Global, regional, and national time trends in cancer mortality attributable to high fasting plasma glucose: an age-period cohort analysis.

BACKGROUND: High fasting plasma glucose (HFPG) is the fastest-growing risk factor for cancer deaths worldwide. We reported the cancer mortality attributable to HFPG at global, regional, and national levels over the past three decades and associations with age, period, and birth cohort. METHODS: Data for this study were retrieved from the Global Burden of Disease Study 2019, and we used age-period-cohort modelling to estimate age, cohort and period effects, as well as net drift (overall annual percentage change) and local drift (annual percentage change in each age group). RESULTS: Over the past 30 years, the global age-standardized mortality rate (ASMR) attributable to HFPG has increased by 27.8%. The ASMR in 2019 was highest in the male population in high sociodemographic index (SDI) areas (8.70; 95% CI, 2.23-18.04). The net drift for mortality was highest in the female population in low SDI areas (2.33; 95% CI, 2.12-2.55). Unfavourable period and cohort effects were found across all SDI quintiles. Cancer subtypes such as "trachea, bronchus, and lung cancers", "colon and rectal cancers", "breast cancer" and "pancreatic cancer" exhibited similar trends. CONCLUSIONS: The cancer mortality attributable to HFPG has surged during the past three decades. Unfavourable age-period-cohort effects on mortality were observed across all SDI quintiles, and the cancer mortality attributable to HFPG is expected to continue to increase rapidly in the future, particularly in lower SDI locations. This is a grim global public health issue that requires immediate attention.

Structural alterations of the salience network in patients with insular glioma.

OBJECTIVE: The structural alteration that occurs within the salience network (SN) in patients with insular glioma is unclear. Therefore, we aimed to investigate the changes in the topological network and brain structure alterations within the SN in patients with insular glioma. METHODS: We enrolled 46 patients with left insular glioma, 39 patients with right insular glioma, and 21 demographically matched healthy controls (HCs). We compared the topological network, gray matter (GM) volume, and fractional anisotropy (FA) between HCs and patients after controlling for the effects of age and gender. RESULTS: Patients with insular glioma showed topological network decline mainly in the insula, basal ganglia region, and anterior cingulate cortex (ACC). Compared with HCs, patients primarily showed GM volume increased in the ACC, inferior temporal gyrus (ITG), superior temporal gyrus (STG), temporal pole: middle temporal gyrus (TPOmid), insula, middle temporal gyrus (MTG), middle frontal gyrus, and superior occipital gyrus (SOG), but decreased in TPOmid, ITG, temporal pole: superior temporal gyrus, and SOG. FA declined mainly in the STG, MTG, ACC, superior frontal gyrus, and SOG, and also showed an increased cluster in SOG. CONCLUSIONS: FA represents the integrity of the white matter. In patients with insular glioma, decreased FA may lead to the destruction of the topological network within the SN, which in turn may lead to the decrease of network efficiency and brain function, and the increase of GM volume may compensate for these changes. Overall, this pattern of structural changes provides new insight into the compensation model of insular glioma.

Intakes of omega-3 fatty acids and risks of all-cause and cause-specific mortality in people with diabetes: a cohort study based on NHANES 1999-2014.

AIMS: Intake of omega-3 fatty acids is associated with several health benefits, but the specific benefits in populations with diabetes have yet to be elucidated. Therefore, this study aimed to explore the relationship between intake of omega-3 fatty acids and mortality in people with diabetes. METHODS: This was a prospective cohort study and included 4854 participants with diabetes (mean age, 57.92 years; 50.9% male) from the National Health and Nutrition Examination Survey (1999-2014). Eicosapentaenoic (EPA) and docosahexaenoic (DHA) acid intake were used as alternative markers of omega-3 fatty acids intake and calculated based on the sum of the 24-h dietary recall interviews and dietary supplements. Mortality data were ascertained by linkage to National Death Index records by December 31, 2015. Cox proportional hazard models and restricted cubic spline were used to assess the relationship between EPA and DHA intake and all-cause and cause-specific mortality. Statistical analyses were performed using R 4.2.0 software. RESULTS: Compared with participants with a lower EPA + DHA intake, participants who had a higher EPA + DHA intake tended to be Non-Hispanic Black; were more likely to be obese; and had higher daily energy intake and family income. During 34,386 person-years of follow-up, 1102 deaths were documented, including 266 cardiovascular disease deaths and 152 cancer deaths. In multivariable regression analyses with adjustment of confounding factors, higher EPA + DHA intake was significantly and linearly related to lower all-cause mortality: there was a 25% reduced risk of all-cause mortality. CONCLUSIONS: Higher omega-3 fatty acid intake was independently related to lower all-cause mortality in individuals with diabetes, suggesting an adequate intake of omega-3 fatty acids may prevent premature death among the population with diabetes.

