Xuexian Fang

Promises and Challenges of Big Data Computing in Health Sciences

With the development of smart devices and cloud computing, more and more public health data can be collected from various sources and can be analyzed in an unprecedented way. The huge social and academic impact of such developments caused a worldwide buzz for big data. In this review article, we summarized the latest applications of Big Data in health sciences, including the recommendation systems in healthcare, Internet-based epidemic surveillance, sensor-based health conditions and food safety monitoring, Genome-Wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL), inferring air quality using big data and metabolomics and ionomics for nutritionists. We also reviewed the latest technologies of big data collection, storage, transferring, and the state-of-the-art analytical methods, such as Hadoop distributed file system, MapReduce, recommendation system, deep learning and network Analysis. At last, we discussed the future perspectives of health sciences in the era of Big Data. We explained the steps for Big Data projects: 1. Formulate your question; 2. Find the right ways (smart devices, Internet, hospitals ?) to collect your data; 3. Store the data; 4. Analyze your data; 5. Generate the analysis report with vivid visualization. 6. Evaluate the project: problem solved or start over. The latest applications of Big Data in health sciences were reviewed. The cutting edge computational technologies of big data collection, storage, transferring, and the state-of-the-art analytical methods were introduced. The future perspectives of health sciences in the era of Big Data were discussed.

Landscape of dietary factors associated with risk of gastric cancer: A systematic review and dose-response meta-analysis of prospective cohort studies

BACKGROUND The associations between dietary factors and gastric cancer risk have been analysed by many studies, but with inconclusive results. We conducted a meta-analysis of prospective studies to systematically investigate the associations. METHODS Relevant studies were identified through searching Medline, Embase, and Web of Science up to June 30, 2015. We included prospective cohort studies of intake of dietary factors with risk estimates and 95% confidence intervals for gastric cancer. RESULTS Seventy-six prospective cohort studies were eligible and included in the analysis. We ascertained 32,758 gastric cancer cases out of 6,316,385 participants in relations to intake of 67 dietary factors, covering a wide ranging of vegetables, fruit, meat, fish, salt, alcohol, tea, coffee, and nutrients, during 3.3 to 30 years of follow-up. Evidence from this study indicates that consumption of total fruit and white vegetables, but not total vegetables, was inversely associated with gastric cancer risk. Both fruit and white vegetables are rich sources of vitamin C, which showed significant protective effect against gastric cancer by our analysis too. Furthermore, we found concordant positive associations between high-salt foods and gastric cancer risk. In addition, a strong effect of alcohol consumption, particularly beer and liquor but not wine, on gastric cancer risk was observed compared with nondrinkers. Dose-response analysis indicated that risk of gastric cancer was increased by 12% per 5 g/day increment of dietary salt intake or 5% per 10 g/day increment of alcohol consumption, and that a 100 g/day increment of fruit consumption was inversely associated with 5% reduction of risk. CONCLUSION This study provides comprehensive and strong evidence that there are a number of protective and risk factors for gastric cancer in diet. Our findings may have significant public health implications with regard to prevention of gastric cancer and provide insights into future cohort studies and the design of related clinical trials.

Obesity and iron deficiency: a quantitative meta-analysis

Hypoferraemia (i.e. iron deficiency) was initially reported among obese individuals several decades ago; however, whether obesity and iron deficiency are correlated remains unclear. Here, we evaluated the putative association between obesity and iron deficiency by assessing the concentration of haematological iron markers and the risks associated with iron deficiency in both obese (including overweight) subjects and non‐overweight participants. We performed a systematic search in the databases PubMed and Embase for relevant research articles published through December 2014. A total of 26 cross‐sectional and case–control studies were analysed, comprising 13,393 overweight/obese individuals and 26,621 non‐overweight participants. Weighted or standardized mean differences of blood iron markers and odds ratio (OR) of iron deficiency were compared between the overweight/obese participants and the non‐overweight participants using a random‐effects model. Compared with the non‐overweight participants, the overweight/obese participants had lower serum iron concentrations (weighted mean difference [WMD]: −8.37 μg dL−1; 95% confidence interval [CI]: −11.38 to −5.36 μg dL−1) and lower transferrin saturation percentages (WMD: 2.34%, 95% CI: −3.29% to −1.40%). Consistent with this finding, the overweight/obese participants had a significantly increased risk of iron deficiency (OR: 1.31; 95% CI: 1.01–1.68). Moreover, subgroup analyses revealed that the method used to diagnose iron deficiency can have a critical effect on the results of the association test; specifically, we found a significant correlation between iron deficiency and obesity in studies without a ferritin‐based diagnosis, but not in studies that used a ferritin‐based diagnosis. Based upon these findings, we concluded that obesity is significantly associated with iron deficiency, and we recommend early monitoring and treatment of iron deficiency in overweight and obese individuals. Future longitudinal studies will help to test whether causal relationship exists between obesity and iron deficiency.

