In prior studies of cumulative risk factor exposure, self-identified race was independently associated with incident cardiovascular disease (CVD). A recent study suggests clinical, demographic, and socioeconomic factors explain racial differences. We used propensity score matching to study race as an independent incident CVD risk factor.

The purpose of this study was to assess race as an independent risk factor for incident CVD.

We analyzed CARDIA (Coronary Artery Risk Development in Young Adults) study data using propensity score matching of White and Black women, and, separately, White and Black men, with respect to known CVD risk factors.

Black men (n = 487), compared to White men (n = 487), had higher risk of CVD (HR: 2.30; 95% CI: 1.36-3.89; P = 0.0014), stroke (HR: 5.00; 95% CI: 1.45-17.3; P = 0.0047), and congestive heart failure (CHF) (HR: 3.60; 95% CI: 1.34-9.70; P = 0.0067). Black women (n = 640), compared to White women (n = 640), had higher CVD risk (HR: 2.36; 95% CI: 1.17-4.78; P = 0.014) and stroke risk (HR: 2.80; 95% CI: 1.01-7.77; P = 0.039) and borderline significantly higher CHF risk (HR: 3.50; 95% CI: 0.73-16.9; P = 0.096). Risk of coronary heart disease did not differ significantly by race in either sex. Multivariable analyses showed racial differences in the associations of multiple risk factors with incident CVD events independent of other known CVD risk factors.

Propensity score matching analyses demonstrate that race is an independent risk factor for incident CVD and its components, CHF, and stroke. Multivariable analyses suggest racial differences in Black vs White risk factor impact as the possible cause. Reasons for these differences remain to be explored.

Relevant calcified coronary artery disease (CCAD) may be present in around 20% of patients undergoing percutaneous coronary interventions, and it is known to add procedural challenges and risks. Careful patient selection and specific expertise in multimodality imaging and plaque modification techniques are required to plan and adopt the most appropriate therapeutic strategy. This review aims to present the contemporary clinical approach and procedural planning for CCAD patients, describing the available tools and strategies in view of the most recent scientific evidence.

There has been a renewed interest in identifying heterogenous treatment effects (HTEs) to guide personalized medicine. The objective was to illustrate the use of a step-by-step transparent parametric data-adaptive approach (the generalized HTE approach) based on the G-computation algorithm to detect heterogenous subgroups and estimate meaningful conditional average treatment effects (CATE). The following seven steps implement the generalized HTE approach: Step 1: Select variables that satisfy the backdoor criterion and potential effect modifiers; Step 2: Specify a flexible saturated model including potential confounders and effect modifiers; Step 3: Apply a selection method to reduce overfitting; Step 4: Predict potential outcomes under treatment and no treatment; Step 5: Contrast the potential outcomes for each individual; Step 6: Fit cluster modeling to identify potential effect modifiers; Step 7: Estimate subgroup CATEs. We illustrated the use of this approach using simulated and real data. Our generalized HTE approach successfully identified HTEs and subgroups defined by all effect modifiers using simulated and real data. Our study illustrates that it is feasible to use a step-by-step parametric and transparent data-adaptive approach to detect effect modifiers and identify meaningful HTEs in an observational setting. This approach should be more appealing to epidemiologists interested in explanation.

We review the utility of coronary artery calcium (CAC) scoring in personalized risk assessment and initiation of cardiovascular disease risk modifying therapy.

Many populations - including South Asians, patients with cancer, patients with human immunodeficiency virus (HIV), younger patients, and elderly patients - were not included during the conception of the current risk stratification tools. CAC scoring may allow clinicians to risk-stratify these individuals and help initiate preventive therapy in higher risk populations. Furthermore, CAC scoring may be able to be integrated into current imaging practices to allow for more ubiquitous and equitable screening practices.

CAC scoring is an additional, objective metric that may allow for nuanced and personalized risk assessment of future atherosclerotic cardiovascular disease (ASCVD) events.

Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive model for cf-PWV based on brachial-ankle pulse wave velocity (baPWV) and other the accessible clinical parameters. This model aims to allow patients to estimate their cf-PWV in advance without the need for direct measurement. We selected participants of the Northern Shanghai community from 2013 to 2022 as the study object. The Pearson correlation coefficient was employed for correlation analysis in feature selection. The linear regression models demonstrated low root mean square error (RMSE), error term (ε), and R2 values, indicating good predictive performance. A Cox proportional hazards model revealed a significant association between machine learning-predicted cf-PWV and mortality risk, supporting the validity of prediction model. Using a threshold of cf-PWV greater than 10 m/s as the criterion, a classification prediction model was developed. Shapley Additive Explanations (SHAP) analysis was then applied to the Gradient Boosting model to elucidate the predictive mechanism of the optimal model. Without precise instruments, doctors often cannot determine a patient's cf-PWV. When the cf-PWV value predicted by the machine learning algorithm is high, patients can be recommended for more precise measurements to confirm the prediction and emphasize the importance of follow-up health management and psychological support. It is feasible to use a machine learning algorithm based on baPWV and other readily available clinical parameters to predict cf-PWV.

We aimed to identify the characteristics of patients with diabetes who can derive cognitive benefits from intensive blood pressure (BP) treatment using machine learning methods.

Using data from the Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORD-MIND) study, 1349 patients with type 2 diabetes who underwent BP treatment (intensive treatment targeting a systolic BP <120 mmHg vs. standard treatment targeting <140 mmHg) were included in the machine learning analysis. Seventy-nine variables correlated with diabetes and cognitive function were used to build the causal forest and causal tree models for identifying heterogeneous BP treatment effects on cognitive decline.

Our analyses identified four variables including urinary albumin-to-creatinine ratio (UACR, mg/g), Framingham 10-year cardiovascular risk score (FRS, %), triglycerides (TG, mmol/L) and diabetes duration, that categorized the participants into five subgroups with different risk benefits for cognitive decline from BP treatments. Subgroup 1 (UACR ≥65 mg/g) had an absolute risk reduction (ARR) of 15.36% (95% CI, 5.01%-25.46%) from intensive versus standard BP treatment (hazard ratio [HR], 0.36; 95% CI, 0.18-0.73). Subgroup 2 (UACR <65 mg/g, FRS ≥26%, TG <2.3 mmol/L and diabetes duration ≥9 years) had an ARR of 14.74% (95% CI, 4.56%-24.59%) from intensive versus standard BP treatment (HR, 0.34; 95% CI, 0.15-0.77). No significant benefits were found for other subgroups.

Patients with type 2 diabetes with high UACR, or with low UACR and low TG, but high predicted cardiovascular risk and long diabetes duration were likely to derive cognitive benefits from intensive BP treatment.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

The effect of different functional dependency types on cardiovascular disease (CVD) is largely unknown. Here, we aimed to investigate the association between functional dependence and CVD among middle-aged and older adults by conducting a cross-sectional and longitudinal study.

The study sample comprised 16,459 individuals of ≥40 years (including 10,438 without CVD) who had participated in the 2011 China Health and Retirement Longitudinal Study (CHARLS). Functional dependence was categorized based on the "interval-of-need" method, while CVD was defined as physician-diagnosed heart disease or stroke. Cox proportional hazard regression was employed to assess the effects of functional dependence on CVD. Moreover, patients were grouped according to the functional status changes, and the impact of these changes on CVD was observed. Heterogeneity, subgroup, and interaction analyses were used to evaluate the consistency of the study findings. Finally, a mediation analysis was performed to estimate the potential mediation effects on the relationship between functional dependence and CVD risk.

CVD prevalence in the overall study population was 13.73 % (2260/16,459), while its prevalence among individuals with functional independence, low dependency, medium dependency, and high dependency was 9.60 % (1085/11,302), 14.25 % (119/835), 17.72 % (115/649), and 25.01 % (941/3763), respectively. Additionally, medium (odds ratio: 1.33, 95 % confidence interval: 1.06-1.68) and high functional dependency (1.55, 95 % CI: 1.38-1.75) were associated with CVD. A total of 2987 (28.62 %) participants with CVD were identified during the 9-year follow-up, with 4.85 % (145/2987) of the CVD cases being attributed to functional dependence. The individuals with medium (HR: 1.20, 95 % CI: 1.01-1.44) and high functional dependency (1.25, 95 % CI: 1.14-1.37) were more likely to develop CVD than their peers with functional independence. Furthermore, persistent functional dependence (HR: 1.72, 95 % CI: 1.52-1.94) and transition from functional independence to dependence (1.79, 95 % CI: 1.61-1.98) were associated with a higher CVD risk than continuous functional independence. Hypertension and diabetes may partially mediate CVD caused by functional dependence.

