Evidence Synthesis for Complex Interventions Using Meta-Regression Models.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.

The Relationship Between Vitamin D Intake and Serum 25-hydroxyvitamin D in Young Children: A Meta-Regression to Inform WHO/FAO Vitamin D Intake Recommendations.

BACKGROUND: This work was commissioned by the World Health Organization and Food and Agriculture Organization to inform their update on the vitamin D requirements for children aged <4 y. OBJECTIVES: The objective of this work was to undertake multilevel and multivariable dose-response modeling of serum 25-hydroxyvitamin D (25OHD) to total vitamin D intake in children aged <4 y with the goal of deriving updated vitamin D requirements for young children. METHODS: Systematically identified randomized controlled trials among healthy children from 2 wk up to 3.9 y of age provided with daily vitamin D supplements or vitamin D-fortified foods were included. Linear and nonlinear random effects multilevel meta-regression models with and without covariates were fitted and compared. Interindividual variability was included by simulating the individual serum 25OHD responses. The percentage of individuals reaching set minimal and maximal serum 25OHD thresholds was calculated and used to derive vitamin D requirements. RESULTS: A total of 31 trials with 186 data points from North America, Europe, Asia, and Australasia/Oceania, with latitudes ranging from 61°N to 38°S, and with participants of likely mostly light or medium skin pigmentation, were included. In 29 studies the children received vitamin D supplements and in 2 studies the children received vitamin D-fortified milk with or without supplements. The dose-response relationship between vitamin D intake and serum 25OHD was best fitted with the unadjusted quadratic model. Adding additional covariates, such as age, did not significantly improve the model. At a vitamin D intake of 10 µg/d, 97.3% of the individuals were predicted to achieve a minimal serum 25OHD threshold of 28 nmol/L. At a vitamin D intake of 35 µg/d, 1.4% of the individuals predicted to reach a maximal serum 25OHD threshold of 200 nmol/L. CONCLUSIONS: In conclusion, this paper details the methodological steps taken to derive vitamin D requirements in children aged <4 y, including the addition of an interindividual variability component.

Assessing social cognition in patients with schizophrenia and healthy controls using the reading the mind in the eyes test (RMET): a systematic review and meta-regression.

The reading the mind in the eyes test (RMET) - which assesses the theory of mind component of social cognition - is often used to compare social cognition between patients with schizophrenia and healthy controls. There is, however, no systematic review integrating the results of these studies. We identified 198 studies published before July 2020 that administered RMET to patients with schizophrenia or healthy controls from three English-language and two Chinese-language databases. These studies included 41 separate samples of patients with schizophrenia (total n = 1836) and 197 separate samples of healthy controls (total n = 23 675). The pooled RMET score was 19.76 (95% CI 18.91-20.60) in patients and 25.53 (95% CI 25.19-25.87) in controls (z = 12.41, p < 0.001). After excluding small-sample outlier studies, this difference in RMET performance was greater in studies using non-English v. English versions of RMET (Chi [Q] = 8.54, p < 0.001). Meta-regression analyses found a negative association of age with RMET score and a positive association of years of schooling with RMET score in both patients and controls. A secondary meta-analysis using a spline construction of 180 healthy control samples identified a non-monotonic relationship between age and RMET score - RMET scores increased with age before 31 and decreased with age after 31. These results indicate that patients with schizophrenia have substantial deficits in theory of mind compared with healthy controls, supporting the construct validity of RMET as a measure of social cognition. The different results for English versus non-English versions of RMET and the non-monotonic relationship between age and RMET score highlight the importance of the language of administration of RMET and the possibility that the relationship of aging with theory of mind is different from the relationship of aging with other types of cognitive functioning.

Glycaemic control and macrovascular and microvascular outcomes: A systematic review and meta-analysis of trials investigating intensive glucose-lowering strategies in people with type 2 diabetes.

