Dataset Information

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

ABSTRACT: Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.

SUBMITTER: Nobis L

PROVIDER: S-EPMC6603440 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

Nobis Lisa L Manohar Sanjay G SG Smith Stephen M SM Alfaro-Almagro Fidel F Jenkinson Mark M Mackay Clare E CE Husain Masud M

NeuroImage. Clinical 20190619

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Her ...[more]

PMID: 31254939

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Project description:ObjectiveTo construct applicable models suitable for predicting the risk of suicidal behavior among individuals with depression, particularly on the progression from no history of suicidal behavior to suicide attempts, as well as from suicidal ideation to suicide attempts.MethodsBased on a prospective cohort from the UK Biobank, a total of 55,139 individuals aged 50 and above with depression were enrolled in the study, among whom 29,528 exhibited suicidal behavior. Specifically, they were divided into control (25,611), suicidal ideation (24,361), and suicide attempt (5167) groups. Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of important features for distinguishing suicidal ideation and suicide attempts. We used the Gradient Boosting Decision Tree (GBDT) algorithm with stratified 10-fold cross-validation and grid-search to construct the prediction models for suicidal ideation or suicide attempts. To address the dataset imbalance in classifying suicide attempts, we used random under-sampling. The SHapley Additive exPlanations (SHAP) were used to estimate the important variables in the GBDT model.ResultsSignificant differences in sociodemographic, economic, lifestyle, and psychological factors were observed across the three groups. Each classifier optimally utilized 8-11 features. Overall, the algorithms predicting suicide attempts demonstrated slightly higher performance than those predicting suicidal ideation. The GBDT classifier achieved the highest accuracy, with AUROC scores of 0.914 for suicide attempts and 0.803 for suicidal ideation. Distinctive predictive factors were identified for each group: while depression's inherent characteristics crucially distinguished the suicidal ideation group from controls, some key predictors, including the age of depression onset and childhood trauma events, were identified for suicide attempts.ConclusionsWe established applicable machine learning-based models for predicting suicidal behavior, particularly suicide attempts, in individuals with depression, and clarified the differences in predictors between suicidal ideation and suicide attempts.

Project description:ImportanceEfforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN).ObjectiveWe compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort.Design/setting/participantsCross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM.ExposureDiagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%.Main outcomes and measuresTotal retinal, mRNFL and GC-IPL thickness.Results74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 μm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 μm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 μm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 μm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 μm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 μm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 μm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 μm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness.ConclusionGC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN.

Dataset Information

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

Publications

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

Similar Datasets

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