Aging and obesity are crucial contributing factors to the development and progressive worsening of T2DM. The large population of obese elderly people with diabetes has caused a tremendous health crisis worldwide. Using lipidomics, many lipid metabolites have been detected and implicated as critical components that can be used to explain the complex interactions between aging, obesity, insulin resistance (IR), and T2DM [6].
To identify associations between lipids and T2DM incidence, several large-scale lipidomic cohort studies have been performed in adults [25–28]. Given the large number of obese older adults with T2DM, this study only included overweight/obese elderly adults with NGT, IGT, and T2DM and presented a comprehensive lipidomic evaluation.
In this study, a high-coverage non-targeted absolute quantitative lipidomics approach was used to investigate the plasma lipidome profiles of 148 overweight/obese elderly participants who with NGT, IGT or T2DM. Our findings showed that patients with IGT and T2D had increased concentrations of DG, TG, PC, sphingomyelins (SM), and Cer species. These results are in agreement with the findings of cross-sectional studies, which revealed that IGT and T2D induced significantly elevated levels of PCs, DGs, and TGs [29, 30]. Our results are also in agreement with data from prospective studies, which showed that PCs, DGs, and TGs were positively associated with the progression to dysglycemia and T2DM [14, 25, 26, 31].
TGs and DGs showed the most significant association with T2D and IGT. Our analysis is in line with those of previous studies, which showed that TG(46:1), TG(48:1), TG(48:2), TG(51:1), and TG (52:1) were positively associated with T2DM and prediabetes in an American Indian cohort [27], and positively associated with T2DM in Chinese[26] and Caucasian cohorts [32].
Chain length and desaturation of fatty acid moieties in lipid molecules complicate the assignment of biological roles to lipid classes [33]. In the PREDIMED trial [34], odd-chain TGs (C53, C55) were negatively associated with diabetes risk, and consistent results were discovered in a large-scale prospective lipidomics study [26]. In the same study, Lu et al. described TGs with 48–50 carbon atom numbers and 2–3 double bonds as risk factors for the development of T2DM [26]. Additionally, a cluster of TG species with saturated and monounsaturated acyl chains were identified to be associated with the prevalence and incidence of DM [32, 35]. Increased levels of saturated long-chain fatty acids are known to contribute to insulin resistance and T2DM [36]. We systematically examined IGT- and T2DM-associated alterations in the number of carbon atoms and double bonds in the various lipid classes that were investigated. Significant changes in the number of carbon atoms and the degree of unsaturation were observed in the PCs and TGs. Patients with IGT and T2DM tend to have increased levels of TGs with longer carbon atom numbers (C44–50) and low double bond numbers (n [C = C] = 0–2).
TG- and DG-mediated insulin resistance is the unifying molecular mechanism that explains the most common forms of IR associated with obesity and aging, as well as T2DM [33, 37, 38]. Different views were put forward when researchers discovered that inhibiting PE production and the subsequent accumulation of DG and TG retained insulin sensitivity and increased mitochondrial biogenesis and muscle oxidative capacity in knockout mouse muscles [39]. They believe that phospholipids, rather than DGs or TGs, are probable modulators of IR in muscles [6].
We found that the levels of multiple glycerophospholipids, including PC (10:0/11:4), PC (14:0/10:1), PS (11:0/16:0), PS (11:0/18:0), PI (16:0/16:0), and PI (16:0/16:1), changed significantly in individuals with IGT and T2DM. Although previous studies have reported associations of PC, PE, PS, and PI species with IR, T2D, and related traits [26, 27, 35, 40], some studies have shown inconsistent results as PCs and PEs were increased in some studies [41, 42] but were decreased in others [30, 31]. In young adults (aged 18–34 years), independent of age and BMI, PI (16:0/16:0) and PI (16:0/16:1) were positively associated with insulin AUC in men and homeostatic model assessment of insulin resistance (HOMA-IR) in both women and men [40]. PI (16:0/16:1) was also found to be positively associated with prediabetes over a 5-year follow-up period [27].
Furthermore, for PCs, we observed that IGT and T2DM induced increased concentrations of even-chain (C32, C34, C36, C38, C40, and C52) PCs. In contrast to the IGT group, the levels of PCs with shorter chain lengths (C21, C24, C27, C29, and C30) decreased in the T2DM group. A relationship between diabetes risk and the carbon number and double bond content among PCs was also identified by Rhee et al. They depicted a downsloping pattern in which PCs with relatively lower carbon number and double bond content were most significantly elevated in patients with T2DM compared to the levels in controls [32]. LPCs, such as LPC (18:0), LPC (18:1), and LPC (18:2), were negatively correlated with T2DM, while no significant change in LPCs was found in the present study.
Cer is the precursor of ganglioside and SM, and lipidomic profiling has revealed relationships between their levels, aging, obesity, and diabetes [6, 18]. In the present study, we found that the levels of most Cers in the IGT group were reduced and the levels of gangliosides GM3 and SMs were elevated (Fig. 2b). Compared with IGT, T2DM induced a higher proportion of Cers, less increased SMs, and slightly decreased ganglioside GM3 (Fig. 3b). These results suggest a shift in the balance of sphingolipid metabolism as diabetes progresses. Further, saturated SMs (C34:0, C36:0, C38:0, C40:0) and unsaturated sphingomyelins (C34:1, C36:1, C42:3) were reported to be risk factors for IR and incident T2D among 1974 ethnically Chinese individuals [43]. Our study showed that the levels of SM(d36:0), SM(d38:0), SM(d39:0), SM(d42:7), and SM(d44:4) were higher in patients with T2DM but not in patients with IGT.
Machine learning algorithms have been increasingly recognized as enabling techniques for selecting biomarkers for various human diseases [44]. Finally, we successfully selected potential biomarkers to distinguish the NGT, IGT, and T2DM groups in overweight/obese elderly individuals using three machine learning algorithms. Interestingly, PA (18:2/10:4) and Cer (m32:0) were the common biomarkers of IGT and T2DM. The prediction models differentiate the three groups yielding good AUCs.
The current study had several limitations. First, although a high-coverage and the most flexible and efficient system we used, we carried out only non-targeted lipidomics analysis. The sample size of this study was also relatively small. Second, to ensure the stability and reliability of the lipidomics analysis, we carried out a strict quality control evaluation of the results, while the cross-sectional study that lacked prospective lipidomics data weakened the credibility of the results.
In summary, our high-coverage non-targeted absolute quantitative lipidomic analysis revealed novel lipidomic patterns in overweight/obese elderly individuals with IGT and T2DM. A panel of differential lipids was successfully identified as a potential biomarker in patients with IGT and T2DM. The lipidomic profile may improve our understanding of the extent and complexity of lipid dysregulation in obesity, aging, and diabetes and provide new insights into the underlying molecular mechanisms of diabetes.