title: Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.
abstract: Dyslipidemia is an independent risk factor for type 2 diabetes, although exactly which of the many plasma lipids contribute to this remains unclear. We therefore investigated whether lipid profiling can inform diabetes prediction by performing liquid chromatography/mass spectrometry-based lipid profiling in 189 individuals who developed type 2 diabetes and 189 matched disease-free individuals, with over 12 years of follow up in the Framingham Heart Study. We found that lipids of lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. This pattern was strongest for triacylglycerols (TAGs) and persisted after multivariable adjustment for age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. A combination of 2 TAGs further improved diabetes prediction. To explore potential mechanisms that modulate the distribution of plasma lipids, we performed lipid profiling during oral glucose tolerance testing, pharmacologic interventions, and acute exercise testing. Levels of TAGs associated with increased risk for diabetes decreased in response to insulin action and were elevated in the setting of insulin resistance. Conversely, levels of TAGs associated with decreased diabetes risk rose in response to insulin and were poorly correlated with insulin resistance. These studies identify a relationship between lipid acyl chain content and diabetes risk and demonstrate how lipid profiling could aid in clinical risk assessment.
authors.affiliation: Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts 02129, USA.
authors.forename: Eugene P
authors.initials: EP
authors.lastname: Rhee
authors.forename: Susan
authors.initials: S
authors.lastname: Cheng
authors.forename: Martin G
authors.initials: MG
authors.lastname: Larson
authors.forename: Geoffrey A
authors.initials: GA
authors.lastname: Walford
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Lewis
authors.forename: Elizabeth
authors.initials: E
authors.lastname: McCabe
authors.forename: Elaine
authors.initials: E
authors.lastname: Yang
authors.forename: Laurie
authors.initials: L
authors.lastname: Farrell
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: O'Donnell
authors.forename: Steven A
authors.initials: SA
authors.lastname: Carr
authors.forename: Ramachandran S
authors.initials: RS
authors.lastname: Vasan
authors.forename: Jose C
authors.initials: JC
authors.lastname: Florez
authors.forename: Clary B
authors.initials: CB
authors.lastname: Clish
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Wang
authors.forename: Robert E
authors.initials: RE
authors.lastname: Gerszten
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood / Q000737:chemistry; D016022:Case-Control Studies; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000175:diagnosis* / Q000209:etiology; D050171:Dyslipidemias / Q000097:blood / Q000150:complications; D005080:Exercise Test; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D007328:Insulin; D007333:Insulin Resistance / Q000502:physiology*; D008055:Lipids / Q000097:blood* / Q000737:chemistry; D008297:Male; D008875:Middle Aged; D015394:Molecular Structure; D011237:Predictive Value of Tests; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood* / Q000737:chemistry
chemical_list: D015415:Biomarkers; D007328:Insulin; D008055:Lipids; D014280:Triglycerides