The dramatic increase in the prevalence of obesity and its accompanying comorbidities are major health concerns in Korea. Obesity is defined as a body mass index ≥25 kg/m² in Korea. Current estimates are that 32.8% of adults are obese: 36.1% of men and 29.7% of women. The prevalence of being overweight and obese in national surveys is increasing steadily. Early detection and the proper management of obesity are urgently needed. Weight loss of 5% to 10% is the standard goal. In obese patients, control of cardiovascular risk factors deserves the same emphasis as weight-loss therapy. Since obesity is multifactorial, proper care of obesity requires a coordinated multidisciplinary treatment team, as a single intervention is unlikely to modify the incidence or natural history of obesity.

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Osteoclasts are multinucleated cells formed mainly on bone surfaces in response to cytokines by fusion of bone marrow-derived myeloid lineage precursors that circulate in the blood. Major advances in understanding of the molecular mechanisms regulating osteoclast formation and functions have been made in the past 20 years since the discovery that their formation requires nuclear factor-kappa B (NF-κB) signaling and that this is activated in response to the essential osteoclastogenic cytokine, receptor activator of NF-κB ligand (RANKL), which also controls osteoclast activation to resorb (degrade) bone. These studies have revealed that RANKL and some pro-inflammatory cytokines, including tumor necrosis factor, activate NF-κB and downstream signaling, including c-Fos and nuclear factor of activated T-cells, cytoplasmic 1 (NFATc1), and inhibition of repressors of NFATc1 signaling, to positively regulate osteoclast formation and functions. However, these cytokines also activate NF-κB signaling that can limit osteoclast formation through the NF-κB signaling proteins, TRAF3 and p100, and the suppressors of c-Fos/NFATc1 signaling, IRF8, and RBP-J. This paper reviews current understanding of how NF-κB signaling is involved in the positive and negative regulation of cytokine-mediated osteoclast formation and activation.

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Glucocorticoids are widely used as potent anti-inflammatory and immunosuppressive drugs to treat a wide range of diseases. However, they are also associated with a number of side effects, including new-onset hyperglycemia in patients without a history of diabetes mellitus (DM) or severely uncontrolled hyperglycemia in patients with known DM. Glucocorticoid-induced diabetes mellitus (GIDM) is a common and potentially harmful problem in clinical practice, affecting almost all medical specialties, but is often difficult to detect in clinical settings. However, scientific evidence is lacking regarding the effects of GIDM, as well as strategies for prevention and treatment. Similarly to nonsteroid-related DM, the principles of early detection and risk factor modification apply. Screening for GIDM should be considered in all patients treated with medium to high doses of glucocorticoids. Challenges in the management of GIDM stem from wide fluctuations in postprandial hyperglycemia and the lack of clearly defined treatment protocols. Together with lifestyle measures, hypoglycemic drugs with insulin-sensitizing effects are indicated. However, insulin therapy is often unavoidable, to the point that insulin can be considered the drug of choice. The treatment of GIDM should take into account the degree and pattern of hyperglycemia, as well as the type, dose, and schedule of glucocorticoid used. Moreover, it is essential to instruct the patient and/or the patient's family about how to perform the necessary adjustments. Prospective studies are needed to answer the remaining questions regarding GIDM.

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The prevalence of diabetes is increasing globally, particularly in Asia. According to the 2013 Diabetes Atlas, an estimated 366 million people are affected by diabetes worldwide; 36% of those affected live in the Western Pacific region, with a significant proportion in East Asia. The reasons for this marked increase in the prevalence of diabetes can be extrapolated from several distinct features of the Asian region. First, the two most populated countries, China and India, are located in Asia. Second, Asians have experienced extremely rapid economic growth, including rapid changes in dietary patterns, during the past decades. As a result, Asians tend to have more visceral fat within the same body mass index range compared with Westerners. In addition, increased insulin resistance relative to reduced insulin secretory function is another important feature of Asian individuals with diabetes. Young age of disease onset is also a distinctive characteristic of these patients. Moreover, changing dietary patterns, such as increased consumption of white rice and processed red meat, contributes to the deteriorated lifestyle of this region. Recent studies suggest a distinctive responsiveness to novel anti-diabetic agents in Asia; however, further research and efforts to reverse the increasing prevalence of diabetes are needed worldwide.

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A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

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Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease affecting 30% of the general population and 40% to 70% of obese individuals. Adipose tissue plays a crucial role in its pathogenesis, as it produces and secretes pro- and anti-inflammatory cytokines called adipokines. Adiponectin and leptin have well-determined actions in terms of NAFLD pathophysiology. Adiponectin deficiency is associated with a pro-inflammatory condition, as it is observed in obesity and other metabolic disorders. On the other hand, increased leptin levels, above the normal levels, act as a pro-inflammatory stimulus. Regarding other adipokines (resistin, visfatin, chemerin, retinol-binding protein 4, irisin), data about their contribution to NAFLD pathogenesis and progression are inconclusive. In addition, pharmacological agents like thiazolidinediones (pioglitazone and rosiglitazone), that are used in the management of NAFLD exert favourable effects on adipokine levels, which in turn may contribute to the improvement of liver function. This review summarizes the current knowledge and developments in the association between adipokines and NAFLD and discusses possible therapeutic implications targeting the modulation of adipokine levels as a potential tool for the treatment of NAFLD.

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  World Journal of Gastroenterology.2018; 24(29): 3204.     CrossRef
• Thiazolidinedione induces a therapeutic effect on hepatosteatosis by regulating stearoyl‑CoA desaturase‑1, lipase activity, leptin and resistin
  Hessah Al‑Muzafar, Kamal Amin
  Experimental and Therapeutic Medicine.2018;[Epub]     CrossRef
• Impact of weight cycling on CTRP3 expression, adipose tissue inflammation and insulin sensitivity in C57BL/6J mice
  Xin Li, Li Jiang, Miao Yang, Yu‑Wen Wu, Jia‑Zhong Sun
  Experimental and Therapeutic Medicine.2018;[Epub]     CrossRef
• Analysis of non‑alcoholic fatty liver disease microRNA expression spectra in rat liver tissues
  Jiao Nie, Chang‑Ping Li, Jue‑Hong Li, Xia Chen, Xiaoling Zhong
  Molecular Medicine Reports.2018;[Epub]     CrossRef