Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

LDL cholesterol burden in elderly patients with familial hypercholesterolemia: Insights from real-world data

Background and aims

Familial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein cholesterol (LDL-C) and increased risk of premature coronary heart disease (CHD). While current LDL-C levels usually guides therapy, the cumulative exposure to LDL-C (the LDL-C burden) is suggested to offer a more precise estimate of cardiovascular risk in people with FH. Therefore, using real-world data, this study aimed to estimate the LDL-C burden at different ages in elderly FH patients with and without CHD, and to assess the LDL-C burden at CHD onset.

Methods

Data was retrospectively collected from the medical records of elderly (>60 years) FH patients at the Lipid Clinic in Oslo. The LDL-C burden (mM-years) was estimated based on repeated LDL-C measurements and information on lipid-lowering medication. Time-weighted average (TWA) LDL-C was calculated as LDL-C burden divided by years.

Results

We included 112 FH patients, of which 55 (49 %) had experienced at least one CHD-event, and 58 (52 %) were females. Median age at first and last visit were 50 years and 68 years, respectively, with a median of 9 (range; 2-14) available LDL-C measurements. Subjects with CHD had higher LDL-C burden at all ages tested (45, 50 and 60 years) compared with the non-CHD group (p < 0.01, also after adjusting for sex), and had higher TWA LDL-C before treatment at the Lipid Clinic (p = 0.004), but not during follow-up (p = 0.6). There were no sex differences in LDL-C burden at all ages tested, also after adjusting for CHD (p > 0.1). However, women had higher TWA LDL-C during follow-up at the Lipid Clinic (p = 0.01). Median LDL-C burden at CHD onset was 352 mM-years; numerically lower in women than in men (320 vs. 357 mM-years, respectively. p = 0.1).

Conclusion

Elderly FH patients with CHD had higher estimated LDL-C burden compared with FH patients without CHD, due to higher burden prior to treatment, highlighting the importance of earlydetection and treatment.

Plasma Ceramide C24:0/C16:0 Ratio is Associated with Improved Survival in Patients with Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a high fatality rate, with surgery as the only curative treatment. Identification of new biomarkers related to survival may help guide discovery of new pathophysiologic pathways and potential therapeutic targets. As long-chain ceramides have been linked to tumor proliferation, we sought to determine if ceramide levels were prognostic in PDAC.

METHODS

Patients from two phase I studies of PDAC were followed for all-cause mortality. Ceramide levels (C24:0, C22:0, and C16:0) were quantified before treatment and at study intervals. Multivariable Cox regression models assessed the association of ceramide levels and mortality after adjusting for other univariable predictors, including time-dependent tumor resection. The ability of repeated ceramide measures to discriminate patients at risk for mortality was also assessed using multivariable modeling and the c-statistic.

RESULTS

Higher plasma C16:0 concentration was associated with higher all-cause mortality in univariable and multivariable analysis (adjusted hazard ratio [aHR] 1.41, 95% confidence interval [CI] 1.09-1.82; p < 0.01). In contrast, a higher plasma C24:0/C16:0 ratio was associated with lower all-cause mortality in multivariable analysis (aHR 0.69, 95% CI 0.49-0.97; p = 0.032). Discrimination of mortality was significantly improved with the addition of either plasma C16:0 or C24:0/C16:0 levels, with optimal discrimination occurring using repeated measures of the C24:0/C16:0 ratio (c-statistic 0.73 vs. c-statistic 0.66; p < 0.001).

CONCLUSIONS

Higher plasma C16:0 and lower C24:0/C16:0 ratios are independently associated with mortality in PDAC and show an ability to improve discrimination of mortality in this deadly disease. Further studies are needed to confirm this association and evaluate this novel pathway for potential therapeutic targets.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study

Aims

Cardiovascular disease (CVD) may not be detected in time with conventional clinical approaches. Abnormal gait patterns have been associated with pathological conditions and can be monitored continuously by gait video. We aim to test the association between non-contact, video-based gait information and general CVD status.

Methods and results

Individuals undergoing confirmatory CVD evaluation were included in a prospective, cross-sectional study. Gait videos were recorded with a Kinect camera. Gait features were extracted from gait videos to correlate with the composite and individual components of CVD, including coronary artery disease, peripheral artery disease, heart failure, and cerebrovascular events. The incremental value of incorporating gait information with traditional CVD clinical variables was also evaluated. Three hundred fifty-two participants were included in the final analysis [mean (standard deviation) age, 59.4 (9.8) years; 25.3% were female]. Compared with the baseline clinical variable model [area under the receiver operating curve (AUC) 0.717, (0.690-0.743)], the gait feature model demonstrated statistically better performance [AUC 0.753, (0.726-0.780)] in predicting the composite CVD, with further incremental value when incorporated with the clinical variables [AUC 0.764, (0.741-0.786)]. Notably, gait features exhibited varied association with different CVD component conditions, especially for peripheral artery disease [AUC 0.752, (0.728-0.775)] and heart failure [0.733, (0.707-0.758)]. Additional analyses also revealed association of gait information with CVD risk factors and the established CVD risk score.

Conclusion

We demonstrated the association and predictive value of non-contact, video-based gait information for general CVD status. Further studies for gait video-based daily living CVD monitoring are promising.

The association between metabolic syndrome and insulin resistance with risk of cardiovascular events in different states of cardiovascular health status

AIMS/INTRODUCTION

The aim was to examine the joint effect of metabolic syndrome (MetS) and insulin resistance (IR) with ideal cardiovascular health (iCVH) status on incident cardiovascular diseases (CVDs).

