Øystein Dunker

Introduction/Aims: High quality reference limits for nerve conduction studies (NCS) are essential for diagnosis of neuromuscular disorders. Examining healthy controls to calculate reference limits directly is expensive and time consuming. Indirect methods, including extrapolated norms (E‐norms), extrapolated reference values (E‐Ref), and multivariate extrapolated reference values (MeRef), use historical hospital data instead. These methods sort the historical measurements in increasing order, creating so‐called S‐curves, and select normal measurements based on the shape of these S‐curves. Current versions of these methods have several limitations, and we aimed to improve them by modifying how they select normal measurements. Methods: E‐norms, E‐Ref, and MeRef were modified with new S‐curve selection algorithms. The modified versions of the methods were used to calculate reference limits for common NCS measurements from a large historical database. The results were compared to reference limits calculated from 680 healthy subjects using Youden's J statistic and z‐score deviation. Results: The modified methods provided reference limits with Youden's J > 0.8 and z ‐score deviation < 0.9 for most types of NCS measurements and similar or higher Youden's J than the unmodified methods. In most cases, the methods required at least 500 measurements and fewer than 20% abnormal measurements for good performance. Discussion: Changing the S‐curve selection algorithms improves E‐norms, E‐Ref, and MeRef. The modifications require a sufficient number and proportion of normal measurements in the historical database. When these prerequisites are met, a combination of indirect methods can be used when developing reference limits from historical patient data.

Neuropathic pain is a complex chronic condition with a multifactorial etiology that includes genetic susceptibility. However, the genetic basis of neuropathic pain remains poorly understood. We aimed to investigate genetic variants associated with the presence and intensity of neuropathic pain using genome-wide association analyses in the GeNeuP cohort, comprised of 1146 deeply phenotyped individuals with peripheral neuropathy from Norway. Genotyping was performed using the Illumina Global Screening Array, and analyses were conducted to test associations with the presence and intensity of neuropathic pain. No significant associations were detected at the genome-wide significance level ( P < 5 × 10 ⁻⁸ ) for either the presence or intensity of neuropathic pain. However, at the subthreshold level ( P < 10 ⁻⁶ ), 3 single nucleotide polymorphisms (annotated to the CHRDL1 and MCF2L gene and a long non-coding RNA) were associated with intensity of neuropathic pain. A targeted candidate gene analysis of 163 genes previously implicated in neuropathic pain and neuropathy did not yield significant associations. These results highlight the complexity of the genetic architecture underlying neuropathic pain and the challenges in identifying common variants with detectable effects. The identification of subthreshold associations of genes involved in synaptic plasticity is intriguing and merits further investigation. Larger studies with refined phenotyping will be essential to validate these signals and to advance understanding of the genetic contributors to neuropathic pain.

Introduction/Aims: The association between body mass index (BMI) and nerve conduction study (NCS) amplitudes has not been adequately studied in overweight and obese subjects. Our aim was to estimate the impact of high BMI on NCS amplitudes and NCS summary measures.Methods: One hundred and seventy‐five patients with previous gastric bypass surgery and 86 subjects from the general population had clinical evaluations and NCS of upper and lower limbs. Subjects with diabetes, mononeuropathy, or polyneuropathy (PNP) were excluded. Eighty‐three former patients with a mean age of 48 years (SD 8), BMI mean = 34.3 kg/m² (range 22–60) and 62 control subjects from the general population with a mean age of 55 years (SD 11), BMI mean = 26.6 kg/m² (range 19–40), were analyzed. Several Z‐compounds were calculated from NCS parameters. Multiple linear regression models compared the impact of BMI on NCS amplitude measurements, corrected for height and age, and NCS‐based Z‐compounds.Results: Most sensory amplitudes and tibial nerve motor amplitude decreased significantly with increasing BMI. The magnitudes of association were comparable for BMI and age. A new Z‐compound for sensory amplitudes correlated moderately strongly with BMI for subjects with BMI < 30 kg/m² and BMI ≥ 30 kg/m² (standardized beta 0.55 and 0.47 respectively, p < 0.0005).Discussion: Sensory NCS amplitudes depend on BMI in obese and non‐obese subjects. Lower reference limits for several sensory amplitudes should probably be adjusted for BMI. Reference limits for amplitude‐dependent Z‐compounds should be corrected for BMI. The findings have relevance for all patients investigated with nerve conduction studies, in particular, patients with suspected axonal polyneuropathies.

Introduction There is a need for simple and cheap diagnostic tools for diabetic polyneuropathy (DPN). We aimed to assess the diagnostic accuracy of the 5.07/10 g monofilament test in patients referred to polyneuropathy assessments, as well as to examine how disease severity, age, sex and neuropathic pain (NP) impact diagnostic accuracy. Research design and methods Five Norwegian university hospitals recruited patients with diabetes aged 18–70 referred to neurological outpatient clinics for polyneuropathy assessments. The 5.07/10 g Semmes-Weinstein monofilament examination (SWME) was validated against the Toronto consensus for diagnosing diabetic neuropathies; the results were stratified by age, sex and NP. Disease severity was graded by a combined nerve conduction study (NCS) Z-score, and logistic regression was applied to assess whether disease severity was a predictor of diagnostic accuracy. Results In total, 506 patients were included in the study. Global sensitivity was 0.60 (95% CI 0.55, 0.66), specificity 0.82 (95% CI 0.75, 0.87), positive and negative predictive values were 0.86 (95% CI 0.81, 0.90) and 0.52 (95% CI 0.46, 0.58), respectively, positive and negative likelihood ratios were 3.28 (95% CI 2.37, 4.53) and 0.49 (95% CI 0.42, 0.57), respectively. The SWME was less sensitive in females (0.43), had lower specificity in patients with NP (0.56), and performed worse in patients ≥50 years. NCS-based disease severity did not affect diagnostic accuracy (OR 1.15, 95% CI 0.95, 1.40). Conclusions This multicenter study demonstrates poor diagnostic performance for the 5.07/10 g SWME in patients with diabetes referred to polyneuropathy assessments; it is particularly unsuited for female patients and those with NP. The diagnostic accuracy of the SWME was not influenced by NCS-based disease severity, demonstrating that it does not perform better in patients with later stages of DPN. We do not recommend the use of the 5.07/10 g monofilament in the evaluation of patients with diabetes referred to polyneuropathy assessments.