Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration.

Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.

Corrigendum: Research trends and hotspots of glial fibrillary acidic protein within the area of Alzheimer's disease: a bibliometric analysis.

[This corrects the article DOI: 10.3389/fnagi.2023.1196272.].

Research trends and hotspots of glial fibrillary acidic protein within the area of Alzheimer's disease: a bibliometric analysis.

Objective: Our aim was to analyze the trends and hotspots on glial fibrillary acidic protein (GFAP) within the area of Alzheimer's disease (AD) by using a bibliometric method, which is currently missing. Methods: All articles and reviews on GFAP within the area of AD from inception to December 31, 2022, were searched from the Web of Science Core Collection. Full records were derived, imported into Microsoft Excel, and analyzed by BIBLIOMETRC, VOSviewer, and CiteSpace. Results: In total, 2,269 publications, including 2,166 articles, were ultimately included. The number of publications from 81 countries/regions and 527 academic journals increased annually. The top three prolific countries and institutions were the USA, China, and England, the University of Gothenburg (Sweden), Universidade Federal Rio Grande do Sul (Brasilia), and UCL Queen Square Institute of Neurology (England). Henrik Zetterberg from the University of Gothenburg, Kaj Blennow from the University of Gothenburg, and Alexei Verkhratsky from the University of Manchester were the top three prolific and cited authors; Journal of Alzheimer's Disease, Brain Research, and Neuroscience contributed the most publications. The top key areas of research included "molecular, biology, and genetics" and "molecular, biology, and immunology," and the top published and linked meaningful keywords included oxidative stress, inflammation/neuroinflammation, microglia, hippocampus, amyloid, cognitive impairment, tau, and dysfunction. Conclusion: Based on the bibliometric analysis, the number of publications on GFAP within the area of AD has been rapidly increasing, especially in the past several years. Oxidative stress and inflammation are research hotspots, and GFAP in body fluids, especially blood, could be used for large-scale screening for AD.

Establishment of a reference interval for total carbon dioxide using indirect methods in Chinese populations living in high-altitude areas: A retrospective real-world analysis.

BACKGROUND: Hypoxia leads to different concentrations of the bicarbonate buffer system in Tibetan people. Indirect methods were used to establish the reference interval (RI) for total carbon dioxide (tCO2) based on big data from the adult population of Tibet, a high-altitude area in Western China. METHODS: Anonymous tCO2 test data (n = 442,714) were collected from the People's Hospital of the Tibet Autonomous Region from January 2018, to December 2021. Multiple linear regression and variance component analyses were performed to assess the effects of sex, age, and race on tCO2 levels. Indirect methods, including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR, were used to calculate the total RI and ethnicity-partitioned RI. RESULTS: A total of 230,821 real-world tCO2 test results were eligible. Sex, age, and race were significantly associated with the tCO2 levels. The total and ethnically-partitioned RIs estimated using the five indirect methods were comparable. The total RI of tCO2 was 14-24 mmol/L (calculated using Hoffmann and refineR) and 15-24 mmol/L (Bhattacharya, EM and kosmic). For Han nationality, the RIs were 14-25 mmol/L (calculated using Hoffmann and Bhattacharya), 16-23 mmol/L (EM), 15-24 mmol/L (kosmic), and 14.2-24.5 mmol/L (refineR). For the Tibetan population, the RIs were 14-24 mmol/L (calculated using Hoffmann and refineR), 15-24 mmol/L (Bhattacharya and kosmic), and 15-23 mmol/L (EM). The established RIs were significantly lower than those living at lower altitudes area (22-29 mmol/L) that was provided by the manufacturer. CONCLUSION: The tCO2 RI of the populations living on the Tibetan Plateau was significantly lower than those at the lower altitudes. The RIs established using indirect methods are suitable for clinical applications in Tibet.

Utilization of five data mining algorithms combined with simplified preprocessing to establish reference intervals of thyroid-related hormones for non-elderly adults.

