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Yoo TK, Lee MY, Sung KC. The Risk of Coronary Artery Calcification according to Different Lipid Parameters and Average Lipid Parameters. J Atheroscler Thromb 2024; 31:1194-1214. [PMID: 38417908 PMCID: PMC11300743 DOI: 10.5551/jat.64600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/08/2024] [Indexed: 03/01/2024] Open
Abstract
AIM We compared the association between the baseline and average lipid parameters over time and the coronary artery calcification (CAC) risk. METHODS Participants who underwent annual (biannual) health examinations and coronary artery computed tomography to measure CAC at least twice between March 2010 and December 2019, with a baseline CAC of 0, were included. The levels of apolipoprotein B (ApoB), Apolipoprotein A-I (ApoA1), ApoB/ApoA1, non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), TG/HDL-C, and TC/HDL-C were measured or calculated. The remnant cholesterol (RC) levels were calculated. The average lipid parameters before study entry were calculated using data from 2002 to 2010. The participants were divided into quartiles (Q) according to the parameter values. Cox proportional hazard modeling, adjusted for confounding factors, compared the CAC risk of the highest quartile to the lowest quartile. RESULTS Among 29,278 participants (mean age, 39.19±5.21; men, 88.27%), 2,779 developed CAC >0. The highest quartile of ApoB showed a numerically strong association with CAC risk, compared with the lowest quartile of ApoB (Q1: reference; Q2: HR,1.41, 95% CI,1.25-1.59; Q3: HR,1.97, 95% CI,1.75-2.21; Q4: HR,2.72, 95% CI,2.41-3.07). RC showed a modest association with CAC risk (Q1: reference; Q2: HR,1.13, 95% CI,0.99-1.28; Q3: HR,1.3, 95% CI,1.15-1.47; Q4: HR,1.7, 95% CI,1.51-1.91). The strength of the association was comparable between the parameters at baseline and the average lipid parameters over time. CONCLUSIONS A high ApoB level showed a strong association with CAC risk compared with the lowest ApoB quartile. The baseline lipid parameters can predict CAC development as effectively as the average of multiple measurements can.
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Affiliation(s)
- Tae Kyung Yoo
- Department of Medicine, MetroWest Medical Center, Framingham, MA, USA
| | - Mi Yeon Lee
- Division of Biostatistics, Department of R&D Management, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ki-Chul Sung
- Division of Cardiology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Bergstrand S, Jonasson H, Fredriksson I, Larsson M, Östgren CJ, Strömberg T. Association between cardiovascular risk profile and impaired microvascular function in a Swedish middle-aged cohort (the SCAPIS study). Eur J Prev Cardiol 2024; 31:1152-1161. [PMID: 38333959 DOI: 10.1093/eurjpc/zwae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/04/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
AIMS The aim was to investigate the relationship between microvascular function, cardiovascular risk profile, and subclinical atherosclerotic burden. METHODS AND RESULTS The study enrolled 3809 individuals, 50-65 years old, participating in the population-based observational cross-sectional Swedish CArdioPulmonary bioImage Study. Microvascular function was assessed in forearm skin using an arterial occlusion and release protocol determining peak blood oxygen saturation (OxyP). Cardiovascular risk was calculated using the updated Systematic Coronary Risk Evaluation [SCORE2; 10-year risk of fatal and non-fatal cardiovascular disease (CVD) events]. The OxyP was compared with coronary artery calcification score (CACS) and to plaques in the carotid arteries. Individuals with OxyP values in the lowest quartile (Q1; impaired microvascular function) had a mean SCORE2 of 5.8% compared with 3.8% in those with the highest values of OxyP (Q4), a relative risk increase of 53%. The risk of having a SCORE2 > 10% was five times higher for those in Q1 (odds ratio: 4.96, 95% confidence interval: 2.76-8.93) vs. Q4 when adjusting for body mass index and high-sensitivity C-reactive protein. The OxyP was lower in individuals with CACS > 0 and in those with both carotid plaques and CACS > 0, compared with individuals without subclinical atherosclerotic burdens (87.5 ± 5.6% and 86.9 ± 6.0%, vs. 88.6 ± 5.8%, P < 0.01). CONCLUSION In a population without CVD or diabetes mellitus, impaired microvascular function is associated with cardiovascular risk profiles such as higher SCORE2 risk and CACS. We suggest that OxyP may serve as a microcirculatory functional marker of subclinical atherosclerosis and CVD risk that is not detected by structural assessments.
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Affiliation(s)
- Sara Bergstrand
- Department of Health, Medicine and Caring Sciences, Linköping University, Building 511, Entrance 75, Room 13.1309 Campus US, 581 83 Linköping, Sweden
| | - Hanna Jonasson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ingemar Fredriksson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Perimed AB, Järfälla, Stockholm, Sweden
| | - Marcus Larsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Carl Johan Östgren
- Department of Health, Medicine and Caring Sciences, Linköping University, Building 511, Entrance 75, Room 13.1309 Campus US, 581 83 Linköping, Sweden
- Centre of Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Tomas Strömberg
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
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Lei MH, Hsu YC, Chung SL, Chen CC, Chen WC, Chen WM, Jao AT, Hsiao JF, Hsu JT, Wu SY. Assessing mortality risk in Type 2 Diabetes patients with prolonged ASCVD risk factors: the inclusive Poh-Ai predictive scoring system with CAC Score integration. Diabetol Metab Syndr 2024; 16:104. [PMID: 38764060 PMCID: PMC11103845 DOI: 10.1186/s13098-024-01341-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
PURPOSE To enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited. METHODS A retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40-80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups. RESULTS In our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401-1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001). CONCLUSION The Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.
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Affiliation(s)
- Meng-Huan Lei
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Yu-Chen Hsu
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Sheng-Liang Chung
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Chao-Chin Chen
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Wei-Cheng Chen
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Wan-Ming Chen
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
| | - An-Tzu Jao
- Big Data Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Ju-Feng Hsiao
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan
| | - Jen-Te Hsu
- Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan.
| | - Szu-Yuan Wu
- Big Data Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan.
- Department of Food Nutrition and Health Biotechnology, College of Medical and Health Science, Asia University, Taichung, Taiwan.
- Division of Radiation Oncology, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan.
- Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan.
- Centers for Regional Anesthesia and Pain Medicine, Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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Hoori A, Al-Kindi S, Hu T, Song Y, Wu H, Lee J, Tashtish N, Fu P, Gilkeson R, Rajagopalan S, Wilson DL. Enhancing cardiovascular risk prediction through AI-enabled calcium-omics. Sci Rep 2024; 14:11134. [PMID: 38750142 PMCID: PMC11096314 DOI: 10.1038/s41598-024-60584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P < 0.001). The calcium-omics model identified 73.5% of MACE cases in the high-risk group, a 13.2% improvement as compared to Agatston, suggesting that calcium-omics could be used to better identity candidates for intensive follow-up and therapies. The categorical net-reclassification index was NRI = 0.153. Our findings from this exploratory study suggest the utility of calcium-omics in improved risk prediction. These promising results will pave the way for more extensive, multi-institutional studies of calcium-omics.
