| Title |
The Interaction of Genetic Predisposition and Socioeconomic Position With Type 2 Diabetes Mellitus
|
|---|---|
| Published in |
Psychosomatic Medicine, April 2018
|
| DOI | 10.1097/psy.0000000000000562 |
| Pubmed ID | |
| Authors |
Sander K.R. van Zon, Sijmen A. Reijneveld, Peter J. van der Most, Morris A. Swertz, Ute Bültmann, Harold Snieder |
| Abstract |
A strong genetic predisposition for type 2 diabetes mellitus (T2DM) may aggravate the negative effects of low socioeconomic position (SEP) in the etiology of the disorder. This study aimed to examine cross-sectional and longitudinal associations and interactions of a genetic risk score (GRS) and SEP with T2DM, and to investigate whether clinical and behavioral risk factors can explain these associations and interactions. We used data from 13,027 genotyped participants from the Lifelines study. The GRS was based on single-nucleotide polymorphisms (SNPs) genome-wide associated with T2DM and was categorized into tertiles. SEP was measured as educational level. T2DM was based on biological markers, recorded medication use, and self-reports. Cross-sectional and longitudinal associations, and interactions, between the GRS and SEP on T2DM were examined. The combination of a high GRS and low SEP had the strongest association with T2DM in cross-sectional (OR: 3.84; 95% CI: 2.28, 6.46) and longitudinal analyses (HR: 2.71; 1.39, 5.27), compared to a low GRS and high SEP. Interaction between a high GRS and a low SEP was observed in cross-sectional (relative excess risk due to interaction: 1.85; 0.65, 3.05) but not in longitudinal analyses. Clinical and behavioral risk factors mostly explained the observed associations and interactions. A high GRS combined with a low SEP provides the highest risk for T2DM. These factors also exacerbated each other's impact cross-sectionally but not longitudinally. Preventive measures should target individual and contextual factors of this high-risk group to reduce the risk of T2DM. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 8 | 22% |
| Student > Master | 6 | 17% |
| Student > Ph. D. Student | 4 | 11% |
| Student > Doctoral Student | 2 | 6% |
| Librarian | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 11 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 19% |
| Psychology | 3 | 8% |
| Nursing and Health Professions | 3 | 8% |
| Materials Science | 2 | 6% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 6 | 17% |
| Unknown | 14 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2018.
All research outputs
#15,097,241
of 25,382,440 outputs
Outputs from Psychosomatic Medicine
#1,770
of 2,498 outputs
Outputs of similar age
#180,804
of 343,807 outputs
Outputs of similar age from Psychosomatic Medicine
#13
of 21 outputs
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