Real-world study of the concomitant use of biphasic insulin aspart 30/70 with GLP-1 receptor agonist versus first-generation basal insulin with GLP-1 receptor agonist in type 2 diabetes.

AIMS: Combining insulin with a glucagon-like peptide-1 receptor agonist (GLP-1RA) to treat type 2 diabetes (T2D) is common. While many studies have investigated concomitant therapy with basal insulin+GLP-1RA, few have reported on premixed insulin+GLP-1RA. We aimed to address this gap using data from the Clinical Practice Research Datalink Aurum database in England. METHODS: This retrospective cohort study with propensity score matching assessed glycaemic levels and other clinical outcomes in people with T2D, comparing biphasic insulin aspart 30/70 (BIAsp 30) + GLP-1RA with basal insulin (insulin detemir/glargine U100) + GLP-1RA (from 2006 to 2021). RESULTS: In total, 4770 eligible people were identified; 1511 had a BIAsp 30 + GLP-1RA regimen and were propensity score-matched to an equal number receiving basal+GLP-1RA. There was no significant difference in glycated haemoglobin (HbA1c) reduction between cohorts at 6 months (p = 0.15), with a decrease of -1.07 (95% CI: -1.16; -0.98) %-points (-11.7 mmol/mol [95% CI: -12.7; -10.7]) in the BIAsp 30 + GLP-1RA cohort, versus -0.97 (95% CI: -1.07; -0.88) %-points (-10.6 mmol/mol [95% CI: -11.7; -9.6]) in the basal+GLP-1RA cohort. Body mass index (BMI) decreased by -0.35 kg/m2 (95% CI: -0.52;-0.18) at 6 months with BIAsp 30 + GLP-1RA, versus -0.72 kg/m2 (95% CI: -0.90;-0.54) with basal+GLP-1RA (p = 0.003). BMI was influenced by the initiation sequence of GLP-1RA in relation to insulin (p < 0.0001). Hypoglycaemia rates were low and not significantly different between cohorts. CONCLUSIONS: Combining BIAsp 30 + GLP-1RA provides glycaemic control with no significant difference to that of propensity score-matched people receiving basal insulin+GLP-1RA, with no increase in hypoglycaemia risk or weight gain.

Real-world study of ethnic differences in glycaemic control and clinical characteristics among insulin-naïve people with type 2 diabetes initiating biphasic insulin aspart 30/70: A retrospective, observational cohort study in England.

AIMS: This study investigated the ethnic differences in glycaemic levels and clinical characteristics among insulin-naïve people with type 2 diabetes (T2D) initiating biphasic insulin aspart 30/70 (BIAsp 30) in primary practice in England. MATERIALS AND METHODS: Retrospective, observational cohort study utilizing data from the Clinical Practice Research Datalink Aurum database, including White, South Asian, Black and Chinese insulin-naïve adults with T2D, initiating BIAsp 30. The index date was that of the first BIAsp 30 prescription. Endpoints included change in glycated haemoglobin (HbA1c) and body mass index (BMI) 6 months post index. RESULTS: In total, 11 186 eligible people were selected (9443 White, 1116 South Asian, 594 Black, 33 Chinese). HbA1c decreased across all subgroups 6 months post index: estimated %-point changes [95% CI of -2.32 (-2.36; -2.28) (White); -1.91 (-2.02; -1.80) (South Asian); -2.55 (-2.69; -2.40) (Black); and -2.64 (-3.24; -2.04) (Chinese)]. The BMI increased modestly 6 months post index in all subgroups [estimated changes (95% CI) kg/m2 : White, 0.92 (0.86; 0.99); South Asian, 0.60 (0.41; 0.78); Black, 1.41 (1.16; 1.65); and Chinese, 0.32 (-0.67; 1.30)]. In the overall population, hypoglycaemic event rates increased from 0.92 events per 100 patient-years before the index to 3.37 events per 100 patient-years post index; event numbers were too low to be analysed by subgroup. CONCLUSIONS: Among insulin-naïve people with T2D initiating BIAsp 30, clinically meaningful HbA1c reductions in all ethnicities were observed. Some ethnic groups had larger reductions than others, but differences were small. In all groups, small BMI increases were seen, with small differences observed between groups. Hypoglycaemia rates were low.

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients.

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).

Comorbidity landscape of the Danish patient population affected by chromosome abnormalities.

