Dataset Information

Diabetic polyneuropathy is a risk factor for decline of lower extremity strength in patients with type 2 diabetes.

ABSTRACT:

Aims/introduction

The present study elucidated the effect of diabetic polyneuropathy (DPN) on lower extremity strength in a wide age range of type 2 diabetes patients.

Materials and methods

Participants (n = 1,442) were divided into three age groups (30-49 years, 50-69 years and 70-87 years), and comparisons were made separately for each sex. Lower extremity strength was measured in terms of knee extension force (KEF) with a hand-held dynamometer. KEF was compared according to the presence or absence of DPN. Furthermore, the effect of DPN on KEF with other diabetic complications (diabetic retinopathy and diabetic nephropathy), diabetes status (diabetes duration and glycated hemoglobin) and habitual behavior (regular exercise, smoking and drinking behaviors) as explanatory variables was analyzed using multiple regression analysis in several models.

Results

The frequency of DPN differed among age groups, ranging from 14.3 to 49.6%, and increasing with age. There was no significant difference in KEF between patients aged 30-49 years with and without DPN. However, among both men and women aged 50-69 years and 70-87 years, patients with DPN showed significantly diminished KEF (11.0-12.9% and 11.9-16.6%, respectively) compared with those without DPN (P < 0.01-0.001). In women aged 50-69 years and 70-87 years, and in men aged 50-69 years, DPN was a significant explanatory variable for KEF in all multiple regression analysis models.

Conclusion

DPN might reinforce a KEF decline in middle-aged and elderly type 2 diabetes patients.

SUBMITTER: Nomura T

PROVIDER: S-EPMC5754538 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

Diabetic polyneuropathy is a risk factor for decline of lower extremity strength in patients with type 2 diabetes.

Nomura Takuo T Ishiguro Tomoyasu T Ohira Masayoshi M Ikeda Yukio Y

Journal of diabetes investigation 20170506 1

<h4>Aims/introduction</h4>The present study elucidated the effect of diabetic polyneuropathy (DPN) on lower extremity strength in a wide age range of type 2 diabetes patients.<h4>Materials and methods</h4>Participants (n = 1,442) were divided into three age groups (30-49 years, 50-69 years and 70-87 years), and comparisons were made separately for each sex. Lower extremity strength was measured in terms of knee extension force (KEF) with a hand-held dynamometer. KEF was compared according to the ...[more]

PMID: 28296226

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Project description:To examine the association between use of statin and nonstatin cholesterol-lowering medications and risk of nontraumatic major lower extremity amputations (LEAs) and treatment failure (LEA or death).A retrospective cohort of patients with Type I and Type 2 diabetes mellitus (diabetes) was followed for 5 years between 2004 and 2008. The follow-up exposure duration was divided into 90-day periods. Use of cholesterol-lowering agents, diabetic medications, hemoglobin A1c, body mass index, and systolic and diastolic blood pressures were observed in each period. Demographic factors were observed at baseline. Major risk factors of LEA including peripheral neuropathy, peripheral artery disease, and foot ulcers were observed at baseline and were updated for each period. LEA and deaths were assessed in each period and their hazard ratios (HRs) were estimated. The study took place in the U.S. Department of Veterans Affairs Healthcare system, and the subjects consisted of cholesterol drug-naïve patients with Type I or II diabetes who were treated in the U.S. Department of Veterans Affairs Healthcare system in 2003 and were <65 years old at the end of follow-up.Of 83,953 patients in the study cohort, 217 (0.3%) patients experienced a major LEA and 11,716 (14.0%) patients experienced an LEA or death (treatment failure) after a mean follow-up of 4.6 years. Compared with patients who did not use cholesterol-lowering agents, statin users were 35% to 43% less likely to experience an LEA (HR, 0.65; 95% confidence interval [CI], 0.42-0.99) and a treatment failure (HR, 0.57; 95% CI, 0.54-0.60). Users of other cholesterol-lowering medications were not significantly different in LEA risk (HR, 0.95; 95% CI, 0.35-2.60) but had a 41% lower risk of treatment failure (HR, 0.59; 95% CI, 0.51-0.68).This is the first study to report a significant association between statin use and diminished amputation risk among patients with diabetes. In this nonrandomized cohort, beneficial effects of statin therapy were similar to that seen in large-scale clinical trial experience. For LEA risk, those given nonstatins did not have a statistically significant benefit and its effect on LEA risk was much smaller compared with statins. Unanswered questions to be explored in future studies include a comparison of statins of moderate vs high potency in those with high risk of coronary heart disease and an exploration of whether the effects seen in this study are simply effects of cholesterol-lowering or possibly pleiotropic effects.

Project description:BACKGROUND:Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems. METHODS:For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification. RESULTS:It was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control. CONCLUSION:Depending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.

Dataset Information

Diabetic polyneuropathy is a risk factor for decline of lower extremity strength in patients with type 2 diabetes.

Aims/introduction

Materials and methods

Results

Conclusion

Publications

Diabetic polyneuropathy is a risk factor for decline of lower extremity strength in patients with type 2 diabetes.

Similar Datasets

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