth systematic search of the literature has been conducted for this narrative review. The systematic search of PubMed and Cochrane databases yielded 3993 articles, of which 629 underwent full-text screening. A total of 78 studies were included in the final qualitative synthesis and data evaluation. The pathophysiology of the cardiovascular sequelae of Long COVID syndrome and the cardiac manifestations and complications of Long COVID syndrome are critically evaluated. In addition, potential cardiovascular risk factors are assessed, and preventive methods and treatment options are examined in this review.

This systematic review poignantly summarizes the evidence from the available literature regarding the cardiovascular manifestations of Long COVID syndrome and reviews potential mechanistic pathways, diagnostic approaches, preventive measures, and treatment options.

Long COVID is a complex, heterogeneous syndrome affecting over four hundred million people globally. There are few recommendations, and no formal training exists for medical professionals to assist with clinical evaluation and management of patients with Long COVID. More research into the pathology, cellular, and molecular mechanisms of Long COVID, and treatments is needed. The goal of this work is to disseminate essential information about Long COVID and recommendations about definition, diagnosis, treatment, research and social issues to physicians, researchers, and policy makers to address this escalating global health crisis.

A 3-round modified Delphi consensus methodology was distributed internationally to 179 healthcare professionals, researchers, and persons with lived experience of Long COVID in 28 countries. Statements were combined into specific areas: definition, diagnosis, treatment, research, and society.

The survey resulted in 187 comprehensive statements reaching consensus with the strongest areas being diagnosis and clinical assessment, and general research. We establish conditions for diagnosis of different subgroups within the Long COVID umbrella. Clear consensus was reached that the impacts of COVID-19 infection on children should be a research priority, and additionally on the need to determine the effects of Long COVID on societies and economies. The consensus on COVID and Long COVID is that it affects the nervous system and other organs and is not likely to be observed with initial symptoms. We note, biomarkers are critically needed to address these issues.

This work forms initial guidance to address the spectrum of Long COVID as a disease and reinforces the need for translational research and large-scale treatment trials for treatment protocols.

Both the global prevalence of diabetes and the frequency of natural and man-made disasters are increasing. Of all chronic diseases, the consequences of sudden loss of medical supplies are most serious for those with diabetes, with people living with type 1 diabetes being at risk of death within a few days without insulin. This review considers how to prepare for and respond to sudden reductions in medical supplies to those with diabetes. Recent experiences with the COVID-19 pandemic in India, the war in Ukraine and the war/blockade in the Tigray region of Ethiopia are described, and the importance of prevention, preparedness, response and recovery are discussed. It is hoped that lessons from these and other disasters and ongoing advocacy and other actions may help to mitigate the risks of significant morbidity and mortality for people with diabetes in disaster-impacted regions across the world.

Coronavirus disease 2019 (COVID-19) has been linked to the development of post-COVID-19 conditions (PCCs). We investigated whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection increases the risk of selected PCCs or death up to 1 year after infection, separately in the wild-type (WT), Alpha-transition, Delta, and Omicron eras and by vaccination status.

We used health records of the Veterans Health Administration to emulate a hypothetical target trial of SARS-CoV-2 infection versus no infection. Veterans who tested positive for SARS-CoV-2 between March 2020 and April 2022 (n = 430 160) were matched 1:1 to veterans who had not tested positive for SARS-CoV-2. All-cause mortality and cumulative incidence of 32 potential PCCs were ascertained at 31-180 and 181-365 days after infection or matched index date.

From 31 to 180 days, the cumulative incidence of death and all organ-level PCCs was greater in infected versus uninfected participants, with cumulative incidence differences lower in the Omicron than in the WT era and lower in vaccinated than in unvaccinated persons. In the Omicron era, the cumulative incidence of death and most PCCs from day 181-365 were higher in infected than in uninfected participants only among unvaccinated but not among vaccinated persons.

Excess burden of PCCs and mortality persisted 31-180 days after infection in the Omicron era, albeit at a lower level than in the WT and Delta eras. Excess burden of mortality and most PCCs was much lower 181-365 days after infection and was observed in the Omicron era only among unvaccinated persons, suggesting a protective effect of vaccination.

