ArticleFrontiers in endocrinology2024
A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.
- Prevalence, risk factors, and early prediction of cognitive impairment in patients with diabetes mellitus: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Risk factors for mild cognitive impairment in type 2 diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Development and validation of a diagnostic model for mild cognitive impairment in patients with type 2 diabetes mellitus and concomitant white matter hyperintensities.BMC neurology · 2026Article
- A nomogram as a screening tool for mild cognitive impairment in middle-aged and elderly patients with atrial fibrillation.Scientific reports · 2026Article
- A nomogram prediction model incorporating noninvasive lens AGEs and conventional biochemical indicators for assessing and predicting diabetic kidney disease.Scientific reports · 2026Article
- A review of risk factors and intervention measures for comorbidity of diabetes and mental disorders.Frontiers in psychiatry · 2026Review
- Article
- Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain.BMC nursing · 2025Article
- Predicting Mild Cognitive Impairment in Type 2 Diabetes: A Machine Learning Approach.Journal of diabetes research · 2025Article
- Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models.PloS one · 2025Article
- Risk Prediction Models for Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: A high risk of developing mild cognitive impairment (MCI) is faced by elderly patients with type 2 diabetes mellitus (T2DM). In this study, independent risk factors for MCI in elderly patients with T2DM were investigated, and an individualized nomogram model was developed. Methods: In this study, clinical data of elderly patients with T2DM admitted to the endocrine ward of the hospital from November 2021 to March 2023 were collected to evaluate cognitive function using the Montreal Cognitive Assessment scale. To screen the independent risk factors for MCI in elderly patients with T2DM, a logistic multifactorial regression model was employed. In addition, a nomogram to detect MCI was developed based on the findings of logistic multifactorial regression analysis. Furthermore, the accuracy of the prediction model was evaluated using calibration and receiver operating characteristic curves. Finally, decision curve analysis was used to evaluate the clinical utility of the nomogram. Results: In this study, 306 patients were included. Among them, 186 patients were identified as having MCI. The results of multivariate logistic regression analysis demonstrated that educational level, duration of diabetes, depression, glycated hemoglobin, walking speed, and sedentary duration were independently correlated with MCI, and correlation analyses showed which influencing factors were significantly correlated with cognitive function (p <0.05). The nomogram based on these factors had an area under the curve of 0.893 (95%CI:0.856-0.930)(p <0.05), and the sensitivity and specificity were 0.785 and 0.850, respectively. An adequate fit of the nomogram in the predictive value was demonstrated by the calibration plot. Conclusions: The nomogram developed in this study exhibits high accuracy in predicting the occurrence of cognitive dysfunction in elderly patients with T2DM, thereby offering a clinical basis for detecting MCI in patients with T2DM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.