Estimated Regression Coefficients Research Articles

A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score.

Polygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in PRS causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of PRS and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model to the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.

The Effect of Interpersonal Skills, Intrapersonal Skills, Suprapersonal Skills, and Career Adaptability on Employability Skills in Indonesian Aviation Polytechnic Cadets

This study aims to determine the influence effect of Interpersonal skills, Intrapersonal skills, Suprapersonal skills, and Career adaptability on Employability skills in Aviation Polytechnic Cadets. This research was conducted in seven official schools under the Indonesian Civil Aviation Human Resources Development Centre. The sample of this study amounted to 300 cadets who were determined using purposive random sampling. Data collection techniques consisted of distributing questionnaires and documentation. The data analysis technique in this study uses the Structural Equation Model (SEM), which consists of a measurement model and a structural model. The measurement model produces research constructs that fit the data. The structural model describes the causal or predictive relationship between exogenous and endogenous variables. The exogenous variables consist of Intrapersonal skills, Interpersonal skills, Suprapersonal skills, and Career adaptability. The endogenous variable is Employability skills. Structural model testing uses the SEM-PLS Program. The structural model analysis shows that the model fits significantly with the data, with the standardized regression coefficient (SRW) estimates being significant (p<0.001). The results showed: (1) There is a significant positive influence between Intrapersonal Skills on Career Adaptability; (2) There is a significant positive influence between Interpersonal Skills on Career Adaptability; (3) There is a significant positive influence between Suprapersonal Skills on Career Adaptability; (4) There is a significant positive influence between Career Adaptability on Employability Skills; (5) There is a significant positive influence between Intrapersonal Skills on Employability Skills through the mediator Career Adaptability; (6) There is a significant positive influence between Interpersonal Skills on Employability Skills through the mediator Career Adaptability; (7) There is a significant positive influence between Suprapersonal Skills on Employability Skills through the mediator Career Adaptability. The research implies that it is recommended that lecturers in teaching not only pay attention to academic aspects (cognitive) but also pay attention to other aspects such as Intrapersonal, Interpersonal, Suprapersonal, Employability, and Career adaptability.

Challenges in estimating the counterfactual placebo HIV incidence rate from a registration cohort: The PrEPVacc trial.

There is increasing recognition that the interpretation of active-controlled HIV prevention trials should consider the counterfactual placebo HIV incidence rate, that is, the rate that would have been observed if the trial had included a placebo control arm. The PrEPVacc HIV vaccine and pre-exposure prophylaxis trial (NCT04066881) incorporated a pre-trial registration cohort partly for this purpose. In this article, we describe our attempts to model the counterfactual placebo HIV incidence rate from the registration cohort. PrEPVacc was conducted at four study sites in three African countries. During the set up of the trial, potential participants were invited to join a registration cohort, which included HIV testing every 3 months. The trial included a non-inferiority comparison of two daily, oral pre-exposure prophylaxis regimens (emtricitabine/tenofovir disoproxil fumarate, emtricitabine/tenofovir alafenamide fumarate), administered for a target duration of 26 weeks (until 2 weeks after the third of four vaccinations). We developed a multi-variable Poisson regression model to estimate associations in the registration cohort between HIV incidence and baseline predictors (socio-demographic and behavioural variables) and time-dependent predictors (calendar time, time in follow-up). We then used the estimated regression coefficients together with participant characteristics in the active-controlled pre-exposure prophylaxis trial to predict a counterfactual placebo incidence rate. Sensitivity analyses regarding the effect of calendar period were conducted. A total of 3255 participants were followed up in the registration cohort between July 2018 and October 2022, and 1512 participants were enrolled in the trial between December 2020 and March 2023. In the registration cohort, 106 participants were diagnosed with HIV over 3638 person-years of follow-up (incidence rate = 2.9/100 person-years of follow-up (95% confidence interval: 2.4-3.5)). The final statistical model included terms for study site, gender, age, occupation, sex after using recreational drugs, time in follow-up, and calendar period. The estimated effect of calendar period was very strong, an overall 37% (95% confidence interval: 19-51) decline per year in adjusted analyses, with evidence that this effect varied by study site. In sensitivity analyses investigating different assumptions about the precise effect of calendar period, the predicted counterfactual placebo incidence ranged between 1.2 and 3.7/100 person-years of follow-up. In principle, the use of a registration cohort is one of the most straightforward and reliable methods for estimating the counterfactual placebo HIV incidence. However, the predictions in PrEPVacc are complicated by an implausibly large calendar time effect, with uncertainty as to whether this can be validly extrapolated over the period of trial follow-up. Other limitations are discussed, along with suggestions for mitigating these in future studies.

