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From syndrome X to cardiometabolic risk: clinical and public health implications

Published online by Cambridge University Press:  18 July 2019

Jean-Pierre Després*
Affiliation:
Centre de recherche sur les soins et les services de première ligne–Université Laval, Québec, QC, Canada Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Québec, QC, Canada Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, QC, Canada
*
Corresponding author: Jean-Pierre Després, email Jean-Pierre.Despres.ciussscn@ssss.gouv.qc.ca
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Abstract

Although the first description of a syndrome defined by the co-existence of atherogenic and diabetogenic metabolic abnormalities is debated in the literature, it was Gerald Reaven who proposed, in his landmark 1988 Banting award lecture, that a significant proportion of individuals (with diabetes or not) were characterised by insulin resistance causing prejudice to cardiovascular health. However, Reaven was influenced by seminal observations made more than 50 years earlier by Himsworth who proposed that there were two forms of diabetes (insulin resistant v. insulin sensitive). Reaven went further in proposing the theory that insulin resistance was the most prevalent cause of CVD associated with metabolic abnormalities that he named syndrome X. Because there was a syndrome X documented in cardiology, the term evolved to insulin resistance syndrome. As Reaven could also find insulin-resistant individuals in non-obese subjects, he did not include obesity as a feature of syndrome X. Imaging studies then revealed that excess adipose tissue in the abdominal cavity, a condition described as visceral obesity, was the form of overweight/obesity associated with insulin resistance and its related abnormalities. As obesity risk assessment and management remain largely based on body weight (BMI) and weight loss, it is proposed that our clinical approaches and public health messages should be revisited. First, patients should be educated about the importance of monitoring their waistline as a crude index of abdominal adiposity. Secondly, public health approaches focussing on ‘lifestyle vital signs’ including achieving healthy waistlines rather than healthy body weights should be developed.

Type
Conference on ‘Optimal diet and lifestyle strategies for the management of cardio-metabolic risk’
Copyright
Copyright © The Author 2019

We are undoubtedly going through an epidemic of chronic lifestyle diseases. For instance, the prevalence of obesity keeps increasing all over the world and is closely related to chronic metabolic diseases such as type 2 diabetes, hypertension and CVD(1Reference Gonzalez-Muniesa, Martinez-Gonzalez and Hu3). Recent data suggest that in a few years, half a billion people worldwide will have to live with type 2 diabetes(2). Clearly, our lifestyle habits have radically changed over the past century as sedentary behaviours now kill more people than smoking(Reference Wen and Wu4). Accordingly, overall nutritional quality has deteriorated with overconsumption of processed foods with added sugar, salt and refined carbohydrates(Reference Hruby, Manson and Qi5,Reference Mozaffarian6) . Thus, although sophisticated healthcare systems of many affluent countries can provide a decent life expectancy to their citizens, life expectancy being healthy and free from chronic diseases has not followed(Reference Fries7), leading to prohibitive costs associated with medical treatments and procedures to keep these patients alive(Reference Mozaffarian8). Consequently, an expanding proportion of our population lives longer while nevertheless carrying the burden of being afflicted by costly chronic diseases.

Pioneers in the field of endocrinology and metabolism have generated concepts and hypotheses that remain very relevant to the afore-mentioned issues. For instance, in 1988, while giving his famous Banting award lecture, Reaven was the first to propose that the most prevalent cause of CVD was not an elevated cholesterol concentration but rather a constellation of abnormalities related to a reduced responsiveness to insulin(Reference Reaven9). Reaven proposed that insulin resistance, which can be assessed or estimated by various techniques in vivo was (1) a prevalent condition in the population (about 25 %); (2) associated with a typical dyslipidaemic state (high TAG and low HDL-cholesterol concentrations) as well as with elevated blood pressure and fasting hyperinsulinaemia and (3) a central component of an atherogenic cluster of metabolic abnormalities which was a common cause of CVD(Reference Reaven9,Reference Reaven10) . It is also appropriate to point out that Harold Himsworth was a major influence in the Reaven proposal as the former was the first to suggest, in the 1930s, that there were two forms of patients with diabetes (those who were insulin sensitive and those who were insulin resistant)(Reference Himsworth11). Reaven extended that notion and proposed that insulin resistance could also be found in the non-diabetic population.

