A network-based study reveals multimorbidity patterns in people with type 2 diabetes.

Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM's prevalent comorbidities and their interrelationships using network analysis. Using electronic medical records (EMRs) from 496,408 Chinese patients with T2DM, we constructed male and female global multimorbidity networks and age- and sex-specific networks. Employing diverse network metrics, we assessed the structural properties of these networks. Furthermore, we identified hub, root, and burst diseases within these networks while scrutinizing their temporal trends. Our findings uncover interconnected T2DM comorbidities manifesting as emergence in clusters or age-specific outbreaks and core diseases in each sex that necessitate timely detection and intervention. This data-driven methodology offers a comprehensive comprehension of T2DM's multimorbidity, providing hypotheses for clinical considerations in the prevention and therapeutic strategies.

Insulin use and gout risk among patients with type 2 diabetes mellitus: a real-world cohort study in Shanghai, China.

OBJECTIVES: The effect of insulin use on gout risk remains unknown. This study aimed to investigate the association between insulin use and gout risk among patients with type 2 diabetes mellitus (T2DM). METHODS: Based on the Shanghai Link Healthcare Database, patients with newly diagnosed T2DM, with or without insulin exposure, were identified from January 1, 2014 to December 31, 2020, and followed until December 31, 2021. Apart from the original cohort, we also established a 1:2 propensity score-matched cohort. A time-dependent Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for gout incidence associated with insulin exposure. RESULTS: A total of 414,258 patients with T2DM, including 142,505 insulin users and 271,753 insulin non-users, were enrolled in this study. After a median follow-up of 4.08 years (interquartile range, 2.46-5.90 years), the incidence of gout was significantly higher in insulin users than in insulin non-users (319.35 versus 302.20 cases per 100,000 person-years; HR 1.09, 95% CI 1.03-1.16). The results were robust in propensity score-matched cohort, sensitivity analyses, and stratified analysis of aspirin. In other stratified analyses, the association between insulin use and increased gout risk was found only in patients who were female, or aged 40-69 years, or without hypertension, dyslipidemia, ischemic heart disease, chronic lung disease, kidney disease, or not using diuretic. CONCLUSIONS: Insulin use is associated with a significantly increased risk of gout among patients with T2DM. Key Points • The first real-world study to investigate the effect of insulin use on gout risk. • Insulin use is associated with a significantly increased risk of gout among patients with type 2 diabetes mellitus.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China.

BACKGROUND: All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap. METHODS: Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots). RESULTS: A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots. CONCLUSIONS: This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

Insulin therapy and biliary tract cancer: insights from real-world data.

Objective: The association between insulin therapy and the risk of biliary tract cancer (BTC) is uncertain. We aimed to assess this risk in type 2 diabetic patients. Methods: Using electronic medical data from the Shanghai Hospital Link database, 202,557 patients with type 2 diabetes (164,997 insulin never-users and 37,560 insulin ever-users) were identified in this study between January 1, 2013, and December 31, 2016, with follow-up until December 31, 2019. By propensity score matching, an ever-user was matched with a never-user. Cox proportional hazards regression analysis was used to estimate risk ratios (HRs) and 95% CIs for three subtypes of BTC (intrahepatic cholangiocarcinoma (ICC), extrahepatic cholangiocarcinoma (ECC), and gallbladder cancer (GBC)). Results: At a mean follow-up of 5.33 years, 143 cases of BTC were observed. The crude incidence rates (per 100,000 person-years) of ECC, ICC, and GBC in ever-users:never-users were 10.22:3.63, 2.04:2.04, and 8.17:6.01, respectively. Insulin therapy was associated with an increased risk of ECC (HR, 4.10; 95% CI, 1.54-10.92; P = 0.005) compared to patients who never used insulin. No statistically significant results were observed for insulin and ICC/GBC. Consistent results were also found in the original cohort. Conclusions: The relationship between insulin therapy and BTC is type-specific. Further studies are warranted to provide evidence on the identification of ECC risk groups among type 2 diabetic patients.

Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study.

