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Record W4288452510 · doi:10.2337/dc21-2130

Relation of Change or Substitution of Low- and No-Calorie Sweetened Beverages With Cardiometabolic Outcomes: A Systematic Review and Meta-analysis of Prospective Cohort Studies

2022· review· en· W4288452510 on OpenAlexafffund
Jennifer J. Lee, Tauseef Khan, Néma McGlynn, Vasanti Malik, James O. Hill, Lawrence A. Leiter, Per Bendix Jeppesen, Dario Rahelić, Hana Kahleová, Jordi Salas‐Salvadó, Cyril W.C. Kendall, John L. Sievenpiper

Bibliographic record

VenueDiabetes Care · 2022
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversity of SaskatchewanSt. Michael's HospitalUniversity of Toronto
FundersInstitute of Nutrition, Metabolism and DiabetesBanting and Best Diabetes Centre, University of TorontoEuropean Association for the Study of DiabetesNovo NordiskCanadian Institutes of Health ResearchNational Honey BoardAstraZenecaLoblaw Companies LimitedEli Lilly and CompanyCity and County of San FranciscoNational Institutes of HealthDiabetes CanadaInternational Sweeteners AssociationGeneral Mills
KeywordsMedicineMeta-analysisCalorieProspective cohort studyDiabetes mellitusCohort studyLow calorieType 2 diabetesSystematic reviewMEDLINEEnvironmental healthInternal medicineFood scienceEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Adverse associations of low- and no-calorie sweetened beverages (LNCSB) with cardiometabolic outcomes in observational studies may be explained by reverse causality and residual confounding. PURPOSE: To address these limitations we used change analyses of repeated measures of intake and substitution analyses to synthesize the association of LNCSB with cardiometabolic outcomes. DATA SOURCES: MEDLINE, Embase, and the Cochrane Library were searched up to 10 June 2021 for prospective cohort studies with ≥1 year of follow-up duration in adults. STUDY SELECTION: Outcomes included changes in clinical measures of adiposity, risk of overweight/obesity, metabolic syndrome, type 2 diabetes (T2D), cardiovascular disease, and total mortality. DATA EXTRACTION: Two independent reviewers extracted data, assessed study quality, and assessed certainty of evidence using GRADE. Data were pooled with a random-effects model and expressed as mean difference (MD) or risk ratio (RR) and 95% CI. DATA SYNTHESIS: A total of 14 cohorts (416,830 participants) met the eligibility criteria. Increase in LNCSB intake was associated with lower weight (5 cohorts, 130,020 participants; MD -0.008 kg/year [95% CI -0.014, -0.002]). Substitution of LNCSB for sugar-sweetened beverages (SSB) was associated with lower weight (three cohorts, 165,579 participants; MD, -0.12 [-0.14, -0.10,] kg/y) and lower incidence of obesity (OB) (one cohort, 15,765 participants; RR 0.88 [95% CI 0.88, 0.89]), coronary heart disease (six cohorts, 233,676 participants; 0.89 [0.81, 0.98]), cardiovascular disease mortality (one cohort, 118,363 participants; 0.95 [0.90, 0.99]), and total mortality (one cohort, 118,363 participants; 0.96 [0.94, 0.98]) with no adverse associations across other outcomes. Substitution of water for SSB showed lower weight (three cohorts, 165,579 participants; MD -0.10 kg/year [-0.13, -0.06]), lower waist circumference (one cohort, 173 participants; -2.71 cm/year [-4.27, -1.15]) and percent body fat (one cohort, 173 participants; -1.51% per year [-2.61, -0.42]), and lower incidence of OB (one cohort, 15,765 participants; RR 0.85 [0.75, 0.97]) and T2D (three cohorts, 281,855 participants; 0.96 [0.94, 0.98]). Substitution of LNCSB for water showed no adverse associations. LIMITATIONS: The evidence was low to very low certainty owing to downgrades for imprecision, indirectness, and/or inconsistency. CONCLUSIONS: LNCSB were not associated with cardiometabolic harm in analyses that model the exposure as change or substitutions. The available evidence provides some indication that LNCSB in their intended substitution for SSB may be associated with cardiometabolic benefit, comparable with the standard of care, water.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0160.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.125
GPT teacher head0.362
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations67
Published2022
Admission routes2
Has abstractyes

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