Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain ß-amyloid pathology.

BACKGROUND: Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer's disease (AD). METHODS: We examined plasma Aß42, Aß40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain ß-amyloid (Aß) pathology. RESULTS: Our computational models accurately predict brain Aß positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aß positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories. CONCLUSIONS: The innovative integrated models provide opportunity to assess Aß pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.

The numbers of people with Alzheimer's disease are increasing. People with Alzheimer's disease have changes in the brain as well as cognitive impairment, which is when a person has difficulty remembering, learning, concentrating, or making decisions. Innovative medicines and new treatments all target people with early Alzheimer's disease. However, the methods used currently to diagnose Alzheimer's disease are expensive and can be unpleasant for patients. We studied Chinese people with no cognitive impairment, some cognitive decline, mild cognitive impairment, Alzheimer's disease and non-Alzheimer's disease dementia. We established a computational model that can predict the changes seen in the brain in people with Alzheimer's disease from information including results of blood and memory tests. This non-invasive and cost-effective approach might improve early identification of those with Alzheimer's disease.

Influence of CYP3A5, IL-10 polymorphisms and metabolism rate on tacrolimus exposure in renal post-transplant recipients.

Aim: To investigate the influence of CYP3A5 and IL-10 polymorphisms on tarcolimus metabolism and renal function for renal transplantation recipients at a stable period. Methods: CYP3A5 and IL-10 polymorphisms, together with other clinical factors, were collected for 149 renal transplantation patients at postoperative stable period. Statistics analysis was performed to explore key factors affecting tarcolimus metabolism. Results: CYP3A5 6986A >G and IL-10 -819C >T significantly impacted tacrolimus metabolism (p < 0.001). CYP3A5 6986A >G G allele and IL-10 -819C >T T allele were associated with poorer tacrolimus metabolic capability. Patients with various tacrolimus metabolism rates presented little difference in renal functions at stable period. Conclusion: Genotyping of CYP3A5 and IL-10 might benefit the precision dosage of tacrolimus for renal transplantation recipients.

Little Change in Functional Brain Networks Following Acute Levodopa in Drug-Naïve Parkinson's Disease.

OBJECTIVE: The objective of this study was to investigate the effects of levodopa on functional brain networks in Parkinson's disease. METHODS: We acquired resting state functional magnetic resonance imaging in 30 drug-naïve participants with Parkinson's disease and 20 age-matched healthy controls. Each participant was studied following administration of a single oral dose of either levodopa or placebo in a randomized, double-blind, crossover design. RESULTS: The greatest observed differences in functional connectivity were between Parkinson's disease versus control participants, independent of pharmacologic intervention. By contrast, the effects of levodopa were much smaller and detectable only in the Parkinson's disease group. Moreover, although levodopa administration in the Parkinson's disease group measurably improved motor performance, it did not increase the similarity of functional connectivity in Parkinson's disease to the control group. CONCLUSIONS: We found that a single, small dose of levodopa did not normalize functional connectivity in drug-naïve Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

New statistical method identifies cytokines that distinguish stool microbiomes.

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website.

Detecting associations between intact connectomes and clinical covariates using recursive partitioning object-oriented data analysis.

Many neuroscientists are interested in how connectomes (graphical representations of functional connectivity between areas of the brain) change in relation to covariates. In statistics, changes like this are analyzed using regression, where the outcomes or dependent variables are regressed onto the covariates. However, when the outcome is a complex object, such as connectome graphs, classical regression models cannot be used. The regression approach developed here to work with complex graph outcomes combines recursive partitioning with the Gibbs distribution. We will only discuss the application to connectomes, but the method is generally applicable to any graphical outcome. The method, called Gibbs-RPart, partitions the covariate space into a set of nonoverlapping regions such that the connectomes within regions are more similar than they are to the connectomes in other regions. This paper extends the object-oriented data analysis paradigm for graph-valued data based on the Gibbs distribution, which we have applied previously to hypothesis testing to compare populations of connectomes from distinct groups (see the work of La Rosa et al).

Breath carbonyl compounds as biomarkers of lung cancer.

