The link between psoriasis and obstructive sleep apnea (OSA) has not been confirmed. We aimed to investigate the relationship between psoriasis and OSA. We conducted a systematic review and meta-analysis of case-control, cross-sectional, and cohort studies on the association between psoriasis and OSA. We searched MEDLINE and Embase for relevant studies on May 11, 2019. The Newcastle-Ottawa Scale was used to evaluate the risk of bias of included studies. We performed random-effects model meta-analysis to calculate pooled odds ratio (ORs) with 95% confidence intervals (CIs) for case-control and cross-sectional studies as well as pooled incidence rate ratio (IRR) with 95% CIs for cohort studies in association between psoriasis and OSA. A total of 4 case-control or cross-sectional studies and 3 cohort studies with a total of 5,840,495 subjects were included. We identified a significantly increased odds for OSA in psoriasis patients , and significantly increased risk for psoriasis in OSA patients . In conclusion, our study identified a bidirectional association between psoriasis and OSA. Sleep quality should be inquired in patients with psoriasis. Respirologist consultation or polysomnography may be indicated for those presenting with night snoring, recurrent awaking, and excessive daytime sleepiness. The prevalence was estimated 0.5–11.4% in adults2. Psoriasis is considered as a multisystemic disease involving proinflammatory cytokines such as tumor necrosis factor (TNF), interleukin (IL)-1, and IL-174. Various comorbidities have been linked to psoriasis including cardiovascular disease, metabolic syndrome, chronic kidney disease, uveitis, thyroid diseases, vitiligo, and inflammatory bowel disease11.Psoriasis is a chronic inflammatory skin disease with characteristic feature of sharply circumscribed erythematous plaques with silvery scales on the trunk and limbs13. OSA is characterized with night snoring, recurrent awaking, and excessive daytime sleepiness14. The gold standard test for diagnosing OSA is polysomnography15. The prevalence of OSA ranges from 3 to 17%12. Risk factors of OSA include obesity, aging, male gender, anatomical predisposition, and alcohol consumption13. OSA induces systemic inflammatory and increases the risk of hypertension, stroke, cardiovascular disease, and metabolic disorder, in particular of diabetes mellitus type 2 and metabolic syndrome regardless of the obesity17.Obstructive sleep apnea (OSA), also called obstructive sleep apnea-hypopnea syndrome, is a chronic disorder of intermittent upper airway collapse during sleep resulting in recurrent hypoxia19. The association between psoriasis and OSA has been examined in many studies but the results were limited and inconsistent27. The objective of this study was to assess the evidence regarding the bidirectional association between psoriasis and OSA.Psoriasis and OSA share a common pathogenesis of inflammatory and immune imbalance28. We have registered the study protocol with PROSPERO .We conducted a systematic review and meta-analysis of observational studies  on the association between psoriasis and OSA. The reporting of this study followed the Meta-analysis of Observational studies in Epidemiology (MOOSE) guidelinesWe searched the MEDLINE and Embase for relevant studies from inception to May 11, 2019. The search terms included psoriasis and sleep apnea and their synonyms. The search strategy is shown in the Supplement. No geographic or language limitations were imposed.The criteria for studies to be included were as follows: (1) observational studies investigating the association between psoriasis and OSA, including case-control, cross-sectional, and cohort studies; (2) research on human subjects; (3) the case group consisted of patients with psoriasis and the control group consisted of people without psoriasis in studies that examined the odds or risk of OSA in psoriasis patients; the study group was composed of patients with OSA and the control group was composed of people without OSA in studies that evaluated the odds or risk of psoriasis in OSA patients. We screened titles and abstracts for the initial study selection and obtained the full text of potentially eligible studies to confirm if they met our inclusion criteria. Two authors (T.G. and Y.F.) independently selected studies and disagreement was resolved by consulting the other author (C.C.).We extracted the following data from the included studies: first author, year of publication, country of study, number and gender of study subjects, definition of the case group, definition of outcome, and risk estimates including hazard ratio (HR), incidence rate ratio (IRR), and odds ratio (OR) with 95% confidence interval (CI) on the association between psoriasis and OSA.29. For case-control and cross-sectional studies, we examined the following eight domains: adequacy of case definition, representativeness of cases, selection of controls, definitions of controls, comparability of cases and controls, ascertainment of exposure, same method of ascertainment for cases and controls, and non-response rate. For cohort studies, we evaluated the following eight domains: representativeness of exposed cohort, selection of non-exposed cohort, ascertainment of exposure, absence of outcome of interest at start of study, comparability of cohorts, assessment of outcome, follow-up duration, and adequacy of follow up of cohorts.The risk of bias of included studies was assessed by using the Newcastle-Ottawa Scale30. The statistical heterogeneity was assessed by the I2 statistic across the included studies. An I2 of >50% represents substantial heterogeneity31. We chose random-effects model for meta-analyses because clinical heterogeneity was anticipated. Therefore the DerSimonian and Laird method that takes between-study variability into account was used to obtain the pooled OR and IRR estimates32. Also, we conducted a sensitivity analysis after excluding studies that were rated with a high risk of bias.The Review Manage version 5.3  was used for conducting all analyses. We calculated the OR with 95% CI in included case-control and cross-sectional studies and IRR in included cohort studies. If OR was not reported in an included study, we calculated the crude OR based on published data for example the number of events in the case and control groups. The most fully adjusted OR, HR, or IRR were adopted if reported. We performed meta-analyses to evaluate the bidirectional association between psoriasis and OSA. For included case-control and cross-sectional studies, we calculated pooled OR to examine the association between the two diseases. For cohort studies, we treated HR as IRR and calculated the pooled IRRThe PRISMA flow chart of study selection is shown in Fig. 33. Two studies were rated with an unclear risk of bias in the ‘representativeness of cases’ and ‘selection of controls’ domains because patients were selected from hospital not community35. We rated two studies as unclear risk of bias in the ‘comparability of cases and controls’ domain because only age and gender, but not body mass index, were controlled35. Moreover, three studies were rated as unclear risk in the ‘ascertainment of exposure’ domain because only medical records or ICD codes were employed34. In three included cohort studies, we rated the Cohen 2015 study at high risk of bias in the ‘representativeness of exposed cohort’ domain . One included cohort study illustrated a consistently increased risk for OSA in patients with mild psoriasis , severe psoriasis , and psoriatic arthritis 25.One cohort study27 and one case-control study34 found a consistent increase in psoriasis among patients with OSA. The meta-analysis of three included cohort studies with 5,544,674 study subjects showed a significant association of OSA with psoriasis . The Cohen 2015 study was rated with a high risk of bias in the representativeness of the exposed cohort because all the study subjects were nurses23. However, the association of OSA with psoriasis remained positive after excluding the Cohen 2015 study . One case-control study showed a significantly increased odds for psoriasis in relation to OSA 34.Conversely, three cohort studies35 and a 13-fold increased odds for prevalent psoriasis in relation to OSA34. Similarly, the evidence from an included cohort study demonstrated consistently increased risk for incident OSA among patients with mild and severe psoriasis as well as psoriatic arthritis25. Conversely, OSA patients were 2.52-fold more likely to develop incident psoriasis when compared with non-OSA controls27. Our meta-analysis possesses high generalizability because of including studies from various ethnicities for example the United States, France, Denmark, Greece, Israel, and Taiwan.To the best of our knowledge, the present study is the first meta-analysis investigating the bidirectional association between psoriasis and OSA. The evidence from included case-control and cross-sectional studies found a 2.6-fold greater odds for prevalent OSA in relation to psoriasis36. Patients with psoriasis have been found to have increased body mass index (BMI) than healthy controls37. Besides, OSA was related to obesity and thus weight loss is the recommended fist-line therapy39. Obesity may promote upper airway inflammation, reduce muscle contractibility, and induce airway collapse during sleep40. BMI has been correlated with psoriasis severity37 and OSA26. Among most of included studies in our meta-analysis34, the confounding from obesity has been considered and BMI was adjusted in the statistical analysis. The bidirectional association between psoriasis and OSA remained significant after adjustment for obesity or BMI. Only one included case-control study of Tsai 2011 did not contain any information about BMI or body weight33. Another included cross-sectional study of Sacmaci 2019 did not perform adjustment for obesity, but there was no significant difference in BMI between psoriasis patients and non-psoriasis controls 35. Therefore, confounding by obesity could not fully explain the association between psoriasis and OSA.Obesity is a trigger for inflammation