Gestational diabetes mellitus (GDM) develops during pregnancy, affecting 1–14% of all pregnancies depending on diagnostic criteria and the population characteristics (Chen et al., 2018; Melchior et al., 2017). The majority of women with a history of GDM were not known to have overt diabetes before pregnancy and return to non-diabetes post-delivery. However, women with a history of GDM are ~7 times more likely to develop type 2 diabetes (T2D) during the child-bearing years compared to women who had no previous GDM (Chen et al., 2018; Bellamy et al., 2009; Gunderson et al., 2007). In fact, it is estimated that 35–50% of women with GDM may progress to T2D within 10 years after delivery (Bellamy et al., 2009; Tobias, 2018). Within 15 to 25 years, the lifetime maternal risk for overt diabetes is estimated to reach >50% (American Diabetes Association, 2019; Kim et al., 2002). Therefore, it is critical to uncover the underlying metabolic changes and understand the distinctive pathophysiology in T2D progression/development following GDM.

In the past decade, omics-based approaches have been used to discover novel metabolic fluctuations in humans, providing insight into pathophysiology of disease and identifying biomarkers of future disease including diabetes (Sas et al., 2015; Khan et al., 2019; Allalou et al., 2016). In particular, lipidomics has emerged as a more specialized omics platform that enables the measurement of a wide spectrum of lipid species. This approach has greatly expanded our understanding of the complexity of lipid dysregulation in metabolic diseases. Recently, an increasing number of lipidomics studies have aimed to link lipid dysregulation to diabetes pathology (Meikle et al., 2013; Lu et al., 2018; Rhee et al., 2011; Lu et al., 2019; Meikle et al., 2014; Alshehry et al., 2016; Lu et al., 2016; Suvitaival et al., 2018; Razquin et al., 2018). In the Framingham Heart Study cohort, more than 100 lipid analytes were measured and a group of triacylglycerols (low total carbon number and carbon double bonds) were found to be associated with increased risk of T2D (Rhee et al., 2011). In the PREDIMED trial, 207 plasma lipids were measured in which lysophosphatidylcholines (LPCs), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesteryl esters (CEs) were found to be inversely associated with T2D risk while triacylglycerols (TAGs), diacylglycerol (DAGs) and phosphatidylethanolamine (PEs) were positively associated with T2D risk (Razquin et al., 2018). A total of 277 plasma lipids were analyzed using a lipidomics approach in Finnish males in which five lipids were selected to predict progression to Type 2 diabetes (T2D) (Suvitaival et al., 2018). In this cohort, higher levels of specific TAGs and diacyl- phospholipids and lower levels of alkylacyl-phosphatidylcholines were also observed in those who progressed to T2D (Suvitaival et al., 2018). In a very recent lipidomics study of a Chinese cohort, 250 lipids were tested and 38 significantly associated with T2D risk, including TAGs, LPCs, PCs, polyunsaturated fatty acid (PUFA)–plasmalogen phosphatidylethanolamines (PUFA-PEps), and CEs (Lu et al., 2019). A lipid panel including six lipids significantly improved T2D prediction compared to that achieved by conventional risk factors (Lu et al., 2019). In all of these studies, the positive association of TAG/DAG and T2D risk was consistently reported. However, a convergence on other specific lipids were not evident. This could be due to the differences in study design, cohort background and methodology including, importantly, limitations in coverage - expressed lipids in each study were not consistent.

Lipidomics has also been performed in GDM cohorts, including the measurement of 181 lipids in serum samples obtained from GDM women in their early second trimester. Four lipid biomarkers (TG(51:1), TG(48:1), PC(32:1), and PCae(40:4)) were identified for GDM prediction with a moderate accuracy 71% (Lu et al., 2016). Another lipidomic study measuring ~300 lipid species in blood samples from 104 women with recent GDM at 12 weeks post-delivery, of whom 21 cases later developed T2D, showed 84% accuracy in T2D prediction based on three lipids [i.e., PE(P-36:2), PS38:4, CE20:4] in combination with six other risk factors (i.e., age, BMI, prenatal fasting glucose, postpartum fasting glucose, total triglycerides, and total cholesterol) (Lappas et al., 2015). Our team identified seven lipids from early postpartum blood samples to predict later incident T2D with an AUC of 0.92 in a relatively small subset of women with recent GDM in our large prospective cohort (55 matched pairs of incident cases controls) (Khan et al., 2019). To date however, no consensus has been achieved in terms of lipidomic dysregulation in GDM progression to T2D, likely due to limitation in the coverage of lipidome, cohort size, clinical data including diagnosis and follow-up years. Lipidomic changes within a large prospective cohort of women with GDM followed from the early postpartum period have not been evaluated. A comprehensive evaluation of lipidomic changes in relation to progression to T2D could elucidate the pathogenesis of transition from GDM to T2D, and thereby improve our understanding of the clinical targets for therapeutic interventions.

