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Insulin resistance as a predictor of cardiovascular diseases
Resistência à insulina como preditivo das doenças cardiovasculares
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The World Health Organization estimates that CVDs account for 31% of all global deaths, increasing to over 50% mortality among individuals with diabetes mellitus.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">1</span></a> This chronic state of hyperglycemia is normally associated with defects in insulin secretion and insulin action, also known as insulin resistance (IR), or both. Moreover, defects in uptake and oxidation of glucose, glycogen synthesis, and in the ability to suppress lipid oxidation are some of the metabolic alterations due to the IR state. These metabolic events consequently trigger oxidative stress and inflammatory responses that ultimately lead to cell damage. Thus, in hyperglycemia and IR states, metabolic, structural and functional alterations have been shown to affect multiple tissues, including the heart, skeletal muscle, liver, and adipose tissue. In the heart and vasculature, the aforementioned IR-induced changes have been associated with the incidence of diabetic cardiomyopathy, coronary artery disease, myocardial ischemia, and ultimately heart failure. Despite this strong link between IR and increased CVD incidence, the proportion of CVD that can be attributed to IR remains unclear. Through a meta-analysis, IR measured using the homeostasis model assessment (HOMA), a model of glucose-insulin feedback system in the overnight-fasted state,<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">2</span></a> was shown to be a good predictor of CVD.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">3</span></a> This method has been widely used in clinical practice to measure IR, however due to cost-effectiveness and the instability of insulin in blood, the efficacy of HOMA-IR is questionable. Thus, for clinical practice a simple, robust and accessible marker as a surrogate for IR to address this pathology and predict cardiovascular risk is needed. Triglyceride-glucose index, a product of fasting triglycerides (TG) and glucose, and TG-to-high density lipoprotein cholesterol (HDL) ratio (TG/HDL) have been more recently suggested as an alternative to measure IR with improved efficiency.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">4</span></a> Moreover, some anthropometric measurements, such as body mass index (BMI) and waist circumference (WC), are also used to identify IR.<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">5</span></a> However, in the current issue, Nunes et al.<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">6</span></a> found that there was a better correlation between lipid accumulation product (LAP) and HOMA-IR than with BMI, neck circumference (NC), waist-hip ratio (WHR) and sagittal abdominal diameter (SAD), in elderly population. Although these measurements are easy to obtain, anthropometric measurements are not comprehensive in reflecting obesity and metabolic abnormalities for each individual. In this scenario, LAP emerges as a result of the correlation between waist measurements and fasting TG levels, thus reflecting both anatomic and physiological alterations related to lipid accumulation. The authors therefore suggest LAP as a useful and simple clinical marker to assess cardiometabolic risk factors. Indeed, in a cross-sectional study including non-diabetic subjects, LAP was found to be closely associated with HOMA-IR.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">7</span></a> Recent studies have also corroborated the direct correlation between LAP and IR, as well as increased incidence of CVD.<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">8,9</span></a> Thus, LAP, a single and inexpensive index, has been suggested as a precise marker that outperforms BMI and WC in identifying IR in a large population range, showing a strong association with CVD risk factors.</p><p id="par0010" class="elsevierStylePara elsevierViewall">Considering the close association between LAP, diabetes and metabolic syndrome, and the fact that this index takes into account TG circulating levels, it is expected to be strongly correlated with an altered circulating lipid profile. Recent studies report that higher LAP is related to abnormal total cholesterol, high density lipoprotein cholesterol (HDL-C) levels, TG to HDL-C ratio and malondialdehyde.