International Journal of Obesity最新文献

Menopause accelerates metabolic dysfunction, including (pre-)diabetes, obesity and visceral adiposity. However, the effects of endocrine vs. chronological aging in this progression are poorly understood. We hypothesized that menopause, especially in the context of middle-age, would exacerbate the metabolic effects of a high fat diet. Using young-adult and middle-aged C57BL/6J female mice, we modeled diet-induced obesity via chronic administration of high fat (HF) diet vs. control diet. We modeled peri-menopause/menopause via injections of 4-vinylcyclohexene diepoxide, which accelerates ovarian failure vs. vehicle. We performed glucose tolerance tests 2.5 and 7 months after diet onset, during the peri-menopausal and menopausal phases, respectively. Peri-menopause increased the severity of glucose intolerance and weight gain in middle-aged, HF-fed mice. Menopause increased weight gain in all mice regardless of age and diet, while chronological aging drove changes in adipose tissue distribution towards more visceral vs. subcutaneous adiposity. These data are in line with clinical data showing that post-menopausal women are more susceptible to metabolic dysfunction and suggest that greater chronological age exacerbates the effects of endocrine aging (menopause). This work highlights the importance of considering both chronological and endocrine aging in studies of metabolic health.

Background: Internalized weight bias is the belief in negative, weight-based stereotypes and the application of these stereotypes to oneself. These negative stereotypes have harmful impacts on people with overweight/obesity, and weight-based discrimination is well-documented across a variety of settings. Given poor outcomes associated with internalized weight bias, particularly among individuals with obesity, it is necessary to validate measures assessing internalized weight bias among diverse samples. The present study sets out to investigate measurement invariance properties across weight status (women with vs. without overweight/obesity) and race (White vs. Asian; White vs. bi- or multi-racial) of the Modified Weight Bias Internalization Scale (WBIS-M), an 11 item self-report measure.

Methods: Participants were 746 racially/ethnically diverse women across the weight spectrum (24.9% with overweight/obesity). Confirmatory factor analyses of the WBIS-M were initially performed among the full sample, and all sub-samples. Each model showed good to excellent descriptive model fit. Subsequent analyses examined factor loadings and item thresholds of the WBIS-M to assess metric, threshold, and scalar invariance. Invariance was determined by assessing changes in Comparative Fit Index (ΔCFI $\le$ -0.010), Root Mean Square Error of Approximation (ΔRMSEA $\le$ 0.015), and Standardized Root Mean Square Residuals (ΔSRMR $\le$ 0.030).

Results: Based on these previously established statistical cutoffs, the WBIS-M showed invariance across weight status and racial groups in the present sample. The current results lend support for use of the WBIS-M to measure internalized weight bias in women who do and do not have overweight/obesity, and among White, Asian, and bi- or multi-racial women.

Conclusion: This may inform future studies that wish to utilize the WBIS-M, such as investigations of mean level differences in internalized weight bias. These findings may have clinical applications in the treatment and prevention of obesity, given the heightened levels of internalized weight bias and weight-based discrimination faced by individuals with higher body weights.

Background: The triglyceride-glucose (TyG) index performs better at reflecting insulin resistance when combined with waist circumference (WC), body mass index (BMI), and waist-to-height ratio (WHtR) than when used alone. This study aimed to prospectively examine the relationships between TyG, TyG-BMI, TyG-WC, and TyG-WHtR with the incidence of myocardial infarction (MI) and its subtypes.

Methods: This cohort study included 370,390 participants from the UK Biobank. The Cox proportional hazards model and restricted cubic spline regression model were used to assess the associations of TyG, TyG-BMI, TyG-WC, and TyG-WHtR with MI, ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI). The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were employed to examine the predictive value of four indicators.

