Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review

BACKGROUND: People with type 2 diabetes have a higher risk of cardiovascular disease morbidity and mortality. We aim to distil the evidence, summarize the developments, and identify the gaps in relevant research on predicting cardiovascular disease in type 2 diabetes people using AI techniques in the last ten years. METHODS: A systematic search was carried out for literature published between 1st January 2010 and 30th May 2021 in five medical and scientific databases, including Medline, EMBASE, Global Health (CABI), IEEE Xplore and Web of Science Core Collection. All English language studies describing AI models for predicting cardiovascular diseases in adults with type 2 diabetes were included. The retrieved studies were screened and the data from included studies were extracted by two reviewers. The survey and synthesis of extracted data were conducted based on predefined research questions. IJMEDI checklist was used for quality assessment. RESULTS: From 176 articles identified by the search, 5 studies with sample sizes ranging from 560 to 203,517 met our inclusion criteria. The models predicted the risk of multiple cardiovascular diseases over 5 or 10 years. Ensemble learning, particularly random forest, is the most used algorithm in these models and consistently provided competitive performance. Commonly used features include age, body mass index, blood pressure measurements, and cholesterol measurements. Only one study carried out external validation. The area under the receiver operating characteristic curve for derivation cohorts varied from 0.69 to 0.77. AI models achieved better performance than conventional models in some specific scenarios. CONCLUSIONS: AI technologies seem to show promising performance (AUROC in external validation: 0.75 compared to 0.69 from conventional risk scores) for cardiovascular disease prediction in type 2 diabetes people. However, only one of the reviewed models conducted an external validation. Quality of reporting was low in general, and all models lack reproducibility and reusability

Two-year effectiveness of a stepped-care depression prevention intervention and predictors of incident depression in primary care patients with diabetes type 2 and/or coronary heart disease and subthreshold depression; data from the Step-Dep cluster randomized controlled trial

Introduction Major depressive disorders (MDD), diabetes mellitus type 2 (DM2) and coronary heart disease (CHD) are leading contributors to the global burden of disease and often co-occur. Objectives To evaluate the two-year effectiveness of a stepped-care intervention to prevent MDD compared to usual care and to develop a prediction model for incident depression in DM2 and/or CHD patients with subthreshold depression. Methods Data of 236 Dutch primary care DM2/CHD patients with subthreshold depression (Patient Health Questionnaire 9 (PHQ-9) score ≥6, no current MDD according to the Mini International Neuropsychiatric Interview (DSM-IV criteria)), who participated in the Step-Dep trial were used. A PHQ-9 score of ≥10 at minimally one measurement during follow-up (at 3, 6, 9, 12 and 24 months) was used to determine the cumulative incidence of MDD. Potential demographic and psychological predictors were measured at baseline via web-based self-reported questionnaires and evaluated using a multivariable logistic regression model. Model performance was assessed with the Hosmer–Lemeshow test, Nagelkerke’s R2 explained variance and Area Under the Receiver Operating Characteristic curve (AUC). Bootstrapping techniques were used to internally validate our model. Results 192 patients (81%) were available at two-year follow-up. The cumulative incidence of MDD was 97/192 (51%). There was no statistically significant overall treatment effect over 24 months of the intervention (OR 1.37; 95% CI 0.52; 3.55). Baseline levels of anxiety, depression, the presence of >3 chronic diseases and stressful life-events predicted the incidence of MDD (AUC 0.80 interquartile range (IQR) 0.79-0.80; Nagelkerke’s R2 0.34 IQR 0.33-0.36). Conclusion A model with four factors predicted depression incidence during two-year follow-up in patients with DM2/CHD accurately, based on the AUC. The Step-Dep intervention did not influence the incidence of MDD. Future depression prevention programs should target patients with these four predictors present, and aim to reduce both anxiety and depressive symptoms

External validation and extension of a diagnostic model for obstructive coronary artery disease: A cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination in Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort

__Objective__ To externally validate and extend a recently proposed prediction model to diagnose obstructive coronary artery disease (CAD), with the ultimate aim to better select patients for coronary angiography. __Design__ Analysis of individual baseline data of a prospective cardiology cohort. __Setting__ Single-centre secondary and tertiary cardiology clinic. __Participants__ 4888 patients with suspected CAD, without known previous CAD or other heart diseases, who underwent an elective coronary angiography between 2004 and 2008 as part of the prospective Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. Relevant data were recorded as in routine clinical practice. __Main outcome measures__ The probability of obstructive CAD, defined as a stenosis of minimally 50% diameter in at least one of the main coronary arteries, estimated with the predictors age, sex, type of chest pain, diabetes status, hypertension, dyslipidaemia, smoking status and laboratory data. Missing predictor data were multiply imputed. Performance of the suggested models was evaluated according to discrimination (area under the receiver operating characteristic curve, depicted by the c statistic) and calibration. Logistic regression modelling was applied for model updating. __Results__ Among the 4888 participants (38% women and 62% men), 2127 (44%) had an obstructive CAD. The previously proposed model had a c statistic of 0.69 (95% CI 0.67 to 0.70), which was lower than the expected c statistic while correcting for case mix (c=0.80). Regarding calibration, there was overprediction of risk for high-risk patients. All logistic regression coefficients were smaller than expected, especially for the predictor â € chest pain'. Ext

The clinical value of metabolic syndrome and risks of cardiometabolic events and mortality in the elderly: The Rotterdam study

Background: To evaluate the clinical value of metabolic syndrome based on different definitions [American Heart Association/National Heart, Lung and Blood Institute (AHA/NHLBI), International Diabetes Federation (IDF) and European Group for the Study of Insulin Resistance (EGIR)] in middle-aged and elderly populations. Methods: We studied 8643 participants from the Rotterdam study (1990-2012; mean age 62.7; 57.6% female), a large prospective population-based study with predominantly elderly participants. We performed cox-proportional hazards models for different definitions, triads within definitions and each separate component for the risk of incident type 2 diabetes mellitus, coronary heart disease, stroke, cardiovascular- and all-cause mortality. Results: In our population of 8643 subjects, metabolic syndrome was highly prevalent (prevalence between 19.4 and 42.4%). Metabolic syndrome in general was associated with incident type 2 diabetes mellitus (median follow-up of 6.8years, hazard ratios 3.13-3.78). The associations with coronary heart disease (median follow-up of 7.2years, hazard ratios 1.08-1.32), stroke (median follow-up of 7.7years, hazard ratios 0.98-1.32), cardiovascular mortality (median follow-up of 8.2years, ratios 0.95-1.29) and all-cause mortality (median follow-up of 8.7years, hazard ratios 1.05-1.10) were weaker. AHA/NHLBI- and IDF-definitions showed similar associations with clinical endpoints compared to the EGIR, which was only significantly associated with incident type 2 diabetes mellitus. All significant associations disappeared after correcting metabolic syndrome for its individual components. Conclusions: Large variability exists between and within definitions of the metabolic syndrome with respect to risk of clinical events and mortality. In a relatively old population the metabolic syndrome did not show an additional predictive value on top of its individual components. So, besides as a manner of easy identification of high risk patients, the metabolic syndrome does not seem to add any predictive value for clinical practice

Genomic insights into the causes of type 2 diabetes.

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research

Independent Associations of Fasting Insulin, Glucose, and Glycated Haemoglobin with Stroke and Coronary Heart Disease in Older Women

From a prospective study of women aged 60-79 years, Debbie Lawlor and colleagues conclude that insulin resistance is an important risk factor for coronary heart disease and stroke

International variation in outcomes among people with cardiovascular disease or cardiovascular risk factors and impaired glucose tolerance: insights from the NAVIGATOR Trial

