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1
Kamlesh, Fatania. "Identifying Obesity Subtypes, Related Biomarkers, and Heterogeneity." International Journal of Pharmaceutical and Clinical Research 16, no. 7 (2024): 1323–31. https://doi.org/10.5281/zenodo.13207011.
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Globally, obesity is a severe medical condition that requires novel strategies and acknowledged international agreement to treat illnesses that result in morbidity. Examining the diverse relationships between the different adult obesity phenotypes was the goal of this review. To distinguish between biomarkers, an analysis was conducted on proteins and related genes in each group. There is currently no clear consensus in nomenclature, despite the fact that a number of terminologies are used for classification and characterisation within this disorder. The most important categories that were examined were sarcopenic obesity, metabolically abnormal, normal weight, metabolically healthy obese, and metabolically abnormal obese. These phenotypes don’t specify specific genotypes, epigenetic gene regulation, or inflammatory protein combinations. Numerous other genes have been related to obesity, yet it is still worthwhile to check. Since there are no meaningful biomarkers, the outcomes of those for diagnosis are not very predictive. It is critical to reach agreement on the nomenclature and attributes applied to obesity subtypes. Finding certain molecular biomarkers is also necessary for more accurate detection of obesity subtypes.
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Yen, Chia-Liang, та Chi-Chang K. Shieh. "Aberrant glycogen synthase kinase 3β phosphorylation contributes to abnormal cytokine secretion in obese subjects. (116.29)". Journal of Immunology 186, № 1_Supplement (2011): 116.29. http://dx.doi.org/10.4049/jimmunol.186.supp.116.29.
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Abstract To clarify the role of obesity in the immune responses, we investigated the relationship between insulin-induced signaling and Toll-like Receptor (TLR)-induced leukocyte cytokine secretion in obese subjects. Peripheral blood samples were collected from metabolically normal (MNO) and metabolically abnormal (MAO) obese subjects and stimulated with TLR agonists. We found elevated IL-1β, IL-10 and decreased IL-6, IFN-γ secretion in both MNO and MAO leukocytes with TLR stimulation when compared with normal leukocytes. Changes in TLR-induced cytokine secretion correlated with biochemical markers including fasting glucose, insulin, homeostasis model assessment (HOMA) index, and body mass index (BMI). Moreover, peripheral blood mononuclear cells (PBMCs) collected from MAO subjects showed enhanced basal Akt / Glycogen Synthase Kinase 3β (GSK3β) phosphorylation which was not further increased by insulin and lipopolysaccharide (LPS). We also found IκB accumulation in PBMCs isolated from normal subjects treated with GSK3β inhibitor lithium chloride. These findings indicate that TLR-induced cytokine secretion is affected by obesity even in subjects who have not reached the criteria of metabolic syndrome. Enhanced leukocyte GSK3β phosphorylation may contribute to aberrant immune response in obese subjects.
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Lin, I.-Hsin, Yi-Ping Yu, Tuyen Van Duong, et al. "Effect of Obesity and Metabolic Health Status on Metabolic-Associated Steatotic Liver Disease among Renal Transplant Recipients Using Hepatic Steatosis Index." Nutrients 16, no. 19 (2024): 3344. http://dx.doi.org/10.3390/nu16193344.
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Background/Objectives: Obesity and metabolic conditions increase the risk of metabolic-associated steatotic liver disease (MASLD). This study examined the risk of MASLD in 137 renal transplant recipients (RTRs) from a single-center hospital on the basis of their obesity and metabolic health status. Methods: Participants were categorized into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically abnormal nonobese (MANO), and metabolically abnormal obese (MAO). MASLD was assessed using the hepatic steatosis index (HSI), calculated as 8 × (aspartate aminotransferase/alanine aminotransferase ratio) + body mass index + 2 (if diabetic) + 2 (if woman). The HSI scores were 29.50 ± 4.55, 38.08 ± 5.44, 33.61 ± 5.23, and 39.86 ± 4.13 in the MHNO, MHO, MANO, and MAO groups, respectively (p < 0.05). Results: Overall, 25.55% of the participants (57.14% men) were classified as having MASLD (HSI > 36). A multivariate-adjusted regression analysis revealed significantly higher HSI scores in the MAO group than in the MHNO group. Both MHO and MANO groups also had significantly higher HSI scores. The odds ratios for more severe MASLD were 2.74 (95% CI: 0.88–8.52) for the MANO group and 74.59 (95% CI: 13.29–418.68) for the MAO group compared with the MHNO group. Conclusions: These findings suggest that RTRs with obesity have a higher risk of MASLD, but even those with a normal weight and metabolic abnormalities are at increased risk.
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Pacifico, Lucia, Eleonora Poggiogalle, Francesco Costantino, et al. "Acylated and nonacylated ghrelin levels and their associations with insulin resistance in obese and normal weight children with metabolic syndrome." European Journal of Endocrinology 161, no. 6 (2009): 861–70. http://dx.doi.org/10.1530/eje-09-0375.
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BackgroundGhrelin, a peptide mainly derived from the stomach, plays a pivotal role in the regulation of food intake, energy metabolism, and storage, as well as in insulin sensitivity. Ghrelin circulates in acylated (A-Ghr) and nonacylated (NA-Ghr) forms, and their potential differential associations with insulin resistance (IR) in childhood obesity remain undefined.ObjectiveWe investigated the associations of ghrelin forms with IR in normal weight and obese children and the impact of metabolic syndrome (MS) on their plasma values.DesignA total of 210 children in four subgroups of normal weight/obese children with and without components of MS were studied. Fasting blood glucose, insulin, lipid profile, and acylated and total ghrelin were examined. IR was determined by a homeostasis model assessment (HOMA) of IR.ResultsIn the entire population, plasma insulin and HOMA-IR were associated negatively with T-Ghr and NA-Ghr, but positively with the ratio of A/NA-Ghr after adjustment for age, gender, and Tanner stage. Obese metabolically abnormal children had lower T-Ghr and NA-Ghr, but comparable A-Ghr and a higher A/NA-Ghr ratio than obese metabolically normal subjects. Compared with lean healthy children, lean metabolically abnormal subjects had higher A-Ghr and the A/NA-Ghr ratio, but comparable T-Ghr and NA-Ghr. A multiple regression analysis showed that A-Ghr and the A/NA-Ghr ratios were positively associated with HOMA-IR, independent of age, gender, Tanner stage, and body mass index (or waist circumference) and other components of MS.ConclusionsA-Ghr excess may negatively modulate insulin action in obese and nonobese children, and may contribute to the association of IR and MS.
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Korac, Aleksandra, Biljana Srdic-Galic, Ana Stancic, Vesna Otasevic, Bato Korac, and Aleksandra Jankovic. "Adipokine signatures of subcutaneous and visceral abdominal fat in normal-weight and obese women with different metabolic profiles." Archives of Medical Science 17, no. 2 (2021): 323–36. http://dx.doi.org/10.5114/aoms/92118.
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IntroductionMetabolic syndrome arises from abnormal adipose function accompanied by insulin resistance. As early factors reflecting/impacting lipid storage dysfunction of adipose tissues, we sought to determine adipokine levels in subcutaneous and visceral adipose tissues (SAT and VAT).Material and methodsGene and protein expression of leptin, adiponectin, and resistin were analyzed in SAT and VAT of normal-weight and overweight/obese women, subclassified according to insulin resistance index, triglyceride, total-, LDL- and HDL-cholesterol levels into metabolically healthy and “at risk” groups.ResultsCompared with normal-weight women, obese women had higher serum leptin levels (P<0.05), as well as increased leptin gene and protein expression in VAT. Conversely, expression levels of leptin were lower in SAT of obese women, and minor in the SAT of “at risk” groups of women, compared with weight-matched healthy groups. In addition, lower adiponectin levels were detected in SAT of metabolically healthy obese women (P<0.01), and lower in SAT and VAT (P<0.05) of “at risk” obese women compared to healthy, obese women. Significant differences in resistin levels were only observed in obese women; resistin gene expression was higher in VAT and SAT of obese, compared to normal-weight women. However, higher gene expression was not consistent with protein expression of resistin.ConclusionsLow adiponectin in both examined adipose tissues and inappropriate leptin expression levels in SAT appear to be important characteristics of obesity-related metabolic syndrome. Intriguingly, this adipokine dysregulation is primary seen in SAT, suggesting that endocrine dysfunction in this abdominal depot may be an early risk sign of metabolic syndrome.
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Brochu, Martin, André Tchernof, Isabelle J. Dionne, et al. "What Are the Physical Characteristics Associated with a Normal Metabolic Profile Despite a High Level of Obesity in Postmenopausal Women?1." Journal of Clinical Endocrinology & Metabolism 86, no. 3 (2001): 1020–25. http://dx.doi.org/10.1210/jcem.86.3.7365.
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Although obesity is often associated with insulin resistance and a cluster of metabolic disturbances, the existence of a subgroup of healthy but obese individuals has been postulated. It is unclear why some obese individuals fail to show traditional risk factors associated with the insulin resistance syndrome despite having a very high accumulation of body fat. To address this issue, we identified and studied a subgroup of metabolically normal but obese (MNO) postmenopausal women to gain insight into potential physiological factors that may protect them against the development of obesity-related comorbidities. We carefully examined the metabolic characteristics of 43 obese, sedentary postmenopausal women (mean ± sd, 58.0± 6.0 yr). Subjects were classified as MNO or as metabolically abnormal obese (MAO) based on an accepted cut-point for insulin sensitivity (measured by the hyperinsulinemic/euglycemic clamp technique). Thereafter, we determined 1) body composition (fat mass and lean body mass), 2) body fat distribution (abdominal visceral and sc adipose tissue areas, midthigh sc adipose tissue and muscle attenuation), 3) plasma lipid-lipoprotein levels, 4) plasma glucose and insulin concentrations, 5) resting blood pressure, 6) peak oxygen consumption, 7) physical activity energy expenditure, and 8) age-related onset of obesity with a questionnaire as potential modulators of differences in the risk profile. We identified 17 MNO subjects who displayed high insulin sensitivity (11.2 ± 2.6 mg/min·kg lean body mass) and 26 MAO subjects with lower insulin sensitivity (5.7 ± 1.1 mg/min·kg lean body mass). Despite comparable total body fatness between groups (45.2 ± 5.3% vs. 44.8 ± 6.6%; P = NS), MNO individuals had 49% less visceral adipose tissue than MAO subjects (141 ± 53 vs. 211 ± 85 cm2; P &lt; 0.01). No difference was noted between groups for abdominal sc adipose tissue (453 ± 126 vs. 442 ± 144 cm2; P = NS), total fat mass (38.1 ± 10.6 vs. 40.0 ± 11.8 kg), muscle attenuation (42.2± 2.6 vs. 43.6 ± 4.8 Houndsfield units), and physical activity energy expenditure (1060 ± 323 vs. 1045 ± 331 Cal/day). MNO subjects had lower fasting plasma glucose and insulin concentrations and lower insulin levels during the oral glucose tolerance test (P values ranging between 0.01–0.001). No difference was observed between groups for 2-h glucose levels and glucose area during the oral glucose tolerance test. MNO subjects showed lower plasma triglycerides and higher high density lipoprotein cholesterol concentrations than MAO individuals (P &lt; 0.01 in both cases). Results from the questionnaire indicated that 48% of the MNO women presented an early onset of obesity (&lt;20 yr old) compared with 29% of the MAO subjects (P = 0.09). Stepwise regression analysis showed that visceral adipose tissue and the age-related onset of obesity explained 22% and 13%, respectively, of the variance observed in insulin sensitivity (total r2 = 0.35; P &lt; 0.05 in both cases). Our results support the existence of a subgroup of obese but metabolically normal postmenopausal women who display high levels of insulin sensitivity despite having a high accumulation of body fat. This metabolically normal profile is associated with a lower accumulation of visceral adipose tissue and an earlier age-related onset of obesity.
