The sex-dependent impact of adipose tissue and inflammation on chronic pain - A cross-sectional study from the all of us research program.

Emerging evidence suggests an association between chronic pain and elevated body fat. We sought to determine if individuals with higher body fat, measured by hip circumference (HC) and waist circumference (WC), are at risk for chronic pain when they demonstrate higher expression of inflammatory markers. We investigated the incidence and severity of pain in patients with varying WC/HC and inflammatory markers (C-Reactive Protein, IL-6, leptin) using the NIH-sponsored All of Us Database. For each inflammatory marker and sex, participants were divided into four groups based on combinations of normal/high marker levels and small/large WC/HC. We used statistical analysis to compare WC/HC and pain severity (mean NRS pain score) between groups of the same sex. In females, but not males, combinations of elevated CRP with large WC/HC exerted additive effects on the incidence of chronic pain (p < 0.01) and severe pain (p < 0.001), as well as on the severity of pain evaluated by the mean NRS pain score (p < 0.01). This relationship held true for females with high IL-6 or leptin and large WC or HC (p < 0.001 for chronic pain and severe pain incidence, and p < 0.05 for pain severity). Neither IL-6 nor leptin showed any significant impact on pain in males. Obesity status and CRP exert additive prognostic effects for chronic pain in females, but not in males. The concomitant evaluation of other inflammatory factors, such as IL-6 or leptin in females, may further augment the prediction of chronic pain. PERSPECTIVE: This article investigates the relationship between chronic pain, obesity, and inflammatory markers. It could help elucidating sex difference in pain mechanisms, as well as the risk factors for chronic pain, potentially improving patient diagnosis, follow-up and treatment.

Investigation of the intraoperative cortical responses to spinal motor mapping in a patient with chronic pain.

Intraoperative neurophysiological monitoring (IONM) in spinal cord stimulation (SCS) surgery for chronic pain is shown to provide effective guidance during device placement. Electromyography (EMG) is used to determine the laterality of the paddle. In some SCS cases, laterality cannot be obtained via EMG due to patient physiology. Electroencephalography (EEG) is already used in IONM to monitor cortical responses. Here, we show proof-of-concept of assessing the responses of epidurally evoked EMGs simultaneously with EEGs to determine laterality during IONM using a high-resolution (HR) SCS paddle. An 8-column HR-SCS paddle was acutely placed at T9-T10 interspace in patients with failed back surgery syndrome. EMG signals from 18 muscle groups were recorded simultaneously with 60-channel EEG signals at various stimulation amplitudes (0-10 mA). Particular attention was paid to regions associated with pain including the somatosensory cortex (S1), prefrontal cortex (PFC), and motor cortex (M1). When left and right lateral contacts were stimulated at low amplitudes (1-2 mA), significant changes were seen in θ, α, and ß powers in the contralateral PFC but not in M1 or S1. There was a significant correlation between M1 and contralateral contacts in α power. At higher currents (7-8 mA), right-sided contacts resulted in α power change. We found significant differences in α, θ, and ß powers in PFC for contralateral stimulation of the lateral SCS contacts at low amplitudes and in α power at higher amplitudes. The changes in PFC suggest the potential of EEG for understanding a cortical mechanism of action of SCS and provide insight into the pathophysiology of chronic pain.NEW & NOTEWORTHY Here, we present proof of concept of assessing the responses of epidurally evoked electromyography simultaneously with scalp electroencephalography to determine whether both laterality and insights into pain mechanisms can be elucidated. With stimulation, significant changes were seen in θ, α, and ß band power in the contralateral prefrontal cortex and in α power in the motor cortex. We provide insight into the mechanism of action of SCS in preventing pain in this patient.

Improved Selectivity in Eliciting Evoked Electromyography Responses With High-Resolution Spinal Cord Stimulation.