OBJECTIVE: Lung cancer dysregulations impart oxidative stress which results in important metabolic products in the form of volatile organic compounds (VOCs) in exhaled breath. The objective of this work is to use statistical classification models to determine specific carbonyl VOCs in exhaled breath as biomarkers for detection of lung cancer. MATERIALS AND METHODS: Exhaled breath samples from 85 patients with untreated lung cancer, 34 patients with benign pulmonary nodules and 85 healthy controls were collected. Carbonyl compounds in exhaled breath were captured by silicon microreactors and analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The concentrations of carbonyl compounds were analyzed using a variety of statistical classification models to determine which compounds best differentiated between the patient sub-populations. Predictive accuracy of each of the models was assessed on a separate test data set. RESULTS: Six carbonyl compounds (C(4)H(8)O, C(5)H(10)O, C(2)H(4)O(2), C(4)H(8)O(2), C(6)H(10)O(2), C(9)H(16)O(2)) had significantly elevated concentrations in lung cancer patients vs. CONTROLS: A model based on counting the number of elevated compounds out of these six achieved an overall classification accuracy on the test data of 97% (95% CI 92%-100%), 95% (95% CI 88%-100%), and 89% (95% CI 79%-99%) for classifying lung cancer patients vs. non-smokers, current smokers, and patients with benign nodules, respectively. These results were comparable to benchmarking based on established statistical and machine-learning methods. The sensitivity in each case was 96% or higher, with specificity ranging from 64% for benign nodule patients to 86% for smokers and 100% for non-smokers. CONCLUSION: A model based on elevated levels of the six carbonyl VOCs effectively discriminates lung cancer patients from healthy controls as well as patients with benign pulmonary nodules.

Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data.

BACKGROUND: In this study, we empirically evaluated the consistency and accuracy of five different methods to detect differentially expressed genes (DEGs) based on microarray data. METHODS: Five different methods were compared, including the t-test, significance analysis of microarrays (SAM), the empirical Bayes t-test (eBayes), t-tests relative to a threshold (TREAT), and assumption adequacy averaging (AAA). The percentage of overlapping genes (POG) and the percentage of overlapping genes related (POGR) scores were used to rank the different methods on their ability to maintain a consistent list of DEGs both within the same data set and across two different data sets concerning the same disease. The power of each method was evaluated based on a simulation approach which mimics the multivariate distribution of the original microarray data. RESULTS: For smaller sample sizes (6 or less per group), moderated versions of the t-test (SAM, eBayes, and TREAT) were superior in terms of both power and consistency relative to the t-test and AAA, with TREAT having the highest consistency in each scenario. Differences in consistency were most pronounced for comparisons between two different data sets for the same disease. For larger sample sizes AAA had the highest power for detecting small effect sizes, while TREAT had the lowest. DISCUSSION: For smaller sample sizes moderated versions of the t-test can generally be recommended, while for larger sample sizes selection of a method to detect DEGs may involve a compromise between consistency and power.

Retrospective review of intrauterine device in adolescent and young women.

STUDY OBJECTIVE: To examine our experience with intrauterine device (IUD) use in adolescents and young women. DESIGN: Retrospective descriptive study evaluating outcomes after IUD insertion for patients 21 years or less over an 8-year period. SETTING: Three sites including a Pediatric and Adolescent gynecology private practice, a Title X clinic, and community based, grant funded clinic serving a high risk teen population. PARTICIPANTS: Females from menarche to age 21. MAIN OUTCOMES MEASURED: The probability of IUD retention, differences in IUD retention probabilities between two age groups, and risk factors for IUD removal, expulsion, and infection were evaluated. RESULTS: 233 records showed 50% of the <18-year-old age group and 71.5% of the 18-21-year-old group had their IUD in place at 5 years. Age was found to be a significant factor for removal (P < 0.001), with under 18-year-olds at greater risk of removal/expulsion (hazard ratio (HR) = 2.85). Parity (RR = 5.6 for nulliparous vs multiparous patients, P < 0.001) and prior STI (RR = 5.5, P < 0.001) were significant risk factors for infection. Nulliparous patients were at higher risk of expulsion (P = 0.045), though age was not a statistically significant risk factor. CONCLUSIONS: The rate of continuation was lower in adolescents under 18 compared to 18-21-year-olds, but was still higher than for other hormonal contraceptives. Despite this groups' high risk for STI the IUD did not increase the risk of infection and may offer some degree of protection. IUDs appear to be a safe option in young adolescents (<18 years old) and nulliparous women.