Alessio Crippa

Coffee Consumption and Mortality From All Causes, Cardiovascular Disease, and Cancer: A Dose-Response Meta-Analysis

Several studies have analyzed the relationship between coffee consumption and mortality, but the shape of the association remains unclear. We conducted a dose-response meta-analysis of prospective studies to examine the dose-response associations between coffee consumption and mortality from all causes, cardiovascular disease (CVD), and all cancers. Pertinent studies, published between 1966 and 2013, were identified by searching PubMed and by reviewing the reference lists of the selected articles. Prospective studies in which investigators reported relative risks of mortality from all causes, CVD, and all cancers for 3 or more categories of coffee consumption were eligible. Results from individual studies were pooled using a random-effects model. Twenty-one prospective studies, with 121,915 deaths and 997,464 participants, met the inclusion criteria. There was strong evidence of nonlinear associations between coffee consumption and mortality for all causes and CVD (P for nonlinearity < 0.001). The largest risk reductions were observed for 4 cups/day for all-cause mortality (16%, 95% confidence interval: 13, 18) and 3 cups/day for CVD mortality (21%, 95% confidence interval: 16, 26). Coffee consumption was not associated with cancer mortality. Findings from this meta-analysis indicate that coffee consumption is inversely associated with all-cause and CVD mortality.

Milk Consumption and Mortality from All Causes, Cardiovascular Disease, and Cancer: A Systematic Review and Meta-Analysis.

Results from epidemiological studies of milk consumption and mortality are inconsistent. We conducted a systematic review and meta-analysis of prospective studies assessing the association of non-fermented and fermented milk consumption with mortality from all causes, cardiovascular disease, and cancer. PubMed was searched until August 2015. A two-stage, random-effects, dose-response meta-analysis was used to combine study-specific results. Heterogeneity among studies was assessed with the I2 statistic. During follow-up periods ranging from 4.1 to 25 years, 70,743 deaths occurred among 367,505 participants. The range of non-fermented and fermented milk consumption and the shape of the associations between milk consumption and mortality differed considerably between studies. There was substantial heterogeneity among studies of non-fermented milk consumption in relation to mortality from all causes (12 studies; I2 = 94%), cardiovascular disease (five studies; I2 = 93%), and cancer (four studies; I2 = 75%) as well as among studies of fermented milk consumption and all-cause mortality (seven studies; I2 = 88%). Thus, estimating pooled hazard ratios was not appropriate. Heterogeneity among studies was observed in most subgroups defined by sex, country, and study quality. In conclusion, we observed no consistent association between milk consumption and all-cause or cause-specific mortality.

Fish consumption and risk of rheumatoid arthritis: a dose-response meta-analysis

IntroductionThe association between fish consumption and rheumatoid arthritis (RA) is unclear. The aim of this paper was to summarize the available evidence on the association between fish consumption and risk of RA using a dose-response meta-analysis.MethodsRelevant studies were identified by a search of MEDLINE and EMBASE through December 2013, with no restrictions. A random-effects dose-response meta-analysis was conducted to combine study specific relative risks. Potential non-linear relation was investigated using restricted cubic splines. A stratified analysis was conducted by study design.ResultsSeven studies (four case-controls and three prospective cohorts) involving a total of 174 701 participants and 3346 cases were included in the meta-analysis. For each one serving per week increment in fish consumption, the relative risk (RR) of RA was 0.96 (95% confidence interval (CI) 0.91 to 1.01). Results did not change when stratifying by study design. No heterogeneity or publication bias was observed. When fish consumption was modeled using restricted cubic splines, the risk of RA was 20 to 24% lower for 1 up to 3 servings per week of fish (RR =0.76, 95% CI: 0.57 to 1.02) as compared to never consumption.ConclusionsResults from this dose-response meta-analysis showed a non-statistically significant inverse association between fish consumption and RA.

Meta‐Analysis of Potassium Intake and the Risk of Stroke

Background The possibility that lifestyle factors such as diet, specifically potassium intake, may modify the risk of stroke has been suggested by several observational cohort studies, including some recent reports. We performed a systematic review and meta‐analysis of existing studies and assessed the dose–response relation between potassium intake and stroke risk. Methods and Results We reviewed the observational cohort studies addressing the relation between potassium intake, and incidence or mortality of total stroke or stroke subtypes published through August 6, 2016. We carried out a meta‐analysis of 16 cohort studies based on the relative risk (RR) of stroke comparing the highest versus lowest intake categories. We also plotted a pooled dose–response curve of RR of stroke according to potassium intake. Analyses were performed with and without adjustment for blood pressure. Relative to the lowest category of potassium intake, the highest category of potassium intake was associated with a 13% reduced risk of stroke (RR=0.87, 95% CI 0.80–0.94) in the blood pressure–adjusted analysis. Summary RRs tended to decrease when original estimates were unadjusted for blood pressure. Analysis for stroke subtypes yielded comparable results. In the spline analysis, the pooled RR was lowest at 90 mmol of potassium daily intake (RRs=0.78, 95% CI 0.70–0.86) in blood pressure–adjusted analysis, and 0.67 (95% CI 0.57–0.78) in unadjusted analysis. Conclusions Overall, this dose–response meta‐analysis confirms the inverse association between potassium intake and stroke risk, with potassium intake of 90 mmol (≈3500 mg)/day associated with the lowest risk of stroke.