MATERIALS AND METHODS

The study included 6,240 Iranian adults ≥30 years, free of prior cardiovascular disease. Ideal cardiovascular health was determined based on American Heart Association's Life Simple 7. Metabolic syndrome was defined according to the Joint Interim Statement Criteria, and insulin resistance was defined as HOMA-IR ≥1.85 in women and ≥2.17 in men. Multivariable Cox proportional hazard ratios (HRs) were applied to examine the impact of metabolic syndrome, and insulin resistance at various levels of iCVH status.

RESULTS

During the median follow-up of 14.0 years, 909 cases of cardiovascular disease occurred. Metabolic syndrome and insulin resistance were significantly associated with incident cardiovascular disease events. In the poor and intermediate status, metabolic syndrome increased cardiovascular disease events with HRs of 1.83 and 1.57, respectively; the corresponding values for insulin resistance in the mentioned categories were 1.91 and 1.25, respectively (P values < 0.05). In the intermediate and poor iCVH status, hypertriglyceridemia was linked to a 40% and 35% higher risk of cardiovascular disease, the corresponding values for low HDL-C was 20% and 60%, respectively (P values < 0.05). Although adding metabolic syndrome, its dyslipidemia and insulin resistance to iCVH status in both poor and intermediate status significantly improve the prediction of cardiovascular disease using net reclassification improvement (P values < 0.05), the value of C-index did not change.

CONCLUSIONS

Metabolic syndrome and the dyslipidemia component had a negligible but significant improvement in the prediction of cardiovascular disease among individuals with non-optimal iCVH status.

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Aims

Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population.

Methods and results

A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively.

Conclusion

Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.

Residual risks and evolving atherosclerotic plaques

Atherosclerotic disease of the coronary and carotid arteries is the primary global cause of significant mortality and morbidity. The chronic occlusive diseases have changed the epidemiological landscape of health problems both in developed and the developing countries. Despite the enormous benefit of advanced revascularization techniques, use of statins, and successful attempts of targeting modifiable risk factors, like smoking and exercise in the last four decades, there is still a definite "residual risk" in the population, as evidenced by many prevalent and new cases every year. Here, we highlight the burden of the atherosclerotic diseases and provide substantial clinical evidence of the residual risks in these diseases despite advanced management settings, with emphasis on strokes and cardiovascular risks. We critically discussed the concepts and potential underlying mechanisms of the evolving atherosclerotic plaques in the coronary and carotid arteries. This has changed our understanding of the plaque biology, the progression of unstable vs stable plaques, and the evolution of plaque prior to the occurrence of a major adverse atherothrombotic event. This has been facilitated using intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy in the clinical settings to achieve surrogate end points. These techniques are now providing exquisite information on plaque size, composition, lipid volume, fibrous cap thickness and other features that were previously not possible with conventional angiography.

Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes

The QRISK cardiovascular disease (CVD) risk assessment model is not currently optimized for patients with type 2 diabetes (T2DM). We aim to identify if the abundantly available repeatedly measured data for patients with T2D improves the predictive capability of QRISK to support the decision-making process regarding CVD prevention in patients with T2DM. We identified patients with T2DM aged 25 to 85, not on statin treatment and without pre-existing CVD from the IQVIA Medical Research Data United Kingdom primary care database and then followed them up until the first diagnosis of CVD, ischemic heart disease, or stroke/transient ischemic attack. We included traditional, nontraditional risk factors and relevant treatments for our analysis. We then undertook a Cox's hazards model accounting for time-dependent covariates to estimate the hazard rates for each risk factor and calculated a 10-year risk score. Models were developed for males and females separately. We tested the performance of our models using validation data and calculated discrimination and calibration statistics. The study included 198,835 (180,143 male with 11,976 outcomes and 90,466 female with 8,258 outcomes) patients. The 10-year predicted survival probabilities for females was 0.87 (0.87 to 0.87), whereas the observed survival estimates from the Kaplan-Meier curve for all female models was 0.87 (0.86 to 0.87). The predicted and observed survival estimates for males were 0.84 (0.84 to 0.84) and 0.84 (0.83 to 0.84) respectively. The Harrell's C-index of all female models and all male models were 0.71 and 0.69 respectively. We found that including time-varying repeated measures, only mildly improved CVD risk prediction for T2DM patients in comparison to the current practice standard. We advocate for further research using time-varying data to identify if the involvement of further covariates may improve the accuracy of currently accepted prediction models.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.

Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study

BACKGROUND

High-sensitivity cardiac troponin testing is a promising tool for cardiovascular risk prediction, but whether serial testing can dynamically predict risk is uncertain. We evaluated the trajectory of cardiac troponin I in the years prior to a cardiovascular event in the general population, and determine whether serial measurements could track risk within individuals.

METHODS

In the Whitehall II cohort, high-sensitivity cardiac troponin I concentrations were measured on three occasions over a 15-year period. Time trajectories of troponin were constructed in those who died from cardiovascular disease compared to those who survived or died from other causes during follow up and these were externally validated in the HUNT Study. A joint model that adjusts for cardiovascular risk factors was used to estimate risk of cardiovascular death using serial troponin measurements.

RESULTS

In 7,293 individuals (mean 58 ± 7 years, 29.4% women) cardiovascular and non-cardiovascular death occurred in 281 (3.9%) and 914 (12.5%) individuals (median follow-up 21.4 years), respectively. Troponin concentrations increased in those dying from cardiovascular disease with a steeper trajectory compared to those surviving or dying from other causes in Whitehall and HUNT (Pinteraction < 0.05 for both). The joint model demonstrated an independent association between temporal evolution of troponin and risk of cardiovascular death (HR per doubling, 1.45, 95% CI,1.33-1.75).

CONCLUSIONS

Cardiac troponin I concentrations increased in those dying from cardiovascular disease compared to those surviving or dying from other causes over the preceding decades. Serial cardiac troponin testing in the general population has potential to track future cardiovascular risk.

Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study

BACKGROUND

Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.

METHODS

We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models.