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Affiliation(s)
- Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Sadeer Al-Kindi
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Tao Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Yingnan Song
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Hao Wu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Nour Tashtish
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Robert Gilkeson
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Sanjay Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Onnis C, Virmani R, Kawai K, Nardi V, Lerman A, Cademartiri F, Scicolone R, Boi A, Congiu T, Faa G, Libby P, Saba L. Coronary Artery Calcification: Current Concepts and Clinical Implications. Circulation 2024; 149:251-266. [PMID: 38227718 PMCID: PMC10794033 DOI: 10.1161/circulationaha.123.065657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Coronary artery calcification (CAC) accompanies the development of advanced atherosclerosis. Its role in atherosclerosis holds great interest because the presence and burden of coronary calcification provide direct evidence of the presence and extent of coronary artery disease; furthermore, CAC predicts future events independently of concomitant conventional cardiovascular risk factors and to a greater extent than any other noninvasive biomarker of this disease. Nevertheless, the relationship between CAC and the susceptibility of a plaque to provoke a thrombotic event remains incompletely understood. This review summarizes the current understanding and literature on CAC. It outlines the pathophysiology of CAC and reviews laboratory, histopathological, and genetic studies, as well as imaging findings, to characterize different types of calcification and to elucidate their implications. Some patterns of calcification such as microcalcification portend increased risk of rupture and cardiovascular events and may improve prognosis assessment noninvasively. However, contemporary computed tomography cannot assess early microcalcification. Limited spatial resolution and blooming artifacts may hinder estimation of degree of coronary artery stenosis. Technical advances such as photon counting detectors and combination with nuclear approaches (eg, NaF imaging) promise to improve the performance of cardiac computed tomography. These innovations may speed achieving the ultimate goal of providing noninvasively specific and clinically actionable information.
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Affiliation(s)
- Carlotta Onnis
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato s.s. 554 Monserrato (Cagliari) 09045, ITALY
| | - Renu Virmani
- Department of Cardiovascular Pathology, CVPath Institute, 19 Firstfield Road, Gaithersburg, MD
| | - Kenji Kawai
- Department of Cardiovascular Pathology, CVPath Institute, 19 Firstfield Road, Gaithersburg, MD
| | - Valentina Nardi
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
| | - Amir Lerman
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
| | | | - Roberta Scicolone
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato s.s. 554 Monserrato (Cagliari) 09045, ITALY
| | - Alberto Boi
- Department of Cardiology, Azienda Ospedaliera Brotzu, Cagliari Italy
| | - Terenzio Congiu
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Ospedale San Giovanni di Dio (Cagliari) 09100 ITALY
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Ospedale San Giovanni di Dio (Cagliari) 09100 ITALY
| | - Peter Libby
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato s.s. 554 Monserrato (Cagliari) 09045, ITALY
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Qiu Y, Hao W, Guo Y, Guo Q, Zhang Y, Liu X, Wang X, Nie S. The association of lipoprotein (a) with coronary artery calcification: A systematic review and meta-analysis. Atherosclerosis 2024; 388:117405. [PMID: 38101270 DOI: 10.1016/j.atherosclerosis.2023.117405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 11/20/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND AND AIMS Coronary artery calcification (CAC) is a crucial pathophysiological characteristic of atherosclerosis. The association between lipoprotein (a) [Lp(a)] and CAC is inconsistent. We aimed to assess the relationship between Lp(a) and CAC by exploring the association between elevated Lp(a) and CAC prevalence, the relationship between Lp(a) level and CAC prevalence, and the correlation between elevated Lp(a) and CAC progression. METHODS We searched the PubMed, Web of Science, and EMBASE databases up to November 01, 2023. Studies exploring the association between serum Lp(a) and CAC (quantified using the Agatston score) were included. Association between Lp(a) level or elevated Lp(a) (higher than the cutoff values of 30 mg/dL, 50 mg/dL, or the highest quartile ranging from 33 to 38.64 mg/dL) and prevalence [CAC score >0 or >100, log (CAC score+1) >0] or progression (an increase in CAC score >0 or ≥100) of CAC were analysed. Odds ratios and 95% confidence intervals were calculated using a random-effects model. RESULTS 40,073 individuals from 17 studies were included. Elevated Lp(a) was associated with a higher prevalence of CAC (OR, 1.31; 95% CI, 1.06 to 1.61; p = 0.01). As a continuous variable, Lp(a) level was positively correlated with the prevalence of CAC (OR, 1.05; 95% CI, 1.02 to 1.08; p = 0.003). Furthermore, elevated Lp(a) was associated with greater CAC progression (OR, 1.54; 95% CI, 1.23 to 1.92; p = 0.0002). CONCLUSIONS This meta-analysis suggested that Lp(a) is associated with prevalence and progression of CAC. Further studies are required to explore whether Lp(a)-lowering therapy could prevent or inhibit CAC, ultimately reducing coronary artery disease risk.
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Affiliation(s)
- Yuyao Qiu
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Wen Hao
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yingying Guo
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Qian Guo
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yushi Zhang
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xiaochen Liu
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xiao Wang
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
| | - Shaoping Nie
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Miller RJH, Gransar H, Rozanski A, Dey D, Al‐Mallah M, Chow BJW, Kaufmann PA, Cademartiri F, Maffei E, Han D, Slomka PJ, Berman DS. Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation. J Am Heart Assoc 2023; 12:e031601. [PMID: 38108259 PMCID: PMC10863788 DOI: 10.1161/jaha.123.031601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. METHODS AND RESULTS The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; P=0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; P<0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P=0.005). CONCLUSIONS We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.
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Affiliation(s)
- Robert J. H. Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Libin Cardiovascular Institute of AlbertaUniversity of CalgaryCalgaryAlbertaCanada
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Division of Cardiology and Department of MedicineMount Sinai Morningside HospitalMount Sinai Heart and the Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Mouaz Al‐Mallah
- Houston Methodist DeBakey Heart and Vascular CenterHoustonTX
| | - Benjamin J. W. Chow
- Departments of Medicine (Cardiology and Nuclear Medicine) and RadiologyUniversity of Ottawa Heart InstituteOttawaOntarioCanada
| | - Philipp A. Kaufmann
- Department of Nuclear MedicineUniversity Hospital Zurich, University of ZurichZurichSwitzerland
| | | | - Erica Maffei
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) SYNLAB SDNNaplesItaly
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Piotr J. Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Daniel S. Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
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Ding H, Zhu J, Tian Y, Xu L, Song L, Shi Y, Mu D, Chen R, Liu H, Liu B. Relationship between the triglyceride-glucose index and coronary artery calcification in asymptomatic, non-diabetic patients undergoing maintenance hemodialysis. Ren Fail 2023; 45:2200849. [PMID: 37133817 PMCID: PMC10158539 DOI: 10.1080/0886022x.2023.2200849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
OBJECTIVE Coronary artery calcification (CAC) is positively and independently associated with cardiovascular disease (CVD) in patients undergoing maintenance hemodialysis (MHD). Insulin resistance is independently associated with CAC and is an important risk factor for CVD. The triglyceride-glucose (TyG) index is a reliable biomarker of insulin resistance. This cross-sectional, observational study aimed to investigate the relationship between the TyG index and CAC in asymptomatic non-diabetic patients undergoing MHD. METHODS The quantitative coronary artery calcification score (CACS) was calculated and expressed using the Agatston score. The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multiple Poisson regression analysis, Spearman correlation analysis, and receiver operating characteristic (ROC) curves were used to investigate the relationship between the TyG index and CAC. RESULTS The 151 patients were divided into three groups according to the tertiles of the TyG index. With an increase in the TyG index, the CACS significantly increased (Spearman's rho = 0.414, p < 0.001). Poisson regression analysis indicated that the TyG index was independently related to the presence of CAC (prevalence ratio, 1.281 [95% confidence interval, 1.121-1.465], p < 0.001). Furthermore, ROC curve analysis showed that the TyG index was of value in predicting the CAC in asymptomatic non-diabetic patients undergoing MHD, with an area under the curve of 0.667 (p = 0.010). CONCLUSION The TyG index is independently related to the presence of CAC in asymptomatic, non-diabetic patients undergoing MHD.
