Cardiovascular Disease Risk Across a Spectrum of Adverse Plasma Lipid Combinations by Gender and Glycemic Status.

High triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C) and high non-HDL-C levels are risk factors for cardiovascular disease (CVD). It is unclear whether the combinations of their adverse changes are related with CVD risk in different gender and diabetes status, particularly in Chinese population. This study aims to evaluate the CVD risk associated with different adverse lipid combinations. A total of 38,989 participants from Chinese Multicenter Longitudinal Health Management Cohorts (mean age 42 years; 62% male) without baseline CVD were followed up for incident CVD from 2007 to 2015. Participants with various combinations of baseline TG, non-HDL-C, and HDL-C levels within- or out of range according to Adult Treatment Panel III were grouped into 8 distinct lipid categories. Cox models estimated the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of different lipid categories for CVD. After multivariable adjustment, a low level of HDL-C combined with either a high level of non-HDL-C alone or TG alone were associated with increased CVD risk with adjusted HRs (95% CIs) of 1.77 (1.36 to 2.30) and 2.08 (1.30 to 3.34) in male participants. Diabetic participants with high non-HDL-C and low HDL-C levels (adjusted HR 2.93, 95% CI 1.15 to 7.46), and non-diabetic participants with high TG and low HDL-C levels (adjusted HR 1.73, 95% CI 1.33 to 2.26) had greater risk of incident CVD. These relations remained significant when limited analysis to participants with normal LDL-C levels of <3.4 mmol/L, indicating the various combinations of out-of-range lipid profiles other than LDL-C are associated with different CVD risk and the associations depend on gender and glycemic status.

Postnatal length and weight growth velocities according to Fenton reference and their associated perinatal factors in healthy late preterm infants during birth to term-corrected age: an observational study.

BACKGROUND: Optimum early postnatal growth is critical for early and later health of preterm infants. Postnatal length and weight growth velocities and their associated perinatal factors in healthy late preterm infants without restriction of neonatal complications and nutritional problems have not been widely studied. METHODS: As part of ongoing longitudinal follow-up study of growth and development of preterm infants in Shandong Qianfoshan Hospital in China, 599 healthy late preterm infants without neonatal complications and nutritional problems were sampled from 795 preterm infants born in January 2014 to April 2017. Perinatal factors, growth parameters, growth velocities(ΔLengthZ and ΔWeightZ: Z-score changes of length and weight) during birth and term-corrected age were documented. Associated variables of growth velocities were analyzed by bivariate and multivariate regression analyses. Adjusted ΔLengthZ and ΔWeightZ were compared between/among subgroups of associated variables using analysis of covariance. Catch-up growth were defined as ΔLengthZ or ΔWeightZ > 0.67. RESULTS: The mean ΔLengthZ and ΔWeightZ were 0.28, 0.65, respectively. Catch-up growth of length and weight was ubiquitous(30.7, 46.2%, respectively). Faster length growth velocity was associated with male, larger postmenstrual age(PMA) at birth, younger mother and larger PMA at visit; Faster weight growth velocity was associated with male, unfavorable intrauterine growth status defined by birth weight percentile(Small-for-Gestational-Age(P90)), twin and larger PMA at visit. When adjusted for associated co-variables, weight catch-up growth existed in subgroups of 36 weeks PMA at birth, male, twin and SGA, while AGA almost reached this standard with mean adjusted ΔWeightZ as 0.66. Although none of these subgroups got length catch-up growth standard, infants of 36 weeks PMA at birth had statistically rapider length growth velocity than 34 and 35 weeks PMA at birth subgroups(mean adjusted ΔLengthZs of 34, 35 and 36 weeks subgroups: 0.10, 0.22, 0.38, respectively). CONCLUSIONS: Postnatal length and weight growth velocities of healthy late preterm infants from birth to term-corrected age were much superior than that of Fenton reference, especially for weight, with ubiquitous catch-up growth. Different associated factors for length and weight growth signified the necessity of constructing more detailed growth standards by specific stratification for associated factors.

Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000-2015.

BACKGROUND: Malaria prevalence in Cameroon is a major public health problem both at the regional and urban-rural geographic scale. In 2016, an estimated 1.6 million confirmed cases, and 18,738 cases were reported in health facilities and communities respectively, with about 8000 estimated deaths. Several studies have estimated malaria prevalence in Cameroon using the analytical techniques at the regional scale. We aimed at identifying malaria clusters and hotspots at the urban-rural geographic scale from the Demographic and Health Survey (DHS) data for households between 2000 and 2015 using ArcGIS for intervention programs. METHODS: To identify malaria hotspots and analyze the pattern of distribution, we used the optimized hotspots toolset and spatial autocorrelation respectively in ArcGIS 10.3 for desktop. We also used Pearson's Correlation analysis to identify associative environmental factors using the R-software 3.4.1. RESULTS: The spatial distribution of malaria showed statistically significant clustered pattern for the year 2000 and 2015 with Moran's indexes 0.126 (P < 0.001) and 0.187 (P < 0.001) respectively. Meanwhile, the years 2005 and 2010 with Moran's indexes 0.001 (P = 0.488) and 0.002 (P = 0.318) respectively, had a random malaria distribution pattern. There exist varying degrees of malaria clusters and statistically significant hotspots in the urban-rural areas of the 12 administrative regions. Malaria cases were associated with population density and some environmental covariates; rainfall, enhanced vegetation index and composite lights (P < 0.001). CONCLUSION: This study identified urban-rural areas with high and low malaria clusters and hotspots. Our maps can be used as supportive tools for effective malaria control and elimination, and investments in malaria programs and research, malaria prevention, diagnosis and treatment, surveillance, should pay more attention to urban-rural geographic scale.

Development and validation of a nomogram for predicting survival in Chinese han patients with resected colorectal cancer.

BACKGROUND: Estimates of survival after curative colorectal cancer (CRC) surgery are the basis of patient care and treatment planning. A nomogram is a useful tool for individualized cancer prognosis. METHODS: A total of 2450 patients with nonmetastatic CRC were included to develop a nomogram. Prognostic factors were identified and integrated by the Cox proportional hazards model. Then, we developed and validated a prognostic nomogram. The performance of this model was assessed by the concordance index (C-index) and a calibration curve. The nomogram was internally validated by bootstrapping and externally validated with a separate database of 299 patients from The Cancer Genome Atlas. RESULTS: Age, T stage, N stage, histological type, tumor location, lymph-vascular invasion, preoperative carcinoembryonic antigen, and sample lymph nodes were integrated into the nomogram. The C-index of the nomogram for predicting overall survival was higher than that of the seventh edition American Joint Committee on Cancer TNM staging system (training data set, 0.76 vs 0.68, respectively; P < 0.001; validation data set, 0.78 vs 0.69, respectively; P = 0.003). CONCLUSION: We developed a prognostic nomogram for patients with nonmetastatic CRC, which could provide a more individualized outcome prognostication than that afforded by the TNM staging system by using common clinicopathologic factors.