BACKGROUND: Despite the extensive research on data mining algorithms, there is still a lack of a standard protocol to evaluate the performance of the existing algorithms. Therefore, the study aims to provide a novel procedure that combines data mining algorithms and simplified preprocessing to establish reference intervals (RIs), with the performance of five algorithms assessed objectively as well. METHODS: Two data sets were derived from the population undergoing a physical examination. Hoffmann, Bhattacharya, Expectation Maximum (EM), kosmic, and refineR algorithms combined with two-step data preprocessing respectively were implemented in the Test data set to establish RIs for thyroid-related hormones. Algorithm-calculated RIs were compared with the standard RIs calculated from the Reference data set in which reference individuals were selected following strict inclusion and exclusion criteria. Objective assessment of the methods is implemented by the bias ratio (BR) matrix. RESULTS: RIs of thyroid-related hormones are established. There is a high consistency between TSH RIs established by the EM algorithm and the standard TSH RIs (BR = 0.063), although EM algorithms seems to perform poor on other hormones. RIs calculated by Hoffmann, Bhattacharya, and refineR methods for free and total triiodo-thyronine, free and total thyroxine respectively are close and match the standard RIs. CONCLUSION: An effective approach for objectively evaluating the performance of the algorithm based on the BR matrix is established. EM algorithm combined with simplified preprocessing can handle data with significant skewness, but its performance is limited in other scenarios. The other four algorithms perform well for data with Gaussian or near-Gaussian distribution. Using the appropriate algorithm based on the data distribution characteristics is recommended.

Reference intervals for LC-MS /MS measurements of plasma renin activity, aldosterone, angiotensin II, and 24-hour urinary aldosterone in Northern Chinese Han population.

BACKGROUND: Examination of aldosterone to Renin Ratio (ARR) and plasma aldosterone concentration (PAC) or 24-h urinary aldosterone excretion (24-h UALD) was the necessary tests in confirmatory tests for primary aldosteronism (PA). We developed a combined liquid chromatography-tandem mass spectrometry method (LC-MS/MS) for plasma renin activity (PRA), PAC, and angiotensin II (Ang II) and investigated their reference intervals (RIs) in northern Chinese Han population. The RIs of 24-h UALD excretion were also studied using LC-MS/MS. METHODS: A total of 309 healthy volunteers were recruited in 3 cities in China. PRA, PAC, Ang II, and 24-h UALD were measured using the laboratory-developed LC-MS/MS. Multiple linear regression and the variance component model were applied to determine if the RI needed to be split. The RIs of PRA, PAC, and Ang II were determined using the nonparametric percentile method. RESULTS: The laboratory-developed LC-MS/MS method was verified and showed good performance. Standard deviation ratio (SDR) sex for PAC and SDR region for Ang II are 0.466 and 0.407, respectively, indicating that the RIs of PAC and Ang II must be divided by sex and region, respectively. In addition, the SDR 24hUK for 24-h UALD is 0.579, indicating that the RI of 24-h UALD must be partitioned by urine potassium. CONCLUSION: RIs were established for tests related to the renin-angiotensin-aldosterone system in the apparently healthy northern Chinese Han population by the LC-MS/MS method.

Development and clinical evaluation of an online automated quality control system for improving laboratory quality management.

BACKGROUND: We developed an efficient online automated quality control (AUTO QC) system and tested its feasibility on automatic laboratory assembly lines. METHODS: AUTO QC is based on developed quality control software (Smart QC) and designed adaptable consumables before. We applied the system to two assembly lines in our laboratory. Using third-party quality control samples, we evaluated the impact of the online AUTO QC system on out-of-control rate, biosecurity risk, turnaround time (TAT) and cost. RESULTS: AUTO QC significantly decreased the occurrence rate of the Westgard quality control rules 13S/22S/R4s and 12S, representing out-of-control and warning, respectively. The out-of-control rates were reduced by 58%, and the potential biosecurity risk of the samples decreased by 90%. The AUTO QC implementation also reduced the median TAT (by 7 min), the number of full-time employees and the cost of the quality control samples (by 45%). CONCLUSIONS: The total laboratory AUTO QC system can improve the quality and stability of QC testing and reduce cost.

How far is the goal of applying ß-amyloid in cerebrospinal fluid for clinical diagnosis of Alzheimer's disease with standardization of measurements?

Cerebrospinal fluid (CSF) ß-amyloid (Aß) is important for early diagnosis of Alzheimer's disease (AD). However, the cohort distributions and cut-off values have large variation across different analytical assays, kits, and laboratories. In this review, we summarize the cut-off values and diagnostic performance for CSF Aß1-42 and Aß1-42/Aß1-40, and explore the important effect factors. Based on the Alzheimer's Association external quality control program (AAQC program), the peer group coefficient of variation of manual ELISA assays for CSF Aß1-42 was unsatisfied (>20%). Fully automated platforms with better performance have recently been developed, but still not widely applied. In 2020, the certified reference material (CRM) for CSF Aß1-42 was launched; however, the AAQC 2021-round results did not show effective improvements. Thus, further development and popularization of CRM for CSF Aß1-42 and Aß1-40 are urgently required. Standardizing the diagnostic procedures of AD and related status and the pre-analytical protocols of CSF samples, improving detection performance of analytical assays, and popularizing the application of fully automated platforms are also important for the establishment of uniform cut-off values. Moreover, each laboratory should verify the applicability of uniform cut-off values, and evaluate whether it is necessary to establish its own population- and assay-specific cut-off values.