Influence of single observations on the choice of the penalty parameter in ridge regression

Penalized regression methods such as ridge regression heavily rely on the choice of the penalty parameter, which is often computed via cross-validation. Discrepancies in the value of the penalty parameter may lead to substantial differences in regression coefficient estimates and predictions. This paper investigates the effect of single observations on the optimal choice of the penalty parameter, showing how the presence of influential points can change it dramatically. The points are distinguished as ‘expanders’ or ‘shrinkers’ based on their effect on the model complexity. The approach supplies a visual exploratory tool to identify influential points, naturally implementable for high-dimensional data where traditional approaches usually fail. Applications to simulated and real data examples, both low- and high-dimensional, are presented. The visual tool is implemented in the R package influridge.

Examining maternal social perceptions and stress responses during pregnancy.

Pregnant women are sensitive to the emotions and cues present in social interactions. They may exhibit heightened stress responses when support is lacking in a harmonious environment. The objective of this study was to examine the interaction of family harmony and social support and its association with stress responses among pregnant women. A total of 556 pregnant women were recruited to participate in this study. Psychological stress was measured using the Perceived Stress Scale - 10 (PSS-10). We assessed their perceived levels of family harmony (Family Harmony Scale-5: FHS-5) and social support (Multidimensional Scale of Perceived Social Support; MSPSS). They also provided blood samples for measurement of interleukin-6 (IL-6) as a marker of inflammation. Moderation analysis was conducted with estimation of regression coefficients and 95 % confidence intervals. After adjusting for covariates, the interaction of family harmony and social support in association with IL-6 levels was significant (β = -0.23, p = .045); however, its association with psychological stress was not significant. Specifically, the lack of support within a harmonious family environment was linked to elevated levels of IL-6 in pregnant women (β = 0.26, p = .023). Social interactions lacking supportive gestures have the potential to exacerbate inflammatory responses in pregnant women. Findings underscore the importance of supportive social interactions in promoting the mental well-being of pregnant women.

Adaptive Learning in Robust Linear Regression With a Semiparametric Skew‐Normal Scale Mixture Distribution

ABSTRACTThe ordinary least squares method is commonly used for estimating regression coefficients for the linear regression. However, this approach is highly sensitive to influential outliers and loses efficiency when the error follows a skewed or heavy‐tailed distribution. To address these limitations, we propose the adoption of semiparametric skew‐normal scale mixture distributions for the error. By using a nonparametric maximum likelihood estimator for the scale factor in skew‐normal scale mixture, we can mitigate the risk of misspecification issues and produce robust estimators without any model selection procedure. Furthermore, we present simulation studies and real data analyses to demonstrate the performance of the proposed method.

Sample size calculations based on day-to-day variability of stress biomarkers in persons with dementia and their family caregivers

Introduction: Accurate estimates of intra-individual variability are necessary for proper design of clinical trials and epidemiological studies where the stress biomarkers cortisol and dehydroepiandrosterone sulfate (DHEA-S) are measured for dyads of persons with dementia (PWDs) and their family caregivers (FCGs). The aim is to determine the number of consecutive sampling days required to detect effect differences in clinical trials, and to accurately estimate regression coefficients in epidemiological studies where stress biomarkers are exposure variables in regression models with future disease as outcome. Methods: Clinical trial data from dyads of PWDs and their FCGs were used. Salivary cortisol and DHEA-S samples were collected five days a week, for eight consecutive weeks. From this data, we created formulas and graphical tools for the number of required sampling days needed to detect effect differences, and we calculated number of days needed for regression coefficients to be estimated with<10% bias. Results: A total of 5791 salivary samples from 34 dyads were used. For morning cortisol, five consecutive sampling days at baseline and an equal number of days at study termination is sufficient to detect a treatment difference>5% of baseline level with>20 dyads per group. When stress biomarkers are used in epidemiological studies at least six consecutive sampling days are required. Conclusion: Based on a large number of consecutive measurements of stress biomarkers we calculated the sufficient numbers of sampling days for clinical trials and for epidemiological studies to produce credible results. Our findings will aid researchers in the study design phase.