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Balcázar-Hernandez, Lourdes, Lourdes Basurto, Leticia Manuel-Apolinar, et al. "Pattern of Adiponectin, Osteocalcin, Irisin, FGF-21, and MCP-1 According to the Body Size Phenotype: Could They Be Markers of Metabolic Health in Mexican-Mestizo Middle-Aged Women?" Metabolites 11, no. 11 (2021): 771. http://dx.doi.org/10.3390/metabo11110771.
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Variations in levels of some adipokines, myokines, osteokines, hepatokines and inflammatory cytokines contribute to abnormal glucose and lipid metabolism. The aim of this study was to determine the pattern of adiponectin, osteocalcin (OCN), irisin, FGF-21, and MCP-1 according to the body size phenotype of middle-aged women, and their associations with BMI, visceral adipose tissue (VAT), and HOMA-IR. A cross-sectional study in 265 women aged from 40 to 65 years was performed. The biochemical characteristics were evaluated in metabolically healthy normal weight, metabolically unhealthy normal weight, metabolically healthy obese, and metabolically unhealthy obese women. There was an association of OCN with BMI (r = −0.107; p = 0.047); adiponectin with BMI (r = −0.217; p = 0.001), insulin (r = −0.415; p = 0.0001), HOMA-IR (r = −0.429; p = 0.0001), and VAT (r = −0.134; p = 0.025); irisin with BMI (r = 0.604; p = 0.001), insulin (r = 0.446; p = 0.0001), HOMA-IR (r = 0.452; p = 0.0001), and VAT (r = 0.645; p = 0.0001); FGF−21 with insulin (r = −0.337; p= 0.030) and HOMA-IR (r = −0.341; p = 0.03); and MCP-1 with BMI (r = 0.481; p = 0.0001), VAT (r = 0.497; p = 0.001), insulin (r = 0.298; p= 0.001), and HOMA-IR (r = 0.255; p = 0.004). A multivariate analysis showed that an elevation of OCN (OR 1.4 (95%CI 1.06–1.81)) and a reduction of adiponectin (OR 0.9 (0.84–0.96)) were associated factors for a metabolic unhealthy phenotype in normal weight participants. Likewise, higher irisin (OR 1.007 (1.003–1.011)) and MCP-1 (1.044 (1.008–1.083)) were risk factors for a metabolic unhealthy phenotype in woman with obesity. OCN, adiponectin, irisin, FGF-21, and MCP-1 are associated with some metabolic parameters such as BMI, HOMA-IR, and VAT, and could be possible biomarkers of an unhealthy metabolic phenotype in middle-aged women.
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Pelczyńska, Marta, Ewa Miller-Kasprzak, Marcin Piątkowski, et al. "The Role of Adipokines and Myokines in the Pathogenesis of Different Obesity Phenotypes—New Perspectives." Antioxidants 12, no. 12 (2023): 2046. http://dx.doi.org/10.3390/antiox12122046.
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Obesity is a characteristic disease of the twenty-first century that is affecting an increasing percentage of society. Obesity expresses itself in different phenotypes: normal-weight obesity (NWO), metabolically obese normal-weight (MONW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). A range of pathophysiological mechanisms underlie the occurrence of obesity, including inflammation, oxidative stress, adipokine secretion, and other processes related to the pathophysiology of adipose tissue (AT). Body mass index (BMI) is the key indicator in the diagnosis of obesity; however, in the case of the NWO and MONW phenotypes, the metabolic disturbances are present despite BMI being within the normal range. On the other hand, MHO subjects with elevated BMI values do not present metabolic abnormalities. The MUO phenotype involves both a high BMI value and an abnormal metabolic profile. In this regard, attention has been focused on the variety of molecules produced by AT and their role in the development of obesity. Nesfatin-1, neuregulin 4, myonectin, irisin, and brain-derived neurotrophic factor (BDNF) all seem to have protective effects against obesity. The primary mechanism underlying the action of nesfatin-1 involves an increase in insulin sensitivity and reduced food intake. Neuregulin 4 sup-presses lipogenesis, decreases lipid accumulation, and reduces chronic low-grade inflammation. Myonectin lowers the amount of fatty acids in the bloodstream by increasing their absorption in the liver and AT. Irisin stimulates the browning of white adipose tissue (WAT) and consequently in-creases energy expenditure, additionally regulating glucose metabolism. Another molecule, BDNF, has anorexigenic effects. Decorin protects against the development of hyperglycemia, but may also contribute to proinflammatory processes. Similar effects are shown in the case of visfatin and chemerin, which may predispose to obesity. Visfatin increases adipogenesis, causes cholesterol accumulation in macrophages, and contributes to the development of glucose intolerance. Chemerin induces angiogenesis, which promotes the expansion of AT. This review aims to discuss the role of adipokines and myokines in the pathogenesis of the different obesity phenotypes.
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Badour, Sanaa, Rodrigo Valderrãbano, Juliana Ferri-Guerra, Diana Barb, and Rajesh Garg. "PSUN55 Abnormal Body Fat Distribution in Lean Individuals with Metabolic Abnormalities." Journal of the Endocrine Society 6, Supplement_1 (2022): A255—A256. http://dx.doi.org/10.1210/jendso/bvac150.525.
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Abstract Background Truncal obesity has long been considered an essential driver of metabolic abnormalities, but it is common for lean individuals with body mass index (BMI) &lt;25 kg/m2 to have one or more metabolic abnormalities. We hypothesized that metabolically unhealthy lean (MUL) individuals have a different distribution of fat compared to their metabolically healthy lean (MHL) counterparts. Methods We performed a retrospective analysis of body composition data obtained from a repository of dual-energy X-ray absorptiometry scans performed during routine clinical practice at a University Bone Clinic. Metabolic data were retrieved from electronic medical records within one calendar year of the index scan. Those with at least 2 components of the metabolic syndrome: blood pressure &gt;130/85 mmHg, triglyceride &gt;150 mg/dL, HDL &lt;50 mg/dL for women and &lt;40 mg/dL for men, fasting plasma glucose &gt;100 mg/dL (or HbA1c &gt;5.7) were considered MUL, while those with 1 component or less were considered MHL. Waist circumference was not considered in this study. Adults with a BMI less than 25 kg/m2 were included in the analysis. Unadjusted and adjusted binary logistic regression were used to analyze the association between metabolic abnormalities and various anthropometric indexes. Results The study population consisted of 119 lean adults with a mean age of 62±14 years and a mean BMI of 22±1.9 kg/m2. Majority of participants were white (88%, n=105) and included postmenopausal women (85%, n=101). A large proportion were of Hispanic ethnicity (41%, n=70). There were 69 (58%) MHL and 50 (42%) MUL individuals. MUL and MHL groups had comparable percentage of total body fat (34±6.6 versus 34±5.6, p=0.360), fat mass index (8.2±1.6 versus 8.1±1.5, p=0.360) and appendicular lean mass (kg/m2) (5.9±0.8 versus 5.8±0.7, p=0.420). However, compared to MHL, MUL subjects had significantly higher visceral adipose tissue (cm2) (87±40 versus 9±29, p=0.004), trunk-to-leg fat ratio (0.89±0.18 versus 0.77±0.13, p&lt;0.001), trunk-to-limb fat ratio (1.00±0.29 versus 0.82±0.18, p&lt;0.001) and android-to-gynoid fat ratio (0.90±0.16 versus 0.81±0.15, p=0.010). After adjusting for age and sex, the odds of being MUL compared to MHL (odds ratio [OR (95% confidence interval (CI))]) increased for every standard deviation increase in visceral adipose tissue (OR=1.75 [1.13-2.73]), trunk-to-leg fat ratio (OR=2.28 [1.30-4.00]), trunk-to-limb fat ratio (OR=2.43 [1.37-4.32]) and android-to-gynoid fat ratio (OR=1.80 [1.07-3.03]). Conclusion Metabolically unhealthy subjects with normal BMI had markedly increased visceral adipose tissue and truncal redistribution of body fat without an increase in total body fat. These findings highlight the importance of using body morphometry measures, rather than total body fat, to assess CVD risk in lean individuals. Our findings suggest that the phenotype of lean individuals with metabolic derangements is not very different from that of obese individuals who have metabolic syndrome as per the National Cholesterol Education Program Adult Treatment Panel-III definition. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.
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Naher, Sabikun, M. Mozammel Hoque, and Hasan Imam. "Obesity phenotypes and their adipocyte dysfunction among the attendants at outpatient department." Bangabandhu Sheikh Mujib Medical University Journal 11, no. 2 (2018): 112. http://dx.doi.org/10.3329/bsmmuj.v11i2.36507.
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<p>The present study was aimed to find out the frequency of obesity phenotypes and to correlate adipocyte dysfunction with different obesity phenotypes. A total of 1,507 apparently healthy adults attending the outpatient department were grouped into three based on body mass index which were then further categorized into six groups according to metabolically unhealthy or healthy phenotypes by the presence or absence of metabolic syndrome. Of them, metabolically obese normal weight, overweight, healthy obese and unhealthy obese groups were included as obesity phenotypes. Then by purposive sampling, leptin and adiponectin concentration of 184 subjects with different phenotypes were measured to find out the adipocyte dysfunction. Metabolically obese overweight followed by metabolically unhealthy obese were more prevalent i,e., 24.9% and 19.5%. All obesity phenotypes except metabolically obese normal weight were significantly documented with adipocyte dysfunction (p&lt;0.05).</p>
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Wu, Shenghui, Susan P. Fisher-Hoch, Belinda Reninger, Kristina Vatcheva, and Joseph B. McCormick. "Metabolic Health Has Greater Impact on Diabetes than Simple Overweight/Obesity in Mexican Americans." Journal of Diabetes Research 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4094876.
