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A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from

A non-Gaussian smoothing (NGS) technique is developed for filtering low count transmission (TR) data to be used for attenuation correction (AC) of positron emission tomography (PET) studies. The method is based on a statistical technique known as the generalized linear mixed model that allows an inverse link function that avoids the inversion of the observed transmission data. The NGS technique has been implemented in the sinogram domain in one-dimensional mode as angle-by-angle computation. To make it adaptive as a function of the TR count statistics we also develop and validate an objective procedure to choose an optimal smoothing parameter. The technique is assessed using experimental phantoms, simulating PET whole-body studies, and applied to real patient data. Different experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR counting statistic are considered. The method is evaluated, in terms of mean squared error (MSE), by comparing pixel by pixel the distribution for high counts statistics TR scan (1 h) with the corresponding counts distribution for low count statistics TR scans (e.g., 1 min). The smoothing parameter selection is shown to have high efficiency, meaning that it tends to choose values close to the unknown best value. Furthermore, the counts distribution of emission (EM) images, reconstructed with AC generated using low count TR data (1 min), are within 5% of the corresponding EM images reconstructed with AC generated using the high count statistics TR data (1 h). An application to a real patient whole-body PET study shows the promise of the technique for routine use. Index Terms--Attenuation, Poisson data, regression, roughness penalty, sinogram, smoothing.

A statistical model involving machine calibration factors in PET is developed based on the analysis of the correlation structure of the blank scan data. These factors include the nonuniform detector efficiencies and the geometric elects. The crystal interference effect is shown to be the main feature of the correlation function. A likelihood based theory is developed and the maximum likelihood estimates of the factors are derived. In the application of the model, estimated machine effects are used to calibrate the transmission scan and a robust smoothing is then applied to get the attenuation correction factor. The proposed technique is illustrated with studies on the GE Advance PET Scanner.

Background and purpose: The objective was to assess the ability of eight commonly measured blood markers to serve as prognostic biomarkers in amyotrophic lateral sclerosis (ALS). Methods: A cohort study was conducted of 399 individuals with newly diagnosed ALS between 2006 and 2011 in Stockholm, Sweden. Information on eight blood markers, including creatinine, albumin, haemoglobin, C‐reactive protein (CRP), glucose, potassium, sodium and calcium, measured at or after the date of ALS diagnosis, was collected. The Cox regression model and joint model were used to explore the associations between biomarkers and risk of mortality. Results: The mean age at ALS diagnosis was 66.25 years and 58% of the patients were male. A lower than median level of serum creatinine [hazard ratio (HR) 1.67; 95% confidence interval (CI) 1.31–2.12] or albumin (HR 1.49, 95% CI 1.13–1.96) whereas a higher than median level of log‐transformed CRP (HR 1.33, 95% CI 1.04–1.71) or glucose (HR 1.34, 95% CI 1.01–1.78) at baseline was associated with a higher mortality risk. Taking all available measurements after ALS diagnosis into account, an association was found between per standard deviation (SD) decrease in serum creatinine (HR 2.23, 95% CI 1.81–2.75) or albumin (HR 1.83, 95% CI 1.43–2.36) as well as per SD increase of log(CRP) (HR 1.96, 95% CI 1.58–2.43) or glucose (HR 1.61, 95% CI 1.21–2.12) and a higher mortality risk. No clear association was found for haemoglobin, potassium, sodium or calcium. Conclusions: This study suggests that serum creatinine, albumin, CRP and glucose measured at the time of ALS diagnosis as well as their temporal changes after ALS diagnosis could serve as additional prognostic biomarkers for ALS. Their values in routine clinical practice and clinical trials of ALS need to be investigated further. [ABSTRACT FROM AUTHOR]

