INTRODUCTION
Type 2 diabetes (T2D) accelerates vascular injury and atherogenesis. Accordingly, T2D is a powerful risk factor for atherosclerotic cardiovascular disease (ASCVD) and its complications, including cardiovascular death, myocardial infarction (MI), and ischemic stroke. However, whereas the aggregate population-level impact of T2D on ASCVD risk is substantial, there is enormous heterogeneity in individual risk of ASCVD among patients with T2D, in part because of their varied clinical characteristics and the diverse pathogenetic mechanisms driving their glucose dysregulation. This variability in risk is seen both in patients with established ASCVD and in those without manifest ASCVD and underscores the importance of effective risk stratification in this population. Refining cardiovascular risk assessment of patients with T2D allows clinicians to appropriately focus the intensity of preventive efforts and allows patients to make informed decisions about managing their own cardiovascular risk.
Multiple tools exist for stratifying risk of ASCVD among patients with T2D, including clinical risk scores, blood-based biomarkers, and cardiovascular imaging studies. In this chapter, we will review the current landscape of instruments used to evaluate ASCVD risk in patients with T2D across the spectrum of primary and secondary prevention. Risk stratification for other types of cardiovascular complications of T2D, including heart failure (see Chapter 15 ), is covered in other chapters.
TYPE 2 DIABETES AND ATHEROSCLEROTIC CARDIOVASCULAR DISEASE
Epidemiology of Atherosclerotic Cardiovascular Disease Risk in Type 2 Diabetes
Many studies dating back to the 1970s have demonstrated the epidemiologic link between T2D and risk of ASCVD (see also Chapter 6 ). Although conventional risk factors for ASCVD, including hypertension and dyslipidemia, tend to cluster with T2D, this clustering does not fully account for the excess atherothrombotic risk seen with T2D. In a large meta-analysis of nearly 700,000 individuals from 102 prospective studies, there was an approximate twofold excess in risk of coronary heart disease, stroke, and other vascular diseases conferred by the presence of T2D, independent from other conventional risk factors.
Older studies suggested that these adverse vascular effects resulted in comparable ASCVD risk profiles between diabetic patients without prior MI and nondiabetic patients with prior MI, leading to the notion that T2D should be considered a coronary heart disease risk equivalent. However, with greater attention to the importance of intensive risk factor modification in patients with diabetes, incidence rates of ASCVD-related morbidity and mortality have steadily declined over the last three decades, such that T2D alone no longer has the same prognostic significance as a prior MI. Nevertheless, due to the dramatic increase in the prevalence of T2D over that same time period, the overall burden of T2D-related atherothrombotic complications has continued to climb.
Mechanisms of Atherosclerotic Cardiovascular Disease Risk in Type 2 Diabetes
Multiple molecular and cellular mechanisms are responsible for diabetes-associated atherosclerosis (see also Chapters 7 and 19 ). For example, both hyperglycemia and insulin resistance directly contribute to the formation of advanced glycation end-products and reactive oxygen species (ROS), as well as mitochondrial dysfunction. These processes collectively result in endothelial dysfunction, vascular inflammation, and activation of vascular smooth muscle cells. The dyslipidemia that generally accompanies T2D further accelerates atherogenesis, because oxidized low-density lipoprotein (LDL) particles are deposited in the subendothelial layer of affected portions of the vasculature. Dysfunctional endothelium promotes activation and adhesion of monocytes, which exit the circulation and engulf these oxidized LDL particles, forming lipid-laden foam cells that drive progression of atherosclerotic lesions. Indeed, studies comparing the composition of atherosclerotic plaques in patients with and without T2D have demonstrated that the former contain more lipid-rich atheroma and macrophage infiltration.
Beyond accelerating atherogenesis, diabetes directly promotes atherothrombosis through several mechanisms. For instance, the vascular inflammation that drives atherosclerotic progression also contributes to atherosclerotic plaque instability. In addition, diabetes promotes platelet activation and the release of thrombogenic substances contained within platelet granules. Finally, diabetes increases circulating concentrations of multiple prothrombotic proteins and decreases the concentrations of anticoagulant proteins. Collectively, the prothrombotic milieu that accompanies T2D supports thrombus formation at the site of ruptured atherosclerotic plaques.
