Female ; Humans ; Endodermal Sinus Tumor/pathology* ; Adenocarcinoma/pathology* ; Choriocarcinoma/pathology*

Objective: To investigate the clinicopathological characteristics of esophageal carcinoma with gland duct differentiation. Methods: The clinical, morphologic and immunohistochemical (IHC) features of eight cases of esophageal carcinoma with gland duct differentiation diagnosed from 2012 to 2022 at the First Affiliated Hospital of Soochow University were summarized. Results: There were four males and four females, with a mean age of 68.5 (range 59-82) years. Two tumors were located in middle esophagus, five in the lower esophagus, and one in the cardia. The mean diameter was 2.4 cm (range 0.6-4.5 cm). The tumor had a bilayer epithelial structure, including the inner luminal epithelium and the outer basal epithelium. Immunohistochemistry showed that CK7 (8/8) and CK18 (8/8) were positive in the inner epithelium. p40 (8/8), p63 (8/8) and CK5/6 (8/8) were positive in the outer epithelium. SMA, calponin and CD117 were all negative. p53 mutants were found in all eight cases (strong and diffuse positivity in 6/8; complete loss of expression in 2/8). No columnar metaplasia, intestinal metaplasia and ectopic gastric mucosa were observed in the surface squamous epithelium in the cases. The mean follow-up time was 21.5 months (range 5-51 months). Seven patients survived and one patient died 31 months after surgery due to recurrence and liver metastasis. Conclusion: Esophageal carcinoma with esophageal gland duct differentiation is a rare tumor with unique histologic and IHC characteristics.
Male ; Female ; Humans ; Middle Aged ; Aged ; Aged, 80 and over ; Esophageal Neoplasms/pathology* ; Epithelium/pathology* ; Metaplasia/metabolism* ; Carcinoma/pathology*

Humans ; Herpesvirus 4, Human ; Epstein-Barr Virus Infections/complications* ; Cholangiocarcinoma/pathology* ; Carcinoma, Squamous Cell ; Bile Ducts, Intrahepatic/pathology* ; Bile Duct Neoplasms/pathology*

Humans ; Thyroid Cancer, Papillary ; Neck/pathology* ; Lymph Nodes/pathology* ; Thyroid Neoplasms/pathology* ; Lymphoma, T-Cell/pathology*

Objective: This study estimated nationally representative medical expenditures of gynecologic cancers, described treatment patterns and assessed key risk factors associated with the economic burden in the United States. Methods: A retrospective repeated measures design was used to estimate the effect of gynecologic cancers on medical expenditures and utilization among women. Data were extracted from the Medical Expenditure Panel Survey (weighted sample of 609,787 US adults) from 2007 to 2014. Using the behavioral model of health services utilization, characteristics of cancer patients were examined and compared among uterine, cervical, and ovarian cancer patients. Multivariable linear regression models were conducted on medical expenditure with a prior logarithmic transformation. Results: The estimated annual medical expenditure attributed to gynecologic cancers was $3.8 billion, with an average cost of $6,293 per patient. The highest annual cost per person was ovarian cancer ($13,566), followed by uterine cancer ($6,852), and cervical cancer ($2,312). The major components of medical costs were hospital inpatient stays (53%, $2.03 billion), followed by office-based visits (15%, $559 million), and outpatient visits (13%, $487 million). Two key prescription expenditures were antineoplastic hormones (10.3%) and analgesics (9.2%). High expenditures were significantly associated with being a married woman (p<0.001), having private health insurance (p<0.001), being from a low- and middleincome family (p<0.001), or living in the Midwest or the South (p<0.001). Conclusion The key risk factors and components were well described for the economic burden of gynecologic cancers. With a growing population of cancer patients, efforts to reduce the burden of gynecologic cancers are warranted.

