J. Glenn et al. Nature Medicine 10.1038/s41591-023-02294-8 (2023)5. The BoP approach was designed to address key problems in current analytical frameworks. To provide helpful information to users making decisions around risk exposure, the approach systematically estimates a flexible mean risk–outcome function, avoiding making strong assumptions such as log-linearity of the relationship. Complementing the mean risk–outcome relationship, BoP analysis provides a BoP risk function (BoPRF) that represents the lowest estimate of excess harmful risk associated with a risk factor, incorporating unexplained between-study heterogeneity after accounting for known variation in study design characteristics. From the BoPRF, we calculate summary risk–outcome scores and star-rating measures that present conservative estimates of the risk–outcome relationship and enable comparisons across different risk–outcome pairs.We, together with the Global Burden of Disease Study and Burden of Proof (BoP) collaborators, have published a BoP Capstone Methods paper and suite of associated meta-analyses that introduce and apply a new framework for synthesizing evidence to evaluate relationships between selected risk factors and health outcomes6 argue that our inclusion of between-study heterogeneity improperly inflates uncertainty intervals and that our star-rating summary measure is too simplistic. They further suggest that we wrongly assume risk–outcome relationships to be non-log-linear. We welcome this opportunity to engage in discourse and clarify misunderstandings. We all agree on the importance of using methods that summarize the available risk–outcome evidence in a way that most accurately captures the health risks people experience because of risk factor exposures.In their Matters Arising on the BoP papers, Glenn et al.7. The status quo in meta-analytic research—where the inclusion of more studies creates increased precision in the estimate even if they are divergent—is counterintuitive. Classic confidence intervals by themselves often fail to capture what matters to scientists and the public because they largely ignore variation in estimates of effects between studies. We believe the BoP approach addresses this problem by providing a complementary summary measure that incorporates between-study heterogeneity to obtain a prediction interval at the study level1. This interval answers the question: ‘If a large criterion standard study was conducted today, how varied might its findings be, based on all available information?’ In our reports5 and visualizations , we present both classically reported uncertainty intervals and the BoP uncertainty interval. The BoP approach provides new insight, allowing users to differentiate between research fields where results are consistent from those where they are not.With respect to the critique by Glenn et al. that our method improperly inflates uncertainty intervals, we note that in classic meta-analysis methods, between-study heterogeneity affects posterior uncertainty only marginally by enlarging the reported standard errors. As studies accumulate in the literature, even if each study contradicts the previous one, classic posterior uncertainty around effect size estimates continues to shrink, making the basis for health risk guidance ever more certain despite apparent disagreement between studies. The phenomena of strong recommendations in spite of highly variable conclusions of emerging studies lead, justifiably, to broad critiques of the entire field9 and the Cochrane collaboration10 currently use to evaluate study quality. The BoP approach provides a mechanism to account for differences between study-level characteristics, to the extent that such differences are known and can be encoded to explain between-study heterogeneity. Any remaining unexplained between-study heterogeneity then contributes to the overall rating of effect size and evidence strength. Unexplained variation in results that remains after accounting for study-level covariates can (and should) contribute to a lower risk score and lower star ratings. We believe that users of the results will appreciate this transparency.Specific study results may vary in part due to study design, length of follow-up and other topic-specific observable variables that correlate exactly to categories that Grading of Recommendations, Assessment, Development, and Evaluations1 clearly show, when the dose–response relationship is log-linear, we recover that shape. A major contribution of the BoP method is that it allows more nuanced assessment of the underlying risk, letting the data inform the shape of the relationship rather than the analyst imposing it. Applying the BoP approach to analyze the risk–outcome pairs included in the Global Burden of Disease Study, we see that most relationships are not in fact log-linear, but some are, including systolic blood pressure versus heart disease 4. We suggest that indiscriminately assuming log-linearity is a generic problem in many canonical systematic reviews15, which may assume a log-linear relationship for computational convenience. Shortcomings of previous methodological attempts to account for non-log-linear relationships are discussed in the methods appendix of the Capstone paper1 and illustrated using simulation.Glenn et al. incorrectly conclude that we assume that relationships between red meat consumption and chronic disease are not log-linear. A central principle in the BoP approach is to forgo the assumption that risks are log-linear; however, this is very different from assuming risks are ‘not log-linear’. As simulation studies in the Capstone paper1. The critique stated here applies to studies that do not consider diet substitution  and to any meta-analyses that use such studies. Unfortunately, detailed individual-level information is rarely available, although ongoing efforts to synchronize and collaborate between investigators are commendable and promising. Nonetheless, most work in meta-analysis relies on extracting information across studies where only results were reported and individual-level data are unavailable.Because of the inherent limitations around correcting for study-level bias in meta-analyses and, in terms of dietary risk analyses, including studies that do not carefully consider alternative foods or conduct substitution analyses, Glenn et al. suggest that a better approach would be to obtain primary, individual-level data for all original studies and use standard methods to define variables and adjust for covariates. Pooled studies can be helpful for exploring bias and answering myriad nuanced questions that can be addressed with primary detailed data shared among investigators. However, depending on how pooled studies are conducted, the results may also mask a large amount of heterogeneity in findings across studies that deserves explanation. We share Glenn et al.’s concern about the underlying literature and note it as a limitation17, not to mention the disagreements surrounding myriad other risk–outcome pairs17. The BoP approach incorporates divergent study results and study designs, accounts for explainable differences and allows comparison between strength of evidence across different scientific domains.We respect that these investigators feel strongly about the health risks of red meat consumption and recognize that they are looking for an explanation as to why the results of the BoP analysis do not agree with their previous beliefs. Hypotheses stand only until they are refuted; therein lies the dynamic of scientific debate and progress. There are certainly groups that feel differently from Glenn et al.


The mortality rate of patients with heart failure (HF) remains high, and when heart failure occurs, blood urea nitrogen (BUN) is involved in the perfusion of renal blood flow. Some studies have shown an association between heart failure prognosis and blood urea nitrogen, but the results of some other studies were inconsistent. Therefore, we conducted a comprehensive meta-analysis to investigate the value of BUN on the prognosis of patients with heart failure.A computerized systematic search of all English literature was performed in four databases, PubMed, Cochrane, Embase and Web of Science, from their inception to May 2022. The data of BUN were classified into continuous and categorical variables after passing the inclusion and exclusion criteria. The BUN data of both types were extracted separately into stata15.0 for statistical analysis.RR = 2.29, 95% CI:1.42–3.70, P < 0.001). The results showed statistical significance in multifactorial and univariate groups, the prospective cohort, and European and Asian groups. When BUN was used as a continuous variable, the risk of death in heart failure was 1.02 times higher for each unit increase in BUN . Subgroup analysis showed statistical significance in retrospective cohort, American and Asian.A total of 19 cohort studies involving 56,003 patients were included. When BUN was used as a categorical variable, the risk of death in heart failure was 2.29 times higher for high levels of BUN than for low levels of BUN (High BUN is an independent predictor of all-cause mortality in heart failure. Lower BUN was associated with better prognosis in patients with heart failure. Among them, serum creatinine is the most commonly used indicator, which can be filtered through the glomerulus. When creatinine concentration is elevated, renal function deteriorates with an decreased glomerular filtration rate. However, the increase in serum creatinine may also be caused by hypermyolysis. Therefore, serum creatinine cannot fully reflect heart failure . CystatiBlood urea