Evidence map›Paper›PMID 38076414›Full record

ArticleLancet regional health. Americas2023

Prevalence of prediabetes in Mexico: a retrospective analysis of nationally representative surveys spanning 2016-2022.

Carlos A Fermín-Martínez, César Daniel Paz-Cabrera, Martín Roberto Basile-Alvarez, Paulina Sánchez Castro, Alejandra Núñez-Luna, Jerónimo Perezalonso-Espinosa, Daniel Ramírez-García, Neftali Eduardo Antonio-Villa, Arsenio Vargas-Vázquez, Luisa Fernández-Chirino and 5 more

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

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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

15 authors.

Carlos A Fermín-MartínezResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
César Daniel Paz-CabreraResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Martín Roberto Basile-AlvarezResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Paulina Sánchez CastroResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Alejandra Núñez-LunaResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Jerónimo Perezalonso-EspinosaEspecialidad en Medicina Preventiva, Instituto Nacional de Salud Pública, Mexico City, Mexico.
Daniel Ramírez-GarcíaResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Neftali Eduardo Antonio-VillaDepartmento de Endocrinología, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico.
Arsenio Vargas-VázquezEspecialidad en Medicina Preventiva, Instituto Nacional de Salud Pública, Mexico City, Mexico.
Luisa Fernández-ChirinoResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
Karime Berenice Carrillo-HerreraFacultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Leslie Alitzel Cabrera-QuintanaFacultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Rosalba Rojas-MartínezInstituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico.
Jacqueline A SeiglieDiabetes Unit, Massachusetts General Hospital, Harvard Medical School, MA, USA.
Omar Yaxmehen Bello-ChavollaResearch Division, Instituto Nacional de Geriatría, Mexico City, Mexico.

Funding

REACH-Es: Adapting a digital health tool to improve diabetes medication adherence among Latino adultsK23DK135798 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Jacqueline Anne Seiglie · 2023 to 2026
$784k
NIDDK NIH HHS K23 DK135798
6 · The paper itself

Abstract

Background: Characterizing prediabetes phenotypes may be useful in guiding diabetes prevention efforts; however, heterogeneous criteria to define prediabetes have led to inconsistent prevalence estimates, particularly in low- and middle-income countries. Here, we estimated trends in prediabetes prevalence in Mexico across different prediabetes definitions and their association with prevalent cardiometabolic conditions. Methods: We conducted a serial cross-sectional analysis of National Health and Nutrition Surveys in Mexico (2016-2022), totalling 22 081 Mexican adults. After excluding individuals with diagnosed or undiagnosed diabetes, we defined prediabetes using ADA (impaired fasting glucose [IFG] 100-125 mg/dL and/or HbA1c 5.7-6.4%), WHO (IFG 110-125 mg/dL), and IEC criteria (HbA1c 6.0-6.4%). Prevalence trends of prediabetes over time were evaluated using weighted Poisson regression and its association with prevalent cardiometabolic conditions with weighted logistic regression. Findings: The prevalence of prediabetes (either IFG or high HbA1c [ADA]) in Mexico was 20.9% in 2022. Despite an overall downward trend in prediabetes (RR 0.973, 95% CI 0.957-0.988), this was primarily driven by decreases in prediabetes by ADA-IFG (RR 0.898, 95% CI 0.880-0.917) and WHO-IFG criteria (RR 0.919, 95% CI 0.886-0.953), while prediabetes by ADA-HbA1c (RR 1.055, 95% CI 1.033-1.077) and IEC-HbA1C criteria (RR 1.085, 95% CI 1.045-1.126) increased over time. Prediabetes prevalence increased over time in adults >40 years, with central obesity, self-identified as indigenous or living in urban areas. For all definitions, prediabetes was associated with an increased risk of cardiometabolic conditions. Interpretation: Prediabetes rates in Mexico from 2016 to 2022 varied based on defining criteria but consistently increased for HbA1c-based definitions and high-risk subgroups. Funding: This research was supported by Instituto Nacional de Geriatría in Mexico. JAS was supported by NIH/NIDDK Grant# K23DK135798.

Indexed as

Cardiometabolic riskGlucose metabolismInsulin resistancePrediabetes

Identifiers

PMID38076414
PMCPMC10701418

What OpenQuestion holds

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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.