Cardio-metabolic risk factors play a crucial role in the development of cardiovascular and metabolic diseases. Basal metabolic rate (BMR) is a fundamental physiological parameter that affects energy expenditure and might contribute to variations in these risk factors. However, the exact relationship between BMR and cardio-metabolic risk factors has remained unclear.

We employed Mendelian Randomization (MR) analysis to explore the association between BMR (N: 534,045) and various cardio-metabolic risk factors, including body mass index (BMI, N: 681,275), fasting glucose (N: 200,622), high-density lipoprotein (HDL) cholesterol (N = 403,943), low-density lipoprotein (LDL) cholesterol (N = 431,167), total cholesterol (N: 344,278), and triglycerides (N: 441,016), C-reactive protein (N: 436,939), waist circumference (N: 232,101), systolic blood pressure (N: 810,865), diastolic blood pressure (N: 810,865), glycated haemoglobin (N: 389,889), and N-terminal prohormone brain natriuretic peptide (N: 21,758). We leveraged genetic variants strongly associated with BMR as instrumental variables to investigate potential causal relationships, with the primary analysis using the Inverse Variance Weighted (IVW) method.

Our MR analysis revealed compelling evidence of a causal link between BMR and specific cardio-metabolic risk factors. Specifically, genetically determined higher BMR was associated with an increased BMI (β = 0.7538, 95% confidence interval [CI]: 0.6418 to 0.8659, p < 0.001), lower levels of HDL cholesterol (β = -0.3293, 95% CI: 0.4474 to -0.2111, p < 0.001), higher levels of triglycerides (β = 0.1472, 95% CI: 0.0370 to 0.2574, p = 0.0088), waist circumference (β = 0.4416, 95% CI: 0.2949 to 0.5883, p < 0.001), and glycated haemoglobin (β = 0.1037, 95% CI: 0.0080 to 0.1995, p = 0.0377). However, we did not observe any significant association between BMR and fasting glucose, LDL cholesterol, total cholesterol, C-reactive protein, systolic blood pressure, diastolic blood pressure, or N-terminal prohormone brain natriuretic peptide (all p-values>0.05).

This MR study provides valuable insights into the relationship between BMR and cardio-metabolic risk factors. Understanding the causal links between BMR and these factors could have important implications for the development of targeted interventions and therapies.

The purpose of this review is to provide an overview of the current quality of diabetes care in the USA, discuss barriers to guideline-recommended treatment adherence, and outline strategies for the improvement in care.

Current treatment guidelines highlight the importance of glycemic control, use of novel medications with proven cardiovascular efficacy, and multifactorial cardiovascular risk factor intervention for the treatment of diabetes and associated complications. Albeit proven evidence for these guidelines, the vast majority of patients with diabetes remain insufficiently treated. Interventions to improve outcomes require focus on care delivery systems, physician behavior, and patient-centered approaches. De-fragmenting care systems to form collaborative, multi-specialty teams, use of standardized and comprehensive treatment algorithms, development of quality assessment tools, avoiding physician therapeutic inertia, and addressing patient barriers, including lack of perceived benefit, insufficient diabetes education and access to care, and medication costs, represent key objectives to improve diabetes care and outcomes. Clinical research in standardized trials has proven the feasibility to reduce morbidity and mortality associated with diabetes. Implementing models of care to disseminate these encouraging research findings to the wider population and to overcome barriers to achieving guideline-recommended treatment goals should be the objective to improve our current quality of diabetes care in the USA.

The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD).

To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates.

Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1).

In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.

We sought to assess the effect of hemoglobin A1c (HbA1c) on the outcomes of a primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI).

Many studies have shown the diagnostic and predictive value of HbA1c levels in patients with acute myocardial infarction. We assessed the prognostic role of HbA1c in short-term and long-term mortality in 796 consecutive patients with STEMI.

