Prevalence and clinical characteristics of hypertension and metabolic syndrome in newly diagnosed patients with ketosis-onset diabetes: a cross-sectional study

The clinical value of glycosylated hemoglobin level in newly diagnosed ketosis-prone type 2 diabetes

Objective

To evaluate the clinical value of glycosylated hemoglobin (HbA1c) in newly diagnosed ketosis-prone type 2 diabetes (KPD).

Methods

A total of 330 patients with newly diagnosed type 2 diabetes (T2DM) hospitalized in our department with an average age of 48.72 ± 13.07 years old were selected and divided into T2DM group (193 cases) and KPD group (137 cases) according to whether they were combined with ketosis. According to the quartile level of HbA1c, they were divided into group A (HbA1c < 8.90%, 84 cases), group B (8.90%≤HbA1c < 10.70%, 86 cases), group C (10.70%≤HbA1c ≤ 12.40%, 85 cases) and group D (HbA1c > 12.40%, 75 cases). The general clinical features, laboratory indicators and islet function of each group were compared. Spearman correlation analysis was used to explore the correlation between HbA1c and β- Hydroxybutyric acid (β- HB) and islet function. ROC curve was used to analyze the sensitivity and specificity of HbA1c in diagnosing KPD, and the optimal tangent point was obtained.

Results

HbA1c, β-HB, FFA, RBG, insulin dosage, GSP, OGTT (0, 0.5, 1, 2, 3h) in KPD group were significantly higher than those in T2DM group (P< 0.001). HDL-C, IRT (0, 0.5, 1, 2, 3h), HOMA-β, HOMA-IR, HOMA-IS, ΔC30/ΔG30, AUC insulin were significantly lower than those in T2DM group (P< 0.001). With the increase of HbA1c level, the incidence of ketosis, β-HB, FFA and insulin dosage increased, while IRT (0, 0.5, 1, 2, 3h), ΔC30/ΔG30, AUC insulin, HOMA-β and HOMA-IS decreased accordingly (P< 0.001). In all newly diagnosed T2DM patients, Spearman correlation analysis showed that HbA1c was positively correlated with β-HB (r=0.539, P < 0.001), and was negatively correlated with HOMA-β (r=-0.564, P < 0.001), HOMA-IS (r=-0.517, P < 0.01, P < 0.001), HOMA-IR (r=-0.177, P < 0.001), ΔC30/ΔG30 (r=-0.427, P < 0.01) and AUC insulin (r=-0.581, P < 0.001). In ROC curve analysis, the optimal threshold for the diagnosis of KPD was 10.15%, Youden index was 0.616, area under the curve (AUC) was 0.882, sensitivity = 92.70%, specificity = 70.50%.

Conclusion

In newly diagnosed T2DM patients, if HbA1c > 10.15%, it is more likely to develop KPD. Monitoring HbA1c level is conducive to timely detection of high-risk individuals with KPD and taking appropriate measures to prevent the occurrence and development of the disease.

High Atherogenic Risk in Ketosis-Prone Type 2 Diabetic Individuals with Ketosis Episodes: A Cross-Sectional Study

Purpose

Diabetes is an important contributor to the progression of atherosclerosis (AS). We aimed to investigate the correlation between ketosis episodes and lipid-related parameters in patients with new-onset ketosis-prone type 2 diabetes (KPT2D), further attempting to assess the impact of ketosis episodes on AS.

Patients and Methods

A cross-sectional study of 147 subjects with new-onset diabetes was performed, including 65 KPT2D subjects (KPT2D group) and 82 non-ketotic type 2 diabetes (T2D) (T2D group) subjects. Anthropometric and biochemical parameters were measured in all subjects. Calculation of atherogenic index of plasma (AIP) by traditional lipid parameters.

Results

The AIP (P = 0.008) level and the percentage of AIP ≥ 0.24 (P = 0.026) in subjects with KPT2D were higher than in subjects with T2D. The apoA1 (P = 0.001) levels were significantly lower in patients with KPT2D than in patients with T2D. In the KPT2D group, plasma ketones were positively correlated with AIP (P = 0.023) and negatively correlated with apoA1 (P = 0.002). Univariate logistic regression suggested that plasma ketone (OR = 1.704, P = 0.040) was an important related factor for the AS in subjects with KPT2D. Multiple linear regression suggested plasma ketone was significantly positive with AIP (β = 0.437, P = 0.020). In multiple linear regression analysis suggests that apolipoprotein A1 (β = -0.335, P = 0.033) is strongly associated with ketotic episodes in newly diagnosed ketosis-prone type 2 diabetic patients.