nitrogen is involved in changes in renal perfusion and better reflects the progression of heart failure than traditional indicators such as blood creatinine . For eve2.2.1.We systematically searched all the results from the databases of Embase, PubMed, Cochrane, and Web of Science since their inception until May 2022 by combining subject terms with free words. The search terms were “heart failure” or “heart decompensation” or “cardiac failure” or “myocardial failure” combined with “BUN” or “blood urea nitrogen” or “serum urea nitrogen” (See the appendix for the search strategy).2.2.For this study, our inclusion criteria were (1) study type: cohort study; (2) subjects: all patients ≥18 years old and diagnosed with heart failure according to clinical criteria; (3) exposure: blood urea nitrogen levels provided in admitted heart failure patients; (4) outcome measure: all-cause mortality.in vitro studies; (2) systematic reviews, conference abstracts, letters, and case reports; (3) non-English literature; and (4) duplicate studies .Exclusion criteria included (1) animal studies and The retrieved studies were imported into EndNote for literature screening. After removing duplicate studies, all literature was screened based on title, abstract, and data type. The literature was screened separately by two researchers and categorized as eligible, uncertain, or ineligible based on the inclusion and exclusion criteria. The full texts of articles that were jointly classified as “eligible” by both researchers was summarized for our review and assessment. Therefore, a third researcher was not required to verify the inconsistency of the literature.2.3.After the search was completed, we used an Excel spreadsheet to summarize the basic information in the 19 articles and extracted the first author, year of publication, study design, study type, study location, follow-up time, all-cause mortality, and univariate and multifactorial analyses of blood urea nitrogen levels. In terms of literature quality assessment, we fully considered the impact of the following six aspects: (1) study participants, (2) loss to follow-up, (3) measurement of risk factors, (4) measurement and control of confounding variables, (5) outcomes measurement, and (6) analyses and reports. Each aspect was classified to a high, moderate, or low risk of bias. In this paper, a study was considered to be of high quality if four or above aspects including the loss to follow-up and the confounders were evaluated with low risk of bias .2.4.RR values pooled across studies. The heterogeneity of included studies was investigated by the Cochran Q-test. Fixed-effects and random-effects models were selected by p-value and I2 value in the forest plot. If I2 > 50% and p < 0.1, the random-effects model was adopted; if I2 < 50% and p > 0.1, the fixed-effects model was employed  and 95% CIwere used to evaluate the effect of blood urea nitrogen on the prognosis of heart failure based on the data with key characteristics from the data table, and the evaluation index was the employed . When he3.3.1.The initial search yielded 6,945 papers, and 1,966 duplicates were excluded through the initial screening process in The basic information and literature quality evaluation of the included studies is shown in 3.2.RR = 2.29, 95% CI: 1.42–3.70, P < 0.001), and the difference was statistically significant. Since I2 = 96.4%, a random-effects model was chosen for analysis . The risk of developing heart failure with high levels of blood urea nitrogen was 4.98 times higher in the multiivariate group . In contrast, in retrospective cohort studies, high and low levels of blood urea nitrogen had no statistical significance in predicting death from heart failure   .RR = 1.48,95% CI:0.78–2.81, p = 0.23). The results were statistically significant in the European group, where the risk of death from heart failure was 2.96 times higher in the high blood urea nitrogen group than in the low group . The risk of death from blood urea nitrogen in the Asian high-level group was 2.43 times higher than that in the low-level group , and the results were statistically significant . This indicated that the risk of death from heart failure increased by 1.02 times for each unit increase in blood urea nitrogen I2 = 93.7%. The random-effects model was selected for analysis  as well as in the univariate group   .RR = 1.01,95% CI:1.00–1.02, p = 0.03). In the retrospective cohort studies , it was illustrated that a one-unit increase in blood urea nitrogen was associated with a 1.03 fold increase in the risk of death from heart failure  showed no statistical significance. In the European group , blood urea nitrogen levels had no statistical significance in predicting death from heart failure. While the results of the American group were statistically significant, as the risk of death from heart failure increased by 1.05 times for each unit increase in blood urea nitrogen . One unit increase in blood urea nitrogen in the Asian group was associated with 1.02 times the risk of death from heart failure.   .3.4.When blood urea nitrogen was analyzed as a categorical and continuous variable, respectively, sensitivity analysis showed that the values of the studies combining the remaining outcomes after removing each group separately remained within the intervals of (95% CI:1.42–3.70) and (95% CI:1.01–1.03), indicating that the stability of blood urea nitrogen levels on all-cause mortality in heart failure patients was not affected by any of the studies , 11.p = 0.64) and Egger's test (p = 0.009) suggested a publication bias in Egger's test. To correct for publication bias, we applied the trim and fill method in random effects, and the correction showed a good symmetry of the funnel plot with an RR value of 1.057 (95% CI:1.04–1.073). The corrected funnel plot was not significantly different from the initial model, and publication bias did not affect the results (p = 0.35) and Egger's test indicated (p = 0.211) that there was no publication bias  is known to be secreted from ventricular cardiomyocytes. When heart failure occurs, the increased secretion of BNP can inhibit the activation of RASS system, thereby increasing glomerular filtration rate and decreasing the preload of patients with heart failure. A large number of studies have shown that the increase of BNP is associated with the poor prognosis of patients with heart failure, and BNP is the gold standard for the prognosis of early heart failure. In order to explore more biomarkers for heart failure with similar mechanism of action as BNP for clinical use, BUN may be an important indicator for the prognosis of heart failure in the future since the mechanisms of action of BUN and BNP are similar and they had good specificity .Studies have shown that changes in blood urea nitrogen are closely related to the deterioration of renal function in heart failure patients. The prognosis of heart failure can be reflected by monitoring blood urea nitrogen in response to renal perfusion . Blood uIn this meta-analysis, due to the large heterogeneity in the forest plot, we conducted analyses exploring the sources of heterogeneity in three subgroups: univariate and multivariate groups, prospective and retrospective cohorts, and different continents. The results showed that the heterogeneity was 89.9% for univariate analysis when blood urea nitrogen was used as a categorical variable, while the size of heterogeneity could not be counted in only one literature for multifactorial analysis. The high heterogeneity in univariate analysis may be due to the fact that indicators such as patient's diet, weight and age influence the prognosis of heart failure patients, resulting in high heterogeneity. Heterogeneity was 98.1% in the retrospective cohort study and 0 in the prospective cohort, suggesting that study type was the source of heterogeneity, which may be owing to the presence of missing patient data and incomplete records in retrospective cohort studies.Finally, the heterogeneity among continents is 92.9% in Asia, 0 in Europe and 74.2% in America, which may be due to the ethnic variability among individuals, the high level of development in Europe, the inclusion of the same type of heart failure patients, and the low number of deaths from heart failure. In contrast, more patients with heart failure were included in Asia and America with more types of heart failure, leading to high heterogeneity. Heterogeneity in multifactorial analysis was great when blood urea nitrogen was used as a continuous-type variable, which may be related to the different indicators included in each article during the multifactorial analysis. Heterogeneity remained higher in retrospective cohort studies than in prospective cohort studies, with the least heterogeneity in South America when analyzed between continents, which may be a result of South America being in an underdeveloped country with less research on heart failure.Our study also had limitations. First, the type of heart failure varied among the independent studies included but mainly was acute heart failure, which is a source of clinical variation. Second, multifactorial analysis was not performed in some studies, so whether blood urea nitrogen is an independent predictor of prognostic risk in heart failure remains to be discussed. Finally, patients in these studies were followed up at different time points, which is also a source of variability.In summary, blood urea nitrogen level is a strong predictor of prognosis in hospitalized patients with heart failure. More research is needed to explore whether lowering blood urea nitrogen could be a therapeutic target for patients with heart failure.