The diverse pathophysiological mechanisms by which diabetes contributes to ASCVD underscores the complexity of a disease that paradoxically is defined by a single metabolite—glucose. Mirroring the variable cardiovascular clinical risk profiles of patients with T2D, the pathways driving diabetes-associated atherothrombosis are also highly variable across individuals with T2D. Understanding the basis for this heterogeneity has been and remains an important area of investigation.
HETEROGENEITY OF CARDIOVASCULAR RISK PROFILES IN DIABETES
The varied clinical profiles of patients with diabetes can be characterized through different lenses. The conventional classification system for diabetes is not inherently defined by associations with cardiovascular risk, but rather by the suspected mechanism of dysglycemia. Type 1 diabetes (T1D) results from autoimmune destruction of pancreatic β-cells resulting in absolute insulin deficiency, whereas T2D is a nonautoimmune process characterized by varying degrees of insulin resistance and deficiency (see also Chapters 1 and 5 ). Other atypical forms of diabetes that do not fit cleanly in the category of T1D or T2D are also part of the current classification system, including some with known genetic causes (e.g., mature-onset diabetes of the young) and some related to diseases of the exocrine pancreas. With deeper understanding of the genetic and metabolomic architecture of different subgroups of patients with diabetes, these categories are continuing to evolve.
Another emerging paradigm for classifying diabetes has been to leverage advances in machine learning techniques to define data-driven metabolic subphenotypes with the goal of expanding our understanding of underlying disease mechanisms and potentially guiding therapeutic approaches. One such effort identified five novel clusters of individuals with adult-onset diabetes using six variables that can be monitored in clinical practice. Interestingly, these clusters not only have distinct clinical features but are associated with varying risk of diabetes-related complications, including coronary heart disease. The heterogeneity in the cardiovascular risk profiles of these clusters highlights the important link between metabolic characteristics and cardiovascular disease manifestations and suggests that this type of framework may eventually be important for precision cardiovascular treatment approaches.
Another more clinically oriented view of the heterogeneous profiles of patients with diabetes is to classify them directly in relation to cardiovascular risk factor control. This concept stems from the evidence-based understanding that multifactorial risk factor management can prevent or delay diabetes-related cardiovascular complications, which has led professional society guidelines to focus not only on glycemic control but also on the management of blood pressure, lipids, and kidney disease risk in this population. In a large cohort study from the Swedish National Diabetes Register that included more than 270,000 individuals with T2D followed for a median of 5.7 years, individuals were categorized by age and according to the presence of five cardiovascular risk factors—elevated glycated hemoglobin (HbA 1c ), elevated LDL cholesterol, albuminuria, smoking, and elevated blood pressure. When all five of these risk factors were within target ranges, individuals with T2D had only marginal or no excess risk of death, stroke, and MI compared to the general population. However, with each successive increase in the number of risk factors not within the target range, there was a stepwise increase in ASCVD risk. Moreover, age was an important effect modifier of this relationship, such that the incremental cardiovascular risk associated with having poorly controlled risk factors was especially pronounced in younger patients.
Thus, while T2D is a powerful risk factor for ASCVD and its complications, cardiovascular risk is not uniformly elevated in patients with T2D. This increased but varying risk underscores the importance of individualized risk assessment as well as patient-centered considerations when managing cardiovascular risk in patients with diabetes.
PREVENTIVE THERAPIES TO REDUCE CARDIOVASCULAR RISK IN PATIENTS WITH TYPE 2 DIABETES
A broad range of therapeutic strategies, including lifestyle interventions, pharmacologic therapies, and procedural interventions (e.g., coronary artery bypass grafting) have been shown to reduce the risk of cardiovascular complications in patients with T2D (see also Chapters 3, 4, 12, and 13 ). Over the last decade, the results of multiple large cardiovascular outcomes trials motivated by regulatory directives to demonstrate cardiovascular safety in at-risk populations (see also Chapter 11 ), have specifically expanded the evidence base supporting the use of certain antihyperglycemic therapies. The emergence of these pharmacotherapies, which include sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1 RAs), as well as other novel agents, including lipid-lowering therapies and antiplatelet agents, have dramatically altered the treatment armamentarium for cardiovascular primary and secondary prevention in T2D.
The full spectrum of therapies used to manage cardiovascular risk in patients with T2D is covered in detail in later chapters. Here, we provide a general overview of these preventive therapies and their effects on ASCVD outcomes to frame the discussion around the value of ASCVD risk stratification.