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Purpose. To compare the risk for diabetes in each of 4 categories of metabolic health and BMI.Methods. Participants were drawn from the Cameron County Hispanic Cohort, a randomly selected Mexican American cohort in Texas on the US-Mexico border. Subjects were divided into 4 phenotypes according to metabolic health and BMI: metabolically healthy normal weight, metabolically healthy overweight/obese, metabolically unhealthy normal weight, and metabolically unhealthy overweight/obese. Metabolic health was defined as having less than 2 metabolic abnormalities. Overweight/obese status was assessed by BMI higher than 25 kg/m2. Diabetes was defined by the 2010 ADA definition or by being on a diabetic medication.Results. The odds ratio for diabetes risk was 2.25 in the metabolically healthy overweight/obese phenotype (95% CI 1.34, 3.79), 3.78 (1.57, 9.09) in the metabolically unhealthy normal weight phenotype, and 5.39 (3.16, 9.20) in metabolically unhealthy overweight/obese phenotype after adjusting for confounding factors compared with the metabolically healthy normal weight phenotype.Conclusions. Metabolic health had a greater effect on the increased risk for diabetes than overweight/obesity. Greater focus on metabolic health might be a more effective target for prevention and control of diabetes than emphasis on weight loss alone.
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Kaur, Akaal, Desmond G. Johnston, and Ian F. Godsland. "Does metabolic health in overweight and obesity persist? – Individual variation and cardiovascular mortality over two decades." European Journal of Endocrinology 175, no. 2 (2016): 133–43. http://dx.doi.org/10.1530/eje-16-0095.
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Objective Overweight and obese individuals may be metabolically healthy, but attention needs to be given to long-term persistence of this trait and any associated variation in cardiovascular risk. Design Cross-sectional and longitudinal variation in metabolic health and associated cardiovascular mortality were analysed in 1099 white European-origin normal-weight and overweight or obese males followed for 20years. Methods Definitions of metabolic health were based on LDL and HDL cholesterol, triglycerides, blood pressure, fasting glucose and cardiovascular risk. Insulin resistance (e.g. HOMA-IR) and sub-clinical inflammation (ESR and white blood cell count) were explored. Cardiovascular mortality risks and persistence of metabolic health status were evaluated. Results There were 87 cardiovascular deaths. Insulin resistance was increased in metabolically healthy overweight or obese participants (median HOMA-IR 2.63, 95% CI: 1.79–3.65, P<0.001) relative to normal-weight participants (median HOMA-IR 1.67, 95% CI: 1.08–2.67, P<0.001) as was sub-clinical inflammation but metabolically healthy overweight or obese individuals were not at increased risk of cardiovascular mortality compared with the metabolically healthy normal-weight individuals (hazard ratio 1.13, 95% CI: 0.34–3.72, P=0.8). The proportions of initially metabolically healthy overweight or obese who remained metabolically healthy for visits 2, 3 and 4 were 54, 48 and 39% respectively, and for initially normal-weight individuals, 68, 51 and 41%. A lower proportion of metabolically healthy overweight or obese individuals remained metabolically healthy at visit 2 compared with normal-weight individuals (P=0.007), but proportions converged thereafter. Conclusions Despite being insulin resistant and having greater sub-clinical inflammation, and despite instability in metabolic health status, metabolically healthy overweight or obese individuals were at no greater risk of cardiovascular mortality than their normal-weight equivalents.
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Lejawa, Mateusz, Kamila Osadnik, Zenon Czuba, Tadeusz Osadnik, and Natalia Pawlas. "Association of Metabolically Healthy and Unhealthy Obesity Phenotype with Markers Related to Obesity, Diabetes among Young, Healthy Adult Men. Analysis of MAGNETIC Study." Life 11, no. 12 (2021): 1350. http://dx.doi.org/10.3390/life11121350.
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Adipose tissue secretes many regulatory factors called adipokines. Adipokines affect the metabolism of lipids and carbohydrates. They also influence the regulation of the immune system and inflammation. The current study aimed to evaluate the association between markers related to obesity, diabesity and adipokines and metabolically healthy and unhealthy obesity in young men. The study included 98 healthy participants. We divided participants into three subgroups based on body mass index and metabolic health definition: 49 metabolically healthy normal-weight patients, 27 metabolically healthy obese patients and 22 metabolically unhealthy obese patients. The 14 metabolic markers selected were measured in serum or plasma. The analysis showed associations between markers related to obesity, diabesity and adipokines in metabolically healthy and unhealthy obese participants. The decreased level of adipsin (p < 0.05) was only associated with metabolically healthy obesity, not with metabolically unhealthy obesity. The decreased level of ghrelin (p < 0.001) and increased level of plasminogen activator inhibitor-1 (p < 0.01) were only associated with metabolically unhealthy obesity, not with metabolically healthy obesity. The decreased level of adiponectin and increased levels of leptin, c-peptide, insulin and angiopoietin-like 3 protein were associated with metabolically healthy and unhealthy obesity. In conclusion, our data show that metabolically healthy obesity was more similar to metabolically unhealthy obesity in terms of the analyzed markers related to obesity and diabesity.
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Gómez-Zorita, Saioa, Maite Queralt, Maria Angeles Vicente, Marcela González, and María P. Portillo. "Metabolically healthy obesity and metabolically obese normal weight: a review." Journal of Physiology and Biochemistry 77, no. 1 (2021): 175–89. http://dx.doi.org/10.1007/s13105-020-00781-x.
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Bershteĭn, L. M., and I. G. Kovalenko. "Metabolically healthy obese subjects and metabolic signs of obesity in subjects with normal body weight: what is behind?" Problems of Endocrinology 56, no. 3 (2010): 47–51. http://dx.doi.org/10.14341/probl201056347-51.
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The current concept of obesity as a heterogeneous condition is discussed. Such interpretation provides a basis for distinguishing a group of obese subjects without metabolic disturbances (metabolically healthy obese (MHO) subjects) and another one including subjects with normal body weight and signs of hormonal and metabolic disorders (metabolically obese normal weight (MONW) subjects). A separate subgroup is represented by obese patients with deficit of muscular mass and force (sarcopenic obesity). Potential mechanisms underlying development of these conditions are considered along with selected applied aspects of this problem.
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Jung, Younhea, Gyoung Nyun Kim, Eun Byeol Oh, Kyoung Ohn, and Jung Il Moon. "Metabolic Health, Obesity, and Intraocular Pressure." Journal of Clinical Medicine 12, no. 5 (2023): 2066. http://dx.doi.org/10.3390/jcm12052066.
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Obesity has been associated with increased intraocular pressure (IOP), but the results are inconsistent. Recently, a subgroup of obese individuals with good metabolic profiles were suggested to have better clinical outcomes than normal-weight individuals with metabolic diseases. The relationships between IOP and different combinations of obesity and metabolic health status have not been investigated. Therefore, we investigated the IOP among groups with different combinations of obesity status and metabolic health status. We examined 20,385 adults aged 19 to 85 years at the Health Promotion Center of Seoul St. Mary’s Hospital between May 2015 and April 2016. Individuals were categorized into four groups according to obesity (body mass index (BMI) ≥ 25 kg/m2) and metabolic health status (defined based on prior medical history or abdominal obesity, dyslipidemia, low high-density lipoprotein cholesterol, high blood pressure, or high fasting blood glucose levels upon medical examination). ANOVA and ANCOVA were performed to compare the IOP among the subgroups. The IOP of the metabolically unhealthy obese group (14.38 ± 0.06 mmHg) was the highest, followed by that of the metabolically unhealthy normal-weight group (MUNW, 14.22 ± 0.08 mmHg), then, the metabolically healthy groups (p < 0.001; 13.50 ± 0.05 mmHg and 13.06 ± 0.03 mmHg in the metabolically healthy obese (MHO) and metabolically healthy normal-weight groups, respectively). Subjects who were metabolically unhealthy showed higher IOP compared to their counterparts who were metabolically healthy at all BMI levels, and there was a linear increase in IOP as the number of metabolic disease components increased, but no difference between normal-weight vs. obese individuals. While obesity, metabolic health status, and each component of metabolic disease were associated with higher IOP, those who were MUNW showed higher IOP than those who were MHO, which indicates that metabolic status has a greater impact than obesity on IOP.
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Jakubiak, Grzegorz K., Kamila Osadnik, Mateusz Lejawa, Sławomir Kasperczyk, Tadeusz Osadnik, and Natalia Pawlas. "Oxidative Stress in Association with Metabolic Health and Obesity in Young Adults." Oxidative Medicine and Cellular Longevity 2021 (June 26, 2021): 1–19. http://dx.doi.org/10.1155/2021/9987352.
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Introduction. Obesity is one of the most important public health problems in the world. Among obese people, there are those who, apart from excessive body weight, do not exhibit other metabolic dysfunctions, have a lower risk of developing cardiovascular diseases (CVDs), and show lower mortality. According to the theory, they are referred as metabolically healthy obese (MHO), in contrast to metabolically unhealthy obese (MUO). Metabolic disturbances occurring with obesity have been well established to be associated with oxidative stress. Aim. The purpose of this study was to analyse the association between selected anthropometric and biochemical parameters with oxidative stress in MHO, MUO, and normal weight young adults. Material and Methods. Individuals with age between 18 and 36 years with no history of chronic diseases and use of medicaments, with or without obesity, participated in the study. Complete blood counts, biochemical measurements, and parameters of oxidative stress such as total antioxidant capacity (TAC), total oxidative status (TOS), oxidative stress index (OSI), serum concentration of malondialdehyde (MDA), ceruloplasmin, thiol groups and lipid hydroperoxides (LPH), concentration of lipofuscin (LPS) in erythrocytes, and the activity of superoxide dismutase (SOD) were measured. Results. 422 patients who met the inclusion criteria were enrolled in the study. Among the study participants, 208 people (49.29%) were offspring of patients with angiographically confirmed coronary artery disease. Among the participants, 16 patients have been included in the group of metabolically healthy obese (MHO) people and 61 patients in the group of metabolically unhealthy obese (MUO) people and 345 patients had normal body weight. Significant differences between metabolically unhealthy obese and normal weight patients, as well as between women and men, have been found. Conclusions. We showed significant differences in the selected parameters of oxidative stress between metabolically unhealthy obese (MUO) individuals and young volunteers with normal body weight as well as without significant medical history.
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Bertran, Laia, Jordi Capellades, Sonia Abelló, Carmen Aguilar, Teresa Auguet, and Cristóbal Richart. "Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study." PLOS ONE 19, no. 5 (2024): e0303569. http://dx.doi.org/10.1371/journal.pone.0303569.
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There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.
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Kruger, Herculina Salome, Zelda De Lange-Loots, Iolanthé Marike Kruger, and Marlien Pieters. "The Metabolic Profiles of Metabolically Healthy Obese and Metabolically Unhealthy Obese South African Adults over 10 Years." International Journal of Environmental Research and Public Health 19, no. 9 (2022): 5061. http://dx.doi.org/10.3390/ijerph19095061.
