Volume 18 · Issue 03 · 2026

Type 2 diabetes mellitus (T2DM) has become a major global public health challenge. The unique geography and climate of cold regions confer significant regional heterogeneity on the occurrence and progression of T2DM. Although preliminary data have been accumulated in domestic and international studies on T2DM in cold areas, the absence of systematic comparative analyses has prevented the full exploitation of its potential clinical value. To address this gap, The Chinese Collaborative Group on Type 2 Diabetes Mellitus Management in Cold Regions of China convened experts in relevant fields to develop the Expert consensus on differences in clinical characteristics and management models of type 2 diabetes mellitus in cold regions between China and international communities. This consensus systematically compares the similarities and differences between Chinese and international cold regions T2DM from the perspectives of epidemiology, clinical characteristics, risk factors, and management models, thereby providing new perspectives and strategies for T2DM prevention and control in China′s cold regions.

To investigate the correlation between elevated blood urea nitrogen (BUN) levels and peripheral nerve dysfunction in patients with type 2 diabetes mellitus (T2DM) and normal glomerular filtration rate.

This was a prospective cohort study. Patients with T2DM and normal estimated glomerular filtration rate (eGFR) [≥90 ml·min^-1·(1.73 m²)^-1] were consecutively recruited through convenience sampling from the Department of Endocrinology at Nantong First People′s Hospital between January 2021 and May 2025. Data on height, weight, BUN levels, albumin, and glycated hemoglobin A_1c (HbA_1c) were collected. Body mass index (BMI) was calculated on the basis of the recorded height and weight. Electromyography was utilized to assess parameters of peripheral nerve function, specifically nerve latency, amplitude, and conduction velocity (NCV) of the peripheral nerves, including the median nerve (MN), ulnar nerve (UN), common peroneal nerve (CPN), posterior tibial nerve (PTN), superficial peroneal nerve (SPN), and sural nerve (SN). After the standardization of functional parameters using Z-scores, composite Z-scores were calculated for nerve latency, amplitude, and NCV. Furthermore, an annual follow-up was performed with the enrolled patients. All subjects were divided into four subgroups based on the quartiles of baseline BUN levels: Q₁ group (BUN range: 1.80-4.68 mmol/L), Q₂ group (BUN range: 4.69-5.77 mmol/L), Q₃ group (BUN range: 5.78-6.91 mmol/L) and Q₄ group (BUN range: 6.92-12.86 mmol/L). One-way analysis of variance with linear polynomial contrasts, the Jonckheere-Terpstra test, and the chi-squared test with linear-by-linear association were employed to assess the trend in clinical data corresponding to the increasing quartiles of baseline BUN levels. Additionally, Pearson′s correlation analysis was conducted to evaluate the relationship between baseline BUN levels and peripheral nerve function parameters. Furthermore, multivariate linear regression analysis was utilized to determine whether baseline BUN serves as an independent factor associated with peripheral nerve dysfunction (prolonged latency, decreased amplitude, and reduced NCV) in patients with T2DM, as well as to investigate the potential association between elevated baseline BUN levels and the progression of peripheral nerve dysfunction in these patients.

A total of 1 338 qualified patients with T2DM were recruited, with 333 patients in Q₁ group, 334 patients in Q₂ group, 335 patients in Q₃ group and 336 patients in Q₄ group. From Q₁, Q₂, Q₃ to Q₄ group, the overall composite Z-score for latency showed a significant increasing trend, whereas the overall composite Z-scores for amplitude and NCV exhibited a significant decreasing trend (P for trend <0.001). Moreover, Pearson′s correlation analysis showed that the baseline BUN level was positively correlated with overall composite Z-score of the neural latency (r=0.305, P<0.001) and negatively correlated with overall composite Z-scores of the neural amplitude and NCV (r=-0.315 and -0.280, both P<0.001). Furthermore, multivariate linear regression analysis revealed that elevated baseline BUN levels were linked to higher overall composite Z-score for latency (β=0.170, t=4.895, P<0.001) and lower overall composite Z-scores for amplitude (β=-0.188, t=-5.866, P<0.001) and NCV (β=-0.154, t=-4.681, P<0.001) independent of BMI, albumin levels, HbA_1c and other clinical covariates. Among the recruited patients, 187 completed the annual follow-up. After controlling for BMI, albumin, HbA_1c and other clinical variables through multivariate linear regression analysis, elevated baseline BUN levels were associated with a decline in NCV in the lower limbs: CPN motor NCV (β=-0.241, t=-3.026, P=0.003), PTN motor NCV (β=-0.210, t=-2.563, P=0.012), SPN sensory NCV (β=-0.274, t=-3.322, P=0.001), and SN sensory NCV (β=-0.209, t=-2.475, P=0.015).

