Category: Statistics

SSM Popul Health. 2024 Oct 11;28:101716. doi: 10.1016/j.ssmph.2024.101716. eCollection 2024 Dec.

ABSTRACT

BACKGROUND: The relationship between education and cardiometabolic biomarkers is contextually dependent on both inter-country and intra-country factors. This study aimed to examine educational differences in cardiometabolic biomarkers among middle-aged and older adults in the US, Mexico, China, and India, and whether this relationship is modified by urbanicity of residence.

METHODS: Data were from contemporary cross-sectional waves of the US Health and Retirement Study (HRS; 2016/17, n = 19,608), the Mexican Health and Aging Study (MHAS; 2015, n = 12,356), the China Health and Retirement Longitudinal Study (CHARLS; 2015/16, n = 13,268), and the Longitudinal Aging Study in India (LASI; 2017/19, n = 47,838). To account for substantial variations in educational distribution across the four countries, we measured education attainment in two ways: by categorizing education levels into binary classifications (‘lower education: lower secondary education or below’ vs. ‘higher education: upper secondary education or above’) to assess absolute education attainment, and by using within-country percentile ranks to capture relative education attainment. We assessed educational differences in four cardiometabolic biomarkers: body mass index (BMI), systolic blood pressure (SBP), glycated haemoglobin (HbA1c), and total cholesterol. We tested whether urbanicity of residence modified the relationship between education and these cardiometabolic biomarkers.

RESULTS: The proportion of individuals with higher education was 82.6% in the US, 15.6% in Mexico, 10.6% in China, and 16.8% in India. In the US, higher education was associated with lower SBP (-2.74 mmHg, 95% CI: -3.62, -1.86) and HbA1c (-0.14%, 95% CI: -0.20, -0.08), but higher total cholesterol (3.33 mg/dL, 95% CI: 1.41, 5.25). In Mexico, higher education was associated with lower BMI only (-0.51 kg/m², 95% CI: -0.76, -0.26). In China, higher education was not associated with any biomarker. In India, higher education was associated with higher BMI (1.61 kg/m², 95% CI: 1.49, 1.73), SBP (1.67 mmHg, 95% CI: 1.16, 2.18), and HbA1c (0.35%, 95% CI: 0.19, 0.51). The association between education and cardiometabolic biomarkers was modified by urbanicity in China and India but not in the US or Mexico. In both China and India, relationships between education and cardiometabolic biomarkers were stronger among rural residents compared to those among urban residents. Results based on relative education attainment showed similar patterns in terms of the direction of the effect estimates, despite some discrepancies in statistical significance.

INTERPRETATION: There is a complex relationship between education and cardiometabolic biomarkers across countries and by urbanicity of residence. This complexity underscores the importance of accounting for contextual factors when devising strategies to enhance cardiometabolic health in various settings.

PMID:39484632 | PMC:PMC11525230 | DOI:10.1016/j.ssmph.2024.101716

Int J Hepatol. 2024 Oct 24;2024:3879328. doi: 10.1155/2024/3879328. eCollection 2024.

ABSTRACT

Background and aims: Focal liver lesions (FLL) are one of the most common indications for hepatology and hepatobiliary surgery consultation. In this retrospective study, we aim to assess if contrast-enhanced ultrasound (CEUS) can address diagnostic dilemmas in the evaluation of indeterminate liver lesions by identifying characteristics of indeterminate FLL on CEUS and correlating these with cross-sectional imaging and pathology findings. Methods: We retrospectively reviewed all patients who underwent CEUS evaluation for liver lesions over a 28-month period (Oct 2020 to Jan 2023) at the University of Kentucky. To assess the relationship between CEUS results and the corresponding CT, MRI, and/or pathologic findings, the McNemar-Bowker tests were performed. Results: Twenty-nine patients were included (after two exclusions from a total n of 31). Mean age was 54 years, 62% were female, and 48% had underlying cirrhosis. Of the 29 patients with initial cross-sectional imaging, the initial results showed malignancy or likely malignant lesion in 6 patients and benign or likely benign lesion in 6 patients. The remaining 17 patients had inconclusive/indeterminate results. CEUS clarified an “indeterminate” CT/MRI result 15 times out of 17 (88.2%), moving the diagnosis to “benign” 11 times while suggesting “malignant” only four times. When aggregating indeterminate cross-sectional results with either benign or malignant categories suggested by CEUS, CEUS never reversed a benign CT/MRI diagnosis but often reversed a malignant CT/MRI diagnosis. Conclusion: CEUS provided a definitive diagnosis of indeterminate liver lesions in approximately 90% of patients and avoided the need for biopsy in most patients. In cases where the liver lesions were biopsied, CEUS accurately distinguished malignant versus benign lesions as confirmed by biopsy findings. CEUS, therefore, has the potential to provide a precise diagnosis for the majority of indeterminate lesions.

