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- McCaffery, Jeanne M., et al.
(författare)
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Replication of the Association of BDNF and MC4R Variants With Dietary Intake in the Diabetes Prevention Program
- 2017
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Ingår i: Psychosomatic Medicine. - : Lippincott Williams & Wilkins. - 0033-3174 .- 1534-7796. ; 79:2, s. 224-233
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Genomewide association studies (GWAS) have identified consistent associations with obesity, with a number of studies implicating eating behavior as a primary mechanism. Few studies have replicated genetic associations with dietary intake. This study evaluates the association between obesity susceptibility loci and dietary intake. Methods: Data were obtained as part of the Diabetes Prevention Program (DPP), a clinical trial of diabetes prevention in persons at high risk of diabetes. The association of 31 genomewide association studies identified obesity risk alleles with dietary intake, measured through a food frequency questionnaire, was investigated in 3,180 participants from DPP at baseline. Results: The minor allele at BDNF, identified as protective against obesity, was associated with lower total caloric intake (beta = -106.06, SE = 33.13; p = .0014) at experimentwide statistical significance (p = .0016), whereas association of MC4R rs571312 with higher caloric intake reached nominal significance (beta = 61.32, SE = 26.24; p = .0194). Among non-Hispanic white participants, the association of BDNF rs2030323 with total caloric intake was stronger (beta = -151.99, SE = 30.09; p < .0001), and association of FTO rs1421085 with higher caloric intake (beta = 56.72, SE = 20.69; p = .0061) and percentage fat intake (beta = 0.37, SE = 0.08; p =. 0418) was also observed. Conclusions: These results demonstrate with the strength of independent replication that BDNF rs2030323 is associated with 100 to 150 greater total caloric intake per allele, with additional contributions of MC4R and, in non-Hispanic white individuals, FTO. As it has been argued that an additional 100 kcal/d could account for the trends in weight gain, prevention focusing on genetic profiles with high dietary intake may help to quell adverse obesity trends.
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| 2. |
- Varga, Tibor V., et al.
(författare)
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Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes : A machine learning approach in the Diabetes Prevention Program
- 2021
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Ingår i: BMJ Open Diabetes Research and Care. - : BMJ. - 2052-4897. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes. Research design and methods Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility. Results Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study. Conclusions NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study. Trial registration number Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.
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