RESULTS

In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86-0.87 at 5 years, 0.79-0.81 at 10 years) than using baseline or last observed CS data (0.80-0.86 at 5 years, 0.73-0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering.

CONCLUSION

Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

Joint longitudinal and time-to-event modelling compared with standard Cox modelling in patients with type 2 diabetes with and without established cardiovascular disease: An analysis of the EXSCEL trial

AIM

To demonstrate the gain in predictive performance when cardiovascular disease (CVD) risk prediction tools (RPTs) incorporate repeated rather than only single measurements of risk factors.

MATERIALS AND METHODS

We used data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial to compare the quality of predictions of future major adverse cardiovascular events (MACE) with the Cox proportional hazards model (using single values of risk factors) compared to the Bayesian joint model (using repeated measures of risk factors). The risk of MACE was calculated in patients with type 2 diabetes with and without established CVD. We assessed the predictive ability of the following cardiovascular risk factors: glycated haemoglobin, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides, estimated glomerular filtration rate, low-density lipoprotein cholesterol (LDL-C), total cholesterol, and systolic blood pressure (SBP) using the time-dependent area under the receiver-operating characteristic curve (aROC) for discrimination and the time-dependent Brier score for calibration.

RESULTS

In participants without history of CVD, the aROC of SBP increased from 0.62 to 0.69 when repeated rather than only single measurements of SBP were incorporated into the predictive model. Similarly, the aROC increased from 0.67 to 0.80 when repeated rather than only single measurements of both SBP and LDL-C were incorporated into the predictive model. For all other investigated cardiovascular risk factors, the measures of discrimination and calibration both improved when using the joint model as compared to the Cox proportional hazards model. The improvement was evident in participants with and without history of CVD but was more pronounced in the latter group.

CONCLUSIONS

The analysis demonstrates that the joint modelling approach, considering trajectories of cardiovascular risk factors, provides superior predictive performance compared to standard RPTs that use only a single timepoint.

Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records

BACKGROUND

Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.

METHODS

We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means.

RESULTS

The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005).

CONCLUSION

Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or Competing Risks

Background and Aims

Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, their discrimination ability is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there are no data on CV risk functions including time-dependent covariates together with other variables. Our aim was to examine the effect of including time-dependent covariates, competing risks, and treatments in coronary risk prediction.

Methods

Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35-74 years without previous history of cardiovascular disease were included (n = 8470). Coronary and stroke events and mortality due to other CV causes or to cancer were recorded during follow-up (median = 12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with basal measurement of classical risk factors, treatments, or competing risks. Models were cross-validated and compared for discrimination (area under ROC curve), calibration (Hosmer-Lemeshow test), and reclassification (categorical net reclassification index).

Results

Cancer mortality was the highest cumulative-incidence event. Adding cholesterol and hypertension treatment to classical risk factors improved discrimination of coronary events by 2% and reclassification by 7-9%. The inclusion of competing risks and/or 2 measurements of risk factors provided similar coronary event prediction, compared to a single measurement of risk factors.

Conclusion

Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary risk prediction does not improve with 2 measurements of covariates or inclusion of competing risks.

Landmarking 2.0: Bridging the gap between joint models and landmarking

The problem of dynamic prediction with time‐dependent covariates, given by biomarkers, repeatedly measured over time, has received much attention over the last decades. Two contrasting approaches have become in widespread use. The first is joint modeling, which attempts to jointly model the longitudinal markers and the event time. The second is landmarking, a more pragmatic approach that avoids modeling the marker process. Landmarking has been shown to be less efficient than correctly specified joint models in simulation studies, when data are generated from the joint model. When the mean model is misspecified, however, simulation has shown that joint models may be inferior to landmarking. The objective of this article is to develop methods that improve the predictive accuracy of landmarking, while retaining its relative simplicity and robustness. We start by fitting a working longitudinal model for the biomarker, including a temporal correlation structure. Based on that model, we derive a predictable time‐dependent process representing the expected value of the biomarker after the landmark time, and we fit a time‐dependent Cox model based on the predictable time‐dependent covariate. Dynamic predictions based on this approach for new patients can be obtained by first deriving the expected values of the biomarker, given the measured values before the landmark time point, and then calculating the predicted probabilities based on the time‐dependent Cox model. We illustrate the approach in predicting overall survival in liver cirrhosis patients based on prothrombin index.

Changes in blood pressure according to stature in Mexican adults

OBJECTIVE:

To determine the possible existence of differences in blood pressure change over time according to stature in Mexican adults.

METHODS:

We analyzed the National Household Living Standards Survey databases following household members between 2005 and 2009. We selected participants who were between 20 and 40 years old (n = 7,130). We estimated multilevel models with random intercept to analyze differences in blood pressure changes according to stature. We adjusted the models for age, locality size, geographic region, per capita family income, waist-to-height ratio, physical activity, alcohol consumption, smoking, and use of antihypertensive drugs.

RESULTS:

In both sexes, baseline blood pressure tended to be lower as stature decreased. The differences were maintained in both the crude and adjusted models. In men, the increases in systolic pressure over time tended to be higher as stature increased.

CONCLUSIONS:

Contrary to what studies observed in high-income countries, in Mexico blood pressure was positively associated with stature.

