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 journalArticle

16 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.

LanguageEnglish
Article number67
JournalLipids in Health and Disease
Volume15
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

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

ASJC Scopus subject areas

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

Cite this

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",
month = "1",
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doi = "10.1186/s12944-016-0234-3",
language = "English",
volume = "15",
journal = "Lipids in health and disease",
issn = "1476-511X",
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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

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, 01.01.2016.

Research output: Contribution to journalArticle

TY - JOUR

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

T2 - Lipids in health and disease

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 - 2016/1/1

Y1 - 2016/1/1

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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JO - Lipids in health and disease

JF - Lipids in health and disease

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