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Research on how the COVID-19 pandemic, societal restrictions, and healthcare services barriers have impacted patients with hypertension is limited. This study aimed to evaluate trends in alcohol-related disorders, other alcohol-associated conditions, and deaths among patients with hypertension during the pandemic (March 2020–Feb 2022) compared to the pre-pandemic period (March 2018–Feb 2020) in Region Stockholm, Sweden. This exploratory descriptive time series analysis was conducted among adults diagnosed with hypertension between 2015 and 2018. Data were obtained from the Swedish National Patient Register (specialist care) and the Stockholm Region’s primary care database. The quarterly period prevalence of diagnoses or cumulative incidence of acute diagnoses and deaths was presented. The study included 168,963 patients with hypertension (57% females). Overall, no profound shifts in alcohol-related disorders or mortality were observed during the pandemic. However, noteworthy trends were: alcohol-related disorder diagnoses in primary care increased among females (3.2/1000 compared to 2.8–3.1/1000 pre-pandemic), while rates of alcohol dependency decreased in specialist care, particularly among males (3.5–4.1/1000 compared to 4.1–5.1/1000 pre-pandemic). Alcohol-related disorders and deaths remained higher in males than in females during both periods. Among other alcohol-associated conditions, cardiovascular disease prevalence increased in both sexes in primary care and in male patients in specialist care, whereas mental illness decreased in both sexes. This study highlights the need for continued prevention of hazardous alcohol use among patients with hypertension and monitoring of cardiovascular risk factors. Further research on hypertensive patients is needed, as the pandemic-related health impacts may not become apparent until many years later.

Objectives Long COVID, defined as diverse symptoms persisting > 3 months post-infection, remains a major post-pandemic healthcare burden. Here we investigate risk factor posed by pre-existing respiratory symptoms and illnesses for development of long COVID, with focus on individuals with mild-to-moderate COVID-19 at the primary infection, that did not require hospitalization during the primary SARS-CoV-2 infection. Methods This case-control study was designed to investigate the prevalence of respiratory system-related diagnoses in adult; non-hospitalized long COVID patients (cases) compared to matched controls without a history of long COVID. Data was extracted from the Stockholm Region’s database (VAL) and included diagnoses 12 months pre- and 6 months post-long COVID diagnosis as well as pre-pandemic diagnoses (year 2019). Conditional logistic regression models were applied. Results Patients with Long COVID displayed higher frequencies of pre-pandemic respiratory conditions (year 2019) as well as 12 months before long COVID diagnosis compared to controls, including acute upper respiratory tract infections (men: Odds ratio (OR) 2.47, women: OR 2.22), asthma (men: OR 1.76, women: OR 1.95), and bronchitis (men: OR 2.15, women: OR 2.71). ORs for asthma were the highest 12 months before long COVID diagnosis (men: OR 4.18, women: OR 3.76). Conclusion Patients with Long COVID with a mild-to-moderate primary SARS-CoV-2 infection had higher prevalence of pre-existing respiratory conditions than controls, suggesting that respiratory diseases including asthma were a significant risk factor for long COVID also in the non-hospitalized population. Understanding the link between common respiratory conditions managed in primary care, including asthma and bronchitis, and long COVID is vital for refining clinical strategies and improving outcomes in post-viral conditions. Key take-home message Pre-pandemic respiratory diagnoses, including asthma, were more common among individuals later diagnosed with long COVID compared with matched controls. In line with previous literature, most long COVID cases in our cohort were female.

Background The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis. Methods This population-based case–control study included subjects aged 18–65 years from Sweden. Stochastic gradient boosting was used to develop a predictive model using diagnoses received in primary care, hospitalization due to acute COVID- 19, and prescribed medication. The variables with normalized relative influence (NRI) ≥ 1% showed were considered predictive. Odds ratios of marginal effects (OR ME ) were calculated. Results The study included 47,568 PASC cases and controls. More females ( n  = 5113) than males ( n  = 2815) were diagnosed with PASC. Key predictive factors identified in both sexes included prior hospitalization due to acute COVID- 19 (NRI 16.1%, OR ME 18.8 for females; NRI 41.7%, OR ME 31.6 for males), malaise and fatigue (NRI 14.5%, OR ME 4.6 for females; NRI 11.5%, OR ME 7.9 for males), and post-viral and related fatigue syndromes (NRI 10.1%, OR ME 21.1 for females; NRI 6.4%, OR ME 28.4 for males). Conclusions Machine learning can predict PASC based on previous diagnoses and medications. Use of this AI method could support diagnostics of PASC in primary care and provide insight into PASC etiology.