Glucose-Lowering Medications With Cardiovascular Benefit
Because of its efficacy for lowering glucose and its generally favorable safety profile, metformin has traditionally been viewed as the foundational pharmacologic therapy for patients with T2D. However, while multiple preclinical and clinical studies suggest a potential atheroprotective effect of metformin, there are limited studies directly evaluating the effects of metformin on hard clinical outcomes. In the 10-year posttrial monitoring study of the United Kingdom Prospective Diabetes Study (UKPDS), patients treated with metformin ( n = 342) had a significantly lower risk of MI (risk ratio [RR], 0.67; 95% confidence interval [CI], 0.51–0.89) and all-cause mortality (RR, 0.71; 95% CI, 0.59–0.89) compared to those randomized to nonintensive glucose control. Nevertheless, in the larger A Diabetes Outcome Progression Trial (ADOPT), which randomized 4360 patients with newly diagnosed T2D to metformin, glyburide, or rosiglitazone, metformin use did not reduce the risk of cardiovascular events relative to the other agents over a median follow-up of 4 years. Notably, both trials enrolled low-risk populations and thus were limited by low event rates.
Both SGLT2i and GLP-1 RA were also initially introduced as antihyperglycemic agents. SGLT2i lower blood glucose by enhancing urinary glucose excretion, and GLP-1 RAs do so by augmenting glucose-dependent insulin secretion and glucagon suppression. Importantly, the cardiovascular outcomes trials evaluating these agents have demonstrated their powerful cardioprotective properties independent of their glucose-lowering effects, which has shifted the focus of their use towards comprehensive cardiovascular risk reduction.
In a meta-analysis of six cardiovascular outcomes trials of four SGLT2i agents enrolling a combined 46,969 patients with T2D, the SGLT2i class was shown to reduce the risk of cardiovascular death by 15% (hazard ratio [HR], 0.85; 95% CI, 0.78–0.93) and of major adverse cardiovascular events (MACE), a composite of cardiovascular death, MI, or ischemic stroke, by 10% (HR, 0.90; 95% CI, 0.85–0.90). Although the pathophysiological mechanisms underlying each component of this composite outcome may be heterogeneous, treatment effects on MACE are generally viewed as modifying ASCVD risk. SGLT2i also robustly reduces the risk of hospitalization for heart failure by 32% (HR, 0.68; 95% CI, 0.61–0.76) and of kidney disease progression by 38% (HR, 0.62; 95% CI, 0.56–0.70).
In a meta-analysis of eight GLP-1 RA trials, comprising 60,080 patients with T2D, the GLP-1 RA class was shown to reduce cardiovascular death by 13% (HR, 0.87; 95% CI, 0.80–0.94) and MACE by 14% (HR, 0.86; 95% CI, 0.80–0.93). Notably, there is clinically important heterogeneity within the GLP-1 RA class. Specifically, semaglutide, dulaglutide, and liraglutide have the most favorable cardiovascular outcomes data, and are therefore the agents recommended by current guidelines. Compared to the SGLT2i, the effects of GLP-1 RA on hospitalization for heart failure (HR, 0.89; 95% CI, 0.82–0.98) and kidney disease progression (HR, 0.86; 95% CI, 0.72–1.02) are more modest. However, GLP-1 RA are more effective than SGLT2i for reducing HbA 1c and body weight. Thus while the benefits with respect to ASCVD risk reduction are quite similar between the two classes, selection between these agents is often driven by considerations related to their other salutary benefits.
Cardiovascular Risk Factor Management
In addition to using glucose-lowering therapies with known cardiovascular benefits, managing other ASCVD risk factors that tend to cluster with T2D, including hypertension and dyslipidemia, is an important aspect of the multifactorial approach to reducing cardiovascular risk in T2D.
Individual clinical trials testing strategies of intensive blood pressure lowering in patients with T2D have yielded varying results. Nevertheless, the totality of the data suggests that targeting lower blood pressures reduces the risk of ASCVD events, with the trade-off of increasing the risk of adverse events related to episodes of hypotension (e.g., syncope). Thus guidelines recommend a blood pressure goal of <130/80 mm Hg in all patients with T2D if this target can be safely attained.