Background: Regional differences in risk of diabetes mellitus and cardiovascular outcomes in people with impaired glucose tolerance are poorly characterized. Our objective was to evaluate regional variation in risk of new‐onset diabetes mellitus, cardiovascular outcomes, and treatment effects in participants from the NAVIGATOR (Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research) trial. Methods and Results: NAVIGATOR randomized people with impaired glucose tolerance and cardiovascular risk factors or with established cardiovascular disease to valsartan (or placebo) and to nateglinide (or placebo) with a median 5‐year follow‐up. Data from the 9306 participants were categorized by 5 regions: Asia (n=552); Europe (n=4909); Latin America (n=1406); North America (n=2146); and Australia, New Zealand, and South Africa (n=293). Analyzed outcomes included new‐onset diabetes mellitus; cardiovascular death; a composite cardiovascular outcome of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke; and treatment effects of valsartan and nateglinide. Respective unadjusted 5‐year risks for new‐onset diabetes mellitus, cardiovascular death, and the composite cardiovascular outcome were 33%, 0.4%, and 4% for Asia; 34%, 2%, and 6% for Europe; 37%, 4%, and 8% for Latin America; 38%, 2%, and 6% for North America; and 32%, 4%, and 8% for Australia, New Zealand, and South Africa. After adjustment, compared with North America, European participants had a lower risk of new‐onset diabetes mellitus (hazard ratio 0.86, 95% CI 0.78–0.94; P=0.001), whereas Latin American participants had a higher risk of cardiovascular death (hazard ratio 2.68, 95% CI 1.82–3.96; P<0.0001) and the composite cardiovascular outcome (hazard ratio 1.48, 95% CI 1.15–1.92; P=0.003). No differential interactions between treatment and geographic location were identified. Conclusions: Major regional differences regarding the risk of new‐onset diabetes mellitus and cardiovascular outcomes in NAVIGATOR participants were identified. These differences should be taken into account when planning global trials