Background and purpose: Previous animal studies have suggested a disrupted intestinal microbiome in amyotrophic lateral sclerosis (ALS). Due to the known effect of antibiotics on gut microflora, the potential role of antibiotics use on the risk of ALS deserves an investigation. Methods: A nested case–control study was conducted using several Swedish national registers. In all, 2484 ALS patients diagnosed between 1 July 2006 and 31 December 2013 were included as cases, and five controls per case individually matched to the case by sex, birth year and area of residence were randomly selected from the general Swedish population. Information on antibiotics prescriptions before ALS diagnosis was extracted from the Prescribed Drug Register for both cases and controls. A conditional logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Results: After accounting for potential diagnostic delay in ALS by excluding all prescriptions within 1 year before diagnosis, any antibiotics use was associated with a higher risk of ALS. The ORs (95% CIs) were 1.06 (0.94–1.19), 1.13 (1.00–1.28) and 1.18 (1.03–1.35) when comparing 1, 2–3 and ≥4 prescriptions to no prescription (P for trend = 0.0069). Similar results were noted for antibiotics used for respiratory infections and urinary tract as well as skin and soft tissue infections. Amongst different individual antibiotics, the risk of ALS was especially increased in relation to more than two prescriptions of beta‐lactamase sensitive penicillin (OR 1.28; 95% CI 1.10–1.50). Conclusions: Use of antibiotics, especially repeated, might be associated with a higher subsequent risk of ALS. [ABSTRACT FROM AUTHOR]

A common goal of epidemiologic research is to study how two exposures interact in causing a binary outcome. Causal interaction is defined as the presence of subjects for which the causal effect of one exposure depends on the level of the other exposure. For binary exposures, it has previously been shown that the presence of causal interaction is testable through additive statistical interaction. However, it has also been shown that the magnitude of causal interaction, defined as the proportion of subjects for which there is causal interaction, is generally not identifiable. In this article, we derive bounds on causal interactions, which are applicable to binary outcomes and categorical exposures with arbitrarily many levels. These bounds can be used to assess the magnitude of causal interaction, and serve as an important complement to the statistical test that is frequently employed. The bounds are derived both without and with an assumption about monotone exposure effects. We present an application of the bounds to a study of gene-gene interaction in rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Background:Long-term daily use of aspirin has been associated with reduced cancer mortality. To explore this association, we compared tumour TNM characteristics among aspirin users with those among non-users.Methods:From the Swedish Cancer Register, we identified patients diagnosed with colorectal, lung, prostate and breast cancers between 2006 and 2009 and matched them to the Swedish Prescribed Drug Register to obtain information on low-dose aspirin use prior to diagnosis. Contingency table and logistic regression analyses were used to test for association and obtain odds ratios (ORs).Results:We identified 17 041 colorectal, 9766 lung, 29 770 prostate and 20 299 breast cancer patients. The proportion of low-dose aspirin users was ∼26% among colorectal, lung and prostate cancer patients and ∼14% among breast cancer patients. Adjusted for age, gender, education level and place of residence, low-dose aspirin use was associated with lower tumour extent (T) for colorectal and lung cancers (P<0.0001) but not for prostate and breast cancers. The adjusted OR of aspirin use for the T4 vs T1 categories was ∼0.7 (95% confidence interval (CI) 0.6-0.8). For all cancers, we found no evidence of association of aspirin use with nodal involvement (N). Except for a borderline result in prostate cancer (OR ∼0.9; 95% CI 0.8-1.0), aspirin use was associated with a lower rate of metastatic disease (ORs ∼0.6-0.8). Among the histological subgroups of lung cancer, significant differences in tumour extent were observed most clearly within the adenocarcinoma subgroup (OR ∼0.6, 95% CI 0.5-0.8), although numbers of other subtypes were more limited; and there was a significant reduction of ∼20-30% in the odds of metastasis among the aspirin users across the subgroups.Conclusion:Use of low-dose aspirin in the year prior to diagnosis was found to be associated with lower tumour extent and fewer metastatic disease for colorectal and lung cancers. For these cancers, the benefit of aspirin use appears to be during both early and late cancer progression. [ABSTRACT FROM AUTHOR]

