Framingham Cardiovascular Risk Prediction and Metabolic Syndrome Using Different Alternative Methods to Measure Insulin Resistance

Dissertation for MPH degree

Abstract

Context

Insulin resistance is an important risk factor in cardiovascular and metabolic disorders, including type 2 diabetes, metabolic syndrome, and cardiovascular disease. Direct methods to identify insulin resistance are invasive, complex, time-consuming and expensive. Several alternative methods of measuring insulin resistance have been introduced to be used in epidemiological studies with a large sample size.

Objective

To examine 9 alternative methods of measuring insulin resistance based on fasting values ??to predict Framingham cardiovascular risk and metabolic syndrome in Qazvin

Method

480 men and 502 women aged 20-72 living in Minodar region of Qazvin participated in this study. 10-year cardiovascular risk was calculated by the Framingham risk scoring system. ATP III and JIS diagnostic criteria were used to define metabolic syndrome. 9 alternative indices for measuring insulin resistance, including HOMA-IR, QUICKI, FIRI, IGR, ISI basal, Bennett's SI, McAuley, Raynaud and TyG were investigated. The ROC curve of these indices was drawn and compared for the risk of Framingham and metabolic syndrome.

Findings

88.9% of the Framingham cardiovascular risk population was less than 10% and 1.1% had a risk of more than 20%. 24.9% of the population had metabolic syndrome according to the ATP III criterion and 33.2% of the population had metabolic syndrome according to the JIS criterion. TyG and McAuley indices had the highest power to identify cardiovascular risk greater than 10%. The TyG index had the most power to identify the metabolic syndrome.

Conclusion

There is a significant difference between alternative indices for measuring insulin resistance to detect cardiovascular risk and metabolic syndrome. Longitudinal studies are necessary to confirm the power of alternative indices for measuring insulin resistance in the diagnosis of cardiovascular disease.

Key words

Insulin resistance, metabolic syndrome, ROC curve, type 2 diabetes

Statement of the problem and importance of the research

In recent years, non-communicable diseases have increased significantly. So that the World Health Organization has attributed about 60% of the annual death in the world to these diseases and this problem is observed in all countries (WHA55.23, 2002). It is predicted that by 2020, the mortality rate will increase to 73% and the rate of non-communicable disease will increase to 60% (WHA55.23, 2002).

Cardiovascular diseases are one of the major causes of death worldwide, and as the first cause of death, account for one third of all deaths in the world. In addition to high mortality, these diseases also cause significant complications and are among the causes of certain disabilities, especially in old age. The most important risk factors for cardiovascular diseases are improper nutrition, inactivity, smoking, obesity, high blood pressure, diabetes, and blood lipid disorders, all of which are rooted in an inappropriate lifestyle. Framingham scoring system can be used to determine the risk of cardiovascular diseases. This system estimates the risk of cardiovascular diseases in the next 10 years. (;Ghotbi et al, 2007 NCEP, 2002).

The trend of diabetes as the most common disease caused by metabolic disorder is increasing in recent years, so that from 1995 to 2025, the population of people with it will increase by 122%. At the beginning of the 21st century, 150 million people in the world and 2 million people in Iran suffer from it. This disease accounts for 9% of all deaths in the world with 4 million deaths per year, and in many countries it is considered the most important cause of blindness and the leading cause of amputation and chronic kidney failure at the age of 20-70.By implementing diabetes prevention measures and correcting lifestyles, diabetes can be reduced to two thirds of cases (Ghotbi et al, 2007).

Metabolic syndrome is a set of risk factors including obesity, high blood pressure, dyslipidemia, and increased plasma glucose. Metabolic syndrome is strongly associated with the development of cardiovascular disease, insulin resistance, and diabetes mellitus (Seneff et al, 2011; Tsouli et al, 2006; Wilson et al, 2005; Eckel et al, 2005). Metabolic syndrome has become a modern epidemic whose prevalence is increasing (Meshkani et al, 2011). The most important cause of metabolic syndrome is insulin resistance, which initially causes postprandial hyperinsulinemia, then fasting hyperinsulinemia, and finally leads to hyperglycemia. People with metabolic syndrome are 1.5-3 times more likely to suffer from cardiovascular diseases and 3-5 times more likely to suffer from type 2 diabetes (Eckel, 2012).

Insulin resistance is one of the most important risk factors for cardiometabolic diseases, including type 2 diabetes, metabolic syndrome and cardiovascular diseases (Vaccaro et al, 2004; Radikova, 2003; Esteghamati et al, 2010). Insulin resistance is a key component of cardiovascular risk factors (Lorenzo et al, 2010). On the other hand, insulin resistance is the key pathological link between obesity, type 2 diabetes and metabolic syndrome (Mart?nez-Larrad et al, 2012). Several risk factors such as obesity, physical inactivity, body fat tissue distribution, age and hyperinsulinemia may be signs of insulin resistance. Insulin resistance predicts the development of type 2 diabetes even in normal people in terms of glucose tolerance (Radikova, 2003).

The direct and gold standard method for evaluating insulin resistance is the euglycemic hyperinsulinemic clamp method (Radikova, 2003; DeFronzo, 1979). But it is invasive, complex, difficult and expensive (DeFronzo et al, 1979; Lorenzo et al, 2010). Therefore, multiple and simple alternative indicators using the measurement of fasting insulin and/or glucose levels alone or in combination with insulin and glucose in various glucose tolerance test samples have been introduced and used in addition to using other metabolic variables such as triglycerides (Lorenzo et al, 2010; DeFronzo et al, 1979; Guerrero-Romero et al, 2010; Radikova et al, 2006; Preethi et al. et al., 2011). HOMA-IR, QUICKI, FIRI, IGR, ISI basal, Bennett's SI, McAuley, Raynaud, TyG, Belfiore's ISI(gly) basal, IGR2h, ISI2h, Gutt's ISI0,120, Avignon's SiM, Stumvoll (0,120), Stumvoll with demographics, Stumvoll MCROGTT, Stumvoll ISIOGTT, Belfiore's ISI(gly)area, SIISOGTT, Matsuda are among the alternative methods that have been introduced during the past years (Raynaud et al, 1999; Matthews et al, 1985; Duncan et al, 1995; Hanson et al, 2000; Sluiter et al, 1976; Katz et al, 2000; Anderson et al, 1995; McAuley et al, 2001; Belfiore et al, 1998; Gutt et al, 2000; Stumvoll et al, 2007; Bastard et al, 1999. Most studies have compared the accuracy of one or more of these methods with euglycemic clamp. 1999; Duncan et al., 1976; McAuley et al., 1998; Avignon et al., 1999; Stumvoll et al., 2001; Stumvoll et al., 2000; Bastard et al., 2007; Matsuda et al., 1999). But less attention has been paid to their accuracy in diagnosing cardiovascular risk or metabolic syndrome. The number of studies conducted in this field is very limited (Lorenzo et al, 2010; Mart?nez-Larrad et al, 2012) and a similar Iranian study was not available in the literature review. For this reason, the study of prediction of cardiovascular risk of Framingham and metabolic syndrome using different alternative methods of measuring insulin resistance in Minoodar region of Qazvin was designed and proposed.