The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings. Medical screening tests are used to identify possible disease before signs or symptoms present, such as HIV antibody testing, or to identify the presence of a condition that has not otherwise been identified, such as in depression screening. The accuracy of a screening test is evaluated by comparing against a reference standard that is thought to represent the true status of the target condition. Accuracy is typically characterized by sensitivity or true positive rate (TPR), which is the probability of a positive screen given that the patient has the condition, and 1-specificity or false positive rate (FPR), which is the probability of a positive screen given that the patient does not have the condition. When screening test results are ordinal or continuous, a threshold is set to classify test results as positive or negative.1. Such meta-analyses must consider the intrinsic correlation between TPR and FPR across studies. This is because selecting a lower threshold for classifying positive screening results would simultaneously increase the TPR of the test but also its FPR while a higher threshold would have the opposite effect. The bivariate random effects model (BREM) is commonly used because it allows for simultaneous estimation of TPR and FPR with the random effects assumed to have a joint normal distribution2.Meta-analyses of test accuracy pool results from primary studies to attempt to overcome imprecision due to small samples, conduct subgroup analyses that were not feasible in the primary studies, and estimate variance within and between studies3, whereas 4.The inter-study heterogeneity or variability of TPR and FPR is an important output from a meta-analysis and may be characterized by several different metrics. The most direct is the between-study variance, often denoted as 5. The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy regards the prediction interval as the best graphical depiction of the magnitude of heterogeneity6. In the bivariate case, prediction intervals for TPR and FPR are represented as regions in two-dimensional space where this set of values has a predetermined probability of containing a new two-dimensional vector of estimates from a new study comparable to those in the pool7.Another way to describe heterogeneity is the prediction interval. This represents an estimated range of values that has a predetermined probability of containing the estimate of interest from a new study sampled from the same population as used to fit the model. The use of prediction intervals has important advantages in that it summarizes point estimates and variance components from the BREM as the interval considers overall mean estimates of TPR and FPR, their standard errors and between-study variance8. Meta-analyses assessing the diagnostic accuracy of the PHQ-9 and other similar screening questionnaires often report a confidence interval around pooled estimates of TPR and FPR10. The confidence interval contains the true value of the mean measure, here the pooled estimate of TPR or FPR with probability 0.95. Consequently, other authors have argued that prediction intervals are more useful since they provide information on the range of possible accuracy values that may be encountered in a future study12. Thus, a prediction interval and a confidence interval are not the same thing and serve different purposes. The confidence interval is a measure of precision that indicates how accurately we have estimated the pooled sensitivity or specificity based on the standard error and depends on the number of studies in the meta-analysis13. On the other hand, the prediction interval measures dispersion, is based on the standard deviation that shows how much the measures vary across different populations, and is not related to the number of studies in the analysis13. The prediction interval is more informative when it comes to heterogeneity, describing the extent of dispersion in the context of sensitivity or specificity13. A clinician using the results from a meta-analysis on the diagnostic accuracy of the PHQ-9 would have a better idea of how the diagnostic accuracy varied across studies, and indeed, how the PHQ-9 might be expected to perform in a new study, or setting such as the physician’s practice.The Patient Health Questionnaire-9 (PHQ-9) is a self-report depression symptom questionnaire that consists of nine items, each scored 0 to 3 , that can be used for depression screening. A standard threshold score of 10 or greater has been shown to maximize the sum of sensitivity and specificity (TPR and 1-FPR)Because diagnostic accuracy is represented by the true positive rate (TPR) and the false positive rate (FPR), a prediction region is used rather than two prediction intervals. The region, which is elliptical in the logit space, takes into account the correlation between logit(TPR) and logit(FPR) which is reflected in the orientation and size of the minor axes of the prediction ellipse. The orientation of the ellipse relates to the “slope” linking logit(TPR) and logit(FPR) observations. The strength of the correlation is depicted by the width of the ellipses about their minor axis. Moreover, the prediction region makes explicit that some combinations of TPR and FPR are unlikely, whereas the two intervals do not.12. The objective of the present study was to illustrate the use of prediction regions for TPR and FPR of the PHQ-9 as a numerical and graphical depiction of the heterogeneity in an individual participant meta-analysis (IPDMA) and investigate how these regions (1) change as more studies are included in the BREM, and (2) vary across subgroups.Despite several sources suggesting that prediction intervals be used to quantify and describe heterogeneity and the range of accuracy values, they are still underused14 and results have been reported8. The present analysis extends the work specified in the protocol by characterizing heterogeneity in the study pool via prediction regions constructed from the BREM, and using the region to describe the range of likely mean measures of TPR and FPR from an unseen study similar to those in the pool (with probability 0.95).This study is a secondary analysis of an IPDMA. For the main IPDMA registered in PROSPERO (CRD42014010673), a protocol was published14.For the original IPDMA, studies were eligible for inclusion if: (1) they included PHQ-9 scores, (2) they included major depression classification based on a validated diagnostic interview, (3) the time interval between administration of the PHQ-9 and the diagnostic interview was no more than 2 weeks, and (4) participants were at least 18-years old and recruited outside of psychiatric settings. The studies and data included in the dataset were selected from the results of an online search strategy from 2000 to 2016. Eligible studies were assessed independently by two investigators. For further details on the search and selection processes, refer to the published protocol15.For each study in the PHQ-9 IPDMA dataset, generalized linear models were fitted to estimate TPR and FPR and their respective 95% CIs. From these, forest plots were produced for a qualitative assessment of heterogeneity. All analyses were completed in R16. As these measures are described in two-dimensional ROC-space, confidence and prediction regions are analyzed instead of their one-dimensional analogues: confidence, and prediction intervals. For each model, 95% confidence and prediction regions were constructed following the method described by Chew7  presented cases of major depression and 1794  were correctly identified as cases using the standard PHQ-9 cutoff score of ≥ 10.A summary of the individual participant data can be found in Additional File Studies published up to and including the cutoff dates of 2009, 2011 and 2013 included 19, 28 and 49 of the 58 available studies and 28.2%, 59.4% and 81.8% of total participants respectively. At the 2009 cutoff, the TPR and FPR were 0.86 and 0.18 (see Table There was some evidence that between-study standard deviation estimates for FPR decreased from entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaThe change in the estimated correlation between logit(TPR) and logit(FPR) is reflected in the orientation and size of the minor axes of the prediction ellipses in the top row of Fig. Female participants represented 57% of participants. For this subgroup, BREM estimates for TPR and FPR were 0.84 and 0.19. For the male subjects, BREM estimates were 0.80 and 0.14 for TPR and FPR respectively , while scattered, showed less variability than the TPR estimates. The shape of the prediction region reflects the underlying positive correlation of these measures. Looking at Fig. Both confidence and prediction regions considering only the one-dimensional confidence/prediction intervals could be misleading, if interpreted naively. As an example, in Fig. 8. Both prediction regions for male and female groups span a comparable length parallel to either axis, although the shape of the ellipse in logit-ROC or the slanted border in ROC space differ between groups to some extent. This may be a depiction of the observed difference in the point estimates for correlation for both fixed and random effects between the female and male groups. The location of TPR and FPR estimates by age subgroup did not differ greatly from the overall population estimates. These regions were similar in size and shape: the largest region corresponding to the age group between 18 and 35 years old, being widest in the TPR direction, and the smallest corresponding to the 51 to 64 cohorts. While categorizing age has some downsides, it allowed us to present prediction regions by age category and improved interpretation of results.The subgroup analyses aided in investigating possible sources of heterogeneity among the study pool. Prediction regions by subgroups can reveal some differences that might be hard to appreciate when only one-dimensional prediction intervals are used. Subgroup analysis by sex revealed no statistically significant differences between the point estimates of mean TPR and FPR between the female and male groups or when compared to overall population estimates; this coincides with the results of the main PHQ-9 IPDMA17. In meta-analyses that aim to estimate drug efficacy for a certain condition, prediction intervals provide information about its possible effects in a new, similar sample to the ones in the study pool. IntHout, Ioannidis, Rovers and Goeman report that prediction intervals which include the null value suggest that intervention effects could be null or even in the opposite direction of the intended effect11. In a meta-analysis, reporting only a confidence interval around an overall pooled estimate may mask the possibility that, in some setting, treatments are ineffective12. In the context of diagnostic accuracy, both TPR and FPR are sought to be different than 0.5. If either measure were to take on this value, the test would distinguish cases of major depression no better than a coin flip. Based on the prediction intervals in this study, it seems unlikely that in settings similar to the ones in the 58 studies available, both TPR and FPR are equal to 0.5. Prediction intervals have been reported to also aid in drawing conclusions from studies of varying size, instead of relying on the results of large studies18.The use of prediction intervals has been suggested in the literature as a complete summary of a random effects meta-analysis and a proper characterization of heterogeneityThe present study had the advantage of access to a sizable data set of individual participant data collected from a large number of studies. The presence of heterogeneity was evident from preliminary analyses and later corroborated by graphical inspection of the prediction regions.The use of prediction regions allowed us to shed light regarding previously unseen trends in the data. In the present analysis, the varying correlations between TPR and FPR as more studies were added to the model and across subgroups were of special interest as they had noticeable effects on the shape of the prediction regions. The present analysis used prediction regions to investigate heterogeneity in the study pool and revealed greater heterogeneity regarding TPR estimates as compared to FPR estimates. Prediction regions display the full range of variability in the data, which is essential for making predictions, and uncovering trends which may have been otherwise unknown to the researcher; thus, supporting the recommendation by authors of using prediction regions as the most adequate summary of the results of a meta-analysis.Supplementary Tables.Supplementary Information.