PMID:39484627 | PMC:PMC11527524 | DOI:10.1155/2024/3879328

bioRxiv [Preprint]. 2024 Oct 21:2024.10.17.618550. doi: 10.1101/2024.10.17.618550.

ABSTRACT

Functional Positron Emission Tomography (fPET) with (bolus plus) constant infusion of [18F]-fluorodeoxyglucose FDG), known as fPET-FDG, is a recently introduced technique in human neuroimaging, enabling the detection of dynamic glucose metabolism changes within a single scan. However, the statistical analysis of fPET-FDG data remains challenging because its signal and noise characteristics differ from both classic bolus-administration FDG PET and from functional Magnetic Resonance Imaging (fMRI), which together compose the primary sources of inspiration for analytical methods used by fPET-FDG researchers. In this study, we present an investigate of how inaccuracies in modeling baseline FDG uptake can introduce artifactual patterns to detrended TAC residuals, potentially introducing spurious (de)activations to general linear model (GLM) analyses. By combining simulations and empirical data from both constant infusion and bolus-plus-constant infusion protocols, we evaluate the effects of various baseline modeling methods, including polynomial detrending, regression against the global mean time-activity curve, and two analytical methods based on tissue compartment model kinetics. Our findings indicate that improper baseline removal can introduce statistically significant artifactual effects, although these effects characterized in this study (~2-8%) are generally smaller than those reported by previous literature employing robust sensory stimulation (~10-30%). We discuss potential strategies to mitigate this issue, including informed baseline modeling, optimized tracer administration protocols, and careful experimental design. These insights aim to enhance the reliability of fPET-FDG in capturing true metabolic dynamics in neuroimaging research.

PMID:39484579 | PMC:PMC11526866 | DOI:10.1101/2024.10.17.618550

bioRxiv [Preprint]. 2024 Oct 26:2024.10.24.619733. doi: 10.1101/2024.10.24.619733.

ABSTRACT

BACKGROUND: Schizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, and persistent neurobiological abnormalities. Sex-related differences in clinical presentation imply separate brain substrates. The present study characterized deep brain morphology using shape features to understand the independent effects of diagnosis and sex on the brain, and to determine whether the neurobiology of schizophrenia varies as a function of sex.

METHODS: This study analyzed multi-site archival data from 1,871 male (M) and 955 female (F) participants with SCZ, and 2,158 male and 1,877 female healthy controls (CON) from twenty-three cross-sectional samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each site’s data for seven deep brain regions obtained from T1-weighted structural MRI scans. Effect sizes were calculated for the following main contrasts: 1) Sex effects;2) Diagnosis-by-Sex interaction; 3) within sex tests of diagnosis; 4) within diagnosis tests of sex differences. Meta-regression models between brain structure and clinical variables were also computed separately in men and women with schizophrenia.

RESULTS: Mass univariate meta-analyses revealed more concave-than-convex shape differences in all regions for women relative to men, across diagnostic groups ( d = -0.35 to 0.20, SE = 0.02 to 0.07); there were no significant diagnosis-by-sex interaction effects. Within men and women separately, we identified more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum in SCZ ( d = -0.30 to 0.30, SE = 0.03 to 0.10). Within CON and SZ separately, we found more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate ( d = -0.30 to 0.20, SE = 0.03 to 0.09). Meta-regression models revealed similarly small, but significant relationships, with medication and positive symptoms in both SCZ-M and SCZ-F.

