Since the role of the liver in metabolic dysfunction, including type 2 diabetes mellitus, was demonstrated, studies on non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD) have shown associations between fatty liver disease and other metabolic diseases. Unlike the exclusionary diagnostic criteria of NAFLD, MAFLD diagnosis is based on the presence of metabolic dysregulation in fatty liver disease. Renaming NAFLD as MAFLD also introduced simpler diagnostic criteria. In 2023, a new nomenclature, steatotic liver disease (SLD), was proposed. Similar to MAFLD, SLD diagnosis is based on the presence of hepatic steatosis with at least one cardiometabolic dysfunction. SLD is categorized into metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related/-associated liver disease, alcoholrelated liver disease, specific etiology SLD, and cryptogenic SLD. The term MASLD has been adopted by a number of leading national and international societies due to its concise diagnostic criteria, exclusion of other concomitant liver diseases, and lack of stigmatizing terms. This article reviews the diagnostic criteria, clinical relevance, and differences among NAFLD, MAFLD, and MASLD from a diabetologist’s perspective and provides a rationale for adopting SLD/MASLD in the Fatty Liver Research Group of the Korean Diabetes Association.

Since the role of the liver in metabolic dysfunction, including type 2 diabetes mellitus, was demonstrated, studies on non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD) have shown associations between fatty liver disease and other metabolic diseases. Unlike the exclusionary diagnostic criteria of NAFLD, MAFLD diagnosis is based on the presence of metabolic dysregulation in fatty liver disease. Renaming NAFLD as MAFLD also introduced simpler diagnostic criteria. In 2023, a new nomenclature, steatotic liver disease (SLD), was proposed. Similar to MAFLD, SLD diagnosis is based on the presence of hepatic steatosis with at least one cardiometabolic dysfunction. SLD is categorized into metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related/-associated liver disease, alcoholrelated liver disease, specific etiology SLD, and cryptogenic SLD. The term MASLD has been adopted by a number of leading national and international societies due to its concise diagnostic criteria, exclusion of other concomitant liver diseases, and lack of stigmatizing terms. This article reviews the diagnostic criteria, clinical relevance, and differences among NAFLD, MAFLD, and MASLD from a diabetologist’s perspective and provides a rationale for adopting SLD/MASLD in the Fatty Liver Research Group of the Korean Diabetes Association.

Since the role of the liver in metabolic dysfunction, including type 2 diabetes mellitus, was demonstrated, studies on non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD) have shown associations between fatty liver disease and other metabolic diseases. Unlike the exclusionary diagnostic criteria of NAFLD, MAFLD diagnosis is based on the presence of metabolic dysregulation in fatty liver disease. Renaming NAFLD as MAFLD also introduced simpler diagnostic criteria. In 2023, a new nomenclature, steatotic liver disease (SLD), was proposed. Similar to MAFLD, SLD diagnosis is based on the presence of hepatic steatosis with at least one cardiometabolic dysfunction. SLD is categorized into metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related/-associated liver disease, alcoholrelated liver disease, specific etiology SLD, and cryptogenic SLD. The term MASLD has been adopted by a number of leading national and international societies due to its concise diagnostic criteria, exclusion of other concomitant liver diseases, and lack of stigmatizing terms. This article reviews the diagnostic criteria, clinical relevance, and differences among NAFLD, MAFLD, and MASLD from a diabetologist’s perspective and provides a rationale for adopting SLD/MASLD in the Fatty Liver Research Group of the Korean Diabetes Association.

Since the role of the liver in metabolic dysfunction, including type 2 diabetes mellitus, was demonstrated, studies on non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD) have shown associations between fatty liver disease and other metabolic diseases. Unlike the exclusionary diagnostic criteria of NAFLD, MAFLD diagnosis is based on the presence of metabolic dysregulation in fatty liver disease. Renaming NAFLD as MAFLD also introduced simpler diagnostic criteria. In 2023, a new nomenclature, steatotic liver disease (SLD), was proposed. Similar to MAFLD, SLD diagnosis is based on the presence of hepatic steatosis with at least one cardiometabolic dysfunction. SLD is categorized into metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related/-associated liver disease, alcoholrelated liver disease, specific etiology SLD, and cryptogenic SLD. The term MASLD has been adopted by a number of leading national and international societies due to its concise diagnostic criteria, exclusion of other concomitant liver diseases, and lack of stigmatizing terms. This article reviews the diagnostic criteria, clinical relevance, and differences among NAFLD, MAFLD, and MASLD from a diabetologist’s perspective and provides a rationale for adopting SLD/MASLD in the Fatty Liver Research Group of the Korean Diabetes Association.

Novel strategies are required to reduce the risk of developing diabetes and/or clinical outcomes and complications of diabetes. In this regard, the role of the circadian system may be a potential candidate for the prevention of diabetes. We reviewed evidence from animal, clinical, and epidemiological studies linking the circadian system to various aspects of the pathophysiology and clinical outcomes of diabetes. The circadian clock governs genetic, metabolic, hormonal, and behavioral signals in anticipation of cyclic 24-hour events through interactions between a “central clock” in the suprachiasmatic nucleus and “peripheral clocks” in the whole body. Currently, circadian rhythmicity in humans can be subjectively or objectively assessed by measuring melatonin and glucocorticoid levels, core body temperature, peripheral blood, oral mucosa, hair follicles, rest-activity cycles, sleep diaries, and circadian chronotypes. In this review, we summarized various circadian misalignments, such as altered light-dark, sleep-wake, rest-activity, fasting-feeding, shift work, evening chronotype, and social jetlag, as well as mutations in clock genes that could contribute to the development of diabetes and poor glycemic status in patients with diabetes. Targeting critical components of the circadian system could deliver potential candidates for the treatment and prevention of type 2 diabetes mellitus in the future.