Conclusion

Ketosis episodes in patients with KPT2D were significantly and positively associated with elevated AIP levels and reduced apoA1 levels. Frequent ketosis episodes may accelerate the progression of AS.

Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes

Background

Ketosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develop and validate a novel nomogram to predict KPD.

Methods

In this retrospective study, clinical data of a total of 398 newly diagnosed type 2 diabetes in our hospital who met our research standards with an average age of 48.75 ± 13.86 years years old from January 2019 to December 2022 were collected. According to the occurrence of ketosis, there were divided into T2DM groups(228 cases)with an average age of 52.19 ± 12.97 years, of whom 69.74% were male and KPD groups (170cases)with an average age of 44.13 ± 13.72 years, of whom males account for 80.59%. Univariate and multivariate logistic regression analysis was performed to identify the independent influencing factors of KPD and then a novel prediction nomogram model was established based on these independent predictors visually by using R4.3. Verification and evaluation of predictive model performance comprised receiver-operating characteristic (ROC) curve, corrected calibration curve, and clinical decision curve (DCA).

Results

4 primary independent predict factors of KPD were identified by univariate and multivariate logistic regression analysis and entered into the nomogram including age, family history, HbA1c and FFA. The model incorporating these 4 predict factors displayed good discrimination to predict KPD with the area under the ROC curve (AUC) of 0.945. The corrected calibration curve of the nomogram showed good fitting ability with an average absolute error =0.006 < 0.05, indicating a good accuracy. The decision analysis curve (DCA) demonstrated that when the risk threshold was between 5% and 99%, the nomogram model was more practical and accurate.

Conclusion

In our novel prediction nomogram model, we found that age, family history, HbA1c and FFA were the independent predict factors of KPD. The proposed nomogram built by these 4 predictors was well developed and exhibited powerful predictive performance for KPD with high discrimination, good accuracy, and potential clinical applicability, which may be a useful tool for early screening and identification of high-risk population of KPD and therefore help clinicians in making customized treatment strategy.

Development and Validation of a Risk Prediction Model for Ketosis-Prone Type 2 Diabetes Mellitus Among Patients Newly Diagnosed with Type 2 Diabetes Mellitus in China

Background

We established a nomogram for ketosis-prone type 2 diabetes mellitus (KP-T2DM) in the Chinese adult population in order to identify high-risk groups early and intervene in the disease progression in a timely manner.

Methods

We reviewed the medical records of 924 adults with newly diagnosed T2DM from January 2018 to June 2021. All patients were randomly divided into the training and validation sets at a ratio of 7:3. The least absolute shrinkage and selection operator regression analysis method was used to screen the predictors of the training set, and the multivariable logistic regression analysis was used to establish the nomogram prediction model. We verified the prediction model using the receiver operating characteristic (ROC) curve, judged the model's goodness-of-fit using the Hosmer-Lemeshow goodness-of-fit test, and predicted the risk of ketosis using the decision curve analysis.

Results

A total of 21 variables were analyzed, and four predictors-hemoglobin A1C, 2-hour postprandial blood glucose, 2-hour postprandial C-peptide, and age-were established. The area under the ROC curve for the training and validation sets were 0.8172 and 0.8084, respectively. The Hosmer-Lemeshow test showed that the prediction model and validation set have a high degree of fit. The decision curve analysis curve showed that the nomogram had better clinical applicability when the threshold probability of the patients was 0.03-0.79.

Conclusion

The nomogram based on hemoglobin A1C, 2-hour postprandial blood glucose, 2-hour postprandial C-peptide, and age has good performance and can serve as a favorable tool for clinicians to predict KP-T2DM.