CONCLUSIONS: Sex-specific variation is an overriding feature of deep brain shape regardless of disease status, underscoring persistent patterns of sex differences observed both within and across diagnostic categories, and highlighting the importance of including it as a critical variable in studies of neurobiology. Future work should continue to explore these dimensions independently to determine whether these patterns of brain morphology extend to other aspects of neurobiology in schizophrenia, potentially uncovering broader implications for diagnosis and treatment.

KEY POINTS: Statistical analyses revealed significant main effects for diagnosis and sex in deep brain shape morphology. Among patients with schizophrenia, there was a pattern of thinning and surface contraction in the bilateral hippocampus, amygdala, accumbens, and thalamus, and a pattern of significant thickening and surface expansion in the bilateral putamen and pallidum compared to healthy control participants. Between males and females, there was a pattern of significant thinning and surface contraction in the bilateral thalamus, pallidum, putamen, and amygdala in females compared to males.There was no significant interaction between diagnosis and biological sex, suggesting that sex differences in deep brain shape and asymmetry among patients with schizophrenia reflect those observed in healthy individuals.Small but statistically significant relationships exist between brain structure and clinical correlates of schizophrenia were similar for both men and women with the disease, such that higher CPZ was associated with shape-derived thinning and surface contraction in the caudate, accumbens, hippocampus, amygdala, and thalamus, and elevated positive symptoms were associated with shape-derived thinning and surface contraction in the bilateral caudate, right hippocampus, and right amygdala.

PMID:39484539 | PMC:PMC11526939 | DOI:10.1101/2024.10.24.619733

bioRxiv [Preprint]. 2024 Oct 26:2024.10.25.619982. doi: 10.1101/2024.10.25.619982.

ABSTRACT

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (200-300 V/m). We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be practical.

PMID:39484517 | PMC:PMC11527135 | DOI:10.1101/2024.10.25.619982

bioRxiv [Preprint]. 2024 Oct 22:2024.10.21.619532. doi: 10.1101/2024.10.21.619532.

ABSTRACT

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the “rating widget” – a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

PMID:39484445 | PMC:PMC11526949 | DOI:10.1101/2024.10.21.619532

bioRxiv [Preprint]. 2024 Oct 23:2024.10.23.619767. doi: 10.1101/2024.10.23.619767.

ABSTRACT

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets – containing association data for all variants in the analysis – are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

PMID:39484403 | PMC:PMC11526975 | DOI:10.1101/2024.10.23.619767

bioRxiv [Preprint]. 2024 Oct 22:2024.10.19.619237. doi: 10.1101/2024.10.19.619237.

ABSTRACT

The structural connections of the brain’s white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.g., cognitive scores). Our approach analyzes tissue microstructure measures based on quantile-specific bundle regions . These regions are defined according to the quantiles of fractional anisotropy (FA) from the periphery to the core of a population fiber bundle, which pools all individuals’ bundles. To investigate how fiber bundle tissue microstructure relates to covariates of interest, we employ the statistical technique of quantile regression. Unlike ordinary regression, which only models a conditional mean, quantile regression models the conditional quantiles of a response variable. This enables the proposed analysis, where a quantile regression is fitted for each quantile-specific bundle region. To demonstrate FMQ Regression, we perform an illustrative study in a large healthy young adult tractography dataset derived from the Human Connectome Project-Young Adult (HCP-YA), focusing on particular bundles expected to relate to particular aspects of cognition and motor function. Importantly, our analysis considers sex-specific effects in brain-behavior associations. In comparison with a traditional method, Automated Fiber Quantification (AFQ), which enables FA analysis in regions defined along the trajectory of a bundle, our results suggest that FMQ Regression is much more powerful for detecting brain-behavior associations. Importantly, FMQ Regression finds significant brain-behavior associations in multiple bundles, including findings unique to males or to females. In both males and females, language performance is significantly associated with FA in the left arcuate fasciculus, with stronger associations in the bundle’s periphery. In males only, memory performance is significantly associated with FA in the left uncinate fasciculus, particularly in intermediate regions of the bundle. In females only, motor performance is significantly associated with FA in the left and right corticospinal tracts, with a slightly lower relationship at the bundle periphery and a slightly higher relationship toward the bundle core. No significant relationships are found between executive function and cingulum bundle FA. Our study demonstrates that FMQ Regression is a powerful statistical approach that can provide insight into associations from bundle periphery to bundle core. Our results also identify several brain-behavior relationships unique to males or to females, highlighting the importance of considering sex differences in future research.