Development of the Aslam-Ahmad estimator for the Cox proportional and hazards regression model with multicollinearity

In the current article, we develop the Aslam-Ahmad estimator i.e. the modified Liu-ridge-type estimator as proposed by Aslam and Ahmad [Aslam M, Ahmad S. The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity. Commun Statist Simul Comput. 2022] for the Cox proportional hazards regression model. It is common to use the maximum partial likelihood approach for the parameter estimation of the Cox proportional hazards model. However, when there is considerable multicollinearity among the regressors, this estimator is unable to give stable estimates for the unknown regression coefficients. To vanquish the problem, the literature suggests many alternative biased estimators. Following this, we propose to tailor the Aslam-Ahmad estimator for the Cox proportional hazards model and define its properties. The performance of the proposed estimator with the available estimators has been evaluated by the Monte Carlo simulations using the scalar mean squared error criterion. A numerical example has also been provided for the illustration.

High-dimensional semiparametric mixed-effects model for longitudinal data with non-normal errors

Difficulties may arise when analyzing longitudinal data using mixed-effects models if nonparametric functions are present in the linear predictor component. This study extends semiparametric mixed-effects modeling in cases when the response variable does not always follow a normal distribution and the nonparametric component is structured as an additive model. A novel approach is proposed to identify significant linear and non-linear components using a double-penalized generalized estimating equation with two penalty terms. Furthermore, the iterative approach intends to enhance the efficiency of estimating regression coefficients by incorporating the calculation of the working covariance matrix. The oracle properties of the resulting estimators are established under certain regularity conditions, where the dimensions of both the parametric and nonparametric components increase as the sample size grows. We perform numerical studies to demonstrate the efficacy of our proposal.

Impacts of clinical backgrounds and intervention strategies on duration of intravenous antibiotics treatments in patients diagnosed with calculous pyelonephritis: A single-center retrospective study

ObjectivesThere are limited information that need to do appropriate treatment including duration of antibiotic treatments, timing of urinary drainage and pathogenesis of bacteria in calculous pyelonephritis. In the present study, we investigated real-world data on clinical features and succeeded treatment strategies in calculous pyelonephritis cases in our hospital, then, aimed to make predictive model estimating duration of intravenous antibiotics treatment. MethodsParticipants were 163 consecutive patients diagnosed with calculous pyelonephritis who underwent antibiotics treatments between 2017 and 2023 in our in-patients’ clinic. Candidates for explanatory variables that may affect duration of antibiotic treatments were age, gender, body mass index, stone location, stone size, septic status, blood culture, urine drainage, indwelling urethral catheter, diabetes mellitus and steroid intake. ResultsDuration of intravenous antibiotics treatment was 6 days in median (IQR: 4–8 days). Indwelling DJ stent or percutaneous nephrostomy were undergone in 74 (45.4 %) patients. Multiple regression analysis revealed that gender, age, indwelling urethral catheter, septic status and management of urine drainage independently affected essential duration of intravenous antibiotics treatment and regression coefficient estimates of those factors respectively were 0.998, 0.890, 2.487, 1.462, 1.293 with constant of 2.464. ConclusionsOur preliminary multiple regression models for predicting duration of intravenous antibiotics treatment may be useful to judge the timing of changing treatment strategies for patients who would not improve at around estimated intravenous antibiotics treatment periods. If vital signs were stable, it may be acceptable to judge urine drainage from above the urinary stone at around two days after intravenous antibiotic treatments.

