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Gamma-glutamyl transpeptidase (GGT) is the most widely used biomarker in the early diagnosis of biliary atresia (BA), but its diagnostic efficacy is questionable in different sub-populations. We aim to identify subgroups that are defined by specific variables with cut-offs and can significantly affect the diagnostic efficacy of GGT for detecting BA.Clinical data from 1273 infants with neonatal obstructive jaundice (NOJ) diagnosed between January 2012 and December 2017 at the Children's Hospital of Fudan University were enrolled, reviewed, and analyzed. Random forest-based Virtual Twins method was used to identify potential subgroups.Hemoglobin (HGB) and fasting gallbladder filling were selected as defining variables. The diagnostic efficacy of GGT was significantly better (AUC = 0.855) for patients with hemoglobin (HGB) ≤ 105 g/L and a gallbladder that was not or poorly filled. Diagnostic efficacy was worst in the subgroup defined by HGB 105 g/L (AUC = 0.722). The inclusion of interaction terms between GGT and the subgroups in a logistic regression model significantly improved (p = 0.002) prediction performance.This study provides evidence that the diagnostic efficacy of GGT can differ significantly across different subgroups. Therefore, a GGT diagnostic result should be interpreted cautiously when patients belong to subgroups with low diagnostic efficacy. The development of a prediction model and/or clinical diagnostic pathway for early detection of BA should also account for the heterogeneous diagnostic efficacy of GGT.

Objective: To examine whether and how sex and age modify the association between accelerometer-based physical activity (PA) and metabolic syndrome (MetS) among American (US) adults. Method: Adults aged ≥20 years old who participated in the mobile center examination during 2003–2006 in the National Health and Nutrition Examination Survey were included for analysis. The total minutes per day of moderate-to-vigorous PA (MVPA) was estimated using ActiGraph. Multivariable logistic regression was used to estimate the odds ratio (OR) of having MetS at an increasing MVPA time. The modification effects of gender and age on the association between MetS and MVPA time were examined by testing for two-way and three-way interaction terms of MVPA time, sex, and age in the model after adjusting for relevant covariates. Results: The prevalence of MetS generally decreased with the MVPA time and was lower in females than in males, although the sex difference varied across age groups. After adjusting for demographic and lifestyle covariates, there was a significant sex difference in how an increased MVPA time lowered the odds of MetS. This interactive effect also varied with age. MVPA benefitted young and middle-age populations up until about 65 years old for both sexes, and the protective effect weakened with age. Although the effect of MVPA was stronger for males than females at young ages, the rate at which it attenuated was quicker in males. The OR of MetS between males and females per unit change of MVPA time was 0.73 (95% CI: [0.57, 0.93]) at age = 25 years, compared to OR = 1.00 (95% CI: [0.88, 1.16]) at age = 60 years. Before the age of 50, the gender difference in the protective effect on MetS was larger at low MVPA levels and became smaller at higher MVPA levels. The male advantage was quite stable with an increasing MVPA time for ages 50–60, and no longer significant at older ages. Conclusions: Young and middle-age populations benefitted from MVPA, lowering the risk of MetS for both sexes. A longer MVPA time was associated with a greater decrease in the risk of MetS in young men than in women, but the sex difference reduced with age and was no longer apparent in older populations.

Background: Prior studies on the relationship between bowel health and mortality have generally focused on the individual association of stool frequency or consistency with mortality but did not present a joint association. Therefore, we aimed to systematically evaluate the individual and joint associations of stool frequency and consistency with all-cause and cause-specific mortality in this study. Methods: A total of 14,574 participants from the National Health and Nutrition Examination Survey 2005–2010 were incorporated in this analysis. Survey sample-weighted Cox proportional hazards models adjusted for potential confounders were used to estimate hazard ratios (HRs) between bowel health measures and mortality risks. Results: During a median of 7.6 years of follow-up, 1502 deaths occurred, including 357 cancer deaths and 284 cardiovascular disease (CVD) deaths. The bowel habit of the most participants was 7 times/week (50.7%), and the most common type was “Like a sausage or snake, smooth and soft” (51.8%). Stool frequency displayed a parabolic relationship with all-cause mortality, and less than 7 times/week is a significant risk factor for mortality (HR for 1 time/week: 1.43, p-values = 0.04. HR for 6 times/week: 1.05, p-value = 0.03). Analyzing the joint association of stool frequency and consistency on mortality clarified the limitations of only inspecting the effects of either individual factor. Compared with 7 times/week of normal stool, infrequent soft stools at 4 times/week were associated with 1.78-, 2.42-, and 2.27-times higher risks of all-cause, cancer, and CVD mortality, respectively. Conclusion: Analyses of bowel health should consider the joint effects of stool frequency and stool consistency. Self-appraisal of stool frequency and consistency may be a simple but useful tool for informing about major chronic illnesses.

Background: There is limited research on the associations between serum nutritional biomarkers and mortality risk in patients with metabolic syndrome (MetS). Existing studies merely investigated the single-biomarker effect. Thus, this study aimed to investigate the combined effect of nutritional biomarker mixtures and mortality risk using the Bayesian kernel machine regression (BKMR) model in patients with MetS. Methods: We included the MetS patients, defined according to the 2018 Guideline on the Management of Blood Cholesterol from the National Health and Nutrition Examination Survey (NHANES) 2001–2006. A total of 20 serum nutritional biomarkers were measured and evaluated in this study. The Cox proportional hazard model and restricted cubic spline models were used to evaluate the individual linear and non-linear association of 20 nutritional biomarkers with mortality risk. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixture of nutritional biomarkers and mortality risk. Results: A total of 1455 MetS patients had a median age of 50 years (range: 20–85). During a median of 17.1-year follow-up, 453 (24.72%) died: 146 (7.20%) caused by CVD and 87 (5.26%) by cancer. Non-linear and linear analyses indicated that, in total, eight individual biomarkers (α-carotene, β-carotene, bicarbonate, lutein/zeaxanthin, lycopene, potassium, protein, and vitamin A) were significantly associated with all-cause mortality (all p-values < 0.05). Results from BKMR showed an association between the low levels of the mixture of nutritional biomarkers and high risk of all-cause mortality with the estimated effects ranging from 0.04 to 0.14 (referent: medians). α-Carotene (PIP = 0.971) and potassium (PIP = 0.796) were the primary contributors to the combined effect of the biomarker mixture. The nutritional mixture levels were found to be negatively associated with the risk of cardiovascular disease (CVD) mortality and positively associated with the risk of cancer mortality. After it was stratified by nutrients, the mixture of vitamins showed a negative association with all-cause and CVD mortality, whereas the mixture of mineral-related biomarkers was positively associated with all-cause and cancer mortality. Conclusion: Our findings support the evidence that nutritional status was associated with long-term health outcomes in MetS patients. It is necessary for MetS patients to be concerned with certain nutritional status (i.e., vitamins and mineral elements).

Objective To assess the efficacy and safety of the new oral anticoagulants versus warfarin in patients with atrial fibrillation by the meta-analyses performed for 5 studies ARISTOTLE, ENGAGE AF-TIMI 48, RE-LY, ROCKET-AF, and J-ROCKET. Methods The events including primary efficacy endpoint (stroke and systemic embolism), ischemic stroke, hemorrhagic stroke, all-cause mortality, and myocardial infarction were used for efficacy analysis and those including major bleeding, intracranial hemorrhage, and gastrointestinal bleeding were used for safety analysis. Instead of combining both doses to 1 meta-analysis, the high-dose groups of RE-LY (150 mg twice daily) and ENGAGE AF-TIMI 48 (60 mg twice daily) were combined with the single dose studies ARISTOTLE, ROCKET-AF, and J-ROCKET. A separate meta-analysis was done for the low-dose groups of RE-LY (110 mg twice daily) and ENGAGE AF-TIMI 48 (30 mg twice daily). Results The high-dose regimen had better performance than low dose in efficacy. In addition, low-dose regimen demonstrated to significantly reduce the risk of hemorrhagic stroke, all-cause mortality, and intracranial hemorrhage. Conclusions The new oral anticoagulants demonstrated promising alternatives to warfarin in prevention of stroke in patients with atrial fibrillation.

Correspondence analysis (CA) and principal component analysis (PCA) are often used to describe multivariate data. In certain applications they have been used for estimation in latent variable models. The theoretical basis for such inference is assessed in generalized linear models where the linear predictor equals αj + xiβj or aj — bj (xi — xiβj 2, (i - 1,…,n; j = 1,…, m) and xi is treated as a latent fixed effect. The PCA and CA eigenvectors/column scores are evaluated as estimators of βj and uj and as estimators of uj. With m fixed and n ↑ ∞, consistent estimators cannot be obtained due to the incidental parameters problem unless sufficient “moment” conditions are imposed on xi. PCA is equivalent to maximum likelihood estimation for the linear Gaussian model and gives a consistent estimator of βj (up to a scale change) when the second sample moment of xi is positive and finite in the limit. It is inconsistent for Poisson and Bernoulli distributions, but when bj is constant, its first and/or sec...

Methods were developed to evaluate the performance of a decision-tree model used to predict landscape-level patterns of potential forest vegetation in central New York State. The model integrated environmental databases and knowledge on distribution of vegetation. Soil and terrain decision-tree variables were derived by processing state-wide soil geographic databases and digital terrain data. Variables used as model inputs were soil parent material, soil drainage, soil acidity, slope position, slope gradient, and slope azimuth. Landscapescale maps of potential vegetation were derived through sequential map overlay operations using a geographic information system (GIS). A verification sample of 276 field plots was analyzed to determine: (1) agreement between GIS-derived estimates of decision-tree variables and direct field measurements, (2) agreement between vegetation distributions predicted using GIS-derived estimates and using field observations, (3) effect of misclassification costs on prediction agreement, (4) influence of particular environmental variables on model predictions, and (5) misclassification rates of the decision-tree model. Results indicate that the prediction model was most sensitive to drainage and slope gradient, and that the imprecision of the input data led to a high frequency of incorrect predictions of vegetation. However, in many cases of misclassification the predicted vegetation was similar to that of the field plots so that the cost of errors was less than expected from the misclassification rate alone. Moreover, since common vegetation types were more accurately predicted than rare types, the model appears to be reasonably good at predicting vegetation for a randomly selected plot in the landscape. The error assessment methodology developed for this study provides a useful approach for determining the accuracy and sensitivity of landscape-scale environmental models, and indicates the need to develop appropriate field sampling procedures for verifying the predictions of such models.

Two methods of analysis are compared to estimate the treatment effect of a comparative study where each treated individual is matched with a single control at the design stage. The usual matched-pairs analysis accounts for the pairing directly in its model, whereas regression adjustment ignores the matching but instead models the pairing using a set of covariates. For a normal linear model, the estimated treatment effect from the matched-pairs analysis (paired t-test) is more efficient. For a Bernoulli logistic model, matched-pairs analysis performs better when the sample size is small, but is inferior to logistic regression for large sample sizes.