Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank.

Background: Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited. Methods: To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n = 677) to age- and sex-matched controls (n = 2708). Findings: SCZ cases exhibited higher genetic predisposition to SCZ (OR = 1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR = 0.88, 95% CI 0.88-0.96) and C-reactive protein (OR = 0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases. Interpretation: These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data. Funding: EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.

Self- and informant-reported personality traits and vaccination against COVID-19.

As COVID-19 vaccines' accessibility has grown, so has the role of personal choice in vaccination, and not everybody is willing to vaccinate. Exploring personality traits' associations with vaccination could highlight some person-level drivers of, and barriers to, vaccination. We used self- and informant-ratings of the Five-Factor Model domains and their subtraits (a) measured approximately at the time of vaccination with the 100 Nuances of Personality (100NP) item pool (N = 56,575) and (b) measured on average ten years before the pandemic with the NEO Personality Inventory-3 (NEO-PI-3; N = 3,168). We tested individual domains' and either items' (in the 100NP sample) or facets' (in the NEO-PI-3 sample) associations with vaccination, as well as their collective ability to predict vaccination using elastic net models trained and tested in independent sample partitions. Although the NEO-PI-3 domains and facets did not predict vaccination ten years later, the domains correlated with vaccination in the 100NP sample, with vaccinated people scoring slightly higher on neuroticism and agreeableness and lower on openness, controlling for age, sex, and education. Collectively, the five domains predicted vaccination with an accuracy of r = .08. Associations were stronger at the item level. Vaccinated people were, on average, more science-minded, politically liberal, respectful of rules and authority, and anxious but less spiritual, religious, and self-assured. The 100NP items collectively predicted vaccination with r = .31 accuracy. We conclude that unvaccinated people may be a psychologically heterogeneous group and highlight some potential areas for action in vaccination campaigns.

Mapping of the gene network that regulates glycan clock of ageing.

Glycans are an essential structural component of immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation), a trait which was shown to be associated with ageing. We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Upregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.

Risk of SARS-CoV-2 infection and hospitalization in individuals with natural, vaccine-induced and hybrid immunity: a retrospective population-based cohort study from Estonia.

A large proportion of the world's population has some form of immunity against SARS-CoV-2, through either infection ('natural'), vaccination or both ('hybrid'). This retrospective cohort study used data on SARS-CoV-2, vaccination, and hospitalization from national health system from February 2020 to June 2022 and Cox regression modelling to compare those with natural immunity to those with no (Cohort1, n = 94,982), hybrid (Cohort2, n = 47,342), and vaccine (Cohort3, n = 254,920) immunity. In Cohort 1, those with natural immunity were at lower risk for infection during the Delta (aHR 0.17, 95%CI 0.15-0.18) and higher risk (aHR 1.24, 95%CI 1.18-1.32) during the Omicron period than those with no immunity. Natural immunity conferred substantial protection against COVID-19-hospitalization. Cohort 2-in comparison to natural immunity hybrid immunity offered strong protection during the Delta (aHR 0.61, 95%CI 0.46-0.80) but not the Omicron (aHR 1.05, 95%CI 0.93-1.1) period. COVID-19-hospitalization was extremely rare among individuals with hybrid immunity. In Cohort 3, individuals with vaccine-induced immunity were at higher risk than those with natural immunity for infection (Delta aHR 4.90, 95%CI 4.48-5.36; Omicron 1.13, 95%CI 1.06-1.21) and hospitalization (Delta aHR 7.19, 95%CI 4.02-12.84). These results show that risk of infection and severe COVID-19 are driven by personal immunity history and the variant of SARS-CoV-2 causing infection.

Genetic insights into the age-specific biological mechanisms governing human ovarian aging.

There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.

networkGWAS: a network-based approach to discover genetic associations.