Functional dependence is associated with high CVD risk. Therefore, appropriate healthcare attention must be directed towards functionally dependent populations to protect their cardiovascular health.

Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level.

We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421).

Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman's ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention.

Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.

Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of death worldwide and in Taiwan. It is highly prevalent and has a tremendous impact on global health. Therefore, the Taiwan Society of Cardiology developed these best-evidence preventive guidelines for decision-making in clinical practice involving aspects of primordial prevention including national policies, promotion of health education, primary prevention of clinical risk factors, and management and control of clinical risk factors. These guidelines cover the full spectrum of ASCVD, including chronic coronary syndrome, acute coronary syndrome, cerebrovascular disease, peripheral artery disease, and aortic aneurysm. In order to enhance medical education and health promotion not only for physicians but also for the general public, we propose a slogan (2H2L) for the primary prevention of ASCVD on the basis of the essential role of healthy dietary pattern and lifestyles: "Healthy Diet and Healthy Lifestyles to Help Your Life and Save Your Lives". We also propose an acronym of the modifiable risk factors/enhancers and relevant strategies to facilitate memory: " ABC2D2EFG-I'M2 ACE": Adiposity, Blood pressure, Cholesterol and Cigarette smoking, Diabetes mellitus and Dietary pattern, Exercise, Frailty, Gout/hyperuricemia, Inflammation/infection, Metabolic syndrome and Metabolic dysfunction-associated fatty liver disease, Atmosphere (environment), Chronic kidney disease, and Easy life (sleep well and no stress). Some imaging studies can be risk enhancers. Some risk factors/clinical conditions are deemed to be preventable, and healthy dietary pattern, physical activity, and body weight control remain the cornerstone of the preventive strategy.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research.

Vascular calcification (VC) is the ectopic deposition of calcium-containing apatite within vascular walls, exhibiting a high prevalence in older adults, and those with diabetes or chronic kidney disease. VC is a subclinical cardiovascular risk trait that increases mortality and functional deterioration. However, effective treatments for VC remain largely unavailable despite multiple attempts. Part of this therapeutic nihilism results from the failure to appreciate the diversity of VC as a pathological complex, with unforeseeable variations in morphology, risk associates, and anatomical and molecular pathogenesis, affecting clinical management strategies. VC should not be considered a homogeneous pathology because accumulating evidence refutes its conceptual and content uniformity. Here, we summarize the pathophysiological sources of VC heterogeneity from the intersecting pathways and networks of cellular, subcellular, and molecular crosstalk. Part of these pathological connections are synergistic or mutually antagonistic. We then introduce clinical implications related to the VC heterogeneity concept. Even within the same individual, a specific artery may exhibit the strongest tendency for calcification compared with other arteries. The prognostic value of VC may only be detectable with a detailed characterization of calcification morphology and features. VC heterogeneity is also evident, as VC risk factors vary between different arterial segments and layers. Therefore, diagnostic and screening strategies for VC may be improved based on VC heterogeneity, including the use of radiomics. Finally, pursuing a homogeneous treatment strategy is discouraged and we suggest a more rational approach by diversifying the treatment spectrum. This may greatly benefit subsequent efforts to identify effective VC therapeutics.

