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Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

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., (© 2024. The Author(s).)

Polypharmacy has generally been assessed by raw counts of different drugs administered concomitantly to the same patients; not with respect to the likelihood of dosage-adjustments. To address this aspect of polypharmacy, the objective of the present study was to identify co-medications associated with more frequent dosage adjustments. The data foundation was electronic health records from 3.2 million inpatient admissions at Danish hospitals (2008-2016). The likelihood of dosage-adjustments when two drugs were administered concomitantly were computed using Bayesian logistic regressions. We identified 3,993 co-medication pairs that associate significantly with dosage changes when administered together. Of these pairs, 2,412 (60%) did associate with readmission, mortality or longer stays, while 308 (8%) associated with reduced kidney function. In comparison to co-medications pairs that were previously classified as drug-drug interactions, pairs not classified as drug-drug interactions had higher odds ratios of dosage modifications than drug pairs with an established interaction. Drug pairs not corresponding to known drug-drug interactions while still being associated significantly with dosage changes were prescribed to fewer patients and mentioned more rarely together in the literature. We hypothesize that some of these pairs could be associated with yet to be discovered interactions as they may be harder to identify in smaller-scale studies., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: S.B. reports ownerships in Intomics A/S, Hoba Therapeutics Aps, Novo Nordisk A/S, Lundbeck A/S and managing board membership in Intomics A/S outside the submitted work. The other authors have declared that no competing interests exist., (Copyright: © 2023 Leal Rodríguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer., (© 2023. The Author(s).)

Background: Patients diagnosed with ischemic heart disease (IHD) are becoming increasingly multi-morbid, and studies designed to analyze the full spectrum are few., Methods: Disease trajectories, defined as time-ordered series of diagnoses, were used to study the temporality of multi-morbidity. The main data source was The Danish National Patient Register (NPR) comprising 7,179,538 individuals in the period 1994-2018. Patients with a diagnosis code for IHD were included. Relative risks were used to quantify the strength of the association between diagnostic co-occurrences comprised of two diagnoses that were overrepresented in the same patients. Multiple linear regression models were then fitted to test for temporal associations among the diagnostic co-occurrences, termed length two disease trajectories. Length two disease trajectories were then used as basis for constructing disease trajectories of three diagnoses., Results: In a cohort of 570,157 IHD disease patients, we identified 1447 length two disease trajectories and 4729 significant length three disease trajectories. These included 459 distinct diagnoses. Disease trajectories were dominated by chronic diseases and not by common, acute diseases such as pneumonia. The temporal association of atrial fibrillation (AF) and IHD differed in different IHD subpopulations. We found an association between osteoarthritis (OA) and heart failure (HF) among patients diagnosed with OA, IHD, and then HF only., Conclusions: The sequence of diagnoses is important in characterization of multi-morbidity in IHD patients as the disease trajectories. The study provides evidence that the timing of AF in IHD marks distinct IHD subpopulations; and secondly that the association between osteoarthritis and heart failure is dependent on IHD., (© 2022. The Author(s).)