Characterization of ferroptosis in murine models of hemochromatosis

Ferroptosis is a recently identified iron‐dependent form of nonapoptotic cell death implicated in brain, kidney, and heart pathology. However, the biological roles of iron and iron metabolism in ferroptosis remain poorly understood. Here, we studied the functional role of iron and iron metabolism in the pathogenesis of ferroptosis. We found that ferric citrate potently induces ferroptosis in murine primary hepatocytes and bone marrow–derived macrophages. Next, we screened for ferroptosis in mice fed a high‐iron diet and in mouse models of hereditary hemochromatosis with iron overload. We found that ferroptosis occurred in mice fed a high‐iron diet and in two knockout mouse lines that develop severe iron overload (Hjv–/– and Smad4Alb/Alb mice) but not in a third line that develops only mild iron overload (Hfe–/– mice). Moreover, we found that iron overload–induced liver damage was rescued by the ferroptosis inhibitor ferrostatin‐1. To identify the genes involved in iron‐induced ferroptosis, we performed microarray analyses of iron‐treated bone marrow–derived macrophages. Interestingly, solute carrier family 7, member 11 (Slc7a11), a known ferroptosis‐related gene, was significantly up‐regulated in iron‐treated cells compared with untreated cells. However, genetically deleting Slc7a11 expression was not sufficient to induce ferroptosis in mice. Next, we studied iron‐treated hepatocytes and bone marrow–derived macrophages isolated from Slc7a11–/– mice fed a high‐iron diet. Conclusion: We found that iron treatment induced ferroptosis in Slc7a11–/– cells, indicating that deleting Slc7a11 facilitates the onset of ferroptosis specifically under high‐iron conditions; these results provide compelling evidence that iron plays a key role in triggering Slc7a11‐mediated ferroptosis and suggest that ferroptosis may be a promising target for treating hemochromatosis‐related tissue damage. (Hepatology 2017;66:449–465).

Dietary intake of heme iron and risk of cardiovascular disease: A dose–response meta-analysis of prospective cohort studies

BACKGROUND AND AIMS Iron is thought to play a fundamentally important role in the development of cardiovascular disease (CVD). This meta-analysis was performed to investigate the dose-response association between dietary intake of iron (including heme and non-heme iron) and the risk of CVD. METHODS AND RESULTS We performed a search of the PubMed and Embase databases for prospective cohort studies of the association between dietary iron intake and CVD risk. Thirteen articles comprising 252,164 participants and 15,040 CVD cases were eligible for inclusion. Heme iron intake was associated significantly with increased risk of cardiovascular disease, and the pooled relative risk (RR) for each 1 mg/day increment was 1.07 (95% confidence interval: 1.01 to 1.14, I² = 59.7%). We also found evidence of a curvilinear association (P < 0.05 for non-linearity). In contrast, we found no association between CVD risk and dietary non-heme (0.98, 0.96 to 1.01, I² = 15.8%) or total iron (1.00, 0.94 to 1.06, I² = 30.4%). Subgroup analyses revealed that the association between heme iron intake and CVD risk was stronger among non-fatal cases (1.19, 1.07-1.33) and American patients (1.31, 1.11-1.56). CONCLUSIONS Higher dietary intake of heme iron is associated with an increased risk of cardiovascular disease, whereas no association was found between CVD and non-heme iron intake or total iron intake. These findings may have important public health implications with respect to preventing cardiovascular disease.

Pleiotropic actions of iron balance in diabetes mellitus

As an essential element, iron plays a central role in many physiological processes, including redox balance, inflammation, energy metabolism, and environment sensing. Perturbations in iron homeostasis are associated with several conditions, including hyperglycemia and diabetes, both of which have been studied in patients and animal models. To clarify the pleiotropic role of iron homeostasis in diabetes development, the early studies on diseases with iron-overload, studies on clinical iron depletion therapies, associations between iron-related genetic polymorphisms and diabetes, and etiological mechanisms underlying iron perturbations-impaired insulin secretion and insulin sensitivity were carefully reviewed and discussed. Hereditary hemochromatosis, transfusion-dependent thalassemia, and excess heme iron intake can increase the risk of developing diabetes. Genetically modified mice and mice fed a high-iron diet present with discrepant phenotypes due to differences in tissue iron distribution. Moreover, several genetic polymorphisms related to iron homeostasis have been associated with the risk of developing diabetes. Tightly controlled iron metabolism is essential for insulin secretion and insulin sensitivity, and iron overload in pancreatic islets alters reactive oxygen species (ROS) generation, as well as hypoxia-inducible factor-1α (HIF-1α) stability and adenosine triphosphate (ATP) synthesis, thereby impairing the function and viability of β-cells. Decreased levels of adiponectin, macrophage-mediated inflammation, and ROS-mediated liver kinase B1 (LKB1)/adenosine monophosphate-activated protein kinase (AMPK) activation can contribute to iron overload-induced insulin resistance, whereas iron deficiency could also participate in obesity-related inflammation, hypoxia, and insulin resistance. Because iron homeostasis is closely correlated with many metabolic processes, future studies are needed in order to elucidate the finely tuned network among iron homeostasis, carbohydrate and lipid metabolism, inflammation, and hypoxia.

