Dataset Information

The epidemiology and risk factors of chronic polyneuropathy.

ABSTRACT: Polyneuropathy is a disabling condition of the peripheral nerves, characterized by symmetrical distal numbness and paresthesia, often accompanied with pain and weakness. Although the disease is often encountered in neurological clinics and is well known by physicians, incidence and prevalence rates are not well known. We searched EMBASE, Medline, Web-of-science, Cochrane, PubMed Publisher, and Google Scholar, for population-based studies investigating the prevalence of polyneuropathy and its risk factors. Out of 5119 papers, we identified 29 eligible studies, consisting of 11 door-to-door survey studies, 7 case-control studies and 11 cohort/database studies. Prevalence of polyneuropathy across these studies varies substantially. This can partly be explained by differences in assessment protocols and study populations. The overall prevalence of polyneuropathy in the general population seems around 1% and rises to up to 7% in the elderly. Polyneuropathy seemed more common in Western countries than in developing countries and there are indications that females are more often affected than males. Risk factor profiles differ across countries. In developing countries communicable diseases, like leprosy, are more common causes of neuropathy, whereas in Western countries especially diabetes, alcohol overconsumption, cytostatic drugs and cardiovascular disease are more commonly associated with polyneuropathy. In all studies a substantial proportion of polyneuropathy cases (20-30%) remains idiopathic. Most of these studies have been performed over 15 years ago. More recent evidence suggests that the prevalence of polyneuropathy in the general population has increased over the years. Future research is necessary to confirm this increase in prevalence and to identify new and potentially modifiable risk factors.

SUBMITTER: Hanewinckel R

PROVIDER: S-EPMC4756033 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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The epidemiology and risk factors of chronic polyneuropathy.

Hanewinckel Rens R van Oijen Marieke M Ikram M Arfan MA van Doorn Pieter A PA

European journal of epidemiology 20151223 1

Polyneuropathy is a disabling condition of the peripheral nerves, characterized by symmetrical distal numbness and paresthesia, often accompanied with pain and weakness. Although the disease is often encountered in neurological clinics and is well known by physicians, incidence and prevalence rates are not well known. We searched EMBASE, Medline, Web-of-science, Cochrane, PubMed Publisher, and Google Scholar, for population-based studies investigating the prevalence of polyneuropathy and its ris ...[more]

PMID: 26700499

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Project description:Background and purposeChronic axonal polyneuropathy is a common disease, but the etiology remains only partially understood. Previous etiologic studies have identified clinical risk factors, but genetic evidence supporting causality between these factors and polyneuropathy are largely lacking. In this study, we investigate whether there is a genetic association of clinically established important risk factors (diabetes, body mass index [BMI], vitamin B12 levels, and alcohol intake) with chronic axonal polyneuropathy.MethodsThis study was performed within the population-based Rotterdam Study and included 1565 participants (median age = 73.6 years, interquartile range = 64.6-78.8, 53.5% female), of whom 215 participants (13.7%) had polyneuropathy. Polygenic scores (PGSs) for diabetes, BMI, vitamin B12 levels, and alcohol intake were calculated at multiple significance thresholds based on published genome-wide association studies.ResultsHigher PGSs of diabetes, BMI, and alcohol intake were associated with higher prevalence of chronic axonal polyneuropathy, whereas higher PGS of vitamin B12 levels was associated with lower prevalence of polyneuropathy. These effects were most pronounced for PGSs with lenient significance thresholds for diabetes and BMI (odds ratio [OR]diabetes, p < 1.0 = 1.21, 95% confidence interval [CI] = 1.05-1.39 and ORBMI, p < 1.0 = 1.21, 95% CI = 1.04-1.41) and for the strictest significance thresholds for vitamin B12 level and alcohol intake (OR vitamin B12, p < 5e-6 = 0.79, 95% CI = 0.68-0.92 and ORalcohol, p < 5e-8 = 1.17, 95% CI = 1.02-1.35). We did not find an association between different PGSs and sural sensory nerve action potential amplitude, nor between individual lead variants of PGSp < 5e-8 and polyneuropathy.ConclusionsThis study provides evidence for polygenic associations of diabetes, BMI, vitamin B12 level, and alcohol intake with chronic axonal polyneuropathy. This supports the hypothesis of causal associations between well-known clinical risk factors and polyneuropathy.

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

The epidemiology and risk factors of chronic polyneuropathy.

Publications

The epidemiology and risk factors of chronic polyneuropathy.

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