A total of 796 patients with STEMI undergoing primary PCI were prospectively enrolled between December 2013 and June 2015. The patients were divided into three groups on the basis of admission HbA1c levels: group I (HbA1c≤5.6%), group II (HbA1c 5.7-6.4%), and group III (HbA1c≥6.5%). The in-hospital and 2-year cardiovascular (CV) mortality and morbidity of all three patient groups were followed up. A significant association was found between HbA1c level and 2-year primary clinical outcomes, including CV mortality, heart failure/shock, and major adverse cardiovascular event (P<0.001).

HbA1c is an independent predictor of the in-hospital and long-term mortality, nonfatal reinfarction, and target vessel revascularization in STEMI patients treated with primary PCI.

We aimed at assessing the impact of increased HbA1c (≥6.5%) on 1-year mortality in consecutive patients with ST Elevation Myocardial Infarction (STEMI) submitted to primary percutaneous coronary intervention (PCI).

The study population comprises 1205 STEMI patients treated with primary PCI and consecutively admitted to our Center from 1 January 2004 to 31 December 2011.

Two hundred and seventy-six patients with previously diagnosed diabetes (276/1205, 22.9%, Group A), 78 patients without previously known diabetes and HbA1c at least 6.5% (78/1205, 6.5%, Group B) and 851 patients without previously known diabetes and HbA1c less than 6.5% (851/1205, 70.1%, Group C).At Cox regression analysis, HbA1c at least 6.5% was not related to 1-year postdischarge mortality in patients with previously diagnosed diabetes nor in those without previously known diabetes.Kaplan-Meier survival curve analysis showed that patients in Group A exhibited the lowest survival rate, while patients in Group B (i.e. patients without previously known diabetes and with HbA1c ≥6.5%) showed a significant reduction in their survival rate since 6 months after discharge.

In a large series of STEMI patients submitted to primary PCI, HbA1c levels were not related with outcomes at multivariable analysis.

Diabetes is underdiagnosed and undertreated among acute myocardial infarction (AMI) patients. The early diagnosis and treatment of diabetes during AMI facilitates improved risk stratification, use of appropriate revascularization strategies and secondary prevention medications, and timely initiation of glycemic therapy. Accurate diagnostic methods, such as hemoglobin A1c, should be evaluated for hospitalized AMI patients. In addition, efforts to improve the uptake of diabetes screening and management in the hospitalized setting should occur. Possible actions include the use of clinical information systems to generate physician reminders for diabetes detection and management, audit and feedback programs, and professional society initiatives to address diabetes screening and therapy initiation through clinical guidelines and performance measures. Through the application of both these and other efforts listed in the manuscript, the rates of undiagnosed and undertreated diabetes among AMI patients can be significantly reduced, which would lead to an improvement in both diabetic and cardiovascular outcomes.

In population-based studies, including diabetic and nondiabetic cohorts, glycated hemoglobin A1c (HbA1c) has been reported as an independent predictor of all-cause and cardiovascular disease mortality. Data on the prognostic role of HbA1c in patients with acute myocardial infarction (MI) are not univocal since they stem from studies which mainly differ in patients' selection criteria, therapy (thrombolysis vs mechanical revascularization) and number consistency. The present review is focused on available evidence on the prognostic significance of HbA1c measured in the acute phase in patients with ST-elevation myocardial infarction (STEMI) submitted to primary percutaneous coronary intervention (PCI). We furthermore highlighted the role of HbA1c as a screening tool for glucose intolerance in patients with STEMI. According to available evidence, in contemporary cohorts of STEMI patients submitted to mechanical revascularization, HbA1c does not seem to be associated with short and long term mortality rates. However, HbA1c may represent a screening tool for glucose intolerance from the early phase on in STEMI patients. On a pragmatic ground, an HbA1c test has several advantages over fasting plasma glucose or an oral glucose tolerance test in an acute setting. The test can be performed in the non-fasting state and reflects average glucose concentration over the preceding 2-3 mo. We therefore proposed an algorithm based on pragmatic grounds which could be applied in STEMI patients without known diabetes in order to detect glucose intolerance abnormalities from the early phase. The main advantage of this algorithm is that it may help in tailoring the follow-up program, by helping in identifying patients at risk for the development of glucose intolerance after MI. Further validation of this algorithm in prospective studies may be required in the contemporary STEMI population to resolve some of these uncertainties around HbA1c screening cutoff points.

