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REMOVING PLEIOTROPIC SIGNALS REVEAL DISEASE-SPECIFIC GENETIC ARCHITECTURE IN NOISY, SHALLOW BIOBANK PHENOTYPES 去除多效性信号揭示了嘈杂的浅生物库表型中疾病特异性遗传结构

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.547

Hyunkyung Kim , Na Cai , Andy Dahl

Pleiotropy is pervasive in complex traits, and understanding it is necessary to characterize shared vs specific genetic effects. Specific effects point to the core biology of a trait, which is especially challenging to characterize in heterogeneous traits such as major depressive disorder (MDD). Exploiting shared effects, on the other hand, can improve statistical power to detect genetic effects and exploit them for polygenic prediction. Large multi-trait genetic datasets, like the UK Biobank, provide opportunities to jointly model these shared and specific effects across thousands of related traits.

However, the standard approach to understand pleiotropy–genetic correlation–is overly simplistic as it only captures genome-wide aggregate similarity. While more recent approaches have extended genetic correlation to locus-level measures or factor models spanning many traits, it remains challenging to separate trait-specific effects from those that are broadly shared across related phenotypes. For example, genetic effects on alcohol use, and neuroticism will affect MDD, yet they are not specific to MDD nor likely to shed light on its core etiology. Here, we develop a Bayesian matrix factorization approach to address these limitations by partitioning high-dimensional pleiotropic relationships into effects that are shared vs specific to a focal trait of interest.

First, we applied our approach to simulated data to demonstrate it can reliably separate genetic effects that are specific to a trait vs that are mediated through secondary traits. Our approach outperforms other factorization-based approaches, such as conditioning on phenome-wide PCs. We then applied our approach to identify MDD-specific genetic effects in UK Biobank by accounting for shared genetic effects across 216 MDD-relevant traits. Specifically, we excluded the best-available measure, LifetimeMDD, and evaluated our ability to recapitulate this measure from two lower-quality measures, a GP-based measure and ICD10-based depression. We first show that our approach yields more specific phenotypes, which are more correlated to LifetimeMDD (R2s increase from 0.551 and 0.272 to 0.634 for the GP and ICD10 measures, respectively). Next, we showed that our approach yields better polygenic scores to predict LifetimeMDD (R2s increase from 0.081 and 0.035 to 0.097 for the GP and ICD10 measures, respectively; both p_bootstrap < .01).

Overall, our approach can be applied to any large-scale, noisy biobank phenotypes to improve their disorder-specificity. This is an important step toward bridging the gap between carefully-phenotyped datasets and shallowly-phenotyped datasets, which is essential for deriving powerful and specific genetic associations in complex traits.

多效性在复杂性状中普遍存在，了解共同遗传效应与特定遗传效应的特征是必要的。特定效应指向一个特征的核心生物学，这在异质性特征（如重度抑郁症（MDD））中尤其具有挑战性。另一方面，利用共享效应可以提高检测遗传效应的统计能力，并利用它们进行多基因预测。大型多性状遗传数据集，如英国生物银行，提供了在数千个相关性状中共同建模这些共享和特定影响的机会。然而，理解多效性——遗传相关——的标准方法过于简单，因为它只捕获全基因组的总相似性。虽然最近的方法已经将遗传相关性扩展到基因座水平的测量或跨越许多性状的因子模型，但将性状特异性效应与在相关表型中广泛共享的效应分开仍然具有挑战性。例如，基因对酒精使用和神经质的影响会影响重度抑郁症，但它们不是重度抑郁症所特有的，也不太可能阐明其核心病因。在这里，我们开发了一种贝叶斯矩阵分解方法，通过将高维多效关系划分为共享效应和特定于感兴趣的焦点特征的效应来解决这些限制。首先，我们将我们的方法应用于模拟数据，以证明它可以可靠地分离特定于性状的遗传效应与通过次要性状介导的遗传效应。我们的方法优于其他基于因式分解的方法，例如在全域pc上进行条件反射。然后，我们通过计算216个mdd相关性状的共享遗传效应，应用我们的方法在UK Biobank中识别mdd特异性遗传效应。具体来说，我们排除了最好的测量方法，LifetimeMDD，并从两个较低质量的测量方法（基于gp的测量方法和基于icd10的抑郁方法）中评估了我们概括该测量方法的能力。我们首先表明，我们的方法产生了更具体的表型，这些表型与LifetimeMDD更相关（GP和ICD10测量的R2s分别从0.551和0.272增加到0.634）。接下来，我们证明了我们的方法可以产生更好的多基因分数来预测LifetimeMDD （GP和ICD10测量的R2s分别从0.081和0.035增加到0.097；两者都是p_bootstrap <； 0.01）。总的来说，我们的方法可以应用于任何大规模的、嘈杂的生物库表型，以提高它们的疾病特异性。这是弥合仔细表型数据集和浅表型数据集之间差距的重要一步，这对于在复杂性状中获得强大和特定的遗传关联至关重要。

