Evidence map›Paper›PMID 38950403›Full record

Observational studyAnnals of internal medicine2024

Medication-Induced Weight Change Across Common Antidepressant Treatments : A Target Trial Emulation Study.

Joshua Petimar, Jessica G Young, Han Yu, Sheryl L Rifas-Shiman, Matthew F Daley, William J Heerman, David M Janicke, W Schuyler Jones, Kristina H Lewis, Pi-I D Lin and 4 more

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in Annals of internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Joshua PetimarDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (J.P., J.G.Y.).ORCID 0000-0002-3025-5931
Jessica G YoungDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (J.P., J.G.Y.).ORCID 0000-0002-2758-6932
Han YuDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.).
Sheryl L Rifas-ShimanDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.).ORCID 0000-0003-0033-920X
Matthew F DaleyInstitute for Health Research, Kaiser Permanente Colorado, Denver, Colorado (M.F.D.).ORCID 0000-0003-1309-4096
William J HeermanDepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee (W.J.H.).ORCID 0000-0002-9706-6860
David M JanickeDepartment of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, Florida (D.M.J.).ORCID 0000-0002-8493-610X
W Schuyler JonesDivision of Cardiology, Duke University Department of Medicine, and Duke University Medical Center, Duke Clinical Research Institute, Durham, North Carolina (W.S.J.).ORCID 0000-0002-7288-9596
Kristina H LewisDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina (K.H.L.).ORCID 0000-0002-4921-7230
Pi-I D LinDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.).ORCID 0000-0003-3564-4255
Carly PrenticeFaith Family Medical Center, Nashville, Tennessee (C.P.).
John W MerrimanDepartment of Medicine, College of Medicine, University of Florida, Gainesville, Florida (J.W.M.).ORCID 0000-0001-7891-9285
Sengwee TohDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.).ORCID 0000-0002-5160-0810
Jason P BlockDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.).ORCID 0000-0001-7660-9064

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Ian Michael Carroll · 1999 to 2026
$31.6M
Medications and Weight Gain in PCORnet: The MedWeight StudyR01DK120598 · NIDDK · HARVARD PILGRIM HEALTH CARE, INC. · PI BLOCK, JASON PERRY · 2020 to 2023
$2.9M
NIDDK NIH HHS P30 DK056350NIDDK NIH HHS R01 DK120598
6 · The paper itself

Abstract

backgroundAntidepressants are among the most commonly prescribed medications, but evidence on comparative weight change for specific first-line treatments is limited.

objectiveTo compare weight change across common first-line antidepressant treatments by emulating a target trial.

designObservational cohort study over 24 months.

settingElectronic health record (EHR) data from 2010 to 2019 across 8 U.S. health systems.

participants183 118 patients. MEASUREMENTS: Prescription data determined initiation of treatment with sertraline, citalopram, escitalopram, fluoxetine, paroxetine, bupropion, duloxetine, or venlafaxine. The investigators estimated the population-level effects of initiating each treatment, relative to sertraline, on mean weight change (primary) and the probability of gaining at least 5% of baseline weight (secondary) 6 months after initiation. Inverse probability weighting of repeated outcome marginal structural models was used to account for baseline confounding and informative outcome measurement. In secondary analyses, the effects of initiating and adhering to each treatment protocol were estimated.

resultsCompared with that for sertraline, estimated 6-month weight gain was higher for escitalopram (difference, 0.41 kg [95% CI, 0.31 to 0.52 kg]), paroxetine (difference, 0.37 kg [CI, 0.20 to 0.54 kg]), duloxetine (difference, 0.34 kg [CI, 0.22 to 0.44 kg]), venlafaxine (difference, 0.17 kg [CI, 0.03 to 0.31 kg]), and citalopram (difference, 0.12 kg [CI, 0.02 to 0.23 kg]); similar for fluoxetine (difference, -0.07 kg [CI, -0.19 to 0.04 kg]); and lower for bupropion (difference, -0.22 kg [CI, -0.33 to -0.12 kg]). Escitalopram, paroxetine, and duloxetine were associated with 10% to 15% higher risk for gaining at least 5% of baseline weight, whereas bupropion was associated with 15% reduced risk. When the effects of initiation and adherence were estimated, associations were stronger but had wider CIs. Six-month adherence ranged from 28% (duloxetine) to 41% (bupropion). LIMITATION: No data on medication dispensing, low medication adherence, incomplete data on adherence, and incomplete data on weight measures across time points.

conclusionSmall differences in mean weight change were found between 8 first-line antidepressants, with bupropion consistently showing the least weight gain, although adherence to medications over follow-up was low. Clinicians could consider potential weight gain when initiating antidepressant treatment. PRIMARY FUNDING SOURCE: National Institutes of Health.

Indexed as

Antidepressive AgentsWeight GainAdultAgedBupropionCitalopramDuloxetine HydrochlorideFemaleHumansMaleMiddle AgedAntidepressive AgentsBupropionCitalopramDuloxetine Hydrochloride

Identifiers

PMID38950403
PMCPMC11819980

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.