Frequentist performances of Bayesian prediction intervals for random-effects meta-analysis.

The prediction interval has been increasingly used in meta-analyses as a useful measure for assessing the magnitude of treatment effect and between-studies heterogeneity. In calculations of the prediction interval, although the Higgins-Thompson-Spiegelhalter method is used most often in practice, it might not have adequate coverage probability for the true treatment effect of a future study under realistic situations. An effective alternative candidate is the Bayesian prediction interval, which has also been widely used in general prediction problems. However, these prediction intervals are constructed based on the Bayesian philosophy, and their frequentist validities are only justified by large-sample approximations even if noninformative priors are adopted. There has been no certain evidence that evaluated their frequentist performances under realistic situations of meta-analyses. In this study, we conducted extensive simulation studies to assess the frequentist coverage performances of Bayesian prediction intervals with 11 noninformative prior distributions under general meta-analysis settings. Through these simulation studies, we found that frequentist coverage performances strongly depended on what prior distributions were adopted. In addition, when the number of studies was smaller than 10, there were no prior distributions that retained accurate frequentist coverage properties. We also illustrated these methods via applications to two real meta-analysis datasets. The resultant prediction intervals also differed according to the adopted prior distributions. Inaccurate prediction intervals may provide invalid evidence and misleading conclusions. Thus, if frequentist accuracy is required, Bayesian prediction intervals should be used cautiously in practice.

Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm Validation Study.

BACKGROUND: Performing systematic reviews is a time-consuming and resource-intensive process. OBJECTIVE: We investigated whether a machine learning system could perform systematic reviews more efficiently. METHODS: All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network-based artificial intelligence engine (Concept Encoder, Fronteo Inc). The primary endpoint was work saved over sampling at 95% recall (WSS@95%). RESULTS: Among 145 candidate reviews of randomized controlled trials, 8 reviews fulfilled the inclusion criteria. For these 8 reviews, the machine learning system significantly reduced the literature screening workload by at least 6-fold versus that of manual screening based on WSS@95%. When machine learning was initiated using 2 correct articles that were randomly selected by a researcher, a 10-fold reduction in workload was achieved versus that of manual screening based on the WSS@95% value, with high sensitivity for eligible studies. The area under the receiver operating characteristic curve increased dramatically every time the algorithm learned a correct article. CONCLUSIONS: Concept Encoder achieved a 10-fold reduction of the screening workload for systematic review after learning from 2 randomly selected studies on the target topic. However, few meta-analyses of randomized controlled trials were included. Concept Encoder could facilitate the acquisition of evidence for clinical guidelines.

Sodium-glucose co-transporter-2 inhibitors as add-on therapy to insulin for type 1 diabetes mellitus: Systematic review and meta-analysis of randomized controlled trials.

New treatments for type 1 diabetes are an unmet need. We investigated the efficacy and safety of adding sodium-glucose co-transporter-2 (SGLT2) inhibitors to insulin for type 1 diabetes by conducting a meta-analysis of prospective randomized, placebo-controlled trials. A search of electronic databases up to October 2017 identified 1361 studies, of which 14 were investigated (N = 4591). Meta-analysis showed that SGLT2 inhibitor therapy significantly reduced glycated haemoglobin (HbA1c) concentration by 0.4% (95% confidence interval [CI] 0.35, 0.46; P < .001, I2 = 0%), fasting plasma glucose by 1.14 mmol/L (95% CI 0.8,1.47), body weight by 2.68 kg (95% CI 2.0, 3.36), and systolic blood pressure by 3.37 mmHg (95% CI 1.46, 5.28). In addition, bolus insulin decreased by 3.6 units/day (95% CI 2.0, 5.3), and basal insulin decreased by 4.2 units/day (95% CI 2.2, 6.3). Continuous glucose monitoring showed a decrease in glucose excursions compared with placebo, with reduced variation of mean blood glucose, glucose standard deviation, and mean amplitude of glucose excursion. There was no significant increase in the rate of hypoglycaemia or severe hypoglycaemia; however, SGLT2 inhibitor therapy increased diabetic ketoacidosis (odds ratio [OR] 3.38) and genital tract infection (OR 3.44). Add-on SGLT2 inhibitor therapy might be advantageous for type 1 diabetes, but its use should be considered carefully.

Biosimilar vs originator insulins: Systematic review and meta-analysis.

