Confirmation of the Absence of Somogyi Effect in Patients with Type 2 Diabetes by Retrospective Continuous Glucose Monitoring Systems.

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 (P < 0.001). 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, P < 0.001). 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.

The impact of dusk phenomenon on total glucose exposure in Chinese people with type 2 diabetes.

ABSTRACT: 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 HbA1c, 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.

Self-Monitoring of Blood Glucose to Assess Dawn Phenomenon in Chinese People with Type 2 Diabetes Mellitus.

Aims. We investigated whether self-monitoring of blood glucose could be used to assess dawn phenomenon in Chinese people with type 2 diabetes mellitus (T2DM). Methods. A total of 306 people with T2DM underwent continuous glucose monitoring and self-monitoring of blood glucose for 72 h. A linear model was used to fit the optimal linear formula of the magnitude of dawn phenomenon (ΔDawn) and self-monitoring of blood glucose values. Results. The prevalence of dawn phenomenon was similar within different oral antidiabetic drug groups (42.5%, 31.5%, and 40.9%, P = 0.216). Multiple variable linear regression showed that prebreakfast, prelunch, and predinner glucose measurements were independently and significantly correlated with ΔDawn. The linear formula between ΔDawn and blood glucose was as follows: ΔDawn (mg/dL) = 0.557 × prebreakfast - 0.065 × prelunch - 0.164 × predinner - 20.894 (mg/dL) (adjusted R2 = 0.302, P = 0.000). Conclusions. Dawn phenomenon could be partly assessed by blood glucose self-monitoring in Chinese people with T2DM using the abovementioned formula. The incidence of dawn phenomenon was similar among patients in different oral antidiabetic drug groups.

Cigarette Smoking Is Associated with a Lower Prevalence of Newly Diagnosed Diabetes Screened by OGTT than Non-Smoking in Chinese Men with Normal Weight.

Different studies have produced conflicting results regarding the association between smoking and diabetes mellitus, and detailed analysis of this issue in Chinese males based on nationwide samples is lacking. We explored the association between cigarette smoking and newly-diagnosed diabetes mellitus (NDM) in Chinese males using a population-based case-control analysis; 16,286 male participants without previously diagnosed diabetes were included. Prediabetes and NDM were diagnosed using the oral glucose tolerance test. The cohort included 6,913 non-smokers (42.4%), 1,479 ex-smokers (9.1%) and 7,894 current smokers (48.5%). Age-adjusted glucose concentrations (mmol/L) were significantly lower at fasting and 120 min in current smokers than non-smokers (5.25 vs. 5.30, 6.46 vs. 6.55, respectively, both P < 0.01). After adjustment for demographic and behavioral variables (age, region, alcohol consumption status, physical activity, education, and family history of diabetes), logistic regression revealed significant negative associations between smoking and NDM in males of a normal weight (BMI < 25 kg/m2: adjusted odds ratio [AOR] = 0.75, P = 0.007; waist circumference < 90 cm: AOR = 0.71, P = 0.001) and males living in southern China (AOR = 0.75, P = 0.009), but not in males who were overweight/obese, males with central obesity, or males living in northern China. Compared to non-smokers, current smokers were less likely to be centrally obese or have elevated BP (AOR: 0.82 and 0.74, both P < 0.05), and heavy smokers (≥ 20 pack-years) were less likely to have elevated TG (AOR = 0.84, P = 0.012) among males of a normal weight. There were no significant associations between quitting smoking and metabolic disorders either among males of a normal weight or males who were overweight/obese. In conclusion, smokers have a lower likelihood of NDM than non-smokers among Chinese males with a lower BMI/smaller waist.