Recent genome-wide association studies have reported the discoveries of genetic variants of small to moderate effects. However, most studies of complex diseases face a great challenge because the number of significant variants is less than what is required to explain the disease heritability. A new approach is needed to recognize all potential discoveries in the data. In this paper, we present a practical model-free procedure to estimate the number of true discoveries as a function of the number of top-ranking SNPs together with the confidence bounds. This approach allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Estimation of genetic and environmental contributions to cancers falls in the framework of generalized linear mixed modelling with several random effect components. Computational challenges remain, however, in dealing with binary or survival phenotypes. In this paper, we consider the analysis of melanoma onset in a population of 2.6 million nuclear families in Sweden, for which none of the current survival-based methodologies is feasible. We treat the disease outcome as a binary phenotype, so that the standard proportional hazard model leads to a generalized linear model with the complementary-log link function. For rare diseases this link is very close to the probit link, and thus allows the use of marginal likelihood for the estimation of the variance components. We correct for the survival length bias by censoring the parent generation within each family at the time they attain the same cumulative hazard as the child generation, thus improving the validity of the estimates. Our finding that childhood shared environment in addition to genetic factors had a considerable effect on the development of melanoma is consistent with epidemiological studies. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climatic phenomena such as the El-Niño–southern oscillation and the north Atlantic oscillation are results of complex interactions between atmospheric and oceanic processes. Understanding the interactions has enabled scientists to give early warning of the forthcoming phenomena, thereby reducing damage caused by them. Statistical methods have played an important role in revealing effects of these phenomena on different regions of the world. One such method is maximum covariance analysis (MCA). Two apparent weaknesses are associated with MCA. Firstly, it tends to produce estimates with a low signal-to-noise ratio, especially when the sample size is small. Secondly, there has been no objective way of incorporating incomplete records, which are frequently encountered in climatology and oceanographic data-bases. We introduce an MCA which incorporates a smoothing procedure on the estimates. The introduction of the smoothing procedure is shown to improve the signal-to-noise ratio on the estimates. The estimation of smoothing parameters is carried out by using a penalized likelihood approach, which makes the inclusion of incomplete records quite straightforward. The methodology is applied to investigate the association between Irish winter precipitation and sea surface temperature anomalies around the world. The results show relationships between Irish precipitation anomalies and the El-Niño–southern oscillation and the north Atlantic oscillation phenomena. [ABSTRACT FROM AUTHOR]

Given the promise of rich biological information in microarray data we will expect an increasing demand for a robust, practical and well-tested methodology to provide patient prognosis based on gene expression data. In standard settings, with few clinical predictors, such a methodology has been provided by the Cox proportional hazard model, but no corresponding methodology is available to deal with the full set of genes in microarray data. Furthermore, we want the procedure to be able to deal with the general survival data that include censored information. Conceptually such a procedure can be constructed quite easily, but its implementation will never be straightforward due to computational problems. We have developed an approach that relies on an extension of the Cox proportional likelihood that allows random effects parameters. In this approach, we use the full set of genes in the analysis and deal with survival data in the most general way. We describe the development of the model and the steps in the implementation, including a fast computational formula based on a subsampling of the risk set and the singular value decomposition. Finally, we illustrate the methodology using a data set obtained from a cohort of breast cancer patients. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A penalized likelihood approach to the estimation of calibration factors in positron emission tomography (PET) is considered, in particular the problem of estimating the efficiency of PET detectors. Varying efficiencies among the detectors create a non-uniform performance and failure to account for the non-uniformities would lead to streaks in the image, so efficient estimation of the non-uniformities is desirable to reduce the propagation of noise to the final image. The relevant data set is provided by a blank scan, where a model may be derived that depends only on the sources affecting non-uniformities: inherent variation among the detector crystals and geometric effects. Physical considerations suggest a novel mixed inverse model with random crystal effects and smooth geometric effects. Using appropriate penalty terms, the penalized maximum likelihood estimates are derived and an efficient computational algorithm utilizing the fast Fourier transform is developed. Data-driven shrinkage and smoothing parameters are chosen to minimize an estimate of the predictive loss function. Various examples indicate that the approach proposed works well computationally and compares well with the standard method. [ABSTRACT FROM AUTHOR]

The CAST was a randomized, double-blind placebo-controlled multicentre trial of antiarrhythmic medications designed to suppress ventricular arrhythmias in patients after an acute myocardial infarction (MI). A collection of 21 items derived from established scales was used to assess aspects of quality of life in CAST. The questions focused on symptoms, mental health, physical functioning, social functioning, life satisfaction, and life expectancy. Additional aspects included exposure to major stressful life events, and perceived social support and social integration. Work status was also recorded. Using the baseline values of 1465 (98%) out of 1498 patients enrolled in the CAST main study between 15 June 1987 and 19 April 1989, the reliability and validity of the scales used in CAST were computed. High internal consistency reliability (> or = 0.70) was found for Symptoms, Mental Health, and Physical Functioning. The discriminative validity, in particular for Symptoms, Mental Health, Physical and Social Functioning, showed that patients with heart failure and previous MI, as well as those suffering from angina and dyspnea, had a worse quality of life than those patients who were not experiencing these symptoms. It was concluded that the scales selected to form the CAST quality of life questionnaire were both reliable and clinically valid for this patient population and therefore could be used to detect disease progression and treatment effects. [ABSTRACT FROM AUTHOR]