Biosimilar insulins have expanded the treatment options for diabetes. We compared the clinical efficacy and safety of biosimilar insulins with those of originator insulins by conducting a meta-analysis. A random-effects meta-analysis was performed on randomized controlled trials comparing biosimilar and originator insulins in adults with diabetes. Studies were obtained by searching electronic databases up to December 2017. Ten trials, in a total of 4935 patients, were assessed (2 trials each on LY2963016, MK-1293, Mylan's insulin glargine and SAR342434, and 1 trial each on FFP-112 and Basalog). The meta-analysis found no differences between long-acting biosimilar and originator insulins with regard to reduction in glycated haemoglobin at 24 weeks (0.04%, 95% confidence interval [CI] -0.01, 0.08; P for efficacy = .14, I2 = 0%) or at 52 weeks (0.03%, 95% CI -0.04, 0.1), or reduction in fasting plasma glucose (0.08 mmol/L, 95% CI 0.36, 0.53), hypoglycaemia (odds ratio 0.99, 95% CI 0.96, 1.03), mortality, injection site reactions, insulin antibodies and allergic reactions. Analyses stratified by type of diabetes and prior insulin use yielded similar findings. Similarly, no significant differences were found between short-acting biosimilar and originator insulins. In summary, our meta-analysis showed no significant differences in clinical efficacy and safety, including immune reactions, between biosimilar and originator insulins. Biosimilar insulins can increase access to modern insulin therapy and reduce medical costs.

Interaction between type 2 diabetes polygenic risk and physical activity on cardiovascular outcomes.

AIMS: The beneficial effects of exercise on reducing the risk of cardiovascular disease are established. However, the potential interaction between genetic risk for type 2 diabetes and physical activity on cardiovascular outcomes remains elusive. We aimed to investigate the effect of type 2 diabetes genetic risk-physical activity interaction on cardiovascular outcomes in individuals with diabetes. METHODS AND RESULTS: Using the UK Biobank cohort, we investigated the effect of type 2 diabetes genetic risk-physical activity interaction on three-point and four-point major adverse cardiovascular events (MACE), in 25 701 diabetic participants. We used a polygenic risk score for type 2 diabetes (PRS_T2D) as a measure of genetic risk for type 2 diabetes. We observed a significant interaction between PRS_T2D and physical activity on cardiovascular outcomes (three-point MACE: P trend for interaction = 0.0081; four-point MACE: P trend for interaction = 0.0037). Among participants whose PRS_T2D was in the first or second quartile, but not in the third or fourth quartile, each 10 metabolic equivalents (METs) hours per week of physical activity decreased the risk of three-point or four-point MACE. Furthermore, restricted cubic spline analysis indicated that intense physical activity (>80 METs hours per week, which was self-reported by 12.7% of participants) increased the risk of cardiovascular outcomes among participants whose PRS_T2D was in the fourth quartile. Sub-group analysis suggested that negative impact of intense physical activity was observed only in non-insulin users. CONCLUSION: The beneficial effect of physical activity on cardiovascular outcomes disappeared among those with high genetic risk for type 2 diabetes.

The beneficial effects of exercise on reducing the risk of cardiovascular disease are established. However, whether genetic risk for type 2 diabetes influences the effect of physical activity on cardiovascular outcomes in individuals with diabetes remains elusive. We aimed to investigate interaction between genetic risk for type 2 diabetes and physical activity on major adverse cardiovascular events (MACE) in individuals with diabetes.The beneficial effect of physical activity on cardiovascular outcomes disappeared among diabetic individuals with high genetic risk for type 2 diabetes, due to significant gene­environment interaction; in this subpopulation, intense physical activity was associated with increased risk of cardiovascular outcomes.Personalized exercise recommendations tailored to avoid excessively intense exercise, in combination with genetic screening of high-risk individuals, would be required.

Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study.