and has been linked to chronic inflammatory diseases19. OSA is considered a systemic inflammatory disorder. High levels of systemic inflammatory molecules are part of mechanism leading to OSA42. The activities of IL-17, TNF, IL-6, IL-7 and C-reactive protein are significantly increased in patients with OSA compare to obese patients45. These pro-inflammatory cytokines decreased after continuous positive airway pressure (CPAP) treatment44. Studies have shown that the circadian rhythm of TNF release was significantly disturbed in patients with OSA47. The inflammatory process may predispose them to the development of psoriasis27. Elevated circulating levels of IL-17, TNF, IL-6, and IL-22 were also associated with psoriasis48. One previous study found that treatment with etanercept significantly improved symptoms of OSA in moderate to severe psoriasis49. The increase of IL-17 is related to atherosclerotic vascular disease, which is a risk factor for OSA50. Moreover, psoriasis causes pruritus and may lead to sleep disturbance, which contributes to systemic inflammation and consequently induces OSA53.The bidirectional association between psoriasis and OSA suggest the shared common systemic inflammatory pathogenic pathways55. Some scholars assumed that psoriasis-related itching and pain could disrupt sleep and thus increase autonomic activation, which possibly induces OSA54. On the other hand, Gabryelska et al. proposed low grade inflammation in OSA patients with elevated levels of IL-1 and TNF could stimulate hypothalamus and increase hypothalamic-pituitary-adrenal activity with resultant surging of autonomic activity56. The inflammation and autonomic activation involved in OSA may be a risk factor for psoriasis55.Autonomic activation is considered an important factor between OSA and psoriasis57. HIF-1α, a transcriptional regulator of cell oxygen metabolism, is a specific marker for diagnosing OSA58. The levels of HIF-1α correlate with the severity of OSA and can be effectively reduced by CPAP treatment57. Besides, vascular risk response to hypoxia was detected in OSA patients. A significant upregulation of NF-κB, HIF-1α, endothelial nitric oxide synthase, vascular cell adhesion molecule 1, and vascular endothelial growth factor (VEGF) were expressed in the skin biopsy specimens of OSA patients59. Increased circulation of VEGF promotes angiogenesis which is also an important process in initiating psoriasis60. VEGF is a marker of tissue response to hypoxia and activated by HIF-1α59. There is evidence indicating activation of excess HIF-1α and VEGF in psoriatic skin when compare to normal skin62. In addition, HIF-1α is involved in T-cell regulation and survival which are crucial in psoriasis formation62. The effects of hypoxia with elevated levels of HIF-1α and VEGF may explain the association between OSA and psoriasis.Intermittent hypoxia during sleep in OSA patients induces oxidative stress. Some markers including nuclear factor-κB (NF-κB) and hypoxia-inducible factor-1α (HIF-1α) could detect the oxygen stress and activate the inflammatory pathway33 and misclassification bias might have been present. Second, high statistical heterogeneity was detected as to the association of psoriasis with OSA (I2 = 89%; see Fig. There are a few limitations of the present study. First, OSA was identified by the use of ICD codes in four included studiesIn conclusion, the evidence to date supports a bidirectional association between psoriasis and OSA. All patients with psoriasis should be informed about the risk of OSA, and vice versa. OSA is a comorbidity of psoriasis that should not be ignored, and sleep quality should be inquired in patients with psoriasis. Respirologist consultation or polysomnography may be indicated for those presenting with night snoring, daytime sleepiness, and insomnia.Supplementary Table S1.


Air pollution is a major issue that poses a health threat worldwide. Although several studies investigated the adverse effects of air pollution on various diseases, few have directly demonstrated the effects on pneumonia. Therefore, we performed a systematic review and meta-analysis on the associations between short-term exposure of air pollutants and hospital admission or emergency room (ER) visit for pneumonia.A literature search was performed using PubMed, Embase, and Web of Science up to April 10, 2020. Pooled estimates were calculated as % increase with 95% confidence intervals using a random-effects model. A sensitivity analysis using the leave-one-out method and subgroup analysis by region were performed.3 increment in PM2.5 and PM10 resulted in a 1.0% (95% CI: 0.5–1.5) and 0.4% (95% CI: 0.2–0.6) increase in hospital admission or ER visit for pneumonia, respectively. Every 1 ppm increase of CO and 10 ppb increase of NO2, SO2, and O3 was associated with 4.2% (95% CI: 0.6–7.9), 3.2% (95% CI: 1.3–5.1), 2.4% (95% CI: − 2.0-7.1), and 0.4% (95% CI: 0–0.8) increase in pneumonia-specific hospital admission or ER visit, respectively. Except for CO, the sensitivity analyses yielded similar results, demonstrating the robustness of the results. In a subgroup analysis by region, PM2.5 increased hospital admission or ER visit for pneumonia in East Asia but not in North America.A total of 21 studies were included in the analysis. Every 10 μg/m2. Based on the results, stricter intervention policies regarding air pollution and programs for protecting human respiratory health should be implemented.By combining the inconsistent findings of several studies, this study revealed the associations between short-term exposure of air pollutants and pneumonia-specific hospital admission or ER visit, especially for PM and NOThe online version contains supplementary material available at 10.1186/s12940-020-00687-7. Pneumonia is a common but potentially life-threatening disease with a high incidence around the world . It is cAir pollution still poses a major health threat worldwide. It has been reported that over 90% of the world’s population live in areas where the air pollution level exceeds the World Health Organization (WHO) guideline limits . Air polThe air pollution can increase the onset risk of pneumonia, including both hospitalization , 10 and Although several studies systemically reviewed the effects of particulate matter (PM) on hospital admissions for respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD) , 14, few2.5, PM10, SO2, NO2, CO, and O3) and hospital admission or ER visit for pneumonia. The search strategy is detailed in Supplementary Table 1, Additional file The literature search was performed using PubMed, Embase, and Web of Science for studies on the association between short-term exposure to air pollution and hospital admission or ER visit for pneumonia up to April 10, 2020. The search included keywords related to air pollution (PMStudies were included if they: (1) were original studies published in peer-reviewed journals, (2) investigated the short-term effects  of air pollutants on hospital admission or ER visit for pneumonia, (3) provided sufficient information to calculate regression estimates and 95% confidence intervals (CIs), (4) used time-series or case-crossover study design, and (5) were published in English. Exclusion criteria were: (1) reviews, commentaries, or editorials; (2) in vitro or in vivo studies; (3) studies on children only; (4) studies under special conditions  without overall estimates; or (5) studies on combined outcomes with other respiratory diseases. If there were overlapping data, only the most recent and comprehensive data were included in the meta-analysis.Data were extracted independently by two researchers and discrepancies were resolved by consensus. The following information was extracted from each study: name of the first author, publication year, study setting, study design, number of cases, percentages of male and elderly patients, air pollutants studied, outcome level, and study results. If there were multiple lag estimates for the same exposure, only one estimate was selected to prevent over-representation of a single study in the meta-analysis. For multiple lag estimates, a priori lag selection protocol devised by Atkinson et al. was used with the following priorities: (1) the lag that the author focused on in the abstract or stated a priori, (2) the lag with the most statistical significance (positive or negative), and (3) the lag that showed the largest effect estimate (positive or negative) .Due to the lack of validated scales for quality assessment of time-series and case-crossover studies, we adapted the quality assessment approach developed by Mustafic et al. . Three c3 of PM2.5 and PM10 concentrations; 1 ppm of CO; 10 ppb of SO2, NO2, and O3. To transform the estimate, the following equation was used: odds ratio (OR)standardized = ORincrement(10)/increment [p-value < 0.05 was considered statistically significant. Heterogeneity between studies was assessed by a chi square-based Q test and I2 test. A random-effects model (DerSimonian-Laird method) was applied to consider the heterogeneity within and between studies and to give a more conservative estimate of statistical confidence [We used % increase with 95% CIs as a measure of effect size. To pool the results, all estimates were standardized to an increase of 10 μg/mriginal) . As the nfidence . Publicanfidence . Sensitinfidence .n = 4), different outcomes (n = 3), combined outcomes (n = 31), irrelevant studies (n = 10), not providing short-term effects (n = 3), not providing overall effects (n = 10), conducted in children (n = 1), unable to extract data (n = 1), and overlapping studies (n = 19). Finally, 21 studies were included for meta-analysis [The study selection process is summarized in Fig. ies n = 1. Finallydies n = , not proanalysis .Fig. 1Fn = 7), followed by China (n = 4). Most studies used the ICD code for pneumonia diagnosis; almost 60% of the studies examined hospital admission, while the rest examined ER visit. The number of cases in each study ranged from 325 to 4.2 million. In terms of study quality, about 43% of studies were regarded as good quality, while the rest were intermediate quality.The