Elevated baseline BUN levels were not only independently associated with increased nerve latency and decreased nerve amplitude and NCV, but also can predict the decline in NCV in the lower limbs at the annual follow-up in patients with T2DM and normal eGFR. These findings suggest that elevated BUN levels represent a significant risk factor for peripheral nerve dysfunction in these patients.

To investigate the association between serum EPS-15-interacting protein 1 (EPSIN1) and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM), and to explore population heterogeneity.

This was a cross-sectional study. Participants were from two independent cross-sectional datasets (a discovery set and a validation set). Discovery set: from March 2024 to March 2025, a total of 268 hospitalized T2DM patients from the Department of Endocrinology of Gansu Provincial Hospital were consecutively enrolled. During the same period, 137 healthy individuals from the hospital′s health check-up center were included as a normal control (NC) group. T2DM patients were divided into the T2DM-only group (144 cases) and the DR group (124 cases). The DR group was further divided into non-proliferative DR (NPDR, 64 cases) and proliferative DR (PDR, 60 cases) subgroups. Validation set: to evaluate the discriminative ability of EPSIN1 among T2DM patients, 102 T2DM patients from the same department were enrolled from April to October 2025. Serum levels of EPSIN1 and the following clinical biochemical indicators were measured: triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (Scr), serum uric acid (SUA), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), fasting plasma glucose (FPG), fasting insulin (FINS), and glycated hemoglobin A_1c (HbA_1c). The estimated glomerular filtration rate (eGFR) was calculated using a formula, and the homeostasis model assessment of insulin resistance (HOMA-IR) was computed. Pearson correlation analysis was used to examine the associations between EPSIN1 and general characteristics/biochemical indicators. Univariate and multivariate logistic stepwise regression analyses were performed to identify influencing factors for DR. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of EPSIN1 for DR. Subgroup analysis and interaction tests were conducted to explore heterogeneity.

In the discovery set, EPSIN1 levels were positively correlated with age (r=0.21), diabetes duration (r=0.43), and SUA (r=0.27), and negatively correlated with eGFR (r=-0.25) (all P<0.001). Multivariate logistic regression showed that SUA (OR=1.02, 95%CI 1.01-1.03), eGFR (OR=0.96, 95%CI 0.92-0.98), and EPSIN1 (OR=1.05, 95%CI 1.04-1.07) were influencing factors for DR. EPSIN1 levels increased progressively with DR progression (F=366.867, P<0.001). The ROC curve showed that the area under the curve (AUC) of EPSIN1 for diagnosing DR was 0.82 (95%CI 0.75-0.89), with a sensitivity of 80.90%, a specificity of 82.10%, and an optimal cutoff value of 519.76 ng/L. This efficacy was confirmed in the validation set (AUC=0.80, 95%CI 0.69-0.90). Subgroup analysis revealed that the association between EPSIN1 and DR was stronger in females (OR=1.08, 95%CI 1.04-1.11), patients with diabetes duration ≤7 years (OR=1.08, 95%CI 1.04-1.13), aged ≥55 years (OR=1.08, 95%CI 1.04-1.13), and patients with HbA_1c<8.58% (OR=1.09, 95%CI 1.04-1.13).

Serum EPSIN1 is an influencing factor for DR in T2DM patients and is positively correlated with DR severity. EPSIN1 demonstrates good diagnostic performance, which was validated in an independent set. Moreover, the association between EPSIN1 and DR is more significant in female T2DM patients, those with a disease duration ≤7 years, aged ≥55 years, and with HbA_1c<8.58%.

To investigate the correlation between the free triiodothyronine/free thyroxine (FT3/FT4) ratio and body composition distribution as well as insulin resistance (IR) in euthyroid patients with type 2 diabetes mellitus (T2DM).