It is expected but unknown whether machine-learning models can outperform regression models, such as a logistic regression (LR) model, especially when the number and types of predictor variables increase in electronic health records (EHRs). We aimed to compare the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and LR with the least absolute shrinkage and selection operator (LR-LASSO) for unplanned readmission. We used EHRs of patients discharged alive from 38 hospitals in 2015-2017 for derivation and in 2018 for validation, including basic characteristics, diagnosis, surgery, procedure, and drug codes, and blood-test results. The outcome was 30-day unplanned readmission. We created six patterns of data tables having different numbers of binary variables (that ≥5% or ≥1% of patients or ≥10 patients had) with and without blood-test results. For each pattern of data tables, we used the derivation data to establish the machine-learning and LR models, and used the validation data to evaluate the performance of each model. The incidence of outcome was 6.8% (23,108/339,513 discharges) and 6.4% (7,507/118,074 discharges) in the derivation and validation datasets, respectively. For the first data table with the smallest number of variables (102 variables that ≥5% of patients had, without blood-test results), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the last data table with the largest number of variables (1543 variables that ≥10 patients had, including blood-test results), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720), suggesting that the difference between GBDT and LR-LASSO was small and their 95% confidence intervals overlapped. In conclusion, GBDT generally outperformed LR-LASSO to predict unplanned readmission, but the difference of c-statistic became smaller as the number of variables was increased and blood-test results were used.

FTO Obesity Variant-Exercise Interaction on Changes in Body Weight and BMI: The Taiwan Biobank Study.

CONTEXT: Gene-exercise interaction on cross-sectional body mass index (BMI) has been extensively studied and is well established. However, gene-exercise interaction on changes in body weight/BMI remains controversial. OBJECTIVE: To examine the interaction between the FTO obesity variant and regular exercise on changes in body weight/BMI. PARTICIPANTS: Taiwan Biobank participants aged 30-70 years (N = 20 906) were examined at both baseline and follow-up visit (mean follow-up duration: 3.7 years). MAIN OUTCOME MEASURES: The interaction between the FTO obesity variant rs1421085 and regular exercise habit (no exercise, ≤20 metabolic equivalent of tasks (METs)/week exercise, >20 METs/week exercise) on changes in body weight/BMI. RESULTS: Individuals with the risk allele of rs1421085 gained more weight and increased BMI than those without the risk allele if they did not exercise. In contrast, individuals with the risk allele gained less weight and BMI if they exercised regularly, indicating an interaction between rs1421085 and regular exercise habit (P = .030 for Δbody weight and P = .034 for ΔBMI). The effect of exercise on maintaining body weight was larger in those with the risk allele of rs1421085. When we focused on individuals without regular exercise at baseline, individuals with the risk allele again tended to lose more weight than those with a nonrisk allele if they had acquired an exercise habit by the follow-up visit. CONCLUSION: The beneficial effect of exercise is greater in individuals genetically prone to obesity due to the interaction between the FTO obesity variant rs1421085 and regular exercise on changes in body weight and BMI.

Influence diagnostics and outlier detection for meta-analysis of diagnostic test accuracy.

Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta-analyses, some studies may have markedly different characteristics from the others and potentially be inappropriate to include. The inclusion of these "outlying" studies might lead to biases, yielding misleading results. In addition, there might be influential studies that have notable impacts on the results. In this article, we propose Bayesian methods for detecting outlying studies and their influence diagnostics in DTA meta-analyses. Synthetic influence measures based on the bivariate hierarchical Bayesian random effects models are developed because the overall influences of individual studies should be simultaneously assessed by the two outcome variables and their correlation information. We propose four synthetic measures for influence analyses: (a) relative distance, (b) standardized residual, (c) Bayesian p-value, and (d) influence statistic on the area under the summary receiver operating characteristic curve. We also show that conventional univariate Bayesian influential measures can be applied to the bivariate random effects models, which can be used as marginal influential measures. Most of these methods can be similarly applied to the frequentist framework. We illustrate the effectiveness of the proposed methods by applying them to a DTA meta-analysis of ultrasound in screening for vesicoureteral reflux among children with urinary tract infections.

Myocardial infarction in type 2 diabetes using sodium-glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning.