While exploring factors associated with insulin resistance, Reaven also noted that he could observe individuals with obesity who were nevertheless insulin sensitive whereas he could find insulin resistance in non-obese subjects; this is why he did not include obesity as one of his features of syndrome X. Imaging studies providing more sophisticated and accurate measurements of body composition initiated more than 30 years ago have since shed some light on this issue.

For instance, in 1983, Matsuzawa and co-workers in Japan were the first to report, using images generated by computed tomography, that there were remarkable differences in the way people would store abdominal fat (abdominal computed tomography images showing that some individuals had a large accumulation of subcutaneous adipose tissue (SAT) whereas others had considerable amount of intra-abdominal or visceral adipose tissue (VAT) with little subcutaneous fat)(Reference Tokunaga, Matsuzawa and Ishikawa12). Inspired by these results, we began, in the mid-80s, to systematically scan abdomens of asymptomatic men and women with the use of computed tomography. At that time, we quickly reached the conclusion that there were indeed substantial individual variations in VAT v. SAT accumulation. On average, men were found to have twice the amount of VAT compared to premenopausal women, whereas middle-aged men and women had more VAT than young adults(Reference Lemieux, Prud'homme and Bouchard13,Reference Kvist, Chowdhury and Grangard14) . Reviewing our early work on the topic would be beyond the scope of the present paper and the reader is referred to previous review papers on the topic(Reference Després, Moorjani and Lupien15Reference Tchernof and Després19). Fig. 1 summarises the constellation of metabolic abnormalities that we found to be associated with excess VAT and not with SAT. Thus, our early findings have contributed to explain why Reaven could not find an association between obesity and features of his syndrome X: excess VAT, not excess BMI per se, was the main driver of the dysmetabolic state of syndrome X. Subcutaneous obesity, in the absence of excess VAT, was not found to be associated with substantial deteriorations in insulin resistance and related metabolic abnormalities. Many imaging studies (using computed tomography or MRI) have since confirmed that excess VAT is a key correlate of the features of Reaven syndrome X(Reference Després, Lemieux and Bergeron17,Reference Després and Lemieux20Reference Smith24) . There is also evidence that lower body or gluteofemoral fat is negatively associated with the risk of CVD among individuals with obesity in the Dallas Heart Study(Reference Neeland, Turer and Ayers25). These results suggest that subcutaneous fat, particularly lower body subcutaneous fat, may not cause any prejudice to cardiometabolic health and may even be protective against the development of cardiometabolic outcomes. Such notion is fully consistent with the findings of a study by Klein et al. (Reference Klein, Fontana and Young26) conducted more than 15 years ago in a sample of women with obesity reporting that liposuction of a substantial amount of SAT was not associated with improvements in the cardiometabolic risk profile.

Fig. 1. Simple overview of atherogenic and diabetogenic complications associated with an excess amount of visceral adipose tissue (identified in dark grey within the abdominal muscle wall) increasing the risk of developing type 2 diabetes and CVD (CVD).

Because there is a syndrome X in cardiology (clinical symptoms of CHD without evidence of CHD from angiographic investigations)(Reference Cheng27,Reference Kemp28) , the term insulin resistance syndrome has also been used to describe the constellation of metabolic abnormalities first described by Reaven. Furthermore, as measuring insulin resistance cannot be performed in primary care, Grundy and co-workers then proposed at the beginning of the millennium the use of some simple clinical tools to identify individuals who would be very likely to be characterised by the abnormalities of insulin resistance: the metabolic syndrome was born (National Cholesterol Education Program Adult Treatment Panel III)(29). Because insulin resistance is frequently found among individuals with abdominal obesity and because we had previously proposed that a large waistline combined with elevated plasma TAG concentrations was predictive of visceral obesity(Reference Lemieux, Pascot and Couillard30), the committee proposed that simple variables such as waist circumference, TAG, HDL-cholesterol, blood pressure and glucose level could be used to discriminate individuals likely to be insulin resistant. Countless studies that have since compared individuals showing at least three out of these five clinical criteria v. those not meeting these criteria, the vast majority of them confirming that a clinical diagnosis of the metabolic syndrome was predictive of an increased risk of CVD(Reference Galassi, Reynolds and He31,Reference Gami, Witt and Howard32) . Thus, although its clinical relevance has been questioned(Reference Gale33,Reference Kahn, Buse and Ferrannini34) , a clinical diagnosis of the metabolic syndrome is useful to at least identify the subgroup of overweight or obese individuals more likely to be characterised by an excess of VAT and related metabolic abnormalities(Reference Després, Lemieux and Bergeron17,Reference Després, Cartier and Côté35,Reference Lemieux, Poirier and Bergeron36) .

