Volume 11 · Issue 08 · 2019

This article reviews the development trend of diabetes and its complications in China, summarizes the international practice and experience of diabetes complications prevention and treatment, and thinks about the work and research of diabetes complications prevention and treatment in China, and puts forward the promotion strategy. It is suggested to improve the awareness rate, treatment rate and risk factor control rate of diabetes; Promote early screening and early detection of diabetic complications; Strengthen lifestyle intervention and diabetes self-management support; Optimize the monitoring system of diabetic complications and influencing factors.

Patients with type 1 diabetes are a high risk group of hypoglycemia, with an early onset age and a long course of disease. Recurrent hypoglycemia not only reduces treatment compliance, but also leads to impaired brain function and increased risk of cardiovascular and cerebrovascular related death. Therefore, how to balance the blood sugar standard and reduce the risk of hypoglycemia has always been the key and difficult point in the treatment of type 1 diabetes. In recent years, the development of medical technology and the deepening of related research have provided new basis and technical support for the prevention and control of hypoglycemia. Based on the latest clinical evidence, this paper systematically expounds the current situation, harm and prevention and control strategies of hypoglycemia in patients with type 1 diabetes, aiming at strengthening the awareness of hypoglycemia prevention in the treatment of type 1 diabetes, and providing clinical practice basis for optimizing the comprehensive management of blood glucose.

20% of islet function in normal people is enough to maintain glucose metabolism, and only if more than 80% of islet function is lost, diabetes will occur. The redundancy system of pancreatic islet reference industrial design belongs to the "five redundancy" framework, so there should be no problem of functional depletion. However, the reality is that most people over 60 years old have glucose metabolism disorders. As for the cause of diabetes, genetics can only explain the incidence of 10% of patients with type 2 diabetes. The vast majority of the causes stem from the fact that modern lifestyle exceeds the physiological limit set by human genes. Specifically, the intake of a large amount of carbohydrates exceeds the load that pancreatic islets can bear. Therefore, a low-carb diet can (partially) restore the original "service life" of pancreatic islets and prevent and treat diabetes.

To compare coverage of diabetes management and blood glucose, blood pressure and blood lipid control and the related influencing factors between patients with diabetes in rural and urban-rural joint areas of Songjiang district, Shanghai.

Two rural and two urban-rural joint towns from Songjiang district were selected to collect relevant socio-economic and community health services information. Demographics, physical examination and laboratory tests results were collected from 3 298 patients with diabetes in communities. The t-test, chi-square test, partial-correlation test and multivariate logistic test were used for statistical analysis.

The number of community health staff per 10 000 people in rural group was higher than that in rural-urban joint group (6.56 vs 1.42). The coverage rate of diabetes management in rural group was higher than that of urban-rural joint group (39.35% vs 18.09%). In rural group, the control rates of blood glucose (HbA_1c<7%), blood pressure (SBP/DBP<130/80 mmHg, 1 mmHg=0.133 kPa), and blood lipid (LDL-C<2.6 mmol/L) in patients with diabetes were 45.9%, 33.2%, and 51.0%, respectively, and only 8.5% of patients achieved all three targets (combined control rate). In urban-rural joint group, these rates were 47.4%, 20.8%, 49.4%, and 4.6% respectively. After controlling for the age, the adjustedOR of uncontrolled combined rate urban-rural joint group was 1.841 (95%CI 1.375-2.464). Based on the multivariate logistic regression analysis, the independent risk factors for the combined control rate included age (OR=1.02, 95%CI 1.01-1.04), duration of diabetes (OR=1.07, 95%CI 1.03-1.10), and central obesity (OR=1.71, 95%CI 1.25-2.34).

The combined control rate of blood glucose, blood pressure and blood lipid in rural group is higher than that in urban-rural joint group. Age, duration of diabetes, and central obesity are important influencing factors of the comprehensive targets control of diabetes management. It is suggested to strengthen the health resources in urban-rural joint areas from the government.

To explore the effects of parental smoking (passive smoking) on glucose metabolism in children and adolescents.