This review provides a broad overview of lessons learned in the five years since COVID-19 was identified. It is a bimodal disease, starting with an initially virus-driven phase, followed by resolution or ensuing inappropriate immune activation causing severe inflammation that is no longer strictly virus dependent. Humoral immunity is beneficial for preventing or attenuating the early stage, without benefit once the later stage begins. Neutralizing antibodies elicited by natural infection or vaccination are short-lived and highly vulnerable to viral sequence variation. By contrast, cellular immunity, particularly the CD8+ T cell arm, has a role in preventing or attenuating severe disease, is far less susceptible to viral variation, and is longer-lived than antibodies. Finally, an ill-defined phenomenon of prolonged symptoms after acute infection, termed "long COVID," is poorly understood but may involve various immunologic defects that are hyperactivating or immunosuppressive. Remaining issues include needing to better understand the immune dysregulation of severe disease to allow more tailored therapeutic interventions, developing antibody strategies that cope with the viral spike sequence variability, prolonging vaccine efficacy, and unraveling the mechanisms of long COVID to design therapeutic approaches.

SARS-CoV-2 infection leads to post-acute sequelae that can affect nearly every organ system, including the immune system. However, whether an infection with SARS-CoV-2 is associated with increased risk of future infections with other pathogens is not yet fully characterised. In this study, we aimed to test the association between a positive test for COVID-19, compared with a negative test, and rates of future infections with other pathogens.

We used the US Department of Veterans Affairs health-care databases to build a spatiotemporally aligned cohort of 231 899 people with a positive COVID-19 test and 605 014 with a negative COVID-19 test (test-negative control group) between Nov 1, 2021, and Dec 31, 2023. We first did a discovery approach to map the associations between those with a positive COVID-19 test versus a negative test and laboratory-based outcomes of infectious illnesses. We then compared rates of a prespecified set of infectious disease outcomes between those with and without a positive COVID-19 test. To evaluate the specificity of the findings to COVID-19, we compared the rates of a prespecified set of infectious disease outcomes in a spatiotemporally aligned cohort of people admitted to hospital for COVID-19 (n=12 450) versus those admitted for seasonal influenza (n=3293). Outcomes were ascertained 30 days after the date of the first test until the end of follow-up (365 days after the first test plus 30 days, death, or July 18, 2024, whichever came first). An inverse probability weighting approach was used to balance demographic and health characteristics across cohorts. Log-binomial regression models were used to estimate risk ratios (RRs) and 95% CIs.

In the 12 months of follow-up, compared with participants who had a negative test for COVID-19, people with COVID-19 who did not require admission to hospital during the acute phase of infection had increased test positivity rates for bacterial infections (in blood, urine, and respiratory cultures) and viral diseases (including Epstein-Barr virus, herpes simplex virus reactivation, and respiratory viral infections). People who were positive for COVID-19 and admitted to hospital also had increased rates of bacterial infections in blood, respiratory, and urine biospecimens, and viral infections in blood and respiratory biospecimens. Analyses of prespecified outcomes showed that, compared with the test-negative control group, participants with a positive COVID-19 test who were not admitted to hospital had significantly increased rates of outpatient diagnosis of infectious illnesses (RR 1·17 [95% CI 1·15-1·19]), including bacterial, fungal, and viral infections; outpatient respiratory infections (1·46 [1·43-1·50]); and admission to hospital for infectious illnesses (1·41 [1·37-1·45]), including for sepsis and respiratory infections; the rates of prespecified outcomes were generally higher among those who were admitted to hospital for COVID-19 during the acute phase. Compared with people admitted to hospital for seasonal influenza, those admitted for COVID-19 had higher rates of admission to hospital for infectious illnesses (1·24 [1·10-1·40]), admission to hospital for sepsis (RR 1·35 [1·11-1·63]), and in-hospital use of antimicrobials (1·23 [1·10-1·37]).

Our results suggest that a positive test for COVID-19 (vs a negative test) was associated with increased rates of diagnosis of various infections in the 12 months following an acute SARS-CoV-2 infection. The putative long-term effects of COVID-19 on the immune system and the propensity for infection with other pathogens should be further evaluated in future studies.

US Department of Veterans Affairs.

In British Columbia (BC), Canada, COVID-19 and associated control measures impacted routine care for patients with diabetes. Some of these measures may have impacted timely screening and diagnosis of type 2 diabetes. We assessed the impact of control measures on screening and diagnosis of type 2 diabetes in BC.