AIM: We aimed to determine the macrovascular and microvascular outcomes of intensive versus standard glucose-lowering strategies in type 2 diabetes (T2D) and investigate the relationships between these outcomes and trial arm glycated haemoglobin (HbA1c) reduction. MATERIALS AND METHODS: In this systematic review and meta-analysis, we identified relevant trials from MEDLINE, Embase, the Cochrane Library, and bibliographies up to August 2023. Macrovascular and microvascular outcomes, along with safety outcomes, were evaluated. Pooled study-specific hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated, and meta-regression was employed to analyse the relationships between outcomes and HbA1c reduction. RESULTS: We included 11 unique RCTs involving 51 469 patients with T2D (intensive therapy, N = 26 691; standard therapy, N = 24 778). Intensive versus standard therapy reduced the risk of non-fatal myocardial infarction (MI) (HR 0.84; 95% CI 0.75-0.94) with no difference in the risk of major adverse cardiovascular events (HR 0.97; 95% CI 0.92-1.03) and other adverse cardiovascular outcomes. Intensive versus standard therapy reduced the risk of retinopathy (HR 0.85; 0.78-0.93), nephropathy (HR 0.71; 0.58-0.87) and composite microvascular outcomes (HR 0.88; 0.77-1.00). Meta-regression analyses showed modest evidence of inverse linear relationships between HbA1c reduction and the outcomes of major adverse cardiovascular events, non-fatal MI, stroke and retinopathy, but these were not statistically significant. CONCLUSIONS: In people with T2D, intensive glucose control was associated with a reduced risk of non-fatal MI and several microvascular outcomes, particularly retinopathy and nephropathy. The lack of an effect of intensive glucose-lowering on most macrovascular outcomes calls for a more comprehensive approach to managing cardiovascular risk factors alongside glycaemic control.

Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta-regression analysis of 120 randomized controlled trials.

AIMS: To assess the potential for precision medicine in type 2 diabetes by quantifying the variability of body weight as response to pharmacological treatment and to identify predictors which could explain this variability. METHODS: We used randomized clinical trials (RCTs) comparing glucose-lowering drugs (including but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo from four recent systematic reviews. RCTs reporting on body weight after treatment to allow for calculation of its logarithmic standard deviation (log[SD], i.e., treatment response heterogeneity) in verum (i.e., treatment) and placebo groups were included. Meta-regression analyses were performed with respect to variability of body weight after treatment and potential predictors. RESULTS: A total of 120 RCTs with a total of 43 663 participants were analysed. A slightly larger treatment response heterogeneity was shown in the verum groups, with a median log(SD) of 2.83 compared to 2.79 from placebo. After full adjustment in the meta-regression model, the difference in body weight log(SD) was -0.026 (95% confidence interval -0.044; 0.008), with greater variability in the placebo groups. Scatterplots did not show any slope divergence (i.e., interaction) between clinical predictors and the respective treatment (verum or placebo). CONCLUSIONS: We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs for body weight reduction in type 2 diabetes. The precision medicine approach may thus be of limited value in this setting.

Glycaemic control and macrovascular and microvascular outcomes in type 2 diabetes: Systematic review and meta-analysis of cardiovascular outcome trials of novel glucose-lowering agents.

AIM: Using a systematic review and meta-analysis of placebo-controlled cardiovascular outcome trials (CVOTs) of newer glucose-lowering agents [sodium-glucose cotransporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs), and dipeptidyl peptidase-4 inhibitors (DPP-4is)] in type 2 diabetes (T2D), we aimed to determine the macrovascular and microvascular outcomes of these agents and clarify the relationships between glycated haemoglobin (HbA1c) reduction and risk of these outcomes. MATERIALS AND METHODS: Randomized controlled trials were identified from MEDLINE, Embase and the Cochrane Library until September 2023. Study-specific hazard ratios with 95% confidence intervals (CIs) were pooled, and meta-regression was used to assess the relationships between outcomes and between trial arm HbA1c reductions. RESULTS: Twenty unique CVOTs (six SGLT-2is, nine GLP-1RAs, five DPP-4is), based on 169 513 participants with T2D, were eligible. Comparing SGLT-2is, GLP-1RAs and DPP-4is with placebo, the hazard ratios (95% CIs) for 3-point major adverse cardiovascular events were 0.88 (0.82-0.94), 0.85 (0.79-0.92) and 1.00 (0.94-1.06), respectively. SGLT-2is and GLP-1RAs consistently reduced the risk of several macrovascular and microvascular complications, particularly kidney events. DPP-4is showed no macrovascular benefits. There was potential evidence of an inverse linear relationship between HbA1c reduction and 3-point major adverse cardiovascular event risk (estimated risk per 1% reduction in HbA1c: 0.84, 95% CI 0.67-1.06; p = .14; R2 = 14.2%), which was driven by the component of non-fatal stroke (R2 = 100.0%; p = .094). There were non-significant inverse linear relationships between HbA1c reduction and the risk of several vascular outcomes. CONCLUSIONS: SGLT-2is and GLP-1RAs showed consistent risk reductions in macrovascular and microvascular outcomes. The vascular benefits of SGLT-2is and GLP-1RAs in patients with T2D extend beyond mere glycaemic control.

