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Adalimumab is the only FDA-approved biologic for hidradenitis suppurativa (HS). In the setting of increasing obesity rates worldwide, the relationship between adalimumab efficacy for HS and BMI is essential to understand. We assessed this relationship through markers of disease severity and inflammation.Institutional review board-approved retrospective chart review of Montefiore/Einstein HS Center (HSC) patients (n = 57) treated with adalimumab. The relationship between BMI and adalimumab efficacy was assessed through disease severity (HS-Physician Global Assessment [HS-PGA] 0 and Numerical Rating Scale Pain [NRS-Pain]) and inflammatory markers (erythrocyte sedimentation rate [ESR], C-reactive protein [CRP], and interleukin-6 [IL-6]). A BMI ≥ 30 is defined as obese; BMI 30 is defined as non-obese.The mean age was 35.8 ± 13.0 years. After adalimumab therapy, those with BMI 30 experienced significant reductions in HS-PGA (-1.5 ± 0.9; P 0.0001) and NRS-Pain (-1.6 ± 2.1; P 0.0001), as well as mean decreases in inflammatory markers ESR, CRP, and IL-6 (-17.90 ± 23.6, -0.71 ± 1.4, -5.88 ± 7.9, respectively; P 0.05). Obese patients (BMI ≥ 30) experienced mean increases in HS-PGA (+0.22 ± 0.8; P 0.05) and NRS-Pain scores (+1.41 ± 3.5; P 0.05) as well as mean increases in ESR, CRP, and IL-6 (+2.62 ± 28.3, +0.44 ± 3.0, +2.35 ± 6.9, respectively; P 0.05). Comparing the cohorts, differences in changes in HS-PGA, NRS-Pain, ESR, and IL-6 after therapy are significantly different (P 0.05).We identified significantly lower efficacy of adalimumab in HS patients with BMI ≥ 30 compared to those with BMI 30. Those with BMI ≥ 30 demonstrated signs of both clinical and physiological deterioration while on adalimumab. Future studies are needed to examine adalimumab dosing for HS patients with high BMI, as well as a critical reconsideration of weight-based therapies.

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and newly generated midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ~26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture helps refine neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

A novel case of eruptive vellus hair cysts arising during radiation therapy and a brief review of the literature

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ∼26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.