Journal of Obesity & Metabolic Syndrome最新文献

Adipose tissue is a complex metabolic and endocrine organ that plays a central role in systemic energy homeostasis. While single-cell and single-nucleus RNA sequencing have revealed remarkable cellular heterogeneity within adipose tissue depots, these approaches lack spatial context, limiting the ability to understand how cellular organization and microenvironmental cues shape adipose tissue biology. Spatial transcriptomics (ST) has emerged as a powerful technology to overcome this barrier by allowing one to map gene expression directly within intact tissue sections. Recent advances in ST platforms now permit analysis at a high resolution, enabling interrogation of adipocyte subpopulations, stromal progenitors, immune cell infiltration, and tissue remodeling. In this review, we provide an overview of current ST technologies, computational strategies for analysis, and recent applications for understanding adipose tissue biology. We further highlight key opportunities for ST to address unanswered questions surrounding adipogenic niches, depot-specific remodeling, and immune cell interactions. Together, these advances position ST as a transformative tool for dissecting the architecture and function of adipose tissue in health and metabolic disease.

Background: The 2024 Obesity Fact Sheet provides an updated overview of the prevalence of obesity, abdominal obesity, underweight, and obesity as defined by body fat percentage and low muscle mass.

Methods: This study included participants who underwent general health examinations provided by the National Health Insurance Service between 2013 and 2022 (17,220,905 in 2022) and 4,425 participants in the Korea National Health and Nutrition Examination Survey (2022).

Results: In 2022, prevalence rates of obesity and abdominal obesity were 38.4% (males 49.6%, females 27.7%) and 24.5% (males 31.3%, females 18.0%), respectively. Although the prevalence of obesity increased across all classes, class II and III obesity increased notably by 1.6-fold and 2.6-fold, respectively, compared with 2013. The prevalence of underweight was highest among adults aged 20 to 24 and those aged ≥85. Across all age groups, the prevalence of underweight was consistently higher among females than in males. The prevalence of obesity, defined as body fat percentage, was 41.5% (46.1% in males and 37.1% in females), whereas that of low muscle mass was 16.8%.

Conclusion: This study presents updated data on the prevalence of obesity, abdominal obesity, underweight, and obesity defined by body fat percentage and low muscle mass. These findings highlight the need for timely strategies for obesity prevention and management, as well as the importance of addressing underweight status among young adults and older individuals. Moreover, our findings emphasize the complexity of obesity assessment, addressing the need for more detailed evaluation of body composition.

Background: This study explores how relative skeletal muscle mass is associated with the development of metabolic dysfunction-associated steatotic liver disease (MASLD) and the remission of baseline MASLD in a community-based population cohort.

Methods: The study included 1,544 participants with an average age of 58 years. All participants underwent baseline and follow-up assessments in 2015 or 2016. Appendicular skeletal muscle mass was measured using an automatic bioelectrical impedance analysis (BIA), and total skeletal muscle mass was calculated using the BIA equation. Relative skeletal muscle mass was evaluated in two ways: divided by weight and divided by visceral fat area (VFA). Liver fat content was assessed using ultrasonography, and the NAFLD fibrosis score was calculated to quantify the degree of liver fibrosis.

Results: During a median follow-up of 2.1 years, each one-standard deviation increase in relative total skeletal muscle mass was associated with a decreased risk of MASLD incidence among males (hazard ratio [HR], 0.56; 95% confidence interval [CI], 0.43 to 0.74, adjusted for weight; and HR, 0.23; 95% CI, 0.13 to 0.42, adjusted for VFA) and females (HR, 0.62; 95% CI, 0.47 to 0.83, adjusted for weight; and HR, 0.37; 95% CI, 0.19 to 0.70, adjusted for VFA). In both sexes, the increase in relative appendicular skeletal muscle mass was also associated with a reduced MASLD risk. We found statistically significant inverse associations between relative skeletal muscle mass and both liver fat content and liver fibrosis.

Conclusion: Low relative muscle mass is associated with an increased risk of MASLD incidence and persistence. Therefore, increasing skeletal muscle mass over time might aid in the prevention and management of MASLD.