MOTIVATION: While the search for associations between genetic markers and complex traits has led to the discovery of tens of thousands of trait-related genetic variants, the vast majority of these only explain a small fraction of the observed phenotypic variation. One possible strategy to overcome this while leveraging biological prior is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffer from a vast search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. RESULTS: To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to network-based genome-wide association studies using mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated P-values, which are obtained through circular and degree-preserving network permutations. networkGWAS successfully detects known associations on diverse synthetic phenotypes, as well as known and novel genes in phenotypes from Saccharomycescerevisiae and Homo sapiens. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. AVAILABILITY AND IMPLEMENTATION: https://github.com/BorgwardtLab/networkGWAS.git.

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients.

The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.

A retrospective cohort study of incidence and risk factors for severe SARS-CoV-2 breakthrough infection among fully vaccinated people.

SARS-CoV-2 vaccination is currently the mainstay in combating the COVID-19 pandemic. However, there are still people among vaccinated individuals suffering from severe forms of the disease. We conducted a retrospective cohort study based on data from nationwide e-health databases. The study included 184,132 individuals who were SARS-CoV-2 infection-naive and had received at least a primary series of COVID-19 vaccination. The incidence of BTI (breakthrough infection) was 8.03 (95% CI [confidence interval] 7.95⎼8.13/10,000 person-days), and for severe COVID-19 it was 0.093 (95% CI 0.084⎼ 0.104/10,000 person-days). The protective effect of vaccination against severe COVID-19 remained constant for up to six months, and the booster dose offered an additional pronounced benefit (hospitalization aHR 0.32, 95% CI 0.19⎼0.54). The risk of severe COVID-19 was higher among those ≥ 50 years of age (aHR [adjusted hazard ratio] 2.06, 95% CI 1.25⎼3.42) and increased constantly with every decade of life. Male sex (aHR 1.32, 95% CI 1.16⎼1.45), CCI (The Charlson Comorbidity Index) score ≥ 1 (aHR 2.09, 95% CI 1.54⎼2.83), and a range of comorbidities were associated with an increased risk of COVID-19 hospitalization. There are identifiable subgroups of COVID-19-vaccinated individuals at high risk of hospitalization due to SARS-CoV-2 infection. This information is crucial to driving vaccination programs and planning treatment strategies.

Lessons learned during the process of reporting individual genomic results to participants of a population-based biobank.

The return of individual genomic results (ROR) to research participants is still in its early phase, and insight on how individuals respond to ROR is scarce. Studies contributing to the evidence base for best practices are crucial before these can be established. Here, we describe a ROR procedure conducted at a population-based biobank, followed by surveying the responses of almost 3000 participants to a range of results, and discuss lessons learned from the process, with the aim of facilitating large-scale expansion. Overall, participants perceived the information that they received with counseling as valuable, even when the reporting of high risks initially caused worry. The face-to-face delivery of results limited the number of participants who received results. Although the participants highly valued this type of communication, additional means of communication need to be considered to improve the feasibility of large-scale ROR. The feedback collected sheds light on the value judgements of the participants and on potential responses to the receipt of genetic risk information. Biobanks in other countries are planning or conducting similar projects, and the sharing of lessons learned may provide valuable insight and aid such endeavors.

Precise, Genotype-First Breast Cancer Prevention: Experience With Transferring Monogenic Findings From a Population Biobank to the Clinical Setting.

Although hereditary breast cancer screening and management are well accepted and established in clinical settings, these efforts result in the detection of only a fraction of genetic predisposition at the population level. Here, we describe our experience from a national pilot study (2018-2021) in which 180 female participants of Estonian biobank (of >150,000 participants in total) were re-contacted to discuss personalized clinical prevention measures based on their genetic predisposition defined by 11 breast cancer-related genes. Our results show that genetic risk variants are relatively common in the average-risk Estonian population. Seventy-five percent of breast cancer cases in at-risk subjects occurred before the age of 50 years. Only one-third of subjects would have been eligible for clinical screening according to the current criteria. The participants perceived the receipt of genetic risk information as valuable. Fluent cooperation of project teams supported by state-of-art data management, quality control, and secure transfer can enable the integration of research results to everyday medical practice in a highly efficient, timely, and well-accepted manner. The positive experience in this genotype-first breast cancer study confirms the value of using existing basic genomic data from population biobanks for precise prevention.