We used data from the BC COVID-19 Cohort, which includes COVID-19 and healthcare administrative data on all residents of BC. We assessed and compared screening (≥40 yrs) and diagnosis (≥18 yrs) of diabetes among the adult population during the pandemic period (1 April 2020-31 December 2022), with 1 January 2016-31 March 2020 used as a historical reference period. We used interrupted time series with generalized additive models to evaluate the impact of policy measures on screening and diagnoses trends.

We observed an initial decline in the mean number of screenings and diagnoses. In the third post-policy phase (January 2022-December 2022), there was a 4.8% (-5.1, 15.4) increase in screenings while after an initial reduction in diabetes diagnoses, we observed a significant increase of 31.6% (17.8, 46.6) in the third post-policy phase. Further stratification by age and sex showed the entire increase in diagnoses trends was driven by younger females with a 56.4% (25.1, 92.9) and 58.7% (38.2, 81.3) increment in diagnoses in the 18-29 and 40-49 age groups, respectively.

The initial reduced number of screenings and diagnoses followed by the significant upward trend in diabetes diagnoses in the later post-policy phase have important clinical and public health implications. Further research is needed to understand the post-pandemic increase in diabetes among females.

Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episodes of COVID-19 are significantly more likely to be hospitalized in the following months. The pathophysiological mechanisms for delayed complications are still poorly understood, with a dissociation seen between ongoing symptoms and objective measures of cardiopulmonary health. COVID-19 is anticipated to alter the long-term trajectory of many chronic cardiovascular and pulmonary diseases, which are common among those at risk of severe disease.

This study aims to use a single, integrated device-MouthLab, which measures 10 vital health parameters in 60 seconds-and a cloud-based proprietary analytics engine to develop and validate the Aidar Decompensation Index (AIDI), to predict decompensation in health among patients who previously had severe COVID-19.

Overall, 200 participants will be enrolled. Inclusion criteria are patients in the US Department of Veterans Affairs health care system; "severe" COVID-19 infection during the acute phase, defined as requiring hospitalization, within 3-6 months before enrollment; aged ≥18 years; and having 1 of 6 prespecified chronic conditions. All participants will be instructed to use the MouthLab device to capture daily physiological data and complete monthly symptom surveys. Structured data collection tables will be developed to extract the clinical characteristics of those who experience decompensation events (DEs). The performance of the AIDI will depend on the magnitude of difference in physiological signals between those experiencing DEs and those who do not, as well as the time until a DE (ie, the closer to the event, the easier the prediction). Information about demographics, symptoms (Medical Research Council Dyspnea Scale and Post-COVID-19 Functional Status Scale), comorbidities, and other clinical characteristics will be tagged and added to the biomarker data. The resultant predicted probability of decompensation will be translated into the AIDI, where there will be a linear relationship between the risk score and the AIDI. To improve prediction accuracy, data may be stratified based on biological sex, race, ethnicity, or underlying clinical characteristics into subgroups to determine if there are differences in performance and detection lead times. Using appropriate algorithmic techniques, the study expects the model to have a sensitivity of >80% and a positive predicted value of >70%.

Recruitment began in January 2023, and at the time of manuscript submission, 204 patients have been enrolled. Publication of the complete results and data from the study is expected in 2025.

The focus on identifying predictor variables using a combination of biosensor-derived physiological features should enable the capture of heterogeneous characteristics of complications related to post-COVID-19 condition across diverse populations.

ClinicalTrials.gov NCT05220306; https://clinicaltrials.gov/study/NCT05220306.

Host metabolic fitness is a critical determinant of infectious disease outcomes. Obesity, aging, and other related metabolic disorders are recognized as high-risk disease modifiers for respiratory infections, including coronavirus infections, though the underlying mechanisms remain unknown. Our study highlights fatty acid-binding protein 4 (FABP4), a key regulator of metabolic dysfunction and inflammation, as a modulator of SARS-CoV-2 pathogenesis, correlating strongly with disease severity in COVID-19 patients. We demonstrate that loss of FABP4 function, by genetic or pharmacological means, reduces SARS-CoV-2 replication and disrupts the formation of viral replication organelles in adipocytes and airway epithelial cells. Importantly, FABP4 inhibitor treatment of infected hamsters diminished lung viral titers, alleviated lung damage and reduced collagen deposition. These findings highlight the therapeutic potential of targeting host metabolism in limiting coronavirus replication and mitigating the pathogenesis of infection.