Bayesian ridge estimators based on a vine copula-based prior in Poisson and negative binomial regression models

Poisson and negative binomial regressions are popular methods for modelling the relationship between a count variable and explanatory variables. In the presence of multicollinearity, ridge regression is an alternative to the maximum likelihood estimation for regression coefficients. Furthermore, ridge estimators are interpreted as the Bayesian posterior mean (or mode) when the regression coefficients follow a multivariate normal prior. However, using the multivariate normal prior may not effectively estimate regression coefficients, especially in the presence of interaction terms. This study proposes vine copula-based priors for Bayesian ridge estimators in Poisson and negative binomial regression models. The simulations and data analysis results indicate that for the Poisson model with equidispersion and the negative binomial model with overdispersion, the Clayton and Gumbel copula priors of the Archimedean family achieve superior performance than the multivariate normal prior and Gaussian copula prior.

NLRP3 inflammasome-induced pyroptosis and serum ASC specks are increased in patients with cardiogenic shock.

Cardiogenic shock (CS) is characterized by impaired cardiac function, very high mortality, and limited treatment options. The proinflammatory signaling during different phases of CS is incompletely understood. We collected serum and plasma (n = 44) as well as freshly isolated peripheral blood mononuclear cells (PBMCs, n = 7) of patients with CS complicating acute myocardial infarction on admission and after revascularization (24, 48, and 72 h) and of healthy controls (serum and plasma, n = 75; PBMCs, n = 12). PBMCs of patients with CS had increased gene expression of NLRP3, CASP1, PYCARD, IL1B, and IL18 and showed increased rates of pyroptosis (control, 4.7 ± 0.3 vs. 9.9 ± 1.7% in patients with CS, P = 0.02). Serum interleukin (IL)-1β levels were increased after revascularization. IL-18 and IL-6 were higher in patients with CS than in healthy controls but comparable before and after revascularization. Proinflammatory apoptosis-associated speck-like proteins containing CARD (ASC) specks were elevated in the serum of patients with CS on admission and increased after revascularization (admission, 11.1 ± 4.4 specks/µL; after 24 h, 19.0 ± 3.9, P = 0.02). ASC specks showed a significant association with 30-day mortality in patients with CS (P < 0.05). The estimated regression coefficients and odds ratios indicated a positive relationship between ASC specks and mortality (odds ratio: 1.029, 95% confidence interval, 1.000 to 1.072; P = 0.02). Pyroptosis and circulating ASC specks are increased in patients with CS and are particularly induced after reperfusion. This underscores their potential role as a biomarker for poor outcomes in patients with CS. ASC specks represent promising new therapeutic targets for patients with CS with high inflammatory burden.NEW & NOTEWORTHY The expression of NLR family pyrin domain containing-3 (NLRP3) inflammasome-related genes and the rate of pyroptosis are increased in PBMCs from patients with CS. Furthermore, patients with CS are characterized by higher serum concentrations of ASC specks and IL-1β, IL-6, and IL-18. This current study adds circulating ASC specks to the portfolio of biomarkers for the identification of patients with a high inflammatory burden paving the way for precision medicine approaches to improve clinical outcomes.

An Econometric Evaluation of the Variation effect of the Nigerian Rate of Exchange on the Economic Progression

This research work investigated an econometric evaluation of the variation effect of the Nigerian rate of exchange on the economic progression between the years 1986-2017. A secondary source of data was employed in this work, which was extracted from the National Bureau Statistical Bulletin published by the Central Bank of Nigeria. Maximum Likelihood estimation of the R-software was carried out to evaluate the parameter estimate of the multiple regression coefficients and analyzed the influence of the explanatory variables (Gross Domestic Product, Unemployment and Inflation on the rate of Exchange). It was observed that the Gross Domestic Product and Unemployment rate influenced negatively on the Exchange rate but significant with (β = -1.646, t = -2.409, sig.val = 0.0228) and (β = - 0.8602, t = -3.302, sig.val = 0.00263) respectively. Nevertheless Inflation rate had positive influence on the rate of Exchange but not significant with (β = 1.311, t = 0.852, sig.val = 0.40122). The Coefficient of determination (R2 = 82.7) implies that the variation of the Nigerian rate of exchange has significant effect on the economic progression between the years 1986-2017. However, the paper recommends the following; a control of the sequential rising of inflation so as to have a stabilized rate of exchange and a promising economy for the citizens, adequate security, effective fiscal and monetary policies, as well as the provision of infrastructural facilities so that foreign investors will be attracted to invest in Nigeria.

