Pub Date : 2025-11-01Epub Date: 2025-10-21DOI: 10.1016/S0140-6736(25)01293-0
Toby Pillinger, Atheeshaan Arumuham, Robert A McCutcheon, Enrico D'Ambrosio, Georgios Basdanis, Marco Branco, Richard Carr, Valeria Finelli, Toshi A Furukawa, Siobhan Gee, Adrian Heald, Sameer Jauhar, Zihan Ma, Valentina Mancini, Calum Moulton, Georgia Salanti, David M Taylor, Anneka Tomlinson, Allan H Young, Orestis Efthimiou, Oliver D Howes, Andrea Cipriani
<p><strong>Background: </strong>Antidepressants induce physiological alterations; however, the degree to which these occur in treatment with various antidepressants is unclear. We aimed to compare and rank antidepressants based on physiological side-effects by synthesising data from randomised controlled trials (RCTs).</p><p><strong>Methods: </strong>We searched MEDLINE, EMBASE, PsycINFO, ClinicalTrials.gov, and the US Food and Drug Administration (FDA) website from database inception to April 21, 2025. We included single-blinded and double-blinded RCTs comparing antidepressants and placebo in acute monotherapy of any psychiatric disorder. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in weight; total cholesterol; glucose; heart rate; systolic and diastolic blood pressure; corrected QT interval (QTc); sodium; potassium; aspartate transferase (AST); alanine transaminase (ALT); alkaline phosphatase (ALP); bilirubin; urea; and creatinine. We did meta-regressions to examine study-level associations between physiological change and age, sex, and baseline weight. We estimated the correlation between depressive symptom severity change and metabolic parameter change.</p><p><strong>Findings: </strong>Of 26 252 citations, 151 studies and 17 FDA reports met inclusion criteria. The overall sample included 58 534 participants, comparing 30 antidepressants with placebo. Median treatment duration was 8 weeks (IQR 6·0-8·5). We observed clinically significant differences between antidepressants in terms of metabolic and haemodynamic effects, including an approximate 4 kg difference in weight-change between agomelatine and maprotiline, over 21 beats-per-minute difference in heart rate change between fluvoxamine and nortriptyline, and over 11 mmHg difference in systolic blood pressure between nortriptyline and doxepin. Paroxetine, duloxetine, desvenlafaxine, and venlafaxine were associated with increases in total cholesterol and, for duloxetine, glucose concentrations, despite all drugs reducing bodyweight. There was strong evidence of duloxetine, desvenlafaxine, and levomilnacipran increasing AST, ALT, and ALP concentrations, although the magnitudes of these alterations were not considered clinically significant. We did not find strong evidence of any antidepressant affecting QTc, or concentrations of sodium, potassium, urea, and creatinine to a clinically significant extent. Higher baseline bodyweight was associated with larger antidepressant-induced increases in systolic blood pressure, ALT, and AST, and higher baseline age was associated with larger antidepressant-induced increases in glucose. We did not observe an association between changes in depressive symptoms and metabolic disturbance.</p><p><strong>Interpretation: </strong>We found strong evidence that antidepressants differ markedly in their physiological effects, particularly for cardiometabolic parameters. Treatment guidelines should be updated to ref
{"title":"The effects of antidepressants on cardiometabolic and other physiological parameters: a systematic review and network meta-analysis.","authors":"Toby Pillinger, Atheeshaan Arumuham, Robert A McCutcheon, Enrico D'Ambrosio, Georgios Basdanis, Marco Branco, Richard Carr, Valeria Finelli, Toshi A Furukawa, Siobhan Gee, Adrian Heald, Sameer Jauhar, Zihan Ma, Valentina Mancini, Calum Moulton, Georgia Salanti, David M Taylor, Anneka Tomlinson, Allan H Young, Orestis Efthimiou, Oliver D Howes, Andrea Cipriani","doi":"10.1016/S0140-6736(25)01293-0","DOIUrl":"10.1016/S0140-6736(25)01293-0","url":null,"abstract":"<p><strong>Background: </strong>Antidepressants induce physiological alterations; however, the degree to which these occur in treatment with various antidepressants is unclear. We aimed to compare and rank antidepressants based on physiological side-effects by synthesising data from randomised controlled trials (RCTs).</p><p><strong>Methods: </strong>We searched MEDLINE, EMBASE, PsycINFO, ClinicalTrials.gov, and the US Food and Drug Administration (FDA) website from database inception to April 21, 2025. We included single-blinded and double-blinded RCTs comparing antidepressants and placebo in acute monotherapy of any psychiatric disorder. