Post-prandial lipid levels for assessing target goal achievement in type 2 diabetic patients taking statin

Comparison of fasting and random lipid profiles among subjects with type 2 diabetes mellitus: an outpatient-based cross-sectional study in Bangladesh

BACKGROUND

Despite the wide acceptability of fasting lipid profiles in practice, emerging evidence suggests that random lipid profiles might be a convenient alternative for lipid measurement. The objective of the present study was to compare the fasting and random lipid profile among subjects with type 2 diabetes mellitus (T2DM).

METHODS

The present cross-sectional study included 1543 subjects with T2DM visiting several endocrinology outpatient clinics throughout Bangladesh from January to December 2021. The fasting lipid profile was measured in the morning following 8-10 h of overnight fasting, and the random lipid profile was measured at any time of the day, irrespective of the last meal. The values of fasting and random lipids were compared using the Wilcoxon signed-rank test and Spearman rank correlation coefficients.

RESULTS

In this study, a good level of correlation was observed between fasting and random lipid levels [r = 0.793, p < 0.001 for triglyceride (TG); r = 0.873, p < 0.001 for low-density lipoprotein cholesterol (LDL-C); r = 0.609, p < 0.001 for high-density lipoprotein cholesterol (HDL-C); and r = 0.780, p < 0.001 for total cholesterol (TC)]. In addition, TG and TC levels increased by 14% and 0.51%, respectively, in the random state compared to the fasting state (p- <0.05), while LDL-C levels decreased by 0.71% (p-value 0.42). No change was noticed in the HDL-C level. The difference between fasting and random lipid profiles was similar irrespective of patients' age, sex, BMI, glucose-lowering drug(s), and lipid-lowering therapy.

CONCLUSIONS

Random lipid profile correlates significantly with fasting lipid profile with little difference. Hence, it might be a reliable alternative for fasting lipid profile in patients with T2DM.

Carbohydrate-rich Meals Have no Impact on Post-prandial Lipid Parameters in Indians with Subclinical and Overt Primary Hypothyroidism

BACKGROUND AND AIMS

The impact of altered cholesterol metabolism on post-prandial lipids in Indians with hypothyroidism is not known. This study evaluated the impact of overt primary hypothyroidism (OPH) and subclinical hypothyroidism (ScH) on post-prandial lipids after a standardised, carbohydrate-rich, mixed meal.

METHODS

Endocrinology outpatients were screened for possible inclusion into the study. Patients >18 years of age with hypothyroidism who were not taking levothyroxine and who did not present with any comorbidities underwent biochemical evaluation following a carbohydrate-rich, mixed meal. Assessments included total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), triglycerides, lipoprotein-A (Lp-A), apolipoprotein-A1 (apo-A1), apolipoprotein-B (apo-B), insulin and fasting glucose. Assessments were carried out 1 hour, 2 hours and 4 hours after the meal. Patients were compared against healthy matched controls recruited from healthcare professionals in the hospital (asymptomatic and apparently healthy nursing staff, reception staff and ward staff).

RESULTS

Data from 194 patients (161 with ScH and 33 with OPH) and 40 euthyroid controls were analysed. Anthropometry, body mass index, glycaemia and insulin resistance were comparable among patients with OPH and ScH, and controls. LDL-C and Lp-A were significantly higher in those with OPH, compared with ScH and controls, at baseline, 1 hour, 2 hours and 4 hours after mixed meal consumption (all p<0.05). There was progressive and similar decline in post-prandial TC, LDL-C and Lp-A in all three groups. Triglycerides were similar among the OPH, ScH and control groups, both in fasting and post-prandial state, with a progressive and similar increase in post-prandial triglycerides in all three groups.

CONCLUSION

This study demonstrated that severity of hypothyroidism had no impact on post-prandial TC, LDL-C and Lp-A. In addition, hypothyroidism had no impact on post-prandial triglycerides. Therefore, we conclude that lipid profile can be reliably estimated in a non-fasting state in individuals with ScH and OPH.

Bioanalytical techniques in nontargeted clinical lipidomics

Lipidomic analysis aims at comprehensive characterization of molecular lipids in biological systems. Due to the central role of lipid metabolism in many devastating diseases, lipidomics is being increasingly applied in biomedical research. Over the past years, advances in analytical techniques and bioinformatics enabled increasingly comprehensive and accurate coverage of lipids both in tissues and biofluids, yet many challenges remain. This review highlights recent progress in the domain of analytical lipidomics, with main emphasis on non-targeted methodologies for large scale clinical applications, as well as discusses some of the key challenges and opportunities in this field.

Agreement Between Fasting and Postprandial LDL Cholesterol Measured with 3 Methods in Patients with Type 2 Diabetes Mellitus

BACKGROUND

LDL cholesterol (LDL-C) is a modifiable cardiovascular disease risk factor. We used 3 LDL-C methods to study the agreement between fasting and postprandial LDL-C in type 2 diabetes (T2DM) patients.

METHODS

We served 74 T2DM patients a standardized meal and sampled blood at fasting and 1.5, 3.0, 4.5, and 6.0 h postprandially. We measured LDL-C by use of modified β quantification (MBQ), the Friedewald equation (FE), and a direct homogeneous assay (DA). We evaluated agreement using 95% limits of agreement (LOA) within ±0.20 mmol/L (±7.7 mg/dL).

RESULTS

LDL-C concentrations at all postprandial times disagreed with those at fasting for all methods. In 66 patients who had complete measurements with all LDL-C methods, maximum mean differences (95% LOA) in postprandial vs fasting LDL-C were −0.16 mmol/L (−0.51; 0.19) [−6.2 mg/dL (−19.7; 7.3)] with MBQ at 3 h; −0.36 mmol/L (−0.89; 0.17) [−13.9 mg/dL (−34; 6.6)] with FE at 4.5 h; and −0.24 mmol/L (−0.62; 0.05) [−9.3 mg/dL (−24; 1.9)] with DA at 6.0 h. In postprandial samples, FE misclassified 38% of patients (two-thirds of statin users) into lower Adult Treatment Panel III (ATP III) risk categories. Greater disagreement between fasting and postprandial LDL-C was observed in individuals with postprandial triglyceride concentrations &gt;2.08 mmol/L (&gt;184 mg/dL) and in women (interactions: P ≤ 0.038).

CONCLUSIONS

Differences up to 0.89 mmol/L (34 mg/dL) between fasting and postprandial LDL-C concentrations, with postprandial LDL-C concentrations usually being lower, were found in T2DM by 3 different LDL-C methods. Such differences are potentially relevant clinically and suggest that, irrespective of measurement method, postprandial LDL-C concentrations should not be used to assess cardiovascular disease risk.