Despite the importance of the gut microbiome to health, the role of human genetic variation in shaping its composition remains poorly understood. Here we report genome-wide association analyses of harmonized metagenomic data from 16,017 adults in four Swedish population-based studies, with replication in 12,652 people from the Norwegian HUNT study. We identified variants in the OR51E1-OR51E2 locus, encoding sensors for microbiome-derived fatty acids, associated with microbial richness. We further identified 15 study-wide significant genetic associations (P < 5.4 × 10-11) involving eight loci and 14 common bacterial species, of which 11 associations at six loci were replicated. The results confirm previously reported associations at LCT, ABO and FUT2, and provide evidence for new loci MUC12, CORO7-HMOX2, SLC5A11, FOXP1 and FUT3-FUT6, with supporting data from metabolomics and gene expression analyses. Our findings link gut microbial variation genetically to gastrointestinal functions, including enteroendocrine fatty acid sensing, bile composition and mucosal layer composition.

Disruptions in gut microbiome are implicated in cardiometabolic disorders and other health outcomes. Antibiotics are known gut microbiome disruptors, but their long-term consequences remain underexplored. Here we combined individual-level data from the Swedish Prescribed Drug Register with fecal metagenomes of 14,979 adults to examine the association between oral antibiotic use over 8 years and gut microbiome. In multivariable confounder-adjusted regression models, antibiotic use <1 year before fecal sampling was associated with the greatest reduction in species diversity, but significant associations were also observed for use 1-4 and 4-8 years earlier. Clindamycin, fluoroquinolones and flucloxacillin accounted for most of the associations with the abundance of individual species. Use of these antibiotics 4-8 years earlier was associated with altered abundance of 10-15% of the species studied; penicillin V, extended-spectrum penicillins and nitrofurantoin were associated with only a few species. Similar results were found comparing one antibiotic course 4-8 years before sampling versus none in the past 8 years. These findings indicate that antibiotics may have long-lasting consequences for the gut microbiome.

Sleep and sleep disturbances, such as sleep apnoea and insomnia, are linked to mental health, particularly, depressive symptoms. It remains unclear however, if sleep fragmentation or hypoxia is primarily linked to depressive symptoms and how insomnia symptoms affect this relationship in samples free of clinical referral bias. We analysed cross-sectional data from two harmonised, population-based cohorts: the Sleep and HEalth in women (SHE) study and the Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study (ntotal = 1238; 68% males, 57 ± 12 years, BMI 28.0 ± 4.2 kg/m2). Objective measures of OSA (apnoea-hypopnoea index; AHI, oxygen desaturation index; ODI) and sleep fragmentation variables (sleep efficiency; SE% and wake after sleep onset; WASO) were measured, along with subjective data on insomnia symptoms and depressive symptoms. Logistic regression models were used to assess associations while adjusting for age, sex, BMI, alcohol and physical activity. Pathway analyses further explored potential mediating relationships. Depressive symptoms were present in 17% of the sample. We did not find a link between intermittent hypoxia variables (AHI, ODI) and depressive symptoms. In contrast, reduced SE% (OR 0.84; 0.72-0.98) and increased WASO (1.26; 1.06-1.51) were significantly related to depressive symptoms. When including adjustment for insomnia in the model, these associations were no longer significant. While sleep fragmentation was associated with depressive symptoms, insomnia symptoms appear to be a key factor. Assessing insomnia in depression and vice versa and addressing these conditions, may be useful for reducing symptom burden.