Aims: Some hypoglycemic therapies are associated with lower risk of cardiovascular outcomes. We investigated the incidence of cardiovascular disease among patients with type 2 diabetes using antidiabetic drugs from three classes, which were sodium-glucose co-transporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dipeptidyl peptidase-4 inhibitors (DPP-4is).Materials and methods: We compared the risk of myocardial infarction (MI) among these drugs and developed a machine learning model for predicting MI in patients without prior heart disease. We analyzed US health plan data for patients without prior MI or insulin therapy who were aged ≥40 years at initial prescription and had not received oral antidiabetic drugs for ≥6 months previously. After developing a machine learning model to predict MI, proportional hazards analysis of MI incidence was conducted using the risk obtained with this model and the drug classes as explanatory variables.Results: We analyzed 199,116 patients (mean age: years), comprising 110,278 (58.6) prescribed DPP-4is, 43,538 (55.1) prescribed GLP-1RAs and 45,300 (55.3) prescribed SGLT-2is. Receiver operating characteristics analysis showed higher precision of machine learning over logistic regression analysis. Proportional hazards analysis by machine learning revealed a significantly lower risk of MI with SGLT-2is or GLP-1RAs than DPP-4is (hazard ratio: 0.81, 95% confidence interval: 0.72-0.91, p = .0004 vs. 0.63, 0.56-0.72, p < .0001). MI risk was also significantly lower with GLP-1RAs than SGLT-2is (0.77, 0.66-0.90, p = .001).Limitations: All patients analyzed were covered by US commercial health plans, so information on patients aged ≥65 years was limited and the socioeconomic background may have been biased. Also, the observation period differed among the three classes of drugs due to differing release dates.Conclusions: Machine learning analysis suggested the risk of MI was 37% lower for type 2 diabetes patients without prior MI using GLP-1RAs versus DPP-4is, while the risk was 19% lower for SGLT-2is versus DPP-4is.

Understanding the experiences of long-term maintenance of self-worth in persons with type 2 diabetes in Japan: a qualitative study.

OBJECTIVE: Persons with type 2 diabetes are often stigmatised for having what is considered a lifestyle-related disease. Accordingly, some blame themselves for their condition, resulting in feelings of low self-worth that ultimately impact their self-management behaviours. However, there are no studies examining why some do not blame themselves for their condition and manage to maintain their self-worth in relation to their illness. This study aimed to explore an understanding of how such persons experience the maintenance of self-worth in relation to their illness over the lifelong course of treatment. DESIGN: A cross-sectional qualitative study. Face-to-face semistructured interviews were conducted with a purposive sampling strategy. The data was analysed using a qualitative descriptive method that involved concurrent data collection and constant comparative analysis. SETTING: Two tertiary-level hospitals in Japan. PARTICIPANTS: Thirty-three outpatients with type 2 diabetes who currently had good glycaemic control but had previously had poor glycaemic control. RESULTS: Three themes explaining the maintenance of self-worth were identified: (1) Participants gained 'control' over their illness by living a 'normal life.' They found a way to eat preferred foods, dine out with family and friends, travel and work as usual; (2) Participants discovered the positive aspects of type 2 diabetes, as they felt 'healthier' from the treatment and felt a sense of security and gratitude for the care they received from healthcare professionals; (3) Participants discovered a new sense of self-worth by moving towards goals for type 2 diabetes treatment and experienced inner growth through positive lifestyle choices. CONCLUSIONS: The process of restoring and maintaining self-worth should be brought to the attention of healthcare professionals in diabetes care. These professionals could help patients discover positive self-representations through diabetes treatment (eg, a realisation that one does not lack self-control) and could aid in increasing patient engagement in diabetes self-management.

Glycemic control, mortality, and hypoglycemia in critically ill patients: a systematic review and network meta-analysis of randomized controlled trials.

PURPOSE: It is unclear whether tight glycemic control is warranted in all critically ill adults. We employed network meta-analysis to examine the risk of mortality and hypoglycemia associated with different glycemic control targets in critically ill adults. METHODS: Electronic databases were searched up to 2016 for randomized controlled trials comparing various insulin regimens in critically ill adults with hyperglycemia. Two reviewers independently extracted information and evaluated quality with the Cochrane risk-of-bias tool. Four glycemic control groups were compared: tight (blood glucose: 4.4 < 6.1 mmol/l), moderate (6.1 < 7.8 mmol/l), mild (7.8 < 10.0 mmol/l), and very mild (10.0 to < 12.2 mmol/l). Network meta-analysis was performed by a frequentist approach with multivariate random effects meta-analysis. RESULTS: Thirty-six randomized trials (17,996 patients) were identified. Compared with very mild control, tight control did not reduce the risk of short-term mortality [relative risk (RR) 0.94 (95 % CI 0.83-1.07, p = 0.36)], and neither did mild control [RR 0.88 (0.73-1.06), p = 0.18] or moderate control [RR 1.1 (0.66-1.84), p = 0.72]. However, severe hypoglycemia (<2.2 mmol/l) was more frequent with tight control than very mild control [RR 5.49 (3.22-9.38), p < 0.001] or mild control [RR 4.47 (2.5-8.03), p < 0.001]. Stratified analyses (cause of death, ICU type, time period, or diabetes) did not find significant between-group differences. Ranking analysis revealed the following hierarchy for avoiding death (highest to lowest rank): mild control, tight control, and very mild control. CONCLUSIONS: Network meta-analysis showed no mortality benefit of tight glycemic control in critically ill patients, but fivefold more hypoglycemia versus mild or very mild control.

J-curve relation between daytime nap duration and type 2 diabetes or metabolic syndrome: A dose-response meta-analysis.

Adequate sleep is important for good health, but it is not always easy to achieve because of social factors. Daytime napping is widely prevalent around the world. We performed a meta-analysis to investigate the association between napping (or excessive daytime sleepiness: EDS) and the risk of type 2 diabetes or metabolic syndrome, and to quantify the potential dose-response relation using cubic spline models. Electronic databases were searched for articles published up to 2016, with 288,883 Asian and Western subjects. Pooled analysis revealed that a long nap (≥60 min/day) and EDS were each significantly associated with an increased risk of type 2 diabetes versus no nap or no EDS (odds ratio 1.46 (95% CI 1.23-1.74, p < 0.01) for a long nap and 2.00 (1.58-2.53) for EDS). In contrast, a short nap (<60 min/day) was not associated with diabetes (p = 0.75). Dose-response meta-analysis showed a J-curve relation between nap time and the risk of diabetes or metabolic syndrome, with no effect of napping up to about 40 minutes/day, followed by a sharp increase in risk at longer nap times. In summary, longer napping is associated with an increased risk of metabolic disease. Further studies are needed to confirm the benefit of a short nap.

Daytime Napping and the Risk of Cardiovascular Disease and All-Cause Mortality: A Prospective Study and Dose-Response Meta-Analysis.

STUDY OBJECTIVES: To summarize evidence about the association between daytime napping and the risk of cardiovascular disease and all-cause mortality, and to quantify the potential dose-response relation. DESIGN: Meta-analysis of prospective cohort studies. METHODS AND RESULTS: Electronic databases were searched for articles published up to December 2014 using the terms nap, cardiovascular disease, and all-cause mortality. We selected well-adjusted prospective cohort studies reporting risk estimates for cardiovascular disease and all-cause mortality related to napping. Eleven prospective cohort studies were identified with 151,588 participants (1,625,012 person-years) and a mean follow-up period of 11 years (60% women, 5,276 cardiovascular events, and 18,966 all-cause deaths). Pooled analysis showed that a long daytime nap (≥ 60 min/day) was associated with a higher risk of cardiovascular disease (rate ratio [RR]: 1.82 [1.22-2.71], P = 0.003, I(2) = 37%) compared with not napping. All-cause mortality was associated with napping for ≥ 60 min/day (RR: 1.27 [1.11-1.45], P < 0.001, I(2) = 0%) compared with not napping. In contrast, napping for < 60 min/day was not associated with cardiovascular disease (P = 0.98) or all-cause mortality (P = 0.08). Meta-analysis demonstrated a significant J-curve dose-response relation between nap time and cardiovascular disease (P for nonlinearity = 0.01). The RR initially decreased from 0 to 30 min/day. Then it increased slightly until about 45 min/day, followed by a sharp increase at longer nap times. There was also a positive linear relation between nap time and all-cause mortality (P for non-linearity = 0.97). CONCLUSIONS: Nap time and cardiovascular disease may be associated via a J-curve relation. Further studies are needed to confirm the efficacy of a short nap.

Successfully achieving target weight loss influences subsequent maintenance of lower weight and dropout from treatment.

OBJECTIVES: The influence of the amount and rate of weight loss on subsequently regaining weight and dropout from treatment in severely obese patients targeting 5% weight loss was investigated. METHODS: A total of 120 consecutive hospital patients with severe obesity (BMI: 42 ± 9 kg/m(2) ) participated in an inpatient program targeting 5% weight loss that involved goal setting, charting weight four times daily, and diet and exercise. They were followed after discharge to assess subsequent regaining of weight and dropout. RESULTS: Mean weight loss was 4.9 ± 2.4% after a mean of 19 days in the hospital, and 43% of the patients achieved the target weight loss (>5%). Over the median 2-year follow-up period, greater than 5% in-hospital weight loss was associated with a significantly lower risk of regaining weight after adjustment for various factors (>5% to ≤7% loss: hazard ratio 0.30 [0.11-0.85] for regaining all of the lost weight and 0.32 [0.13-0.78] for regaining half of the lost weight). No significant relation between the amount or rate of weight loss and dropout from subsequent outpatient treatment was seen. CONCLUSIONS: Successfully achieving the target weight loss in a comprehensive program predicts subsequent maintenance of lower weight without increasing the risk of dropout. Successful in-hospital weight loss might increase the motivation of obese patients.