In the present study, lipidomics of 1008 lipid species from 15 lipid classes and 296 fatty acids was measured in a well-characterised prospective cohort with recent GDM pregnancy and no diabetes, followed from 6 to 9 weeks post-delivery (baseline), retested with OGTTs for 2 years and followed via clinical laboratory testing and diagnoses up to 8 years later. Our aims were to systematically investigate lipidomic dysregulation in the transition from no diabetes to incident T2D following a GDM pregnancy and uncover lipid markers that may facilitate the early prediction of T2D incidence with clinical risk factors.

In the present study, lipidomic profiling was used to assess the lipid changes at early post-partum (6 to 9 weeks) in a well-characterized, racially and ethnically diverse prospective cohort of postpartum women with recent GDM. A lipid signature associated with future diabetes risk was uncovered which contributes new knowledge to understanding the aetiology of diabetes in women associated with GDM. Importantly, our data indicate that women with recent GDM who later develop new onset T2D have clear differences in their lipidome compared to controls after delivery. This clearly shows they already exhibit lipid dysregulation in the early post-partum period.

Among the 311 lipids positively associated with progression to T2D, we found 293 belonging to TAG classes. This is equivalent to an impressive 57.2% of all measured TAGs (293 out of 512) (Figure 2B). In addition, among the lipids associated with the most significant T2D risk, 91% of them were TAGs (97 out of 107) (Figure 3A). This finding fits our clinical measurements showing elevated TAGs in T2D incident cases (Table 1) and is consistent with other studies (Khan et al., 2019; Meikle et al., 2013; Rhee et al., 2011; Lu et al., 2019; Meikle et al., 2014; Alshehry et al., 2016; Suvitaival et al., 2018; Razquin et al., 2018; Lappas et al., 2015). TAGs, belonging to neutral lipids, are the energy storage in adipocytes and are an efficient energy source for muscle. In plasma, TAGs enable the bidirectional flow of fat from adipose tissue storage and blood glucose from the liver. Therefore, it is not surprising that TAGs outweigh other lipids as the dominant lipid species in terms of reflecting the changes of lipid metabolism in the body. The source of TAGs could be from food intake or endogenous TAG biosynthesis, such as lipogenesis. Our KEGG analysis demonstrated that the glycerolipid metabolism pathway was upregulated, suggesting the accumulation of TAGs could be attributed to the up-regulation of TAG biosynthesis (Figure 5). It was reported high sugar could stimulate de novo lipogenesis in liver thereby increasing serum TAG level (Schwarz et al., 2003). This process could be activated directly through transcriptional factor carbohydrate responsive element binding protein (ChREBP) to promote expression of lipogenic enzymes. Alternatively, lipogenesis could also be regulated by insulin through sterol regulatory element binding protein-1 (SREBP1). The elevated level of plasma hexose and insulin in those incident T2D cases at baseline could be associated with the enhanced endogenous lipogenesis.

In contrast, classes of glycerophospholipids (PC and LPC classes) are inversely associated with T2D risk (Figure 2B). Glycerophospholipids (through DAG) and TAGs share the same precursor glycerol-3-phosphate. Therefore, the downward trend in glycerophospholipids could be linked to the up-regulation of TAG biosynthesis. In addition to the phospholipids, an impressive 62% of measured sphingolipids (31 out of 50 tested) were inversely associated with T2D risk (Figure 2B). Particularly SM(18:1), SM(20:1), SM(24:1), HCER(24:1), and LCER(16:0) were among the lipids with the most significant risk associated with diabetes (Figure 3A). KEGG analysis revealed that sphingolipid metabolism was the most significantly down-regulated (p=2.11E-05), further supporting the inverse association between sphingolipids and diabetes risk. So far, the relationship between sphingolipids and T2D risk has not been unequivocally ascertained. Several cross-sectional clinical studies have shown that CERs (upstream node of the sphingolipids pathway) are elevated in obese subjects with T2D (Meikle et al., 2013; Lemaitre et al., 2018; Lopez et al., 2013; Haus et al., 2009). We and others, however, have previously shown a negative association of SMs (downstream node of the whole pathway) with diabetes risk (Khan et al., 2019; Allalou et al., 2016; Razquin et al., 2018; Fall et al., 2016; Floegel et al., 2013). Further biological testing in humans and models of diabetes risk are required to validate the association between sphingolipids and diabetes onset.

