Hypothalamic MRI-derived microstructure is associated with neurocognitive aging in humans.

The hypothalamus regulates homeostasis across the lifespan and is emerging as a regulator of aging. In murine models, aging-related changes in the hypothalamus, including microinflammation and gliosis, promote accelerated neurocognitive decline. We investigated relationships between hypothalamic microstructure and features of neurocognitive aging, including cortical thickness and cognition, in a cohort of community-dwelling older adults (age range 65-97 years, n=124). Hypothalamic microstructure was evaluated with two magnetic resonance imaging diffusion metrics: mean diffusivity (MD) and fractional anisotropy (FA), using a novel image processing pipeline. Hypothalamic MD was cross-sectionally positively associated with age and it was negatively associated with cortical thickness. Hypothalamic FA, independent of cortical thickness, was cross-sectionally positively associated with neurocognitive scores. An exploratory analysis of longitudinal neurocognitive performance suggested that lower hypothalamic FA may predict cognitive decline. No associations between hypothalamic MD, age, and cortical thickness were identified in a younger control cohort (age range 18-63 years, n=99). To our knowledge, this is the first study to demonstrate that hypothalamic microstructure is associated with features of neurocognitive aging in humans.

Reduced healthcare access contributes to delay of care in endometrial cancer.

OBJECTIVE: We aimed to characterize delays to care in patients with endometrioid endometrial cancer and the role healthcare access plays in these delays. METHODS: A chart review was performed of patients with endometrioid endometrial cancer who presented with postmenopausal bleeding at a diverse, urban medical center between 2006 and 2018. The time from symptom onset to treatment was abstracted from the medical record. This interval was subdivided to assess for delay to presentation, delay to diagnosis, and delay to treatment. RESULTS: We identified 484 patients who met the inclusion criteria. The median time from symptom onset to treatment was 4 months with an interquartile range of 2 to 8 months. Most patients had stage I disease at diagnosis (88.6%). There was no significant difference in race/ethnicity or disease stage at time of diagnosis between different groups. Patients who had not seen a primary care physician or general obstetrician-gynecologist in the year before symptom onset were more likely to have significantly delayed care (27.7% vs 14.3%, p = 0.02) and extrauterine disease (20.2% vs 4.9%, p < 0.01) compared to those with established care. Black and Hispanic patients were more likely to experience significant delays from initial biopsy to diagnosis. CONCLUSIONS: Delays exist in the evaluation of endometrial cancer. This delay is most pronounced in patients without an established outpatient primary care provider or obstetrician-gynecologist.

Age of first exposure to soccer heading: Associations with cognitive, clinical, and imaging outcomes in the Einstein Soccer Study.

Introduction: The objective of this study is to assess the role of age at first exposure (AFE) to soccer heading as a predictor of known adverse associations of recent and longer-term heading with brain microstructure, cognitive, and behavioral features among adult amateur soccer players. Methods: The sample included 276 active amateur soccer players (196 male and 81 female) aged 18-53 years old. AFE to soccer heading was treated as a binary variable, dichotomized at ≤ 10 years vs. >10 years old, based on a recently promulgated US Soccer policy, which bans heading for athletes ages 10 and under. Results: We found that soccer players who began heading at age 10 or younger performed better on tests of working memory (p = 0.03) and verbal learning (p = 0.02), while accounting for duration of heading exposure, education, sex, and verbal intelligence. No difference in brain microstructure or behavioral measures was observed between the two exposure groups. Discussion: The findings indicate that, among adult amateur soccer players, AFE to heading before age 10 compared to later start of heading, is not associated with adverse outcomes, and may be associated with better cognitive performance in young adulthood. Cumulative heading exposure across the lifespan, rather than early life exposure, may drive risk for adverse effects and should be the focus of future longitudinal studies to inform approaches to enhance player safety.

Low molecular weight serum cell-free DNA concentration is associated with clinicopathologic indices of poor prognosis in women with uterine cancer.