Dose-response meta-analysis of differences in means

BackgroundMeta-analytical methods are frequently used to combine dose-response findings expressed in terms of relative risks. However, no methodology has been established when results are summarized in terms of differences in means of quantitative outcomes.MethodsWe proposed a two-stage approach. A flexible dose-response model is estimated within each study (first stage) taking into account the covariance of the data points (mean differences, standardized mean differences). Parameters describing the study-specific curves are then combined using a multivariate random-effects model (second stage) to address heterogeneity across studies.ResultsThe method is fairly general and can accommodate a variety of parametric functions. Compared to traditional non-linear models (e.g. Emax, logistic), spline models do not assume any pre-specified dose-response curve. Spline models allow inclusion of studies with a small number of dose levels, and almost any shape, even non monotonic ones, can be estimated using only two parameters. We illustrated the method using dose-response data arising from five clinical trials on an antipsychotic drug, aripiprazole, and improvement in symptoms in shizoaffective patients. Using the Positive and Negative Syndrome Scale (PANSS), pooled results indicated a non-linear association with the maximum change in mean PANSS score equal to 10.40 (95 % confidence interval 7.48, 13.30) observed for 19.32 mg/day of aripiprazole. No substantial change in PANSS score was observed above this value. An estimated dose of 10.43 mg/day was found to produce 80 % of the maximum predicted response.ConclusionThe described approach should be adopted to combine correlated differences in means of quantitative outcomes arising from multiple studies. Sensitivity analysis can be a useful tool to assess the robustness of the overall dose-response curve to different modelling strategies. A user-friendly R package has been developed to facilitate applications by practitioners.

Goodness of fit tools for dose-response meta-analysis of binary outcomes.

Goodness of fit evaluation should be a natural step in assessing and reporting dose–response meta‐analyses from aggregated data of binary outcomes. However, little attention has been given to this topic in the epidemiological literature, and goodness of fit is rarely, if ever, assessed in practice. We briefly review the two‐stage and one‐stage methods used to carry out dose–response meta‐analyses. We then illustrate and discuss three tools specifically aimed at testing, quantifying, and graphically evaluating the goodness of fit of dose–response meta‐analyses. These tools are the deviance, the coefficient of determination, and the decorrelated residuals‐versus‐exposure plot. Data from two published meta‐analyses are used to show how these three tools can improve the practice of quantitative synthesis of aggregated dose–response data. In fact, evaluating the degree of agreement between model predictions and empirical data can help the identification of dose–response patterns, the investigation of sources of heterogeneity, and the assessment of whether the pooled dose–response relation adequately summarizes the published results.

A new measure of between‐studies heterogeneity in meta‐analysis

Assessing the magnitude of heterogeneity in a meta-analysis is important for determining the appropriateness of combining results. The most popular measure of heterogeneity, I(2) , was derived under an assumption of homogeneity of the within-study variances, which is almost never true, and the alternative estimator, R^I, uses the harmonic mean to estimate the average of the within-study variances, which may also lead to bias. This paper thus presents a new measure for quantifying the extent to which the variance of the pooled random-effects estimator is due to between-studies variation, R^b, that overcomes the limitations of the previous approach. We show that this measure estimates the expected value of the proportion of total variance due to between-studies variation and we present its point and interval estimators. The performance of all three heterogeneity measures is evaluated in an extensive simulation study. A negative bias for R^b was observed when the number of studies was very small and became negligible as the number of studies increased, while R^I and I(2) showed a tendency to overestimate the impact of heterogeneity. The coverage of confidence intervals based upon R^b was good across different simulation scenarios but was substantially lower for R^I and I(2) , especially for high values of heterogeneity and when a large number of studies were included in the meta-analysis. The proposed measure is implemented in a user-friendly function available for routine use in r and sas. R^b will be useful in quantifying the magnitude of heterogeneity in meta-analysis and should supplement the p-value for the test of heterogeneity obtained from the Q test. Copyright

Letter: coffee consumption and gallstone disease - a cautionary note on the assignment of exposure values in dose-response meta-analyses.