The World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments.

We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19.

During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05-0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00-1.09; P=0.04) showed a significant association with long COVID after 12-18 months in a multivariable logistic regression analysis.

Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

Characterizing the paradigm and impact of long COVID is crucial for addressing this worldwide health challenge. This study aimed to investigate the prevalence of long COVID one year after primary Omicron infection and characterize differences in long-term health consequence between participants with persistent long COVID and those who fully recovered.

This a community-based cross-sectional study conducted from December 2023 to March 2024 at the China-Japan Friendship Hospital and 16 administrative districts in Beijing. 12,789 participants infected with Omicron between December 2022 and January 2023 were recruited through stratified multistage random sampling and included in the final analysis. Of them, 376 participants with persistent long COVID and 229 without long COVID were matched for further physical examinations. The primary outcome was the prevalence of long COVID one year after infection. Secondary outcomes included muscle strength, exercise capacity, health-related quality of life (HRQoL), mental health, work status, laboratory tests, and examinations.

Among 12,789 participants (media [IQR] age, 48.4 [37.3 to 61.4] years; 7817 females [61.1%]), 995 of them (7.8%) experienced long COVID within one year, with 651 (5.1%) having persistent symptoms. Fatigue (598/995 [60.1%]) and post-exertional malaise (367/995 [36.9%]) were the most common symptoms. Brain fog had the lowest resolution proportion as 4.2% within one year. The odds of long COVID increased with reinfections (odds ratios for one reinfection 2.592 [95% CI: 2.188 to 3.061]; two or more: 6.171 [3.227 to 11.557]; all p < 0.001). Participants with persistent long COVID had markedly lower muscle strength (upper-limb: 26.9 ± 12.4 vs. 29.1 ± 14.5 Kg; lower-limb: 40.0 [27.0 to 62.0] vs. 43.0 [28.0 to 59.0] s), worse exercise capacity and poorer HRQoL, and meaningful difference in laboratory tests results compared to those without long COVID. They also exhibited significantly higher proportions of abnormal lung function (FEV1 %pred<80%: 13.0% vs. 2.0%; DLco %pred<80%: 32.7% vs. 19.9%) and lung imaging abnormalities (23.5% vs. 13.6%).

The considerable health burden of long COVID and the progression of neurological symptoms following Omicron infection warrant close monitoring. Utilizing professional questionnaires and developing reliable diagnostic tools are necessary for improving diagnosis and treatment of long COVID.

This work was supported by Beijing Research Center for Respiratory Infectious Diseases (BJRID2024-012), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2022-I2M-CoV19-005/CIFMS 2021-I2M-1-048), the National Natural Science Foundation of China (82241056/82200114/82200009), the New Cornerstone Science Foundation.

To estimate the time trends of treated diabetes incidence, prevalence and mortality in France from 2013 to 2019 and to compare with the Covid-19 pandemic period (2020-2021).

Using the French National Health Data System, people with treated diabetes ≥ 45 years-old were identified based on their medications. Annual time trends over 2013-2019 were estimated using Poisson log-linear model controlled for age, year and region for prevalence (aPTT), incidence (aITT) and mortality (aMTT). Numbers of incident cases and deaths in 2020-2021 were estimated from these trends, and compared with those observed.

Over 2013-2019, incidence and mortality declined significantly in men, aITT=-0.61 % (-0.95;-0.26); aMTT=-0.52 % (-0.81;-0.22), leading to a stable prevalence, aPTT=0.18 % (-0.03;0.40). In women, the fall in incidence was more marked, aITT=-1.45 % (-1.95;-0.95), mortality was stable, aMTT=-0.19 % (-0.54;0.15), leading to a significant decrease in prevalence, aPTT=-0.31 % (-0.60;-0.02). Compared with people not treated for diabetes, the relative risk of mortality increased significantly in men over the 2013-2019 period, from 1.38 (1.37;1.39) to 1.42 (1.41;1.43), while the risk remained stable in women, from 1.45 (1.44;1.46) to 1.46 (1.45;1.47). In 2020, there were 7,458 and 4,404 additional deaths and 3,550 and 4,919 new cases in respectively men and women. In 2021, there were 11,576 and 6,371 additional deaths and 30,057 and 26,169 new cases in respectively men and women.