Novel Early Pregnancy Multimarker Screening Test for Preeclampsia Risk Prediction.

Preeclampsia (PE) is a common pregnancy-linked disease, causing preterm births, complicated deliveries, and health consequences for mothers and offspring. We have previously developed 6PLEX, a multiplex assay that measures PE-related maternal serum biomarkers ADAM12, sENG, leptin, PlGF, sFlt-1, and PTX3 in a single test tube. This study investigated the potential of 6PLEX to develop novel PE prediction models for early pregnancy. We analyzed 132 serum samples drawn at 70-275 gestational days (g days) from 53 pregnant women (PE, n = 22; controls, n = 31). PE prediction models were developed using a machine learning strategy based on the stepwise selection of the most significant models and incorporating parameters with optimal resampling. Alternative models included also placental FLT1 rs4769613 T/C genotypes, a high-confidence risk factor for PE. The best performing PE prediction model using samples collected at 70-98 g days comprised of PTX3, sFlt-1, and ADAM12, the subject's parity and gestational age at sampling (AUC 0.94 [95%CI 0.84-0.99]). All cases, that developed PE several months later (onset 257.4 ± 15.2 g days), were correctly identified. The model's specificity was 80% [95%CI 65-100] and the overall accuracy was 88% [95%CI 73-95]. Incorporating additionally the placental FLT1 rs4769613 T/C genotype data increased the prediction accuracy to 93.5% [AUC = 0.97 (95%CI 0.89-1.00)]. However, 6PLEX measurements of samples collected at 100-182 g days were insufficiently informative to develop reliable PE prediction models for mid-pregnancy (accuracy <75%). In summary, the developed model opens new horizons for first-trimester PE screening, combining the easily standardizable 6PLEX assay with routinely collected antenatal care data and resulting in high sensitivity and specificity.

A Principal Component Informed Approach to Address Polygenic Risk Score Transferability Across European Cohorts.

One important confounder in genome-wide association studies (GWASs) is population genetic structure, which may generate spurious associations if not properly accounted for. This may ultimately result in a biased polygenic risk score (PRS) prediction, especially when applied to another population. To explore this matter, we focused on principal component analysis (PCA) and asked whether a population genetics informed strategy focused on PCs derived from an external reference population helps in mitigating this PRS transferability issue. Throughout the study, we used two complex model traits, height and body mass index, and samples from UK and Estonian Biobanks. We aimed to investigate 1) whether using a reference population (1000G) for computation of the PCs adjusted for in the discovery cohort improves the resulting PRS performance in a target set from another population and 2) whether adjusting the validation model for PCs is required at all. Our results showed that any other set of PCs performed worse than the one computed on samples from the same population as the discovery dataset. Furthermore, we show that PC correction in GWAS cannot prevent residual population structure information in the PRS, also for non-structured traits. Therefore, we confirm the utility of PC correction in the validation model when the investigated trait shows an actual correlation with population genetic structure, to account for the residual confounding effect when evaluating the predictive value of PRS.

Using genetic variation to disentangle the complex relationship between food intake and health outcomes.

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.

Long-term mortality following SARS-CoV-2 infection: A national cohort study from Estonia.