Atherosclerosis develops over many years and its underlying mechanisms are still not fully understood. Plasma metabolomics across the different stages of development may help identify biomarkers that clarify disease pathways and improve early risk assessment. We performed untargeted plasma metabolomics using ultra-performance liquid chromatography-mass spectrometry in 8,146 participants without cardiovascular disease from the population-based SCAPIS cohort. Associations of 1,171 circulating metabolites with subclinical coronary atherosclerosis burden, assessed using coronary computed tomography angiography and quantified by segment involvement score, were assessed using multivariable models. Metabolites associated with coronary atherosclerosis were then evaluated in independent cohorts representing later stages of the atherosclerotic disease continuum: imminent myocardial infarction (MIMI, n=2,018), and coronary plaque burden and vulnerability in myocardial infarction survivors (PROSPECT II, n=898). Twelve metabolites, including phosphate, malate, sphingomyelins, amino acids, and one uncharacterized feature, were robustly associated with subclinical coronary atherosclerosis independent of traditional risk factors. Notably, sphingomyelins showed inverse associations with subclinical atherosclerosis, imminent myocardial infarction, and the presence of vulnerable plaques. Malate, N-acetyl-isoputreanine and an uncharacterized molecule (X-25790) were positively associated with subclinical coronary atherosclerosis and with imminent myocardial infarctions. These findings reveal a metabolomic signature across the atherosclerosis continuum, highlighting candidate biomarkers that may enhance understanding of disease mechanisms and aid risk stratification.

BACKGROUND:Higher meat intake has been associated with adverse health outcomes, including cardiovascular disease (CVD). This study investigated plasma metabolites associated with meat intake and their relation with cardiometabolic biomarkers, subclinical CVD markers, and incident CVD. METHODS:Associations between self-reported meat intake and 1272 plasma metabolites were investigated in the SCAPIS cohort (n = 8,819; ages 50-64). Meat-associated metabolites were further examined for relation with subclinical CVD markers in the POEM cohort (n = 502; age 50) and incident CVD in the EpiHealth cohort (n = 2,278; ages 45-75; 107 incident cases over 9.6 years follow-up). Meat intake was categorized into white, unprocessed red, and processed red meat. Linear regression analyzed associations between meat intake, metabolites and cardiometabolic biomarkers, and subclinical CVD markers, while Cox models evaluated association between meat-associated metabolites and incident CVD. RESULTS:After correction for multiple testing, 458, 368, and 403 metabolites were associated with white, unprocessed red, and processed red meat, respectively. Processed red meat-associated metabolites were associated with higher levels of fasting insulin, hemoglobin A1c, and lipoprotein(a), and were inversely associated with maximal oxygen consumption. Two metabolites, 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (hazard ratios (HR: 1.32; 95 % CI: 1.08, 1.62)) and glutamine degradant (HR: 1.35; 95 % CI: 1.07, 1.72), that were inversely associated with intake of all meat types, were also associated with a higher risk of incident CVD. CONCLUSIONS:This study provides comprehensive analysis of self-reported meat intake and plasma metabolites. The findings may enhance our understanding of the relationship between meat intake and CVD, and provide insights into underlying mechanisms.

Objective Meat intake is suggested to affect gut microbiome composition and the risk of chronic diseases. We aimed to identify meat-associated gut microbiome features and their association with host factors. Design Gut microbiota species were profiled by deep shotgun metagenomics sequencing in 9,669 individuals. Intake of white meat, unprocessed red meat, and processed red meat was assessed using a food frequency questionnaire. The associations of meat intake with alpha-diversity and relative abundance of gut microbiota species were tested using linear regression models with adjustment for dietary fiber intake, body mass index, and other potential confounders. Meat-associated species were further assessed for association with enrichment of microbial gene function, meat-associated plasma metabolites, and clinical biomarkers. Results Higher intake of processed red meat was associated with reduced alpha microbial diversity. White meat, unprocessed, and processed red meat intakes were associated with 36, 14, and 322 microbiota species, respectively. Species associated with processed red meat were enriched for bacterial pathways like amino acid degradation, while those negatively linked were enriched for pathways like homoacetogenesis. Furthermore, species positively associated with processed red meat were to a large extent associated with reduced trimethylamine N-oxide and glutamine levels but increased creatine and carnitine metabolites, fasting insulin and glucose, C-reactive protein, apolipoprotein A1, and triglyceride levels and higher blood pressure. Conclusion This largest to date population-based study on meat and gut microbiota suggests that meat intake, particularly processed red meat, may modify the gut microbiota composition, functional capacity, and health-related biomarkers.