PURPOSE: Most chromosome abnormality patients require long-term clinical care. Awareness of mosaicism and comorbidities can potentially guide such health care. Here we present a population-wide analysis of direct and inverse comorbidities affecting patients with chromosome abnormalities. METHODS: We extracted direct and inverse comorbidities for the 11 most prevalent chromosome abnormalities from the Danish National Patient Registry (covering 6.9 million patients hospitalized between 1994 and 2015): trisomy 13, 18, and 21, Klinefelter (47,XXY), triple X, XYY, Turner (45,X), Wolf-Hirschhorn, Cri-du-chat, Angelman, and Fragile X syndromes (FXS). We also performed four sub-analyses for male/female Down syndrome (DS) and FXS and non-mosaic/mosaic DS and Turner syndrome. RESULTS: Our data cover 9,003 patients diagnosed with at least one chromosome abnormality. Each abnormality showed a unique comorbidity signature, but clustering of their profiles underlined common risk profiles for chromosome abnormalities with similar genetic backgrounds. We found that DS had a decreased risk for three inverse cancer comorbidities (lung, breast, and skin) and that male FXS and non-mosaic patients have a much more severe phenotype than female FXS and mosaic patients, respectively. CONCLUSION: Our study underlines the importance of considering mosaicism, sex, and the associated comorbidity profiles of chromosome abnormalities to guide long-term health care of affected patients.

MicroRNAs, Regulatory Networks, and Comorbidities: Decoding Complex Systems.

MicroRNAs (miRNAs) are small noncoding RNAs involved in the posttranscriptional regulation of messenger RNAs (mRNAs). Each miRNA targets a specific set of mRNAs. Upon binding the miRNA inhibits mRNA translation or facilitate mRNA degradation. miRNAs are frequently deregulated in several pathologies including cancer and cardiovascular diseases. Since miRNAs have a crucial role in fine-tuning the expression of their targets, they have been proposed as biomarkers of disease progression and prognostication.In this chapter we discuss different approaches for computational predictions of miRNA targets based on sequence complementarity and integration of expression data. In the last section of the chapter we discuss new opportunities in the study of miRNA regulatory networks in the context of temporal disease progression and comorbidities.

TEMPORAL ORDER OF DISEASE PAIRS AFFECTS SUBSEQUENT DISEASE TRAJECTORIES: THE CASE OF DIABETES AND SLEEP APNEA.

Most studies of disease etiologies focus on one disease only and not the full spectrum of multimorbidities that many patients have. Some disease pairs have shared causal origins, others represent common follow-on diseases, while yet other co-occurring diseases may manifest themselves in random order of appearance. We discuss these different types of disease co-occurrences, and use the two diseases "sleep apnea" and "diabetes" to showcase the approach which otherwise can be applied to any disease pair. We benefit from seven million electronic medical records covering the entire population of Denmark for more than 20 years. Sleep apnea is the most common sleep-related breathing disorder and it has previously been shown to be bidirectionally linked to diabetes, meaning that each disease increases the risk of acquiring the other. We confirm that there is no significant temporal relationship, as approximately half of patients with both diseases are diagnosed with diabetes first. However, we also show that patients diagnosed with diabetes before sleep apnea have a higher disease burden compared to patients diagnosed with sleep apnea before diabetes. The study clearly demonstrates that it is not only the diagnoses in the patient's disease history that are important, but also the specific order in which these diagnosis are given that matters in terms of outcome. We suggest that this should be considered for patient stratification.

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality.

Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 'Other sepsis'. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data.

Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci.

We simultaneously investigated the genetic landscape of ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis and ulcerative colitis to investigate pleiotropy and the relationship between these clinically related diseases. Using high-density genotype data from more than 86,000 individuals of European ancestry, we identified 244 independent multidisease signals, including 27 new genome-wide significant susceptibility loci and 3 unreported shared risk loci. Complex pleiotropy was supported when contrasting multidisease signals with expression data sets from human, rat and mouse together with epigenetic and expressed enhancer profiles. The comorbidities among the five immune diseases were best explained by biological pleiotropy rather than heterogeneity (a subgroup of cases genetically identical to those with another disease, possibly owing to diagnostic misclassification, molecular subtypes or excessive comorbidity). In particular, the strong comorbidity between primary sclerosing cholangitis and inflammatory bowel disease is likely the result of a unique disease, which is genetically distinct from classical inflammatory bowel disease phenotypes.