Glycerophospholipids (through DAG) and TAGs share the same precursor glycerol-3-phosphate (G3P). The higher G3P induced by higher plasma glucose levels could shift the acyl-CoA to lipogenesis from sphingolipids and phospholipids pathways. Therefore, in those incident T2D cases, the downward trend in glycerophospholipids and sphingolipids could be associated with the up-regulation of TAG biosynthesis. In normal physiological conditions, de novo lipogenesis mainly occurs in the liver and adipose tissue and is a minor contributor to serum TAG homeostasis. However, an up-regulated lipogenesis could break the balance causing lipidemia. In addition, down-regulation of glycerophospholipids and sphingolipids biosynthesis impairs the integrity of cell membrane structure, which might contribute to insulin resistance. Although higher glucose level could correlate with higher TAG, TAG is not simply an indirect measure of glucose. Instead, increased TAG along with decreased phospholipids and sphingolipids could be an early sign of up-regulated endogenous de novo lipogenesis, a driving force of T2D.

Investigating the composition of the fatty acids in the lipids showed long chain SFA myristic acid (C14:0) and palmitic acid (C16:0) were positively associated with T2D risk. Previously, palmitic acids were reported to cause pancreatic beta cell dysfunction and were shown to be associated with diabetes (Oh et al., 2018; Nemecz et al., 2018). A previous study on a large prospective cohort EPIC-InterAct case suggested that even-chain SFA in phospholipids were positively associated with diabetes risk while odd-chain SFA had a negative association (Forouhi et al., 2014). Similarly, we detected odd-chain SFA from phospholipids were negatively associated with T2D risk. However, the association between even-chain SFAs and T2D risk was more complicated depending on the lipid classes from which they were derived. Even-chain SFAs from glycerol lipids (TAGs and DAGs) were positively associated with T2D risk while those from sphingolipids had a negative association. No significant association to T2D risk was detected in even-chain SFAs from phospholipids (Figure 4B). Odd-chain SFAs (C15:0 and C17:0) are mainly exogenously derived from dairy fat intake (Smedman et al., 1999; Wolk et al., 1998; Hodson et al., 2008). In contrast, even-chain SFAs are from an endogenous source, such as increased lipolysis from adipose tissue or de-novo lipogenesis from excess carbohydrates (Hodson et al., 2008; Siler et al., 1999; Hudgins et al., 1998; King et al., 2006; Hudgins et al., 1996).

In addition to the carbon numbers of fatty acids, we also showed the association between the degree of fatty acid unsaturation (number of double bonds) and diabetes. MUFAs, particularly those from sphingolipids, were negatively associated with T2D risk; however，PUFAs from TAGs were positively associated. These findings suggest that fatty acids from different sources and lipid classes have opposite influences on diabetes risk. This would provide novel insight into the role of lipid metabolism in diabetes onset and further develop guidelines for a healthy diet to prevent diabetes.

In addition to investigating the pathology of diabetes onset, we also developed an 11-lipid panel to predict future diabetes. Traditional clinical parameters such as FPG and 2hPG can achieve a prediction power AUC of 0.775. However, when we combine our lipid panel with FPG and 2hPG, we can improve the prediction power from 0.775 to 0.842. Among those 11 lipids, 10 belong to TAG and one is PC. These results suggest that specific metabolites of the TAG and PC classes play important roles in the early detection of women who will transition from GDM to T2D. Since diabetes is a metabolic disorder involving dysmetabolism of carbohydrate, lipids and amino acids, it is not surprising that biomarkers of both carbohydrate and lipid metabolism can improve the predictive power over carbohydrate metabolism alone. Based on our data, we would envision that adding a specific lipidomic signature to existing clinical parameters for testing, perhaps including other metabolites (ie. biogenic amines and amino acids) will provide a more accurate assessment of future T2D risk. Nonetheless, our study provides an important clinical application for early prediction of diabetes when most GDM women return to normoglycemia after delivery. The early prediction will contribute to early intervention and prevention of diabetes.

