External validation of QDSCORE® for predicting the 10-year risk of developing Type 2 diabetes

OBJECTIVEThe authors aimed to develop the first clinical grading scale for patients with surgically treated spontaneous supratentorial intracerebral hemorrhage (ICH).METHODSA nationwide multicenter study including 401 ICH patients surgically treated by craniotomy and evacuation of a spontaneous supratentorial ICH was conducted between January 1, 2011, and December 31, 2015. All neurosurgical centers in Sweden were included. All medical records and neuroimaging studies were retrospectively reviewed. Independent predictors of 30-day mortality were identified by logistic regression. A risk stratification scale (the Surgical Swedish ICH [SwICH] Score) was developed using weighting of independent predictors based on strength of association.RESULTSFactors independently associated with 30-day mortality were Glasgow Coma Scale (GCS) score (p = 0.00015), ICH volume ≥ 50 mL (p = 0.031), patient age ≥ 75 years (p = 0.0056), prior myocardial infarction (MI) (p = 0.00081), and type 2 diabetes (p = 0.0093). The Surgical SwICH Score was the sum of individual points assigned as follows: GCS score 15–13 (0 points), 12–5 (1 point), 4–3 (2 points); age ≥ 75 years (1 point); ICH volume ≥ 50 mL (1 point); type 2 diabetes (1 point); prior MI (1 point). Each increase in the Surgical SwICH Score was associated with a progressively increased 30-day mortality (p = 0.0002). No patient with a Surgical SwICH Score of 0 died, whereas the 30-day mortality rates for patients with Surgical SwICH Scores of 1, 2, 3, and 4 were 5%, 12%, 31%, and 58%, respectively.CONCLUSIONSThe Surgical SwICH Score is a predictor of 30-day mortality in patients treated surgically for spontaneous supratentorial ICH. External validation is needed to assess the predictive value as well as the generalizability of the Surgical SwICH Score.

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Plasma microRNA signature associated with retinopathy in patients with type 2 diabetes

Scientific Reports ◽

10.1038/s41598-021-83047-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Donato Santovito ◽

Lisa Toto ◽

Velia De Nardis ◽

Pamela Marcantonio ◽

Rossella D’Aloisio ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Vision Loss ◽

External Validation ◽

Diabetic Patients ◽

Disease Biomarkers ◽

Response To Stress ◽

Severity Of The Disease ◽

Plasma Microrna

AbstractDiabetic retinopathy (DR) is a leading cause of vision loss and disability. Effective management of DR depends on prompt treatment and would benefit from biomarkers for screening and pre-symptomatic detection of retinopathy in diabetic patients. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression which are released in the bloodstream and may serve as biomarkers. Little is known on circulating miRNAs in patients with type 2 diabetes (T2DM) and DR. Here we show that DR is associated with higher circulating miR-25-3p (P = 0.004) and miR-320b (P = 0.011) and lower levels of miR-495-3p (P < 0.001) in a cohort of patients with T2DM with DR (n = 20), compared with diabetic subjects without DR (n = 10) and healthy individuals (n = 10). These associations persisted significant after adjustment for age, gender, and HbA1c. The circulating levels of these miRNAs correlated with severity of the disease and their concomitant evaluation showed high accuracy for identifying DR (AUROC = 0.93; P < 0.001). Gene ontology analysis of validated targets revealed enrichment in pathways such as regulation of metabolic process (P = 1.5 × 10–20), of cell response to stress (P = 1.9 × 10–14), and development of blood vessels (P = 2.7 × 10–14). Pending external validation, we anticipate that these miRNAs may serve as putative disease biomarkers and highlight novel molecular targets for improving care of patients with diabetic retinopathy.

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Interpretable Machine Learning Algorithm Reveals Novel Gut Microbiome Features in Predicting Type 2 Diabetes

Current Developments in Nutrition ◽

10.1093/cdn/nzaa062_016 ◽

2020 ◽

Vol 4 (Supplement_2) ◽

pp. 1559-1559

Author(s):

Wanglong Gou ◽

Chu-Wen Ling ◽

Yan He ◽

Zengliang Jiang ◽

Yuanqing Fu ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Gut Microbiome ◽

