Current technologies enable high-throughput “snapshots” of the lipidome (13
). Here, we have applied LC/MS-based lipid profiling to the FHS to identify a plasma signature of diabetes risk. We show that TAGs of lower carbon number and double bond content were associated with an increased risk of type 2 diabetes, whereas TAGs of higher carbon number and double bond content were associated with a decreased risk of type 2 diabetes. A similar pattern was noted for other lipid classes, including LPCs, LPEs, and PCs. The results of physiologic and pharmacologic experiments suggest that the divergent risk embedded in plasma triglycerides is due in part to the heterogeneous relationship between individual TAGs and insulin action. Nevertheless, select TAGs and other lipid analytes remained significant disease predictors, after adjusting for insulin (as well as other biochemical and clinical risk factors) and among the subset of subjects in the lowest quartile of HOMA-IR.
Several lines of evidence demonstrate that lipid profiling helps clarify the relationship between plasma TAGs and insulin action. In the acute setting, we showed that TAGs of lower carbon number and double bond content decreased with OGTT, whereas TAGs of relatively higher carbon number and double bond content increased. These findings were not appreciated during recent metabolomic surveys of oral glucose ingestion (9
). Glipizide administration resulted in the same dynamic TAG pattern, highlighting insulin rather than glucose as the proximate cause of the observed changes. The inverse pattern was elicited by acute metformin intake, which decreased plasma glucose and insulin levels. Exercise, which is known to acutely improve insulin sensitivity at the tissue level (11
), demonstrated the same TAG response as OGTT and glipizide administration. In a small study of 19 individuals, Schwab et al. have shown that the sustained increase in insulin sensitivity associated with diet-induced weight loss over 33 weeks is also associated with this pattern of TAG changes (18
These observations are further corroborated by the relationship between plasma TAGs and insulin resistance. In fasting pre-OGTT FHS samples, we showed that TAGs of lower carbon number and double bond content — i.e., TAGs that fall in response to insulin action — were elevated in the setting of insulin resistance. Further, insulin-resistant individuals had a blunted decrease in these TAGs during OGTT. TAGs of higher carbon number and double bond content, which increase in response to insulin action, had the weakest correlation with insulin resistance. Taken together, these data show that individual TAGs respond differentially to insulin activity and sensitivity, both acutely and over time.
We demonstrated a positive relationship between each TAG’s correlation with insulin resistance and its ability to predict type 2 diabetes in FHS (Figure D). Contrary to the prevailing view of bulk triglycerides as an adverse risk factor, we identified specific TAGs that are associated with either an increased or decreased risk of diabetes. Further, these risk markers were altered up to 12 years prior to disease onset. Integrating the positive and negative risk captured by a TAG of relatively lower carbon number and double bond content (TAG 50:0) and a TAG of relatively higher carbon number and double bond content (TAG 58:10) further improved diabetes prediction. Finally, lipid profiling applied to individuals with and without type 2 diabetes demonstrated that the TAG risk pattern identified in FHS persists in established disease (Figure C).
The results of MS/MS analyses demonstrate that the lipid analytes associated with increased diabetes risk are predominantly composed of saturated and monounsaturated fatty acids, whereas lipids associated with decreased diabetes risk are composed of polyunsaturated fatty acids (Figure ). These data are consistent with prior studies of diabetes prediction, which have relied on the measurement of derivatized fatty acids, following hydrolysis of plasma lipids (19
). By contrast, our approach is able to view acyl chains in their natural context, across distinct macromolecular species. For instance, dynamic changes after glucose ingestion were notable among TAGs but not SMs, PCs, or CEs (data not shown). This finding directs attention toward TAG-specific mechanisms of acute insulin action. As an example, the increasing proportion of polyunsaturated fatty acids in TAGs during OGTT has been attributed to insulin-mediated inhibition of hormone sensitive lipase: the subsequent decrease in saturated and monounsaturated free fatty acid release from adipose tissue increases the relative amount of polyunsaturated free fatty acids available to the liver for TAG assembly (17
). In contrast to the TAG-predominant response to OGTT, the relationship between diabetes risk and acyl chain composition in fasting pre-OGTT plasma was identified across several lipid classes. The breadth of this finding draws attention to general pathways of lipoprotein assembly. For example, insulin is known to increase the hepatic expression of various fatty acid desaturases, including SCD1, D5D, and D6D (24
), in animals. Whether decreased desaturase activity due to insulin resistance contributes to the lipid risk pattern observed in humans remains unclear.
Although we highlight the upstream significance of insulin action, we note that our conditional logistic regression model adjusts for baseline differences in fasting insulin as well as age, sex, BMI, fasting glucose, total triglycerides, and HDL cholesterol. Further, the downsloping TAG risk pattern persisted in the comparison between cases and controls in the lowest quartile of HOMA-IR (Figure B). Finally, dietary differences, as culled from a detailed questionnaire, do not account for differences in lipid profiles between cases and controls. These findings raise the possibility that select lipid predictors not only convey very subtle metabolic disturbances but could also play a causal role in disease pathogenesis.
Several technical limitations warrant mention. First, we acknowledge that our platform does not provide comprehensive coverage of the plasma lipidome. However, by focusing on abundant plasma lipids, we were able to measure more than 100 analytes, while using only 10 μl of valuable archived samples; this feature may facilitate its clinical implementation. Second, Kotronen et al. (28
) have shown that lipid profiling of distinct lipoprotein fractions can also inform the relationship between individual lipids and insulin resistance. Although such an approach can provide valuable biologic insights, lipoprotein fractionation is impractical for high-throughput biomarker applications. Finally, our platform does not provide absolute quantitation of lipid analytes. We note, however, that the major thrust of the present findings involves a pattern
of diabetes risk and the effect of insulin action on this pattern, as opposed to the absolute quantitation of any specific analyte. The design of assays for specific lipids (e.g., incorporating isotope-labeled standards and using chromatography specifically designed for TAG separation) will permit absolute quantitation and improve the precision of select measurements.
More work is required to extend and validate our findings. The case-control design of our study resulted in the selection of high-risk controls, i.e., individuals with similar baseline metabolic risk factors as cases but who did not later develop type 2 diabetes. As a result, our study is unable to provide formal estimation of the predictive ability of specific lipids in the general population, in which there is a wide spectrum of disease risk. In addition, our study samples included middle-aged to older individuals of predominantly European ancestry, which may limit the generalizability of our findings to younger individuals or other racial/ethnic groups. Although TAG profiling alone may not be sufficiently specific to identify individuals who go on to develop type 2 diabetes, it may contribute to clinical models of diabetes prediction that contain other variables. Thus, future efforts will be directed at measuring specific TAGs in more diverse cohorts and adjudicating to what extent these biomarkers add to established risk predictors at a population level. Finally, further study on how medications, diet, and exercise modulate the lipidome over time may provide insight into their salutary effects on metabolic risk.
In summary, we have applied LC/MS-based profiling to identify a lipidomic signature of diabetes risk. This pattern is most notable among TAGs and is at least in part attributable to the graded relationship between specific TAGs and insulin resistance. These findings, however, do not merely recapitulate available metrics of metabolic risk: select TAGs at each end of the risk signature identify individuals at either an increased or decreased risk of diabetes, above and beyond information provided by age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. Combining the positive- and negative-risk information in select TAGs further improves risk prediction. Future work will be required to more precisely assess the predictive findings in additional cohorts and to determine whether our findings represent an early marker, effector, or both, of nascent metabolic disease.