The number of non-intubated general anesthesia outside the operating room is growing as the increasing demand for comfort treatment. Non-intubated general anesthesia outside the operating room requires rapid onset of anesthesia, smoothness, quick recovery, and few postoperative complications. Traditional anesthetic regimens  have severe respiratory and circulatory depression and many systemic adverse effects. In this paper, we compare the effectiveness and safety of propofol and subclinical doses of esketamine with other traditional regimens applied to non-intubated general anesthesia through a systematic review and meta-analysis.We searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and Sinomed databases for the period from January 2000 to October 2022. We rigorously screened the literature according to predefined inclusion and exclusion criteria, while risk assessment of the studies was performed using The Cochrane Collaboration’s tool, and statistical analysis of the data was performed using RevMan 5.4 software. The main outcome indicators we evaluated were the various hemodynamic parameters and incidence of various adverse effects between the experimental and control groups after induction of anesthesia.After a rigorous screening process, a total of 14 papers were included in the final meta-analysis. After risk bias assessment, three of the papers were judged as low risk and the others were judged as having moderate to high risk. Forest plots were drawn for a total of 16 indicators. Meta-analysis showed statistically significant differences in HR’ WMD 3.27 , MAP’ WMD 9.68 , SBP’ WMD 5.42 , DBP’ WMD 4.02 , propofol dose’ SMD -1.39 , hypotension’ RR 0.30 , bradycardia’ RR 0.33 , hypoxemia or apnea’ RR 0.45 , injection pain’ RR 0.28 , intraoperative choking’ RR 0.62 , intraoperative body movements’ RR 0.48  and overall incidence of adverse reactions’ RR 0.52 .The indicators that were not statistically different were time to wake up’ WMD − 0.55 , nausea and vomiting 0.84’ RR , headache and dizziness’ RR 1.57  and neuropsychiatric reaction’ RR 1.05 . The funnel plot showed that the vast majority of studies fell within the funnel interval, but the symmetry was relatively poor.In non-intubated general anesthesia, the combination of subclinical doses of esketamine and propofol did reduce circulatory and respiratory depression, injection pain, and other adverse effects, while the incidence of esketamine’s own side effects such as neuropsychiatric reactions did not increase, and the combination of the two did not cause the occurrence of new and more serious adverse reactions, and the combination of the two was safe and effective.PROSPREO registration number: CRD 42022368966. With the increasing popularity of comfort medicine, many people are choosing general anesthesia when they receive treatment, such as painless gastroenteroscopy, painless bronchoscopy, painless abortion, etc . Many prPropofol is now basically one of the essential drugs for non-intubated general anesthesia outside the operating room, with rapid onset of action, smooth induction, rapid metabolism, and no side effects such as involuntary muscle tremors, etc . AlthougEsketamine, as the right-hand molecular structure of ketamine, acts like ketamine mainly by antagonizing the N-methyl-D-aspartate receptor . HoweverA computer-based search of databases including PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, Sinomed. The search strategy was as follows:(“Propofol”) AND (“Esketamine”). The date parameter for the search was set from January 2000 to October 2022.The inclusion criteria are as follows: (a) randomized controlled trials on humans, (b) non-intubated general anesthesia without any muscle relaxants, (c) only two anesthetics, propofol and esketamine, were used in the experimental group (d) subclinical doses (0.5 mg/kg less) of esketamine, and (e) adults over 18 years old.The exclusion criteria are as follows: (a) data missing and (b) repeatedly published articles.All included articles were assessed independently by the review authors using The Cochrane Collaboration’s tool for assessing risk of bias. Disagreements and discrepancies were solved through a consensus discussion with correspondence author. The risk of bias was graded as high, uncertain or low according to the following seven critical domains with respect to The Cochrane Collaboration’s tool for assessing risk of bias: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting and other bias. The Cochrane Collaboration’s tool for assessing risk of bias proposes an approach for summarizing the risk of bias according to the seven domains, which was used judiciously. A study was judged to have low risk of bias when there was low risk of bias for all key domains and plausible bias was unlikely to seriously alter the results. Unclear risk of bias for one or more key domains resulted in an overall unclear risk of bias for the study, while high risk of bias for any domain resulted in an overall high risk of bias for the study.To clarify the efficacy and incidence of adverse effects of different drug regimens in non-intubated anesthesia, we extracted basic information for each article (including author and year of publication), number of subjects and age groups, trial design protocol , and results on the incidence of various adverse effects Statistical analysis of the data was performed using RevMan 5.4 (Cochrane Collaboration). According to the heterogeneity test results, the effect model is determined. I2 ≥ 50% indicates greater heterogeneity, and the random effect model is selected; I2 < 50% indicates that the heterogeneity is within the acceptable range, and the fixed effect model is selected. When P < 0.05, it was considered that there were significant differences in the changes of each outcome indices. The weighted mean difference (WMD) is used to represent the results of measurements using the same unit of measurement; otherwise, the standardized mean difference (SMD) is used to represent the results. All results were expressed with 95% confidence intervals (CI). Sensitivity analysis was used to assess the reliability and stability of the results. Funnel plots were drawn, and the publication bias was evaluated by the symmetry of the funnel plot and concentration of literature to the midline.Pubmed searched 35 articles, Embase searched 135 articles, Cochrane Library searched 82 articles, web of science searched 32 articles, CNKI searched 74 articles, Wanfang searched 60 articles, VIP searched 37 articles, and Sinomed searched 56 articles, and a total of 14 articles were included in this review after being screened strictly according to the inclusion and exclusion criteria, and the screening process of these articles is shown in Fig. The bias risk assessment tool recommended by the Cochrane Handbook for Systematic Reviews of Interventions was used to evaluate the quality of the included literatures, and the results are shown in Figs. The results of the meta-analysis are summarized in Table We will analyze publication bias by plotting funnel plots using fixed-effect models with statistically significant meta-analysis’s results, including hypotension, bradycardia, intraoperative cough, and neuropsychiatric response, and the results are shown in Fig. Based on the results of the meta-analysis, we can conclude that the combination of propofol and subclinical doses of esketamine in non-intubated general anesthesia does result in smoother hemodynamic (heart rate and blood pressure) fluctuations and a reduced incidence of hypotension and bradycardia in patients, as we previously hypothesized. Propofol has a significant depressant effect on the cardiovascular system, resulting in a decrease in cardiac output, cardiac index, per-beat index and total peripheral resistance during induction of anesthesia, due to the dual effect of peripheral vasodilation and direct cardiac depression . The symOne of the non-negligible side effects of clinical induction doses of esketamine is the neuropsychiatric reaction during the awakening period, which is due to the ability of esketamine to activate prefrontal glutamate neurotransmission . The useBased on the results of the meta-analysis, we also found that the combination of propofol and subclinical doses of esketamine attenuated the incidence of intraoperative choking and intraoperative somatic movements, but the combination did not show differences in time to awakening, nausea and vomiting, and headache and dizziness compared with other pairing regimens. The combination of propofol and esketamine does not theoretically conflict with each other because they act on different receptor pathways, and their pharmacological properties suggest that they should compensate for each other’s deficiencies. The results of the meta-analysis confirmed that the overall incidence of adverse reactions was lower with subclinical doses of esketamine and propofol than with other combinations, and that no more serious side effects occurred.In clinical practice, propofol is used in a wide range, and the dose has to be adjusted according to the patient’s specific situation, and the actual clinical dose range is about 1-2.5 mg/kg . our speWe found through our search that although there have been many randomized controlled trials that have confirmed that subclinical doses of esketamine and propofol combined significantly reduce the incidence of various adverse reactions compared with the combination of propofol and other drugs, no higher level of evidence has been found to validate this conclusion. Therefore, this meta-analysis is groundbreaking, although it still has many imperfections. We look forward to the emergence of more randomized controlled trials in the future to further update and improve the issues analyzed in this meta.Our study is not without limitations. First, most of the studies we included were from China, which may lead to poor extrapolation of the findings and potentially large publication bias. Second, we included studies with a wide range of subjects’ ages and did not strictly distinguish between younger and older adults, which may have affected the results to some extent, so we excluded one study with all subjects in the older age group and found that the meta results for each outcome indicator containing this study did not reverse after excluding this study, suggesting that this study did not seriously affect the results of the meta. We would have liked to perform a subgroup analysis because the drugs used in the control group and propofol were different in each study, but we abandoned the subgroup analysis because of the small sample size of each subgroup.In non-intubated general anesthesia, the combination of subclinical doses of esketamine and propofol did reduce circulatory and respiratory depression, injection pain, and other adverse effects, while the incidence of esketamine’s own side effects such as neuropsychiatric reactions did not increase, and the combination of the two did not cause the occurrence of new and more serious adverse reactions, and the combination of the two was safe and effective.