characteristics of included studies are shown in Table 2.5, PM10, NO2, and CO were associated with an increased risk of hospital admission or ER visit for pneumonia  and 0.4%  increase in hospital admission or ER visit for pneumonia, respectively. In addition, every 1 ppm increase of CO was associated with 4.2%  increase in hospital admission or ER visit for pneumonia. For every 10 ppb increase of NO2, SO2, and O3 increased pneumonia-specific hospital admission or ER visit by 3.2% , 2.4% , and 0.4% , respectively.Among the air pollutants analyzed, PMP > 0.05 for all analyses). Sensitivity analysis was performed by sequentially excluding each study. With the exception of CO, all pollutants, which obtained statistical significance in the main analyses, showed similar results, indicating that no individual study significantly affected the pooled results. The ranges of increase were 0.9–1.1% for PM2.5, 0.4–0.5% for PM10, and 2.4–4.0% for NO2. When excluding the three largest studies, which had more than 1 million cases [3 increase in PM2.5 and PM10 and 10 ppb in O3 was 1.1% , 0.5% , and 5.5% , respectively.The publication bias was assessed using the funnel plot and Begg’s test  increase in hospital admission or ER visit for pneumonia, whereas the effect estimate for North America was considerably smaller than that for East Asia; yet the confidence intervals still exhibited considerable overlap . For PM10, East Asia and North America showed a 0.3% (95% CI: 0.1–0.4) and 0.4% (95% CI: 0.3–0.6) increase for every 10 μg/m3 increase, respectively; for O3, the corresponding values were 0.9% (95% CI: − 0.5-2.4) and 0.2% (95% CI: − 0.8-1.2). In terms of SO2, NO2 and CO, there were no available studies conducted on North America.As there were six and five studies for PM2.5 and PM10 showed significant associations with pneumonia-specific hospital admission or ER visit for all lag days (lag 0 to lag 5) and the largest association was observed for lag 3 and lag 5, respectively  and 0.4% (95% CI: 0.2–0.6), respectively. For NO2 and CO, the largest association was observed for lag 2  and lag 5 , respectively. On the contrary, SO2 and O3 levels did not show significant associations for all lag days (lag 0 to lag5), and the % increase range for SO2 and O3 was − 0.3 to 1.9 and − 1.0 to 0.7, respectively.When combining results with the same lag day, PM2.5, PM10, NO2, and CO) and hospital admission or ER visit for pneumonia, although no such association was identified regarding SO2 and O3. Except for CO, the sensitivity analyses yielded similar results, demonstrating the robustness of the results. In a subgroup analysis by region, PM2.5 increased hospital admission or ER visit for pneumonia in East Asia but not in North America.In this meta-analysis, we demonstrated a significant association between air pollutants  and Th1-type cytokines (interleukin-12 and interferon-γ) [Several studies have suggested that PM is related to inflammation. According to Gordon et al.feron-γ) . Elevateferon-γ) , 47.2.5 exposure can damage the mucociliary system [2.5, which can be inhaled more deeply into the lungs, is considered more harmful to health than PM10, the latter can also cause pneumonia in a similar manner [There are two possible mechanisms that mainly account for the increased risk of pneumonia induced by PM: altered immunity and oxidative stress, both of which are closely linked to inflammation. The respiratory system possesses multiple tiers of immunity to defend against harmful airborne particles and microorganisms . Howevery system , suppresy system , and impy system , wherebyy system . Althougr manner .Oxidative stress is another factor believed to play an important role in the pathogenesis of PM-induced pneumonia. Several studies have shown that acute exposure to PM triggers pulmonary oxidative stress. PM can directly generate reactive oxygen species on the surface, alter mitochondrial function, dysregulate antioxidant enzymes , increase other oxidases in the lungs , and activate metabolic enzyme activity including cytochrome P450s and glutathione S-transferase , 54. The2, a free-radical gaseous component of indoor and outdoor air pollution, was associated with increased risk of pneumonia hospital admission or ER visit in this study. As NO2 is linked to nitrosative stress in the lungs, it can lead to airway injury [2 results in increased susceptibility to both bacterial and viral infections [2 and pneumonia.Along with PM, NOy injury . Furtherfections , 58, whi, CO can trigger proinflammatory responses in the airways [CO was another air pollutant associated with pneumonia. According to Ghio et al. airways . In addi airways , 60, 61. airways , Phosri  airways , and San airways  were exc2.5 was associated with risk of hospital admission or ER visit for pneumonia in East Asia but not in North America. The two regions are known to have remarkably different concentrations of PM2.5 with East Asia generally having a higher exposure to PM2.5 and its consequent health burden than North America [2.5 on pneumonia in different countries, further research is required on the effects of air pollutants between different regions.Subgroup analysis by region showed that an increase of PM America . In addi10, no association between air pollutants and pneumonia was affected by the confounder-adjusting method. Although meta-regression analysis showed that the confounder-adjusting method affected the pooled estimates for PM10, most studies on PM10 yielded the same score (2 point); therefore, the result was not confirmative.We performed meta-regression with the confounder-adjusting method, which is assessed on a 0 to 3 scale by the third component of quality assessment (described in the method section). Except for PMThis meta-analysis has some limitations that should be considered when interpreting the results. First, included studies used the air pollutant levels obtained from monitoring stations rather than personal exposures. Second, considerable heterogeneity was observed. Third, due to the lack of information from individual studies, some potential factors, which could affect the risk of pneumonia-specific hospital admission or ER visit , could not be adjusted.2. Based on the results, stricter intervention policies regarding air pollution and programs for protecting human respiratory health should be implemented.To our knowledge, this is the first systematic review and meta-analysis to evaluate the acute effects of air pollutants on hospital admission or ER visit for pneumonia. By combining the inconsistent findings of several studies, this study revealed the associations between short-term exposure of air pollutants and pneumonia-specific hospital admission or ER visit, especially for PM and NOAdditional file 1: Supplementary Table 1. Search strategy. Supplementary Table 2. Meta-analyses of association between air pollutants and hospital admission or emergency room visit for pneumonia in the same lag day. Supplementary Figure 1. Funnel plot of association between air pollutants and hospital admission or emergency room visit for pneumonia. a. Funnel plot of association between PM2.5 and hospital admission or emergency room visit for pneumonia. b. Funnel plot of association between PM10 and hospital admission or emergency room visit for pneumonia. c. Funnel plot of association between SO2 and hospital admission or emergency room visit for pneumonia. D. funnel plot of association between NO2 and hospital admission or emergency room visit for pneumonia. e. Funnel plot of association between CO and hospital admission or emergency room visit for pneumonia. f. Funnel plot of association between O3 and hospital admission or emergency room visit for pneumonia. Supplementary Figure 2. Subgroup analysis by region for the association between air pollutants and hospital admission or emergency room visit for pneumonia. a. Subgroup analysis by region for the association between PM2.5 and hospital admission or emergency room visit for pneumonia. b. Subgroup analysis by region for the association between PM10 and hospital admission or emergency room visit for pneumonia. c. Subgroup analysis by region for the association between O3 and hospital admission or emergency room visit for pneumonia. Supplementary Figure 3. Subgroup analysis by study design for the association between air pollutants and hospital admission or emergency room visit for pneumonia. a. Subgroup analysis by study design for the association between PM2.5 and hospital admission or emergency room visit for pneumonia. b. Subgroup analysis by study design for the association between PM10 and hospital admission or emergency room visit for pneumonia. c. Subgroup analysis by study design for the association between SO2 and hospital admission or emergency room visit for pneumonia. d. Subgroup analysis by study design for the association between NO2 and hospital admission or emergency room visit for pneumonia. e. Subgroup analysis by study design for the association between CO and hospital admission or emergency room visit for pneumonia. f. Subgroup analysis by study design for the association between O3 and hospital admission or emergency room visit for pneumonia. Supplementary Figure 4. Subgroup analysis by study quality for the association between air pollutants and hospital admission or emergency room visit for pneumonia. a. Subgroup analysis by study quality for the association between PM2.5 and hospital admission or emergency room visit for pneumonia. b. Subgroup analysis by study quality for the association between PM10 and hospital admission or emergency room visit for pneumonia. c. Subgroup analysis by study quality for the association between SO2 and hospital admission or emergency room visit for pneumonia. d. Subgroup analysis by study quality for the association between NO2 and hospital admission or emergency room visit for pneumonia. e. Subgroup analysis by study quality for the association between CO and hospital admission or emergency room visit for pneumonia. f. Subgroup analysis by study quality for the association between O3 and hospital admission or emergency room visit for pneumonia.


