Reference intervals for thyroid-stimulating hormone, free thyroxine, and free triiodothyronine in elderly Chinese persons

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.

A preliminary study on the reference intervals of serum tumor marker in apparently healthy elderly population in southwestern China using real-world data

BACKGROUND

The aim is to establish and verify reference intervals (RIs) for serum tumor markers for an apparently healthy elderly population in Southwestern China using an indirect method.

METHODS

Data from 35,635 apparently healthy elderly individuals aged 60 years and above were obtained in West China Hospital from April 2020 to December 2021. We utilized the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate outliers. Subgroups are divided according to gender and age to examine the division of RIs. The Z-test was used to compare differences between groups, and 95% distribution RIs were calculated using a nonparametric method.

RESULTS

In the study, we observed that the RIs for serum ferritin and Des-γ-carboxy prothrombin (DCP) were wider for men, ranging from 64.18 to 865.80 ng/ml and 14.00 to 33.00 mAU/ml, respectively, compared to women, whose ranges were 52.58 to 585.88 ng/ml and 13.00 to 29.00 mAU/ml. For other biomarkers, the overall RIs were established as follows: alpha-fetoprotein (AFP) 0-6.75 ng/ml, carcinoembryonic antigen (CEA) 0-4.85 ng/ml, carbohydrate antigen15-3 (CA15-3) for females 0-22.00 U/ml, carbohydrate antigen19-9 (CA19-9) 0-28.10 U/ml, carbohydrate antigen125 (CA125) 0-20.96 U/ml, cytokeratin 19 fragment (CYFRA21-1) 0-4.66 U/ml, neuron-specific enolase (NSE) 0-19.41 ng/ml, total and free prostate-specific antigens (tPSA and fPSA) for males 0-5.26 ng/ml and 0-1.09 ng/ml. The RIs for all these biomarkers have been validated through our rigorous processes.

CONCLUSION

This study preliminarily established 95% RIs for an apparently healthy elderly population in Southwestern China. Using real-world data and an indirect method, simple and reliable RIs for an elderly population can be both established and verified, which are suitable for application in various clinical laboratories.

Incidence and Determinants of Spontaneous Normalization of Subclinical Hypothyroidism in Older Adults

CONTEXT

With age, the prevalence of subclinical hypothyroidism rises. However, incidence and determinants of spontaneous normalization remain largely unknown.

OBJECTIVE

To investigate incidence and determinants of spontaneous normalization of TSH levels in older adults with subclinical hypothyroidism.

DESIGN

Pooled data were used from the (1) pretrial population and (2) in-trial placebo group from 2 randomized, double-blind, placebo-controlled trials (Thyroid Hormone Replacement for Untreated Older Adults With Subclinical Hypothyroidism Trial and Institute for Evidence-Based Medicine in Old Age thyroid 80-plus thyroid trial).

SETTING

Community-dwelling 65+ adults with subclinical hypothyroidism from the Netherlands, Switzerland, Ireland, and the United Kingdom.

PARTICIPANTS

The pretrial population (N = 2335) consisted of older adults with biochemical subclinical hypothyroidism, defined as ≥1 elevated TSH measurement (≥4.60 mIU/L) and a free T4 within the laboratory-specific reference range. Individuals with persistent subclinical hypothyroidism, defined as ≥2 elevated TSH measurements ≥3 months apart, were randomized to levothyroxine/placebo, of which the in-trial placebo group (N = 361) was included.

MAIN OUTCOME MEASURES

Incidence of spontaneous normalization of TSH levels and associations between participant characteristics and normalization.

RESULTS

In the pretrial phase, TSH levels normalized in 60.8% of participants in a median follow-up of 1 year. In the in-trial phase, levels normalized in 39.9% of participants after 1 year of follow-up. Younger age, female sex, lower initial TSH level, higher initial free T4 level, absence of thyroid peroxidase antibodies, and a follow-up measurement in summer were independent determinants for normalization.