One of the most prevalent sleep disorders affecting the individual’s daily life is obstructive sleep apnea (OSA), for which obesity is a major risk factor. Several novel lipid indices have been suggested to have associations with OSA, among which visceral adiposity index (VAI), atherogenic index of plasma (AIP), and lipid accumulation product (LAP) are the most important ones. Herein, the current study aimed to systematically investigate the association between these indices and OSA.Four international databases, including PubMed, Scopus, the Web of Science, and Embase were searched in order to find relevant studies that investigated LAP, VAI, or AIP in OSA and compared them with non-OSA cases or within different severities of OSA. Random-effect meta-analysis was used to generate the standardized mean difference (SMD) and 95% confidence interval (CI) of the difference in lipid indices between OSA and non-OSA cases. Moreover, the pooled area under the receiver operating characteristic curves (AUCs) observed in individual studies for diagnosis of OSA based on these lipid indices were calculated by random-effect meta-analysis.P < 0.01). Moreover, AIP also increased in higher severities of OSA. Regarding LAP, a higher LAP was observed in OSA/patients with high risk for OSA rather than in controls/low risk for OSA . VAI was also increased in OSA based on results from two studies.Totally 14 original studies were included, comprised of 14,943 cases. AIP, LAP, and VAI were assessed in eight, five, and five studies, respectively. Overall, these lipid indices had acceptable diagnostic ability . Meta-analysis revealed that AIP was significantly higher in patients with OSA (SMD 0.71, 95% CI 0.45 to 0.97, These findings suggest that composite lipid indices are increased in OSA. Also, these indices can have the potential beneficiary diagnostic and prognostic ability in OSA. Future studies can confirm these findings and enlighten the role of lipid indices in OSA.The online version contains supplementary material available at 10.1186/s12944-023-01859-3. Obstructive sleep apnea (OSA) is defined by alternating episodes of hypopnea and breathing stops during sleep . AccordiOSA is commonly recognized as cardiovascular morbidity and mortality risk factor . A numbePatients with heart disease have a higher prevalence of OSA. Also, cardiovascular diseases (CVDs) are better predicted by visceral fat distribution than by body mass index (BMI) , 13. In With consideration, CVD is highly common among OSA patients, and dyslipidemia and obesity are important factors for CVD; however, to the best of our knowledge, the correlation between composite lipid indices and OSA has not been systematized. Moreover, although several diagnostic and prognostic biomarkers have been investigated in OSA –19, therThis study adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 protocol . The stuThe four major international databases, Scopus, Medline (PubMed), Embase, and Web of Science (ISI), were comprehensively searched on 11 May 2023. The search syntax was composed within each database using keywords and common acronyms for "obstructive sleep apnea" such as "sleep-disordered breathing" OR "OSA" OR "SAS" OR "SDB" combined with word alterations of "lipid indices" such as "atherogenic index" OR "visceral adiposity index" OR "lipid accumulation product" OR "atherogenic index of plasma"  prospective, retrospective, or cross-sectional studies on human subjects with OSA; (2) written in English; (3) assessed at least one of the target composite lipid indices including VAI, LAP, or AIP and compared them between OSA and non-OSA cases or within different severities of OSA, characterized by apnea–hypopnea index (AHI) . MoreoveThe exclusion criteria were 1) non-OSA articles; 2) reviews; 3) studies not reporting any composite lipid indices; 4) conference abstracts; 5) animal or in vitro studies.Two reviewers (AHB and AK) independently scrutinized titles and abstracts and retrieved records based on the eligibility criteria. Consecutively, the selected articles went through full-text evaluation for inclusion in the review. In case of any disagreement, a third researcher (PB) intervened and made the final decision. The data extraction sheet included the first author’s name, any of the present composite lipid indices values , the diagnostic ability of composite lipid indices (area under the receiver operating characteristic curves (AUCs), sensitivity, specificity, and cut-off), the population characteristics in each arm (OSA patients and controls), the sample size in each arm, the year of publication, the design of the study, mean age, BMI, and male percentage, and key qualitative and quantitative findings.The scoring system of the Newcastle Ottawa scale (NOS)  was implP of 0.05 as the statistical significance level. Given the varied measurement method for each of the lipid indices, the standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using random-effect meta-analysis  to make uniform comparisons  for a 1-standard deviation increase in AIP, which remained significant after adjusting for covariates.Bikov et al.  found thFive studies with 10,875 individuals evaluated the role of LAP in OSA which all were cross-sectional studies , 34, 36.P < 0.01, Fig. I2: 80.7%). Although the funnel plot did not show any asymmetry for publication bias . Sensitivity analysis found that the primary results are accurate .Bikov et al.  found thP < 0.001).Bianchi et al.  used theIn a cross-sectional study, Bikov et al.  found anThe study by Bikov et al.  found AUFive studies with 11,195 individuals assessed the association between VAI and OSA , 34, 36 P < 0.01). In line with this study, Chen et al. [Two studies by Bikov et al.  and Chenn et al.  found hiP < 0.05 for controls vs. severe, moderate vs. severe, and mild vs. severe). Mazzuca et al. [P = 0.154). Moreover, they found no difference in VAI between males and females (P = 0.698).Bikov et al.  found tha et al.  found inIn a cross-sectional study, Bikov et al.  found anThe study by Bikov et al.  found AUDuring the recent decade, the investigation of different lipid parameters and composite lipid indices in OSA patients has become an area of interest for research. In this paper, we determined the relationship between three composite lipid indices, including AIP, LAP, VAI, and OSA. In addition to acceptable AUC for diagnosis of OSA, we found significantly higher levels of these indices in patients with OSA. Although this was observed in individual studies as well, our results can emphasize the need for focus on these indices as the pooled effect was also significant.All composite lipid indices evaluated in this study showed acceptable performance for the diagnosis of OSA (AUC > 0.7). Despite the fact that AUCs were almost similar for all three indices reported, LAP had a slightly higher AUC. Several studies investigated the diagnostic ability of anthropometric indices , WC, and BMI) before –39. WangOSA, which is defined by repeated upper airway collapse resulting in intermittent hypoxemia, is a significant risk factor for hypertension, heart disease, metabolic syndrome, insulin resistance, impaired glucose tolerance, and the onset of type 2 diabetes mellitus , 28. It AIP is believed to show better prediction of cardiovascular outcomes than other single lipid components , 28. MeaThe following processes are hypothesized to be able to explain the connection between OSA severity and the AIP index. First, CIH could raise TG levels by encouraging the liver's production of TG-related proteins and enzymes. Simultaneously, the elevated sympathetic tone in OSA patients may alter the body's level of endocrine hormones, which may impact lipoprotein lipase synthesis and result in a decrease in serum HDL-C concentration, raising AIP. Furthermore, prior research suggested that blocking the alpha-1 receptor could increase serum HDL-C and lower TG levels. Therefore, hyper-sympathetic tone observed in OSA patients may also contribute to elevated AIP levels .In two sizable male populations from East Asia, Shumizu et al. and Wu et al. demonstrated a link between the TG/HDL-C ratio and OSA. The obvious flaw in these two studies is that they only looked at men, even though gender also plays a role in the relationship between OSA and dyslipidemia, particularly AIP , 41. ThiAnother important issue to consider is that the most popular lipid-lowering drugs, statins, may increase HDL-C levels in addition to decreasing total cholesterol; however, this effect varies amongst medications. To resolve this bias, statin-using patients were not included in Bikov et al.'s investigations, and they discovered that this factor did not impact the findings regarding the relationship between AIP and OSA .In 2005, the LAP was first presented which was found to be a more accurate predictor of CVD than BMI. It was then verified as a helpful clinical sign to forecast upcoming cardiovascular events and cardiovascular death. It represents the buildup of lipids in the abdominal region and has been shown to be more accurate at predicting diabetes than BMI , 27.The best methods available for quantifying visceral fat are computed tomography (CT) scan and magnetic resonance imaging (MRI). Given their expensive price, it is challenging to use them broadly. LAP is an easy-to-use indicator that just needs the serum TG level and WC that can distinguish between subcutaneous and visceral adiposity accurately by describing the excessive buildup of fat . There cBased on the Berlin questionnaire, Bianchi et al. showed higher LAP in patients who were at high risk of OSA , but BikRecent research demonstrates that imaging methods can accurately measure the visceral fat distribution pattern. Despite CT's ability to be the gold standard for fat distribution discrimination, its high cost and radiation exposure prevent its widespread use in public health and clinical settings. Also, numerous studies have shown that BMI and WC are unreliable for predicting the function and distribution of visceral fat. Determining straightforward metrics that can be consistently used in routine clinical practice as substitute markers of visceral adiposity and indications of elevated cardiometabolic risk have therefore received special attention , 43.VAI, first introduced in 2010, was calculated using several metabolic markers. It was shown to have a strong correlation with metabolic syndrome, insulin resistance, and CVD , 30. In It has been documented how OSA affects inflammatory indicators. Meanwhile, interleukin-6, high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-alpha (TNF-α), and adiponectin levels have been found to be elevated, and visceral adipose tissue (VAT) accumulation and so VAI has been linked to these changes in obese patients. The positive association between TNF-α and VAI was also supported by the evidence that was available , 46.VAI was higher in OSA patients in comparison to controls; however, the increase was only statistically significant in moderate-to-severe OSA, according to Bikov et al. (2022). Additionally, the link between VAI and OSA was no longer significant after controlling for age, gender, and BMI. They believe that this marker may be of limited relevance in clinical practice due to the difficulties of calculating VAI compared to LAP . In ChenIn contrast to the aforementioned studies, Mazzuka et al. revealed that there was little association between VAI and the severity of OSA. As blood pressure factor is not included in calculating VAI, unlike metabolic syndrome, and the lack of statistically significant differences in HDL-C with increasing the severity of OSA are all possible explanations for the poor correlation between AHI and VAI and the low sensitivity in OSA patients . AccordiThe current study is the first that systematically reviewed and analyzed the composite lipid indices in OSA patients. The precise and comprehensive search of major databases and the independent screening of the results by two reviewers made this study strong and reliable. Most of the included studies were cross-sectional or cohort, which allowed for uniform evaluation of study qualification using NOS scaling. There are also some limitations to this study that should be mentioned. First of all, the small number of studies included in the meta-analysis and the small number of subgroups, introduce a potential source of bias. Because of the study design of included papers, no causal relationship can be drawn and the clinical importance of these lipid indices in OSA is better to be evaluated in interventional trials. Second, as a diagnostic tool, some included studies have used polygraphy and some others have used polysomnography which has increased heterogeneity in the results. Another important defect is the evaluation failure of factors like lipid-lowering medications, diet, regular exercise, gender effect, and menopausal status that can affect the OSA severity, in most of the studies included. Fourth, due to the low number of studies reporting sensitivity and specificity, we were not able to conduct meta-analyses for these measures, hence, the results of each individual study were reported descriptively. Finally, composite lipid indices were measured only at the baseline of studies and more frequent evaluation of them could offer more valuable data.As OSA is associated with plenty of other comorbidities and could affect remarkably on patients’ quality of life and function, its diagnosis and control are important medical and health issues. According to our results, AIP, LAP, and VAI all are associated with OSA, and also their amounts are progressively higher in higher severities of the disease, so they can be considered valuable diagnostic and prognostic factors for this prevalent disease. It can help clinicians in diagnosing and determining the prognosis of OSA. However, because of the novelty of this topic and the small number of included studies, more research should be conducted to strengthen and support this relevance.Additional file 1: Supplementary Table 1. Search details. Supplementary Table 2. Qualities of included studies based on the NOS system. Supplementary Figure 1. Sensitivity analysis for meta-analysis of AUCs for diagnosing OSA. Supplementary Figure 2. Funnel plot for meta-analysis of AIP between OSA patients and healthy controls. Supplementary Figure 3. Sensitivity analysis for meta-analysis of AIP between OSA patients and healthy controls. Supplementary Figure 4. Funnel plot for meta-analysis of LAP between OSA patients and healthy controls. Supplementary Figure 5. Sensitivity analysis for meta-analysis of LAP between OSA patients and healthy controls.