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Affiliation(s)
- Hong Ding
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Jinhua Zhu
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Ying Tian
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Li Xu
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Lei Song
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Ying Shi
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Dongxing Mu
- Institute of Nephrology, People's Hospital of Yangzhong city, Zhenjiang, Jiangsu Province, China
| | - Ruoxin Chen
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, Jiangsu Province, China
| | - Hong Liu
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, Jiangsu Province, China
| | - Bicheng Liu
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, Jiangsu Province, China
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Hoori A, Al-Kindi S, Hu T, Song Y, Wu H, Lee J, Tashtish N, Fu P, Gilkeson R, Ra-jagopalan S, Wilson DL. Enhancing cardiovascular risk prediction through AI-enabled calcium-omics. ARXIV 2023:arXiv:2308.12224v1. [PMID: 37664409 PMCID: PMC10473778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction. Methods We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics. Results Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153. Conclusions AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk.
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Affiliation(s)
- Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Sadeer Al-Kindi
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Tao Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Yingnan Song
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Hao Wu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Nour Tashtish
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Robert Gilkeson
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Sanjay Ra-jagopalan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106, USA
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10
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Kasahara K, Kerby RL, Zhang Q, Pradhan M, Mehrabian M, Lusis AJ, Bergström G, Bäckhed F, Rey FE. Gut bacterial metabolism contributes to host global purine homeostasis. Cell Host Microbe 2023; 31:1038-1053.e10. [PMID: 37279756 PMCID: PMC10311284 DOI: 10.1016/j.chom.2023.05.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/25/2023] [Accepted: 05/10/2023] [Indexed: 06/08/2023]
Abstract
The microbes and microbial pathways that influence host inflammatory disease progression remain largely undefined. Here, we show that variation in atherosclerosis burden is partially driven by gut microbiota and is associated with circulating levels of uric acid (UA) in mice and humans. We identify gut bacterial taxa spanning multiple phyla, including Bacillota, Fusobacteriota, and Pseudomonadota, that use multiple purines, including UA as carbon and energy sources anaerobically. We identify a gene cluster that encodes key steps of anaerobic purine degradation and that is widely distributed among gut-dwelling bacteria. Furthermore, we show that colonization of gnotobiotic mice with purine-degrading bacteria modulates levels of UA and other purines in the gut and systemically. Thus, gut microbes are important drivers of host global purine homeostasis and serum UA levels, and gut bacterial catabolism of purines may represent a mechanism by which gut bacteria influence health.
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Affiliation(s)
- Kazuyuki Kasahara
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Robert L Kerby
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Qijun Zhang
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Meenakshi Pradhan
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Margarete Mehrabian
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
| | - Fredrik Bäckhed
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
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11
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Vazirian F, Sadeghi M, Wang D, Javidi Dashtbayaz R, Gholoobi A, Samadi S, Mohammadpour AH. Correlation between osteoprotegerin and coronary artery calcification in diabetic subjects: a systematic review of observational studies. BMC Cardiovasc Disord 2023; 23:96. [PMID: 36809976 PMCID: PMC9942374 DOI: 10.1186/s12872-023-03123-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/15/2023] [Indexed: 02/23/2023] Open
Abstract
Coronary artery calcification (CAC) is one of the critical cardiovascular complications that lead to elevated morbidity and mortality among patients with type 2 diabetes (T2M). The association between osteoprotegerin (OPG) and CAC could potentially provide a reasonable chance for preventive therapy in type 2 diabetic patients and benefit the rate of mortality. Since measurement of CAC score is relatively expensive and requires radiation exposure, the current systematic review aims to provide clinical evidence for evaluating the prognostic role of OPG in determining CAC risk among subjects with T2M. Web of Science, PubMed, Embase, and Scopus, were investigated until July 2022. We assessed human studies investigating the association of OPG with CAC in type 2 diabetic patients. Quality assessment was performed by Newcastle-Ottawa quality assessment scales (NOS). Out of 459 records, 7 studies remained eligible to be included. Observational studies that provided odds ratio (OR) estimates with 95% confidence intervals (CIs) for the association between OPG and the risk of CAC were analyzed by random-effects model. In order to provide a visual summary of our findings, the estimation of pooled OR from cross-sectional studies was reported as 2.86 [95% CI 1.49-5.49], which is consistent with the findings of the cohort study. Results revealed that the association between OPG and CAC was significant among diabetic patients. OPG is hypothesized to be a potential marker in predicting the presence of high coronary calcium score among subjects with T2M that could be recognized as a novel target for further pharmacological investigations.
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Affiliation(s)
- Fatemeh Vazirian
- School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Masoumeh Sadeghi
- Department of Epidemiology, Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Dongdong Wang
- Department of Medicine, Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada
| | - Reza Javidi Dashtbayaz
- Department of Cardiovascular Diseases, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Arash Gholoobi
- Department of Cardiovascular Diseases, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sara Samadi
- Department of Clinical Pharmacy, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Hooshang Mohammadpour
- Department of Clinical Pharmacy, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran. .,Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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12
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Semenov VV, Bosdriesz JR, Kuryata O. The Predictive Value of Serum Aldosterone Level for Coronary Artery Calcium Score in Patients with Chronic Kidney Disease: A Single-center Study. Prague Med Rep 2023; 124:242-254. [PMID: 37736948 DOI: 10.14712/23362936.2023.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023] Open
Abstract
Patients with chronic kidney disease (CKD) have high cardiovascular risk (CVR), which is often underestimated by conventional tools. The coronary artery calcium score (CACS) significantly improves CVR stratification by conventional tools, but it is often not available in low-resources settings. Aldosterone may be a cheaper alternative to CACS for CVR assessment in CKD patients. The aim was to assess the ability of serum aldosterone level to predict CACS in patients with CKD in comparison to standard predictors. This single-center study included 57 patients aged 40 to 67 years with CKD (estimated glomerular filtration rate [eGFR] ≥45 ml/min) and arterial hypertension. Serum aldosterone, sex, age, body mass index, blood pressure, total cholesterol, eGFR, and proteinuria were used for prediction of CACS>0 Agatston units (AU) and CACS>100 AU. The area under the curve (AUC) with 95% confidence intervals (CI) and the mean Brier scores were examined for predictors of CACS. Aldosterone predicted a CACS>100 AU (AUC = 0.72, 95% CI: 0.56-0.88), but not a CACS>0 AU. Age predicted a CACS>100 AU (AUC = 0.80, 95% CI: 0.67-0.93) and a CACS>0 AU (AUC = 0.75, 95% CI: 0.62-0.89). The addition of aldosterone to age for prediction of a CACS>100 AU improved the mean Brier score, compared to the model with age alone, from 0.16 to 0.14, but not the AUC (0.83, 95% CI: 0.70-0.95). Aldosterone was a significant predictor of a CACS>100 AU in patients with CKD, but aldosterone was not a better predictor than age alone.