Purpose: The aim of Copenhagen Hospital Biobank-Cardiovascular Disease Cohort (CHB-CVDC) is to establish a cohort that can accelerate our understanding of CVD initiation and progression by jointly studying genetics, diagnoses, treatments and risk factors., Participants: The CHB-CVDC is a large genomic cohort of patients with CVD. CHB-CVDC currently includes 96 308 patients. The cohort is part of CHB initiated in 2009 in the Capital Region of Denmark. CHB is continuously growing with ~40 000 samples/year. Patients in CHB were included in CHB-CVDC if they were above 18 years of age and assigned at least one cardiovascular diagnosis. Additionally, up-to 110 000 blood donors can be analysed jointly with CHB-CVDC. Linkage with the Danish National Health Registries, Electronic Patient Records, and Clinical Quality Databases allow up-to 41 years of medical history. All individuals are genotyped using the Infinium Global Screening Array from Illumina and imputed using a reference panel consisting of whole-genome sequence data from 8429 Danes along with 7146 samples from North-Western Europe. Currently, 39 539 of the patients are deceased., Findings to Date: Here, we demonstrate the utility of the cohort by showing concordant effects between known variants and selected CVDs, that is, >93% concordance for coronary artery disease, atrial fibrillation, heart failure and cholesterol measurements and 85% concordance for hypertension. Furthermore, we evaluated multiple study designs and the validity of using Danish blood donors as part of CHB-CVDC. Lastly, CHB-CVDC has already made major contributions to studies of sick sinus syndrome and the role of phytosterols in development of atherosclerosis., Future Plans: In addition to genetics, electronic patient records, national socioeconomic and health registries extensively characterise each patient in CHB-CVDC and provides a promising framework for improved understanding of risk and protective variants. We aim to include other measurable biomarkers for example, proteins in CHB-CVDC making it a platform for multiomics cardiovascular studies., Competing Interests: Competing interests: The authors affiliated with deCODE genetics/Amgen are employed by the company., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

It is unknown how sequential drug patterns convey information on a patient's health status and treatment guidelines rarely account for this. Drug-agnostic longitudinal analyses of prescription trajectories in a population-wide setting are needed. In this cohort study, we used 24 years of data (1.1 billion prescriptions) from the Danish prescription registry to model the risk of sequentially redeeming a drug after another. Drug pairs were used to build multistep longitudinal prescription trajectories. These were subsequently used to stratify patients and calculate survival hazard ratios between the stratified groups. The similarity between prescription histories was used to determine individuals' best treatment option. Over the course of 122 million person-years of observation, we identified 9 million common prescription trajectories and demonstrated their predictive power using hypertension as a case. Among patients treated with agents acting on the renin-angiotensin system we identified four groups: patients prescribed angiotensin converting enzyme (ACE) inhibitor without change, angiotensin receptor blockers (ARBs) without change, ACE with posterior change to ARB, and ARB posteriorly changed to ACE. In an adjusted time-to-event analysis, individuals treated with ACE compared to those treated with ARB had lower survival probability (hazard ratio, 0.73 [95% CI, 0.64-0.82]; P < 1 × 10 -16 ). Replication in UK Biobank data showed the same trends. Prescription trajectories can provide novel insights into how individuals' drug use change over time, identify suboptimal or futile prescriptions and suggest initial treatments different from first line therapies. Observations of this kind may also be important when updating treatment guidelines., (© 2021. The Author(s).)

Background: Novel immunotherapies targeting cancer-associated truncated O-glycans Tn (GalNAcα-Ser/Thr) and STn (Neu5Acα2-6GalNacα-Ser/Thr) are promising strategies for cancer treatment. However, no comprehensive, antibody-based mapping of truncated O-glycans in tumours exist to guide drug development., Methods: We used monoclonal antibodies to map the expression of truncated O-glycans in >700 tissue cores representing healthy and tumour tissues originating from breast, colon, lung, pancreas, skin, CNS and mesenchymal tissue. Patient-derived xenografts were used to evaluate Tn expression upon tumour engraftment., Results: The Tn-antigen was highly expressed in breast (57%, n = 64), colorectal (51%, n = 140) and pancreatic (53%, n = 108) tumours, while STn was mainly observed in colorectal (80%, n = 140) and pancreatic (56%, n = 108) tumours. We observed no truncated O-glycans in mesenchymal tumours (n = 32) and low expression of Tn (5%, n = 87) and STn (1%, n = 75) in CNS tumours. No Tn-antigen was found in normal tissue (n = 124) while STn was occasionally observed in healthy gastrointestinal tissue. Surface expression of Tn-antigen was identified across several cancers. Tn and STn expression decreased with tumour grade, but not with cancer stage. Numerous xenografts maintained Tn expression., Conclusions: Surface expression of truncated O-glycans is limited to cancers of epithelial origin, making Tn and STn attractive immunological targets in the treatment of human carcinomas., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities., Competing Interests: IK, CS, KB, PH, AH, PJ, LJ, CR, MP, RE, HA, TA, JB, NG, KB, OP, FP, TH, RB, PR, SB No competing interests declared, (© 2019, Kirk et al.)