Lipidomic data have been deposited in Harvard Dataverse: https://doi.org/10.7910/DVN/KUDDSF. Source data files have been provided for Figures 2, 3, 4 and 6 as supporting files.

1. 1. Wheeler MB

   (2020) Harvard Dataverse

   SWIFT lipidomics data.

   https://doi.org/10.7910/DVN/KUDDSF

1. 1. Allalou A
   2. Nalla A
   3. Prentice KJ
   4. Liu Y
   5. Zhang M
   6. Dai FF
   7. Ning X
   8. Osborne LR
   9. Cox BJ
   10. Gunderson EP
   11. Wheeler MB

   (2016) A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes

   Diabetes 65:2529–2539.

   https://doi.org/10.2337/db15-1720

   • PubMed
   • Google Scholar
2. 1. Alshehry ZH
   2. Mundra PA
   3. Barlow CK
   4. Mellett NA
   5. Wong G
   6. McConville MJ
   7. Simes J
   8. Tonkin AM
   9. Sullivan DR
   10. Barnes EH
   11. Nestel PJ
   12. Kingwell BA
   13. Marre M
   14. Neal B
   15. Poulter NR
   16. Rodgers A
   17. Williams B
   18. Zoungas S
   19. Hillis GS
   20. Chalmers J
   21. Woodward M
   22. Meikle PJ

   (2016) Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus

   Circulation 134:1637–1650.

   https://doi.org/10.1161/CIRCULATIONAHA.116.023233

   • PubMed
   • Google Scholar
3. 1. American Diabetes Association

   (2019) 14. management of diabetes in pregnancy: Standards of Medical Care in Diabetes-2019

   Diabetes Care 42:S165–S172.

   https://doi.org/10.2337/dc19-S014

   • PubMed
   • Google Scholar
4. 1. Bellamy L
   2. Casas J-P
   3. Hingorani AD
   4. Williams D

   (2009) Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis

   The Lancet 373:1773–1779.

   https://doi.org/10.1016/S0140-6736(09)60731-5

   • Google Scholar
5. 1. Carpenter MW
   2. Coustan DR

   (1982) Criteria for screening tests for gestational diabetes

   American Journal of Obstetrics and Gynecology 144:768–773.

   https://doi.org/10.1016/0002-9378(82)90349-0

   • Google Scholar
6. 1. Chen Q
   2. Francis E
   3. Hu G
   4. Chen L

   (2018) Metabolomic profiling of women with gestational diabetes mellitus and their offspring: Review of metabolomics studies

   Journal of Diabetes and its Complications 32:512–523.

   https://doi.org/10.1016/j.jdiacomp.2018.01.007

   • Google Scholar
7. 1. Chong J
   2. Soufan O
   3. Li C
   4. Caraus I
   5. Li S
   6. Bourque G
   7. Wishart DS
   8. Xia J

   (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

   Nucleic Acids Research 46:W486–W494.

   https://doi.org/10.1093/nar/gky310

   • Google Scholar
8. 1. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus

   (2003) Report of the expert committee on the diagnosis and classification of diabetes mellitus

   Diabetes Care 26 Suppl 1:S5–S20.

   https://doi.org/10.2337/diacare.26.2007.s5

   • PubMed
   • Google Scholar
9. 1. Fall T
   2. Salihovic S
   3. Brandmaier S
   4. Nowak C
   5. Ganna A
   6. Gustafsson S
   7. Broeckling CD
   8. Prenni JE
   9. Kastenmüller G
   10. Peters A
   11. Magnusson PK
   12. Wang-Sattler R
   13. Giedraitis V
   14. Berne C
   15. Gieger C
   16. Pedersen NL
   17. Ingelsson E
   18. Lind L

   (2016) Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes

   Diabetologia 59:2114–2124.

   https://doi.org/10.1007/s00125-016-4041-1

   • Google Scholar
10. 1. Floegel A
    2. Stefan N
    3. Yu Z
    4. Muhlenbruch K
    5. Drogan D
    6. Joost H-G
    7. Fritsche A
    8. Haring H-U
    9. Hrabe de Angelis M
    10. Peters A
    11. Roden M
    12. Prehn C
    13. Wang-Sattler R
    14. Illig T
    15. Schulze MB
    16. Adamski J
    17. Boeing H
    18. Pischon T

    (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach

    Diabetes 62:639–648.