Precision medicine became the key strategy in development priority of science and technology in China. The large population-based cohorts become valuable resources in preventing and treating major diseases in the population, which can contribute scientific evidence for personalized treatment and precise prevention. The fundamental question of the achievements above, therefore, is how to construct a large population-based cohort in a standardized way. The Chinese Preventive Medicine Association co-ordinated experienced researchers from Peking University and other well-known institutes to write up two group standards Technical specification of data processing for large population-based cohort study (T/CPMA 001-2018) and Technical specification of data security for large population-based cohort study (T/CPMA 002-2018), on data management. The standards are drafted with principles of emphasizing their scientific, normative, feasible, and generalizable nature. In these two standards, the key principles are proposed, and technical specifications are recommended in data standardization, cleansing, quality control, data integration, data privacy protection, and database security and stability management in large cohort studies. The standards aim to guide the large population-based cohorts that have been or intended to be established in China, including national cohorts, regional population cohorts, and special population cohorts, hence, to improve domestic scientific research level and the international influence, and to support decision-making and practice of disease prevention and control.
China ; Cohort Studies ; Delivery of Health Care ; Humans ; Population Surveillance ; Quality Control ; Reference Standards

Objective: To describe the genetic structure of populations in different areas of China, and explore the effects of different strategies to control the confounding factors of the genetic structure in cohort studies. Methods: By using the genome-wide association study (GWAS) on data of 4 500 samples from 10 areas of the China Kadoorie Biobank (CKB), we performed principal components analysis to extract the first and second principal components of the samples for the component two-dimensional diagram generation, and then compared them with the source of sample area to analyze the characteristics of genetic structure of the samples from different areas of China. Based on the CKB cohort data, a simulation data set with cluster sample characteristics such as genetic structure differences and extensive kinship was generated; and the effects of different analysis strategies including traditional analysis scheme and mixed linear model on the inflation factor (λ) were evaluated. Results: There were significant genetic structure differences in different areas of China. Distribution of the principal components of the population genetic structure was basically consistent with the geographical distribution of the project area. The first principal component corresponds to the latitude of different areas, and the second principal component corresponds to the longitude of different areas. The generated simulation data showed high false positive rate (λ=1.16), even if the principal components of the genetic structure was adjusted or the area specific subgroup analysis was performed, λ could not be effectively controlled (λ>1.05); while, by using a mixed linear model adjusting for the kinship matrix, λ was effectively controlled regardless of whether the genetic structure principal component was further adjusted (λ=0.99). Conclusions: There were large differences in genetic structure among populations in different areas of China. In molecular epidemiology studies, bias caused by population genetic structure needs to be carefully treated. For large cohort data with complex genetic structure and extensive kinship, it is necessary to use a mixed linear model for association analysis.
China ; Genetic Structures ; Genome-Wide Association Study ; Humans ; Linear Models ; Principal Component Analysis

Objective: To evaluate the association of educational level with anthropometric measurements at different adult stages and their long-term changes in adults who participated in the second re-survey of China Kadoorie Biobank (CKB). Methods: The present study excluded participants who were aged >65 years, with incomplete or extreme measurement values, or with major chronic diseases at baseline survey or re-survey. The weight at age 25 years was self-reported. Body height, body weight and waist circumference at baseline survey (2004-2008) and re-survey (2013-2014) were analyzed. Results: The present study included 3 427 men and 6 320 women. Both body weight and waist circumference (WC) increased with age. From age 25 years to baseline survey (mean age 45.2±6.5), the mean weight change per 5-year was (1.70±2.63) kg for men and (1.27±2.10) kg for women. From baseline survey to re-survey (53.2±6.5), the mean changes per 5-year for body weight were (1.12±2.61) kg for men and (0.90±2.54) kg for women; and that for WC was (3.20±3.79) cm for men and (3.83±3.85) cm for women. Among women, low educational level was consistently associated with higher body mass index (BMI) and WC at age 25 years, baseline survey and re-survey. Among men, low educational level was associated with higher BMI at age 25 years. At baseline survey and re-survey, the educational level in men was not statistically associated with BMI; but men who completed junior or senior high school showed slight higher WC and increase of WC from baseline survey to re-survey than other male participants. Conclusions: Body weight and WC increased with age for both men and women. The associations of educational level with BMI and WC were different between men and women.
Adult ; Asian People/statistics & numerical data* ; Body Height ; Body Mass Index ; Body Weight ; China/epidemiology* ; Educational Status ; Female ; Humans ; Male ; Middle Aged ; Obesity/ethnology* ; Risk Factors ; Sex Distribution ; Waist Circumference/ethnology*