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Affiliation(s)
- Viktor V Semenov
- Department of Internal Medicine 2 and Phthisiology, Dnipro State Medical University, Dnipro, Ukraine.
| | - Jizzo R Bosdriesz
- ERA-EDTA Registry, Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Olexandr Kuryata
- Department of Internal Medicine 2 and Phthisiology, Dnipro State Medical University, Dnipro, Ukraine
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13
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Soschynski M, Hagar MT, Taron J, Krauss T, Ruile P, Hein M, Nührenberg T, Russe MF, Bamberg F, Schlett CL. Update for the Performance of CT Coronary Angiography. ROFO-FORTSCHR RONTG 2022; 194:613-624. [PMID: 35231938 DOI: 10.1055/a-1747-3554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Coronary CT angiography (cCTA) is a class 1 recommendation in the current guidelines by the European Society of Cardiology (ESC) for excluding significant coronary artery stenosis. To achieve optimal image quality at a low radiation dose, the imaging physician may choose different acquisition modes. Therefore, the consensus guidelines by the Society of Cardiovascular Computed Tomography (SCCT) provide helpful guidance for this procedure. METHOD The article provides practical recommendations for the application and acquisition of cCTA based on the current literature and our own experience. RESULTS AND CONCLUSION According to current ESC guidelines, cCTA is recommended in symptomatic patients with a low or intermediate clinical likelihood for coronary artery disease. We recommend premedication with beta blockers and nitrates prior to CT acquisition under certain conditions even with the latest CT scanner generations. The most current CT scanners offer three possible scan modes for cCTA acquisition. Heart rate is the main factor for selecting the scan mode. Other factors may be coronary calcifications and body mass index (BMI). KEY POINTS · CCTA is a valid method to exclude coronary artery disease in patients with a low to intermediate clinical likelihood.. · Even with the latest generation CT scanners, premedication with beta blockers and nitrates can improve image quality at low radiation exposure.. · Current CT scanners usually provide retrospective ECG gating and prospective ECG triggering. Dual-source scanners additionally provide a "high pitch" scan mode to scan the whole heart during one heartbeat, which may also be achieved using single-source scanners with broad detectors in some cases.. · Besides the available scanner technology, the choice of scan mode primarily depends on heart rate and heart rate variability (e. g., arrhythmia).. CITATION FORMAT · Soschynski M, Hagar MT, Taron J et al. Update for the Performance of CT Coronary Angiography. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1747-3554.
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Affiliation(s)
- Martin Soschynski
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
| | - Muhammad Taha Hagar
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
| | - Jana Taron
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany.,Cardiac MR PET CT Program, Massachusetts General-Hospital, Harvard Medical School, Boston, United States
| | - Tobias Krauss
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
| | - Philipp Ruile
- Department of Cardiology & Angiology II, University Heart Center Freiburg-Bad Krozingen, Germany
| | - Manuel Hein
- Department of Cardiology & Angiology II, University Heart Center Freiburg-Bad Krozingen, Germany
| | - Thomas Nührenberg
- Department of Cardiology & Angiology II, University Heart Center Freiburg-Bad Krozingen, Germany
| | - Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Germany
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14
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Osawa K. The role of cardiac injury biomarkers as risk predictors for peripheral artery disease: Where do we go from here? Int J Cardiol 2021; 345:127-128. [PMID: 34687804 DOI: 10.1016/j.ijcard.2021.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Kazuhiro Osawa
- Department of General Internal Medicine 3, Kawasaki Medical School General Medical Center, 2-6-1 Nakasange, Kita-ku, Okayama 700-8505, Japan.
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15
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Javaid A, Mitchell JD, Villines TC. Predictors of Coronary Artery Calcium and Long-Term Risks of Death, Myocardial Infarction, and Stroke in Young Adults. J Am Heart Assoc 2021; 10:e022513. [PMID: 34743556 PMCID: PMC8751911 DOI: 10.1161/jaha.121.022513] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/16/2021] [Indexed: 01/02/2023]
Abstract
Background Coronary artery calcium (CAC) is well-validated for cardiovascular disease risk stratification in middle to older-aged adults; however, the 2019 American College of Cardiology/American Heart Association guidelines state that more data are needed regarding the performance of CAC in low-risk younger adults. Methods and Results We measured CAC in 13 397 patients aged 30 to 49 years without known cardiovascular disease or malignancy between 1997 and 2009. Outcomes of myocardial infarction (MI), stroke, major adverse cardiovascular events (MACE; MI, stroke, or cardiovascular death), and all-cause mortality were assessed using Cox proportional hazard models, controlling for baseline risk factors (including atrial fibrillation for stroke and MACE) and the competing risk of death or noncardiac death as appropriate. The cohort (74% men, mean age 44 years, and 76% with ≤1 cardiovascular disease risk factor) had a 20.6% prevalence of any CAC. CAC was independently predicted by age, male sex, White race, and cardiovascular disease risk factors. Over a mean of 11 years of follow-up, the relative adjusted subhazard ratio of CAC >0 was 2.9 for MI and 1.6 for MACE. CAC >100 was associated with significantly increased hazards of MI (adjusted subhazard ratio, 5.2), MACE (adjusted subhazard ratio, 3.1), stroke (adjusted subhazard ratio, 1.7), and all-cause mortality (hazard ratio, 2.1). CAC significantly improved the prognostic accuracy of risk factors for MACE, MI, and all-cause mortality by the likelihood ratio test (P<0.05). Conclusions CAC was prevalent in a large sample of low-risk young adults. Those with any CAC had significantly higher long-term hazards of MACE and MI, while severe CAC increased hazards for all outcomes including death. CAC may have utility for clinical decision-making among select young adults.
