Tag: pubmed

ArXiv. 2023 Oct 31:arXiv:2310.20527v1. Preprint.

ABSTRACT

PURPOSE: To study the effect of proton linear energy transfer (LET) on rib fracture in breast cancer patients treated with pencil-beam scanning proton therapy (PBS) using a novel tool of dose-LET volume histogram (DLVH).

METHODS: From a prospective registry of patients treated with post-mastectomy proton therapy to the chest wall and regional lymph nodes for breast cancer between 2015 and 2020, we retrospectively identified rib fracture cases detected after completing treatment. Contemporaneously treated control patients that did not develop rib fracture were matched to patients 2:1 considering prescription dose, boost location, reconstruction status, laterality, chest wall thickness, and treatment year. The DLVH index, V(d, l), defined as volume(V) of the structure with at least dose(d) and LET(l), was calculated. DLVH plots between the fracture and control group were compared. Conditional logistic regression (CLR) model was used to establish the relation of V(d, l) and the observed fracture at each combination of d and l. The p-value derived from CLR model shows the statistical difference between fracture patients and the matched control group. Using the 2D p-value map, the DLVH features associated with the patient outcomes were extracted.

RESULTS: Seven rib fracture patients were identified, and fourteen matched patients were selected for the control group. The median time from the completion of proton therapy to rib fracture diagnosis was 12 months (range 5 to 14 months). Two patients had grade 2 symptomatic rib fracture while the remaining 5 were grade 1 incidentally detected on imaging. The derived p-value map demonstrated larger V(0-36 Gy[RBE], 4.0-5.0 keV/um) in patients experiencing fracture (p<0.1).

CONCLUSIONS: In breast cancer patients receiving PBS, a larger volume of chest wall receiving moderate dose and high LET may result in increased risk of rib fracture.

PMID:37961731 | PMC:PMC10635309

bioRxiv. 2023 Oct 27:2023.10.27.564319. doi: 10.1101/2023.10.27.564319. Preprint.

ABSTRACT

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with anincreased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson’s ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10 ^-60 ). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

PMID:37961722 | PMC:PMC10634875 | DOI:10.1101/2023.10.27.564319

Res Sq. 2023 Nov 1:rs.3.rs-3496738. doi: 10.21203/rs.3.rs-3496738/v1. Preprint.

ABSTRACT

Background: Recent decades have seen an exponential rise in global obesity prevalence, with rates nearly doubling in a span of forty years. A comprehensive knowledge base regarding the systemic effects of obesity is required to create new preventative and therapeutic agents effective at combating the current obesity epidemic. Previous studies of diet-induced obesity utilizing mouse models have demonstrated a difference in bodyweight gain by sex. In such studies, female mice gained significantly less weight than male mice when given the same high fat (HF) diet, indicating a resistance to diet-induced obesity. Research has also shown sex differences in gut microbiome composition between males and females, indicated to be in part a result of sex hormones. Understanding metabolic differences between sexes could assist in the development of new measures for obesity prevention and treatment. This study aimed to characterize sex differences in weight gain, plasma lipid profiles, fecal microbiota composition, and fecal short chain fatty acid levels. We hypothesized a role for the gut microbiome in these sex differences that would be normalized following microbiome depletion. Methods: A mouse model was used to study these effects. Mice were divided into treatment groups by sex, diet, and presence/absence of an antibiotic cocktail to deplete genera in the gut microbiome. We hypothesized that sex differences would be present both in bodyweight gain and systemic measures of obesity, including hormone and circulating free fatty acid levels. Results: We determined statistically significant differences for sex and/or treatment for the outcome measures. We confirm previous findings in which male mice gained significantly more weight than female mice fed the same high fat diet. However, sex differences persisted following antibiotic administration for microbiome depletion. Conclusions: We conclude that sex differences in the gut microbiome may contribute to sex differences in obesity, but they do not explain all of the differences.

PMID:37961721 | PMC:PMC10635401 | DOI:10.21203/rs.3.rs-3496738/v1

medRxiv. 2023 Oct 23:2023.10.23.23297392. doi: 10.1101/2023.10.23.23297392. Preprint.

ABSTRACT

BACKGROUND: Postpartum Depression (PPD) is a major health challenge with potentially devastating maternal and physical health outcomes. Development of diabetes mellitus has been hypothesized as one the potential adverse effects of PPD among mothers in the postpartum period but this association has not been adequately studied. This study aimed at determining prevalence of postpartum depression and its association with diabetes mellitus among mothers in Mbarara District, southwestern Uganda.