This systematic review and meta-analysis examines the association of major food sources of fructose-containing sugars with incident metabolic syndrome. What is the association of major food sources of fructose-containing sugars with incident metabolic syndrome?In this systematic review and meta-analysis of 13 prospective studies including 49 591 participants, the adverse association of sugar-sweetened beverages with incident metabolic syndrome did not extend to other major food sources of fructose-containing sugars. Yogurt, fruit, 100% fruit juice, and mixed fruit juice all had a protective association with incident metabolic syndrome.Generalized statements on the adverse effects of fructose-containing sugars cannot be extrapolated from sugar-sweetened beverage results, as assessment of other important food sources of fructose-containing sugars show protective associations with metabolic syndrome incidence. Sugar-sweetened beverages (SSBs) are associated with increased risk of metabolic syndrome (MetS). However, the role of other important food sources of fructose-containing sugars in the development of MetS remains unclear.To examine the association of major food sources of fructose-containing sugars with incident MetS.sugar-sweetened beverages, fruit drink, yogurt, metabolic syndrome, and prospective study.MEDLINE, Embase, and Cochrane Library were searched from database inception to March 24, 2020, in addition to manual searches of reference lists from included studies using the following search terms: Inclusion criteria included prospective cohort studies of 1 year or longer that investigated the association of important food sources of fructose-containing sugars with incident MetS in participants free of MetS at the start of the study.Q statistic) and quantified (I2 statistic). Dose-response analyses were performed using a 1-stage linear mixed-effects model. The certainty of the evidence was assessed using GRADE . Results were reported according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines.Study quality was assessed using the Newcastle-Ottawa Scale. Extreme quantile risk estimates for each food source with MetS incidence were pooled using a random-effects meta-analysis. Interstudy heterogeneity was assessed (Cochran Pooled risk ratio (RR) of incident MetS (pairwise and dose response).Thirteen prospective cohort studies  that assessed 8 fructose-containing foods and MetS were included. An adverse linear dose-response association for SSBs  and an L-shaped protective dose-response association for yogurt  and fruit  was found. Fruit juices (mixed and 100%) had a U-shaped dose-response association with protection at moderate doses . Honey, ice cream, and confectionary had no association with MetS incidence. The certainty of the evidence was moderate for SSBs, yogurt, fruit, mixed fruit juice, and 100% fruit juice and very low for all other food sources.The findings of this meta-analysis suggest that the adverse association of SSBs with MetS does not extend to other food sources of fructose-containing sugars, with a protective association for yogurt and fruit throughout the dose range and for 100% fruit juice and mixed fruit juices at moderate doses. Therefore, current policies and guidelines on the need to limit sources of free sugars may need to be reexamined. This systematic review and dose-response meta-analysis of prospective cohort studies examines the association of food sources of fructose-containing sugars and incident MetS and evaluates the strength and quality of the evidence using GRADE .7Fructose-containing sugars  in the diet have been implicated as potential contributing factors to increased MetS risk.Cochrane Handbook for Systematic Reviews of Interventions.8 Results were reported according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines.10 The study protocol was registered at ClinicalTrials.gov.11 Data sources included MEDLINE, Embase, and the Cochrane Library from database inception to March 24, 2020. Manual search of the reference lists from included studies supplemented the database search. Search terms reflected the most consumed food sources of fructose-containing sugars  , the outcome of interest , and the study design  . The GRADE approach was used to assess the overall certainty and strength of the evidence, ranging from high to very low certainty (eAppendix 1 in the 7Two independent reviewers (Z.S.A. and T.A.K) extracted relevant data, including sample size, participant characteristics, food source of fructose-containing sugars, exposure levels, follow-up duration, number of MetS cases, covariates in fully adjusted models, and the relative risk (RR) with 95% CIs of incident MetS per category of intake, median dose in each category, and funding source, dual-sequentially. Studies were assessed for risk of bias using the Newcastle-Ottawa Scale.17 Dose-response analyses were conducted in Stata software, version 16 (StataCorp) using drmeta.18 Each food source of fructose-containing sugar was considered as an independent exposure. Risk ratios (RRs) of extreme quantiles from the most adjusted models were used for pairwise analyses.8 When studies used continuous RRs per dose, we imputed the extreme quantiles from other publications of the same or similar cohort. Hazard ratios and odds ratios were converted to RRs based on the recommended method by Zhang and Yu19  using dmetar.20 A fixed-effects model was used if the number of studies was 5 or fewer.21 Unit-of-analysis error  was addressed by dividing participants equally among the multiple comparisons and readjusting the log SEs.8 Interstudy heterogeneity was assessed using the Cochran Q (χ2) statistic and quantified by the I2 statistic, where I2 of 50% or greater and P < .10 determined by the Q statistic represented evidence of substantial heterogeneity.8 Sources of heterogeneity were assessed by sensitivity analyses that involved the systematic removal of each study for food sources with more than 2 cohorts. If 10 or more cohort comparisons were available, a priori subgroup analyses were performed.Summary estimates were determined by natural log transforming and pooling the RRs using the DerSimonian and Laird random-effects model.23 with significance set at P < .10. In the presence of publication bias, the Duval and Tweedie trim and fill method was used.24If 10 or more cohort comparisons were available, studies were assessed for publication bias by visual inspection of funnel plots and formal testing using the Begg and Egger tests,26 Doses were defined as the mean consumption in each reported category or quantile. We reported nonlinear associations for a study if results of the Wald test for departure from linearity were significant at P < .10 (2-sided).27 The significance for the main pooled RR for the pairwise analyses was based on P < .05.Dose responses were modeled using RRs (95% CIs) from dose categories to determine the shape of the association between the dose of the fructose-containing foods and the risk of MetS (eAppendix 3 in the 40 (49 591 participants and 14 205 cases) with data from 8 unique prospective cohorts met the inclusion criteria (38), mixed fruit juice , 100% fruit juice , fruit , yogurt , honey , ice cream , and confectionary . Prospective cohort studies that assessed grain and grain-based products or other fruit- or dairy-based products with incident MetS were not identified.Thirteen reportscriteria . Eight m40 Studies included data from the US,36 Spain,39 Iran,38 and South Korea.40 Participants ranged from adolescents to older adults . Appelhans et al28 exclusively studied a female cohort. The mean (SD) duration of follow-up was 5.7 (3.3) years . Fruit juice was considered to be mixed fruit juice if the study combined fruit drinks and fruit juice or did not specify the kind of fruit juice (100% fruit juice or fruit drink). Yogurt was considered a source of fructose given that more than 70% of the yogurt products are flavored41 and consumers prefer yogurt products with a moderate (approximately 7%-10%) concentration of added sucrose.44 MetS was defined using the Adult Treatment Panel III,40 harmonized criteria,39 or a continuous scale33 . Fruit and yogurt intake had an inverse association with incident MetS . No association was found between mixed fruit juice, 100% fruit juice, honey, ice cream, or confectionary with MetS incidence.30 resulted in nonsignificant evidence of interstudy heterogeneity for yogurt; however, it did not significantly affect the pooled estimate. Because none of the comparisons had 10 or more cohorts, subgroup analyses were not performed.eTable 5 in the 38 with a dose range of 0 to 680 mL/d, demonstrated an adverse linear dose-response association between SSB intake and MetS , with no evidence for departure from linearity (P = .27) (P = .27) .38) and 100% fruit juice (1 cohort32) indicate a U-shaped, significant, nonlinear dose-response association with incident MetS, with the curve suggesting a maximum protection between 75 and 150 mL. There was no protective association after 200 mL/d for mixed fruit juice intake and after 175 mL/d for 100% fruit juice. The estimated RR for 125 mL/d was 0.58  for mixed fruit juice and 0.77  for 100% fruit juice.Data for mixed fruit juice  was 0.61 .Data from 2 cohorts35 with a dose range of 0 to 129 g/d of yogurt intake found an L-shaped, protective, nonlinear dose-response association with incident MetS, with the curve suggesting a sharp reduction of RR until 80 g/d. The estimated RR for 85 g (one-third cup serving) was 0.66 .Data from 3 cohorts37 with a dose range of 8 to 84 g/d found no evidence of a dose-response association with incident MetS . Relevant data were not available to assess the dose-response association for honey and ice cream.Confectionary data from 2 cohortsI2 = 68% for SSBs and 73% for fruit juice), the RR estimates for SSB studies were all in the same direction with considerable overlap. In addition, the heterogeneity observed with 100% fruit juice was explained by the nonlinear dose-response model. Therefore, these 2 