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Record W4414861752 · doi:10.1007/s13300-025-01802-y

The Mechanisms of Inflammatory Factors and the Total Load of Cerebral Small Vessel Disease in Diabetic Retinopathy and Cognitive Impairment

2025· article· en· W4414861752 on OpenAlexaboutno aff
Junjun Miao, Shi Chen, Xinyi Sun, Yun She, Lijuan Wang, Siman Liu, Jiangyi Yu, Jing Ge, Zhenguo Qiao

Bibliographic record

VenueDiabetes Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentDiabetes mellitusDiabetic retinopathyDiseaseRetinopathyInflammation

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this study was to explore the roles and methods of inflammatory factors and total load of cerebral small vessel disease (CSVD) in diabetic retinopathy (DR) and cognitive impairment. MATERIALS AND METHODS: In total, 1860 patients with type 2 diabetes mellitus (T2DM) were divided into a DR group and a non-diabetic retinopathy (NDR) group, and nonproliferative DR was divided into mild and moderate-to-severe according to the severity. The patients' baseline data were recorded, and imaging indicators were collected to evaluate CSVD. Monofactor analysis was performed to identify the risk factors associated with DR and cognitive impairment, and a logistic regression model was used to determine independent risk factors. Finally, Nomogram and receiver operating characteristic (ROC) curves were constructed to evaluate the prediction effect of the model. RESULTS: (1) 693 patients (37.26%) had DR and 1167 patients (62.74%) had no DR. In the DR group, hypertension, disease course, low-density lipoprotein cholesterol (LDL-C), uric acid (UA), glycosylated hemoglobin (HbA1c), triglyceride glucose index (TyG), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were all significantly higher than in the NDR group (p < 0.001). Multivariate logistic regression analysis further verified that hypertension, LDL-C, PLR, and SII were independent risk factors for DR. (2) Among 612 patients with nonproliferative DR, the levels of hypertension, UA, HbA1c, TyG index, interleukin-6 (IL-6), monocyte-to-lymphocyte ratio (MLR), and SII in the moderate-to-severe nonproliferative DR group were significantly higher than those in the mild nonproliferative DR group (p < 0.01). (3) Patients with moderate-to-severe nonproliferative DR were divided into a cognitive impairment group and a non-cognitive impairment group. Smoking history, drinking history, fasting blood glucose, HbA1c, TyG index, PLR, MLR, SII, total CSVD magnetic resonance imaging (MRI) load, and white matter hyperintensities (WMHs) were significantly associated with cognitive impairment (p < 0.01). Smoking history, fasting blood glucose, HbA1c, TyG index, SII, total CSVD load, and lacunar infarction (LI) were independent risk factors for cognitive impairment in patients with moderate-to-severe DR. In addition, total MRI load (r = 0.711, p < 0.05), TyG index (r = 0.712, p < 0.05), SII (r = 0.703, p < 0.05), and PLR (r = 0.724, p < 0.05) were significantly negatively correlated with Montreal Cognitive Assessment (MoCA) score. CONCLUSIONS: This study identified hypertension history, LDL-C, PLR, and SII as factors independently associated with the presence of DR in patients with T2DM. In addition, UA, TyG, SII, total CSVD load, and WMHs were significantly associated with more severe stages of DR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.229
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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