    https://doi.org/10.2337/db12-0495

    • Google Scholar
11. 1. Forouhi NG
    2. Koulman A
    3. Sharp SJ
    4. Imamura F
    5. Kröger J
    6. Schulze MB
    7. Crowe FL
    8. Huerta JM
    9. Guevara M
    10. Beulens JWJ
    11. van Woudenbergh GJ
    12. Wang L
    13. Summerhill K
    14. Griffin JL
    15. Feskens EJM
    16. Amiano P
    17. Boeing H
    18. Clavel-Chapelon F
    19. Dartois L
    20. Fagherazzi G
    21. Franks PW
    22. Gonzalez C
    23. Jakobsen MU
    24. Kaaks R
    25. Key TJ
    26. Khaw K-T
    27. Kühn T
    28. Mattiello A
    29. Nilsson PM
    30. Overvad K
    31. Pala V
    32. Palli D
    33. Quirós JR
    34. Rolandsson O
    35. Roswall N
    36. Sacerdote C
    37. Sánchez M-J
    38. Slimani N
    39. Spijkerman AMW
    40. Tjonneland A
    41. Tormo M-J
    42. Tumino R
    43. van der A DL
    44. van der Schouw YT
    45. Langenberg C
    46. Riboli E
    47. Wareham NJ

    (2014) Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study

    The Lancet Diabetes & Endocrinology 2:810–818.

    https://doi.org/10.1016/S2213-8587(14)70146-9

    • Google Scholar
12. 1. Gunderson EP
    2. Lewis CE
    3. Tsai AL
    4. Chiang V
    5. Carnethon M
    6. Quesenberry CP
    7. Sidney S

    (2007) A 20-year prospective study of childbearing and incidence of diabetes in young women, controlling for glycemia before conception: the coronary artery risk development in young adults (CARDIA) Study

    Diabetes 56:2990–2996.

    https://doi.org/10.2337/db07-1024

    • PubMed
    • Google Scholar
13. 1. Gunderson EP
    2. Matias SL
    3. Hurston SR
    4. Dewey KG
    5. Ferrara A
    6. Quesenberry CP
    7. Lo JC
    8. Sternfeld B
    9. Selby JV

    (2011) Study of Women, Infant feeding, and Type 2 diabetes mellitus after GDM pregnancy (SWIFT), a prospective cohort study: methodology and design

    BMC Public Health 11:952.

    https://doi.org/10.1186/1471-2458-11-952

    • Google Scholar
14. 1. Gunderson EP
    2. Hedderson MM
    3. Chiang V
    4. Crites Y
    5. Walton D
    6. Azevedo RA
    7. Fox G
    8. Elmasian C
    9. Young S
    10. Salvador N
    11. Lum M
    12. Quesenberry CP
    13. Lo JC
    14. Sternfeld B
    15. Ferrara A
    16. Selby JV

    (2012) Lactation intensity and postpartum maternal glucose tolerance and insulin resistance in women with recent GDM: the SWIFT cohort

    Diabetes Care 35:50–56.

    https://doi.org/10.2337/dc11-1409

    • PubMed
    • Google Scholar
15. 1. Gunderson EP
    2. Kim C
    3. Quesenberry CP
    4. Marcovina S
    5. Walton D
    6. Azevedo RA
    7. Fox G
    8. Elmasian C
    9. Young S
    10. Salvador N
    11. Lum M
    12. Crites Y
    13. Lo JC
    14. Ning X
    15. Dewey KG

    (2014) Lactation intensity and fasting plasma lipids, lipoproteins, non-esterified free fatty acids, leptin and adiponectin in postpartum women with recent gestational diabetes mellitus: The SWIFT cohort

    Metabolism 63:941–950.

    https://doi.org/10.1016/j.metabol.2014.04.006

    • Google Scholar
16. 1. Gunderson EP
    2. Hurston SR
    3. Ning X
    4. Lo JC
    5. Crites Y
    6. Walton D
    7. Dewey KG
    8. Azevedo RA
    9. Young S
    10. Fox G
    11. Elmasian CC
    12. Salvador N
    13. Lum M
    14. Sternfeld B
    15. Quesenberry CP
    16. Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy Investigators

    (2015) Lactation and progression to type 2 diabetes mellitus after gestational diabetes mellitus: a prospective cohort study

    Annals of Internal Medicine 163:889–898.