An affordable approach to classifying type 2 diabetes based on fasting plasma glucose, TyG index and BMI: a retrospective cohort study of NHANES Data from 1988 to 2014.

BACKGROUND: The ß-cell function and insulin resistance required by existing methods of classifying type 2 diabetes are not routinely adopted in most medical institutions of developing countries and regions. This study aims to propose a novel, affordable classification approach and evaluate its predictive ability for several health and mortality outcomes, including cardiovascular health (CVH), retinopathy, chronic kidney disease (CKD), nonalcoholic fatty liver disease (NAFLD), advanced liver fibrosis, and mortality caused by all-cause, cardiovascular disease (CVD), cancer. METHODS: Based on 4060 participants with diabetes (aged ≥ 30 at the time of diagnosis) selected from the National Health and Nutrition Examination Survey III & 1999-2014, we proposed a novel, but simple classification approach based on the threshold of fasting plasma glucose (FPG), triglyceride-glucose (TyG) index and body mass index (BMI). We used logistic regression model to assess its predictability for diabetes complications, and Cox regression model to estimate the mortality risks. RESULTS: By utilizing this approach, we characterized the subjects into four subgroups: subgroup A (obesity-related), which accounts for 37% of the total, subgroup B (age-related), 38%, subgroup C (insulin resistance), 20%, and subgroup D (severe insulin deficiency), 5%. Subjects in subgroup D had a higher risk of retinopathy, in subgroup B had a lower risk of poor cardiovascular health, nonalcoholic fatty liver disease, and advanced liver fibrosis, in subgroup C had a higher risk of all-cause mortality. CONCLUSIONS: This study proposes an affordable and practical method for classifying patients with type 2 diabetes into different subgroups, with a view to yield a high predictability of patient outcomes and to assist clinicians in providing better treatment.

Validation of type 2 diabetes subgroups by simple clinical parameters: a retrospective cohort study of NHANES data from 1999 to 2014.

OBJECTIVES: To verify whether a simplified method based on age, body mass index (BMI) and glycated haemoglobin (HbA1c) is feasible in classifying patients with type 2 diabetes (T2D), and evaluate the predictive ability of subgroups in several health and mortality outcomes. DESIGN: Retrospective cohort study. SETTING: The National Health and Nutrition Examination Survey 1999-2014 cycle. PARTICIPANTS: A total of 1960 participants with diabetes and the age at diagnosis greater than 30. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants with T2D were assigned to previously defined (by Ahlqvist) subgroups based on five variables: age, BMI, HbA1c, homoeostasis model assessment (HOMA) 2 estimates of ß-cell function (HOMA2-B), and insulin resistance (HOMA2-IR), and on three variables: age, BMI and HbA1c. The classification performances of the three variables were evaluated based on 10-fold cross validation, with accuracy, precision and recall as evaluation criteria. Outcomes were assessed using logistic regression and Cox regression analysis. RESULTS: Without HOMA measurements, it is difficult to identify severe insulin-resistant diabetes, but other subgroups can be ideally identified. There is no significant difference between the five variables and the three variables in the ability to predict the prevalence of poor cardiovascular health (CVH), chronic kidney disease, non-alcoholic fatty liver disease and advanced liver fibrosis, and the risk of all-cause, cardiovascular disease and cancer-related mortality (p>0.05), except the prevalence of poor CVH in mild age-related diabetes (p<0.05). CONCLUSIONS: A simple classification based on age, BMI and HbA1c could be used to identify T2D with several health and mortality risks, which is accessible in most individuals with T2D. Due to its simplicity and practicality, more patients with T2D can benefit from subgroup specific treatment paradigms.

Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging.

