BackgroundMalaria 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.MethodsTo 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.ResultsThe 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).ConclusionThis 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.
BackgroundC-reactive protein (CRP) has been used as a biomarker of chronic low-grade inflammation in observational studies. We aimed to determine whether genetically determined CRP was associated with hundreds of human phenotypes to guide anti-inflammatory interventions.MethodsWe used individual data from the UK Biobank to perform a phenome-wide two-stage least squares (2SLS) Mendelian randomization (MR) analysis for CRP with 879 diseases. Summary-level data from the FinnGen consortium were utilized to perform phenome-wide two-sample MR analysis on 821 phenotypes. Systematic two-sample MR methods included MR-IVW, MR-WME, MR-Mod, and MR-PRESSO as sensitivity analyses combined with multivariable MR to identify robust associations. Genetic correlation analysis was applied to identify shared genetic risks.ResultsWe found genetically determined CRP was robustly associated with 15 diseases in the UK Biobank and 11 diseases in the FinnGen population (P < 0.05 for all MR analyses). CRP was positively associated with tongue cancer, bronchitis, hydronephrosis, and acute pancreatitis and negatively associated with colorectal cancer, colon cancer, cerebral ischemia, electrolyte imbalance, Parkinson’s disease, epilepsy, anemia of chronic disease, encephalitis, psychophysical visual disturbances, and aseptic necrosis of bone in the UK Biobank. There were positive associations with impetigo, vascular dementia, bipolar disorders, hypercholesterolemia, vertigo, and neurological diseases, and negative correlations with degenerative macular diseases, metatarsalgia, interstitial lung disease, and idiopathic pulmonary fibrosis, and others. in the FinnGen population. The electrolyte imbalance and anemia of chronic disease in UK Biobank and hypercholesterolemia and neurological diseases in FinnGen pass the FDR corrections. Neurological diseases and bipolar disorders also presented positive genetic correlations with CRP. We found no overlapping causal associations between the populations. Previous causal evidence also failed to support these associations (except for bipolar disorders).ConclusionsGenetically determined CRP was robustly associated with several diseases in the UK Biobank and the FinnGen population, but could not be replicated, suggesting heterogeneous and non-repeatable effects of CRP across populations. This implies that interventions at CRP are unlikely to result in decreased risk for most human diseases in the general population but may benefit specific high-risk populations. The limited causal evidence and potential double-sided effects remind us to be cautious about CRP interventions.
Gastric cancer (GC) is a type of cancer that is commonly diagnosed worldwide due to a lack of early diagnostic, prognostic and therapeutic targets for this disease. The aim of the present study was to examine the expression levels of five long non-coding RNAs, namely PTPRG antisense RNA 1 (PTPRG-AS1), forkhead box P4 antisense RNA 1 (FOXP4-AS1), bladder cancer-associated transcript 2 (BLACAT2), ZXF2 and upregulated in colorectal cancer (UCC), to study their associations with patient characteristics and assess their prognostic efficacy, in order to determine the possibility of their application as GC biomarkers. The expression levels of long non-coding RNAs (lncRNAs) were determined by reverse transcription-quantitative PCR analysis of 61 pairs of GC tissues and adjacent healthy gastric mucosa tissues and GC cell lines. The Chi-square test was conducted to assess the associations of lncRNA expression levels with clinical characteristics of patients. The effect of UCC on GC cell proliferation was determined using in vitro functional experiments. The prognostic efficacy of FOXP4-AS1, BLACAT2 and UCC were examined in the Gene Expression Profiling Interactive Analysis database and those of PTPRG-AS1 were examined in the Kaplan Meier Plot database. Gene alteration frequencies of PTPRG-AS1 and BLACAT2 in GC were identified using the cBioPortal for Cancer Genomics. PTPRG-AS1, FOXP4-AS1, BLACAT2, ZXF2 and UCC were found to be upregulated in GC cell lines and GC tissues compared with adjacent normal tissues. PTPRG-AS1 and ZXF2 expression levels were associated with the expression status of the cell proliferation marker Ki67. UCC promoted the proliferation of GC cells in vitro and was associated with lymph node metastasis. Increased expression of FOXP4-AS1 indicated a favorable outcome in terms of disease-free survival, whereas high expression of PTPRG-AS1 was associated with poor survival rates for patients in different GC risk groups. BLACAT2 gene mutation was associated with poor disease-free survival outcome for patients with GC. The results suggest that PTPRG-AS1, FOXP4-AS1, BLACAT2, ZXF2 and UCC are potential biomarkers for the detection of GC at the molecular level and may be used as potential targets for GC therapy. The individual roles of these lncRNAs may be utilized for prognostic predictions.
Background Limited studies have compared the association between various physical measurements and the risk of cancer or cardiovascular disease (CVD). We aim to explore the best‐individualized indicators of cancer and CVD risk assessment. Methods From May 2004 to December 2017, a community‐based cohort in China involving 100 280 participants were enrolled. BMI, height, body surface area (BSA), and body fat percentage (BFP) were compared in parallel about cancer and CVD risk with the multivariable‐adjusted Cox proportional hazard regression model. Results Within the follow‐up period, 3107 (3.10%) were diagnosed with cancer and 3721 (3.71%) had CVD. Per‐level increased (in tertile: T1, T2, and T3 level) BSA, height, and BFP was positively associated with the risk of overall cancer [HR (95% CI): 1.10 (1.05‐1.15), 1.12 (1.07‐1.18), and 1.10 (1.03‐1.16), respectively], whereas BMI was insignificant. Compared with the reference group (T2), the highest BSA level (T3) was positively associated with overall cancer incidence for both male [HR (95% CI): 1.28 (1.13‐1.45)] and female [HR (95% CI): 1.13 (1.00‐1.28)]. The BSA, height, and BFP also significantly associated with some site‐specific cancers including thyroid, stomach, breast, urinary system, and skin cancer. Meanwhile, BFP presented a strong positive association with overall CVD [HR (95% CI): 1.22 (1.15‐1.30) in trend] in both gender and associated with nearly all CVD subtypes especially the myocardial infarction and heart failure. Conclusion BSA, height, and BFP have more sensitivity in assessing cancer risk and BFP shows the largest hazard ratios for CVD incident. We provided valuable evidence for the application of height, BSA, and BFP in routine healthcare practice. These encouraging findings should be tested in more well‐defined studies for risk prediction.
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