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Affiliation(s)
- Aamir Javaid
- Division of Cardiovascular MedicineUniversity of Virginia HealthCharlottesvilleVA
| | | | - Todd C. Villines
- Division of Cardiovascular MedicineUniversity of Virginia HealthCharlottesvilleVA
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16
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Xiong H, Wang L, Jin F, Zhang B, Wang X, Chang X, Zhao LP. Association of cystatin C with coronary artery calcification in patients undergoing multidetector computed tomography. Medicine (Baltimore) 2021; 100:e26761. [PMID: 34397720 PMCID: PMC8322554 DOI: 10.1097/md.0000000000026761] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/08/2021] [Indexed: 01/04/2023] Open
Abstract
Cystatin C is associated with atherosclerosis, but the relationship between cystatin C and coronary artery calcification (CAC) is uncertain. The purpose of this study was to evaluate the predictive value of cystatin C on the occurrence and severity of CAC.A total of 1447 hospitalized patients with coronary computed tomography angiography were selected in this study. According to the CAC score (CACS), patients were divided into calcification group (with CAC, n = 749) and control group (without CAC, n = 698). The calcification group was further divided into low calcification group (CACS < 100, n = 407), medium calcification group (CACS 100-400, n = 203), and high calcification group (CACS≥400, n = 139).Patients with CAC had higher cystatin C level than those in control group (P < .05). With the increase of calcification score, the cystatin C level showed an upward trend. The cystatin C level in the high calcification group was significantly higher than those in the low and medium calcification group (P < .05). ROC curve analysis showed that cystatin C had a high predictive value for the occurrence of CAC [area under the curve 0.640, 95% confidence interval (95% CI) 0.591-0.690, cut-off value 0.945 mg/L, sensitivity 0.683, specificity 0.558, P < .05] and severe CAC (area under the curve 0.638, 95% CI 0.550-0.762, cut-off value 0.965 mg/L, sensitivity 0.865, specificity 0.398, P < .05). Multivariate logistic regression analysis showed that cystatin C was an independent predictor of severe CAC (AOR 3.748, 95% CI 1.138-10.044, P < .05).Cystatin C was significantly associated with the occurrence and severity of CAC, suggesting that cystatin C had the potential as a predictor of CAC.
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Affiliation(s)
- Hui Xiong
- Department of Cardiology
- Emergency Department of Xuguan District
| | - Li Wang
- Department of Cardiology
- Emergency Department of Xuguan District
| | | | - Bo Zhang
- Radiology Department, The Second Affiliated Hospital of Soochow University, Suzhou City, China
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17
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Campos-Staffico AM, Cordwin D, Ding Y, Lester CA, Brook RD, Luzum JA, Dorsch MP. Fewer patients receive recommendations for pharmacotherapy in primary prevention using the 2018 atherosclerotic cardiovascular disease risk estimator. Prev Med 2021; 148:106555. [PMID: 33862035 PMCID: PMC8573994 DOI: 10.1016/j.ypmed.2021.106555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 04/05/2021] [Accepted: 04/11/2021] [Indexed: 11/23/2022]
Abstract
Shortly after the introduction of the 2013 original Pooled Cohort Equation (PCE), an overestimation of risk was suggested. As such, the updated 2018 PCE was developed to more accurately assess atherosclerotic cardiovascular disease (ASCVD) risk in the population. Hence, this study aims to compare drug prescribing recommendations in a large, real-world patient population, depending on which PCE is used to estimate 10-year ASCVD risk. This retrospective cohort study identified 20,843 patients aged between 40 and 75 years with no previous ASCVD. The 10-year ASCVD risk score was assessed by using both PCE. Patients were assigned to the four risk categories according to the 2018 ACC/AHA guideline. The percentage of patients qualifying for guideline-recommended primary prevention with statins and/or anti-hypertensives were compared between both PCE. Risk reclassification occurred in 26.7% of patients overall (n = 5571), of which 98.1% (n = 5466) were assigned to lower risk categories with the updated PCE. Non-diabetic (14.0%) patients no longer met the threshold for recommending statins as primary prevention with the updated PCE. Likewise, 13.8% of patients with stage I hypertension no longer met the threshold for recommending antihypertensive drugs with the updated PCE. In conclusion, risk reclassification occurred among 26.7% of patients overall, mostly due to lower risk categories assigned by the updated PCE. Up to 14.0% of patients no longer met the threshold for recommending statin therapy and/or antihypertensive drugs by using the updated PCE. These findings suggest that using the updated PCE could translate into fewer patients receiving pharmacotherapy for ASCVD primary prevention.
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Affiliation(s)
| | - David Cordwin
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yuting Ding
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Corey A Lester
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert D Brook
- Division of Cardiovascular Medicine, Department of Internal Medicine, Medical School, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jasmine A Luzum
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael P Dorsch
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
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18
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Eriksson H, Torén K, Rosengren A, Andersson E, Söderberg M. Psychosocial job exposure and risk of coronary artery calcification. PLoS One 2021; 16:e0252192. [PMID: 34033665 PMCID: PMC8148350 DOI: 10.1371/journal.pone.0252192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 05/11/2021] [Indexed: 11/27/2022] Open
Abstract
Purpose The aim was to examine potential associations between psychosocial job exposures, evaluated with the Job Demand-Control-model, and presence of coronary artery calcium. Methods We performed a cross-sectional study using the Swedish CArdioPulmonary bioImage Study,(SCAPIS)pilot study. Coronary artery calcium was assessed through computed tomography of the coronary arteries and with coronary artery scoring, CACS. Main outcome was CACS ≥100 compared to CACS 0. Job demand and control was analysed according to the standard categorization of the two variables into: high strain, active, passive and low strain (reference). Associations between these variables and CACS were calculated with prevalence ratios (PR) using Cox regression with robust variance, 95% confidence intervals (CI) and adjusted for age, smoking, education, socioeconomic area and metabolic syndrome. Results In total 777 participants were used in our analyses, for which 20% of the men and 5% of the women had CACS ≥100, respectively. The PR of having CACS ≥100 was non-significantly elevated for men in high strain jobs 1.54 (95% CI 0.88–2.69) and in active jobs 1.67 (95% CI 0.92–3.06), adjusted for covariates. For women there was no association between exposure to high strain and having CACS ≥100 PR 1.02 (95% CI 0.24–4.31). Among women reporting passive job, the PR was non-significantly elevated, 2.40 (95% CI 0.83–6.92), adjusted for covariates. Conclusion The statistical power of the study was limited, but our results suggests the possibility that exposure to a high strain or an active job situation may increase the risk of CACS in men, while in women, it may rather be exposure to passive job.
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Affiliation(s)
- Helena Eriksson
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- * E-mail:
| | - Kjell Torén
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Annika Rosengren
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Andersson
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mia Söderberg
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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19
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Si Y, Fan W, Han C, Liu J, Sun L. Atherogenic Index of Plasma, Triglyceride-Glucose Index and Monocyte-to-Lymphocyte Ratio for Predicting Subclinical Coronary Artery Disease. Am J Med Sci 2021; 362:285-290. [PMID: 33989563 DOI: 10.1016/j.amjms.2021.05.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/15/2020] [Accepted: 05/03/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND The atherogenic index of plasma (AIP), triglyceride-glucose (TyG) index, and monocyte-to-lymphocyte ratio (MLR) are strongly associated with atherogenesis of the coronary artery. This study aimed to investigate the association of the AIP, TyG index, and MLR with subclinical coronary artery disease (CAD) and evaluate their ability to predict subclinical CAD. METHODS A total of 697 asymptomatic patients were enrolled in this study and assigned to the subclinical CAD group (n=332) and control group (n=365). The clinical data, coronary artery calcification score, and calculated AIP, TyG index, and MLR were collected by graduate students in the cardiology division. Multivariate logistic regression models were set up to assess the risk factors for subclinical CAD. RESULTS The AIP, TyG index and MLR values were higher in the subclinical CAD group than in the control group (all P<0.05). In addition to the classic independent clinical risk factors, increased AIP, TyG index and MLR values were all independent risk factors for subclinical CAD (all P<0.05). The AUCs were higher after combining clinical risk factors than the AIP, TyG index, or MLR alone (all P<0.05). CONCLUSIONS The AIP, TyG index and MLR are independent risk factors for subclinical CAD, which can be useful for improving the diagnosis and prevention of CAD.