Lowering LDL cholesterol is the cornerstone of ASCVD risk management. In a meta-analysis of 18,686 individuals with T2D from 14 randomized trials of statin therapy followed for a mean of 4.3 years, there was a 13% proportional reduction (rate ratio [RR], 0.87; 99% CI, 0.76–1.00; p =.008) in vascular mortality and a 21% proportional reduction in major vascular events (RR, 0.79; 99% CI, 0.72–0.86; p <0.0001) per mmol/L reduction in LDL cholesterol. Thus statin therapy is recommended for all patients with T2D. For patients with T2D and ASCVD, the addition of ezetimibe or a PCSK9 inhibitor is recommended when an LDL cholesterol goal of <55 mg/dL is not achieved on maximum tolerated statin therapy. The benefit of these therapies with respect to ASCVD risk reduction has been demonstrated in subgroup analyses of patients with T2D enrolled in the pivotal cardiovascular outcomes trials of these agents.
Finally, antiplatelet therapy including aspirin and P2Y12 inhibitors, used as monotherapy or in combination, plays an important role in reducing ASCVD risk in patients with T2D by directly addressing the pathobiological mechanisms of platelet activation and thrombosis. Multiple studies support their use in patients with T2D for both primary and secondary ASCVD prevention. However, the optimal selection of agent(s) and duration of treatment depends on individual risk profile.
VALUE OF RISK STRATIFICATION IN PATIENTS WITH TYPE 2 DIABETES
Balancing Risks and Benefits in Clinical Decision Making
While clinical management guidelines recommend that certain interventions be broadly implemented in all patients with T2D (e.g., healthy lifestyle modifications), they suggest that others be used more selectively according to individual patient considerations. This is because, for most interventions, there is an inherent trade-off between the potential benefits of a therapy and the risks associated with its use. In some circumstances, these risks may be dominantly related to safety of the therapy. For example, antiplatelet therapies increase the risk of major bleeding events, including intracranial hemorrhage. Similarly, intensive blood pressure lowering increases the risk of symptomatic hypotension, which can lead to syncope and falls. Other “risks” may include costs of certain therapies to individual patients. Such financial considerations also become relevant when addressing T2D-associated ASCVD risk at the population level. For instance, it may not be feasible to scale certain costly interventions to reach the entire population, or even the majority, of patients with T2D given the prevalence of the disease. Finally, considerations related to overall pill burden and other patient factors, such as each individual’s unique preferences and values, can all influence the decision to implement or not implement a given intervention. For clinicians to integrate this complex set of factors when having patient-centered discussions and making clinical decisions about initiating certain therapies, it is often helpful to quantify individual ASCVD risk and the anticipated cardiovascular risk reduction from a therapy.
Relative Versus Absolute Risk Reduction With Cardiovascular Therapies
The results of cardiovascular outcomes trials are generally reported using measures of relative effect (e.g., hazard ratios) accompanied by a measure of the precision of the estimate (e.g., 95% CI). In this formulation, the incidence of a particular outcome during the trial observation period among patients randomized to an investigational therapy is presented as a proportion of the incidence of that outcome in patients randomized to a control group. The relative risk reduction (RRR) refers to the magnitude by which this ratio deviates from 1. For example, the estimated RRR for a therapy with HR 0.80 (95% CI, 0.70–0.90) would be 20%, with 95% confidence that the true RRR value falls between 10% and 30%. On the other hand, absolute risk reduction (ARR) refers to the difference in the incidence of an outcome between treatment groups over a given period of time.
Consider the example of a clinical trial in which the observed cumulative incidence of MACE at 5 years is 15% in patients randomly allocated to an investigational therapy, compared to 20% in patients randomly allocated to placebo. Here, the RRR would be approximately 25% based on the ratio of the cumulative incidences between treatment groups (i.e., 0.15/0.20 = 0.75; RRR = 1.00–0.75 = 0.25, or 25%) ( NB: the exact estimate of the RRR will depend on the specific modeling approach ). In this example, the ARR would be approximately 5% (i.e., 0.20–0.15 = 0.05, or 5%). If, instead, the observed 5-year cumulative incidence rates were 6% and 8%, respectively, the RRR would still be approximately 25% (i.e., 0.06/0.08 = 0.75, and 1.00–0.75 = 0.25, or 25%), but the ARR would only be approximately 2% (i.e., 0.08–0.06 = 0.02, or 2%). This example underscores the fact that the underlying risk of the study population, as reflected by the placebo event rate, significantly influences ARR but generally has little effect on RRR.
