What are the distinctive features of anti-neurofascin 155 antibody CIDP?

Cranial nerve hypertrophy in IgG4 anti-neurofascin 155 antibody-positive polyneuropathy.

Franques J, Chapon F, Devaux J, Mathis S.

Neurology 2017; 88:e52.

Abstract

Anti-NF155 IgG4 antibodies are associated with a younger age at onset, sensory ataxia, tremor, and a poor response to IVIg treatment. Nerve hypertrophy has been reported in up to half of patients with CIDP, particularly in anti-NF155 antibody-positive patients. Hypertrophy of cervical and lumbosacral roots/plexuses has also been observed. Neurologists should be aware that patients with CIDP and anti-NF155 antibodies may demonstrate diffuse cranial nerve hypertrophy.

This reference is included in the neurochecklist:

Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP): investigations

Abstract link

By Dr. Jana – http://docjana.com/saltatory-conduction/ ; https://www.patreon.com/posts/4374048, CC BY 4.0, Link
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Is the HPV vaccine a cause of small fiber neuropathy?

Hypothesis: Human papillomavirus vaccination syndrome-small fiber neuropathy and dysautonomia could be its underlying pathogenesis.

Martínez-Lavín M.

Clin Rheumatol 2015; 34:1165-1169.

ABSTRACT

Hypothesis:

Vaccination has been one of the most effective public health measures in the history of medicine. However, seemingly inexplicit adverse reactions have been described after the injection of the newer vaccines vs. human papillomavirus (HPV). The symptoms more often reported are chronic pain with paresthesias, headaches, fatigue, and orthostatic intolerance.

Argument:

Adverse reactions appear to be more frequent after HPV vaccination when compared to other type of immunizations. Different isolated cases and small series have described the development of complex regional pain syndrome (CRPS), postural orthostatic tachycardia syndrome (POTS), and fibromyalgia after HPV vaccination. These are illnesses often difficult to diagnose that have overlapping clinical features. Sympathetic nervous system dysfunction seems to play a major role in the pathogenesis of these syndromes. Also, small fiber neuropathy has been recently recognized in CRPS, POTS, and fibromyalgia. This article forwards the hypothesis that small fiber neuropathy and dysautonomia could be the common underlying pathogenesis to the group of rare, but severe reactions that follow HPV vaccination.

Conclusion:

Clinicians should be aware of the possible association between HPV vaccination and the development of these difficult to diagnose painful dysautonomic syndromes.

This reference is now included in the neurochecklist:

Small fiber neuropathy: causes

Abstract link

Neurons, confocal fluorescence microscopy. ZEISS Microscopy on Flikr. https://www.flickr.com/photos/zeissmicro/8695004301

Is tafamidis really beneficial in familial amyloid polyneuropathy?

Long-term treatment of transthyretin familial amyloid polyneuropathy with tafamidis: a clinical and neurophysiological study.

Planté-Bordeneuve V, Gorram F, Salhi H, et al.

J Neurol 2017; 264:268-276.

ABSTRACT

Background:

Tafamidis is a transthyretin (TTR) stabilizer recently approved to slow the neurologic impairment in TTR familial amyloid polyneuropathy (TTR-FAP). The pivotal studies on Tafamidis reported encouraging results on the short term, in the early onset Val30Met-TTR-FAP patients at an early stage of the neuropathy. However, the effect of the drug in the non-Val30Met patients, at a more advanced stage of the disease and on the long term, is less known.

Methods:

In this study, we report the effect of Tafamidis in 43 TTR-FAP patients with a variety of pathogenic mutations, including 53% of non-Val30Met variants, at different stages of neuropathy followed on the long term. General and neurological assessment was performed in a standardized protocol every 6-12 months along with neurophysiological variables, including testing of small nerve fibres. The mean follow-up under treatment was 2 years with a subset of 26 patients treated for 3 years.

Results:

Overall, Tafamidis was well tolerated. A significant clinical deterioration of the neuropathy and the patient’s general condition was observed across the 3 years follow-up, although neurophysiological parameters remained stable for the first 2 years. In contrast, patients had a significant increase of BMI under treatment. Deterioration of the neuropathy correlated to an older age at disease onset or treatment initiation and to poor clinical status at baseline. A higher BMI at baseline was associated with a lower progression of the neuropathy. About one-third of the patients who received 3 years of tafamidis had still preserved walking capacity or good clinical condition, suggesting that tafamidis slowed the disease progression in some patients.

Conclusions:

Overall, our work shows that tafamidis is well tolerated in TTR-FAP but does not prevent the steady progression of the neuropathy on the long term. Age, neurologic status, and general condition at baseline appear to be best predictors of tafamidis efficacy on the neurological function.

This reference is cited in the neurochecklist:

Familial amyloid polyneuropathy (FAP): TTR: management

Abstract link

By Boku wa Kage – based on PDB ID 2BEG, rendered with PyMol (www.pymol.org), GFDL, Link

The 52 variants of CMT… and their practical checklists

Jean-Marie Charcot, Pierre Marie, and Howard Henry Tooth will be confounded to see what has become of the disease they described hundreds of years ago. Charcot-Marie Tooth disease (CMT) was a simple and straightforward disease then, with easily recognisable features such as the ‘classic’ high arched foot (pes cavus), the hammer toes, and the inverted champagne glass appearance of the leg.