Because measurement of insulin resistance is not included as a criterion of the metabolic syndrome, Reaven had also expressed concerns about the metabolic syndrome as a useful concept in clinical practice(Reference Reaven37Reference Reaven39). However, as the key point to be made is that insulin resistance is a central abnormality associated with an atherogenic and diabetogenic cluster of metabolic abnormalities, we have also suggested that such constellation should be called the Reaven syndrome(Reference Després, Lemieux and Bergeron17). Hopefully, history will fix this issue and make sure that the seminal work of this pioneer is recognised(Reference Després40).

Critiques of the metabolic syndrome

Another critique addressed to the metabolic syndrome has been to question its added value in clinical practice(Reference Gale33,Reference Kahn, Buse and Ferrannini34) . For instance, a clinical diagnosis of the metabolic syndrome (presence) does not provide information about its severity(Reference Després, Cartier and Côté35). In addition, although many studies including meta-analyses have shown that patients with the metabolic syndrome are at increased risk of CVD compared to those without the metabolic syndrome(Reference Galassi, Reynolds and He31,Reference Gami, Witt and Howard32) , to what extent its diagnosis provides further information about absolute risk after consideration for traditional risk factors is uncertain(Reference Després and Lemieux20). For instance, HDL-cholesterol, blood pressure and glucose (or diabetes) are already considered in global risk assessment algorithms(Reference D'Agostino, Vasan and Pencina41Reference Conroy, Pyorala and Fitzgerald43). On that basis, we have proposed that the presence of the metabolic syndrome most often predicts an increase in relative CVD risk and that its presence combined with classical CVD risk factors should be considered in the evaluation of global cardiometabolic risk (Fig. 2)(Reference Després and Lemieux20). The reader is referred to previous reviews for a more complete discussion of this issue(Reference Després, Lemieux and Bergeron17,Reference Després and Lemieux20,Reference Després, Poirier and Bergeron44) .

Fig. 2. Contribution of metabolic syndrome to global cardiometabolic risk (CMR). (a) Under this model, metabolic syndrome is considered as a multiplex risk factor that cannot be used as a risk calculator but rather as one component of global CMR. (b) This model shows the contribution of visceral adiposity as the driving force behind the most prevalent form of the metabolic syndrome. (c) This model shows the added value of hypertriglyceridaemic (hyperTG) waist as a simple clinical tool to identify individuals most likely to be characterised by visceral obesity. Under this model, hyperTG waist alone does not assess the global risk but is useful as its presence further increases the global risk associated with traditional CVD risk factors. Adapted from(Reference Després and Lemieux20).

Visceral adiposity: a key feature in the Reaven syndrome

Why does an excess of VAT cause prejudice to health? Currently, three non-mutually exclusive scenarios have been proposed(Reference Després, Lemieux and Bergeron17,Reference Tchernof and Després19,Reference Després and Lemieux20) . First, VAT has a peculiar metabolism compared to subcutaneous fat. It becomes hypertrophic when enlarged, exposing the liver through the portal circulation to high concentrations of glycerol and NEFA. Secondly, when enlarged, VAT become infiltrated with inflammatory macrophages which contribute to the pro-inflammatory state of visceral obesity. Thirdly, and most importantly, evidence also suggests that excess VAT is a marker of the relative inability of SAT to act as a protective ‘metabolic sink’ when facing an energy surplus. Under this scenario, when the capacity of SAT becomes saturated, the overflow of lipids leads to their accumulation elsewhere, not only in VAT but also in the liver, the heart, the skeletal muscle, the kidney, the pancreas, etc., a phenomenon referred to as ectopic fat deposition(Reference Després18,Reference Tchernof and Després19,Reference Neeland, Poirier and Després23,Reference Britton and Fox45Reference Karpe and Pinnick47) . Under this model, excess VAT can be considered as an excellent marker and a consequence of dysfunctional adipose tissue, explaining why it is often accompanied by ectopic fat deposition.