CONCLUSION

Because TSH levels spontaneously normalized in a large proportion of older adults with subclinical hypothyroidism (also after confirmation by repeat measurement), a third measurement may be recommended before considering treatment.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01660126 and Netherlands Trial Register, NTR3851.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

Population-wide analysis of hospital laboratory tests to assess seasonal variation and temporal reference interval modification

We identified mortality-, age-, and sex-associated differences in relation to reference intervals (RIs) for laboratory tests in population-wide data from nearly 2 million hospital patients in Denmark and comprising more than 300 million measurements. A low-parameter mathematical wave-based modification method was developed to adjust for dietary and environment influences during the year. The resulting mathematical fit allowed for improved association rates between re-classified abnormal laboratory tests, patient diagnoses, and mortality. The study highlights the need for seasonally modified RIs and presents an approach that has the potential to reduce over- and underdiagnosis, affecting both physician-patient interactions and electronic health record research as a whole.

Free thyroxine measurement in clinical practice: how to optimize indications, analytical procedures, and interpretation criteria while waiting for global standardization

Thyroid dysfunctions are among the most common endocrine disorders and accurate biochemical testing is needed to confirm or rule out a diagnosis. Notably, true hyperthyroidism and hypothyroidism in the setting of a normal thyroid-stimulating hormone level are highly unlikely, making the assessment of free thyroxine (FT4) inappropriate in most new cases. However, FT4 measurement is integral in both the diagnosis and management of relevant central dysfunctions (central hypothyroidism and central hyperthyroidism) as well as for monitoring therapy in hyperthyroid patients treated with anti-thyroid drugs or radioiodine. In such settings, accurate FT4 quantification is required. Global standardization will improve the comparability of the results across laboratories and allow the development of common clinical decision limits in evidence-based guidelines. The International Federation of Clinical Chemistry and Laboratory Medicine Committee for Standardization of Thyroid Function Tests has undertaken FT4 immunoassay method comparison and recalibration studies and developed a reference measurement procedure that is currently being validated. However, technical and implementation challenges, including the establishment of different clinical decision limits for distinct patient groups, still remain. Accordingly, different assays and reference values cannot be interchanged. Two-way communication between the laboratory and clinical specialists is pivotal to properly select a reliable FT4 assay, establish reference intervals, investigate discordant results, and monitor the analytical and clinical performance of the method over time.

Preliminary observation of thyroid function changes in subclinical thyroid diseases

BACKGROUND

It is estimated that 1.95% and 5.55% of adults in China suffer from subclinical thyroid diseases, which is difficult to diagnose and treat.

OBJECTIVE

This study aimed to explore the development and prognosis of subclinical thyroid diseases to provide a reference from our single center experience.

METHODS

A total of 240 cases from April 2019 to August 2021 in the laboratory information system database of Huanghua Development Boai Hospital were retrospectively analyzed. Binary logistic regression was conducted to analyze odds ratio (OR) of subclinical thyroid disease types returning to a normal state.

RESULTS

Among the patients hypothyroidism Ia and hyperthyroidism Ia were the most common type with conversion to the normal state (P< 0.001). TSH level of patients with conversion to a normal state was significantly lower than that of those who developed to abnormal disease (P= 0.015). The OR values of hyperthyroidism Ia and hypothyroidism Ia that returned to a normal state compared with hyperthyroidism Ib were 2.659 (1.159 ∼ 6.096, P= 0.021) and 3.138 (1.1.278 ∼ 7.709, P= 0.013), respectively. The OR value of hypothyroidism Ib that returned to normal compared with hyperthyroidism Ib was 0.629 (0.131 ∼ 3.010, P= 0.561). Thyroid hormone levels, age, and gender at first diagnosis were not impact factor for prognosis of subclincal thyroid disease (P> 0.05).

CONCLUSION

Cases with grade hypothyroidism Ia and hyperthyroidism Ia are more likely to revert to normal state than other subclinical thyroid diseases. TSH reference range should be explored for diagnosis and treatment.

Age-specific serum thyrotropin reference range for the diagnosis of subclinical hypothyroidism and its association with lipid profiles in the elderly population

