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 language | English (US) |
|---|---|
| Article number | 67 |
| Journal | Lipids in Health and Disease |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2016 |
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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
}
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
UR - http://www.scopus.com/inward/record.url?scp=85007569921&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007569921&partnerID=8YFLogxK
U2 - 10.1186/s12944-016-0234-3
DO - 10.1186/s12944-016-0234-3
M3 - Article
C2 - 27044508
AN - SCOPUS:85007569921
VL - 15
JO - Lipids in Health and Disease
JF - Lipids in Health and Disease
SN - 1476-511X
IS - 1
M1 - 67
ER -