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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Ich möchte kurz voranstellen, dass ich als Umweltwissenschaftlerin ausgebildet bin, in der Epidemiologie promoviert und mich daraufhin der Wissensgeschichte und -soziologie und insbesondere den Science & Technology Studies zugewandt habe. Mein Interesse an den alltäglichen Praktiken der Wissensbildung und technischen Formalisierungsprozessen in der Ernährungsepidemiologie ist zum einen inspiriert durch den practice turn und den material turn in der Wissenschaftsforschung, zum andern aber auch durch meine eigene Forschungserfahrung in der Epidemiologie. Im Hinblick auf Epigenetik und Ernährung als Medium der Übertragung frage ich nach so banalen Dingen wie: "Wie werden Äpfel in Experimenten formalisiert?", um einigen konkreten Arbeitsweisen der Postgenomik innerhalb des Feldes Nahrung - Ernährung - Stoffwechsel nachzugehen. Im Sinne der bereits erwähnten Unterscheidung zwischen intra- und intergenerationaler Bedeutung des Begriffs Epigenetik von Testa und Boniolo geht es in den folgenden Beispielen primär um intragenerationale Epigenetik. Allerdings gibt es auch in der Ernährungsepidemiologie durchaus Forschung zu inter- oder transgenerationalen Effekten, hier werden oft die Langzeitstudien genannt, in denen die Folgen des niederländischen Hungerwinters 1944, während der deutschen Besatzung, als die Wehrmacht die Versorgungswege der Bevölkerung blockierte, über mehrere Generationen untersucht wurden (vgl. auch den Beitrag von Guy Vergères).
Background: Unlike metastatic colorectal cancer (CRC) there are to date few reports concerning the predictive value of molecular biomarkers on the clinical outcome in stage II/III CRC patients receiving adjuvant chemotherapy. Aim of this study was to assess the predictive value of proteins related with the EGFR- and VEGFR- signalling cascades in these patients.
Methods: The patients' data examined in this study were from the collective of the 5-FU/FA versus 5-FU/FA/irinotecan phase III FOGT-4 trial. Tumor tissues were stained by immunohistochemistry for VEGF-C, VEGF-D, VEGFR-3, Hif-1 α, PTEN, AREG and EREG expression and evaluated by two independent, blinded investigators. Survival analyses were calculated for all patients receiving adjuvant chemotherapy in relation to expression of all makers above.
Results: Patients with negative AREG and EREG expression on their tumor had a significant longer DFS in comparison to AREG/EREG positive ones (p< 0.05). The benefit on DFS in AREG-/EREG- patients was even stronger in the group that received 5-FU/FA/irinotecan as adjuvant treatment (p=0.002). Patients with strong expression of PTEN profited more in terms of OS under adjuvant treatment containing irinotecan (p< 0.05). Regarding markers of the VEGFR- pathway we found no correlation of VEGF-C- and VEGFR-3 expression with clinical outcome. Patients with negative VEGF-D expression had a trend to live longer when treated with 5-FU/FA (p=0.106). Patients who were negative for Hif-1 α, were disease-free in more than 50% at the end of the study and showed significant longer DFS-rates than those positive for Hif-1 α (p=0.007). This benefit was even stronger at the group treated with 5-FU/FA/irinotecan (p=0.026). Finally, AREG-/EREG-/PTEN+ patients showed a trend to live longer under combined treatment combination.
Conclusions: The addition of irinotecan to adjuvant treatment with 5-FU/FA does not provide OS or DFS benefit in patients with stage II/III CRC. Nevertheless, AREG/EREG negative, PTEN positive and Hif-1 α negative patients might profit significantly in terms of DFS from a treatment containing fluoropyrimidines and irinotecan. Our results suggest a predictive value of these biomarkers concerning adjuvant chemotherapy with 5-FU/FA +/− irinotecan in stage II/III colorectal cancer.
HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders
(2021)
Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10−3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Background: Multiple studies have focused on medical and pharmacological treatments and outcome predictors of patients with status epilepticus (SE). However, a sufficient understanding of recurrent episodes of SE is lacking. Therefore, we reviewed recurrent SE episodes to investigate their clinical characteristics and outcomes in patients with relapses.
Methods: In this retrospective, multicenter study, we reviewed recurrent SE patient data covering 2011 to 2017 from the university hospitals of Frankfurt and Marburg, Germany. Clinical characteristics and outcome variables were compared among the first and subsequent SE episodes using a standardized form for data collection.
Results: We identified 120 recurrent SE episodes in 80 patients (10.2% of all 1177 episodes). The mean age at the first SE episode was 62.2 years (median 66.5; SD 19.3; range 21–91), and 42 of these patients were male (52.5%). A mean of 262.4 days passed between the first and the second episode. Tonic–clonic seizure semiology and a cerebrovascular disease etiology were predominant in initial and recurrent episodes. After subsequent episodes, patients showed increased disability as indicated by the modified Rankin Scale (mRS), and 9 out of 80 patients died during the second episode (11.3%). Increases in refractory and super-refractory SE (RSE and SRSE, respectively) were noted during the second episode, and the occurrence of a non-refractory SE (NRSE) during the first SE episode did not necessarily provide a protective marker for subsequent non-refractory episodes. An increase in the use of intravenous-available anti-seizure medication (ASM) was observed in the treatment of SE patients. Patients were discharged from hospital with a mean of 2.8 ± 1.0 ASMs after the second SE episode and 2.1 ± 1.2 ASMs after the first episode. Levetiracetam was the most common ASM used before admission and on discharge for SE patients.
Conclusions: This retrospective, multicenter study used the mRS to demonstrate worsened outcomes of patients at consecutive SE episodes. ASM accumulations after subsequent SE episodes were registered over the study period. The study results underline the necessity for improved clinical follow-ups and outpatient care to reduce the health care burden from recurrent SE episodes.