SIRS, We read with interest the dose–response meta-analysis by Zhang et al., where the authors reported a nonlinear inverse association between coffee consumption and risk of gallstone disease (Pnon-linearity <0.05). 1 This analysis was based on four prospective cohort studies that presented results for several categories of coffee consumption. According to common practice, Zhang et al. assigned the mid-point of each category as a proxy for the exposure level. For the highest open-ended category, however, the mid-point was set at 1.5 times the lower boundary, which is quite unusual and may lead to too high values of coffee consumption (up to 9 cups/ day). In this letter, we would like to discuss the consequences of that exposure assignment and, by doing so, draw attention to the importance of sensitivity analyses in dose–response meta-analyses. Data were reanalysed using different methods of exposure assignment. In addition to that of Zhang et al. (which referred to a meta-analysis on egg consumption), we used a method proposed in two recent meta-analyses on coffee consumption. 6 This method is different in only one respect; it assumes that the highest openended category has the same amplitude as the preceding one, which may lead to more reasonable values of coffee consumption (up to 6.5 cups/day). For comparison, we obtained the actual median values directly from the authors of the original studies (up to 7 cups/day). Statistical analyses were conducted with the R package dosresmeta. Compared to the median values, Zhang et al.’s midpoints overestimated the coffee consumption in the highest categories by at least 22%. In contrast, the alternative mid-points were much closer to the median values ( 8%). As expected, we observed a non-linear inverse association between coffee consumption and risk of gallstone disease when using Zhang et al.’s mid-points (Pnon-linearity <0.05) (Figure 1). However, there was no evidence of a nonlinear association when using the other methods of exposure assignment, neither the alternative mid-points (Pnon-linearity = 0.60) nor the median values (Pnon-linearity = 0.69). Furthermore, using either of these two methods, the inverse association was stronger than that reported by Zhang et al. For example, the hazard ratio (95% confidence interval) for 6 cups/day vs. 0 cups/day was 0.66 (0.51–0.85) when we used the median values [previously reported to be 0.75 (0.64–0.88)]. The issues we have discussed in this letter do not change Zhang et al.’s conclusion that coffee consumption is related to a decreased risk of gallstone disease. However, they do change the interpretation of the dose– response results, that is, the shape and magnitude of the exposure-disease association (which is highly relevant given the widespread consumption of coffee and the high incidence of gallstones). More broadly, this letter exemplifies the general importance of exposure assignment in dose–response meta-analyses. Whenever possible, we encourage metaanalysts to retrieve information on the exposure distribution directly from the original authors. We also recommend that sensitivity analyses are routinely performed to examine whether the assigned exposure values have a strong influence on the dose–response results.

Estimates of Mortality Benefit From Ideal Cardiovascular Health Metrics: A Dose Response Meta‐Analysis

Background Several studies have shown an inverse relationship between ideal cardiovascular health (CVH) and mortality. However, there are no studies that pool these data to show the shape of the relationship and quantify the mortality benefit from ideal CVH. Methods and Results We conducted a systematic internet literature search of multiple databases including MEDLINE, Web of Science, Embase, CINAHL, and Scopus for longitudinal studies assessing the relationship between ideal CVH and mortality in adults, published between January 1, 2010, and May 31, 2017. We included studies that assessed the relationship between ideal CVH and mortality in populations that were initially free of cardiovascular disease. We conducted a dose‐response meta‐analysis generating both study‐specific and pooled trends from the correlated log hazard ratio estimates of mortality across categories of ideal CVH metrics. A total of 6 studies were included in the meta‐analysis. All of the studies indicated a linear decrease in (cardiovascular disease and all‐cause) mortality with increasing ideal CVH metrics. Overall, each unit increase in CVH metrics was associated with a pooled hazard ratio for cardiovascular disease mortality of 0.81 (95% confidence interval, 0.75–0.87), while each unit increase in ideal CVH metrics was associated with a pooled hazard ratio of 0.89 (95% confidence interval, 0.86–0.93) for all‐cause mortality. Conclusions Our meta‐analysis showed a strong inverse linear dose‐response relationship between ideal CVH metrics and both all‐cause and cardiovascular disease–related mortality. This study suggests that even modest improvements in CVH is associated with substantial mortality benefit, thus providing a strong public health message advocating for even the smallest improvements in lifestyle.

One-stage dose–response meta-analysis for aggregated data

The standard two-stage approach for estimating non-linear dose–response curves based on aggregated data typically excludes those studies with less than three exposure groups. We develop the one-stage method as a linear mixed model and present the main aspects of the methodology, including model specification, estimation, testing, prediction, goodness-of-fit, model comparison, and quantification of between-studies heterogeneity. Using both fictitious and real data from a published meta-analysis, we illustrated the main features of the proposed methodology and compared it to a traditional two-stage analysis. In a one-stage approach, the pooled curve and estimates of the between-studies heterogeneity are based on the whole set of studies without any exclusion. Thus, even complex curves (splines, spike at zero exposure) defined by several parameters can be estimated. We showed how the one-stage method may facilitate several applications, in particular quantification of heterogeneity over the exposure range, prediction of marginal and conditional curves, and comparison of alternative models. The one-stage method for meta-analysis of non-linear curves is implemented in the dosresmeta R package. It is particularly suited for dose–response meta-analyses of aggregated where the complexity of the research question is better addressed by including all the studies.