For many years, cardiovascular disease (CVD) has been the leading cause of death around the world. Often associated with CVD are comorbidities such as obesity, abnormal lipid profiles and insulin resistance. Insulin is a key hormone that functions as a regulator of cellular metabolism in many tissues in the human body. Insulin resistance is defined as a decrease in tissue response to insulin stimulation thus insulin resistance is characterized by defects in uptake and oxidation of glucose, a decrease in glycogen synthesis, and, to a lesser extent, the ability to suppress lipid oxidation. Literature widely suggests that free fatty acids are the predominant substrate used in the adult myocardium for ATP production, however, the cardiac metabolic network is highly flexible and can use other substrates, such as glucose, lactate or amino acids. During insulin resistance, several metabolic alterations induce the development of cardiovascular disease. For instance, insulin resistance can induce an imbalance in glucose metabolism that generates chronic hyperglycemia, which in turn triggers oxidative stress and causes an inflammatory response that leads to cell damage. Insulin resistance can also alter systemic lipid metabolism which then leads to the development of dyslipidemia and the well-known lipid triad: (1) high levels of plasma triglycerides, (2) low levels of high-density lipoprotein, and (3) the appearance of small dense low-density lipoproteins. This triad, along with endothelial dysfunction, which can also be induced by aberrant insulin signaling, contribute to atherosclerotic plaque formation. Regarding the systemic consequences associated with insulin resistance and the metabolic cardiac alterations, it can be concluded that insulin resistance in the myocardium generates damage by at least three different mechanisms: (1) signal transduction alteration, (2) impaired regulation of substrate metabolism, and (3) altered delivery of substrates to the myocardium. The aim of this review is to discuss the mechanisms associated with insulin resistance and the development of CVD. New therapies focused on decreasing insulin resistance may contribute to a decrease in both CVD and atherosclerotic plaque generation.

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Association between insulin resistance and the development of cardiovascular disease

Diabetes is a global endemic with rapidly increasing prevalence in both developing and developed countries. The American Diabetes Association has recommended glycated hemoglobin (HbA1c) as a possible substitute to fasting blood glucose for diagnosis of diabetes. HbA1c is an important indicator of long-term glycemic control with the ability to reflect the cumulative glycemic history of the preceding two to three months. HbA1c not only provides a reliable measure of chronic hyperglycemia but also correlates well with the risk of long-term diabetes complications. Elevated HbA1c has also been regarded as an independent risk factor for coronary heart disease and stroke in subjects with or without diabetes. The valuable information provided by a single HbA1c test has rendered it as a reliable biomarker for the diagnosis and prognosis of diabetes. This review highlights the role of HbA1c in diagnosis and prognosis of diabetes patients.

https://journals.sagepub.com/doi/pdf/10.4137/BMI.S38440

Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients

The relationship between obesity and cognitive impairment is important given the globally ageing population in whom cognitive decline and neurodegenerative disorders will carry grave individual, societal and financial burdens. This review examines the evidence for the link between obesity and cognitive function in terms of both the immediate effects on cognitive performance, and effects on the trajectory of cognitive ageing and likelihood of dementia. In mid-life, there is a strong association between obesity and impaired cognitive function. Anthropometric measures of obesity are also associated with reduced neural integrity (e.g. grey and white matter atrophy). Increasing age coupled with the negative metabolic consequences of obesity (e.g. type 2 diabetes mellitus) are likely to significantly contribute to cognitive decline and incidence of dementia. Stress is identified as a potential risk factor promoting abdominal obesity and contributing to impaired cognitive function. However, the potentially protective effects of obesity against cognitive decline in older age require further examination. Finally, surgical and whole diet interventions, which address obesity may improve cognitive capacity and confer some protection against later cognitive decline. In conclusion, obesity and its comorbidities are associated with impaired cognitive performance, accelerated cognitive decline and neurodegenerative pathologies such as dementia in later life. Interventions targeting mid-life obesity may prove beneficial in reducing the cognitive risks associated with obesity.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8D58FA03B576F4E4A2AB18208930192B/S0029665117002014a.pdf/div-class-title-the-relationship-between-obesity-and-cognitive-health-and-decline-div.pdf

The relationship between obesity and cognitive health and decline.

