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The Big Diabetes Lie

How I Healed my Diabetes

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Obesity has been further classified into stage I (BMI ranging from 30.0 to 34.9 kg/m2), stage II (BMI from 35.0 to 39.9 kg/m2), and stage III (if >40.0 kg/m2 or morbid obesity). The most accepted direct measures of body fat include underwater weighing, bioimpedance, and dual energy X-ray absorpti-ometry. However, these tests are not widely available and not suitable for routine clinical practice, reason why BMI is the preferred alternative. However, one must point out that BMI is a simple and inexpensive way to quantify body fat, but that ethnicity, age, gender, cardiorespiratoy fitness, and body fat distribution are important factors that may modify considerably the health risks associated with obesity. For example, females and older adults have a higher proportion of adiposity for any given BMI compared to younger subjects, in particular males (30) . Meta-analysis examining the impact of ethnicity has concluded that estimates of body fat using BMI overestimate in African-Americans the percentage of body fat relative to that of Caucasians (31).

While between 1960 and 2002 the average height has increased by 1.0-1.5 in. in the general adult population, weight has increased by ~25 lbs in both genders in the same period (or 14.8% in men and 17.2% in women) (32). Both reduced levels of physical activity (33) and increased caloric intake (34) appear to account for this. Recent information from the 2002 Behavioral Risk Factor Surveillance Survey (BRFSS), an annual survey conducted by the Centers for Disease Control (CDC), has estimated that 59.2% of Americans are either overweight or obese (29, 35). Obesity alone affects 60 million of adult Americans. Data from the most recent National Center for Health statistics using NHANES data report almost two out of three Americans as being overweight or obese (65.2%). Both sources coincide in that the "epidemic" of obesity is increasing at an alarming rate, particularly in Hispanics and African-Americans women and in socially disadvantaged groups, particularly those with the poorest levels of education and lowest income (35, 36). In this regard, the social network appears to play a key role in the development of obesity as reported in a longitudinal follow-up between 1971 and 2003 from the Framingham Heart Study, in which the chances of a person becoming obese increased between 37% and 57% if he or she had a spouse, sibling, or friend who became obese in a given interval (37). The United States leads the world as the country with more overweight and obese subjects at 64.5%, followed closely by Mexico, Australia, and the United Kingdom, with estimates that 60% of the increased incidence of diabetes can be attributed just to weight gain (36). We live in the paradox of a world in which more people are overweight and obese than those undernourished (about one billion compared to 850 million, respectively) (17). Health care expenditures also increase significantly once the BMI >30 kg/m2 (35, 36). In the United States, medical care expenses for obesity-associated conditions were estimated to be $117 billion or near 10% of the total health care costs.

Excess adiposity is also believed to be the driving force behind the development of early CVD and increased overall mortality observed in obese individuals in population-based studies (33, 35, 38-47). Obesity is associated with a reduced life span, with 100,000-400,000 excess deaths per year, depending on the models used to assess the impact of obesity (38, 47, 48). Mortality increases sharply as the BMI exceeds 30 kg/m2 (12, 33, 35, 38, 46, 47, 49, 50). It has been recently suggested that poor car-diorespiratory fitness may be more important than adiposity itself in older adults, being independent of overall or abdominal obesity, highlighting the importance of functional capacity beyond simple measures of adiposity such as BMI (51) . Moreover, it has been estimated that soon low levels of physical activity and poor dietary habits will overtake tobacco as the leading cause of death in the United States (52). In the Framingham Heart Study, middle-aged overweight subjects had an average 7-year reduction in life expectancy (38).

Recently, two large studies have assessed how obesity early in life may predict future CHD and overall life expectancy. Baker et al. (53) reported that in a large cohort of 276,835 Danish schoolchildren ages 7 through 13, there was a linear association between increasing BMI and risk of CHD, so that per each 1-unit increase in BMI at age 13 there was a 15% higher risk of CHD in adulthood.

Consistent with the deleterious effects of obesity, van Dam et al. (39) recently reported that increased adiposity at age 18 in women is associated with increased mortality later in life. The authors assessed body weight in 102,400 women from the Nurses' Health Study II and followed them for 12 years. They found that there was a 1.6-fold and 2.8 increase in mortality rates among overweight and obese women, respectively, compared to women with a BMI between 18.5 and 21.9 kg/m2 at age 18. Obesity is also associated with significant functional impairment (50, 54), another factor that predisposes to a more sedentary lifestyle and contributes to reduced CV fitness and a higher risk of CVD.