Large Scale ◽

Validation Cohort ◽

Learning Algorithm ◽

External Validation ◽

Positive Association ◽

Interpretable Machine Learning

Abstract Objectives The gut microbiome-type 2 diabetes (T2D) relationship among human cohorts have been controversial. We hypothesized that this limitation could be addressed by integrating the cutting-edge interpretable machine learning framework and large-scale human cohort studies. Methods 3 independent cohorts with >9000 participants were included in this study. We proposed a new machine learning-based analytic framework — using LightGBM to infer the relationship between incorporated features and T2D, and SHapley Additive explanation(SHAP) to identified microbiome features associated with the risk of T2D. We then generated a microbiome risk score (MRS) integrating the threshold and direction of the identified microbiome features to predict T2D risk. Results We finally identified 15 microbiome features (two of them are indicators of microbial diversity, others are taxa-related features) associated with the risk of T2D. The identified T2D-related gut microbiome features showed superior T2D prediction accuracy compared to host genetics or traditional risk factors. Furthermore, we found that the MRS (per unit change in MRS) consistently showed positive association with T2D risk in the discovery cohort (RR 1.28, 95%CI 1.23-1.33), external validation cohort 1 (RR 1.23, 95%CI 1.13-1.34) and external validation cohort 2 (GGMP, RR 1.12, 95%CI 1.06-1.18). The MRS could also predict future glucose increment. We subsequently identified dietary and lifestyle factors which could prospectively modulate the microbiome features, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Conclusions Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiome features may serve as potential drug targets for T2D in future. Funding Sources This study was funded by National Natural Science Foundation of China (81903316, 81773416), Westlake University (101396021801) and the 5010 Program for Clinical Researches (2007032) of the Sun Yat-sen University (Guangzhou, China).

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External validation of type 2 diabetes computer simulation models: definitions, approaches, implications and room for improvement—a protocol for a systematic review

Systematic Reviews ◽

10.1186/s13643-017-0664-7 ◽

2017 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Katherine Ogurtsova ◽

Thomas L. Heise ◽

Ute Linnenkamp ◽

Charalabos-Markos Dintsios ◽

Stefan K. Lhachimi ◽

...

Keyword(s):

Systematic Review ◽

Type 2 Diabetes ◽

Computer Simulation ◽

External Validation ◽

Simulation Models ◽

Computer Simulation Models

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External Validation of the UKPDS Risk Engine in Incident Type 2 Diabetes: A Need for New Type 2 Diabetes-Specific Risk Equations

Diabetes Care ◽

10.2337/dc13-1159 ◽

2013 ◽

Vol 37 (2) ◽

pp. 537-545 ◽

Cited By ~ 24

Author(s):

C. A. Bannister ◽

C. D. Poole ◽

S. Jenkins-Jones ◽

C. L. Morgan ◽

G. Elwyn ◽

...

Keyword(s):

Type 2 Diabetes ◽

External Validation ◽

Incident Type ◽

Specific Risk ◽

Incident Type 2 Diabetes ◽

New Type ◽

Risk Engine

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Prediction of Ten-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus: Derivation in Nanjing, China and External Validation in Scotland, UK

SSRN Electronic Journal ◽

10.2139/ssrn.3732142 ◽

2020 ◽

Author(s):

Cheng Wan ◽

Steph Read ◽

Honghan Wu ◽

Shan Lu ◽

Xin Zhang ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Type 2 Diabetes Mellitus ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

External Validation

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Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2019-000735 ◽

2019 ◽

Vol 7 (1) ◽

pp. e000735 ◽

Cited By ~ 1

Author(s):

Dahai Yu ◽

Jin Shang ◽

Yamei Cai ◽

Zheng Wang ◽

Xiaoxue Zhang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Nephropathy ◽

Risk Score ◽

Cardiovascular Mortality ◽

Validation Cohort ◽

External Validation ◽

Cardiovascular Death ◽

Individual Risk ◽

Derivation Cohort

ObjectiveTo derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).Research design and methodsTwo independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.ResultsThe 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.The UKPDS risk score substantially underestimated cardiovascular mortality.ConclusionsA new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.

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Novel Model Predicts Diabetic Nephropathy in Type 2 Diabetes

American Journal of Nephrology ◽

10.1159/000505145 ◽

2019 ◽

Vol 51 (2) ◽

pp. 130-138 ◽

Cited By ~ 2

Author(s):

Shimin Jiang ◽

Jinying Fang ◽

Tianyu Yu ◽

Lin Liu ◽

Guming Zou ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Nephropathy ◽

Kidney Disease ◽

External Validation ◽

Curve Analysis ◽

Calibration Plot ◽

The Novel ◽

Decision Curve Analysis ◽

Novel Model

Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904–0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.

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