Results: The hazard ratios (HRs) and 95% confidence intervals (CIs) of MI in the highest quartiles for TyG, TyG-BMI, TyG-WC, and TyG-WHtR were 1.36 (1.28-1.44), 1.47 (1.39-1.56), 1.53 (1.43-1.64), and 1.58 (1.48-1.68) in the fully-adjusted model. Comparable findings were observed when the outcomes were reclassified as STEMI or NSTEMI. However, the associations of TyG-BMI, TyG-WC, and TyG-WHtR with the risk of STEMI were weaker than MI and NSTEMI. A linear dose-response association between TyG and the risk of MI and NSTEMI were demonstrated. TyG-BMI, TyG-WC, and TyG-WHtR all showed nonlinear patterns in their associations with the risk of MI, STEMI, and NSTEMI. TyG-WC was most effective in diagnosing MI (AUC: 0.648, 95% CI: 0.644-0.653), STEMI (AUC: 0.631, 95% CI: 0.622-0.639), and NSTEMI (AUC: 0.647, 95% CI: 0.641-0.654).

Conclusion: The TyG index was linearly associated with increased risk of MI and NSTEMI, whereas TyG-BMI, TyG-WC, and TyG-WHtR were nonlinearly associated with increased risk of MI and NSTEMI. There were distinct patterns in the relationships between these indicators with STEMI. TyG-WC provided the best diagnostic effectiveness for MI, STEMI, and NSTEMI.

Background: Numerous studies have demonstrated that the onset of type 2 diabetes (T2D) is linked to the reduction in ß-cell mass caused by apoptosis, a process initiated by endoplasmic reticulum (ER) stress. The aim of this study was to investigate the associations between single nucleotide polymorphisms (SNPs) in the ATF6 gene (activating transcription factor 6), a key sensor of ER stress, and T2D susceptibility.

Methods: The study involved 3229 unrelated individuals, including 1569 patients with T2D and 1660 healthy controls from Central Russia. Four functionally significant intronic SNPs, namely rs931778, rs90559, rs2341471, and rs7517862, were genotyped using the MassARRAY-4 system.

Results: The rs2341471-G/G genotype of ATF6 was found to be associated with an increased risk of T2D (OR = 1.61, 95% CI 1.37-1.90, P_FDR < 0.0001). However, a BMI-stratified analysis showed that this genotype and haplotypes CGGA and TAGA are associated with T2D risk exclusively in subjects with obesity or overweight (P_FDR < 0.05). Despite these patients being found to have higher consumption of high-carbohydrate and high-calorie diets compared to normal-weight individuals (P < 0.0001), the influence of the rs7517862 polymorphism on T2D risk was observed independently of these dietary habits. Functional SNP annotation revealed the following: (1) the rs2341471-G allele is associated with increased ATF6 expression; (2) the SNP is located in a region exhibiting enhancer activity epigenetically regulated in pancreatic islets; (3) the rs2341471-G was predicted to create binding sites for 18 activating transcription factors that are part of gene-regulatory networks controlling glucose metabolism and maintaining proteostasis.

Conclusions: The present study revealed, for the first time, a strong association between the rs2341471-G/G ATF6 genotype and an increased risk of type 2 diabetes in people with obesity or overweight, regardless of known dietary risk factors. Further research is needed to support the potential of silencing the ATF6 gene as a means for the treatment and prevention of type 2 diabetes.

Background: Cardiometabolic diseases are risk factors for COVID-19 severity. The extent that cardiometabolic health represents a modifiable factor to mitigate the short- and long-term consequences from SARS-CoV-2 remains unclear. Our objective was to evaluate the associations between intraindividual variability of cardiometabolic health indicators and COVID-19 related hospitalizations and post-COVID conditions (PCC) among a relatively healthy population.

Methods: This retrospective, multi-site cohort study was a post-hoc analysis among individuals with cardiometabolic health data collected during routine blood donation visits in 24 US states (2009-2018) and who responded to COVID-19 questionnaires (2021-2023). Intraindividual variability of blood pressure (systolic, diastolic), total circulating cholesterol, and body mass index (BMI) were defined as the coefficient of variation (CV) across all available donation timepoints (ranging from 3 to 74); participants were categorized into CV quartiles. Associations were evaluated by multivariable binomial regressions.