There are several reasons that the results of clinical trials are generally reported using measures of relative effect. First, since RRR is much less influenced by the underlying risk of the study population, it is much easier to compare RRRs than it is to compare ARRs across study populations with very different risk profiles. Second, whereas the RRR of a cardiovascular therapy is generally stable over time, ARR varies as a direct function of treatment duration, which is also highly variable across trials. Moreover, when clinicians use therapies for ASCVD risk prevention in patients with T2D, they are often considering timelines of potential benefit that far exceed the typical duration of most clinical trials.
Although measures of relative treatment effect are favored when describing the results of clinical trials, quantification of anticipated ARR from a cardiovascular therapy becomes important when translating the results to clinical practice. Specifically, when clinicians weigh the magnitude of potential risk against the magnitude of potential benefit for an individual patient, it is the absolute risk-benefit calculus that is being considered. To put a quantitative point on this concept, the number needed to treat is calculated using the formula 1/ARR (where ARR is expressed as a ratio). Thus, as the ARR increases in magnitude, the number of individuals who need to be treated to prevent an ASCVD event decreases, meaning that the impact of using that therapy in a particular patient population is greater. For these reasons, cardiovascular professional society guidelines emphasize that “the goal of the clinician is to match the intensity of preventive efforts with an individual’s absolute risk of a future ASCVD event.”
CLINICAL ATHEROSCLEROTIC CARDIOVASCULAR DISEASE RISK ASSESSMENT IN PATIENTS WITH TYPE 2 DIABETES
The simplest and most widely used framework for characterizing a patient’s absolute cardiovascular risk profile is according to whether the patient has an established history of ASCVD, in which case the goal is to prevent recurrent events (secondary prevention), or no known ASCVD, in which case the goal is to prevent first events (primary prevention). Since patients who have had prior ASCVD events are at much higher risk for future events and generally require more intensive therapy, there are separate clinical practice guidelines for primary versus secondary prevention. Nevertheless, there is substantial ASCVD risk heterogeneity within these groups, as well as important overlap between them.
To more precisely estimate absolute risk of adverse cardiovascular events, multiple clinical risk tools have been developed and implemented in clinical practice. Despite the similar objectives of these scores, they are quite varied with respect to their target patient populations, clinical variables, and cardiovascular outcomes of interest. For instance, some are designed to predict broad composite cardiovascular outcomes reflecting multiple pathobiological processes, including arrhythmias and heart failure in addition to ASCVD events (i.e., MI and ischemic stroke). While broad composite outcomes may capture multidimensional cardiovascular risk, lack of outcome specificity may limit a score’s utility for guiding treatment selection, since many cardiovascular therapies only modify certain types of cardiovascular risk. As an example, most lipid-lowering therapies are highly effective for reducing atherothrombotic risk but not for reducing the risk of heart failure or arrhythmias.
Most available clinical scores for ASCVD risk assessment are designed to be used in broad populations that include patients with and without T2D. The American College of Cardiology (ACC)/American Heart Association (AHA) Pooled Cohort Equation was developed using data from several large, racially and geographically diverse North American cohorts, and is designed to predict 10-year risk of coronary heart disease death, MI, or stroke in patients without preexisting ASCVD. Similarly, the QRISK3 and QR4 scores, which were developed in the United Kingdom using electronic medical record data, predict 10-year risk of ischemic heart disease, cerebrovascular disease, or transient ischemic attack in individuals without prior ASCVD. Each of these risk tools are intended for use in primary prevention, and each includes diabetes as a risk indicator in the model. However, because they are not tailored to patients with T2D, they do not include well-established T2D-specific ASCVD risk factors (e.g., diabetes duration, HbA 1c , albuminuria), which may impact their accuracy in patients with T2D. To address this limitation, the AHA developed and validated updated risk models for predicting incident cardiovascular disease and cardiovascular disease subtypes (ASCVD and heart failure), known as the PREVENT equations, which have the option of incorporating hemoglobin A1c and albuminuria. In addition to these scores intended for use in the general population, several T2D-specific risk tools have been developed to more precisely estimate risk in patients with T2D.