food sources were not downgraded for inconsistency. The certainty of evidence of no association was very low for honey, ice cream, and confectionary because of downgrades for serious imprecision, indirectness for honey, ice cream, and confectionary with no upgrades.The GRADE certainty of evidence was moderate for adverse association for SSBs and protective association for mixed fruit juice, 100% fruit juice, fruit, and yogurt with MetS risk attributable to upgrades for dose-response gradient . AlthougIn our systematic review and meta-analysis, 13 prospective cohort studies (including 49 591 participants and 14 205 MetS cases) found that SSB intake was associated with an increased risk for MetS incidence, whereas yogurt and fruit were associated with a reduced risk. Mixed fruit juice and 100% fruit juice had a U-shaped association with MetS, presenting a protective association between 75 and 150 mL/d and an adverse association for more than 175 to 200 mL/d. No association was found between honey, ice cream, and confectionary and MetS incidence.6 Previous meta-analyses46 found a 20% and 46% increased MetS risk with higher SSB consumption from 3 prospective and 8 cross-sectional studies, respectively. Our findings expand on current findings by the inclusion of 7 prospective cohorts and the assessment of dose response, which found a 14% increased risk of MetS incidence per 355-mL daily serving of SSBs.The adverse association of SSB intake and MetS incidence in our study is consistent with the current literature.47 Furthermore, SSBs are a source of liquid calories, which can have a lower effect on satiety compared with solid foods, resulting in increased energy intake, weight gain, and downstream complications related to MetS.48 Although the prospective studies40 included in our SSB analysis controlled for potential confounding factors, all except 1 study28 controlled for total energy intake, and 2 studies36 did not adjust for adiposity, an important risk factor and component of MetS.3 Thus, residual and unmeasured confounding could have contributed to the observed adverse association.The association between SSB and incident MetS may reflect a general unhealthy lifestyle whereby individuals with greater SSB intake are likely to have a poorer diet quality, higher caloric intake, and a sedentary lifestyle.49 highlighted that higher dairy consumption was inversely associated with MetS incidence by 14% among 7 prospective cohorts with a dose-response reduction with incremental intake. Our findings broadly concur with these results. This protective association of yogurt may be attributable to its rich micronutrient composition. Calcium, a major nutrient in yogurt, decreases fat absorption, lowers triglyceride concentration, improves the overall ratio of high-density lipoprotein to low-density lipoprotein.50 In addition, dairy-derived saturated fats have anti-inflammatory properties and potentially improve insulin sensitivity and glycemic response.51 Furthermore, the probiotic bacteria found in yogurt products have been linked to modulating gut microbiota through the reduction of pathogenic bacteria while increasing metabolite production and modulating various inflammatory reactions, all of which can aid in reducing the risk of MetS.52Conversely, yogurt had a protective association against MetS incidence, with a dose-dependent benefit with intakes of 60 to 80 g/d. The nonlinear findings indicate that the association above 85 g/d plateaus, and data are lacking to suggest any benefit associated with increasing intake beyond this dose. The role of yogurt, or more broadly dairy intake, and MetS has gained attention during the past decade. A meta-analysis53 and blood pressure54; however, evidence on the dose range with MetS is limited.40 Most fruit intake and MetS studies56 are cross-sectional in design or are assessed in combination with vegetable intake, making it difficult to determine the association of specific fructose-containing fruits with MetS. One such meta-analysis57 of cross-sectional studies found that fruit intake had a protective association with MetS risk.Similarly, fruit consumption presented a protective association against MetS incidence, with the greatest dose benefit at 300 to 450 g/d . Fruit intake is protective for some components of MetS, including waist circumference58 and the potential for harm at higher doses may be attributable to the consumption of excess calories outweighing any benefit of these bioactive nutrients.59We identified a U-shaped dose-dependent association with mixed fruit juice and 100% fruit juice intake, showing protective associations against MetS with intakes less than 200 mL. The benefit of 100% fruit juice seen at moderate doses may be attributable to the range of fruit-derived nutrients and bioactive compounds in fruit juice,47Mixed fruit juices are a combination of fruit drinks (which are similar to SSBs because they are sugary drinks without the accompanying nutrients) and 100% fruit juice. The observed moderate doses of intake may represent the beneficial nutrients from natural fruit within the mixed fruit juice, thus indicating an association similar to that of 100% fruit juices rather than SSBs. The lack of linear association in 100% fruit juice and mixed fruit juice underscores that without consideration of the dose-response association, a naive analysis of extreme intakes assumes a false-linear association and fails to detect important dose ranges for protection or harm.60 to our knowledge, only 1 prospective cohort study30 assessed honey with MetS incidence and found no significant association. Similarly, the current limited evidence indicates that ice cream30 and confectionary37 were not significantly associated with MetS incidence. Future data might clarify our association, particularly for confectionaries, for which CIs did not eliminate significant harm.Furthermore, honey, ice cream, and confectionary intake was not associated with MetS incidence. Although animal models suggest potential protective effects of honey in MetS,The protective and neutral association in our results highlight 2 important considerations. First, the small beneficial effects of some foods might be driven by catalytic doses of fructose intake. Second, the food composition is important. SSBs are without beneficial nutrients and thus offer an unchecked source of fructose-containing sugar, whereas in other foods , nutrients other than sugars  may offer protection that might overcome harms from added sugars. More data are needed to enable a complete dose-response assessment and reveal dose ranges for increased or reduced risk, depending on the balance between nutrient matrixes vs excess sugars.There are numerous strengths associated with our study. To our knowledge, this study is the first meta-analysis to comprehensively compare major food sources of fructose-containing sugars with incident MetS in prospective cohort studies. We conducted a thorough literature search, performed quantitative synthesis, and assessed the certainty of the evidence using GRADE. Selected studies included a large sample size, long follow-up durations, and adjustment for multiple lifestyle factors. We also assessed dose responses for all food sources and identified ranges and cutoffs for benefit and harm.61 The estimates were all in the same direction, and there was considerable overlap for SSB and yogurt. The nonlinear dose-response model explained the heterogeneity for yogurt and 100% fruit juice. Honey, ice cream, and confectionary were downgraded for serious indirectness for the inability to assess inconsistency because only 1 study was available for each exposure. Furthermore, they were downgraded for serious imprecision, indicating no association with MetS incidence in the extreme quantile analysis. The CIs were wide and could not conclude clinically important harm for confectionary or clinically important benefit or harm for honey and ice cream. In our dose-response analysis, we found a significant linear dose response of harm for SSBs and a nonlinear dose response of benefit for mixed fruit juice, 100% fruit juice, fruit, and yogurt, leading to an upgrade for the certainty of evidence. Data were not available for grain and grain-based products, a leading source of sugar.62This study also had some limitations. The observational nature of prospective cohort studies may result in unmeasured and residual confounding and may suffer from reverse causality. Thus, GRADE evaluation for observational studies is low certainty of evidence. Although SSBs, yogurt, and 100% fruit juice had substantial interstudy heterogeneity, we did not consider this as a serious inconsistency.64 Thus, well-intentioned policies and guidelines to limit sources of free sugars, such as fruit juice or sweetened yogurts, based on evidence from SSBs may need to be reexamined with a food-based lens, such as those of the new Canada’s Food Guide65 or Scientific Advisory Committee on Nutrition.66Our study provides supporting evidence that increased SSB consumption is associated with MetS incidence. Generalizing statements on the adverse effects of fructose-containing sugars, however, cannot be extrapolated to other major food sources of fructose-containing sugars. Furthermore, our dose-response assessment found that mixed and 100% fruit juice presented consistent dosage for benefit that align with some national nutrition guidelines, suggesting that a 150-mL intake may contribute toward the recommended daily fruit consumption.Additional prospective studies are needed to improve our estimates and better understand the dose-response association between important food sources of fructose-containing sugars and MetS. Moreover, high-quality, large randomized clinical trials are needed on other fructose-containing foods. Furthermore, studies of whole diets and dietary patterns that consist of various food sources of fructose-containing sugars with cardiometabolic-related health outcomes can also contribute to the evidence of the association of these diets with MetS.