Longitudinal trajectories of blood lipid levels in an ageing population sample of Russian Western-Siberian urban population

This study investigated 12-year blood lipid trajectories and whether these trajectories are modified by smoking and lipid lowering treatment in older Russians. To do so, we analysed data on 9,218 Russian West-Siberian Caucasians aged 45-69 years at baseline participating in the international HAPIEE cohort study. Mixed-effect multilevel models were used to estimate individual level lipid trajectories across the baseline and two follow-up examinations (16,445 separate measurements over 12 years). In all age groups, we observed a reduction in serum total cholesterol (TC), LDL-C and non-HDL-C over time even after adjusting for sex, statin treatment, hypertension, diabetes, social factors and mortality (P<0.01). In contrast, serum triglyceride (TG) values increased over time in younger age groups, reached a plateau and decreased in older age groups (> 60 years at baseline). In smokers, TC, LDL-C, non-HDL-C and TG decreased less markedly than in non-smokers, while HDL-C decreased more rapidly while the LDL-C/HDL-C ratio increased. In subjects treated with lipid-lowering drugs, TC, LDL-C and non-HDL-C decreased more markedly and HDL-C less markedly than in untreated subjects while TG and LDL-C/HDL-C remained stable or increased in treatment naïve subjects. We conclude, that in this ageing population we observed marked changes in blood lipids over a 12 year follow up, with decreasing trajectories of TC, LDL-C and non-HDL-C and mixed trajectories of TG. The findings suggest that monitoring of age-related trajectories in blood lipids may improve prediction of CVD risk beyond single measurements.

Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults

Absolute risks of stroke are typically estimated using measurements of cardiovascular disease risk factors recorded at a single visit. However, the comparative utility of single versus sequential risk factor measurements for stroke prediction is unclear. Risk factors were recorded on three separate visits on 13,753 individuals in the prospective China Kadoorie Biobank. All participants were stroke-free at baseline (2004-2008), first resurvey (2008), and second resurvey (2013-2014), and were followed-up for incident cases of first stroke in the 3 years following the second resurvey. To reflect the models currently used in clinical practice, sex-specific Cox models were developed to estimate 3-year risks of stroke using single measurements recorded at second resurvey and were retrospectively applied to risk factor data from previous visits. Temporal trends in the Cox-generated risk estimates from 2004 to 2014 were analyzed using linear mixed effects models. To assess the value of more flexible machine learning approaches and the incorporation of longitudinal data, we developed gradient boosted tree (GBT) models for 3-year prediction of stroke using both single measurements and sequential measurements of risk factor inputs. Overall, Cox-generated estimates for 3-year stroke risk increased by 0.3% per annum in men and 0.2% per annum in women, but varied substantially between individuals. The risk estimates at second resurvey were highly correlated with the annual increase of risk for each individual (men: r = 0.91, women: r = 0.89), and performance of the longitudinal GBT models was comparable with both Cox and GBT models that considered measurements from only a single visit (AUCs: 0.779-0.811 in men, 0.724-0.756 in women). These results provide support for current clinical guidelines, which recommend using risk factor measurements recorded at a single visit for stroke prediction.

Multi-morbidity and blood pressure trajectories in hypertensive patients: A multiple landmark cohort study

BACKGROUND

Our knowledge of how to better manage elevated blood pressure (BP) in the presence of comorbidities is limited, in part due to exclusion or underrepresentation of patients with multiple chronic conditions from major clinical trials. We aimed to investigate the burden and types of comorbidities in patients with hypertension and to assess how such comorbidities and other variables affect BP levels over time.

METHODS AND FINDINGS

In this multiple landmark cohort study, we used linked electronic health records from the United Kingdom Clinical Practice Research Datalink (CPRD) to compare systolic blood pressure (SBP) levels in 295,487 patients (51% women) aged 61.5 (SD = 13.1) years with first recorded diagnosis of hypertension between 2000 and 2014, by type and numbers of major comorbidities, from at least 5 years before and up to 10 years after hypertension diagnosis. Time-updated multivariable linear regression analyses showed that the presence of more comorbidities was associated with lower SBP during follow-up. In hypertensive patients without comorbidities, mean SBP at diagnosis and at 10 years were 162.3 mm Hg (95% confidence interval [CI] 162.0 to 162.6) and 140.5 mm Hg (95% CI 140.4 to 140.6), respectively; in hypertensive patients with ≥5 comorbidities, these were 157.3 mm Hg (95% CI 156.9 to 157.6) and 136.8 mm Hg (95% 136.4 to 137.3), respectively. This inverse association between numbers of comorbidities and SBP was not specific to particular types of comorbidities, although associations were stronger in those with preexisting cardiovascular disease. Retrospective analysis of recorded SBP showed that the difference in mean SBP 5 years before diagnosis between those without and with ≥5 comorbidities was -9 mm Hg (95% CI -9.7 to -8.3), suggesting that mean recorded SBP already differed according to the presence of comorbidity before baseline. Within 1 year after the diagnosis, SBP substantially declined, but subsequent SBP changes across comorbidity status were modest, with no evidence of a more rapid decline in those with more or specific types of comorbidities. We identified factors, such as prescriptions of antihypertensive drugs and frequency of healthcare visits, that can explain SBP differences according to numbers or types of comorbidities, but these factors only partly explained the recorded SBP differences. Nevertheless, some limitations have to be considered including the possibility that diagnosis of some conditions may not have been recorded, varying degrees of missing data inherent in analytical datasets extracted from routine health records, and greater measurement errors in clinical measurements taken in routine practices than those taken in well-controlled clinical study settings.

CONCLUSIONS

BP levels at which patients were diagnosed with hypertension varied substantially according to the presence of comorbidities and were lowest in patients with multi-morbidity. Our findings suggest that this early selection bias of hypertension diagnosis at different BP levels was a key determinant of long-term differences in BP by comorbidity status. The lack of a more rapid decline in SBP in those with multi-morbidity provides some reassurance for BP treatment in these high-risk individuals.

Value of total cholesterol readings earlier versus later in life to predict cardiovascular risk

BACKGROUND

Prognostic implications of blood cholesterol may differ at different stages of life. This cohort study compares the value of total cholesterol (TC) readings earlier versus later in life for the prediction of coronary atherosclerosis, cardiovascular events, and cardiovascular death.