Background: We aimed to investigate the moderating effects of obesity, age, and sex on the association between sleep duration and the development of diabetes in Asians. Methods: We analyzed data from a cohort of the Korean Genome and Epidemiology Study conducted from 2001 to 2020. After excluding shift workers and those with diabetes at baseline, 7,407 participants were stratified into three groups according to sleep duration: ≤5 hoursight, >5 to 7 hoursight (reference), and >7 hoursight. The Cox proportional hazards analyses were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes mellitus (T2DM). Subgroup analyses were performed according to obesity, age, and sex. Results: During 16 years of follow-up, 2,024 cases of T2DM were identified. Individuals who slept ≤5 hight had a higher risk of incident diabetes than the reference group (HR, 1.17; 95% CI, 1.02 to 1.33). The subgroup analysis observed a valid interaction with sleep duration only for obesity. A higher risk of T2DM was observed in the ≤5 hoursight group in non-obese individuals, men, and those aged <60 years, and in the >7 hoursight group in obese individuals (HRs were 1.34 [95% CI, 1.11 to 1.61], 1.22 [95% CI, 1 to 1.49], and 1.18 [95% CI, 1.01 to 1.39], respectively). Conclusion This study confirmed the effect of sleep deprivation on the risk of T2DM throughout the 16-year follow-up period. This impact was confined to non-obese or young individuals and men. We observed a significant interaction between sleep duration and obesity.

Background: There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods: This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics. Results: Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without. Conclusion We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV.

Alcoholic liver cirrhosis (ALC) is caused by chronic alcohol overconsumption and might be linked to dysregulated immune responses in the gut-liver axis. However, there is a lack of comprehensive research on levels and functions of innate lymphocytes including mucosalassociated invariant T (MAIT) cells, NKT cells, and NK (NK) cells in ALC patients. Thus, the aim of this study was to examine the levels and function of these cells, evaluate their clinical relevance, and explore their immunologic roles in the pathogenesis of ALC. Peripheral blood samples from ALC patients (n = 31) and healthy controls (HCs, n = 31) were collected. MAIT cells, NKT cells, NK cells, cytokines, CD69, PD-1, and lymphocyte-activation gene 3 (LAG-3) levels were measured by flow cytometry. Percentages and numbers of circulating MAIT cells, NKT cells, and NK cells were significantly reduced in ALC patients than in HCs. MAIT cell exhibited increased production of IL-17 and expression levels of CD69, PD-1, and LAG-3. NKT cells displayed decreased production of IFN-γ and IL-4. NK cells showed elevated CD69 expression. Absolute MAIT cell levels were positively correlated with lymphocyte count but negatively correlated with C-reactive protein. In addition, NKT cell levels were negatively correlated with hemoglobin levels. Furthermore, log-transformed absolute MAIT cell levels were negatively correlated with the Age, Bilirubin, INR, and Creatinine score. This study demonstrates that circulating MAIT cells, NKT cells, and NK cells are numerically deficient in ALC patients, and the degree of cytokine production and activation status also changed. Besides, some of their deficiencies are related to several clinical parameters. These findings provide important information about immune responses of ALC patients.

Background: We investigated whether fasting glucose (FG) variability could predict the risk of dementia. Methods: This cohort study analyzed data from Koreans with diabetes after at least three health examinations by the Korean National Health Insurance Corporation between 2005 and 2010, which included at least one examination between 2009 and 2010. A total of 769,554 individuals were included, excluding those aged <40 years and those with dementia. FG variability was measured using the variability independent of the mean (FG-VIM). The incidence of dementia was defined by the International Classification of Diseases 10th Revision codes and prescription of anti-dementia medication and was subdivided into Alzheimer’s disease (AD) and vascular dementia (VD). Results: During the 6.9-year follow-up, 54,837, 41,032, and 6,892 cases of all-cause dementia, AD, and VD, respectively, were identified. Cox proportional regression analyses showed that as the FG-VIM quartile increased, the risk of dementia serially increased after adjustment for metabolic factors, income status, and diabetes-related characteristics, including the mean FG. Participants in FG-VIM quartile 4 showed a 18%, 19%, and 17% higher risk for all-cause dementia, AD, and VD, respectively, than those in quartile 1; this particularly included non-obese patients with a longer duration of diabetes, high FG levels, dyslipidemia, and those taking glucose-lowering medications. Conversely, the baseline FG status and dementia showed a U-shaped association. Conclusion Increased FG variability over 5 years can predict the risk of dementia in individuals with diabetes in Korea. This finding was more pronounced in patients with less favorable metabolic profiles.

Self-monitoring of capillary blood glucose is important for controlling diabetes. Recently, a laser lancing device (LMT-1000) that can collect capillary blood without skin puncture was developed. We enrolled 150 patients with type 1 or 2 diabetes mellitus. Blood sampling was performed on the same finger on each hand using the LMT-1000 or a conventional lancet. The primary outcome was correlation between glucose values using the LMT-1000 and that using a lancet. And we compared the pain and satisfaction of the procedures. The capillary blood sampling success rates with the LMT-1000 and lancet were 99.3% and 100%, respectively. There was a positive correlation (r=0.974, P<0.001) between mean blood glucose levels in the LMT-1000 (175.8±63.0 mg/dL) and conventional lancet samples (172.5±63.6 mg/dL). LMT-1000 reduced puncture pain by 75.0% and increased satisfaction by 80.0% compared to a lancet. We demonstrated considerable consistency in blood glucose measurements between samples from the LMT-1000 and a lancet, but improved satisfaction and clinically significant pain reduction were observed with the LMT-1000 compared to those with a lancet.