Using the preferred linear model, we found significant increased risks of coronary heart disease (CHD) mortality and CVD mortality as well as combined fatal and non-fatal CHD, CVD, carotid atherosclerosis disease and hypertension in those exposed to drinking water with an As concentration of 10 µg/L compared to the referent (drinking water As concentration of 1 µg/L) population. Notwithstanding limitations included, the observed significant increased risks of CVD endpoints arising from As concentrations in drinking water between 1 µg/L and the 10 µg/L suggests further lowering of this guideline value should be considered.To the best of our knowledge, a dose-response meta-analysis of the relationship between cardiovascular disease (CVD) and arsenic (As) exposure at drinking water As concentrations lower than the WHO provisional guideline value (10 µg/L) has not been published yet. We conducted a systematic review and meta-analyses to estimate the pooled association between the relative risk of each CVD endpoint and low-level As concentration in drinking water both linearly and non-linearly using a random effects dose-response model. In this study, a significant positive association was found between the risks of most CVD outcomes and drinking water As concentration for both linear and non-linear models ( Originating from either geological or anthropogenic activities, arsenic (As) has been widely recognized since the 1950s as one of the most serious human carcinogens . UbiquitThe relationship between CVD and As exposure at As concentrations in drinking water greater than 100 µg/L is well established ,18,20,21Existing meta-analyses and systematic reviews that include the lower end of the As exposure range have not clearly and quantitatively specified any increased risk below 10 µg/L of water As concentration possibly because of a lack of epidemiological data. These reviews notably include a) a qualitative systematic review of the epidemiologic evidences on the association of CVD and low, moderate and high As exposure , b) a meDue to the recent publication of a number of key epidemiological studies  buildingWe searched ISI Web of Science for studies published before December 2019 on the association between CVD risks including overall CVD, coronary heart disease (CHD), stroke, carotid atherosclerosis disease, hypertension and associated clinic markers and As exposure and for the general population. The medical subject headings (MeSH) used were “arteriosclerosis”, “atherosclerosis", “carotid artery diseases”, “cardiovascular diseases”, “cerebrovascular disease”, “cerebrovascular disorders”, “coronary artery disease”, “heart diseases”, “hypertension”, “infarct”, “ischemia”, “ischemic heart disease”, “myocardial infarction”, “peripheral vascular diseases”, “peripheral arterial disease”, “stroke” and were combined with “arsenic”, “arsenic poisoning”, “arsenicals”, “arsenite” and “arsenate”. The terms were combined with the Boolean operators "OR" or "AND". The reference lists and cross-references of eligible studies were also searched as were the bibliographies of recent systematic reviews and meta-analyses reported by Navas-Acien et al. , Phung eStudies not published in English, related to specific scenario like childhood exposure or exposure during pregnancy or occupational exposure, or based on a specific form of As like arsenic trioxide  were excluded. The review of literature was conducted in accordance with the techniques specified by Stroup et al.  for metaBased on the abstract review, studies were firstly excluded if (a) they were case reports or case series; (b) no original data were presented; (c) had no human records; or (d) the outcome measures were not related with mortality or morbidity risk of relevant CVD types and/or clinical indicators considered in this study. The full text of studies which was regarded as potentially eligible was then assessed and further excluded if they were (a) not related with dietary As exposure or not for the general population; (b) no available relative risk  and measures of variability of different types of CVD were mentioned; or c) no categorical As exposure (at least three categories) was used . The metFor each study that appeared to meet the inclusion criteria, we extracted data on the number of deaths or cases, person-years or total number of population, the measured risk values  and their statistical uncertainty ) for each As concentration category. In addition, we recorded the descriptive information including authors’ names, year of study publication, sampling method, sample size, study design, and outcome ascertainment as well as the adjustment for confounders.Similar to the method used by Moon et al. , we combinary As  using Eqn et al. .A random-effects dose-response meta-analysis was conducted to estimate the pooled dose-response relationship between the log-transformed relative risk of each CVD outcome and water As concentrations from the summarized data of multiple studies using the package ‘dosresmeta’ in R statistical software ,65. ThisDose-response relationships for individual studies with at least three concentration categories were plotted using the ‘ggplot’ function in the ‘ggplot2’ package in R and the pooled dose-response relationship for each CVD endpoint was plotted using the ‘matplot’ function in R to visualize the predicted relative risks at higher As level compared with the referent concentration (1 µg/L).2 and adjusted R2) and Akaike’s information criterion (AIC) values via the ‘gof’ function in the ‘dosresmeta’ package in R. Furthermore, dose-response relationships for individual studies were overprinted by the pooled dose-response relationship for each CVD endpoint to visually test the model goodness-of-fit. Heterogeneity was assessed by P-heterogeneity (p < 0.05 being significant), I2-statistics  and Cochran’s Q-statistic [Forest plots were used to show the pooled relative risks (95% CI) for both individual studies and the overall one, comparing risk for chronic exposure to drinking water As of 10 µg/L with 1 µg/L using the ‘forestplot’ function of the ‘metafor’ package in R, with the sizes of the squares of individual study relative risks weighted by the inverse variance of the log-relative risk within each model. Specifically, both the individual and the overall pooled relative risks (95% CIs) in the forest plots were calculated via the dose-response meta-analysis through the ‘dosresmeta’ package in R. Model goodness-of-fit was assessed using deviance tests, coefficient of determination  was plotted against the standard error of log-relative risk, with the funnel centred at the overall model estimate.Finally, sensitivity analyses were conducted. On one hand, to test the reliability and suitability of combining different exposure media, we conducted sensitivity analysis to estimate the relationships between CVD risks and drinking water As both linearly and non-linearly by excluding studies which did not provide water As concentrations directly. On the other hand, with the intention of analysing the effects of low-level As exposure, we also excluded studies with As concentration higher than 100 µg/L and conducted subgroup analysis only for low to moderate As concentrations (taken here to be < 100 µg/L), modelling its linear and non-linear associations with the CVD risks.All our data analysis was performed using R statistical software , version 3.4.3  and R Studio (R Studio Desktop 1.1.423) ,77,78.For our systematic review and meta-analysis, we identified 28 studies which satisfied the inclusion criteria . There wAmong all these studies, there were 13 cohort studies ,80,81,82We have included studies from As-endemic areas, such as Bangladesh ,20,67,70We observed substantial differences in the methods of exposure assessment across studies : while mIn this study, the different CVD outcomes considered were: carotid atherosclerosis ,72,73, CAssociation between CVD risks and As exposure could vary by age, gender, smoking status, alcohol consumption, physical activity, diabetes and obesity status, education level as well as some other socio-economic factors, all of which might be either powerful predictors of CVD risks or important factors in As toxicity ,92,93,94Of the 28 selected studies, 22 studies included dose-response meta-analysis of mortality risk, of which there were seven for CHD, eight for CVD and seven for stroke. The 21 studies on combined fatal and non-fatal CVD risks included three studies on carotid atherosclerosis disease, four studies on CHD, two studies on CVD, eight studies on hypertension and four studies on stroke. Apart from those quantifying the risk of different CVD types, we also considered four studies for CVD clinic markers, including two studies for pulse blood pressure and two studies for QT prolongation .p < 0.05) (p < 0.05) was also obtained for the combined fatal and non-fatal risk of CHD, CVD, carotid atherosclerosis disease and hypertension with the relative risks of these four endpoints at 10 µg/L of water As concentration were 1.405 (95% CI: 1.183–1.667), 1.411 (95% CI: 1.242–1.603), 1.936 (95% CI: 1.403–2.671) and 1.231 (95% CI: 1.043–1.452), respectively, compared with 1 µg/L as the reference  . Compare < 0.05) . Similareference .p < 0.05) for CHD and CVD mortality risks as well as the combined fatal and non-fatal risk of CHD and carotid atherosclerosis disease, significant increases in pooled relative risks at 10 µg/L drinking water As compared with 1 µg/L as a reference were only found for the mortality risk of CHD   .2 statistic, Cochran’s Q-statistic and p-values for heterogeneity as presented in p-values < 0.05 and I2 > 50%).Combined with the estimated Ip-value, R2, adjusted R2 and AIC, as well as the difference between dose-response relationships for individual studies and the pooled dose-response relationship for each CVD endpoint, have been used for the goodness-of-fit assessment , deviance tests indicated no overall lack of fit for other CVD endpoints. In addition, there was only a slight increase in the R2 and the adjusted R2 for non-linear models compared with linear ones for most CVD endpoints, indicating that non-linear models explained more the variation of CVD risks. Linear models are considered a better fit for all the CVD endpoints due to their lower AIC values.Deviance, sessment . Accordip-values for Egger’s Regression Test lower than 0.05  ,33,40,97To reduce model misspecification, we conducted both a constant linear and a smooth non-linear model  via a two-stage ’dosresmeta’ function. Somewhat consistent with previous meta-analysis researches ,24, diffp-value for non-linear trend lower than 0.05); it is, therefore, important to bear in mind that the non-linear analysis of As effects should not be neglected, calling for future individual-level studies with sufficient data on low level As exposure and adverse health effects.Importantly, our study could provide guidelines for future modelling assessment in epidemiological studies. We found the linear model better using AIC as the selection criteria depicting the associations of low-level As concentration and CVD outcomes. Such a conclusion has already been confirmed by several epidemiological and biological studies ,53,61. HUnfortunately, our study also has several limitations, the most important of which might be the unavoidable heterogeneity in our meta-analysis. We assumed that the above-mentioned substantial differences in underlying population characteristics, adjustment for confounding, methods for outcome ascertainment and exposure assessment might be responsible for such heterogeneity. It has been well-documented that assessing As exposure via a biomarker of internal dose is of great importance as it could provide an integrated measure of all sources of exposure, not only from drinking water, but also from diet, soil and air particles ,101. It It is also important to understand that even though several perspective studies, such as the ones conducted by D’Ippoliti et al.  and ErsbThese findings shed light, at least in part, on low-level As exposure induced health risks, suggesting that a consideration of downward revision of the WHO provisional guide value may be indicated to protect better human health and to reduce the economic burden arising from CVD related outcomes, even in countries such as the UK, where relatively few individuals consume drinking water with more than 10 µg/L As , but oveOur meta-analysis indicates an overall positive association between CVD risks and low level (1 µg/L to 10 µg/L) As concentration in drinking water. For the general population, those with chronic exposure to As in drinking water at the WHO provisional guideline value limit (10 µg/L) compared to a reference population with 1 µg/L As are modelled to have significant excess CHD (50% (95% CI: 15%–95%)) and CVD (17% (95% CI: 5%–31%)) mortality risks, as well as significant excess combined fata land non-fatal risks of CHD (41 % (95% CI: 18%–67%)), CVD (41% (95% CI: 24%–60%)), carotid atherosclerosis (94% (95% CI: 40%–167%)) and hypertension (23% (95% CI: 4%–45%)). Given the high mortality and morbidity of CVD risks in the world , such an