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引用次数: 0

POPULATION MEDICAL GENOMICS IN LATIN AMERICA 拉丁美洲人口医学基因组学

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.453

Andres Moreno-Estrada

Global health efforts require genetic profiling and deep phenotyping from diverse populations to better understand individuals’ variation associated with disease and tackle population-specific health problems. The Mexican Biobank Project is generating the most comprehensive nationwide genomic resource from a Latin American admixed population to reveal the evolutionary processes shaping the current diversity of the Mexican population and the genetic basis of chronic and infectious diseases.

全球卫生工作需要对不同人群进行基因分析和深度表型分析，以便更好地了解与疾病相关的个体变异，并解决特定人群的健康问题。墨西哥生物银行项目正在从拉丁美洲混合人口中产生最全面的全国基因组资源，以揭示形成当前墨西哥人口多样性的进化过程以及慢性和传染病的遗传基础。

引用次数: 0

SINGLE-CELL MULTIOMIC APPROACHES FOR UNDERSTANDING HUMAN BRAIN VARIABILITY IN HEALTH AND DISEASE 单细胞多组学方法用于理解人类大脑在健康和疾病中的变异性

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.509

Margarita Behrens

Diversity and individual variability are essential to human cognitive function. Identifying the conserved and variable transcriptomic and epigenomic signatures of the brain’s cellular components is critical for understanding the neurobiological basis of individual variation and how this changes with age and in mental disorders. We will discuss results from a multiomic single-cell epigenome and transcriptome analyses performed on brain samples with sex and age diversity, and show they provide new insight into the diversity of brain-cell molecular identity across individuals. As well, we will discuss age-related changes in the epigenome of specific cell-types in relation to neurological disorders.

多样性和个体可变性对人类认知功能至关重要。识别大脑细胞成分的保守和可变转录组学和表观基因组特征对于理解个体变异的神经生物学基础以及这种变异如何随年龄和精神障碍而变化至关重要。我们将讨论对具有性别和年龄多样性的脑样本进行的多组单细胞表观基因组和转录组分析的结果，并表明它们为个体之间脑细胞分子身份的多样性提供了新的见解。此外，我们将讨论与神经系统疾病相关的特定细胞类型表观基因组的年龄相关变化。

引用次数: 0

DEVELOPMENTAL ORIGINS OF PSYCHIATRIC RISK: DISSECTING GENE REGULATION THROUGH SINGLE-CELL MULTIOMIC ANALYSIS 精神疾病风险的发育起源：通过单细胞多组学分析剖析基因调控

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.510

Panos Roussos

Psychiatric disorders such as schizophrenia, bipolar disorder, major depression, and autism spectrum disorder frequently originate from disruptions in neurodevelopmental processes, many of which unfold long before clinical symptoms emerge. However, dissecting the molecular and regulatory mechanisms that underlie these early developmental perturbations has remained a major challenge, particularly due to limited access to human brain tissue across the lifespan and the complexity of brain cellular diversity.

To address this, we applied state-of-the-art single-nucleus multi-omic technologies—simultaneously profiling gene expression and chromatin accessibility—to construct high-resolution atlases spanning key stages of human brain development and adulthood. Our datasets encompass over one million nuclei from multiple brain regions, including the dorsolateral prefrontal cortex (DLPFC), a region central to cognition and psychiatric vulnerability, and the olfactory epithelium (OE), a regenerative sensory tissue with neurogenic potential. These integrative datasets enable unprecedented insights into dynamic transcriptional programs and epigenetic regulation during neurodevelopment and aging.

Through trajectory inference and enhancer-gene regulatory network reconstruction, we identified stage-specific transcription factors and cell-type-specific cis-regulatory modules that guide neuronal and glial lineage commitment. We discovered striking convergence in gene regulatory dynamics between olfactory sensory neuron development and early-stage cortical excitatory neurons, suggesting that the OE may serve as a surrogate system to model human neurodevelopment. Furthermore, integrating our regulatory maps with genome-wide association study (GWAS) loci for major psychiatric disorders allowed us to prioritize putative causal genes and regulatory elements operating at specific developmental windows.