Background: From 1968 to 2002, Singapore experienced an almost four-fold increase in prostate cancer incidence. This paper examines the incidence, mortality and survival patterns for prostate cancer among all residents in Singapore from 1968 to 2002.Methods: This is a retrospective population-based cohort study including all prostate cancer cases aged over 20 (n = 3613) reported to the Singapore Cancer Registry from 1968 to 2002. Age-standardized incidence, mortality rates and 5-year Relative Survival Ratios (RSRs) were obtained for each 5-year period. Follow-up was ascertained by matching with the National Death Register until 2002. A weighted linear regression was performed on the log-transformed age-standardized incidence and mortality rates over period.Results: The percentage increase in the age-standardized incidence rate per year was 5.0%, 5.6%, 4.0% and 1.9% for all residents, Chinese, Malays and Indians respectively. The percentage increase in age-standardized mortality rate per year was 5.7%, 6.0%, 6.6% and 2.5% for all residents, Chinese, Malays and Indians respectively. When all Singapore residents were considered, the RSRs for prostate cancer were fairly constant across the study period with slight improvement from 1995 onwards among the Chinese.Conclusion: Ethnic differences in prostate cancer incidence, mortality and survival patterns were observed. There has been a substantial improvement in RSRs since the 1990s for the Chinese. [ABSTRACT FROM AUTHOR]

Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in the clinical development. The main concerns in dose-finding study are to identify a minimum effective dose (MED) in anesthesia studies and maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, we propose two modifications of Firth's logistic regression using reparametrization, called reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, we develop a method on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals., (© 2024 John Wiley & Sons Ltd.)

Objective: To investigate the association between infections and disability worsening in people with multiple sclerosis (MS) treated with either B-cell depleting therapy (rituximab) or interferon-beta/glatiramer acetate (IFN/GA)., Methods: This cohort study spanned from 2000 to 2021, using data from the Swedish MS Registry linked to national health care registries, comprising 8,759 rituximab and 7,561 IFN/GA treatment episodes. The risk of hospital-treated infection was estimated using multivariable Cox models. The association between infections and increase in Expanded Disability Status Scale (EDSS) scores was assessed using a doubly robust generalized estimating equations model. Additionally, a piece-wise exponential model analyzed events of increased disability beyond defined cut-off values, controlling for relapses, and MRI activity., Results: Compared with IFN/GA, rituximab displayed increased risk of both inpatient- and outpatient-treated infections (hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.50-2.90 and HR, 1.37; 95% CI, 1.13-1.67, respectively). An inpatient-treated infection was associated with a 0.19-unit increase in EDSS (95% CI, 0.12-0.26). Degree of worsening was greatest for progressive MS, and under IFN/GA treatment, which unlike rituximab, was more commonly associated with MRI activity. After controlling for relapses and MRI activity, inpatient-treated infections were associated with disability worsening in people with relapsing-remitting MS treated with IFN/GA (HR, 2.01; 95% CI, 1.59-2.53), but not in those treated with rituximab., Interpretation: Compared to IFN/GA, rituximab doubled the infection risk, but reduced the risk of subsequent disability worsening. Further, the risk of worsening after hospital-treated infection was greater with progressive MS than with relapsing-remitting MS. Infection risk should be considered to improve long term outcomes. ANN NEUROL 2024., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Bone marrow (BM) evaluation is the de facto standard for diagnosis, molecular analysis, risk stratification, and therapy response assessment in acute myeloid leukemia (AML), but in patients with a high number of circulating blast cells, the peripheral blood (PB) sample could provide similar information as BM. However, there is no large-scale molecular study comparing the two specimens in terms of their gene expression profiles, cellular heterogeneities, and ex-vivo drug sensitivity., Methodology: We used (i) the BEAT-AML cohort each with detailed molecular data; (ii) cell-type deconvolution to estimate leukemic and immune cell proportions between specimen types; (iii) differential expression (DE) and drug-cell type association analysis; and (iv) logistic regression models to assess the association between induction therapy response, cell-type composition and first-line drug treatment., Results: Results: We identified 207 patients having BM and 116 patients having PB samples. There was a total of 1271 DE genes (false discovery rate < 0.05) between BM and PB; the top enriched pathways in terms of DE genes belong to the immune system pathways. Aggregated ex-vivo drug response profiles from the two specimens were largely similar, as were the cellular components, except for the GMP-like cell type (17% in BM vs. 5% in PB, p -value = 2 × 10 -7 ). Among the specimen-specific results, the GMP-like subtype was associated with multiple drug resistance in BM and the ProMono-like subtype in PB. Several cell types were associated with the response to induction therapy, but the impact of specimen type on the interaction of cell type and cytarabine-associated induction therapy was not statistically significant for most cell types., Results: Conclusions: Even though there are molecular and cellular differences between BM and PB samples, they show many similarities in ex-vivo drug response profiles, indicating the clinical utility of the substantially less-invasive PB samples., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). eJHaem published by British Society for Haematology and John Wiley & Sons Ltd.)