The incidence and prevalence of metabolic and musculoskeletal diseases are increasing. Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance, inflammation, advanced glycation end-product accumulation and increased oxidative stress. These characteristics can negatively affect various aspects of muscle health, including muscle mass, strength, quality and function through impairments in protein metabolism, vascular and mitochondrial dysfunction, and cell death. Sarcopenia is a term used to describe the age-related loss in skeletal muscle mass and function and has been implicated as both a cause and consequence of T2DM. Sarcopenia may contribute to the development and progression of T2DM through altered glucose disposal due to low muscle mass, and also increased localized inflammation, which can arise through inter- and intramuscular adipose tissue accumulation. Lifestyle modifications are important for improving and maintaining mobility and metabolic health in individuals with T2DM and sarcopenia. However, evidence for the most effective and feasible exercise and dietary interventions in this population is lacking. In this review, we discuss the current literature highlighting the bidirectional relationship between T2DM and sarcopenia, highlight current research gaps and treatments, and provide recommendations for future research.

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Sarcopenia and type 2 diabetes mellitus: a bidirectional relationship.

Background Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions. Methods and findings Using data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals’ usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain). Conclusions Our AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the “information gain” achieved by considering more risk factors in the predictive model was significantly higher than the “modeling gain” achieved by adopting complex predictive models.

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Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

Diabetes mellitus is a chronic condition that occurs when the body cannot produce enough or effectively use of insulin. Compared with individuals without diabetes, patients with type 2 diabetes mellitus have a considerably higher risk of cardiovascular morbidity and mortality, and are disproportionately affected by cardiovascular disease. Most of this excess risk is it associated with an augmented prevalence of well-known risk factors such as hypertension, dyslipidaemia and obesity in these patients. However the improved cardiovascular disease in type 2 diabetes mellitus patients can not be attributed solely to the higher prevalence of traditional risk factors. Therefore other non-traditional risk factors may be important in people with type 2 diabetes mellitus. Cardiovascular disease is increased in type 2 diabetes mellitus subjects due to a complex combination of various traditional and non-traditional risk factors that have an important role to play in the beginning and the evolution of atherosclerosis over its long natural history from endothelial function to clinical events. Many of these risk factors could be common history for both diabetes mellitus and cardiovascular disease, reinforcing the postulate that both disorders come independently from “common soil”. The objective of this review is to highlight the weight of traditional and non-traditional risk factors for cardiovascular disease in the setting of type 2 diabetes mellitus and discuss their position in the pathogenesis of the excess cardiovascular disease mortality and morbidity in these patients.

Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength?

To achieve good metabolic control in diabetes and keep long term, a combination of changes in lifestyle and pharmacological treatment is necessary. Achieving near-normal glycated hemoglobin significantly, decreases risk of macrovascular and microvascular complications. At present there are different treatments, both oral and injectable, available for the treatment of type 2 diabetes mellitus (T2DM). Treatment algorithms designed to reduce the development or progression of the complications of diabetes emphasizes the need for good glycaemic control. The aim of this review is to perform an update on the benefits and limitations of different drugs, both current and future, for the treatment of T2DM. Initial intervention should focus on lifestyle changes. Moreover, changes in lifestyle have proven to be beneficial, but for many patients is a complication keep long term. Physicians should be familiar with the different types of existing drugs for the treatment of diabetes and select the most effective, safe and better tolerated by patients. Metformin remains the first choice of treatment for most patients. Other alternative or second-line treatment options should be individualized depending on the characteristics of each patient. This article reviews the treatments available for patients with T2DM, with an emphasis on agents introduced within the last decade.