Background: Research on the relationship between physical activity and medical expenses among individuals with obesity has been sparse. This study investigates that association using nationwide data from Korea.

Methods: We analyzed data from the National Sample Cohort of the Korean National Health Insurance Service, including 112,531 adults with obesity who underwent at least two health screenings within a 2-year interval between 2009 and 2015. Participants were categorized into four groups based on changes in their regular physical activity during 2 years: non-exercisers, quitters, starters, and maintainers. A two-part model was used to assess the average and excess annual medical expenses per person, including outpatient and hospitalization expenses.

Results: Lower levels of physical activity correlated with an increase in both average and excess annual medical expenses per person (P for trend <0.001). Non-exercisers showed the highest average annual medical expenses per person, followed by quitters, starters, and maintainers. The most substantial increases in overall medical expenses were observed among quitters, followed by non-exercisers, starters, and maintainers. These associations were prominent in individuals aged ≥65 years and those with both type 2 diabetes mellitus and hypertension.

Conclusion: In this nationwide study of individuals with obesity, lower physical activity levels were associated with increased medical expenses. Consistently engaging in physical activity might significantly lower medical expenses, particularly among elderly people and individuals with comorbidities. These findings highlight the importance of promoting sustained physical activity as a strategy for managing healthcare costs among individuals with obesity.

Although the prevalence of obesity is increasing worldwide, related treatment remains a complex challenge that requires multidimensional approaches. Recent advancements in artificial intelligence (AI) have led to the development of multimodal methods capable of integrating diverse types of data. These AI approaches utilize both multimodal data integration and multidimensional feature representations, enabling personalized, data-driven strategies for obesity management. AI can support obesity management through applications such as risk prediction, clinical decision support systems, large language models, and digital therapeutics. Several studies have shown that these AI-based weight loss programs can achieve significant weight reduction and behavioral changes. These AI systems can induce behavioral modifications through continuous personalized feedback and improve accessibility for people in underserved areas. However, these AI technologies must address issues such as data privacy and security, transparency and accountability, and consider the potential widening health disparities between individuals who have access to AI technology and those who do not, as well as strategies for sustained user engagement. Conducting long-term clinical trials and evaluations of cost-effectiveness across diverse, large-scale populations would facilitate the effective application of AI in obesity management, ultimately contributing to improvements in public health.

Background: As obesity increases, the burden of obesity-related comorbidities also rises. However, the prevalence of obesity-related comorbidities among individuals in Korea has not been evaluated.

Methods: Data from the 2007 to 2022 Korean National Health and Nutrition Examination Surveys database were analyzed (n=93,761). The prevalence of hypertension, diabetes, dyslipidemia, steatotic liver disease (SLD), and chronic kidney disease (CKD) was analyzed based on the presence of obesity and central obesity. The prevalence of obesity-related comorbidities was examined according to age and sex.

Results: The prevalence of obesity has steadily increased from 31.5% in 2007-2009 to 37.4% in 2020-2022, with a more pronounced rise in men and those aged 19 to 39 years. Among individuals with obesity, the prevalence of hypertension, diabetes, dyslipidemia, CKD, and SLD has also increased. The proportion of metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD with increased alcohol intake have risen. The increase in CKD prevalence was particularly prominent in the young (19 to 39 years) and middle-aged (40 to 59 years) groups. Similar trends were observed when analyzing data based on central obesity.

Conclusion: With the increase in obesity, the prevalence of obesity-related comorbidities in the Korean population has risen. Young and middle-aged individuals with obesity are particularly vulnerable to these comorbidities, highlighting the need for early intervention and targeted healthcare strategies.

Adipocytes play a central role in energy balance by integrating lipid storage, thermogenesis, and endocrine regulation. Their diversity-comprising white, brown, and beige adipocytes-emerges through tightly coordinated transcriptional and epigenetic programs. In addition to transcription factors, epigenetic mechanisms such as DNA methylation and histone modifications mediated by acetyltransferases, methyltransferases, and demethylases shape the chromatin states that govern adipocyte differentiation and function. The concept of metabolic memory refers to the long-lasting imprint that environmental and nutritional cues make on cells and tissues; it can facilitate rapid adaptation to subsequent challenges but also predispose organisms to metabolic dysfunction and related complications. Recent technological advances have revealed that cold exposure and obesity leave epigenomic marks on adipocytes, providing mechanistic insights into how metabolic memory is encoded. This review highlights the fundamental principles of the adipose tissue epigenome, the regulation of adipocyte identity, and how epigenomic memory links environmental history to long-term metabolic health.

Adipose tissue is a dynamic immunometabolic organ whose cellular heterogeneity and functional plasticity are central to systemic energy balance and metabolic regulation. Disruption of immune-adipocyte interactions is closely linked to the development of obesity and related metabolic disorders. In this review, we summarize current advances in understanding of the immune landscape in adipose tissue, with an emphasis on the distinct roles of immune cell subsets. Recent approaches including global and single-cell transcriptomic analysis, spatial profiling, and lineage tracing have expanded our ability to characterize these populations. We further highlight mechanisms through which immune cells influence adipocyte turnover, lipid handling, and thermogenesis, as well as reciprocal signals from adipocytes such as cytokines, lipid mediators, extracellular vesicles, and nutrient exchange. This bidirectional crosstalk governs adipose tissue remodeling and determines the occurrence of metabolic homeostasis or dysfunction. Finally, we provide perspectives into the ways in which these interactions may guide the identification of novel therapeutic targets for obesity and metabolic disease.

The global obesity epidemic can no longer be explained by personal choice or caloric excess alone. Mounting evidence points to underlying neurobehavioral dysfunction, exacerbated by environments engineered to promote overconsumption. Modern obesity is driven by five interrelated neurobehavioral factors: cue-evoked eating, habitual-context eating, food addiction, emotional eating, and restrained eating. These maladaptive eating patterns arise from a decoupling of homeostatic and hedonic brain circuits in an obesogenic environment. This review synthesizes evidence from neuroimaging, behavioral experiments, and animal studies to illustrate how each factor contributes to obesity risk and relapse after weight loss. We further discuss emerging interventions -including digital therapeutics (DTx) and electroceuticals-that target these drivers with increasing precision. DTx platforms deliver scalable, phenotype-informed interventions through cognitive-behavioral modules, real-time monitoring, and artificial intelligence-driven coaching. Electroceutical strategies, including non-invasive brain stimulation and vagus nerve modulation, show promise in reshaping dysfunctional circuits. Finally, we propose a neurobehavioral subtyping model to guide personalized obesity treatment, integrating brain-based phenotyping with multimodal interventions. This framework may offer a path toward sustained and mechanism-driven obesity care.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a liver manifestation of diabetes that is often associated with obesity and insulin resistance, with hyperglycemia worsening its progression. Recent studies have shown a bidirectional relationship between MASLD and diabetes: MASLD contributes to insulin resistance, and hyperglycemia accelerates the progression of MASLD to metabolic dysfunction-associated steatohepatitis (MASH). Hepatokines upregulated by overnutrition and hyperglycemia are implicated in the link between liver steatosis and insulin resistance in skeletal muscle, highlighting inter-organ crosstalk in the progression of both MASLD and diabetes. In individuals with diabetes, hyperglycemia and free fatty acid influx promote de novo lipogenesis and enhance lipid oxidation and oxidative phosphorylation in the liver, potentially leading to increased oxidative stress, inflammation, and fibrosis. Transcriptome analyses of human MASH and diabetic MASH model animals have revealed liver endothelial cell damage in diabetic conditions. Most drugs proven effective for MASH in randomized controlled trials are antidiabetic agents. Recently, pioglitazone, glucagon-like peptide-1 (GLP-1) receptor agonists, and dual agonists of glucose-dependent insulinotropic polypeptide and GLP-1 have been recommended as preferred options for glycemic control in MASH patients with type 2 diabetes mellitus. Meanwhile, the efficacy of sodium-glucose cotransporter 2 inhibitors for MASH has also been reported, primarily in East Asia. Given the diversity in MASLD/MASH pathology among populations, ranging from lean to obese individuals with and without diabetes, population-specific approaches might help elucidate the pathogenesis of MASLD/MASH and develop treatment strategies.