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Am J Clin Nutr. 2015 Nov;102(5):1266-78. doi: 10.3945/ajcn.114.101238. Epub 2015 Sep 9.

Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians.

Author information

1
Departments of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA; amfretts@uw.edu.
2
Department of Mathematics, Computer Science, and Cooperative Engineering, University of St. Thomas, Houston, TX;
3
Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Sciences Center, Houston, TX;
4
Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA;
5
Department of Biostatistics, Boston University School of Public Health, Boston, MA;
6
Department of Genetics, Division of Statistical Genomics, School of Medicine, Washington University, St. Louis, MO;
7
Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece;
8
Department of Clinical Sciences Genetic and Molecular Epidemiology Unit and.
9
USDA/Agricultural Research Service Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX;
10
Department of Internal Medicine and.
11
Institute of Behavioral Sciences.
12
Department of Clinical Sciences, Lund University, Malmö, Sweden;
13
Department of Epidemiology and.
14
Department of Food and Environmental Sciences, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland;
15
Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA;
16
Biostatistics, and Cardiovascular Health Research Unit, University of Washington, Seattle, WA;
17
Department of Clinical Sciences Genetic and Molecular Epidemiology Unit and Department of Biobank Research.
18
Department of Epidemiology, Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC;
19
Nutritional Epidemiology Program, Jean Mayer-USDA Human Nutrition Research Center on Aging, and.
20
William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom;
21
Nutrition and Genomics Laboratory, Tufts University, Boston, MA;
22
Laboratory of Epidemiology and Population Sciences and.
23
Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland;
24
Center for Public Health Genomics, Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA;
25
Department of Epidemiology and Netherlands Genomics Initiative, Leiden, Netherlands;
26
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland;
27
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN;
28
Department of Medicine Brigham and Women's Hospital, Harvard Medical School, Boston MA and.
29
Department of Epidemiology and Department of Nutrition, Harvard School of Public Health, Boston, MA;
30
Jean Mayer-USDA Human Nutrition Research Center on Aging, and Nutrition and Genomics Laboratory, Tufts University, Boston, MA;
31
Laboratory of Neurogenetics, National Institute of Aging, Bethesda, MD; Department of Clinical Physiology.
32
Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA;
33
Department of Epidemiology.
34
School of Medicine, and Tampere University Hospital, University of Tampere, Tampere, Finland;
35
Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, MA;
36
Department of Odontology, and.
37
Jean Mayer-USDA Human Nutrition Research Center on Aging, and Nutrition and Genomics Laboratory, Tufts University, Boston, MA; Department of Epidemiology and Population Genetics, Cardiovascular Research Center, Madrid, Spain; IMDEA Food Institute, Madrid, Spain;
38
Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC;
39
Department of Epidemiology and Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands;
40
William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom; Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia;
41
Department of Clinical Chemistry, Fimlab Laboratories, School of Medicine, and.
42
Departments of Epidemiology, Medicine, Health Services and Cardiovascular Health Research Unit, University of Washington, Seattle, WA; Group Health Research Institute, Group Health Cooperative, Seattle, WA.
43
Department of Biostatistics, Boston University School of Public Health, Boston, MA; Framingham Heart Study, Framingham, MA;
44
Department of Clinical Sciences Genetic and Molecular Epidemiology Unit and Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA;
45
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL;
46
Laboratory of Neurogenetics, National Institute of Aging, Bethesda, MD;
47
Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland; Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; General Practice Unit, Helsinki University Central Hospital, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland;
48
Clinical Epidemiology Unit and Diabetes Research Unit, General Medicine Division, Massachusetts General Hospital, Boston, MA; and.
49
Departments of Epidemiology, Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA; New York Academy of Medicine, New York, NY.

Abstract

BACKGROUND:

Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.

OBJECTIVE:

We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.

DESIGN:

Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.

RESULTS:

Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.

CONCLUSION:

The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

KEYWORDS:

diet; gene–diet interaction; glucose; insulin; meat intake; meta-analysis

PMID:
26354543
PMCID:
PMC4625584
DOI:
10.3945/ajcn.114.101238
[Indexed for MEDLINE]
Free PMC Article

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