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Affiliation(s)
- Yueqiao Si
- Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, HeBei, China
| | - Wenjun Fan
- Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, HeBei, China
| | - Chao Han
- Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, HeBei, China
| | - Jingyi Liu
- Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, HeBei, China
| | - Lixian Sun
- Department of Cardiology, The Affiliated Hospital of Chengde Medical University, Chengde, HeBei, China.
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20
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Abidov A, Chehab O. Cardiovascular risk assessment models: Have we found the perfect solution yet? J Nucl Cardiol 2020; 27:2375-2385. [PMID: 30793251 DOI: 10.1007/s12350-019-01642-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Accepted: 01/28/2019] [Indexed: 12/27/2022]
Affiliation(s)
- Aiden Abidov
- Division of Cardiology/Department of Internal Medicine, Wayne State University, Detroit, MI, USA.
- Cardiology Section/Department of Internal Medicine, John D. Dingell VA Medical Center, 4646 John R., Detroit, MI, 48201, USA.
| | - Omar Chehab
- Division of Cardiology/Department of Internal Medicine, Wayne State University, Detroit, MI, USA
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21
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Khan MMH, Fujiyoshi A, Shiino A, Hisamatsu T, Torii S, Suzuki S, Kunimura A, Segawa H, Kadota A, Ohkubo T, Nozaki K, Miura K, Ueshima H. The Association Between Coronary Artery Calcification and Subclinical Cerebrovascular Diseases in Men: An Observational Study. J Atheroscler Thromb 2020; 27:995-1009. [PMID: 31969522 PMCID: PMC7508726 DOI: 10.5551/jat.51284] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Aim: Coronary artery calcification (CAC) is an independent predictor of stroke and dementia, in which subclinical cerebrovascular diseases (SCVDs) play a vital pathogenetic role. However, few studies have described the association between CAC and SCVDs. Therefore, the aim of this study was to assess the clinical relationship between CAC and SCVDs in a healthy Japanese male population. Methods: In this observational study, 709 men, free of stroke, were sampled from a city in Japan from 2010 to 2014. CAC was scored using the Agatston method. The following SCVDs were assessed using magnetic resonance imaging: intracranial arterial stenosis (ICAS), lacunar infarction, deep and subcortical white matter hyperintensity (DSWMH), periventricular hyperintensity (PVH), and microbleeds. The participants were categorized according to CAC scores as follows: no CAC (0), mild CAC (1–100), and moderate-to-severe CAC (> 100). The adjusted odds ratios of prevalent SCVDs were computed in reference to the no-CAC group using logistic regression. Results: The mean (standard deviation) age of the participants was 68 (8.4) years. Participants in the moderate-to-severe CAC category showed significantly higher odds of prevalent lacunar infarction, DSWMH, and ICAS in age-adjusted and risk-factor-adjusted models. Microbleeds and PVH, in contrast, did not show any significant associations. The trends for CAC with lacunar infarction, DSWMH, and ICAS were also significant (all P-values for trend ≤ 0.02). Conclusions: Higher CAC scores were associated with higher odds of lacunar infarction, DSWMH, and ICAS. The presence and degree of CAC may be a useful indicator for SCVDs involving small and large vessels.
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Affiliation(s)
- Md Maruf Haque Khan
- Department of Public Health, Shiga University of Medical Science.,Center for Epidemiologic Research in Asia, Shiga University of Medical Science.,Department of Public Health and Informatics, Bangabandhu Sheikh Mujib Medical University
| | - Akira Fujiyoshi
- Department of Public Health, Shiga University of Medical Science.,Department of Hygiene, Wakayama Medical University
| | - Akihiko Shiino
- Molecular Neuroscience Research Center, Shiga University of Medical Science
| | - Takashi Hisamatsu
- Department of Environmental Medicine and Public Health, Shimane University
| | - Sayuki Torii
- Department of Public Health, Shiga University of Medical Science
| | - Sentaro Suzuki
- Department of Public Health, Shiga University of Medical Science
| | - Ayako Kunimura
- Department of Public Health, Shiga University of Medical Science
| | - Hiroyoshi Segawa
- Center for Epidemiologic Research in Asia, Shiga University of Medical Science
| | - Aya Kadota
- Department of Public Health, Shiga University of Medical Science
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine
| | - Kazuhiko Nozaki
- Department of Neurosurgery, Shiga University of Medical Science
| | - Katsuyuki Miura
- Department of Public Health, Shiga University of Medical Science.,Center for Epidemiologic Research in Asia, Shiga University of Medical Science
| | - Hirotsugu Ueshima
- Department of Public Health, Shiga University of Medical Science.,Center for Epidemiologic Research in Asia, Shiga University of Medical Science
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22
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Strauss HW, Nakahara T, Narula N, Narula J. Vascular Calcification: The Evolving Relationship of Vascular Calcification to Major Acute Coronary Events. J Nucl Med 2019; 60:1207-1212. [PMID: 31350320 DOI: 10.2967/jnumed.119.230276] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022] Open
Abstract
Calcification in a coronary artery is accepted as definite evidence of coronary atherosclerosis. The extent and density of calcification, as combined in the Agatston score, is associated with the risk of a patient experiencing a major acute coronary event. Atherosclerosis occurs because damaged endothelial cells allow low-density lipoprotein cholesterol (LDLc) to leak into subintimal tissue. Proteoglycans in subendothelial collagen have a high affinity for LDLc, retaining the lipoprotein cholesterol complex. As the endothelial damage is repaired, the subintimal LDLc is trapped. Retained LDLc induces an inflammatory response in the overlying endothelium, causing the endothelium to express chemotactic peptides. Chemotactic peptides attract circulating monocytes, which follow the concentration gradient, enter the tissue, and become tissue macrophages to phagocytize and digest the irritating LDLc in the atheroma. In the process of digesting LDLc, enzymes in the macrophages oxidize the LDLc complex. Oxidized LDL is toxic to macrophages; when present in sufficient quantity, it may cause death of macrophages, contributing to inflammation in the atheroma. In a necrotic inflammatory lesion, the regulatory mechanisms that control tissue concentrations of calcium and phosphorus are lost, allowing the solubility product of calcium phosphate to be exceeded, resulting in the formation of microscopic calcium-phosphate crystals. With ongoing inflammation, additional calcium-phosphate crystals are formed, which may aggregate. When these aggregated calcium phosphate crystals exceed 1 mm, the lesions become visible on clinical CT as coronary calcifications. Serial gated CT scans of the heart have demonstrated that once formed, CT-visible calcifications do not decrease significantly in size but may increase.