High levels of ambient fine particulate matter (PM2.5) might increase the risk of death due to cardiovascular diseases (CVDs). As a critical risk factor for CVDs, dyslipidemia can cause CVDs or exacerbate pre-existing ones. This study aimed to investigate whether a short-time exposure to PM2.5 leads to dyslipidemia (HyperTC, HyperLDL-C, HyperTG and HypoHDL-C) in adults. The serum lipid data were provided by the Sichuan Provincial People's Hospital Medical Examination Center. We included 309,654 subjects aged 18-79 between May 10, 2015, and May 10, 2017. An advanced distributed lag nonlinear model (DLNM) was applied to investigate the acute and lag effects of ambient PM2.5 on the risk of dyslipidemia. This study was also stratified by sex, age, BMI and season to examine potential effect modification. We observed that the associations between an interquartile increase in PM2.5 (43 µg/m3) and dyslipidemia were [relative risk (RR); 95% confidence interval (CI)]: 1.042 (1.013, 1.071) for HyperLDL-C and 1.027 (1.006, 1.049) for HyperTC at lag0 day. The lag effects were found at lag6 day for HyperLDL-C, in lag4-6 days for HyperTC and lag4-7 days for HyperTG. Short-term exposure to ambient PM2.5 was related to dyslipidemia and the effect modification was observed in the subgroup analysis. The female and normal-weight populations were more susceptible to the risks of PM2.5 on HyperLDL-C and HyperTC.

Association between Lipid Profiles and Serum Urate: A Cross-Sectional Study in Southwestern China.

OBJECTIVE: The relationship between lipid profiles and serum urate has not been fully investigated. This study aims to investigate the sex- and age-specific association between lipid profiles and serum urate. METHODS: This was a cross-sectional study involving 122,351 participants aged 18-99 years from a check-up centre in Southwestern China. Generalized additive models and smooth curve fitting were conducted to explore the association between components of lipid profiles and serum urate. Furthermore, multivariate linear and logistic regression models were also performed. RESULTS: In generalized additive models, the fitted smoothing curves showed that serum urate fluctuated in a small range with total cholesterol, LDL-C, or HDL-C raising. After adjusting for confounders, the differences in serum urate progressively increased with raising serum triglycerides quartiles. The likelihood (odds ratio, OR) for developing serum urate > 420 µmol/L significantly increased in the highest quartile of triglycerides than in the lowest quartile, in hypertriglyceridemia than in normal triglycerides, and with 1 mmol/L increment in triglycerides in all sex- and age-specific groups. Furthermore, the increased OR (95% confidence interval) was higher in females than in males compared with their respective controls. CONCLUSIONS: Serum urate and the likelihood for developing serum urate >420 µmol/L increased with triglycerides raising. Females were in a higher likelihood for developing serum urate >420 µmol/L than males with raising triglycerides. With changes in total cholesterol, LDL-C, or HDL-C, serum urate fluctuated in a small range.

Reconsidering the HbA1c Cutoff for Diabetes Diagnosis Based on a Large Chinese Cohort.

INTRODUCTION: The HbA1c has been considered as the 'gold standard' in diabetes diagnosis and management, however, age, gender and body mass index (BMI) might have certain effects on HbA1c. We are aiming to further investigate the correlation between age and HbA1c, and whether it was affected by gender and BMI. METHODS: A cross-sectional survey including 135,893 nondiabetic individuals who took the physical examination between 2013 and 2017 was conducted. The subjects were grouped by gender, age and BMI, and the interactive and independent effects of the 3 factors on the HbA1c were detected. The median and 95% confidence interval (CI) of HbA1c levels were calculated. RESULTS: The HbA1c levels gradually increased along with age, both in female and male, and there is a positive association between BMI and the HbA1c. The difference on HbA1c in gender was associated with both age and BMI, the age-related increase in HbAlc was accentuated in the subgroup with higher BMI, and there was a marked accentuation of the positive association between BMI and HbA1c as age increased. In almost all the young and middle-aged (aged 20-59) subgroups, the 97.5th percentiles of HbA1c levels were lower than 6.5%, suggesting that the single HbA1c cutoff value is probably not applicable to the young and middle-aged population. CONCLUSIONS: We recommend that the effects of age, gender and BMI should be taken into consideration when using HbA1c for the diagnosis and management of diabetes, especially in the young and middle-aged population.

