Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Epicardial Adipose Tissue and Major Adverse Cardiovascular Events in Myocardial Infarction Patients with and without Diabetes

BACKGROUND

Epicardial adipose tissue (EAT) accumulation plays a key role in the progression myocardial infarction (MI) and diabetes. Diabetic patients have elevated risk of major adverse cardiac events (MACEs) compared to non-diabetic patients. We aimed to investigate the prognostic value of EAT volume in MI patients with and without diabetes.

METHODS

This study included 458 MI patients who underwent cardiac computed tomography (CT) imaging and received successful stent implantation. EAT volume was quantified with cardiac CT imaging. Sub-study stratification of patients by diabetes status was further analyzed. Cox proportional hazards regression models were applied to evaluate the association between EAT volume and MACEs.

RESULTS

Diabetes was identified in 135 of the 458 patients (29.5%). EAT volume was significantly higher in diabetes than non-diabetes. During a median follow-up of 1154 days, MACEs occurred more frequently in patients with versus without diabetes. EAT volume was independent predictor of MACEs in all MI patients after adjustment for risk factors, and showed good predictive value in the evaluation of MACEs. Moreover, EAT volume was also significantly associated with MACEs after adjustment for risk factors in diabetes and non-diabetes in the subgroup analysis.

CONCLUSION

MI patients with diabetes had higher EAT volume and experienced higher rate of MACEs compared to non-diabetes. EAT volume is an independent risk of prognosis of MI, regardless of the diabetes status.

Long-term cardiovascular risks and the impact of statin treatment on socioeconomic inequalities: a microsimulation model

BACKGROUND

UK cardiovascular disease (CVD) incidence and mortality have declined in recent decades but socioeconomic inequalities persist.

AIM

To present a new CVD model, and project health outcomes and the impact of guideline-recommended statin treatment across quintiles of socioeconomic deprivation in the UK.

DESIGN AND SETTING

A lifetime microsimulation model was developed using 117 896 participants in 16 statin trials, 501 854 UK Biobank (UKB) participants, and quality-of-life data from national health surveys.

METHOD

A CVD microsimulation model was developed using risk equations for myocardial infarction, stroke, coronary revascularisation, cancer, and vascular and non-vascular death, estimated using trial data. The authors calibrated and further developed this model in the UKB cohort, including further characteristics and a diabetes risk equation, and validated the model in UKB and Whitehall II cohorts. The model was used to predict CVD incidence, life expectancy, quality-adjusted life years (QALYs), and the impact of UK guideline-recommended statin treatment across socioeconomic deprivation quintiles.

RESULTS

Age, sex, socioeconomic deprivation, smoking, hypertension, diabetes, and cardiovascular events were key CVD risk determinants. Model-predicted event rates corresponded well to observed rates across participant categories. The model projected strong gradients in remaining life expectancy, with 4-5-year (5-8 QALYs) gaps between the least and most socioeconomically deprived quintiles. Guideline-recommended statin treatment was projected to increase QALYs, with larger gains in quintiles of higher deprivation.

CONCLUSION

The study demonstrated the potential of guideline-recommended statin treatment to reduce socioeconomic inequalities. This CVD model is a novel resource for individualised long-term projections of health outcomes of CVD treatments.

Statin therapy in individuals with intermediate cardiovascular risk

BACKGROUND

As intermediate cardiovascular risk group accounts for a large part of the total population, determining appropriate cholesterol target in this population is critical. Herein, we investigated the optimal low-density lipoprotein cholesterol (LDL-C) level in individuals with intermediate cardiovascular risk after statin therapy.

METHODS

This was a nationwide observational and validation cohort study (median duration of follow-up: 7.5 and 8.7 years, respectively), using data from the Korean National Health Insurance Service and a tertiary hospital database. Among individuals who underwent regular health examinations, those with ≥2 cardiovascular risk factors except diabetes mellitus, LDL-C 100-189 mg/dL, and newly used statins were enrolled. Of the 358,694 screened people, 57,594 met the inclusion criteria, of whom 27,793 were finally analyzed. The study population was stratified according to post-treatment LDL-C levels as follows: <100, 100-119, 120-139, and ≥ 140 mg/dL. The primary outcome variable was composite cardiovascular events (myocardial infarction, coronary revascularization, and ischemic stroke). From the patients screened of Severance Hospital cohort, 1859 meeting inclusion criteria were used for validation.

RESULTS

The rates of composite events ranged from 7.74 to 9.10 (mean 8.38)/1000 person-years in the three lower LDL-C groups. Adjusted hazard ratios (aHRs) ranged from 0.78 to 0.95 in the three groups with lower LDL-C, and a lower event risk was more evident in the groups that achieved LDL-C levels <120 mg/dL (p = 0.001-0.009). The total mortality risk did not differ between groups. In the validation cohort, the mean rate of composite events was 10.83/1000 person-years. aHRs ranged from 0.52 to 0.78 in the groups with lower LDL-C, and a lower risk was more obvious in patients who achieved LDL-C levels <100 mg/dL (p = 0.006-0.03).

CONCLUSIONS

Individuals with intermediate cardiovascular risk who achieved LDL-C levels <120 mg/dL after statin therapy had lower event risk. This result provides clinically useful evidence on target LDL-C levels in this population.

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed

OBJECTIVES

Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement.

STUDY DESIGN AND SETTING

We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs.

RESULTS

Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD.

CONCLUSION

Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

Independent relevance of adiposity measures to coronary heart disease risk among 0.5 million adults in UK Biobank

BACKGROUND

Evidence on body fat distribution shows opposing effects of waist circumference (WC) and hip circumference (HC) for coronary heart disease (CHD). We aimed to investigate the causality and the shape of such associations.

METHODS

UK Biobank is a prospective cohort study of 0.5 million adults aged 40-69 years recruited between 2006 and 2010. Adjusted hazard ratios (HRs) for the associations of measured and genetically predicted body mass index (BMI), WC, HC and waist-to-hip ratio with incident CHD were obtained from Cox models. Mendelian randomization (MR) was used to assess causality. The analysis included 456 495 participants (26 225 first-ever CHD events) without prior CHD.

RESULTS

All measures of adiposity demonstrated strong, positive and approximately log-linear associations with CHD risk over a median follow-up of 12.7 years. For HC, however, the association became inverse given the BMI and WC (HR per usual SD 0.95, 95% CI 0.93-0.97). Associations for BMI and WC remained independently positive after adjustment for other adiposity measures and were similar (1.14, 1.13-1.16 and 1.18, 1.15-1.20, respectively), with WC displaying stronger associations among women. Blood pressure, plasma lipids and dysglycaemia accounted for much of the observed excess risk. MR results were generally consistent with the observational, implying causality.

CONCLUSIONS

Body fat distribution measures displayed similar associations with CHD risk as BMI except for HC, which was inversely associated with CHD risk (given WC and BMI). These findings suggest that different measures of body fat distribution likely influence CHD risk through both overlapping and independent mechanisms.

Burden of type 1 and type 2 diabetes and high fasting plasma glucose in Europe, 1990-2019: a comprehensive analysis from the global burden of disease study 2019

Introduction

With population aging rampant globally, Europe faces unique challenges and achievements in chronic disease prevention. Despite this, comprehensive studies examining the diabetes burden remain absent. We investigated the burden of type 1 and type 2 diabetes, alongside high fasting plasma glucose (HFPG), in Europe from 1990-2019, to provide evidence for global diabetes strategies.

