Causal relationship between serum uric acid and abnormal blood pressure based on the panel model study: A 5-year cohort study.

BACKGROUND AND AIMS: To investigate the relationship between elevated serum uric acid (SUA) levels and blood pressure (BP). METHODS AND RESULTS: Based on the Beijing Health Management Cohort, 5276 health examination people were enrolled. Cross-lagged model was used to explore the relationship between SUA levels and blood pressure. The results showed: (1) increased SUA and increased systolic blood pressure (SBP): ① The path coefficients from baseline SUA to follow-up SBP were statistically significant in both the general population (ß = 0.034, P < 0.05) and men (ß = 0.048, P < 0.05). The path coefficients from baseline SBP to follow-up SUA were not statistically significant in either the general population (ß = 0.010, P > 0.05) or men (ß = 0.011, P > 0.05). ② The path coefficients from baseline SUA to follow-up SBP and from baseline SBP to follow-up SUA were not statistically significant in women with BMI ≥ 25 kg/m2 and BMI < 25 kg/m2. (2) Increased SUA and diastolic blood pressure (DBP): ① There was no statistical significance between the path coefficients from baseline DBP to follow-up SUA and the path coefficients from baseline SUA to follow-up DBP. ② In men and women, BMI ≥ 25 kg/m2 and BMI < 25 kg/m2, the path coefficients from baseline DBP to follow-up SUA and from baseline SUA to follow-up DBP were not statistically significant. CONCLUSIONS: SUA can increase blood pressure in the general male population; no reverse time sequence relationship was found. The temporal relationships between SUA levels and SBP abnormalities were different in the sex and BMI subgroups. No bidirectional causal temporal relationship was found between SUA elevation and DBP abnormality.

Association between plasma homocysteine and hypertension: Results from a cross-sectional and longitudinal analysis in Beijing's adult population from 2012 to 2017.

Plasma homocysteine (Hcy) levels are associated with elevated blood pressure. However, the causal association between Hcy levels and the risk of hypertension remains ambiguous. Taking the study design effect into consideration, this study aimed to investigate this issue through a cross-sectional and longitudinal analysis. Data were obtained from the Beijing Health Management Cohort study, which conducted routine health check-ups from 2012 to 2017. Multivariate logistic regression was used for the cross-sectional analysis, and a quadratic inference function approach was performed for the longitudinal analysis. A total of 30 376 subjects (mean age = 50.0 years) were included in the cross-sectional analysis, and a subgroup of 3913 subjects without hypertension at baseline was included in the longitudinal analysis. After adjusting for potential confounders, the risk of hypertension increased with Hcy levels in the cross-sectional analysis using the traditional definition of hypertension (OR = 1.262, 95% CI: 1.155-1.378, Q2 vs Q1; OR = 1.458, 95% CI: 1.335-1.593, Q3 vs Q1; OR = 1.520, 95% CI: 1.388-1.664, Q4 vs Q1) and the 2017 hypertension definition (OR = 1.159, 95% CI: 1.067-1.259, Q2 vs Q1; OR = 1.328, 95% CI: 1.221-1.445, Q3 vs Q1; OR = 1.328, 95% CI: 1.217-1.449, Q4 vs Q1). The longitudinal analysis showed that hypertension risk increased in the third quartile of Hcy (OR = 1.268, 95% CI: 1.030-1.560, Q3 vs Q1). Elevated total plasma Hcy may be used as a predictive biomarker for hypertension. Attention should be paid to gender-specific mechanisms when issuing precise precautions.

Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer's disease.

