Heart failure (HF) has been recognized as a common complication of diabetes, with a prevalence of up to 22% in individuals with diabetes and increasing incidence rates. Data also suggest that HF may ...develop in individuals with diabetes even in the absence of hypertension, coronary heart disease, or valvular heart disease and, as such, represents a major cardiovascular complication in this vulnerable population; HF may also be the first presentation of cardiovascular disease in many individuals with diabetes. Given that during the past decade, the prevalence of diabetes (particularly type 2 diabetes) has risen by 30% globally (with prevalence expected to increase further), the burden of HF on the health care system will continue to rise. The scope of this American Diabetes Association consensus report with designated representation from the American College of Cardiology is to provide clear guidance to practitioners on the best approaches for screening and diagnosing HF in individuals with diabetes or prediabetes, with the goal to ensure access to optimal, evidence-based management for all and to mitigate the risks of serious complications, leveraging prior policy statements by the American College of Cardiology and American Heart Association.
Abstract
This review takes an inclusive approach to microvascular dysfunction in diabetes mellitus and cardiometabolic disease. In virtually every organ, dynamic interactions between the ...microvasculature and resident tissue elements normally modulate vascular and tissue function in a homeostatic fashion. This regulation is disordered by diabetes mellitus, by hypertension, by obesity, and by dyslipidemia individually (or combined in cardiometabolic disease), with dysfunction serving as an early marker of change. In particular, we suggest that the familiar retinal, renal, and neural complications of diabetes mellitus are late-stage manifestations of microvascular injury that begins years earlier and is often abetted by other cardiometabolic disease elements (eg, hypertension, obesity, dyslipidemia). We focus on evidence that microvascular dysfunction precedes anatomic microvascular disease in these organs as well as in heart, muscle, and brain. We suggest that early on, diabetes mellitus and/or cardiometabolic disease can each cause reversible microvascular injury with accompanying dysfunction, which in time may or may not become irreversible and anatomically identifiable disease (eg, vascular basement membrane thickening, capillary rarefaction, pericyte loss, etc.). Consequences can include the familiar vision loss, renal insufficiency, and neuropathy, but also heart failure, sarcopenia, cognitive impairment, and escalating metabolic dysfunction. Our understanding of normal microvascular function and early dysfunction is rapidly evolving, aided by innovative genetic and imaging tools. This is leading, in tissues like the retina, to testing novel preventive interventions at early, reversible stages of microvascular injury. Great hope lies in the possibility that some of these interventions may develop into effective therapies.
Graphical Abstract
Graphical Abstract
In each of the 6 tissues reviewed, we highlight the reciprocal relationship(s) between the tissue’s somatic cells and the microvasculature serving them. Together these components function as a microvascular unit. Early in their course, diabetes mellitus and cardiometabolic disease disrupt these microvascular units and produce tissue dysfunction. Over time, this disruption leads to common (eg, increased endothelial barrier permeability, pericyte loss, capillary rarefaction, disordered angiogenesis, etc.) as well as tissue-specific (eg, microglia activation in the central nervous system and retina, sympathetic overactivity, and perivascular adipose inflammation in peripheral tissues) microvascular injury responses that are orchestrated by a host of both systemic and local signaling processes.
Abstract
Context
Interventions that decrease mean glucose have reduced rates of micro- and macrovascular complications in type 1 diabetes (T1D). However, the difference in cardiovascular risk between ...people with T1D and the general population endures, suggesting that factors beyond hemoglobin A1C (HbA1c) normalization drive cardiovascular outcomes.
Objective
To determine whether various HbA1c metrics predict anatomic cardiovascular disease (CVD) risk factors and/or CVD events in people with T1D.
Methods
We used linear regression to analyze the relationship of several HbA1c metrics to anatomic CVD risk factors and then used Cox regression to model their relationship to incident CVD events in the CACTI Study (ClinicalTrials.gov Identifier: NCT00005754).