Association of Levels of Physical Activity With Risk of Parkinson Disease: A Systematic Review and Meta-analysis

Key Points Question What is the association between physical activity and the risk of Parkinson disease? Findings In this systematic review and meta-analysis of more than half a million unique participants, physical activity, particularly moderate to vigorous physical activity, was associated with a significant reduction in Parkinson disease risk. This association was stronger among men than women. Meaning Physical activity may be an important protective factor for preventing the development of Parkinson disease among at-risk men; thus, large prospective studies should be performed to examine this association and to investigate the factors that underlie the observed sex difference.

Serum ferritin in combination with prostate-specific antigen improves predictive accuracy for prostate cancer

Ferritin is highly expressed in many cancer types. Although a few studies have reported an association between high serum ferritin levels and an increased risk of prostate cancer, the results are inconsistent. Therefore, we performed a large case-control study consisting of 2002 prostate cancer patients and 951 control patients with benign prostatic hyperplasia (BPH). We found that high ferritin levels were positively associated with increased serum prostate-specific antigen (PSA) levels and prostate cancer risk; each 100 ng/ml increase in serum ferritin increased the odds ratio (OR) by 1.20 (95% CI: 1.13−1.36). In the prostate cancer group, increased serum ferritin levels were significantly correlated with higher Gleason scores (p < 0.001). Notably, serum PSA values had even higher predictive accuracy among prostate cancer patients with serum ferritin levels > 400 ng/ml (Gleason score + total PSA correlation: r = 0.38; Gleason score + free PSA correlation: r = 0.49). Moreover, using immunohistochemistry, we found that prostate tissue ferritin levels were significantly higher (p < 0.001) in prostate cancer patients (n = 129) compared to BPH controls (n = 31). Prostate tissue ferritin levels were also highly correlated with serum ferritin when patients were classified by cancer severity (r = 0.81). Importantly, we found no correlation between serum ferritin levels and the inflammation marker C-reactive protein (CRP) in prostate cancer patients. In conclusion, serum ferritin is significantly associated with prostate cancer and may serve as a non-invasive biomarker to complement the PSA test in the diagnosis and prognostic evaluation of prostate cancer.

Dietary intake of heme iron and body iron status are associated with the risk of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES Some potential role of iron overload in the development of diabetes mellitus have been suggested. Our study aimed to systematically assess the association between the risk of gestational diabetes mellitus (GDM) and iron intakes/body iron status. METHODS AND STUDY DESIGN PubMed and Web of Science were searched for relevant articles. Relative risks (RR) of GDM in relation to dietary iron intakes and body iron stores were pooled with the random-effects model. Weighted mean differences of iron blood markers between GDM and non-GDM individuals were also analyzed. RESULTS Twenty-five studies were included in the qualitative analysis, and 23 studies with 29,378 participants and 3,034 GDM patients were included in the quantitative analysis. Dietary intake of heme iron was significantly associated with GDM risk (RR=1.65, 95% CI: 1.28 to 2.12), and the pooled RR for each 1mg/day increment of heme iron intake was 1.38 (95% CI: 1.19 to 1.61). No association between GDM and the intakes of nonheme iron, total iron, or supplemental iron was detected. Body iron stores, as represented by serum ferritin level, were correlated with GDM risk (RR=1.64, 95% CI: 1.27 to 2.11). Moreover, the concentrations of both serum ferritin and serum iron were increased in GDM patients, compared with non-GDM individuals. CONCLUSIONS Increased dietary intake of heme iron and body iron status are positively associated with the risk of GDM development in pregnant women. Future studies are warranted to better understand the role of iron in GDM development.

Cardiomyocyte-specific deletion of ferroportin using MCK-Cre has no apparent effect on cardiac iron homeostasis

a Department of Nutrition, Research Center for Nutrition andHealth, Institute ofNutrition and Food Safety, School of PublicHealth, School ofMedicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310058, China b The First Affiliated Hospital, Institute of Translational Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Medicine, Zhejiang University, Hangzhou 310058, China c Department of Nutrition, Research Center for Nutrition and Health, College of Public Health, Zhengzhou University, Zhengzhou 450001, China d Shandong Provincial Collaborative Innovation Center for Neurodegenerative Disorders, Qingdao University, 266071, China