A single faecal microbiota transplantation modulates the microbiome and improves clinical manifestations in a rat model of colitis

Inflammatory bowel disease; Faecal microbiota transplantation; Rat model of colitisEnfermedad inflamatoria intestinal; Trasplante de microbiota fecal; Modelo de colitis en ratasMalaltia inflamatòria intestinal; Trasplantament de microbiota fecal; Model de colitis en ratesBackground: Faecal microbiota transplantation (FMT) is a novel potential therapy for inflammatory bowel diseases, but it is poorly characterised. Methods: We evaluated the performance of the mouse and rat as a pre-clinical model for human microbiota engraftment. We then characterised the effect of a single human stool transfer (HST) on a humanised model of DSS-induced colitis. Colonic and faecal microbial communities were analysed using the 16S rRNA approach and clinical manifestations were assessed in a longitudinal setting. Findings: The microbial community of rats showed greater similarity to that of humans, while the microbiome of mice showed less similarity to that of humans. Moreover, rats captured more human microbial species than mice after a single HST. Using the rat model, we showed that HST compensated faecal dysbiosis by restoring alpha-diversity and by increasing the relative abundance of health-related microbial genera. To some extent, HST also modulated the microbial composition of colonic tissue. These faecal and colonic microbial communities alterations led to a relative restoration of colon length, and a significant decrease in both epithelium damage and disease severity. Remarkably, stopping inflammation by removing DSS before HST caused a faster and greater recovery of both microbiome and clinical manifestation features. Interpretation: Our results indicate that the rat outperforms the mouse as a model for human microbiota engraftment and show that the efficacy of HST can be enhanced when inflammation stimulation is withdrawn. Finally, our findings support a new therapeutic strategy based on the use FMT combined with anti inflammatory drugs.Study funded by the Instituto de Salud Carlos III/FEDER (PI17/00614), a government agency. The funder had no role in study design, data collection, data analysis, interpretation or writing of the report

Sex Differences in Multimorbidity, Inappropriate Medication and Adverse Outcomes of Inpatient Care : MoPIM Cohort Study

There is no published evidence on the possible differences in multimorbidity, inappropriate prescribing, and adverse outcomes of care, simultaneously, from a sex perspective in older patients. We aimed to identify those possible differences in patients hospitalized because of a chronic disease exacerbation. A multicenter, prospective cohort study of 740 older hospitalized patients (≥65 years) was designed, registering sociodemographic variables, frailty, Barthel index, chronic conditions (CCs), geriatric syndromes (GSs), polypharmacy, potentially inappropriate prescribing (PIP) according to STOPP/START criteria, and adverse drug reactions (ADRs). Outcomes were length of stay (LOS), discharge to nursing home, in-hospital mortality, cause of mortality, and existence of any ADR and its worst consequence. Bivariate analyses between sex and all variables were performed, and a network graph was created for each sex using CC and GS. A total of 740 patients were included (53.2% females, 53.5% ≥85 years old). Women presented higher prevalence of frailty, and more were living in a nursing home or alone, and had a higher percentage of PIP related to anxiolytics or pain management drugs. Moreover, they presented significant pairwise associations between CC, such as asthma, vertigo, thyroid diseases, osteoarticular diseases, and sleep disorders, and with GS, such as chronic pain, constipation, and anxiety/depression. No significant differences in immediate adverse outcomes of care were observed between men and women in the exacerbation episode

Multimorbidity patterns in COVID-19 patients and their relationship with infection severity : MRisk-COVID study

Several chronic conditions have been identified as risk factors for severe COVID-19 infection, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess their relationship with infection severity/mortality. The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were subjected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within each stratum. Bivariate analyses were performed to assess the relationship between severity/mortality and age, sex, and multimorbidity clusters. Severe infection was reported in 9.5% (95% CI: 9.0-9.9) of the patients, and death occurred in 3.9% (95% CI: 3.6-4.2). We identified multimorbidity clusters related to severity/mortality in most age groups from 21 to 65 years. In males, the cluster with highest percentage of severity/mortality was Heart-liver-gastrointestinal (81-90 years, 34.1% severity, 29.5% mortality). In females, the clusters with the highest percentage of severity/mortality were Diabetes-cardiovascular (81-95 years, 22.5% severity) and Psychogeriatric (81-95 years, 16.0% mortality). This study characterized several multimorbidity clusters in COVID-19 patients based on sex and age, some of which were found to be associated with higher rates of infection severity/mortality, particularly in younger individuals. Further research is encouraged to ascertain the role of specific multimorbidity patterns on infection prognosis and identify the most vulnerable morbidity profiles in the community. Registered 4 August 2021 (retrospectively registered)