Tooth loss is prevalent in older adults and associated with functional capacity decline. Studies on the susceptibility of some individuals to the effects of tooth loss are lacking. This study aimed to investigate the heterogeneity of the association between tooth loss and higher-level functional capacity in older Japanese individuals employing a machine learning approach. This is a prospective cohort study using the data of adults aged ≥65 y in Japan (N = 16,553). Higher-level functional capacity, comprising instrumental independence, intellectual activity, and social role, was evaluated using the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC). The scale ranged from 0 (lowest function) to 13 (highest function). Doubly robust targeted maximum likelihood estimation was used to estimate the population-average association between tooth loss (having <20 natural teeth) and TMIG-IC total score after 6 y. The heterogeneity of the association was evaluated by estimating conditional average treatment effects (CATEs) using the causal forest algorithm. The result showed that tooth loss was statistically significantly associated with lower TMIG-IC total scores (population-average effect: -0.14; 95% confidence interval, -0.18 to -0.09). The causal forest analysis revealed the heterogeneous associations between tooth loss and lower TMIG-IC total score after 6 y (median of estimated CATEs = -0.13; interquartile range = 0.12). The high-impact subgroup (i.e., individuals with estimated CATEs of the bottom 10%) were significantly more likely to be older and male, had a lower socioeconomic status, did not have a partner, and had poor health conditions compared with the low-impact subgroup (i.e., individuals with estimated CATEs of the top 10%). This study found that heterogeneity exists in the association between tooth loss and lower scores on functional capacity. Implementing tooth loss prevention policy and clinical measures, especially among vulnerable subpopulations significantly affected by tooth loss, may reduce its burden more effectively.

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

Esophageal variceal bleeding is a severe complication associated with liver cirrhosis and typically necessitates endoscopic hemostasis. The current standard treatment is endoscopic variceal ligation (EVL), and Western guidelines recommend antibiotic prophylaxis following hemostasis. However, given the improvements in prognosis for variceal bleeding due to advancements in the management of bleeding and treatments of liver cirrhosis and the global concerns regarding the emergence of multidrug-resistant bacteria, there is a need to reassess the use of routine antibiotic prophylaxis after hemostasis.

To evaluate the effectiveness of antibiotic prophylaxis in patients treated for EVL.

We conducted a 13-year observational study using the Tokushukai medical database across 46 hospitals. Patients were divided into the prophylaxis group (received antibiotics on admission or the next day) and the non-prophylaxis group (did not receive antibiotics within one day of admission). The primary outcome was composed of 6-wk mortality, 4-wk rebleeding, and 4-wk spontaneous bacterial peritonitis (SBP). The secondary outcomes were each individual result and in-hospital mortality. A logistic regression with inverse probability of treatment weighting was used. A subgroup analysis was conducted based on the Child-Pugh classification to determine its influence on the primary outcome measures, while sensitivity analyses for antibiotic type and duration were also performed.

Among 980 patients, 790 were included (prophylaxis: 232, non-prophylaxis: 558). Most patients were males under the age of 65 years with a median Child-Pugh score of 8. The composite primary outcomes occurred in 11.2% of patients in the prophylaxis group and 9.5% in the non-prophylaxis group. No significant differences in outcomes were observed between the groups (adjusted odds ratio, 1.11; 95% confidence interval, 0.61-1.99; P = 0.74). Individual outcomes such as 6-wk mortality, 4-wk rebleeding, 4-wk onset of SBP, and in-hospital mortality were not significantly different between the groups. The primary outcome did not differ between the Child-Pugh subgroups. Similar results were observed in the sensitivity analyses.

No significant benefit to antibiotic prophylaxis for esophageal variceal bleeding treated with EVL was detected in this study. Global reassessment of routine antibiotic prophylaxis is imperative.

With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction.

Cardiovascular disease (CVD) seriously jeopardizes human health worldwide, thereby needing the establishment of an effective CVD prediction model that can be of great significance for controlling the risk of the disease and safeguarding the physical and mental health of the population.

Considering the UCI heart disease dataset as an example, initially, a single machine learning prediction model was constructed. Subsequently, six methods such as Pearson, chi-squared, RFE and LightGBM were comprehensively used for the feature screening. On the basis of the base classifiers, Soft Voting fusion and Stacking fusion was carried out to build a prediction model for cardiovascular diseases, in order to realize an early warning and disease intervention for high-risk populations. To address the data imbalance problem, the SMOTE method was adopted to process the data set, and the prediction effect of the model was analyzed using multi-dimensional and multi-indicators.

In the single classifier model, the MLP algorithm performed optimally on the preprocessed heart disease dataset. After feature selection, five features eliminated. The ENSEM_SV algorithm that combines the base classifiers to determine the prediction results by soft voting on the results of the classifiers achieved the optimal value on five metrics such as Accuracy, Jaccard_Score, Hamm_Loss, AUC, etc., and the AUC value reached 0.951. The RF, ET, GBDT, and LGB algorithms were employed in the first stage sub-model composed of base classifiers. The AB algorithm was selected as the second stage model, and the ensemble algorithm ENSEM_ST, obtained by Stacking fusion of the two stages exhibited the best performance on 7 indicators such as Accuracy, Sensitivity, F1_Score, Mathew_Corrcoef, etc., and the AUC reached 0.952. Furthermore, a comparison of the algorithms' classification effects based on different training set occupancy was carried out. The results indicated that the prediction performance of both the fusion models was better than the single models, and the overall effect of ENSEM_ST fusion was stronger than the ENSEM_SV fusion.

The fusion model established in this study improved the overall classification accuracy and stability of the model to a significant extent. It has a good application value in the predictive analysis of CVD diagnosis, and can provide a valuable reference in the disease diagnosis and intervention strategies.

Polymyxin B hemadsorption (PMX-HA) reduces blood endotoxin levels, but characteristics of patients with sepsis likely to benefit from PMX-HA are not well known. We sought to identify patient subgroups likely to benefit from PMX-HA.

We retrospectively identified 1911 patients with sepsis from a retrospective observational study in Japan (the JSEPTIC-DIC study) and 286 patients with endotoxemic septic shock from a randomized controlled trial in North America that restricted patients to those with high endotoxin activity (the EUPHRATES trial). We applied the machine learning-based causal forest model to the JSEPTIC-DIC cohort to investigate heterogeneity in treatment effects of PMX-HA on 28-day survival after adjusting for potential confounders and ascertain the best criteria for PMX-HA use. The derived criteria for targeted therapy by PMX-HA were validated using the EUPHRATES trial cohort.

The causal forest model revealed heterogeneity in treatment effects of PMX-HA. Since patients having higher treatment effects were more likely to have severe coagulopathy and hyperlactatemia, we identified the potential treatment targets of PMX-HA as patients with PT-INR > 1.4 or lactate > 3 mmol/L. In the EUPHRATES trial cohort, PMX-HA use on the targeted subpopulation (75% of all patients) was significantly associated with higher 28-day survival (PMX-HA vs. control, 68% vs. 52%; treatment effect of PMX-HA, + 16% [95% CI + 2.2% to + 30%], p = 0.02).

Abnormal coagulation and hyperlactatemia in septic patients with high endotoxin activity appear to be helpful to identify patients who may benefit most from PMX-HA. Our findings will inform enrollment criteria for future interventional trials targeting patients with coagulopathy and hyperlactatemia.

We previously reported a novel manual method for measuring bone mineral density (BMD) in coronary artery calcium (CAC) scans and validated our method against Dual X-Ray Absorptiometry (DEXA). Furthermore, we have developed and validated an artificial intelligence (AI) based automated BMD (AutoBMD) measurement as an opportunistic add-on to CAC scans that recently received FDA approval. In this report, we present evidence of equivalency between AutoBMD measurements in cardiac vs lung CT scans.

AI models were trained using 132 cases with 7649 (3 mm) slices for CAC, and 37 cases with 21918 (0.5 mm) slices for lung scans. To validate AutoBMD against manual measurements, we used 6776 cases of BMD measured manually on CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA). We then used 165 additional cases from Harbor UCLA Lundquist Institute to compare AutoBMD in patients who underwent both cardiac and lung scans on the same day.

Mean±SD for age was 69 ± 9.4 years with 52.4% male. AutoBMD in lung and cardiac scans, and manual BMD in cardiac scans were 153.7 ± 43.9, 155.1 ± 44.4, and 163.6 ± 45.3 g/cm3, respectively (p = 0.09). Bland-Altman agreement analysis between AutoBMD lung and cardiac scans resulted in 1.37 g/cm3 mean differences. Pearson correlation coefficient between lung and cardiac AutoBMD was R2 = 0.95 (p < 0.0001).

Opportunistic BMD measurement using AutoBMD in CAC and lung cancer screening scans is promising and yields similar results. No extra radiation plus the high prevalence of asymptomatic osteoporosis makes AutoBMD an ideal screening tool for osteopenia and osteoporosis in CT scans done for other reasons.