Background and Purpose: Evidence has accumulated to support the early involvement of altered gastrointestinal (GI) function in neurodegenerative disease. However, risk of Alzheimer disease (AD) and Parkinson disease (PD) among individuals with a GI biopsy of normal mucosa or nonspecific inflammation is unknown., Methods: This matched cohort study included all individuals in Sweden with a GI biopsy of normal mucosa (n = 480,346) or nonspecific inflammation (n = 655,937) during 1965-2016 (exposed group) as well as their individually matched population references and unexposed full siblings. A flexible parametric model and stratified Cox model were used to estimate hazard ratio (HR) and its 95% confidence interval (CI)., Results: Individuals with normal mucosa or nonspecific inflammation had a higher risk of AD and PD during the 20 years after biopsy. Compared with the population references, individuals with normal mucosa had an increased risk of AD (incidence rate [IR] difference = 13.53 per 100,000 person-years, HR [95% CI] = 1.15 [1.11-1.20]) and PD (IR difference = 6.72, HR [95% CI] = 1.16 [1.10-1.23]). Elevated risk was also observed for nonspecific inflammation regarding AD (IR difference = 13.28, HR [95% CI] = 1.11 [1.08-1.14]) and PD (IR difference = 6.83, HR [95% CI] = 1.10 [1.06-1.14]). Similar results were observed in subgroup and sensitivity analyses and when comparing with their unexposed siblings., Conclusions: Individuals with a GI biopsy of normal mucosa or nonspecific inflammation had an increased risk of AD and PD. This adds new evidence of the early involvement of GI dysfunction in neurodegenerative disease., (© 2022 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background: Several psychiatric disorders have been associated with increased risk of cardiovascular disease (CVD), however, the role of familial factors and the main disease trajectories remain unknown., Methods: In this longitudinal cohort study, we identified a cohort of 900,240 patients newly diagnosed with psychiatric disorders during January 1, 1987 and December 31, 2016, their 1,002,888 unaffected full siblings, and 1:10 age- and sex-matched reference population from nationwide medical records in Sweden, who had no prior diagnosis of CVD at enrolment. We used flexible parametric models to determine the time-varying association between first-onset psychiatric disorders and incident CVD and CVD death, comparing rates of CVD among patients with psychiatric disorders to the rates of unaffected siblings and matched reference population. We also used disease trajectory analysis to identify main disease trajectories linking psychiatric disorders to CVD. Identified associations and disease trajectories of the Swedish cohort were validated in a similar cohort from nationwide medical records in Denmark (N = 875,634 patients, same criteria during January 1, 1969 and December 31, 2016) and in Estonian cohorts from the Estonian Biobank (N = 30,656 patients, same criteria during January 1, 2006 and December 31, 2020), respectively., Findings: During up to 30 years of follow-up of the Swedish cohort, the crude incidence rate of CVD was 9.7, 7.4 and 7.0 per 1000 person-years among patients with psychiatric disorders, their unaffected siblings, and the matched reference population. Compared with their siblings, patients with psychiatric disorders experienced higher rates of CVD during the first year after diagnosis (hazard ratio [HR], 1.88; 95% confidence interval [CI], 1.79-1.98) and thereafter (1.37; 95% CI, 1.34-1.39). Similar rate increases were noted when comparing with the matched reference population. These results were replicated in the Danish cohort. We identified several disease trajectories linking psychiatric disorders to CVD in the Swedish cohort, with or without mediating medical conditions, including a direct link between psychiatric disorders and hypertensive disorder, ischemic heart disease, venous thromboembolism, angina pectoris, and stroke. These trajectories were validated in the Estonian Biobank cohort., Interpretation: Independent of familial factors, patients with psychiatric disorders are at an elevated risk of subsequent CVD, particularly during first year after diagnosis. Increased surveillance and treatment of CVDs and CVD risk factors should be considered as an integral part of clinical management, in order to reduce risk of CVD among patients with psychiatric disorders., Funding: This research was supported by EU Horizon 2020 Research and Innovation Action Grant, European Research Council Consolidator grant, Icelandic Research fund, Swedish Research Council, US NIMH, the Outstanding Clinical Discipline Project of Shanghai Pudong, the Fundamental Research Funds for the Central Universities, and the European Union through the European Regional Development Fund; the Research Council of Norway; the South-East Regional Health Authority, the Stiftelsen Kristian Gerhard Jebsen, and the EEA-RO-NO-2018-0535., Competing Interests: OAA declares receiving grants or contracts from the South-East Norway Health Authority, NIH and KG Jebsen Stiftelsen, receiving consulting fees from Biogen, Cortechs.ai and Milken. OAA gets Speaker's honorarium from Janssen, Lundbeck and Sunovion, and has a patent on Intranasal Administration (US20160310683 A1). OAA participated in advisory board as National PI for JANSSEN trial depression, MAPS trial PTSD and BI trial schizophrenia. OAA declares having stock at Cortechs.ai. UAV declares receiving support from EPA2023, ISTSS2022 as keynote speaker, and serves on a NordForsk expert committee on Long COVID. FF declares receiving grants from the 10.13039/501100004359Swedish Research Council, Swedish Research Council for Health, Workinglife and Welfare, 10.13039/501100002794Swedish Cancer Society, 10.13039/501100000781European Research Council and the US CDC. FF declares getting payment from 10.13039/501100004359Swedish Research Council for grant review. All other authors declare no competing interests., (© 2023 The Author(s).)

