Evidence map›Paper›PMID 27617202›Full record

ReviewMolecular metabolism2016

Interactions between host genetics and gut microbiome in diabetes and metabolic syndrome.

Siegfried Ussar, Shiho Fujisaka, C Ronald Kahn

Abstract readReview
In one paragraph

Review in Molecular metabolism, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 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

76 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Protective Effects ofToxics · 2025
    Article
  6. Frontiers in microbiology · 2025
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16 more citing papers are in PubMed but not listed here.

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.

Siegfried UssarJRG Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, 85748 Munich/Garching, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany.
Shiho FujisakaFirst Department of Internal Medicine, University of Toyama, 930-0194 Toyama, Japan.
C Ronald KahnSection on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA.

Funding

SPECIAL ASSAY COREP30DK036836 · NIDDK · JOSLIN DIABETES CENTER · PI ROHIT N. KULKARNI · 1986 to 2026
$50.5M
STRUCTURE-FUNCTION RELATIONSHIPS IN THE ALIMENTARY TRACTP30DK034854 · NIDDK · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI JONATHAN C KAGAN · 1986 to 2026
$32.4M
INSULIN RECEPTOR STRUCTURE AND TURNOVERR37DK031036 · NIDDK · JOSLIN DIABETES CENTER · PI KAHN, C RONALD · 2000 to 2019
$12.9M
Insulin Receptor Substrates and Insulin ActionR01DK033201 · NIDDK · JOSLIN DIABETES CENTER · PI KAHN, C RONALD · 1986 to 2016
$10.6M
The Insulin Receptor and Its Signaling MechanismsR01DK031036 · NIDDK · JOSLIN DIABETES CENTER · PI C RONALD KAHN · 1986 to 2026
$4.2M
NIDDK NIH HHS P30 DK034854NIDDK NIH HHS P30 DK036836NIDDK NIH HHS R01 DK031036NIDDK NIH HHS R01 DK033201NIDDK NIH HHS R37 DK031036
6 · The paper itself

Abstract

backgroundDiabetes, obesity, and the metabolic syndrome are multifactorial diseases dependent on a complex interaction of host genetics, diet, and other environmental factors. Increasing evidence places gut microbiota as important modulators of the crosstalk between diet and development of obesity and metabolic dysfunction. In addition, host genetics can have important impact on the composition and function of gut microbiota. Indeed, depending on the genetic background of the host, diet and other environmental factors may produce different changes in gut microbiota, have different impacts on host metabolism, and create different interactions between the microbiome and the host. SCOPE OF REVIEW: In this review, we highlight how appropriate animal models can help dissect the complex interaction of host genetics with the gut microbiome and how diet can lead to different degrees of weight gain, levels of insulin resistance, and metabolic outcomes, such as diabetes, in different individuals. We also discuss the challenges of identifying specific disease-associated microbiota and the limitations of simple metrics, such as phylogenetic diversity or the ratio of Firmicutes to Bacteroidetes. MAJOR

conclusionsUnderstanding these complex interactions will help in the development of novel treatments for microbiome-related metabolic diseases. This article is part of a special issue on microbiota.

Indexed as

EnvironmentHost geneticsMetabolic syndromeMicrobial diversityMicrobiomeMicrobiotaObesity

Identifiers

PMID27617202
PMCPMC5004229

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.