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SignalP ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) is a very popular prediction method for signal peptides, the intrinsic signals that make proteins secretory. The SignalP web server has existed since 1995 and is now in its sixth major version. In this historical account, we (three authors who have taken part in the entire journey plus the first author of the latest version) describe the differences between the versions and discuss the various decisions taken along the way.
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Internet , Sinais Direcionadores de Proteínas , Software , Biologia Computacional/métodos , HumanosRESUMO
AIMS/HYPOTHESIS: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes. METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose. RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone. CONCLUSIONS/INTERPRETATION: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
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Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal-fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genes HAND2, TBX3 and RAP2C/FRMD7 at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.
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Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Hemorragia Pós-Parto , Humanos , Feminino , Hemorragia Pós-Parto/genética , Gravidez , Predisposição Genética para Doença , Loci Gênicos , Receptores de Progesterona/genética , Receptores de Progesterona/metabolismoRESUMO
AIMS: Heterogeneity in the rate of ß-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. METHODS: We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in ß-cell mass measured as fasting C-peptide. RESULTS: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in ß-cell function. The second signature was related to translation and viral infection was inversely associated with change in ß-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid ß-cell decline. CONCLUSIONS: Features that differ between individuals with slow and rapid decline in ß-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.
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Diabetes Mellitus Tipo 1 , Células Secretoras de Insulina , Humanos , Diabetes Mellitus Tipo 1/imunologia , Diabetes Mellitus Tipo 1/patologia , Células Secretoras de Insulina/patologia , Células Secretoras de Insulina/metabolismo , Feminino , Masculino , Adulto , Progressão da Doença , Biomarcadores/análise , Seguimentos , Adolescente , Adulto Jovem , Prognóstico , Proteômica , Peptídeo C/análise , Peptídeo C/sangue , Criança , Pessoa de Meia-Idade , Genômica , MultiômicaRESUMO
AIMS/HYPOTHESIS: The gut microbiome is implicated in the disease process leading to clinical type 1 diabetes, but less is known about potential changes in the gut microbiome after the diagnosis of type 1 diabetes and implications in glucose homeostasis. We aimed to analyse potential associations between the gut microbiome composition and clinical and laboratory data during a 2 year follow-up of people with newly diagnosed type 1 diabetes, recruited to the Innovative approaches to understanding and arresting type 1 diabetes (INNODIA) study. In addition, we analysed the microbiome composition in initially unaffected family members, who progressed to clinical type 1 diabetes during or after their follow-up for 4 years. METHODS: We characterised the gut microbiome composition of 98 individuals with newly diagnosed type 1 diabetes (ND cohort) and 194 autoantibody-positive unaffected family members (UFM cohort), representing a subgroup of the INNODIA Natural History Study, using metagenomic sequencing. Participants from the ND cohort attended study visits within 6 weeks from the diagnosis and 3, 6, 12 and 24 months later for stool sample collection and laboratory tests (HbA1c, C-peptide, diabetes-associated autoantibodies). Participants from the UFM cohort were assessed at baseline and 6, 12, 18, 24 and 36 months later. RESULTS: We observed a longitudinal increase in 21 bacterial species in the ND cohort but not in the UFM cohort. The relative abundance of Faecalibacterium prausnitzii was inversely associated with the HbA1c levels at diagnosis (p=0.0019). The rate of the subsequent disease progression in the ND cohort, as assessed by change in HbA1c, C-peptide levels and insulin dose, was associated with the abundance of several bacterial species. Individuals with rapid decrease in C-peptide levels in the ND cohort had the lowest gut microbiome diversity. Nineteen individuals who were diagnosed with type 1 diabetes in the UFM cohort had increased abundance of Sutterella sp. KLE1602 compared with the undiagnosed UFM individuals (p=1.2 × 10-4). CONCLUSIONS/INTERPRETATION: Our data revealed associations between the gut microbiome composition and the disease progression in individuals with recent-onset type 1 diabetes. Future mechanistic studies as well as animal studies and human trials are needed to further validate the significance and causality of these associations.
