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Diabetes and obesity are the main metabolic drivers of peripheral neuropathy

Objective To determine the associations between individual metabolic syndrome (MetS) components and peripheral neuropathy in a large population‐based cohort from Pinggu, China. Methods A cross‐sectional, randomly selected, population‐based survey of participants from Pinggu, China was performed. Met...

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Published in:Annals of clinical and translational neurology 2018-04, Vol.5 (4), p.397-405
Main Authors: Callaghan, Brian C., Gao, LeiLi, Li, Yufeng, Zhou, Xianghai, Reynolds, Evan, Banerjee, Mousumi, Pop‐Busui, Rodica, Feldman, Eva L., Ji, Linong
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container_title Annals of clinical and translational neurology
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creator Callaghan, Brian C.
Gao, LeiLi
Li, Yufeng
Zhou, Xianghai
Reynolds, Evan
Banerjee, Mousumi
Pop‐Busui, Rodica
Feldman, Eva L.
Ji, Linong
description Objective To determine the associations between individual metabolic syndrome (MetS) components and peripheral neuropathy in a large population‐based cohort from Pinggu, China. Methods A cross‐sectional, randomly selected, population‐based survey of participants from Pinggu, China was performed. Metabolic phenotyping and neuropathy outcomes were performed by trained personnel. Glycemic status was defined according to the American Diabetes Association criteria, and the MetS using modified consensus criteria (body mass index instead of waist circumference). The primary peripheral neuropathy outcome was the Michigan Neuropathy Screening Instrument (MNSI) examination. Secondary outcomes were the MNSI questionnaire and monofilament testing. Multivariable models were used to assess for associations between individual MetS components and peripheral neuropathy. Tree‐based methods were used to construct a classifier for peripheral neuropathy using demographics and MetS components. Results The mean (SD) age of the 4002 participants was 51.6 (11.8) and 51.0% were male; 37.2% of the population had normoglycemia, 44.0% prediabetes, and 18.9% diabetes. The prevalence of peripheral neuropathy increased with worsening glycemic status (3.25% in normoglycemia, 6.29% in prediabetes, and 15.12% in diabetes, P < 0.0001). Diabetes (odds ratio [OR] 2.60, 95% CI 1.77–3.80) and weight (OR 1.09, 95% CI 1.02–1.18) were significantly associated with peripheral neuropathy. Age, diabetes, and weight were the primary splitters in the classification tree for peripheral neuropathy. Interpretation Similar to previous studies, diabetes and obesity are the main metabolic drivers of peripheral neuropathy. The consistency of these results reinforces the urgent need for effective interventions that target these metabolic factors to prevent and/or treat peripheral neuropathy.
doi_str_mv 10.1002/acn3.531
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Methods A cross‐sectional, randomly selected, population‐based survey of participants from Pinggu, China was performed. Metabolic phenotyping and neuropathy outcomes were performed by trained personnel. Glycemic status was defined according to the American Diabetes Association criteria, and the MetS using modified consensus criteria (body mass index instead of waist circumference). The primary peripheral neuropathy outcome was the Michigan Neuropathy Screening Instrument (MNSI) examination. Secondary outcomes were the MNSI questionnaire and monofilament testing. Multivariable models were used to assess for associations between individual MetS components and peripheral neuropathy. Tree‐based methods were used to construct a classifier for peripheral neuropathy using demographics and MetS components. Results The mean (SD) age of the 4002 participants was 51.6 (11.8) and 51.0% were male; 37.2% of the population had normoglycemia, 44.0% prediabetes, and 18.9% diabetes. The prevalence of peripheral neuropathy increased with worsening glycemic status (3.25% in normoglycemia, 6.29% in prediabetes, and 15.12% in diabetes, P &lt; 0.0001). Diabetes (odds ratio [OR] 2.60, 95% CI 1.77–3.80) and weight (OR 1.09, 95% CI 1.02–1.18) were significantly associated with peripheral neuropathy. Age, diabetes, and weight were the primary splitters in the classification tree for peripheral neuropathy. Interpretation Similar to previous studies, diabetes and obesity are the main metabolic drivers of peripheral neuropathy. The consistency of these results reinforces the urgent need for effective interventions that target these metabolic factors to prevent and/or treat peripheral neuropathy.</description><identifier>ISSN: 2328-9503</identifier><identifier>EISSN: 2328-9503</identifier><identifier>DOI: 10.1002/acn3.531</identifier><identifier>PMID: 29687018</identifier><language>eng</language><publisher>United States: John Wiley &amp; Sons, Inc</publisher><subject>Diabetes ; Metabolism ; Obesity ; Peripheral neuropathy</subject><ispartof>Annals of clinical and translational neurology, 2018-04, Vol.5 (4), p.397-405</ispartof><rights>2018 The Authors. published by Wiley Periodicals, Inc on behalf of American Neurological Association.</rights><rights>2018. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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subjects Diabetes
Metabolism
Obesity
Peripheral neuropathy
title Diabetes and obesity are the main metabolic drivers of peripheral neuropathy
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