The current study aimed to clarify the association between household polluting cooking fuels and adverse birth outcomes using previously published articles.In this systematic review and meta-analysis, a systematic literature search in PubMed, Embase, Web of Science, and Scopus databases were undertaken for relevant studies that had been published from inception to 16 January 2023. We calculated the overall odds ratio (OR) and 95% confidence interval (CI) for adverse birth outcomes  associated with polluting cooking fuels . Subgroup analysis and meta-regression were also conducted.P < 0.01; cross-sectional study: P < 0.01) and sample size (≥ 1000: P < 0.01) were the covariates associated with heterogeneity. Cooking fuel types (mixed fuel: P < 0.05) were the potentially heterogeneous source in the SGA analysis.We included 16 cross-sectional, five case–control, and 11 cohort studies in the review. Polluting cooking fuels were found to be associated with LBW , SGA , stillbirth , and PTB . The results of most of the subgroup analyses were consistent with the main results. In the meta-regression of LBW, study design (cohort study: The use of household polluting cooking fuels could be associated with LBW, SGA, stillbirth, and PTB. The limited literature, observational study design, exposure and outcome assessment, and residual confounding suggest that further strong epidemiological evidence with improved and standardized data was required to assess health risks from particular fuels and technologies utilized. Household air pollution (HAP) is often considered to be a major public health problem in low- and middle-income countries (LMICs) . HAP has2.5), carbon monoxide (CO), and polycyclic aromatic hydrocarbons (PAHs), which can influence fetal development , small for gestational age (SGA), stillbirth, and preterm birth (PTB) . For exaelopment , 24.A growing number of epidemiological studies focus on the association between HAP and adverse birth outcomes. Two meta-analyses have pooled the effects of solid fuel use and adverse birth outcomes , 26. BotThe current systematic review and meta-analysis scrutinized the published adverse birth outcomes data related to polluting cooking fuel use from previous research publications using previously published search. We used the standard method of the preferred reporting items for systematic review and meta-analysis (PRISMA) protocols 2009 for conducting this review study . The stuWe searched the PubMed, Embase, Web of Science, and Scopus databases for articles reporting HAP and adverse birth outcomes published up to 16 January 2023. In the present review, HAP is defined as chemical, biological, and physical contamination of house air and derived from the use of polluting fuel  for cooking . An infan = 3) (n = 2) (n = 3) (n = 2)  original studies, (2) conducted in the human population, and (3) quantified the association between HAP exposure during pregnancy and adverse birth outcomes . Studies were excluded for the following reasons: (1) the outcome was due to other factors  but not polluting cooking fuels ; (2) studies reported other outcomes those were not of interest; (3) the subjects from the control group were not exposed to clean fuels  or were exposed to polluting fuels; (4) studies did not report relative risk (RR) or odds ratio (OR); (5) conference abstract, letter, or protocol; (6) not published in English. Based on the inclusion and exclusion criteria, we excluded several unqualified studies from previous meta-analyses. The reasons for exclusion are as follows: reported the impact of other factors  on adverse birth outcomes (n = 3) –53; the  (n = 2) , 55; did (n = 3) –58; publ (n = 2) , 60.Data from all included studies were independently extracted by two reviewers and cross-checked to avoid errors. The following information was extracted from the publications: author, publication year, location of the study, study population, study design, study period, exposure, outcome, covariates adjustment, the frequency distribution of exposure and outcome, comparator, exposure, and outcome assessment method. We also extracted risk estimates of association relating polluting cooking fuel to adverse birth outcomes for pooled analysis. RR or OR with precision [95% confidence interval (CI)] was extracted from included studies. The fully adjusted effect estimates were used for analysis when both unadjusted and adjusted estimates were provided.The methodological quality of cohort and case–control studies was assessed by the Newcastle–Ottawa scale  . Each stI2 statistic was defined to assess the heterogeneity across studies. We reported the heterogeneity as low, moderate, or high with I2 values of 25, 50, or 75%, respectively , Embase (n = 421), Web of Science (n = 943), and Scopus (n = 798). An additional article was identified by a manual search of reference lists of included studies (n = 1). After excluding duplicates (n = 959), 1,536 records were excluded based on title and abstract. We obtained 109 articles for in-depth evaluation, of which 77 were excluded after reviewing the full text. Ultimately, 32 studies were enrolled in our final systematic review and meta-analysis.n = 11) (n = 5) (n = 16) (n = 29)  or “no exposure” to polluting fuels (n = 3) . In addition, three studies also directly measured the concentrations of kitchen air pollutants, such as CO (2.5 (10) (A summary of the characteristics of 32 publications is presented in n = 11) , 47, 68, (n = 5) , 70, 72,(n = 16) , 71, 73.(n = 16) , 47, 69 ch as CO , PM2.5  , and quaDetailed quality assessments are shown in I2 = 75.0%, P < 0.001) (P < 0.001)  (P = 0.468)  (P = 0.584)  (P = 0.568)  with high heterogeneity (< 0.001) . We foun< 0.001) . After a< 0.001) . No sign= 0.468) . For sti= 0.269) . We foun= 0.584) . Seven s= 0.464) . No evid= 0.568) .To explore possible sources of heterogeneity across eligible studies, we performed subgroup analyses for LBW and SGA . NotablyP < 0.01; cross-sectional study: P < 0.01) and sample size  were the covariates associated with heterogeneity. Cooking fuel types (mixed fuel: P < 0.05) were covariates associated with the heterogeneity in the SGA analysis.In the meta-regression analysis of LBW , we founI2 reduced to 15.9%)   and SGA o 15.9%) .In a previous meta-analysis, Pope et al. reported that indoor air pollution from solid fuel use was associated with a 38% increased risk of LBW and a 51% increased risk of stillbirth . Amegah 2), which have been shown to be associated with SGA in several meta-analyses  and nitrogen dioxide , and most of the women in Africa use charcoal (85.4–93.5%), whereas women in Latin America mainly use liquefied petroleum gas (69.1–97.6%) . HoweverDifferent fuel types may influence the strength of the association between polluting fuels with LBW and SGA. The results demonstrated that biomass fuels had a larger pooled OR for the association with LBW than fossil fuels. However, when various polluting cooking fuels were grouped together, the association was not significant. Studies have shown that the concentrations of pollutants released from coal are lower than those released from biomass , 79. A lThere was high heterogeneity in the present study, although the meta-regression analyses found several covariates associated with heterogeneity, which was still uncontrolled after subgroup analyses. Moreover, sensitivity analysis was performed to explore the potential source of heterogeneity in LBW and SGA. However, the source of heterogeneity was not found in LBW. This may be attributed to the number of included studies that were not sufficient to detect the source of heterogeneity. In the future, more qualified original research is needed to explore the source of heterogeneity. For SGA, sensitivity analysis revealed that after excluding Chaudhary's study , the het2, sulfur dioxide (SO2), and PAHs  could produce considerable exposure misclassification for exposure settings and inaccurate correlation assessments between exposure and outcomes. By directly measuring and recording the concentration of kitchen air pollutants, we can avoid misclassification of exposures and obtain accurate exposure–response relationships. However, only three of included studies directly measured the concentration of kitchen air pollutants , 38, 40.Our findings suggest that polluting cooking fuel use is associated with an increased risk of LBW, SGA, stillbirth, and PTB. Different polluting fuel types may influence the strength of the association between polluting fuel use and LBW and SGA. Moving forward, we encourage more prospective studies to thoroughly investigate the association between polluting cooking fuel use and adverse birth outcomes. On exposure assessment, a more direct method of measuring, such as household or portable air quality monitors, will help quantify exposure information and an exposure–response relationship.The original contributions presented in the study are included in the article/ML: conceptualization, data curation, software, formal analysis, and writing—original draft. TL, CM, JF, ZZ, and YW: writing—reviewing and editing. YX: writing—reviewing and editing and supervising. YZ: writing—reviewing and editing and funding acquisition. CJ: conceptualization, writing—reviewing and editing, supervision, and funding acquisition. All authors contributed to the article and approved the submitted version.