PURPOSE: To extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis. METHODS: Two groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features. Statistical analysis was conducted using SPSS (version 20.0), including the Shapiro-Wilk test and Wilcoxon signed-rank test, which were used to test significant differences in each feature between the tumor and healthy regions. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data distribution so as to avoid tumor selection bias. The Gini impurity index in random forests (RFs) was used to select the top five out of all features. Based on the five features, three classification models were built respectively with three machine learning classifiers: RF, support vector machine (SVM), and back propagation (BP) neural network. RESULTS: Sixteen of the 25 features were significantly different between the tumor and healthy areas. Through the Gini impurity index in RFs, standard deviation, first-order moment, variance, third-order absolute moment, and third-order central moment were selected to build the classification model. The classification model trained using the SVM classifier achieved the best performance, with sensitivity, specificity, and area under the curve of 94.04%, 92.3%, and 0.932, respectively. CONCLUSION: Texture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.

The analysis of differential diagnosis of benign and malignant thyroid nodules based on ultrasound reports.

BACKGROUND: Thyroid cancer is a common endocrine tumor, the incidence of which is increasing each year. Early diagnosis and treatment can effectively prevent thyroid cancer. This article uses Chinese's ultrasound reports to determine the value of early diagnosis. METHODS: The clinical data center of the First Affiliated Hospital of Nanjing Medical University was screened for patients diagnosed with a thyroid nodule, who had undergone a thyroid function test, ultrasound records and pathological assessment. A total of 811 patients with a total of 1,290 pathologically confirmed nodules (506 benign and 784 malignant) were enrolled. Logistic regression was used to analyze the variables that significantly affected malignant nodules. The sensitivity and specificity of ultrasound thyroid imaging-reporting and data system (TI-RADS) classification results for benign and malignant tumors were calculated. RESULTS: The age of the patients had a very significant difference in the classification of benign and malignant nodules (P<0.001), and the marital status was significantly different (P<0.05). Gender and medical insurance had no significant effect (P>0.05). Thyroglobulin (TG), free thyroxine (FT4), and free triiodothyronine (FT3) had significant effects (P=0.003) on the incidence of malignant nodules in patients, while thyroid-stimulating hormone (TSH) had no significant effect (P>0.05). Ultrasound analysis showed a Youden's index of 78.97%, a positive predictive value of 93.20%, and a negative predicted value of 84.10% at the most excellent classification effect. The sensitivity was 89.0%, the specificity was 89.9%; much greater than the classification model based on the thyroid function test (sensitivity =80.6%, specificity =55.8%). CONCLUSIONS: The present study verifies the effectiveness of using TI-RADS classification for diagnosis of benign and malignant thyroid nodules, and explores the use of new analysis methods for clinical data. To reduce dependence on the doctors, ultrasound image data and clinical phenotypic data can be further used to assist clinical decision making.

Hepatitis B virus infection and the risk of cancer among the Chinese population.

The relationship between hepatitis B virus (HBV) and nonhepatocellular cancers remains inconclusive. This large case-control study aimed to assess the associations between HBV infection status and multiple cancers. Cases (n = 50 392) and controls (n = 11 361) were consecutively recruited from 2008 to 2016 at the First Affiliated Hospital of Nanjing Medical University. Multivariable adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were estimated using logistic regression by adjusting age and gender. A meta-analysis based on published studies was also performed to verify the associations. Of these, 12.1% of cases and 5.5% of controls were hepatitis B surface antigen (HBsAg) seropositive. We observed significant associations between HBsAg seropositivity and esophagus cancer (aOR [95% CI] = 1.32 [1.13-1.54]), stomach cancer (1.46 [1.30-1.65]), hepatocellular carcinoma (HCC; 39.11 [35.08-43.59]), intrahepatic and extrahepatic bile duct carcinoma (ICC and ECC; 3.83 [2.58-5.67] and 1.72 [1.28-2.31]), pancreatic cancer (PaC; 1.37 [1.13-1.65]), non-Hodgkin lymphoma (NHL; 1.88 [1.61-2.20]) and leukemia (11.48 [4.05-32.56]). Additionally, compared to participants with HBsAg-/anti-HBs-/anti-HBc-, participants with HBsAg-/anti-HBs-/anti-HBc+, indicating past HBV-infected, had an increased risk of esophagus cancer (aOR [95% CI] = 1.46 [1.24-1.73]), stomach cancer (1.20 [1.04-1.39]), HCC (4.80 [3.95-5.84]) and leukemia (15.62 [2.05-119.17]). Then the overall meta-analysis also verified that HBsAg seropositivity was significantly associated with stomach cancer (OR [95% CI] = 1.23 [1.14-1.33]), ICC (4.05 [2.78-5.90]), ECC (1.73 [1.30-2.30]), PaC (1.26 [1.09-1.46]), NHL (1.95 [1.55-2.44]) and leukemia (1.54 [1.26-1.88]). In conclusion, both our case-control study and meta-analysis confirmed the significant association of HBsAg seropositivity with stomach cancer, ICC, ECC, PaC, NHL and leukemia. Of note, our findings also suggested that the risk of stomach cancer elevated for people whoever exposed to HBV.

Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system.

Automated electrocardiogram (ECG) diagnosis could be a useful aid for clinical use. We applied a deep learning method to build a system for automated detection and classification of ECG signals. We first trained a convolutional neural network (CNN) to detect cardiovascular disease in ECG signals using a training data set of 259,789 ECG signals collected from the cardiac function rooms of a tertiary care hospital. The CNN classification was validated using an independent test data set of 18,018 ECG signals. The labels used covered >90% of clinical diagnoses. The system grouped ECGs into 18 classifications-17 different types of abnormalities and normal ECG. The overall accuracy of the model was tested and found to be close to 95%; the accuracy for diagnosis of normal rhythm/atrial fibrillation was 99.15%. The proposed CNN model could help reduce misdiagnosis and missed diagnosis in primary care settings and also improve efficiency and save manpower cost for large general hospitals.

Association between type 2 diabetes and cancer incidence in China: data in hospitalized patients from 2006 to 2013.

BACKGROUND: Evidence of a role for type 2 diabetes in overall cancer risk is limited in ethnic Chinese populations. We therefore investigated whether there is an association between diabetes and cancer incidence. METHODS: All type 2 diabetes and cancer hospitalized patients from the First Affiliated Hospital of Nanjing Medical University between 2006.01 and 2013.12 were eligible for the study. Our research used healthcare information technology and statistical methods to analyze the clinical data of hospitalized patients and explored the relationship between diabetes and cancer. Participants with fasting glucose ≥126 mg/dL, or taking hypoglycemic medications, were classed as having type 2 diabetes. Cancer incidence was established through regular follow-up interviews and medical records. Data were entered into Excel and a database was set up with ACCESS software. Clinical information such as demographics like gender, age, occupation, marriage, insurance and etc., diagnoses, and prescription record were chosen and analyzed. SPSS software was also used for statistical analysis. RESULTS: The number of patients with both diabetes and cancer rose from 220 cases in 2006 to 1,623 cases in 2013. The proportion of cancer patients with diabetes has also increased every year. Younger participants (aged ≤50 years) with diabetes had a greater risk of all cancers [P<0.005, odds ratio (OR) >3.4]. And cancer patients with diabetes occurs more frequently in male patients than in female patients, especially since 2009 the proportion has increased more evidently (P<0.005, OR >1.4). Further analysis showed that the level of blood lipid in patients with diabetes mellitus and cancer was significantly different from that in patients with simple diabetes mellitus (P<0.05). CONCLUSIONS: Our results clearly demonstrate a positive association between diabetes and cancer, especially in younger individuals aged less than 50 years. This finding highlights a need for greater awareness among public health workers and physicians of the importance of effective control of diabetes in the younger population.

Prevalence of hypertension, diabetes, and dyslipidemia, and their additive effects on myocardial infarction and stroke: a cross-sectional study in Nanjing, China.