Collectively, our findings highlight the power of multiomic single-cell analysis in unraveling the developmental origins of psychiatric disease. By linking genetic risk to temporally defined regulatory programs and accessible cell types, this work lays a foundation for future efforts to pinpoint disease mechanisms and therapeutic targets. Moreover, our demonstration that accessible neurogenic tissues can recapitulate key features of brain development opens new avenues for modeling psychiatric risk in vivo.

精神疾病，如精神分裂症、双相情感障碍、重度抑郁症和自闭症谱系障碍，通常源于神经发育过程的中断，其中许多在临床症状出现之前就已经出现了。然而，解剖这些早期发育扰动背后的分子和调控机制仍然是一个主要挑战，特别是由于在整个生命周期中对人类脑组织的访问有限以及脑细胞多样性的复杂性。为了解决这个问题，我们应用了最先进的单核多组学技术——同时分析基因表达和染色质可及性——构建了跨越人类大脑发育和成年期关键阶段的高分辨率图谱。我们的数据集涵盖了来自多个大脑区域的100多万个细胞核，包括背外侧前额叶皮层（DLPFC），一个认知和精神脆弱性的中心区域，以及嗅觉上皮（OE），一个具有神经源性潜力的再生感觉组织。这些综合数据集使我们能够前所未有地深入了解神经发育和衰老过程中的动态转录程序和表观遗传调控。通过轨迹推断和增强基因调控网络重建，我们确定了指导神经元和神经胶质谱系承诺的阶段特异性转录因子和细胞类型特异性顺式调控模块。我们发现嗅觉感觉神经元发育和早期皮层兴奋性神经元之间的基因调控动力学具有显著的趋同性，这表明OE可以作为模拟人类神经发育的替代系统。此外，将我们的调控图谱与主要精神疾病的全基因组关联研究（GWAS）位点相结合，使我们能够优先考虑在特定发育窗口操作的推定因果基因和调控元件。总的来说，我们的发现强调了多组单细胞分析在揭示精神疾病的发育起源方面的力量。通过将遗传风险与暂时定义的调控程序和可获得的细胞类型联系起来，这项工作为未来确定疾病机制和治疗靶点的努力奠定了基础。此外，我们的研究表明，可接近的神经源性组织可以概括大脑发育的关键特征，这为在体内模拟精神疾病风险开辟了新的途径。

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引用次数: 0

THE ROLE OF SEX IN POLYGENIC RISK FOR SEX-BIASED BRAIN DISORDERS 性别在性别偏倚性脑部疾病多基因风险中的作用

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.552

Arielle Crestol , Dennis van der Meer , Nadine Parker , Ann-Marie de Lange , Espen Hagen , Hannah Oppenheimer , Stener Nerland , Edith Breton , Christian K Tamnes , Ole A. Andreassen , Ingrid Agartz , Claudia Barth

Sex differences in genetic vulnerability have been implicated in psychiatric and neurodegenerative disorders, yet their specific impact on brain health and clinical risk remains poorly understood. Leveraging data from up to 220,836 women and 187,651 men from the UK Biobank (aged 39–81), we assessed how sex and polygenic risk scores (PRSs) for major depressive disorder (PRSMDD), Alzheimer’s disease (PRSAD), schizophrenia (PRSSCZ), and Parkinson’s disease (PRSPD) relate to case-control status and brain age gap (BAG), a neuroimaging marker of brain health. We tested for sex differences in PRSs and performed regression analyses examining associations between sex, PRSs, and either case-control status or BAG. We then compared models with sex-pooled and sex-specific PRSs to explore whether sex-specific PRSs can improve predictive accuracy. While PRSSCZ was higher in women compared to men, no other sex differences were found between PRSs. Our most striking finding was a significant PRSAD-by-sex interaction, in which PRSAD conferred greater risk for AD diagnosis in women compared to men. Consistently, the women-only PRSAD model outperformed the sex-pooled model, while no differences were observed between sex-pooled and men-only models. By contrast, sex-pooled PRS models outperformed sex-specific PRS models for MDD, SCZ, and PD. No sex-by-PRS interactions were significantly associated with BAG. However, men presented with higher BAG values than women, indicative of an older brain age. Further, higher PRSMDD, higher PRSAD, and lower PRSPD were each associated with higher BAG, irrespective of sex. Finally, BAG model performance did not differ between sex-pooled and sex-specific PRS models. Our findings highlight that sex moderates AD genetic risk for diagnostic status in middle-to-late-life adults, and as such, tailoring PRSs by sex may improve risk assessment for AD. While sex-specific PRSs offered limited value for the other disorders, our findings suggest that the value of sex-specific PRSs will likely grow with increased statistical power.