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in weight; total cholesterol; glucose; heart rate; systolic and diastolic blood pressure; corrected QT interval (QTc); sodium; potassium; aspartate transferase (AST); alanine transaminase (ALT); alkaline phosphatase (ALP); bilirubin; urea; and creatinine. We did meta-regressions to examine study-level associations between physiological change and age, sex, and baseline weight. We estimated the correlation between depressive symptom severity change and metabolic parameter change.</p><p><strong>Findings: </strong>Of 26 252 citations, 151 studies and 17 FDA reports met inclusion criteria. The overall sample included 58 534 participants, comparing 30 antidepressants with placebo. Median treatment duration was 8 weeks (IQR 6·0-8·5). We observed clinically significant differences between antidepressants in terms of metabolic and haemodynamic effects, including an approximate 4 kg difference in weight-change between agomelatine and maprotiline, over 21 beats-per-minute difference in heart rate change between fluvoxamine and nortriptyline, and over 11 mmHg difference in systolic blood pressure between nortriptyline and doxepin. Paroxetine, duloxetine, desvenlafaxine, and venlafaxine were associated with increases in total cholesterol and, for duloxetine, glucose concentrations, despite all drugs reducing bodyweight. There was strong evidence of duloxetine, desvenlafaxine, and levomilnacipran increasing AST, ALT, and ALP concentrations, although the magnitudes of these alterations were not considered clinically significant. We did not find strong evidence of any antidepressant affecting QTc, or concentrations of sodium, potassium, urea, and creatinine to a clinically significant extent. Higher baseline bodyweight was associated with larger antidepressant-induced increases in systolic blood pressure, ALT, and AST, and higher baseline age was associated with larger antidepressant-induced increases in glucose. We did not observe an association between changes in depressive symptoms and metabolic disturbance.</p><p><strong>Interpretation: </strong>We found strong evidence that antidepressants differ markedly in their physiological effects, particularly for cardiometabolic parameters. Treatment guidelines should be updated to ref","PeriodicalId":18014,"journal":{"name":"The Lancet","volume":" ","pages":"2063-2077"},"PeriodicalIF":88.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145367701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-18Epub Date: 2025-10-12DOI: 10.1016/S0140-6736(25)01330-3
<p><strong>Background: </strong>Comprehensive, comparable, and timely estimates of demographic metrics-including life expectancy and age-specific mortality-are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study-part of the latest GBD release, GBD 2023-aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time.</p><p><strong>Methods: </strong>We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950-2023. For the first time, we used complete birth history data for ages 5-14 years, age-specific sibling history data for ages 15-49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution.</p><p><strong>Findings: </strong>In 2023, 60·1 million (95% UI 59·0-61·1) deaths occurred globally, of which 4·67 million (4·59-4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2-38·4) over the 1950-2
{"title":"Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950-2023: a demographic analysis for the Global Burden of Disease Study 2023.","authors":"","doi":"10.1016/S0140-6736(25)01330-3","DOIUrl":"10.1016/S0140-6736(25)01330-3","url":null,"abstract":"<p><strong>Background: </strong>Comprehensive, comparable, and timely estimates of demographic metrics-including life expectancy and age-specific mortality-are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study-part of the latest GBD release, GBD 2023-aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time.</p><p><strong>Methods: </strong>We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950-2023. For the first time, we used complete birth history data for ages 5-14 years, age-specific sibling history data for ages 15-49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution.</p><p><strong>Findings: </strong>In 2023, 60·1 million (95% UI 59·0-61·1) deaths occurred globally, of which 4·67 million (4·59-4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2-38·4) over the 1950-2","PeriodicalId":18014,"journal":{"name":"The Lancet","volume":" ","pages":"1731-1810"},"PeriodicalIF":88.5,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12535839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145301671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-18Epub Date: 2025-10-12DOI: 10.1016/S0140-6736(25)01637-X