By Benefros at English WikipediaOwn work (Original text: Own work, originally from en.wikipedia; description page is/was here.), CC BY-SA 3.0, Link

What was once a clear clinical entity has however morphed into a complex genetic maze. CMT has literally evolved into a hydra with 52 genetically distinct heads (and I thought I will never use literally in this blog). Half of this number is made up by the 26 forms of CMT type 2, which now runs from CMT2A to CMT2Z! Where to go after Z is anyones guess?

CC BY 2.0, Link

What neurologists could easily recognise and classify by distinctive clinical features now runs rings around them with complex neurophysiological cut-off points.

By JanbroggerOwn work, Public Domain, Link

In tackling the increasingly complex phenomenon of CMT, Neurochecklists has attempted to demystify the disease with a variety of simple checklists such as:

The genetic classification of CMT

The distinctive features of CMT

The differentiating features of CMT

The investigations of CMT

The management of CMT

Or you may wish to dive in properly and explore individual CMT subtypes. In which case, here are the 52 varieties of CMT and their genetic mutations, all linked to their checklists. Watch out for SPG11!

CMT1: Autosomal dominant demyelinating

CMT1A: PMP22 

CMT1B: MPZ

CMT1C: LITAF/SIMPLE 

CMT1D: EGR2 

CMT1E: PMP22 

CMT1F: NEFL 

CMT2: Autosomal dominant axonal

CMT2A: MFN2  

CMT2B: RAB7 

CMT2C: TRPV4

CMT2D: GARS 

CMT2E: NEFL 

CMT2F: HSPB1 

CMT2G (old term for CMT2P)

CMT2H: GDAP1 

CMT2I: MPZ 

CMT2J: MPZ 

CMT2K: GDAP1 and JPH1 

CMT2L: HSPB8 

CMT2M: DNM2 

CMT2N: AARS 

CMT20: DYNC1H1 

CMT2P: LRSAM1 

CMT2Q: DHTKD1 

CMT2R: TRIM2 

CMT2S: IGHMBP2 

CMT2T: MME 

CMT2U: MARS 

CMT2V: NAGLU 

CMT2W: HARS 

CMT2X: SPG11

CMT2Y: VCP 

CMT2Z: MORC2

CMT2B1 and CMT2B2: Autosomal recessive axonal

CMT2B1: LMNA

CMT2B2: MED25 

CMT4: Autosomal recessive demyelinating

CMT4A: GDAP1 

CMT4B1: MTMR2 

CMT4B2: SBF2 

CMT4B3: SBF1 

CMT4C: SH3TC2

CMT4D: NDRG1 

CMT4E: MPZ and EGR2

CMT4F: PRX 

CMT4G: HK1 

CMT4H: FGD4 

CMT4I (non-existent)

CMT4J: FIG4 

CMT3,5 and 6

CMT3: PMP22, MPZ, EGR2, PXN, GJB1

CMT5: MFN2

CMT6: MFN2

CMTX: X-linked

CMTX1: JGB1 

CMTX2: mutation unknown

CMTX3: BSCL2

CMTX4: AIFM1 

CMTX5: PRPS1

CMTX6: PDK3 

CMTDI: Autosomal dominant intermediate

CMTDIA: mutation unknown

CMTDIB: DNM2

CMTDIC: YARS 

CMTDID: MPZ 

CMTDIE: INF2 

CMTDIF: GNB4 

CMTRI: Autosomal recessive intermediate

CMT2RIA: GDAP 

CMT2RIB: KARS

CMT2RIC: PLEKHG5 

CMT2RID: COX6A1 

 

Explore these and more on Neurochecklists

 

How does CMT involve the central nervous system?

Cerebral white matter abnormalities in patients with Charcot-Marie-Tooth disease

Lee M, Park CH, Chung HK, et al

Ann Neurol 2017; 81:147-151

Abstract

Here, we report the structural evidence of cerebral white matter abnormalities in Charcot-Marie-Tooth (CMT) patients and the relationship between these abnormalities and clinical disability.

Brain diffusion tensor imaging (DTI) was performed in CMT patients with demyelinating (CMT1A/CMT1E), axonal (CMT2A/CMT2E), or intermediate (CMTX1/DI-CMT) peripheral neuropathy. Although all patients had normal brain magnetic resonance imaging, all genetic subgroups except CMT1A had abnormal DTI findings indicative of significant cerebral white matter abnormalities: decreased fractional anisotropy and axial diffusivity, and increased radial diffusivity.

DTI abnormalities were correlated with clinical disability, suggesting that there is comorbidity of central nervous system damage with peripheral neuropathy in CMT patients.

 

You may also check out:

Loss of coupling distinguishes GJB1 mutations associated with CNS manifestations of CMT1X from those without CNS manifestations.

Abrams CK, Goman M, Wong S, Scherer SS, Kleopa KA, Peinado A, Freidin MM.