A total of 3 510 subjects (including 1 577 boys and 1 573 girls) aged 6-18 years were recruited from the cohort of the Beijing Child and Adolescent Metabolic Syndrome (BCAMS) Study. Self-reported information on parental smoking, family history of diabetes mellitus, lifestyle and social-economic factors were collected by questionnaire. Fasting plasma glucose (FPG) and fasting insulin were measured and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated to estimate insulin resistance. Subjects with at least one parent smoking were defined as passive smokers, and with no parent smoking were non-exposure. Covariance analysis and Logistic regression models were used to analyze the relationship between passive smoking and glucose metabolism.

After adjusting for potential confounders, passive smokers had higher body mass index (BMI) than non-exposure in both boys and girls (F=12.371-37.871, all P<0.05). Compared with non-exposure, the insulin levels were increased by 16.2% for boys and 8.3% for girls, and the HOMA-IR were increased by 18.5% for boys and 9.4% for girls in passive smokers, with adjusting for age and pubertal stage (F=5.088-15.128, all P<0.05). After further adjusting for lifestyle, social-economic factors, parents′ BMI and family history of diabetes, the differences in insulin and HOMA-IR between the two groups of boys were still statistically significant (F=6.441, 7.067, all P<0.05), while these differences disappeared after further adjustment for BMI (all P>0.05). Regarding FPG, passive smoking was still associated with higher level of FPG with further adjustment for BMI in boys (F=4.998, P<0.05) and the risk of impaired fasting glucose were increased by 42.3% (OR=1.423, 95%CI:1.012-2.002, P<0.05); The difference in FPG between the two groups of girls was not significant (P>0.05).

The relationship between parental smoking and insulin resistance in children and adolescents may be associated with increased obesity, but the effect of parental smoking on FPG in boys is independent of obesity.

To investigate the incidence and risk factors of nocturnal hypoglycemia in type 1 diabetes mellitus (T1DM), and to explore whether nocturnal hypoglycemia can be evaluated by daily capillary blood glucose profiles.

A total of 137 T1DM patients hospitalized in Nanjing Drum Tower Hospital between May 2013 and August 2018 were recruited and received continuous glucose monitoring (CGM) for 3 consecutive days during stable therapy period, meanwhile capillary blood glucose measurements (before and after 3 meals as well as at bedtime) were recorded. From data of CGM, the patients were divided into nocturnal hypoglycemia group and non-hypoglycemia group. The demographics, laboratory measurements and dynamic blood glucose parameters were compared between these two groups. Receiver operating characteristic curve (ROC) was used to analyze the optimal cut-off points of fasting blood glucose, postprandial blood glucose, bedtime blood glucose and BGn was used to predict nocturnal hypoglycemia. Multiple regression analysis was used to evaluate the risk factors of nocturnal hypoglycemia.

(1) Totally, 179 hypoglycemic profiles monitored by CGM were recorded in 137 patients with T1DM, including 50 nocturnal hypoglycemic episodes occurred in 31 patients (27.9%). (2) Logistic regression analysis indicated that glycated hemoglobin A_1c (HbA_1c), glycated albumin and mean blood glucose were independent negatively correlated with nocturnal hypoglycemia (OR=0.784, 0.021, 0.751, P<0.05), while standard deviation of blood glucose and low blood glucose index were independent positively correlated with nocturnal hypoglycemia (OR=1.641, 3.004, P<0.05). (3) Of the daily capillary blood glucose profiles, levels of fasting blood glucose, blood glucose after dinner and blood glucose at bedtime were independently negative correlated with nocturnal hypoglycemia (OR=0.257, 0.685, 0.708, P<0.05). The cut-off values for prediction of nocturnal hypoglycemia were as follows: levels of fasting glucose was 5.8 mmol/L [sensitivity 80%, specificity 90%, area under curvel (AUC) 0.91], levels of blood glucose after dinner was 8.2 mmol/L (sensitivity 57%, specificity 76%, AUC 0.72), levels of blood glucose at bedtime was 6.7 mmol/L (sensitivity 64%, specificity 90%, AUC 0.80). (4) Based on the capillary blood glucose profiles before meals and at bedtime, a model was established for the prediction of nocturnal hypoglycemia: BGn=bedtime blood glucose × (1-SD_SM/MBG_SM). When BGn was 5.2 mmol/L, the specificity increased to 93% and AUC was 0.81.