The overdiagnosis of subclinical hypothyroidism (SCH) in the elderly has driven researchers to establish age-specific thyroid stimulating hormone (TSH) intervals to precisely evaluate the prevalence of SCH. Moreover, abnormal lipid profiles, an insidious manifestation of SCH, show various impacts on different age groups. This study aimed to establish an age-specific TSH reference range to clarify the spectrum of SCH in the elderly. The prevalence of dyslipidemia and the age-specific association between TSH and lipid profiles were analyzed to elucidate the relationship between SCH and dyslipidemia. This cross-sectional study enrolled 2460 participants aged ≥ 65 years via cluster sampling. All participants received physical, laboratory tests and thyroid ultrasound examination and completed the questionnaire. The chi-square test was used to analyze variations of dyslipidemia prevalence among different groups. The Cochran-Armitage trend test was applied for testing the linear trends of age-specific prevalence of dyslipidemia among different TSH intervals in each age group. After adjusting for confounding factors, the age-specific association between TSH and lipid profiles was identified using multi-variate linear regression analysis. The TSH reference ranges in the 65-70 age group, 71-80 age group and > 80 age group were 0.65-5.51 mIU/L, 0.85-5.89 mIU/L and 0.78-6.70 mIU/L, respectively. Using these age-specific reference ranges, the prevalence of SCH in the whole population was 3.74%, which was significantly lower than the prevalence based on the laboratory reference range (10.28%). In the 65-70 age group, only the prevalence of high total cholesterol (TC) increased significantly with the age-specific TSH intervals, and TSH was positively associated with TC and low-density lipoprotein cholesterol (LDL-C). In the 71-80 and > 80 age groups, the prevalence of high TC, high triglycerides (TGs), and high LDL-C increased significantly with elevated TSH reference ranges. The levels of TC, TGs, and LDL-C were also positively associated with TSH level in 71-80 age group. However, such an association disappeared in > 80 age group. An age-specific reference range for TSH can effectively prevent the overdiagnosis of SCH in the elderly. Aging could somewhat attenuate the impact of TSH on lipid profiles.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01751-1.

Seasonal variation in thyroid function in over 7,000 healthy subjects in an iodine-sufficient area and literature review

Context

Seasonal variation in thyroid function, especially serum free triiodothyronine (FT3) and free thyroxine (FT4) levels, in healthy subjects remains unclear.

Methods

We examined thyroid function, including serum FT3 and FT4 levels, in healthy Japanese subjects using data of more than 7,000 health check-up participants and applied the analysis of means with transformed ranks (ANOMTR) to compare each month. In addition, we reviewed reports published in the last 2 decades.

Results

The median serum thyrotropin (TSH) level was the highest in January (1.61 mIU/L), and the lowest in May (1.16 mIU/L). ANOMTR revealed that serum TSH levels are high in winter and low in summer. Conversely, the median serum FT3 level was higher in July than in other months, and the ANOMTR plot demonstrated serum FT3 levels to be significantly higher in summer and lower in winter. In contrast, serum FT4 levels were more consistent throughout the year, but statistically, those in February and March, October, and November were higher than those in other months. ANOMTR revealed variations in serum FT4 levels to be small through the year but biphasic.

Conclusions

Taken together with previous reports, our study demonstrated seasonal changes in the serum TSH levels to be high in winter in the northern hemisphere; however, the serum FT3 differed among countries, and those of Japanese, an iodine-sufficient country, were high in summer. In contrast, FT4 levels were more consistent. These changes should be taken into account to precisely evaluate thyroid function.

Establishing reference intervals for islet autoantibodies in Han Chinese type 1 diabetes

Currently, islet autoantibodies (IAbs) constitute the most reliable marker for detecting the autoimmune process of type 1 diabetes (T1D). However, there are no appropriate reference intervals (RIs) to interpret the results of IAbs in China. In this study, we aimed to establish the RIs of four common IAbs based on the Han Chinese population and evaluate their clinical diagnostic values in patients with T1D. We collected 177 blood samples from healthy volunteers to detect the levels of IAbs directed against insulin (IAA), glutamic acid decarboxylase-65 (GADA), insulinoma antigen 2 (IA-2A), and zinc transporter-8 (ZnT8A) using a chemiluminescence immunoassay. RIs were calculated using nonparametric 95th percentile intervals in accordance with the Clinical and Laboratory Standards Institute guidelines, and their clinical diagnostic values were evaluated by detecting the levels of IAbs of 140 blood samples from patients with T1D in a clinical setting. We defined 138 individuals as the apparently healthy population from the 177 healthy volunteers based on the exclusion criteria. No association between the levels of the four IAbs and gender (p > .05) and age (p > .05) were found in the apparently healthy population. The combined RIs for GADA, IA-2A, ZnT8A, and IAA were 0-1.78 IU/mL, 0-3.91 IU/mL, 0-2.36 AU/mL, and 0-0.58 COI, respectively. Overall, the diagnostic efficiency for the four IAbs, especially for GADA and IAA, were improved by using the RIs established in this study. The RIs for IAbs established in this study will be a valuable tool for disease diagnosis and the therapeutic management of T1D in a clinical setting.