In this regard, studies that have focused on liver fat have also shown that non-alcoholic fatty liver disease has become a prevalent condition and a source of major concern, being the most common cause of liver failure and transplant(Reference Hardy, Oakley and Anstee48,Reference Byrne and Targher49) . Excess liver fat (which can now be non-invasively measured by magnetic resonance spectroscopy) has also been associated with the features of the insulin resistance syndrome(Reference Hardy, Oakley and Anstee48,Reference Fabbrini, Sullivan and Klein50,Reference Lim, Taskinen and Boren51) . We have therefore been interested in deciphering the contributions of VAT v. liver fat in modulating cardiometabolic risk. Results obtained from a large cardiometabolic imaging study (The International Study of Prediction of Intra-abdominal adiposity and its Relationships with Cardiometabolic risk/Intra-abdominal Adiposity) have first confirmed that both VAT and liver fat were independently associated with type 2 diabetes(Reference Smith, Borel and Nazare52). However, results from this cohort have also revealed that the most prevalent form of excess fatty liver was found among subjects with excess VAT whereas elevated liver fat in the absence of excess VAT was a much less prevalent condition(Reference Baillot, Nazare and Borel53). Therefore, from a clinical standpoint, considering the increasing prevalence of non-alcoholic fatty liver disease, we believe that it is important to emphasise to clinicians that its most prevalent form is found among men and women with visceral obesity.

Along with the seminal observations of Himsworth, it is also important to point out that about 75 % of patients with type 2 diabetes are characterised by some excess of VAT/ectopic fat, whereas about 25 % of them do not show substantial VAT/ectopic fat deposition. Thus, despite their diabetes, this less prevalent subgroup of patients with almost normal levels of VAT/ectopic fat is at a lower cardiometabolic risk than patients with both diabetes and excess VAT/ectopic fat, a finding concordant with previous observations that features of the metabolic syndrome, often but not always present in patients with type 2 diabetes, contribute to exacerbating their CVD risk(Reference Alexander, Landsman and Teutsch54).

Assessment and management of visceral obesity in clinical practice: time for a paradigm shift

Traditionally, as many clinical obesity guidelines consider obesity as a disease, the rationale for its treatment has been very simple: as excess body weight/fat is bad, weight loss must be targeted and is the criterion to assess therapeutic success. From the evidence reviewed earlier, it is proposed that a paradigm shift is necessary. First, patients who are overweight or obese are quite heterogeneous regarding their health risk and the term ‘metabolically healthy obesity’ has even been coined to describe a subgroup of patients who may be at a much lower health risk than expected from their obesity(Reference Lavie, De Schutter and Milani55). However, to go as far as stating that they are metabolically healthy is heavily debated as it depends upon how we define ‘healthy’(Reference Després56) but these individuals are certainly at lower risk than patients with excess VAT(Reference Kramer, Zinman and Retnakaran57). These lower risk patients characterised by subcutaneous obesity are likely to benefit less from weight loss (in terms of improvement of their CVD risk factors) than individuals with an excess of VAT and liver fat. In this regard, lifestyle intervention studies involving diet and exercise have shown that a substantial loss of VAT can be achieved in patients with initially high levels of VAT and that such changes could sometime be observed in the absence of weight loss(Reference Rao, Pandey and Garg58Reference Ross and Bradshaw60). Under those circumstances, a given lifestyle modification programme will not only produce a loss of VAT but it may also generate an increase in muscle mass, leading to trivial changes in body weight(Reference Ross and Bradshaw60,Reference Davidson, Hudson and Kilpatrick61) . On that basis, we have previously proposed that weight loss may not optimally assess the responsiveness of some overweight/obese individuals with excess VAT and that waist circumference may be more appropriate to evaluate the loss of unhealthy body fat induced by a lifestyle modification programme(Reference Després62). Thus, waist loss may be clinically more relevant than weight loss. A legitimate question to be asked is: What is the magnitude of waist reduction required to generate improvements in the indices of cardiometabolic health? Additional lifestyle intervention studies will be needed to specifically address this issue but results achieved so far are encouraging. For instance, although they did not target a reduction in their participants' waistlines as a primary endpoint, the two well-known Diabetes Prevention Study(Reference Tuomilehto, Lindstrom and Eriksson63) and the Diabetes Prevention Program(Reference Knowler, Barrett-Connor and Fowler64) reported average reductions in waist circumference of about 4 cm, such change being associated with a substantial reduction in the incidence of type 2 diabetes (−58 %) in the intervention arms. In a lifestyle intervention study specifically conducted in abdominally obese men, we also reported substantial improvements in the indices of cardiometabolic health associated with a reduction of waist circumference slightly >5 cm over 3 years(Reference Borel, Nazare and Baillot65). Lastly, the workplace health lifestyle intervention programme conducted by our laboratory and producing substantial improvements in the cardiometabolic health profile of blue- and white-collar workers also generated an average reduction of 4 cm in the waistline of participants over a period of 3 months(Reference Lévesque, Vallières and Poirier66). Although further work will be required to fully address this issue, the afore-mentioned results suggest that even a waist circumference reduction of moderate magnitude (≥4 cm) could generate substantial benefits in terms of cardiometabolic health.