遗传易感性的性别差异与精神和神经退行性疾病有关，但它们对大脑健康和临床风险的具体影响仍知之甚少。利用来自英国生物银行（UK Biobank）的多达220,836名女性和187,651名男性（39-81岁）的数据，我们评估了重度抑郁症（PRSMDD）、阿尔茨海默病（PRSAD）、精神分裂症（PRSSCZ）和帕金森病（PRSPD）的性别和多基因风险评分（PRSs）与病例对照状态和脑年龄差距（BAG）（脑健康的神经影像学标志物）的关系。我们测试了PRSs的性别差异，并进行了回归分析，检验了性别、PRSs和病例对照状态或BAG之间的关系。然后，我们比较了性别池和性别特异性PRSs模型，以探索性别特异性PRSs是否可以提高预测准确性。虽然女性的PRSSCZ高于男性，但在prsz之间没有发现其他性别差异。我们最引人注目的发现是PRSAD与性别的显著相互作用，其中PRSAD赋予女性比男性更大的AD诊断风险。一致地，仅限女性的PRSAD模型优于性别混合模型，而性别混合模型和仅限男性的模型之间没有差异。相比之下，性别混合的PRS模型在MDD、SCZ和PD方面的表现优于性别特异性的PRS模型。无性别- prs交互作用与BAG显著相关。然而，男性的BAG值高于女性，表明大脑年龄较大。此外，不论性别，较高的PRSMDD、较高的PRSAD和较低的PRSPD均与较高的BAG相关。最后，BAG模型的性能在性别池和性别特定的PRS模型之间没有差异。我们的研究结果强调，在中老年成年人中，性别调节AD诊断状态的遗传风险，因此，按性别定制PRSs可能会改善AD的风险评估。虽然性别特异性PRSs对其他疾病的价值有限，但我们的研究结果表明，性别特异性PRSs的价值可能会随着统计能力的提高而增加。

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引用次数: 0

LONGITUDINAL DATA ANALYSIS REVEALS SEX DIFFERENCES IN GENETIC RISK FACTORS UNDERLYING WORSENING DEPRESSIVE SYMPTOMS AND COGNITIVE DECLINE IN OLDER ADULTS 纵向数据分析揭示了老年人抑郁症状恶化和认知能力下降的遗传风险因素的性别差异

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.520

Jessica Dennis, Karanvir Singh

Neuropsychiatric symptoms such as depression and apathy are among the earliest signs of dementia, are more common in females than in males, and worsen in-step with cognitive impairment. These symptoms are caused by the brain changes associated with dementia, are difficult to treat, and are not just a predictable consequence of the effects of dementia on quality of life. We sought to understand the genetic and non-genetic factors that influence depressive symptom emergence and progression across the cognitive decline spectrum. We analyzed repeated measures of cognitive and depressive symptoms spanning up to 9 years from ∼8,500 adults aged 65+ at baseline participating in the Canadian Longitudinal Study on Aging. We modeled repeated measures using hierarchical (mixed effect) models and latent class growth models, which allow for multiple possible latent growth trajectories within the study population. We found that known dementia genetic risk factors (APOE e4 allele count and a polygenic score for Alzheimer’s disease) associated with faster cognitive decline in both sexes. Using latent class growth models, we identified three different depressive symptom trajectory groups (persistent low, N=7346; persistent high, N=447; and new-onset, N=974), and we found that faster cognitive decline increased the risk of new-onset depressive symptoms to a greater extent in females than in males, in models adjusted for sociodemographic factors. APOE e4 carriers were more likely to experience new-onset depressive symptoms and cognitive decline compared to non-carriers, regardless of sex, but the polygenic score for Alzheimer’s disease predicted new-onset depressive symptoms and cognitive decline uniquely in females. Our results suggest that new-onset depressive symptoms and accelerated cognitive decline capture a dementia prodrome, especially in females. Moreover, our approach demonstrates the value of longitudinal data analysis in genetic studies for understanding unique patient subgroups, which can be leveraged in future GWAS efforts.

Disclosure: Nothing to disclose.