Results: Overall, 3344 participants provided 42,090 donations (median 9 [IQR 5, 17]). The median age was 48 years (38, 56) at the first study donation. 1.2% (N = 40) were hospitalized due to COVID-19 and 15.5% (N = 519) had PCC. Higher BMI variability was associated with greater risk of COVID-19 hospitalization (4th quartile aRR 4.15 [95% CI 1.31, 13.11], p = 0.02; 3rd quartile aRR 3.41 [95% CI 1.09, 10.69], p = 0.04). Participants with higher variability of BMI had greater risk of PCC (4th quartile aRR 1.29 [95% CI 1.02, 1.64]; p = 0.04). Intraindividual variability of blood pressure (systolic, diastolic) and total circulating cholesterol were not associated with COVID-19 hospitalization or PCC risk (all p > 0.05). From causal mediation analysis, the association between the highest quartiles of BMI variability and PCC was not mediated by hospitalization (p > 0.05).

Conclusions: Higher intraindividual variability of BMI was associated with COVID-19 hospitalization and PCC risk. Our findings underscore the need for further elucidating mechanisms that explain these associations and importance for consistent maintenance of body weight.

Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.

Background: The ongoing debate regarding the need for screening Cushing's syndrome (CS) in patients with obesity continues. The objectives of this study were to establish the prevalence of CS in the population with obesity and assess how metabolic health status influences cortisol levels following the 1 mg dexamethasone suppression test (DST).

Methods: This retrospective study included 1008 patients with obesity who underwent screening with the 1 mg DST for CS. These patients were categorized into two groups as metabolically healthy obesity (MHO) and unhealthy obesity (MUO).

Results: Out of the 1008 patients, 779 (77.3%) belonged to the MUO group. Within the entire study cohort, 12 (1.2%) patients exhibited a cortisol level of ≥ 1.8 after the 1 mg DST. Cortisol levels following the 1 mg DST were also significantly higher in the MUO group than in the MHO group (p = 0.001). Among these 12 patients, 11 were presenting a MUO phenotype. Hypercortisolism was definitively diagnosed in two patients, resulting in an overall prevalence of 0.2%. The 1 mg DST demonstrated a specificity of 99% and 100% sensitivity for screening for CS.

Conclusions: While the 1 mg DST is a practical screening test for CS with high specificity in obesity, the number of CS cases detected remains relatively low. Therefore, it may be more reasonable and applicable to screen patients with MUO phenotype rather than all individuals with obesity.

Background/objectives: Melanocortins mediate their biological functions via five different melanocortin receptors (MC1R - MC5R). MC1R is expressed in the skin and leukocytes, where it regulates skin pigmentation and inflammatory responses. MC1R is also present in the liver and white adipose tissue, but its functional role in these tissues is unclear. This study aimed at determining the regulatory role of MC1R in fatty acid metabolism.

Methods: Male recessive yellow (Mc1r^e/e) mice, a model of global MC1R deficiency, and male hepatocyte-specific MC1R deficient mice (Mc1r LKO) were fed a chow or Western diet for 12 weeks. The mouse models were characterized for body weight and composition, liver adiposity, adipose tissue mass and morphology, glucose metabolism and lipid metabolism. Furthermore, qPCR and RNA sequencing analyses were used to investigate gene expression profiles in the liver and adipose tissue. HepG2 cells and primary mouse hepatocytes were used to study the effects of pharmacological MC1R activation.

Results: Chow- and Western diet-fed Mc1r^e/e showed increased liver weight, white adipose tissue mass and plasma triglyceride (TG) concentration compared to wild type mice. This phenotype occurred without significant changes in food intake, body weight, physical activity or glucose metabolism. Mc1r LKO mice displayed a similar phenotype characterized by larger fat depots, increased adipocyte hypertrophy and enhanced accumulation of TG in the liver and plasma. In terms of gene expression, markers of de novo lipogenesis, inflammation and apoptosis were upregulated in the liver of Mc1r LKO mice, while enzymes regulating lipolysis were downregulated in white adipose tissue of these mice. In cultured hepatocytes, selective activation of MC1R reduced ChREBP expression, which is a central transcription factor for lipogenesis.

Conclusions: Hepatocyte-specific loss of MC1R disturbs fatty acid metabolism in the liver and leads to an obesity phenotype characterized by enhanced adipocyte hypertrophy and TG accumulation in the liver and circulation.