N-(Carboxymethyl)lysine (CML) is an advanced glycation end product formed on protein by combined nonenzymatic glycation and oxidation (glycoxidation) reactions. We now report that CML is also formed during metal-catalyzed oxidation of polyunsaturated fatty acids in the presence of protein. During copper-catalyzed oxidation in vitro, the CML content of low density lipoprotein increased in concert with conjugated dienes but was independent of the presence of the Amadori compound, fructoselysine, on the protein. CML was also formed in a time-dependent manner in RNase incubated under aerobic conditions in phosphate buffer containing arachidonate or linoleate; only trace amounts of CML were formed from oleate. After 6 days of incubation the yield of CML in RNase from arachidonate was 0.7 mmol/mol lysine compared with only 0.03 mmol/mol lysine for protein incubated under the same conditions with glucose. Glyoxal, a known precursor of CML, was also formed during incubation of RNase with arachidonate. These results suggest that lipid peroxidation, as well as glycoxidation, may be an important source of CML in tissue proteins in vivo and that CML may be a general marker of oxidative stress and long term damage to protein in aging, atherosclerosis, and diabetes.

The insulin resistance syndrome (IRS) is associated with dyslipidemia and increased cardiovascular disease risk. A novel method for detailed analyses of lipoprotein subclass sizes and particle concentrations that uses nuclear magnetic resonance (NMR) of whole sera has become available. To define the effects of insulin resistance, we measured dyslipidemia using both NMR lipoprotein subclass analysis and conventional lipid panel, and insulin sensitivity as the maximal glucose disposal rate (GDR) during hyperinsulinemic clamps in 56 insulin sensitive (IS; mean ± SD: GDR 15.8 ± 2.0 mg · kg⁻¹ · min⁻¹, fasting blood glucose [FBG] 4.7 ± 0.3 mmol/l, BMI 26 ± 5), 46 insulin resistant (IR; GDR 10.2 ± 1.9, FBG 4.9 ± 0.5, BMI 29 ± 5), and 46 untreated subjects with type 2 diabetes (GDR 7.4 ± 2.8, FBG 10.8 ± 3.7, BMI 30 ± 5). In the group as a whole, regression analyses with GDR showed that progressive insulin resistance was associated with an increase in VLDL size (r = −0.40) and an increase in large VLDL particle concentrations (r = −0.42), a decrease in LDL size (r = 0.42) as a result of a marked increase in small LDL particles (r = −0.34) and reduced large LDL (r = 0.34), an overall increase in the number of LDL particles (r = −0.44), and a decrease in HDL size (r = 0.41) as a result of depletion of large HDL particles (r = 0.38) and a modest increase in small HDL (r = −0.21; all P < 0.01). These correlations were also evident when only normoglycemic individuals were included in the analyses (i.e., IS + IR but no diabetes), and persisted in multiple regression analyses adjusting for age, BMI, sex, and race. Discontinuous analyses were also performed. When compared with IS, the IR and diabetes subgroups exhibited a two- to threefold increase in large VLDL particle concentrations (no change in medium or small VLDL), which produced an increase in serum triglycerides; a decrease in LDL size as a result of an increase in small and a reduction in large LDL subclasses, plus an increase in overall LDL particle concentration, which together led to no difference (IS versus IR) or a minimal difference (IS versus diabetes) in LDL cholesterol; and a decrease in large cardioprotective HDL combined with an increase in the small HDL subclass such that there was no net significant difference in HDL cholesterol. We conclude that 1) insulin resistance had profound effects on lipoprotein size and subclass particle concentrations for VLDL, LDL, and HDL when measured by NMR; 2) in type 2 diabetes, the lipoprotein subclass alterations are moderately exacerbated but can be attributed primarily to the underlying insulin resistance; and 3) these insulin resistance-induced changes in the NMR lipoprotein subclass profile predictably increase risk of cardiovascular disease but were not fully apparent in the conventional lipid panel. It will be important to study whether NMR lipoprotein subclass parameters can be used to manage risk more effectively and prevent cardiovascular disease in patients with the IRS.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image–language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP’s accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.