This cross-sectional study enrolled adult T2DM patients with normal thyroid function and complete clinical data, hospitalized in the Metabolic Diseases Department of Chu Hsien-I Memorial Hospital of Tianjin Medical University between August 2016 and October 2024. Data were collected on FT3, FT4, glycated hemoglobin A_1c (HbA_1c), fasting plasma glucose (FPG), fasting C-peptide (FCP), height and muscle mass of the upper limbs, lower limbs, and trunk. The limb-to-trunk muscle ratio and skeletal muscle index (SMI) were calculated. The adjusted homeostasis model assessment of insulin resistance (HOMA-IR) was derived using FPG and FCP values. Participants were divided into four groups based on quartiles of FT3/FT4: Q₁ group (FT3/FT4<0.25), Q₂ group (0.25≤FT3/FT4<0.31), Q₃ group (0.31≤FT3/FT4<0.42), Q₄ group (FT3/FT4≥0.42). Intergroup comparisons were performed using one-way analysis of variance (ANOVA), Kruskal-Wallis H test, and χ² test. Ordered logistic regression was employed to identify factors influencing FT3/FT4.

A total of 295 euthyroid T2DM patients were included. Significant differences were observed among the four groups in adjusted HOMA-IR (H=61.544), HbA_1c (F=3.147), limb-to-trunk muscle ratio (F=139.086), and SMI (F=7.190) (P<0.05). Ordered logistic regression revealed that adjusted HOMA-IR (OR=1.339, 95%CI 1.024-1.752), HbA_1c (OR=0.815, 95%CI 0.705-0.941), limb-to-trunk muscle ratio (OR=0.214, 95%CI 0.154-0.298), and SMI (OR=1.343, 95%CI 1.172-1.537) were influencing factors of FT3/FT4.

In euthyroid T2DM patients, the limb-to-trunk muscle ratio was negatively associated with the FT3/FT4 ratio, whereas SMI and adjusted HOMA-IR were positively associated with FT3/FT4.

To investigate the joint evaluative value of the triglyceride-glucose (TyG) index and insulin dose adjustment consistency rate for glycemic attainment during short-term intensive insulin therapy (SIIT) in patients with newly diagnosed type 2 diabetes mellitus (T2DM), and to construct a stratified intervention model.

This study employed a cross-sectional design. Newly diagnosed T2DM patients who were hospitalized and received SIIT in the Endocrinology Departments of the First Affiliated Hospital of the University of Science and Technology of China and Changshu First People′s Hospital between January 2021 and February 2023 were enrolled. Data on glycated hemoglobin A_1c (HbA_1c), triglyceride (TG), fasting plasma glucose (FPG), and seven-point daily fingertip blood glucose measurements were collected. The basal/bolus insulin dose adjustment consistency rate, TyG index, and calculated time in range (cTIR) were calculated. Based on cTIR results, participants were divided into two groups: the cTIR-achieved group (cTIR≥70%) and the cTIR-not-achieved group (cTIR<70%). Differences between the two groups were compared using the two independent samples t test, Mann-Whitney U test, or chi-square test as appropriate. Binary logistic regression analysis was performed to identify factors associated with achieving glycemic target. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated to evaluate the value of combining the TyG index and insulin dose consistency rate of bolus insulin adjustments in predicting whether patients would achieve glycemic target.

A total of 224 newly diagnosed T2DM patients were finally included, with 74 in the cTIR-achieved group and 150 in the cTIR-not-achieved group. Compared to the cTIR-not-achieved group, the cTIR-achieved group had significantly lower HbA_1c and TyG index at disease onset (both P<0.05). Multivariate binary logistic regression analysis revealed that bolus insulin dose adjustment consistency (OR=1.058, 95%CI 1.033-1.087), age (OR=0.961, 95%CI 0.934-0.987), HbA_1c level (OR=0.803, 95%CI 0.656-0.967), and TyG index (OR=0.410, 95%CI 0.243-0.659) were independent influencing factors for achieving glycemic target after SIIT. The model constructed based on the TyG index and bolus insulin adjustment consistency yielded an AUC of 0.807 (95%CI 0.731-0.884) for assessing in-hospital glycemic target achievement.

The combination of the TyG index and insulin dose adjustment consistency rate effectively enhances the assessment for achieving glycemic targets with SIIT in newly diagnosed T2DM patients.

To develop and validate a nomogram model for moderate-to-severe hepatic steatosis in patients with type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD).