While patients with diabetes mellitus (DM) have more extensive coronary disease and worse survival after acute myocardial infarction (AMI) than patients without DM, data on whether they experience more angina are conflicting.

We examined angina prevalence over the year following AMI among 3367 patients, including 1080 (32%) with DM, from 24 US hospitals enrolled in the TRIUMPH registry from 2005 to 2008.

Patients with vs. without DM were more likely to be treated with antianginal medications both at discharge and over follow up. Despite more aggressive angina therapy, patients with vs. without DM had higher prevalence and severity of angina prior to AMI (49 vs. 43%, p = 0.001) and at each follow-up assessment, although rates of angina declined in both groups over time. In a hierarchical, multivariable, repeated-measures model that adjusted for multiple demographic and clinical factors including severity of coronary disease and in-hospital revascularization, DM was associated with a greater odds of angina over the 12 months of follow up; this association increased in magnitude over time (12-month OR 1.18, 95% CI 1.01-1.37; DM*time pinteraction = 0.008).

Contrary to conventional wisdom, angina is more prevalent and more severe among patients with DM, both prior to and following AMI. This effect is amplified over time and independent of patient and treatment factors, including the presence of multivessel disease and coronary revascularization. This increased burden of angina may be due to more diffuse nature of coronary disease, more rapid progression of coronary disease over time, or greater myocardial demand among DM patients.

Incident diabetes mellitus (DM) is important to recognize in patients with acute myocardial infarction (AMI). To develop an efficient screening strategy, we explored the use of random plasma glucose (RPG) at admission and fasting plasma glucose (FPG) to select patients with AMI for glycosylated hemoglobin (HbA1c) testing.

Prospective registry of 1574 patients with AMI not taking glucose-lowering medication from 24 US hospitals. All patients had HbA1c measured at a core laboratory and admission RPG and ≥2 FPGs recorded during hospitalization. We examined potential combinations of RPG and FPG and compared these with HbA1c≥6.5%-considered the gold standard for DM diagnosis in these analyses.

An RPG>140 mg/dL or FPG≥126 mg/dL had high sensitivity for DM diagnosis. Combining these into a screening protocol (if admission RPG>140, check HbA1c; or if FPG≥126 on a subsequent day, check HbA1c) led to HbA1c testing in 50% of patients and identified 86% with incident DM (number needed to screen (NNS)=3.3 to identify 1 case of DM; vs NNS=5.6 with universal HbA1c screening). Alternatively, using an RPG>180 led to HbA1c testing in 40% of patients with AMI and identified 82% of DM (NNS=2.7).

We have established two potential selective screening methods for DM in the setting of AMI that could identify the vast majority of incident DM by targeted screening of 40-50% of patients with AMI with HbA1c testing. Using these methods may efficiently identify patients with AMI with DM so that appropriate education and treatment can be promptly initiated.

To evaluate the relationship between A1C and glucose therapy intensification (GTI) in patients with diabetes mellitus (DM) hospitalized for acute myocardial infarction (AMI).

A1C was measured as part of routine care (clinical A1C) or in the core laboratory (laboratory A1C, results unavailable to clinicians). GTI predictors were identified using hierarchical Poisson regression.

Of 1,274 patients, 886 (70%) had clinical A1C and an additional 263 had laboratory A1C measured. Overall, A1C was <7% in 419 (37%), 7-9% in 415 (36%), and >9% in 315 patients (27%). GTI occurred in 31% of patients and was more frequent in those with clinical A1C both before (34 vs. 24%, P < 0.001) and after multivariable adjustment (relative risk 1.34 [95% CI 1.12-1.62] vs. no clinical A1C).

Long-term glucose control is poor in most AMI patients with DM, but only a minority of patients undergo GTI at discharge. Inpatient A1C assessment is strongly associated with intensification of glucose-lowering therapy.

In 518 consecutive STEMI non-diabetic patients, glycated hemoglobin>6.5% was not associated with increased short and long term mortality, but was associated with higher admission glucose values, worse in-hospital glycemic control and a higher incidence of acute insulin resistance (HOMA index).