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Obesity is associated with an increased cardiometabolic risk, but some individuals maintain metabolically healthy obesity (MHO). The aims were to follow a cohort of black South African adults over a period of 10 years to determine the proportion of the group that maintained MHO over 10 years, and to compare the metabolic profiles of the metabolically healthy and metabolically unhealthy groups after the follow-up period. The participants were South African men (n = 275) and women (n = 642) from the North West province. The prevalence of obesity and the metabolic syndrome increased significantly. About half of the metabolically healthy obese (MHO) adults maintained MHO over 10 years, while 46% of the women and 43% of men became metabolically unhealthy overweight/obese (MUO) at the end of the study. The metabolic profiles of these MHO adults were similar to those of the metabolically healthy normal weight (MHNW) group in terms of most metabolic syndrome criteria, but they were more insulin resistant; their CRP, fibrinogen, and PAI-1act were higher and HDL-cholesterol was lower than the MHNW group. Although the metabolic profiles of the MUO group were less favourable than those of their counterparts, MHO is a transient state and is associated with increased cardiometabolic risk.
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Narankiewicz, Dariusz, Josefina Ruiz-Nava, Veronica Buonaiuto, et al. "Utility of Liver Function Tests and Fatty Liver Index to Categorize Metabolic Phenotypes in a Mediterranean Population." International Journal of Environmental Research and Public Health 17, no. 10 (2020): 3518. http://dx.doi.org/10.3390/ijerph17103518.
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The aim of this study was to analyze the utility of liver function tests (LFT) and fatty liver index (FLI), a surrogate marker of non-alcoholic fatty liver disease, in the categorization of metabolic phenotypes in a Mediterranean population. A cross-sectional study was performed on a random representative sample of 2233 adults assigned to a health center in Málaga, Spain. The metabolic phenotypes were determined based on body mass index (BMI) categorization and the presence or absence of two or more cardiometabolic abnormalities (high blood pressure, low high-density lipoprotein (HDL) cholesterol, hypertriglyceridemia, pre-diabetes) or type 2 diabetes. No difference was observed between metabolically healthy and metabolically abnormal phenotypes on LFT. The mean FLI of the population was 41.1 ± 28.6. FLI was significantly higher (p < 0.001) in the metabolically abnormal phenotypes in all BMI categories. The proportion of individuals with pathological FLI (≥60) was significantly higher in the metabolically abnormal overweight and obese phenotypes (p < 0.001). On a multivariate model adjusted for sex, age, and waist circumference, a significant correlation was found between pathological FLI and metabolically abnormal phenotypes in the overweight and obese BMI categories. Area under the curve (AUC) of FLI as a biomarker was 0.76, 0.74, and 0.72 for the metabolically abnormal normal-weight, overweight, and obese groups, respectively. Liver biochemistry is poorly correlated with metabolic phenotypes. Conversely, a good correlation between FLI, as a marker of non-alcoholic fatty liver disease (NAFLD), and metabolically abnormal phenotypes in all BMI ranges was found. Our study suggests that FLI may be a useful marker for characterizing metabolically abnormal phenotypes in individuals who are overweight or obese.
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Sánchez-Iñigo, Laura, David Navarro-González, Alejandro Fernández-Montero, Juan Pastrana-Delgado, and JA Martínez. "Risk of incident ischemic stroke according to the metabolic health and obesity states in the Vascular-Metabolic CUN cohort." International Journal of Stroke 12, no. 2 (2016): 187–91. http://dx.doi.org/10.1177/1747493016672083.
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Background Whether obesity is a major risk factor for cardiovascular disease in the absence of metabolic comorbidities remains under debate. Indeed, some obese individuals may be at low risk of metabolic-related complications, while normal-weight individuals may not be “healthy.” Aims To assess the incidence of ischemic stroke according to the metabolic health and obesity states of 5171 participants from the Vascular-Metabolic CUN cohort. Methods A Cox proportional-hazard analysis was conducted to estimate the hazard ratio and their 95% confidence interval of stroke according to the metabolic health and obesity states based on TyG index and Adult Treatment Panel-III criteria, during 9.1 years of follow-up. Results After 50,056.2 person-years of follow-up, 162 subjects developed an ischemic stroke (incidence rate 3.23 per 1000 person-years). Metabolically healthy obese subjects did not show greater risk of stroke, while metabolically unhealthy participants, obese and non-obese, had an increased risk of stroke, compared with healthy non-obese. The hazard ratios for the multivariable adjusted model were 1.55 (95% CI: 1.36–1.77) and 1.86 (95% CI: 1.57–2.21), respectively. Conclusions Metabolically unhealthy individuals exhibited a greater risk of ischemic stroke than metabolically healthy obese individuals.
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Sultana, Sharmin, Md Matiur Rahman, Rinky Rani Saha, Md Golam Ahad, Jakia Sultana Shila, and Md Aminul Haque Khan. "Microalbuminuria in Metabolic Obesity: A Cross-Sectional Study in a Selected Tertiary Care Hospital of Bangladesh." Journal of Enam Medical College 10, no. 3 (2022): 159–68. http://dx.doi.org/10.3329/jemc.v10i3.59357.
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Background: Overweight and obese individuals may have no cardiometabolic risk whereas normal weight individuals may present with cardiometabolic risk. The term‘Metabolic obesity’ has been floated to identify hidden metabolic risks irrespective of BMI. The pathophysiology of metabolic obesity can be explained by microvascular dysfunction and microalbuminuria is a wellknown marker of microvascular dysfunction. Objective: The objective of this study was to find out the association of microalbuminuria with metabolic obesity in Bangladeshi adult subjects. Materials and Methods: This cross- sectional analytical study included 200 individuals who attended outpatient department in Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka from March 2018 to February 2019. The study subjects were divided into metabolically obese (metabolically unhealthy) group and metabolically non-obese (metabolically healthy) group by metabolic syndrome (MetS) criteria. Metabolic syndrome was defined according to the South Asian Modified-National Cholesterol Education Program (NCEP). Microalbuminuria was defined as a urinary albumin to creatinine of 30 to 300 mg/gm. Demographic profile, BP, height, weight, waist circumference etc. were measured and fasting blood glucose, serum triglyceride, serum HDL-C were estimated and albumin to creatinine ratio (ACR) was calculated. Statistical analysis was done using SPSS version 22.0. Results: The frequencies of metabolically obese (metabolically unhealthy) group and metabolically non-obese (metabolically healthy) group were 128 (64%) and 72 (36%) respectively. Mean values for age (p value 0.001), body mass index (p value 0.027), waist circumference (p<0.001), systolic blood pressure (SBP) (p<0.001) and diastolic blood pressure (DBP) (p<0.001), fasting blood glucose (p<0.001) and triglycerides (p<0.001) were significantly higher in the metabolically obese group compared to metabolically non-obese group. Among the study subjects, the prevalence of microalbuminuria was 32.5% and prevalence of microalbuminuria was found very high (38.3%) in metabolically obese group, whereas microalbuminuria in metabolically non-obese group was found 22.2%, which was statistically significant (p value 0.02). Our results showed that diastolic BP (p<0.001), systolic BP (p<0.001), fasting blood sugar (p<0.001) and triglyceride (p<0.008) were significantly correlated with microalbuminuria. In the logistic regression analysis, diastolic BP (p value 0.015) and FBS (p value 0.039) were significantly associated with microalbuminuria. After harmonization of statistical analysis, our study indicated that elevated blood pressure and fasting blood sugar had strong association with microalbuminuria and are likely to be critical components that lead a substantial number of subjects to the prestage of metabolic obesity in the Bangladeshi adult population. Conclusion: Our study concludes that the prevalence of microalbuminuria is significantly high in metabolic obesity (metabolically unhealthy) in Bangladeshi adult population. Association of microalbuminuria with metabolic obesity is mainly attributed to high diastolic blood pressure and fasting blood glucose. J Enam Med Col 2020; 10(3): 159-168
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Naher, S., SS Sejooti, MM Hoque, et al. "Metabolic Phenotyping Using Metabolic Syndrome Criteria and Cardiometabolic Disabilities Criteria in Bangladeshi Adult Population." Bangladesh Journal of Medical Biochemistry 11, no. 1 (2019): 1–8. http://dx.doi.org/10.3329/bjmb.v11i1.39762.
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Obesity has become a global epidemic and has been found to be associated with numerous comorbidities. Body mass index (BMI) based classification of obesity is simple but co-morbidities do not affect all obese and overweight people. The present study was aimed to find out the frequency of metabolic phenotypes in different BMI groups using metabolic syndrome (MetS) criteria and cardiometabolic disabilities (CA) criteria and also to find out an appropriate method for defining metabolic health among adult population attending out patient department of Bangabandhu Sheikh Mujib Medical University (BSMMU). This cross-sectional analytical study was carried out in the Department of Biochemistry, Bangabandhu Sheikh Mujib Medical University (BSMMU) from March 2016 to February 2017. By non probability sampling, a total of 1023 study subjects were selected from apparently healthy adult individuals attending outpatient department of BSMMU. The study subjects were grouped into three body mass index classes and also further categorized into six groups according to metabolically unhealthy or healthy phenotypes by presence or absence of metabolic syndrome (MetS) criteria as well as cardiometabolic disabilities (CA) criteria respectively. Then agreement among different metabolic phenotypes based on these two criteria were observed. Frequency of different metabolic phenotypes i,e metabolically healthy normal weight (MHNW), metabolically obese normal weight (MONW), metabolically healthy over weight (MHOW), metabolically obese over weight (MOOW), metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) were 12.3%, 6.9%, 21.4%, 27.7%, 7.7%, 23.9% by MetS criteria and 7.7%, 11.5%, 11.6%, 37.4%, 6.1%, 25.6% by CA criteria respectively. MOOW followed by MUO were found to be predominant among all phenotypes. Fare agreement was found between two criteria in case of normal weight and overweight groups and good agreement was found in case of obese groups. From this study, it may be concluded that, attention should be given to the metabolically obese phenotypes in different BMI classes to reduce co-morbidities.
 Bangladesh J Med Biochem 2018; 11(1): 1-8
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Huang, Lam O., Ruth J. F. Loos, and Tuomas O. Kilpeläinen. "Evidence of genetic predisposition for metabolically healthy obesity and metabolically obese normal weight." Physiological Genomics 50, no. 3 (2018): 169–78. http://dx.doi.org/10.1152/physiolgenomics.00044.2017.
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Obesity has evolved into a global pandemic that constitutes a major threat to public health. The majority of obesity-related health care costs are due to cardiometabolic complications, such as insulin resistance, dyslipidemia, and hypertension, which are risk factors for Type 2 diabetes and cardiovascular disease. However, many obese individuals, often called metabolically healthy obese (MHO), seem to be protected from these cardiometabolic complications. Conversely, there is a group of individuals who suffer from cardiometabolic complications despite being of normal weight; a condition termed metabolically obese normal weight (MONW). Recent large-scale genomic studies have provided evidence that a number of genetic variants show an association with increased adiposity but a favorable cardiometabolic profile, an indicator for the genetic basis of the MHO and MONW phenotypes. Many of these loci are located in or near genes that implicate pathways involved in adipogenesis, fat distribution, insulin signaling, and insulin resistance. It has been suggested that a threshold for subcutaneous adipose tissue expandability may be at play in the manifestation of MHO and MONW, where expiry of adipose tissue storage capacity could lead to ectopic lipid accumulation in non-adipose tissues such as liver, muscle, heart, and pancreatic beta cells. Understanding the genetic aspects of the mechanisms that underpin MHO and MONW is crucial to define appropriate public health action points and to develop effective intervention measures.