Obesity and Body Fat Distribution

Another important aspect of adiposity is the distribution of body fat. The waist circumference has been adopted as the practical way to measure central adiposity, but the accurate measurement of visceral fat calls for the use of imaging techniques such a magnetic resonance imaging (MRI) or computed tomography (CT). By these techniques, abdominal fat is typically measured with a single cut at the L4-5 vertebral bodies, although estimation of the total visceral fat volume is a more accurate approach (55). High carbohydrate diets are known to promote hepatic very low-density lipoprotein (VLDL) oversecretion. In women, body fat deposition is primarily peripheral in the gluteo-femoral subcutaneous region. Adipose tissue expands in this area and the lower abdomen in overweight and obese women ("pear-shape" fat distribution). A key observation was that obese women with a predominantly upper-body fat distribution had much greater rates of lipolysis and free fatty acid (FFA) turnover than those with lower body obesity (56). In contrast to women, fat takes more frequently a more central distribution in overweight and obese men ("apple-shape"). As with women with a more central fat distribution, this has been associated with insulin resistance, more visceral fat accumulation, higher triglycerides, and lower high-density lipoprotein-cholesterol (HDL-C) levels (57). Subjects with "apple-shaped" fat distribution have a higher risk of CVD possibly related to the important metabolic differences between visceral and subcutaneous adipose tissue. This has made central obesity a criterion of significant value for the diagnosis of the MS (see discussion below).

Abdominal obesity is also associated with a greater risk of developing T2DM (58) . The Paris Prospective Study was the first large prospective study to confirm the close relationship between adipose tissue insulin resistance (i.e., elevated plasma FFA concentration) and the deterioration of glucose tolerance over time (59). Visceral fat is believed to be more prone to lipolysis in response to counterregulatory hormones and more resistant to the antilipolytic effect of insulin (57, 60). Moreover, it has been speculated that because it drains directly into the portal vein, FFA derived from the visceral bed would have a more direct impact on liver metabolism than fat from peripheral (subcutaneous) lipolysis. In any case, the role of visceral fat to overall CV risk remains highly controversial (57, 61-63). For example, in a recent cross-sectional study across 21 research centers in Europe, simultaneously measuring insulin sensitivity by the gold-standard in euglycemic insulin clamp technique and the clustering of risk factors associated with the MS, it was not possible to isolate different measures of adiposity (BMI, fat mass, or fat distribution) as more prominent than the others as causative factors for insulin resistance or related CV risk factors (63) . We have found a closer correlation between visceral and liver fat accumulation than when compared with BMI or subcutaneous fat .55, 64). However, while the "portal hypothesis" is appealing to the development of hepatic insulin resistance by visceral adipose tissue, the finding that in healthy obese individuals the contribution of visceral fat to the overall FFA pool increases only modestly (from 10% to only 25% compared to lean subjects) (65), suggests that expansion of subcutaneous fat adipose tissue also plays an important role in the development of hepatic insulin resistance. Moreover, as FFA from visceral fat contributes only with 5% or less to the peripheral plasma FFA pool (65) , it is unlikely to be a primary responsible for peripheral (muscle) insulin resistance, again highlighting the damaging effect of overall adiposity

(visceral and subcutaneous) as sources of FFA for subsequent ectopic (i.e., muscle, liver, P-cells) fat deposition. It is also possible that the deleterious role of visceral fat is mediated not so much by FFA but primarily by the release a number of adipocytokines [tumor necrosis factor-a (TNF-a), leptin, interleukins (IL), etc.] that have been shown to promote insulin resistance (66, 67), offering a unifying explanation for how a rather modest amount of adipose tissue (i.e., 10-15% of total body fat) may hold the potential to impair hepatic and peripheral (muscle) insulin action.

Obesity and the Insulin Resistance (Metabolic) Syndrome

In recent years, there has been great interest in the concept of a clustering of risk factors for CVD occurring in a given individual to a greater degree than expected by chance. This clustering, commonly referred to as the "metabolic syndrome," has clinical manifestations frequently observed in obesity and is believed to be associated with underlying insulin resistance in the majority, but not all, of individuals. In its original conception, "syndrome X" (68) or the "insulin resistance syndrome" (69) provided a framework to understand the relationship or association between insulin resistance, multiple metabolic abnormalities, and the development of T2DM. Interest in this "metabolic" clustering of risk factors evolved into an aggregation of risk factors (obesity, elevated TG (triglycerides) and low HDL-C, hypertension, insulin resistance and abnormal fasting or 2-h plasma glucose levels, and others) used primarily to predict in a given individual the risk of CVD.

Many studies have shown that the clustering of risk factors identifies subjects more likely to develop CVD (33, 49, 57, 70-77). In a landmark study by Lakka et al. (78), they prospectively followed 1,209 middle-aged Finnish men without CVD at baseline and showed that after 11.4 years of follow-up, those with the MS [defined using either National Cholesterol Education Program (NCEP) or WHO criteria] were 2.9-times more likely to die of CHD and had 1.9-fold higher CVD mortality rates. More recently, the San Antonio Heart Study examined the relationship between gender, the MS (NCEP definition) and diabetes in their ability to predict CHD mortality over 15.5 years of follow-up in 4,996 men and women (71). Relative to women with neither diabetes nor MS, women with both had a 14-fold increased risk of CHD mortality whereas men had only a fourfold increased risk, respectively, gender being a strong modifier of the joint effect of diabetes and MS on CHD mortality. Still, three aspects are under considerable debate regarding the MS: (1) whether the association of multiple risk factors in the MS adds to CVD prediction more than the sum of its individual components; (2) which is the precise role of insulin resistance in its pathogenesis, and (3) which parameters/ risk factors would best serve as predictors of CV disease (or T2DM) and what are their optimal cutoffs (79-81). Regarding the first issue, it is unlikely that epidemiological studies alone using multiple regression analysis and other approaches will give us a definitive answer as to whether the whole (i.e., MS) will be more predictive than the sum of the parts (i.e., individual risk factors). This is because the mutual interaction of these metabolic abnormalities (i.e., the "embedded" impact of obesity and/ or insulin resistance on atherogenic dyslipidemia, hypertension, or the plasma glucose concentration, among other factors) will likely make impossible that any statistical analysis will be able to dissect and quantify the relative contribution of these closely intertwined CV risk factors to overall CVD.

As for the role of insulin resistance in the pathogenesis of the MS, while controversial, it offers so far the best unifying hypothesis based on a large amount of basic and clinical data, with many prospective studies indicating that insulin resistance is an independent risk factor that strongly predicts future CV morbidity and mortality (44, 49, 57, 66, 73, 82-84). Crude measurements of insulin resistance in some studies (i.e., such as a fasting plasma insulin level or the HOMA model, that is primarily a measure hepatic HOMA (homeostasis model assessment)) HOMA insulin resistance (but not of muscle or adipose tissue insulin sensitivity) may erroneously conclude that insulin resistance does not play a role. Alternatively, the effect of insulin resistance may already be accounted for by its impact on driving hepatic VLDL production (increasing plasma triglycerides) and promoting a high HDL-C turnover (lowering HDL) (85, 86). Insulin resistance may also be promoting pancreatic P-cell failure and progressive hyperglycemia in individuals genetically predisposed to T2DM, so that when these variables are included in multiple regression analysis, the fasting insulin is no longer an "independent" risk factor to predict risk of CVD or T2DM. The role of insulin resistance has also been questioned on the grounds that not all patients with insulin resistance develop the MS, and also that not all patients with the MS are insulin-resistant. However, this reasoning is quite naive as to expect that every subject with insulin resistance will develop the MS, because disease always depends on a permissive factor (i.e., insulin resistance in the case of the MS) plus the diminished reserve to a given insult by the target organ (i.e., the vascular bed in atherosclerosis, the liver in nonalcoholic fatty liver disease (NAFLD), the ovary in polycystic ovary syndrome (PCOS), and the pancreatic P-cell in T2DM). In other words, insulin resistance create a fertile soil for end-organ damage and the genetic make-up determines the susceptibility to this permissive environment, in the same way that nobody questions today the roles of hypertension and dyslipidemia to the development of CVD, although many CV events will never develop even in the presence of this well-established risk factors.

Some of the confusion among the role of the MS arises on the emphasis placed on its different components/risk factors and variable cut-offs adopted by different organizations: the NCEP, the IDF, the Group for the Study of Insulin Resistance (EGIR), the American Association of Clinical Endocrinologists (AACE), and the WHO [elegantly reviewed by Meigs (84)]. These include obesity and/or a measure of waist circumference, fasting glucose [and some a measure of insulin resistance (EGIR) (WHO) or 2-h glucose (WHO)], triglycerides/HDL-C and elevated blood pressure. While obesity is important in all definitions, depending on the emphasis put on other risk factors, they appear to be defining slightly different populations. For example, the definition most widely held by clinicians is the NCEP ATP III 2005, which combines any of three out of five risk factors to meet the criteria. The criteria and cut-offs of the NCEP are fasting plasma glucose >100 mg/dL, central obesity (>35 in in women and >40 in in men), low plasma HDL-C (£40 mg/dL in males and <50 mg/dL in women); plasma triglycerides >150 mg/dL, and blood pressure >130 or >80 mmHg (or pharmacological treatment of any of these risk factors). This is a rather "lipid-centric" definition as most meet the MS criteria from having obesity and —one to two of the lipid criteria. It is limited also by not taking into account the weight of the different factors or by using measurements of insulin resistance, although its simplicity has made it a valuable tool for primary care physicians and for use in large epidemiological studies. Other definitions acknowledge directly or indirectly the importance of insulin resistance and the likelihood that it may have an important pathogenic role. The IDF requires abnormal waist-circumference as the driving criteria, emphasizing the role of abdominal/central obesity as a surrogate for insulin resistance. The EGIR and WHO definitions aim at identifying those with insulin resistance for its diagnosis, measured by the gold-standard euglycemic insulin clamp technique [or at least a fasting insulin in the top 25% (EGIR)], although these measurements are not costly and difficult to obtain by clinicians. In an attempt to assist physicians in clinical practice, AACE includes as criteria several conditions strongly associated with insulin resistance, such as NAFLD, polycystic ovary disease, and acanthosis nigricans. This has very practical implications and serves to increase awareness among doctors and patients on the importance of conditions with apparent no relation to the development of T2DM and CVD.