An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods.

BACKGROUND: Currently, the direct method is the main approach for establishment of reference interval (RI). However, only a handful of studies have described the effects of sample size on establishment of RI and estimation of sample size. We describe a novel approach for estimation of the sample size when establishing RIs using the transformed parametric and non-parametric methods. METHODS: A total of 3,697 healthy participants were enrolled in this study. We adopted a two-layer nested loop sample size estimation method to determine the effects of sample size on RI, using thyroid-related hormone as an example. The sample size was selected as the calculation result when the width of the confidence interval (CI) of the upper and lower limit of the RI were both stably < 0.2 times the width of RI. Then, we calculated the sample size for establishing RIs via transformed parametric and non-parametric methods for thyroid-related hormones. RESULTS: Sample sizes for thyroid stimulating hormone (TSH), as required by parametric and non-parametric methods to establish RIs were 239 and 850, respectively. Sample sizes required by the transformed parametric method for free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3) and total thyroxine (TT4) were all less than 120, while those required by the non-parametric method were more than 120. CONCLUSION: We describe a novel approach for estimating sample sizes for establishment of RI. A corresponding open-source code has been developed and is available for applications. The established method is suitable for most analytes, with evidence based on thyroid-related hormones indicating that different sample sizes are required to establish RIs using different methods for analytes with different variations.

Retraction notice to "Pulmonary route of administration is instrumental in developing therapeutic interventions against respiratory diseases" [Saudi Pharm. J. 28(12) (2020) 1655-1665].

[This retracts the article DOI: 10.1016/j.jsps.2020.10.012.].

Validation and comparison of five data mining algorithms using big data from clinical laboratories to establish reference intervals of thyroid hormones for older adults.

AIM: To establish Reference intervals (RIs) of thyroid-related hormones in older adults using five data mining algorithms and to assess the applicability of each algorithm. METHODS: RIs for thyroid-related hormones in older adults were established using five data mining algorithms based on physical examination and patient data. The results of these algorithms were compared to those of RIs established using healthy older adults recruited following strict inclusion and exclusion criteria. The bias ratio (BR) matrix was used to compare the limits of RIs established using different algorithms. RESULTS: Consistency across different algorithms in physical examination data was found to be greater than that of outpatient data. The transformed Hoffmann, transformed Bhattacahrya, kosmic and refineR algorithms showed good performance in calculating RIs from physical examination data. The RIs of Thyroid Stimulating Hormone (TSH) established using Expectation maximization (EM) and patient data were highly consistent with the RIs established using data from healthy older adults. CONCLUSION: This study recommends the use of transformed Hoffmann, transformed Bhattacahrya, kosmic, and refineR algorithms which are based on physical examination data to establish RIs for thyroid-related hormones in older adults. However, if patient data is used, then an EM algorithm combined with Box-Cox transformation is recommended for data with obvious skewness.

Establishment of Reference Interval and Aging Model of Homocysteine Using Real-World Data.

Objective: The level of Homocysteine (Hcy) in males is generally higher than that of females, but the same reference interval (RI) is often used in clinical practice. This study aims to establish a sex-specific RI of Hcy using five data mining algorithms and compare these results. Furthermore, age-related continuous RI was established in order to show the relationship between Hcy concentration distribution and age. Methods: A total of 20,801 individuals were included in the study and Tukey method was used to identify outliers in subgroups by sex and age. Multiple linear regression and standard deviation ratio (SDR) was used to determine whether the RI for Hcy needs to be divided by sex and age. Five algorithms including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR were utilized to establish the RI of Hcy. Generalized Additive Models for Location Scale and Shape (GAMLSS) algorithm was used to determine the aging model of Hcy and calculate the age-related continuous RI. Results: RI of Hcy needed to be partitioned by sex (SDR = 0.735 > 0.375). RIs established by Hoffmann, Bhattacharya, EM (for females) and kosmic are all within the 95% CI of reference limits established by refine R. The Sex-specific aging model of Hcy showed that the upper limits of the RI of Hcy declined with age beginning at age of 18 and began to rise approximately after age of 40 for females and increased with age for males. Conclusion: The RI of Hcy needs to be partitioned by sex. The RIs established by the five data mining algorithms showed good consistency. The dynamic sex and age-specific model of Hcy showed the pattern of Hcy concentration with age and provide more personalized tools for clinical decisions.

