Search Results (100)

Type 2 diabetes mellitus (T2DM) involves chronic hyperglycemia, insulin resistance, low-grade inflammation, and oxidative stress that drive cardiometabolic and renal damage despite current therapies. Sodium–glucose cotransporter (SGLT) inhibitors have reshaped the treatment landscape, but residual risk and safety concerns highlight the need for new agents that combine glucose-lowering efficacy with redox–inflammatory modulation. LASSBio-1986 is a synthetic N-acylhydrazone (NAH) derivative designed as a gliflozin-like scaffold with the potential to interact with SGLT1/2 while also influencing oxidative and inflammatory pathways. Here, we integrated in silico and in vivo approaches to characterize LASSBio-1986 as a multifunctional antidiabetic lead in murine models of glucose dysregulation. PASS and target class prediction suggested a broad activity spectrum and highlighted transporter- and stress-related pathways. Molecular docking indicated high-affinity binding to both SGLT1 and SGLT2, with a modest energetic preference for SGLT2, and ADME/Tox predictions supported favorable oral drug-likeness. In vivo, intraperitoneal LASSBio-1986 improved oral glucose tolerance and reduced glycemic excursions in an acute glucose challenge model in C57BL/6 mice, while enhancing hepatic and skeletal muscle glycogen stores. In a dexamethasone-induced insulin-resistance model, LASSBio-1986 improved insulin sensitivity, favorably modulated serum lipids, attenuated thiobarbituric acid-reactive substances (TBARS), restored reduced glutathione (GSH) levels, and rebalanced pro- and anti-inflammatory cytokines in metabolic tissues, with efficacy broadly comparable to dapagliflozin. These convergent findings support LASSBio-1986 as a preclinical, multimodal lead that targets SGLT-dependent glucose handling while mitigating oxidative and inflammatory stress in models relevant to T2DM. Chronic disease models, formal toxicology, and pharmacokinetic studies, particularly with oral dosing, will be essential to define its translational potential. Full article

The flavor and aroma of kiwifruit are largely influenced by the concentration of Volatile Organic Compounds (VOCs). To analyze the volatile profiles and identify characteristic aroma compounds, this study utilized Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) to analyze the aromatic compounds sourced from seven major production regions in China and New Zealand, covering red-, green-, and yellow-fleshed varieties. A total of 77 VOCs were identified, with esters, aldehydes, and ketones as the dominant classes. Significant regional and varietal differences were observed: red-fleshed kiwifruits from Yunnan exhibited high levels of 2-Vinyl-5-methylfuran, Ethyl formate, and 1-Penten-3-one; green-fleshed fruits from Shaanxi were rich in Limonene and Methyl hexanoate, and those from Yunnan were rich in 1-Propanol and 1-Hexanol; and yellow-fleshed fruits from Henan were characterized by Methyl salicylate and 3-Hydroxy-2-butanone. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully classified kiwifruits by origin and variety, confirming the stability and predictive power of the model (Q2Y > 0.97). This study also elucidated the key metabolic pathways—including lipid oxidation, amino acid degradation, and terpenoid metabolism—underlying the formation of these characteristic VOCs. These findings provide a theoretical foundation for the biochemical regulation of kiwifruit flavor and support the development of origin-tracing and quality-assessment tools based on VOC fingerprints. Full article

Metabolic syndrome (MetS) represents a global health challenge mainly driven by chronic low-grade inflammation and persistent oxidative stress (OS). Current therapeutic and nutritional strategies often fail to resolve these interconnected core pathologies due to the multifactorial nature of MetS. Anthocyanins (ACNs), a class of potent dietary flavonoids, offer significant promise due to their established pleiotropic effects, including robust antioxidant activity through modulation of the Nrf2/ARE pathway, anti-inflammatory effects via NF-κB suppression, and overall support for glucose and lipid homeostasis. However, the therapeutic efficacy of ACNs is characterized by interindividual variability, which is intrinsically linked to their low systemic bioavailability. This heterogeneity in the response is due to the complex interplay between genetic polymorphisms affecting absorption, distribution, metabolism, and excretion (ADME), as well as the specific biotransformation capacity of the gut microbiome. This review proposes that achieving the full clinical potential of ACNs requires moving beyond conventional nutritional advice. We propose that precision nutrition, which integrates multi-omics data (e.g., genomics, metagenomics, and metabolomics), can determine the individual phenotype, predict functional metabolic response, and tailor safer and effective ACN-rich interventions. This integrated, multifactorial approach is essential for optimizing the antioxidant and metabolic benefits of ACNs for the prevention and management of MetS and its associated pathologies. Full article

