The prevalences of impaired fasting glucose and diabetes mellitus in working age men of North China: Anshan Worker Health Survey

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290-0.7392) for the training cohort and 0.7336 (95% CI 0.7285-0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

Epidemiology of Isolated Impaired Glucose Tolerance Among Adults Aged Above 50 Years in Rural China

INTRODUCTION

Isolated impaired glucose tolerance (i-IGT) is a subtype of prediabetes in which an individual demonstrates elevated 2-h post-glucose load glucose levels but normal fasting plasma glucose levels. However, few studies have explored the prevalence and risk factors of i-IGT among adults in rural China. Thus, we aimed to explore the prevalence and risk factors of i-IGT among adults ≥50 years old in a low-income, rural population in China.

MATERIALS AND METHODS

Individuals aged ≥50 years with normal fasting plasma glucose levels were included in the final analysis. Fasting and 2-h venous blood samples were collected to assess the selected parameter measurements.

RESULTS

A total of 2175 individuals were included in this study. The i-IGT prevalence was 22.9% and significantly higher among females than among males (P<0.05). Older age [odds ratio (OR), 1.606; 95% confidence interval (CI), 1.101-2.342; P=0.014), hypertension (OR, 1.554; 95% CI, 1.152-2.019; P=0.004), and central obesity (OR, 1.395; 95% CI, 1.099-1.771; P=0.006) were associated with i-IGT. Moreover, white blood cell (OR, 1.089; 95% CI, 1.009-1.175; P=0.029), high-sensitivity C-reactive protein (OR, 1.049; 95% CI, 1.020-1.078; P=0.001), serum uric acid (OR, 1.0003; 95% CI, 1.001-1.004; P=0.001), triglyceride (OR, 1.540; 95% CI, 1.105-2.147; P=0.011), and alanine aminotransferase (OR, 1.012; 95% CI, 1.004-1.021; P=0.004) levels were also linked to i-IGT in the analyzed population.

CONCLUSION

Health promotion education and a standardized approach to managing body weight, BP, and lipid and uric acid levels would benefit this low-income population in rural China for reducing the risk of cardiovascular disease.

Lifestyle-related predictors affecting prediabetes and diabetes in 20-30-year-old young Korean adults

OBJECTIVES

To investigate lifestyle-related predictors of prediabetes and diabetes in young adults aged 20–39 years using data from the 2014-2016 Korea National Health and Nutrition Examination Survey (KNHANES).

METHODS

This study is a cross-sectional, secondary analysis using the KNHANES data. Participants were classified into normal group (fasting plasma glucose [FPG] <100 mg/dL and/or hemoglobin A1c [HbA1c] <5.7%), prediabetes group (FPG 100-125 mg/dL and/or HbA1c 5.7-6.4%), and diabetes group (FPG ≥126 mg/dL and/or HbA1c ≥ 6.5%). The data were statistically analyzed using SPSS software.

RESULTS

Out of 4,190 participants, 27.7% of men and 16.3% of women were in the prediabetes group and 1.4% of men and 1.3% of women were in the diabetes group. Logistic regression confirmed that age and obesity are predictors of prediabetes and diabetes in both men and women. Additionally low physical activity and low education level are predictors of prediabetes in men and women, respectively (p<0.05).

CONCLUSIONS

This study has found that age and increased obesity are predictors of elevated blood glucose in young men and women in their 20s and 30s. A strategy to lower obesity by promoting physical activity in men in their 30s is essential to prevent metabolic syndrome and progression to prediabetes.

Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China: a multicenter observational study

BACKGROUND

Blood glucose control is closely related to type 2 diabetes mellitus (T2DM) prognosis. This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network (EN) with a machine-learning algorithm to predict glycemic control.

METHODS

Basic information, biochemical indices, and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017. An EN regression was used to address variable collinearity. Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status. Additionally, a stepwise logistic regression was performed to compare the machine learning models.