Imaging-defined atherosclerosis represents an intermediate phenotype of atherosclerotic cardiovascular disease (ASCVD). Genome-wide association studies (GWAS) on directly measured coronary plaques using coronary computed tomography angiography (CCTA) are scarce. In the so far largest population-based cohort with CCTA data, we performed a GWAS on coronary plaque burden as determined by the segment involvement score (SIS) in 24,811 European individuals. We identified 20 significant independent genetic markers for SIS, three of which were found in loci not implicated in ASCVD before. Further GWAS on coronary artery calcification showed similar results to that of SIS, whereas a GWAS on ultrasound-assessed carotid plaques identified both shared and non-shared loci with SIS. In two-sample Mendelian randomization studies using SIS-associated markers in UK Biobank and CARDIoGRAMplusC4D, one extra coronary segment with atherosclerosis corresponded to 1.8-fold increased odds of myocardial infarction. This GWAS data can aid future studies of causal pathways in ASCVD.

Background & Aims:A quarter of the world population is estimated to have metabolic dysfunction-associated steatotic liver disease. Here, we aim to understand the impact of liver trait-associated genetic variants on fat content and tissue volume across organs and body compartments and on a large set of biomarkers. Methods:Genome-wide association analyses were performed on liver fat and liver volume estimated with magnetic resonance imaging in up to 27,243 unrelated European participants from the UK Biobank. Identified variants were assessed for associations with fat fraction and tissue volume in >2 million 'Imiomics' image elements in 22,261 individuals and with circulating biomarkers in 310,224 individuals. Results:We confirmed four liver fat and nine liver volume previously reported genetic variants (p values <5 × 10-8). We further found evidence suggestive of a novel liver volume locus, ADH4, where each additional T allele increased liver volume by 0.05 SD (SE = 0.01, p value = 3.3 × 10-8). The Imiomics analyses showed that liver fat-increasing variants were specifically associated with fat fraction of the liver tissue (p values <2.8 × 10-3) and with higher inflammation, liver and renal injury biomarkers, and lower lipid levels. Associations of liver volume variants with fat content, tissue volume, and biomarkers were more heterogeneous, for example the liver volume-increasing alleles at CENPW and PPP1R3B were associated with higher skeletal muscle volumes and were more pronounced in men, whereas the GCKR variant was negatively associated with lower skeletal muscle volumes in women (p values <2.8 × 10-3). Conclusions:Liver fat-increasing variants were mostly linked to fat fraction of the liver and were positively associated with some adverse metabolic biomarkers and negatively with lipids. In contrast, liver volume-associated variants showed a less consistent pattern across organs and biomarkers. Impact and implications:Liver fat and liver volume are common metabolic traits with a strong genetic component, yet the extent to which they exert organ-specific vs. systemic effects remains poorly defined. By integrating genome-wide association analyses and high-resolution neck-to-knee magnetic resonance imaging data through the Imiomics framework, this study reveals distinct genetic architectures for liver fat and liver volume, including sex-specific effects. These findings provide new insights into the biological, organ-level, tissue-specific, and systemic characteristics of steatotic liver disease and its genetic determinants. The results may inform the development of precision imaging genetic approaches, biomarker discovery, and stratified risk assessment strategies, while reinforcing the importance of incorporating sex-specific analyses in future research and clinical applications.