Results
In linear regression models adjusted for age, sex, and T1D duration, baseline Hba1c (b = 0.3998, P = 0.0236), mean HbA1c (b = 0.5385, P = 0.0109), and HbA1c SD (b = 1.1521, P = 0.0068) were each positively associated with square root transformed coronary artery calcium volume. Conversely, only mean HbA1c (b = 1.659, P = 0.0048) positively associated with pericardial adipose tissue volume. In survival models adjusted for age, sex, and T1D duration, baseline HbA1c hazard ratio (HR): 1.471, 95% CI: 1.257-1.721, mean HbA1c (HR: 1.850, 95% CI: 1.511-2.264), time-varying HbA1c (HR: 1.500, 95% CI: 1.236-1.821), and HbA1c SD (HR: 1.665, 95% CI: 1.022-2.711) each independently predicted CVD events over 14.3 ± 5.2 person-years of follow-up.
Conclusions/interpretation
We found that various HbA1c metrics positively correlated with CAC volume and independently predicted incident CVD events in the CACTI T1D cohort. These associations with CVD events persisted for baseline HbA1c, mean HbA1c, and time-varying HbA1c even after adjustment for numerous CVD risk factors.
Introduction: Interventions that decrease mean glucose have reduced rates of type 1 diabetes (T1D) vascular complications, but the difference in cardiovascular disease (CVD) risk between people with ...T1D and the general population endures. This suggests that factors beyond hemoglobin A1c (HbA1c) normalization may drive T1D CVD outcomes. HbA1c variability is an established predictor of T1D microvascular disease, but thus far only the FinnDiane Study has implicated it as a risk factor for T1D CVD events. No studies have examined the relationship of HbA1c variability to both CVD risk factors and events in a prospective T1D cohort.
Methods: We used multivariable logistic regression to analyze the Coronary Artery Calcification in Type 1 Diabetes (CACTI) T1D cohort (N=597) . Primary outcomes were square root-transformed coronary artery calcium (CAC) volume and CVD events. CVD events were defined as myocardial infarction, coronary artery bypass grafting, angioplasty, or cardiovascular death. HbA1c variability was defined as a 1-standard deviation increment in HbA1c and calculated as standard deviation of all HbA1c values for each subject.
Results: After adjustment for age, sex, and T1D duration, HbA1c variability predicted both CVD events (HR: 1.562, 95% CI: 1.052-2.318, P=0.0269) and CAC volume (rpartial= 0.3346, P<0.001) , while mean HbA1c predicted CVD events (HR: 1.399, 95% CI: 1.079-1.816, P=0.0114) but not CAC volume (rpartial= 0.0934, P=0.2886) . Further investigation revealed that the relationship between HbA1c variability and CVD events was strongest in those with good glycemic control (HR: 6.330 at mean HbA1c of 6.0%, HR: 3.920 at mean HbA1c of 7.0%, HR: 2.428 at mean HbA1c of 8.0%, HR: 1.5at mean HbA1c of 9.0%, and HR: 0.931 at mean HbA1c of 10.0%) .
Conclusion: HbA1c variability may be a risk factor for both coronary atherosclerosis and CVD events in T1D, and the appropriate care strategy for this population may include maintaining an HbA1c value over time that is both at-target and stable.
Disclosure
W.B.Horton: None. J.K.Snell-bergeon: Stock/Shareholder; GlaxoSmithKline plc.
