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. 2020 Oct;34(10):107653.
doi: 10.1016/j.jdiacomp.2020.107653. Epub 2020 Jun 11.

The metabolic drivers of neuropathy in India

Evan L Reynolds  1 , Brian C Callaghan  2 , Mousumi Banerjee  3 , Eva L Feldman  4 , Vijay Viswanathan  5
Affiliations

The metabolic drivers of neuropathy in India

Evan L Reynolds et al. J Diabetes Complications. 2020 Oct.

Abstract

Aims: To determine the association between the metabolic syndrome (MetS) and neuropathy in Chennai, India.

Methods: We recruited participants attending the M.V. Hospital for Diabetes. Neuropathy was defined using the Michigan Neuropathy Screening Instrument combined index and MetS was defined using the updated National Cholesterol Education Program criteria. Multivariable logistic regression models were used to assess the associations between individual metabolic components and neuropathy.

Results: Of the 652 participants (42% female and mean (SD) age of 45.5 (9.7)) included in the study, the prevalence of neuropathy was 9.8%. Neuropathy prevalence increased with worsening glycemic status (p < 0.01), but not as the number of MetS components increased (p = 0.12). Among normoglycemic participants, an increasing neuropathy trend was observed as the number of MetS components increased (p = 0.04). Multivariable logistic regression found that diabetes (OR:3.41,1.28-9.11) was associated with neuropathy, but waist circumference was not (OR:1.002,0.88-1.14).

Conclusions: Similar to previous studies, diabetes was the most important metabolic risk factor for neuropathy in a population from Chennai, India. In contrast to other population-based studies, waist circumference was not associated with neuropathy. Whether the distribution of obesity affects nerves differently in Indian populations requires future studies with more precise anthropometric measures.

Keywords: Diabetes mellitus; Metabolic syndrome; Neuropathy; Obesity.

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Conflict of interest statement

Disclosures Dr. Reynolds, Dr. Banerjee and Dr. Viswanathan report no disclosures. Dr. Feldman consults for Novartis. Dr. Callaghan consults for a PCORI grant, DynaMed, the Immune Tolerance Network, and performs medical legal consultations including consultations for the Vaccine Injury Compensation Program.

Figures

Figure 1
Figure 1. Neuropathy prevalence stratified by glycemic status
The prevalence of neuropathy as determined by primary (MNSI index) and secondary neuropathy outcomes (MNSI examination, questionnaire, monofilament, and biothesiometer) stratified by glycemic status (normoglycemia, pre-diabetes, diabetes).
Figure 2
Figure 2. Boxplot of predicted probabilities for neuropathy based on the primary multivariable logistic regression, stratified by glycemic status
Predicted probabilities for neuropathy (MNSI Index>3.29) based on the multivariable logistic regression model, stratified by glycemic status.
Figure 3
Figure 3. The prevalence of neuropathy with increasing metabolic syndrome components stratified by glycemic status.
Neuropathy was defined as those with a Michigan Neuropathy Screening Instrument (MNSI) Index score >3.29. Glycemic status was determined by the glucose tolerance test according to the Expert Committee on the diagnosis and classification of diabetes mellitus. Metabolic syndrome components were defined using the updated NCEP criteria.
See this image and copyright information in PMC

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