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Headache disorders are the most common disorders of the nervous system. The lifetime prevalence of headache disorders show that some individuals never experience headache. The etiology of complete freedom from headache is not known. To assess genetic variants associated with complete freedom from headache, we performed a genome-wide association study of individuals who have never experienced a headache. We included 63,992 individuals (2,998 individuals with complete freedom from headache and 60,994 controls) from the Danish Blood Donor Study Genomic Cohort. Participants were included in two rounds, from 2015 to 2018 and in 2020. We discovered a genome-wide significant association, with the lead variant rs7904615[G] in ADARB2 (EAF = 27%, OR = 1.20 [1.13-1.27], p = 3.92 × 10-9). The genomic locus was replicated in a non-overlapping cohort of 13,032 individuals (539 individuals with complete freedom from headache and 12,493 controls) from the Danish Blood Donor Study Genomic Cohort (p < 0.05, two-sided). Participants for the replication were included from 2015 to 2020. In conclusion, we show that complete freedom from headache has a genetic component, and we suggest that ADARB2 is involved in complete freedom from headache. The genomic locus was specific for complete freedom from headache and was not associated with any primary headache disorders.
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Doadores de Sangue , Estudo de Associação Genômica Ampla , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos de Coortes , Dinamarca/epidemiologia , Loci Gênicos , Predisposição Genética para Doença , Cefaleia/genética , Polimorfismo de Nucleotídeo Único , Proteínas de Ligação a RNA/genéticaRESUMO
AIMS/HYPOTHESES: Glucagon and glucagon-like peptide-1 (GLP-1) are derived from the same precursor; proglucagon, and dual agonists of their receptors are currently being explored for the treatment of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD). Elevated levels of endogenous glucagon (hyperglucagonaemia) have been linked with hyperglycaemia in individuals with type 2 diabetes but are also observed in individuals with obesity and MASLD. GLP-1 levels have been reported to be largely unaffected or even reduced in similar conditions. We investigated potential determinants of plasma proglucagon and associations of glucagon receptor signalling with metabolic diseases based on data from the UK Biobank. METHODS: We used exome sequencing data from the UK Biobank for ~410,000 white participants to identify glucagon receptor variants and grouped them based on their known or predicted signalling. Data on plasma levels of proglucagon estimated using Olink technology were available for a subset of the cohort (~40,000). We determined associations of glucagon receptor variants and proglucagon with BMI, type 2 diabetes and liver fat (quantified by liver MRI) and performed survival analyses to investigate if elevated proglucagon predicts type 2 diabetes development. RESULTS: Obesity, MASLD and type 2 diabetes were associated with elevated plasma levels of proglucagon independently of each other. Baseline proglucagon levels were associated with the risk of type 2 diabetes development over a 14 year follow-up period (HR 1.13; 95% CI 1.09, 1.17; n=1562; p=1.3×10-12). This association was of the same magnitude across strata of BMI. Carriers of glucagon receptor variants with reduced cAMP signalling had elevated levels of proglucagon (ß 0.847; 95% CI 0.04, 1.66; n=17; p=0.04), and carriers of variants with a predicted frameshift mutation had higher levels of liver fat compared with the wild-type reference group (ß 0.504; 95% CI 0.03, 0.98; n=11; p=0.04). CONCLUSIONS/INTERPRETATION: Our findings support the suggestion that glucagon receptor signalling is involved in MASLD, that plasma levels of proglucagon are linked to the risk of type 2 diabetes development, and that proglucagon levels are influenced by genetic variation in the glucagon receptor, obesity, type 2 diabetes and MASLD. Determining the molecular signalling pathways downstream of glucagon receptor activation may guide the development of biased GLP-1/glucagon co-agonist with improved metabolic benefits. DATA AVAILABILITY: All coding is available through https://github.com/nicwin98/UK-Biobank-GCG.
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Essential tremor (ET) is a prevalent neurological disorder with a largely unknown underlying biology. In this genome-wide association study meta-analysis, comprising 16,480 ET cases and 1,936,173 controls from seven datasets, we identify 12 sequence variants at 11 loci. Evaluating mRNA expression, splicing, plasma protein levels, and coding effects, we highlight seven putative causal genes at these loci, including CA3 and CPLX1. CA3 encodes Carbonic Anhydrase III and carbonic anhydrase inhibitors have been shown to decrease tremors. CPLX1, encoding Complexin-1, regulates neurotransmitter release. Through gene-set enrichment analysis, we identify a significant association with specific cell types, including dopaminergic and GABAergic neurons, as well as biological processes like Rho GTPase signaling. Genetic correlation analyses reveals a positive association between ET and Parkinson's disease, depression, and anxiety-related phenotypes. This research uncovers risk loci, enhancing our knowledge of the complex genetics of this common but poorly understood disorder, and highlights CA3 and CPLX1 as potential therapeutic targets.
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Tremor Essencial , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Tremor Essencial/genética , Humanos , Polimorfismo de Nucleotídeo Único , Loci GênicosRESUMO
CONTEXT: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. OBJECTIVE: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. METHODS: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. RESULTS: Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. CONCLUSION: These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.