This was a cross-sectional study. A total of 213 T2DM patients admitted to China-Japan Union Hospital of Jilin University from February 2025 to October 2025 were prospectively enrolled. Clinical data such as age, sex, body mass index (BMI), disease duration, and laboratory indicators including alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), glycated hemoglobin A_1c (HbA_1c), uric acid (UA) were collected. All patients underwent ultrasound attenuation imaging (ATI) to measure the attenuation coefficient (AC) and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) examinations. Moderate-to-severe hepatic steatosis was defined as MRI-PDFF≥11.3%. Patients were randomly divided into a training set (152 cases) and a validation set (61 cases) at a ratio of 7∶3 based on the caret package in R language. In the training set, independent predictors were screened using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis to construct a nomogram model. The model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

There were no statistically significant differences in the baseline indicators between the training set and the validation set (all P>0.05). In the training set, patients were divided into the non-or mild hepatic steatosis group (MRI-PDFF<11.3%, 108 cases) and the moderate or severe hepatic steatosis group (MRI-PDFF≥11.3%, 44 cases) based on MRI-PDFF results. Statistically significant differences were observed between the two groups in age, gender, body mass index, disease duration, ALT, AST, GGT, TG, LDL-C, FPG, UA, AC, and MRI-PDFF (all P<0.05). Multivariate logistic regression analysis showed that age (OR=0.479, 95%CI 0.289-0.792), AC (OR=3.147, 95%CI 1.796-5.512), and GGT (OR=2.185, 95%CI 1.357-3.519) and UA (OR=2.239, 95%CI 1.332-3.765) were predictors of moderate and above hepatic steatosis, and a nomogram model was constructed based on the above four predictors. The areas under the ROC curves (AUC) for the training set and validation set were 0.876 (95%CI 0.821-0.931) and 0.838 (95%CI 0.738-0.937), respectively. Calibration curves and DCA indicated that the model had good calibration and clinical net benefit.

The nomogram model constructed in this study demonstrates good predictive performance for moderate-to-severe hepatic steatosis in patients with T2DM and MASLD.

To investigate the changes in pulmonary function and their associated factors in patients with type 2 diabetes mellitus (T2DM) in the Lhasa region, China.

This was a case-control study. A total of 90 T2DM patients who were treated as outpatients or inpatients in the Department of Endocrinology, Xizang Autonomous Region People′s Hospital from September 2023 to October 2024 were enrolled as the T2DM group. Meanwhile, 90 healthy subjects were selected as the control group at a ratio of 1∶1. General data including ethnicity, sex, age, height, weight, body mass index (BMI), and duration of diabetes were collected. For patients in the T2DM group, estimated glomerular filtration rate (eGFR), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), glycated hemoglobin A_1c (HbA_1c), and fasting plasma glucose (FPG) were measured. Pulmonary function indices of both groups were collected, including vital capacity (VC), forced vital capacity (FVC), forced expiratory volume in one second (FEV₁), ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC), peak expiratory flow (PEF), maximal expiratory flow at 25% of vital capacity (MEF₂₅), maximal expiratory flow at 50% of vital capacity (MEF₅₀), maximal expiratory flow at 75% of vital capacity (MEF₇₅), maximal mid-expiratory flow (MEF_75/25), maximal voluntary ventilation (MVV), total lung capacity (TLC), diffusing capacity of the lung for carbon monoxide (DLCO), and diffusing capacity of the lung for carbon monoxide per unit alveolar volume (DLCO/VA). Independent samples t-test, Mann-Whitney U test, and chi-square test were used to compare the differences in general data and pulmonary function indices between the two groups. Spearman correlation analysis was performed to analyze the related factors of pulmonary function changes, and multiple linear regression analysis was further conducted based on these factors to explore the influencing factors of pulmonary function.

Compared with the control group, the pulmonary function indices including FVC, FEV₁, PEF, MEF₇₅, MEF₅₀, MEF₂₅, MEF_75/25, and TLC in the T2DM group were significantly decreased (all P<0.05). Spearman correlation analysis showed that FEV₁, PEF, MEF₇₅, and MEF₅₀ were negatively correlated with the duration of diabetes (r=-0.265, -0.269, -0.316, -0.245, all P<0.05), and DLCO/VA was negatively correlated with FPG (r=-0.301, P<0.05). With the increase of the duration of T2DM, FVC, MVV, FEV₁, PEF, MEF₅₀, and MEF₇₅ decreased significantly (all P<0.05). VC, FVC, FEV₁, PEF, MVV, and TLC were positively correlated with BMI (r=0.369, 0.349, 0.333, 0.224, 0.249, 0.234, all P<0.05). VC and FVC were negatively correlated with eGFR (r=-0.268, -0.257, both P<0.05). VC, FVC, and PEF were positively correlated with TC (r=0.263, 0.234, 0.331, all P<0.05). PEF and MEF₇₅ were positively correlated with LDL-C (r=0.332, 0.218, both P<0.05). Multiple linear regression analysis showed that VC, FVC, and FEV₁ were linearly negatively correlated with the duration of diabetes and eGFR, and linearly positively correlated with BMI. DLCO/VA was linearly positively correlated with the duration of diabetes and eGFR, and linearly negatively correlated with FPG (all P<0.05).

