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The Journal of Clinical Endocrinology & Metabolism Vol. 92, No. 2 396-398
Copyright © 2007 by The Endocrine Society

CONTROVERSY IN CLINICAL ENDOCRINOLOGY

Metabolic Syndrome: A Solution in Search of a Problem

Ele Ferrannini

Department of Internal Medicine and National Research Council Institute of Clinical Physiology, University of Pisa School of Medicine, 56100 Pisa, Italy

Address all correspondence and requests for reprints to: E. Ferrannini, M.D., Department of Internal Medicine, Via Savi, 8, 56126 Pisa, Italy. E-mail: ferranni{at}ifc.cnr.it.


   Introduction
Top
 Introduction
The Beginning
 The Follow-Up
 Results
 The Prospect
 References
 
The other day I saw Mrs. R. G., admitted for shortness of breath and fatigue. "A lady of 59," recited the young intern, "has had type 2 diabetes for 15 yr, treated with metformin plus glibenclamide, and hypertension, well-controlled on an angiotensin-converting enzyme inhibitor-thiazide combination. Her body mass index is 30.5 kg/m2, serum triglycerides are 2.2 mmol/liter and high-density lipoprotein cholesterol is 0.99 mmol/liter." Then, with a triumphant glance at me, "metabolic syndrome," she stated. A clear message, I thought: the metabolic syndrome is here to stay. A captivating concept, a catchy name, the right blend of mystery ("syndrome") and novelty, a reputation of global applicability, in consonance with other global epidemics (obesity and diabetes): all ingredients of success. How did it happen?


   The Beginning
Top
 Introduction
 The Beginning
The Follow-Up
 Results
 The Prospect
 References
 
In 1988 Reaven (1) formalized the concept that insulin resistance clusters with glucose intolerance, dyslipidemia, and hypertension to enhance cardiovascular disease (CVD) risk. These abnormalities did not just segregate with insulin resistance but were related to it by cause-effect relationships, some clearer than others and many still undergoing active investigation. In other words, much less a probabilistic phenomenon than a mechanistic theory: the insulin resistance syndrome (IRS). In IRS, the primacy of insulin resistance (Fig. 1Go) is posited on the grounds that insulin resistance is an effective transducer of environmental influences, with obesity (especially visceral) (2), cardiorespiratory fitness (3), and stress (4) being the most important ones. On the effector side, insulin exerts potent actions not only in pathways in glucose homeostasis but also on lipid turnover, blood pressure control, and vascular reactivity (nonglucose actions in Fig. 1Go). Moreover, chronic hyperinsulinemia—the in vivo adaptive response to insulin resistance—has been shown to have pathogenic potential in its own right [for example, by down-regulating insulin action (5), strengthening antinatriuresis (6), or stimulating the adrenergic nervous system (7)], thereby creating reinforcement circuits in the network (8). These facts are supported by a wealth of experimental and clinical investigation (extensively reviewed in Ref. 9). However, it is crucial to emphasize that just as insulin resistance alone is insufficient to alter glucose tolerance—for which some degree of ß-cell dysfunction is required—insulin resistance/hyperinsulinemia is neither strictly necessary nor sufficient to alter lipid metabolism, blood pressure, or vascular function. Each of these homeostatic systems is under multifactorial control, and defects in one or more steps of its effector pathway (other factors in Fig. 1Go) are necessary to drive the system out of control. Also, each of these systems is redundant, with plenty of interactions. For example, a genetic deficiency in glucokinase (or lipoprotein lipase) expression can be enough to impair glucose tolerance (or triglyceride metabolism), and at the same time induce insulin resistance (secondary to glucotoxicity or substrate competition, respectively): the resulting pathophysiological picture would be indistinguishable from one of primary insulin resistance.


Figure 1
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  		FIG. 1. Simplified schematization of the domains of the syndrome. Note that genetic influences are not indicated because they apply to each element in the schema. CR fitness, Cardiorespiratory fitness; Endoth. Dysfun., endothelial dysfunction. See text for further explanation.

 
Thus, the proper domain of IRS is pathophysiology (hatched area in Fig. 1Go): a network in which pulling one node or arm out of the normal boundaries will drag other nodes and arms into the pathological range or just below it. A pathophysiological syndrome will result, with a composition presumably rather variable from case to case depending on the initiating event(s).


   The Follow-Up
Top
 Introduction
 The Beginning
 The Follow-Up
Results
 The Prospect
 References
 
In the 1990s, the pathophysiological IRS transmuted into the clinical metabolic syndrome. What caused this shift? Was it because eminent scientific societies rushed out all-purpose definitions or because of the vested interests of the pharmaceutical industry? Was it disease mongering [akin to other, proven or suspected, cases (10)]? While these factors may have played some part, the driving motive may have been less ignoble.

In fact, hyperinsulinemia predicts diabetes, dyslipidemia (11), and, to a lesser extent, hypertension (12), and is an independent, if weak, CVD predictor (13): the question arises, can one exploit IRS to anticipate and, hopefully, prevent disease? In this case, new knowledge generates a legitimate inquiry; after all, scientists are increasingly expected to try and translate science into practice in areas of major medical and social concern. If this was the project, however, it has gone somewhat astray.