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Diabetes Mellitus Tipo 2 , Dieta , Peptídeo 1 Semelhante ao Glucagon , Estilo de Vida , Estado Pré-Diabético , Humanos , Masculino , Feminino , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/metabolismo , Peptídeo 1 Semelhante ao Glucagon/sangue , Peptídeo 1 Semelhante ao Glucagon/metabolismo , Estudos Transversais , Pessoa de Meia-Idade , Estado Pré-Diabético/sangue , Estado Pré-Diabético/metabolismo , Idoso , Adulto , Resistência à Insulina , Jejum/sangue , Obesidade/sangue , Obesidade/metabolismo , Estudos de Coortes , Glicemia/metabolismo , Glicemia/análise , Adiposidade/fisiologiaRESUMO
Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.
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Exposição Ambiental , Expossoma , Humanos , Biologia MolecularRESUMO
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Cirrose Hepática , Humanos , Cirrose Hepática/genética , Neoplasias Hepáticas/genética , Carcinoma Hepatocelular/genética , Alanina Transaminase/sangue , Polimorfismo de Nucleotídeo Único , Masculino , Lipase/genética , Feminino , gama-Glutamiltransferase/genética , Proteínas de Membrana/genética , Estudos de Coortes , Estudos de Casos e Controles , Herança Multifatorial/genética , Fatores de Risco , Variação GenéticaRESUMO
About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.
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BACKGROUND: Variation in laboratory healthcare data due to seasonal changes is a widely accepted phenomenon. Seasonal variation is generally not systematically accounted for in healthcare settings. This study applies a newly developed adjustment method for seasonal variation to analyze the effect seasonality has on machine learning model classification of diagnoses. METHODS: Machine learning methods were trained and tested on ~ 22 million unique records from ~ 575,000 unique patients admitted to Danish hospitals. Four machine learning models (adaBoost, decision tree, neural net, and random forest) classifying 35 diseases of the circulatory system (ICD-10 diagnosis codes, chapter IX) were run before and after seasonal adjustment of 23 laboratory reference intervals (RIs). The effect of the adjustment was benchmarked via its contribution to machine learning models trained using hyperparameter optimization and assessed quantitatively using performance metrics (AUROC and AUPRC). RESULTS: Seasonally adjusted RIs significantly improved cardiovascular disease classification in 24 of the 35 tested cases when using neural net models. Features with the highest average feature importance (via SHAP explainability) across all disease models were sex, C- reactive protein, and estimated glomerular filtration. Classification of diseases of the vessels, such as thrombotic diseases and other atherosclerotic diseases consistently improved after seasonal adjustment. CONCLUSIONS: As data volumes increase and data-driven methods are becoming more advanced, it is essential to improve data quality at the pre-processing level. This study presents a method that makes it feasible to introduce seasonally adjusted RIs into the clinical research space in any disease domain. Seasonally adjusted RIs generally improve diagnoses classification and thus, ought to be considered and adjusted for in clinical decision support methods.
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Doenças Cardiovasculares , Humanos , Doenças Cardiovasculares/diagnóstico , Laboratórios , Instalações de Saúde , Confiabilidade dos Dados , Aprendizado de MáquinaRESUMO
Artificial intelligence (AI) and algorithms are heralded as significant solutions to the widening gap between the rising healthcare needs of ageing and multi-morbid populations and the scarcity of resources to provide such care. Objective: This article investigates how the PMHnet algorithm - an AI prognostication tool developed in Denmark to predict the one-year all-cause mortality risk for patients hospitalized with ischemic heart disease - was presented to cardiologists working in the hospital setting, and how they responded to this novel decision-support tool. Methods: Empirically, we draw upon ethnographic fieldwork in the Danish-led international research project, PM Heart, which since 2019 has developed the PMHnet algorithm and implemented the software into the electronic health record system in hospitals in Eastern Denmark (the Capital Region and Region Zealand). Results: Paying careful attention to the hopes and concerns of cardiologists who will have to embrace and adapt to algorithmic tools in their everyday work of diagnosing and treating patients, we identify three analytical themes meriting attention when AI is implemented in healthcare: 1) the re-negotiation of agency and autonomy in human-algorithm relations, 2) accountability in algorithmic prognostication and 3) the complex relationship between association and causation actualized by predictive algorithms. From these analytical themes, we elicit methodological questions to guide future ethnographic explorations of how AI and advanced algorithms are put to use in the healthcare system, with what implications, and for whom. Conclusion: We conclude that local, qualitative investigations of how algorithms are used, embraced and contested in everyday clinical practice are needed in order to understand their implications - good and bad, intended and unintended - for clinicians, patients and healthcare provision.