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">10</span></a> Similar results were obtained in the study by Nunes et al., where a positive correlation between low levels of HDL-C as well as high levels of TG and increased levels of LAP was demonstrated. Thus, to gain a deeper knowledge of the etiology of metabolic disorders, considerable efforts have been made to shift CVD research toward the study of dysregulated lipid metabolism by characterizing the circulating lipidome. Very recent data showed, through plasma lipidomic profiling via liquid chromatography-tandem mass spectrometry, that lysoglycerophospholipids and sphingolipids are good lipid biomarkers associated with metabolic risk factors and consequent good biomarkers for CVD.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">11</span></a> Moreover, work carried out in the Strong Heart Family Study (SHFS), a well-characterized cohort study conducted in a population at high risk of diabetes, demonstrated that higher levels of plasma ceramides were associated with higher fasting plasma insulin and HOMA-IR.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">12</span></a> Accordingly, due to the complex phenotype and dynamics of chronic metabolic-related diseases, affected by multiple genetic and environmental factors, scientific and clinical communities have made efforts to find the molecular etiology behind these diseases. In this scenario, metabolomics appears as a powerful phenotyping tool, which is described as a comprehensive analysis of all small molecules present in a biological system. These types of approach provide an integrated view of the metabolic profiles due to the sensitive detection of molecular changes over time as result of intrinsic and extrinsic factors.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">11</span></a> Thus, metabolites, as single molecules or integrated in a comprehensive signature, are promising targets for efficient diagnosis, prognosis and therapeutic approaches, to overcome the limitation of the current conventional clinical markers during risk assessment and stratification. The application of this technology can therefore contribute to decipher molecular mechanisms underlying metabolic diseases. In line with this, recent results obtained from machine learning classifiers, based on the plasma lipidomes, enabled the accurate distinction between different atherosclerosis-related CVD, including ischemic stroke and systemic lupus erythematosus, based on the lipid profile. Notably, this tool was also sensitive to statin treatment, showing a distinct lipid fingerprint from the CVD cohorts.<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">13</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In conclusion, the work developed by Nunes et al. has contributed to the field of metabolic disorder diagnosis, such as IR, revealing the LAP measurement as a valuable tool for the assessment of IR. Moreover, due to its tight correlation with CVD, this marker can predict cardiometabolism risk in elderly population, thus paving the way for the development of alternative methodologies to accurately measure the odds of cardiovascular events.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0020" class="elsevierStylePara elsevierViewall">The author has no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Conflicts of interest" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:13 [ 0 => array:3 [ "identificador" => "bib0070" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Insulin resistance and hyperglycaemia in cardiovascular disease development" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "M. 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| Ano/Mês | Html | Total | |
|---|---|---|---|
| 2024 Novembro | 9 | 8 | 17 |
| 2024 Outubro | 42 | 45 | 87 |
| 2024 Setembro | 45 | 33 | 78 |
| 2024 Agosto | 52 | 32 | 84 |
| 2024 Julho | 35 | 37 | 72 |
| 2024 Junho | 38 | 38 | 76 |
| 2024 Maio | 36 | 18 | 54 |
| 2024 Abril | 37 | 37 | 74 |
| 2024 Maro | 30 | 21 | 51 |
| 2024 Fevereiro | 33 | 23 | 56 |
| 2024 Janeiro | 26 | 29 | 55 |
| 2023 Dezembro | 27 | 30 | 57 |
| 2023 Novembro | 37 | 27 | 64 |
| 2023 Outubro | 32 | 17 | 49 |
| 2023 Setembro | 21 | 24 | 45 |
| 2023 Agosto | 20 | 18 | 38 |
| 2023 Julho | 28 | 16 | 44 |
| 2023 Junho | 26 | 27 | 53 |
| 2023 Maio | 31 | 33 | 64 |
| 2023 Abril | 23 | 4 | 27 |
| 2023 Maro | 55 | 23 | 78 |
| 2023 Fevereiro | 44 | 21 | 65 |
| 2023 Janeiro | 28 | 14 | 42 |
| 2022 Dezembro | 47 | 14 | 61 |
| 2022 Novembro | 50 | 32 | 82 |
| 2022 Outubro | 49 | 31 | 80 |
| 2022 Setembro | 24 | 28 | 52 |
| 2022 Agosto | 40 | 32 | 72 |
| 2022 Julho | 36 | 35 | 71 |
| 2022 Junho | 30 | 30 | 60 |
| 2022 Maio | 24 | 25 | 49 |
| 2022 Abril | 35 | 37 | 72 |
| 2022 Maro | 34 | 49 | 83 |
| 2022 Fevereiro | 24 | 30 | 54 |
| 2022 Janeiro | 25 | 31 | 56 |
| 2021 Dezembro | 22 | 28 | 50 |
| 2021 Novembro | 34 | 38 | 72 |
| 2021 Outubro | 27 | 62 | 89 |
| 2021 Setembro | 32 | 35 | 67 |
| 2021 Agosto | 105 | 106 | 211 |
| 2021 Julho | 58 | 49 | 107 |
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