Dataset Information

Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.

ABSTRACT: Neuropathic pain is common in diabetic peripheral neuropathy (DN), probably caused by pathogenic ion channel gene variants. Therefore, we performed molecular inversion probes-next generation sequencing of 5 transient receptor potential cation channels, 8 potassium channels and 2 calcium-activated chloride channel genes in 222 painful- and 304 painless-DN patients. Twelve painful-DN (5.4%) patients showed potentially pathogenic variants (five nonsense/frameshift, seven missense, one out-of-frame deletion) in ANO3 (n = 3), HCN1 (n = 1), KCNK18 (n = 2), TRPA1 (n = 3), TRPM8 (n = 3) and TRPV4 (n = 1) and fourteen painless-DN patients (4.6%-three nonsense/frameshift, nine missense, one out-of-frame deletion) in ANO1 (n = 1), KCNK18 (n = 3), KCNQ3 (n = 1), TRPA1 (n = 2), TRPM8 (n = 1), TRPV1 (n = 3) and TRPV4 (n = 3). Missense variants were present in both conditions, presumably with loss- or gain-of-functions. KCNK18 nonsense/frameshift variants were found in painless/painful-DN, making a causal role in pain less likely. Surprisingly, premature stop-codons with likely nonsense-mediated RNA-decay were more frequent in painful-DN. Although limited in number, painful-DN patients with ion channel gene variants reported higher maximal pain during the night and day. Moreover, painful-DN patients with TRP variants had abnormal thermal thresholds and more severe pain during the night and day. Our results suggest a role of ion channel gene variants in neuropathic pain, but functional validation is required.

SUBMITTER: Sleczkowska M

PROVIDER: S-EPMC9266298 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.

Ślęczkowska Milena M Almomani Rowida R Marchi Margherita M de Greef Bianca T A BTA Sopacua Maurice M Hoeijmakers Janneke G J JGJ Lindsey Patrick P Salvi Erika E Bönhof Gidon J GJ Ziegler Dan D Malik Rayaz A RA Waxman Stephen G SG Lauria Giuseppe G Faber Catharina G CG Smeets Hubert J M HJM Gerrits Monique M MM

International journal of molecular sciences 20220628 13

Neuropathic pain is common in diabetic peripheral neuropathy (DN), probably caused by pathogenic ion channel gene variants. Therefore, we performed molecular inversion probes-next generation sequencing of 5 transient receptor potential cation channels, 8 potassium channels and 2 calcium-activated chloride channel genes in 222 painful- and 304 painless-DN patients. Twelve painful-DN (5.4%) patients showed potentially pathogenic variants (five nonsense/frameshift, seven missense, one out-of-frame ...[more]

PMID: 35806193

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Project description:Neuropathic pain is a frequent feature of diabetic peripheral neuropathy (DPN) and small fiber neuropathy (SFN). Resolving the genetic architecture of these painful neuropathies will lead to better disease management strategies, counselling and intervention. Our aims were to profile ten sodium channel genes (SCG) expressed in a nociceptive pathway in painful and painless DPN and painful and painless SFN patients, and to provide a perspective for clinicians who assess patients with painful peripheral neuropathy. Between June 2014 and September 2016, 1125 patients with painful-DPN (n = 237), painless-DPN (n = 309), painful-SFN (n = 547) and painless-SFN (n = 32), recruited in four different centers, were analyzed for SCN3A, SCN7A-SCN11A and SCN1B-SCN4B variants by single molecule Molecular inversion probes-Next Generation Sequence. Patients were grouped based on phenotype and the presence of SCG variants. Screening of SCN3A, SCN7A-SCN11A, and SCN1B-SCN4B revealed 125 different (potential) pathogenic variants in 194 patients (17.2%, n = 194/1125). A potential pathogenic variant was present in 18.1% (n = 142/784) of painful neuropathy patients vs. 15.2% (n = 52/341) of painless neuropathy patients (17.3% (n = 41/237) for painful-DPN patients, 14.9% (n = 46/309) for painless-DPN patients, 18.5% (n = 101/547) for painful-SFN patients, and 18.8% (n = 6/32) for painless-SFN patients). Of the variants detected, 70% were in SCN7A, SCN9A, SCN10A and SCN11A. The frequency of SCN9A and SCN11A variants was the highest in painful-SFN patients, SCN7A variants in painful-DPN patients, and SCN10A variants in painless-DPN patients. Our findings suggest that rare SCG genetic variants may contribute to the development of painful neuropathy. Genetic profiling and SCG variant identification should aid in a better understanding of the genetic variability in patients with painful and painless neuropathy, and may lead to better risk stratification and the development of more targeted and personalized pain treatments.

Project description:BackgroundTo improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors.MethodsThe Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy.ResultsRandom Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN.ConclusionsMachine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.

Project description:BackgroundPainful Diabetic Peripheral Neuropathy (PDPN) is a common complication of diabetes, it severely affects the quality of life of patients. Acupuncture has been shown to be effective in the treatment of PDPN. To evaluate the efficacy and safety of acupuncture for pain relief in patients diagnosed with diabetic peripheral neuropathy, we conducted a systematic review and meta-analysis.MethodWe thoroughly searched specific databases, which included PUBMED, EMBASE, Web of Science, the Cochrane Library, the Chinese Biomedical Literature Database, the Chinese National Knowledge Infrastructure, China Science and Technology Journal Database and the Wanfang Data. All randomized controlled trials of acupuncture therapy for PDPN with pain change scales were included. Included studies were assessed for methodological quality according to the risk of bias from the Cochrane handbook. Meta-analyses were carried out to analyze the outcomes, subgroup analyses, sensitivity analyses, and funnel plot analyses were undertaken.ResultsThis systematic review evaluated a total of 25 trials of acupuncture therapy in combination with conventional treatment, involving a total of 1,561 patients with PDPN. According to the results, among 16 trials using VAS scores with a total of 1,552 patients, 2 acupoint injection trials (MD -2.38, 95% CI: -2.76 to -2.01, p < 0.00001), 12 acupuncture trials (MD -1. 31, 95% CI: -1.60 to -1.02, p < 0.00001) and 2 moxibustion trials showed that acupuncture therapy combined with conventional treatment improved pain better than conventional treatment (MD -2.50, 95% CI: -2.76 to -2.24, p < 0.00001). In the subgroup analysis of the acupuncture group, the results of the 5 trials in which the location of acupuncture was only in the limbs (MD -1.27, 95% CI: -1.54 to -1.01, p < 0.00001) and the 7 trials both in limbs and torso (MD -1.38, 95% CI: -1.81 to -0.95, p < 0.00001) also demonstrated that acupuncture was effective in pain improvement.ConclusionThis meta-analysis analyzed the possible efficacy of acupuncture in combination with conventional treatment for pain in diabetic peripheral neuropathy, particularly when acupoints are located in the limbs. However, there are limitations to this meta-analysis and future clinical studies are needed to confirm these findings.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023449447, identifier (CRD42023449447).

Dataset Information

Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.

Publications

Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.

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