PMID:39484397 | PMC:PMC11526951 | DOI:10.1101/2024.10.19.619237

bioRxiv [Preprint]. 2024 Oct 21:2024.10.17.618945. doi: 10.1101/2024.10.17.618945.

ABSTRACT

Optimizing prevention and early detection of cancer requires understanding the number, types and timing of driver mutations. To quantify this, we exploited the elevated cancer incidence and mutation rates in germline BRCA1 and BRCA2 (gBRCA1/2) carriers. Using novel statistical models, we identify genomic deletions as the likely rate-limiting mutational processes, with 1-3 deletions required to initiate breast and ovarian tumors. gBRCA1/2 -driven hereditary and sporadic tumors undergo convergent evolution to develop a similar set of driver deletions, and deletions explain the elevated cancer risk of gBRCA1/2 -carriers. Orthogonal mutation timing analysis identifies deletions of chromosome 17 and 13q as early, recurrent events. Single-cell analyses confirmed deletion rate differences in gBRCA1/2 vs. non-carrier tumors as well as cells engineered to harbor gBRCA1/2 . The centrality of deletion-associated chromosomal instability to tumorigenesis shapes interpretation of the somatic evolution of non-malignant tissue and guides strategies for precision prevention and early detection.

PMID:39484366 | PMC:PMC11526986 | DOI:10.1101/2024.10.17.618945

Front Aging Neurosci. 2024 Oct 17;16:1406573. doi: 10.3389/fnagi.2024.1406573. eCollection 2024.

ABSTRACT

BACKGROUND AND AIMS: The relationship between the ABCA7 gene and Alzheimer’s disease (AD) has been widely studied across various populations. However, the results have been inconsistent. This meta-analysis aimed to evaluate the association of ABCA7 polymorphisms with AD risk, including specific subtypes such as late-onset Alzheimer’s disease (LOAD).

METHODS: Relevant studies were identified through comprehensive database searches, and the quality of each study was assessed using the Newcastle-Ottawa Scale (NOS). Allele and genotype frequencies were extracted from the included studies. The pooled odds ratios (OR) with corresponding 95% confidence intervals (CI) were calculated using random-effects or fixed-effects models. Multiple testing corrections were conducted using the false discovery rate (FDR) method. The Cochran Q statistic and I² metric were used to evaluate heterogeneity between studies, while Egger’s test and funnel plots were employed to assess publication bias.

RESULTS: A total of 36 studies, covering 21 polymorphisms and involving 31,809 AD cases and 44,994 controls, were included in this meta-analysis. NOS scores ranged from 7 to 9, indicating high-quality studies. A total of 11 SNPs (rs3764650, rs3752246, rs4147929, rs3752232, rs3752243, rs3764645, rs4147934, rs200538373, rs4147914, rs4147915, and rs115550680) in ABCA7 were significantly associated with AD risk. Among these SNPs, two (rs3764650 and rs3752246) were also found to be related to the late-onset AD (LOAD) subtype. In addition, two SNPs (rs4147929 and rs4147934) were associated with the susceptibility to AD only in non-Hispanic White populations. A total of 10 SNPs (rs3764647, rs3752229, rs3752237, rs4147932, rs113809142, rs3745842, rs3752239, rs4147918, rs74176364, and rs117187003) showed no significant relationship with AD risk. Sensitivity analyses confirmed the reliability of the original results, and heterogeneity was largely attributed to deviations from Hardy-Weinberg equilibrium, ethnicity, and variations between individual studies.

CONCLUSION: The available evidence suggests that specific ABCA7 SNPs may be associated with AD risk. Future studies with larger sample sizes will be necessary to confirm these results.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier: CRD42024540539.

PMID:39484364 | PMC:PMC11524920 | DOI:10.3389/fnagi.2024.1406573