Background: Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance., Methods: A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques., Results: By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis., Conclusions: The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/ ., (© 2023. The Author(s).)

Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions., Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00100-6., Competing Interests: Conflict of interestsThe authors declare no competing financial interest., (© The Author(s) 2023.)

A link between neurodegenerative diseases and infections has been previously reported. However, it is not clear to what extent such link is caused by confounding factors or to what extent it is intimately connected with the underlying conditions. Further, studies on the impact of infections on mortality risk following neurodegenerative diseases are rare. We analysed two data sets with different characteristics: (i) a community-based cohort from the UK Biobank with 2023 patients with multiple sclerosis, 2200 patients with Alzheimer's disease, 3050 patients with Parkinson's disease diagnosed before 1 March 2020 and 5 controls per case who were randomly selected and individually matched to the case; (ii) a Swedish Twin Registry cohort with 230 patients with multiple sclerosis, 885 patients with Alzheimer's disease and 626 patients with Parkinson's disease diagnosed before 31 December 2016 and their disease-free co-twins. The relative risk of infections after a diagnosis of neurodegenerative disease was estimated using stratified Cox models, with adjustment for differences in baseline characteristics. Causal mediation analyses of survival outcomes based on Cox models were performed to assess the impact of infections on mortality. Compared with matched controls or unaffected co-twins, we observed an elevated infection risk after diagnosis of neurodegenerative diseases, with a fully adjusted hazard ratio (95% confidence interval) of 2.45 (2.24-2.69) for multiple sclerosis, 5.06 (4.58-5.59) for Alzheimer's disease and 3.72 (3.44-4.01) for Parkinson's disease in the UK Biobank cohort, and 1.78 (1.21-2.62) for multiple sclerosis, 1.50 (1.19-1.88) for Alzheimer's disease and 2.30 (1.79-2.95) for Parkinson's disease in the twin cohort. Similar risk increases were observed when we analysed infections during the 5 years before diagnosis of the respective disease. Occurrence of infections after diagnosis had, however, relatively little impact on mortality, as mediation of infections on mortality (95% confidence interval) was estimated as 31.89% (26.83-37.11%) for multiple sclerosis, 13.38% (11.49-15.29%) for Alzheimer's disease and 18.85% (16.95-20.97%) for Parkinson's disease in the UK Biobank cohort, whereas it was 6.56% (-3.59 to 16.88%) for multiple sclerosis, -2.21% (-0.21 to 4.65%) for Parkinson's disease and -3.89% (-7.27 to -0.51%) for Alzheimer's disease in the twin cohort. Individuals with studied neurodegenerative diseases display an increased risk of infections independently of genetic and familial environment factors. A similar magnitude of risk increase is present prior to confirmed diagnosis, which may indicate a modulating effect of the studied neurological conditions on immune defences., Competing Interests: The authors report no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined P $$ P $$ -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses., (© 2022 John Wiley & Sons Ltd.)