Objective: This study aimed to apply the Random Forest machine learning model using oxidative stress biomarkers to classify breast cancer status and assess sentinel lymph node (SLN) metastasis, a pathology of high incidence and mortality that represents a major public health challenge. Methods: The breast cancer classification cohort included 188 women with infiltrating ductal carcinoma and 78 healthy volunteers. For SLN metastasis assessment, a subset of 29 women with metastases and 57 controls (n = 86) was used. Data preprocessing and the SMOTE technique were applied to balance the classes in the metastasis set, achieving a perfect balance of 171 examples (57 per class). Random Forest model with a leave-one-out validation strategy was employed and oxidative stress biomarkers (e.g., lipid peroxidation, total antioxidant capacity, superoxide dismutase, catalase, glutathione peroxidase) were used. Results: The model achieved high accuracy (0.996) in classifying breast cancer, representing a substantial improvement over current screening methods such as mammography. In contrast, its performance in detecting SLN metastases was more limited (accuracy = 0.854), likely reflecting the inherent complexity and heterogeneity of the metastatic process. Moreover, these estimates derive from a retrospective case–control cohort and should not be viewed as a substitute for, or a direct comparison with, population-based mammography screening, which would require dedicated prospective validation. Conclusions: The findings underscore the model’s robust performance in distinguishing women with breast cancer from healthy volunteers, but highlight significant gaps in its ability to diagnose metastatic disease. Future research should integrate additional biomarkers, longitudinal data, and explainable artificial intelligence (XAI) methods to improve clinical interpretability and accuracy in metastasis prediction, moving towards precision medicine. Full article

Background/Objectives: Pregnancy represents a unique physiological state marked by extensive metabolic adaptations, particularly in lipid pathways essential for maternal adjustments, fetal development, and postpartum recovery. This study aimed to explore these changes through untargeted lipidomic profiling. Methods: This observational, comparative, non-interventional clinical study included 107 women, of which 65 were in the third trimester of pregnancy (mean age 27.9 ± 5 years) and 42 were in the early postpartum period (≤7 days, mean age 28.9 ± 5.9 years). Inclusion criteria were singleton, term pregnancies (37–41 weeks) with neonates weighing > 2500 g and no associated pregnancy-related pathologies; exclusion criteria included multiple gestation, use of lipid-altering medications, maternal age > 40 years, or diagnosed pregnancy complications. Plasma samples were analyzed using High-Performance Liquid Chromatography–Quadrupole Time-Of-Flight–Electrospray Ionization (positive mode)–Mass Spectrometry, data were processed with MetaboAnalyst 6.0 using multivariate and univariate analyses (Partial Least Squares–Discriminant Analysis, Volcano Plot, Random Forest, Receiver Operating Characteristic analysis), with statistical significance set at p < 0.05. Results: Multivariate analysis demonstrated a clear separation between groups with high predictive accuracy as reflected by strong classification metrics (Accuracy = 0.90, R² = 0.75, Q² = 0.68). Several discriminative lipids were consistently identified across statistical models, including 2-Methoxyestrone (AUC = 0.861), Eicosanedioic acid (AUC = 0.854), and Pregnenolone sulfate (AUC = 0.843). These biomarkers were further categorized into five major lipid classes: steroid hormones, long-chain fatty acids, lysophospholipids, ceramides/sphingolipids, and glycerolipids. Conclusions: Untargeted lipidomic profiling revealed distinct metabolic signatures that differentiate late pregnancy from early post-partum states. The identification of robust lipid biomarkers with high discriminative performance highlights their potential utility in maternal health monitoring, obstetric risk assessment, and postpartum recovery surveillance. Full article