Pulmonary function in T2DM patients in Lhasa is significantly decreased compared with that of the control group, primarily presenting as restrictive ventilatory dysfunction and small airway dysfunction. The duration of diabetes, FPG, BMI, and eGFR are significant factors associated with pulmonary function impairment in T2DM patients in Lhasa.

To investigate the prevalence, awareness, and influencing factors of dysglycemia among children and adolescents in Henan Province.

This cross-sectional study was conducted from February to November 2023. A total of 27 768 children and adolescents aged 6-18 years were enrolled from 18 counties/districts in Henan Province using a multi-stage stratified cluster sampling method. Data on demographic characteristics, behavioral habits, and chronic disease status were collected via questionnaires. Physical measurements including height, weight, waist circumference, and blood pressure were obtained, and fasting blood samples were collected to detect fasting plasma glucose (FPG) and lipid levels. The Rao-Scott χ² test was used to compare prevalence differences of all age groups (6-8 years, 9-11 years, 12-14 years, 15-18 years). Multivariate linear regression and logistic regression models based on complex sampling design were applied to analyze the influencing factors of FPG levels and dysglycemia.

In 2023, the average FPG level of children and adolescents in Henan Province was (5.09±0.49) mmol/L, with 357 cases of dysglycemia, corresponding to a detection rate of 1.23%. The FPG level and the detection rate of impaired fasting glucose in boys of all age groups were higher than those in girls (all P<0.05). The detection rate of dysglycemia peaked in the 12-14 years age group. Notably, only 34.17% (122/357) of individuals with dysglycemia were aware of their blood glucose status. Multivariate linear analysis showed that females (β=-0.085), different age groups (9-11 years: β=0.089; 12-14 years: β=0.086; 15-18 years: β=-0.049), rural residence (β=0.052), obesity (simple obesity: β=0.042; combined obesity: β=0.057), and hypertension (β=0.001) were influencing factors of FPG levels. Meanwhile, multivariate logistic regression revealed that age 9-14 years (9-11 years: OR=1.993; 12-14 years: OR=2.406), rural residence (OR=1.452), combined obesity (OR=1.596), hypertension (OR=1.007), and dyslipidemia (OR=1.005) were associated with an increased risk of dysglycemia among children and adolescents (all P<0.05).

The prevalence of dysglycemia among children and adolescents in Henan Province varies by sex and age, and is influenced by obesity and hypertension. The low awareness of blood glucose levels is a pressing issue that requires urgent attention.

To investigate the magnitude and trends of the disease burden of chronic kidney disease due to type 2 diabetes mellitus (CKD-T2DM) in China from 1990 to 2021.

This study was a cross-sectional study. Based on the Global Burden of Disease Study 2021 database, we analyzed the incidence and prevalence of CKD-T2DM in China, with stratification by sex and age. Log-linear regression and Joinpoint regression models were used to calculate the estimated annual percentage change (EAPC) and average annual percentage change (AAPC), respectively, to evaluate trends in CKD-T2DM disease burden. Decomposition analysis was applied to attribute absolute changes in the number of cases to three major factors: population aging, population growth, and epidemiological shifts. Furthermore, an autoregressive integrated moving average (ARIMA) model was employed to project the future trends in the CKD-T2DM disease burden through 2050.

In 2021, the number of prevalent and incident cases of CKD-T2DM in China was 20.91 million and 350 000, respectively, with corresponding rates of 1 470 and 25 per 100 000 population. The disease burden and its growth rate were slightly higher in females than in males. From 1990 to 2021, the EAPC for the prevalence rate and the incidence rate was 1.46% (95%CI 1.30%-1.62%) and 2.96% (95%CI 2.80%-3.12%), respectively; while the corresponding AAPC was 1.13% (95%CI 0.88%-1.39%) and 2.69% (95%CI 2.60%-2.79%), respectively. In 2021, age groups with significant increases in the number of prevalent and incident cases were those aged >50 years and >65 years, respectively. Population aging contributed the most to changes in the burden cases (prevalence: 91.06%; incidence: 75.04%). ARIMA models projected that the disease burden will continue to rise through 2050.