Measuring insulin resistance directly (by the glucose clamp technique) is difficult; at the population level, it has been performed only in a few hundred Pima Indians followed-up for some years (14). Using fasting plasma insulin levels as a proxy for insulin resistance introduces confounding, because of the partly different physiology of hyperinsulinemia and insulin resistance (15), as well as lack of measurement standardization across studies. These practical hurdles have prompted the search for cheap, easy surrogates of insulin resistance: among them, the waist girth or the waist-to-hip ratio seemed best in certain epidemiological studies (16). Thus, anthropometric measures have tended to replace insulin resistance in new definitions of the syndrome before research could conclusively establish whether these traits are equivalent or distinct and, in the latter case, how so in relation to the components of the cluster. A protean "metabolic syndrome" has then morphed out through different definitions (World Health Organization, National Cholesterol Education Program–Adult Treatment Panel, International Diabetes Federation, etc.) adopting mixtures of anthropometric, pathophysiological, and clinical criteria: predictors (waist girth, insulin, triglycerides) and outcomes (diabetes, hypertension) have been dichotomized (thresholds rather than continuous variables), assembled (any two of three or three of five criteria), and even prioritized (e.g. waist girth first, then any two of three) in somewhat arbitrary fashion.

Next, the temptation to reanalyze available datasets with incident diabetes or CVD as the outcome by using some definition (or multiple definitions) of the syndrome as the risk factor has been difficult to resist. The literature has been flooded with epidemiological analyses purporting to show an independent predictivity of one or the other metabolic syndrome for incident diabetes and CVD (>17,000 PubMed entries).

The final turn has been to upgrade the metabolic syndrome to disease status—certified with a classification code (first in the United States, next in Europe)—and then propel it into the clinical arena worldwide, where, as my intern attested, it has been received with much favor.


   Results
Top
 Introduction
 The Beginning
 The Follow-Up
 Results
The Prospect
 References
 
Current evidence suggests the following. 1) The stability of the metabolic syndrome over time is ill defined. It may display a relatively high rate of spontaneous regression (as is the case with impaired glucose tolerance). In the only relevant study (17), the prevalence of the metabolic syndrome did not increase in Mexico City between 1990 and 1992 and 1997 and 1999 despite increasing central obesity. 2) The metabolic syndrome offers little substantial advantage in CVD risk prediction over available algorithms (e.g. the Framingham score). A careful meta-analysis has shown that, depending on definition (and modifications thereof), sample size, subject selection, duration of follow-up, outcome event, and type of statistical analysis, using the metabolic syndrome as a predictor may provide some improvement in risk assessment (18). To predict diabetes, however, the current definitions of metabolic syndrome do not offer any significant advantage over other algorithms (19, 20) (although they efficiently detect impaired glucose tolerance) (21). 3) Which component of the syndrome carries what weight has not been established. 4) That the syndrome as a whole counts more than the sum of its parts is still unproven.

Last, it is popular to claim that the concept of the metabolic syndrome has been, and continues to be, very useful to the medical community to enhance awareness of risk clustering and to promote thorough screening in individuals presenting a CVD risk factor. Whereas this benefit sounds likely, to my knowledge no study has formally addressed this issue; diverting attention from major, established CVD risk may actually be a possible untoward consequence of focusing on the metabolic syndrome.


   The Prospect
Top
 Introduction
 The Beginning
 The Follow-Up
 Results
 The Prospect
References
 
If someone presents with overt diabetes, hypertension, and dyslipidemia, one can certainly call that metabolic syndrome regardless of any specific criteria or definition. It would, however, be an abbreviation, not a diagnosis; prognosis and treatment of that metabolic syndrome would be today what they were before metabolic syndrome was undeservedly granted a disease code.

It remains possible that some combination of subclinical abnormalities, more or less closely related to insulin resistance/hyperinsulinemia/visceral obesity, may signal a significant surplus of CVD risk that is not predicted by the classical risk engines (Framingham, UKPDS, PROCAM, etc.). This hypothesis must be rigorously tested with the use of powerful tools: extensive datasets, robust statistical models, and cross-validation. Picking out predictors from the physical/lifestyle domain (e.g. waist circumference as a proxy of visceral adiposity, resting heart rate as a proxy of cardio-respiratory fitness, etc.) and/or from the large pool of biochemical markers (e.g. C-reactive protein, adiponectin, high-density lipoprotein cholesterol, triglycerides, ApoA/ApoB ratio, fibrinogen, etc.) does not require assumptions about etiology or pathogenesis. So long as the aim is to configure a risk syndrome (22), all that matters is the ability of its components to consistently and substantially contribute to eroding unexplained CVD risk. Hypothesis testing should, in my opinion, involve the following steps (see Table 1Go): 1) The purpose of the syndrome, whether prediction of diabetes or CVD, should be specified. 2) The criteria should be unambiguously defined (23). 3) Physiological parameters should not be dichotomized unless independent evidence proves the existence of a threshold in their relation to risk. 4) Modeling should explore nonlinearities and weighting. 5) Established predictors (e.g. age, familial diabetes, or premature cardiovascular disease, etc.) should be included in the model. Of special import would be to arrive at the proof that the cluster is stronger than the sum of its components. This would be an emergent property: behavior that is not fully predicted by structure, like brain function (24). Such a result would imply that some risk factors act not in an additive but multiplicative fashion. For example, having an abnormal lipid profile may amplify CVD risk in a hypertensive individual (e.g. the notion of familial dyslipidemic hypertension) (25).