Although physical activity (PA) has been recognized as a favourable factor in the prevention of various diseases, including certain forms of cancer, the relationship between PA and gastric cancer (GC) is not yet fully understood. This study aims to provide data from a pooled analysis of case-control studies within the Stomach cancer Pooling (StoP) Project to estimate the association between leisure-time PA and the occurrence of GC.Six case-control studies from StoP project collected data on leisure-time PA, for a total of 2,343 cases and 8,614 controls. Subjects were classified into three leisure-time PA categories, either none/low, intermediate or high, based on study-specific tertiles. We used a two-stage approach. Firstly, we applied multivariable logistic regression models to obtain study-specific odds ratios (ORs) and corresponding 95% confidence intervals (CIs) then, we used a random-effect models to obtain pooled effect estimates. We performed stratified analyses according to demographic, lifestyle and clinical covariates.The meta-analysis showed ORs of GC with no significant differences between intermediate vs low and high vs low PA level . GC risk estimates did not strongly differ across strata of selected covariates except for age ≤ 55 years old (high vs low level: OR 0.72 [95%CI 0.55–0.94]) and for control population-based studies (high vs low level: OR 0.79 [95%CI 0.68–0.93]).No association was found between leisure time PA and GC, apart from a slight suggestion of decreased risk below age 55 and in control population-based studies. These results may reflect specific characteristics of GC at a younger age, or the presence of a cohort effect mediating and interacting with socioeconomic determinants of GC The different distribution of PA levels among hospitalized controls could have led to an underestimated effect of PA on GC risk. H. Pylori infection, plus other dietary and environmental factors [Gastric cancer (GC) is one of the most common cancers worldwide, particularly in low income countries . Over th factors , 3. Howe factors . Physica factors . In part factors . It is r factors . PA is a factors . PA has  factors . Among p factors –14. Ther factors  and it i factors , and to Overall, ten out of over 30 studies included in the latest release (number 2.1) of the StoP dataset collected data on leisure-time PA –25. HoweThe questionnaires usually included demographic and lifestyle data on PA, cigarette smoking, alcohol use, dietary habits, and family history of cancer. Additional data were obtained from cancer registries or hospital medical records. All data were collected and standardized according to a pre-specified format at the data-pooling center.Table 1. Studies reported [The main characteristics of PA variables are outlined in reported –21 the dreported , 20, 21 reported , 18, 19 reported , 28, forThe relationship between PA and GC was evaluated using a two-stage approach . FirstlyI2 statistics measures [H. pylori status . Since only some studies had information on H. pylori infection, we also carried out calculations using the random-effects model and performed sensitivity analyses including only those individuals that were H. pylori positive. Heterogeneity tests were performed across all the strata estimates. Statistical analyses were carried out using STATA software, version 16.In the second phase, a random-effect model was applied in order to estimate summary (pooled) effect measures. Heterogeneity across studies was assessed with the Q and measures . In ordeTable 2.The main characteristics of the 2,343 cases and 8,614 controls included in the present analysis are reported in H. pylori status, the data were only available for three study centers .The majority of study subjects both among cases and controls showed low levels of PA (54.3% among cases and 53.3% among controls). On average, cases were older than controls (62.9 vs 58.9 years of age) and of lower social classes (39.7% in cases vs 35.6% in controls). The proportion of current smokers was similar across cases (20.0%) and controls (20.2%), while cases were more than twice as likely as to consume high levels of alcoholic beverages . The vast majority were non-cardia site (64.7%). Regarding information on Table 3.The ORs and 95% CI for GC according to leisure PA levels are presented in  with the exception for age strata, the histological type of GC and the type of control. Among subjects ≤ 55 years, high levels of PA showed an OR of 0.72 (95%CI 0.55–0.94) . For the intestinal type of GC we obtained an OR of 1.38 (95%CI 1.03–1.84) and 1.81 (95%CI 1.17–2.78) for the intermediate and high level of PA, respectively, compared to the low levels . For control population-based studies, high levels of PA showed an OR of 0.79 (0.68–0.93) .Overall, we did not find significant differences across PA levels  reported no differences between low levels of PA and intermediate or high levels over all strata with the only exception for family history of GC. Subjects with family history of GC performing high levels of PA, respect to low level of PA, had an OR of 0.18 (95%CI 0.03–0.96).The analysis restricted to H. pylori may be involved in this difference as it is also more frequently reported among low people of low socioeconomic status [H. pylori [We did not find an association between leisure-time PA and GC risk with no substantial differences in the risk estimates according to occupational PA strata. Our results do not parallel the findings from a meta-analyses  that incc status , 42. In . pylori .A limitation is represented by recreational PA data collected by study-specific questionnaires with different definitions and classifications. To have comparable variables, we defined three levels of PA intensity based on defined thresholds and wherever possible using the standard ones . This miMoreover, the stratified analysis regarding the intestinal type of GC showed an opposing trend respect to both the overall analysis, on association of GC and PA, and the analysis stratified by categories of age which may be due to an under-adjustment. Furthermore, adjustments for social class position in our statistical models are of particular importance in the present research and a role of under-adjustment is possible.Lastly, we have three studies with hospital-based controls. These controls may have a different distribution of PA levels because individuals who are hospitalized may be more likely to have underlying health conditions that affect their ability to engage in PA. This may have underestimated the effect of PA on GC risk.We did not find an association between PA levels and GC risk, apart from some suggestion of decreased risk below age 55. This may reflect specific characteristics of GC at a younger age, or the presence of a cohort effect mediating and interacting with socioeconomic determinants of GC. A decreased risk also emerged when we analyzed control population-based studies separately, possibly due to the distribution of PA levels in the hospitalized controls leading to an underestimation of the effect of PA on GC risk.S1 TableStomach cancer pooling (StoP) Project consortium.(DOCX)Click here for additional data file.