Sci Rep 2017; 7:40166.

These references are now included in the neurochecklist:

Charcot Marie Tooth disease (CMT): differentiating features

Abstract link 1

Abstract link 2

By NIH / U.S. National Library of Medicine - http://resource.nlm.nih.gov/101425121, Public Domain, Link
By NIH / U.S. National Library of Medicine – http://resource.nlm.nih.gov/101425121, Public Domain, Link
By en:Eugène Pirou - [1], Public Domain, Link
By en:Eugène Pirou[1], Public Domain, Link

Is obesity a risk factor for peripheral neuropathy?

Association between metabolic syndrome components and polyneuropathy in an obese population

Callaghan BC, Xia R, Reynolds E, et al.

JAMA Neurol 2016; 73:1468-1476.

Abstract

IMPORTANCE:

Past studies have shown an association between metabolic syndrome and polyneuropathy, but the precise components that drive this association remain unclear.

OBJECTIVES:

To determine the prevalence of polyneuropathy stratified by glycemic status in well-characterized obese and lean participants and investigate the association of specific components of metabolic syndrome with polyneuropathy.

METHODS:

We performed a cross-sectional, observational study from November 1, 2010, to December 31, 2014, in obese participants (body mass index [calculated as weight in kilograms divided by height in meters squared] of 35 or more with no comorbid conditions or 32 or more with at least 1 comorbid condition) from a weight management program and lean controls from a research website. The prevalence of neuropathy, stratified by glycemic status, was determined, and a Mantel-Haenszel χ2 test was used to investigate for a trend. Logistic regression was used to model the primary outcome of polyneuropathy as a function of the components of metabolic syndrome after adjusting for demographic factors. Participants also completed quantitative sudomotor axon reflex testing, quantitative sensory testing, the neuropathy-specific Quality of Life in Neurological Disorders instrument, and the short-form McGill Pain Questionnaire.

RESULTS:

We enrolled 102 obese participants (mean [SD] age, 52.9 [10.2] years; 48 men and 54 women; 45 with normoglycemia [44.1%], 31 with prediabetes [30.4%], and 26 with type 2 diabetes [25.5%]) and 53 lean controls (mean [SD] age, 48.5 [9.9] years; 16 men and 37 women). The prevalence of polyneuropathy was 3.8% in lean controls (n = 2), 11.1% in the obese participants with normoglycemia (n = 5), 29% in the obese participants with prediabetes (n = 9), and 34.6% in the obese participants with diabetes (n = 9) (P < .01 for trend). Age (odds ratio, 1.09; 95% CI, 1.02-1.16), diabetes (odds ratio, 4.90; 95% CI, 1.06-22.63), and waist circumference (odds ratio, 1.24; 95% CI, 1.00-1.55) were significantly associated with neuropathy in multivariable models. Prediabetes (odds ratio, 3.82; 95% CI, 0.95-15.41) was not significantly associated with neuropathy.

CONCLUSIONS:

The prevalence of polyneuropathy is high in obese individuals, even those with normoglycemia. Diabetes, prediabetes, and obesity are the likely metabolic drivers of this neuropathy.

 

This reference is now included in the neurochecklist:

Chronic idiopathic axonal polyneuropathy (CIAP): features

screen-shot-2016-12-24-at-23-32-53

Abstract link

Obesity health risk. Mike Licht on Flikr. https://www.flickr.com/photos/notionscapital/6980588184
Obesity health risk. Mike Licht on Flikr. https://www.flickr.com/photos/notionscapital/6980588184

Is there a shortcut to suspecting inflammatory neuropathy?

 Rapid screening for inflammatory neuropathies by standardized clinical criteria

Karam C, Tramontozzi LA III.

Neurol Clin Pract 2016; 6:384-388.

Abstract

Background: Delay in recognition and treatment of inflammatory neuropathies increases morbidity and mortality. We have developed and standardized 3 clinical screening criteria that rapidly detect inflammatory neuropathies.

Methods: We reviewed all patients with definite large fiber neuropathy in 2 different patient populations: 1 from a private neurology clinic and the other from a tertiary care center. Patients were divided into 2 groups: those with an inflammatory neuropathy and those with a noninflammatory neuropathy. We specifically noted the 3 key neuropathy characteristics: onset, distribution, and associated systemic features (ODS). We studied the sensitivity and specificity of ODS in differentiating between inflammatory and noninflammatory neuropathies.

Results: A total of 206 patients were included: 51 from the private clinic and 155 from the tertiary care center. The sensitivity of using ODS in detecting an inflammatory neuropathy was 96% and the specificity was 85%. The positive predictive value of ODS was 0.8 and negative predictive value was 0.97.

Conclusions: Rapid screening for inflammatory neuropathies by ODS clinical criteria is highly sensitive and has a high negative predictive value for noninflammatory neuropathies. ODS uses simple clinical criteria to rapidly screen for patients with a potentially treatable form of neuropathy and accelerate their diagnostic evaluation.

Classification of evidence: This study provides Class IV evidence that 3 neuropathy characteristics—onset, distribution, and associated systemic features—accurately identify patients with inflammatory neuropathies.

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