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  		TABLE 1. Problems with the current paradigm and potential solutions

 
Once a general risk engine capable of capturing cardiometabolic risk with high specificity and accuracy is compiled and validated in multiple datasets from different populations, it could be reduced to a clinical algorithm by peeling off complex or costly measures in a statistically controlled fashion (26). Whereas not yet a disease, a risk syndrome identified by this tool would definitely serve the purpose of designing preventive measures. In addition, it would fertilize pharmaceutical research, as new drugs may target the cluster rather than one or the other of its components. Typically, insulin sensitizers or weight-reducing agents may hit nodal points of the cluster (insulin resistance and ectopic fat, respectively), thereby improving multiple abnormalities. The clinical algorithm could prove useful to monitor efficacy and time course of new treatments.

The point is, the task still waits to be completed. Should it succeed, we will have a name for it: metabolic syndrome.


   Footnotes
 
Abbreviations: CVD, Cardiovascular disease; IRS, insulin resistance syndrome.

Received May 3, 2006.

Accepted October 27, 2006.


   References
Top
 Introduction
 The Beginning
 The Follow-Up
 Results
 The Prospect
 References
 

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  2. Lebovitz HE, Banerji MA 2005 Point: visceral adiposity is causally related to insulin resistance. Diabetes Care 28:2322–2325[Free Full Text]
  3. LaMonte MJ, Barlow CE, Jurca R, Kampert JB, Church TS, Blair SN 2005 Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome: a prospective study of men and women. Circulation 112:505–512
  4. Bjorntorp P 1995 Insulin resistance: the consequence of a neuroendocrine disturbance? Int J Obes Relat Metab Disord Suppl 1:S6–S10
  5. Del Prato S, Leonetti F, Simonson DC, Sheehan P, Matsuda M, DeFronzo RA 1994 Effect of sustained physiologic hyperinsulinaemia and hyperglycaemia on insulin secretion and insulin sensitivity in man. Diabetologia 37:1025–1035[Medline]
  6. Ferrannini E 1995 The phenomenon of insulin resistance: its possible relevance to hypertensive disease. In: Laragh JH, Brenner BM, eds. Hypertension: pathophysiology, diagnosis, and management. 2nd ed. New York: Raven Press; 2281–2300
  7. Muscelli E, Emdin M, Natali A, Pratali L, Camastra S, Gastaldelli A, Baldi S, Carpeggiani C, Ferrannini E 1998 Autonomic and hemodynamic responses to insulin in lean and obese humans. J Clin Endocrinol Metab 83:2084–2090[Abstract/Free Full Text]
  8. Ferrannini E 2006 Is insulin resistance the cause of the metabolic syndrome? Ann Med 38:42–51[CrossRef][Medline]
  9. Reaven GM, Laws A 1999 Insulin resistance. The metabolic syndrome X. In: Contemporary endocrinology. Totowa, NJ: Humana Press
  10. Moynihan R, Henry D 2006 The fight against disease mongering: generating knowledge for action. PLoS Med 3:e191
  11. Haffner SM, Valdez RA, Hazuda HP, Mitchell BD, Morales PA, Stern MP 1992 Prospective analysis of the insulin-resistance syndrome (syndrome X). Diabetes 41:715–722[Abstract]
  12. Haffner SM, Ferrannini E, Hazuda HP, Stern MP 1992 Clustering of cardiovascular risk factors in confirmed prehypertensive individuals. Hypertension 20:38–45[Abstract/Free Full Text]
  13. Hu G, Qiao Q, Tuomilehto J, Eliasson M, Feskens EJ, Pyorala K, DECODE Insulin Study Group 2004 Plasma insulin and cardiovascular mortality in non-diabetic European men and women: a meta-analysis of data from eleven prospective studies. Diabetologia 47:1245–1256[Medline]
  14. Lillioja S, Mott DM, Spraul M, Ferraro R, Foley JE, Ravussin E, Knowler WC, Bennett PH, Bogardus C 1993 Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus. Prospective studies of Pima Indians. N Engl J Med 329:1988–1992[Abstract/Free Full Text]
  15. Ferrannini E, Balkau B 2002 Insulin: in search of a syndrome. Diabet Med 19:724–729[CrossRef][Medline]
  16. Despres JP 2006 Is visceral obesity the cause of the metabolic syndrome? Ann Med 38:52–63[CrossRef][Medline]
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  18. Ford ES 2005 Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care 28:1769–1778[Abstract/Free Full Text]
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  20. Saaristo T, Peltonen M, Lindstrom J, Saarikoski L, Sundvall J, Eriksson JG, Tuomilehto J 2005 Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res 2:67–72[Abstract/Free Full Text]
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