RESULTS

The well-controlled blood glucose rate was 45.82% in North China. The multivariable analysis found that hypertension history, atherosclerotic cardiovascular disease history, exercise, and total cholesterol were protective factors in glycosylated hemoglobin (HbA1c) control, while central adiposity, family history, T2DM duration, complications, insulin dose, blood pressure, and hypertension were risk factors for elevated HbA1c. Before the dimensional reduction in the EN, the areas under the curve of RF, SVM, and BP were 0.73, 0.61, and 0.70, respectively, while these figures increased to 0.75, 0.72, and 0.72, respectively, after dimensional reduction. Moreover, the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models (the sensitivity and accuracy of logistic were 0.52 and 0.56; RF: 0.79, 0.70; SVM: 0.84, 0.73; BP-ANN: 0.78, 0.73, respectively).

CONCLUSIONS

More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications. The EN and machine learning algorithms are alternative choices, in addition to the traditional logistic model, for building predictive models of blood glucose control in patients with T2DM.

Interaction analysis of systolic blood pressure and glycosylated hemoglobin in diabetic retinopathy: A Chinese sample

Background: The objective of this study was to evaluate (1) the association of blood pressure (BP) profiles and glucose profiles with diabetic retinopathy (DR) and (2) the interaction between BP profiles and glucose profiles in DR in a Chinese sample.

Methods: This was a cross-sectional study of 984 DR patients. Demographic data, BP profiles, glucose profiles, renal function parameters, and medical histories were recorded. All the participants underwent testing for DR with digital nonmydriatic fundus photography and image analysis, using a standardized protocol. A multiple variable logistic regression (MLR) was employed to detect the associations and interactions of BP profiles and glucose profiles with DR.

Results: Systolic blood pressure (SBP) and glycated hemoglobin (HbA1c) were significantly correlated with DR ([Formula: see text] for SBP, [Formula: see text]; [Formula: see text] for HbA1c, [Formula: see text]). An interaction between SBP and HbA1c was detected in a multiple logistic regression (MLR) model after adjustment for relevant potential confounders ([Formula: see text], [Formula: see text]).

Conclusion: The present study suggested that SBP and HbA1c were independently and significantly associated with DR and that SBP interacted with HbA1c to impact on DR.

Sugar in mind: Untangling a sweet and sour relationship beyond type 2 diabetes

It is widely recognised that type 2 diabetes (T2D) represents a major disease burden but it is only recently that its role in neurodegeneration has attracted more attention. This research has shown that T2D is associated with impaired cerebral health, cognitive decline and dementia. However, the impact on the brain of progressive metabolic changes associated with the pre-clinical development of the disease is less clear. The aim of this review is to comprehensively summarise how the emergence of risk factors and co-morbid conditions linked to the development of T2D impact cerebral health. Particular attention is directed at characterising how normal but elevated blood glucose levels in individuals without T2D contribute to neurodegenerative processes, and how the main risk factors for T2D including obesity, physical activity and diet modulate these effects. Where available, evidence from the animal and human literature is contrasted, and sex differences in risk and outcomes are highlighted.

Highly Selective and Potent Human β-Secretase 2 (BACE2) Inhibitors against Type 2 Diabetes: Design, Synthesis, X-ray Structure and Structure-Activity Relationship Studies

Herein we present the design, synthesis, and biological evaluation of potent and highly selective β-secretase 2 (memapsin 1, beta-site amyloid precursor protein cleaving enzyme 2, or BACE 2) inhibitors. BACE2 has been recognized as an exciting new target for type 2 diabetes. The X-ray structure of BACE1 bound to inhibitor 2 a {N³ -[(1S,2R)-1-benzyl-2-hydroxy-3-[[(1S,2S)-2-hydroxy-1-(isobutylcarbamoyl)propyl]amino]propyl]-5-[methyl(methylsulfonyl)amino]-N¹ -[(1R)-1-phenylpropyl]benzene-1,3-dicarboxamide} containing a hydroxyethylamine isostere was determined. Based on this structure, a computational docking study was performed which led to inhibitor 2 a-bound BACE2 models. These were used to optimize the potency and selectivity of inhibitors. A systematic structure-activity relationship study led to the identification of determinants of the inhibitors' potency and selectivity toward the BACE2 enzyme. Inhibitors 2 d [N³ -[(1S,2R)-1-benzyl-2-hydroxy-3-[[(1S,2S)-2-hydroxy-1-(isobutylcarbamoyl)pentyl]amino]propyl]-N¹ -methyl-N¹ -[(1R)-1-phenylpropyl]benzene-1,3-dicarboxamide; K_i =0.031 nm, selectivity over BACE1: ≈174 000-fold] and 3 l [N¹ -((2S,3R)-3-hydroxy-1-phenyl-4-((3-(trifluoromethyl)benzyl)amino)butan-2-yl)-N³ ,5-dimethyl-N³ -((R)-1-phenylethyl)isophthalamide; K_i =1.6 nm, selectivity over BACE1: >500-fold] displayed outstanding potency and selectivity. Inhibitor 3 l is nonpeptide in nature and may pave the way to the development of a new class of potent and selective BACE2 inhibitors with clinical potential.