Secondly, as high-risk patients with excess VAT often have a diet of low overall nutritional quality and are too often sedentary, it would be important, in addition to monitoring waist circumference changes over time, to target overall nutritional quality and level of physical activity. Discussion of results obtained in a health promotion programme conducted in our laboratory(Reference Lévesque, Vallières and Poirier66Reference Lévesque, Poirier and Després68) has shown the value of using simple field tools to rapidly and reliably measure overall nutritional quality using a food-based questionnaire and level of physical activity(Reference Lévesque, Vallières and Poirier66,Reference Lévesque, Poirier and Després67) and that these two lifestyle metrics were powerful correlates of waist circumference and related cardiometabolic risk.

As cardiorespiratory fitness is currently the most powerful variable to discriminate cardiometabolic risk(Reference Kodama, Saito and Tanaka69Reference Ross, Blair and Arena71), it has been proposed that we should find ways to measure it in primary care, even with the use of non-exercise equations(Reference Ross, Blair and Arena71). Again, results from our workplace health programme have shown that four simple lifestyle metrics (waist circumference, cardiorespiratory fitness measured using a simple submaximal exercise test, overall nutritional quality and level of physical activity) were powerful predictors of traditional CVD risk factors measured in primary care such as cholesterol, blood pressure and HbA1c(Reference Lévesque, Vallières and Poirier66Reference Lévesque, Poirier and Després68). On that basis, we propose that the implementation of simple tools to (1) identify patients most likely to be viscerally obese; (2) assess ‘lifestyle vital signs’, should be tested in the context of primary care to evaluate the added value of not only focussing on excess body weight and weight loss but also of assessing key metrics related to patients health, irrespective of their body weight.

Finally, in a world where the lay public is bombarded by the media about the risk of obesity and the importance of healthy body weight, consideration should be given to (1) the stigma of considering obesity as a disease; (2) the pitfalls of considering obesity as a homogenous entity; (3) the positive message associated with empowering patients with new notions and tools where we go beyond excess weight and weight loss, while we rather emphasise the importance of key lifestyle habits that have an impact not only of their health but also on their waistline and their cardiorespiratory fitness, irrespective of the BMI. Thus, consideration should be given to aligning public health messages to recent scientific evidence.

Acknowledgements

Dr Jean-Pierre Després is the Scientific Director of the International Chair on Cardiometabolic Risk which is based at Université Laval.

Financial Support

The work of the author has been funded by the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada, the Canadian Diabetes Association, the Fonds de recherche du Québec-Santé and the Fondation de l'Institut universitaire de cardiologie et de pneumologie de Québec.

Conflict of Interest

None.

References

1.N. C. D. Risk Factor Collaboration (2016) Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 387, 13771396.CrossRefGoogle Scholar
2.N. C. D. Risk Factor Collaboration (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 387, 15131530.CrossRefGoogle Scholar
3.Gonzalez-Muniesa, P, Martinez-Gonzalez, MA, Hu, FB et al. (2017) Obesity. Nat Rev Dis Primers 3, 17034.CrossRefGoogle ScholarPubMed
4.Wen, CP & Wu, X (2012) Stressing harms of physical inactivity to promote exercise. Lancet 380, 192193.CrossRefGoogle ScholarPubMed
5.Hruby, A, Manson, JE, Qi, L et al. . (2016) Determinants and consequences of obesity. Am J Public Health 106, 16561662.CrossRefGoogle ScholarPubMed
6.Mozaffarian, D (2016) Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 133, 187225.CrossRefGoogle ScholarPubMed
7.Fries, JF (1980) Aging, natural death, and the compression of morbidity. N Engl J Med 303, 130135.CrossRefGoogle ScholarPubMed
8.Mozaffarian, D (2011) Achieving cardiovascular health: a bleak outlook or tremendous potential? J Am Coll Cardiol 57, 16971699.CrossRefGoogle ScholarPubMed
9.Reaven, GM (1988) Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37, 15951607.CrossRefGoogle ScholarPubMed
10.Reaven, GM (2011) Insulin resistance: the link between obesity and cardiovascular disease. Med Clin North Am 95, 875892.CrossRefGoogle ScholarPubMed
11.Himsworth, HP (1936) Diabetes mellitus: Its differentiation into insulin-sensitive and insulin-insensitive types. Lancet 227, 127130.CrossRefGoogle Scholar
12.Tokunaga, K, Matsuzawa, Y, Ishikawa, K et al. (1983) A novel technique for the determination of body fat by computed tomography. Int J Obes 7, 437445.Google ScholarPubMed
13.Lemieux, S, Prud'homme, D, Bouchard, C et al. (1993) Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr 58, 463467.CrossRefGoogle ScholarPubMed
14.Kvist, H, Chowdhury, B, Grangard, U et al. (1988) Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 48, 13511361.CrossRefGoogle ScholarPubMed
15.Després, JP, Moorjani, S, Lupien, PJ et al. (1990) Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 10, 497511.CrossRefGoogle ScholarPubMed
16.Després, JP (1993) Abdominal obesity as important component of insulin-resistance syndrome. Nutrition 9, 452459.Google ScholarPubMed
17.Després, JP, Lemieux, I, Bergeron, J et al. (2008) Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol 28, 10391049.CrossRefGoogle ScholarPubMed
18.Després, JP (2012) Body fat distribution and risk of cardiovascular disease: an update. Circulation 126, 13011313.CrossRefGoogle ScholarPubMed
19.Tchernof, A & Després, JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93, 359404.CrossRefGoogle ScholarPubMed
20.Després, JP & Lemieux, I (2006) Abdominal obesity and metabolic syndrome. Nature 444, 881887.CrossRefGoogle ScholarPubMed
21.Shah, RV, Murthy, VL, Abbasi, SA et al. (2014) Visceral adiposity and the risk of metabolic syndrome across body mass index: the MESA Study. JACC Cardiovasc Imaging 7, 12211235.CrossRefGoogle ScholarPubMed
22.Matsuzawa, Y (2008) The role of fat topology in the risk of disease. Int J Obes (Lond) 32, Suppl. 7, S83S92.CrossRefGoogle Scholar
23.Neeland, IJ, Poirier, P & Després, JP (2018) Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation 137, 13911406.CrossRefGoogle ScholarPubMed
24.Smith, U (2015) Abdominal obesity: a marker of ectopic fat accumulation. J Clin Invest 125, 17901792.CrossRefGoogle ScholarPubMed
25.Neeland, IJ, Turer, AT, Ayers, CR et al. (2015) Body fat distribution and incident cardiovascular disease in obese adults. J Am Coll Cardiol 65, 21502151.CrossRefGoogle ScholarPubMed
26.Klein, S, Fontana, L, Young, VL et al. (2004) Absence of an effect of liposuction on insulin action and risk factors for coronary heart disease. N Engl J Med 350, 25492557.CrossRefGoogle ScholarPubMed
27.Cheng, TO (2007) Cardiac syndrome X versus metabolic syndrome X. Int J Cardiol 119, 137138.CrossRefGoogle ScholarPubMed
28.Kemp, HG Jr (1973) Left ventricular function in patients with the anginal syndrome and normal coronary arteriograms. Am J Cardiol 32, 375376.CrossRefGoogle ScholarPubMed
29.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (2001) Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285, 24862497.CrossRefGoogle Scholar
