Serum electrolyte levels in relation to macrovascular complications in Chinese patients with diabetes mellitus

BACKGROUND: The prevalence of diabetes in China is increasing rapidly. However, scarce data are available on serum electrolyte levels in Chinese adults with diabetes, especially in those with cardiovascular complications. This study measured serum electrolyte levels and examined their relationship with macrovascular complications in Chinese adults with diabetes. METHODS: The three gender- and age-matched groups were enrolled into this analysis, which were 1,170 subjects with normal glucose regulation (NGR), 389 with impaired glucose regulation (IGR) and 343 with diabetes. Fasting plasma glucose (FPG), 2-hour post-load plasma glucose (2hPG), glycosylated hemoglobin A1c (HbA1c) and serum electrolyte levels were measured. Data collection included ankle brachial index results. RESULTS: Serum sodium and magnesium levels in the diabetes group were significantly decreased compared to the NGR group (sodium: 141.0 ± 2.4 vs. 142.1 ± 2.0 mmol/l; magnesium: 0.88 ± 0.08 vs. 0.91 ± 0.07 mmol/l, all P < 0.01), while the serum calcium level was significantly increased (2.36 ± 0.11 vs. 2.33 ± 0.09 mmol/l, P < 0.01). Multiple linear regression showed that serum sodium and magnesium levels in the diabetes group were negatively correlated with FPG, 2hPG and HbA1c (sodium: Std β = −0.35, -0.19, -0.25; magnesium: Std β = −0.29, -0.17, -0.34, all P < 0.01), while the serum calcium level was positively correlated with HbA1c (Std β = 0.17, P < 0.05). In diabetic subjects, serum sodium, magnesium and potassium levels were decreased in the subjects with the elevation of estimated glomerular filtration rates (P < 0.05). ANCOVA analysis suggested that serum magnesium level in subjects with diabetic macrovascular complications was significantly decreased compared with diabetic subjects without macrovascular complications after the effect of some possible confounding being removed (P < 0.05). CONCLUSIONS: Serum sodium and magnesium levels were decreased in Chinese subjects with diabetes, while the observed increase in calcium level correlated with increasing glucose level. Diabetic patients with macrovascular complications had lower serum magnesium level than those with no macrovascular complications

Ten-year changes in the prevalence of overweight, obesity and central obesity among the Chinese adults in urban Shanghai, 1998–2007 — comparison of two cross-sectional surveys

BACKGROUND: In China, obesity is expected to increase rapidly in both urban and rural areas. However, there have been no comprehensive reports on secular trends in obesity prevalence among Chinese adults in urban Shanghai, which is the largest city in southern China. METHODS: In 1998–2001 and again in 2007–2008, two independent population-based cross-sectional surveys were conducted in Shanghai to investigate the prevalence of metabolic disorders. These surveys obtained height, waist circumference (WC), and weight measurements for Chinese adults aged between 20 and 74 years who lived in urban communities. From the 1998–2001 survey, 4,894 participants (2,081 men and 2,813 women, mean age: 48.9 years) were recruited, and 4,395 participants (1,599 men and 2,796 women, mean age: 49.8 years) were recruited from the 2007–2008 survey. Using the World Health Organization criteria, overweight was defined as 25 kg/m(2) ≤ BMI < 30 kg/m(2) and obesity as BMI ≥ 30 kg/m(2). Central obesity was defined as WC ≥ 90 cm in men or ≥85 cm in women. The differences in prevalence of obesity, central obesity and overweight between the two surveys were tested using multivariable logistic regression analyses. RESULTS: Compared to the 1998–2001 survey, in the 2007–2008 survey the BMI distribution for men and the WC distribution for both genders is shifted significantly to the right along the x-axis (all p < 0.001). Over the ten years, the prevalence of combined overweight and obesity increased 24% (from 31.5% to 39.1%, p < 0.001) in men, but decreased 8% (from 27.3% to 25.0%; p < 0.01) in women. The prevalence of central obesity increased 40% in men (from 19.5% to 27.3%; p < 0.01), but the increase was not significant in women (15.0% to 17.1%; p = 0.051). In the total population, only central obesity showed a significant change between the populations in the two surveys, increasing 29% (from 17.3% to 22.4%; p < 0.001). CONCLUSIONS: Over this 10 year period, central obesity increased significantly in the Shanghai adult population. However, the prevalence of combined overweight and obesity was significantly increased in men but not in women

