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Supplementary Tables 1-3 from Mitochondrial Genetic Background Modifies Breast Cancer Risk

Inefficient mitochondrial electron transport chain (ETC) function has been implicated in the vicious cycle of reactive oxygen species (ROS) production that may predispose an individual to late onset diseases, such as diabetes, hypertension, and cancer. Mitochondrial DNA (mtDNA) variations may affect the efficiency of ETC and ROS production, thus contributing to cancer risk. To test this hypothesis, we genotyped 69 mtDNA variations in 156 unrelated European-American females with familial breast cancer and 260 age-matched European-American female controls. Fisher's exact test was done for each single-nucleotide polymorphism (SNP)/haplogroup and the P values were adjusted for multiple testing using permutation. Odds ratio (OR) and its 95% confidence interval (95% CI) were calculated using the Sheehe correction. Among the 69 variations, 29 were detected in the study subjects. Three SNPs, G9055A (OR, 3.03; 95% CI, 1.63–5.63; P = 0.0004, adjusted P = 0.0057), A10398G (OR, 1.79; 95% CI, 1.14–2.81; P = 0.01, adjusted P = 0.19), and T16519C (OR, 1.98; 95% CI, 1.25–3.12; P = 0.0030, adjusted P = 0.0366), were found to increase breast cancer risk; whereas T3197C (OR, 0.31; 95% CI, 0.13–0.75; P = 0.0043, adjusted P = 0.0526) and G13708A (OR, 0.47; 95% CI, 0.24–0.92; P = 0.022, adjusted P = 0.267) were found to decrease breast cancer risk. Overall, individuals classified as haplogroup K show a significant increase in the risk of developing breast cancer (OR, 3.03; 95% CI, 1.63–5.63; P = 0.0004, adjusted P = 0.0057), whereas individuals bearing haplogroup U have a significant decrease in breast cancer risk (OR, 0.37; 95% CI, 0.19–0.73; P = 0.0023, adjusted P = 0.03). Our results suggest that mitochondrial genetic background plays a role in modifying an individual's risk to breast cancer. [Cancer Res 2007;67(10):4687–94]

Objective This study examined the mediating roles of empathy and self-compassion in the relationship between parental attachment, prosocial behavior, and antisocial behavior among Chinese adolescents after the Jiuzhaigou earthquake. Methods A total of 411 adolescents (M = 14.73, SD = 0.91) were assessed 16 months after the Jiuzhaigou earthquake using self-report questionnaires of trauma exposure, parental attachment, empathy, self-compassion, prosocial behavior, and antisocial behavior. Results The parental attachment had a positive effect on prosocial behavior via empathy and positive self-compassion, as well as via an indirect path from empathy to positive self-compassion. Parental attachment had a negative effect on antisocial behavior via empathy and negative self-compassion, and had an indirect path from empathy to negative self-compassion. Conclusions These findings indicate that enhancing parental attachment may promote prosocial behavior and reduce antisocial behavior of post-earthquake adolescents. Empathy and self-compassion play significant mediating roles between parental attachment, prosocial behavior, and antisocial behavior.

It is a common practice in public health research that multiple biomarkers are collected to diagnose or predict a disease outcome. A natural question is how to combine multiple biomarkers to improve the diagnostic accuracy. It has been shown by Neyman-Pearson lemma that the likelihood ratio statistic achieves the optimal AUC in theory. However, practical difficulty often lies in the estimation of the multivariate density functions. We propose three novel methods for the biomarker combination, with the idea of breaking down the joint densities to a series of univariate densities. The marginal likelihood ratio approach only assumes the marginal distribution of each biomarker. While the conditional likelihood ratio (CLR) and pseudo likelihood ratio (PLR) approaches assume the conditional distributions of a marker given others, and hence make use of the correlation structure to estimate the combination rules. The proposed methods make it much easier to assume and validate the univariate distributions of a biomarker than making multivariate distributional assumptions. Extensive simulation studies demonstrate that the CLR and the PLR approaches outperform many existing methods, and are therefore recommended for practical use. The proposed methods are motivated by and applied to a biomarker study to diagnose childhood autism/autism spectrum disorder.

To better describe and understand the time dynamics in functional data analysis, it is often desirable to recover the partial derivatives of the random surface. A novel approach is proposed based on marginal functional principal component analysis to derive the representation for partial derivatives. To obtain the Karhunen-Loeve expansion of the partial derivatives, an adaptive estimation is explored. Asymptotic results of the proposed estimates are established. Simulation studies show that the proposed methods perform well in finite samples. Application to the human mortality data reveals informative time dynamics in mortality rates.

Previous studies indicated that self-compassion can alleviate posttraumatic stress disorder (PTSD) and promote posttraumatic growth (PTG). Rumination is a cognitive process in which individuals repeatedly think about traumatic events and their consequences, which includes invasive rumination (IR) and deliberate rumination (DR). IR is a process of thoughts over traumatic event that invades the cognitive world in an undesired state, whereas DR is an individual’s conscious and repeated thinking on events. IR and DR have also been shown to have effects on PTSD and PTG. Studies have examined the relationship between self-compassion, IR, deliberate rumination DR, PTSD, and PTG, respectively, but only a few studies have examined their combined roles in PTSD and PTG among adolescents who have experienced an earthquake. In addition, recent studies have shown that the positive and negative components of self-compassion have different effects on post-traumatic stress responses, but few studies have explored the mechanisms of positive and negative self-compassion on PTSD and PTG in adolescents who experience earthquakes. This study examines the mediating roles of rumination in the relationship between self-compassion, PTSD and PTG among Chinese adolescents after the Jiuzhaigou earthquake. Ten months after the Jiuzhaigou earthquake, a questionnaire survey was conducted among 621 middle school students in Jiuzhaigou county. The participants were assessed by using the following systems: Trauma Exposure Questionnaire, Self-Compassion Scale, Event-Related Rumination Inventory, PTSD Checklist for DSM-5, and Posttraumatic Growth Inventory. When we controlled for the gender, age, and trauma exposure in the direct effect model, positive self-compassion (PSC) had a negative effect on PTSD and a positive effect on PTG. Moreover, negative self-compassion (NSC) had a positive effect on PTSD, but the effect on PTG was nonsignificant. In the indirect model, PSC had an indirect and positive effect on PTSD and PTG via deliberate rumination (DR). NSC had an indirect and positive effect on PTSD and PTG via invasive rumination (IR), whereas NSC also had an indirect and negative effect on PTSD and PTG, as well as via an indirect path from IR to DR on PTSD and PTG. Findings indicated that increased PSC may reduce PTSD and be beneficial for PTG. NSC may exacerbate PTSD. Rumination plays a significant mediating role between self-compassion, PTSD, and PTG.

S-palmitoylation is a reversible posttranslational modification in which a 16-carbon saturated fatty acid (generally palmitate) is attached to specific cysteine residues in polypeptides via thioester linkage. Dynamic S-palmitoylation (palmitoylation-depalmitoylation), like phosphorylation-dephosphorylation, regulates the function of numerous proteins, especially in the brain. While a family of 23 palmitoyl-acyl transferases (PATS), commonly known as ZDHHCs, catalyze S-palmitoylation of proteins, the thioesterases, localized either in the cytoplasm (eg, APT1) or in the lysosome (eg, PPT1) mediate depalmitoylation. Previously, we reported that APT1 requires dynamic S-palmitoylation for shuttling between the cytosol and the plasma membrane. APT1 depalmitoylated H-Ras to regulate its signaling pathway that stimulates cell proliferation. Although we demonstrated that APT1 catalyzed its own depalmitoylation, the ZDHHC(s) that S-palmitoylated APT1 had remained unidentified. We report here that ZDHHC5 and ZDHHC23 catalyze APT1 S-palmitoylation. Intriguingly, lysosomal Ppt1-deficiency in Cln1-/- mouse, a reliable animal model of INCL, markedly reduced ZDHHC5 and ZDHHC23 levels. Remarkably, in the brain of these mice decreased ZDHHC5 and ZDHHC23 levels suppressed membrane-bound APT1, thereby, increasing plasma membrane-localized H-Ras, which activated its signaling pathway stimulating microglia proliferation. Increased inflammatory cytokines produced by microglia together with increased complement C1q level contributed to the transformation of astrocytes to neurotoxic A1 phenotype. Importantly, neuroinflammation was ameliorated by treatment of Cln1-/- mice with a PPT1-mimetic small molecule, N-tert(Butyl)hydroxylamine (NtBuHA). Our results revealed a novel pathway to neuropathology in an INCL mouse model and uncovered a previously unrecognized mechanism of the neuroprotective actions of NtBuHA and its potential as a drug target.

A major emphasis in precision medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. And as such, enormous resources and innovative clinical trials designs in oncology are devoted to identifying predictive biomarkers. Predictive biomarkers are ones that will identify patients that are more likely to respond to specific therapies and they are usually discovered through retrospective analysis from large randomized phase II or phase III trials. One important design to consider is the stratified biomarker design, where patients will have their specimens obtained at baseline and the biomarker status will be assessed prior to random assignment. Regardless of their biomarker status, patients will be randomized to either an experimental arm or the standard of care arm. The stratified biomarker design can be used to test for a treatment-biomarker interaction in predicting a time-to event outcome. Many biomarkers, however, are derived from tissues from patients, and their levels may be heterogeneous. As a result, biomarker levels may be measured with error and this would have an adverse impact on the power of a stratified biomarker clinical trial. We present a trial design and an analysis framework for the stratified biomarker design. We show that the naïve test is biased and provide bias-corrected estimators for computing the sample size and the 95% confidence interval when testing for a treatment-biomarker interaction in predicting a time to event outcome. We propose a sample size formula that adjusts for misclassification and apply it in the design of a phase III clinical trial in renal cancer.

This article concerns evaluating the effectiveness of a continuous diagnostic biomarker against a continuous gold standard that is measured with error. Extending the work of Obuchowski (2005, 2016), Wu et al (2016) suggested an accuracy index and proposed an estimator for the index with error-prone standard when the reliability coefficient is known. Combining with additional measurements (without measurement errors) on the continuous gold standard collected from some subjects, this article proposes two adaptive estimators of the accuracy index when the reliability coefficient is unknown, and further establish the consistency and asymptotic normality of these estimators. Simulation studies are conducted to compare various estimators. Data from an intervention trial on glycemic control among children with type 1 diabetes are used to illustrate the proposed methods.

Inactivating mutations in the PPT1 gene encoding palmitoyl-protein thioesterase-1 (PPT1) underlie the CLN1 disease, a devastating neurodegenerative lysosomal storage disorder. The mechanism of pathogenesis underlying CLN1 disease has remained elusive. PPT1 is a lysosomal enzyme, which catalyzes the removal of palmitate from S-palmitoylated proteins (constituents of ceroid lipofuscin) facilitating their degradation and clearance by lysosomal hydrolases. Thus, it has been proposed that Ppt1-deficiency leads to lysosomal accumulation of ceroid lipofuscin leading to CLN1 disease. While S-palmitoylation is catalyzed by palmitoyl acyltransferases (called ZDHHCs), palmitoyl-protein thioesterases (PPTs) depalmitoylate these proteins. We sought to determine the mechanism by which Ppt1-deficiency may impair lysosomal degradative function leading to INCL pathogenesis. Here we report that in Ppt1(−/−) mice, which mimic CLN1 disease, low level of inositol 3-phosphate receptor-1 (IP3R1) that mediates Ca(++)-transport from the ER to the lysosome dysregulated lysosomal Ca(++) homeostasis. Intriguingly, the transcription factor NFATC4, which regulates IP3R1-expression, required S-palmitoylation for trafficking from the cytoplasm to the nucleus. We identified two palmitoyl acyltransferases, ZDHHC4 and ZDHHC8, which catalyzed S-palmitoylation of NFATC4. Notably, in Ppt1(−/−) mice, reduced ZDHHC4 and ZDHHC8 levels markedly lowered S-palmitoylated NFATC4 (active) in the nucleus, which inhibited IP3R1-expression, thereby, dysregulating lysosomal Ca(++) homeostasis. Consequently, Ca(++)-dependent lysosomal enzyme activities were markedly suppressed. Impaired lysosomal degradative function impaired autophagy, which caused lysosomal storage of undigested cargo. Importantly, IP3R1-overexpression in Ppt1(−/−) mouse fibroblasts ameliorated this defect. Our results reveal a previously unrecognized role of Ppt1 in regulating lysosomal Ca(++)-homeostasis and suggest that this defect contributes to pathogenesis of CLN1 disease.

Background/Objectives. Reward-related eating is hypothesized to underlie risk for weight gain in obesogenic environments, but its role is unknown during pregnancy and postpartum when weight change is normative, but excess weight gain and weight retention are common. This study examined associations of self-reported reward-related eating, self-regulation, and the home food environment with excessive gestational weight gain (GWG) and postpartum weight change. Subjects/Methods. Participants in the Pregnancy Eating Attributes Study observational cohort were enrolled at ≤12 weeks pregnancy and followed through one-year postpartum (458 recruited; 367 retained through delivery). Participants completed four measures of reward-related eating – Modified Yale Food Addiction Scale, Power of Food Scale, Multiple Choice Procedure, and a Reinforcing Value of Food Questionnaire; two measures of self-regulation – Barratt Impulsiveness Scale and Delay of Gratification Inventory; and a Home Food Inventory. Measured weight and skinfolds were obtained. Multinomial logistic and multiple linear regression analyses estimated associations of reward-related eating, self-regulation, and home food environment with excessive GWG, gestational fat gain, postpartum weight change, and percent of GWG retained. Results. Excessive GWG was associated with food reinforcement intensity, but not with any other measure of reward-related eating, self-regulation, or home food environment. Greater gestational fat gain was associated only with higher Multiple Choice Procedure. Postpartum weight change and percent of GWG retained were associated with greater Delay of Gratification and obesogenic home food environment, but not with any measure of reward-related eating or with impulsivity. Conclusions. Findings do not support the hypothesis that self-reported reward-related eating is associated with weight outcomes in pregnancy and postpartum but indicate a relation of Delay of Gratification with postpartum weight retention. Further research using both surveys and objective measures of reward-related eating is needed to advance our understanding of the relation of reward-related eating with weight changes during this critical period of a woman’s life.

Assuming the proportional hazards model and non-informative censoring, the full likelihood approach is used to obtain two new residuals. The first residual is based on the ideas used in obtaining score-type residuals similar to the partial likelihood approach. The second type of residual is based on the concept of deviance residuals. Extensive simulations are conducted to compare the performance of the residuals from the full likelihood based approach with those of the partial likelihood method. We demonstrate through simulation studies that the full likelihood based residuals are more efficient than their partial likelihood counterpart in identifying potential outliers when the censoring proportion is high. The graphical techniques are used to illustrate the applications of these residuals using some examples.

This article concerns the problem of estimating a continuous distribution in a diseased or nondiseased population when only group-based test results on the disease status are available. The problem is challenging in that individual disease statuses are not observed and testing results are often subject to misclassification, with further complication that the misclassification may be differential as the group size and the number of the diseased individuals in the group vary. We propose a method to construct nonparametric estimation of the distribution and obtain its asymptotic properties. The performance of the distribution estimator is evaluated under various design considerations concerning group sizes and classification errors. The method is exemplified with data from the National Health and Nutrition Examination Survey study to estimate the distribution and diagnostic accuracy of C-reactive protein in blood samples in predicting chlamydia incidence.

Receiver operating characteristic (ROC) curve is commonly used to evaluate and compare the accuracy of classification methods or markers. Estimating ROC curves has been an important problem in various fields including biometric recognition and diagnostic medicine. In real applications, classification markers are often developed under two or more ordered conditions, such that a natural stochastic ordering exists among the observations. Incorporating such a stochastic ordering into estimation can improve statistical efficiency (Davidov and Herman, 2012). In addition, clustered and correlated data arise when multiple measurements are gleaned from the same subject, making estimation of ROC curves complicated due to within-cluster correlations. In this article, we propose to model the ROC curve using a weighted empirical process to jointly account for the order constraint and within-cluster correlation structure. The algebraic properties of resulting summary statistics of the ROC curve such as its area and partial area are also studied. The algebraic expressions reduce to the ones by Davidov and Herman (2012) for independent observations. We derive asymptotic properties of the proposed order-restricted estimators and show that they have smaller mean-squared errors than the existing estimators. Simulation studies also demonstrate better performance of the newly proposed estimators over existing methods for finite samples. The proposed method is further exemplified with the fingerprint matching data from the National Institute of Standards and Technology Special Database 4.

Semicontinuous data, characterized by a sizable number of zeros and observations from a continuous distribution, are frequently encountered in health research concerning food consumptions, physical activities, medical and pharmacy claims expenditures, and many others. In analyzing such semicontinuous data, it is imperative that the excessive zeros be adequately accounted for to obtain unbiased and efficient inference. Although many methods have been proposed in the literature for the modeling and analysis of semicontinuous data, little attention has been given to clustering of semicontinuous data to identify important patterns that could be indicative of certain health outcomes or intervention effects. We propose a Bernoulli-normal mixture model for clustering of multivariate semicontinuous data and demonstrate its accuracy as compared to the well-known clustering method with the conventional normal mixture model. The proposed method is illustrated with data from a dietary intervention trial to promote healthy eating behavior among children with type 1 diabetes. In the trial, certain diabetes friendly foods (eg, total fruit, whole fruit, dark green and orange vegetables and legumes, whole grain) were only consumed by a proportion of study participants, yielding excessive zero values due to nonconsumption of the foods. Baseline foods consumptions data in the trial are used to explore preintervention dietary patterns among study participants. While the conventional normal mixture model approach fails to do so, the proposed Bernoulli-normal mixture model approach has shown to be able to identify a dietary profile that significantly differentiates the intervention effects from others, as measured by the popular healthy eating index at the end of the trial.

Objective To investigate the effect of hypercortisolism on the developing brain we performed clinical, cognitive, and psychological evaluation of children with Cushing disease (CD) at diagnosis and 1 year after remission. Study design Prospective study of 41 children with CD. Children completed diverse sets of cognitive measures before and 1 year after remission. Neuropsychological evaluation included the Wechsler Intelligence Scale, California Verbal Learning Test, Trail Making Test, the combined subset scores of Wide Range Achievement Test and Woodcock-Johnson Psychoeducational Battery Test of Achievement, and the Behavioral Assessment System for Children. Results Comprehensive cognitive evaluations at baseline and 1 year following cure revealed significant decline mostly in nonverbal skills. Decrements occurred in most of the various indices that measure all aspects of cognitive function and younger age and early pubertal stage largely contributed to most of this decline. Results indicated that age at baseline was associated with positive regression weights for changes in scores for verbal, performance, and full intelligence quotient (IQ) scores and for subtests arithmetic, picture completion, coding, block design, scores; indicating that older age at baseline was associated with less of a deterioration in cognitive scores from pre- to posttreatment. Conclusion Our findings suggest that chronic glucocorticoid excess and accompanying secondary hormonal imbalances followed by eucortisolemia have detrimental effects on cognitive function in the developing brain; younger age and pubertal stage are risk factors for increased vulnerability, while older adolescents have cognitive vulnerabilities like that of adult patients affected with CD.

ObjectivesTo evaluate the individual and combined associations of five modifiable risk factors with risk of type 2 diabetes among women with a history of gestational diabetes mellitus and examine whether these associations differ by obesity and genetic predisposition to type 2 diabetes.DesignProspective cohort study.SettingNurses’ Health Study II, US.Participants4275 women with a history of gestational diabetes mellitus, with repeated measurements of weight and lifestyle factors and followed up between 1991 and 2009.Main outcome measureSelf-reported, clinically diagnosed type 2 diabetes. Five modifiable risk factors were assessed, including not being overweight or obese (body mass index of the modified Alternate Healthy Eating Index), regular exercise (≥150 min/week of moderate intensity or ≥75 min/week of vigorous intensity), moderate alcohol consumption (5.0-14.9 g/day), and no current smoking. Genetic susceptibility for type 2 diabetes was characterised by a genetic risk score based on 59 single nucleotide polymorphisms associated with type 2 diabetes in a subset of participants (n=1372).ResultsOver a median 27.9 years of follow-up, 924 women developed type 2 diabetes. Compared with participants who did not have optimal levels of any of the risk factors for the development of type 2 diabetes, those who had optimal levels of all five factors had >90% lower risk of the disorder. Hazard ratios of type 2 diabetes for those with one, two, three, four, and five optimal levels of modifiable factors compared with none was 0.94 (95% confidence interval 0.59 to 1.49), 0.61 (0.38 to 0.96), 0.32 (0.20 to 0.51), 0.15 (0.09 to 0.26), and 0.08 (0.03 to 0.23), respectively (PtrendtrendConclusionsAmong women with a history of gestational diabetes mellitus, each additional optimal modifiable factor was associated with an incrementally lower risk of type 2 diabetes. These associations were seen even among individuals who were overweight/obese or were at greater genetic susceptibility.

Background: Previous studies indicate that social support, self-compassion, and posttraumatic growth (PTG) can affect prosocial behaviour and antisocial behaviour, but few studies have examined their combined role in prosocial and antisocial behaviour among adolescents who have experienced traumatic events. Objective: This study examined the mediating roles of self-compassion and PTG in the relationship between social support and prosocial and antisocial behaviour among Chinese adolescents after the Ya’an earthquake. Method: Four and a half years after the Ya’an earthquake, 492 students aged 13 to 18 in Lushan County (98.6% of the 499 students surveyed) were assessed using the following system: Measures of Trauma Exposure Questionnaire, Social Support Questionnaire, Self-Compassion Scale, Posttraumatic Growth Inventory, and Child Behaviour Problems Questionnaire. Results: When we controlled for gender, age, and traumatic exposure, social support had a positive effect on prosocial behaviour. Moreover, social support had an indirect and positive effect on prosocial behaviour via positive self-compassion and PTG, as well as via an indirect path from positive self-compassion to PTG, but social support had a negative effect on antisocial behaviour via PTG, as well as via an indirect path from positive self-compassion to PTG on antisocial behaviour. Social support also had a positive effect on antisocial behaviour via negative self-compassion. Conclusion: Findings suggest that increased support may be beneficial for prosocial behaviour and reduce antisocial behaviour. Self-compassion and PTG play a significant mediating role between social support, prosocial behaviour, and antisocial behaviour., HIGHLIGHTS • This study examined the mediating roles of self-compassion (positive and negative self-compassion) and PTG in the relationship between social support and prosocial and antisocial behavior among Chinese adolescents after the Ya’an earthquake.

We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman's, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings.

Author(s): Hinkle, Stefanie N; Rawal, Shristi; Bjerregaard, Anne Ahrendt; Halldorsson, Thor I; Li, Mengying; Ley, Sylvia H; Wu, Jing; Zhu, Yeyi; Chen, Liwei; Liu, Aiyi; Grunnet, Louise Groth; Rahman, Mohammad L; Kampmann, Freja Bach; Mills, James L; Olsen, Sjurdur F; Zhang, Cuilin | Abstract: BackgroundArtificially sweetened beverages (ASBs) are commonly consumed and recommended for individuals at high risk for cardiometabolic diseases; however, the health effects of ASBs remain contradictory. Given that cross-sectional analyses are subject to reverse causation, prospective studies with long-term follow-up are needed to evaluate associations between ASBs and cardiometabolic health, especially among high-risk individuals.ObjectiveThe aim of this study was to examine associations of ASB intake and cardiometabolic health among high-risk women with prior gestational diabetes mellitus (GDM).MethodsWe included 607 women with GDM from the Danish National Birth Cohort (DNBC; 1996-2002) who completed a clinical exam 9-16 y after the DNBC pregnancy for the Diabetes a Women's Health (DWH) Study (2012-2014). We assessed ASB intake using FFQs completed during the DNBC pregnancy and at the DWH Study clinical exam. We examined cardiometabolic outcomes at the DWH clinical exam. We estimated percentage differences in continuous cardiometabolic markers and RRs for clinical endpoints in association with ASB intake both during pregnancy and at follow-up adjusted for prepregnancy BMI, diet, and lifestyle factors. Sensitivity analyses to account for reverse causation were performed.ResultsIn pregnancy and at follow-up, 30.4% and 36.4% of women regularly (≥2 servings/wk) consumed ASB, respectively. Consumption of ASBs, both during pregnancy and at follow-up, was associated with higher glycated hemoglobin (HbA1c), insulin, HOMA-IR, triglycerides, liver fat, and adiposity and with lower HDL at follow-up. After adjustment for covariates, particularly prepregnancy BMI, the majority of associations between ASB intake in pregnancy and outcomes at follow-up became null with the exception of HbA1c. ASB intake at follow-up (≥1 serving/d compared with l1 serving/mo) was associated with higher HbA1c (6.5%; 95% CI: 1.9, 11.3; P-trend = 0.007); however, associations were not upheld in sensitivity analyses for reverse causation.ConclusionsAmong Danish women with a history of GDM, ASB intake was not significantly associated with cardiometabolic profiles.

Multiple endpoints are often naturally clustered based on their scientific interpretations. Tests that compare these clustered outcomes between independent groups may lose efficiency if the cluster structures are not properly accounted for. For the two-sample generalized Behrens-Fisher hypothesis concerning multiple endpoints we propose a cluster-adjusted multivariate test procedure for the comparison and demonstrate its gain in efficiency over test procedures that ignore the clusters. Data from a dietary intervention trial are used to illustrate the methods.