Intensive evaluation of referred unclassified neuropathies yields improved diagnosis

Dyck, Peter J.; Oviatt, Karen F.; Lambert, Edward H.

doi:10.1002/ana.410100304

Cited by 301 publications

(166 citation statements)

References 3 publications

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“…Even in patients with type 2 diabetes, glycemic control has only a small effect on the prevention of peripheral neuropathy 6. Furthermore, a significant proportion of patients with peripheral neuropathy have no known underlying cause 7, 8, 9, 10. Therefore, a better understanding of the underlying causes is needed to inform the development of new disease modifying treatments.…”

Section: Introductionmentioning

confidence: 99%

Diabetes and obesity are the main metabolic drivers of peripheral neuropathy

Callaghan

Gao

Li³

et al. 2018

Ann Clin Transl Neurol

158

110

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ObjectiveTo determine the associations between individual metabolic syndrome (MetS) components and peripheral neuropathy in a large population‐based cohort from Pinggu, China.MethodsA 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.ResultsThe 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.InterpretationSimilar 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.

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Section: Introductionmentioning

confidence: 99%

Diabetes and obesity are the main metabolic drivers of peripheral neuropathy

Callaghan

Gao

Li³

et al. 2018

Ann Clin Transl Neurol

158

110

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“…Hereditary polyneuropathy represents 42% of 205 cases of undiagnosed neuropathy 20 and 30% of 402 consecutive patients with polyneuropathy 21 in two separate neuropathy specialty clinics. Thus, hereditary polyneuropathy was recognized to account a Extrapolated total number and mutation frequencies recalculated for the total number.…”

Section: Discussion Prioritizing Molecular Testingmentioning

confidence: 99%

Charcot-Marie-Tooth disease and related hereditary polyneuropathies: Molecular diagnostics determine aspects of medical management

Szigeti

García

Lupski

2006

Genetics in Medicine

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Purpose: An evidence-based approach was used to determine the frequency distribution of genes contributing to the Charcot-Marie-Tooth (CMT) disease phenotype. Methods: We performed a combined analysis of 11 populationbased studies from various ethnic backgrounds to generate an evidence-based testing scheme. To estimate the relative frequencies of the responsible genes for which population-based studies are not available, we used our cohort of clinically classified patients with CMT and related neuropathies collected before the availability of genetic testing. Results: Similar mutation frequencies were detected in the various studies, revealing a uniform distribution of pathogenic mutations. In CMT1 70% of patients harbor the CMT1A duplication, followed by GJB1 mutations at 8.8%. MPZ and PMP22 mutations are less common, identified on average in 2.9% and 1.5% of patients,respectively. Other genes not tested in population-based studies contribute to less than 1% of disease individually.In CMT2 MFN2 mutations are the most common, although population-based studies are not yet available. Conclusion:CMT represents a heterogeneous group of disorders at the molecular level. Nevertheless, testing for the CMT1A duplication (i.e., duplication of PMP22) alone yields an accurate molecular diagnosis in approximately half of all patients. If one further specifies the clinical type (demyelinating vs. axonal), the yield of detecting a molecular defect increases to 75% to 80% in the demyelinating or CMT1 group with a screening test that evaluates for CMT1A duplication/hereditary neuropathy with liability to pressure palsies deletion and GJB1 point mutations. Case vignette: A 35-year-old man presents with a 5-year history of numbness in his feet and occasional tripping. Family history reveals that he has an 8-year-old daughter. He and his wife lost a 5-year-old daughter 1 year ago, who developed acute lymphoblastic leukemia and became paralyzed after the first dose of chemotherapy. His wife is expecting another child. Genet MedSigns and symptoms of peripheral neuropathy are often identified during office visits to both the neurologist and generalist physician. When neuropathy presents in the context of a systemic illness, such as diabetes or uremia, the acquired nature is easily recognized. However, in the absence of overt systemic illness, the ability to distinguish acquired from inherited neuropathies on clinical grounds is often challenging, particularly in sporadic disease.Charcot-Marie-Tooth (CMT) and related peripheral neuropathies represent a heterogeneous group of hereditary disorders of the peripheral nervous system with an estimated frequency of 1 in 2500 individuals. 1 CMT is characterized by slow and progressive weakness of the legs followed, in some cases, by hand involvement. On the basis of motor nerve conduction velocities (NCVs), two major types can be distinguished: the demyelinating form (CMT1), which is characterized by symmetrically slowed NCV (usually Ͻ 38 m/sec; normal is Ͼ 45 m/sec), and the axonal form (CMT...

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“…This estimate is, however, based on very uncertain assumptions. A recent report stated that CIDPrepresents a significant number of initially-undiagnosed acquired neuropathies, with estimates varying from 10 to 20% (9). Moreover, our recent survey (10) revealed that the prevalence rate is roughly estimated to be around 2 to 3 per 100,000 of population in the northern Honshu area.…”

mentioning

confidence: 81%

Chronic Inflammatory Demyelinating Poly neuropathy: Treatable Hypertrophic Neuropathy

Baba

1999

Intern. Med.

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Intensive evaluation of referred unclassified neuropathies yields improved diagnosis

Cited by 301 publications

References 3 publications

Diabetes and obesity are the main metabolic drivers of peripheral neuropathy

Diabetes and obesity are the main metabolic drivers of peripheral neuropathy

Charcot-Marie-Tooth disease and related hereditary polyneuropathies: Molecular diagnostics determine aspects of medical management

Chronic Inflammatory Demyelinating Poly neuropathy: Treatable Hypertrophic Neuropathy

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