The study is aimed to assess whether the addition of contourlet-based hippocampal magnetic resonance imaging (MRI) texture features to multivariant models improves the classification of Alzheimer's disease (AD) and the prediction of mild cognitive impairment (MCI) conversion, and to evaluate whether Gaussian process (GP) and partial least squares (PLS) are feasible in developing multivariant models in this context. Clinical and MRI data of 58 patients with probable AD, 147 with MCI, and 94 normal controls (NCs) were collected. Baseline contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters based on MRI, and regional CMgl measurement based on fluorine-18 fluorodeoxyglucose-positron emission tomography were included to develop GP and PLS models to classify different groups of subjects. GPR1 model, which incorporated MRI texture features and was based on GPG, performed better in classifying different groups of subjects than GPR2 model, which used the same algorithm and had the same data as GPR1 except that MRI texture features were excluded. PLS model, which included the same variables as GPR1 but was based on the PLS algorithm, performed best among the three models. GPR1 accurately predicted 82.2% (51/62) of MCI convertors confirmed during the 2-year follow-up period, while this figure was 53 (85.5%) for PLS model. GPR1 and PLS models accurately predicted 58 (79.5%) vs. 61 (83.6%) of 73 patients with stable MCI, respectively. For seven patients with MCI who converted to NCs, PLS model accurately predicted all cases (100%), while GPR1 predicted six (85.7%) cases. The addition of contourlet-based MRI texture features to multivariant models can effectively improve the classification of AD and the prediction of MCI conversion to AD. Both GPR and LPS models performed well in the classification and predictive process, with the latter having significantly higher classification and predictive accuracies. Advances in knowledge: We combined contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters, and regional CMgl measurement to develop models using GP and PLS algorithms to classify AD patients.

Association of Waist Circumference Gain and Incident Prediabetes Defined by Fasting Glucose: A Seven-Year Longitudinal Study in Beijing, China.

The risk of incident prediabetes with gain in waist circumference (WC) has not been addressed among Chinese adults. A total of 7951 participants who underwent health check-ups at the Beijing Physical Examination Center and Beijing Xiaotangshan hospital were recruited in 2009 and followed up in 2016. Participants were classified into four groups according to categories of percent WC gain: ≤-2.5%, -2.5-2.5%, 2.5-5%, and >5%. The effect of WC gain on prediabetes was evaluated using modified Poisson regression models. Over seven years of follow-up, we identified 1034 prediabetes cases (413 women). Compared with a WC gain of ≤-2.5%, participants with a WC gain of >5% have a higher risk of prediabetes, be they male (non-abdominal obesity at baseline group: RR = 1.57, 95% CI: 1.10-2.24, abdominal obesity at baseline group: RR = 1.66, 95% CI: 1.20-2.30) or female (non-abdominal obesity at baseline group: RR = 1.74, 95% CI: 1.14-2.64, abdominal obesity at baseline group: RR = 2.47, 95% CI: 1.43-4.28). In conclusion, the risk of prediabetes increased significantly with increasing WC for both genders in the Chinese population. Lifestyle interventions aiming at preventing abdominal obesity are urgently needed to reduce the increasing burden of prediabetes, diabetes, and its complications.

BMI and BMI Changes to All-cause Mortality among the Elderly in Beijing: a 20-year Cohort Study.

OBJECTIVE: To explore the association between body mass index (BMI) and all-cause mortality among the elderly in Beijing. METHODS: This analysis was based on the Beijing multidimensional longitudinal study of aging (BLSA), which included 2,090 subjects over 55 years old and was followed-up from 1992 to 2012. BMI-mortality curves were drawn to find the optimal BMI range with the lowest mortality. Cox proportional hazard models were used to obtain the hazard ratios (HRs) for BMI and BMI changes in the overall population and in specific stratified populations. RESULTS: During follow-up, 1,164 deaths were recorded; BMI-mortality curve was U-shaped, with the lowest mortality at a BMI of approximately 25 kg/m2. After adjusting for gender, age, smoking, drinking and some pre-existing diseases, HRs for underweight, overweight and obesity compared with normal weight were 1.372 (95% CI: 1.154-1.631), 0.767 (95% CI: 0.666-0.884) and 0.871 (95% CI: 0.830-1.246), respectively. HR for BMI drop was 3.245 (95% CI: 0.824-12.772) in the underweight group and 1.892 (95% CI: 0.830-1.246) in the normal weight group, HR for BMI rise was 1.795 (95% CI: 1.243-2.591) in normal weight group and 1.962 (95% CI: 1.202-3.203) in the overweight group. CONCLUSION: Keeping BMI in an overweight status and stable is related to a reduced mortality.

Longitudinal Associations between Triglycerides and Metabolic Syndrome Components in a Beijing Adult Population, 2007-2012.

