Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder affecting people worldwide. It is characterized by several key features, including hyperinsulinemia, hyperglycemia, hyperlipidemia, and dysbiosis. Epidemiologic studies have shown that T2DM is closely associated with the development and progression of cancer. T2DM-related hyperinsulinemia, hyperglycemia, and hyperlipidemia contribute to cancer progression through complex signaling pathways. These factors increase drug resistance, apoptosis resistance, and the migration, invasion, and proliferation of cancer cells. Here, we will focus on the role of hyperinsulinemia, hyperglycemia, and hyperlipidemia associated with T2DM in cancer development. Additionally, we will elucidate the potential molecular mechanisms underlying their effects on cancer progression. We aim to identify potential therapeutic targets for T2DM-related malignancies and explore relevant directions for future investigation.

Objective: To construct and validate prediction models for the risk of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus. Methods: Patients with type 2 diabetes mellitus hospitalized over the period between January 2010 and September 2018 were retrospectively collected. Eighteen baseline demographic and clinical characteristics were used as predictors to train five machine-learning models. The model that showed favorable predictive efficacy was evaluated at annual follow-ups. Multi-point data of the patients in the test set were utilized to further evaluate the model’s performance. We also assessed the relative prognostic importance of the selected risk factors for DR outcomes. Results: Of 7943 collected patients, 1692 (21.30%) developed DR during follow-up. Among the five models, the XGBoost model achieved the highest predictive performance with an AUC, accuracy, sensitivity, and specificity of 0.803, 88.9%, 74.0%, and 81.1%, respectively. The XGBoost model’s AUCs in the different follow-up periods were 0.834 to 0.966. In addition to the classical risk factors of DR, serum uric acid (SUA), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), estimated glomerular filtration rate (eGFR), and triglyceride (TG) were also identified to be important and strong predictors for the disease. Compared with the clinical diagnosis method of DR, the XGBoost model achieved an average of 2.895 years prior to the first diagnosis. Conclusion: The proposed model achieved high performance in predicting the risk of DR among patients with type 2 diabetes mellitus at each time point. This study established the potential of the XGBoost model to facilitate clinicians in identifying high-risk patients and making type 2 diabetes management-related decisions.

The updated definition of hypertension by the American College of Cardiology (ACC) and the American Heart Association (AHA) is an important paradigm shift and has lead to extensive discussion. We aimed to examine the association between the updated blood pressure (BP) categories and the risk of cardiovascular diseases (CVDs) with potential modifications from other cardiovascular health metrics (CVHMs). This prospective study included 91,204 participants ≥40 years recruited from 20 community sites across mainland China. Participants were followed up during 2010-2016 for CVD events including nonfatal myocardial infarction, stroke, heart failure, and cardiovascular death. BP categories were defined according to the 2017 ACC/AHA guideline and CVHMs included smoking, physical activity, diet, body-mass index, total cholesterol, and fasting glucose. Overall, 1,985 major CVD events occurred during a mean follow-up of 3.7 years. Having more ideal CVHMs significantly reduced the risk of CVD events in both stage 1 and stage 2 hypertension. Compared with participants without hypertension, participants having ≥4 ideal CVHMs were no longer associated with an increased CVD risk in stage 1 hypertension (HR=1·04; 95% CI=0·83-1·31), but less so in stage 2 hypertension (HR=1·90, 95% CI=1·70-2·13). Such pattern of association was more evident in participants aged <60 years (P for interaction <0·05). Stage 1 hypertension defined by the ACC/AHA identifies individuals at increased CVD risk, which can be attenuated by achieving more preferable cardiovascular health, especially in adults aged <60 years.

Background. Diabetes mellitus is a common metabolic disease and the prevalence is increasing rapidly. Thyroid disorders including subclinical hypothyroidism (SCH) and low triiodothyronine (T3) syndrome are frequently observed in diabetic patients. We conducted a study to explore thyroid function in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN). Methods. We included 103 healthy volunteers, 100 T2DM patients without DN, and 139 with DN. Physical examinations including body mass index and blood pressure and laboratory measurements including renal function, thyroid function, and glycosylated hemoglobin were conducted. Results. Patients with DN had higher thyroid stimulating hormone (TSH) levels and lower free T3 (FT3) levels than those without DN (p < 0.01). The prevalence of SCH and low FT3 syndrome in patients with DN was 10.8% and 20.9%, respectively, higher than that of controls and patients without DN (p < 0.05). Through Pearson correlation or Spearman rank correlation analysis, in patients with DN, there were positive correlations in TSH with serum creatinine (r = 0.363, p = 0.013) and urinary albumin-to-creatinine ratio (r = 0.337, p = 0.004), and in FT3 with estimated glomerular filtration rate (eGFR) with statistical significance (r = 0.560, p < 0.001). Conclusions. High level of TSH and low level of FT3 were observed in T2DM patients with DN. Routine monitoring of thyroid function in patients with DN is necessary, and management of thyroid dysfunction may be a potential therapeutic strategy of DN.

Dyslipidaemia has always been regarded as the cornerstone of arteriosclerosis and is related to the pathogenesis of renal insufficiency. However, it is unclear which routinely available lipid parameter is related to urinary albumin to creatinine ratio (UACR). The purpose of this study was to examine the lipid abnormalities associated with UACR in the general population in China. The present study was nested in an ongoing Risk Evaluation of cAncers in Chinese diabetic Individuals: A lONgitudinal (REACTION) study, which was designed to demonstrate the association of abnormal glucose metabolism with the risk of cancer in the Chinese population. This cross-sectional study included 34, 569 subjects (11, 390 males and 23, 179 females) from 8 different regional community cohorts, with an average age of 57.9 years. The UACR data were divided into the < 25% group, the 25–49% group, the 50–74% group, and the ≥ 75% group according to the quartile division of the centre where the subjects visited. The lipid classes were defined according to the guidelines for the prevention and treatment of dyslipidaemia in Chinese adults. Multiple logistic regression analysis was used to evaluate the association of the lipid parameters and UACR. Multivariable regression analysis revealed that compared with the other lipid parameters, triglycerides (TG) showed an adjusted odds ratio that was significant in model 1–4. This relationship was attenuated after adjusting for Haemoglobin A1c (HbA1c) and blood pressure (BP), but TG ≥ 2.3 mmol/L was still significantly associated with UACR in total subjects and in both men and women (OR: 1.131, 95% CI 1.065–1.203, P < 0.001 in total subjects; OR: 1.134, 95% CI 1.022–1.258, P = 0.017 in men; OR: 1.129, 95% CI 1.046–1.219, P = 0.002 in women). In the stratified analysis, elevated TG was significantly associated with increased urinary albumin in subjects with eGFR ≥ 90 mL/min per 1.73 m2, 5.6 ≤ FBG < 7.0 or 7.8 ≤ PBG < 11.1 mmol/L, 24 ≤ BMI < 28 kg/m2, 120 ≤ SBP < 140 and/or 80 ≤ DBP < 90 mmHg. We conclude that high TG levels rather than total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or non-high-density lipoprotein cholesterol levels are associated with UACR in the general population in China.