Evidence map›Paper›PMID 40320535›Full record

ArticleNutrition journal2025

Identifying and characterizing shared and ethnic background site-specific dietary patterns in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Roberta De Vito, Briana Stephenson, Daniela Sotres-Alvarez, Anna-Maria Siega-Riz, Josiemer Mattei, Maria Parpinel, Brandilyn A Peters, Sierra A Bainter, Martha L Daviglus, Linda Van Horn and 1 more

Abstract read
In one paragraph

Article in Nutrition journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  9. Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024
    Article
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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

11 authors.

Roberta De Vito *Department of Biostatistics and Data Science Institute, Brown University, Providence, RI, USA.ORCID http://orcid.org/0000-0003-0639-5341
Briana Stephenson *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-6147-1039
Daniela Sotres-AlvarezDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0002-3226-6140
Anna-Maria Siega-RizDepartments of Nutrition and Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.ORCID http://orcid.org/0000-0002-1303-4248
Josiemer MatteiDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5424-8245
Maria ParpinelDepartment of Medicine - DMED, Università degli Studi di Udine, Udine, Italy.ORCID http://orcid.org/0000-0003-1309-4467
Brandilyn A PetersDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.ORCID http://orcid.org/0000-0002-1534-2578
Sierra A BainterDepartment of Psychology, University of Miami, Coral Gables, FL, USA.ORCID http://orcid.org/0000-0001-7461-0803
Martha L DaviglusInstitute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA.ORCID http://orcid.org/0000-0002-6791-8727
Linda Van HornDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.ORCID http://orcid.org/0000-0002-1362-5806
Valeria EdefontiBranch of Medical Statistics, Biometry and Epidemiology "G. A. Maccacaro", Department of Clinical Sciences and Community Health - Dipartimento Di Eccellenza 2023 - 2027, Università degli Studi di Milano, Via Celoria 22, 20133, Milan, Italy. valeria.edefonti@unimi.it.ORCID http://orcid.org/0000-0002-1995-1477

Funding

Bayesian Variable Selection Methods to Accelerate Identification of Important Psychological Predictors and Neural Substrates of PsychopathologyK01MH122805 · NIMH · UNIVERSITY OF MIAMI CORAL GABLES · PI BAINTER, SIERRA · 2020 to 2024
$659k
Ministero dell'Istruzione, dell'Università e della Ricerca PRIN 20227YCB5PNHLBI NIH HHS HHSN268201300001INHLBI NIH HHS HHSN268201300002INHLBI NIH HHS HHSN268201300003INHLBI NIH HHS HHSN268201300004INHLBI NIH HHS N01- HC-65236 Northwestern UnivNIH HHS K01MH122805NIH HHS NIGMS/NIH COBRE CBHD P20GM109035NIMH NIH HHS K01 MH122805Università degli Studi di Milano Young Investigator Grant 2020
6 · The paper itself

Abstract

backgroundA posteriori dietary patterns (DPs) are critical for capturing actual dietary behaviour. However, assessing their reproducibility across (sub)populations requires novel modelling approaches beyond descriptive statistics. Multi-study factor analysis derives DPs that are shared among all studies/subpopulations and those specific to a study or subpopulation of interest. Bayesian implementation of the multi-study factor analysis (BMSFA) is more flexible than frequentist as it imposes fewer assumptions and improves factor selection.

methodsWe applied BMSFA to 24-h dietary recalls from the baseline visit (2008-2011) of the US Hispanic Community Health Study/Study of Latinos (n = 16,415). The analysis was conducted on 42 common nutrients to identify shared and subpopulation-specific DPs. Subpopulations were defined based on the cross-classification of ethnic background (Cuban, Dominican Republic, Mexican, Puerto Rican, Central and South American) and study site (Bronx, Chicago, Miami, San Diego) resulting in 12 Ethnic Background Site (EBS) categories. Regression analysis characterized DPs in terms of food groups, overall diet quality, socio-demographic/lifestyle factors, adjusting for survey design.

resultsWe identified four shared DPs across all EBS categories: Plant-based foods, Processed foods, Dairy products, and Seafood. Additionally, twelve EBS-specific DPs were identified-one for each EBS category. Most EBS-specific DPs were further grouped into overarching profiles: Animal vs. vegetable source, Animal source only, and Poultry vs. dairy products, to capture nuances within animal-based DPs. Puerto Rican background participants from Chicago expressed a strikingly different DP from all others (i.e., high on beta-carotene and low on starch/iron/thiamin). Higher overall diet quality was observed with increasing categories of Plant-based foods, Seafood, and the "Puerto Rican background - Chicago" EBS-specific DP, whereas increasing categories of Dairy products, Processed foods, and the remaining EBS-specific DPs were related to lower diet quality. Compared to non-US-born participants, US-born individuals had significantly higher adjusted mean scores in absolute value for most DPs. Specifically, they exhibited lower adherence to the Plant-based foods and Dairy products DPs but higher adherence to Processed foods, Seafood, and six EBS-specific DPs.

conclusionsThe BMSFA successfully captured sources of dietary homogeneity and heterogeneity among US Hispanic/Latino adults across ethnic backgrounds and study sites. The study highlighted the crucial role of nativity on DPs.

Indexed as

DietFeeding BehaviorHispanic or LatinoAdultBayes TheoremDiet SurveysFactor Analysis, StatisticalFemaleHumansMaleMiddle AgedUnited StatesBayesian analysisBayesian multi-study factor analysisDietary patternsFactor analysisHispanic Community Health Study/Study of LatinosHispanics/LatinosMulti-study factor analysisReproducibility of dietary patterns

Identifiers

PMID40320535
PMCPMC12051310

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LicenceCC BY-NC-ND
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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.