대사 질환 동시 이환의 심혈관계 질환 위험 평가 및 기계학습 예측 모형 개발

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2022. 8. 박수경.연구 배경: 인구의 고령화와 서구형 생활양식으로 인해 대사 질환 동시 이환 (고혈압, 당뇨병, 및 고지혈증 등을 포함한 두가지 이상의 대사 질환을 가진 것으로 정의)의 유병률이 증가하고 있다. 이러한 대사성 질환은 심혈관계 질환의 위험 증가와 연관된다. 2016년 Global Burden of Disease에 따르면, 심혈관계 질환에 의한 사망은 21세기 주요 사망 원인이며, 우리나라에서는 암에 이어 두번째로 높은 사망원인을 차지한다. 세계보건기구 (The World Health Organization)에서는 음주, 흡연, 비만, 신체 활동, 건강한 식습관을 심혈관계 질환의 예방 가능한 요인으로 지정한 바 있다. 이에 대사 질환 동시 이환에 대한 연구가 필요하다. 따라서, 이 연구의 목적은 1) 한국에서의 대사성 질환과 동시 이환의 유병률을 추정하고; 2) 대사 동시 이환 심혈관계 가족력과 심혈관계 발생 위험을 평가하고, 3)대사 동시 이환에 따른 심혈관계 사망에 대해 생활습관 요인 미치는 영향을 평가하고; 4) 생활 습관 변화와 대사 증후군의 연관성을 확인하고; 5) 대사 동시 이환에 대한 기계학습을 기반으로 한 건강 연령 및 질병 위험 예측 모형을 개발하는 것이다. 연구 방법: 본 연구는 한국인유전체역학조사사업 (KoGES)의 도시기반 (Health examinee-Gem Study, HEXA), 농촌기반 (Cardiovascular disease association study, CAVAS), 지역사회기반 (Ansan and Ansung Study, 2001-2014)를 주로 사용하였고, 추가로 미국 국민건강영양조사 (US National Health and Nutrition Examination Survey, NHANES 2003-2014), 한국국민건강영양조사 (Korea NHANES, KNHANES 2007-2014), 아시아 코호트 연구 (Asia Cohort Consortium)를 사용하였다. 통계방법으로는, 세계보건기구의 세계표준인구를 이용한 직접 표준화 방법을 이용해 대사성 질환의 연령표준화 유병률을 산출하였다. 연구 대상자의 일반적인 특성은 연속형 변수의 경우 Student’s t-test, 범주형 변수의 경우 Chi-squared test를 시행하여 비교하였다. 콕스 비례 위험 회귀 분석과 로지스틱 회귀 분석을 수행하여 hazard ratios (HRs), odds ratio (ORs), 95% confidence interval을 추정하였다. 위험 예측 모형의 경우, training set (전체 대상자의 70%)에서 콕스 비례 회귀 분석, random survival forest 기반 모형을 각각 구축하고, test set (전체 대상자의 30%)에서 concordance index (c-index)를 이용해 각 모형의 성능을 평가하였다. 건강 연령 예측 모형의 경우, 10-fold validation을 사용한 elastic net 방법을 이용해 모형을 구축하였다. 연구 결과: 한국과 미국의 대사성 질환과 동시 이환을 비교한 결과, 한국이 미국보다 대사 동시 이환의 유병률이 낮았다. 한국과 미국에서 가장 흔한 대사 질환 조합은 고혈압과 비만이었다. 한국 인구 중 농촌 지역에 거주하는 인구는 도시 지역에 거주하는 인구보다 대사 동시 이환 유병률이 더 높은 것으로 나타났다. 대사 동시 이환, 심혈관계 질환 가족력, 그리고 심근경색과 뇌졸중을 포함한 심혈관계 질환의 위험 연구 결과는 다음과 같다. 고혈압, 당뇨병, 고지혈증이 있고, 심혈관계 가족력이 있는 대상자는 심혈관계 질환 가족력과 질병이 없는 대상자에 비해 유의하게 심혈관계 질환 (HR 2.88, 95% CI: 1.96-4.24), 심근경색 (HR 3.30, 95% CI: 2.06-5.29), 뇌졸중 (HR 2.52, 95% CI: 1.33-4.79) 위험이 증가하는 것을 확인했다 심혈관대사 질환 동시 이환을 가진 대상자에서 생활 습관 요인이 심혈관계 질환 관련 사망에 미치는 영향 연구에서는, ‘비흡연’, ‘금주’, ‘체질량 지수 18.5–27.4kg/m2’를 건강 상태로 정의하여 건강한 생활 습관 점수를 산출했다. 생활 습관 요인 중 금연은 심혈관계 질환 사망 위험 감소와 가장 강한 연관성을 보였다. 고혈압, 당뇨병, 관상동맥질환이 있는 대상자에서는 건강한 생활 습관 점수가 1씩 증가할 때마다 심혈관계 사망위험이 24% (HR 0.76, 95% CI: 0.63-0.93)씩 감소했다. 2개 이상의 심혈관계 대사질환이 있는 대상의 경우, 건강한 생활 습관 요인은 3가지 모두 있는 경우 심혈관계 질환 사망 (HR 0.51, 95% CI: 0.42-0.61)과 심혈관계 질환으로 인한 조기 사망위험(HR 0.38, 95% CI: 0.27-0.54)의 감소에 유의한 영향이 있었다. 지역사회기반 연구자료를 이용한 반복 측정된 생활 습관 요인의 변화에 따른 대사 증후군 위험 연구에서는, 하루 흡연 개피수의 증가 (HR 1.49, 95% CI: 1.09-2.03), 음주량의 light/moderate에서 heavy로 증가는 (HR 1.42, 95% CI: 1.10-1.84) 대사 증후군의 발생 위험의 증가와 유의한 연관성을 보였다. 새롭게 비만 된 대상자는 꾸준히 적정 체중을 유지하는 대상자에 비해 대사성 증후군 (HR 1.88, 95% CI: 1.44-2.45)의 발생 위험의 증가와 유의한 관계를 보였다. 보다 정밀한 개인 맞춤 건강 상태 예측 및 개선을 위해 기계 학습 기반 질병 예측 모형을 개발과 대사 동시 이환에 대한 예측 변수로서의 건강연령을 개발한 연구에 따르면, 실제 연령에 비해 젊은 건강 연령을 가진 경우, 당뇨병 (HR = 0.63, 95% CI: 0.55–0.72), 고혈압 (HR = 0.74, 95% CI: 0.68–0.81), 당뇨병과 고혈압 동시 이환 (HR = 0.65, 95% CI: 0.47–0.91) 위험도가 낮은 것으로 나타났다. 기계학습기반 예측 모형 연구 결과, 기계 학습 기반의 고혈압과 당뇨병 동시 이환 모형은 높은 통계적 질병 예측력을 보이는 것으로 나타났다. 연구 결론: 본 연구는 한국 인구집단에서 심혈관계 질환 발생 및 사망의 위험을 줄이기 위해 대사 동시 이환에 대한 연구에 대한 필요성을 강조한다. 본 연구에서는 동시 이환을 가진 대상자 중 특히 심혈관계 질환 가족력이 있는 경우에 심혈관계 질환의 발생 위험이 증가하는 것을 확인하였다. 또한 심혈관계 대사 질환 동시 이환을 가진 대상자라도, 금연, 금주, 표준 체질량 지수 유지와 같은 건강한 생활 습관은 심혈관계 질환으로 인한 사망과 조기 사망 위험 감소와 연관성이 있었다. 또한, 건강한 생활습관으로의 변화를 통해 대사 증후군의 위험을 줄이는 데 도움이 되는 것을 확인하였다. 이러한 요인들을 기반으로 기계학습을 이용하여 구축된 질병 예측 모형과 건강연령은 우리나라에서의 대사 질환 동시 이환에 대한 고위험군을 파악하고 이를 미리 예방함으로써, 건강증진을 통해 질병 부담을 줄이는 효과적인 도구로 활용될 수 있을 것으로 기대된다.Introduction: The growing aging population and westernized lifestyle have increased the prevalence of disease comorbidity, which is defined as having more than two metabolic diseases including hypertension (HTN), diabetes mellitus (DM), dyslipidemia (LIP), obesity, and metabolic syndrome (MetS). The combination of these diseases is related to an increased risk of cardiovascular disease (CVD) outcomes. The Global Burden of Disease 2016 Study reported that CVD are by far the leading cause of death globally and one of the major health challenges of the 21st century. In Korea, CVD is the second largest cause of death following cancer. As those diseases share risk factors, the World Health Organization (WHO) designated healthy lifestyle, including alcohol reduction, weight loss, smoking cessation, physical activity, and healthy diet, as modifiable factors of CVDs. Thus, it is necessary to estimate the amount of comorbidity prevalence, identify the combined association of metabolic comorbidity and other risk factors (family history of CVD and lifestyle factors) with CVD outcomes, and develop predictive model for comorbidity for detecting the high-risk of metabolic comorbidity and preventing the future risk of CVD through intervention strategies. Methods: This study mainly used population-based cohort study from the Korea Genome and Epidemiology Study (KoGES) including Health Examinee-Gem study (HEXA), cardiovascular disease association study (CAVAS), and Ansan and Ansung Study from 2001-2014, in addition to United States (US) National Health and