Influence of Health Behaviors and Occupational Stress on Prediabetic State among Male Office Workers

This study examined the influence of health behaviors and occupational stress on the prediabetic state of male office workers, and identified related risks and influencing factors. The study used a cross-sectional design and performed an integrative analysis on data from regular health checkups, health questionnaires, and a health behavior-related survey of employees of a company, using Spearman&rsquo;s correlation coefficients and multiple logistic regression analysis. The results showed significant relationships of prediabetic state with health behaviors and occupational stress. Among health behaviors, a diet without vegetables and fruits (Odds Ratio (OR) = 3.74, 95% Confidence Interval (CI) = 1.93⁻7.66) was associated with a high risk of prediabetic state. In the subscales on occupational stress, organizational system in the 4th quartile (OR = 4.83, 95% CI = 2.40⁻9.70) was significantly associated with an increased likelihood of prediabetic state. To identify influencing factors of prediabetic state, the multiple logistic regression was performed using regression models. The results showed that dietary habits (&beta; = 1.20, p = 0.002), total occupational stress score (&beta; = 1.33, p = 0.024), and organizational system (&beta; = 1.13, p = 0.009) were significant influencing factors. The present findings indicate that active interventions are needed at workplace for the systematic and comprehensive management of health behaviors and occupational stress that influence prediabetic state of office workers.

Prevalence, awareness, treatment, control of type 2 diabetes mellitus and risk factors in Chinese rural population: the RuralDiab study

The study aimed to investigate prevalence, awareness, treatment and control of type 2 diabetes mellitus (T2DM), and to explore potential risk factors in rural areas of China. A total of 16413 individuals aged 18–74 years in rural districts were recruited from the Rural Diabetes, Obesity and Lifestyle (RuralDiab) study for the epidemiological research. Meanwhile, a meta-analysis including 7 published studies was conducted to validate the result of the cross-sectional study. The rates of crude and age-standardized prevalence, awareness, treatment and control of T2DM were 12.19%, 67.00%, 62.35%, 22.20% and 6.98%, 60.11%, 54.85%, 18.77%, respectively. The prevalence, awareness, treatment and control of T2DM displayed increased trends with age (P_trend < 0.01) and were strongly associated with education, drinking, more vegetable and fruit intake, physical activity, family history of diabetes, body mass index (BMI). The results of this meta-analysis showed that the pooled prevalence, awareness, treatment and control of T2DM in China countryside were 7.3% (5.3–9.4%), 57.3% (36.9–77.6%), 48.4% (32.4–64.5%) and 21.0% (9.9–32.1%), respectively. The prevalence of T2DM was high with inadequate awareness, treatment and control of T2DM in China rural areas. Healthy lifestyles should be advocated to reduce prevalence and improve awareness, treatment, and control of T2DM in Chinese rural residents.

Identification of Risk Factors Affecting Impaired Fasting Glucose and Diabetes in Adult Patients from Northeast China

BACKGROUND

Besides genetic factors, the occurrence of diabetes is influenced by lifestyles and environmental factors as well as trace elements in diet materials. Subjects with impaired fasting glucose (IFG) have an increased risk of developing diabetes mellitus (DM). This study aimed to explore risk factors affecting IFG and diabetes in patients from Northeast China.