BACKGROUND: Longitudinal associations between triglycerides (TG) and other metabolic syndrome (MetS) components have rarely been reported. The purpose was to investigate the longitudinal association between TG and other MetS components with time. METHODS: The longitudinal study was established in 2007 on individuals who attended health check-ups at Beijing Tongren Hospital and Beijing Xiaotangshan Hospital. Data used in this study was based on 7489 participants who had at least three health check-ups over a period of 5-year follow up. Joint model was used to explore longitudinal associations between TG and other MetS components after adjusted for age. RESULTS: There were positive correlations between TG and other MetS components except for high density lipoprotein (HDL), and the correlations increased with time. A negative correlation was displayed between TG and HDL, and the correlation also increased with time. Among all five pairs of TG and other MetS components, the marginal correlation between TG and body mass index (BMI) was the largest for both men and women. The marginal correlation between TG and fasting plasma glucose was the smallest for men, while the marginal correlation between TG and diastolic blood pressure was the smallest for women. CONCLUSIONS: The longitudinal association between TG and other MetS components increased with time. Among five pairs of TG and other MetS components, the longitudinal correlation between TG and BMI was the largest. It is important to closely monitor subjects with high levels of TG and BMI in health check-up population especially for women, because these two components are closely associated with development of hypertension, diabetes, cardiovascular disease and other metabolic diseases.

Education-Related Parameters in High Myopia: Adults versus School Children.

PURPOSE: Since high myopia in the younger generation may differ etiologically from high myopia in older generations, we examined whether education-related parameters differ between high myopia in today´s school children and high pathological myopia in today´s elderly generation. METHODS: The investigation included the adult populations of the population-based Beijing Eye Study (BES) (3468 adults;mean age:64.6±9.8years;range:50-93years) and Central India Eye and Medical Study (CIEMS) (4711 adults;age:49.±13.2years;range:30-100years), and the children and teenager populations of the Shandong Children Eye Study (SCES) (6026 children;age:9.7±3.3years;range:4-18years;cycloplegic refractometry), Gobi Desert Children Eye Study (1565;age:11.9±3.5years;range:6-21 years;cycloplegic refractometry), Beijing Pediatric Eye Study (681 children;age:7.7±1.6years;range:5-13 years;non-cycloplegic refractometry,calculation of axial length to corneal curvature radius ratio), Beijing Children Eye Study (15066 children;age:13.2±3.4years;range:7-18years;non-cycloplegic refractometry), Beijing High School Teenager Eye Study (4677 children;age:16.9±0.7years;range:16-18years;non-cycloplegic refractometry). RESULTS: In the BES and CIEMS, educational level did not differ significantly between, or was significantly lower in the highly myopic group (myopic refractive error ≥6 diopters) than in the non-highly myopic group. In all non-adult study populations, higher prevalence of high myopia was significantly associated with higher degree of education related parameters such as attendance of high-level schools, and more time spent for indoors near work versus time spent outdoors. CONCLUSIONS: Comparing associations of old or genetic high myopia in adults with new or acquired high myopia in school children revealed that education-related parameters did not show a clear association with old or genetic high myopia, while in contrast, new high myopia showed strong associations with education. It confirms previous studies that the two forms of high myopia not only differed in age of onset, but also in associations with education as well. The data support the notion of two types of high myopia. Future studies may assess whether the risk of pathologic myopic maculopathy and high myopia associated open-angle glaucoma differs between both types of high myopia.

Risk profiles for metabolic syndrome and its transition patterns for the elderly in Beijing, 1992-2009.

There have been few reports on the development of metabolic disorders, especially when they are considered as a cluster. The purpose of this study was to describe risk profiles for metabolic syndrome (MetS) in elderly dwellers in Beijing, and to find their transition patterns over time. Data were derived from Beijing longitudinal study of aging, a community-based cohort study hosted by Xuanwu hospital. There were 3,257 elderly people aged 55 years or over recruited in 1992. MetS was assessed for the years 1992, 2000, and 2009. Finally, 363 subjects with complete information for components of MetS in the three years were included in the study. The criteria of MetS recommended by the joint interim statement criteria were adopted. Latent transition analysis was used to calculate the transition probabilities between adjacent visits. A risk typology consisting of four time-invariant groups was detected based on the components of MetS for all subjects. Low MetS risk group, BP risk group, BP-HDL risk group, and BP-FPG-TG risk group were found. The probability of staying at the same status was higher at the two intervals across 18 years. Four latent groups were extracted based on three assessments for the components of MetS, together with their transition patterns. Findings suggested various trajectories for MetS components. Different combinations of intervention strategy might be needed for MetS risk groups.