Methods

Disease burden estimates due to type 1 and type 2 diabetes and HFPG were extracted from the GBD 2019 across Eastern, Central, and Western Europe. We analyzed trends from 1990 to 2019 by Joinpoint regression, examined correlations between diabetes burden and Socio-demographic indices (SDI), healthcare access quality (HAQ), and prevalence using linear regression models. The Population Attributable Fraction (PAF) was used to described diabetes risks.

Results

In Europe, diabetes accounted for 596 age-standardized disability-adjusted life years (DALYs) per 100,000 people in 2019, lower than globally. The disease burden from type 1 and type 2 diabetes was markedly higher in males and escalated with increasing age. Most DALYs were due to type 2 diabetes, showing regional inconsistency, highest in Central Europe. From 1990-2019, age-standardized DALYs attributable to type 2 diabetes rose faster in Eastern and Central Europe, slower in Western Europe. HFPG led to 2794 crude DALYs per 100,000 people in 2019. Type 1 and type 2 diabetes burdens correlated positively with diabetes prevalence and negatively with SDI and HAQ. High BMI (PAF 60.1%) and dietary risks (PAF 34.6%) were significant risk factors.

Conclusion

Europe's diabetes burden was lower than the global average, but substantial from type 2 diabetes, reflecting regional heterogeneity. Altered DALYs composition suggested increased YLDs. Addressing the heavy burden of high fasting plasma glucose and the increasing burden of both types diabetes necessitate region-specific interventions to reduce type 2 diabetes risk, improve healthcare systems, and offer cost-effective care.

Comparison of the European and US guidelines for lipid-lowering therapy in primary prevention of cardiovascular disease

AIMS

Population-wide impacts of new guidelines in the primary prevention of atherosclerotic cardiovascular disease (ASCVD) should be explored in independent cohorts. Assess and compare the lipid-lowering therapy eligibility and predictive classification performance of 2016 and 2021 European Society of Cardiology (ESC), 2019 American Heart Association/American College of Cardiology (AHA/ACC), and 2022 US Preventive Services Task Force (USPSTF) guidelines.

METHODS AND RESULTS

Participants from the CoLaus|PsyCoLaus study, without ASCVD and not taking lipid-lowering therapy at baseline. Derivation of 10-year risk for ASCVD using Systematic COronary Risk Evaluation (SCORE1), SCORE2 [including SCORE2-Older Persons (SCORE2-OP)], and pooled cohort equation. Computation of the number of people eligible for lipid-lowering therapy based on each guideline and assessment of discrimination and calibration metrics of the risk models using first incident ASCVD as an outcome. Among 4,092 individuals, 158 (3.9%) experienced an incident ASCVD during a median follow-up of 9 years (interquartile range, 1.1). Lipid-lowering therapy was recommended or considered in 40.2% (95% confidence interval, 38.2-42.2), 26.4% (24.6-28.2), 28.6% (26.7-30.5), and 22.6% (20.9-24.4) of women and in 62.1% (59.8-64.3), 58.7% (56.4-61.0), 52.6% (50.3-54.9), and 48.4% (46.1-50.7) of men according to the 2016 ESC, 2021 ESC, 2019 AHA/ACC, and 2022 USPSTF guidelines, respectively. 43.3 and 46.7% of women facing an incident ASCVD were not eligible for lipid-lowering therapy at baseline according to the 2021 ESC and 2022 USPSTF, compared with 21.7 and 38.3% using the 2016 ESC and 2019 AHA/ACC, respectively.

CONCLUSION

Both the 2022 USPSTF and 2021 ESC guidelines particularly reduced lipid-lowering therapy eligibility in women. Nearly half of women who faced an incident ASCVD were not eligible for lipid-lowering therapy.

The effect of sedentary time on cardiovascular disease risk in middle-aged and elderly patients with type 2 diabetes mellitus

Sedentary lifestyle has become quite prevalent lately, and it has been associated with cardiovascular diseases (CVDs). CVD is a primary cause of premature death in patients with type 2 diabetes mellitus (T2DM). Some studies have focused on the association between sedentary behavior and blood glucose among T2DM patients. However, the occurrence and development of CVD involves many factors, such as blood glucose, blood lipid and so on. Therefore, we comprehensively examined the association of sedentary time with overall CVD risk and various metabolic risk factors in T2DM patients. A total of 775 middle-aged and elderly patients with T2DM were assessed. Framingham risk equation was employed to assess their overall CVD risk, while the sedentary time was self-reported. Demographic data and anthropometric and cardiac metabolic indicators were separately analyzed for both genders. The median age of the respondents was 55 (range: 45-75) years, and 39.23% were women. The overall risk of CVD in women was lower than that in men. Linear regression analysis revealed that sedentary time was significantly positively correlated with overall CVD risk and triglyceride level, but not with diastolic blood pressure and glycosylated hemoglobin and high-density lipoprotein cholesterol (HDL-C) levels. However, the correlation of sedentary time with fasting blood glucose level, body mass index, total cholesterol, and low-density lipoprotein cholesterol was only detected in women. In middle-aged and elderly patients with T2DM, prolonged sedentary time may increase the triglyceride levels and the overall risk of CVD. The adverse effects of sedentary time on fasting blood glucose, body mass index, total cholesterol, and low-density lipoprotein cholesterol may exhibit sex-based differences, as they were detected only in women.

The Moli-sani risk score, a new algorithm for measuring the global impact of modifiable cardiovascular risk factors

BACKGROUND

Cardiovascular (CV) disease is preventable through interventions targeting modifiable factors. Most algorithms based on modifiable CV risk factors (CV-rf) have been developed in US populations and do not account for the role of diet. We aimed to assess an algorithm based on modifiable CV-rf including diet, using data from an Italian population.

METHODS

To derive the Moli-sani Risk Score (MRS), we used data on 16,656 men and women (age ≥ 35 y) from the population of the Moli-sani Study. The Risk-and-Prevention-Study, Italy (N = 8606) acted as external validation cohort and the Life's-Simple-7 score was used as benchmark. The MRS targeted at fatal or non-fatal CV events and included 9 common modifiable CV-rf.

RESULTS

After 8.1 years (median) of follow-up, 816 events occurred in the derivation cohort. The MRS was calculated as a weighted sum of its 9 components, with weights reflecting the strength of the association. In comparison with individuals in the first, those in the fourth quartile of the score had hazard ratio (HR) for CV events equal to 3.18 (95%CI: 2.54-3.97). One more point in the score was associated with 7% (6%-8%) and 4% (3%-5%) higher hazard of events in the derivation and validation cohort, respectively. The MRS performed better than the Life's Simple-7 for discrimination.

CONCLUSION

We propose the Moli-sani Risk Score, a validated, performing algorithm able to measure the combined impact that modifiable CV-rf have on CV risk. The score can be used to design preventive interventions, quantify the effectiveness of interventions, and compare different preventive strategies.

Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records

OBJECTIVE

To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds.

METHODS AND ANALYSIS

eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40-69 years in a population in England for invitation to a formal CVD risk assessment.

RESULTS

Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events.

CONCLUSIONS

The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events.

Cardiovascular Risk Scores among Asymptomatic Adults with Haemophilia

BACKGROUND

The mortality rate of Brazilian people with haemophilia (PwH) is decreasing, but the relative incidence of deaths associated with cardiovascular disease (CVD) is increasing.