BACKGROUND:There is mounting evidence supporting the role of the microbiota in hypertension from experimental studies and population-based studies. We aimed to investigate the relationship between specific characteristics of the gut microbiome and 24-h ambulatory blood pressure measurements. METHODS:The association of gut microbial species and microbial functions, determined by shotgun metagenomic sequencing of fecal samples, with 24-h ambulatory blood pressure measurements in 3695 participants and office blood pressure was assessed in multivariable-adjusted models in 2770 participants without antihypertensive medication from the Swedish CArdioPulmonary bioImage Study. RESULTS:Gut microbiome alpha diversity was negatively associated with diastolic blood pressure variability. Additionally, four microbial species were associated with at least one of the 24-h blood pressure traits. Streptococcus sp001556435 was associated with higher systolic blood pressure, Intestinimonas massiliensis and Dysosmobacter sp001916835 with lower systolic blood pressure, Dysosmobacter sp001916835 with lower diastolic blood pressure, and ER4 sp900317525 with lower systolic blood pressure variability. Moreover, office blood pressure data from a subsample without ambulatory blood pressure measurements replicated the association of Intestinimonas massiliensis with systolic blood pressure and Dysosmobacter sp001916835 with diastolic blood pressure. Species associated with 24-h blood pressure were linked to a similar pattern of metabolites. CONCLUSIONS:In this large cross-sectional analysis, gut microbiome alpha diversity negatively associates with diastolic blood pressure variability, and four gut microbial species associate with 24-h blood pressure traits.

Background: Higher meat intake has been associated with adverse health outcomes, including cardiovascular disease (CVD). However, the mechanisms by which meat consumption increases CVD risk remain unclear. We used metabolomics data from a large population-based study to identify plasma metabolites associated with self-reported meat intake and associations with cardiometabolic biomarkers, subclinical CVD markers and incident CVD. Methods: We investigated the association between self-reported meat intake and 1272 plasma metabolites measured using ultra-high-performance liquid chromatography coupled with mass spectrometry in the SCAPIS (n=8,819; aged 50-64) cohort. Meat-associated metabolites were further analyzed in relation with subclinical CVD markers in the POEM cohort (n=502, all aged 50) and with incident CVD in the EpiHealth cohort (n=2,278; aged 45-75; 107 incident cases over 9.6 years follow-up). Meat intake was assessed through food frequency questionnaire, and categorized into white, unprocessed red, and processed red meat. We analyzed associations between meat intake and metabolites, meat-associated metabolites with cardiometabolic biomarkers, and subclinical CVD markers employing linear regression, adjusting for demographics and lifestyle factors. Cox proportional hazards analysis evaluated the associations between meat-associated metabolites and CVD incident. Results: After correction for multiple testing, we identified 458, 368, and 403 metabolites associated with self-reported white, unprocessed red and processed red meat intake, respectively. Metabolites positively associated with all three meat types were related with higher plasma levels of apolipoprotein A1, C-reactive protein, and increased intima-media thickness, while metabolites negatively associated were related with higher fasting insulin levels. Processed red meat-associated metabolites were related with higher levels of fasting insulin, glycated hemoglobin, and lipoprotein(a) and were inversely related with maximal oxygen consumption. Two metabolites, 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (HR: 1.32; 95% CI: 1.08, 1.62) and glutamine degradant (HR: 1.35; 95% CI: 1.07, 1.72), associated with higher intakes of all three meat types were also related with a higher risk of incident CVD. Conclusion: This study identified hundreds of metabolites associated with self-reported intake of different meat types. Processed red meat increasing metabolites were associated with worse glycemic measures and reduced cardiovascular function. These findings may enhance our understanding of the relationship between meat intake and CVD, providing insights into underlying mechanisms. ### Competing Interest Statement JA has served on advisory boards for Astella, AstraZeneca, and Boehringer Ingelheim, and has received lecturing fees from AstraZeneca and Novartis, all of which are unrelated to the present work. JS reports direct or indirect stock ownership in companies (Anagram kommunikation AB, Sence Research AB, Symptoms Europe AB, MinForskning AB) providing services to companies and authorities in the health sector including Amgen, AstraZeneca, Bayer, Boehringer, Eli Lilly, Gilead, GSK, Goteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linkoping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma, WeMind. The remaining authors declare no competing interests. ### Funding Statement We acknowledge the financial support from the European Research Council [ERC-STG-2018-801965 (TF); ERC-CoG-2014-649021 (MO-M)], the Swedish Research Council [VR 2019-00977 (SCL), 2019-01471 (TF), 2018-02784 (MO-M), 2019-01015 (JA), 2020-00243 (JA), 2019-01236 (GE), 2022-01460 (SA)], the Swedish Heart-Lung Foundation [Hjärt-Lungfonden, 20230687 (TF), , 20200711 (MO-M), 20180343 (JA)], 20200173 (GE)], the Swedish Cancer Society [Cancerfonden, 2021 (SCL)],FORMAS [2020-00989 (SA)], Erik, Karin och Gösta Selanders Stiftelse [2020 (SA)], Åke Wibergs Stiftelse [2020 (SA)], Marcus Borgström Foundation [2020 (SA)], EFSD/Novo Nordisk [2020 (SA)], EpiHealth [2022 (SA)]. The main funding body of The Swedish CArdioPulmonary bioImage Study (SCAPIS) is the Swedish Heart and Lung Foundation. The study is also funded by the Knut and Alice Wallenberg Foundation, the Swedish Research Council, VINNOVA (Sweden's Innovation agency), the University of Gothenburg and Sahlgrenska University Hospital, Karolinska Institutet and Region Stockholm, Linköping University and University Hospital, Lund University and Skåne University Hospital, Umeå University and University Hospital, Uppsala University and University Hospital. We would like to acknowledge the help of Biobank Sweden and the local biobank facilities for their services in handling of biological samples and biobanking. The computations and data handling were enabled by resources in project sens2019512 provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), funded by the Swedish Research Council through grant agreement no. 2022-06725. The EpiHealth study is funded as a strategic research area by the Swedish government. POEM was funded by Uppsala University Hospital. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained from the Swedish Ethical Review Authority (DNR 2023-07352-01, DNR 2009-057, DNR 2018-315). All participants from each of the three studies provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The dataset supporting the findings of this research article was provided by the SCAPIS, EPIHEALTH, and POEM Data Access Board and are not publicly available due to confidentiality. Data can be shared upon reasonable request to the corresponding author, but only after obtaining permission from the Swedish Ethical Review Authority (https://etikprovningsmyndigheten.se)