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Seasonality in laboratory healthcare data is associated with possible under- and overdiagnoses of patients in the clinic. Here, we present a protocol to analyze electronic health record data for seasonality patterns and adjust existing reference intervals for these changes using R software. We describe steps for preprocessing population-wide patient laboratory data into a single dataset. We then detail steps for defining strata, normalizing to median, and fitting data to selected functions. For complete details on the use and execution of this protocol, please refer to Muse et al. (2023).1.
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Estações do Ano , HumanosRESUMO
BACKGROUND: The emerging use of biomarkers in research and tailored care introduces a need for information about the association between biomarkers and basic demographics and lifestyle factors revealing expectable concentrations in healthy individuals while considering general demographic differences. METHODS: A selection of 47 biomarkers, including markers of inflammation and vascular stress, were measured in plasma samples from 9876 Danish Blood Donor Study participants. Using regression models, we examined the association between biomarkers and sex, age, Body Mass Index (BMI), and smoking. RESULTS: Here we show that concentrations of inflammation and vascular stress biomarkers generally increase with higher age, BMI, and smoking. Sex-specific effects are observed for multiple biomarkers. CONCLUSION: This study provides comprehensive information on concentrations of 47 plasma biomarkers in healthy individuals. The study emphasizes that knowledge about biomarker concentrations in healthy individuals is critical for improved understanding of disease pathology and for tailored care and decision support tools.
Blood-based biomarkers are circulating molecules that can help to indicate health or disease. Biomarker levels may vary depending on demographic and lifestyle factors such as age, sex, smoking status, and body mass index. Here, we examine the effects of these demographic and lifestyle factors on levels of biomarkers related to activation of the immune system and cardiovascular stress. Measurements of 47 different proteins were performed on blood samples from nearly 10,000 healthy Danish blood donors. Measurement data were linked with questionnaire data to assess effects of lifestyle. We found that immune activation and vascular stress generally increased with age, BMI, and smoking. As these measurements are from healthy blood donors they can serve as a reference for expectable effects and inflammation levels in healthy individuals. Knowledge about the healthy state is important for understanding disease progression and optimizing care.
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Two-thirds of all human conceptions are lost, in most cases before clinical detection. The lack of detailed understanding of the causes of pregnancy losses constrains focused counseling for future pregnancies. We have previously shown that a missense variant in synaptonemal complex central element protein 2 (SYCE2), in a key residue for the assembly of the synaptonemal complex backbone, associates with recombination traits. Here we show that it also increases risk of pregnancy loss in a genome-wide association analysis on 114,761 women with reported pregnancy loss. We further show that the variant associates with more random placement of crossovers and lower recombination rate in longer chromosomes but higher in the shorter ones. These results support the hypothesis that some pregnancy losses are due to failures in recombination. They further demonstrate that variants with a substantial effect on the quality of recombination can be maintained in the population.
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Proteínas Nucleares , Complexo Sinaptonêmico , Humanos , Feminino , Gravidez , Complexo Sinaptonêmico/metabolismo , Proteínas Nucleares/metabolismo , Estudo de Associação Genômica Ampla , Proteínas Cromossômicas não Histona/metabolismo , Recombinação Genética , MeioseRESUMO
Social trust is a heritable trait that has been linked with physical health and longevity. In this study, we performed genome-wide association studies of self-reported social trust in n = 33,882 Danish blood donors. We observed genome-wide and local evidence of genetic similarity with other brain-related phenotypes and estimated the single nucleotide polymorphism-based heritability of trust to be 6% (95% confidence interval = (2.1, 9.9)). In our discovery cohort (n = 25,819), we identified one significantly associated locus (lead variant: rs12776883) in an intronic enhancer region of PLPP4, a gene highly expressed in brain, kidneys, and testes. However, we could not replicate the signal in an independent set of donors who were phenotyped a year later (n = 8063). In the subsequent meta-analysis, we found a second significantly associated variant (rs71543507) in an intergenic enhancer region. Overall, our work confirms that social trust is heritable, and provides an initial look into the genetic factors that influence it.
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Doadores de Sangue , Estudo de Associação Genômica Ampla , Humanos , Confiança , Fenótipo , Dinamarca , Polimorfismo de Nucleotídeo Único , Predisposição Genética para DoençaRESUMO
Background: Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival. Methods: First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed. Results: The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found. Conclusions: This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.