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Shaharyar, Sameer, Lara L. Roberson, Omar Jamal, et al. "Obesity and Metabolic Phenotypes (Metabolically Healthy and Unhealthy Variants) Are Significantly Associated with Prevalence of Elevated C-Reactive Protein and Hepatic Steatosis in a Large Healthy Brazilian Population." Journal of Obesity 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/178526.
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Background. Among the obese, the so-called metabolically healthy obese (MHO) phenotype is thought to confer a lower CVD risk as compared to obesity with typical associated metabolic changes. The present study aims to determine the relationship of different subtypes of obesity with inflammatory-cardiometabolic abnormalities.Methods. We evaluated 5,519 healthy, Brazilian subjects (43±10years, 78% males), free of known cardiovascular disease. Those with <2 metabolic risk factors (MRF) were considered metabolically healthy, and those with BMI ≥ 25 kg/m2and/or waist circumference meeting NCEP criteria for metabolic syndrome as overweight/obese (OW). High sensitivity C reactive protein (hsCRP) was measured to assess underlying inflammation and hepatic steatosis (HS) was determined via abdominal ultrasound.Results. Overall, 40% of OW individuals were metabolically healthy, and 12% normal-weight had ≥2 MRF. The prevalence of elevated CRP (≥3 mg/dL) and HS in MHO versus normal weight metabolically healthy group was 22% versus 12%, and 40% versus 8% respectively (P<0.001). Both MHO individuals and metabolically unhealthy normal weight (MUNW) phenotypes were associated with elevated hsCRP and HS.Conclusion. Our study suggests that MHO and MUNW phenotypes may not be benign and physicians should strive to treat individuals in these subgroups to reverse these conditions.
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Chulkov, V. S., E. S. Gavrilova, V. S. Chulkov, E. D. Pankova, S. A. Martynov, and V. I. Manzhos. "Cardiometabolic risk factors and renin-angiotensin system polymorphisms in young individuals with various metabolic phenotypes." "Arterial’naya Gipertenziya" ("Arterial Hypertension") 28, no. 1 (2022): 58–66. http://dx.doi.org/10.18705/1607-419x-2022-28-1-58-66.
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Background. The increased prevalence of obesity and associated cardiometabolic diseases attract attention worldwide. Renin-angiotensin system can link obesity and cardiovascular and metabolic diseases.Objective. To access a comprehensive assessment of cardiometabolic risk factors and gene polymorphisms of the renin-angiotensin system in metabolic phenotypes among young individuals.Design and methods. The sample consisted of 251 individuals, who were divided into four groups: group 1 — metabolically healthy individuals with normal body mass index (BMI) (n = 62); group 2 — metabolically unhealthy individuals with normal BMI (n = 57); group 3 — metabolically healthy overweight/obese individuals (n = 16); group 4— metabolically unhealthy overweight/ obese individuals (n = 116). All participants answered a questionnaire designed for this study. Anthropometric, clinical and biochemical parameters were assessed. The following polymorphisms were evaluated:, A1166C polymorphism of the angiotensin II type 1 receptor gene (rs5186), M235T polymorphism of the angiotensinogen gene (rs699), T174M polymorphism of the angiotensinogen gene (rs4762), I/D polymorphism of the angiotensinconverting enzyme gene (rs4340).Results. In young individuals with metabolically unhealthy overweight/ obesity, a higher frequency of coexistent abdominal obesity and hypertension was found in combination with a higher frequency of the allele T of AGT 235M/T. The greater differences in carbohydrate and lipid metabolism in combination with a higher serum levels of leptin and low serum concentrations of adiponectin were also found in young individuals with metabolically unhealthy overweight/obesity.
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Garrido-Acosta, Osvaldo, Brisa Oro-López, Nathalie Molina-López, Adriana Altamirano-Bautista, and Adelfo Reyes-Ramírez. "Development of Metabolically Obese but Normal Weight (MONW) Wistar Rats by Oral Administration of Hypercaloric Emulsion." Pharmaceutical and Chemical Journal 5, no. 5 (2018): 15–22. https://doi.org/10.5281/zenodo.13923891.
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Obesity is defined as the accumulation of adipose tissue, overweight and obesity are defined on the basis of body mass index and this is accompanied by an increased prevalence of comorbidities as type 2 diabetes, cardiovascular and metabolic disease in the population. The standard relationship between weight status and metabolic health is not applicable for some subtypes of individuals, the subtypes are known as “metabolically obese, but normal-weight”, and others are the “metabolically healthy, but obese” phenotypes. Regarding the problem of obesity there are areas of progress, greater social and political awareness, development of new treatments, and an extended base of evidence for its prevention and intervention. In contrast, the prevalence of this has increased rapidly in the last two decades, largely to the lifestyle of large cities. For the study of overweight, obesity and its physiopathology consequences, in addition to clinical studies in humans, are used animal models for understand the evolution of disease and support the development of therapies, the animal models, the current animal models generate rats with phenotype metabolically obese with overweight. In this research we proposed to evaluate the effect of intragastric administration of a hypercaloric emulsion composed of fructose syrup and edible vegetable oil on development of obesity in rats, we obtained rats with phenotype metabolically healthy but obesity only in eight weeks with adiposity index of 11.5 vs 5.9 of control group and 8.3 on sham group with fructose drinking solution.
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Brandão, Inês, Maria João Martins, and Rosário Monteiro. "Metabolically Healthy Obesity—Heterogeneity in Definitions and Unconventional Factors." Metabolites 10, no. 2 (2020): 48. http://dx.doi.org/10.3390/metabo10020048.
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The concept of heterogeneity among obese individuals in their risk for developing metabolic dysfunction and associated complications has been recognized for decades. At the origin of the heterogeneity idea is the acknowledgement that individuals with central obesity are more prone to developing type 2 diabetes and cardiovascular disease than those with peripheral obesity. There have been attempts to categorize subjects according to their metabolic health and degree of obesity giving rise to different obese and non-obese phenotypes that include metabolically unhealthy normal-weight (MUHNW), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Individuals belonging to the MHO phenotype are obese according to their body mass index although exhibiting fewer or none metabolic anomalies such as type 2 diabetes, dyslipidemia, hypertension, and/or unfavorable inflammatory and fribinolytic profiles. However, some authors claim that MHO is only transient in nature. Additionally, the phenotype categorization is controversial as it lacks standardized definitions possibly blurring the distinction between obesity phenotypes and confounding the associations with health outcomes. To add to the discussion, the factors underlying the origin or protection from metabolic deterioration and cardiometabolic risk for these subclasses are being intensely investigated and several hypotheses have been put forward. In the present review, we compare the different definitions of obesity phenotypes and present several possible factors underlying them (adipose tissue distribution and cellularity, contaminant accumulation on the adipose tissue, dysbiosis and metabolic endotoxemia imposing on to the endocannabinoid tone and inflammasome, and nutrient intake and dietary patterns) having inflammatory activation at the center.
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Bermudez, Valmore, Joselyn Rojas, Juan Salazar, et al. "Biochemical and clinical characterization of metabolic phenotypes: a cross-sectional study from Maracaibo city, Venezuela." F1000Research 7 (February 27, 2018): 230. http://dx.doi.org/10.12688/f1000research.13897.1.
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Background:In 1980, Reuben Andresen observed that in certain individuals, obesity did not increase mortality, introducing an atypical phenotype called “healthy obese”. Other studies reported that 10-15 % of lean individuals presented insulin resistance, hyperglycemia and dyslipidemia. The objective of this study was to evaluate biochemical and clinical characteristics of metabolic phenotypes in Maracaibo city.Methods:A descriptive, cross-sectional study with a randomized multistage sampling was performed including 1226 non diabetic individuals from both sexes. For phenotype definition, the subjects were first classified according to their BMI into Normal-Weight, Overweight and Obese; then divided in metabolically healthy and unhealthy using a two-step analysis cluster. To evaluate the relationship with coronary risk, a multiple logistic regression model was performed.Results:In the studied population, 5.2% (n=64) corresponded to unhealthy lean subjects, and 17.4% (n=217) to healthy obese subjects. Metabolically unhealthy normal-weight (MUNW) phenotype was found in males in 53.3% in contrast to 51.3% of metabolically unhealthy obese (MUO) phenotype found in females. An association between metabolically unhealthy phenotypes and a higher risk of a coronary event was found, especially for obese individuals (MHO: OR=1.85 CI95%: 1.11-3.09; p=0.02 and MUO: OR=2.09 CI95%: 1.34-3.28; p<0.01).Conclusion:Individuals with atypical metabolic phenotypes exist in Maracaibo city. Related factors may include insulin resistance, basal glucose levels, and triglycerides levels. Lastly, cardiovascular risk exhibited by healthy obese individuals should be classified in categories of major coronary risk related to lean subjects.
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Bonikowske, Amanda R., Maria Irene Barillas Lara, Katlyn E. Koepp, et al. "Fat Mass Index Better Identifies Metabolic Syndrome: Insights from Patients in Early Outpatient Cardiac Rehabilitation." Journal of Clinical Medicine 8, no. 12 (2019): 2147. http://dx.doi.org/10.3390/jcm8122147.
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Body mass index (BMI) does not differentiate fat and lean mass or the distribution of adipose tissue. The purpose of this study was to examine the prevalence of metabolic syndrome (MetS) among patients entering outpatient cardiac rehabilitation (CR) across fat mass index (FMI) categories compared with BMI. This retrospective cross-sectional study evaluated dual-energy x-ray absorptiometry in 483 CR patients from 1 January 2014, through 31 December 2017. Clinical data were extracted from the electronic health record. Patients were grouped by FMI and BMI categories. Mean (SD) age of patients was 64.3 (14) years. The normal FMI category had 15 patients; excess fat, 74; and obese, 384. In contrast, 93, 174, and 216 were in the normal, overweight, and obese BMI categories, respectively. Prevalence of MetS was 0 (0%) in normal, 5 (1%) in excess fat, and 167 (54%) in obese FMI, with 97% in the obese category. MetS prevalence was 4 patients (0.8%) in normal, 39 (8%) in overweight, and 129 (27%) in obese BMI categories, with 75% of MetS in the obese category. FMI more accurately classified CR patients with metabolically abnormal fat (p < 0.001). FMI is a more sensitive index than BMI for metabolically abnormal fat of outpatient CR patients.
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Rotar, O., A. Konradi, S. Shalnova, et al. "[OP.2A.03] METABOLICALLY HEALTHY OBESITY AND METABOLICALLY OBESE NORMAL WEIGHT IN RUSSIAN POPULATION." Journal of Hypertension 34 (September 2016): e16. http://dx.doi.org/10.1097/01.hjh.0000491368.63715.51.