Background: The objective of this study was to describe 12-month mortality following SARS-CoV-2 infection compared with a reference population with no history of SARS-CoV-2. Methods: Nationwide cohort study using electronic health care data on SARS-CoV-2 RNA positive cases (n= 66,287) and reference group subjects (n=254,969) with linkage to SARS-CoV-2 testing and death records. Findings: People infected with SARS-COV-2 had more than three times the risk of dying over the following year compared with those who remained uninfected (aHR 3·1, 95%CI 2·9-3·3). Short-term mortality (up to 5 weeks post-infection) was significantly higher among COVID-19 group (1623·0/10 000) than in the reference group (118/10 000). For COVID-19 cases aged 60 years or older, increased mortality persisted until the end of the first year after infection, and was related to increased risk for cardiovascular (aHR 2·1, 95%CI 1·8-2·3), cancer (aHR 1·5, 95%CI 1·2-1·9), respiratory system diseases (aHR 1·9, 95%CI 1·2-3·0), and other causes of death (aHR 1·8, 95%CI 1·4-2·2). Interpretation: Increased risk of death from SARS-CoV-2 is not limited to the acute illness: SARS-CoV-2 infection carries a substantially increased mortality in the following 12 months. This excess death mainly occurs in older people and is driven by broad array of causes of death. Funding: Research was carried out with the support of Estonian Research Council (grants PRG1197, PRG198), European Regional Development Fund (RITA 1/02-120) and European Social Fund via IT Academy program.

A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk.

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year's OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

Effectiveness and feasibility of cardiovascular disease personalized prevention on high polygenic risk score subjects: a randomized controlled pilot study.

Aims: The aim of this study was to evaluate the effect of the intervention by proactively sharing a patient's high polygenic risk score (PRS) for coronary artery disease (CAD). Outcomes included: (i) reduction in cardiovascular disease (CVD) risk factors over 12 months; (ii) difference in purchased prescriptions of lipid-lowering and anti-hypertensive drugs between intervention group and control group subjects; and (iii) opinion of the participating physicians and subjects on PRS usefulness. Methods and results: This randomized controlled trial was conducted among middle-aged subjects with a top 20% CAD PRS in a family medicine setting. Participants were selected from 26 953 Estonian Biobank cohort participants. Subjects were informed and counselled about their PRS score and CAD risk using the visual tool at baseline (Visit I), counselling session (Visit II), and on the final Visit III at 12 months. The primary endpoint was not significantly different. However, the intervention group participants had a significantly higher probability of initiating statin treatment compared with the controls. Their levels of LDL-cholesterol (LDL-C) were significantly decreased compared with baseline on Visit III and significantly lower than in the control group. The vast majority of participating family physicians believe that finding out about genetic risks will affect the subject's lifestyle and medication compliance. Conclusion: Most of our outcome measures were in favour of this intervention. Participants achieved larger changes in cholesterol and blood pressure values. The vast majority (98.4%) of family physicians are interested in continuing to use genetic risk assessment in practice.

Evaluating the prognostic performance of a polygenic risk score for breast cancer risk stratification.

BACKGROUND: Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value. OBJECTIVES: To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only. METHODS: We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance. RESULTS: A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50-62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75-85% PRS-group, 1.34 for the 85-95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model's AUC was 0.720 (95% CI: 0.675-0.765) for 3-year and 0.704 (95% CI: 0.670-0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years. CONCLUSION: The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.

Mendelian Randomization Identifies the Potential Causal Impact of Dietary Patterns on Circulating Blood Metabolites.

Nutrition plays an important role in the development and progress of several health conditions, but the exact mechanism is often still unclear. Blood metabolites are likely candidates to be mediating these relationships, as their levels are strongly dependent on the frequency of consumption of several foods/drinks. Understanding the causal effect of food on metabolites is thus of extreme importance. To establish these effects, we utilized two-sample Mendelian randomization using the genetic variants associated with dietary traits as instrumental variables. The estimates of single-nucleotide polymorphisms' effects on exposures were obtained from a recent genome-wide association study (GWAS) of 25 individual and 15 principal-component dietary traits, whereas the ones for outcomes were obtained from a GWAS of 123 blood metabolites measured by nuclear magnetic resonance spectroscopy. We identified 413 potentially causal links between food and metabolites, replicating previous findings, such as the association between increased oily fish consumption and higher DHA, and highlighting several novel associations. Most of the associations were related to very-low-density, intermediate-density (IDL), and low-density lipoproteins (LDL). For example, we found that constituents of IDL particles and large LDL particles were raised by coffee and alcohol while lowered by an overall healthier diet and fruit consumption. Our findings provide a strong base of evidence for planning future RCTs aimed at understanding the role of diet in determining blood metabolite levels.