30.Lemieux, I, Pascot, A, Couillard, C et al. (2000) Hypertriglyceridemic waist. A marker of the atherogenic metabolic triad (hyperinsulinemia, hyperapolipoprotein B, small, dense LDL) in men? Circulation 102, 179184.CrossRefGoogle ScholarPubMed
31.Galassi, A, Reynolds, K & He, J (2006) Metabolic syndrome and risk of cardiovascular disease: a meta-analysis. Am J Med 119, 812819.CrossRefGoogle ScholarPubMed
32.Gami, AS, Witt, BJ, Howard, DE et al. (2007) Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol 49, 403414.CrossRefGoogle ScholarPubMed
33.Gale, EA (2005) The myth of the metabolic syndrome. Diabetologia 48, 16791683.CrossRefGoogle ScholarPubMed
34.Kahn, R, Buse, J, Ferrannini, E et al. (2005) The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 28, 22892304.CrossRefGoogle Scholar
35.Després, JP, Cartier, A, Côté, M et al. (2008) The concept of cardiometabolic risk: bridging the fields of diabetology and cardiology. Ann Med 40, 514523.CrossRefGoogle ScholarPubMed
36.Lemieux, I, Poirier, P, Bergeron, J et al. (2007) Hypertriglyceridemic waist: a useful screening phenotype in preventive cardiology? Can J Cardiol 23, Suppl. B, 23B31B.CrossRefGoogle ScholarPubMed
37.Reaven, GM (2005) The metabolic syndrome: requiescat in pace. Clin Chem 51, 931938.CrossRefGoogle ScholarPubMed
38.Reaven, GM (2006) The metabolic syndrome: is this diagnosis necessary? Am J Clin Nutr 83, 12371247.CrossRefGoogle ScholarPubMed
39.Reaven, GM (2011) The metabolic syndrome: time to get off the merry-go-round? J Intern Med 269, 127136.CrossRefGoogle ScholarPubMed
40.Després, JP (2018) The Reaven syndrome: a tribute to a giant. Nat Rev Endocrinol 14, 319320.CrossRefGoogle ScholarPubMed
41.D'Agostino, RB Sr, Vasan, RS, Pencina, MJ et al. (2008) General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117, 743753.CrossRefGoogle ScholarPubMed
42.Assmann, G, Schulte, H, Cullen, P et al. (2007) Assessing risk of myocardial infarction and stroke: new data from the Prospective Cardiovascular Munster (PROCAM) study. Eur J Clin Invest 37, 925932.CrossRefGoogle ScholarPubMed
43.Conroy, RM, Pyorala, K, Fitzgerald, AP et al. (2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24, 9871003.CrossRefGoogle ScholarPubMed
44.Després, JP, Poirier, P, Bergeron, J et al. (2008) From individual risk factors to the metabolic syndrome to global cardiometabolic risk. Eur Heart J 10, Suppl. B, B24B33.CrossRefGoogle Scholar
45.Britton, KA & Fox, CS (2011) Ectopic fat depots and cardiovascular disease. Circulation 124, e837e841.CrossRefGoogle ScholarPubMed
46.Morelli, M, Gaggini, M, Daniele, G et al. (2013) Ectopic fat: the true culprit linking obesity and cardiovascular disease? Thromb Haemost 110, 651660.CrossRefGoogle ScholarPubMed
47.Karpe, F & Pinnick, KE (2015) Biology of upper-body and lower-body adipose tissue – link to whole-body phenotypes. Nat Rev Endocrinol 11, 90100.CrossRefGoogle ScholarPubMed
48.Hardy, T, Oakley, F, Anstee, QM et al. (2016) Nonalcoholic fatty liver disease: Pathogenesis and disease spectrum. Annu Rev Pathol 11, 451496.CrossRefGoogle ScholarPubMed
49.Byrne, CD & Targher, G (2015) NAFLD: a multisystem disease. J Hepatol 62, Suppl. 1, S47S64.CrossRefGoogle ScholarPubMed
50.Fabbrini, E, Sullivan, S & Klein, S (2010) Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology 51, 679689.CrossRefGoogle ScholarPubMed
51.Lim, S, Taskinen, MR & Boren, J (2019) Crosstalk between nonalcoholic fatty liver disease and cardiometabolic syndrome. Obes Rev 20, 599611.CrossRefGoogle ScholarPubMed
52.Smith, JD, Borel, AL, Nazare, JA et al. (2012) Visceral adipose tissue indicates the severity of cardiometabolic risk in patients with and without type 2 diabetes: results from the INSPIRE ME IAA study. J Clin Endocrinol Metab 97, 15171525.CrossRefGoogle ScholarPubMed
53.Baillot, A, Nazare, JA, Borel, AL et al. (2018) Visceral adipose tissue vs. liver fat as drivers of cardiometabolic risk: The INSPIRE ME IAA Study. https://2018.obesityweek.com/abstract/visceral-adipose-tissue-vs-liver-fat-as-drivers-of-cardiometabolic-risk-the-inspire-me-iaa-study/Google Scholar
54.Alexander, CM, Landsman, PB, Teutsch, SM et al. (2003) NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes 52, 12101214.CrossRefGoogle ScholarPubMed