OBJECTIVES

We aimed to describe the CVD risk score of PwH according to Pooled Cohort Equations Risk (PCER) Calculator tool and its treatment recommendations. We also compared the PCER estimates with the respective Framingham Risk Score (FRS).

METHODS

This cross-sectional study included male PwH ≥ 40 years treated at the Comprehensive Haemophilia Treatment Centre of Pernambuco (Recife/Brazil). PwH with a previous CVD event or a low-density lipid cholesterol ≥ 5.0 mmol/L were excluded. Interviews, medical file reviews, and blood tests were performed. The PCER tool was used to estimate the CVD risk and compare it with the respective FRS. A p-value < 0.05 was accepted as statistically significant.

RESULTS

Thirty PwH were included. Median age was 51.5 [interquartile range-IQR; 46.0-59.5] years. The prevalence of obesity, systemic arterial hypertension, diabetes mellitus, hypertriglyceridaemia, hypercholesterolaemia, and hypoHDLaemia were 20%, 67%, 24%, 14%, 47%, and 23%, respectively. The median PCER score was 6.9% [IQR; 3.1-13.2], with 50% having a high risk (PCER ≥ 7.5%). Statin use was suggested for 54% of PwH. Blood pressure was poorly controlled in 47% of PwH. The agreement between PCER and FRS was 80% (κ = 0.60; p = 0.001).

CONCLUSIONS

Half of the male people with haemophilia aged 40 years or older had a 10-year high risk of developing CVD with strong recommendations to improve control of dyslipidaemia and blood pressure.

Intima-media thickness at the near or far wall of the common carotid artery in cardiovascular risk assessment

Aims

Current guidelines recommend measuring carotid intima-media thickness (IMT) at the far wall of the common carotid artery (CCA). We aimed to precisely quantify associations of near vs. far wall CCA-IMT with the risk for atherosclerotic cardiovascular disease (CVD, defined as coronary heart disease or stroke) and their added predictive values.

Methods and results

We analysed individual records of 41 941 participants from 16 prospective studies in the Proof-ATHERO consortium {mean age 61 years [standard deviation (SD) = 11]; 53% female; 16% prior CVD}. Mean baseline values of near and far wall CCA-IMT were 0.83 (SD = 0.28) and 0.82 (SD = 0.27) mm, differed by a mean of 0.02 mm (95% limits of agreement: -0.40 to 0.43), and were moderately correlated [r = 0.44; 95% confidence interval (CI): 0.39-0.49). Over a median follow-up of 9.3 years, we recorded 10 423 CVD events. We pooled study-specific hazard ratios for CVD using random-effects meta-analysis. Near and far wall CCA-IMT values were approximately linearly associated with CVD risk. The respective hazard ratios per SD higher value were 1.18 (95% CI: 1.14-1.22; I² = 30.7%) and 1.20 (1.18-1.23; I² = 5.3%) when adjusted for age, sex, and prior CVD and 1.09 (1.07-1.12; I² = 8.4%) and 1.14 (1.12-1.16; I²=1.3%) upon multivariable adjustment (all P < 0.001). Assessing CCA-IMT at both walls provided a greater C-index improvement than assessing CCA-IMT at one wall only [+0.0046 vs. +0.0023 for near (P < 0.001), +0.0037 for far wall (P = 0.006)].

Conclusions

The associations of near and far wall CCA-IMT with incident CVD were positive, approximately linear, and similarly strong. Improvement in risk discrimination was highest when CCA-IMT was measured at both walls.

The cost-effectiveness of a uniform versus age-based threshold for one-off screening for prevention of cardiovascular disease

The objective of this article was to assess the cost-effectiveness of screening strategies for cardiovascular diseases (CVD). A decision analytic model was constructed to estimate the costs and benefits of one-off screening strategies differentiated by screening age, sex and the threshold for initiating statin therapy ("uniform" or "age-adjusted") from the Spanish NHS perspective. The age-adjusted thresholds were configured so that the same number of people at high risk would be treated as under the uniform threshold. Health benefit was measured in quality-adjusted life years (QALY). Transition rates were estimated from the European Prospective Investigation into Cancer and Nutrition (EPIC-CVD), a large multicentre nested case-cohort study with 12 years of follow-up. Unit costs of primary care, hospitalizations and CVD care were taken from the Spanish health system. Univariate and probabilistic sensitivity analyses were employed. The comparator was no systematic screening program. The base case model showed that the most efficient one-off strategy is to screen both men and women at 40 years old using a uniform risk threshold for initiating statin treatment (Incremental Cost-Effectiveness Ratio of €3,274/QALY and €6,085/QALY for men and women, respectively). Re-allocating statin treatment towards younger individuals at high risk for their age and sex would not offset the benefit obtained using those same resources to treat older individuals. Results are sensitive to assumptions about CVD incidence rates. To conclude, one-off screening for CVD using a uniform risk threshold appears cost-effective compared with no systematic screening. These results should be evaluated in clinical studies.

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.

SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe

AIMS

To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe.

METHODS AND RESULTS

SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m2, and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m2, and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively.

CONCLUSION

SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe.

Serum Level of Cytokeratin 18 (M65) as a Prognostic Marker of High Cardiovascular Disease Risk in Individuals with Non-Alcoholic Fatty Liver Disease

Alterations in apoptosis, as reflected by circulating Cytokeratin 18 (CK18), are involved in the progression of non-alcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis and atherogenesis. We aimed to explore the discriminant accuracy of Cytokeratin 18 (CK18, including M65 and M30 forms) for an elevated fatty liver index (FLI) as a validated proxy of NAFLD, and cardiovascular disease (CVD) risk in the general population. Both serum CK18 forms were measured using a commercial immunoassay in randomly selected samples from 312 participants of the PREVEND general population cohort. FLI ≥ 60 was used to indicate NAFLD. Framingham Risk Score (FRS) and the SCORE2 were used to estimate the 10-year risk of CVD. The Receiver Operating Characteristic (ROC) curve, linear/logistic regression models, and Spearman's correlations were used. Intricate associations were found between CK18, FLI, and CVD risk scores. While M30 was the only independent predictor of FLI ≥ 60, M65 best discriminated NAFLD individuals at very-high 10-year CVD risk according to SCORE2 (AUC: 0.71; p = 0.001). Values above the predefined manufacturer cutoff (400 U/L) were associated with an independent 5-fold increased risk (adjusted odds ratio: 5.44, p = 0.01), with a negative predictive value of 93%. Confirming that NAFLD is associated with an increased CVD risk, our results in a European general population-based cohort suggest that CK18 M65 may represent a candidate biomarker to identify NAFLD individuals at low CVD risk.

Early Detection Is the Best Prevention-Characterization of Oxidative Stress in Diabetes Mellitus and Its Consequences on the Cardiovascular System

Previous studies demonstrated an important role of oxidative stress in the pathogenesis of cardiovascular disease (CVD) in diabetic patients due to hyperglycemia. CVD remains the leading cause of premature death in the western world. Therefore, diabetes mellitus-associated oxidative stress and subsequent inflammation should be recognized at the earliest possible stage to start with the appropriate treatment before the onset of the cardiovascular sequelae such as arterial hypertension or coronary artery disease (CAD). The pathophysiology comprises increased reactive oxygen and nitrogen species (RONS) production by enzymatic and non-enzymatic sources, e.g., mitochondria, an uncoupled nitric oxide synthase, xanthine oxidase, and the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX). Considering that RONS originate from different cellular mechanisms in separate cellular compartments, adequate, sensitive, and compartment-specific methods for their quantification are crucial for early detection. In this review, we provide an overview of these methods with important information for early, appropriate, and effective treatment of these patients and their cardiovascular sequelae.

CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer

PURPOSE

Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort.

MATERIALS AND METHODS

We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study.

RESULTS

The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years.

CONCLUSION

To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.

Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction

BACKGROUND

The Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown.

METHODS

In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration.

RESULTS

The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD.

CONCLUSIONS

We demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.

Artificial intelligence in atherosclerotic disease: Applications and trends

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

Estimating individual lifetime risk of incident cardiovascular events in adults with Type 2 diabetes: an update and geographical calibration of the DIAbetes Lifetime perspective model (DIAL2)

AIMS

The 2021 European Society of Cardiology cardiovascular disease (CVD) prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding intensified preventive treatment options in adults with Type 2 diabetes, e.g. the DIAbetes Lifetime perspective model (DIAL model). The aim of this study was to update the DIAL model using contemporary and representative registry data (DIAL2) and to systematically calibrate the model for use in other European countries.

METHODS AND RESULTS

The DIAL2 model was derived in 467 856 people with Type 2 diabetes without a history of CVD from the Swedish National Diabetes Register, with a median follow-up of 7.3 years (interquartile range: 4.0-10.6 years) and comprising 63 824 CVD (including fatal CVD, non-fatal stroke and non-fatal myocardial infarction) events and 66 048 non-CVD mortality events. The model was systematically recalibrated to Europe's low- and moderate-risk regions using contemporary incidence data and mean risk factor distributions. The recalibrated DIAL2 model was externally validated in 218 267 individuals with Type 2 diabetes from the Scottish Care Information-Diabetes (SCID) and Clinical Practice Research Datalink (CPRD). In these individuals, 43 074 CVD events and 27 115 non-CVD fatal events were observed. The DIAL2 model discriminated well, with C-indices of 0.732 [95% confidence interval (CI) 0.726-0.739] in CPRD and 0.700 (95% CI 0.691-0.709) in SCID.

CONCLUSION

The recalibrated DIAL2 model provides a useful tool for the prediction of CVD-free life expectancy and lifetime CVD risk for people with Type 2 diabetes without previous CVD in the European low- and moderate-risk regions. These long-term individualized measures of CVD risk are well suited for shared decision-making in clinical practice as recommended by the 2021 CVD ESC prevention guidelines.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

The association between the body height and cardiovascular diseases: a retrospective analysis of 657,310 outpatients in Germany

Background

Cardiovascular disease (CVD) represents the leading cause of death worldwide. The identification of individuals at increased risk of CVD is essential to reduce its morbidity and mortality globally. Based on existing data on a potential association between the individual body height and the risk for CVD, we investigated this association in a large cohort of outpatients in Germany.

Methods

A total of 657,310 adult outpatients with available body height data from the Disease Analyzer (IQVIA) database were included in Germany between 2019 and 2021. The prevalence of common CVD diagnoses (hypertension, coronary heart disease, atrial fibrillation and flutter, heart failure, ischemic stroke, and venous thromboembolism) was evaluated as a function of the patients’ body height stratified by age and sex.

Results

In both sexes, the prevalence of hypertension, coronary heart disease, heart failure, and ischemic stroke was higher among patients of smaller body height. In contrast, the prevalence of atrial fibrillation and venous thromboembolism was higher in taller patients. In age- and BMI-adjusted logistic regression analyses, an increased body height was negatively associated with coronary heart disease (OR = 0.91 in women and OR = 0.87 in men per 10-cm increase in height) and strongly positively associated with atrial fibrillation (OR = 1.25 in women and men) and venous thromboembolism (OR = 1.23 in women and OR = 1.24 in men).

Conclusion

We present the first data from a large cohort of outpatients in Germany providing strong evidence for an association between the body height and common CVD. These data should stimulate a discussion as to how far the body height should be implemented as a parameter in stratification tools to assess CVD risk in order to further reduce cardiovascular morbidity and mortality in the future.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Breast artery calcification as an opportunistic predictor of cardiovascular disease

BACKGROUND

Atherosclerotic cardiovascular disease is still the leading cause of mortality for women. Breast cancer screening with mammography is recommended in all women aged over 40 years.

AIMS

Whether breast artery calcification (BAC) is associated with cardiovascular disease is not clear. We aimed to evaluate the association between BAC and the presence of coronary atherosclerosis determined by CT.

METHODS

All patients who underwent both mammography and coronary CT angiography between January 2010 and December 2016 were screened, and patients with a duration of less than 12 months between CT and mammography were included.

RESULTS

A total of 320 women were included and BAC was detected in 47 (14.6%) patients. BAC was correlated with age and CT coronary calcium score. Both the frequency of critical coronary artery stenosis (34% vs 10.6%; p = 0.001) and CT coronary calcium score (5.5 vs 0; p = 0.001) was significantly higher in patients with BAC. The absence of BAC was a strong predictor of the absence of significant coronary artery disease (p = 0.001). BAC was independently associated with all-cause mortality after excluding patients with breast cancer (HR: 5.32; p = 0.013).

CONCLUSION

Breast artery calcification is associated with coronary calcium score and significant coronary stenosis. A high BAC score is related to increased mortality.

Development, validation and comparison of multivariable risk scores for prediction of total stroke and stroke types in Chinese adults: a prospective study of 0.5 million adults

BACKGROUND AND PURPOSE

Low-income and middle-income countries have the greatest stroke burden, yet remain understudied. This study compared the utility of Framingham versus novel risk scores for prediction of total stroke and stroke types in Chinese adults.

METHODS

China Kadoorie Biobank (CKB) is a prospective study of 512 726 adults, aged 30-79 years, recruited from 10 areas in China in 2004-2008. By 1 January 2018, 43 234 incident first stroke cases (36 310 ischaemic stroke (IS); 8865 haemorrhagic stroke (HS)) were recorded in 503 842 participants with no history of stroke at baseline. We compared the predictive utility of the Framingham Stroke Risk Profile (FSRP) with novel CKB stroke risk scores and included recalibration, refitting, stratifying by study area and addition of other risk factors. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) and calibration was assessed using Greenwood-Nam-D'Agostino χ² statistics.

RESULTS

Incidence of total stroke varied fivefold by area in China. The FSRP had good discrimination for total stroke (AUC (95% CI); men: 0.78 (0.77 to 0.79), women: 0.77 (95% CI 0.76 to 0.78)), but poor calibration (χ²; men: 1,825, women: 3,053), substantially underestimating absolute risks. Recalibration reduced χ² by >80%, but did not improve discrimination. Refitting the FSRP did not materially improve discrimination, but further improved calibration. Stratification by area improved discrimination (AUC; men: 0.82 (0.82 to 0.83); women: 0.82 (0.82 to 0.83)), but not calibration. Adding other risk factors yielded modest, but statistically significant, improvements in the AUCs. The findings for IS and HS were similar to those for total stroke.

