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Abstract
Aim
To examine the associations between bone turnover markers and periodontitis in two cross‐sectional population‐based studies.
Materials and Methods
We used data from two independent adult samples (N = 4993), collected within the Study of Health in Pomerania project, to analyse cross‐sectional associations of N‐procollagen type 1 amino‐terminal propeptide (P1NP), C‐terminal cross‐linking telopeptide, osteocalcin, bone‐specific alkaline phosphatase (BAP), fibroblast growth factor 23, wingless‐type mouse mammary tumour virus integration site family member 5a (WNT5A), and sclerostin values with periodontitis. Confounder‐adjusted gamma and fractional response regression models were applied.
Results
Positive associations were found for P1NP with mean pocket probing depth (PPD; eβ=1.008; 95% confidence interval [CI]: 1.001–1.015), mean clinical attachment loss (mean CAL; eβ=1.027; 95% CI: 1.011–1.044), and proportion of sites with bleeding on probing (%BOP; eβ=1.055; 95% CI: 1.005–1.109). Similar associations were seen for BAP with %BOP (eβ=1.121; 95% CI: 1.042–1.205), proportion of sites with PPD ≥4 mm (%PPD4) (eβ=1.080; 95% CI: 1.005–1.161), and sclerostin with %BOP (eβ=1.308; 95% CI: 1.005–1.704). WNT5A was inversely associated with mean PPD (eβ=0.956; 95% CI: 0.920–0.993) and %PPD4 (eβ=0.794; 95% CI: 0.642–0.982).
Conclusions
This study revealed scattered associations of P1NP, BAP, WNT5A, and sclerostin with periodontitis, but the results are contradictory in the overall context. Associations reported in previous studies could not be confirmed.
Introduction
Neurofilament light (NfL) can be detected in blood of healthy individuals and at elevated levels in those with different neurological diseases. We investigated if the choice of biological matrix can affect results when using NfL as biomarker in epidemiological studies.
Method
We obtained paired serum and EDTA-plasma samples of 299 individuals aged 37–67 years (BiDirect study) and serum samples of 373 individuals aged 65–83 years (MEMO study). In BiDirect, Passing–Bablok analyses were performed to assess proportional and systematic differences between biological matrices. Associations between serum or EDTA-plasma NfL and renal function (serum creatinine, serum cystatin C, glomerular filtration rate, and kidney disease) were investigated using linear or logistic regression, respectively. All regression coefficients were estimated (1) per one ng/L increase and (2) per one standard deviation increase (standardization using z-scores). In MEMO, regression coefficients were estimated (1) per one ng/L increase of serum or calculated EDTA-plasma NfL and (2) per one standard deviation increase providing a comparison to the results from BiDirect.
Results
We found proportional and systematic differences between paired NfL measurements in BiDirect, i.e., serum NfL [ng/L] = −0.33 [ng/L] + 1.11 × EDTA-plasma NfL [ng/L]. Linear regression coefficients for the associations between NfL and renal function did not vary between the different NfL measurements. In MEMO, one standard deviation increase in serum NfL was associated with greater changes in the outcomes than in BiDirect.
Conclusion
Although there are differences between serum and EDTA-plasma NfL, results can be used interchangeably if standardized values are used.
Disregarded Measurement Uncertainty Contributions and Their Magnitude in Measuring Plasma Glucose
(2020)
Background:
Each measurement is subject to measurement uncertainty (MU). Consequently, each measurement of plasma glucose concentration used for diagnosis and monitoring of diabetes mellitus (DM) is affected. Although concepts and methods of MU are well established in many fields of science and technology, they are presently only incompletely implemented by medical laboratories, neglecting MU of target values of internal quality control (IQC) materials.
Methods:
An empirical and practical approach for the estimation of MU based on the analysis of routine IQC using control samples with assigned target values is presented. Its feasibility is demonstrated exemplarily by analyzing IQC data from one year obtained for glucose employing the hexokinase method with IQC of two different concentrations.
Results:
Combined relative extended (k = 2) MU comprising bias, coefficient of variation (CV), and MU of the target values assigned to control materials were about 9% with a lower (~ 56 mg/dL; ~3.1 mmol/L) and 8% with a higher (~ 346 mg/dL; ~19.2 mmol/L) concentration sample, analyzing IQC of one year from three different devices.
Conclusions:
Estimation of MU in this study is quite reliable due to the large number of IQC data from one year. The MU of the target values of the commercial control material in this study was considerably larger than other MU contributions, ie, standard deviation and bias. In the future, the contribution of MU of commercial IQC should be addressed more carefully and technologies to measure glucose should be geared toward smaller MU possible, as needed, especially for glucose concentration measurements in diagnosis and management of DM.