55.Lavie, CJ, De Schutter, A & Milani, RV (2015) Healthy obese versus unhealthy lean: the obesity paradox. Nat Rev Endocrinol 11, 5562.CrossRefGoogle ScholarPubMed
56.Després, JP (2012) What is ‘metabolically healthy obesity’?: from epidemiology to pathophysiological insights. J Clin Endocrinol Metab 97, 22832285.CrossRefGoogle Scholar
57.Kramer, CK, Zinman, B & Retnakaran, R (2013) Are metabolically healthy overweight and obesity benign conditions?: a systematic review and meta-analysis. Ann Intern Med 159, 758769.CrossRefGoogle ScholarPubMed
58.Rao, S, Pandey, A, Garg, S et al. (2019) Effect of exercise and pharmacological interventions on visceral adiposity: a systematic review and meta-analysis of long-term randomized controlled trials. Mayo Clin Proc 94, 211224.CrossRefGoogle ScholarPubMed
59.Gepner, Y, Shelef, I, Schwarzfuchs, D et al. (2018) Effect of distinct lifestyle interventions on mobilization of fat storage pools: CENTRAL Magnetic Resonance Imaging Randomized Controlled Trial. Circulation 137, 11431157.CrossRefGoogle ScholarPubMed
60.Ross, R & Bradshaw, AJ (2009) The future of obesity reduction: beyond weight loss. Nat Rev Endocrinol 5, 319325.CrossRefGoogle ScholarPubMed
61.Davidson, LE, Hudson, R, Kilpatrick, K et al. (2009) Effects of exercise modality on insulin resistance and functional limitation in older adults: a randomized controlled trial. Arch Intern Med 169, 122131.CrossRefGoogle ScholarPubMed
62.Després, JP (2015) Obesity and cardiovascular disease: weight loss is not the only target. Can J Cardiol 31, 216222.CrossRefGoogle Scholar
63.Tuomilehto, J, Lindstrom, J, Eriksson, JG et al. (2001) Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 344, 13431350.CrossRefGoogle ScholarPubMed
64.Knowler, WC, Barrett-Connor, E, Fowler, SE et al. (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346, 393403.Google ScholarPubMed
65.Borel, AL, Nazare, JA, Baillot, A et al. (2017) Cardiometabolic risk improvement in response to a 3-yr lifestyle modification program in men: contribution of improved cardiorespiratory fitness vs. weight loss. Am J Physiol Endocrinol Metab 312, E273E281.CrossRefGoogle ScholarPubMed
66.Lévesque, V, Vallières, M, Poirier, P et al. (2015) Targeting abdominal adiposity and cardiorespiratory fitness in the workplace. Med Sci Sports Exerc 47, 13421350.CrossRefGoogle ScholarPubMed
67.Lévesque, V, Poirier, P, Després, JP et al. (2015) Assessing and targeting key lifestyle cardiovascular risk factors at the workplace: effect on hemoglobin A1c levels. Ann Med 47, 605614.CrossRefGoogle ScholarPubMed
68.Lévesque, V, Poirier, P, Després, JP et al. (2017) Relation between a simple lifestyle risk score and established biological risk factors for cardiovascular disease. Am J Cardiol 120, 19391946.CrossRefGoogle ScholarPubMed
69.Kodama, S, Saito, K, Tanaka, S et al. (2009) Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. JAMA 301, 20242035.CrossRefGoogle ScholarPubMed
70.Myers, J (2014) New American Heart Association/American College of Cardiology guidelines on cardiovascular risk: when will fitness get the recognition it deserves? Mayo Clin Proc 89, 722726.CrossRefGoogle Scholar
71.Ross, R, Blair, SN, Arena, R et al. (2016) Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association. Circulation 134, e653e699.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Simple overview of atherogenic and diabetogenic complications associated with an excess amount of visceral adipose tissue (identified in dark grey within the abdominal muscle wall) increasing the risk of developing type 2 diabetes and CVD (CVD).

Figure 1

Fig. 2. Contribution of metabolic syndrome to global cardiometabolic risk (CMR). (a) Under this model, metabolic syndrome is considered as a multiplex risk factor that cannot be used as a risk calculator but rather as one component of global CMR. (b) This model shows the contribution of visceral adiposity as the driving force behind the most prevalent form of the metabolic syndrome. (c) This model shows the added value of hypertriglyceridaemic (hyperTG) waist as a simple clinical tool to identify individuals most likely to be characterised by visceral obesity. Under this model, hyperTG waist alone does not assess the global risk but is useful as its presence further increases the global risk associated with traditional CVD risk factors. Adapted from(20).