METHODS: This was a facility based cross sectional study of 309 mothers between 6 ^th week to 6 ^th month after childbirth. Using proportionate stratified consecutive sampling, mothers were enrolled from postnatal clinics of two health facilities, Mbarara Regional Referral Hospital and Bwizibwera Health center IV. PPD was diagnosed using the Mini-International Neuropsychiatric Interview (MINI 7.0.2) for the Diagnostic and Statistical Manual of Mental Disorders, 5 ^th Edition (DSM-5). Diabetes mellitus was diagnosed by measuring Hemoglobin A1c (HbA1c). Logistic regression was used to determine the association of PPD and diabetes mellitus among mothers.

RESULTS: The study established that PPD prevalence among mothers of 6 ^th weeks to 6 ^th months postpartum period in Mbarara was 40.5% (95% CI: 35.1-45.1%). A statistically significant association between postpartum depression and diabetes mellitus in mothers between 6 weeks and 6 months postpartum was established. The prevalence of diabetes mellitus among mothers with PPD was 28% compared to 13.6% among mothers without PPD Mothers with PPD had 3 times higher odds of being newly diagnosed with diabetes between 6 weeks and 6 months postpartum as compared to those without PPD during the same period (aOR=3.0, 95% CI: 1.62-5.74, p=0.001).

CONCLUSION AND RECOMMENDATIONS: Postpartum women within 6 ^th weeks to 6 ^th months have higher risks of developing diabetes mellitus. Research is needed to determine if targeted diabetes mellitus screening, prevention interventions and management will help reduce the burden.

PMID:37961709 | PMC:PMC10635159 | DOI:10.1101/2023.10.23.23297392

medRxiv. 2023 Oct 24:2023.10.23.23297398. doi: 10.1101/2023.10.23.23297398. Preprint.

ABSTRACT

BACKGROUND: CVD prediction models do not perform well in people with diabetes. We therefore aimed to identify novel predictors for six facets of CVD, (including coronary heart disease (CHD), Ischemic stroke, heart failure (HF), and atrial fibrillation (AF)) in people with T2DM.

METHODS: Analyses were conducted using the UK biobank and were stratified on history of CVD and of T2DM: 459,142 participants without diabetes or a history of CVD, 14,610 with diabetes but without CVD, and 4,432 with diabetes and a history of CVD. Replication was performed using a 20% hold-out set, ranking features on their permuted c-statistic.

RESULTS: Out of the 600+ candidate features, we identified a subset of replicated features, ranging between 32 for CHD in people with diabetes to 184 for CVD+HF+AF in people without diabetes. Classical CVD risk factors (e.g. parental or maternal history of heart disease, or blood pressure) were relatively highly ranked for people without diabetes. The top predictors in the people with diabetes without a CVD history included: cystatin C, self-reported health satisfaction, biochemical measures of ill health (e.g. plasma albumin). For people with diabetes and a history of CVD top features were: self-reported ill health, and blood cell counts measurements (e.g. red cell distribution width). We additionally identified risk factors unique to people with diabetes, consisting of information on dietary patterns, mental health and biochemistry measures. Consideration of these novel features improved risk classification, for example per 1000 people with diabetes 133 CVD and 165 HF cases appropriately received a higher risk.

CONCLUSION: Through data-driven feature selection we identified a substantial number of features relevant for prediction of cardiovascular risk in people with diabetes, the majority of which related to non-classical risk factors such as mental health, general illness markers, and kidney disease.

PMID:37961704 | PMC:PMC10635178 | DOI:10.1101/2023.10.23.23297398

bioRxiv. 2023 Oct 23:2023.10.22.563476. doi: 10.1101/2023.10.22.563476. Preprint.

ABSTRACT

Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.

PMID:37961684 | PMC:PMC10634715 | DOI:10.1101/2023.10.22.563476

bioRxiv. 2023 Nov 5:2023.11.03.565343. doi: 10.1101/2023.11.03.565343. Preprint.

ABSTRACT

Human induced pluripotent stem cell-derived sensory neurons (hiPSC-SNs) and human dorsal root ganglia (hDRG) neurons are popular tools in the field of pain research; however, few groups make use of both approaches. For screening and analgesic validation purposes, important characterizations can be determined of the similarities and differences between hDRG and hiPSC-SNs. This study focuses specifically on electrophysiology properties of hDRG in comparison to hiPSC-SNs. We also compared hDRG and hiPSC-SNs from both male and female donors to evaluate potential sex differences. We recorded neuronal size, rheobase, resting membrane potential, input resistance, and action potential waveform properties from 83 hiPSCs-SNs (2 donors) and 108 hDRG neurons (9 donors). We observed several statistically significant electrophysiological differences between hDRG and hiPSC-SNs, such as size, rheobase, input resistance, and several actional potential (AP) waveform properties. Correlation analysis also revealed many properties that were positively or negatively correlated, some of which were differentially correlated between hDRG and hiPSC-SNs. This study shows several differences between hDRG and hiPSC-SNs and allows better understanding of the advantages and disadvantages of both for use in pain research. We hope this study will be a valuable resource for pain researchers considering the use of these human in vitro systems for mechanistic studies and/or drug development projects.

