Identification of independent risk factors for diabetic neuropathy progression in patients with type 2 diabetes mellitus

Huang, Linli; Shen, Xiaodong; Huang, Lingning; Yan, Sunjie; Wu, Peiwen

doi:10.1177/03000605211044366

Cited by 10 publications

(10 citation statements)

References 22 publications

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“…In line with our findings, Huang et al. ( 40 ) reported that the use of insulin is an independent risk factor for DPN in patients with type 2 diabetes. On the other hand, metformin possibly attenuated neuropathic pain in a diabetic rat model ( 57 ).…”

Section: Discussionsupporting

confidence: 93%

“…Insulin was shown to possibly augment the mechanical responsiveness to mechanical stimuli and induce excitation in peripheral nerves in an in vitro study (56). In line with our findings, Huang et al (40) reported that the use of insulin is an independent risk factor for DPN in patients with type 2 diabetes. On the other hand, metformin possibly attenuated neuropathic pain in a diabetic rat model (57).…”

Section: Discussionsupporting

confidence: 85%

“…Variability of fasting glucose was reported to be associated with DPNP, peripheral artery disease, and mortality (37)(38)(39). Retinopathy was reported to be associated with DPN, CKD, and mortality (40,41). Endothelial dysfunction, a well-known CV risk factor, was also reported to be associated with DPN (42).…”

Section: Discussionmentioning

confidence: 99%

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Scores of peripheral neuropathic pain predicting long-term mortality in patients with type 2 diabetes: A retrospective cohort study

2022

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ObjectivesDiabetic peripheral neuropathic pain (DPNP) is a prevalent chronic complication in patients with diabetes. Using a questionnaire is helpful for DPNP screening in outpatients. In this retrospective cohort, we aimed to examine whether DPNP diagnosed based on scoring questionnaires could predict long-term mortality in outpatients with type 2 diabetes.MethodsWe enrolled 2318 patients who had joined the diabetes pay-for-performance program and completed the annual assessments, including both the identification pain questionnaire (ID pain) and Douleur Neuropathique en 4 questionnaire (DN4), between January 2013 and October 2013. Information on registered deaths was collected up to August 2019.ResultsThere was high consistency in the scores between the ID pain and DN4 (r = 0.935, P < 0.001). During the median follow-up of 6.2 years (interquartile range: 5.9−6.4 years), 312 patients deceased. Patients with an ID pain score of ≥ 2 had a higher mortality risk than those with a score of < 2 (hazard ratio [HR] = 1.394, 95%CI: 1.090−1.782), and patients with a DN4 score of ≥ 4 had a higher mortality risk than those with a score of < 4 (HR = 1.668, 95% confidence interval [CI]: 1.211−2.297). Patients consistently diagnosed with DPNP by the ID pain and DN4 had a significantly higher mortality risk (HR = 1.713, 95% CI: 1.223−2.398, P = 0.002), but not those discrepantly diagnosed with DPNP (P = 0.107), as compared with those without DPNP.ConclusionsBoth the ID pain and DN4 for DPNP screening were predictive of long-term mortality in patients with type 2 diabetes. However, a discrepancy in the diagnosis of DPNP weakened the power of mortality prediction.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 85%

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Scores of peripheral neuropathic pain predicting long-term mortality in patients with type 2 diabetes: A retrospective cohort study

2022

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“…The model comprised eight variables identified as independent predictors of diabetic neuropath including glycemic control (FBG), other comorbidities, alcohol drinking, Hypertension, type of treatment for DM, physical activity, WBC and RBC count. This result is supported by previous studies conducted [ 36 , 41 , 46 , 61 – 63 ].…”

Section: Discussionsupporting

confidence: 91%

Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005–2021

Gelaw,

Muche,

Alem

et al. 2023

PLoS ONE

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Background Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians. Objective Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005–2021. Methods A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model. Results The incidence proportion of diabetic neuropathy among DM patients was 21.29% (95% CI; 18.59, 24.25). In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment, white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2% (95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7 (95% CI; 67.2%, 75.9%). It had less optimism coefficient (0.015). To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2% (95% CI; 65.8%, 74.6%). The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. Conclusion The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.

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Section: Discussionsupporting

confidence: 91%

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Gelaw¹,

Muche

Alem

et al. 2022

Preprint

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Background:-Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians Objective:- Prediction of the risk for diabetic neuropathy among diabetes mellitus patients, in selected referral hospitals, Amhara regional state, northwest Ethiopia,2022 Methods:- A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model Results:-The incidence proportion of diabetic neuropathy among DM patients was 21.29 %( 95% CI; 18.59, 24.25).In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment , white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2 %( 95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7(95% CI; 67.2 %, 75.9%).It had less optimism coefficient (0.015).To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2 %( 95 % CI; 65.8%, 74.6%).The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. Conclusion:- The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.

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Identification of independent risk factors for diabetic neuropathy progression in patients with type 2 diabetes mellitus

Cited by 10 publications

References 22 publications

Scores of peripheral neuropathic pain predicting long-term mortality in patients with type 2 diabetes: A retrospective cohort study

Scores of peripheral neuropathic pain predicting long-term mortality in patients with type 2 diabetes: A retrospective cohort study

Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005–2021

Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

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