Aims: To explore the clinical factors and urinary metabolites that predict biopsy-confirmed diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM).
Methods: Data from the medical records of 126 patients with T2DM who underwent kidney biopsy between January 2010 and October 2020 at a single-center were retrospectively reviewed to investigate the clinical factors that predict DN. Urine samples were collected to perform urine metabolomics in patients with T2DM divided by biopsy-confirmed DN, immunoglobulin A, and membranous nephropathy, and a control group of healthy participants. Each group comprised 11 age- and sex-matched participants. A prediction model was developed using a combination of clinical factors and urinary metabolites, and a multivariate receiver operating characteristic (ROC) analysis was conducted.
Results: Age, presence of proliferative diabetic retinopathy, T2DM duration, and hemoglobin A1c levels were clinical factors predictive of DN. Four urinary metabolites (alanine, choline, N-phenylacetylglycine, and trigonelline) had variable importance in projection scores > 1 and were predictive of DN. When conducting multivariate ROC analysis with a combination of clinical factors and urinary metabolites, the area under the curve was 1.000.
Conclusions: The combination of clinical factors and urinary metabolites is highly valuable for predicting biopsy-confirmed DN in patients with T2DM.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.diabres.2023.110986 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study.
JMIR Form Res
January 2025
Vaccine Study Center, Northern California Division of Research, Kaiser Permanente, Oakland, CA, United States.
Background: Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial.
View Article and Find Full Text PDFInternet-Based Dementia Prevention Intervention (DementiaRisk): Protocol for a Randomized Controlled Trial and Knowledge Translation.
JMIR Res Protoc
January 2025
McMaster University, Hamilton, ON, Canada.
Background: Research has shown that engaging in a range of healthy lifestyles or behavioral factors can help reduce the risk of developing dementia. Improved knowledge of modifiable risk factors for dementia may help engage people to reduce their risk, with beneficial impacts on individual and public health. Moreover, many guidelines emphasize the importance of providing education and web-based resources for dementia prevention.
View Article and Find Full Text PDFExploring the Psychological and Physiological Insights Through Digital Phenotyping by Analyzing the Discrepancies Between Subjective Insomnia Severity and Activity-Based Objective Sleep Measures: Observational Cohort Study.
JMIR Ment Health
January 2025
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the advent of wearable technology has enabled continuous, objective sleep monitoring in natural environments. However, the relationship between subjective insomnia severity and objective sleep parameters remains unclear.
View Article and Find Full Text PDFMultimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.
JMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFDesigning the First Pregnancy Guaranteed Income Program in the United States: Qualitative Needs Assessment and Human-Centered Design to Develop the Abundant Birth Project.
JMIR Form Res
January 2025
Sexual Health and Reproductive Equity Program, School of Social Welfare, University of California, Berkeley, Berkeley, CA, United States.
Background: Racial inequities in pregnancy outcomes persist despite investments in clinical, educational, and behavioral interventions, indicating that a new approach is needed to address the root causes of health disparities. Guaranteed income during pregnancy has the potential to narrow racial health inequities for birthing people and infants by alleviating financial stress.
Objective: We describe community-driven formative research to design the first pregnancy-guaranteed income program in the United States-the Abundant Birth Project (ABP).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!