Nutrition Examination Survey 2003-2014 (NHANES), Korea NHANES (KNHAENS) 2007-2014, and Asia Cohort Consortium (ACC) study. For the statistical analyses, direct standardization methods using the WHO world standard population was performed to estimate the age-standardized prevalence of metabolic diseases. The baseline characteristics were compared using Chi-squared test for categorical variables and Student’s t-test for continuous variables. Cox proportional hazards regression analysis was performed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of CVD outcomes. To calculate the odds ratios (ORs) of metabolic diseases, logistic regression models were used. For prediction model, cox proportional hazard regression, and random survival forest (RSF) models were developed in the training set (70% of the total population) and performance evaluations of each model were performed in the test set (30% of the total population) with concordance statistics (c-index). For self-assessed biological age (BA) prediction model, elastic net regression analysis with 10-fold cross validation was performed. Results: According to the comparison of the prevalence of metabolic disease and comorbidity in Korea and the US, Korea had a lower prevalence of metabolic comorbidity than the US. In both Korean and the US population, the most common combination was HTN and obesity. Among the Korean population, individuals living in rural areas had the higher comorbidity prevalence than those who lived in urban areas. In the association between metabolic comorbidity, family history of CVD, and the risk of CVD study, we found that individuals with DM, HTN, LIP, and a positive family history of CVD had a 2.88-fold increased risk of CVD, a 3.30-fold increased risk of MI, and a 2.52-fold increased risk of stroke compared to the individuals with a negative family history of CVD and none of metabolic diseases. In the impact of lifestyle factors with cardiometabolic disease (CMDs) such as HTN, DM, coronary heart disease (CHD), and stroke on CVD death study, the healthy lifestyle status was defined as ‘never smoker’, ‘never drinker’, and ‘body mass index (BMI) 18.5–27.4kg/m2’in Asian population. Among the lifestyle factors, non-smoking had the strongest association with decreasing risk of all cause and CVD death among the healthy lifestyle factors. A significant association of healthy lifestyle score with lower CVD death was observed among individuals with HTN, DM, and CHD (HR 0.76, 95% CI: 0.63-0.93). For individuals with cardiometabolic comorbidity, having three of healthy lifestyle factors was significantly associated with decrease in CVD (HR 0.51, 95% CI: 0.42-0.61) and premature CVD death (HR 0.38, 95% CI: 0.27-0.54). Based on the repeated measurements for assessing change in lifestyle factors study, unhealthy lifestyle modification including increased dose of cigarette smoking (HR 1.49, 95% CI: 1.09-2.03) and increased their intensity of consumption from light/moderate to heavy had a significantly increased risk for MetS (HR 1.42, 95% CI: 1.10-1.84). For obesity, individuals who newly became obesity had a significant increase in risk for MetS (HR 1.88, 95% CI: 1.44-2.45). For improving the individualized health status, we developed machine learning-based disease prediction model and self-assessed BA as a predictor for metabolic comorbidity. We found that compared to the individuals in same BA as chronological age (CA) group, those in younger BA than CA group were associated with a decreased risk of DM (HR = 0.63, 95% CI: 0.55–0.72), HTN (HR = 0.74, 95% CI: 0.68–0.81), and combination of HTN and DM (HR = 0.65, 95% CI: 0.47–0.91). For machine learning-based disease prediction model study, predictive models achieved a high discriminatory ability for comorbidity of HTN and DM. Conclusions: This study highlights the necessity of accounting to metabolic comorbidity to reduce the future risk of CVD outcomes in Korean population. Although individuals already have had cardiometabolic comorbidity, healthy lifestyles (smoking cessation, abstaining from alcohol, and maintaining BMI) are effective to reduce the further risk of CVD death. Moreover, lifestyle changes help to decrease the risk of a cluster of metabolic conditions. At last, machine learning-based self-assessed BA and disease prediction model may be an effective indicator for identifying the high-risk group and decreasing burden of metabolic comorbidities in Korea through prevention.I. Introduction 1 1.1. Background 1 1.2. Objectives 9 1.3. Hypothesis 11 II. Materials and methods 14 2.1. Data source 14 2.2. Study population 16 2.3. Key variables 24 2.4. Statistical analysis 32 III. Results 39 3.1. Prevalence study 39 3.2. Family history of CVD and the risk of CVD study 49 3.3. Lifestyle factors, and the risk of CVD death study 59 3.4. Change in lifestyle factors study 71 3.5. Biological age study 84 3.6. Prediction model study 93 IV. Discussions 105 4.1. Key findings 105 4.2. Comparison to previous studies 108 4.3. Strengths and limitations 117 V. Conclusion 122 References 123 Appendix 145 Abstract in Korean 181 Acknowledgment 185박