CONCLUSIONS

The FSRP reliably differentiated Chinese adults with incident stroke, but substantially underestimated the absolute risks of stroke. Novel local risk prediction equations that took account of differences in stroke incidence within China enhanced risk prediction of total stroke and major stroke pathological types.

Gut Microbially Produced Indole-3-Propionic Acid Inhibits Atherosclerosis by Promoting Reverse Cholesterol Transport and Its Deficiency Is Causally Related to Atherosclerotic Cardiovascular Disease

BACKGROUND

Accumulating evidence has shown that disorders in the gut microbiota and derived metabolites affect the development of atherosclerotic cardiovascular disease (ASCVD). However, which and how specific gut microbial metabolites contribute to the progression of atherosclerosis and the clinical relevance of their alterations remain unclear.

METHODS

We performed integrated microbiome-metabolome analysis of 30 patients with coronary artery disease (CAD) and 30 age- and sex-matched healthy controls to identify CAD-associated microbial metabolites, which were then assessed in an independent population of patients with ASCVD and controls (n=256). We further investigate the effect of CAD-associated microbial metabolites on atherosclerosis and the mechanisms of the action.

RESULTS

Indole-3-propionic acid (IPA), a solely microbially derived tryptophan metabolite, was the most downregulated metabolite in patients with CAD. Circulating IPA was then shown in an independent population to be associated with risk of prevalent ASCVD and correlated with the ASCVD severity. Dietary IPA supplementation alleviates atherosclerotic plaque development in ApoE^-/- mice. In murine- and human-derived macrophages, administration of IPA promoted cholesterol efflux from macrophages to ApoA-I through an undescribed miR-142-5p/ABCA1 (ATP-binding cassette transporter A1) signaling pathway. Further in vivo studies demonstrated that IPA facilitates macrophage reverse cholesterol transport, correlating with the regulation of miR-142-5p/ABCA1 pathway, whereas reduced IPA production contributed to the aberrant overexpression of miR-142-5p in macrophages and accelerated the progression of atherosclerosis. Moreover, the miR-142-5p/ABCA1/reverse cholesterol transport axis in macrophages were dysregulated in patients with CAD, and correlated with the changes in circulating IPA levels.

CONCLUSIONS

Our study identify a previously unknown link between specific gut microbiota-derived tryptophan metabolite and ASCVD. The microbial metabolite IPA/miR-142-5p/ABCA1 pathway may represent a promising therapeutic target for ASCVD.

Prediction performance and fairness heterogeneity in cardiovascular risk models

Prediction models are commonly used to estimate risk for cardiovascular diseases, to inform diagnosis and management. However, performance may vary substantially across relevant subgroups of the population. Here we investigated heterogeneity of accuracy and fairness metrics across a variety of subgroups for risk prediction of two common diseases: atrial fibrillation (AF) and atherosclerotic cardiovascular disease (ASCVD). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large datasets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity across subpopulations defined by age, sex, and presence of preexisting disease, with fairly consistent patterns across both scores. For example, using CHARGE-AF, discrimination declined with increasing age, with a concordance index of 0.72 [95% CI 0.72-0.73] for the youngest (45-54 years) subgroup to 0.57 [0.56-0.58] for the oldest (85-90 years) subgroup in Explorys. Even though sex is not included in CHARGE-AF, the statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65-74 years subgroup with a value of - 0.33 [95% CI - 0.33 to - 0.33]. We also observed weak discrimination (i.e., < 0.7) and suboptimal calibration (i.e., calibration slope outside of 0.7-1.3) in large subsets of the population; for example, all individuals aged 75 years or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify the behavior of clinical risk models within specific subpopulations so they can be used appropriately to facilitate more accurate, consistent, and equitable assessment of disease risk.

Socioeconomic Deprivation: An Important, Largely Unrecognized Risk Factor in Primary Prevention of Cardiovascular Disease

Background:

Socioeconomic deprivation is associated with higher cardiovascular morbidity and mortality. Whether deprivation status should be incorporated in more cardiovascular risk estimation scores remains unclear. This study evaluates how socioeconomic deprivation status affects the performance of 3 primary prevention cardiovascular risk scores.

Methods:

The Generation Scotland Scottish Family Health Study was used to evaluate the performance of 3 cardiovascular risk scores with (ASSIGN [Assessing cardiovascular risk using SIGN (Scottish Intercollegiate Guidelines Network) guidelines to ASSIGN preventive treatment]) and without (SCORE2 [Systematic Coronary Risk Evaluation 2 algorithm], Pooled Cohort Equations) socioeconomic deprivation as a covariate in the risk prediction model. Deprivation was defined by Scottish Index of Multiple Deprivation score. The predicted 10-year risk was evaluated against the observed event rate for the cardiovascular outcome of each risk score. The comparison was made across 3 groups defined by the deprivation index score consisting of group 1 defined as most deprived, group 3 defined as least deprived, and group 2, which consisted of individuals in the middle deprivation categories.

Results:

The study population consisted of 15 506 individuals (60.0% female, median age of 51). Across the population, 1808 (12%) individuals were assigned to group 1 (most deprived), 8119 (52%) to group 2, and 4708 (30%) to group 3 (least deprived), and 871 (6%) individuals had a missing deprivation score. Risk scores based on models that did not include deprivation status significantly under predicted risk in the most deprived (6.43% observed versus 4.63% predicted for SCORE2 [P=0.001] and 6.69% observed versus 4.66% predicted for Pooled Cohort Equations [P<0.001]). Both risk scores also significantly overpredicted the risk in the least deprived group (3.97% observed versus 4.72% predicted for SCORE2 [P=0.007] and 4.22% observed versus 4.85% predicted for Pooled Cohort Equations [P=0.028]). In contrast, no significant difference was demonstrated in the observed versus predicted risk when using the ASSIGN risk score, which included socioeconomic deprivation status in the risk model.

Conclusions:

Socioeconomic status is a largely unrecognized risk factor in primary prevention of cardiovascular disease. Risk scores that exclude socioeconomic deprivation as a covariate under- and overestimate the risk in the most and least deprived individuals, respectively. This study highlights the importance of incorporating socioeconomic deprivation status in risk estimation systems to ultimately reduce inequalities in health care provision for cardiovascular disease.

[Effectiveness of statin treatment strategies for primary prevention of cardiovascular diseases in a community-based Chinese population: A decision-analytic Markov model]

OBJECTIVE

To evaluate the effectiveness of statin treatment strategies based on risk assessment for the primary prevention of cardiovascular diseases by the Western guidelines in a community-based Chinese population from economically developed areas using data from the Chinese electronic health records research in Yinzhou (CHERRY) study.

METHODS

A Markov model was used to evaluate the effectiveness of the following statin treatment strategies, including: (1) usual care without cardiovascular risk assessment(Strategy 0); (2) using the World Health Organization (WHO) non-laboratory-based risk charts with statin treatment for high-risk group (risk ≥ 20%) (Strategy 1); (3) using the WHO laboratory-based risk charts with statin treatment for high-risk group (risk ≥ 20%) (Strategy 2); and (4) using the Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR) model with statin treatment for high-risk group (risk ≥ 10%, Strategy 3). According to the guidelines, adults in the medium-risk group received lifestyle intervention, and adults in the high-risk group received life-style intervention and statin treatment under these strategies. The Markov model simulated different strategies for ten years (cycles) using parameters from the CHERRY study, published data, meta-analyses and systematic reviews for Chinese. The number of cardiovascular events or deaths, as well as the number need to treat (NNT) with statin per cardiovascular event or death prevented, were calculated to compare the effectiveness of different strategies. One-way sensitivity analysis on the uncertainty of incidence rate of cardiovascular diseases, and probabilistic sensitivity analysis on the uncertainty of hazard ratios of interventions were conducted.