Future prospective studies will allow to "fine tune" the currently used MS criteria. From a practical perspective, many practitioners give value to the MS criteria by assisting them in searching for clusters of CV risk factors in patients who are overweight, or have either CVD or T2DM. It also helps them at the time of choosing a pharmacological agent to treat a given CV risk factor. For example, one may avoid treating one CV risk factor with an agent that may have deleterious effects on other risk factors, if alternative treatments are available. For example, beta blockers may increase the plasma cholesterol and plasma glucose levels and have been associated with increased incidence of T2DM in epidemiological and intervention studies (4); use of an angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) may be preferable in the setting of uncomplicated hypertension and diabetes as both have no deleterious effects on glucose or lipid metabolism and have been reported to reduce the incidence of T2DM, with added benefits on preservation of renal function which is already at greater risk of damage in the setting of obesity, MS or T2DM.

The Challenge of Predicting the Development of T2DM

While the MS is a tool to predict CV risk, in the wake of the diabetes epidemic there has been significant interest about its ability to also predict T2DM, with several studies in Caucasians (73, 75, 87) and ethnic minorities (74, 88) confirming its value in this regard. This is important because if we can find the optimal way to identify early-on subjects that will develop T2DM later in life, aggressive lifestyle and/or pharmacological interventions before the development of hyperglycemia are likely to be very cost-effective (discussed at the end of the chapter).

A recent meta-analysis showed that the presence of MS increases the likelihood of developing T2DM by two to fourfold (49). However, it is less sensitive than direct tests aimed at identifying subjects at risk of developing T2DM (88). In testing a predictive tool for a disease, rather than estimating the relative risk, it is standard to use the area under the receiver-operator-characteristic curve (ROC) to determine the accuracy of a test to discriminate individuals that will develop a disease from those that will not. The ROC is always a balance between the ability of the test to correctly identify subjects (true positive rate) from its error in doing so (false positive), depending on the sensitivity and specificity set by the different parameters in the model. The gold-standard to predict T2DM has traditionally been the oral glucose tolerance test (OGTT), but it is somewhat inconvenient for widespread clinical use. Given the epidemic of T2DM, studies that have examined the value of the MS criteria to predict T2DM have concluded that it provides a reasonable prediction of future T2DM with a ROC between 0.75 and 0.82 (76, 89-91). Of note, a ROC of 0.5 is considered simple chance discrimination while a perfect screening test would have an ROC of 1.0. However, the use of impaired fasting glucose (IFG) plays a key role in driving the predictive value of the different MS criteria, so that when required as one of the screening criteria for the prediction of T2DM, it greatly enhances the predictive value of the MS and it diminishes significantly when excluded (73). However, models tailored to predict T2DM (that do not necessarily need to incorporate an OGTT in their model) are more accurate than the MS with ROCs of 0.84-0.85, such as the San Antonio Diabetes Prediction Model (SAPDM) (76, 89) and the Framingham Offspring Study database in middle-aged Caucasians (92). Recently, the use of the 1-h OGTT, or factoring-in insulin resistance to insulin secretion during an OGTT, provided a slightly better prediction for T2DM compared to the SAPDM (ROC 0.86 vs. 0.80) or 2-h OGTT (ROC 0.79, both p < 0.001), but with the caveat of requiring invasive testing (OGTT) for the diagnosis of T2DM (91).

One can expect in the future that the MS will have improved accuracy to predict T2DM if ethnicity (minorities being more prone to T2DM) and a family history (FH) of T2DM (a strong predictor of insulin resistance and diminished pancreatic P-cell reserve) are taken into consideration. For example, African-Americans tend to have higher blood pressure, lower triglycerides, and higher HDL-C (in contrast to higher triglycerides in Japanese) while insulin resistance and T2DM are more common in Mexican-Americans and American-Indians (14). In high-risk populations (minorities, those with a FH of T2DM or gestational diabetes, obese patients with features of the MS), it may be cost-effective to use a minimally invasive and simple test such as an OGTT to establish an early diagnosis and consider intervention (23). It is still quite a tragedy that diabetes continues to be diagnosed late and that still today about one-third of patients with T2DM are unaware of having the condition.

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