    https://doi.org/10.7326/M15-0807

    • PubMed
    • Google Scholar
17. 1. Haus JM
    2. Kashyap SR
    3. Kasumov T
    4. Zhang R
    5. Kelly KR
    6. Defronzo RA
    7. Kirwan JP

    (2009) Plasma ceramides are elevated in obese subjects with type 2 diabetes and correlate with the severity of insulin resistance

    Diabetes 58:337–343.

    https://doi.org/10.2337/db08-1228

    • PubMed
    • Google Scholar
18. 1. Hodson L
    2. Skeaff CM
    3. Fielding BA

    (2008) Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake

    Progress in Lipid Research 47:348–380.

    https://doi.org/10.1016/j.plipres.2008.03.003

    • Google Scholar
19. 1. Hudgins LC
    2. Hellerstein M
    3. Seidman C
    4. Neese R
    5. Diakun J
    6. Hirsch J

    (1996) Human fatty acid synthesis is stimulated by a eucaloric low fat, high carbohydrate diet

    Journal of Clinical Investigation 97:2081–2091.

    https://doi.org/10.1172/JCI118645

    • Google Scholar
20. 1. Hudgins LC
    2. Seidman CE
    3. Diakun J
    4. Hirsch J

    (1998) Human fatty acid synthesis is reduced after the substitution of dietary starch for sugar

    The American Journal of Clinical Nutrition 67:631–639.

    https://doi.org/10.1093/ajcn/67.4.631

    • Google Scholar
21. 1. Khan SR
    2. Mohan H
    3. Liu Y
    4. Batchuluun B
    5. Gohil H
    6. Al Rijjal D
    7. Manialawy Y
    8. Cox BJ
    9. Gunderson EP
    10. Wheeler MB

    (2019) The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes

    Diabetologia 62:687–703.

    https://doi.org/10.1007/s00125-018-4800-2

    • Google Scholar
22. 1. Kim C
    2. Newton KM
    3. Knopp RH

    (2002) Gestational diabetes and the incidence of type 2 diabetes: a systematic review

    Diabetes Care 25:1862–1868.

    https://doi.org/10.2337/diacare.25.10.1862

    • PubMed
    • Google Scholar
23. 1. King IB
    2. Lemaitre RN
    3. Kestin M

    (2006) Effect of a low-fat diet on fatty acid composition in red cells, plasma phospholipids, and cholesterol esters: investigation of a biomarker of total fat intake

    The American Journal of Clinical Nutrition 83:227–236.

    https://doi.org/10.1093/ajcn/83.2.227

    • Google Scholar
24. 1. Lappas M
    2. Mundra PA
    3. Wong G
    4. Huynh K
    5. Jinks D
    6. Georgiou HM
    7. Permezel M
    8. Meikle PJ

    (2015) The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics

    Diabetologia 58:1436–1442.

    https://doi.org/10.1007/s00125-015-3587-7

    • PubMed
    • Google Scholar
25. 1. Lemaitre RN
    2. Yu C
    3. Hoofnagle A
    4. Hari N
    5. Jensen PN
    6. Fretts AM
    7. Umans JG
    8. Howard BV
    9. Sitlani CM
    10. Siscovick DS
    11. King IB
    12. Sotoodehnia N
    13. McKnight B

    (2018) Circulating sphingolipids, insulin, HOMA-IR, and HOMA-B: the strong heart family study

    Diabetes 67:1663–1672.

    https://doi.org/10.2337/db17-1449

    • PubMed
    • Google Scholar
26. 1. Löfgren L
    2. Ståhlman M
    3. Forsberg G-B
    4. Saarinen S
    5. Nilsson R
    6. Hansson GI

    (2012) The BUME method: a novel automated chloroform-free 96-well total lipid extraction method for blood plasma

    Journal of Lipid Research 53:1690–1700.

    https://doi.org/10.1194/jlr.D023036

    • Google Scholar
27. 1. Lopez X
    2. Goldfine AB
    3. Holland WL
    4. Gordillo R
    5. Scherer PE

    (2013) Plasma ceramides are elevated in female children and adolescents with type 2 diabetes

    Journal of Pediatric Endocrinology and Metabolism 26:995–998.

    https://doi.org/10.1515/jpem-2012-0407

    • Google Scholar
28. 1. Lu L
    2. Koulman A
    3. Petry CJ
    4. Jenkins B
    5. Matthews L
    6. Hughes IA
    7. Acerini CL
    8. Ong KK
    9. Dunger DB