RESULTS

Totally 225 811 Chinese adults aged 40-79 years without cardiovascular diseases at baseline were enrolled. In contrast to the usual care without risk assessment-based statin treatment strategy, Strategy 1 using the WHO non-laboratory-based risk charts could prevent 3 482 [95% uncertainty interval (UI): 2 110-4 661] cardiovascular events, Strategy 2 using the WHO laboratory-based risk charts could prevent 3 685 (95%UI: 2 255-4 912) events, and Strategy 3 using the China-PAR model could prevent 3 895 (95%UI: 2 396-5 181) events. NNTs with statin per cardiovascular event prevented were 22 (95%UI: 14-54), 21 (95%UI: 14-52), and 27 (95%UI: 17-67), respectively. Strategy 3 could prevent more cardiovascular events, while Strategies 1 and 2 required fewer numbers need to treat with statin per cardiovascular event prevented. The results were consistent in the sensitivity analyses.

CONCLUSION

The statin treatment strategies based on risk assessment for the primary prevention of cardiovascular diseases recommended by the Western guidelines could achieve substantive health benefits in adults from developed areas of China. Using the China-PAR model for cardiovascular risk assessment could prevent more cardiovascular diseases while using the WHO risk charts seems more efficient.

[Effectiveness of statin treatment strategies for primary prevention of cardiovascular diseases in a community-based Chinese population: A decision-analytic Markov model]

OBJECTIVE

To evaluate the effectiveness of statin treatment strategies based on risk assessment for the primary prevention of cardiovascular diseases by the Western guidelines in a community-based Chinese population from economically developed areas using data from the Chinese electronic health records research in Yinzhou (CHERRY) study.

METHODS

A Markov model was used to evaluate the effectiveness of the following statin treatment strategies, including: (1) usual care without cardiovascular risk assessment(Strategy 0); (2) using the World Health Organization (WHO) non-laboratory-based risk charts with statin treatment for high-risk group (risk ≥ 20%) (Strategy 1); (3) using the WHO laboratory-based risk charts with statin treatment for high-risk group (risk ≥ 20%) (Strategy 2); and (4) using the Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR) model with statin treatment for high-risk group (risk ≥ 10%, Strategy 3). According to the guidelines, adults in the medium-risk group received lifestyle intervention, and adults in the high-risk group received life-style intervention and statin treatment under these strategies. The Markov model simulated different strategies for ten years (cycles) using parameters from the CHERRY study, published data, meta-analyses and systematic reviews for Chinese. The number of cardiovascular events or deaths, as well as the number need to treat (NNT) with statin per cardiovascular event or death prevented, were calculated to compare the effectiveness of different strategies. One-way sensitivity analysis on the uncertainty of incidence rate of cardiovascular diseases, and probabilistic sensitivity analysis on the uncertainty of hazard ratios of interventions were conducted.

RESULTS

Totally 225 811 Chinese adults aged 40-79 years without cardiovascular diseases at baseline were enrolled. In contrast to the usual care without risk assessment-based statin treatment strategy, Strategy 1 using the WHO non-laboratory-based risk charts could prevent 3 482 [95% uncertainty interval (UI): 2 110-4 661] cardiovascular events, Strategy 2 using the WHO laboratory-based risk charts could prevent 3 685 (95%UI: 2 255-4 912) events, and Strategy 3 using the China-PAR model could prevent 3 895 (95%UI: 2 396-5 181) events. NNTs with statin per cardiovascular event prevented were 22 (95%UI: 14-54), 21 (95%UI: 14-52), and 27 (95%UI: 17-67), respectively. Strategy 3 could prevent more cardiovascular events, while Strategies 1 and 2 required fewer numbers need to treat with statin per cardiovascular event prevented. The results were consistent in the sensitivity analyses.

CONCLUSION

The statin treatment strategies based on risk assessment for the primary prevention of cardiovascular diseases recommended by the Western guidelines could achieve substantive health benefits in adults from developed areas of China. Using the China-PAR model for cardiovascular risk assessment could prevent more cardiovascular diseases while using the WHO risk charts seems more efficient.

Role of Traditional Cardiovascular Risk Factors after Initiation of Statin Therapy: A PharmLines Inception Cohort Study

Background

Multiple studies and meta-analyses examined the role of traditional risk factors for cardiovascular events in statin treatment-naive patients. Nowadays, millions receive such therapy for the primary prevention of cardiovascular events (CVE).

Objective

CVEs still occur in patients on primary preventive statin therapy. Therefore, further risk stratification within these patients is urgently needed.

Methods

Using the unique linkage between biomedical data and prescription data from the PharmLines Initiative, we assessed the role of several risk factors used in cardiovascular risk models, using a time-dependent Cox PH model, in the occurrence of drug treatment of CVEs after initiation of statin therapy.

Results

Among 602 statin therapy starters, 11% received drug treatment for CVE within an average follow-up period of 832 days. After multivariable modelling, cholesterol levels and blood pressure at baseline were no longer associated, whereas self-reported diabetes and increasing age were highly associated with the outcome when on statin therapy (hazard ratio (HR): 3.01, 95% confidence interval (95% CI): 1.48-6.12 and 1.04; 95% CI: 1.01-1.07, respectively). Males, smokers, and nonadherent patients had increased risks (HR 1.6, 1.12, and 1.18, resp.), though not statistically significant.

Conclusion

Drug treatment for CVEs after statin initiation is increased in patients with diabetes type 2, in aged patients, males, smokers, and those with poor adherence, while there was no association with baseline cholesterol levels and blood pressure. These factors should be taken into account during the monitoring of statin therapy and may lead to changes in statin treatment or risk-related lifestyle factors.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

Aortic Pulse Wave Velocity as Adjunct Risk Marker for Assessing Cardiovascular Disease Risk: Prospective Study

BACKGROUND

Aortic pulse wave velocity is a noninvasive measure of aortic stiffness and arterial aging. Its current value in cardiovascular risk estimation practice is unknown. We aimed to establish whether aortic pulse wave velocity identified individuals with higher risk of incident major adverse cardiovascular events and improved performance of the American Heart Association/American College of Cardiology atherosclerotic cardiovascular disease risk score.

METHODS

This prospective analysis included 3837 Whitehall II cohort participants screened in 2008 to 2009, and followed for 11.7 years (mean=10.3, SD=1.81), without history of stroke, myocardial infarction, or coronary heart disease.

RESULTS

Mean age of the sample was 65.0 years (SD=5.6), 2831 participants (73.8%) were male and mean atherosclerotic cardiovascular disease risk score was 13.8%. At the end of follow-up, 411 individuals (10.7%) had suffered a major cardiovascular event. Those in the highest aortic pulse wave velocity quartile were at high risk (hazard ratio, 2.99 [95% CI, 2.25-3.97]) and reached the threshold for statin medication (7.5% risk) after 5 years whereas others reached it after 10 years (difference P<0.001). The addition of aortic pulse wave velocity to the risk score improved the C statistic (0.68 versus 0.67, P=0.03) and net reclassification index (4.6%, P=0.04 and 11.3%, P=0.02).