BACKGROUND: Serum cell-free DNA (cfDNA) holds promise as a non-invasive cancer biomarker. The objective of this study was to evaluate the association of cfDNA concentration with clinicopathologic variables of poor prognosis and overall survival among women with uterine cancer compared to benign cancer-free controls. METHODS: cfDNA was extracted from the serum of 91 women with multiple uterine cancer histologies and 22 post-menopausal controls without cancer. Low molecular weight (LMW) cfDNA was separated from contaminating genomic high molecular weight cfDNA using paramagnetic bead purification and its concentration was measured using fluorometric quantification. Clinicopathologic data was abstracted from the electronic medical record. The association between serum cfDNA concentration, clinicopathologic variables, and overall survival was assessed using linear regression modelling, Cox proportional hazards modelling, and the Kaplan-Meier method. RESULTS: Median total serum cfDNA concentration for the cohort was 69.2 ng/mL (IQR 37.4, 132.3) and median LMW cfDNA concentration was 23.8 ng/mL (IQR 14.9, 44.4). There were no significant differences in total serum cfDNA concentration with any clinicopathologic variables. However, LMW cfDNA concentration was significantly higher in serum of women with cancer (25.8 ng/mL IQR 16.0, 49.6) compared to benign controls (15.5 ng/mL IQR 9.3, 25.8 ng/mL) (p < 0.01). It is also significantly higher among women with early stage cancer than benign controls (p < 0.01). There were also significant associations between LMW cfDNA concentration and stage of cancer (p = 0.01) and histology (p = 0.02). Patients with leiomyosarcoma and carcinosarcoma had higher cfDNA concentrations than those with endometrioid cancer. Over a median follow-up of 51.9 months, 75th percentile for overall survival for women with cancer was 24.0 months. Higher LMW cfDNA concentrations is associated with lower survival among women with cancer (p < 0.01). CONCLUSIONS: Serum LMW cfDNA concentration is associated with overall survival in women with uterine cancer, and it is higher among women with uterine cancer compared to those of controls.

Detecting, quantifying, and discriminating the mechanism of mosaic chromosomal aneuploidies using MAD-seq.

Current approaches to detect and characterize mosaic chromosomal aneuploidy are limited by sensitivity, efficiency, cost, or the need to culture cells. We describe the mosaic aneuploidy detection by massively parallel sequencing (MAD-seq) capture assay and the MADSEQ analytical approach that allow low (<10%) levels of mosaicism for chromosomal aneuploidy or regional loss of heterozygosity to be detected, assigned to a meiotic or mitotic origin, and quantified as a proportion of the cells in the sample. We show results from a multi-ethnic MAD-seq (meMAD-seq) capture design that works equally well in populations of diverse racial and ethnic origins and how the MADSEQ analytical approach can be applied to exome or whole-genome sequencing data, revealing previously unrecognized aneuploidy or copy number neutral loss of heterozygosity in samples studied by the 1000 Genomes Project, cell lines from public repositories, and one of the Illumina Platinum Genomes samples. We have made the meMAD-seq capture design and MADSEQ analytical software open for unrestricted use, with the goal that they can be applied in clinical samples to allow new insights into the unrecognized prevalence of mosaic chromosomal aneuploidy in humans and its phenotypic associations.

Nanoparticle-Encapsulated Doxorubicin Demonstrates Superior Tumor Cell Kill in Triple Negative Breast Cancer Subtypes Intrinsically Resistant to Doxorubicin.

The effect of size and release kinetics of doxorubicin-nanoparticles on anti-tumor efficacy was evaluated in a panel of human cancer cell lines, including triple-negative breast cancer (TNBC) cells that frequently demonstrate resistance to doxorubicin. Different nano-formulations of sol-gel-based Doxorubicin containing nanoparticles were synthesized. Increased cell kill in chemoreffactory triple-negative breast cancer cells was associated with the smallest size of nanoparticles and the slowest release of Dox. Modeling of dose-response parameters in Dox-sensitive versus Dox-resistant lines demonstrated increased EMax and area under the curve in Dox-resistant mesenchymal TNBC cells, implying potentially favorable activity in this molecular subtype of breast cancer. Mesenchymal TNBC cells demonstrated a high rate of fluorescent bead uptake suggestive of increased endocytosis, which may partially account for the enhanced efficacy of Dox-np in this subtype. Thus, manipulation of size and release kinetics of this nanoparticle platform is associated with enhanced dose-response metrics and tumor cell kill in therapeutically recalcitrant TNBC cell models. This platform is easily customizable and warrants further exploration.

Genotype copy number variations using Gaussian mixture models: theory and algorithms.

Copy number variations (CNVs) are important in the disease association studies and are usually targeted by most recent microarray platforms developed for GWAS studies. However, the probes targeting the same CNV regions could vary greatly in performance, with some of the probes carrying little information more than pure noise. In this paper, we investigate how to best combine measurements of multiple probes to estimate copy numbers of individuals under the framework of Gaussian mixture model (GMM). First we show that under two regularity conditions and assume all the parameters except the mixing proportions are known, optimal weights can be obtained so that the univariate GMM based on the weighted average gives the exactly the same classification as the multivariate GMM does. We then developed an algorithm that iteratively estimates the parameters and obtains the optimal weights, and uses them for classification. The algorithm performs well on simulation data and two sets of real data, which shows clear advantage over classification based on the equal weighted average.

Detecting multiple causal rare variants in exome sequence data.

Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants. Weaknesses of the exome sequence data and the relative strengths and weaknesses of the five approaches are discussed.

Computing power and sample size for case-control association studies with copy number polymorphism: application of mixture-based likelihood ratio test.