Multistep estimators of the between-study covariance matrix under the multivariate random-effects model for meta-analysis.

A wide variety of methods are available to estimate the between-study variance under the univariate random-effects model for meta-analysis. Some, but not all, of these estimators have been extended so that they can be used in the multivariate setting. We begin by extending the univariate generalised method of moments, which immediately provides a wider class of multivariate methods than was previously available. However, our main proposal is to use this new type of estimator to derive multivariate multistep estimators of the between-study covariance matrix. We then use the connection between the univariate multistep and Paule-Mandel estimators to motivate taking the limit, where the number of steps tends toward infinity. We illustrate our methodology using two contrasting examples and investigate its properties in a simulation study. We conclude that the proposed methodology is a fully viable alternative to existing estimation methods, is well suited to sensitivity analyses that explore the use of alternative estimators, and should be used instead of the existing DerSimonian and Laird-type moments based estimator in application areas where data are expected to be heterogeneous. However, multistep estimators do not seem to outperform the existing estimators when the data are more homogeneous. Advantages of the new multivariate multistep estimator include its semi-parametric nature and that it is computationally feasible in high dimensions. Our proposed estimation methods are also applicable for multivariate random-effects meta-regression, where study-level covariates are included in the model.

The Ceiling Effects of EQ-5D-3L and 5L in General Population Health Surveys: A Systematic Review and Meta-Analysis.

OBJECTIVES: This review aims to examine the ceiling effects of EQ-5D-3L (3L) and EQ-5D-5L (5L) in general adult populations and identify the factors influencing these effects. METHODS: We searched 8 databases for observational studies published in English from inception to 24 July 2023. Ceiling effects were calculated by dividing the number of participants reporting full health at dimension or profile level by the total sample size. Subgroup analysis and meta-regression using the metafor package in R software were performed. RESULTS: We identified 94 studies from 70 articles, including 4 543 647 adults across 37 countries. The global pooled proportion of individuals reporting full health ("11111") was 56% (95% CI 51%-62%) for 3L and 49% (95% CI 44%-54%) for 5L. The self-care dimension showed the highest ceiling effects (3L: 97%; 5L: 94%), whereas pain/discomfort had the lowest (3L: 69%; 5L: 60%). The ceiling effects in East/South-East Asia were higher than in Europe by 25% (95% CI 18%-32%) in 3L and 9% (95% CI -2%-20%) in 5L. Adjusting for mean age and proportion of males, significant regional differences persisted in the overall profile level of 3L, in all 3L dimensions (except for self-care), and 5L dimensions (except for pain/discomfort and anxiety/depression). CONCLUSIONS: This review highlights significant ceiling effects in the EQ-5D, especially in Asian populations. The 5L version exhibited fewer ceiling effects than the 3L, indicating its superiority for general population surveys. Further research is crucial to understand the disparities in self-reported health outcomes between Asians and other populations.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. RESULTS: The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.

The association between inflammatory biomarkers and low back disorder: a systematic review and meta-analysis.