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, involving neuroinflammation and T cell infiltration in the central nervous system. However, the contribution of T cell responses to the pathology of the disease is not fully understood. Here we show, by flow cytometric analysis of blood and cerebrospinal fluid (CSF) samples of a cohort of 89 newly diagnosed ALS patients in Stockholm, Sweden, that T cell phenotypes at the time of diagnosis are good predictors of disease outcome. High frequency of CD4 + FOXP3 - effector T cells in blood and CSF is associated with poor survival, whereas high frequency of activated regulatory T (Treg) cells and high ratio between activated and resting Treg cells in blood are associated with better survival. Besides survival, phenotypic profiling of T cells could also predict disease progression rate. Single cell transcriptomics analysis of CSF samples shows clonally expanded CD4 + and CD8 + T cells in CSF, with characteristic gene expression patterns. In summary, T cell responses associate with and likely contribute to disease progression in ALS, supporting modulation of adaptive immunity as a viable therapeutic option., (© 2022. The Author(s).)

Obesity has a highly complex genetic architecture, making it difficult to understand the genetic mechanisms, despite the large number of discovered loci via genome-wide association studies (GWAS). Omics techniques have provided a better resolution to view this problem. As a proxy of cell-level biology, extracellular vesicles (EVs) are useful for studying cellular regulation of complex phenotypes such as obesity. Here, in a well-established Scottish cohort, we utilized a novel technology to detect surface proteins across millions of single EVs in each individual's plasma sample. Integrating the results with established obesity GWAS, we inferred 78 types of EVs carrying one or two of 12 surface proteins to be associated with adiposity-related traits such as waist circumference. We then verified that particular EVs' abundance is negatively correlated with body adiposity, while no association with lean body mass. We also revealed that genetic variants associated with protein-specific EVs capture 2-4-fold heritability enrichment for blood cholesterol levels. Our findings provide evidence that EVs with specific surface proteins have phenotypic and genetic links to obesity and blood lipids, respectively, guiding future EV biomarker research., (© The Author(s) 2022. Published by Oxford University Press.)

An individualized cancer therapy is ideally chosen to target the cancer's driving biological pathways, but identifying such pathways is challenging because of their underlying heterogeneity and there is no guarantee that they are druggable. We hypothesize that a cancer with an activated druggable cancer-specific pathway (DCSP) is more likely to respond to the relevant drug. Here we develop and validate a systematic method to search for such DCSPs, by (i) introducing a pathway activation score (PAS) that integrates cancer-specific driver mutations and gene expression profile and drug-specific gene targets, (ii) applying the method to identify DCSPs from pan-cancer datasets, and (iii) analyzing the correlation between PAS and the response to relevant drugs. In total, 4,794 DCSPs from 23 different cancers have been discovered in the Genomics of Drug Sensitivity in Cancer database and validated in The Cancer Genome Atlas database. Supporting the hypothesis, for the DCSPs in acute myeloid leukemia, cancers with higher PASs are shown to have stronger drug response, and this is validated in the BeatAML cohort. All DCSPs are publicly available at https://www.meb.ki.se/shiny/truvu/DCSP/., (© The Author(s) 2022. Published by Oxford University Press GigaScience.)