BACKGROUND: This study aimed to investigate the prevalence and risk factors for hypertension, diabetes, and dyslipidemia, and to evaluate their additive effects on myocardial infarction (MI) and stroke in Nanjing in East China. METHODS: A multistage, stratified random cluster sampling method was used to select representative participants. All eligible participants completed questionnaires, physical measurements, and blood tests. Multivariable and univariable logistic regression analyses were used to identify associated risk factors and evaluate additive effects on cardiovascular events, respectively. RESULTS: Hypertension was the most prevalent chronic disease among 11,036 participants enrolled (18.5%), followed by dyslipidemia (8.3%) and diabetes (6.0%). The prevalence of hypertension was higher in men than in women while no sex-related difference was observed in the prevalence of diabetes and dyslipidemia. Older age and higher body mass index were risk factors for all three diseases. Sex, central obesity, smoking, number of family members, salt intake, and family history of hypertension were associated with hypertension; central obesity, smoking, alcohol assumption, and family history of diabetes correlated with diabetes; and female sex, higher education, and alcohol assumption were risk factors for dyslipidemia. Hypertension complicated with dyslipidemia conferred more risk of MI and stroke than independent effects. Diabetes also contributed to risk based on hypertension or dyslipidemia. CONCLUSIONS: The burden of hypertension and diabetes has stopped increasing. However, total cholesterol (TC) concentration in the population has not been well controlled. A more comprehensive approach to managing dyslipidemia, hypertension, and diabetes needs to be developed, especially for individuals with multiple cardiovascular risk factors.

Detecting pioglitazone use and risk of cardiovascular events using electronic health record data in a large cohort of Chinese patients with type 2 diabetes.

BACKGROUND: Pioglitazone may have a protective effect against cardiovascular disease risk among type 2 diabetes (T2D) patients, but evidence from China is lacking. This study investigated the association using electronic health records (EHR) data from a Chinese cohort of T2D patients. METHODS: All T2D patients from the First Affiliated Hospital of Nanjing Medical University who were prescribed at least one oral antidiabetic drug and were aged ≥18 years between 1 July 2005 and 30 June 2017 were eligible for inclusion (n = 71 783). Pioglitazone use was determined in 6-month study intervals, with outcome events of myocardial infarction (MI), ischemic stroke, and heart failure. Poisson regression was used to estimate adjusted rate ratios (RRs) with 95% confidence intervals (CIs). RESULTS: In multivariable analysis adjusted for potential confounders, pioglitazone use, compared with no use, was associated with a significant 39% decreased risk of MI (RR = 0.61; 95% CI = 0.42-0.90; P = 0.012). Pioglitazone use was also associated with a non-significant reduction in risk of heart failure or stroke. When MI, heart failure, and stroke were combined as a composite outcome, pioglitazone use was associated with a 30% decrease in risk (RR = 0.70; 95% CI = 0.56-0.88; P = 0.002). CONCLUSIONS: This study demonstrates that applying informatics tools to a large EHR database could be a good way to efficiently conduct clinical observational research. In addition, the findings validated the favorable effect of pioglitazone on the risk of MI among T2D patients in China, with the use of pioglitazone decreasing the risk of MI among those with T2D.

Analysis of treatment pathways for three chronic diseases using OMOP CDM.

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the "Treatment Pathways in Chronic Disease" protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.

Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches.

BACKGROUND: The wide adoption of electronic health record systems (EHRs) in hospitals in China has made large amounts of data available for clinical research including breast cancer. Unfortunately, much of detailed clinical information is embedded in clinical narratives e.g., breast radiology reports. The American College of Radiology (ACR) has developed a Breast Imaging Reporting and Data System (BI-RADS) to standardize the clinical findings from breast radiology reports. OBJECTIVES: This study aims to develop natural language processing (NLP) methods to extract BI-RADS findings from breast ultrasound reports in Chinese, thus to support clinical operation and breast cancer research in China. METHODS: We developed and compared three different types of NLP approaches, including a rule-based method, a traditional machine learning-based method using the Conditional Random Fields (CRF) algorithm, and deep learning-based approaches, to extract all BI-RADS finding categories from breast ultrasound reports in Chinese. RESULTS: Using a manually annotated dataset containing 540 reports, our evaluation shows that the deep learning-based method achieved the best F1-score of 0.904, when compared with rule-based and CRF-based approaches (0.848 and 0.881 respectively). CONCLUSIONS: This is the first study that applies deep learning technologies to BI-RADS findings extraction in Chinese breast ultrasound reports, demonstrating its potential on enabling international collaborations on breast cancer research.

The analysis of the disease spectrum in China.

Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006-2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health.