BACKGROUND Disruptions in gut microbiota have been implicated in cardiometabolic disorders and other health outcomes. Antibiotics are known gut microbiota disruptors, but their long-term consequences on taxonomic composition of the gut microbiome remain underexplored. METHODS We investigated associations between register-based oral antibiotic use over 8 years and gut microbiota composition assessed with fecal shotgun metagenomics in 15,131 adults from the Swedish population-based studies SCAPIS, MOS, and SIMPLER. We applied multivariable regression models with the number of prescriptions in three pre-specified periods before fecal sampling (<1 year, 1-4, 4-8 years) as the main exposures and adjusted for sociodemographics, lifestyle, and comorbidities. Secondary analyses included participants with only one antibiotic course or none. RESULTS Antibiotic use <1 year before fecal sampling was associated with the greatest reduction in gut microbiota species diversity; however, antibiotic use 1-4 years and 4-8 years earlier was also associated with decreased diversity. Clindamycin, fluoroquinolones, and flucloxacillin accounted for most of the associations between antibiotic use and the abundance of individual species across all periods. Use of these three antibiotics 4-8 years earlier was associated with altered abundance of 10-14% of the species studied; use of penicillin V, extended-spectrum penicillins, and nitrofurantoin were associated with altered abundance of only a few species. Similar results were found when comparing one antibiotic course 4-8 years before sampling vs. none in the past 8 years. CONCLUSION Commonly prescribed antibiotics like clindamycin, fluoroquinolones, and the narrow-spectrum flucloxacillin appear to have long-lasting consequences for the gut microbiota. ### Competing Interest Statement J.S. reports direct or indirect stock ownership in companies (Anagram Kommunikation AB, Sence Research AB, Symptoms Europe AB, MinForskning AB) that provide services not related to the present work to companies and authorities in the health sector, including Amgen, AstraZeneca, Bayer, Boehringer, Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma, and WeMind. J.Ä. has served on the advisory boards for Astella, AstraZeneca, and Boehringer Ingelheim and has received lecturing fees from AstraZeneca and Novartis, all unrelated to the present work. J.F.L. has also received financial support from M.S.D. to develop a paper reviewing national healthcare registers in China, has ongoing discussions with M.S.D. about unrelated IBD research, and receives funding for celiac disease research from Takeda. The remaining authors declare no competing interests. The remaining authors declare no competing interests. ### Funding Statement Financial support was obtained in the form of grants from the European Research Council [ERC-STG-2018-801965 (T.F.); ERC-CoG-2014-649021 (M.O.-M.); ERC-STG-2015-679242 (J.G.S.)], the Swedish Heart-Lung Foundation [Hjärt-Lungfonden, 2019-0505 (T.F.); 2018-0343 (J.Ä.); 2020-0711 (M.O.-M.)], the Swedish Research Council [VR, 2019-01471 (T.F.), 2018-02784 (MO-M), 2018-02837 (M.O.-M.), 2019-01015 (J.Ä.), 2020-00243 (J.Ä.), 2022-01460 (S.A.), and EXODIAB 2009-1039 (M.O.-M.)], the Swedish Research Council for Sustainable Development [FORMAS, 2020-00989 (S.A.)], Göran Gustafsson foundation [2016 (T.F.)], Axel and Signe Lagerman's foundation (T.F.), the A.L.F. governmental grant 2018-0148 (M.O.-M.), The Novo Nordic Foundation NNF20OC0063886 (M.O.-M.), The Swedish Diabetes Foundation DIA 2018-375 (M.O.-M.), Center of Clinical Research (CKF) in Region Dalarna (J.Ä.), Epihealth (S.A.), and governmental funding of clinical research within the Swedish National Health Service (J.G.S.). B.K. is supported by a Gullstrand fellow grant from the Uppsala University Hospital. We acknowledge the Swedish Heart-Lung Foundation, the main funding body of SCAPIS. Funding for the SCAPIS study was also provided by the Knut and Alice Wallenberg Foundation, the Swedish Research Council and VINNOVA (Sweden's innovation agency), the University of Gothenburg and Sahlgrenska University Hospital, Karolinska Institutet and Stockholm County Council, Linköping University and University Hospital, Lund University and Skåne University Hospital, Umeå University and University Hospital, Uppsala University and University Hospital. We thank SIMPLER for the providing facilities and experimental support and Anna-Karin Kolseth and Niclas Håkansson for assistance. SIMPLER receives funding through the Swedish Research Council under grant no 2017-00644, 2017-06100, 2021-00160, and Stiftelsen Olle Engkvist Byggmästare. The computations and data handling were enabled by resources in project sens2019512 and simp2023007 provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), funded by the Swedish Research Council through grant agreement no. 2022-06725. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained from the Swedish Ethical Review Authority (DNR 2018-315 B and amendments 2020-06597 and 2022-06460-02, DNR 2012-594 and the amendment 2017-768 and 2020-05611, DNR 2022-06137-01 and amendment DNR 2023-04785-02). All participants from each of the three studies provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data supporting the conclusions of this article were provided by the SCAPIS, SIMPER, and MOS and are not shared publicly due to confidentiality. Data will be shared upon reasonable request to the corresponding author only after permission from the Swedish Ethical Review Authority (https://etikprovningsmyndigheten.se) and from the boards of SCAPIS, (https://www.scapis.org/data-access), SIMPER (https://www.simpler4health.se), and MOS (https://www.malmo-kohorter.lu.se/malmo-offspring-study-mos). The code used for the statistical analysis and .csv files of the supplementary tables will be available at https://github.com/MolEpicUU/antibiot_gut.

