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  • 11
    Online Resource
    Online Resource
    Morphometricity as a measure of the neuroanatomical signature of a trait
    Proceedings of the National Academy of Sciences ; 2016
    In:  Proceedings of the National Academy of Sciences Vol. 113, No. 39 ( 2016-09-27)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 39 ( 2016-09-27)
    Abstract: Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1604378113
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2016
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 12
    Online Resource
    Online Resource
    A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk
    American Diabetes Association ; 2018
    In:  Diabetes Care Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894
    Details
    In: Diabetes Care, American Diabetes Association, Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894
    Abstract: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients’ relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2–51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial–Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06–1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS & gt;0.295, 95% CI 1.47–3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    URL: Article
    DOI: 10.2337/dc18-0087
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2018
    detail.hit.zdb_id: 1490520-6
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  • 13
    Online Resource
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    Association Between False Memories and Delusions in Alzheimer Disease
    American Medical Association (AMA) ; 2023
    In:  JAMA Psychiatry Vol. 80, No. 7 ( 2023-07-01), p. 700-
    Details
    In: JAMA Psychiatry, American Medical Association (AMA), Vol. 80, No. 7 ( 2023-07-01), p. 700-
    Abstract: Understanding the mechanisms of delusion formation in Alzheimer disease (AD) could inform the development of therapeutic interventions. It has been suggested that delusions arise as a consequence of false memories. Objective To investigate whether delusions in AD are associated with false recognition, and whether higher rates of false recognition and the presence of delusions are associated with lower regional brain volumes in the same brain regions. Design, Setting, and Participants Since the Alzheimer’s Disease Neuroimaging Initiative (ADNI) launched in 2004, it has amassed an archive of longitudinal behavioral and biomarker data. This cross-sectional study used data downloaded in 2020 from ADNI participants with an AD diagnosis at baseline or follow-up. Data analysis was performed between June 24, 2020, and September 21, 2021. Exposure Enrollment in the ADNI. Main Outcomes and Measures The main outcomes included false recognition, measured with the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog 13) and the Rey Auditory Verbal Learning Test (RAVLT) and volume of brain regions corrected for total intracranial volume. Behavioral data were compared for individuals with delusions in AD and those without using independent-samples t tests or Mann-Whitney nonparametric tests. Significant findings were further explored using binary logistic regression modeling. For neuroimaging data region of interest analyses using t tests, Poisson regression modeling or binary logistic regression modeling and further exploratory, whole-brain voxel-based morphometry analyses were carried out to explore the association between regional brain volume and false recognition or presence of delusions. Results Of the 2248 individuals in the ADNI database, 728 met the inclusion criteria and were included in this study. There were 317 (43.5%) women and 411 (56.5%) men. Their mean (SD) age was 74.8 (7.4) years. The 42 participants with delusions at baseline had higher rates of false recognition on the ADAS-Cog 13 (median score, 3; IQR, 1 to 6) compared with the 549 control participants (median score, 2; IQR, 0 to 4; U  = 9398.5; P  = .04). False recognition was not associated with the presence of delusions when confounding variables were included in binary logistic regression models. An ADAS-Cog 13 false recognition score was inversely associated with left hippocampal volume (odds ratio [OR], 0.91 [95% CI, 0.88-0.94] , P   & amp;lt; .001), right hippocampal volume (0.94 [0.92-0.97], P   & amp;lt; .001), left entorhinal cortex volume (0.94 [0.91-0.97], P   & amp;lt; .001), left parahippocampal gyrus volume (0.93 [0.91-0.96], P   & amp;lt; .001), and left fusiform gyrus volume (0.97 [0.96-0.99], P   & amp;lt; .001). There was no overlap between locations associated with false recognition and those associated with delusions. Conclusions and Relevance In this cross-sectional study, false memories were not associated with the presence of delusions after accounting for confounding variables, and no indication for overlap of neural networks for false memories and delusions was observed on volumetric neuroimaging. These findings suggest that delusions in AD do not arise as a direct consequence of misremembering, lending weight to ongoing attempts to delineate specific therapeutic targets for treatment of psychosis.