METHODS

In a cardiovascular observation study (CVOS) we performed coronary angiography and prospectively recorded cardiovascular events in 1090 patients over up to 19 years. These patients had participated in a health survey (HS) 15 years prior to the CVOS baseline. TC was measured twice, first at the earlier HS and then later at CVOS recruiting.

FINDINGS

Patients in the highest versus the lowest TC-category of the HS had an OR of 4.30 [2.41-7.65] for significant CAD at angiography, a HR of 1.74 [1.10-2.76] for cardiovascular events, and a HR of 7.55 [1.05-54.49] for cardiovascular death after multivariate adjustment. In contrast, TC as measured at the baseline of the CVOS was neither significantly associated with significant CAD (OR= 0.75 [0.49-1.13]) nor with cardiovascular events or death during follow-up (HR= 0.86 [0.62-1.18] and 0.79 [0.41-1.53], respectively). Moreover, the ESC/EAS-SCORE was found to be more powerful in predicting cardiovascular mortality when using earlier instead of later TC, with a continuous net reclassification improvement of 0.301 (p<0.001).

INTERPRETATION

Early measurement not only enables early intervention in keeping with the concept of lifelong exposure to atherogenic lipoproteins. These data also suggest that cardiovascular risk prediction is more accurate if using earlier in life TC readings.

FUNDING

The present study did not receive any particular funding.

The potential for overdiagnosis and underdiagnosis because of blood pressure variability: a comparison of the 2017 ACC/AHA, 2018 ESC/ESH and 2019 NICE hypertension guidelines

OBJECTIVE

To estimate the extent that BP measurement variability may drive over- and underdiagnosis of 'hypertension' when measurements are made according to current guidelines.

METHODS

Using data from the National Health and Nutrition Examination Survey and empirical estimates of within-person variability, we simulated annual SBP measurement sets for 1 000 000 patients over 5 years. For each measurement set, we used an average of multiple readings, as recommended by guidelines.

RESULTS

The mean true SBP for the simulated population was 118.8 mmHg with a standard deviation of 17.5 mmHg. The proportion overdiagnosed with 'hypertension' after five sets of office or nonoffice measurements using the 2017 American College of Cardiology guideline was 3-5% for people with a true SBP less than 120 mmHg, and 65-72% for people with a true SBP 120-130 mmHg. These proportions were less than 1% and 14-33% using the 2018 European Society of Hypertension and 2019 National Institute for Health and Care Excellence guidelines (true SBP <120 and 120-130 mmHg, respectively). The proportion underdiagnosed with 'hypertension' was less than 3% for people with true SBP at least 140 mmHg after one set of office or nonoffice measurements using the 2017 American College of Cardiology guideline, and less than 18% using the other two guidelines.

CONCLUSION

More people are at risk of overdiagnosis under the 2017 American College of Cardiology guideline than the other two guidelines, even if nonoffice measurements are used. Making clinical decisions about cardiovascular prediction based primarily on absolute risk, minimizes the impact of blood pressure variability on overdiagnosis.

The associations between p,p'-DDE levels and plasma levels of lipoproteins and their subclasses in an elderly population determined by analysis of lipoprotein content

BACKGROUND

Lipoproteins at aberrant levels are known to play a role in cardiovascular disease. The metabolite of the insecticide dichlorodiphenyltrichloroethane (DDT), p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), physically associates with lipids and accumulates in adipose tissue. Little is known about which lipoproteins associate with p,p'-DDE. An association between p,p'-DDE exposure and altered levels of circulating lipids was assessed in a large human cohort using a detailed analysis of lipoprotein content.

METHODS

Plasma samples were collected from the subset of 75-year old Swedes in the Prospective Investigation of the Vasculature of Uppsala Seniors (PIVUS) cohort who were not prescribed lipid lowering medication (n = 571). p,p'-DDE concentrations in plasma were measured using high-throughput solid phase extraction and gas chromatography-high resolution mass spectrometry. Analysis of plasma lipoprotein content was performed with nuclear magnetic resonance spectroscopy.

RESULTS

Detectable levels of p,p'-DDE were found in the plasma samples of all subjects. Elevated p,p'-DDE levels were associated with increased concentrations of lipoproteins of all diameters, with the exception of high density lipoprotein (HDL) of diameters between 14.3 nm-10.9 nm. Of the lipoprotein constituents, triglycerides were most uniformly associated with elevated p,p'-DDE across lipoproteins. p,p'-DDE was furthermore associated with apolipoprotein B, but not apolipoprotein A1.

CONCLUSIONS

The positive associations observed between each lipoprotein class and elevated p,p'-DDE support previous data suggesting that p,p'-DDE interacts with lipoproteins within plasma. It is speculated that both physio-chemical and biological mechanisms may explain why p,p'-DDE does not uniformly associate with lipids across lipoproteins.

Age-Specific Associations Between Systolic Blood Pressure and Cardiovascular Disease: A 10-Year Diabetes Mellitus Cohort Study