METHODS

A population-based, cross-sectional survey of chronic diseases and related risk factors was conducted in Jilin Province of Northeast China. All adult residents, aged 18-79, were invited to participate in this survey using the method of multistage stratified random cluster sampling. One hundred thirty-four patients with IFG or DM and 391 healthy control subjects were recruited. We compared demographic factors, body size measurements, healthy-related behaviors, and hair metallic element contents between IFG/diabetes patients and healthy individuals.

RESULTS

IFG/diabetes patients had a greater weight, waist, hip, and body mass index (BMI) than control subjects. Significant differences in the content of zinc (Zn), potassium (K), copper (Ca), and sodium (Na) as well as Cu/Zn ratios between IFG or DM patients and control subjects (p < 0.05) were also observed. Hair Cu, selenium (Se), and Na contents were positively correlated with blood glucose levels (Cu: rs = 0.135, p = 0.002; Se: rs = 0.110, p = 0.012; Na: rs = 0.091, p = 0.038). Polytomous logistic regression adjusting for age, sex, family history of diabetes and BMI, showed that subjects with high BMI were more likely to develop IFG and DM (IFG: OR = 1.15, OR 95% CI = 1.02-1.29; DM: OR = 1.15, OR 95% CI = 1.01-1.33). Moreover, rarely or never eating fruits was a risk factor for DM (OR = 5.46, OR 95% CI = 1.87-15.98) but not for IFG (OR = 1.70, OR 95% CI = 0.72-4.02). Subjects with abdominal obesity or DM history were more susceptible to DM (abdominal obesity: OR = 2.99, OR 95% CI = 1.07-8.37; DM history: OR = 2.69, OR 95% CI = 1.01-7.20). We found that subjects living in Changling County had a significantly lower chance to suffer from IFG (OR and 95% CI for OR: 0.25, 0.08-0.74). Men and 60-69 years old subjects were at increased risk for IFG (male: OR = 3.51, OR 95% CI = 1.34-9.18; age 60-69: OR = 6.64, OR 95% CI = 1.36-32.47). We did not find significant associations of IFG or DM with certain lifestyles (such as eating more meat, exercise or physical activity, smoking, or alcohol drinking) or the content of some metallic elements (such as iron (Fe), Zn , K, calcium (Ca), Na, or magnesium (Mg)).

CONCLUSIONS

This study demonstrated that less or no fruit eating, DM family history, abdominal obesity conferred vulnerability to DM. Living in Changling County, men and 60-69 years old subjects were found to be risk factors for IFG. Subjects with high BMI were more likely to develop IFG and DM.

Incidence Density and Risk Factors of Diabetic Retinopathy Within Type 2 Diabetes: A Five-Year Cohort Study in China (Report 1)

A prospective study was carried out from August 2010 to August 2014 in the community of Fengyutan (China). Six hundred and twenty two T2D subjects were collected. The incidence density of diabetic retinopathy (DR) was 1.81% person-year (95% confidence interval, CI: 1.21–2.43% person-year). After a Cox regression model analysis and adjusted confounding factors, independent predictors related to the incidence of DR including male gender (adjusted hazard ratios, aHR: 1.47, 95% CI: 1.21–1.62), with hypertension (aHR: 1.49, 95%: 1.12–1.73), duration of diabetes > 10 years (aHR: 2.28, 95%: 2.05–2.42), uncontrolled diabetes (aHR: 1.76, 95%: 1.41–2.01), total cholesterol ≥ 200 mg/dL (aHR: 1.54, 95%: 1.34–1.72) and HbA1c ≥ 7% (mmol/mol) (aHR: 2.12, 95%: 1.87–2.32). Duration of T2D revealed the significantly dose-response relationship to the onset of DR. The incidence density of DR in the Chinese community was relatively low in comparison with other studies. More attention should be paid to the T2D patients, especially of male gender, with hypertension, longer duration of diabetes, uncontrolled diabetes, total cholesterol ≥ 200mg/dL and HbA1c ≥ 7% (mmol/mol).