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 journalArticleResearchpeer-review

15 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
Article number67
Number of pages12
JournalLipids in Health and Disease
Volume15
DOIs
Publication statusPublished - 4 Apr 2016
Externally publishedYes

Keywords

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

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.
@article{fda3e747761d425f91bdce3820b05ab6,
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 = "4",
day = "4",
doi = "10.1186/s12944-016-0234-3",
language = "English",
volume = "15",
journal = "Lipids in Health and Disease",
issn = "1476-511X",
publisher = "Springer-Verlag London Ltd.",

}

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, 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, 67, 04.04.2016.

Research output: Contribution to journalArticleResearchpeer-review

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 - 2016/4/4

Y1 - 2016/4/4

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

UR - http://www.scopus.com/inward/record.url?scp=85007569921&partnerID=8YFLogxK

U2 - 10.1186/s12944-016-0234-3

DO - 10.1186/s12944-016-0234-3

M3 - Article

VL - 15

JO - Lipids in Health and Disease

JF - Lipids in Health and Disease

SN - 1476-511X

M1 - 67

ER -