INTRODUCTION: Low back disorder (LBD) is a major cause of disability worldwide. Inflammation results in proliferation of cytokines or consequent degradation products (collectively known as inflammatory biomarkers) that activate pain pathways which can result in non-specific LBD. This systematic review and meta-analysis aim to evaluate the relationship between inflammatory biomarkers and clinical outcomes in patients with LBD. METHODS: The PRISMA guideline was followed for the systematic reivew. Three online databases were searched. Four RCTs and sixteen observational studies with 1142 LBD patients were analysed. The primary outcomes were back and leg pain scores, back-specific disability scores and expression of inflammatory biomarkers. Standardized mean difference (SMD) and their 95% confidence intervals (CI) were evaluated. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to summarize the strength of evidence. RESULTS: Four RCTs and sixteen observational studies were included in the analysis of 1142 patients with LBD. There was a statistically significant reduction in back pain score and IL-1 beta and increase in the expression of CTX-1 and IL-10 levels post treatment. There was a significant relationship between increase in the expression of MCP- and reduction in the expression of hsCRP with increase in back pain. Significant relationship was also observed between increase in the expression of MCP-1 and reduction in the expression of IL-6 with increase in leg pain. Increase in the expression of IL-8 and reduction in the expression of hsCRP was also associated with increased disability score. CONCLUSION: Inflammatory biomarkers play a significant role in the pathogenesis of LBD. CTX-1, IL-10 and IL-1 beta may be responsible for the decrease in back pain scores post treatment. There is a relationship between MCP-1, IL-6, IL-8 and hsCRP with clinical and functional assessments for LBD. Further studies will improve understanding of the pathogenesis of LBD and aid in targeted management strategies.

Quantitative assessment of baseline imbalances in evolocumab and alirocumab trials: a meta-epidemiological study.

BACKGROUND: Baseline imbalances have been identified in randomized trials of evolocumab and alirocumab. Our aim was to quantitatively assess (1) the presence of systematic baseline differences, and (2) the relationship of baseline differences with effects on low-density lipoprotein-cholesterol (LDL-c) and clinical outcomes in the trials. METHODS: We performed a meta-epidemiological study. PubMed, Embase, regulatory reports, ClinicalTrials.gov and company websites were searched for trials. Seven baseline characteristics (mean age, LDL-c, BMI, percentage males, diabetics, smokers, and hypertensives) and five outcomes (LDL-c, major adverse cardiac events, serious adverse events, any adverse events, all-cause mortality) were extracted. We calculated (1) range and distribution of baseline imbalances (sign-test), (2) pooled baseline differences and heterogeneity (meta-analysis), (3) differences in SDs around continuous variables (sign-test and pooling), and (4) the relationship of baseline differences with outcomes (meta-regression). The comparisons of PCSK9-inhibitor groups with either placebo or ezetimibe were analysed separately and combined. RESULTS: We identified 43 trials with 63,193 participants. Baseline characteristics were frequently missing. Many trials showed small baseline imbalances, but some large imbalances. Only baseline BMI showed a statistically significant lower pooled mean for the drug versus placebo groups (MD -0.16; 95% CI -0.24 to -0.09). Heterogeneity in baseline imbalances was present in six placebo- and five ezetimibe-comparisons. Heterogeneity was statistically significant for BMI, males, diabetics and hypertensives in the combined comparisons. There was a statistically significant preponderance for larger SDs in the PCSK9-inhibitor versus control groups (sign-test age 0.014; LDL-c 0.014; BMI 0.049). Meta-regression showed clinically relevant relationships of baseline imbalances in age, BMI and diabetics with the risk of any adverse events and the risk of mortality. Two relationships were statistically significant: A higher mean BMI in the drug versus control group with a decreased risk of mortality (beta - 0.56; 95% CI -1.10 to -0.02), and a higher proportion of diabetics with an increased risk of any adverse events (beta 0.02; 95% 0.01 to 0.04). CONCLUSIONS: Heterogeneous baseline imbalances and systematically different SDs were present in evolocumab and alirocumab trials, so study groups cannot be assumed to be comparable. These findings raise concerns about the design and conduct of the randomization procedures.

Comparing Bayesian hierarchical meta-regression methods and evaluating the influence of priors for evaluations of surrogate endpoints on heterogeneous collections of clinical trials.

BACKGROUND: Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. METHODS: Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. RESULTS: In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. CONCLUSION: Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint.

Risk factors for herpes zoster infections: a systematic review and meta-analysis unveiling common trends and heterogeneity patterns.