This study reports a favorable dynamic of diabetes over 2013-2019 followed by a sharp increase in incidence and mortality in 2020 and 2021. Continued monitoring is necessary to identify long-term trend and potential indirect effect of the pandemic.

This study investigated post-coronavirus disease 2019 (COVID-19) sequelae among a sample of the Chinese population, and determined the statistical significance of correlations between age, sex, number of COVID-19 vaccinations, number of SARS-CoV-2 infections, development of pneumonia, use of specific drugs (namatevir/ritonavir, azvudine, molnupiravir), chronic underlying diseases, and post-COVID-19 sequelae.

Data from 869 patients, who visited the Emergency Department of Beijing Shijitan Hospital (Beijing, China) between December 7, 2022 to January 31, 2024, were collected. The criteria for admission: Age ≥ 14 years old, and diagnosed with ≥ 1 positive result on nucleic acid or antigen tests. Retrospectively analyzed the main manifestations of sequelae, statistical analysis of the relationship between age; sex; underlying diseases; number of COVID-19 vaccinations received; use of specific antiviral drugs for COVID-19 and prognosis, and statistical analyses, including logistic regression, were performed. Differences with P < 0.05 were considered to be statistically significant. Before entering the model, variables are screened using stepwise regression to ensure that the selected variables are the main ones related to the research question, thereby controlling for confounding factors. SPSS version 27 (IBM Corp. Armonk, NY, USA) was used for statistical analysis. In this study, the duration of sequelae ranged from 2 to 13 months.

This study retrospectively analyzed 869 patients (415 male, 454 female), with an average age of 61.52 ± 23.09 years. There were 401 patients without underlying conditions and 468 patients with one or more underlying conditions. Regarding COVID-19 vaccination status, 320 patients were unvaccinated, while 576 patients received at least one dose of the COVID-19 vaccine. Additionally, 514 patients received antiviral medication, while 355 did not receive antiviral treatment. The primary manifestations of post-COVID-19 included shortness of breath, dizziness and weakness, chronic pneumonia, asthma, and reduced sense of smell. Individuals ≥ 70 years of age were more prone to COVID-19 pneumonia. Patients with underlying disease(s) exhibited statistically higher mortality rates after diagnosis of COVID-19. Vaccination against COVID-19 and the use of specific drugs had a statistically significant effect on mortality and the occurrence of post-COVID-19 sequelae. There was no statistically significant difference in COVID-19 infection rates between males and females, although males were more prone to COVID-19 pneumonia. Young women with COVID-19 often experienced no sequelae, and elderly males exhibited a high mortality rate.

Virus-induced antibodies represent a dual-edged sword in the immune response to viral infections. While antibodies are critical for neutralizing pathogens, some can paradoxically exacerbate disease severity through mechanisms such as antibody-dependent enhancement (ADE), autoantibody, and prolonged inflammation. Long coronavirus disease (COVID) and dengue hemorrhagic fever (DHF) exemplify conditions where pathogenic antibodies play a pivotal role in disease progression. Long COVID is associated with persistent immune dysregulation and autoantibody production, leading to chronic symptoms and tissue damage. In DHF, pre-existing antibodies against dengue virus contribute to ADE, amplifying viral replication, immune activation, and vascular permeability. This review explores the mechanisms underlying these pathogenic antibody responses, highlighting the shared pathways of immune dysregulation and comparing the distinct features of both conditions. By examining these studies, we identify key lessons for therapeutic strategies, vaccine design, and future research aimed at mitigating the severe outcomes of viral infections.

