Biological variation of lipoprotein(a) in a diabetic population. Analysis of the causes and clinical implications

Biological variation of cardiovascular biochemical markers in patients with Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a well-established risk factor for cardiovascular diseases. We aimed to identify the biological variation of ten cardiovascular biochemical markers in T2DM patients to aid in their interpretation.

METHODS

Blood samples for evaluating ten biomarkers were collected biweekly from 23 T2DM patients (10 men, 13 women) for three months. The analytical variability and variations of within-subject (CV_I) and between-subject (CV_G) levels were calculated, as well as the analytical performance specifications, reference change value (RCV), and index of individuality (II).

RESULTS

The levels of total cholesterol (CHOL), apolipoprotein A (apoA), homocysteine (HCY), high-sensitivity troponin T (hsTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) differed between males and females (P < 0.05). The CV_Is or CV_Gs of the biomakers were higher than those of healthy participants in Westgard online database, except for hsTnT. Triglyceride (TG), lipoprotein (a) [Lp(a)] and NT-proBNP had relatively high CV_I, CV_G and RCV, whereas CHOL, high-density lipoprotein cholesterol (HDL-C), apoA and HCY showed low variation. Moreover, the II of HDL-C, LP(a), apoA, HCY and hsTnT was <0.6 and other biochemical markers was between 0.6 and 1.4.

CONCLUSION

The cardiovascular biochemical markers in T2DM patients showed higher CV_I or CV_G, except for hsTnT. ApoA had the lowest CV_I and CV_G values. Population-based reference intervals should be used with caution in clinical decision-making for T2DM patients.

Effect of different types and dosages of statins on plasma lipoprotein(a) levels: A network meta-analysis

BACKGROUND AND AIM

Studies differ with respect to the effects of statins and their on lipoprotein(a)[Lp(a)] levels. The aim of the present study was to resolve these differences by determining the effect of various types and dosages of statins on Lp(a) levels.

METHODS

We searched PubMed, Embase and the Cochrane library for randomized controlled trials (RCTs) investigating the efficacy of statins on plasma Lp(a) levels. Study selection, data extraction and risk of bias assessment were conducted independently by four authors. We conducted pairwise meta-analysis and network meta-analysis (NMA). Consistency models were applied to NMA and the ranking probabilities for each treatment's efficacy were calculated. Node-splitting analysis was used to test inconsistency. This study was registered with PROSPERO, number CRD42020167612.

RESULTS

Twenty RCTs with 23,605 participants were included, involving 11 interventions. Most of the included studies presented some risks of bias, especially risks of performance and detection bias. In the pairwise meta-analysis, pooled results showed a small but statistically significant difference between high-intensity rosuvastatin and placebo on Lp(a) levels (MD = 1.81, 95 % CI [0.43, 3.19], P = 0.01). In the NMA, different types and dosages of statins showed no significant effect on the level of Lp(a), and there was no obvious difference between them. Subgroup analysis based on different populations and treatment durations did not provide any statistically significant findings about different statins on Lp(a) levels. Node-splitting analysis showed that no significant inconsistency existed (P > 0.05).

CONCLUSIONS

Statins have no clinically significant effect on Lp(a) levels, and there is no significant difference in the effect on Lp(a) levels between different types and dosages of statins. Moderate-intensity pitavastatin tended to have the best effect on reducing Lp(a) levels; nevertheless, it was insignificant. Our findings highlight the necessity for further study of the effect of statins on Lp(a) levels in future studies.

Rationale for using data on biological variation

The aims of this study are: 1) to use the data included in the biological variation (BV) database to address the usability of BV estimates; and 2) to use different examples from the authors’ laboratories to illustrate the use and the usefulness of BV data in laboratory medicine. The BV database is an essential tool for laboratory management. Examples of application of data derived from BV are given in this paper, such as analytical performance specifications that have been included in various quality control software designed to optimize operative rules; also they have been incorporated as acceptability limits in external quality assurance reports. BV data from pathological status are of utmost interest for monitoring patients and differences between the intra-individual coefficients of variation (CV

Biological variations of some analytes in renal posttransplant patients: a different way to assess routine parameters

BACKGROUND

Biological variation (BV) data of analytes have been used to evaluate the significant changes in serial results (reference change value, RCV) of healthy individuals in clinical laboratories. However, BV data of healthy subjects may not be identical to the analytes of patients with ongoing clinical condition. The aim of this study was to calculate intra-(CVw) (coefficient of variation for intra-individual BV) and inter-individual (CVg) BV, index of individuality, and RCV of nine serum analytes of renal posttransplant patients.

METHODS

Six serum specimens were obtained in an interval of two months in a one-year period from 70 transplant patients who had been stable for three years. Each time creatinine, uric acid, urea, sodium, potassium, calcium, inorganic phosphate, total protein, and albumin of these patients were analyzed with an integrated clinical chemistry/immunoassay auto-analyzer. ANOVA tests were used to calculate the variations. Results were compared with the data of healthy subjects obtained from BV database.

RESULTS

CVw of all nine analytes of the renal transplant patients were higher than the healthy subjects. RCVs of these analytes were calculated as 14.5% for creatinine, 16.5% for urea, 13.7% for urate, 12.57% for albumin, 8.26% for total protein, 3.25% for sodium, 12.81% for potassium, 5.88% for calcium, and 21.57% for inorganic phosphate.

CONCLUSION

RCV concept for predicting the clinical status in posttransplant population represents an optimization of laboratory reporting and could be a valuable tool for clinical decision.