Background The relationship between systolic blood pressure (SBP) and cardiovascular disease (CVD) among patients with diabetes mellitus remains unclear. The study aimed to explore age-specific associations between SBP and CVD. Methods and Results A population-based retrospective cohort study was conducted on 180 492 Chinese adults with type 2 diabetes mellitus in 2008-2010, with follow-up to 2017. Age-specific associations (<50, 50-59, 60-69, and 70-79 years) between the average SBP in the previous 2 years and CVD risk were assessed by adjusted Cox proportional hazards regression with age-specific regression dilution ratios and patient characteristics stratified by subgroups. During a median follow-up of 9.3 years (1.5 million person-years), 32 545 patients developed a CVD, with an incidence rate of 23.4 per 1000 person-years. A positive and log-linear association between SBP and CVD risk was observed among the 4 age groups without evidence of a threshold down to 120 mm Hg, but the magnitude of SBP effect on CVD attenuated with increased age. The CVD risk in the age group <50 years was ≈22% higher than the age group 70 to 79 years (hazard ratio [HR], 1.33 [95% CI, 1.26-1.41] versus HR, 1.09 [95% CI, 1.07-1.11]). Each 10-mm Hg higher SBP was associated with 12% (HR, 1.12 [95% CI, 1.10-1.13]), 11% (HR, 1.11 [95% CI, 1.10-1.13]), and 20% (HR, 1.20 [95% CI, 1.17-1.22]) higher risk of all composite CVD events, individual CVD, and CVD mortality, respectively. Conclusions There is a significant log-linear relationship between baseline SBP and the risk of CVD among patients with diabetes mellitus in China. The risk increases from an SBP of 120 mm Hg onward. Age influences this relationship significantly, with younger patients (<50 years) having a greater risk of CVD for a similar rise in SBP as compared with those who are older. These findings suggest that differential target blood pressures stratified by age maybe useful.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Progression of conventional cardiovascular risk factors and vascular disease risk in individuals: insights from the PROG-IMT consortium

AIMS

Averaged measurements, but not the progression based on multiple assessments of carotid intima-media thickness, (cIMT) are predictive of cardiovascular disease (CVD) events in individuals. Whether this is true for conventional risk factors is unclear.

METHODS AND RESULTS

An individual participant meta-analysis was used to associate the annualised progression of systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol with future cardiovascular disease risk in 13 prospective cohort studies of the PROG-IMT collaboration (n = 34,072). Follow-up data included information on a combined cardiovascular disease endpoint of myocardial infarction, stroke, or vascular death. In secondary analyses, annualised progression was replaced with average. Log hazard ratios per standard deviation difference were pooled across studies by a random effects meta-analysis. In primary analysis, the annualised progression of total cholesterol was marginally related to a higher cardiovascular disease risk (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.00 to 1.07). The annualised progression of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol was not associated with future cardiovascular disease risk. In secondary analysis, average systolic blood pressure (HR 1.20 95% CI 1.11 to 1.29) and low-density lipoprotein cholesterol (HR 1.09, 95% CI 1.02 to 1.16) were related to a greater, while high-density lipoprotein cholesterol (HR 0.92, 95% CI 0.88 to 0.97) was related to a lower risk of future cardiovascular disease events.

CONCLUSION

Averaged measurements of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol displayed significant linear relationships with the risk of future cardiovascular disease events. However, there was no clear association between the annualised progression of these conventional risk factors in individuals with the risk of future clinical endpoints.

Repeating Measurement of Bone Mineral Density when Monitoring with Dual-energy X-ray Absorptiometry: 2019 ISCD Official Position

Bone mineral density (BMD) can be measured at multiple skeletal sites using various technologies to aid clinical decision-making in bone and mineral disorders. BMD by dual-energy X-ray absorptiometry (DXA) has a critical role in predicting risk of fracture, diagnosis of osteoporosis, and monitoring patients. In clinical practice, DXA remains the most available and best validated tool for monitoring patients. A quality baseline DXA scan is essential for comparison with all subsequent scans. Monitoring patients with serial measurements requires technical expertise and knowledge of the least significant change in order to determine when follow-up scans should be repeated. Prior ISCD Official Positions have clarified how and when repeat DXA is useful as well as the interpretation of results. The 2019 ISCD Official Positions considered new evidence and clarifies if and when BMD should be repeated. There is good evidence showing that repeat BMD measurement can identify people who experience bone loss, which is an independent predictor of fracture risk. There is good evidence showing that the reduction in spine and hip fractures with osteoporosis medication is proportional to the change in BMD with treatment. There is evidence that measuring BMD is useful following discontinuation of osteoporosis treatment. There is less documentation addressing the effectiveness of monitoring BMD to improve medication adherence, whether monitoring of BMD reduces the risk of fracture, or effectively discriminates patients who should and should not recommence treatment following an interruption of medication. Further research is needed in all of these areas.

Association of Blood Pressure and Risk of Cardiovascular and Chronic Kidney Disease in Hong Kong Hypertensive Patients

The association between systolic blood pressure, cardiovascular disease, and chronic kidney disease remains unclear. This study aimed to evaluate these relationships. A population-based cohort of 267 469 adult patients with hypertension but without diabetes mellitus, cardiovascular disease, or chronic kidney disease were identified. Using baseline and repeated systolic blood pressure (average of all systolic blood pressure measurements in the past 5 years), the risks of cardiovascular disease and chronic kidney disease associated with systolic blood pressure were evaluated by Cox regression. Subgroup analyses were conducted by baseline characteristics. Over 1.4 million person-years follow-up (median 6 years), 29 500 cardiovascular disease and 30 993 chronic kidney disease events diagnosed. A J-shape association between baseline systolic blood pressure and risks of cardiovascular disease and chronic kidney disease was observed. Using repeated systolic blood pressure, a positive and log-linear association was identified. There was no evidence of a threshold down to the repeated systolic blood pressure of 120 mm Hg. Increases of 10 mm Hg of repeated systolic blood pressure was associated with a 16% (hazard ratio, 1.15; [95% CI, 1.13-1.16]), 11% (1.11; [1.08-1.13]), and 22% (1.22; [1.20-1.24]) higher risk of composite of cardiovascular disease and chronic kidney disease, individual cardiovascular disease and chronic kidney disease, respectively. Strength of the associations was similar across different subpopulations. This study showed that hypertensive patients with elevated repeated systolic blood pressure are at increased risk of cardiovascular disease or chronic kidney disease, irrespective of different characteristics. Very low single measurement of systolic blood pressure may be a potential indicator for poor health, but there seems to be no threshold for usual systolic blood pressure.