Inflammation is associated with the pathophysiology of type 2 diabetes and COVID-19. Phytochemicals have the potential to modulate inflammation, expression of SARS-CoV-2 viral entry receptors (angiotensin-converting enzyme 2 (ACE2) and transmembrane protease, serine 2 (TMPRSS2)) and glucose transport in the gut. This study assessed the impact of phytochemicals on these processes. We screened 12 phytochemicals alongside 10 pharmaceuticals and three plant extracts, selected for known or hypothesised effects on the SARS-CoV-2 receptors and COVID-19 risk, for their effects on the expression of ACE2 or TMPRSS2 in differentiated Caco-2/TC7 human intestinal epithelial cells. Genistein, apigenin, artemisinin and sulforaphane were the most promising ones, as assessed by the downregulation of TMPRSS2, and thus they were used in subsequent experiments. The cells were then co-stimulated with pro-inflammatory cytokines interleukin-1 beta (IL-1β) and tumour necrosis factor-alpha (TNF-α) for ≤168 h to induce inflammation, which are known to induce multiple pathways, including the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. Target gene expression (ACE2, TMPRSS2, SGLT1 (sodium-dependent glucose transporter 1) and GLUT2 (glucose transporter 2)) was measured by droplet digital PCR, while interleukin-1 (IL-6), interleukin-1 (IL-8) and ACE2 proteins were assessed using ELISA in both normal and inflamed cells. IL-1β and TNF-α treatment upregulated ACE2, TMPRSS2 and SGLT1 gene expression. ACE2 increased with the duration of cytokine exposure, coupled with a significant decrease in IL-8, SGLT1 and TMPRSS2 over time. Pearson correlation analysis revealed that the increase in ACE2 was strongly associated with a decrease in IL-8 (r = -0.77, p < 0.01). The regulation of SGLT1 gene expression followed the same pattern as TMPRSS2, implying a common mechanism. Although none of the phytochemicals decreased inflammation-induced IL-8 secretion, genistein normalised inflammation-induced increases in SGLT1 and TMPRSS2. The association between TMPRSS2 and SGLT1 gene expression, which is particularly evident in inflammatory conditions, suggests a common regulatory pathway. Genistein downregulated the inflammation-induced increase in SGLT1 and TMPRSS2, which may help lower the postprandial glycaemic response and COVID-19 risk or severity in healthy individuals and those with metabolic disorders.

The current classification of diabetes had its genesis over 85 years ago, when individuals with diabetes were first subclassified into insulin sensitive and insulin insensitive states based on the response to an oral glucose tolerance test. About 35 years later, the contemporary classifications of type 1 and type 2 diabetes were coined. Today's evidence, however, suggests that multiple etiologic and pathogenic processes lead to both type 1 and type 2 diabetes, reflecting significant heterogeneity in factors associated with initiation, progression, and clinical presentation of each disorder of glucose homeostasis. Further, the current classification fails to recognize what is currently defined as "atypical" diabetes. Heterogeneity of diabetes continues through the life-course of an individual, with modification of prognosis risk (eg, diabetic complications) altered by genetics, life experience, comorbidities, and therapy. Understanding the sources of heterogeneity in diabetes will likely improve diagnosis, prevention, treatment, and prediction of complications in both the medical and public health settings. Such knowledge will help inform progress in the emerging era of precision diabetes medicine. This article presents NIDDK's Heterogeneity of Diabetes Initiative and a corresponding roadmap for future research in type 2 diabetes heterogeneity.

COVID-19 became a pandemic disease in 2020, with multisystem involvement and high renal morbidity during the acute phase. Some affected patients began to present new or persistent symptoms in a condition known as Long COVID. The study aimed to evaluate renal function using clinical and laboratory findings, and to establish the frequency and staging of renal function decline in Long COVID patients, as well as the associated factors.

This is a cross-sectional observational study that selected participants from a Long COVID clinical care program between 2020 and 2022.

A total of 246 patients were selected for this study, and renal function decline was found in 83 (33.7%). Patients over 60 years (29.6%) and those who developed glycaemic alterations (41.8%) exhibited a higher prevalence of renal outcomes in long COVID. Some laboratory test as LDH levels and glycated hemoglobin seems to have a statistic relation with a decrease in renal function (p < 0.05).

A decline in renal function was common in patients with Long COVID in this study, and older age and glycaemic alterations were relevant to this condition. Some laboratory markers can be used to predict this outcome.

To evaluate the all-cause mortality rate and renal outcomes in patients with diabetes and chronic kidney disease (CKD) following hospital discharge for COVID-19.

This single-center prospective observational study included 187 discharged COVID-19 patients with diabetes and CKD, admitted between December 2022 and January 2023 at West China Hospital, Sichuan University. Cox regression analysis was used to assess mortality risk, and logistic regression was applied to identify risk factors for rapid CKD progression after discharge.