Background Post-acute sequelae of SARS-CoV-2 infection, or long COVID, include diverse symptoms and remain a major concern worldwide. This study investigates the occurrence of depression, anxiety, fatigue, and headache 1 year prior to the COVID-19 pandemic (2019), 12 months prior to, and 6 months after long COVID diagnosis in individuals diagnosed with long COVID and matched population-based controls. Methods This case–control study included nonhospitalized individuals diagnosed with long COVID compared with controls without long COVID, matched by age, sex, and neighborhood socioeconomic status. Data were collected from the Stockholm Regional Health Care Data Warehouse (VAL), including diagnoses in 2019, 12 months before, and 6 months after the long COVID diagnosis. Conditional logistic regression was used to calculate odds (OR) ratios and 99% confidence intervals (CI). Results A total of 5589 cases (mean age: 47 years, 69% female) and 47,561 controls were included. Individuals with long COVID had a higher pre-pandemic frequency of the following diagnoses: depression (women: OR 1.57 (1.26–1.97), men: OR 1.40 (0.88–2.23)), anxiety (women: OR 1.65 (1.41–1.93), men: OR 2.10 (1.56–2.84)), fatigue syndrome after viral infection (women: OR 1.96 (0.86–4.48), men: OR 2.22 (0.29–17)), and headache (women: OR 2.45 (1.96–3.05), men: OR 2.89 (1.86–4.50)). Individuals with long COVID also had a higher frequency of these diagnoses 12 months before and 6 months after the long COVID diagnosis was made, regardless of sex. Conclusions Individuals with long COVID had a higher prevalence of depression, anxiety, fatigue, and headache both before and after being diagnosed with long COVID compared with controls without long COVID. The findings suggest an association between mental health vulnerabilities and long COVID, while the frequency of registered mental health diagnoses remained largely similar after the long COVID diagnosis.

Background: This study aimed to estimate absenteeism costs and identify their predictors in non-hospitalized patients in Sweden. Methods: This cross-sectional study’s data were derived from the longitudinal project conducted at Uppsala University Hospital. The mean absenteeism costs due to COVID-19 were calculated using the human capital approach, and a Poisson regression analysis was employed to determine predictors of these costs. Results: The findings showed that the average absenteeism cost due to COVID-19 was USD 1907.1, compared to USD 919.4 before the pandemic (p < 0.001). Notably, the average absenteeism cost for females was significantly higher due to COVID-19 compared to before the pandemic (USD 1973.5 vs. USD 756.3, p = 0.001). Patients who had not fully recovered at the 12-month follow-up exhibited significantly higher costs than those without symptoms at that point (USD 3389.7 vs. USD 546.7, p < 0.001). The Poisson regression revealed that several socioeconomic factors, including age, marital status, country of birth, educational level, smoking status, BMI, and occupation, along with COVID-19-related factors such as severity at onset, pandemic wave, persistent symptoms at the follow-up, and newly introduced treatment for depression after the infection, were significant predictors of the absenteeism costs. Conclusions: Our study reveals that the mean absenteeism costs due to COVID-19 doubled compared to the year preceding the pandemic. This information is invaluable for decision-makers and contributes to a better understanding of the economic aspects of COVID-19.

Background/aim: This study aimed to distinguish different phenotypes of long COVID through the post-COVID syndrome (PCS) score based on long-term persistent symptoms following COVID-19 and evaluate whether these symptoms affect general health and work ability. In addition, the study identified predictors for severe long COVID. Method: This cluster analysis included cross-sectional data from three cohorts of patients after COVID-19: non-hospitalized (n = 401), hospitalized (n = 98) and those enrolled at the post-COVID outpatient’s clinic (n = 85). All the subjects responded to the survey on persistent long-term symptoms and sociodemographic and clinical factors. K-Means cluster analysis and ordinal logistic regression were used to create PCS scores that were used to distinguish patients’ phenotypes. Results: 506 patients with complete data on persistent symptoms were divided into three distinct phenotypes: none/mild (59%), moderate (22%) and severe (19%). The patients with severe phenotype, with the predominating symptoms were fatigue, cognitive impairment and depression, had the most reduced general health status and work ability. Smoking, snuff, body mass index (BMI), diabetes, chronic pain and symptom severity at COVID-19 onset were factors predicting severe phenotype. Conclusion: This study suggested three phenotypes of long COVID, where the most severe was associated with the highest impact on general health status and working ability. This knowledge on long COVID phenotypes could be used by clinicians to support their medical decisions regarding prioritizing and more detailed follow-up of some patient groups.

