Low socioeconomic status (SES) is associated with adverse cardiovascular risk factor patterns and poor outcomes in patients with diabetes. The aim of this study was to determine whether SES is associated with the control of blood glucose, blood pressure, blood cholesterol (3Bs), and diabetic complications in Chinese adults with type 2 diabetes. Data regarding patients’ demographics, social economics, diabetes complications, and cardiovascular risk profiles were analyzed for 25,454 patients. The outcomes of interest were the proportions of patients with HbA1c <7.0 %, blood pressure <140/80 mmHg, total serum cholesterol <4.5 mmol/L, and diabetes complications. Multivariable logistic regression was used for analysis. Of the 25,454 patients, the least educated patients (1695, 6.7 %) had the highest chances of developing cardiovascular diseases (p = 0.048), cerebrovascular diseases (p < 0.001), and retinopathy (p < 0.001). The patients with lowest household income (10,039, 40.8 %) had the highest prevalence of retinopathy (p < 0.001) and neuropathy (p < 0.001). The most educated patients were more likely than the least educated patients to achieve HbA1c <7.0 % [adjusted odds ratio (OR) 1.38; 95 % confidence interval (95 % CI) 1.22–1.56] and 3B goals (adjusted OR 1.30; 95 % CI 1.11–1.53). The patients with highest household income were more likely to achieve BP < 140/80 mmHg (adjusted OR 1.16; 95 % CI 1.07–1.27), but less likely to reach HbA1c < 7.0 % (adjusted OR 0.90; 95 % CI 0.83–0.98) than those lowest income patients. Low SES was associated with poor metabolic control and more diabetes complications in adult patients in China. Individual diabetes management based on the SES of patients is encouraged.

Aim: Gastrointestinal discomfort is the most common adverse event in metformin treatment for type 2 diabetes. The mechanism of action of metformin is associated with gut microbiota. However, the gut microbial community structure related to metformin-induced gastrointestinal adverse events remains unclear. This study aimed to investigate it.Methods: 50 patients with newly diagnosed diabetes were treated with metformin 1500mg/d for 12 weeks. The patients were divided into two groups according to whether gastrointestinal adverse events occurred (group B) or did not occur (group A) after treatment. The fecal bacterial communities and short-chain fatty acids (SCFAs) were sequenced and compared. 70 diabetes mice were randomly divided into 8 groups and treated with metformin (Met), clindamycin (Clin) and/or SCFA, which were the Met+/Clin+, Met+/Clin-, Met-/Clin+, Met-/Clin-, Met+/SCFA+, Met+/SCFA-, Met-/SCFA+ and Met-/SCFA- group. After 4 weeks of metformin treatment, blood glucose, food intake, fecal SCFAs, gut microbiota and gut hormones were measured.Results: Metformin increased the abundance of Phascolarctobacterium, Intestinimonas and Clostridium III. Functional prediction analysis showed that the propanoate metabolism pathway was significantly up-regulated. The concentrations of acetic acid and propanoic acid in feces were significantly increased. The abundance of Clostridium sensu stricto, Streptococcus and Akkermansia induced by metformin in group B was higher than that in group A. The propanoate metabolism pathway and propanoic acid in feces were significantly up-regulated in group B. In the animal experiments, the food intake decreased and glucose control increased in metformin groups compared with those in the control groups. The total GLP-1 level in the Met+/Clin- group was significantly higher than that in the Met-/Clin- group, while there was no statistical difference between the Met-/Clin- and Met+/Clin+ group. The total GLP-1 level in the Met-/SCFA+ group was significantly higher than that in the Met-/SCFA-group, while the levels of total GLP-1 and active GLP-1 in the Met+/SCFA- group and the Met+/SCFA+ group were significantly higher than those in the Met-/SCFA-group.Conclusions: Our data suggest that metformin promotes the secretion of intestinal hormones such as GLP-1 by increasing the abundance of SCFA-producing bacteria, which not only plays an anti-diabetic role, but also may causes gastrointestinal adverse events.

Background. The Somogyi effect is defined as fasting hyperglycemia secondary to nocturnal hypoglycemia. In past decades, this effect proved to be rare or absent. However, many endocrinologists still believe in this phenomenon in clinical practice. Does the Somogyi effect truly exist? We aimed to answer this question with a study based on a larger sample size. Methods. We collected retrospective CGMs data from 2,600 patients with type 2 diabetes with stable treatment of insulin. Nocturnal hypoglycemia was defined as a CGMs sensor glucose of less than 3.9 mmol/L for at least 15 min between 24:00 and 06:00. Morning fasting glucose was compared between people with nocturnal hypoglycemia and without nocturnal hypoglycemia. Results. Valid CGMs data were obtained on 4,705 of 5,200 nights. Morning fasting glucose was observed lower after nights with nocturnal hypoglycemia compared with nights without hypoglycemia ( ). 84 cases presented fasting glucose of more than 7 mmol/L after nocturnal glucose of less than 3.9 mmol/L. Only 27 cases presented fasting glucose of more than 7 mmol/L after nocturnal glucose of less than 3.0 mmol/L. Fasting glucose values below 3.9 mmol/l in the morning were associated with a 100% risk of nocturnal hypoglycemia, while fasting glucose values over 9.6 mmol/l in the morning were associated with no risk of nocturnal hypoglycemia. Correlation analysis showed that the nocturnal glucose nadir was significantly correlated with fasting glucose levels (r = 0.613, ). Conclusions. Our data provided no support for the existence of the Somogyi effect. If fasting glucose exceeds 9.6 mmol/L, we do not have to worry about asymptomatic nocturnal hypoglycemia in patients with type 2 diabetes.

