Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes

Results from diverse cohorts

Manju Mamtani, Hemant Kulkarni, Gerard Wong, Jacquelyn M. Weir, Christopher K. Barlow, Thomas D. Dyer, Laura Almasy, Michael C. Mahaney, Anthony G. Comuzzie, David C. Glahn, Dianna J. Magliano, Paul Zimmet, Jonathan Shaw, Sarah Williams-Blangero, Ravindranath Duggirala, John Blangero, Peter J. Meikle, Joanne E. Curran

    Research output: Contribution to journal › Article

    14 Citations (Scopus)

    Abstract

    Background: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. Methods: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia -The AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. Results: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. Conclusions: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

    Original languageEnglish (US)
    Article number67
    JournalLipids in Health and Disease
    Volume15
    Issue number1
    DOIs
    StatePublished - 2016

    Fingerprint

    Medical problems
    Type 2 Diabetes Mellitus
    Costs and Cost Analysis
    Costs
    Cost effectiveness
    Cost-Benefit Analysis
    Plasmas
    Screening
    Biomarkers
    Lipids
    Prediabetic State
    Metformin
    Set theory
    Lipid Metabolism

    Keywords

    • Diabetes
    • Diagnostic tools
    • Endocrine disorders
    • Genetics
    • Lipidomics

    ASJC Scopus subject areas

    • Endocrinology, Diabetes and Metabolism
    • Endocrinology
    • Clinical Biochemistry
    • Biochemistry, medical

    Cite this

    Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes : Results from diverse cohorts. / Mamtani, Manju; Kulkarni, Hemant; Wong, Gerard; Weir, Jacquelyn M.; Barlow, Christopher K.; Dyer, Thomas D.; Almasy, Laura; Mahaney, Michael C.; Comuzzie, Anthony G.; Glahn, David C.; Magliano, Dianna J.; Zimmet, Paul; Shaw, Jonathan; Williams-Blangero, Sarah; Duggirala, Ravindranath; Blangero, John; Meikle, Peter J.; Curran, Joanne E.

    In: Lipids in Health and Disease, Vol. 15, No. 1, 67, 2016.

    Research output: Contribution to journal › Article

    Mamtani, M, Kulkarni, H, Wong, G, Weir, JM, Barlow, CK, Dyer, TD, Almasy, L, Mahaney, MC, Comuzzie, AG, Glahn, DC, Magliano, DJ, Zimmet, P, Shaw, J, Williams-Blangero, S, Duggirala, R, Blangero, J, Meikle, PJ & Curran, JE 2016, 'Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: Results from diverse cohorts', Lipids in Health and Disease, vol. 15, no. 1, 67. https://doi.org/10.1186/s12944-016-0234-3
    Mamtani M, Kulkarni H, Wong G, Weir JM, Barlow CK, Dyer TD et al. Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: Results from diverse cohorts. Lipids in Health and Disease. 2016;15(1). 67. https://doi.org/10.1186/s12944-016-0234-3
    Mamtani, Manju ; Kulkarni, Hemant ; Wong, Gerard ; Weir, Jacquelyn M. ; Barlow, Christopher K. ; Dyer, Thomas D. ; Almasy, Laura ; Mahaney, Michael C. ; Comuzzie, Anthony G. ; Glahn, David C. ; Magliano, Dianna J. ; Zimmet, Paul ; Shaw, Jonathan ; Williams-Blangero, Sarah ; Duggirala, Ravindranath ; Blangero, John ; Meikle, Peter J. ; Curran, Joanne E. / Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes : Results from diverse cohorts. In: Lipids in Health and Disease. 2016 ; Vol. 15, No. 1.
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    title = "Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: Results from diverse cohorts",
    abstract = "Background: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. Methods: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia -The AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. Results: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 {\%}. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. Conclusions: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.",
    keywords = "Diabetes, Diagnostic tools, Endocrine disorders, Genetics, Lipidomics",
    author = "Manju Mamtani and Hemant Kulkarni and Gerard Wong and Weir, {Jacquelyn M.} and Barlow, {Christopher K.} and Dyer, {Thomas D.} and Laura Almasy and Mahaney, {Michael C.} and Comuzzie, {Anthony G.} and Glahn, {David C.} and Magliano, {Dianna J.} and Paul Zimmet and Jonathan Shaw and Sarah Williams-Blangero and Ravindranath Duggirala and John Blangero and Meikle, {Peter J.} and Curran, {Joanne E.}",
    year = "2016",
    doi = "10.1186/s12944-016-0234-3",
    language = "English (US)",
    volume = "15",
    journal = "Lipids in Health and Disease",
    issn = "1476-511X",
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    TY - JOUR

    T1 - Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes

    T2 - Results from diverse cohorts

    AU - Mamtani, Manju

    AU - Kulkarni, Hemant

    AU - Wong, Gerard

    AU - Weir, Jacquelyn M.

    AU - Barlow, Christopher K.

    AU - Dyer, Thomas D.

    AU - Almasy, Laura

    AU - Mahaney, Michael C.

    AU - Comuzzie, Anthony G.

    AU - Glahn, David C.

    AU - Magliano, Dianna J.

    AU - Zimmet, Paul

    AU - Shaw, Jonathan

    AU - Williams-Blangero, Sarah

    AU - Duggirala, Ravindranath

    AU - Blangero, John

    AU - Meikle, Peter J.

    AU - Curran, Joanne E.

    PY - 2016

    Y1 - 2016

    N2 - Background: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. Methods: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia -The AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. Results: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. Conclusions: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

    AB - Background: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. Methods: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia -The AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. Results: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. Conclusions: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

    KW - Diabetes

    KW - Diagnostic tools

    KW - Endocrine disorders

    KW - Genetics

    KW - Lipidomics

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    U2 - 10.1186/s12944-016-0234-3

    DO - 10.1186/s12944-016-0234-3

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    JF - Lipids in Health and Disease

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