Nonparametric Test for Logistic Regression with Application to Italian Enterprises’ Propensity for Innovation

In this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variables. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model.

Decreases in serum PFAS are associated with decreases in serum lipids: A longitudinal study on a highly exposed population

IntroductionPerfluoroalkyl substances (PFAS) are widely used, ubiquitous and highly persistent man-made chemicals. Previous cross-sectional studies have consistently linked PFAS exposure to alterations in lipid profiles. However, longitudinal investigations are preferred to mitigate issues related to reverse causation and confounding. Hence, we aimed to investigate the association between changes in serum PFAS and changes in serum lipids, while shedding light on potential modifiers of the examined relationships. MethodsWe used data from a health surveillance program offered to residents of a vast area of the Veneto Region (North-Eastern Italy), who had been exposed to PFAS via contaminated drinking water until 2013. We included subjects aged ≥20 years who provided two blood samples over an average 4-year interval (n = 8101). We examined the relationships between changes in PFOA, PFOS and PFHxS and changes in total cholesterol (TC), high-density lipoprotein cholesterol (HDLC) and low-density lipoprotein cholesterol (LDL-C). Linear models were fitted for change in the natural logarithm (ln) of each lipid in relation to the change in the ln of PFAS. From the estimated regression coefficients, we calculated the predicted percentage change in the response for a ln-decrease in PFAS serum concentrations. ResultsOverall concentrations of PFOA, PFOS, PFHxS fell by 62.1 %, 24.4 % and 35.4 % from baseline, while small increases in lipids were observed. Declines in PFAS concentrations were associated with decreases in all lipids. For a ln-decrease in PFOA HDL-C decreased by 1.99 % (95 % CI: 1.28, 2.70), TC by 1.49 % (95 % CI: 0.88, 2.10), and LDL-C by 1.40 % (95 % CI: 0.45, 2.37). ConclusionsWe found a positive association between changes in PFAS concentrations and changes in cholesterol levels, observing the most marked contrasts across sexes and age groups. Our findings support the reversibility of the associations identified in cross-sectional analyses, emphasizing the importance of water treatment measures in mitigating adverse health effects.

Statistical physical view of statistical inference in Bayesian linear regression model.

This paper considers similarities between statistical physics and Bayes inference through the Bayesian linear regression model. Some similarities have been discussed previously, such as the analogy between the marginal likelihood in Bayes inference and the partition function in statistical mechanics. In particular, this paper considers the proposal to associate discrete sample size with inverse temperature [C. H. LaMont and P. A. Wiggins, Phys. Rev. E 99, 052140 (2019)2470-004510.1103/PhysRevE.99.052140]. The previous study suggested that incorporating this similarity motivates the derivation of analogs of thermodynamic functions such as energy and entropy. The study also anticipated that those analogous functions have potential to describe Bayes estimation from physical points of view and to provide physical insights into mechanisms of estimation. This paper incorporates a macroscopic perspective as an asymptotics similar to the thermodynamic limit into the previous suggestion. Its motivation stems from the statistical mechanical concept of deriving thermodynamic functions that characterize macroscopic properties of macroscopic systems. This incorporation not only allows analogs of macroscopic thermodynamic functions to be considered but also suggests a candidate for an analog of inverse temperature with continuity, which is partly consistent with the previous proposal to associate the discrete sample size with inverse temperature. On the basis of this suggestion, we analyze analogs of macroscopic thermodynamic functions for a Bayesian linear regression model which is the basis of various machine learning models. We further investigate, through the behavior of these functions, how Bayes estimation is described from the perspective of physics and what kind of physical insight is obtained. As a result, the estimation of regression coefficients, which is the primary task of regression, appears to be described by the physical picture of balance between decreasing energy and increasing entropy as in equilibrium states of thermodynamic systems. More specifically we observe the physical view of Bayes inference as follows: the estimation succeeds where the effect of decreasing energy is dominant at low temperature. On the other hand, the estimation fails where the effect of increasing entropy is dominant at high temperature.

Nested case–control sampling without replacement

Nested case–control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study.

Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors

Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated within-group such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.

Fractal geometry of culprit coronary plaque images within optical coherence tomography in patients with acute coronary syndrome vs stable angina pectoris.