What is Known and Objective. To explore the effects of traditional Chinese medicine (TCM) combined with levothyroxine (L‐T4) on thyroid autoantibodies, inflammation, and sleep quality in Hashimoto’s thyroiditis patients. Methods. Patients were randomly divided into group A and group B. Group A was treated with L‐T4 alone, while group B was treated with integrated TCM and L‐T4. TCM symptoms were quantified before and after treatment as well as PSQI. Blood samples were taken to detect clinical indicators and thyroid autoantibodies. Cytokines in serum and thyroid tissues were analyzed by ELISA and RT‐PCR. Results and Discussion. Totally, 196 patients were enrolled in the study, and there were no differences between group A and group B at the baseline. TCM treatment effectively reduced the levels of TGAb and TPOAb and was a protective factor for the improvement of Hashimoto’s thyroiditis antibody titers, p < 0.05. The serum expressions of IL‐17A, IL‐6, and IFN‐γ in group B after treatment were lower than those in group A and before, while the IL‐10 level was raised from the baseline, p < 0.05. Similar results were found in the comparison of IL‐17A, IFN‐γ, and IL‐10 in thyroid tissues of group A, B, and control, p < 0.05. Besides, with integrated treatment, all TCM symptoms except for poor memory were improved as well as sleep quality and mood, p < 0.05. However, these changes were not observed before and after treatment with L‐T4 in group A. What is New and Conclusion. The integrated treatment with TCM had a significant effect on thyroid autoantibodies, inflammation, and sleep quality in Hashimoto’s thyroiditis patients and provided a new and effective method for future treatment.

This study was aimed at assessing the impact of the dusk phenomenon on the total glucose exposure in Chinese people with type 2 diabetes. A total of 380 type 2 diabetes who received a retrospective continuous glucose monitoring system (CGMs) for 72 hours were enrolled in our study, 32 of them failed in CGMs. The patients were first divided into 2 groups: dusk phenomenon (n = 95) and non dusk phenomenon group (n = 253). The magnitude of the dusk phenomenon (δDusk) was quantified by pre-dinner glucose minus post-lunch 2 hours glucose. A persistent δDusk ≥ 0 or a once only δDusk < 0 can be diagnosed with the dusk phenomenon. The participants were secondarily matched for the post-lunch 2 hours glucose to assess the impact of the dusk phenomenon on the overall glucose exposure. The impact of the dusk phenomenon was assessed on high-performance liquid chromatography assay (HbA1c) and 24-hour mean glucose. There were 95 of 348 (27.3%) participants with the dusk phenomenon in the overall population, and the median of δDusk level was –0.8 (–1.8, 0.2) mmol/L. The median of glucose differences between the 2 paired groups were 0.4 (–0.4, 1.0)% for HbA_1c, 0.9 (0.2, 1.4) mmol/L for 24 hours mean glucose. The correlation analysis showed no relationship between the magnitude of dawn phenomenon and the dusk phenomenon (r = 0.052, P = .472). The incidence of dusk phenomenon is about 27.3% in people with type 2 diabetes. The impacts of dusk phenomenon on HbA1c and 24-hour mean glucose were about 0.4% and 0.9 mmol/L and the dusk phenomenon was not related with the dawn phenomenon.

Aims/Background: The dawn phenomenon (DP), characterized by spontaneous morning hyperglycemia in type 2 diabetes mellitus (T2DM), may exacerbate post-breakfast glucose excursions. This study investigated the association between DP and postprandial hyperglycemia, referred to as the “extended dawn phenomenon”. Methods: In this cross-sectional study, 500 T2DM patients (glycated hemoglobin A1c (HbA1c) <7.5%) were recruited from Huadong Hospital, Fudan University between 2021 and 2023. A total of 40 participants were excluded due to incomplete data, resulting in 460 patients for final analysis. All participants underwent continuous glucose monitoring (CGM) and were stratified by the magnitude of DP (δDawn, defined as the difference between fasting glucose and nocturnal nadir glucose, with a threshold of ≥1.11 mmol/L). They were then matched by fasting glucose. Glycemic profiles, including peak post-breakfast glucose and time-in-range (TIR; percentage of time within the target glucose range of 3.9–10.0 mmol/L), were compared between groups. Multivariable logistic regression was used to identify determinants of post-breakfast hyperglycemia. Results: Despite comparable fasting glucose levels, patients with DP exhibited higher peak post-breakfast glucose (median (interquartile ranges [IQR]): 9.7 [8.2–10.7] vs 8.9 [8.0–10.0] mmol/L, p = 0.02) and reduced TIR in the overall cohort (94.1% [85.8–100.0] vs 100.0% [92.3–100.0], p < 0.001), although this difference attenuated after matching (p = 0.133). δDawn independently predicted post-breakfast hyperglycemia (odds ratio (OR) = 1.591, 95% confidence interval (CI): 1.283–1.993, p < 0.001), along with HbA1c (OR = 2.322, 95% CI: 1.530–3.566, p < 0.001), homeostatic model assessment for insulin resistance (HOMA-IR) (OR = 1.308, 95% CI: 1.110–1.548, p = 0.001), and homeostasis model assessment of β-cell function (HOMA-β) (OR = 0.990, 95% CI: 0.983–0.997, p = 0.004). Conclusion: DP contributes to prolonged postprandial hyperglycemia, underscoring its role as a potential therapeutic target for optimizing glycemic control in T2DM.