Large language models for diabetes training: a prospective study

Diabetes training; Large language models; Primary diabetes careFormació en diabetis; Grans models de llenguatge; Atenció primària de diabetisFormación en diabetes; Grandes modelos de lenguaje; Atención primaria de diabetesDiabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.5, ChatGPT-4.0, Google Bard, LlaMA-7B, LlaMA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, MedGPT, HuatuoGPT, and Chinese LlaMA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China (NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians (PCPs) without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that ChatGPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, LlaMA-7B, and LlaMA2-7B. For the NCE-CPFC examination, ChatGPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, MedGPT, and ChatGPT-3.5 successfully passed, whereas LlaMA2-7B, HuatuoGPT, Chinese LLaMA2-7B, and LlaMA-7B failed. ChatGPT-4.0 (84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination (improving by 1 %–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0509202 and 2023ZD0509201), National Natural Science Foundation of China (62077037,8238810007, 82022012, 81870598, 62272298 and 82388101), the National Key Research and Development Program of China (2022YFC2502800 and 2022YFC2407000), the Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002), the Chinese Academy of Engineering (2022-XY-08), the Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212700) and Beijing Natural Science Foundation (IS23096)

A Microalbuminuria Threshold to Predict the Risk for the Development of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients

OBJECTIVE: To test the hypothesis that a microalbuminuria (MA) threshold can help predict the risk for the development of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM)_ patients. DESIGN: We conducted a cross-sectional study of 4739 subjects with T2DM and a prospective study of 297 subjects with T2DM in China respectively. METHODS: Clinical and laboratory data were collected and biologic risk factors associated with any DR were analysed. RESULTS: In the cross-sectional study, we found that MA was an independent risk factor for DR development; further, when the patients were divided into MA deciles, odds ratio (ORs) of DR for the patients in the sixth MA decile (10.7 mg/24 h) was 1.579-fold (1.161-2.147) compared to that for patients in the first MA decile. Furthermore, the OR of DR increased with a gradual increase in MA levels. Similarly, in the prospective study, during a mean follow-up of 4.5 years, we found that 51 patients (29.0%) of the 176 subjects with high MA level (10.7-30 mg/24 h) developed DR, while 17 patients (14.1%) of the 121 subjects with lower MA (<10.7 mg/24 h) developed DR, and the relative risk ratio of the development of DR is 2.13(95% CI, 1.58-3.62, P<0.001). CONCLUSION: These data suggest that an MA threshold can predict the risk for the development of DR in type 2 diabetes mellitus, although it is still within the traditionally established normal range

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry

Comparative Agreement Analysis of Differences in 1,5-Anhydroglucitol, Glycated Albumin, and Glycated Hemoglobin A1c Levels between Fasting and Postprandial States in Steamed Bread Meal Test

Background. Our previous study indicated that serum 1,5-anhydroglucitol (1,5-AG) levels slightly increased after a glucose load; therefore, this study was conducted to explore short-term changes in 1,5-AG levels after a steamed bread meal test (SBMT) and compare the agreement of 1,5-AG, glycated albumin (GA), and glycated hemoglobin A1c (HbA1c) levels between fasting and postprandial states after an SBMT. Methods. 104 participants were recruited and underwent a 100 g SBMT. Fasting, 30 min, and 120 min of 1,5-AG, GA, and HbA1c were measured. Results. Levels of 1,5-AG slightly increased from 30 to 120 min after an SBMT (P0.05), and Bland-Altman difference plot showed that 100% of data points for HbA1c30 and HbA1c120 fell within the limits of agreement; 94.2%, 96.2%, 95.2%, and 95.2% of data points for 1,5-AG30, 1,5-AG120, GA30, and GA120 fell within the limits of agreement, respectively. Conclusion. Agreement analyses indicated good stability of 1,5-AG, GA, and HbA1c levels after the SBMT. HbA1c had an optimal stability, which was superior to that of GA or 1,5-AG

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools. In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation, R2/Q2 plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed. RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis