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Adiposity-based Chronic Disease a New Diagnostic Term
Published in James M. Rippe, Lifestyle Medicine, 2019
Michael G. Flynn, Krauss Jeffrey
The health consequences of ABCD are far-reaching and include metabolic, cardiovascular, orthopedic, gastrointestinal, psychiatric, and oncologic risk (Table 41.3). Prediction of specific ABCD sequelae can be difficult. Not all patients with ABCD develop each of the associated complications. Additionally, the degree of adipose tissue accumulation may not correlate with the severity or the incidence of ABCD-associated complications, especially in conditions characterized by abnormalities in the distribution and function of adipose tissue. Some authors propose the existence of a subset of patients with “metabolically healthy” obesity (MHO), who are not at increased risk for cardiovascular disease or type 2 diabetes (T2D), despite the presence of a BMI above 30 kg/m2 (highlighting the problem of defining obesity by a simple arithmetical formula).13 Over time, patients with MHO still demonstrate increased rates of T2D, insulin resistance, and cardiovascular disease.14,15 These patients with MHO are also at risk for other ABCD-associated complications (Table 41.3),16 consistent with a more complex networking model that demonstrates emergent properties.5
Controversies when using mechanical ventilation in obese patients with and without acute distress respiratory syndrome
Published in Expert Review of Respiratory Medicine, 2019
Giulia Bonatti, Chiara Robba, Lorenzo Ball, Pedro Leme Silva, Patricia Rieken Macêdo Rocco, Paolo Pelosi
A large number of patients admitted to ICUs are obese, ranging from 20 to 70% according to different sites [24,25]. Obesity has been associated with increased risk for ARDS [26] . However, ARDS-related mortality is lower in obese compared to non-obese patients [27], a phenomenon that has been termed the ‘obesity paradox’ or ‘reverse epidemiology’ [6]. Different hypothesis have been proposed to explain this paradox in adult human obesity: 1) Obesity is associated with continuous low-grade inflammation, which might protect the lungs from further insult, in the so-called ‘pre-conditioning cloud’ [28]; 2) there are differences in the pathogenesis of inflammatory vascular diseases and of pulmonary host defence after bacterial infection in the lung [29,30]; 3) metabolically healthy obesity is linked to weaker adipose-related inflammatory activity and lower mortality risk compared to individuals with metabolically unhealthy obesity [31]; 4) the high chest wall elastance could redistribute regional transpulmonary pressure, possibly reducing the potential negative effects of MV in an inhomogeneous lung [6]; and 5) the widespread use of prophylactic measures in obese patients might result in earlier admission to the ICU [32,33].
Healthy obesity: time to give up the ghost?
Published in Annals of Human Biology, 2018
The notion that it is possible to be obese yet have no cardio-metabolic complications (e.g. dyslipidaemia and hyperinsulinemia) is attractive to those of us whose body mass index (BMI) has crossed the threshold of 30 kg/m2. However, since the first reports of so-called ‘metabolically healthy obesity’ in the 1980s, numerous studies have shown that such individuals (1) can be rare, depending on the population and diagnostic criteria, (2) transition to being unhealthy more frequently than their non-obese counterparts and (3) have increased risk of various non-communicable diseases (e.g. type 2 diabetes and chronic kidney disease) and higher mortality compared to healthy normal-weight individuals. Literature on the third point has been summarised in systematic reviews and meta-analyses, most of which have reached the same conclusion that healthy obesity is not benign. Despite the strength of this evidence, large-scale epidemiological studies are still frequently published on healthy obesity and disease/mortality risk in leading cardiovascular medicine journals. In September 2017, for example, the Journal of the American College of Cardiology (SJR 2016—11.488; impact factor 2017—19.896) published an analysis among 3.5 million adults, finding that healthy obese individuals had higher risk for incident cardiovascular disease events than their healthy normal weight peers (Caleyachetty et al., 2017). Naturally, the media have a field day in spinning these types of findings; the BBC news, in this instance, going with ‘Fat but fit is a big fat myth’, despite the study not looking at fitness (BBC, 2017).
Serum adiponectin is a potential biomarker for metabolic syndrome in peri-and postmenopausal women
Published in Gynecological Endocrinology, 2020
Puntabut Wattanapol, Patsama Vichinsartvichai, Prirayapak Sakoonwatanyoo
The concept of ‘Metabolically Healthy Obesity’ (MHO) gains its momentum during the past decade. Generally, MHO is defined in an obese individual obesity (BMI ≥30 kg/m2) without any major cardiovascular risk factors and who are not at higher cardiovascular risk than non-obese individuals [47]. The population with MHO has less visceral adipose tissue than the population with metabolically unhealthy obesity in postmenopausal women [48]. In other words, obesity women without abdominal obesity or obesity women with gynoid fat distribution pattern should be metabolically healthy. We challenge the existence of MHO in peri- and postmenopausal women since it might be just the continuum of midlife body changes during menopausal transition. The hormonal changes during menopausal transition and after menopause may have stronger influence on MetS than body fat distribution pattern or BMI. In our study, participants with gynoid fat distribution pattern had significantly lower prevalence of MetS than participants with android fat distribution pattern (9.9% versus 35.9%, p < .001). Further analysis of peri- and postmenopausal women who were overweight and obese revealed the prevalence of metabolic syndrome in women with gynoid fat distribution pattern is half of those with android fat distribution pattern (26.3% versus 55.6%, p = .005) with the lower prevalence of reduced HDL-C and elevated fasting glucose). The difference in the prevalence of MetS among body fat distribution pattern reflexed the influences of hormonal milieu during menopausal transition since the participants with gynoid body fat distribution pattern was younger and more in perimenopausal stage than android body fat distribution pattern counterpart.