Handheld Platform for Sensitive Rosiglitazone Detection: Immunosensor Based on a Time-Based Readout Device

The detection of rosiglitazone (RSG) in food is of great importance since the excessive intake of RSG could cause adverse effects on the human body. Although liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry are the preliminary methods for the detection of hazardous materials in food, they are not suitable for point-of-care or on-site detection. Herein, a time-based readout (TBR) device with an application software (APP) controlled by a smart phone was developed for the sensitive and selective immunoassay of RSG. The homemade TBR device was based on a two-electrode system, where the immune molecule-modified glassy carbon electrode was used as the bioanode, and Prussian blue-modified FTO was used as the cathode. By using Au-modified octahedral Cu₂O with high catalytic activity as mimetic peroxidase, an insulating layer was generated on the cathode by catalyzing 4-chloro-1-naphthol (4-CN) into benzo-4-chlorohexadienone (B4Q). The time to reach a fixed potential varied indirectly with the concentrations of RSG and was recognized by the APP, while the electrochromic property on the cathode was also correspondingly changed. Under optimum conditions, both the square root of the time and the chroma value of the electrochromism exhibited linear responses for the detection of RSG ranging from 5 × 10^-10 to 5 × 10^-7 g/L, while the limits of detection were 8.2 × 10^-11 and 1.3 × 10^-10 g/L, respectively. With easy operation and portability, this TBR device showed a promising application for point-of-care monitoring of hazardous materials in food or the environment.

Association Between Serum Vitamin D Levels and Ketosis Episodes in Hospitalized Patients with Newly Diagnosed Ketosis-Prone Type 2 Diabetes

PURPOSE

This study aimed to investigate the relationship between 25-hydroxyvitamin D (25OHD) and the onset of ketosis in newly diagnosed patients with ketosis-prone type 2 diabetes (KPT2D).

PATIENTS AND METHODS

A total of 162 patients with non-autoimmune newly diagnosed diabetes mellitus were included in this cross-sectional study. Patients were classified into KPT2D (n = 71) or non-ketotic type 2 diabetes (NKT2D, n = 91). Anthropometric parameters, islet functions, biochemical parameters, and body composition were determined in both KPT2D and NKT2D groups. Correlation analysis was performed to determine the associations between 25OHD and plasma ketones. The risk factors associated with ketosis episodes in patients with new-onset KPT2D were evaluated using binary logistic regression analysis.

RESULTS

Vitamin D deficiency was observed in both patients with KPT2D and NKT2D. Compared with the NKT2D group, serum 25OHD values were lower in the participants of the KPT2D group [14.20 (10.68, 19.52) vs 16.98 (13.54,2.96) ng/mL, P = 0.011]. Serum 25OHD was associated with plasma ketones (R = -0.387). Serum 25OHD is an independent protective factor for ketosis or ketoacidosis episodes in patients with new onset of KPT2D (P = 0.037, OR = 0.921).

CONCLUSION

Vitamin D levels are associated with ketosis episodes in patients with KPT2D. Serum 25OHD is an independent protective factor for ketosis episodes in patients with KPT2D.

Prevalence and Gender Differences of Metabolic Syndrome in Young Ketosis-Prone Type 2 Diabetic Individuals: A Retrospective Study

PURPOSE

This study aimed to identify the prevalence and gender differences of metabolic syndrome in young new-onset ketosis-prone type 2 diabetic (KPT2D) individuals.

PATIENTS AND METHODS

A retrospective study was conducted in Shanghai Xin Hua Hospital from 2007 to 2019. A total of 304 patients from 12 to 40 years of age with newly diagnosed diabetes presenting with ketosis were analyzed. The clinical features and laboratory results of KPT2D and type 1 diabetic (T1D) individuals were compared. Prevalence and gender differences of metabolic syndrome in the KPT2D subjects were analyzed.

RESULTS

The prevalence of metabolic syndrome (P < 0.0001) was significantly higher in young KPT2D than T1D subjects. The prevalence of high blood pressure (P < 0.0001), central obesity (P < 0.0001), low plasma HDL-C concentration (P = 0.045), and hypertriglyceridemia (P < 0.0001) was elevated in the KPT2D subjects compared with T1D. Male predominance (89%) was presented in the KPT2D subjects. The prevalence of metabolic syndrome (P = 0.0002) was significantly higher in young male than female KPT2D subjects. The presence of central obesity (P < 0.0001) and high blood pressure (P =0.03) was higher in male KPT2D subjects than female. The presence of serum triglyceride concentrations ≥ 2.3mmol/L was significantly higher (P = 0.011) in male KPT2D subjects than female.

CONCLUSION

Significantly higher prevalence of metabolic syndrome in young KPT2D patients compared with T1D patients could be an important reference for diabetic differential diagnosis. KPT2D presented a higher predominance in young males, who had higher prevalence of metabolic syndrome than young females.