Objective: To examine the prevalence of 'healthy lifestyle' from data extracted from the China Kadoorie Biobank (CKB) of 0.5 million adults from ten areas across China. Methods: After excluding participants with self-reported histories of coronary heart disease, stroke or cancer, a total of 487 198 participants at baseline (2004-2008) and 22 604 participants at second survey (2013- 2014), were included for analysis. 'Healthy lifestyle' was defined as haing the following characteristics: a) never smoking or having stopped smoking for reasons other than illness; b) alcohol drinking <25 g/day (men)/<15 g/day (women); c) diet rich in vegetables, fruits, legumes and fish, but low in red meat; d) upper quarter of the physical activity level; e) body mass index of 18.5-23.9 kg/m(2) and waist circumstance <85 cm (men)/80 cm (women). We calculated the healthy lifestyle scores (HLS) by counting the number of all the healthy lifestyle factors, with a range from 0 to 6. Results: At baseline, prevalence rates of the above five healthy lifestyles (except physical activity) were 70.6%, 92.6%, 8.7%, 52.6% and 59.0%, respectively, with the mean HLS being 3.1±1.2. Most participants (81.4%) had2-4 healthy components, while only 0.7% (0.2% in men and 1.0% in women) of all the participants had all six healthy lifestyles. Participants who were women, at younger age, with more schooling and rural residents, were more likely to adhere to the healthy lifestyle. After ten years, the mean HLS showed a slight decrease. Conclusion: The prevalence of optimal lifestyles in Chinese adults appeared extremely low. Levels of 'healthy lifestyle' varied greatly among those populations with different socio-demographic characteristics across the ten areas in China.
Adult ; Asian People/statistics & numerical data* ; China ; Female ; Healthy Lifestyle ; Humans ; Life Style ; Male ; Prevalence ; Risk Factors

Objective: To study the relationship between the age of menarche and the near-term/long-term obesity in adult women. Methods: We analyzed the baseline data of 30 895 women with complete data on menarche and body measurement that was from the China Kadoorie Biobank (CKB) study from 2004 to 2008. The age of menarche was divided into three groups: ≤12, 13-16 and ≥17 years old. Prematurity was set at age ≤12 years. Multivariate logistic regression was used to analyze the effects of menarche age on the near/long-term obesity in female adults. Results: The average menarche age of respondent appeared as (15.64±1.92) years old, with prematurity number as 1 421, accounting for 4.6% of the total numbers. Regarding the postponing of dates of birth, the age of menarche in women showed an advancing tendency. Among all the adult women under study, 803 developed near-term obesity, with the prevalence as 2.6%, while the number of long-term obesity was 3 738, accounting for 12.1%. Refining factors of age, lifestyle, menopausal status, hypertension and diabetes showed that the menarche age was related to the risks of both short-term and long-term obesity in women and the ORs (95%CI) were 2.45 (1.74-3.45) and 1.99 (1.69-2.34), respectively. There was no multiplicative interaction shown between the menarche age and menopausal status on long-term obesity (P=0.324). Conclusion: Premature menarche appeared a risk factor for near-term/long-term obesity in adult females.
Adolescent ; Adult ; Age Factors ; China/epidemiology* ; Diabetes Mellitus/epidemiology* ; Female ; Humans ; Menarche ; Obesity/epidemiology* ; Risk Factors