Age- and sex-specific reference intervals for thyroid hormones in a Chinese pediatrics: a prospective observational study of 1,279 healthy children

BACKGROUND

Thyroid hormones are essential for early neurocognitive development and growth and development in childhood and adolescence. However, the reference intervals (RIs) for thyroid hormones in Chinese pediatric individuals remain unclear. This study aimed to establish thyroid hormone RIs for a Chinese pediatric population according to appropriate age- and sex-specific partitioning.

METHODS

In this prospective observational study, a total of 1,279 healthy children (singletons, aged from 1 day to 12 years) were recruited, and serum samples were analyzed on a Mindray automated chemiluminescence immunoassay analyzer CL-6000i for thyroid hormone detection, including thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (T3), and total thyroxine (T4). Age and sex-specific RIs were established, and the corresponding 95% confidence intervals (CIs) were calculated in accordance with the Clinical Laboratory Standards Institute (CLSI) document C28-A3 guidelines.

RESULTS

Quantile testing revealed that the median (P50) and RIs [2.5^th percentile (P2.5)-97.5^th percentile (P97.5)] for TSH, FT3, T3, and T4 of males differed significantly from those of females (P<0.05), except for FT4 (P=0.483). For FT3 and T3, the RIs of males were higher than that of females, and the RI of T4 for males was narrower and higher than that of females [P2.5-P97.5: 72.33-171.60 vs. 72.31-176.27 nmol/L; P50: 116.75 vs. 113.47 nmol/L, P=0.011]. RIs for TSH, FT3, FT4, T3, and T4 showed sex- and age-specific properties and displayed a wide variation during the first month of life but gradually narrowed and concentrated with increasing age. In addition, RIs of TSH, FT3, FT4, and T3 in males differed significantly from females in the first month of life (TSH: 1.46-10.87 vs. 1.08-11.35 mIU/L; FT3: 2.96-7.08 vs. 2.35-7.27 pmol/L, FT4: 13.34-28.65 vs. 13.82-31.83 pmol/L; T3: 0.83-2.33 vs. 0.72-2.46 nmol/L). The RI of T4 also exhibited a difference between males and females in the 9- to 12-year age group (59.31-150.72 vs. 63.29-146.94 pmol/L for males and females, respectively).

CONCLUSIONS

Pediatric RIs of thyroid hormones display age- and sex-specific trends. The RIs established in this study will improve the accuracy of TSH assay result interpretations and clinical decision-making in clinical laboratories that utilize the Mindray analytical platform.

Climate and air pollution exposure are associated with thyroid function parameters: a retrospective cross-sectional study

OBJECTIVES

There are still controversies about the impact of climatic and environmental factors on thyroid function parameters in healthy populations. We investigated the relationships between climate, air pollution exposure, and thyroid function fluctuations.

METHODS

We retrospectively reviewed 327,913 individuals attending routine health checks from December 2013 to December 2018. We analyzed the associations between thyroid function and climatic factors using Spearman's correlation analysis. We explored the relationships between thyroid function and air pollution exposure using multiple linear regression analysis, after adjusting for age, sex, season, and outdoor temperature. We also performed subgroup analyses by age and sex and sensitivity analyses of different anti-thyroid peroxidase antibody status.

RESULTS

Thyroid-stimulating hormone (TSH) and free triiodothyronine (FT3) were negatively associated with outdoor temperature (r = - 0.66, P < 0.001; r = - 0.55, P < 0.001), while free thyroxine (FT4) and FT4/FT3 were positively associated with temperature (r = 0.35, P < 0.001; r = 0.79, P < 0.001). An increase of 10 μg/m³ in fine particulate matter ≤ 2.5 μm (PM2.5) was associated with a decrease of 0.12 pmol/L in FT4 and an increase of 0.07 pmol/L in FT3 (both P < 0.01). FT4/FT3 was significantly negatively associated with PM2.5 (coefficient: - 0.06, P < 0.01). These results remained robust in hierarchical analyses and sensitivity analyses.

CONCLUSIONS

Thyroid function parameters are associated with climate and air pollution exposure. These factors may influence variations in thyroid function. Our results also highlight the importance of public health interventions to reduce air pollution.

Lower normal free thyroxine is associated with a higher risk of metabolic syndrome: a retrospective cohort on Chinese population

BACKGROUND

Recently, the relationship between thyroid hormones (THs) across the euthyroid ranges and metabolic syndrome (MetS) has been widely discussed. This study aimed to present specific cutoff values of THs to assess the association between THs and MetS in a euthyroid cohort.

METHODS

Data of 2694 subjects, aged 18-80 years, who attended health examination in Xi'an Electric Power Central Hospital from April 2011 to December 2015 were collected and analyzed. The first cohort enrolled 929 participants (followed up by 2221 person-years totally) to assess correlations between serum thyrotropin (TSH), triiodothyronine (T3), thyroxine (T4) levels and MetS. The second cohort included 698 participants (followed up by 1709 person-years totally) to evaluate relationships between serum free triiodothyronine (FT3), free thyroxine (FT4) levels and MetS. MetS was defined according to the criteria of the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) scientific statements of 2009. Euthyroidism was defined as serum TSH, FT3 and FT4 levels within the reference ranges without taking any thyroid medication.

RESULTS

The cutoff values for TSH, T3, T4, FT3 and FT4 were 2.0mIU/L, 1.9 nmol/L, 117 nmol/L, 4.3 pmol/L and 16 pmol/L, respectively. Participants were categorized into two groups according to cutoff values: the lower-THs group and the higher-THs group. There was no significant difference in the risk of MetS between two groups in TSH, T3, T4 and FT3. The incidence of MetS was significantly higher in lower-FT4 group than higher-FT4 group (1.00 vs 0.622 (0.458, 0.846), P = 0.002). The lower-FT4/higher-TSH group had the highest hazard ratios of MetS. (2.131vs 1.0 (1.380,3.291), P = 0.006).

CONCLUSIONS

Lower normal FT4 (FT4 ≤ 16.0 pmol/L) is an independent risk factor for MetS, and lower normal thyroid function (TSH > 2.0 mIU/L and FT4 ≤ 16.0 pmol/L) is associated with a higher risk of developing MetS.

Seasonal variations in levels of human thyroid-stimulating hormone and thyroid hormones: a meta-analysis

Seasonal dynamics in biological functions of mammals is regulated by melatonin-mediated circannual fluctuations in the secretion of thyroid-stimulating hormone (TSH) and thyroid hormones. Most anatomical and molecular structures responsive to photoperiod and melatonin secretion changes and the associated receptors are preserved in modern humans. This work aimed to determine the seasonal dynamics of TSH and thyroid hormone levels (total triiodothyronine (T3), free triiodothyronine (FT3), thyroxine (T4), free thyroxine (FT4) and to investigate the dependence of these variations on gender, age and amplitude of meteorological fluctuations. A meta-analysis of 13 panel and 7 cross-sectional studies was performed using Review Manager 5.3 (Cochrane Library). We found that circulating TSH levels were higher in winter than in other seasons, and FT4 levels were higher in autumn than in winter. T4 level had no pronounced seasonal dynamics. The level of circulating T3 was significantly higher in winter than in summer and FT3 levels were lower in summer than in autumn and spring. In addition, analysis of TSH seasonal dynamics (winter vs summer) accounting for gender differences showed pronounced increases in TSH levels during winter in women, but not in men; and also significant increases in FT4 levels during summer in men, but not in women. Seasonal dynamics of FT3 and T4 did not depend on gender. Seasonal dynamics of TSH did not change with respect to age. We also found that the extent of the seasonal dynamics of TSH is influenced by the extent of the annual dynamics of the partial density of oxygen in the air, as well as the magnitude of the annual dynamic of meteorological factors that determine it (atmospheric pressure and relative humidity).

Establishment of variation source and age-related reference interval models for 22 common biochemical analytes in older people using real-world big data mining

BACKGROUND

the ageing population has increased in many countries, including China. However, reference intervals (RIs) for older people are rarely established because of difficulties in selecting reference individuals. Here, we aimed to analyse the factors affecting biochemical analytes and establish RI and age-related RI models for biochemical analytes through mining real-world big data.

METHODS

data for 97,220 individuals downloaded from electronic health records were included. Three derived databases were established. The first database included 97,220 individuals and was used to build age-related RI models after identifying outliers by the Tukey method. The second database consisted of older people and was used to establish variation source models and RIs for biochemical analytes. Differences between older and younger people were compared using the third database.

RESULTS

sex was the main source of variation of biochemical analytes for older people in the variation source models. The distributions of creatinine and uric acid were significantly different in the RIs of biochemical analytes for older people established according to sex. Age-related RI models for biochemical analytes that were most affected by age were built and visualized, revealing various patterns of changes from the younger to older people.

CONCLUSION

the study analysed the factors affecting biochemical analytes in older people. Moreover, RI and age-related RI models of biochemical analytes for older people were established to provide important insight into biological processes and to assist clinical use of various biochemical analytes to monitor the status of various diseases for older people.

Gender and age-specific reference intervals of common biochemical analytes in Chinese population: Derivation using real laboratory data

Background

Indirect sampling methods are not only inexpensive but also efficient for establishing reference intervals (RIs) using clinical data. This study was conducted to select fully normal records to establish ageand gender-specific RIs for common biochemical analytes by laboratory data mining.

Methods

In total, 280,206 records from 2014 to 2018 were obtained from Peking Union Medical College Hospital. Common biochemical analytes total protein, albumin, total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), potassium, sodium, chlorine, calcium, urea, glucose, uric acid (UA), inorganic phosphorus, creatinine (Cr), total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol] were measured using an automatic analyzer. Sources of variation were identified by multiple regression analysis. The 2.5th and 97.5th percentiles were calculated as the lower and upper limits of the RIs, respectively.

Results

Gender was the major source of variation among the 13 common biochemical analytes with an rp > 0.15. In contrast to the value listed in the WS/T 404, nearly all RIs established in this study were significantly narrower. Furthermore, age-specific RIs should be determined for DBil, LDH, and urea, whereas gender-specific RIs are suggested for GGT, LDH, and urea.

Conclusions

We recommend that gender-specific RIs should be established for ALT, AST, GGT, DBil, TBil, UA, and Cr as well as genderand age-specific RIs for urea and ALP. Through indirect sampling, ageand gender-specific RIs for common biochemical analytes were established and analyzed.

Data mining: Seasonal fluctuations and associations between thyroid stimulating hormone and lipid profiles

OBJECTIVE

Thyroid stimulating hormone (TSH) is associated with lipid metabolism. In this study, we aimed to evaluate seasonal variations and the association between TSH and lipid profiles based on clinical big data.

METHOD

This observational, retrospective big data study enrolled a total of 20,192 individuals who visited Peking Union Medical College Hospital for routine health check-ups from 2014 to 2018. Demographic, medical history, common biochemical analytes, and thyroid related test data were obtained. A Kruskal-wallis analysis was used to compare the differences in total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) by TSH quartiles. Spearman correlation analysis was used to evaluate the association between TSH and lipid profiles as well as temperature.

RESULTS

TC and LDL did not vary significantly with TSH concentration; however, TG and HDL-C did. TSH concentration showed weak positive correlation with serum TC, TG, and HDL-C but not with LDL-C. Serum TC concentration was positively correlated with TG and LDL-C. TG was positively correlated with LDL-C but negatively correlated with HDL-C. HDL-C was negatively correlated with LDL-C. TSH and lipid profiles showed seasonal fluctuations. Monthly median TSH, TC, and LDL-C peaked in winter and dropped to a minimum in summer. The correlation coefficient (r) between the average monthly temperature and TSH, TC, TG, HDL-C, and LDL-C was -0.424 (p = 0.001), -0.539 (p < 0.001), -0.020 (p = 0.880), -0.199 (p = 0.127), and -0.442 (p < 0.001), respectively.

CONCLUSION

Seasonal variation was observed in both TSH and lipids. Apart from the seasonal variation of TC and LDL-C, our results also have clinical interpretation. It suggested that it may not reflect the real status of lipids during and immediately after the Spring festival. Thus, in order to diagnosis of hypercholesterolemia, re-testing was needed later to provide the precision diagnostic, monitoring and treatment.