Update on the treatment of type 2 diabetes mellitus

Hypoglycemia unawareness (HU) is defined at the onset of neuroglycopenia before the appearance of autonomic warning symptoms It is a major limitation to achieving tight diabetes and reduced quality of life HU occurs in approximately 40% of people with type 1 diabetes mellitus (T1DM) and with less frequency in T2DM Though the aetiology of HU is multifactorial, possible mechanisms include chronic exposure to low blood glucose, antecedent hypoglycaemia, recurrent severe hypoglycaemia and the failure of counter-regulatory hormones Clinically it manifests as the inability to recognise impeding hypoglycaemia by symptoms, but the mechanisms and mediators remain largely unknown Prevention and management of HU is complex, and can only be achieved by a multifactorial intervention of clinical care and structured patient education by the diabetes team Less know regarding the impact of medications on the development or recognition of this condition in patients with diabetes Several medications are thought to worsen or promote HU, whereas others may have an attenuating effect on the problem This article reviews recent advances in how the brain senses and responds to hypoglycaemia, novel mechanisms by which people with insulin-treated diabetes develop HU and impaired counter-regulatory responses The consequences that HU has on the person with diabetes and their family are also described Finally, it examines the evidence for prevention and treatment of HU, and summarizes the effects of medications that may influence it

Mechanisms of hypoglycemia unawareness and implications in diabetic patients.

Both hyperglycemia and hypoglycemia in hospitalized patients are associated with adverse outcomes including increased rates of infection, longer hospital length of stay, and even death. Clinical trials in patients with type 2 diabetes mellitus proved that by improving glycemic control, we can reduce all of them. Insulin is the preferred treatment for glycemic control in most cases, but alternative treatment options that can normalize blood glucose levels without hypoglycemia are being sought. Moreover, hospitalized patients are particularly vulnerable to severe, prolonged hypoglycemia since they may be unable to sense or respond to the early warning signs and symptoms of low blood glucose. Finally, nutritional support, corticosteroid therapy, and surgery increase the risk of hyperglycemia that leads to an increased risk of morbidity and mortality. We review the management of type 2 diabetes mellitus patients who are admitted to the general medical wards of the hospital for a procedure of intercurrent illness.

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Management of hospitalized type 2 diabetes mellitus patients

Objective: the study was designed to describe the clinical features and evolution of the diabetic patients attended in our hospital emergency department with diabetic ketoacidosis (DKA) using the novel Big Data tool Savana. Method: Retrospective descriptive study of the patients attended in the emergency room of the Infanta Leonor University Hospital during the years 2011 to 2016 with diagnosis of CAD. The search was made with Savana. Results: 95 episodes of DKA were diagnosed in 68 patients. Of the total episodes of CAD 57 were in type 1 diabetics (of which 4 were LADA), 25 in type 2 diabetics, 2 in diabetics postpancreatectomy and 12 were new onset of diabetes. Of the total, 61 (64.2%) required hospital admission, of which 23 (24.2%) were admitted to the intensive care unit (ICU). The mean HbA1c was 10.6 ± 2.1%. Three patients required readmission after discharge. Mortality was very low with death in 1 patient simultaneously diagnosed of lung cancer. The triggers of CAD were: 35 cases (36,8%) lack of adherence to treatment, 31 (32.6%) infections, 12 (12.6%) new onset, 8 (8,4%) various causes and 9 (9.5%) the cause could not be determined. They were classified as mild DKA 28%, 38% as moderate and 34% as severe. The duration of admission was not related to the severity of ketoacidosis. Conclusions: DKA is a serious complication that affects both, type 1 and type 2 diabetics patients, with a high percentage of hospital and ICU admissions, although with low mortality in our environment. The lenght of the stay in hospital is not related to the severity of the DKA.

Iciar Martín-Timón

Papers

Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength?

Update on the treatment of type 2 diabetes mellitus

Mechanisms of hypoglycemia unawareness and implications in diabetic patients.

Management of hospitalized type 2 diabetes mellitus patients

Estudio descriptivo de las cetoacidosis atendidas en urgencias de un hospital de la Comunidad de Madrid mediante la herramienta Savana Manager