Long-Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large-Scale Routine Electronic Health Records

Background How measures of long-term exposure to elevated blood pressure might add to the performance of "current" blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination. Methods and Results Using data from UK primary care linked electronic health records, we applied a landmark cohort study design and identified 80 964 people, aged 50 years (derivation cohort=64 772; validation cohort=16 192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid-lowering prescriptions. We used systolic blood pressure recorded up to 10 years before baseline to estimate past systolic blood pressure (mean, time-weighted mean, and variability) and usual systolic blood pressure (correcting current values for past time-dependent blood pressure fluctuations) and examined their prospective relation with incident cardiovascular disease (first hospitalization for or death from coronary heart disease or stroke/transient ischemic attack). We used Cox regression to estimate hazard ratios and applied Bayesian analysis within a machine learning framework in model development and validation. Predictive performance of models was assessed using discrimination (area under the receiver operating characteristic curve) and calibration metrics. We found that elevated past, current, and usual systolic blood pressure values were separately and independently associated with increased incident cardiovascular disease risk. When used alone, the hazard ratio (95% credible interval) per 20-mm Hg increase in current systolic blood pressure was 1.22 (1.18-1.30), but associations were stronger for past systolic blood pressure (mean and time-weighted mean) and usual systolic blood pressure (hazard ratio ranging from 1.39-1.45). The area under the receiver operating characteristic curve for a model that included current systolic blood pressure, sex, smoking, deprivation, diabetes mellitus, and lipid profile was 0.747 (95% credible interval, 0.722-0.811). The addition of past systolic blood pressure mean, time-weighted mean, or variability to this model increased the area under the receiver operating characteristic curve (95% credible interval) to 0.750 (0.727-0.811), 0.750 (0.726-0.811), and 0.748 (0.723-0.811), respectively, with all models showing good calibration. Similar small improvements in area under the receiver operating characteristic curve were observed when testing models on the validation cohort, in sex-stratified analyses, or by using different landmark ages (40 or 60 years). Conclusions Using multiple blood pressure recordings from patients' electronic health records showed stronger associations with incident cardiovascular disease than a single blood pressure measurement, but their addition to multivariate risk prediction models had negligible effects on model performance.

Blood Pressure and the Electronic Health Record: A Work in Progress

See Article Ayala Solares et al

Trajectories of Blood Lipid Concentrations Over the Adult Life Course and Risk of Cardiovascular Disease and All-Cause Mortality: Observations From the Framingham Study Over 35 Years

Background

Elevated total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐C), triglycerides, and non‐high‐density lipoprotein cholesterol (non‐HDL‐C) and low high‐density lipoprotein cholesterol (HDL‐C) concentrations correlate with atherosclerotic cardiovascular disease (ASCVD) and mortality. Therefore, understanding how lipid trajectories throughout adulthood impact ASCVD and mortality risk is essential.

Methods and Results

We investigated 3875 Framingham Offspring participants (54% women, mean age 48 years) attending ≥1 examination between 1979 and 2014. We evaluated longitudinal correlates of each lipid subtype using mixed‐effects models. Next, we clustered individuals into trajectories through group‐based modeling. Thereafter, we assessed the prospective association of lipid trajectories with ASCVD and mortality. Male sex, greater body mass index, and smoking correlated with higher TC, LDL‐C, triglycerides, non‐HDL‐C, and lower HDL‐C concentrations. We identified 5 TC, HDL‐C, and LDL‐C trajectories, and 4 triglycerides and non‐HDL‐C trajectories. Upon follow‐up (median 8.2 years; 199 ASCVD events; 256 deaths), elevated TC (>240 mg/dL), LDL‐C (>155 mg/dL), or non‐HDL‐C (>180 mg/dL) concentrations conferred >2.25‐fold ASCVD and mortality risk compared with concentrations <165 mg/dL, <90 mg/dL, and <115 mg/dL, respectively ([TC hazard ratio (HR)_ASCVD=4.17, 95% CI 1.94–8.99; TC HR_death=2.47, 95% CI 1.28–4.76] [LDL‐C HR_ASCVD=5.09, 95% CI 1.54–16.85; LDL‐C HR_death=4.04, 95% CI 1.84–8.89] [non‐HDL‐C HR_ASCVD=4.60, 95% CI 1.98–10.70; LDL‐C HR_death=3.74, 95% CI 2.03–6.88]). Consistent HDL‐C concentrations <40 mg/dL were associated with greater ASCVD and mortality risk than concentrations >70 mg/dL (HR_ASCVD=3.81, 95% CI 2.04–7.15; HR_death=2.88, 95% CI 1.70–4.89). Triglycerides trajectories were unassociated with outcomes.

Conclusions

Using a longitudinal modeling technique, we demonstrated that unfavorable lipid trajectories over 35 years confer higher ASCVD and mortality risk later in life.

See Editorial Gidding and Allen

Intake of Saturated Fatty Acids Affects Atherogenic Blood Properties in Young, Caucasian, Overweight Women Even without Influencing Blood Cholesterol

Despite a general relation between fat intake and cardiovascular risk factors, the association is often not observed in studies conducted in heterogenic populations, as for population groups, it may differ. The aim of the study was to analyze the associations between dietary fat intake and lipoprotein cholesterol fractions, as well as atherogenic blood properties, in young and middle-aged overweight Caucasian women. In a group of 138 overweight women, the three-day dietary records were assessed, under-reporters were excluded, and lipoprotein cholesterol fractions were analyzed. For the included 24 young (aged 20–40) and 42 middle-age women (aged 40–60), the intakes of fat, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and cholesterol, as well as the PUFA/SFA ratio, were assessed. Afterwards, the analysis of associations with blood levels of total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride, as well as the TC/HDL ratio, HDL/LDL, ratio and Atherogenic Index of Plasma (AIP), were conducted. It was stated that the influence of the dietary fat level on lipoprotein cholesterol fractions as well as atherogenic blood properties in overweight Caucasian women is age dependent. For young, overweight, Caucasian women, the influence of the dietary fat level on the lipoprotein cholesterol fractions was not observed; however, SFA intake influenced atherogenic blood properties. For middle-aged, overweight, Caucasian women, the PUFA intake had an especially important influence in increasing the HDL cholesterol level. For overweight Caucasian women, not only should lipoprotein cholesterol fractions be controlled, but also the AIP calculated—especially for younger women.