    Type of Medium: Online Resource
    ISSN: 2168-622X
    URL: Article
    DOI: 10.1001/jamapsychiatry.2023.1012
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
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  • 14
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    Online Resource
    Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes
    American Diabetes Association ; 2019
    In:  Diabetes Care Vol. 42, No. 2 ( 2019-02-01), p. 192-199
    Details
    In: Diabetes Care, American Diabetes Association, Vol. 42, No. 2 ( 2019-02-01), p. 192-199
    Abstract: There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings. RESEARCH DESIGN AND METHODS Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A] , and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years. RESULTS At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P & lt; 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody–positive, 13% for single autoantibody–positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody–positive, 12% for single autoantibody–positive, and 0.5% for initially autoantibody-negative subjects. CONCLUSIONS Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody–positive identical twins and multiple autoantibody–positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    URL: Article
    DOI: 10.2337/dc18-0288
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1490520-6
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  • 15
    Online Resource
    Online Resource
    Demographic history and rare allele sharing among human populations
    Proceedings of the National Academy of Sciences ; 2011
    In:  Proceedings of the National Academy of Sciences Vol. 108, No. 29 ( 2011-07-19), p. 11983-11988
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 108, No. 29 ( 2011-07-19), p. 11983-11988
    Abstract: High-throughput sequencing technology enables population-level surveys of human genomic variation. Here, we examine the joint allele frequency distributions across continental human populations and present an approach for combining complementary aspects of whole-genome, low-coverage data and targeted high-coverage data. We apply this approach to data generated by the pilot phase of the Thousand Genomes Project, including whole-genome 2–4× coverage data for 179 samples from HapMap European, Asian, and African panels as well as high-coverage target sequencing of the exons of 800 genes from 697 individuals in seven populations. We use the site frequency spectra obtained from these data to infer demographic parameters for an Out-of-Africa model for populations of African, European, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversity that will be discovered as sample sizes are increased. We predict that the number of discovered nonsynonymous coding variants will reach 100,000 in each population after ∼1,000 sequenced chromosomes per population, whereas ∼2,500 chromosomes will be needed for the same number of synonymous variants. Beyond this point, the number of segregating sites in the European and Asian panel populations is expected to overcome that of the African panel because of faster recent population growth. Overall, we find that the majority of human genomic variable sites are rare and exhibit little sharing among diverged populations. Our results emphasize that replication of disease association for specific rare genetic variants across diverged populations must overcome both reduced statistical power because of rarity and higher population divergence.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1019276108
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2011
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 16
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    Online Resource
    Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
    Springer Science and Business Media LLC ; 2023
    In:  Communications Medicine Vol. 3, No. 1 ( 2023-07-20)
    Details
    In: Communications Medicine, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2023-07-20)
    Abstract: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer’s disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. Methods We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer’s Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer’s disease. Results We show that the cohort with a neuroimaging Alzheimer’s phenotype has a cognitive profile in keeping with Alzheimer’s disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. Conclusions This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer’s disease.
    Type of Medium: Online Resource
    ISSN: 2730-664X
    URL: Article
    DOI: 10.1038/s43856-023-00313-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 3096949-9
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  • 17
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    Online Resource
    Long-term efficacy and safety of eculizumab in Japanese patients with generalized myasthenia gravis: A subgroup analysis of the REGAIN open-label extension study
    Elsevier BV ; 2019
    In:  Journal of the Neurological Sciences Vol. 407 ( 2019-12), p. 116419-
    Details
    In: Journal of the Neurological Sciences, Elsevier BV, Vol. 407 ( 2019-12), p. 116419-
    Type of Medium: Online Resource
    ISSN: 0022-510X
    URL: Article
    DOI: 10.1016/j.jns.2019.08.004
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 1500645-1
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  • 18
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    Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
    Proceedings of the National Academy of Sciences ; 2018
    In:  Proceedings of the National Academy of Sciences Vol. 115, No. 7 ( 2018-02-13), p. 1481-1486
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 7 ( 2018-02-13), p. 1481-1486
    Abstract: When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer’s disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1719747115
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2018
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 19
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    Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study
    Elsevier BV ; 2022
    In:  Modern Pathology Vol. 35, No. 10 ( 2022-10), p. 1362-1369
    Details
    In: Modern Pathology, Elsevier BV, Vol. 35, No. 10 ( 2022-10), p. 1362-1369
    Type of Medium: Online Resource
    ISSN: 0893-3952
    URL: Article
    DOI: 10.1038/s41379-022-01104-9
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2041318-X
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  • 20
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    Eculizumab improves fatigue in refractory generalized myasthenia gravis
    Springer Science and Business Media LLC ; 2019
    In:  Quality of Life Research Vol. 28, No. 8 ( 2019-08-15), p. 2247-2254
    Details
    In: Quality of Life Research, Springer Science and Business Media LLC, Vol. 28, No. 8 ( 2019-08-15), p. 2247-2254
    Abstract: To evaluate the effect of eculizumab on perceived fatigue in patients with anti-acetylcholine receptor antibody-positive, refractory, generalized myasthenia gravis (MG) using the Quality of Life in Neurological Disorders (Neuro-QOL) Fatigue subscale, and to evaluate correlations between improvements in Neuro-QOL Fatigue and other clinical endpoints. Methods Neuro-QOL Fatigue, MG Activities of Daily Living (MG-ADL), Quantitative MG (QMG), and the 15-item MG Quality of Life (MG-QOL15) scales were administered during the phase 3, randomized, placebo-controlled REGAIN study (eculizumab, n  = 62; placebo, n  = 63) and subsequent open-label extension (OLE). Data were analyzed using repeated-measures models. Correlations between changes in Neuro-QOL Fatigue and in MG-ADL, QMG, and MG-QOL15 scores were determined at REGAIN week 26. Results At REGAIN week 26, eculizumab-treated patients showed significantly greater improvements in Neuro-QOL Fatigue scores than placebo-treated patients (consistent with improvements in MG-ADL, QMG, and MG-QOL15 scores previously reported in REGAIN). Improvements with eculizumab were sustained through OLE week 52. Correlations between Neuro-QOL Fatigue and MG-QOL15, MG-ADL, and QMG scores were strong for eculizumab-treated patients at REGAIN week 26, and strong, moderate, and weak, respectively, for placebo-treated patients. Conclusions Compared with placebo, eculizumab was associated with improvements in perceived fatigue that strongly correlated with improvements in MG-specific outcome measures. Trial ID Registration : NCT01997229, NCT02301624.
    Type of Medium: Online Resource
    ISSN: 0962-9343 , 1573-2649
    URL: Article
    DOI: 10.1007/s11136-019-02148-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2008960-0
    SSG: 5,1
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