PURPOSE: The burden of herpes zoster (HZ) is substantial and numerous chronic underlying conditions are known as predisposing risk factors for HZ onset. Thus, a comprehensive study is needed to synthesize existing evidence. This study aims to comprehensively identify these risk factors. METHODS: A systematic literature search was done using MEDLINE via PubMed, EMBASE and Web of Science for studies published from January 1, 2003 to January 1, 2023. A random-effects model was used to estimate pooled Odds Ratios (OR). Heterogeneity was assessed using the I2 statistic. For sensitivity analyses basic outlier removal, leave-one-out validation and Graphic Display of Heterogeneity (GOSH) plots with different algorithms were employed to further analyze heterogeneity patterns. Finally, a multiple meta-regression was conducted. RESULTS: Of 6392 considered records, 80 were included in the meta-analysis. 21 different conditions were identified as potential risk factors for HZ: asthma, autoimmune disorders, cancer, cardiovascular disorders, chronic heart failure (CHF), chronic obstructive pulmonary disorder (COPD), depression, diabetes, digestive disorders, endocrine and metabolic disorders, hematological disorders, HIV, inflammatory bowel disease (IBD), mental health conditions, musculoskeletal disorders, neurological disorders, psoriasis, renal disorders, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and transplantation. Transplantation was associated with the highest risk of HZ (OR = 4.51 (95% CI [1.9-10.7])). Other risk factors ranged from OR = 1.17-2.87, indicating an increased risk for all underlying conditions. Heterogeneity was substantial in all provided analyses. Sensitivity analyses showed comparable results regarding the pooled effects and heterogeneity. CONCLUSIONS: This study showed an increased risk of HZ infections for all identified factors.

How does the SARS-CoV-2 reinfection rate change over time? The global evidence from systematic review and meta-analysis.

BACKGROUND: There is a significant increase in the number of SARS-CoV-2 reinfection reports in various countries. However, the trend of reinfection rate over time is not clear. METHODS: We searched PubMed, Web of Science, Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Wanfang for cohort studies, case-control studies, and cross-sectional studies up to March 16, 2023, to conduct a meta-analysis of global SARS-CoV-2 reinfection rate. Subgroup analyses were performed for age, country, study type, and study population, and time-varying reinfection rates of SARS-CoV-2 were estimated using meta-regression. The risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute critical appraisal tool. RESULT: A total of 55 studies involving 111,846 cases of SARS-CoV-2 reinfection were included. The pooled SARS-CoV-2 reinfection rate was 0.94% (95% CI: 0.65 -1.35%). In the subgroup analyses, there were statistically significant differences in the pooled reinfection rates by reinfection variant, and study type (P < 0.05). Based on meta-regression, the reinfection rate fluctuated with time. CONCLUSION: Meta-regression analysis found that the overall reinfection rate increased and then decreased over time, followed by a period of plateauing and then a trend of increasing and then decreasing, but the peak of the second wave of reinfection rate was lower than the first wave. SARS-CoV-2 is at risk of reinfection and the Omicron variant has a higher reinfection rate than other currently known variants. The results of this study could help guide public health measures and vaccination strategies in response to the Coronavirus Disease 2019 (COVID-19) pandemic.

Association of adipokine levels with obesity in periodontal health and disease: A systematic review with meta-analysis and meta-regression.

This study aimed to investigate the levels of serum, gingival crevicular fluid (GCF), and salivary adipokines and their possible relationship with periodontitis and obesity. An electronic search was conducted in the following databases: PubMed/ Medline, Scopus, and EBSCOhost through February 2023. Two independent reviewers screened the titles, abstracts, and full text of all the studies. Studies comparing the levels of adipokines in GCF, serum, and/or saliva in subjects with obesity and periodontitis (group 1), subjects with normal weight and periodontitis (group 2), and subjects with obesity and gingival health (group 3) were included. Meta-analyses and meta-regression were performed on the data from included studies. Seventeen studies with study participants ranging from 30 to 120 were included with subjects in each group ranging from 10 to 40. There was a significant increase in levels of serum TNF-α, leptin, IL-6, and CRP between groups 1 and 2 (p < .05). In GCF, TNF-α and resistin levels were significantly higher (p < .05) in Group 1 vs. 2. Serum level of leptin was higher for group 1 vs. 3 (p < .05). Meta-regression analysis revealed that the obesity definition (body mass index (BMI) cut-off value >25 or >30) was significant for serum resistin (p < .05) and GCF resistin (p < .05) between group 1 and 2. The current analysis indicates that both periodontitis and obesity can modulate the pro-inflammatory cytokines at systemic and local levels. This bidirectional interaction of periodontitis and obesity via the inflammation pathway seems likely plausible. Further studies are required to elucidate this mechanism in more detail.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects.