Charting weight four times daily as an effective behavioural approach to obesity in patients with type 2 diabetes.

Although many obese people successfully lose weight by dieting and/or behavioural therapy, most of them subsequently regain the lost weight. Thus, new therapeutic strategies are required to maintain weight loss. We report a woman with type 2 diabetes and moderate obesity who succeeded in achieving good glycaemic control and long-term weight loss with weaning from insulin therapy, while charting her weight four times daily. This charting method might be useful for long-term maintenance of weight reduction in obese diabetic patients. Obese patients can monitor their irregular weight fluctuations produced by overeating and correct both their food intake and their lifestyle. Further studies, including randomized control trials, will be needed to confirm the efficacy of this approach in patients with type 2 diabetes.

Lung abscess without sepsis in a patient with diabetes with refractory episodes of spontaneous hypoglycemia: a case report and review of the literature.

INTRODUCTION: Hypoglycemia is a cause of considerable morbidity. Although hypoglycemia has been documented in the setting of septic shock and has been associated with higher mortality, hypoglycemia in infection without sepsis has not been reported in the literature. CASE PRESENTATION: A 72-year-old Japanese woman treated with high-dose glucocorticoids for autoimmune hemolytic anemia, as well as intensive insulin therapy for type 2 diabetes, presented with severe hypoglycemia. A lung abscess was diagnosed by imaging studies and treated with intravenous antibiotics. Hypoglycemia spontaneously recurred during lung abscess exacerbations, despite appropriate de-escalation of antidiabetic therapy. Only mild sporadic episodes of hypoglycemia occurred after the lung abscess was controlled. Infection accompanied with malnutrition and immunosuppression, although in the absence of sepsis, may have contributed to hypoglycemia. CONCLUSIONS: Caution is warranted in the management of hypoglycemia in patients with diabetes with the conditions described here, that is malnutrition and immunosuppression, as infection may be a contributing factor.

Chewing betel quid and the risk of metabolic disease, cardiovascular disease, and all-cause mortality: a meta-analysis.

BACKGROUND: Betel nut (Areca nut) is the fruit of the Areca catechu tree. Approximately 700 million individuals regularly chew betel nut (or betel quid) worldwide and it is a known risk factor for oral cancer and esophageal cancer. We performed a meta-analysis to assess the influence of chewing betel quid on metabolic diseases, cardiovascular disease, and all-cause mortality. METHODOLOGY/PRINCIPAL FINDINGS: We searched Medline, Cochrane Library, Web of Science, and Science Direct for pertinent articles (including the references) published between 1951 and 2013. The adjusted relative risk (RR) and 95% confidence interval were calculated using the random effect model. Sex was used as an independent category for comparison. RESULTS: Of 580 potentially relevant studies, 17 studies from Asia (5 cohort studies and 12 case-control studies) covering 388,134 subjects (range: 94 to 97,244) were selected. Seven studies (N = 121,585) showed significant dose-response relationships between betel quid consumption and the risk of events. According to pooled analysis, the adjusted RR of betel quid chewers vs. non-chewers was 1.47 (P<0.001) for obesity (N = 30,623), 1.51 (P = 0.01) for metabolic syndrome (N = 23,291), 1.47 (P<0.001) for diabetes (N = 51,412), 1.45 (P = 0.06) for hypertension (N = 89,051), 1.2 (P = 0.02) for cardiovascular disease (N = 201,488), and 1.21 (P = 0.02) for all-cause mortality (N = 179,582). CONCLUSION/SIGNIFICANCE: Betel quid chewing is associated with an increased risk of metabolic disease, cardiovascular disease, and all-cause mortality. Thus, in addition to preventing oral cancer, stopping betel quid use could be a valuable public health measure for metabolic diseases that are showing a rapid increase in South-East Asia and the Western Pacific.