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Mintoff, Dillon, Rachel Agius, Stephen Fava, and Nikolai P. Pace. "Investigating Adiposity-Related Metabolic Health Phenotypes in Patients with Hidradenitis Suppurativa: A Cross-Sectional Study." Journal of Clinical Medicine 12, no. 14 (2023): 4847. http://dx.doi.org/10.3390/jcm12144847.
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Background: Obesity and hidradenitis suppurativa (HS) are related through meta-inflammation and are both associated with increased cardiometabolic risk. Notwithstanding, cardiometabolic pathology is not uniform in obesity and a subset of individuals with excess adiposity exhibit a healthy metabolic profile. Whilst the incidence of cardiometabolic endpoints and transitions across different adiposity-related body composition phenotypes within several populations and across different ethnicities have been investigated, data regarding metabolic health (MetH) and body composition phenotypes in individuals with HS are lacking. The objective of this study was to evaluate the relationship between different body composition phenotypes in individuals with HS. Methods: This was a cross-sectional study of 632 individuals with and without HS from a population with a high prevalence of both obesity and HS. A total of four body composition phenotypes were generated based on BMI and metabolic status (defined using either the metabolic syndrome definition or the homeostasis model of insulin resistance (HOMA-IR)): metabolically healthy overweight/obese (MHOWOB), metabolically unhealthy overweight/obese (MUOWOB), metabolically healthy normal weight (MHNW), and metabolically unhealthy normal weight (MUNW). Results: Generally, subjects with HS exhibited a worse metabolic profile with higher levels of indices of central adiposity measures (including Visceral Adiposity Index and waist circumference), systolic blood pressure and markers of insulin resistance, as well as a higher prevalence of the metabolic syndrome. Moreover, when sub-stratified into the different body composition phenotypes, individuals with HS typically also demonstrated adverse metabolic characteristics relative to controls matched for both adiposity and metabolic health, particularly in the normal weight category and despite being classified as metabolically healthy. Being metabolically unhealthy in addition to being overweight/obese increases an individual’s risk of HS. Conclusions: Metabolic risk-assessment should be prioritized in the clinical management of individuals with HS even in those who are lean. Patients attending HS clinics provide a valuable opportunity for targeted cardiovascular risk reduction with respect to the management of both obesity and metabolic health.
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Wang, Wenyu, Piers Blackett, Sohail Khan, and Elisa Lee. "Apolipoproteins A-I, B, and C-III and Obesity in Young Adult Cherokee." Journal of Lipids 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/8236325.
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Since young adult Cherokee are at increased risk for both diabetes and cardiovascular disease, we assessed association of apolipoproteins (A-I, B, and C-III in non-HDL and HDL) with obesity and related risk factors. Obese participants (BMI ≥ 30) aged 20–40 years (n=476) were studied. Metabolically healthy obese (MHO) individuals were defined as not having any of four components of the ATP-III metabolic syndrome after exclusion of waist circumference, and obese participants not being MHO were defined as metabolically abnormal obese (MAO). Associations were evaluated by correlation and regression modeling. Obesity measures, blood pressure, insulin resistance, lipids, and apolipoproteins were significantly different between groups except for total cholesterol, LDL-C, and HDL-apoC-III. Apolipoproteins were not correlated with obesity measures with the exception of apoA-I with waist and the waist : height ratio. In a logistic regression model apoA-I and the apoB : apoA-I ratio were significantly selected for identifying those being MHO, and the result (C-statistic = 0.902) indicated that apoA-I and the apoB : apoA-I ratio can be used to identify a subgroup of obese individuals with a significantly less atherogenic lipid and apolipoprotein profile, particularly in obese Cherokee men in whom MHO is more likely.
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Wang, Xiao-Li, Xiang-Yun Chang, Xiao-Xiao Tang, Zhi-Gang Chen, Ting Zhou, and Kan Sun. "Peripheral invariant natural killer T cell deficiency in metabolically unhealthy but normal weight versus metabolically healthy but obese individuals." Journal of International Medical Research 44, no. 6 (2016): 1272–82. http://dx.doi.org/10.1177/0300060516663778.
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Objective To investigate the proportion of circulating invariant natural killer T (iNKT) cells in four body health types. Methods In this cross-sectional study, participants were classified into four body health types according to the body mass index and metabolic status: metabolically healthy and normal weight (MHNW), metabolically unhealthy but normal weight (MUNW), metabolically healthy but obese (MHO), or metabolically unhealthy and obese (MUO). Demographic and clinical characteristics were measured, and the homeostasis model assessment of insulin resistance (HOMA-IR) and visceral adiposity index (VAI) were calculated. The proportion of circulating iNKT cells was also evaluated by flow cytometry. Results The study enrolled 41 MHNW, 37 MUNW, 30 MHO, and 43 MUO participants. Compared with the MHNW group, the MUNW, MHO, and MUO groups had significantly lower iNKT cell proportions. The iNKT cell proportion was significantly higher in the MHO group than the MUNW and MUO groups. The iNKT cell proportion was inversely correlated with high-sensitivity C-reactive protein, HOMA-IR, and VAI values. Conclusion The proportion of iNKT cells was lower in people (lean or obese) with excessive visceral fat accumulation, suggesting that iNKT cell deficiency may be involved in the pathophysiology of obesity-related metabolic disorders.
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Conus, Florence, Rémi Rabasa-Lhoret, and François Péronnet. "Characteristics of metabolically obese normal-weight (MONW) subjects." Applied Physiology, Nutrition, and Metabolism 32, no. 1 (2007): 4–12. http://dx.doi.org/10.1139/h06-092.
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The existence of a subgroup of normal-weight individuals displaying obesity-related phenotypic characteristics was first proposed in 1981. These individuals were identified as metabolically obese but normal weight (MONW). It was hypothesized that these individuals might be characterized by hyperinsulinemia and (or) insulin resistance, as well as by hypertriglyceridemia and high blood pressure despite having a body mass index (BMI) < 25 kg/m2. Such characteristics could confer upon MONW subjects a higher cardiovascular risk; however, scientific data on MONW subjects are scarce since only 9 publications are directly related to this topic. Despite differences in the criteria for identifying MONW subjects and the small number of subjects involved in most of these studies, their consistent results indicate that: (i) the prevalence of the MONW syndrome ranges between 5% and 45%, depending on the criteria used, age, BMI, and ethnicity; (ii) when compared with control subjects, MONW subjects display an altered insulin sensitivity, a higher abdominal and visceral adiposity, a more atherogenic lipid profile, a higher blood pressure, and a lower physical activity energy expenditure; and (iii) MONW subjects are at higher risks for type 2 diabetes and cardiovascular diseases.
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Bermudez, Valmore, Joselyn Rojas, Juan Salazar, et al. "Biochemical and clinical characterization of metabolic phenotypes: a cross-sectional study from Maracaibo city, Venezuela." F1000Research 7 (March 24, 2021): 230. http://dx.doi.org/10.12688/f1000research.13897.3.
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Background: In 1980, Reuben Andresen observed that in certain individuals, obesity did not increase mortality, introducing an atypical phenotype called “healthy obese”. Other studies reported that 10-15 % of lean individuals presented insulin resistance, hyperglycemia and dyslipidemia. The objective of this study was to evaluate biochemical and clinical characteristics of metabolic phenotypes in Maracaibo city. Methods: A descriptive, cross-sectional sub-analysis of The Maracaibo City Metabolic Syndrome Prevalence Study, with a randomized multistage sampling was performed including 1226 non diabetic individuals from both sexes. For phenotype definition, the subjects were first classified according to their BMI into Normal-Weight, Overweight and Obese; then divided in metabolically healthy and unhealthy using a two-step analysis cluster being predictive variables: HOMA2-IR, HOMA2-βcell, triglycerides. To evaluate the relationship with coronary risk, a multiple logistic regression model was performed. Results: In the studied population, 43.9% (n=538) were healthy normal weight, 5.2% (n=64) unhealthy normal weight, 17.4% (n=217) healthy obese and 33.5% (n=411) unhealthy obese subjects. Atypical phenotypes, Metabolically Unhealthy Normal-Weight (MUNW) was more frequent in males (56.3%), whereas Metabolically Unhealthy Obese (MUO) was more frequent in females (51.3%). This phenotypes had a higher coronary event risk, especially for obese individuals (MHO: OR=1.85 CI95%: 1.11-3.09; p=0.02 and MUO: OR=2.09 CI95%: 1.34-3.28; p<0.01). Conclusion: Individuals with atypical metabolic phenotypes are common in Maracaibo city. Related factors may include insulin resistance, basal glucose, and triglycerides levels. Lastly, obese subjects show a higher coronary event risk even those with normal metabolic status.
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Bermudez, Valmore, Joselyn Rojas, Juan Salazar, et al. "Biochemical and clinical characterization of metabolic phenotypes: a cross-sectional study from Maracaibo city, Venezuela." F1000Research 7 (January 4, 2019): 230. http://dx.doi.org/10.12688/f1000research.13897.2.
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Background: In 1980, Reuben Andresen observed that in certain individuals, obesity did not increase mortality, introducing an atypical phenotype called “healthy obese”. Other studies reported that 10-15 % of lean individuals presented insulin resistance, hyperglycemia and dyslipidemia. The objective of this study was to evaluate biochemical and clinical characteristics of metabolic phenotypes in Maracaibo city. Methods: A descriptive, cross-sectional sub-analysis of The Maracaibo City Metabolic Syndrome Prevalence Study, with a randomized multistage sampling was performed including 1226 non diabetic individuals from both sexes. For phenotype definition, the subjects were first classified according to their BMI into Normal-Weight, Overweight and Obese; then divided in metabolically healthy and unhealthy using a two-step analysis cluster being predictive variables: HOMA2-IR, HOMA2-βcell, triglycerides. To evaluate the relationship with coronary risk, a multiple logistic regression model was performed. Results: In the studied population, 43.9% (n=538) were healthy normal weight, 5.2% (n=64) unhealthy normal weight, 17.4% (n=217) healthy obese and 33.5% (n=411) unhealthy obese subjects. Atypical phenotypes, Metabolically Unhealthy Normal-Weight (MUNW) was more frequent in males (56.3%), whereas Metabolically Unhealthy Obese (MUO) was more frequent in females (51.3%). This phenotypes had a higher coronary event risk, especially for obese individuals (MHO: OR=1.85 CI95%: 1.11-3.09; p=0.02 and MUO: OR=2.09 CI95%: 1.34-3.28; p<0.01). Conclusion: Individuals with atypical metabolic phenotypes are common in Maracaibo city. Related factors may include insulin resistance, basal glucose, and triglycerides levels. Lastly, obese subjects show a higher coronary event risk even those with normal metabolic status.
