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The article discusses a study comparing different cardiovascular disease (CVD) risk prediction models and their implications for treatment recommendations. The study found that individuals with the same risk factor profile can receive different risk estimates depending on the algorithm used. The authors argue that this lack of concordance is a limitation of current risk prediction models and treatment approaches. They also highlight the importance of considering the background level of risk in different populations and the need for country-specific decisions and recommendations regarding screening strategies and treatment thresholds. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

We investigated independent associations between telomere length and risk of fracture and arthroplasty in UK Biobank participants. Leukocyte telomere length (LTL) was measured in baseline samples using a validated polymerase chain reaction (PCR) method. We used, in men and women separately, Cox proportional hazards models to calculate the hazard ratio (HR) for incident fracture (any, osteoporotic) or arthroplasty (hip or knee) over 1,186,410 person‐years of follow‐up. Covariates included age, white cell count, ethnicity, smoking, alcohol, physical activity, and menopause (women). In further analyses we adjusted for either estimated bone mineral density (eBMD) from heel quantitative ultrasound, handgrip strength, gait speed, total fat mass (bioimpedance), or blood biomarkers, all measured at baseline (2006–2010). We studied 59,500 women and 51,895 men, mean ± standard deviation (SD) age 56.4 ± 8.0 and 57.0 ± 8.3 years, respectively. During follow‐up there were 5619 fractures; 5285 hip and 4261 knee arthroplasties. In confounder‐adjusted models, longer LTL was associated with reduced risk of incident knee arthroplasty in both men (HR/SD 0.93; 95% confidence interval [CI], 0.88–0.97) and women (0.92; 95% CI, 0.88–0.96), and hip arthroplasty in men (0.91; 95% CI, 0.87–0.95), but not women (0.98; 95% CI, 0.94–1.01). Longer LTL was weakly associated with reduced risk of any incident fracture in women (HR/SD 0.96; 95% CI, 0.93–1.00) with less evidence in men (0.98; 95% CI, 0.93–1.02). Associations with incident outcomes were not materially altered by adjustment for heel eBMD, grip strength, gait speed, fat mass, or blood biomarker measures. In this, the largest study to date, longer LTL was associated with lower risk of incident knee or hip arthroplasty, but only weakly associated with lower risk of fracture. The relative risks were low at a population level, but our findings suggest that common factors acting on the myeloid and musculoskeletal systems might influence later life musculoskeletal outcomes. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict "statin-naive" CVD risk. We analyzed primary care data (2004–2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (aged 40–85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g. number needed to screen to prevent 1 event) against models ignoring statin initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for (versus ignoring) statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in number needed to screen to prevent 1 event. In conclusion, incorporating statin effects from trial results into risk-prediction models enables statin-naive CVD risk estimation and provides moderate gains in predictive ability but had a limited impact on treatment decision-making under current guidelines in this population. [ABSTRACT FROM AUTHOR]

Aims There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods and results Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms. Conclusion Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need. View large Download slide View large Download slide [ABSTRACT FROM AUTHOR]

Objectives. To assess the prevalences across Europe of radiological indices of degenerative inter-vertebral disc disease (DDD); and to quantify their associations with, age, sex, physical anthropometry, areal BMD (aBMD) and change in aBMD with time. Methods. In the population-based European Prospective Osteoporosis Study, 27 age-stratified samples of men and women from across the continent aged 50+ years had standardized lateral radiographs of the lumbar and thoracic spine to evaluate the severity of DDD, using the Kellgren-Lawrence (KL) scale. Measurements of anterior, mid-body and posterior vertebral heights on all assessed vertebrae from T4 to L4 were used to generate indices of end-plate curvature. Results. Images from 10 132 participants (56% female, mean age 63.9 years) passed quality checks. Overall, 47% of men and women had DDD grade 3 or more in the lumbar spine and 36% in both thoracic and lumbar spine. Risk ratios for DDD grades 3 and 4, adjusted for age and anthropometric determinants, varied across a three-fold range between centres, yet prevalences were highly correlated in men and women. DDD was associated with flattened, non-ovoid inter-vertebral disc spaces. KL grade 4 and loss of inter-vertebral disc space were associated with higher spine aBMD. Conclusion. KL grades 3 and 4 are often used clinically to categorize radiological DDD. Highly variable European prevalences of radiologically defined DDD grades 3+ along with the large effects of age may have growing and geographically unequal health and economic impacts as the population ages. These data encourage further studies of potential genetic and environmental causes. [ABSTRACT FROM AUTHOR]

The article presents the discussion on SCORE2 being a risk algorithm estimating 10-year cardiovascular disease (CVD) risk in men and women from different risk regions of Europe including the need for consideration of sex differences in cardiovascular disease risk.