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Estimating the heat-related mortality and morbidity burden in the province of Quebec, Canada.

BACKGROUND: As climate change increases the frequency and intensity of extreme heat events, there is an urgent need to quantify the heat-related health burden. However, most past studies have focussed on a single health outcome (mainly mortality) or on specific heatwaves, thus providing limited knowledge of the total pressure heat exerts on health services. OBJECTIVES: This study aims to quantify the heat-related mortality and morbidity burden for five different health outcomes including all-cause mortality, hospitalizations, emergency department (ED) visits, ambulance transports and calls to a health hotline, using the province of Quebec (Canada) as a case study. METHODS: A two-step statistical analysis was employed to estimate regional heat-health relationships using Distributed Lag Non-Linear Models (DLNM) and pooled estimates using a multivariate meta-regression. Heat burden was quantified by attributable fraction (AF) and attributable number (AN) for two temperature ranges: all heat (above the minimum mortality/morbidity temperature) and extreme heat (above the 95th percentile of temperature). RESULTS: Higher temperatures were associated with greater risk ratios for all health outcomes studied, but at different levels. Significant AF ranging from 2 to 3% for the all heat effect and 0.4-1.0% for extreme heat were found for all health outcomes, except for hospitalizations that had an AF of 0.1% for both heat exposures. The estimated burden of all heat (and extreme heat) every summer across the province was 470 (200) deaths, 225 (170) hospitalizations, 36 000 (6 200) ED visits, 7 200 (1 500) ambulance transports and 15 000 (3 300) calls to a health hotline, all figures significant. DISCUSSION: This new knowledge on the total heat load will help public health authorities to target appropriate actions to reduce its burden now and in the future. The proposed state-of-the-art framework can easily be applied to other regions also experiencing the adverse effects of extreme heat.

A bayesian network meta-analysis to explore modifying factors in randomized controlled trials: what works for whom to reduce depression in nursing home residents?

BACKGROUND: Reviews of depression interventions in nursing home residents resulted in positive findings. However, because of the heterogeneity of the studies, it remains unclear what works for whom. Considering moderator effects may contribute to a comprehensive understanding of depression treatment in residents. Therefore, this study aims to review depression interventions, examining moderator effects of (1) residents' factors, and (2) components specific of interventions. METHODS: A Bayesian network meta-analysis of randomized controlled trials primarily aimed at reducing depressive symptoms among residents was conducted. First, intervention types, e.g., exercise interventions, were compared to care as usual. Second, meta-regression analyses were conducted for moderator effects of residents' factors (i.e., severity of depressive symptoms, physical dependency, and cognitive impairment) and components identified as specific to an intervention (e.g., music, creativity, positivity). RESULTS: Our search across six databases resulted in 118 eligible studies: 16 on neurobiological interventions, 102 on non-pharmacological interventions. Compared to care as usual, cognitive interventions, such as cognitive behavioral therapy and goal-oriented therapy, showed the strongest effects (MD = -1.00, 95% CrI [-1.40 to -0.66]). Furthermore, the severity of depressive symptoms moderated the effect of interventions (ƅ = -0.63, CrI 95% [-1.04 to -0.22]), while none of fifteen identified intervention-specific components did. In residents with a depression diagnosis, there were larger effect sizes for interventions including daily structure, psychoeducation, healthy food, creativity, positivity, and an activating/encouraging environment, whereas interventions focusing on distraction and relaxation had larger effect sizes in those residents without. CONCLUSIONS: By examining the moderator effects, we provided an integrative perspective on the observed variations in effects across different target groups, and components of depression interventions. This approach underscores the complex nature of interventions, emphasizing the need for continued transdisciplinary research, and the exploration of potential moderators. Future investigations should carefully assess residents' factors and choose interventions and their components accordingly.