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Song, Zimin, Meng Gao, Jun Lv, et al. "Metabolically healthy obesity, transition to unhealthy phenotypes, and type 2 diabetes in 0.5 million Chinese adults: the China Kadoorie Biobank." European Journal of Endocrinology 186, no. 2 (2022): 233–44. http://dx.doi.org/10.1530/eje-21-0743.
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Objectives To prospectively assess the association of metabolic health status and its transition with incident diabetes risk across BMI categories. Design Cohort study based on the China Kadoorie Biobank (CKB). Methods The CKB study enrolled 512 715 adults aged 30–79 years from ten diverse areas in China during 2004–2008. After exclusion, 432 763 participants were cross-classified by BMI categories and the metabolic status was followed up for incident diabetes disease. The changes in BMI and metabolic health status were defined from baseline to the second resurvey. Results Type 2 diabetes risk is higher for metabolically healthy obese (MHO) subjects than metabolically healthy normal weight (MHN) individuals (HR: 3.97, 95% CI: 3.64–3.66), and it is highest for those affected by metabolically unhealthy obese (MUO) (HR: 6.47, 95% CI: 6.17–6.79). About 15.26% of participants with MHN converted to metabolically healthy overweight or obesity (MHOO), whereas 48.40% of MHOO remained unconverted throughout the follow-up. In obese or overweight people, the conversion from metabolically healthy to unhealthy might increase the chances of developing diabetes as compared to those with a stable metabolic healthy state (HR: 3.70, 95% CI: 2.99–4.59), while those with persistent metabolic disorders are most likely to have diabetes (HR: 8.32, 95% CI: 7.08–9.78). Conclusions Metabolic healthy is a transient state, and individuals converted from metabolically healthy status to unhealthy phenotypes across all BMI categories might raise the risk of diabetes.
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Rocha, Edrienny Patrícia Alves Accioly, Mandy Vogel, Juraj Stanik, et al. "Serum Uric Acid Levels as an Indicator for Metabolically Unhealthy Obesity in Children and Adolescents." Hormone Research in Paediatrics 90, no. 1 (2018): 19–27. http://dx.doi.org/10.1159/000490113.
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Background: Metabolically healthy obesity (MHO) refers to those individuals who do not show cardiometabolic abnormalities. Our aim was to identify potential clinical and metabolic indicators that may help to distinguish between metabolically healthy and unhealthy individuals amongst overweight and obese children and adolescents. Methods: The study involved 246 overweight/obese and 212 normal-weight individuals enrolled in the LIFE Child study, aged between 6 and 18 years. Overweight/obese individuals without cardiovascular risk factors (fasting serum lipids, blood pressure, and glucose) were classified as MHO. Individuals meeting 1 or more criteria of cardiovascular risk factors were classified as metabolically unhealthy obesity (MUO). Results: Among the 246 overweight/obese individuals, 173 (70%) were MHO and 73 (30%) were MUO. The MHO individuals were younger, more likely to be male, and had lower BMI SDS. In the logistic regression models, uric acid (UA) SDS (OR 1.61, 95% CI 1.1–2.6, p = 0.004), waist circumference SDS (OR 2.50, 95% CI 1.2–6.4, p = 0.017), and C-peptide (OR 4.05, 95% CI 3.5–91, p = 0.003) were significant indicators of MUO. Conclusion: Our results suggest that nearly one-third of overweight/obese children are already identified as MUO. Serum levels of UA can be used as an indicator of unhealthy obesity in youth, where lower levels of UA indicate a lower risk and higher levels suggest a higher risk of MUO. We note that the relevance of identifying potential indicators remains the first most important step in future clinical research.
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Zhou, Ziyi, Solange Parra-Soto, Yujia Lu, et al. "Abstract 4870: Defining metabolically healthy and unhealthy obesity in relation to cancer risk: A prospective cohort study by using a machine learning approach in comparison with conventional definitions in the UK Biobank." Cancer Research 84, no. 6_Supplement (2024): 4870. http://dx.doi.org/10.1158/1538-7445.am2024-4870.
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Abstract Background: The definition of metabolically healthy and unhealthy obesity for cancer risk remains uncertain and controversial. This study proposed a novel classifier based on biomarkers selected using machine learning (ML) and compared its risk stratification with the conventional definition. Methods: A prospective cohort study was conducted of 317,569 UK Biobank participants who were free of cancer and with body mass index (BMI) ≥18.5 kg/m2 at baseline. Individuals were classified into metabolically healthy and non-obese (MHNO), metabolically unhealthy and non-obese (MUNO), metabolically healthy and obese (MHO), and metabolically unhealthy and obese (MUO), according to body mass index (BMI) and six metabolic criteria. For the ML approach, LASSO regularization was used to select a subset from seventeen metabolic biomarkers to optimize C index. Clinical cut-off value was applied to this subset to define MHO. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) of total, obesity-related, type two diabetes (T2D)-related, and 23 site-specific cancers according to the conventional and ML definitions. Findings: Of 21 biomarkers, 17 were selected using LASSO. Compared with MHNO, individuals with MHO had higher risk of obesity-related cancer (HRconventional 1.22, 95% CI 1.15-1.29; HRML 1.23, 95% CI 1.16-1.30) and T2D-related cancer (HRconventional 1.25, 95% CI 1.19-1.39; HRML 1.27, 95% CI 1.20-1.34) after adjusting for sociodemographic and lifestyle factors. ML-defined metabolic status better-stratified individuals with normal weight for risk of total cancer (HRconventional1.02, 95% CI 0.99 -1.06; HRML 1.06, 95% CI 1.02 - 1.10) and some site-specific cancers, e.g., hepatocellular carcinoma (HRconventional 0.89, 95% CI, 0.53-1.48; HRML 2.52, 95% CI, 1.85-3.45). Interpretation: Compared with the conventional definition of metabolic health, a broader array of metabolic markers may help better stratify individuals for cancer risk. Keywords: Obesity, cancer, metabolically healthy obesity, hepatocellular carcinoma. Citation Format: Ziyi Zhou, Solange Parra-Soto, Yujia Lu, Zhe Fang, Kai Wang, Alaina Bever, Liyuan Tao, Fanny Petermann-Rocha, Jirapitcha Boonpor, Naveed Sattar, Carlos Celis-Morales, Jill P. Pell, Frederick K. Ho, Mingyang Song. Defining metabolically healthy and unhealthy obesity in relation to cancer risk: A prospective cohort study by using a machine learning approach in comparison with conventional definitions in the UK Biobank [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4870.
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Buscemi, Silvio, Pierfilippo Chiarello, Carola Buscemi, et al. "Characterization of Metabolically Healthy Obese People and Metabolically Unhealthy Normal-Weight People in a General Population Cohort of the ABCD Study." Journal of Diabetes Research 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9294038.
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There is actually no consensus about the possibility that in some instances, obesity may be a benign metabolically healthy (MH) condition as opposed to a normal-weight but metabolically unhealthy (MUH) state. The aim of this study was to characterize MH condition and to investigate possible associations with metabolic and cardiovascular complications. One thousand nineteen people (range of age 18–90 years) of the cohort of the ABCD_2 study were investigated. Participants were classified as normal weight (BMI < 24.9 kg/m2) or overweight-obese (BMI ≥25 kg/m2); they were also classified as MH in the presence of 0-1 among the following conditions: (a) prediabetes/type 2 diabetes, (b) hypertension, (c) hypertriglyceridemia or low HDL cholesterolemia, and (d) hypercholesterolemia. MUH condition was diagnosed if ≥2 of the conditions listed were found. The prevalence of overweight/obese people was 71.1%, of whom 27.4% were found to be MH. In addition, 36.7% of the normal-weight participants were MUH. HOMA-IR, high sensitivity C-reactive protein, and the carotid intima-media thickness were significantly different in the 4 subgroups (P<0.001), with higher values observed in the MUH normal-weight and obese groups. In conclusion, this study highlights the importance of identifying a MH condition in normal-weight and in obese people in order to offer better treatment.
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Lyu, Young Sang, Youngmin Yoon, Jin Hwa Kim, and Sang Yong Kim. "The Effect of Periodontitis on Body Size Phenotypes in Adults without Diagnosed Chronic Diseases: The Korean National Health and Nutrition Examination Survey 2013–2015." International Journal of Environmental Research and Public Health 21, no. 9 (2024): 1180. http://dx.doi.org/10.3390/ijerph21091180.
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We aimed to examine the correlation between periodontitis and body size phenotypes in 7301 participants without diagnosed chronic diseases in the Korean National Health and Nutrition Examination Survey 2013–2015. The participants were categorized into the following body size phenotype groups based on body mass index and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). The prevalence rates of mild and severe periodontitis were 18.1% and 7.5%, respectively. Patients with periodontitis were older, current smokers, had a lower family income, were less likely to engage in regular tooth brushing or exercise, and had a higher body mass index and glucose levels. Periodontitis was more prevalent in the MANW and MAO groups than in the MHNW and MHO groups. Compared with the MHNW phenotype, the MAO and MANW phenotypes were significantly associated with mild and severe periodontitis, and the MHO phenotype was significantly associated with mild periodontitis. The MANW and MAO phenotypes are independent risk factors for periodontitis in adults without diagnosed chronic diseases. To enhance public health, a greater focus and effective approaches for identifying metabolic disease phenotypes among individuals with periodontal disease may be clinically relevant.
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Graf, Christine, and Nina Ferrari. "Metabolic Health—The Role of Adipo-Myokines." International Journal of Molecular Sciences 20, no. 24 (2019): 6159. http://dx.doi.org/10.3390/ijms20246159.
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Obesity is now a worldwide epidemic. In recent years, different phenotypes of obesity, ranging from metabolically healthy normal weight to metabolically unhealthy obese, were described. Although there is no standardized definition for these phenotypes or for metabolic health, the influence of lifestyle and early-life factors is undisputed. In this context, the ratio of muscle-to-fat tissue seems to play a crucial role. Both adipose tissue and skeletal muscle are highly heterogeneous endocrine organs secreting several hormones, with myokines and adipokines being involved in local autocrine/paracrine interactions and crosstalk with other tissues. Some of these endocrine factors are secreted by both tissues and are, therefore, termed adipo-myokines. High (cardiorespiratory) fitness as a surrogate parameter for an active lifestyle is epidemiologically linked to “better” metabolic health, even in the obese; this may be partly due to the role of adipo-myokines and the crosstalk between adipose and muscle tissue. Therefore, it is essential to consider (cardiovascular) fitness in the definition of metabolically healthy obese/metabolic health and to perform longitudinal studies in this regard. A better understanding of both the (early-life) lifestyle factors and the underlying mechanisms that mediate different phenotypes is necessary for the tailored prevention and personalized treatment of obesity.
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Alfadda, Assim A. "Circulating Adipokines in Healthy versus Unhealthy Overweight and Obese Subjects." International Journal of Endocrinology 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/170434.