The main cause of acute coronary syndrome (ACS) is plaque rupture and thrombus formation. However, it has not been fairly successful to identify vulnerable plaque to rupture using conventional parameters of intravascular imaging modalities. Fractal analysis is one of the mathematical models to examine geometrical features of picture image using a specific parameter called as fractal dimension (FD) which suggests geometric complexity of the image. This study examined FD of the optical coherence tomography (OCT)-derived images of the culprit plaque in patients with ACS vs stable angina pectoris (SAP) to evaluate the feasibility of FD for identifying vulnerable coronary plaques prone to provoke ACS distinguished from stable plaques only provoking SAP. We examined 65 cases (34 ACS patients, 31 SAP patients) in which the culprit lesion was imaged by OCT before percutaneous coronary intervention in patients with ACS and SAP. The culprit plaque lesion in the ACS group had a significantly larger mean lipid arc (203.8 ± 39.4° vs 152.3 ± 34.5°, p < 0.001) and a larger lipid plaque length (12.6 ± 5.1mm vs 7.7 ± 2.7mm, p < 0.001) and a thinner fibrous cap thickness (75.3 ± 22.3μm vs 134.8 ± 53.2μm, p < 0.001) than those in the SAP group. The prevalence of OCT-derived macrophage infiltration (Mph) in the entire culprit coronary vessel as well as that of the OCT-derived thin-cap fibroatheroma (TCFA) at the culprit lesion were significantly greater in the ACS group than those in the SAP group, respectively (Mph: 61.8% vs 35.5%, p = 0.048; TCFA: 44.1% vs 6.4%, p < 0.001). The FD of culprit plaque in the ACS group was significantly greater than in the SAP group (2.401 ± 0.073 vs 2.341 ± 0.051, p < 0.001). In multivariate regression analysis, the presence of Mph was a significant determinant of FD (regression coefficient estimate 0.049, CI 0.018-0.079, p = 0.002). The FD of OCT-derived image of culprit coronary plaque in the ACS group was significantly greater than that in the SAP group, indicating that the culprit plaque in ACS were structurally more complex. Therefore, fractal analysis of coronary OCT images might be clinically useful for identifying coronary plaques prone to provoke ACS.

Genotype and X-chromosome inactivation are associated with disease severity in females with X-linked Alport syndrome.

Male patients with X-linked Alport syndrome (XLAS) generally develop end-stage kidney disease in early or middle adulthood and show distinct genotype-phenotype correlations. Female patients, however, show various phenotypes ranging from asymptomatic to severe with no genotype-phenotype correlations. However, the factors affecting the severity of XLAS in female patients are unclear. Since X-chromosome inactivation (XCI) affects the severity of certain female X-linked diseases, we investigated whether genotype and XCI were associated with XLAS severity in female patients in a large Japanese cohort. Among 139 female patients with genetically diagnosed XLAS at our institution, we conducted XCI analysis on peripheral blood leukocytes using the human androgen receptor assay method and analyzed two cohorts. In 74 adult female patients, we evaluated the correlation between kidney function (creatinine-estimated glomerular filtration rate [Cr-eGFR] optimized for Japanese individuals) and genotype/XCI using multivariable linear regression analysis, and in 65 pediatric female patients, we evaluated the correlation between kidney function (Cr-eGFR optimized for Japanese individuals) and genotype/XCI using multivariable linear regression analysis. We also investigated the correlation between the development of proteinuria (urine protein-to-creatinine ratio above normal for the patient's age) and genotype/XCI using multivariable Cox proportional hazard analysis. In adult female patients, XCI pattern was significantly associated with Cr-eGFR (regression coefficient estimate = -0.53, P=0.004), whereas genotype was not (P=0.892). In pediatric female patients, both genotype and XCI pattern were significant independent risk factors for the development of proteinuria (hazard ratio [HR], 3.702; 95% confidence interval [CI], 1.681-8.150; P=0.001 and HR, 1.043; 95% CI, 1.061-1.070; P=0.001, respectively), whereas both genotype and XCI pattern were not associated with Cr-eGFR (P=0.20, P=0.67, respectively). Genotype and XCI are factors associated with the severity in females with XLAS.