The lagged effect of air pollution on human eosinophils: a distributed lag non-linear model.

OBJECTIVES: The aim of this study was to determine the lag between exposure to air pollutants and changes in human eosinophil counts. MATERIAL AND METHODS: This was a retrospective study employing 246 425 physical examination records dated December 2013 - December 2016 from Chengdu, China. The authors determined the prevalence of individuals with eosinophil counts above the normal reference range each day. A distributed lag non-linear model was used to evaluate the lagged effect of each air pollutant on eosinophil counts. The lagged effects of each air pollutant were counted and presented with smoothing splines. RESULTS: The effects of air pollutants such as particulate matter (PM2.5, aerodynamic diameters <2.5 µm; PM10, aerodynamic diameters <10 µm), nitrogen dioxide (NO2) and ozone (O3) were evaluated. In women, the effects of PM2.5 (RR = 1.154, 95% CI: 1.061-1.255) and PM10 (RR = 1.309, 95% CI: 1.130-1.517) reached the maximum values on lag day 0. In men, there was no significant effect of PM2.5, but significant effects of PM10 were found for lag days 20-28. The effects of NO2 and O3 on eosinophils were not statistically significant for either gender. CONCLUSIONS: The air pollutants of PM10 have a significant effect on human eosinophils for both women and men, but with different temporal patterns, with women showing a lag of 0-5 days and men showing a lag of 20-28 days. In addition, PM2.5 was significant for women with a lag of 0-3 days but it was not significant for men. Int J Occup Med Environ Health. 2020;33(3):299-310.

Cancer risk among patients with type 2 diabetes: A real-world study in Shanghai, China.

BACKGROUND: The aim of this study was to investigate the risk of 23 common types of cancer among patients with type 2 diabetes (T2D) compared with the general Chinese population. METHODS: Based on the Shanghai Hospital Link database, 410 191 patients with T2D (age 20-99 years) were identified from July 2013 to December 2016, and were followed-up for cancer incidence until December 2017. RESULTS: In all, 8485 cases of newly diagnosed cancer were identified. The standardized incidence ratios (SIRs) of total cancer were 1.34 and 1.62 among males and females, respectively. Among males with T2D, the risk of cancer of the prostate (highest SIR of 1.86), blood (leukemia, lymphoma), skin, thyroid, kidney, liver, pancreas, lung, colorectum, and stomach was increased significantly. There was a significant decrease in the risk of esophageal cancer. In females with T2D, there were significantly greater risks of cancer of the nasopharynx (highest SIR of 2.33), liver, esophagus, thyroid, lung, pancreas, blood (lymphoma, leukemia), uterus, colorectum, breast, cervix, and stomach. In contrast, there was significantly decrease risk of gallbladder cancer in females with T2D. CONCLUSIONS: This study shows significantly increased risks of overall and some site-specific cancers among patients with T2D. We suggest that establishing strategies for regular cancer-specific screening and prevention care among patients with T2D are necessary.

Complete Paternal Uniparental Disomy of Chromosome 2 in an Asian Female Identified by Short Tandem Repeats and Whole Genome Sequencing.

Uniparental disomy (UPD) is a rare type of chromosomal aberration that has sometimes been detected in paternity testing. We examined a 3-person family (father, mother, daughter) first by using short tandem repeat markers, which revealed 4 markers, TPOX, D2S1338, D2S1772, and D2S441, on chromosome 2 that were not transmitted in a Mendelian style. We then performed whole genome sequencing (WGS) to determine the range of the UPD. Chromosome 2 in the daughter showed a complete paternal UPD. To the best of our knowledge, this is the 4th case of complete paternal UPD of chromosome 2 with no clinical phenotype. Our study suggests that WGS, when performed to enhance the accuracy and reliability of parentage testing, can provide a powerful method to detect an UPD.