Association of hematological parameters with metabolic syndrome in Beijing adult population: a longitudinal study.

The purposes of the study were to estimate the incidence of metabolic syndrome (MetS) and to systematically evaluate the relationship between hematological parameters and MetS in a 5-year follow-up of Beijing adult population. The longitudinal study included 3,180 adults, aged 20-65 years, who attended health check-ups in Beijing Tongren Hospital in 2007 and 2012. Multivariate logistic regression was conducted to explore the associations between hematological parameters and MetS. The 5-year cumulative incidence of MetS in this sample was 10.82 % (14.22 % for males and 7.59 % for females). Among all the hematological parameters, white blood cell count (WBC) was positively associated with MetS for 20-35-year-old (male OR 1.482, 95 % CI 1.169-2.974; female OR 1.398, 95 % CI 1.145-3.011), and 36-50-year-old (male OR 2.012, 95 % CI 1.290-4.010; female OR 3.400, 95 % CI 1.818-4.528) male and female subjects. Alanine aminotransferase (ALT) was significantly associated with the incidence of MetS for males (20-35-year-old OR 2.080, 95 % CI 1.371-3.159; 36-50-year-old OR 2.421, 95 % CI 1.335-3.412; 51-65-year-old OR 4.267, 95 % CI 1.161-6.781). Low-density lipoprotein cholesterol (LDL-C) was positively associated with MetS for 51-65-year-old (male OR 3.078, 95 % CI 2.468-5.131; female OR 2.140, 95 % CI 1.524-4.359) for male and female subjects. WBC is positively associated with MetS for young adults, while LDL-C is positively associated with MetS for elderly people. ALT is positively associated with MetS for males. Our findings provide further evidence in support of using hematological markers for early detection of individuals at risk for MetS.

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Clinical risk factor analysis for breast cancer: 568,000 subjects undergoing breast cancer screening in Beijing, 2009.

OBJECTIVES: Although there are many reports about the risk of breast cancer, few have reported clinical factors including history of breast-related or other diseases that affect the prevalence of breast cancer. This study explores these risk factors for breast cancer cases reported in Beijing in 2009. MATERIALS AND METHODS: Data were derived from a Beijing breast cancer screening performed in 2009, of 568,000 women, from 16 districts of Beijing, all aged between 40 and 60 years. In this study, multilevel statistical modeling was used to identify clinical factors that affect the prevalence of breast cancer and to provide more reliable evidence for clinical diagnostics by using screening data. RESULTS AND CONCLUSION: Those women who had organ transplants, compared with those with none, were associated with breast cancer with an odds ratio (OR) = 65.352 [95% confidence interval (CI): 8.488-503.165] and those with solid breast mass compared with none had OR = 1.384 (95% CI: 1.022- 1.873). Malignant tendency was strongly associated with increased risk of breast cancer, OR = 207.999(95% CI: 151.950-284.721). The risk of breast cancer increased with age, OR1 = 2.759 (95% CI: 1.837-4.144, 56-60 vs. 40-45), OR2 = 2.047 (95% CI: 1.394-3.077, 51-55 vs. 40-45), OR3 = 1.668 (95% CI: 1.145-2.431). Normal results of B ultrasonic examination show a lower risk among participants, OR= 0.136 (95% CI: 0.085-0.218). Those women with ductal papilloma compared with none were associated with breast cancer, OR=6.524 (95% CI: 1.871-22.746). Therefore, this study suggests that clinical doctors should pay attention to these high-risk factors.

Estimation of sample size and testing power (part 2).

This article introduces definitions of three special tests, namely, non-inferiority test (to verify that the efficacy of the experimental drug is clinically not inferior to that of the positive control drug), equivalence test (to verify that the efficacy of the experimental drug is equivalent to that of the control drug) and superiority test (to verify that the efficacy of the experimental drug is superior to that of the control drug), and methods of sample size estimation under the three different conditions. By specific examples, the article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.