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. [ABSTRACT FROM PUBLISHER]

Aims Because low-grade inflammation may play a role in the pathogenesis of coronary heart disease (CHD), and pro-inflammatory cytokines govern inflammatory cascades, this study aimed to assess the associations of several pro-inflammatory cytokines and CHD risk in a new prospective study, including meta-analysis of prospective studies. Methods and results Interleukin-6 (IL-6), IL-18, matrix metalloproteinase-9 (MMP-9), soluble CD40 ligand (sCD40L), and tumour necrosis factor-α (TNF-α) were measured at baseline in a case-cohort study of 1514 participants and 833 incident CHD events within population-based prospective cohorts at the Danish Research Centre for Prevention and Health. Age- and sex-adjusted hazard ratios (HRs) for CHD per 1-SD higher log-transformed baseline levels were: 1.37 (95% CI: 1.21–1.54) for IL-6, 1.26 (1.11–1.44) for IL-18, 1.30 (1.16–1.46) for MMP-9, 1.01 (0.89–1.15) for sCD40L, and 1.13 (1.01–1.27) for TNF-α. Multivariable adjustment for conventional vascular risk factors attenuated the HRs to: 1.26 (1.08–1.46) for IL-6, 1.12 (0.95–1.31) for IL-18, 1.21 (1.05–1.39) for MMP-9, 0.93 (0.78–1.11) for sCD40L, and 1.14 (1.00–1.31) for TNF-α. In meta-analysis of up to 29 population-based prospective studies, adjusted relative risks for non-fatal MI or CHD death per 1-SD higher levels were: 1.25 (1.19–1.32) for IL-6; 1.13 (1.05–1.20) for IL-18; 1.07 (0.97–1.19) for MMP-9; 1.07 (0.95–1.21) for sCD40L; and 1.17 (1.09–1.25) for TNF-α. Conclusions Several different pro-inflammatory cytokines are each associated with CHD risk independent of conventional risk factors and in an approximately log-linear manner. The findings lend support to the inflammation hypothesis in vascular disease, but further studies are needed to assess causality. [ABSTRACT FROM PUBLISHER]

Chronic obstructive pulmonary disease is known to be associated with systemic inflammation. We examined the longitudinal association of C-reactive protein (CRP) and lung function in a cohort of 18,110 men and women from the European Prospective Investigation Into Cancer in Norfolk who were 40–79 years of age at baseline (recruited in 1993–1997) and followed-up through 2011. We assessed lung function by measuring forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) at baseline, 4 years, and 13 years. Serum CRP levels were measured using a high-sensitivity assay at baseline and the 13-year follow up. Cross-sectional and longitudinal associations of loge-CRP and lung function were examined using multivariable linear mixed models. In the cross-sectional analysis, 1-standard-deviation increase in baseline loge-CRP (about 3-fold higher CRP on the original milligrams per liter scale) was associated with a −86.3 mL (95% confidence interval: −93.9, −78.6) reduction in FEV1. In longitudinal analysis, a 1-standard-deviation increase in loge-CRP over 13 years was also associated with a −64.0 mL (95% confidence interval: −72.1, −55.8) decline in FEV1 over the same period. The associations were similar for FVC and persisted among lifetime never-smokers. Baseline CRP levels were not predictive of the rate of change in FEV1 or FVC over time. In the present study, we found longitudinal observational evidence that suggested that increases in systemic inflammation are associated with declines in lung function. [ABSTRACT FROM PUBLISHER]

Background: Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure-risk relationships, but involves a number of analytical challenges.Methods: This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes.Results: Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure-risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure-risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available.Conclusion: Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses. [ABSTRACT FROM AUTHOR]

The article discusses a study on the predictive capability of quantitative ultrasound (QUS) and dual x-ray absorptiometry (DXA) in terms of the 10-year absolute risk of fracture. It notes that DXA stands as the accepted reference method in bone mass density (BMD) measurement while a decline in BMD is linked to an increased risk of fracture. Under the study, the participants were subjected to either hip BMD measurement and QUS assessment. Results show that QUS assessment exhibited performance that is similar to that of DXA in predicting fracture risks.

The article discusses a study concerning risk prediction for osteoporotic fractures that is based on the femur geometry variables and an analysis of hip structure in women. Analysis software calculated the structural variables in the narrow neck, intertrochanter (IT), and shaft regions. The conclusion is that trochanteric fractures can be predicted by analyzing the parameters in the IT region.

The article discusses the assumption that remodeling balance and remodeling rate are co-regulated in adulthood. The materials and methods used in the study are presented. The results of the study is discussed. In conclusion, results show that remodeling balance and remodeling rate are not co-regulated in adult human bone.