Epstein Barr Virus (EBV) infects 90%-95% of all adults globally and causes ~ 1% of all cancers. Differing proportions of Burkitt’s lymphoma (BL), gastric carcinoma (GC), Hodgkin’s lymphoma (HL) and nasopharyngeal carcinoma (NPC) are associated with EBV. We sought to systematically review the global epidemiological evidence for risk factors that (in addition to EBV) contribute to the development of the EBV-associated forms of these cancers, assess the quality of the evidence, and compare and contrast the cancers.MEDLINE, Embase and Web of Science were searched for studies of risk factors for EBV-associated BL, GC, HL and NPC without language or temporal restrictions. Studies were excluded if there was no cancer-free comparator group or where analyses of risk factors were inadequately documented. After screening and reference list searching, data were extracted into standardised spreadsheets and quality assessed. Due to heterogeneity, a narrative synthesis was undertaken.9916 hits were retrieved. 271 papers were retained: two BL, 24 HL, one GC and 244 NPC. The majority of studies were from China, North America and Western Europe. Risk factors were categorised as dietary, environmental/non-dietary, human genetic, and infection and clinical. Anti-EBV antibody load was associated with EBV-associated GC and BL. Although the evidence could be inconsistent, HLA-A alleles, smoking, infectious mononucleosis and potentially other infections were risk factors for EBV-associated HL. Rancid dairy products; anti-EBV antibody and EBV DNA load; history of chronic ear, nose and/or throat conditions; herbal medicine use; family history; and human genetics were risk factors for NPC. Fresh fruit and vegetable and tea consumption may be protective against NPC.Many epidemiological studies of risk factors in addition to EBV for the EBV-associated forms of BL, GC, HL and NPC have been undertaken, but there is a dearth of evidence for GC and BL. Available evidence is of variable quality. The aetiology of EBV-associated cancers likely results from a complex intersection of genetic, clinical, environmental and dietary factors, which is difficult to assess with observational studies. Large, carefully designed, studies need to be strategically undertaken to harmonise and clarify the evidence.PROSPERO CRD42017059806. Epstein Barr Virus (EBV) is a herpesvirus estimated to infect 90%-95% of all humans . InfectiDespite decades of intensive research, the aetiology of EBV-linked cancers remains unclear. As only a fraction of EBV-infected individuals develop cancer, other factors must also be influential. Several reviews have been undertaken of the biological and epidemiological evidence for the association between EBV and different cancers -16, but In 2016, there were approximately 461 000 incident cases of non-Hodgkin’s lymphoma (NHL) globally . BL is aHL is a lymphoma characterised by the presence of Hodgkin and Reed-Sternberg cells which has various histological appearances. There were an estimated 73 000 incident cases globally in 2016 . 40%-50%With 830 000 deaths and 1.2 million cases globally (2016), GC is a major cause of morbidity and mortality . Around 96 000 cases of cancer of the nasopharynx occurred in 2016 . NPC forGiven the global importance of EBV-associated BL, HL, GC and NPC, their contrasting features yet common link with EBV, and their complex but unclear aetiology, we undertook a systematic review to map the current global epidemiological evidence base of risk factors for the EBV-associated forms of these tumours in addition to EBV itself. We present an evaluative narrative summary of that evidence, a quality assessment, and an appraisal of the critical gaps in the literature.MEDLINE , Embase  and the Web of Science  were searched for studies of risk factors for a) EBV-associated HL and BL and b) EBV-associated NPC and GC in June to August 2017  of risk factors for EBV-associated HL, BL, NPC and/or GC with extractable data and a cancer-free comparator population.Human studies.No date restrictions.No population restrictions.No language restrictions.Exclusion criteria:Animal studies.Studies of risk factors for EBV infection, not EBV-associated cancers.Studies where the tumours were not proven to be EBV positive, unless for NPC, where EBV is thought to be associated with 95% of tumours .Comparator population had cancer or pre-cancerous lesions (including both EBV-negative HL/BL/GC/NPC and other forms of cancer).Gene/protein expression or genetic (human or EBV) studies of samples taken from cancer tissues, due to the potential for post-tumour mutations.Genetics studies where samples were taken from different bodily sites for the cancer and comparator groups.EBV genetic and anti-EBV antibody expression studies where samples were taken cross-sectionally from cancer patients, due to the potential for tumour-induced expression, or where antibody load was not documented.Hits were screened at the title, abstract and full text stages by three reviewers per cancer, with at least a 10% overlap. Disagreements were resolved by an independent reviewer. Data extraction was undertaken by the same three reviewers, with at least 10% overlap. Discrepancies were resolved by consensus.Data were extracted into a pre-designed spreadsheet which included data on the study design, population and risk factors assessed for EBV-associated cancers.The quality of included studies was assessed using a checklist adapted from Downs and Black , as per Due to between-study heterogeneity, the findings of the included publications were narratively synthesised by theme.As a systematic review of previously published data, this work did not require ethical approval.After de-duplication, 5373 hits were retrieved by the lymphomas search and 2823 by the carcinomas search  of publications were very under-powered, with a score of zero or one, ie, less than 80% power (Table S2 in the 171/271 (63.1%) analyses were undertaken using appropriate statistical methods eg, conditional regression for individually matched studies; 65/271 (24.0%) adjusted for the minimal confounder set. Across the publications, none were thought to be at high risk for observer bias for the outcome, but the potential for recall bias was higher , with exceptions being studies of genetic risk factors.Two publications met our inclusion criteria for EBV-associated BL. Both were undertaken on the same population in Uganda in the 1970s. They examined infection and clinical factors, specifically anti-EBV antibody load prior to cancer diagnosis  ,28. OnlyAmong the 24 EBV-associated HL publications, three examined dietary risk factors, 11 infection and clinical factors, eight human genetic factors, and five environmental and non-diet lifestyle. Within these publications, 21 represented eight sets of overlapping or identical study populations (and one did not have enough information to classify) : one setOf the papers partially or totally examining diet as a risk factor for HL  may be a risk factor for HL , weight, height and measures of physical activity were examined as risk factors by three publications ,45,48. POf the eight publications that examined the human genetic factors associated with EBV-associated HL, six focussed on human leukocyte antigens (HLA), with one additionally looking at tumour necrosis factor (TNF) α and β  . IgG antAmong the 244 NPC publications, 45 examined dietary risk factors, 50 infection and clinical factors, 158 human genetic factors, and 56 environmental and non-diet lifestyle. Publications from the same setting and time frame (thus with overlapping populations) were numerous. Two Coghill et al. publications were a conference abstract and a manuscript of the same analyses, thus the abstract is excluded from the following ,55.Among the dietary risk factors papers, there were a series of common factors that were assessed  ear nose and/or throat conditions was associated with NPC risk, as were herbal medicines. Nasal balms, drops, ointments, oils or sprays were generally not associated (five of seven papers).Twenty publications analysed whether a family history of NPC was associated with personal risk, with the balance of evidence on the side of this being a risk factor (15 publications), in line with the genetic evidence presented in Human genetic risk factors for NPC have been thoroughly reviewed elsewhere, particularly by Hildesheim et al. . Table 7Among the environmental and non-dietary risk factors papers, smoking and exposure to dust, smoke and fumes in both home and occupational environments, and socioeconomic status were common factors to analyse  were only captured during the snowballing process, likely due to our decision to follow the STROBE guidelines for indexing when choosing our search terms. We excluded studies of genetic factors where samples were taken from cancer tissues, due to concerns about malignancy-induced genetic changes. This may, however, have excluded some genetics studies of loci that are unlikely to have been mutated in a cancerous cell. It should be noted that, where we document that a publication provides no evidence of an association, this does not mean that the converse is true; some studies were simply under-powered, thus confidence intervals were wide and estimates statistically uncertain. Our sample size calculations did not consider the implications of matching. We only documented analyses answering the declared aims or hypotheses of a publication; while dredging .Regarding the methodological quality of the included publications, we note several common features. Testing of multiple factors was common, increasing the likelihood of chance findings. Slightly over a third of publications used inappropriate statistical methods and only a quarter adjusted for our pragmatically minimal confounder set of age, ethnicity and sex. We acknowledge, however, that studies from certain settings would have recruited from ethnically homogenous populations, removing the need to control for ethnicity. Particular care should be taken interpreting the findings of dietary and environmental factor studies that do not adjust for upstream socioeconomic determinants of these factors and generally of recall bias. A few publications only presented results for factors positively associated with cancer.As this review was specifically one of EBV-associated forms of our chosen cancers, we were limited by the number of studies that did not report EBV status. For GC, this reflects the relatively recent documentation of GC’s EBV-association and the fact that histological samples are not tested for the virus as standard. Without such testing, future meta-analyses will be substantially hampered. The close association between BL and EBV in endemic areas means that we could have included all publications from these regions regardless of EBV tumour status, however we opted for a consistent approach per tumour.The major implications of our findings for public health are as follows. First, some of the critical risk and protective factors documented are modifiable eg, dietary elements, such as the consumption of rancid dairy products. These present opportunities for governmental interventions. Second, risk profiles can be built from these data for use as screening tools in areas of high cancer incidence. Third, our thorough documentation of the literature to date provides a signpost for future studies a) examining promising, but not fully proven, risk or protective factors in a broader span of geographical locations and b) to ensure that causal networks can be thoroughly mapped and thus confounders appropriately adjusted for . FinallyWe document 271 epidemiological publications on risk factors in addition to EBV for the EBV-associated forms of BL, GC, HL and NPC; the majority focussed on NPC. The quality of the available evidence was variable. The aetiology of EBV-associated cancers likely results from a complex intersection of genetic, clinical and dietary factors, which are difficult to pull apart through observational studies. A more strategic approach to building the evidence base should be undertaken with large, well-designed studies, in order to harmonise and clarify the evidence, particularly for GC and BL.