Thraustochytrium aureum ssp. strugatskii, a marine protist belonging to the class Labyrinthulea, exhibits a complex life cycle characterized by alternating motile and vegetative phases. Using an integrative multimodal microscopy approach, we reconstructed its full developmental cycle and analyzed the coordination between cellular morphology, subcellular architecture, and population-level behavior. Transmission and scanning electron microscopy, combined with fluorescence and time-lapse imaging, revealed the dynamics of nuclear division, organelle rearrangement, and zoospore formation. Morphometric analysis of serial ultrathin sections demonstrated distinct changes in mitochondrial distribution, Golgi apparatus, and lipid droplet abundance during transitions between stages. We have shown that vegetative cells undergo synchronized karyokinesis coupled with stable nuclear-to-cytoplasmic ratios, leading to the emergence of multinucleate stages prior to zoospore formation. The integration of ultrastructural and dynamic data enabled us to propose a systems-level model linking metabolic state, morphogenesis, and population structure. This model highlights feedback regulation between nutrient availability, biomass accumulation, and developmental synchronization. Our results establish that T. aureum ssp. strugatskii has good potential to serve as a tractable model organism for systems-level studies of protists and provide an initial framework for predictive modeling of its life cycle under controlled conditions. Full article

Chronic dermatological conditions such as acne vulgaris, androgenetic alopecia (AGA), and alopecia areata (AA) affect hundreds of millions worldwide and contribute substantially to quality-of-life impairment. Despite the availability of systemic retinoids, anti-androgens, and JAK inhibitors, therapeutic responses remain heterogeneous and relapse is common, underscoring the need for biologically grounded stratification. Over the past decade, large genome-wide association studies and functional analyses have clarified disease-specific and cross-cutting mechanisms. In AA, multiple independent HLA class II signals and immune-regulatory loci such as BCL2L11 and LRRC32 establish antigen presentation and interferon-γ/JAK–STAT signalling as central drivers, consistent with clinical responses to JAK inhibition. AGA is driven by variation at the androgen receptor and 5-α-reductase genes alongside WNT/TGF-β regulators (WNT10A, LGR4, RSPO2, DKK2), explaining follicular miniaturisation and enabling polygenic risk prediction. Acne genetics highlight an immune–morphogenesis–lipid triad, with loci in TGFB2, WNT10A, LGR6, FASN, and FADS2 linking follicle repair, innate sensing, and sebocyte lipid metabolism. Barrier modulators such as FLG and OVOL1, first described in atopic dermatitis, further shape inflammatory thresholds across acne and related phenotypes. Together, these findings position genetics not as an abstract catalogue of risk alleles but as a map of tractable biological pathways. They provide the substrate for patient-stratified interventions ranging from JAK inhibitors in AA, to endocrine versus morphogenesis-targeted strategies in AGA, to lipid- and barrier-directed therapies in acne, while also informing cosmetic practices focused on barrier repair, sebaceous balance, and follicle health. Full article

Background: Colorectal cancer (CRC) is significantly associated with multiple metabolic diseases, with plasma metabolites potentially mediating this relationship. This large-scale metabolomics study aims to (1) quantify the genetic correlations and causal effects between 10 metabolic disease-related phenotypes and CRC risk; (2) identify the plasma metabolites mediating these effects; and (3) explore downstream regulatory genes and druggable targets. Methods: Using linkage disequilibrium score regression and two-sample Mendelian randomization, we assessed the causal relationships between each metabolic trait and CRC. A total of 1091 plasma metabolites and 309 metabolite ratios were identified and analyzed for mediating effects by a two-step MR approach. Colocalization analyses evaluated shared genetic loci. The findings were validated in the UK Biobank for metabolite-trait associations. The expression of candidate genes was explored using data from TCGA, GTEx, and GEO. A FADS1-centered protein–protein interaction (PPI) network was constructed via STRING. Results: BMI, waist circumference, basal metabolic rate, insulin resistance and metabolic syndrome exhibited both genetic correlation and causal effects on CRC. Five plasma metabolites—mannonate, the glucose/mannose ratio, plasma free asparagine, 1-linolenoyl-2-linolenoyl-GPC (18:2/18:3), and the mannose/trans-4-hydroxyproline ratio—were identified as shared central mediators. A colocalization analysis showed rs174546 linked CRC and 1-linolenoyl-2-linoleoyl-GPC. Validation in the UK Biobank confirmed the associations between phosphatidylcholine (the lipid class of this metabolite), adiposity measures, and CRC risk. An integrative analysis of TCGA, GTEx, and GEO revealed consistent upregulation of FADS1/2/3 and FEN1 in CRC, with high FADS1 expression predicting a poorer prognosis and showing the distinct cell-type expression in adipose and colon tissue. The PPI network mapping uncovered nine FADS1 interacting proteins targeted by supplements such as α-linolenic acid and eicosapentaenoic acid. Conclusions: This study systematically reveals, for the first time, the shared intermediary plasma metabolites and their regulatory genes in the causal pathway from metabolic diseases to CRC. These findings provide candidate targets for subsequent functional validation and biomarker development. Full article