<p><strong>Background: </strong>For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions.</p><p><strong>Methods: </strong>The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution.</p><p><strong>Findings: </strong>Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicab
{"title":"Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","authors":"","doi":"10.1016/S0140-6736(25)01637-X","DOIUrl":"10.1016/S0140-6736(25)01637-X","url":null,"abstract":"<p><strong>Background: </strong>For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions.</p><p><strong>Methods: </strong>The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution.</p><p><strong>Findings: </strong>Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicab","PeriodicalId":18014,"journal":{"name":"The Lancet","volume":" ","pages":"1873-1922"},"PeriodicalIF":88.5,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12535840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145301734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-18Epub Date: 2025-10-12DOI: 10.1016/S0140-6736(25)01917-8
<p><strong>Background: </strong>Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations.</p><p><strong>Methods: </strong>GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds.</p><p
{"title":"Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","authors":"","doi":"10.1016/S0140-6736(25)01917-8","DOIUrl":"10.1016/S0140-6736(25)01917-8","url":null,"abstract":"<p><strong>Background: </strong>Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations.</p><p><strong>Methods: </strong>GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds.</p><p","PeriodicalId":18014,"journal":{"name":"The Lancet","volume":" ","pages":"1811-1872"},"PeriodicalIF":88.5,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12535838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145301714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-23Epub Date: 2025-08-18DOI: 10.1016/S0140-6736(25)01153-5
Lonnie Pyne, Patrick Rossignol, Cameron Giles, Mats Junek, Patrick B Mark, Martin Gallagher, Janak R de Zoysa, P J Devereaux, Michael Walsh
Background: Mineralocorticoid receptor antagonists can prevent cardiovascular events in patients with heart failure and non-severe chronic kidney disease, but their effects in patients with kidney failure requiring dialysis are uncertain. We aimed to assess the efficacy and safety of mineralocorticoid receptor antagonists in this patient population.
Methods: In this systematic review and meta-analysis, we updated our previous systematic review by searching MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and the Cumulative Index to Nursing and Allied Health Literature for randomised controlled trials published between database inception and March 18, 2025. Trials comparing a mineralocorticoid receptor antagonist with placebo or standard of care in adults (aged ≥18 years) receiving maintenance dialysis were eligible. Studies that did not report an outcome of interest (cardiovascular mortality, heart failure hospitalisation, all-cause mortality, all-cause hospitalisation, hyperkalaemia, gynaecomastia or breast pain, or hypotension) were excluded. Two reviewers independently identified studies, extracted data, and assessed the risk of bias using the Cochrane risk-of-bias tool. The main outcome was cardiovascular mortality assessed using the empirical Bayes random-effects models, stratified by risk-of-bias. The protocol is registered with PROSPERO (CRD420251008119).
Findings: 19 trials of steroidal mineralocorticoid receptor antagonists including 4675 participants met eligibility criteria. Effect estimates differed trials with low and high risk of bias. In four trials with a low risk of bias (n=3562), 264 cardiovascular deaths occurred in 1785 patients in the mineralocorticoid receptor antagonist group compared with 276 of 1777 patients in the control group (odds ratio 0·98 [95% CI 0·80-1·20]; I2=0·0%; τ2=0·0; moderate certainty) resulting in an absolute risk reduction of 1 fewer event per 1000 patients per year (95% CI 14 fewer to 11 more).
Interpretation: Our findings suggest that steroidal mineralocorticoid receptor antagonists have little to no effect on cardiovascular mortality in patients requiring dialysis. There is insufficient information on the effects of steroidal mineralocorticoid receptor antagonists in subgroups of patients requiring dialysis and no information on non-steroidal mineralocorticoid receptor antagonists. Future trials would need to consider the likelihood of only smaller effects or effects limited to patients or events with pathophysiology that is more clearly driven by aldosterone in their design.
Funding: None.