CONCLUSIONS

Our results show that aortic stiffness predicted major adverse cardiovascular events in a cohort of elderly individuals, improving the performance of a widely used cardiovascular disease risk estimator. Aortic pulse wave velocity measurement is scalable, radiation-free, and easy to perform. Further studies on its applicability in cardiovascular disease risk assessment in primary care settings are needed.

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank

OBJECTIVES

To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme.

METHODS

We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%).

RESULTS

In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men.

CONCLUSIONS

The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme.

Differences in cardiovascular risk levels between cleaning staff and hotel housekeepers

Background and objective

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide, with a greater incidence in the most disadvantaged social classes. In this study, we aimed to evaluate the level of cardiovascular risk in cleaning workers.

Methods

This was a descriptive, cross‐sectional study in 46.632 cleaning workers (40.169 women and 6.463 men). Thirty‐one different scales related to cardiovascular risk were studied (14 assessing overweight and obesity, 5 determining the risk of nonalcoholic fatty liver disease, 5 scales of cardiovascular risk, 4 atherogenic indices, and 3 scales of metabolic syndrome, among others). The results obtained were divided between personnel who perform their cleaning tasks in the hotel and catering industry and those in other sectors.

Results

The prevalence of obesity and arterial hypertension in cleaning workers was over 20% in both sexes. A similar amount was observed in moderate or high values on the REGICOR (Registre GIroní del COR) scale. More than 15% presented metabolic syndrome according to the NCEP ATPIII (National Cholesterol Education Program‐Adult Treatment Program III) criteria, while over 10% of women and 20% of men had a high risk of nonalcoholic fatty liver disease assessed with the fatty liver index.

Conclusion

Cardiovascular risk is higher, in both sexes, in the group of cleaning workers who work in companies other than hotels.

Translational research in nephrology: prognosis

Translational research aims at reducing the gap between the results of studies focused on diagnosis, prognosis and therapy, and every day clinical practice. Prognosis is an essential component of clinical medicine. It aims at estimating the risk of adverse health outcomes in individuals, conditional to their clinical and non-clinical characteristics. There are three fundamental steps in prognostic research: development studies, in which the researcher identifies predictors, assigns the weights to each predictor, and assesses the model’s accuracy through calibration, discrimination and risk reclassification; validation studies, in which investigators test the model’s accuracy in an independent cohort of individuals; and impact studies, in which researchers evaluate whether the use of a prognostic model by clinicians improves their decision-making and patient outcome. This article aims at clarifying how to reduce the disconnection between the promises of prognostic research and the delivery of better individual health.

Evaluation of Atherosclerotic Cardiovascular Risk Prediction Models in China: Results From the CHERRY Study

BACKGROUND

Updated American or Chinese guidelines recommended calculating atherosclerotic cardiovascular disease (ASCVD) risk using the Pooled Cohort Equations (PCE) or Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) models; however, evidence on performance of both models in Asian populations is limited.

OBJECTIVES

The authors aimed to evaluate the accuracy of the PCE or China-PAR models in a Chinese contemporary cohort.

METHODS

Data were extracted from the CHERRY (CHinese Electronic health Records Research in Yinzhou) study. Participants aged 40 to 79 years without prior ASCVD at baseline from 2010 to 2016 were included. ASCVD was defined as nonfatal or fatal stroke, nonfatal myocardial infarction, and cardiovascular death. Models were assessed for discrimination and calibration.

RESULTS

Among 226,406 participants, 5362 (2.37%) adults developed a first ASCVD event during a median of 4.60 years of follow-up. Both models had good discrimination: C-statistics in men were 0.763 (95% confidence interval [CI]: 0.754-0.773) for PCE and 0.758 (95% CI: 0.749-0.767) for China-PAR; C-statistics in women were 0.820 (95% CI: 0.812-0.829) for PCE and 0.811 (95% CI: 0.802-0.819) for China-PAR. The China-PAR model underpredicted risk by 20% in men and by 40% in women, especially in the highest-risk groups. However, PCE overestimated by 63% in men and inversely underestimated the risk by 34% in women with poor calibration (both P < 0.001). After recalibration, observed and predicted risks by recalibrated PCE were better aligned.

CONCLUSIONS

In this large-scale population-based study, both PCE and China-PAR had good discrimination in 5-year ASCVD risk prediction. China-PAR outperformed PCE in calibration, whereas recalibration equalized the performance of PCE and China-PAR. Further specific models are needed to improve accuracy in the highest-risk groups.

The effect of non-invasively obtained central blood pressure on cardiovascular outcome in diabetic patients in Assiut University Hospitals

Background

The major cause of morbidity and mortality in diabetes is cardiovascular disease, which is exacerbated by the presence of hypertension. Therefore, proper control of BP in diabetic hypertensive patients is essential. Few studies have specifically investigated the prognostic significance of central BP in Egyptian populations with diabetes and hypertension and its relation with cardiovascular outcome. This study aims to evaluate relation between central BP and diabetic composite cardiovascular complications.

Results

Diabetic patients with CVD were significantly older (p value < 0.01), obese (p value < 0.01) with long duration of diabetes (p value < 0.001) and had significantly higher peripheral and central systolic and diastolic BP and higher AIx@75(p values < 0.01) than those without CVD. Regarding the metabolic parameters, they had significantly higher fasting blood glucose, HbA1c, and higher blood cholesterol levels (p values < 0.001), higher LDL (p value < 0.01), triglycerides levels (p value = 0.014), and microalbuminuria (p value = 0.028). Logistic regression analysis found increased BMI, central systolic BP, and AIx@75 were independent predictors of composite CVD (p values < 0.05).

Conclusions

There is a pattern of favorability towards central rather than peripheral BP indices to predict the occurrence of CVD in diabetic patients.

High-Sensitivity C-Reactive Protein (hS-CRP) levels and pregnancy outcomes in women with unexplained infertility after ovarian stimulation with intrauterine-insemination (OS-IUI) in a multi-center trial

Objective

To determine if chronic inflammation, assessed by basal high-sensitivity C-reactive protein (hs-CRP) levels, is associated with pregnancy outcomes in women with unexplained infertility undergoing ovarian stimulation with intrauterine insemination.

Design

Prospective cohort analysis of the Reproductive Medicine Network’s Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation (AMIGOS) randomized controlled trial.

Setting

Multicenter university-based randomized controlled trial.

Patient(s)

A total of 781 couples with unexplained infertility.

Intervention(s)

Secondary analysis.

Main Outcome Measure(s)

Adjusted risk ratios of live birth, clinical pregnancy, and pregnancy loss rates by hs-CRP levels.

Result(s)

Associations between hs-CRP levels and clinical pregnancy rates were not observed after adjustment for baseline body mass index. There were fewer live births among women with higher hs-CRP levels, although confidence intervals crossed 1.0. The risk of pregnancy loss was greater in women with increased hs-CRP levels (1–3 mg/L: risk ratio [RR], 1.67; 95% confidence interval [CI], 1.00–2.79; >3–10 mg/L: RR, 1.84; 95% CI, 1.06–3.20; and >10 mg/L: RR, 2.14; 95% CI, 1.05–4.36 compared to women with hs-CRP <1 mg/L).