Background: Atherogenic dyslipidemia is defined by the coexistence of high triglyceride concentrations, low levels of high-density lipoprotein cholesterol (HDL-C), and an excess of small, dense particles of low-density lipoprotein cholesterol (LDL-C). This lipid profile is strongly associated with an increased burden of cardiovascular disease and represents a leading cause of global morbidity and mortality. To better capture this risk, composite lipid ratios—including total cholesterol to HDL-C (TC/HDL-C), LDL-C to HDL-C (LDL-C/HDL-C), triglycerides to HDL-C (TG/HDL-C), and the atherogenic dyslipidemia index (AD)—have emerged as robust markers of cardiometabolic health, frequently demonstrating superior predictive capacity compared with isolated lipid measures. Despite extensive evidence linking these ratios to cardiovascular disease, few large-scale studies have examined their association with sociodemographic characteristics, lifestyle behaviors, and social isolation in working populations. Methods: We conducted a cross-sectional analysis of a large occupational cohort of Spanish workers evaluated between January 2021 and December 2024. Anthropometric, biochemical, and sociodemographic data were collected through standardized clinical protocols. Indices of atherogenic risk—namely the ratios TC/HDL-C, LDL-C/HDL-C, TG/HDL-C, and the atherogenic dyslipidemia index (AD)—were derived from fasting lipid measurements. The assessment of lifestyle factors included tobacco use, physical activity evaluated through the International Physical Activity Questionnaire (IPAQ), adherence to the Mediterranean dietary pattern using the MEDAS questionnaire, and perceived social isolation measured by the Lubben Social Network Scale. Socioeconomic classification was established following the criteria proposed by the Spanish Society of Epidemiology. Logistic regression models were fitted to identify factors independently associated with moderate-to-high risk for each lipid indicator, adjusting for potential confounders. Results: A total of 117,298 workers (71,384 men and 45,914 women) were included. Men showed significantly higher odds of elevated TG/HDL-C (OR 4.22, 95% CI 3.70–4.75) and AD (OR 2.95, 95% CI 2.70–3.21) compared with women, whereas LDL-C/HDL-C ratios were lower (OR 0.86, 95% CI 0.83–0.89). Advancing age was positively associated with all lipid ratios, with the highest risk observed in participants aged 60–69 years. Lower social class, smoking, physical inactivity, poor adherence to the Mediterranean diet, and low social isolation scores were consistently linked to higher atherogenic risk. Physical inactivity showed the strongest associations across all indicators, with ORs ranging from 3.54 for TC/HDL-C to 7.12 for AD. Conclusions: Atherogenic dyslipidemia and elevated lipid ratios are strongly associated with male sex, older age, lower socioeconomic status, unhealthy lifestyle behaviors, and reduced social integration among Spanish workers. These findings highlight the importance of workplace-based cardiovascular risk screening and targeted prevention strategies, particularly in high-risk subgroups. Interventions to promote physical activity, healthy dietary patterns, and social connectedness may contribute to lowering atherogenic risk in occupational settings. Full article

Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learning (ML) approaches. Methods: We analyzed 6247 Taiwanese men aged 18–35 years (mean follow-up 5.9 years). For δ-FPG (continuous outcome), random forest, stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net were compared with multiple linear regression using Symmetric mean absolute percentage error (SMAPE), Root mean squared error (RMSE), Relative absolute error(RAE), and Root relative squared error (RRSE) Sensitivity analyses excluded baseline FPG (FPG_base). Shapley additive explanations(SHAP) values provided interpretability, and stability was assessed across 10 repeated train–test cycles with confidence intervals. For prediabetes (binary outcome), an XGBoost classifier was trained on top predictors, with class imbalance corrected by SMOTE-Tomek. Calibration and decision-curve analysis (DCA) were also performed. Results: ML models consistently outperformed regression on all error metrics. FPG_base was the dominant predictor in full models (100% importance). Without FPG_base, key predictors included body fat, white blood cell count, age, thyroid-stimulating hormone, triglycerides, and low-density lipoprotein cholesterol. The prediabetes classifier achieved accuracy 0.788, precision 0.791, sensitivity 0.995, ROC-AUC 0.667, and PR-AUC 0.873. At a high-sensitivity threshold (0.2892), sensitivity reached 99.53% (specificity 47.46%); at a balanced threshold (0.5683), sensitivity was 88.69% and specificity was 90.61%. Calibration was acceptable (Brier 0.1754), and DCA indicated clinical utility. Conclusions: FPG_base is the strongest predictor of glycemic change, but adiposity, inflammation, thyroid status, and lipids remain informative. A dual interpretable ML framework offers clinically actionable tools for screening and risk stratification in young men. Full article