The coevolution of mammalian hosts and their beneficial commensal microbes has led to development of symbiotic host–microbiota relationships 1 . Epigenetic machinery permits mammalian cells to integrate environmental signals 2 ; however, how these pathways are fine-tuned by diverse cues from commensal bacteria is not well understood. Here we reveal a highly selective pathway through which microbiota-derived inositol phosphate regulates histone deacetylase 3 (HDAC3) activity in the intestine. Despite the abundant presence of HDAC inhibitors such as butyrate in the intestine, we found that HDAC3 activity was sharply increased in intestinal epithelial cells of microbiota-replete mice compared with germ-free mice. This divergence was reconciled by the finding that commensal bacteria, including Escherichia coli , stimulated HDAC activity through metabolism of phytate and production of inositol-1,4,5-trisphosphate (InsP 3 ). Both intestinal exposure to InsP 3 and phytate ingestion promoted recovery following intestinal damage. Of note, InsP 3 also induced growth of intestinal organoids derived from human tissue, stimulated HDAC3-dependent proliferation and countered butyrate inhibition of colonic growth. Collectively, these results show that InsP 3 is a microbiota-derived metabolite that activates a mammalian histone deacetylase to promote epithelial repair. Thus, HDAC3 represents a convergent epigenetic sensor of distinct metabolites that calibrates host responses to diverse microbial signals. Phytate metabolism and production of inositol trisphosphate by commensal bacteria activates epithelial histone deacetylase 3 and promotes intestinal repair.

The microbiota influence disease pathogenesis and treatment, however we have limited ability to assess patient status in relation to the microbiota. Here we find that the nitric oxide generating enzyme, nitric oxide synthase 2 (Nos2), is transcriptionally primed in intestinal epithelial cells (IECs), as opposed to immune cells, in inflammatory bowel disease (IBD) patients. Generation of IEC-specific Nos2 knockout mice revealed that epithelial Nos2 activity promoted susceptibility to intestinal disease and sustained a colitogenic microbiota. Epithelial Nos2 increased levels of nitric oxide-derived nitrates and nitrate-metabolizing bacteria in the intestine. Unexpectedly, extra-intestinal nitrates also reflected IEC-intrinsic Nos2 expression, and systemic nitrate concentrations in patients paralleled intestinal Nos2 activation. In fact, temporally inhibiting epithelial Nos2 was sufficient to alter intestinal nitrate homeostasis and inflammation in mice, as well as restrict nitrate production by human intestinal organoids. These data reveal that epithelial nitric oxide metabolism directs host-microbiota dynamics that can alter disease and that monitoring and targeting this axis may benefit patients with IBD.

Allergy and anti-helminth immunity are driven by type 2 responses in mucosal tissues. Tuft cells are key regulators of type 2 immunity, however the factors that control these cells remain poorly understood. Here we find that butyrate-producing commensal bacteria decrease tuft cells in the intestine. Butyrate suppression of tuft cells required the epigenetic modifying enzyme histone deacetylase 3 (HDAC3), suggesting that HDAC3 may promote tuft cell-dependent immunity. Consistent with this, epithelial-intrinsic HDAC3 actively regulated tuft cell expansion in vivo and was required to induce type 2 immune responses during helminth infection. Interestingly, butyrate epigenetically restricted stem cell differentiation into tuft cells, and inhibition of HDAC3 in adult mice and human intestinal organoids was sufficient to block tuft cell expansion. Collectively, these data reveal an epigenetic pathway in stem cells that directs tuft cell differentiation, and highlight a new level of regulation through which commensal bacteria calibrate intestinal immunity.Competing Interest StatementThe authors have declared no competing interest.