Conclusion(s)

This investigation suggests that chronic inflammation may increase the risk of pregnancy loss but not impact the clinical pregnancy rate in women with unexplained infertility undergoing ovarian stimulation with intrauterine insemination. Associations between inflammation and pregnancy outcomes in women with infertility merit further investigation.

Clinical Trial Registration Number

clinicaltrials.gov NCT01044862.

The management correlation between metabolic index, cardiovascular health, and diabetes combined with cardiovascular disease

BACKGROUND

Cardiovascular disease (CVD) has become a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Although there is also evidence that multifactorial interventions to control blood glucose, blood pressure, and lipid profiles can reduce macrovascular complications and mortality in patients with T2DM, the link between these risk factors has not been established.

METHODS

On 10 December 2018, 1,920 people in four cities in Anhui Province were included. Latent category analysis (LCA) was used to explore the clustering mode of HRBs (health risk behaviors). The primary exposure was HRBs and exercise and diet interventions, and the primary outcome was CVD and other variables, including zMS, triglyceride-glucose index (TyG), TyG-WC (waist circumference), TyG-BMI, TG/HDL, and cardiovascular health (CVH). A multivariable logistic regression model was used to establish the relationship between HRBs, exercise, diet interventions, and CVD. Moderate analysis and mediation moderation analysis were employed by the PROCESS method to explore the relationship between these variables. Sensitivity analysis explored the robustness of the model.

RESULTS

The mean age was 57.10 ± 10.0 years old. Overall, CVD affects approximately 19.9% of all persons with T2DM. Macrovascular complications of T2DM include coronary heart disease, myocardial infarction (MI), cardiac insufficiency, and cerebrovascular disease. Elderly age (χ ² = 22.70), no occupation (χ ² = 20.97), medium and high socioeconomic status (SES) (χ ² = 19.92), higher level of TyG-WC (χ ² = 6.60), and higher zMS (χ ² = 7.59) were correlated with high CVD. Many metabolic indices have shown a connection with T2DM combined with CVD, and there was a dose-response relationship between HRB co-occurrence and clustering of HRBs and zMS; there was a dose-response relationship between multifactorial intervention and CVH. In the mediation moderation analysis, there was an association between HRB, gender, TyG, TyG-BMI, and CVD. From an intervention management perspective, exercise and no diet intervention were more significant with CVD; moreover, there was an association between intervention management, gender, zMS, TyG-WC, TyG-BMI, TG/HDL, and CVD. Finally, there was an association between sex, CVH, and CVD. Sensitivity analysis demonstrated that our results were robust.

CONCLUSIONS

CVD is one of the common complications in patients with type 2 diabetes, and its long-term outcome will have more or less impact on patients. Our findings suggest the potential benefits of scaling up multifactorial and multifaceted interventions to prevent CVD in patients with T2DM.

Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4HEN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4HEN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4HEN-COX quintiles, ranging from 0.25% to 7.8%. ML4HEN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.

Cardiovascular risk assessment in people living with HIV compared to the general population

AIMS

We prospectively assessed and compared the accuracy of cardiovascular risk scores in people living with HIV (PLWH) and individuals from the general population.

METHODS AND RESULTS

The Systematic Coronary Risk Evaluation Score 2 (SCORE2), the Pooled Cohort Equations (PCE), and the HIV-specific Data Collection on Adverse events of Anti-HIV Drugs (D:A:D) score were calculated in participants free from atherosclerotic cardiovascular disease (ASCVD) between 2003 and 2009. In total, 6373 [mean age, 40.6 years (SD, 9.9)] PLWH from the Swiss HIV Cohort Study (SHCS) and 5403 [52.8 years (SD, 10.7)] individuals from the CoLaus|PsyCoLaus study were eligible for analysis. We tested discrimination and calibration, and the value of adding HIV-specific factors to scores using the net reclassification improvement (NRI). During mean follow-ups of 13.5 (SD, 4.1) in SHCS and 9.9 (SD, 2.3) years in CoLaus|PsyCoLaus study, 533 (8.4%) and 374 (6.9%) people developed an incident ASCVD, respectively. This translated into age-adjusted incidence rates of 12.9 and 7.5 per 1000 person-year, respectively. In SHCS, SCORE2, PCE, and D:A:D presented comparable discriminative capacities [area under the receiver operating characteristic curve of 0.745 (95% confidence interval, CI, 0.723-0.767), 0.757 (95% CI, 0.736-0.777), and 0.763 (95% CI, 0.743-0.783)]. Adding HIV-specific variables (CD4 nadir and abacavir exposure) to SCORE2 and PCE resulted in an NRI of -0.1% (95% CI, -1.24 to 1, P = 0.83) and of 2.7% (95% CI, 0.3-5.1, P = 0.03), respectively.

CONCLUSIONS

PLWH present a two-fold higher rate of incident ASCVD compared to individuals from the general population. SCORE2 and PCE, which are clinically easier to use (reduced set of variables without adding HIV-specific factors), are valid to predict ASCVD in PLWH.

A 6-CpG validated methylation risk score model for metabolic syndrome: The HyperGEN and GOLDN studies

There has been great interest in genetic risk prediction using risk scores in recent years, however, the utility of scores developed in European populations and later applied to non-European populations has not been successful. The goal of this study was to create a methylation risk score (MRS) for metabolic syndrome (MetS), demonstrating the utility of MRS across race groups using cross-sectional data from the Hypertension Genetic Epidemiology Network (HyperGEN, N = 614 African Americans (AA)) and the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, N = 995 European Americans (EA)). To demonstrate this, we first selected cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously reported to be significantly associated with MetS and/or component conditions in more than one race/ethnic group (CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Second, we calculated the parameter estimates for the 6 CpGs in the HyperGEN data (AA) and used the beta estimates as weights to construct a MRS in HyperGEN (AA), which was validated in GOLDN (EA). We performed association analyses using logistic mixed models to test the association between the MRS and MetS, adjusting for covariates. Results showed the MRS was significantly associated with MetS in both populations. In summary, a MRS for MetS was a strong predictor for the condition across two race groups, suggesting MRS may be useful to examine metabolic disease risk or related complications across race/ethnic groups.

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort

BACKGROUND

Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

METHODS

We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

RESULTS

After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

CONCLUSIONS

Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Challenges in Optimizing Lipid Management in Women

While there are physiologic differences in lipid metabolism in men and women, pharmacologic therapy is very effective in both with similar management strategies recommended in the current guidelines for the management of dyslipidemia. Despite similar guidelines for treatment, studies have shown that women have worse control of dyslipidemia than their male counterparts. This may stem from multiple contributing factors including underestimation of cardiovascular disease risk in women, decreased prescription and utilization of lipid-lowering therapies, decreased medication adherence, and higher risk of statin intolerance, all of which may contribute to lower attainment of lipid targets. Furthermore, heart disease is the leading cause of mortality in women, with heart disease noted an average of 7-10 years later than in men. This has historically led to the misperception that women are protected from heart disease and can be treated less aggressively. In fact, traditional risk factors for atherosclerotic cardiovascular disease often impact risk in women to a greater extent than they do in men. Unique risk factors such as pregnancy-related disorders also contribute to the level of risk and therefore warrant consideration in risk stratification. This review summarizes the efficacy of contemporary lipid-lowering therapies in women versus men and discusses the challenges that arise with lipid management in women along with potential ways to tackle these obstacles.