Background: Yoghurt can modify gastrointestinal disease risk, possibly acting on gut microbiota. Our study aimed at exploring the under-investigated association between yoghurt and gastric cancer (GC). Methods: We pooled data from 16 studies from the Stomach Cancer Pooling (StoP) Project. Total yoghurt intake was derived from food frequency questionnaires. We calculated study-specific odds ratios (ORs) of GC and the corresponding 95% confidence intervals (CIs) for increasing categories of yoghurt consumption using univariate and multivariable unconditional logistic regression models. A two-stage analysis, with a meta-analysis of the pooled adjusted data, was conducted. Results: The analysis included 6278 GC cases and 14,181 controls, including 1179 cardia and 3463 non-cardia, 1191 diffuse and 1717 intestinal cases. The overall meta-analysis revealed no association between increasing portions of yoghurt intake (continuous) and GC . When restricting to cohort studies, a borderline inverse relationship was found . The adjusted and unadjusted OR were 0.92 (95% CI = 0.85–0.99) and 0.78 (95% CI = 0.73–0.84) for any vs. no yoghurt consumption and GC risk. The OR for 1 category of increase in yoghurt intake was 0.96 (95% CI = 0.91–1.02) for cardia, 1.03 (95% CI = 1.00–1.07) for non-cardia, 1.12 (95% CI = 1.07–1.19) for diffuse and 1.02 (95% CI = 0.97–1.06) for intestinal GC. No effect was seen within hospital-based and population-based studies, nor in men or women. Conclusions: We found no association between yoghurt and GC in the main adjusted models, despite sensitivity analyses suggesting a protective effect. Additional studies should further address this association. According to the 2018 World Cancer Research Fund (WCRF), there is strong evidence that high-salt and salt-preserved foods, overweight/obesity and heavy alcohol consumption increase the risk of gastric cancer (GC), while there is some evidence that consumption of grilled or barbecued meat and fish, consumption of processed meat, low intake of fruit and citrus fruit increase GC risk [The anti-cancer properties of yoghurt have been described . A recenHelicobacter pylori (Hp), possibly contributing to limiting the colonisation of the stomach by the bacterium, suggest a possible protective role of yoghurt towards gastric disease [Yoghurt can be beneficial towards gastrointestinal disease by acting positively on the gut microbiota , balanci disease ,8,9,10.  disease .Dairy products, and yoghurt in particular, are poorly investigated in relation to GC and are often excluded from meta-analyses on nutritional epidemiology . Their pThe Stomach Cancer Pooling (StoP) Project Consortium  provideshttp://www.stop-project.org/ (accessed on 15 February 2023)) [The present study is based on the StoP Project Consortium (y 2023)) , which iy 2023)) ,18. The y 2023)) .The current analysis is based on 16 studies with information on yoghurt intake, including two studies from Italy ,21, one Data were harmonised according to a pre-specified format, and completeness and consistency between variables were checked. Yoghurt intake was derived for each study using food frequency questionnaire (FFQ) information. In the original data collection, yoghurt intake was collected either in g per day or portions per day, with country-specific variability . An overall yoghurt intake variable was generated by summing up the different intakes registered in each study, considering as a measurement unit the number of portions per week. A categorical variable was created considering categories of frequency of yoghurt intake .First of all, we run a univariate analysis.Subsequently, multivariable unconditional logistic regression models were used to estimate the odds ratios (OR) of GC and the corresponding 95% confidence intervals (CI) according to yoghurt intake. The logistic regression models included terms for sex, age , smoking status , socioeconomic status , alcohol drinking  and vegetable and fruit intake . A model using the categorical variable for yoghurt was also adjusted for the study centre. This model was fitted on pooled data from 12 studies which shared the full data. An OR estimate for yoghurt intake as a continuous variable was obtained for each of the 16 studies, and then a combined OR was obtained using a random-effect meta-analysis . We chosAlso, we performed a sensitivity analysis by adding a term for total calorie intake to the main model. This analysis was restricted to 10 studies with available information on both yoghurt and caloric intake.The main model was also repeated by using yoghurt intake as a dichotomous variable (no/any weekly intake).Heterogeneity between strata-specific results was assessed using the Q test.p-value less than 0.05 was considered statistically significant All the statistical analyses were performed on STATA, version 16.1  [A TX, USA) .The analysis included 20,459 subjects, comprising 6278 cases and 14,181 controls. In the adjusted model, the association between yoghurt intake and GC was not linear across categories of intake, and no relationship was found by using yoghurt intake as a continuous variable  based on 12 studies. When a dichotomous variable for yoghurt intake was used, we observed an inverse association between any yoghurt intake vs. no yoghurt intake and GC risk . The association was similar when the analysis was restricted to the three cohort studies (not shown in detail).p heterogeneity = 0.005) for the highest category of intake vs. no yoghurt consumption. When the meta-analysis was restricted to the three case-control studies nested in cohort studies (p heterogeneity = 0.311).The meta-analysis of all 16 studies  revealed studies , a signip for heterogeneity = 0.15). Also, a significant positive trend was found for diffuse GC when considering an increase in intake of one portion of yoghurt per week , no relationship was found with intestinal GC , and GC of undefined histology was inversely related to yoghurt consumption , with significant heterogeneity among these values (p for heterogeneity < 0.001).p = 0.736).Additional exploratory analyses by geographic region were limited by the small number of available studies and did not offer further insight into the results.p = 0.3)  .No differences were evidenced between the analysis adjusted and unadjusted by caloric intake.Our pooled analysis of 16 international studies found no association between yoghurt intake and GC risk. Results were consistent across sex, anatomical subsite and histology groups. The analysis restricted to cohort studies showed a moderate inverse relationship between yoghurt intake and GC, while no association was found in hospital-based and population-based case-control studies.While total yoghurt intake was lower among cases than controls, adjusted analyses from logistic regression models did not find an association between yoghurt intake and GC risk. This is mainly due to unbalances in the case:control ratio across studies with a different mean level of yoghurt intake. In fact, the analysis based on univariate models (only adjusted by the study) showed a significant inverse association between any yoghurt intake and GC risk. It is possible that yoghurt consumption may affect GC risk only at higher intakes than those considered in the present pooled analysis . Energy Noticeably, when considering yoghurt intake as a dichotomous variable, GC risk resulted in being significantly decreased, which supports the evidence of healthy properties of yoghurt.There is large variability of locally consumed fermented foods and beverages, which may lead to different effects on human health in different populations. Most traditional fermented foods are natural sources of probiotic microbes, which have been shown to have anti-Hp properties. As pointed out by Nair and coworkers, the geographical difference in GC incidence among countries with a high prevalence of Hp infection  is not fully explained by the virulence of different Hp strains . The autLactobacillus casei DN-114001 is associated with decreased frequency of common respiratory infectious diseases, reduced risk of Clostridium difficile and antibiotic-associated diarrhoea in the elderly, and decreased asthma and rhinitis episodes in children [Bifidobacterium lactis BB12, was associated with increased gastrointestinal well-being, in particular, reducing abdominal bloating, flatulence and discomfort symptoms [Bifidobacterium lactis BB12 plus Lactobacillus acidophilus LA-5, which, at dosage similar to those of commercial products, improved metabolic parameters  and antioxidant status in diabetics [Lactobacillus acidophilus and Bifidobacterium bifidum are associated with higher moisture and low calorie, while Lactobacillus plantarum and Lactobacillus casei determine higher ash, protein, carbohydrate, energy, calcium and phosphorous content in yoghurt [Probiotics have been shown to be effective against different GI diseases, including Hp infection . For exachildren . Anothersymptoms . Also, tiabetics . The proiabetics , changiniabetics . For exa yoghurt . Also, a yoghurt . It is i yoghurt ,45.Next to the effect conveyed by fermentation of live bacteria, the yoghurt matrix contains other classes of nutrients which may exert a beneficial effect, including vitamins and minerals , bioactive fatty acids, and proteins such as whey .p for heterogeneity = 0.891 < Egger’s test: p = 0.923). The authors found similar results among European and Asian studies and by study design [Nutritional cancer epidemiology often lacks investigation on dairy products and yoghurt in particular, and available studies show inconsistent results ,14,15. Ay design .Consistent with our results, a meta-analysis by Guo and coworkers found an inverse association between dairy products intake and GC risk in cohort studies  but no association in case-control studies . ProspecNo specific relationship emerged by anatomical subsite of GC, nor effect modification by sex. This corroborates the hypothesis of the lack of any effect of yoghurt on GC overall. The result we obtained when stratifying by histological type needs to be considered with caution: while a positive association was identified for diffuse GC, an inverse one was observed when considering cases undefined for histological type, suggesting that their redistribution among diffuse and intestinal type would lead to no overall association. It should be considered that data collection on GC epidemiology, including pathology data, is widely heterogeneous among different countries  and amonWe could not identify other studies reporting comparable results for yoghurt intake and anatomical or histological types of GC, limiting the interpretability of the results.Our pooled analysis has several strengths, starting from a large number of studies included. Notably, by pooling data from the participants of the StoP-Consortium, we could address a topic which has to date been poorly investigated. We could account for several potential confounders, given the detailed information available in the dataset.This study also suffers from some limitations. The measure we used was the frequency of yoghurt intake, but portion size can vary by country . Also, we did not adjust for Hp infection, which is an important risk factor of GC, because of missing data. Further, analyses by anatomical subsite and histological type were impaired by the high proportion of unclassified cases, limiting the interpretability of the results we obtained. In addition, most of the included studies had a case-control design, where questionnaires are administered at the moment of cancer diagnosis among the cases, potentially introducing differential recall bias.In addition, we could not account for the fact that yoghurt may vary according to the matrix and milk source . Further, data were too sparse to investigate any difference between different types of yoghurt, including flavoured, frozen, whole milk or low-fat yoghurt. Lastly, while some studies were designed to investigate diet and GC, none were specifically focused on yoghurt. The study was not designed to investigate the role of the type of milk used for yoghurt production , the type and number of microorganisms used in the coagulation process, as well as the time and incubation temperature or additional ingredients  that are incorporated during the production steps.In conclusion, this pooled analysis found no association between yoghurt intake and GC, nor significantly different effects within the anatomical sites or histological types. Cohort studies, as well as analyses treating yoghurt consumption as a dichotomous variable, showed an inverse association between yoghurt intake and GC risk, suggesting that a healthy effect of yoghurt may be evidenced in large prospective studies. The variability in yoghurt composition is an inherent characteristic of this product, which complicates the identification of its effects on human health. Therefore, we recommend caution in interpreting our results, given the lack of information on yoghurt characteristics and composition, which may vary in different populations. The association between yoghurt and GC deserves further investigation, given the potential implication in GC prevention.