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Affiliation(s)
- H William Strauss
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Takehiro Nakahara
- Department of Diagnostic Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Navneet Narula
- Department of Pathology, New York University School of Medicine, New York, New York; and
| | - Jagat Narula
- Division of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
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23
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Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, Goldberg R, Heidenreich PA, Hlatky MA, Jones DW, Lloyd-Jones D, Lopez-Pajares N, Ndumele CE, Orringer CE, Peralta CA, Saseen JJ, Smith SC, Sperling L, Virani SS, Yeboah J. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019; 139:e1082-e1143. [PMID: 30586774 PMCID: PMC7403606 DOI: 10.1161/cir.0000000000000625] [Citation(s) in RCA: 1176] [Impact Index Per Article: 235.2] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Scott M Grundy
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Neil J Stone
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Alison L Bailey
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Craig Beam
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Kim K Birtcher
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Roger S Blumenthal
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Lynne T Braun
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Sarah de Ferranti
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph Faiella-Tommasino
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Daniel E Forman
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Ronald Goldberg
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Paul A Heidenreich
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Mark A Hlatky
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Daniel W Jones
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Donald Lloyd-Jones
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Nuria Lopez-Pajares
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Chiadi E Ndumele
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Carl E Orringer
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Carmen A Peralta
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph J Saseen
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Sidney C Smith
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Laurence Sperling
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Salim S Virani
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph Yeboah
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
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24
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Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, Goldberg R, Heidenreich PA, Hlatky MA, Jones DW, Lloyd-Jones D, Lopez-Pajares N, Ndumele CE, Orringer CE, Peralta CA, Saseen JJ, Smith SC, Sperling L, Virani SS, Yeboah J. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol. J Am Coll Cardiol 2019; 73:e285-e350. [DOI: 10.1016/j.jacc.2018.11.003] [Citation(s) in RCA: 1113] [Impact Index Per Article: 222.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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25
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Miki T, Miyoshi T, Kotani K, Kohno K, Asonuma H, Sakuragi S, Koyama Y, Nakamura K, Ito H. Decrease in oxidized high-density lipoprotein is associated with slowed progression of coronary artery calcification: Subanalysis of a prospective multicenter study. Atherosclerosis 2019; 283:1-6. [DOI: 10.1016/j.atherosclerosis.2019.01.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/15/2019] [Accepted: 01/22/2019] [Indexed: 12/27/2022]
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26
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The rs1805193, rs5361, and rs5355 single nucleotide polymorphisms in the E-selectin gene (SEL-E) are associated with subclinical atherosclerosis: The Genetics of Atherosclerotic Disease (GEA) Mexican study. Immunobiology 2019; 224:10-14. [DOI: 10.1016/j.imbio.2018.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/07/2018] [Accepted: 11/08/2018] [Indexed: 01/08/2023]
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27
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Guo J, Nunley KA, Costacou T, Miller RG, Rosano C, Edmundowicz D, Orchard TJ. Greater progression of coronary artery calcification is associated with clinically relevant cognitive impairment in type 1 diabetes. Atherosclerosis 2019; 280:58-65. [PMID: 30471556 PMCID: PMC6584032 DOI: 10.1016/j.atherosclerosis.2018.11.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/16/2018] [Accepted: 11/07/2018] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND AIMS We assessed the predictive role of coronary artery calcification (CAC) in clinically relevant cognitive impairment in 148 middle-aged individuals with childhood-onset type 1 diabetes (T1D) from the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study. METHODS Baseline CAC was measured in 1996-98 and repeated 4-8 years later. Per extensive neuropsychological testing in 2010-15, 28% (41/148) of participants met the study definition of clinically relevant cognitive impairment (two or more of 7 select test scores ≥1.5SD worse than demographically appropriate published norms). Logistic regression models with backward selection were constructed for statistical analysis. RESULTS Mean age and T1D duration at first CAC measure were 37 and 29 years, respectively. A greater burden of initial CAC was associated with cognitive impairment determined 14 years later. Compared to Agatston score = 0, odds ratio (OR) and 95% confidence intervals (CI) of 0<-100, 100<-300 and >300 were 1.4 (0.6, 3.6), 2.3 (0.6, 9.7), and 7.9 (1.6, 38.5), respectively. With both initial and progression of CAC in the multivariable model, backward selection retained only CAC progression, showing it was significantly associated with cognitive impairment (OR [95% CI]: 1.7 [1.1, 2.9]). In those with an initial CAC>0, CAC density was marginally, inversely, associated with cognitive impairment when controlling for CAC volume (OR [95%CI]: 0.3 (0.1, 1.2), p value = 0.078). CONCLUSIONS Greater CAC burden was associated with clinically relevant cognitive impairment in middle-aged adults with childhood-onset T1D. CAC progression appears to be a more powerful predictor than initial calcification.
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Affiliation(s)
- Jingchuan Guo
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Karen A Nunley
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Caterina Rosano
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel Edmundowicz
- Section of Cardiology, Temple University Hospital, Philadelphia, PA, USA
| | - Trevor J Orchard
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
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28
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Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, Goldberg R, Heidenreich PA, Hlatky MA, Jones DW, Lloyd-Jones D, Lopez-Pajares N, Ndumele CE, Orringer CE, Peralta CA, Saseen JJ, Smith SC, Sperling L, Virani SS, Yeboah J. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2018; 139:e1046-e1081. [PMID: 30565953 DOI: 10.1161/cir.0000000000000624] [Citation(s) in RCA: 254] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Scott M Grundy
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Neil J Stone
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Alison L Bailey
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Craig Beam
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Kim K Birtcher
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Roger S Blumenthal
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Lynne T Braun
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Sarah de Ferranti
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph Faiella-Tommasino
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Daniel E Forman
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Ronald Goldberg
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Paul A Heidenreich
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Mark A Hlatky
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Daniel W Jones
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Donald Lloyd-Jones
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Nuria Lopez-Pajares
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Chiadi E Ndumele
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Carl E Orringer
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Carmen A Peralta
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph J Saseen
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Sidney C Smith
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Laurence Sperling
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Salim S Virani
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
| | - Joseph Yeboah
- ACC/AHA Representative. †AACVPR Representative. ‡ACC/AHA Task Force on Clinical Practice Guidelines Liaison. §Prevention Subcommittee Liaison. ‖PCNA Representative. ¶AAPA Representative. **AGS Representative. ††ADA Representative. ‡‡PM Representative. §§ACPM Representative. ‖‖NLA Representative. ¶¶APhA Representative. ***ASPC Representative. †††ABC Representative
