In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

BACKGROUND AND OBJECTIVES

To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering.

METHODS

We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples.

RESULTS

In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54).

CONCLUSION

LCA achieved higher aRI than other clustering algorithms.

Comorbidity and medication use in patients with angina due to a coronary vasomotion disorder

Background

Microvascular angina and vasospastic angina are disorders of coronary vasomotion. The associations between these conditions, comorbidity and medication use in relatively unselected populations is not well described.

Aim

To describe the proportions of patients with concomitant morbidity and related medication use in an international, contemporary, clinical database.

Methods

TriNetX, a global federated health research network with access to anonymized electronical medical records (EMRs) from participating healthcare organizations including academic medical centres, specialty physician practices, and community hospitals, predominantly in the USA was used. The ICD10 code (I20.1) representing “Angina pectoris with documented spasm” was used as a primary search term. ICD10 codes were also used for cardiorenal and metabolic conditions. Medication use was classified as occurring prior to or on the date of the angina episode. The time-period for defining the analysis population was 01.01.2017–31.12.2019. The population age was ≥18 years.

Results

Data were available on 12,200 individuals (mean (SD) age 63 (13) years; 63% female). The % of individuals with a concomitant diagnosis is described in Table 1. Hypertension occurred in almost two thirds of individuals, an anxiety disorder affected more than one quarter and type 2 diabetes and/or obesity occurred in one fifth. Medication use is described in Table 2. Half of patients received a calcium channel blocker therapy. Nitroglycerin, beta-blockers, and isosorbide mononitrate were less commonly used (45%, 45% and 23%, respectively). Most (58%) patients were prescribed an antacid. Half of patients received statin treatment (50% overall; 36% atorvastatin) and insulin (12%) and metformin (9%) were the most commonly prescribed antidiabetic medications.

Conclusions

Angina associated with coronary spasm associates with female sex and cardio-metabolic risk factors. Contemporary pharmacotherapy for diabetes and statins appear to be under-used.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): AstraZeneca

Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study

BACKGROUND

Multimorbidity is associated with mortality and service use, with specific types of multimorbidity having differential effects. Additionally, multimorbidity is often negatively associated with participation in research cohorts. Therefore, we set out to identify clusters of multimorbidity patients and how they are differentially associated with mortality and service use across age groups in a population-representative sample.

METHODS

Linked primary and secondary care electronic health records contributed by 382 general practices in England to the Clinical Practice Research Datalink (CPRD) were used. The study included a representative set of multimorbid adults (18 years old or more, N = 113,211) with two or more long-term conditions (a total of 38 conditions were included). A random set of 80% of the multimorbid patients (N = 90,571) were stratified by age groups and clustered using latent class analysis. Consistency between obtained multimorbidity phenotypes, classification quality and associations with demographic characteristics and primary outcomes (GP consultations, hospitalisations, regular medications and mortality) was validated in the remaining 20% of multimorbid patients (N = 22,640).

RESULTS

We identified 20 patient clusters across four age strata. The clusters with the highest mortality comprised psychoactive substance and alcohol misuse (aged 18-64); coronary heart disease, depression and pain (aged 65-84); and coronary heart disease, heart failure and atrial fibrillation (aged 85+). The clusters with the highest service use coincided with those with the highest mortality for people aged over 65. For people aged 18-64, the cluster with the highest service use comprised depression, anxiety and pain. The majority of 85+-year-old multimorbid patients belonged to the cluster with the lowest service use and mortality for that age range. Pain featured in 13 clusters.

CONCLUSIONS

This work has highlighted patterns of multimorbidity that have implications for health services. These include the importance of psychoactive substance and alcohol misuse in people under the age of 65, of co-morbid depression and coronary heart disease in people aged 65-84 and of cardiovascular disease in people aged 85+.

Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer's Disease Pathology

BACKGROUND

Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown.

OBJECTIVE

We aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes.

METHODS

We first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677).

RESULTS

We identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T-N-, A+T+N-, A+T-N+, and A+T+N+) from no AD pathology (A-T-N-) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts.

CONCLUSIONS

Our results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo.

Characteristics and outcomes of clusters of multimorbid patients in UK general practice

Background

Current clinical specialities, guidelines and quality of care metrics are organised around single diseases and treatments of multiple conditions are rarely coordinated, resulting in insufficient or even conflicting care. This study uses large scale English general practice (GP) records to identify and characterise clusters of patients based on their multimorbidity to allow better design of health services and highlight groups that require additional interventions.

Methods

This is a retrospective cohort study that includes multimorbid adult patients (N = 113,211), from a random sample of 391,669 English patients with valid GP records in 2012 where 38 long-term conditions were defined. Latent class analysis, stratified by age groups, was used to identify multimorbidity clusters. Class solutions are validated and associations between multimorbidity clusters, patient characteristics, public health service utilisation and mortality are assessed.

Results

Poor socioeconomic status is associated with clusters with higher service use and mortality risk. Physical-mental health co-morbidity is a major component of multimorbidity across all age strata. The clusters with highest age-stratified mortality risk in under 65 year olds were linked to alcohol and substance misuse, whereas in over 65 year olds they were linked to cardiovascular disease. The largest cluster in the 85+ years strata (58%) has the lowest number of morbidities, a low degree of service use and mortality. Consistency was seen across identification and validation data.

Conclusions

We find a clear distinction between morbidity clusters, both in the prevalence of long term conditions within them, and in their associations with outcomes (service use and mortality). Specific health services and interventions might be more effective when targeted on the distinct types of multimorbidity we have identified, with a particular focus on the morbidity clusters associated with the worst patient outcomes.

Key messages

The first study to derive age stratified multimorbidity clusters from a large GP record system, whose patients are representative of the English population. Knowledge about particularly dangerous clusters of multimorbidity, such as those involving alcohol and drug use in 18–64 years old, and cardiovascular disease in those 65 years or older.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis

Background:

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.

Objectives:

This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts.

Methods:

Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis.

Results:

Gene and pathway level models performed similarly to each other and to a model based on demographic information only.

Conclusions:

Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Blood Protein Markers of Neocortical Amyloid-β Burden: A Candidate Study Using SOMAscan Technology

BACKGROUND

Four previously reported studies have tested for association of blood proteins with neocortical amyloid-β burden (NAB). If shown to be robust, these proteins could have utility as a blood test for enrichment in clinical trials of Alzheimer's disease (AD) therapeutics.

OBJECTIVE

This study aimed to investigate whether previously identified blood proteins also show evidence for association with NAB in serum samples from the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL). The study considers candidate proteins seen in cohorts other than AIBL and candidates previously discovered in the AIBL cohort.

METHODS

Our study used the SOMAscan platform for protein quantification in blood serum. Linear and logistic regressions were used to model continuous NAB and dichotomized NAB respectively using single proteins as a predictor. Multiple protein models were built using stepwise regression techniques and support vectors machines. Age and APOEɛ4 carriage were used as covariates for all analysis.

RESULTS

Of the 41 proteins previously reported, 15 AIBL candidates and 20 non-AIBL candidates were available for testing. Of these candidates, pancreatic polypeptide (PPY) and IgM showed a significant association with NAB. Notably, IgM was found to associate with continuous NAB across cognitively normal control subjects.

CONCLUSIONS

We have further demonstrated the association of PPY and IgM with NAB, despite technical differences between studies. There are several reasons for a lack of significance for the other candidates including platform differences and the use of serum rather than plasma samples. To investigate the possibility of technical differences causing lack of replication, further studies are required.

High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a high-resolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence.