The Prospective Studies of Atherosclerosis (Proof-ATHERO) Consortium: Design and Rationale.

Atherosclerosis - the pathophysiological mechanism shared by most cardiovascular diseases - can be directly or indirectly assessed by a variety of clinical tests including measurement of carotid intima-media thickness, carotid plaque, -ankle-brachial index, pulse wave velocity, and coronary -artery calcium. The Prospective Studies of Atherosclerosis -(Proof-ATHERO) consortium (https://clinicalepi.i-med.ac.at/research/proof-athero/) collates de-identified individual-participant data of studies with information on atherosclerosis measures, risk factors for cardiovascular disease, and incidence of cardiovascular diseases. It currently comprises 74 studies that involve 106,846 participants from 25 countries and over 40 cities. In summary, 21 studies recruited participants from the general population (n = 67,784), 16 from high-risk populations (n = 22,677), and 37 as part of clinical trials (n = 16,385). Baseline years of contributing studies range from April 1980 to July 2014; the latest follow-up was until June 2019. Mean age at baseline was 59 years (standard deviation: 10) and 50% were female. Over a total of 830,619 person-years of follow-up, 17,270 incident cardiovascular events (including coronary heart disease and stroke) and 13,270 deaths were recorded, corresponding to cumulative incidences of 2.1% and 1.6% per annum, respectively. The consortium is coordinated by the Clinical Epidemiology Team at the Medical University of Innsbruck, Austria. Contributing studies undergo a detailed data cleaning and harmonisation procedure before being incorporated in the Proof-ATHERO central database. Statistical analyses are being conducted according to pre-defined analysis plans and use established methods for individual-participant data meta-analysis. Capitalising on its large sample size, the multi-institutional collaborative Proof-ATHERO consortium aims to better characterise, understand, and predict the development of atherosclerosis and its clinical consequences.

Heritability analysis with repeat measurements and its application to resting-state functional connectivity.

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

Repeat measurements of uterine artery pulsatility index, mean arterial pressure and serum placental growth factor at 12, 22 and 32 weeks in prediction of pre-eclampsia.

OBJECTIVE: To investigate the potential value of repeat measurements of uterine artery pulsatility index (UtA-PI), mean arterial pressure (MAP) and serum placental growth factor (PlGF) at 12, 22 and 32 weeks' gestation in the prediction of pre-eclampsia (PE) developing after 32 weeks. METHODS: Data were derived from prospective screening for adverse obstetric outcomes in women attending their routine hospital visit at 11-13, 19-24 and/or 30-34 weeks' gestation in two maternity hospitals in England. UtA-PI, MAP and PlGF were measured. Bayes' theorem was used to combine the a-priori risk from maternal factors with UtA-PI, MAP and PlGF multiples of the median values. The performance of screening for PE developing after the 30-34-week visit by UtA-PI, MAP and PlGF measured at 11-13, 19-24 and 30-34 weeks and their combinations was examined. RESULTS: Screening at 30-34 weeks by UtA-PI, MAP and PlGF detected, at a 10% false-positive rate, 79%, 86% and 92% of preterm PE and 42%, 50% and 56% of term PE. The addition of biomarker values at 11-13 and/or 19-24 weeks was not associated with any improvement in the detection rate of preterm PE; in the case of term PE, there was a marginal (< 2%) improvement in detection for UtA-PI and MAP and a modest improvement of about 5% for PlGF. CONCLUSION: Measurements of UtA-PI, MAP and PlGF in the first and/or second trimester have a small or no effect on improving the prediction of PE provided by screening in the early third trimester. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Maternal serum soluble fms-like tyrosine kinase-1 at 22 and 32 weeks in the prediction of pre-eclampsia.

OBJECTIVE: To investigate the potential value of repeat measurements of maternal serum concentration of soluble fms-like tyrosine kinase-1 (sFlt-1) at 22 and 32 weeks' gestation in the prediction of pre-eclampsia (PE) in women delivering after 32 weeks. METHODS: The data were derived from prospective screening for adverse obstetric outcomes in women attending their routine hospital visit at 19-24 and/or 30-34 weeks' gestation in one of two maternity hospitals in England. Serum sFlt-1 was measured in 7565 and 8264 singleton pregnancies at 19-24 and 30-34 weeks, respectively. Bayes' theorem was used to combine the a-priori risk from maternal factors with sFlt-1 multiples of the median (MoM) values. The performance of screening for PE developing after the 30-34-week visit by sFlt-1, measured at 19-24, 30-34 and at both 19-24 and 30-34 weeks was examined. RESULTS: In pregnancies with PE, sFlt-1 in both the second and third trimesters was increased and the deviation from normal was inversely related to the gestational age at which delivery became necessary for maternal or fetal indications. Serum sFlt-1 at 19-24 weeks was not useful in predicting PE beyond the 30-34-week visit, but the addition of sFlt-1 at 19-24 weeks improved the prediction of PE provided by sFlt-1 at 30-34 weeks. Screening by maternal factors and sFlt-1 at 30-34 weeks predicted 94% of preterm PE and 54% of term PE, at a false-positive rate of 10%; this was improved to 99% and 64%, respectively, by the additional measurement of sFlt-1 at 19-24 weeks. CONCLUSIONS: Measurement of sFlt-1 in the second trimester improves the prediction of PE provided by screening in the early third-trimester. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Longitudinal data analysis for genetic studies in the whole-genome sequencing era.

The analysis of whole-genome sequence (WGS) data using longitudinal phenotypes offers a potentially rich resource for the examination of the genetic variants and their covariates that affect complex phenotypes over time. We summarize eight contributions to the Genetic Analysis Workshop 18, which applied a diverse array of statistical genetic methods to analyze WGS data in combination with data from genome-wide association studies (GWAS) from up to four different time points on blood pressure phenotypes. The common goal of these analyses was to develop and apply appropriate methods that utilize longitudinal repeated measures to potentially increase the analytic efficiency of WGS and GWAS data. These diverse methods can be grouped into two categories, based on the way they model dependence structures: (1) linear mixed-effects (LME) models, where the random effect terms in the linear models are used to capture the dependence structures; and (2) variance-components models, where the dependence structures are constructed directly based on multiple components of variance-covariance matrices for the multivariate Gaussian responses. Despite the heterogeneous nature of these analytical methods, the group came to the following conclusions: (1) the use of repeat measurements can gain power to identify variants associated with the phenotype; (2) the inclusion of family data may correct genotyping errors and allow for more accurate detection of rare variants than using unrelated individuals only; and (3) fitting mixed-effects and variance-components models for longitudinal data presents computational challenges. The challenges and computational burden demanded by WGS data were addressed in the eight contributions.

The potential of repeated mean arterial pressure measurements for predicting early- and late-onset pre-eclampsia in twin pregnancies: Prediction model study.

OBJECTIVE: To investigate the contribution of longitudinal mean arterial pressure (MAP) measurement during the first, second, and third trimesters of twin pregnancies to the prediction of pre-eclampsia. METHODS: A retrospective cohort study was conducted on women with twin pregnancies. Historical data between 2019 and 2021 were analyzed, including maternal characteristics and mean artery pressure measurements were obtained at 11-13, 22-24, and 28-33 weeks of gestation. The outcome measures included pre-eclampsia with delivery <34 and ≥34 weeks of gestation. Models were developed using logistic regression, and predictive performance was evaluated using the area under the curve, detection rate at a given false-positive rate of 10%, and calibration plots. Internal validation was conducted via bootstrapping. RESULTS: A total of 943 twin pregnancies, including 36 (3.82%) women who experienced early-onset pre-eclampsia and 93 (9.86%) who developed late-onset pre-eclampsia, were included in this study. To forecast pre-eclampsia during the third trimester, the most accurate prediction for early-onset pre-eclampsia resulted from a combination of maternal factors and MAP measured during this trimester. The optimal predictive model for late-onset pre-eclampsia includes maternal factors and MAP data collected during the second and third trimesters. The areas under the curve were 0.937 (95% confidence interval [CI] 0.894-0.981) and 0.887 (95% CI 0.852-0.921), respectively. The corresponding detection rates were 83.33% (95% CI 66.53%-93.04%) for early-onset pre-eclampsia and 68.82% (95% CI 58.26%-77.80%) for late-onset pre-eclampsia. CONCLUSION: Repeated measurements of MAP during pregnancy significantly improved the accuracy of late-onset pre-eclampsia prediction in twin pregnancies. The integration of longitudinal data into pre-eclampsia screening may be an effective and valuable strategy.