    (2016) An unbiased lipidomics approach identifies early second trimester lipids predictive of maternal glycemic traits and gestational diabetes mellitus

    Diabetes Care 39:2232–2239.

    https://doi.org/10.2337/dc16-0863

    • PubMed
    • Google Scholar
29. 1. Lu Y
    2. Wang Y
    3. Zou L
    4. Liang X
    5. Ong CN
    6. Tavintharan S
    7. Yuan JM
    8. Koh WP
    9. Pan A

    (2018) Serum lipids in association with type 2 diabetes risk and prevalence in a chinese population

    The Journal of Clinical Endocrinology & Metabolism 103:671–680.

    https://doi.org/10.1210/jc.2017-02176

    • PubMed
    • Google Scholar
30. 1. Lu J
    2. Lam SM
    3. Wan Q
    4. Shi L
    5. Huo Y
    6. Chen L
    7. Tang X
    8. Li B
    9. Wu X
    10. Peng K
    11. Li M
    12. Wang S
    13. Xu Y
    14. Xu M
    15. Bi Y
    16. Ning G
    17. Shui G
    18. Wang W

    (2019) High-Coverage targeted lipidomics reveals novel serum lipid predictors and lipid pathway dysregulation antecedent to type 2 diabetes onset in Normoglycemic chinese adults

    Diabetes Care 42:2117–2126.

    https://doi.org/10.2337/dc19-0100

    • PubMed
    • Google Scholar
31. 1. Meikle PJ
    2. Wong G
    3. Barlow CK
    4. Weir JM
    5. Greeve MA
    6. MacIntosh GL
    7. Almasy L
    8. Comuzzie AG
    9. Mahaney MC
    10. Kowalczyk A
    11. Haviv I
    12. Grantham N
    13. Magliano DJ
    14. Jowett JB
    15. Zimmet P
    16. Curran JE
    17. Blangero J
    18. Shaw J

    (2013) Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes

    PLOS ONE 8:e74341.

    https://doi.org/10.1371/journal.pone.0074341

    • PubMed
    • Google Scholar
32. 1. Meikle PJ
    2. Wong G
    3. Barlow CK
    4. Kingwell BA

    (2014) Lipidomics: Potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease

    Pharmacology & Therapeutics 143:12–23.

    https://doi.org/10.1016/j.pharmthera.2014.02.001

    • Google Scholar
33. 1. Melchior H
    2. Kurch-Bek D
    3. Mund M

    (2017) The prevalence of gestational diabetes

    Deutsches ÄRzteblatt International 114:412–418.

    https://doi.org/10.3238/arztebl.2017.0412

    • Google Scholar
34. 1. Nemecz M
    2. Constantin A
    3. Dumitrescu M
    4. Alexandru N
    5. Filippi A
    6. Tanko G
    7. Georgescu A

    (2018) The distinct effects of palmitic and oleic acid on pancreatic beta cell function: the elucidation of associated mechanisms and effector molecules

    Frontiers in Pharmacology 9:1554.

    https://doi.org/10.3389/fphar.2018.01554

    • PubMed
    • Google Scholar
35. 1. Oh YS
    2. Bae GD
    3. Baek DJ
    4. Park EY
    5. Jun HS

    (2018) Fatty Acid-Induced lipotoxicity in pancreatic Beta-Cells during development of type 2 diabetes

    Frontiers in Endocrinology 9:384.

    https://doi.org/10.3389/fendo.2018.00384

    • PubMed
    • Google Scholar
36. 1. Razquin C
    2. Toledo E
    3. Clish CB
    4. Ruiz-Canela M
    5. Dennis C
    6. Corella D
    7. Papandreou C
    8. Ros E
    9. Estruch R
    10. Guasch-Ferré M
    11. Gómez-Gracia E
    12. Fitó M
    13. Yu E
    14. Lapetra J
    15. Wang D
    16. Romaguera D
    17. Liang L
    18. Alonso-Gómez A
    19. Deik A
    20. Bullo M
    21. Serra-Majem L
    22. Salas-Salvadó J
    23. Hu FB
    24. Martínez-González MA

    (2018) Plasma lipidomic profiling and risk of type 2 diabetes in the PREDIMED trial

    Diabetes Care 41:2617–2624.