Predicting Elevated TSH Levels in the Physical Examination Population With a Machine Learning Model.

Objective: The purpose of this study was to predict elevated TSH levels by developing an effective machine learning model based on large-scale physical examination results. Methods: Subjects who underwent general physical examinations from January 2015 to December 2019 were enrolled in this study. A total of 21 clinical parameters were analyzed, including six demographic parameters (sex, age, etc.) and 15 laboratory parameters (thyroid peroxidase antibody (TPO-Ab), thyroglobulin antibody (TG-Ab), etc.). The risk factors for elevated TSH levels in the univariate and multivariate Logistic analyses were used to construct machine learning models. Four machine learning models were trained to predict the outcome of elevated TSH levels one year/two years after patient enrollment, including decision tree (DT), linear regression (LR), eXtreme Gradient boosting (XGBoost), and support vector machine (SVM). Feature importance was calculated in the machine learning models to show which parameter plays a vital role in predicting elevated TSH levels. Results: A total of 12,735 individuals were enrolled in this study. Univariate and multivariate Logistic regression analyses showed that elevated TSH levels were significantly correlated with gender, FT3/FT4, total cholesterol (TC), TPO-Ab, Tg-Ab, creatinine (Cr), and triglycerides (TG). Among the four machine learning models, XGBoost performed best in the one-year task of predicting elevated TSH levels (AUC (0.87(+/- 0.03))). The most critical feature in this model was FT3/FT4, followed by TPO-Ab and other clinical parameters. In the two-year task of predicting TSH levels, none of the four models performed well. Conclusions: In this study, we trained an effective XGBoost model for predicting elevated TSH levels one year after patient enrollment. The measurement of FT3 and FT4 could provide an early warning of elevated TSH levels to prevent relative thyroid diseases.

Establishment of Reference Intervals for Thyroid-Associated Hormones Using refineR Algorithm in Chinese Population at High-Altitude Areas.

Objectives: Diagnosis of thyroid disease among individuals dwelling at high altitude remains a challenge. Reference intervals (RIs) for thyroid-associated hormones among Tibetans living at various high altitudes were established to improve diagnosis. Methods: One thousand two hundred eighty-one subjects were randomly recruited from Nyingchi, Shigatse/Lhasa, and Ali of Tibet. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were measured by the Cobas e601 electrochemiluminescence analyzer. We used multiple linear regression and variance component analysis to assess the effect of sex, age, and altitude on hormones. RIs were established by refineR algorithm and compared with those provided by the manufacturer. Results: Serum TSH was significantly lower in males than in females, while FT3 and FT4 were higher in males. Both FT3 and FT4 decreased with increasing age. FT3 increased with altitude, while TSH and FT4 were less influenced by altitude. The RI for TSH was 0.764-5.784 µIU/ml, while for FT4, the RIs were 12.36-19.38 pmol/L in females and 14.84-20.18 pmol/L in males. The RIs for FT3 at Nyingchi, Shigatse/Lhasa, and Ali in females were 4.09-4.98, 4.31-5.45, and 4.82-5.58 pmol/L, while in males, the values were 4.82-5.41, 4.88-5.95, and 5.26-6.06 pmol/L, respectively. The obtained RIs for TSH and FT4 were generally higher, while that for FT3 was narrower than the RIs provided by Cobas. Conclusions: Specific RIs were established for thyroid-associated hormones among Tibetans, which were significantly different from those provided by the manufacturer.

Copeptin in fluid disorders and stress.

Copeptin, a glycosylated peptide of 39 amino acids, is the C-terminal segment of arginine vasopressin (AVP) precursor peptide, which is consisted of two other fragments, vasopressin and neurophysin Ⅱ. The main physiological functions of AVP are fluid and osmotic balance, cardiovascular homeostasis and regulation of the endocrine stress response. Numerous studies have demonstrated that the endogenous AVP in plasma is a meaningful biomarker to guide diagnosis and therapy of diseases associated with fluids disorders and stress. However, due to its instability, short half-time life in circulation and lack of readily available AVP assays, clinical measurement of AVP is restricted. In contrast to AVP, copeptin which is released in an equimolar mode with AVP from the pituitary, has emerged as a stable and simple-to-measure surrogate marker of AVP and displays excellent potential in diagnosis, differentiation and prognosis of various diseases. This review will discuss the studies on the clinical value of copeptin in different diseases, especially in AVP-dependent fluids disorders, as well as issues and prospects of the application of this potential biomarker.

Long-term ambient PM2.5 exposure associated with cardiovascular risk factors in Chinese less educated population.

INTRODUCTION: Long-term exposure to ambient air pollution is related to major cardiovascular risk factors including diabetes, hypertension, hyperlipidemia and overweight, but with few studies in high-concentration nations like China so far. We aimed to investigate the association between long-term exposure to ambient fine particulate matter (particles with an aerodynamic diameter ≤ 2.5 µm, PM2.5) and major cardiovascular risk factors in China. METHODS: Adult participants with selected biochemical tests were recruited from the Chinese Physiological Constant and Health Condition (CPCHC) survey conducted from 2007 to 2011. Gridded PM2.5 data used were derived from satellite-observed data with adjustment of ground-observed data. District-level PM2.5 data were generated to estimate the association using multivariate logistic regression model and generalized additive model. RESULTS: A total of 19,236 participants from the CPCHC survey were included with an average age of 42.8 ± 16.1 years, of which nearly half were male (47.0%). The annual average PM2.5 exposure before the CPCHC survey was 33.4 (14.8-53.4) µg/m3, ranging from 8.0 µg/m3 (Xiwuqi) to 94.7 µg/m3 (Chengdu). Elevated PM2.5 was associated with increased prevalence of hypertension (odds ratio (OR) =1.022, 95% confidence interval (95%CI): 1.001, 1.043) and decreased prevalence of overweight (OR = 0.926, 95%CI: 0.910, 0.942). Education significantly interacted with PM2.5 in association with all the interesting risk factors. Each 10 µg/m3 increment of PM2.5 was associated with increased prevalence of diabetes (OR = 1.118, 95%CI: 1.037, 1.206), hypertension (OR = 1.101, 95%CI: 1.056, 1.147), overweight (OR = 1.071, 95%CI: 1.030, 1.114) in participants with poor education, but not in well-educated population. PM2.5 exposure was negatively associated with hyperlipidemia in all participants (OR = 0.939, 95%CI: 0.921, 0.957). The results were robust in all the sensitivity analyses. CONCLUSION: Association between long-term PM2.5 exposure and cardiovascular risk factors might be modified by education. PM2.5 was associated with a higher prevalence of diabetes, hypertension, and overweight in a less-educated population with time-expose dependency. Long-term exposure to PM2.5 might be associated with a lower prevalence of hyperlipidemia.

Evaluation of bone metabolism-associated biomarkers in Tibet, China.

AIM: To measure and evaluate the distribution and possible contributing factors of seven bone metabolism-associated biomarkers in Tibet, a plateau province of China. METHODS: A total of 1615 individuals were recruited from Tibet at three different altitudes. The levels and possible contributing factors of serum calcium, serum phosphorus, ALP, 25OHD, PINP, CTX, and PTH were evaluated. RESULTS: In total, 1246 Tibetan adults (males: n = 543) were eventually enrolled in this study. Multiple linear regression recognized age, sex, altitude, and BMI as the major effect factors. The levels of ALP, PINP, and CTX in males continuously decreased with age; however, those in females increased after approximately 39 years of age. Males had higher 25OHD levels (23.9 vs. 15.4 ng/ml) but lower levels of serum phosphorus (1.12 vs. 1.19 mmol/L) and PTH (41.3 vs. 47.4 pg/ml) than females. Before the age of 50, males had higher levels of calcium, ALP, PINP, and CTX than females, and the opposite trend was observed after the age of 50. The highest levels of serum calcium and phosphorus and the lowest levels of PINP and CTX were found in the Shigatse/Lhasa region, suggesting a better bone metabolism status. Compared with reports from plain areas of China, significantly higher levels of PINP (65.3 vs. 49.36 ng/ml) and CTX (0.46 vs. 0.37 ng/ml) were recorded in Tibetan adults. CONCLUSION: A more active bone turnover status was found in Tibetan adults than in individuals from the plain areas of China.

Analysis of gender-specific associations between aldehyde dehydrogenase 2 (ALDH2) rs671 genetic polymorphisms and serum uric acid levels in Han Chinese.

BACKGROUND: Serum uric acid (SUA) is influenced by lifestyle and genetics, and unbalanced SUA levels are linked to various common disorders. While the aldehyde dehydrogenase 2 (ALDH2) rs671 polymorphism appears to be associated with SUA levels, the evidence remains inconclusive. The aim of this study was to examine the distribution of the ALDH2 rs671 polymorphism among Han Chinese in Beijing and determine the association between this polymorphism and SUA. METHODS: A total of 6,461 randomized healthy individuals were included in the study. Biochemical indicators were tested and ALDH2 rs671 polymorphism testing was conducted for subjects enrolled in the study. The distribution of the ALDH2 rs671 polymorphism and the relationship between genotype and the levels of serum lipids and uric acid (UA) were analyzed. RESULTS: The ALDH2 rs671 genotype frequencies were 68.1% (G/G), 29.3% (G/A), and 2.6% (A/A). There was no significant difference in allele distribution between males and females. In males, different ALDH2 genotypes exhibited significant differences in several biochemical analytes, including body mass index (BMI), blood glucose (Glu), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), UA, glutamyl transpeptidase (GGT), and creatinine (Cr) (P<0.05). No such differences were found in females. SUA levels in G/A and A/A-carrying males were significantly lower than those of G/G-carrying males. The effect of the ALDH2 polymorphism on UA was still significant after further adjustment for factors including BMI, Glu, TC, HDL-C, Cr, and GGT. CONCLUSIONS: The ALDH2 polymorphism is related to SUA in Beijing males, and A allele-carrying males have lower SUA levels.

Establishment of Early Pregnancy Related Thyroid Hormone Models and Reference Intervals for Pregnant Women in China Based on Real World Data.

Thyroid hormone reference intervals are crucial for diagnosing and monitoring thyroid dysfunction during early pregnancy, and the dynamic change trend of thyroid hormones during pregnancy can assist clinicians to assess the thyroid function of pregnant women. This study aims to establish early pregnancy related thyroid hormones models and reference intervals for pregnant women. We established two derived databases: derived database* and derived database#. Reference individuals in database* were used to establish gestational age-specific reference intervals for thyroid hormones and early pregnancy related thyroid hormones models for pregnant women. Individuals in database# were apparently healthy non-pregnant women. The thyroid hormones levels of individuals in database# were compared with that of individuals in database* using nonparametric methods and the comparative confidence interval method. The differences in thyroid stimulating hormone and free thyroxine between early pregnant and non-pregnant women were statistically significant (p<0.0001). The reference intervals of thyroid stimulating hormone, free thyroxine and free triiodothyronine for early pregnant women were 0.052-3.393 µIU/ml, 1.01-1.54 ng/dl, and 2.51-3.66 pg/ml, respectively. Results concerning thyroid stimulating hormone and free thyroxine reference intervals of early pregnancy are comparable with those from other studies using the same detection platform. Early pregnancy related thyroid hormones models showed various change patterns with gestational age for thyroid hormones. Early pregnancy related thyroid hormones models and reference intervals for pregnant women were established, so as to provide accurate and reliable reference basis for the diagnosing and monitoring of maternal thyroid disfunction in early pregnancy.

Effect of sample size and the traditional parametric, nonparametric, and robust methods on the establishment of reference intervals: Evidence from real world data.

Sample size and statistical methods are critical for establishing reference intervals (RIs) but they tend to be overlooked. In this study, we used R (3.6.3) to stratify the reference individuals by sex, and then stratified them using the random sampling method. Fourteen sub-data sets with a sample size of 40, 80, 120, 160, 200, 500, 800, 1000, 1500, 2000, 2500, 3000, 3500, and 4000 were extracted, respectively. The sex ratios of all sub-data sets were 1:1. Transformed parametric (using log transformation), nonparametric, and robust approaches as described in the Clinical and Laboratory Standards Institute guidelines were adopted to establish the RIs and the 90% confidence interval of the thyroid-stimulating hormone (TSH) using data from the sub-data sets. The Bland-Altman plot was used to evaluate the consistency of the upper and lower limits of the RIs established using the three methods. The upper and lower limits of TSH RI tended to be stable starting from the data set with a sample size of 1500. The RIs established using the three methods were more consistent when using a sample size greater than or equal to 2000.