BACKGROUND AND OBJECTIVES: As spinal cord stimulation (SCS) offers a therapy for increasing numbers of patients with chronic pain and spinal cord injury, it becomes increasingly important to better understand its somatotopy. In this prospective study, we investigate whether high-resolution SCS (HR-SCS) offers improved selectivity assessed through elicitation of evoked electromyography (EMG) responses as compared with commercial paddle leads. METHODS: Vertical tripole configurations were used to elicit EMG responses in both types of paddles placed for standard-of-care indications between T6 and T10. In HR-SCS, evoked EMG responses in lower extremity/abdominal muscle groups were monitored at 6 to 8 mediolateral sites. All commercial paddle columns were tested. Percentage change in the maximum root mean square value was calculated at a group level. Heat maps were generated to identify responders for each muscle group. Responders were considered patients who had a >50% change in root mean square over baseline. RESULTS: We demonstrated significantly greater motor responses across medial and lateral contacts and greater responder rates consistently at the T6 and T9 levels with HR-SCS as compared with commercial paddles in 18 patients. Distal muscle groups (gastrocnemius and tibialis anterior) and proximal muscle groups (biceps femoris and quadriceps) were selectively activated at both levels. CONCLUSION: We demonstrate that HR-SCS has greater selectivity in eliciting evoked EMG responses in an intraoperative setting. HR-SCS offers recruitment of muscle groups at lateral contacts concurrently with medial contacts. We provide data that HR-SCS may provide higher spatial resolution, which has the potential to allow for personalization of care and treatment of pain syndromes/symptoms which to date have not been effectively treated.

Sex differences in variables affecting short-term success in substance use disorder treatment.

Because of the stigma surrounding patients with substance use disorder (SUD) and difficulties with follow-up, data on outcomes is limited. We explore real-world data from a prospectively collected database to determine characteristics that contribute to the completion of acute treatment. Our cohort consisted of data from 1039 patients treated at a single facility. Success was defined as successful discharge from the program. Failure was defined as relapse or signing out against medical advice during treatment. We examined 43 distinct features collected at time of treatment using multivariate analysis. In the total cohort and both sexes, longer length of stay (p ≤ 0.01) was linked to treatment failure. When we examined the cohort by sex, variables associated with success and failure differed between groups. Among females, goal-directed thinking (p ≤ 0.05) correlated with treatment success. Taking unnecessary risks (p < 0.05), having a detailed suicide plan (p ≤ 0.001), and constricted thinking (p ≤ 0.01) predicted treatment failure. In males, prior arrest for driving under the influence (p ≤ 0.05), and presence of phobias, paranoias, and delusions (p ≤ 0.05) were associated with treatment failure. Identifying patients prone to acute therapy failure may guide more personalized treatment, thereby increasing success rates. When considering SUD treatments for patients, we must stratify based on patient characteristics.

Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform.

A high performance QRS complex detector applicable for wearable healthcare devices is proposed in the present work. Since, higher SNR results in better detection accuracy and lesser number of coefficients reduces the hardware resources as well as power dissipation during on chip implementation. Biorthogonal spline wavelet transform is chosen for the proposed detector as it has high signal to noise ratio (SNR) and uses only four coefficients for decomposition. In the proposed approach, a Biorthogonal wavelet filter bank with fourth level decomposition is first used to separate the different frequency components and then a fourth level wavelet filter bank is used to get the denoised electrocardiogram (ECG) signals. Wavelet filter bank outputs are multiplied and soft threshold method is applied to get the QRS complex peaks by the QRS complex peak detector block. Add and shift multiplier used in the earlier designs has been replaced by a Booth multiplier in our approach to achieve the higher performance. Booth multiplier and QRS complex peak detector blocks have been designed for low hardware complexity, high performance and accurate detection of the QRS complex peaks. Time interval between the consecutive QRS peaks is calculated using the R-R peak time calculator block and the heart rate (HR) by the HR calculator block. Heart Rate Variability (HRV) and arrhythmia are detected based on these heart rate calculations. Proposed design has been tested for its robustness on multiple datasets (namely, MIT-BIH arrhythmia, MIT-BIH noise stress test, and MIT-BIH atrial fibrillation databases). Sensitivity of 99.31%, positive predictivity of 99.19% and the Detection Error Rate (DER) of 1.49% shown by the proposed design makes it preferable for QRS complex detectors used in wearable healthcare devices.