背景：矿皮质激素受体拮抗剂可以预防心力衰竭和非严重慢性肾脏疾病患者的心血管事件，但它们对需要透析的肾衰竭患者的作用尚不确定。我们的目的是评估矿皮质激素受体拮抗剂在该患者群体中的有效性和安全性。方法：在本系统评价和荟萃分析中，我们通过检索MEDLINE、Embase、Cochrane中央对照试验注册库和护理及相关健康文献累积索引，更新了之前的系统评价，检索数据库建立至2025年3月18日期间发表的随机对照试验。在接受维持性透析的成人（年龄≥18岁）中，比较矿皮质激素受体拮抗剂与安慰剂或标准护理的试验是合格的。未报告相关结果（心血管死亡率、心力衰竭住院、全因死亡率、全因住院、高钾血症、妇科乳房发育或乳房疼痛或低血压）的研究被排除在外。两位审稿人独立识别研究，提取数据，并使用Cochrane风险偏倚工具评估偏倚风险。主要结果是心血管死亡率，使用经验贝叶斯随机效应模型进行评估，并按偏倚风险分层。协议注册在PROSPERO （CRD420251008119）。结果：19项甾体矿皮质激素受体拮抗剂的试验，包括4675名参与者符合资格标准。低偏倚风险和高偏倚风险试验的效果估计不同。在4项低偏倚风险试验（n=3562）中，矿皮质激素受体拮抗剂组1785例患者中发生264例心血管死亡，而对照组1777例患者中有276例（优势比0.98 [95% CI 0.80 - 1.20]; I2= 0.0%; τ2= 0.0；中等确定性），导致每年每1000例患者的绝对风险降低1例（95% CI 14 - 11）。解释：我们的研究结果表明，甾体矿皮质激素受体拮抗剂对需要透析的患者的心血管死亡率几乎没有影响。关于类固醇类固醇皮质激素受体拮抗剂对透析患者亚组的影响的信息不足，而关于非类固醇类固醇皮质激素受体拮抗剂的信息则没有。未来的试验将需要考虑只有较小影响的可能性，或影响仅限于患者或病理生理事件，在其设计中更明显是由醛固酮驱动的。资金:没有。
{"title":"Safety and efficacy of steroidal mineralocorticoid receptor antagonists in patients with kidney failure requiring dialysis: a systematic review and meta-analysis of randomised controlled trials.","authors":"Lonnie Pyne, Patrick Rossignol, Cameron Giles, Mats Junek, Patrick B Mark, Martin Gallagher, Janak R de Zoysa, P J Devereaux, Michael Walsh","doi":"10.1016/S0140-6736(25)01153-5","DOIUrl":"https://doi.org/10.1016/S0140-6736(25)01153-5","url":null,"abstract":"<p><strong>Background: </strong>Mineralocorticoid receptor antagonists can prevent cardiovascular events in patients with heart failure and non-severe chronic kidney disease, but their effects in patients with kidney failure requiring dialysis are uncertain. We aimed to assess the efficacy and safety of mineralocorticoid receptor antagonists in this patient population.</p><p><strong>Methods: </strong>In this systematic review and meta-analysis, we updated our previous systematic review by searching MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and the Cumulative Index to Nursing and Allied Health Literature for randomised controlled trials published between database inception and March 18, 2025. Trials comparing a mineralocorticoid receptor antagonist with placebo or standard of care in adults (aged ≥18 years) receiving maintenance dialysis were eligible. Studies that did not report an outcome of interest (cardiovascular mortality, heart failure hospitalisation, all-cause mortality, all-cause hospitalisation, hyperkalaemia, gynaecomastia or breast pain, or hypotension) were excluded. Two reviewers independently identified studies, extracted data, and assessed the risk of bias using the Cochrane risk-of-bias tool. The main outcome was cardiovascular mortality assessed using the empirical Bayes random-effects models, stratified by risk-of-bias. The protocol is registered with PROSPERO (CRD420251008119).</p><p><strong>Findings: </strong>19 trials of steroidal mineralocorticoid receptor antagonists including 4675 participants met eligibility criteria. Effect estimates differed trials with low and high risk of bias. In four trials with a low risk of bias (n=3562), 264 cardiovascular deaths occurred in 1785 patients in the mineralocorticoid receptor antagonist group compared with 276 of 1777 patients in the control group (odds ratio 0·98 [95% CI 0·80-1·20]; I<sup>2</sup>=0·0%; τ<sup>2</sup>=0·0; moderate certainty) resulting in an absolute risk reduction of 1 fewer event per 1000 patients per year (95% CI 14 fewer to 11 more).</p><p><strong>Interpretation: </strong>Our findings suggest that steroidal mineralocorticoid receptor antagonists have little to no effect on cardiovascular mortality in patients requiring dialysis. There is insufficient information on the effects of steroidal mineralocorticoid receptor antagonists in subgroups of patients requiring dialysis and no information on non-steroidal mineralocorticoid receptor antagonists. Future trials would need to consider the likelihood of only smaller effects or effects limited to patients or events with pathophysiology that is more clearly driven by aldosterone in their design.</p><p><strong>Funding: </strong>None.</p>","PeriodicalId":18014,"journal":{"name":"The Lancet","volume":"406 10505","pages":"811-820"},"PeriodicalIF":88.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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