    https://doi.org/10.2337/dc18-0840

    • PubMed
    • Google Scholar
37. 1. Rhee EP
    2. Cheng S
    3. Larson MG
    4. Walford GA
    5. Lewis GD
    6. McCabe E
    7. Yang E
    8. Farrell L
    9. Fox CS
    10. O’Donnell CJ
    11. Carr SA
    12. Vasan RS
    13. Florez JC
    14. Clish CB
    15. Wang TJ
    16. Gerszten RE

    (2011) Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans

    Journal of Clinical Investigation 121:1402–1411.

    https://doi.org/10.1172/JCI44442

    • Google Scholar
38. 1. Sas KM
    2. Karnovsky A
    3. Michailidis G
    4. Pennathur S

    (2015) Metabolomics and Diabetes: Analytical and Computational Approaches

    Diabetes 64:718–732.

    https://doi.org/10.2337/db14-0509

    • Google Scholar
39. 1. Schwarz J-M
    2. Linfoot P
    3. Dare D
    4. Aghajanian K

    (2003) Hepatic de novo lipogenesis in normoinsulinemic and hyperinsulinemic subjects consuming high-fat, low-carbohydrate and low-fat, high-carbohydrate isoenergetic diets

    The American Journal of Clinical Nutrition 77:43–50.

    https://doi.org/10.1093/ajcn/77.1.43

    • Google Scholar
40. 1. Siler SQ
    2. Neese RA
    3. Hellerstein MK

    (1999) De novo lipogenesis, lipid kinetics, and whole-body lipid balances in humans after acute alcohol consumption

    The American Journal of Clinical Nutrition 70:928–936.

    https://doi.org/10.1093/ajcn/70.5.928

    • PubMed
    • Google Scholar
41. 1. Smedman AEM
    2. Gustafsson I-B
    3. Berglund LGT
    4. Vessby BOH

    (1999) Pentadecanoic acid in serum as a marker for intake of milk fat: relations between intake of milk fat and metabolic risk factors

    The American Journal of Clinical Nutrition 69:22–29.

    https://doi.org/10.1093/ajcn/69.1.22

    • Google Scholar
42. 1. Suvitaival T
    2. Bondia-Pons I
    3. Yetukuri L
    4. Pöhö P
    5. Nolan JJ
    6. Hyötyläinen T
    7. Kuusisto J
    8. Orešič M

    (2018) Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men

    Metabolism 78:1–12.

    https://doi.org/10.1016/j.metabol.2017.08.014

    • Google Scholar
43. 1. Tobias DK

    (2018) Prediction and prevention of type 2 diabetes in women with a history of GDM

    Current Diabetes Reports 18:78.

    https://doi.org/10.1007/s11892-018-1063-8

    • PubMed
    • Google Scholar
44. 1. Wolk A
    2. Vessby B
    3. Ljung H
    4. Barrefors P

    (1998) Evaluation of a biological marker of dairy fat intake

    The American Journal of Clinical Nutrition 68:291–295.

    https://doi.org/10.1093/ajcn/68.2.291

    • Google Scholar

Author details

1. Mi Lai

   Department of Physiology, Faculty of Medicine, University of Toronto, Ontario, Canada

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Dana Al Rijjal

   Department of Physiology, Faculty of Medicine, University of Toronto, Ontario, Canada

   Contribution

   Validation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Hannes L Röst

   Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Ontario, Canada

   Contribution

   Software, Supervision, Methodology, Writing - review and editing

   For correspondence

   hannes.rost@utoronto.ca

   Competing interests

   No competing interests declared

4. Feihan F Dai

   Department of Physiology, Faculty of Medicine, University of Toronto, Ontario, Canada

   Contribution

   Conceptualization, Supervision, Validation, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   f.dai@utoronto.ca

   Competing interests

   No competing interests declared

5. Erica P Gunderson

   Kaiser Permanente Northern California, Division of Research, Oakland, United States

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - review and editing

   For correspondence

   Erica.Gunderson@kp.org

   Competing interests

   Erica P Gunderson is affiliated with Kaiser Permanente. EPG has declared a research grant from Janssen Pharmaceuticals Company. EPG has no other financial interests to declare.

6. Michael B Wheeler

   1. Department of Physiology, Faculty of Medicine, University of Toronto, Ontario, Canada
   2. Advanced Diagnostics, Metabolism, Toronto General Research Institute, Ontario, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing - review and editing

   For correspondence

   michael.wheeler@utoronto.ca

   Competing interests

   MBW has declared a research grant from Janssen Pharmaceuticals Company

   "This ORCID iD identifies the author of this article:" 0000-0002-7480-7267

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