Nocturnal hypoglycemia is common in patients with T1DM, and individualized blood glucose control targets should be set. Potential increased risk factors of nocturnal hypoglycemia were as follows: the levels of fasting blood glucose in the morning is below 5.8 mmol/L, the levels of blood glucose at bedtime is below 6.7 mmol/L or the model of BGn is below 5.2 mmol/L.

To investigate and compare clinical features between type 2 diabetes mellitus (T2DM) combined with fulminant type 1 diabetes mellitus (FT1DM) and simple FT1DM.

A case of T2DM combined with FT1DM was reported, and the clinical features of T2DM combined with FT1DM were summarized and analyzed. The clinical characteristics between 21 T2DM combined with FT1DM patients and 161 simple FT1DM patients from Japan and 70 simple FT1DM patients from China were compared, and a t test was used for the comparisons.

(1) Most patients with T2DM combined with FT1DM were male (male∶female was 14∶7) and old. The course of FT1DM was (4.4±1.7) days. All patients had negative islet autoantibodies. (2) Compared with simple FT1DM from Japan, patients with T2DM combined with FT1DM were older [(60.1±11.8) vs (39.1±15.7) years, P<0.001], had higher glycated hemoglobin A_1c (HbA_1c) levels (8.2%±1.6% vs 6.4%±0.9%, P<0.001), and were more obese [body mass index (BMI) was (23.7±5.7) vs (20.7±3.9) kg/m², P=0.032]. (3) Compared with domestic FT1DM, the difference was similar, patients with T2DM combined with FT1DM were older [(60.1±11.8) vs (31.7±11.4) years, P<0.001], had higher HbA_1c levels (8.2%±1.6% vs 6.8%±1.1%, P<0.001), and had longer course of disease [(4.4±1.7) vs (3.1±2.4) d, P=0.032], but there was no statistically significant difference in BMI [(23.7±5.7) vs (21.1±3.7) kg/m², P=0.068].

Double diabetes can be manifested as T2DM combined with FT1DM, which is characterized by a longer course of disease, older age, higher BMI and HbA_1c level compared with simple FT1DM.

To explore the association between cardiovascular autonomic neuropathy (CAN) and bone mineral density in patients with type 2 diabetes mellitus (T2DM).

A total of 564 patients with T2DM admitted to the Department of Endocrinology, Drum Tower Hospital of Nanjing University Medical School between Jan. 2016 and Dec. 2017 were recruited. According to the Ewing test method, 467 patients (males over 50 years and menopausal females) and 97 patients (males younger than 50 years and females in childbearing age) were divided into CAN groups and non-CAN group. Dual-energy X-ray absorptiometry was used to measure bone densities in locations of the total hip, lumbar vertebrae (L1-L4) and femoral neck. T values of corresponding site were recorded in males over 50 years and menopausal females, and Z values were recorded in males younger than 50 years and females in childbearing age. The T/Z value, age, disease duration, fasting blood glucose were compared between these two groups. Independent sample t-test and chi-square test were used for comparison, and covariance analysis and rank sum test were used for bone mineral density analysis. Pearson correlation analysis was used to investigate the correlation between bone mineral density and CAN evaluation parameters. Multiple linear regression analysis was used to investigate the influencing factors of bone density T and Z values.

In males over 50 years and menopausal females, T-value of the total hip bone density [-0.300(-0.900, 0.500) vs 0.100(-0.400, 0.800), Z=-4.937, P<0.01], T-value of lumbar spine bone density (0.07±1.42 vs 0.51±1.37, t=3.384, F=5.602, P<0.05), and T-value of femoral neck bone density (-0.75±0.91 vs -0.40±0.92, t=4.069, F=4.484, P<0.05) were significantly lower in the CAN group than those in the non-CAN group. In males younger than 50 years and females in childbearing age, Z-value of the total hip bone density (0.20±0.81 vs 0.57±0.79, t=2.228, F=7.324, P<0.01) and femoral neck bone mineral density [-0.200(-0.600, 0.400) vs 0.250(-0.300, 1.025), Z=2.248, P<0.05] were significantly lower in the CAN group than those in the non-CAN group. After adjusting for the influencing factors such as duration of disease, age, and estradiol, CAN was still a factor influencing the T value (β=-0.256,SE=0.106, β′=-0.142, t=-2.414, P<0.05) and Z value (β=-0.554,SE=0.206, β′=-0.355, t=-2.687, P<0.05) of total hip bone density.