Adherence to recommendations for secondary prevention medications after myocardial infarction in Estonia: comparison of real-world data from 2004 to 2005 and 2017 to 2018.

BACKGROUND: Relatively high rates of adherence to myocardial infarction (MI) secondary prevention medications have been reported, but register-based, objective real-world data is scarce. We aimed to analyse adherence to guideline-recommended medications for secondary prevention of MI in 2017 to 2018 (period II) and compare the results with data from 2004 to 2005 (period I) in Estonia. METHODS: Study populations were formed based on data from the Estonian Health Insurance Fund's database and on Estonian Myocardial Infarction Register. By linking to the Estonian Medical Prescription Centre database adherence to guideline-recommended medications for MI secondary prevention was assessed for 1 year follow-up period from the first hospitalization due to MI. Data was analysed using the defined daily dosages methodology. RESULTS: Total of 6694 and 6060 cases of MI were reported in periods I and II, respectively. At least one prescription during the follow up period was found for beta-blockers in 81.0% and 83.5% (p = 0.001), for angiotensin converting enzyme inhibitor/angiotensin II receptor blocker (ACEi/ARB) in 76.9% and 66.0% (p < 0.001), and for statins in 44.0% and 67.0% (p < 0.001) of patients in period I and II, respectively. P2Y12 inhibitors were used by 76.4% of patients in period II. The logistic regression analysis adjusted to gender and age revealed that some drugs and drug combinations were not allocated similarly in different age and gender groups. CONCLUSIONS: In Estonia, adherence to MI secondary prevention guideline-recommended medications has improved. But as adherence is still not ideal more attention should be drawn to MI secondary prevention through systematic guideline implementation.

Development of the Gastrointestinal Dysfunction Score (GIDS) for critically ill patients - A prospective multicenter observational study (iSOFA study).

BACKGROUND & AIMS: To develop a five grade score (0-4 points) for the assessment of gastrointestinal (GI) dysfunction in adult critically ill patients. METHODS: This prospective multicenter observational study enrolled consecutive adult patients admitted to 11 intensive care units in nine countries. At all sites, daily clinical data with emphasis on GI clinical symptoms were collected and intra-abdominal pressure measured. In five out of 11 sites, the biomarkers citrulline and intestinal fatty acid-binding protein (I-FABP) were measured additionally. Cox models with time-dependent scores were used to analyze associations with 28- and 90-day mortality. The models were estimated with stratification for study center. RESULTS: We included 540 patients (224 with biomarker measurements) with median age of 65 years (range 18-94), the Simplified Acute Physiology Score II score of 38 (interquartile range 26-53) points, and Sequential Organ Failure Assessment (SOFA) score of 6 (interquartile range 3-9) points at admission. Median ICU length of stay was 3 (interquartile range 1-6) days and 90-day mortality 18.9%. A new five grade Gastrointestinal Dysfunction Score (GIDS) was developed based on the rationale of the previously developed Acute GI Injury (AGI) grading. Citrulline and I-FABP did not prove their potential for scoring of GI dysfunction in critically ill. GIDS was independently associated with 28- and 90-day mortality when added to SOFA total score (HR 1.40; 95%CI 1.07-1.84 and HR 1.40; 95%CI 1.02-1.79, respectively) or to a model containing all SOFA subscores (HR 1.48; 95%CI 1.13-1.92 and HR 1.47; 95%CI 1.15-1.87, respectively), improving predictive power of SOFA score in all analyses. CONCLUSIONS: The newly developed GIDS is additive to SOFA score in prediction of 28- and 90-day mortality. The clinical usefulness of this score should be validated prospectively. TRIAL REGISTRATION: NCT02613000, retrospectively registered 24 November 2015.