抑郁和冷漠等神经精神症状是痴呆症的早期症状，在女性中比在男性中更常见，并随着认知障碍而恶化。这些症状是由与痴呆症相关的大脑变化引起的，很难治疗，而且不仅仅是痴呆症对生活质量影响的可预测后果。我们试图了解影响认知衰退谱系中抑郁症状出现和进展的遗传和非遗传因素。我们分析了参与加拿大老龄化纵向研究的~ 8,500名65岁以上的成年人长达9年的认知和抑郁症状的重复测量。我们使用分层（混合效应）模型和潜在类别增长模型对重复测量进行建模，这些模型允许在研究人群中存在多种可能的潜在增长轨迹。我们发现，已知的痴呆症遗传风险因素（APOE e4等位基因计数和阿尔茨海默病的多基因评分）与两性认知能力下降更快有关。使用潜在类别增长模型，我们确定了三个不同的抑郁症状轨迹组（持续低，N=7346；持续高，N=447；和新发，N=974），我们发现，在调整了社会人口因素的模型中，更快的认知衰退增加了女性新发抑郁症状的风险，其程度大于男性。与非携带者相比，APOE e4携带者更有可能出现新发抑郁症状和认知能力下降，无论性别如何，但阿尔茨海默病的多基因评分仅在女性中预测新发抑郁症状和认知能力下降。我们的研究结果表明，新发的抑郁症状和加速的认知衰退捕获了痴呆的前驱症状，特别是在女性中。此外，我们的方法证明了纵向数据分析在遗传研究中的价值，以了解独特的患者亚组，这可以在未来的GWAS工作中加以利用。披露：没有什么可披露的。

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引用次数: 0

PUTTING POLYGENIC SCORES IN CONTEXT: HOW INTERSECTIONAL FACTORS AFFECT RELATIVE AND ABSOLUTE GENETIC RISK 把多基因得分放在上下文中：交叉因素如何影响相对和绝对遗传风险

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.546

Mihael Cudic , Justin Tubbs , Tian Ge , Jordan Smoller

Background

The clinical utility of polygenic scores (PGS) is known to vary when training and test samples differ in ancestry. Recent work also suggests that variation in demographic and environmental contexts can affect PGS performance. However, little attention has been given to the ubiquitous phenomenon of intersectionality—where individuals simultaneously belong to multiple demographic and environmental contexts—and its impact on relative and absolute genetic risk.

Methods

We examined how lifetime odds ratios (ORs) and absolute risk (AR) for high-PGS individuals differ across intersectional contexts in the UK Biobank (UKB; n = 375,054, British-European-like ancestry), with a particular focus on major depressive disorder (MDD). Additional phenotypes studied included atrial fibrillation, coronary artery disease, type 2 diabetes, hypercholesterolemia, asthma, and obesity. PGS were computed using PRS-CS and current GWAS summary statistics. We analyzed 106 two-way intersections across sociodemographic factors (sex, age, income, smoking, alcohol intake, and deprivation). Replication was conducted in the All of Us Research Program (AoU; African-like: n = 36,552; European-like: n = 99,477).

Results

Odds ratios for major depression varied substantially across sociodemographic contexts, with even greater variability across intersectional combinations. In two-way intersections, the average maximal OR variation was 56% across all phenotypes. Although absolute risk differences were more moderate after adjusting for baseline prevalence within a context, intersectional effects were still observed. For example, high-PGS individuals with low income had an average 1.0 percentage-point lower AR across phenotypes when using a context-aware versus context-unaware model. For major depression specifically, the AR was 3.1 percentage points lower among those also reporting low alcohol intake in UKB (3.6 points lower in AoU European-like ancestry). Results were broadly consistent across cohorts, with the strongest replication for depression risk observed in the European-like All of Us sample.

Conclusion

We show that intersectional contexts can substantially shape relative genetic risk. Future clinical applications may need to consider incorporating contextual effects to enhance the precision and equity of patient-specific genetic risk assessments.

当训练样本和测试样本的血统不同时，多基因评分（PGS）的临床应用会有所不同。最近的研究还表明，人口和环境背景的变化会影响PGS的表现。然而，很少有人注意到普遍存在的交叉性现象，即个体同时属于多种人口和环境背景，以及它对相对和绝对遗传风险的影响。方法我们研究了英国生物银行（UKB; n = 375,054，英国-欧洲血统）中高pgs个体的终生优势比（ORs）和绝对风险（AR）在交叉背景下的差异，特别关注重度抑郁症（MDD）。研究的其他表型包括房颤、冠状动脉疾病、2型糖尿病、高胆固醇血症、哮喘和肥胖。使用PRS-CS和当前GWAS汇总统计计算PGS。我们分析了106个跨社会人口因素（性别、年龄、收入、吸烟、饮酒和贫困）的双向交叉点。在我们所有人研究计划中进行了重复研究（AoU；非洲人：n = 36,552；欧洲人：n = 99,477）。结果重度抑郁症的患病率在不同的社会人口背景下差异很大，在不同的交叉组合中差异更大。在双向交叉中，所有表型的平均最大OR变异为56%。虽然在背景下调整基线患病率后，绝对风险差异更为温和，但仍然观察到交叉效应。例如，当使用上下文感知与上下文不感知模型时，低收入的高pgs个体在表型上的AR平均低1.0个百分点。特别是对于重度抑郁症，在英国报告酒精摄入量低的人中，AR降低了3.1个百分点（在AoU欧洲血统中降低了3.6个百分点）。结果在各个队列中大致一致，在类似欧洲人的“我们所有人”样本中观察到的抑郁症风险的重复性最强。结论本研究表明，交叉背景可以在很大程度上影响相对遗传风险。未来的临床应用可能需要考虑纳入情境效应，以提高患者特异性遗传风险评估的准确性和公平性。