Nutrients involved in one-carbon metabolism may play a key role in pancreatic carcinogenesis. The aim of this study was to examine the association between pancreatic cancer risk and intake or blood levels of vitamins B6, B12 and methionine via meta-analysis.A systematic search was performed in PubMed, Web of Knowledge and Chinese National Knowledge Infrastructure (CNKI) up to April 2020 to identify relevant studies. Risk estimates and their 95% confidence intervals (CIs) were retrieved from the studies and combined by a random-effect model.A total of 18 studies were included in this meta-analysis on the association of vitamin B6, B12 and methionine with pancreatic cancer risk. The combined risk estimate (95% CI) of pancreatic cancer for the highest vs lowest category of vitamin B6 intake and blood pyridoxal 5′-phosphate  levels was 0.63 (0.48–0.79) and 0.65 (0.52–0.79), respectively. The results indicated a non-linear dose-response relationship between vitamin B6 intake and pancreatic risk. Linear dose–response relationship was found, and the risk of pancreatic cancer decreased by 9% for every 10 nmol/L increment in blood PLP levels. No significant association were found between pancreatic cancer risk and vitamin B12 intake, blood vitamin B12 levels, methionine intake and blood methionine levels.Our study suggests that high intake of vitamin B6 and high concentration of blood PLP levels may be protective against the development of pancreatic cancer. Further research are warranted to confirm the results. Pancreatic cancer is one of the most lethal cancer worldwide, with an estimated 458,918 new cases and 432,242 deaths in 2018 .Potential risk factors for pancreatic cancer are dietary nutrients associated with one-carbon metabolism, such as folate, vitamin B6, vitamin B12 and methionine, which may protect against cancer through DNA methylation, nucleotide synthesis, DNA replication and repair. For example, folate may influence gene stability and expression through its essential role in methionine synthesis and in the conversion to S-adenosylmethionine , and vitamin B12 serves as a cofactor in this biochemical reaction , 8. VitaMany epidemiologic studies have assessed one-carbon metabolism-related nutrients associated with pancreatic cancer risk. A significant protective effect of folate on pancreatic cancer was reported in the previous meta-analysis , 13. DueThis meta-analysis follows the standards of quality for reporting systematic review and meta-analysis (PRISMA) . A literTwo reviewers independently reviewed all identified studies, and studies were included in this meta-analysis if they met all the following criteria: (1) case-control, nested case-control study, cohort study design, or randomized controlled trials (RCTs); (2) the exposure of interest was intake of vitamin B6, vitamin B12 or methionine, or serum or plasma levels of them; (3) reported risk estimate and its 95% confidence interval (CI). The exclusion criteria were as follows: 1) experimental study; 2) letters or case reports; 3) articles that provided inadequate data or only information for cancer mortality. If multiple studies from the same general population were available, the study with the largest number of cases was included in this meta-analysis.Data were extracted from each study by two reviewers. For each study, the following information were collected: first author’s name, publication year, the country in which the study was conducted, study design, number of cases, sex, doses, adjusted variables, type of exposure (dietary intake or blood level), and adjusted risk estimates for highest versus lowest level of vitamin B6 or vitamin B12 or methionine, or blood level of them. The result for dietary intake was extracted if both dietary intake and total intake (dietary intake plus supplement) were provided. Considering that pancreatic cancer is a rare disease, the relative risk (RR) was assumed approximately the same as OR, and the RR was used as the study outcome.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). NOS is an eight-item instrument that is used for assessment of the study population, study comparability, follow-up and outcome of interest. The range of possible scores is 0–9, and we assigned scores of < 7 and ≥ 7 for low- and high-quality studies, respectively.The quality of each study was assessed using the Newcastle-Ottawa Scale (NOS), which was recommended by the Cochrane Non-Randomized Studies Methods Working Group  were two-sided with P < 0.05 considered statistically significant. All the above statistical analyses were carried out with STATA 12.0 .Random-effects model was used to compute a combined RR with its 95% CI, which considers both within and between-study variation. Heterogeneity between studies was assessed by Q statistic and the Iectively . A sensiectively  and Eggeectively . For theectively  and Orsiectively . For stuThe detailed steps of literature search were shown in Fig. 2 = 48.3%, p = 0.06). All studies on the association of blood PLP levels with pancreatic cancer risk showed an inverse association, which was statistically significant in 2 studies [2 = 0, p = 0.428). The pooled RRs of pancreatic cancer for the highest vs lowest categories of vitamin B6 intake and blood PLP level were 0.63  and 0.65 , respectively. The Egger’s (P = 0.379) or Begg’s  after excluding the 2 studies, and the inverse association became stronger . The cumulative meta-analysis is the process of repeated meta-analysis of individual studies each time adding a new study. In the present study, the cumulative meta-analysis sorted by publication year showed no significant association existed before 2011, while a significant association between vitamin B6 intake and risk of pancreatic cancer began to exist and became stable from 2013 , geographical region , and number of included cases (≥ 300 or <  300). A statistically significant protective effect of vitamin B6 intake on pancreatic cancer was observed in case-control studies , while no such effect in cohort studies . Also, the inverse associations were found in US , Asia  and Australia , but not in Europe . When stratifying by number of cases, the RR estimates showed vitamin B6 intake was consistently associated with reduced risk of pancreatic cancer (Table heterogeneity = 0.16) in the overall analysis of vitamin B6 intake, with a significant non-linear dose-response relationship . A 10 nmol/ml increment in blood PLP level conferred an RR of 0.91 , indicating that the risk of pancreatic cancer was decreased by 9% for every 10 nmol/L increment in blood PLP levels . The RR estimates from subgroup analyses varied little, showing no significant association between vitamin B12 intake and pancreatic cancer risk . The sensitivity analysis indicated that the study by Larsson et al. [P = 0.779, I2 = 0%) after excluding this study. No publication bias was detected by Begg’s . The subgroup analyses were consistent with the overall results, revealing that methionine intake was not associated with incidence of pancreatic cancer .Figure n et al.  substantThe association between vitamin B6, B12 or methionine and risk of other cancers has been assessed in previous meta-analysis –39. Wu eAs for vitamin B6 intake, our results were consistent with a recent meta-analysis, which suggested that vitamin B6 intake could significantly decrease pancreatic cancer risk  . HoweverFrom a biological point of view, vitamin B6 may play a protective role in the development of pancreatic cancer. Vitamin B6 is a co-factor involved in DNA synthesis and methylation pathway of one-carbon metabolism . Low vitWe did not observe statistically significant associations between vitamin B12 and methionine and the risk of this malignancy. One potential reason vitamin B12 differs from other B vitamins may be because it is derived exclusively from foods of animal origin, and it is simply a marker for consumption of animal protein, which tends to be associated with unhealthy behaviors that may increase the risk of pancreatic cancer. Previous studies have shown that the risk of meat consumption is positively associated with pancreatic cancer risk  and dietThe strengths of our study include a comprehensive assessment of one-carbon metabolism-related nutrients. There are also some weaknesses in our study. First, the meta-analysis can’t solve the problem of confounding factors inherent in the study. Lack of control over confounding factors may bias the results in either direction, in which risk estimates are exaggerated or underestimated, although individual studies in this meta-analysis have considered a wide range of potential confounding factors except for the study by Arendt et al. . Second,In summary, this present meta-analysis demonstrated that among one-carbon metabolism-related factors, high vitamin B6 intake was associated with lower risk of pancreatic cancer in a non-linear dose-response pattern, and serum PLP level were associated with a significant linear decreased risk of pancreatic cancer. Considering that vitamin B6 is present in a wide variety of foods such as beef, liver, tuna, and bananas, this research is expected to offer novel avenues for the primary prevention and control of pancreatic cancer. However, this evidence is mainly derived from case–control studies and the data for the high level of dietary vitamin B6 intake were sparse, further research including randomized clinical trials is needed to examine the association of dietary vitamin B6 intake with risk of pancreatic cancer at high doses and explore the recommended treatment period to reduce the risk of pancreatic cancer.Additional file 1.Additional file 2.Additional file 3.Additional file 4.