Background: Uric acid (UA) has been suggested as a possible biomarker of bipolar disorder (BD) in recent studies. We aimed to provide a clearer comparison of UA levels between BD and major depressive disorder (MDD). Methods: We retrospectively reviewed the medical chart records of psychiatric inpatients aged 19-60 years, whose main discharge diagnoses were either MDD or BD, with an admission between January 1, 2015 and December 31, 2018 at Seoul National University Hospital. Data such as sex, age, body mass index (BMI), medication usage, and serum UA levels were extracted. Patients with medical conditions or on medications that could influence UA levels were excluded. Age, sex, BMI, and psychiatric drug usage were considered in the comparison of serum UA between MDD and BD patients. Results: Our sample consisted of 142 MDD patients and 234 BD patients. The BD patients had significantly higher serum UA levels compared to the MDD patients, without accounting for other confounding variables (5.75 ± 1.56 mg/dL vs. 5.29 ± 1.59 mg/dL, P = 0.006). T-test comparisons between psychiatric medication users and non-users revealed that mood stabilizers and antipsychotics may be relevant confounding factors in our sample analysis. The likelihood of BD diagnosis was significantly correlated with higher UA levels (odds ratio, 1.410; 95% confidence interval, 1.150-1.728; P = 0.001) when accounting for sex, age, and BMI in the logistic regression analysis. Also, accounting for mood stabilizers or antipsychotics, the likelihood of BD diagnosis was still significantly correlated with higher UA levels. Conclusion: Our study confirms that BD patients are significantly more likely to show higher serum UA levels than MDD patients. The high UA levels in BD point to purinergic dysfunction as an underlying mechanism that distinguishes BD from MDD. Further research is recommended to determine whether UA is a trait or a state marker and whether UA correlates with the symptoms and severity of BD.
The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19–65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = −0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.
Targeted protein degraders (TPDs) have expanded the breadth of therapeutic options through both their catalytic mechanism of action and ability to degrade previously “undruggable” target proteins. Prior reports of small-molecule GSPT1 degraders such as CC-90009 in AML demonstrate potent anti-tumor cytotoxicity, but with a potentially narrow therapeutic index. To increase the efficacy vs. tolerability window of TPDs and improve drug delivery, we introduce TPD-Squared (TPD2 TM), a dual-targeted protein degradation approach of combining the catalytic mechanism of targeted protein degradation with the precision of tumor-targeting therapeutic antibodies. We have previously shown in vitro and in vivo efficacy with a HER2-targeted TPD2 conjugate: ORM-5029. Following on that success, we generated conjugates using a CD33-targeting antibody (OR000283) produced by engineering the FAb (H&L) sequences from gemtuzumab onto an IgG1 Fc with N297A variant to inhibit Fc-γR binding. Medicinal chemistry optimization of linker-payloads led to the identification of ORM-6151, which is composed of SMol006, a highly potent GSPT1 degrader conjugated to OR000283 via a novel β-glucuronide releasable linker. ORM-6151 treatment in CD33-expressing cell lines showed picomolar activity with 10-1000-fold greater potency compared to several GSPT1 degrader molecules, including CC-90009 or Mylotarg, and had robust activity in Mylotarg-resistant lines (AML193 and Kasumi6). ORM-6151 also exhibited picomolar potency in in vitro cytotoxicity to primary relapsed/refractory AML patient blasts, with better potency than CC-90009 and Mylotarg. We evaluated ORM-6151 in several in vivo disseminated xenograft models and observed robust efficacy following a single treatment at doses as low as 0.1 mg/kg. Tumor growth inhibition correlated with the degree and duration of GSPT1 depletion and changes in the expression of previously described integrated stress response biomarker genes. In summary, ORM-6151 is a promising, potential therapy for AML and currently in preclinical development as a first-in-class targeted protein degrader therapy with CD33-targeted delivery. Citation Format: James Palacino, Pedro Lee, Hangyeol Jeong, Yeonjoon Kim, Yoojin Song, Uttapol Permpoon, Wesley Wong, Chen Bai, Nathan Fishkin, Khuloud Takouri, Eunjun Yu, Yong Yi, Anna Skaletskaya, Min-Soo Kim, Da-Yeong Kim, Dong-Ki Choi, Peter U. Park. ORM-6151: A first-in-class CD33-antibody enabled GSPT1 degrader for AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2700.
Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = −2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = −2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.
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