Background: Metabolic syndrome (MetS) is a major risk factor for cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), especially in Middle Eastern populations with a high metabolic burden. This study aimed to evaluate the predictive utility of different lipid ratios, including triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C), total cholesterol (TC)/HDL-C, low-density lipoprotein (LDL-C)/HDL-C, and non-HDL-C/HDL-C, for identifying MetS. In addition, we aimed to characterise the underlying metabolic dysregulation using the most predictive lipid ratio by comparing metabolomic profiles between high-risk (T3) and low-risk (T1) groups. Method: We conducted a cross-sectional study using data from 2179 Qatari adults without CVD and/or T2DM. The predictive value of each lipid ratio for MetS was compared. Untargeted metabolomics was performed to profile metabolic changes between T3 and T1. Results: After adjustment for age, sex, and BMI, TG/HDL-C showed the highest discriminative ability for MetS (AUC = 0.896, 95% CI: 0.88–0.91; OR = 4.36, 95% CI: 3.63–5.28, p < 0.0001). In pairwise AUC comparisons, TG/HDL-C outperformed LDL-C/HDL-C (p = 2.6 × 10⁻⁴, after correction for multiple comparisons), with no significant differences versus other ratios. The high-risk group exhibited raised levels of phosphatidylethanolamines, phosphatidylinositols, and diacylglycerols, and lower levels of sphingomyelins and plasmalogens. These lipid classes have been suggested to be implicated in insulin resistance and metabolic dysfunction. Elevated monoacylglycerols were identified in high-TG/HDL-C groups, representing a previously underreported pattern. Conclusions: The TG/HDL-C ratio showed a better association with MetS compared with other lipid ratios and was linked to distinct metabolomic signatures. These findings suggest potential value for early risk evaluation, but longitudinal and mechanistic studies are needed to confirm clinical applicability. Full article

Background: Type 2 diabetes (T2D), the most common form of diabetes, is associated with a significantly elevated risk of cardiovascular and cerebrovascular complications. However, circulating metabolic signatures that reliably predict the transition to insulin resistance, and are potentially linked to increased vascular risk, remain incompletely characterized. Rodent models, particularly those induced by a high-fat diet (HFD) combined with low-dose streptozotocin (STZ), are widely used to study the progression of T2D. However, the systemic metabolic shifts associated with this model, especially at the plasma level, are poorly defined. Methods: In this study, we performed untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomic profiling on plasma samples from control, HFD-only (obese, insulin-sensitive), and HFD + STZ (obese, insulin-resistant) C57BL/6 mice. Results: In the HFD + STZ cohort, plasma profiles showed a global shift toward lipid classes; depletion of aromatic and branched-chain amino acids (BCAAs); accumulation of phenylalanine-derived co-metabolites, consistent with gut–liver axis dysregulation; elevations in glucose, fructose-6-phosphate, and nucleoside catabolites, indicating impaired glucose handling and heightened nucleotide turnover; increased free fatty acids, reflecting membrane remodeling and lipotoxic stress; and higher cAMP, thyroxine, hydrocortisone, and uric acid, consistent with endocrine and redox imbalance. By contrast, HFD-only mice exhibited elevations in aromatic amino acids and BCAAs relative to controls, a pattern compatible with early obesity-associated adaptation while insulin signaling remained partially preserved. KEGG analysis revealed disturbances in carbohydrate metabolism, amino acid degradation, nucleotide turnover, and hormone-related pathways, and HMDB mapping linked these changes to T2D, obesity, heart failure, and renal dysfunction. Conclusion: Collectively, these findings delineate insulin resistance-specific plasma signatures of metabolic inflexibility and inflammatory stress in the HFD + STZ model, distinguishing it from HFD alone and supporting its utility for mechanistic studies and biomarker discovery. Importantly, this plasma metabolomics study shows that insulin-sensitive and insulin-resistant states exhibit distinct variation in circulating metabolites and cardiovascular risk factors, underscoring the translational value of plasma profiling. Full article

Concomitant sensitisation to non-specific lipid transfer proteins (nsLTPs) from olive pollen (Ole e 7) and peach (Pru p 3) has been observed in the south of Spain. In the search for reasons to explain this observation, we studied a potential causal relationship between Human Leukocyte Antigen (HLA) molecules and nsLTP sensitisation. For this purpose, eighteen Ole e 7-monosensitised (MONOLE) patients, 22 Pru p 3-monosensitised (MONPRU) patients, and 22 bisensitised (BI) patients were genotyped for HLA class II alleles. Complementarily, T-cell epitopes were predicted with the Immune Epitope Database analysis tool to test HLA epitope presentation. Our results showed a significant increase in DRB1*11 and DQB1*03 frequencies in MONPRU patients and DRB1*04 frequency in MONOLE patients. Additionally, T-cell epitope analysis revealed high binding affinity between the predicted Pru p 3 epitopes and DRB1*11 and between the predicted Ole e 7 epitopes and DRB1*04, suggesting that presentation of these epitopes may be favoured and predisposing individuals to sensitisation. Conversely, low DQB1*05 frequency and poor binding ability of predicted epitopes from both nsLTPs postulated this allele as a possible protective factor to sensitisation. Variations in the binding affinity between nsLTP epitopes and HLA molecules may underlie individual susceptibility to nsLTP allergy. Full article

Elevated levels of atherogenic lipoproteins are known to be associated with an increased risk of incident and recurrent cardiovascular events. Knowing that the immediate post-acute coronary syndrome (ACS) period is associated with the maximum risk of recurrent events, the gradual escalation of therapy allows the patient to remain above the targets during the most vulnerable period. In addition, the percentage of lipid-lowering levels for each class of drugs is predictable and has a ceiling. Hence, it is prudent to immediately start with a combination of lipid-lowering drugs following ACS according to the baseline lipid levels. Multiple studies with injectable lipid-lowering agents (PCSK9 inhibitors) such as EVOPACS, PACMAN MI, and HUYGENS MI have shown the feasibility of achieving LDL-C goals by day 28 and beneficial plaque modification in non-infarct-related coronary arteries. Recently, a study from India demonstrated that an upfront triple combination of oral lipid-lowering agents was able to achieve LDL-C goals in a majority of patients in the early post-ACS period. This notion is also supported by a few recent lipid-lowering guidelines advocating for an upfront dual combination of a high-intensity statin and ezetimibe following ACS. Henceforth, the goal should not only be the achievement of lipid targets but also their early achievement. However, the impact of this strategy on long-term cardiovascular outcomes is yet to be ascertained. Full article

Abnormalities in plasma lipoproteins observed in patients with diabetes promote atherosclerosis. However, the association between various lipid species and classes and atherosclerosis remains unclear. Here, we aimed to identify the plasma lipid characteristics associated with atherosclerosis progression in patients with diabetes. We performed semi-targeted lipidomic analysis of fasting plasma samples using supercritical fluid chromatography coupled with mass spectrometry in two independent patient groups with type 2 diabetes (n = 223 and 31) and evaluated cross-sectional associations between plasma lipids and carotid intima-media thickness (CIMT). Ten plasma lipid species, including eight diacylglycerols (DGs), and total DG levels were significantly associated with CIMT in both groups. Patients of the former group were partly observed for 5 years, and we investigated associations between DGs and CIMT progression in these patients (n = 101). As a result, 22 DGs among the 26 identified DGs and total DG (β = 0.398, p < 0.001) were significantly associated with the annual change in CIMT. Furthermore, plasma DG levels improved the predictive ability for CIMT progression, with an adjusted R-squared increase of 0.105 [95% confidence interval: 0.010, 0.232] in the models. Plasma DGs are associated with CIMT progression in patients with type 2 diabetes. Measurement of total plasma DG levels may be beneficial in assessing the risk of atherosclerosis progression. Full article