Predicting clinical variables from neuroimages using fused sparse group lasso

Predictive models in which neuroimage features serve as predictors and a clinical variable is modeled as the outcome are good candidates for clinical application because (1) they can exploit dependencies between predictor variables and thus potentially explain more variability in the outcome than a mass univariate approach, and (2) they allow inference at the individual level, such that a prediction can be obtained for a new individual whose data was not used to train the model. This dissertation proposes methods for neuroimaging prediction models that not only aim for predictive accuracy, but also seek interpretability and potential insight into the underlying pathophysiology of neuropsychiatric disorders. In the first part of this dissertation we propose the fused sparse group lasso penalty, which encourages structured, sparse, interpretable solutions by incorporating prior information about spatial and group structure among voxels. We derive optimization steps for fused sparse group lasso penalized regression using the alternating direction method of multipliers algorithm. With simulation studies, we demonstrate conditions under which fusion and group penalties together outperform either of them alone. We then use fused sparse group lasso to predict continuous measures from resting state magnetic resonance imaging data using the Autism Brain Imaging Data Exchange dataset. In the second part of this dissertation we use fused sparse group lasso to predict age from multimodal neuroimaging data in a sample of cognitively normal adults aged 65 and older. In general, we show how the incorporation of prior information via the fused sparse group lasso penalty can enhance the interpretability of neuroimaging predictive models while also yielding good predictive performance. Public health significance: Psychiatric disorders and neurological diseases such as Alzheimer's present a large public health burden. As of yet, there have been relatively few translations of basic neuroscience findings to clinical applications in psychiatry. Prediction models using neuroimaging data can potentially help clinicians with diagnosis and prediction of prognosis and treatment response. Establishing interpretable neuroimaging-based biomarkers can improve our understanding of the neurobiological mechanisms underlying neuropsychiatric disorders and suggest approaches for prevention and treatment

The Impairment of Macrophage-to-Feces Reverse Cholesterol Transport during Inflammation Does Not Depend on Serum Amyloid A

Studies suggest that inflammation impairs reverse cholesterol transport (RCT). We investigated whether serum amyloid A (SAA) contributes to this impairment using an established macrophage-to-feces RCT model. Wild-type (WT) mice and mice deficient in SAA1.1 and SAA2.1 (SAAKO) were injected intraperitoneally with 3H-cholesterol-labeled J774 macrophages 4 hr after administration of LPS or buffered saline. 3H-cholesterol in plasma 4 hr after macrophage injection was significantly reduced in both WT and SAAKO mice injected with LPS, but this was not associated with a reduced capacity of serum from LPS-injected mice to promote macrophage cholesterol efflux in vitro. Hepatic accumulation of 3H-cholesterol was unaltered in either WT or SAAKO mice by LPS treatment. Radioactivity present in bile and feces of LPS-injected WT mice 24 hr after macrophage injection was reduced by 36% (P \u3c 0.05) and 80% (P \u3c 0.001), respectively. In contrast, in SAAKO mice, LPS did not significantly reduce macrophage-derived 3H-cholesterol in bile, and fecal excretion was reduced by only 45% (P \u3c 0.05). Injection of cholesterol-loaded allogeneic J774 cells, but not syngeneic bone-marrow-derived macrophages, transiently induced SAA in C57BL/6 mice. Our study confirms reports that acute inflammation impairs steps in the RCT pathway and establishes that SAA plays only a minor role in this impairment

Impact of Individual Acute Phase Serum Amyloid A Isoforms on HDL Metabolism in Mice

The acute phase (AP) reactant serum amyloid A (SAA), an HDL apolipoprotein, exhibits pro-inflammatory activities, but its physiological function(s) are poorly understood. Functional differences between SAA1.1 and SAA2.1, the two major SAA isoforms, are unclear. Mice deficient in either isoform were used to investigate plasma isoform effects on HDL structure, composition, and apolipoprotein catabolism. Lack of either isoform did not affect the size of HDL, normally enlarged in the AP, and did not significantly change HDL composition. Plasma clearance rates of HDL apolipoproteins were determined using native HDL particles. The fractional clearance rates (FCRs) of apoA-I, apoA-II, and SAA were distinct, indicating that HDL is not cleared as intact particles. The FCRs of SAA1.1 and SAA2.1 in AP mice were similar, suggesting that the selective deposition of SAA1.1 in amyloid plaques is not associated with a difference in the rates of plasma clearance of the isoforms. Although the clearance rate of SAA was reduced in the absence of the HDL receptor, scavenger receptor class B type I (SR-BI), it remained significantly faster compared with that of apoA-I and apoA-II, indicating a relatively minor role of SR-BI in SAA’s rapid clearance. These studies enhance our understanding of SAA metabolism and SAA’s effects on AP-HDL composition and catabolism

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Children’s and adolescents’ rising animal-source food intakes in 1990–2018 were impacted by age, region, parental education and urbanicity

Animal-source foods (ASF) provide nutrition for children and adolescents’ physical and cognitive development. Here, we use data from the Global Dietary Database and Bayesian hierarchical models to quantify global, regional and national ASF intakes between 1990 and 2018 by age group across 185 countries, representing 93% of the world’s child population. Mean ASF intake was 1.9 servings per day, representing 16% of children consuming at least three daily servings. Intake was similar between boys and girls, but higher among urban children with educated parents. Consumption varied by age from 0.6 at <1 year to 2.5 servings per day at 15–19 years. Between 1990 and 2018, mean ASF intake increased by 0.5 servings per week, with increases in all regions except sub-Saharan Africa. In 2018, total ASF consumption was highest in Russia, Brazil, Mexico and Turkey, and lowest in Uganda, India, Kenya and Bangladesh. These findings can inform policy to address malnutrition through targeted ASF consumption programmes.publishedVersio

Incident type 2 diabetes attributable to suboptimal diet in 184 countries

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.8–14.4 million) incident T2D cases, representing 70.3% (68.8–71.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.0–27.1%)), excess refined rice and wheat intake (24.6% (22.3–27.2%)) and excess processed meat intake (20.3% (18.3–23.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.4–87.7%)) and Latin America and the Caribbean (81.8% (80.1–83.4%)); and lowest proportional burdens were in South Asia (55.4% (52.1–60.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally.publishedVersio