The burden of CKD-T2DM in China is severe and shows an upward trend through 2050. Strengthened prevention and management of risk factors are urgently needed, particularly in the context of accelerating population aging.

To explore the current research landscape and future trends in the field of artificial intelligence (AI)-enabled self-management for type 1 diabetes mellitus (T1DM) through a bibliometric analysis.

Scientific articles on AI-enabled T1DM self-management were systematically retrieved from the Web of Science Core Collection (WoSCC), the Scopus abstract and citation database (Elsevier), and PubMed (National Library of Medicine, NLM) from database inception to April 22, 2025. Bibliometric analysis was performed using Excel 2021, VOSviewer v1.6.20, CiteSpace v6.4.R1, R v4.5.0, RStudio v2024.12.1+563, and Bibliometrix to visualize and analyze publications, authors, journals, countries, keywords, and highly cited references in this research field.

A total of 162 articles were included. The included publications spanned 2006-2025, with the number of publications showing a steady upward trend. Among the included journals, the Journal of Diabetes Science and Technology published the largest number of articles. Among the included countries, the United States ranked first in the number of publications. Research hotspots were clustered into three themes: AI-enabled glucose management, AI algorithm technologies, and AI interaction tools. Glucose management mainly involved blood glucose prediction, dietary management, and insulin decision support. The primary AI approaches were machine learning-based methods, while AI interaction tools included embodied robots and mobile devices.

Existing research on AI-enabled T1DM self-management primarily relies on machine learning to support blood glucose prediction, dietary management, and insulin decision support, delivered through mobile devices or embodied robots.

Insulin resistance caused by LMNA gene variants is common in familial lipoatrophy syndrome. This article reports the diagnosis and treatment of a young woman. The patient went to the doctor due to elevated blood sugar. She had a history of dilated cardiomyopathy, "bird-like" face, skin hyperkeratosis and other premature symptoms. The test results suggested that she had insulin resistance with metabolic abnormalities such as diabetes and hypertriglyceridemia. There was no obvious abnormality in body fat content, and the whole exome sequencing results showed that there was a missense mutation of LMNA gene c.551A>T (p.Q184L). Combined with the medical history and related examinations, the diagnosis was finally confirmed as insulin resistance with diabetes, non-classical Werner syndrome and dilated cardiomyopathy. On the basis of routine symptomatic treatment such as improving cardiac function, lowering lipid, and lifestyle intervention, the metabolism was significantly improved after adding idebenone treatment. Through the report of this patient, this paper reveals that LMNA gene mutation can lead to severe insulin resistance without lipoatrophy, and expands its related phenotypic spectrum, hoping to provide a reference for the diagnosis and treatment of such patients.

Diabetic nephropathy (DKD) is a serious microvascular complication of diabetes and imposes a heavy burden on global public health. In recent years, artificial intelligence (AI) technology has shown great potential in the diagnosis and treatment of DKD. This paper systematically combs out the research progress of AI technology in this field, including disease prediction and risk assessment, such as using random forest algorithm to build high-precision prediction model; Imaging analysis, covering the automatic identification and quantification of renal pathology; Biomarker discovery and drug target prediction, mining potential molecular markers and drug targets through omics data; And clinical decision support and management, such as clinical decision support system deployment. At the same time, the current challenges such as insufficient data standardization, poor model interpretability, and lack of external verification are analyzed, and the future directions such as multimodal data integration, interpretable AI, and personalized treatment are looked forward. AI is expected to promote the transformation of DKD diagnosis and treatment model to precision and individualization, but its clinical transformation still needs to overcome multiple obstacles.

As one of the serious complications of diabetes, diabetic foot ulcer (DFU) has become a difficult point in clinical treatment because of its high disability rate and complex pathological mechanism. The pathophysiology of DFU involves neuropathy, vascular disease and chronic inflammatory microenvironment imbalance caused by hyperglycemia, and abnormal pH (alkaline shift) of wound microenvironment is considered to be one of the key factors that hinder healing. Currently, there are few studies on the treatment of DFU by pH adjustment, and there are no definitive guidelines. This article reviews the pathological mechanism of DFU, the influence of pH value on wound healing of DFU and the pH value regulation strategy in DFU treatment, so as to provide reference for clinical and basic research to carry out acidic treatment of DFU.