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: Guidelines for switching to triple combination therapy directly after monotherapy failure are limited. This study investigated the efficacy, long-term sustainability, and safety of either mono or dual add-on therapy using alogliptin and pioglitazone for patients with type 2 diabetes mellitus (T2DM) who did not achieve their target glycemic range with metformin monotherapy. Methods: The Practical Evidence of Antidiabetic Combination Therapy in Korea (PEAK) was a multicenter, placebo-controlled, double-blind, randomized trial. A total of 214 participants were randomized to receive alogliptin+pioglitazone (Alo+Pio group, n=70), alogliptin (Alo group, n=75), or pioglitazone (Pio group, n=69). The primary outcome was the difference in glycosylated hemoglobin (HbA1c) levels between the three groups at baseline to 24 weeks. For durability, the achievement of HbA1c levels <7% and <6.5% was compared in each group. The number of adverse events was investigated for safety. Results: After 24 weeks of treatment, the change of HbA1c in the Alo+Pio, Alo, and Pio groups were –1.38%±0.08%, –1.03%±0.08%, and –0.84%±0.08%, respectively. The Alo+Pio group had significantly lower HbA1c levels than the other groups (P=0.0063, P<0.0001) and had a higher proportion of patients with target HbA1c achievement. In addition, insulin sensitivity and β-cell function, lipid profiles, and other metabolic indicators were also improved. There were no significant safety issues in patients treated with triple combination therapy. Conclusion Early combination triple therapy showed better efficacy and durability than the single add-on (dual) therapy. Therefore, combination therapy with metformin, alogliptin, and pioglitazone is a valuable early treatment option for T2DM poorly controlled with metformin monotherapy.

Diabetes mellitus is a major risk factor for the development of heart failure. Furthermore, the prognosis of heart failure is worse in patients with diabetes mellitus than in those without it. Therefore, early diagnosis and proper management of heart failure in patients with diabetes mellitus are important. This review discusses the current criteria for diagnosis and screening tools for heart failure and the currently recommended pharmacological therapies for heart failure. We also highlight the effects of anti-diabetic medications on heart failure.

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.

Abnormal aggregation of α-synuclein is a key element in the pathogenesis of several neurodegenerative diseases, including Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy. α-synuclein aggregation spreads through various brain regions during the course of disease progression, a propagation that is thought to be mediated by the secretion and subsequent uptake of extracellular α-synuclein aggregates between neuronal cells. Thus, aggregated forms of this protein have emerged as promising targets for disease-modifying therapy for PD and related diseases. Here, we generated and characterized conformation-specific antibodies that preferentially recognize aggregated forms of α-synuclein. These antibodies promoted phagocytosis of extracellular α-synuclein aggregates by microglial cells and interfered with cell-to-cell propagation of α-synuclein. In an α-synuclein transgenic model, passive immunization with aggregate-specific antibodies significantly ameliorated pathological phenotypes, reducing α-synuclein aggregation, gliosis, inflammation, and neuronal loss. These results suggest that conformation-specific antibodies targeting α-synuclein aggregates are promising therapeutic agents for PD and related synucleinopathies.