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引用次数: 0

ELECTRONIC HEALTH RECORDS AND BIOBANKS IN THE ERA OF COMPUTATIONAL AND GENOMIC PSYCHIATRY 计算和基因组精神病学时代的电子健康记录和生物银行

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.486

Franjo Ivankovic Chair , Julia Sealock Co-chair , Carol Mathews Discussant

<div><div>This symposium will explore how the analysis of large-scale electronic health record (EHR) and biobank data is revolutionizing psychiatric research and driving novel discoveries impacting clinical care. Presentations highlight the utility of EHRs to study and identify sociodemographic biases in psychiatric care, leveraging the rich data across collaborative networks of EHRs to describe and validate novel subtypes of psychiatric disorders, using digital phenotyping for precision psychiatry insights, and utilizing the rich genomic data to develop resources to power the next generation of biological insights.</div><div>The symposium will begin with an overview of the PsycheMERGE network, an NIMH-funded collaboration across 11 institutions that leverages the resources and existing infrastructure of biobanks paired with EHRs across the country to provide a translational “sandbox” in which to evaluate the potential clinical impact of psychiatric genetic findings in a low-risk research setting. The talk will discuss how analysis of multimodal data has yielded “real-world” support for the hypothesis of an immune-metabolic subtype of depression with implications for management.</div><div>The following talk will focus on longitudinal modeling in the All of Us (AoU) dataset to replicate the bidirectional association between activity and mood, as measured by the Patient Health Questionnaire (PHQ-9), highlighting the potential of EHR-integrated digital phenotyping to advance precision psychiatry by informing behavior-based prevention and treatment strategies.</div><div>The third talk will present the two-process model of sleep regulation, a well-established model of sleep disruption in depression, and examine how this model directly translates into observable digital health behaviors that are highly associated with EHR-diagnosed depression and represent interpretable mechanistic phenotypes for further genomic and precision psychiatry approaches.</div><div>The final presentation will showcase a novel reference panel built from over 515,000 individuals from AoU and AnVIL data. In addition to being the largest reference panel to date, this panel prioritizes diversity, encompassing over 250,000 samples from non-European ancestries – a representation nearly twice the size of the entire TOPMed reference panel. The panel includes 101,982 of African and 90,553 individuals of admixed American ancestries. This resource will significantly improve the accuracy of genotype imputation, particularly for rare variants and underrepresented populations, empowering novel discoveries in psychiatric genetics. It will be made widely available for phasing and imputing genotype array and low-pass sequencing data through Broad Institute of MIT and Harvard.</div><div>By showcasing these diverse applications of EHRs, we aim to illuminate the power of computational psychiatry and genomics, when combined with rich phenotypic data, to improve diagnosis, treatment, and ultimately

本次研讨会将探讨大规模电子健康记录（EHR）和生物银行数据的分析是如何彻底改变精神病学研究和推动影响临床护理的新发现的。报告强调了电子病历在精神病学护理中研究和识别社会人口统计学偏差的效用，利用电子病历协作网络中的丰富数据来描述和验证精神疾病的新亚型，使用数字表型进行精确的精神病学见解，并利用丰富的基因组数据开发资源以推动下一代生物学见解。研讨会将首先概述PsycheMERGE网络，该网络由nimh资助，由11家机构合作，利用全国各地生物库的资源和现有基础设施与电子病历相结合，提供一个翻译“沙盒”，在低风险研究环境中评估精神病学遗传发现的潜在临床影响。本次讲座将讨论多模态数据分析如何为抑郁症的免疫代谢亚型假说提供了“现实世界”的支持，并对治疗产生了影响。接下来的演讲将重点关注我们所有人（AoU）数据集中的纵向建模，以复制活动和情绪之间的双向关联，由患者健康问卷（PHQ-9）测量，强调ehr集成数字表型通过告知基于行为的预防和治疗策略来推进精确精神病学的潜力。第三个讲座将介绍睡眠调节的双过程模型，一个完善的抑郁症睡眠中断模型，并研究该模型如何直接转化为可观察的数字健康行为，这些行为与ehr诊断的抑郁症高度相关，并为进一步的基因组和精确精神病学方法提供可解释的机制表型。最后的展示将展示一个新颖的参考面板，该面板由来自AoU和AnVIL数据的515,000多人构建而成。除了是迄今为止最大的参考小组外，该小组还优先考虑多样性，包括超过250,000个来自非欧洲血统的样本-这几乎是整个TOPMed参考小组的两倍。该小组包括101,982名非洲人和90,553名美国混血儿。这一资源将显著提高基因型插入的准确性，特别是对于罕见变异和代表性不足的人群，赋予精神病学遗传学的新发现。它将通过麻省理工学院和哈佛大学的Broad研究所广泛用于分相和输入基因型阵列和低通测序数据。通过展示电子病历的这些不同应用，我们的目标是阐明计算精神病学和基因组学的力量，当与丰富的表型数据相结合时，改善诊断、治疗，并最终改善精神疾病患者的生活。

