Evidence map›Paper›PMID 26251918›Full record

ArticleNutrients2015

The Relationship between Dietary Patterns and Metabolic Health in a Representative Sample of Adult Australians.

Lucinda K Bell, Suzanne Edwards, Jessica A Grieger

Abstract read
In one paragraph

Article in Nutrients, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 4 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

25 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. 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

3 authors.

Lucinda K BellNutrition and Dietetics, School of Health Sciences, Faculty of Medicine, Nursing and Health Sciences, Flinders University, Bedford Park 5042, Australia. lucy.bell@flinders.edu.au.
Suzanne EdwardsData Management and Analysis Centre (DMAC), Faculty of Health Sciences, University of Adelaide, Adelaide 5005, Australia. suzanne.edwards@adelaide.edu.au.
Jessica A GriegerRobinson Research Institute, School of Medicine, Faculty of Health Sciences, University of Adelaide, Adelaide 5005, Australia. jessica.grieger@adelaide.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies assessing dietary intake and its relationship to metabolic phenotype are emerging, but limited. The aims of the study are to identify dietary patterns in Australian adults, and to determine whether these dietary patterns are associated with metabolic phenotype and obesity. Cross-sectional data from the Australian Bureau of Statistics 2011 Australian Health Survey was analysed. Subjects included adults aged 45 years and over (n = 2415). Metabolic phenotype was determined according to criteria used to define metabolic syndrome (0-2 abnormalities vs. 3-7 abnormalities), and additionally categorized for obesity (body mass index (BMI) ≥30 kg/m2 vs. BMI <30 kg/m2). Dietary patterns were derived using factor analysis. Multivariable models were used to assess the relationship between dietary patterns and metabolic phenotype, with adjustment for age, sex, smoking status, socio-economic indexes for areas, physical activity and daily energy intake. Twenty percent of the population was metabolically unhealthy and obese. In the fully adjusted model, for every one standard deviation increase in the Healthy dietary pattern, the odds of having a more metabolically healthy profile increased by 16% (odds ratio (OR) 1.16; 95% confidence interval (CI): 1.04, 1.29). Poor metabolic profile and obesity are prevalent in Australian adults and a healthier dietary pattern plays a role in a metabolic and BMI phenotypes. Nutritional strategies addressing metabolic syndrome criteria and targeting obesity are recommended in order to improve metabolic phenotype and potential disease burden.

Indexed as

Body Mass IndexDietAgedAustraliaBlood GlucoseCholesterol, HDLCholesterol, LDLCross-Sectional StudiesEnergy IntakeFemaleFruitHealth SurveysHumansLogistic ModelsMaleMetabolic SyndromeBlood GlucoseCholesterol, HDLCholesterol, LDLTriglyceridesadultsAustralia, national surveybody mass indexdietary patternsmetabolic healthobesity

Identifiers

PMID26251918
PMCPMC4555134

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.