Cardiology relies on a huge amount of data derived from clinical observations, case-specific considerations and randomised controlled trials. At best, medical intervention relieves symptoms and reduces disease consequences or complications. Rarely, it redirects the cause. However, an accelerated array of opportunities within a digital and molecular discourse may mark a medical era which acknowledges individual variation rather than accepts a pragmatic pooling of similarities. This review aims at providing a brief overview of academic achievements, clinical considerations and translational perspectives related to precision cardiology.

Proteolytic processing is an irreversible post-translational modification functioning as a ubiquitous regulator of cellular activity. Protease activity is tightly regulated via control of gene expression, enzyme and substrate compartmentalization, zymogen activation, enzyme inactivation, and substrate availability. Emerging evidence suggests that proteolysis can also be regulated by substrate glycosylation and that glycosylation of individual sites on a substrate can decrease or, in rare cases, increase its sensitivity to proteolysis. Here, we investigated the relationship between site-specific, mucin-type (or GalNAc-type) O -glycosylation and proteolytic cleavage of extracellular proteins. Using in silico analysis, we found that O -glycosylation and cleavage sites are significantly associated with each other. We then used a positional proteomic strategy, terminal amine isotopic labeling of substrates (TAILS), to map the in vivo cleavage sites in HepG2 SimpleCells with and without one of the key initiating GalNAc transferases, GalNAc-T2, and after treatment with exogenous matrix metalloproteinase 9 (MMP9) or neutrophil elastase. Surprisingly, we found that loss of GalNAc-T2 not only increased cleavage, but also decreased cleavage across a broad range of other substrates, including key regulators of the protease network. We also found altered processing of several central regulators of lipid homeostasis, including apolipoprotein B and the phospholipid transfer protein, providing new clues to the previously reported link between GALNT2 and lipid homeostasis. In summary, we show that loss of GalNAc-T2 O -glycosylation leads to a general decrease in cleavage and that GalNAc-T2 O -glycosylation affects key regulators of the cellular proteolytic network, including multiple members of the serpin family., (© 2018 King et al.)

Aberrant expression of O -glycans is a hallmark of epithelial cancers. Mucin-type O- glycosylation is initiated by a large family of UDP-GalNAc:polypeptide N -acetylgalactosaminyltransferases (GalNAc-Ts) that target different proteins and are differentially expressed in cells and organs. Here, we investigated the expression patterns of all of the GalNAc-Ts in colon cancer by analyzing transcriptomic data. We found that GalNAc-T6 was highly up-regulated in colon adenocarcinomas but absent in normal-appearing adjacent colon tissue. These results were verified by immunohistochemistry, suggesting that GalNAc-T6 plays a role in colon carcinogenesis. To investigate the function of GalNAc-T6 in colon cancer, we used precise gene targeting to produce isogenic colon cancer cell lines with a knockout/rescue system for GALNT6 GalNAc-T6 expression was associated with a cancer-like, dysplastic growth pattern, whereas GALNT6 knockout cells showed a more normal differentiation pattern, reduced proliferation, normalized cell-cell adhesion, and formation of crypts in tissue cultures. O- Glycoproteomic analysis of the engineered cell lines identified a small set of GalNAc-T6-specific targets, suggesting that this isoform has unique cellular functions. In support of this notion, the genetically and functionally closely related GalNAc-T3 homolog did not show compensatory functionality for effects observed for GalNAc-T6. Taken together, these data strongly suggest that aberrant GalNAc-T6 expression and site-specific glycosylation is involved in oncogenic transformation., (© 2018 by The American Society for Biochemistry and Molecular Biology, Inc.)