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  1. Fulltext Association of dietary omega-3 fatty acids with prevalence of metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study
    Lai YH, Petrone AB, Pankow JS, Arnett DK, North KE, Ellison RC, et al.
    Clin Nutr, 2013 Dec;32(6):966-9.
    PMID: 23711994 DOI: 10.1016/j.clnu.2013.05.002
    Metabolic syndrome (MetS), characterized by abdominal obesity, atherogenic dyslipidemia, elevated blood pressure, and insulin resistance is a major public health concern in the United States. Omega-3 fatty acids have been relatively well studied in relation to many individual cardiovascular risk factors; however, their effects on MetS are not well established.
  2. Fulltext Lack of association of apolipoprotein E (Apo E) polymorphism with the prevalence of metabolic syndrome: the National Heart, Lung and Blood Institute Family Heart Study
    Lai LY, Petrone AB, Pankow JS, Arnett DK, North KE, Ellison RC, et al.
    Diabetes Metab Res Rev, 2015 Sep;31(6):582-7.
    PMID: 25656378 DOI: 10.1002/dmrr.2638
    OBJECTIVE: Metabolic syndrome (MetS), characterized by abdominal obesity, atherogenic dyslipidaemia, elevated blood pressure and insulin resistance, is a major public health concern in the United States. The effects of apolipoprotein E (Apo E) polymorphism on MetS are not well established.

    METHODS: We conducted a cross-sectional study consisting of 1551 participants from the National Heart, Lung and Blood Institute Family Heart Study to assess the relation of Apo E polymorphism with the prevalence of MetS. MetS was defined according to the American Heart Association-National Heart, Lung and Blood Institute-International Diabetes Federation-World Health Organization harmonized criteria. We used generalized estimating equations to estimate adjusted odds ratios (ORs) for prevalent MetS and the Bonferroni correction to account for multiple testing in the secondary analysis.

    RESULTS: Our study population had a mean age (standard deviation) of 56.5 (11.0) years, and 49.7% had MetS. There was no association between the Apo E genotypes and the MetS. The multivariable adjusted ORs (95% confidence interval) were 1.00 (reference), 1.26 (0.31-5.21), 0.89 (0.62-1.29), 1.13 (0.61-2.10), 1.13 (0.88-1.47) and 1.87 (0.91-3.85) for the Ɛ3/Ɛ3, Ɛ2/Ɛ2, Ɛ2/Ɛ3, Ɛ2/Ɛ4, Ɛ3/Ɛ4 and Ɛ4/Ɛ4 genotypes, respectively. In a secondary analysis, Ɛ2/Ɛ3 genotype was associated with 41% lower prevalence odds of low high-density lipoprotein [multivariable adjusted ORs (95% confidence interval) = 0.59 (0.36-0.95)] compared with Ɛ3/Ɛ3 genotype.

    CONCLUSIONS: Our findings do not support an association between Apo E polymorphism and MetS in a multicentre population-based study of predominantly White US men and women.

  3. Fulltext Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine
    Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, et al.
    Nat Med, 2023 Oct;29(10):2438-2457.
    PMID: 37794253 DOI: 10.1038/s41591-023-02502-5
    Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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