What Is “Metabolically Healthy Obesity”?: From Epidemiology to Pathophysiological Insights
Notice bibliographique
Résumé
Although it is well recognized that obesity is a health hazard, its heterogeneity has remained a challenge in clinical practice (1). For instance, whereas there is a clear link between obesity and complications such as dyslipidemia, hypertension, and type 2 diabetes, not every obese patient is characterized by these risk factors. Obesity is defined by an excess of body fat associated with increased health risk, and its diagnosis is generally made on the basis of a body mass index (BMI) greater than 30 kg/m2. The study by Hamer and Stamatakis (2) provides additional evidence that one can find individuals with a diagnosis of clinical obesity defined on the basis of their BMI but without the expected cardiovascular disease (CVD) risk factors. In their study, this subgroup of participants represented 5.2% of the overall cohort of 22,203 individuals who were followed for an average of 7 yr. Over this period, there were 604 CVD deaths and 1868 all-cause deaths. Results of this well-conducted prospective study are pretty straightforward: obese individuals not characterized by risk factors were not found to be at increased CVD, and their total mortality risk was comparable to that of metabolically healthy nonobese participants. Thus, in the absence of CVD risk factors, obesity by itself does not appear to be a major risk factor for CVD and related mortality. On the other hand, metabolically unhealthy nonobese subjects were at increased risk for CVD and all-cause deaths. Results of this prospective study are based on a large sample size with a considerable number of deaths. The observations are therefore robust. However, one of the key remaining questions to be answered is the following: who are these individuals who are obese from a BMI standpoint but who appear to be able to “get away” with their excess of body weight/fat and remain metabolically healthy? First, the proportion of obese individuals who are nevertheless metabolically healthy is not trivial because they represented about 22% of the sample of obese participants examined in this cohort. Thus, we need to identify them in clinical practice because they do not share the risk burden of their peers. On the other hand, there was also a fairly large subgroup of presumably nonobese individuals (about 25% of participants with a BMI <30 kg/m2) who were characterized by metabolic risk factors and who were at increased risk for CVD and total mortality. Thus, the present study provides further evidence that the BMI is not the optimal anthropometric variable to estimate the health hazards of obesity. As acknowledged by the authors, this large epidemiological study could not afford sophisticated and costly imaging measurements of visceral adiposity and of ectopic fat depots. There is now emerging evidence from several computed tomography and magnetic resonance imaging studies that excess visceral adiposity and liver fat content are two key drivers of cardiometabolic risk associated with a given level of total body fat (3–6). In the present study, it is therefore very likely that obese individuals who were metabolically healthy had low levels of visceral adipose tissue and of liver fat, whereas nonobese metabolically unhealthy individuals probably had increased levels of visceral/ectopic fat. Cross-sectional analyses of large metabolic/imaging studies have revealed considerable individual variation in visceral adiposity/liver fat at any BMI level (3–6). Upcoming prospective analyses of these metabolic imaging studies are currently under way, and they should eventually shed some light on the important role played by body fat distribution, particularly by visceral adipose tissue/liver fat and by other ectopic fat depots in explaining this remarkable metabolic heterogeneity of obesity (7–9). In their discussion, Hamer and Stamatakis (2) state that their “findings suggest that metabolic risk factors are more important predictors of CVD than overall adiposity” and that “this is consistent with recent data from a collaborative analysis of 58 prospective studies”. Although this statement is correct from an epidemiological standpoint, they should be reminded that in the Emerging Risk Factors Collaboration study, adiposity indices were powerful correlates of deteriorated levels of intermediate risk factors (10). These strong associations between body fatness variables and risk factors are not trivial and have important public health/clinical implications: should we therefore let people become obese and rather treat their hypertension, dyslipidemia, and diabetes downstream with pharmacological agents, or should we work upstream to target the driving force behind these complications? Whereas Hamer and Stamatakis (2) appropriately address later in their discussion the importance of early intervention with diet and exercise, we need to reflect on the fact that our current health care system is unfortunately better designed to promote pharmacotherapy of risk factors rather than to help patients reshape and improve their “obesogenic” lifestyle habits. One simple clinical approach that has been proposed to identify the subgroup of individuals with an excess of visceral adipose tissue is the simultaneous measurement and interpretation of elevated waist circumference and plasma triglyceride concentrations, a phenotype referred to as “hypertriglyceridemic waist” (11). It is suggested that hypertriglyceridemic waist could have been helpful to identify nonobese metabolically unhealthy individuals with an excess of visceral adipose tissue. Many studies have now shown that this simple phenotype could discriminate a fairly large proportion of individuals with excess visceral/liver fat and that this condition was also predictive of an increased CVD risk (12, 13). Finally, another powerful modulator of CVD risk associated with a given BMI is the level of physical activity/fitness. Although the authors have adjusted for reported physical activity level using a validated questionnaire, misclassification of activity level is notoriously substantial with reported physical activity, contributing to weaken its possible contribution to the variation in the CVD risk profile of equally obese individuals. One approach to deal with this issue has been to rather directly assess cardiorespiratory fitness. Although cardiorespiratory fitness has a genetic component, it is also a marker of participation to vigorous physical activities (14). Seminal studies conducted by Blair and colleagues (15, 16) have clearly shown that the so-called “fat and fit” individuals are at substantially reduced risk for clinical outcomes such as type 2 diabetes and CVD despite their high level of adiposity. Indeed, these studies have generally shown that a high level of cardiorespiratory fitness is more important as a cardioprotective factor than low adiposity. In this regard, we have reported that fat and fit individuals were also characterized by low levels of visceral adipose tissue and of inflammatory markers (17, 18). Thus, metabolically healthy obese subjects probably have less visceral adipose tissue/ectopic fat and may be more fit, and these two factors could largely explain the absence of increased CVD risk despite their clinical obesity defined on the basis of the BMI. Thus, obesity assessed by the BMI cannot properly estimate CVD and all-cause mortality risk. Furthermore, the therapeutic objective of achieving a normal BMI to prevent/manage cardiometabolic diseases may also be questioned on the basis of the emerging evidence. In this regard, we have recently proposed that increased participation in vigorous physical activity to reduce visceral adiposity/ectopic fat and to maintain a proper level of insulin sensitivity may be more important than achieving a “healthy” body weight defined by the BMI (19). The study by Hamer and Stamatakis (2) provides robust evidence that a paradigm shift is needed: obesity can no longer be assessed the old-fashioned way. Disclosure Summary: The author has received honoraria as a speaker or consulting fees from Abbott, AstraZeneca, GlaxoSmithKline, Pfizer Canada Inc., Merck, Sanofi, Novartis, Theratechnologies, and Torrent Pharma Ltd. Body mass index cardiovascular disease.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,010 |
| Communication savante | 0,006 | 0,012 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».