Funding
National Institutes of Health (R01DK116731) National Institutes of Health (R01HL113029)
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) decreased cardiac events in large clinical trials; however, an understanding of the physiology driving this benefit is lacking. To investigate this, ...we measured micro- and macrovascular insulin sensitivity in 10 adults with T2DM before and after 12 weeks of EMPA 10 mg daily. We also measured endothelial function (FMD), peripheral and central aortic pressures, and skeletal (SM) and cardiac (CM) muscle microvascular perfusion (MBV- microvascular blood volume; MFV-microvascular flow velocity; MBF-microvascular blood flow) before and during a 120 min euglycemic insulin clamp at weeks 0 and 12. We hypothesized that EMPA would diminish micro- and macro-vascular insulin resistance and improve vascular function. At week 0, insulin infusion lowered peripheral SBP (132±6 vs 125±5, p<0.05) but diminished SM perfusion MBV (1.75±0.2 vs 1.29±0.1, p<0.01) MFV (0.08±0.01 vs 0.05±0.01, p<0.001) and MBF (0.14±3 vs 0.07±0.01, p<0.01). After 12 weeks of EMPA, insulin infusion improved FMD (7.6 ± 1.2 vs 8.7 ±1.6%, p <0.03), lowered peripheral SBP (127±6 vs 107±5, p<0.001); DBP (74±4 vs 65±3, p<0.04); MAP (92±3 vs 79±3, p<0.01); and PP (53±5 vs 42±3, p<0.05) as well as aortic SBP (117±5 vs 96±4, p<0.001); DBP (75±4 vs 66±3, p<0.03); MAP (89±3 vs 76±3, p<0.01); and PP (43±4 vs 30±2, p<0.01). In addition, EMPA improved SM perfusion (MBV: 1.29±0.1 vs 2.02±0.3, p<0.02; MFV: 0.05±0.01 vs 0.08±0.01, p<0.02; MBF: 0.07±0.01 vs 0.18±0.04, p<0.03) and CM perfusion (MBV: 4.51±0.6 vs 5.11±0.4, p=0.18; MFV: 0.37±0.04 vs 0.52±0.06, p<0.05; MBF: 1.80±0.4 vs 2.74±0.5, p<0.05). In conclusion, 12 weeks of EMPA in adults with T2D improved endothelial function (FMD), reduced central and peripheral blood pressures, and augmented SM and CM microvascular perfusion in response to insulin. These changes may contribute to the improved CV outcomes seen with SGLT2i therapy.
Disclosure
L.Jahn: None. L.Hartline: None. K.W.Aylor: None. W.B.Horton: None. E.Barrett: None.
Funding
National Institutes of Health (1R01HL142250-01A1)
Abstract
Metformin improves insulin's action on whole-body glucose metabolism in various insulin-resistant populations. The detailed cellular mechanism(s) for its metabolic actions are multiple and ...still incompletely understood. Beyond metabolic actions, metformin also impacts microvascular function. However, the effects of metformin on microvascular function and microvascular insulin action specifically are poorly defined. In this mini-review, we summarize what is currently known about metformin's beneficial impact on both microvascular function and the microvascular response to insulin while highlighting methodologic issues in the literature that limit straightforward mechanistic understanding of these effects. We examine potential mechanisms for these effects based on pharmacologically dosed studies and propose that metformin may improve human microvascular insulin resistance by attenuating oxidative stress, inflammation, and endothelial dysfunction. Finally, we explore several important evidence gaps and discuss avenues for future investigation that may clarify whether metformin's ability to improve microvascular insulin sensitivity is linked to its positive impact on vascular outcomes.
To quantify the accuracy of and clinical events associated with a risk alert threshold for impending hypoglycemia during ICU admissions.
Retrospective electronic health record review of clinical ...events occurring greater than or equal to 1 and less than or equal to 12 hours after the hypoglycemia risk alert threshold was met.
Adult ICU admissions from June 2020 through April 2021 at the University of Virginia Medical Center.
Three hundred forty-two critically ill adults that were 63.5% male with median age 60.8 years, median weight 79.1 kg, and median body mass index of 27.5 kg/m2.
Real-world testing of our validated predictive model as a clinical decision support tool for ICU hypoglycemia.
We retrospectively reviewed 350 hypothetical alerts that met inclusion criteria for analysis. The alerts correctly predicted 48 cases of level 1 hypoglycemia that occurred greater than or equal to 1 and less than or equal to 12 hours after the alert threshold was met (positive predictive value = 13.7%). Twenty-one of these 48 cases (43.8%) involved level 2 hypoglycemia. Notably, three myocardial infarctions, one medical emergency team call, 19 deaths, and 20 arrhythmias occurred greater than or equal to 1 and less than or equal to 12 hours after an alert threshold was met.
Alerts generated by a validated ICU hypoglycemia prediction model had a positive predictive value of 13.7% for real-world hypoglycemia events. This proof-of-concept result suggests that the predictive model offers clinical value, but further prospective testing is needed to confirm this.
We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients.
Retrospective analysis leading ...to model development and validation.
All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record.
Eleven thousand eight hundred forty-seven ICU patient admissions.
The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model.
Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81).
We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.
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