Recent studies suggest that copy number polymorphisms (CNPs) may play an important role in disease susceptibility and onset. Currently, the detection of CNPs mainly depends on microarray technology. For case-control studies, conventionally, subjects are assigned to a specific CNP category based on the continuous quantitative measure produced by microarray experiments, and cases and controls are then compared using a chi-square test of independence. The purpose of this work is to specify the likelihood ratio test statistic (LRTS) for case-control sampling design based on the underlying continuous quantitative measurement, and to assess its power and relative efficiency (as compared to the chi-square test of independence on CNP counts). The sample size and power formulas of both methods are given. For the latter, the CNPs are classified using the Bayesian classification rule. The LRTS is more powerful than this chi-square test for the alternatives considered, especially alternatives in which the at-risk CNP categories have low frequencies. An example of the application of the LRTS is given for a comparison of CNP distributions in individuals of Caucasian or Taiwanese ethnicity, where the LRTS appears to be more powerful than the chi-square test, possibly due to misclassification of the most common CNP category into a less common category.

A gene-model-free method for linkage analysis of a disease-related-trait based on analysis of proband/sibling pairs.

In this paper we investigate the power of finding linkage to a disease locus through analysis of the disease-related traits. We propose two family-based gene-model-free linkage statistics. Both involve considering the distribution of the number of alleles identical by descent with the proband and comparing siblings with the disease-related trait to those without the disease-related-trait. The objective is to find linkages to disease-related traits that are pleiotropic for both the disease and the disease-related-traits. The power of these statistics is investigated for Kofendrerd Personality Disorder-related traits a (Joining/founding cults) and trait b (Fear/discomfort with strangers) of the simulated data. The answers were known prior to the execution of the reported analyses. We find that both tests have very high power when applied to the samples created by combining the data of the three cities for which we have nuclear family data.

Using mixture models to characterize disease-related traits.

We consider 12 event-related potentials and one electroencephalogram measure as disease-related traits to compare alcohol-dependent individuals (cases) to unaffected individuals (controls). We use two approaches: 1) two-way analysis of variance (with sex and alcohol dependency as the factors), and 2) likelihood ratio tests comparing sex adjusted values of cases to controls assuming that within each group the trait has a 2 (or 3) component normal mixture distribution. In the second approach, we test the null hypothesis that the parameters of the mixtures are equal for the cases and controls. Based on the two-way analysis of variance, we find 1) males have significantly (p < 0.05) lower mean response values than females for 7 of these traits. 2) Alcohol-dependent cases have significantly lower mean response than controls for 3 traits. The mixture analysis of sex-adjusted values of 1 of these traits, the event-related potential obtained at the parietal midline channel (ttth4), found the appearance of a 3-component normal mixture in cases and controls. The mixtures differed in that the cases had significantly lower mean values than controls and significantly different mixing proportions in 2 of the 3 components. Implications of this study are: 1) Sex needs to be taken into account when studying risk factors for alcohol dependency to prevent finding a spurious association between alcohol dependency and the risk factor. 2) Mixture analysis indicates that for the event-related potential "ttth4", the difference observed reflects strong evidence of heterogeneity of response in both the cases and controls.

Data mining and computationally intensive methods: summary of Group 7 contributions to Genetic Analysis Workshop 13.

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations.

A method for evaluating the results of Bayesian model selection: application to linkage analyses of attributes determined by two or more genes.

OBJECTIVES: We apply and evaluate the intrinsic Bayes factor (IBF) of Berger and Pericchi [J Am Stat Assoc 1996;91:109-122; Bayesian Statistics, Oxford University Press, vol 5, 1996] to linkage analyses done using the stochastic search variable selection (SSVS) method of George and McCulloch [J Am Stat Assoc 1993;88:881-889] as proposed by Suh et al. [Genet Epidemiol 2001;21(suppl 1):S706-S711]. METHODS: We consider 20 simulations of linkage data obtained under two different generating models. The SSVS is applied to a multiple regression extension [Genet Epidemiol 2001;21(suppl 1): S706-S711] of the Haseman-Elston [Behav Genet 1972;2:3-19; Genet Epidemiol 2000;19:1-17] methods. Four prior distributions are considered. We apply the IBF criterion to those samples where different prior distributions result in different top models. RESULTS: In those samples where three different models were obtained using the four priors, application of the IBFs eliminated one of the two wrong models in 4 out of 5 situations. Further elimination using the IBF criterion for situations with two different subsets did not serve as well. CONCLUSIONS: When different priors result in three or more different subsets of markers, one can use the IBF to get this number down to two for consideration. When two subsets result we recommend that both be considered.

Locating disease genes using Bayesian variable selection with the Haseman-Elston method.

BACKGROUND: We applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models. RESULTS: In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers) according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results. CONCLUSIONS: We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.