Varisara Lapinee

Purpose: The optimal plasma lipoprotein(a) [Lp(a)] cutoff level for predicting atherosclerotic cardiovascular disease (ASCVD) in Southeast Asian populations remains limited. Therefore, our study aimed to identify the optimal plasma Lp(a) cutoff for predicting ASCVD in Thai patients. Patients and Methods: We conducted a retrospective analysis of patients who underwent Lp(a) measurement at Siriraj Hospital between January 2019 and August 2024. Inclusion criteria included Thai ethnicity and age ≥ 15 years. Baseline characteristics, comorbidities, laboratory data, and Lp(a) levels were extracted from medical records. Lp(a) levels were compared between ASCVD and non-ASCVD groups. Odds ratios (OR) for ASCVD and coronary artery disease (CAD) were calculated using Lp(a)< 25 nmol/L as the reference. Results: A total of 2341 patients (age 54.4± 17.7 years, 42.0% male) were included. Among them, 413 (17.6%) had ASCVD, 254 (10.9%) had CAD, 186 (7.9%) had ischemic stroke, 21 (0.9%) had peripheral arterial disease (PAD), and 14 (0.6%) had abdominal aortic aneurysm. Median Lp(a) levels (nmol/L) were significantly higher in patients with ASCVD [37.2 vs 24.4, p< 0.001], CAD [43.8 vs 24.5, p< 0.001], and AS [51.6 vs 25.3, p=0.002] compared to those without diseases. After adjusting for other risk factors, Lp(a)≥ 40 nmol/L was associated with increased risks of ASCVD [OR 1.538 (1.203– 1.958)] and CAD [OR 1.877 (1.407– 2.505)]. A multivariate model incorporating Lp(a)≥ 40 nmol/L with other risk factors demonstrated 70– 80% sensitivity and specificity for predicting ASCVD and CAD. Conclusion: Elevated plasma Lp(a) levels are significantly associated with ASCVD and CAD. An Lp(a) cutoff of≥ 40 nmol/L predicted ASCVD and CAD risk in Thai.

Type 2 diabetes is a heterogeneous disease with strong genetic components. We showed earlier that newly diagnosed type 2 diabetes in Thai patients could be categorized into four clusters. This study aimed to determine the evidence of hereditary factors in these type 2 diabetes clusters. A total of 487 subjects who were diagnosed with type 2 diabetes in two years were enrolled in the Siriraj Diabetes Center, Siriraj Hospital Bangkok, Bangkok, Thailand. They were divided into four clusters as previously described. The associations between patients' characteristics, degree of family history of diabetes (FHD), and type 2 diabetes clusters were tested using multinomial logistic regression. Among four clusters of newly diagnosed type 2 diabetes, there were significant differences in characteristics at baseline, including age at diagnosis, BMI, waist circumference, blood sugar levels, vital signs, triglyceride, HDL, calculated LDL, creatinine and eGFR (all p < .05). A relatively young age at the time of diabetes diagnosis was associated with having second-degree relatives with diabetes (p < .05) in all clusters when using mild age-related diabetes (MARD) cluster with no FHD as a control. Patients in the severe insulin-deficient diabetes (SIDD) cluster had more first-degree relatives with diabetes (odds ratio = 1.85; p = .0354), while patients in the metabolic syndrome diabetes (MSD) cluster (odds ratio = 10.73; p < .001) and the mild obesity-related diabetes (MOD) group (odds ratio = 6.66; p = .002), had more second-degree relatives with diabetes. Genetic factors might have various roles in the pathogenesis of type 2 diabetes, at least in newly diagnosed Thai patients. Our findings supported that genetic heterogeneity contributed to clinical heterogeneity or four different clusters. Further studies are needed in a larger sample size of these patients is needed to identify genetic loci associated with each cluster.