Image1_Causal effect of serum 25-hydroxyvitamin D levels on low back pain: A two-sample mendelian randomization study.TIF

Background: Previous observational studies have suggested the involvement of 25-hydroxyvitamin D [25(OH)D] in chronic pain. However, whether the 25(OH)D is a novel target for management, the causality remains unclear.Methods: A two-sample Mendelian randomization (MR) study was conducted to identify the causal association between 25(OH)D and low back pain (LBP). The primary analysis was revealing causality from serum 25(OH)D level (n = 417,580) on LBP (21,140 cases and 227,388 controls). The replicated analysis was performing MR estimates from circulating 25(OH)D concentration (n = 79,366) on LBP experienced last month (118,471 cases and 343,386 controls). Inverse variance weighted (IVW) was used as the main analysis. In addition, we used weighted median and MR-Egger to enhance the robustness. Sensitivity analysis was conducted to evaluate the robustness of MR results.Results: IVW estimation indicated strong evidence that higher serum 25(OH)D levels exerted a protective effect on LBP (OR = 0.89, 95% CI = 0.83–0.96, p = 0.002). Similar trends were also found in replicate analysis (OR = 0.98, 95% CI = 0.96–1.00, p = 0.07). After meta-analysis combining primary and replicated analysis, the causal effect is significant (p = 0.03). Sensitivity analysis supported that the MR estimates were robust.Conclusion: In our MR study, genetically increased serum 25(OH)D levels were associated with a reduced risk of LBP in the European population. This might have an implication for clinicians that vitamin D supplements might be effective for patients with LBP in clinical practice.</p

DataSheet1_Causal effect of serum 25-hydroxyvitamin D levels on low back pain: A two-sample mendelian randomization study.docx

Background: Previous observational studies have suggested the involvement of 25-hydroxyvitamin D [25(OH)D] in chronic pain. However, whether the 25(OH)D is a novel target for management, the causality remains unclear.Methods: A two-sample Mendelian randomization (MR) study was conducted to identify the causal association between 25(OH)D and low back pain (LBP). The primary analysis was revealing causality from serum 25(OH)D level (n = 417,580) on LBP (21,140 cases and 227,388 controls). The replicated analysis was performing MR estimates from circulating 25(OH)D concentration (n = 79,366) on LBP experienced last month (118,471 cases and 343,386 controls). Inverse variance weighted (IVW) was used as the main analysis. In addition, we used weighted median and MR-Egger to enhance the robustness. Sensitivity analysis was conducted to evaluate the robustness of MR results.Results: IVW estimation indicated strong evidence that higher serum 25(OH)D levels exerted a protective effect on LBP (OR = 0.89, 95% CI = 0.83–0.96, p = 0.002). Similar trends were also found in replicate analysis (OR = 0.98, 95% CI = 0.96–1.00, p = 0.07). After meta-analysis combining primary and replicated analysis, the causal effect is significant (p = 0.03). Sensitivity analysis supported that the MR estimates were robust.Conclusion: In our MR study, genetically increased serum 25(OH)D levels were associated with a reduced risk of LBP in the European population. This might have an implication for clinicians that vitamin D supplements might be effective for patients with LBP in clinical practice.</p

Image2_Causal effect of serum 25-hydroxyvitamin D levels on low back pain: A two-sample mendelian randomization study.TIF

Background: Previous observational studies have suggested the involvement of 25-hydroxyvitamin D [25(OH)D] in chronic pain. However, whether the 25(OH)D is a novel target for management, the causality remains unclear.Methods: A two-sample Mendelian randomization (MR) study was conducted to identify the causal association between 25(OH)D and low back pain (LBP). The primary analysis was revealing causality from serum 25(OH)D level (n = 417,580) on LBP (21,140 cases and 227,388 controls). The replicated analysis was performing MR estimates from circulating 25(OH)D concentration (n = 79,366) on LBP experienced last month (118,471 cases and 343,386 controls). Inverse variance weighted (IVW) was used as the main analysis. In addition, we used weighted median and MR-Egger to enhance the robustness. Sensitivity analysis was conducted to evaluate the robustness of MR results.Results: IVW estimation indicated strong evidence that higher serum 25(OH)D levels exerted a protective effect on LBP (OR = 0.89, 95% CI = 0.83–0.96, p = 0.002). Similar trends were also found in replicate analysis (OR = 0.98, 95% CI = 0.96–1.00, p = 0.07). After meta-analysis combining primary and replicated analysis, the causal effect is significant (p = 0.03). Sensitivity analysis supported that the MR estimates were robust.Conclusion: In our MR study, genetically increased serum 25(OH)D levels were associated with a reduced risk of LBP in the European population. This might have an implication for clinicians that vitamin D supplements might be effective for patients with LBP in clinical practice.</p

Image_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_5_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_3_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_4_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p