Association of Blood Pressure Classification in Korean Young Adults According to the 2017 American College of Cardiology/American Heart Association Guidelines With Subsequent Cardiovascular Disease Events

IMPORTANCE

Among young adults, the association of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) High Blood Pressure Clinical Practice Guidelines with risk of cardiovascular disease (CVD) later in life is uncertain.

OBJECTIVE

To determine the association of blood pressure categories before age 40 years with risk of CVD later in life.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study from the Korean National Health Insurance Service consisted of 2 488 101 adults aged 20 through 39 years with blood pressure measurements taken twice from 2002 through 2005. Starting from January 1, 2006, participants were followed up until the date of CVD diagnosis, death, or December 31, 2015.

EXPOSURES

Participants were categorized by blood pressure readings: normal (systolic, <120 mm Hg; diastolic, <80 mm Hg), elevated (sytolic, 120-129 mm Hg; diastolic, <80 mm Hg), stage 1 hypertension (systolic, 130-139 mm Hg; diastolic, 80-89 mm Hg), and stage 2 hypertension (systolic, ≥140 mm Hg; diastolic, ≥90 mm Hg).

MAIN OUTCOMES AND MEASURES

The primary outcome was CVD defined as 2 or more days of hospitalization due to CVD or death due to CVD. The secondary outcomes were coronary heart disease (CHD) and stroke.

RESULTS

The study population consisted of 2 488 101 participants (median age, 31 years [interquartile range, 27-36 years], 789 870 women [31.7%]). A total of 44 813 CVD events were observed during a median follow-up duration of 10 years. Men with baseline stage 1 hypertension compared with those with normal blood pressure had higher risk of CVD (incidence, 215 vs 164 per 100 000 person-years; difference, 51 per 100 000 person-years [95% CI, 48-55]; adjusted hazard ratio [HR], 1.25 [95% CI, 1.21-1.28]), CHD (incidence, 134 vs 103 per 100 000 person-years; difference, 31 per 100 000 person-years [95% CI, 28-33]; adjusted HR, 1.23 [95% CI, 1.19-1.27]), and stroke (incidence, 90 vs 67 per 100 000 person-years; difference, 23 per 100 000 person-years [95% CI, 21-26]; adjusted HR, 1.30 [95% CI, 1.25-1.36]). Women with baseline stage 1 hypertension compared with those with normal blood pressure had increased risk of CVD (incidence, 131 vs 91 per 100 000 person-years; difference, 40 per 100 000 person-years [95% CI, 35-45]; adjusted HR, 1.27 [95% CI, 1.21-1.34]), CHD (incidence, 56 vs 42 per 100 000 person-years; difference, 14 per 100 000 person-years [95% CI, 11-18]; adjusted HR, 1.16 [95% CI, 1.08-1.25]), and stroke (incidence, 79 vs 51 per 100 000 person-years; difference, 28 per 100 000 person-years [95% CI, 24-32]; adjusted HR [1.37, 95% CI, 1.29-1.46]). Results for state 2 hypertension were consistent.

CONCLUSIONS AND RELEVANCE

Among Korean young adults, stage 1 and stage 2 hypertension, compared with normal blood pressure, were associated with increased risk of subsequent cardiovascular disease events. Young adults with hypertension, defined by the 2017 ACC/AHA criteria, may be at increased risk of cardiovascular disease.

Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk

The benefits of using electronic health records (EHRs) for disease risk screening and personalized health-care decisions are being increasingly recognized. Here we present a computationally feasible statistical approach with which to address the methodological challenges involved in utilizing historical repeat measures of multiple risk factors recorded in EHRs to systematically identify patients at high risk of future disease. The approach is principally based on a 2-stage dynamic landmark model. The first stage estimates current risk factor values from all available historical repeat risk factor measurements via landmark-age-specific multivariate linear mixed-effects models with correlated random intercepts, which account for sporadically recorded repeat measures, unobserved data, and measurement errors. The second stage predicts future disease risk from a sex-stratified Cox proportional hazards model, with estimated current risk factor values from the first stage. We exemplify these methods by developing and validating a dynamic 10-year cardiovascular disease risk prediction model using primary-care EHRs for age, diabetes status, hypertension treatment, smoking status, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol in 41,373 persons from 10 primary-care practices in England and Wales contributing to The Health Improvement Network (1997-2016). Using cross-validation, the model was well-calibrated (Brier score = 0.041, 95% confidence interval: 0.039, 0.042) and had good discrimination (C-index = 0.768, 95% confidence interval: 0.759, 0.777).

Invited Commentary: Quantifying the Added Value of Repeated Measurements

Meaningful inference in epidemiology relies on accurate exposure measurement. In longitudinal observational studies, having more exposure data in the form of repeated measurements in the same individuals adds useful information. But exactly how much do repeated measurements add, incremental to the information provided by baseline measurements? In this issue of the Journal, Paige et al. (Am J Epidemiol. 2017;186(8):899-907 have quantified the value of adding repeated cholesterol and blood pressure measurements to baseline measurements in a meta-analysis of individual participant data from 38 longitudinal cohort studies. Repeated measurements improve prediction significantly, but the magnitude of this gain in information may be less than expected. In research studies and clinical practice, quality of measurement is more important than quantity.