AIMS/HYPOTHESIS:Parenting a child with type 1 diabetes has been associated with stress-related symptoms. This study aimed to elucidate the potential impact on parental risk of major cardiovascular events (MCE) and death. METHODS:In this register-based study, we included the parents of 18,871 children, born 1987-2020 and diagnosed with type 1 diabetes in Sweden at <18 years. The median parental age at the child's diagnosis was 39.0 and 41.0 years for mothers and fathers, respectively. The cohort also encompassed 714,970 population-based matched parental control participants and 12,497 parental siblings. Cox proportional hazard regression models were employed to investigate the associations between having a child with type 1 diabetes and incident MCE and all-cause death, and, as secondary outcomes, acute coronary syndrome and ischaemic heart disease (IHD). We adjusted for potential confounders including parental type 1 diabetes and country of birth. RESULTS:During follow-up (median 12 years, range 0-35), we detected no associations between parenting a child with type 1 diabetes and MCE in mothers (adjusted HR [aHR] 1.02; 95% CI 0.90, 1.15) or in fathers (aHR 1.01; 95% CI 0.94, 1.08). We noted an increased hazard of IHD in exposed mothers (aHR 1.21; 95% CI 1.05, 1.41) with no corresponding signal in fathers (aHR 0.97; 95% CI 0.89, 1.05). Parental sibling analysis did not confirm the association in exposed mothers (aHR 1.01; 95% CI 0.73, 1.41). We further observed a slightly increased hazard of all-cause death in exposed fathers (aHR 1.09; 95% CI 1.01, 1.18), with a similar but non-significant estimate noted in exposed mothers (aHR 1.07; 95% CI 0.96, 1.20). The estimates from the sibling analyses of all-cause death in fathers and mothers were 1.12 (95% CI 0.90, 1.38) and 0.73 (95% CI 0.55, 0.96), respectively. CONCLUSIONS/INTERPRETATION:Having a child diagnosed with type 1 diabetes in Sweden was not associated with MCE, but possibly with all-cause mortality. Further studies are needed to disentangle potential underlying mechanisms, and to investigate parental health outcomes across the full lifespan.