Anatomic Fat Depots and Coronary Plaque Among Human Immunodeficiency Virus-Infected and Uninfected Men in the Multicenter AIDS Cohort Study.

Methods.  In a cross-sectional substudy of the Multicenter AIDS Cohort Study, noncontrast cardiac computed tomography (CT) scanning for coronary artery calcium (CAC) scoring was performed on all men, and, for men with normal renal function, coronary CT angiography (CTA) was performed. Associations between fat depots (visceral adipose tissue [VAT], abdominal subcutaneous adipose tissue [aSAT], and thigh subcutaneous adipose tissue [tSAT]) with coronary plaque presence and extent were assessed with logistic and linear regression adjusted for age, race, cardiovascular disease (CVD) risk factors, body mass index (BMI), and human immunodeficiency virus (HIV) parameters. Results.  Among HIV-infected men (n = 597) but not HIV-uninfected men (n = 343), having greater VAT was positively associated with noncalcified plaque presence (odds ratio [OR] = 1.04, P < .05), with a significant interaction (P < .05) by HIV serostatus. Human immunodeficiency virus-infected men had lower median aSAT and tSAT and greater median VAT among men with BMI <25 and 25-29.9 kg/m(2). Among HIV-infected men, VAT was positively associated with presence of coronary plaque on CTA after adjustment for CVD risk factors (OR = 1.04, P < .05), but not after additional adjustment for BMI. There was an inverse association between aSAT and extent of total plaque among HIV-infected men, but not among HIV-uninfected men. Lower tSAT was associated with greater CAC and total plaque score extent regardless of HIV serostatus. Conclusions.  The presence of greater amounts of VAT and lower SAT may contribute to increased risk for coronary artery disease among HIV-infected persons