How Suitable Are Registry Data for Recurrence Risk Calculations? Validation of Diagnoses on 1,593 Families With Congenital Heart Disease.

BACKGROUND: Congenital heart disease (CHD) occurs in approximately 1% of all live births, and 3% to 8% of these have until now been considered familial cases, defined as the occurrence of two or more affected individuals in a family. The validity of CHD diagnoses in Danish administrative registry data has only been studied previously in highly selected patient populations. These studies identified high positive predictive values (PPVs) and recurrence risk ratios (RRRs-ratio between probabilities of CHD given family history of CHD and no family history). However, the RRR can be distorted if registry data are used indiscriminately. Here, we investigated the consequences of misclassifications for the RRR using validated diagnoses on Danish patients with familial CHD. METHODS: Danish citizens are assigned a civil registration number (CPR number) at birth or immigration, which acts as a unique identifier in the Danish registries, thus enabling connection of information from several registries. Utilizing the CPR number, we identified Danish patients with familial CHD and reviewed each patient's file. We compared diagnoses from the registries with those manually assigned, which enabled calculation of the PPVs of diagnoses in the Danish registries, and from this, we deduced the false discovery rate (FDR). To measure the consequences on the RRR, the FDR was applied to a simulated data set with true RRR values of 2 and 10. RESULTS: We validated diagnoses of 2,442 patients from 1,593 families. Of these, 874 patients were misclassified corresponding to an FDR of 36%. Applying this FDR on the simulated data sets, we found that the RRR decreased from 2 and 10 to 1.4 and 5.1, respectively. Lastly, we estimated that 11% of all cases with CHD were familial. CONCLUSION: We found that approximately one of nine of all cases with CHD are familial, and we also found that 36% of individuals with CHD in administrative medical registries are misclassified, which distort the RRR in simulated scenarios.

Long-term risk of cardiovascular and cerebrovascular disease after removal of the colonic microbiota by colectomy: a cohort study based on the Danish National Patient Register from 1996 to 2014.

OBJECTIVES: The hypothesis of the study was that if the gut microbiota is involved in the development of atherosclerotic cardiovascular and cerebrovascular diseases (CVDs), total colectomy may reduce the long-term risk of CVDs. The aim was therefore to investigate the risk of CVD in patients after a total colectomy compared with patients undergoing other types of surgery, which are not expected to alter the gut microbiota or the CVD risk. SETTING: The Danish National Patient Register including all hospital discharges in Denmark from 1996 to 2014. PARTICIPANTS: Patients (n=1530) aged 45 years and above and surviving 1000 days after total colectomy without CVDs were selected and matched with five control patients who were also free of CVD 1000 days after other types of surgery. The five control patients were randomly selected from each of the three surgical groups: orthopaedic surgery, surgery in the gastrointestinal tract leaving it intact and other surgeries not related to the gastrointestinal tract or CVD (n=22 950). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the first occurring CVD event in any of the seven diagnostic domains (hypertensive disorders, acute ischaemic heart diseases, chronic ischaemic heart disease, cardiac arrhythmias, heart failure, cerebrovascular diseases and other arterial diseases) and the secondary outcomes were the first occurring event within each of these domains. RESULTS: Estimated by Cox proportional hazard models, the HRs of the composite CVD end point for patients with colectomy compared with the control patients were not significantly reduced (HR=0.94, 95% confidence limits 0.85 to 1.04). Among the seven CVD domains, only the risk of hypertensive disorders was significantly reduced (HR=0.85, 0.73 to 0.98). CONCLUSIONS: Colectomy did not reduce the general risk of CVD, but reduced the risk of hypertensive disorders, most likely due to salt and water depletion induced by colectomy. These results encourage a reappraisal of the associations between gut microbiota and CVD.

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients.

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.

A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.

Although countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By mining the medical records of over 110 million patients, we examine the extent to which Mendelian variation contributes to complex disease risk. We detect thousands of associations between Mendelian and complex diseases, revealing a nondegenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci. Using genome-wide association results, we demonstrate that common variants associated with complex diseases are enriched in the genes indicated by this "Mendelian code." Finally, we detect hundreds of comorbidity associations among Mendelian disorders, and we use probabilistic genetic modeling to demonstrate that Mendelian variants likely contribute nonadditively to the risk for a subset of complex diseases. Overall, this study illustrates a complementary approach for mapping complex disease loci and provides unique predictions concerning the etiologies of specific diseases.