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It is now well established that not all obese subjects are at increased risk of cardiometabolic complications; such patients are termed the metabolically healthy obese. Despite their higher-than-normal body fat mass, they are still insulin sensitive, with a favorable inflammatory and lipid profile and no signs of hypertension. It remains unclear which factors determine an individual's metabolic health. Adipose tissue is known to secrete multiple bioactive substances, called adipokines, that can contribute to the development of obesity-associated complications. The goal of this study was to determine whether the circulating adipokine profiles differs between metabolically healthy and metabolically unhealthy overweight and obese subjects, thereby obtaining data that could help to explain the link between obesity and its related cardiometabolic complications. We defined metabolic health in terms of several metabolic and inflammatory risk factors. The serum adiponectin levels were higher in the healthy group and showed a positive correlation with HDL cholesterol levels in the unhealthy group. There were no differences between the two groups in the levels of serum leptin, chemerin and orosomucoid. Accordingly, adiponectin might play a role in protecting against obesity-associated cardiometabolic derangements. More studies are needed to clarify the role of different chemerin isoforms in this system.
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Tang, Minhua, Qi Zhao, Kangqi Yi, et al. "Association between Metabolic Phenotypes of Body Fatness and Incident Stroke: A Prospective Cohort Study of Chinese Community Residents." Nutrients 14, no. 24 (2022): 5258. http://dx.doi.org/10.3390/nu14245258.
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This study aimed to assess the association of body mass index (BMI)-based and waist circumference (WC)-based metabolic phenotypes with the risk of stroke among Chinese community residents. A total of 34,294 participants (mean ± standard deviation age: 56.05 ± 11.26 years) with no previous stroke diagnosis history were included in this cohort study. BMI-based metabolic phenotypes were classified into eight groups: metabolically healthy and normal weight (MHNW), metabolically healthy and underweight (MHUW), metabolically healthy and overweight (MHOW), metabolically healthy and obese (MHO), metabolically unhealthy and normal weight (MUNW), metabolically unhealthy and underweight (MUUW), metabolically unhealthy and overweight (MUOW), and metabolically unhealthy and obese (MUO). WC-based metabolic phenotypes were classified into four groups: metabolically healthy and normal WC (MHNWC), metabolically healthy and oversized WC (MHOWC), metabolically unhealthy and normal WC (MUNWC), and metabolically unhealthy and oversized WC (MUOWC). The association of these phenotypes with developing stroke events was examined using proportional hazards models. A total of 546 cases of first-stroke onset were recorded over a median follow-up time of 4.97 years. Compared with the reference group, the obesity phenotypes showed higher risks for stroke. The adjusted HRs (95% CIs) of MHUW, MHOW, MHO, MUNW, MUUW, MUOW, and MUO phenotypes were 1.01 (0.41, 2.49), 1.47 (1.09, 2.00), 1.33 (0.80, 2.22), 2.49 (1.87, 3.30), 3.92 (1.44, 10.72), 2.14 (1.64, 2.79), and 2.60 (1.91, 3.55), respectively. The adjusted HRs (95% CIs) of MHOWC, MUNWC, and MUOWC were 1.41 (1.02, 1.94), 2.25 (1.76, 2.87), and 2.16 (1.63, 2.87), respectively. The metabolic phenotypes defined by an alternative definition all showed significant positive associations (except for MHUW), with the adjusted HR ranging from 1.51 to 3.08 based on BMI and from 1.68 to 2.24 based on WC. The risk of stroke increased with the increase in metabolic abnormality numbers in different BMI and WC groups (all p trend < 0.001). The present study suggests that maintaining normal body weight or WC and improving metabolic health are of great significance in preventing cerebrovascular diseases.
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Bermúdez, Valmore, Joselyn Rojas, Juan Salazar, et al. "Sensitivity and Specificity Improvement in Abdominal Obesity Diagnosis Using Cluster Analysis during Waist Circumference Cut-Off Point Selection." Journal of Diabetes Research 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/750265.
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Introduction. The purpose of this study was to analyze the influence of metabolic phenotypes during the construction of ROC curves for waist circumference (WC) cutpoint selection.Materials and Methods. A total of 1,902 subjects of both genders were selected from the Maracaibo City Metabolic Syndrome Prevalence Study database. Two-Step Cluster Analysis (TSCA) was applied to select metabolically healthy and sick men and women. ROC curves were constructed to determine WC cutoff points by gender.Results. Through TSCA, metabolic phenotype predictive variables were selected: HOMA2-IR and HOMA2-βcell for women and HOMA2-IR, HOMA2-βcell, and TAG for men. Subjects were classified as healthy normal weight, metabolically obese normal weight, healthy and metabolically disturbed overweight, and healthy and metabolically disturbed obese. Final WC cutpoints were 91.50 cm for women (93.4% sensitivity, 93.7% specificity) and 98.15 cm for men (96% sensitivity, 99.5% specificity).Conclusions. TSCA in the selection of the groups used in ROC curves construction proved to be an important tool, aiding in the detection of MOWN and MHO which cannot be identified with WC alone. The resulting WC cutpoints were <91.00 cm for women and <98.00 cm for men. Furthermore, anthropometry is insufficient to determine healthiness, and, biochemical analysis is needed to properly filter subjects during classification.
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Gaponov, A. M., N. I. Volkova, L. A. Ganenko, et al. "Characteristics of the colonic microbiome in patients with different obesity phenotypes (the original article)." Journal of microbiology, epidemiology and immunobiology 98, no. 2 (2021): 144–55. http://dx.doi.org/10.36233/0372-9311-66.
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Introduction. The concept of heterogeneity in obesity depending on the risk of developing cardiometabolic complications has garnered attention in recent decades, since not everyone with obesity goes on to develop metabolic dysfunction.The aim of the work is to study specific characteristics of colonic microbial communities in patients with different obesity phenotypes and in healthy individuals by employing metagenomics methods.Materials and methods. A total of 265 individuals (44 men and 221 women; mean age 47.1 ± 4.8 years) were enrolled in the study. They were further divided into clinical groups: Healthy normal-weight individuals (n = 129); patients with obesity (n = 136), including metabolically healthy obesity (n = 40) and metabolically unhealthy obesity (n = 55). Quantitative and qualitative assessment of the intestinal microbiome was based on metagenomic analysis. Fecal samples were used to isolate DNA and perform sequencing of the variable v3-v4 region of the 16S rRNA gene.Results. The study revealed statistically significant (p < 0.05) differences between quantitative and qualitative variables in studied phylotypes of colonic microorganisms in healthy individuals without obesity and in patients with different obesity phenotypes.Discussion. Patients with obesity had higher levels of Bacteroidetes, Proteobacteria and lower levels of Actinobacteria, Firmicutes, TM7 (Saccharibacteria), Fusobacteria, and more frequently detected phyla Tenericutes, Planctomycetes and Lentisphaerae compared to healthy individuals. Metabolically healthy obese patients had more rarely detected phylum Lentisphaerae in their colonic microbiome, increased numbers of Firmicutes and reduced numbers of Bacteroidetes compared to metabolically unhealthy obese patients.Conclusion. The findings demonstrate alterations in the colonic microbiome in patients with different obesity phenotypes.
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Karelis, Antony D., David H. St-Pierre, Florence Conus, Remi Rabasa-Lhoret, and Eric T. Poehlman. "Metabolic and Body Composition Factors in Subgroups of Obesity: What Do We Know?" Journal of Clinical Endocrinology & Metabolism 89, no. 6 (2004): 2569–75. http://dx.doi.org/10.1210/jc.2004-0165.
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Abstract Obesity is thought to be a heterogeneous disorder with several possible etiologies; therefore, by examining subtypes of obesity we attempt to understand obesity’s heterogeneous nature. The purpose of this review was to investigate the roles of metabolic, body composition, and cardiovascular disease risk in subtypes of obesity. We briefly consider two subtypes of obesity that have been identified in the literature. One subset of individuals, termed the metabolically healthy, but obese (MHO), despite having large amounts of fat mass compared with at risk obese individuals shows a normal metabolic profile, but remarkably normal to high levels of insulin sensitivity. Preliminary evidence suggests that this could be due at least in part to lower visceral fat levels and earlier onset of obesity. A second subset, termed the metabolically obese, but normal weight (MONW), present with normal body mass index, but have significant risk factors for diabetes, metabolic syndrome, and cardiovascular disease, which could be due to higher fat mass and plasma triglycerides as well as higher visceral fat and liver content. We also briefly consider the potential role of adipose and gastrointestinal hormonal profiles in MHO and MONW individuals, which could lead to a better understanding of potential factors that may regulate their body composition. This information will eventually be invaluable in helping us understand factors that predispose to or protect obese individuals from metabolic and cardiovascular disease. Collectively, a greater understanding of the MHO and MONW individual has important implications for therapeutic decision making, the characterization of subjects in research protocols, and medical education.
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Wang, Fangnian, Hongsheng Liu, Wanda P. Blanton, Anna Belkina, Nathan K. Lebrasseur, and Gerald V. Denis. "Brd2 disruption in mice causes severe obesity without Type 2 diabetes." Biochemical Journal 425, no. 1 (2009): 71–85. http://dx.doi.org/10.1042/bj20090928.
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Certain human subpopulations are metabolically healthy but obese, or metabolically obese but normal weight; such mutations uncouple obesity from glucose intolerance, revealing pathways implicated in Type 2 diabetes. Current searches for relevant genes consume significant effort. We have reported previously a novel double bromodomain protein called Brd2, which is a transcriptional co-activator/co-repressor with SWI/SNF (switch mating type/sucrose non-fermenting)-like functions that regulates chromatin. In the present study, we show that wholebody disruption of Brd2, an unusual MHC gene, causes lifelong severe obesity in mice with pancreatic islet expansion, hyperinsulinaemia, hepatosteatosis and elevated pro-inflammatory cytokines, but, surprisingly, enhanced glucose tolerance, elevated adiponectin, increased weight of brown adipose tissue, heat production and expression of mitochondrial uncoupling proteins in brown adipose tissue, reduced macrophage infiltration in white adipose tissue, and lowered blood glucose, leading to an improved metabolic profile and avoiding eventual Type 2 diabetes. Brd2 is highly expressed in pancreatic β-cells, where it normally inhibits β-cell mitosis and insulin transcription. In 3T3-L1 pre-adipocytes, Brd2 normally co-represses PPAR-γ (peroxisome-proliferator-activated receptor-γ) and inhibits adipogenesis. Brd2 knockdown protects 3T3-L1 adipocytes from TNF-α (tumour necrosis factor-α)-induced insulin resistance, thereby decoupling inflammation from insulin resistance. Thus hypomorphic Brd2 shifts energy balance toward storage without causing glucose intolerance and may provide a novel model for obese metabolically healthy humans.
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Yoo, Hye Jin. "Pathogenesis and Clinical Implications of Metabolically Healthy Obesity (MHO) and Metabolically Obese Normal Weight (MONW) Subjects." Journal of Korean Diabetes 15, no. 1 (2014): 12. http://dx.doi.org/10.4093/jkd.2014.15.1.12.
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