Objective: To assess the status of handgrip strength of elderly population from longevity areas in China, and to analyze the correlative factors of handgrip strength of elderly people. Methods: Data from Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2012 was used, from which1 967 participants aged ≥60 years old with valid data of grip strength value from 8 Chinese longevity areas were included. Information on demographics characteristic, life style and health status was collected using questionnaires. The handgrip strength of both left and right hands were measured by grip dynamometer. The different characteristics of group of participants with different grip strength were compared and then analyzed by adopting the Cumulative odds Logistic regression model to identify main factors associated with hand grip strength. Results: The P₅₀ (P₂₅-P₅₀) of hand grip strength of elderly people from the eight longevity areas was 20 (11-28) kg; The hand grip strength of males was 26 (18-34) kg, which was higher than that of females(14 (9-20) kg) (P<0.001). Cumulative odds Logistic regression model showed that the hand grip strength of females was lower than males, whose β value (95%CI) was-1.22 (-1.43--1.00). The elderly who was at a higher age, smoking, drinking or with anemia, had a comparatively lower handgrip strength, whose β (95%CI) value were separately-0.08(-0.09~-0.07),-0.29(-0.56~-0.02),-0.54(-0.80~-0.28), and-0.41(-0.62~-0.20). And the elderly who had a higher boby mass index, drinking tea and outdoor activities, had a comparatively higher handgrip strength, whose β(95%CI) value were separately 0.28 (0.15-0.40), 0.25(0.03-0.47) and 0.51(0.30-0.71). Conclusion Age and gender were the main correlative factors, lifestyles and physical conditions might also be correlative factors of hand grip strength of the elderly from longevity areas in China.

Objective: To investigate the influence factors of survival outcome among elderly aged ≥80 years old. Methods: In baseline survey in 2009, 930 participants aged ≥80 years old were enrolled from 7 longevity areas, to collect the information of socioeconomic factors, life style, cognitive function, activities of daily living and diseases, as well as physical examination to test biomarkers of blood and urine. The survival status was followed up at 2012 and 2014 survey. Stepwise Cox proportional hazards models were used to screen influence factors of 5-year survival. Results: During 5 years of follow-up, 571 participants died, 133 participants were lost to follow up, and the all-cause mortality was 63.4%. In stepwise Cox proportional hazards models, male, unmarried, self-reported poor life quality, disability in daily life, cognitive impairment, cardiovascular and cerebrovascular diseases, chronic kidney diseases were risk factors for elderly survival outcome, with the HR (95%CI) at 1.75 (1.40-2.12), 1.49 (1.10-2.03), 1.40 (1.16-1.69), 1.37 (1.11-1.70), 1.51 (1.22-1.88), 1.62 (1.18-2.23) and 1.48 (1.23-1.77) respectively. Each 1 year increase in age corresponded to 4% increase in mortality risk (HR (95%CI)=1.04 (1.02-1.05)); each 1 kg/m² increase in BMI corresponded to 5% increase in mortality risk (HR (95%CI)=0.95 (0.93-0.98)); each 1.0×10⁹/L increase in total lymphocyte count (TLC) corresponded to 13% increase in mortality risk (HR (95%CI)=0.87 (0.76-0.99)). Additionally, the mortality risk decreased 19% (HR (95%CI)=0.81 (0.66-0.98)) in participants with regularly physical exercise compared to those without; and the mortality risk decreased 41% (HR (95% CI)=0.59 (0.40-0.88)) in participants with elevated triglycerides (TG, ≥2.26 mmol/L) compared to those without. Conclusion In Chinese longevity areas, some nutritional and immune indices such as relatively higher level of BMI, TLC and TG were independent protective factors for 5-year survival outcome, which was different from general adults and younger elderly.

Objective To explore association between serum albumin level and cognitive performance in elderly Chinese.Methods All the subjects aged ≥65 years in the 8 longevity areas in Chinese longitudinal health longevity survey (CLHLS) were invited to participate the biomedical in-depth CLHLS study,information about subjects' demographic characteristics,lifestyle,prevalence of diseases and health status was collected through household-interview.The cognitive performance was assessed with Mini Mental State Evaluation (MMSE) scale.Health examination was conducted by medical personnel and fasting venous blood samples were collected to detect the levels of triglycerides,total cholesterol,fasting glucose,creatine and blood albumin.MMSE score was compared and the trend was analyzed with generalized linear model.Association between albumin concentration and cognitive impairment was analyzed by logistic regression model.Results Generalized linear model showed that adjusted MMSE score increased from 23.22 in the lowest quartile group to 25.07 in the highest quartile group (P for linear trend ＜0.001).Logistic regression analysis results showed that the higher albumin level was associated with the lower risk of cognitive impairment (P＜ 0.001),the OR decreased linearly with the increasing level of albumin (P＜0.01),with the OR (95％CI) for the lower,higher and highest quartile groups was 0.64(0.45-0.91),0.60(0.40-0.89) and 0.43 (0.27-0.69),respectively,compared with the lowest quartile group.Conclusion High level of serum albumin was associated with low risk of cognitive impairment.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.