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Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, Goldberg R, Heidenreich PA, Hlatky MA, Jones DW, Lloyd-Jones D, Lopez-Pajares N, Ndumele CE, Orringer CE, Peralta CA, Saseen JJ, Smith SC, Sperling L, Virani SS, Yeboah J. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2018; 73:3168-3209. [PMID: 30423391 DOI: 10.1016/j.jacc.2018.11.002] [Citation(s) in RCA: 1002] [Impact Index Per Article: 167.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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den Harder AM, de Jong PA, de Groot MCH, Wolterink JM, Budde RPJ, Iŝgum I, van Solinge WW, Ten Berg MJ, Lutgens E, Veldhuis WB, Haitjema S, Hoefer IE, Leiner T. Commonly available hematological biomarkers are associated with the extent of coronary calcifications. Atherosclerosis 2018; 275:166-173. [PMID: 29920437 DOI: 10.1016/j.atherosclerosis.2018.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 06/01/2018] [Accepted: 06/08/2018] [Indexed: 01/07/2023]
Abstract
BACKGROUND AND AIMS We aimed to improve the understanding of potential associations between commonly available hematological biomarkers and the coronary artery calcification (CAC) score, which may help unravel the pathophysiology of coronary calcifications and subclinical coronary artery disease. METHODS A cross-sectional study was performed within the Utrecht Patient Oriented Database (UPOD). Patients with suspected or known coronary artery disease who underwent CT CAC scoring as well as standard hematology analysis that was part of routine clinical care (within 3 months of CT acquisition) were included. Complete hematology datasets were extracted from hematology analyzers. Linear regression adjusted for potential confounders was used to assess if hematological biomarkers were related to the CAC score. RESULTS In total, 1504 patients were included, of whom 43% (n = 647) had a CAC score of 0. Mean age (±SD) was 53 ± 13 years, and 34% of patients were women. Red blood cell distribution width (RDW, β = 0.20 [0.05-0.36], p=0.007), the fraction of immature reticulocytes (β = 0.97 [0.10-6.43], p=0.004), coefficient of variation of neutrophil lobularity (β = 0.13 [0.01-0.25], p=0.040) and mean lymphocyte cell size (β = 0.21 [0.08-0.34], p=0.001) were positively associated with the CAC score after adjustment for age, sex, body mass index (BMI), diabetes, glomerular filtration rate (GFR) and high-density lipoprotein (HDL). CONCLUSIONS This study confirms the known association of RDW with the CAC score, and presents the fraction of immature reticulocytes, coefficient of variation of neutrophil lobularity, and mean lymphocyte cell size as new markers associated with a higher CAC score.
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Affiliation(s)
- Annemarie M den Harder
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Mark C H de Groot
- Department of Clinical Chemistry and Hematology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Ricardo P J Budde
- Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Ivana Iŝgum
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Wouter W van Solinge
- Department of Clinical Chemistry and Hematology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Maarten J Ten Berg
- Department of Clinical Chemistry and Hematology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Esther Lutgens
- Department of Medical Biochemistry, Academic Medical Center, Amsterdam, the Netherlands; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University, Munich, Germany
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Saskia Haitjema
- Department of Clinical Chemistry and Hematology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Imo E Hoefer
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
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Lin S, Zhang H, Ma A. The association between depression and coronary artery calcification: A meta-analysis of observational studies. J Affect Disord 2018; 232:276-282. [PMID: 29500955 DOI: 10.1016/j.jad.2018.02.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 01/29/2018] [Accepted: 02/15/2018] [Indexed: 12/26/2022]
Abstract
BACKGROUND AND AIMS Cardiovascular diseases and depression are responsible for a great global burden of disease; however, the association between depression and coronary artery calcification (CAC) remain controversial and no quantitative meta-analysis exists. Thus, we performed a meta-analysis aimed to evaluate the association between depression and CAC. METHODS We performed a systematic search strategy using PubMed, Web of science, Embase, China National Knowledge Infrastructure and Cochrane library for relevant observational studies investigating depression and CAC from inception until April 2017. The pooled odds ratios (ORs) with 95% confidence intervals (95%CIs) and subgroup analyses were calculated using a random effects model. RESULTS After screening 411 non-duplicated articles, a total of 15 studies involving 32,884 were included. Our analyses demonstrated a positive association between diagnosed depression and CAC (OR = 1.15; 95% CI: 1.04-1.28; I2 = 80.6), and a non-significant association between depressive symptoms and CAC (OR = 1.02; 95% CI: 0.97-1.07; I2 = 73.5%). In subgroup analysis for cohort studies, the positive association between diagnosed depression and CAC was enhanced (OR = 2.20; 95% CI: 1.33-3.64; I2 = 0). CONCLUSIONS Our study indicated that diagnosed depression was associated with higher odds of CAC. Systematic screening for CAC may be useful to identify clinically depressed patients at higher risk of future cardiovascular diseases.
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Affiliation(s)
- Song Lin
- The College of Public Health, Qingdao University, 38 Dengzhou Road, Qingdao, Shandong 266021, China
| | - Huaqi Zhang
- The College of Public Health, Qingdao University, 38 Dengzhou Road, Qingdao, Shandong 266021, China
| | - Aiguo Ma
- The College of Public Health, Qingdao University, 38 Dengzhou Road, Qingdao, Shandong 266021, China.
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32
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Min YW, Song BG, Kim HS, Kim K, Lee H, Min BH, Lee JH, Son HJ, Rhee PL, Kim JJ. Associations between reflux esophagitis and the progression of coronary artery calcification: A cohort study. PLoS One 2017; 12:e0184996. [PMID: 28981523 PMCID: PMC5628814 DOI: 10.1371/journal.pone.0184996] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 09/04/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Reflux esophagitis (RE) and coronary heart disease (CHD) have common risk factors, including obesity and metabolic syndrome. This study aimed to evaluate the associations between RE and the future CHD risk. METHODS This retrospective cohort study included 8,221 participants who were ≥20 years old, and who underwent esophagogastroduodenoscopy and coronary computed tomography (CT) scans during the same visit and subsequent CT scans between 2003 and 2013. RE was defined as the presence of at least Los Angeles classification grade A mucosal break. CT scan was used to determine the coronary artery calcium (CAC) scores. CAC progression was defined as an increase in the CAC score on a subsequent CT scan. RESULTS RE was present in 984 (12.0%) participants. RE at baseline was associated with CAC progression (odds ratio [OR], 1.253; 95% confidence interval [CI], 1.088-1.444; P = 0.002), and this association persisted after adjusting the model for age, sex, smoking status, and alcohol consumption (OR, 1.175; 95% CI, 1.001-1.378; P = 0.048). This association disappeared when the model was further adjusted for body mass index, diastolic blood pressure, the presence of hypertension, glycated hemoglobin, low-density lipoprotein cholesterol, and triglycerides (OR, 1.088; 95% CI, 0.924-1.281; P = 0.311) which were selected using a stepwise selection procedure from several metabolic variables. CONCLUSIONS Our results suggest that the presence of RE is closely associated with CHD, even though RE is not a direct risk factor for CHD. Metabolic factors may play roles in CAC progression in individuals with RE.
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Affiliation(s)
- Yang Won Min
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byeong Geun Song
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hye Seung Kim
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, South Korea
| | - Kyunga Kim
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, South Korea
| | - Hyuk Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byung-Hoon Min
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jun Haeng Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hee Jung Son
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Center for Health Promotion, Samsung Medical Center, Seoul, South Korea
| | - Poong-Lyul Rhee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- * E-mail:
| | - Jae J. Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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