Temporal trajectories of accompanying comorbidities in patients with type 2 diabetes: a Korean nationwide observational study

Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach

BACKGROUND

Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management.

OBJECTIVE

The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients.

METHODS

This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation.

RESULTS

The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field.

CONCLUSION

The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

Trends in Diabetes Medication Taking and Incidence of Depression in Patients with Type 2 Diabetes: A Retrospective Cohort Study from 2010 to 2018

BACKGROUND

This study examined the trends in diabetes medication taking and its association with the incidence of depression in patients with type 2 diabetes (T2D).

METHOD

A retrospective cohort of Medicare enrollees with regular care in 2010 was defined from 100% Texas Medicare claims. The impact of medication taking on incident depression was evaluated from 2010 to 2018. Cox proportional hazards regressions were used to estimate the association between medication taking and depression.

RESULTS

A total of 72,461 patients with T2D and with regular care were analyzed. Among 60,216 treated patients, the regular medication taking rate slightly increased from 60.8 to 63.2% during the study period. Patients with regular medication taking at baseline had a 9% lower risk of developing depression (hazard ratio [HR]: 0.91, 95% confidence interval [CI]: 0.89-0.94), and the magnitude of the association increased after adjustment of the model for time-varied medication taking (HR: 0.82, 95% CI: 0.79-0.85). The presence of nephropathy had the greatest mediating effect (23.2%) on the association of medication taking and depression.

CONCLUSION

We demonstrated a steady but modest increase in regular diabetes medication taking over a 9-year period and a significant relationship between medication taking and incident depression in patients with T2D.

Factors associated with the use of traditional doses of amitriptyline for chronic pain management: A cross-sectional study

There are studies on the effect of low-dose amitriptyline on pain control, but there is a lack of studies on the use of amitriptyline for chronic pain and the factors associated with the prescription of traditional doses. We used a national sample cohort of patients aged ≥ 18 years who were prescribed amitriptyline along with chronic pain, without psychiatric disorders, between 2002 to 2015. We categorized the prescriptions into 2 groups according to the daily dose: low doses (≤25 mg) and traditional doses (>25 mg). Multivariable logistic regression models were used to identify factors associated with traditional dose prescriptions. Among 177,769 prescriptions for amitriptyline, 15,119 (8.5%) were prescribed for chronic pain. The prevalence of prescriptions and proportion of traditional doses of amitriptyline tended to decrease during the study period. Male sex (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.05-1.13); age 65-80 years (OR 1.12, 95% CI 1.08-1.16), especially ≥ 80 years (OR 1.55, 95% CI 1.45-1.65); headaches (OR 1.18, 95% CI 1.10-1.27), receiving medical aids (OR 2.58, 95% CI 2.46-2.71); and being prescribed benzodiazepines or zolpidem concomitantly (OR 1.10, 95% CI 1.06-1.15) were significantly associated with traditional dose prescriptions of amitriptyline. Although traditional dose prescriptions of amitriptyline have been declining, close monitoring is still required in the presence of the above-mentioned factors.

Suicide Risk in Patients With Diabetes Varies by the Duration of Diabetes: The Korea National Health and Nutrition Examination Survey (2019)

OBJECTIVE

The objectives of this study were to investigate the suicide risk in diabetes patients and evaluate the variations in suicide risk by the duration of diabetes using a large population sample in South Korea.

METHODS

Data from 6,296 adults in the 2019 Korea National Health and Nutrition Examination Survey were included. The suicidal ideation, suicide plans, and suicidal behavior of diabetes patients were compared to the general population. After classifying the patients into ≤1 year, 2 to 9 years, and ≥10 years of diabetes duration, we evaluated the relationship between the duration of diabetes and the suicide risk.

RESULTS

Diabetes patients had higher prevalences of suicidal ideation (9.1%, p<0.001) and suicide plans (3.6%, p<0.001) than the general population. After adjusting for potential confounding factors, suicide plans (adjusted odds ratio [aOR]=2.926, 95% confidence interval [CI]=1.325-6.463) were significantly associated with diabetes. In the 2 to 9 years group of diabetes patients, we found an increase in the risk of suicidal ideation (aOR=2.035, 95% CI=1.129-3.670), suicide plans (aOR=3.507, 95% CI=1.538-7.996), and suicidal behavior (aOR=7.130, 95% CI=2.035-24.978) after adjusting for the covariates. However, no increases in suicide risk were observed ≤1 year and ≥10 years after diabetes diagnosis.

CONCLUSION

In adults, diabetes is associated with an increase in suicide risk. Suicide risk in diabetes patients showed an inverted U-shaped depending upon the duration of diabetes.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.

Drug Repositioning Using Temporal Trajectories of Accompanying Comorbidities in Diabetes Mellitus

BACKGROUND

Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates' effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications.

METHODS

Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records.

RESULTS

We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44).

CONCLUSION

To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.

Rocha C, V. Maurer-Morelli C, Perez Orrico SR, Cirelli JA, Von Zuben FJ, Mantuaneli Scarel-Caminaga R. Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases

OBJECTIVE

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.

METHODS

We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).

RESULTS

We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.

CONCLUSIONS

ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.