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引用次数: 0

Risk of manic switch and suicidal outcomes in bipolar depression treated with esketamine: A one-year retrospective cohort study of 2126 patients. 艾氯胺酮治疗双相抑郁症躁狂转换和自杀结局的风险：一项为期一年的2126例患者回顾性队列研究

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 Epub Date: 2025-08-06 DOI: 10.1016/j.euroneuro.2025.07.006

Ting-Hui Liu, Jheng-Yan Wu, Po-Yu Huang, Wan-Lin Cheng, Chih-Cheng Lai

Objective: To evaluate the safety of esketamine in bipolar depression, focusing on suicide-related outcomes and manic switch across timeframe and demographic subgroups.

Methods: This retrospective cohort study was conducted in May 2025 using data from the TriNetX global collaborative network. Adults with bipolar depression treated with esketamine plus a mood stabilizer were propensity score-matched 1:1 to patients who received mood stabilizers alone. The primary outcomes were suicide-related events and manic switches across 1-7, 1-30, 1-90, 1-180, and 1-365 day intervals.

Results: After matching, 2126 patients (mean age, 47.0 years; 63 % female) were included. Esketamine use was associated with significantly lower risk of suicide-related outcomes at 1-7 days (hazard ratio [HR] = 0.439; 95 % CI, 0.256-0.753), 1-30 days (HR = 0.485; 95 % CI, 0.324-0.726), 1-90 days (HR = 0.641; 95 % CI, 0.456-0.901), and 1-365 days (HR = 0.754; 95 % CI, 0.577-0.985). Risk of manic switch was not increased and was significantly lower at 1-180 days (HR = 0.643; 95 % CI, 0.442-0.935) and 1-365 days (HR = 0.673; 95 % CI, 0.477-0.950). Subgroup analyses showed consistent suicide risk reduction across age, sex, and race. Female patients exhibited a significantly lower risk of manic switch at longer intervals, an effect not observed in males.

Conclusions: Our real-world study suggests that esketamine, when used alongside mood stabilizers, is a safe and potentially effective treatment for bipolar depression, demonstrating sustained anti-suicidal benefits without an increased risk of manic switch across both short- and long-term follow-up and across different patient subgroups.

目的：评估艾氯胺酮治疗双相抑郁症的安全性，重点关注自杀相关结果和跨时间框架和人口亚组的躁狂转换。方法：这项回顾性队列研究于2025年5月进行，使用TriNetX全球协作网络的数据。接受艾氯胺酮加心境稳定剂治疗的成人双相抑郁症患者与单独接受心境稳定剂治疗的患者倾向评分匹配率为1:1。主要结果是自杀相关事件和躁狂转换在1-7天、1-30天、1-90天、1-180天和1-365天的间隔。结果：经配对后，2126例患者(平均年龄47.0岁；（63%为女性）。使用艾氯胺酮与1-7天自杀相关结局的风险显著降低相关(风险比[HR] = 0.439；95% CI, 0.256-0.753)， 1-30天(HR = 0.485；95% CI, 0.324-0.726)， 1-90天(HR = 0.641；95% CI, 0.456-0.901)， 1-365天(HR = 0.754；95% ci, 0.577-0.985)。1 ~ 180天躁狂转换风险未增加，且显著降低(HR = 0.643；95% CI, 0.442-0.935)和1-365天(HR = 0.673；95% ci, 0.477-0.950)。亚组分析显示，不同年龄、性别和种族的自杀风险都有所降低。女性患者在较长的间隔时间内表现出明显较低的躁狂转换风险，而在男性患者中没有观察到这种效果。结论：我们的真实世界研究表明，当与情绪稳定剂一起使用时，艾氯胺酮是一种安全且潜在有效的双相抑郁症治疗方法，在短期和长期随访以及不同患者亚组中均显示出持续的抗自杀益处，而不会增加躁狂转换的风险。