Relationship between rate of glucose or propionate infusion and milk protein yield and concentration in dairy cows: A meta-regression.

Although postruminal glucose infusion into dairy cows has increased milk protein yield in some past experiments, the same trend has not been observed in others. A meta-regression of 64 sets of observations from 29 previously published glucose and propionate infusion studies in dairy cattle, treating study and experiment (study) as random effects, was performed to establish the general effects of glucose equivalent (GlcE) infusion rate on milk true protein (MTP) yield and content, if any, and to identify independent, fixed-effect variables that accounted for the changes in MTP yield and content that were observed. Candidate explanatory variables included rate and site of infusion, diet composition and intake, body weight and lactation stage of the cows, and the change in nutrient intake between GlcE and control treatments. Across all studies, according to a model containing only the random effects of study and experiment, GlcE infusion at an average of 954 g/d increased MTP yield by 26 g/d, on average, whereas mean MTP content was not affected. Backward stepwise elimination of potential explanatory variables from a full mixed model produced a final, reduced model for MTP yield that retained a positive, second-order quadratic effect of infusion rate of GlcE and a positive, linear effect of the change in crude protein intake (CPI) between GlcE treatment and control. This change in CPI due to GlcE infusion ranged from -0.546 to 0.173 kg/d in the dataset. The model fit indicated that when CPI was allowed to drop during GlcE infusion, the effect of GlcE on MTP yield was smaller than when CPI was maintained or increased, in a manifestation of the classic protein:energy interaction. The final reduced model for MTP content contained the same explanatory variables as for MTP yield, plus a negative effect of intravenous compared with gastrointestinal infusion. Overall, the meta-analysis revealed that both MTP yield, and content were positively related to GlcE infusion rate and to the change in CPI between glucose treatment and control.

Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: Systematic Literature Review and Meta-Analysis.

BACKGROUND: The proliferation of digital technology has the potential to transform diabetes management. One of the critical aspects of modern diabetes management remains the achievement of glycemic targets to avoid acute and long-term complications. OBJECTIVE: This study aims to describe the landscape of evidence pertaining to the relative effectiveness or efficacy and safety of various digital interventions for the self-management of type 2 diabetes mellitus (T2DM), with a primary focus on reducing glycated hemoglobin A1c (HbA1c) levels. METHODS: A systematic literature review (SLR) was conducted by searching Embase, MEDLINE, and CENTRAL on April 5, 2022. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers. Eligibility criteria for the SLR included randomized controlled trials (RCTs) and comparative observational studies evaluating interventions containing both human (eg, coaching) and digital components (eg, glucose meter) in adult patients with T2DM. The primary meta-analysis was restricted to studies that reported laboratory-measured HbA1c. In secondary analyses, meta-regression was performed with the intensity of coaching in the digital intervention as a categorical covariate. RESULTS: In total, 28 studies were included in this analysis. Most studies (23/28, 82%) used the reduction of HbA1c levels as the primary end point, either directly or as a part of a multicomponent outcome. In total, 21 studies reported statistically significant results with this primary end point. When stratified into 3 intervention categories by the intensity of the intervention supporting the digital health technology (analyzing all 28 studies), the success rate appeared to be proportional to the coaching intensity (ie, higher-intensity studies reported higher success rates). When the analysis was restricted to RCTs using the comparative improvement of HbA1c levels, the effectiveness of the interventions was less clear. Only half (12/23, 52%) of the included RCTs reported statistically significant results. The meta-analyses were broadly aligned with the results of the SLR. The primary analysis estimated a greater reduction in HbA1c associated with digital interventions compared with usual care (-0.31%, 95% CI -0.45% to -0.16%; P<.001). Meta-regression estimated reductions of -0.45% (95% CI -0.81% to -0.09%; P=.02), -0.29% (95% CI -0.48% to -0.11%; P=.003), and -0.28% (95% CI -0.65% to 0.09%; P=.20) associated with high-, medium-, and low-intensity interventions, respectively. CONCLUSIONS: These findings suggest that reducing HbA1c levels in individuals with T2DM with the help of digital interventions is feasible, effective, and acceptable. One common feature of effective digital health interventions was the availability of timely and responsive personalized coaching by a dedicated health care professional.