PMID:37961669 | PMC:PMC10635102 | DOI:10.1101/2023.11.03.565343

Res Sq. 2023 Oct 27:rs.3.rs-3335211. doi: 10.21203/rs.3.rs-3335211/v1. Preprint.

ABSTRACT

Objective Pathological, age-related loss of muscle function, commonly referred to as sarcopenia, contributes to loss of mobility, impaired independence, as well as increased risk of adverse health events. Sarcopenia has been attributed to changes in both neural and muscular integrity during aging. Current treatment options are primarily limited to exercise and dietary protein fortification, but the therapeutic impact of these approaches are often inadequate. Prior work has suggested that a ketogenic diet (KD) might improve healthspan and lifespan in aging mice. Thus, we sought to investigate the effects of a KD on neuromuscular indices of sarcopenia in aged C57BL/6 mice.

DESIGN: A randomized, controlled pre-clinical experiment consisting of longitudinal assessments performed starting at 22-months of age (baseline) as well as 2, 6 and 10 weeks after the start of a KD vs. regular chow intervention.

SETTING: Preclinical laboratory study.

SAMPLE SIZE: Thirty-six 22-month-old mice were randomized into 2 dietary groups: KD [n = 22 (13 female and 9 male)], and regular chow [n = 15 (7 female and 8 male)].

MEASUREMENTS: Measures included body mass, hindlimb and all limb grip strength, rotarod for motor performance, plantarflexion muscle contractility, motor unit number estimations (MUNE), and repetitive nerve stimulation (RNS) as an index of neuromuscular junction transmission efficacy recorded from the gastrocnemius muscle. At end point, blood samples were collected to assess blood beta-hydroxybutyrate levels.

STATISTICAL ANALYSIS: Two-way ANOVA mixed-effects analysis (time x diet) were performed to analyze grip, rotarod, MUNE, and muscle contractility data. Results Beta-hydroxybutyrate (BHB) was significantly higher at 10 weeks in mice on a KD vs control group (0.83 ± 0.44 mmol/l versus 0.42 ± 0.21 mmol/l, η ² = 0.265, unpaired t-test, p = 0.0060). Mice on the KD intervention demonstrated significantly increased hindlimb grip strength (time x diet, p = 0.0030), all limb grip strength (time x diet, p = 0.0523), and rotarod latency to fall (time x diet, p = 0.0021). Mice treated with the KD intervention also demonstrated significantly greater MUNE (time x diet, p = 0.0064), but no difference in muscle contractility (time x diet, p = 0.5836) or RNS (time x diet, p = 0.9871). Conclusion KD intervention improved neuromuscular and motor function in aged mice. This pre-clinical work suggests that further research is needed to assess the efficacy and physiological effects of a KD on indices of sarcopenia.

PMID:37961656 | PMC:PMC10635299 | DOI:10.21203/rs.3.rs-3335211/v1

bioRxiv. 2023 Oct 30:2023.10.25.563971. doi: 10.1101/2023.10.25.563971. Preprint.

ABSTRACT

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

PMID:37961654 | PMC:PMC10634903 | DOI:10.1101/2023.10.25.563971

bioRxiv. 2023 Nov 3:2023.03.16.533008. doi: 10.1101/2023.03.16.533008. Preprint.

ABSTRACT

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference (1), we develop OASIS (Optimized Adaptive Statistic for Inferring Structure), a family of statistical tests for contingency tables. OASIS constructs a test-statistic which is linear in the normalized data matrix, providing closed form p-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finitesample bounds correctly characterize the test statistic’s p-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. The same method based on OASIS significance calls detects SARS-CoV-2 and Mycobacterium Tuberculosis strains de novo, which cannot be achieved with current approaches. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single cell RNA-sequencing, where under accepted noise models OASIS still provides good control of the false discovery rate, while Pearson’s X ² test consistently rejects the null. Additionally, we show on synthetic data that OASIS is more powerful than Pearson’s X ² test in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations.

SIGNIFICANCE STATEMENT: Contingency tables are pervasive across quantitative research and data-science applications. Existing statistical tests fall short, however; none provide robust, computationally efficient inference and control Type I error. In this work, motivated by a recent advance in reference-free inference for genomics, we propose a family of tests on contingency tables called OASIS. OASIS utilizes a linear test-statistic, enabling the computation of closed form p-value bounds, as well as a standard asymptotic normality result. OASIS provides a partitioning of the table for rejected hypotheses, lending interpretability to its rejection of the null. In genomic applications, OASIS performs reference-free and metadata-free variant detection in SARS-CoV-2 and M. Tuberculosis, and demonstrates robust performance for single cell RNA-sequencing, all tasks without existing solutions.

PMID:37961606 | PMC:PMC10634974 | DOI:10.1101/2023.03.16.533008