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引用次数: 0

LIVING HAPPILY EVER AFTER: WHAT INFLUENCES ENDURING MENTAL HEALTH? 幸福生活：什么影响持久的心理健康？

IF 6.7 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology

Pub Date : 2025-10-01 DOI: 10.1016/j.euroneuro.2025.08.485

Christel M. Middeldorp , Lianne De Vries , Meike Bartels

Enduring mental health (EMH) is a stable state of mental health over time. More insight into factors associated with EMH can aid prevention of mental health problems. Previous research on EMH has primarily focused on the absence of mental health problems, neglecting the equally important dimension of presence of wellbeing. In this study, EMH is defined by the absence of mental health problems and/or the presence of higher wellbeing over time.

Data on twins and their family members registered in the large Netherlands Twin Register will be examined. Individuals will be included when mental health and/or wellbeing data are available in four or more surveys, to create the EMH phenotype. For more than 17,000 individuals genotypes are also available.

Classical twin analyses will be performed to calculate the heritability of EMH. Furthermore, associations will be presented with polygenic scores (PGS) and educational attainment, self esteem, stressful life events, neuroticism, optimism, loneliness, social support, exercise and self reported health. Besides PGS for mental disorders and traits, PGS for educational attainment, wellbeing, neuroticism, loneliness, childhood maltreatment and risky behavior will be included.

First, univariate regressions will be performed. The significantly associated variables will then be included in multiple logistic regression analyses, using family as a grouping variable to account for clustering. Variables as assessed during adolescents and polygenic scores that are related to EMH can be seen as potential predictors for EMH, whereas variables assessed in the last survey in adulthood can be seen as potential outcomes of EMH.

This project follows up on a project using childhood data on enduring mental health (Alrouh et al, under revision for the Journal of the American Academy of Child and Adolescent Psychiatry). Using measures between age 3 and age 12, 37% of the sample had EMH. This was associated with parental education (OR (low) =0.77 [0.70-0.86]; OR (middle) = 0.88 [0.82-0.95]), child academic achievement (OR=1.07 [1.03,1.12]), and child wellbeing (OR=1.44 [1.35,1.54]), and was weakly associated with ADHD PGS. The heritability was estimated at 54%. These findings are now extended with findings in adults, over a longer timespan and with a more comprehensive analysis of factors that could be associated with EMH.

持久心理健康（EMH）是一种长期稳定的心理健康状态。更深入地了解与EMH相关的因素有助于预防心理健康问题。先前对有效市场假说的研究主要集中在心理健康问题的缺失上，而忽视了同样重要的幸福感维度。在这项研究中，EMH被定义为没有心理健康问题和/或随着时间的推移存在更高的幸福感。将审查在大型荷兰双胞胎登记册中登记的双胞胎及其家庭成员的数据。当心理健康和/或幸福数据在四次或更多的调查中可用时，个人将被包括在内，以创建EMH表型。对于17000多个个体，基因型也是可用的。将进行经典的双胞胎分析来计算EMH的遗传力。此外，多基因得分（PGS）与受教育程度、自尊、压力生活事件、神经质、乐观、孤独、社会支持、锻炼和自我报告的健康状况之间存在关联。除了精神障碍和特征方面的PGS，还包括教育程度、幸福感、神经质、孤独感、童年虐待和危险行为方面的PGS。首先，将进行单变量回归。然后将显著相关的变量包含在多重逻辑回归分析中，使用家庭作为分组变量来解释聚类。在青少年时期评估的变量和与EMH相关的多基因评分可以被视为EMH的潜在预测因素，而在成年期最后一次调查中评估的变量可以被视为EMH的潜在结果。这个项目是在一个使用儿童持久心理健康数据的项目的基础上进行的（Alrouh等人，正在为《美国儿童和青少年精神病学学会杂志》进行修订）。在3岁到12岁之间进行测量，37%的样本患有EMH。这与父母教育有关(OR (low) =0.77 [0.70-0.86]；OR（中） = 0.88[0.82-0.95])、儿童学业成绩（OR=1.07[1.03,1.12]）和儿童幸福感（OR=1.44[1.35,1.54]），且与ADHD PGS呈弱相关。遗传率估计为54%。这些发现现在扩展到成年人的研究，在更长的时间跨度和更全面的分析可能与EMH相关的因素。

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引用次数: 0