During the one-year follow-up, the all-cause mortality rate was 26.7%, with a COVID-19-related acute kidney injury (AKI) incidence of 35.3%, and 35.8% of patients experienced rapid CKD progression after discharge. Cox proportional hazards regression indicated that sepsis and mechanical ventilation were major risk factors for post-discharge all-cause mortality. Logistic regression identified baseline eGFR < 60 mL/min/1.73 m² as an independent risk factor for rapid CKD progression.

During the one-year follow-up period, we observed that patients with diabetes and CKD exhibited higher all-cause mortality and experienced rapid deterioration of kidney function after acute infection with COVID-19. This underscores the importance of ongoing longitudinal follow-up to more accurately track the long-term health effects of COVID-19 on patients with diabetes and CKD.

Whether COVID-19 contributes to youth-onset diabetes is controversial, and research in Asia is lacking.

To explore the incidence and severity of diabetes among youths during the COVID-19 pandemic in South Korea.

This cohort study used claims data for January 1, 2017, through February 28, 2022, from the National Health Insurance Service database in South Korea. The incidence of type 1 diabetes (T1D) and type 2 diabetes (T2D) in patients younger than 20 years during the pandemic was analyzed and compared with that during the prepandemic period. The study included incident cases of T1D identified by at least 2 diagnosis codes with at least 2 insulin prescriptions within 1 year and T2D identified by at least 2 diagnosis codes with at least 2 prescriptions of diabetes medication within 1 year. Analyses were performed between January 29 and September 2, 2024.

COVID-19 pandemic and SARS-CoV-2 infection.

The primary outcome was incidence of T1D and T2D, and secondary outcomes included the rate of diabetic ketoacidosis (DKA) and association of new-onset diabetes with SARS-CoV-2 positivity.

The study included 2599 patients with T1D (mean [SD] age, 12.0 [4.8] years; 1235 [47.5%] male) and 11 040 patients with T2D (mean [SD] age, 16.0 [2.8] years; 6861 [62.1%] male). During the pandemic, the incidence rate ratios were 1.19 (95% CI, 1.10-1.29) for T1D and 1.41 (95% CI, 1.36-1.46) for T2D. The incidence rate of DKA at diagnosis increased during the first pandemic year compared with the prepandemic period (T1D, 42.8% [95% CI, 38.5%-47.0%] vs 31.3% [95% CI, 29.0%-33.7%], respectively; T2D, 6.0% [95% CI, 5.0%-7.1%] vs 2.9% [95% CI, 2.5%-3.3%], respectively) but returned to prepandemic levels in the second pandemic year (T1D, 34.5% [95% CI, 30.6%-38.5%]; T2D, 3.2% [95% CI, 2.6%-3.9%]). The hazard ratio for new-onset diabetes associated with SARS-CoV-2 positivity was 0.44 (95% CI, 0.17-1.13) for T1D and 1.08 (95% CI, 0.74-1.57) for T2D.

These findings suggest that the incidence and severity of T1D and T2D among South Korean youths increased during the COVID-19 pandemic. The cohort analysis does not support SARS-CoV-2 infection itself as being directly associated with incident diabetes.

The post-acute sequelae of COVID-19 (PASC) poses a significant health challenge in the post-pandemic world. However, the underlying biological mechanisms of PASC remain intricate and elusive. Network-based methods can leverage electronic health record data and biological knowledge to investigate the impact of COVID-19 on PASC and uncover the underlying biological mechanisms. This study analyzed territory-wide longitudinal electronic health records (from January 1, 2020 to August 31, 2022) of 50 296 COVID-19 patients and a healthy non-exposed group of 100 592 individuals to determine the impact of COVID-19 on disease progression, provide molecular insights, and identify associated biomarkers. We constructed a comorbidity network and performed disease-protein mapping and protein-protein interaction network analysis to reveal the impact of COVID-19 on disease trajectories. Results showed disparities in prevalent disease comorbidity patterns, with certain patterns exhibiting a more pronounced influence by COVID-19. Overlapping proteins elucidate the biological mechanisms of COVID-19's impact on each comorbidity pattern, and essential proteins can be identified based on their weights. Our findings can help clarify the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations, while also yielding potential biomarkers and corresponding treatments for specific disease progression patterns.