The UKPDS Risk Engine (UKPDS-RE) was the first T2D-specific risk calculator ( Table 8.1 ). Developed using data from 4540 patients with T2D and no known ASCVD enrolled in the UKPDS study, the tool includes age, gender, ethnicity, diabetes duration, HbA 1c , smoking status, atrial fibrillation, systolic blood pressure (SBP), total cholesterol, and LDL cholesterol to provide 10-year risk estimates of fatal and nonfatal coronary heart disease and ischemic stroke. Although the UKPDS-RE has been embraced by clinical guidelines since it was developed in 2001, more recent studies have raised concerns about the calibration of the risk model in contemporary T2D cohorts, specifically suggesting that the UKPDS-RE systematically overestimates ASCVD risk. The apparent miscalibration of UKPDS-RE over time likely reflects secular trends in the overall cardiovascular risk profile of patients with T2D owing to improved multifactorial risk factor control, as discussed earlier in this chapter. The ADVANCE risk score is a T2D-specific risk model that was derived using clinical data and adjudicated outcomes from patients with T2D ( n = 7168) without manifest ASCVD enrolled in the ADVANCE trial. The score leverages multiple T2D-specific risk indicators, including diabetes duration, HbA 1c , albuminuria, and microvascular retinal complications, among other clinical variables, to predict a 4-year risk of MI, stroke, or vascular death.
Table 8.1
Diabetes-Specific Models for ASCVD Risk Prediction in Patients With Type 2 Diabetes
| Risk Model | Publication Year | Derivation Cohort | Outcome Predicted by Risk Model | Clinical Variables in Risk Model | ||
|---|---|---|---|---|---|---|
| Data Source | N | Regions Sampled | ||||
| Primary Prevention | ||||||
| UKPDS-Risk Engine ( UKPDS-RE ) | 2001 | UKDPS | 4540 | United Kingdom | Fatal and nonfatal CHD or ischemic stroke | Age, gender, ethnicity, diabetes duration, HbA 1c , smoking status, atrial fibrillation, SBP, total cholesterol, LDL-C |
| Advance Risk Model | 2011 | ADVANCE | 7168 | Asia, Australia, Europe, Canada | MI, stroke, or vascular death | Age at diagnosis, gender, diabetes duration, pulse pressure, retinopathy, atrial fibrillation, HbA 1c , UACR, non-HDL-C, treated hypertension |
| SCORE2-Diabetes | 2023 | SCID, CPRD, UK Biobank, ERFC | 229,460 | Europe, United States | MI, stroke, cardiovascular death | Age, gender, smoking, SBP, total cholesterol, HDL-C, age at diabetes diagnosis, HbA 1c , eGFR |
| Primary and Secondary Prevention | ||||||
| Diabetes Lifetime-Perspective ( DIAL ) Model | 2019 | Swedish National Diabetes Register | 389,366 | Sweden | MI, stroke, vascular death | Age, gender, smoking, SBP, BMI, HbA 1c , eGFR, non-HDL-C, UACR, diabetes duration, insulin use, and history of CVD |
| TIMI Risk Score for Atherothrombosis in Diabetes | 2023 | SAVOR-TIMI 53, DECLARE-TIMI 58 | 23,643 | Africa, Asia, Australia, Europe, North America, South America | Fatal and nonfatal MI or ischemic stroke | Age, gender, waist circumference (or BMI), HbA 1c , insulin use, eGFR, UACR, smoking, SBP, LDL-C, prior CAD, prior MI, PAD, prior ischemic stroke, prior PCI, prior CABG |
ADVANCE , Action in Diabetes and Vascular disease: preterax and diamicron-MR controlled evaluation; CABG , coronary artery bypass grafting; CAD , coronary artery disease; CHD , coronary heart disease; CPRD , Clinical Practice Research Datalink; eGFR , estimated glomerular filtration rate; ERFC , Emerging Risk Factors Collaboration; HbA 1c , glycated hemoglobin; HDL-C , high-density lipoprotein-cholesterol; LDL-C , low-density lipoprotein-cholesterol; MI , myocardial infarction; PAD , peripheral artery disease; PCI , percutaneous coronary intervention; SBP , systolic blood pressure; SCID , Scottish Care Information-Diabetes; UACR , urine albumin-creatinine ratio; UKPDS , United Kingdom Prospective Diabetes Study.