BACKGROUND:Diagnostic testing is essential for disease surveillance and test-trace-isolate efforts. We aimed to investigate if residential area sociodemographic characteristics and test accessibility were associated with Coronavirus Disease 2019 (COVID-19) testing rates. METHODS:We included 426 224 patient-initiated COVID-19 polymerase chain reaction tests from Uppsala County in Sweden from 24 June 2020 to 9 February 2022. Using Poisson regression analyses, we investigated if postal code area Care Need Index (CNI; median 1.0, IQR 0.8-1.4), a composite measure of sociodemographic factors used in Sweden to allocate primary healthcare resources, was associated with COVID-19 daily testing rates after adjustments for community transmission. We assessed if the distance to testing station influenced testing, and performed a difference-in-difference-analysis of a new testing station targeting a disadvantaged neighbourhood. RESULTS:We observed that CNI, i.e. primary healthcare need, was negatively associated with COVID-19 testing rates in inhabitants 5-69 years. More pronounced differences were noted across younger age groups and in Uppsala City, with test rate ratios in children (5-14 years) ranging from 0.56 (95% CI 0.47-0.67) to 0.87 (95% CI 0.80-0.93) across three pandemic waves. Longer distance to the nearest testing station was linked to lower testing rates, e.g. every additional 10 km was associated with a 10-18% decrease in inhabitants 15-29 years in Uppsala County. The opening of the targeted testing station was associated with increased testing, including twice as high testing rates in individuals aged 70-105, supporting an intervention effect. CONCLUSIONS:Ensuring accessible testing across all residential areas constitutes a promising tool to decrease inequalities in testing.

Myocardial infarction is a leading cause of death globally but is notoriously difficult to predict. We aimed to identify biomarkers of an imminent first myocardial infarction and design relevant prediction models. Here, we constructed a new case–cohort consortium of 2,018 persons without prior cardiovascular disease from six European cohorts, among whom 420 developed a first myocardial infarction within 6 months after the baseline blood draw. We analyzed 817 proteins and 1,025 metabolites in biobanked blood and 16 clinical variables. Forty-eight proteins, 43 metabolites, age, sex and systolic blood pressure were associated with the risk of an imminent first myocardial infarction. Brain natriuretic peptide was most consistently associated with the risk of imminent myocardial infarction. Using clinically readily available variables, we devised a prediction model for an imminent first myocardial infarction for clinical use in the general population, with good discriminatory performance and potential for motivating primary prevention efforts.