CAN is an important factor to decrease bone density in patients with T2DM.

To develop a simple risk score to screen diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM).

A multicentric community-based cross-sectional study was carried out in central urban China between Aug 2014 and Nov 2015. A total of 713 patients (approximately 70%) were selected using systematic random sampling method as training samples to formulate the risk score, and the remaining 322 patients (approximately 30%) were used as test samples. Age, duration of diabetes mellitus, history of hypertension, body mass index, waist circumference, physical inactivity and diet control were considered as candidate risk factors. β-coefficients derived from a multiple logistic regression model predicting the presence of DKD were used to calculate the risk score. Cross-validation was used to validate the method for establishment of the risk score.

The risk score was composed of age, duration of diabetes mellitus, and history of hypertension. The area under the receiver operating characteristics curve for DKD was 0.723 (95%CI 0.677-0.769). Comparing the Youden′s Index of different values, the optimal cutoff point was 11 to predict DKD. Using the cut-off value of 11 points to screen DKD, the sensitivity and specificity in test samples (63.6% and 75.8%) was similar as that in training samples (66.7% and 72.8%).

The risk score could be a reliable primary assessment tool to screen DKD in Chinese patients with T2DM.

Type 2 diabetes is mainly manifested by β cell function impairment and insulin resistance. More and more studies have confirmed that the pathogenesis of type 2 diabetes is closely related to inflammation. Psoriasis is considered to be an autoimmune disease, and there may be a common inflammatory pathway in the pathogenesis of the two diseases. As a novel hypoglycemic drug, glucagon-like peptide-1 (GLP-1) receptor agonist is widely used in the treatment of type 2 diabetes. It can act on multiple organs and systems (including pancreas, skin, etc.) to produce anti-inflammatory effects. We observed a case of type 2 diabetes complicated with severe pustular psoriasis treated with GLP-1 receptor agonist, and reviewed the literature reports in recent years to provide evidence for exploring the related mechanism of GLP-1 receptor agonist in the treatment of type 2 diabetes complicated with psoriasis.

Diabetic peripheral neuropathy affects the movement, sensory, and autonomic nerves of patients, which leads to abnormal gait in patients, which is manifested by decreased balance ability, changes in plantar pressure, etc. Gait analysis can obtain kinematics, dynamics and electromyographic data of human walking. Patients with diabetic peripheral neuropathy have characteristic changes in gait parameters, which are associated with the occurrence of falls and diabetic foot. Application of gait analysis plays an important role in early recognition, intervention treatment and prognosis follow-up of patients with diabetic peripheral neuropathy.

Good blood glucose control is the foundation of diabetic management, and blood glucose monitoring is an important part of diabetes management. As a classic monitoring method, self-blood glucose monitoring can help patients obtain instantaneous blood glucose. In the past, it was believed that as long as fasting blood glucose, postprandial blood glucose and glycosylated hemoglobin (HbA) can be controlled well_1c), intensive hypoglycemic treatment can be used to manage blood sugar well. However, as people's understanding of blood glucose management in diabetes continues to deepen, it is found that HbA_1cAnd self-blood glucose monitoring is far from meeting the new demand of "patient-centered" individualized blood glucose management today, and it is necessary to find other ways to supplement the shortcomings of both. Continuous glucose monitoring is increasingly widely used, and its blood glucose indicators are abundant, which can show blood glucose fluctuations and predict hypoglycemia, especially the percentage of time within the range of blood glucose reaching the standard, which has become the preferred index for clinical evaluation of blood glucose control level and prediction of the risk of diabetic complications.

This article introduces the contents of the 8th International Diabetic Foot Forum held by the International Diabetic Foot Working Group in The Hague, Netherlands in 2019 on diabetic foot infection. First, it introduces the conference speeches, keynote speeches, case discussions, oral speeches and poster exchanges on diabetic foot infection. Finally, it introduces in detail the background, updated contents and interpretation of main recommended points of the newly released guidelines on diabetic foot infection by the International Diabetic Foot Working Group in 2019.