The SCORE2-Diabetes risk algorithm was developed to complement the more general SCORE2 risk algorithm. SCORE2-Diabetes is a gender-specific competing risk-adjusted model that includes the conventional ASCVD risk factors included in SCORE2 (age, gender, smoking, SBP, total and HDL cholesterol) along with several diabetes-specific variables (age at diabetes diagnosis, HbA 1c , eGFR). It was developed using data from four European population data sources (Scottish Care Information-Diabetes, Clinical Practice Research Datalink, UK Biobank, and Emerging Risk Factors Collaboration) comprising 229,460 participants, and is designed to predict 10-year risk of incident cardiovascular disease, defined as the composite of cardiovascular mortality, nonfatal MI, or nonfatal stroke, in patients with T2D and no known cardiovascular disease. The 2023 European Society of Cardiology Guidelines for the Management of Cardiovascular Disease in Patients with Diabetes recommend categorizing ASCVD risk in patients with T2D based on the presence of clinically established ASCVD (very high risk); severe target-organ damage (very high risk); or, for patients without established ASCVD or severe target-organ damage, 10-year predicted ASCVD risk using the SCORE2-Diabetes algorithm (≥20% = very high; 10–<20% = high; 5–<10% = moderate; <5% = low). In turn, the guideline-directed targets for LDL-C and use of antihyperglycemic therapies are assigned according to this risk framework ( Fig. 8.1 ).
Atherosclerotic cardiovascular disease ( ASCVD ) risk stratification framework in the 2023 European Society of Cardiology ( ESC ) Guidelines for the management of cardiovascular disease in patients with diabetes. ASCVD , atherosclerotic cardiovascular disease; CV , cardiovascular; CVD , cardiovascular disease; GLP-1 RA , glucagon-like peptide 1 receptor agonist; LDL-C , low-density lipoprotein-cholesterol; SGLT2 , sodium-glucose cotransporter 2; T2DM , type 2 diabetes mellitus; TOD , target organ damage.
From Eur Heart J . 2023;44(39):4043–4140.
In contrast to the UKPDS-RE, ADVANCE, and SCORE2-Diabetes risk scores, which focus exclusively on primary prevention, the Diabetes Lifetime-perspective (DIAL) model and the TIMI Risk Score for Atherothrombosis in Diabetes are designed for use in all individuals with T2D, regardless of preexisting ASCVD status (i.e., both primary and secondary prevention). The DIAL model, which predicts 10-year risk of vascular mortality, MI, or stroke, was developed using the Swedish National Diabetes Register ( n = 389,366) and externally validated in multiple clinical trial cohorts. Risk indicators in the model include age, gender, smoking status, SBP, body mass index (BMI), HbA 1c , estimated glomerular filtration rate (eGFR), non-HDL cholesterol, albuminuria, diabetes duration, insulin use, and history of CVD. An interactive calculator is available at https://U-Prevent.com/ , which combines DIAL model predictions of absolute risk with relative treatment effects from clinical trials to estimate absolute treatment benefit from selected cardiovascular therapies. The TIMI Risk Score for Atherothrombosis in Diabetes was developed and validated in a pooled cohort of 42,181 patients with T2D enrolled in four large cardiovascular outcomes trials. The model predicts risk of fatal and nonfatal MI or ischemic stroke using 16 clinical variables, including 10 variables that apply to all patients with T2D (age, gender, waist circumference [or BMI], HbA 1c , insulin use, eGFR, albuminuria, smoking status, SBP, and LDL-C) and 6 variables that reflect the extent of prior atherosclerotic disease in those with known ASCVD (coronary artery disease, prior MI, PAD, prior ischemic stroke, prior percutaneous coronary intervention, and prior coronary artery bypass grafting). The risk model has good discrimination and is well calibrated in patients with and without established ASCVD. It has also been shown to identify strong gradients of absolute treatment benefit from cardiovascular therapies, including SGLT2i and lipid-lowering therapies (PCSK9i). An online calculator is available at https://timibiostat.shinyapps.io/calculators/ .
Although beyond the scope of this chapter focused on patients with T2D, several clinical risk scores have been developed for use in patients with T1D, including a model for incident CVD that was developed in the Scottish Care Information-Diabetes register ( n = 27,527) and validated in the Swedish National Diabetes Register ( n = 33,183).
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