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

MoPIM cohort study

<p>Potentially Inappropriate Prescription Associated to Multimorbidity Patterns, Using the Explicit STOPP-START Criteria. MoPIM study cohort</p&gt

Multimorbidity patterns of chronic conditions and geriatric syndromes in older patients from the MoPIM multicentre cohort study

To estimate the frequency of chronic conditions and geriatric syndromes in older patients admitted to hospital because of an exacerbation of their chronic conditions, and to identify multimorbidity clusters in these patients. Multicentre, prospective cohort study. Internal medicine or geriatric services of five general teaching hospitals in Spain. 740 patients aged 65 and older, hospitalised because of an exacerbation of their chronic conditions between September 2016 and December 2018. Active chronic conditions and geriatric syndromes (including risk factors) of the patient, a score about clinical management of chronic conditions during admission, and destination at discharge were collected, among other variables. Multimorbidity patterns were identified using fuzzy c-means cluster analysis, taking into account the clinical management score. Prevalence, observed/expected ratio and exclusivity of each chronic condition and geriatric syndrome were calculated for each cluster, and the final solution was approved after clinical revision and discussion among the research team. 740 patients were included (mean age 84.12 years, SD 7.01; 53.24% female). Almost all patients had two or more chronic conditions (98.65%; 95% CI 98.23% to 99.07%), the most frequent were hypertension (81.49%, 95% CI 78.53% to 84.12%) and heart failure (59.86%, 95% CI 56.29% to 63.34%). The most prevalent geriatric syndrome was polypharmacy (79.86%, 95% CI 76.82% to 82.60%). Four statistically and clinically significant multimorbidity clusters were identified: osteoarticular, psychogeriatric, cardiorespiratory and minor chronic disease. Patient-level variables such as sex, Barthel Index, number of chronic conditions or geriatric syndromes, chronic disease exacerbation 3 months prior to admission or destination at discharge differed between clusters. In older patients admitted to hospital because of the exacerbation of chronic health problems, it is possible to define multimorbidity clusters using soft clustering techniques. These clusters are clinically relevant and could be the basis to reorganise healthcare circuits or processes to tackle the increasing number of older, multimorbid patients.

Factors associated to potentially inappropriate prescribing in older patients according to STOPP/START criteria: MoPIM multicentre cohort study

Objectives: The objectives of the present analyses are to estimate the frequency of potentially inappropriate prescribing (PIP) at admission according to STOPP/START criteria version 2 in older patients hospitalised due to chronic disease exacerbation as well as to identify risk factors associated to the most frequent active principles as potentially inappropriate medications (PIMs). Methods: A multicentre, prospective cohort study including older patients (≥65) hospitalized due to chronic disease exacerbation at the internal medicine or geriatric services of 5 hospitals in Spain between September 2016 and December 2018 was conducted. Demographic and clinical data was collected, and a medication review process using STOPP/START criteria version 2 was performed, considering both PIMs and potential prescribing omissions (PPOs). Primary outcome was defined as the presence of any most frequent principles as PIMs, and secondary outcomes were the frequency of any PIM and PPO. Descriptive and bivariate analyses were conducted on all outcomes and multilevel logistic regression analysis, stratified by participating centre, was performed on the primary outcome. Results: A total of 740 patients were included (mean age 84.1, 53.2% females), 93.8% of them presenting polypharmacy, with a median of 10 chronic prescriptions. Among all, 603 (81.5%) patients presented at least one PIP, 542 (73.2%) any PIM and 263 (35.5%) any PPO. Drugs prescribed without an evidence-based clinical indication were the most frequent PIM (33.8% of patients); vitamin D supplement in older people who are housebound or experiencing falls or with osteopenia was the most frequent PPO (10.3%). The most frequent active principles as PIMs were proton pump inhibitors (PPIs) and benzodiazepines (BZDs), present in 345 (46.6%) patients. This outcome was found significantly associated with age, polypharmacy and essential tremor in an explanatory model with 71% AUC. Conclusions: PIMs at admission are highly prevalent in these patients, especially those involving PPIs or BZDs, which affected almost half of the patients. Therefore, these drugs may be considered as the starting point for medication review and deprescription