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Jalei AA, Omar AI, Hassan SA, Hassan YSA, Ahmed NR. Establishment of Reference Intervals for Common Renal and Liver Function Parameters in Healthy Adults in Mogadishu, Somalia: A Cross-Sectional Study. Int J Gen Med 2024; 17:4163-4170. [PMID: 39308973 PMCID: PMC11414748 DOI: 10.2147/ijgm.s480478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 09/08/2024] [Indexed: 09/25/2024] Open
Abstract
Introduction Reference intervals (RIs) are crucial for the accurate interpretating of laboratory test results in clinical settings, serving as benchmarks for evaluating individual health status. This study investigates the influence of sex and age on common liver function tests (LFTs) and renal function tests (RFTs) in healthy adults in Mogadishu, Somalia. Methods A community-based cross-sectional study was carried out from October 2022 to January 2023 on a randomly selected sample of 255 healthy participants from Mogadishu, Somalia. Approximately 5 mL of whole blood was collected from each participant and processed screening of hepatitis B and C, and human immunodeficiency virus, and then biochemical analyses were conducted for common liver and kidney parameters. Results The study found significant sex and age-related differences in the measured LFTs and RFTs parameters. For LFTs, males had higher levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) compared to females (ALT: 11.5 vs 7.5 U/L; AST: 25.5 vs 19.1 U/L; both p < 0.001). Age-related differences were also observed, with individuals aged 30 and above had higher levels of ALT and AST compared to those aged 18-29 (ALT: 10.9 vs 8.5 U/L; AST: 24.3 U/L vs 21.0 U/L, both p < 0.001). For RFTs, males had higher levels of creatinine (0.9 vs 0.7 mg/dL), urea (23.1 vs 16.1 mg/dL), and uric acid (5.2 vs 4.2 mg/dL) than females, all with p < 0.001. Conclusion The study established population specific RIs for common liver and renal function parameters and revealed significant variations across sex and age groups. These findings underscore the importance of developing and using local RIs to ensure accurate clinical interpretation and effective patient management. Further research with larger sample sizes and in diverse regions of Somalia is highly recommended.
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Affiliation(s)
- Abdifatah Abdullahi Jalei
- Faculty of Medicine and Health Sciences, Jamhuriya University of Science and Technology, Mogadishu, Somalia
| | - Abdifetah Ibrahim Omar
- Faculty of Medicine and Health Sciences, Jamhuriya University of Science and Technology, Mogadishu, Somalia
- Jamhuriya Research Center, Jamhuriya University of Science and Technology, Mogadishu, Somalia
| | - Shafie Abdulkadir Hassan
- Faculty of Medicine and Health Sciences, Jamhuriya University of Science and Technology, Mogadishu, Somalia
| | | | - Nur Rashid Ahmed
- Faculty of Medicine and Health Sciences, Jamhuriya University of Science and Technology, Mogadishu, Somalia
- Jamhuriya Research Center, Jamhuriya University of Science and Technology, Mogadishu, Somalia
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Freire MDC, Dias PRTP, Souza TSP, Hirose CK, Araujo PBMC, Neves MFT. Insulin reference intervals in Brazilian adolescents by direct and indirect approaches: validation of a data mining method from laboratory data. J Pediatr (Rio J) 2024; 100:512-518. [PMID: 38670169 PMCID: PMC11361890 DOI: 10.1016/j.jped.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVE To determine reference intervals (RI) for fasting blood insulin (FBI) in Brazilian adolescents, 12 to 17 years old, by direct and indirect approaches, and to validate indirectly determined RI. METHODS Two databases were used for RI determination. Database 1 (DB1), used to obtain RI through a posteriori direct method, consisted of prospectively selected healthy individuals. Database 2 (DB2) was retrospectively mined from an outpatient laboratory information system (LIS) used for the indirect method (Bhattacharya method). RESULTS From DB1, 29345 individuals were enrolled (57.65 % female) and seven age ranges and sex partitions were statistically determined according to mean FBI values: females: 12 and 13 years-old, 14 years-old, 15 years-old, 16 and 17 years-old; and males: 12, 13 and 14 years-old, 15 years-old, 16 and 17 years-old. From DB2, 5465 adolescents (67.5 % female) were selected and grouped according to DB1 partitions. The mean FBI level was significantly higher in DB2, on all groups. The RI upper limit (URL) determined by Bhattacharya method was slightly lower than the 90 % CI URL directly obtained on DB1, except for group female 12 and 13 years old. High agreement rates for diagnosing elevated FBI in all groups on DB1 validated indirect RI presented. CONCLUSION The present study demonstrates that Bhattacharya indirect method to determine FBI RI in adolescents can overcome some of the difficulties and challenges of the direct approach.
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Affiliation(s)
- Monica D C Freire
- Universidade do Estado do Rio de Janeiro, Pós Graduação em Ciências Médicas, Rio de Janeiro, RJ, Brazil.
| | - Paulo R T P Dias
- Universidade Federal Fluminense, Instituto de Saúde Coletiva, Departamento de Epidemiologia e Bioestatística, Niterói, RJ, Brazil; Instituto de Ensino e Pesquisa DASA, São Paulo, SP, Brazil; Universidade do Estado do Rio de Janeiro, Núcleo de Estudos e Pesquisas em Atenção ao Uso de Drogas, Rio de Janeiro, RJ, Brazil
| | - Thiago S P Souza
- Universidade do Estado do Rio de Janeiro, Instituto de Matemática e Estatística, Rio de Janeiro, RJ, Brazil
| | | | - Paula B M C Araujo
- Universidade Federal do Rio de Janeiro, Faculdade de Medicina, Pós-graduação em Endocrinologia, Rio de Janeiro, RJ, Brazil
| | - Mario F T Neves
- Universidade do Estado do Rio de Janeiro, Faculdade de Ciências Médicas, Rio de Janeiro, RJ, Brazil
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Ma C, Yu Z, Qiu L. Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration. Crit Rev Clin Lab Sci 2024; 61:298-316. [PMID: 38146650 DOI: 10.1080/10408363.2023.2291379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023]
Abstract
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.
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Affiliation(s)
- Chaochao Ma
- Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Zheng Yu
- Department of Operations Research and Financial Engineering, Princeton University, Princeton University, Princeton, NJ, USA
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
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Vinnes EW, Alnaes MB, Storaas T. Serum mast cell tryptase reference intervals in European populations. