Nonfasting Lipids for All Patients?

The Importance of Being Aware of Intrinsic Methods' Limitation in Low-Density Lipoprotein Cholesterol Determination to Correctly Identify Cardiovascular Risk: Is Direct Determination Obtained with the Roche System Systematically Overestimating LDL in Very High-Risk Patients with Triglycerides Concentration of Less than 2.25 mmol/L?

BACKGROUND

low-density lipoprotein cholesterol (LDL-C) is a strong cardiovascular risk factor, but the methods used to correctly determine it are is still questioned. The aim of this study was to compare the direct determination of LDL-C levels, obtained with the Roche cobas c system, with LDL-C values calculated through Sampson's and Friedewald's equations in very high-risk patients with triglycerides concentrations of less than 2.25 mmol/L (<200 mg/dL).

METHODS

in 127 consecutive patients with a recent diagnosis of acute coronary syndrome and triglycerides of less than 2.25 mmol/L, plasma LDL-C was measured directly and calculated with Sampson's and Friedewald's equations before hospital discharge, and the data were compared.

RESULTS

median LDL values obtained with the Friedenwald and Sampson equations and with direct determination were 2.31 (IQR 1.59-3.21), 2.36 (IQR 1.66-3.26) and 2.64 (IQR 1.97-3.65) mmol/L, respectively. Direct measurements were higher by 0.35 and 0.40 mmol/L when compared to the levels calculated with Sampson's or Friedewald's equations, respectively (p < 0.01). The correlation between the two equations was almost perfect (rho 0.997) but decreased when the directly determined data were compared to those calculated with Sampson's equation (rho 0.954) or Friedewald's method (rho 0.939).

CONCLUSION

direct determination generated higher values of LDL-C concentration through a probable systematic overestimation.

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.

Exploring the Novelty in Lipid Profiling of Patients: A Non-fasting Approach from Eastern India

Objective To date, no reference interval is available for lipid profile, including total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), or low-density lipoprotein (LDL)-cholesterol, etc., in a non-fasting state. Hence, the study was taken up with the objective of exploring the possibility of establishing a reference interval for non-fasting lipid profile consisting of serum TC, TG, LDL, HDL, and very low-density lipoprotein (VLDL) cholesterol.

Materials and Methods A total of 1,350 apparently healthy subjects, including 636 healthy men and 714 healthy women of 18 years and beyond of age, were enrolled in the study. Reference individuals were recruited using cluster sampling method from various villages and semi-urban regions irrespective of their sex, religion, socioeconomic status, or any other demographic profile, and samples were analyzed in Beckman Coulter AU480 analyzer.

Results The mean age of 1,350 participants was 38.23 ± 15.94 years. We found that all the test parameters require a different reference interval than the established fasting reference range, except for HDL cholesterol in females. The data were subdivided into subjects below 40 years, between 40 and 60 years, and older than 60 years of age. All five parameters in the lipid profile were individually analyzed and were compared age group-wise and gender-wise with the total study population. Significant differences in the various dataset were found.

Conclusion A shift toward non-fasting lipid interval measurement is, thus, a piece of evidence-driven mechanism. Even from a patient's perspective, it sets in ease and convenience in lipid-profile testing, subsequently leading to a more compliant cardiovascular management and monitoring.

How should low-density lipoprotein cholesterol be calculated in 2022?

PURPOSE OF REVIEW

The reference method for low-density lipoprotein-cholesterol (LDL-C) quantitation is β-quantification, a technically demanding method that is not convenient for routine use. Indirect calculation methods to estimate LDL-C, including the Friedewald equation, have been used since 1972. This calculation has several recognized limitations, especially inaccurate results for triglycerides (TG) >4.5 mmol/l (>400 mg/dl). In view of this, several other equations were developed across the world in different datasets.The purpose of this review was to analyze the best method to calculate LDL-C in clinical practice by reviewing studies that compared equations with measured LDL-C.

RECENT FINDINGS

We identified 45 studies that compared these formulae. The Martin/Hopkins equation uses an adjustable factor for TG:very low-density lipoprotein-cholesterol ratios, validated in a large dataset and demonstrated to provide more accurate LDL-C calculation, especially when LDL <1.81 mmol/l (<70 mg/dl) and with elevated TG. However, it is not in widespread international use because of the need for further validation and the use of the adjustable factor. The Sampson equation was developed for patients with TG up to 9 mmol/l (800 mg/dl) and was based on β-quantification and performs well on high TG, postprandial and low LDL-C samples similar to direct LDL-C.

SUMMARY

The choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

Pediatric reference intervals for 32 routine biochemical markers using the siemens healthineers atellica® CH assays in healthy children and adolescents

BACKGROUND

Pediatric reference intervals are essential for test interpretation. With development of newer analytical systems, de novo reference interval establishment is of necessary importance. In the current study, pediatric reference intervals were determined for 32 analytes using Siemens Healthineers Atellica® CH assays in the CALIPER cohort of healthy children and adolescents.

METHODS

Approximately 600 healthy children and adolescents were recruited with informed consent and collected serum samples were analyzed on the Siemens Healthineers Atellica® CH platform. Assays studied included enzymes, proteins, lipids, electrolytes, and additional markers Reference intervals were established according to Clinical and Laboratory Standards Institute (CLSI) guidelines.

RESULTS

Of the 32 parameters, 26 required age partitioning and 18 required sex partitioning. Reference value distributions included consistent increases, decreases, and dynamic variation across the age continuum. Chloride, LDL cholesterol, glucose, lipase, sodium, and triglyceride demonstrated no age or sex-specific differences.

CONCLUSION

The current study expands the clinical utility of the CALIPER database to include 32 Siemens Atellica® chemistry assays. Reference value distributions for Siemens assays mirrored those observed on other comparable assays/systems with few exceptions (e.g. lipase, direct and total bilirubin). These finding support the robustness of previously derived reference intervals in the CALIPER cohort and other global cohorts.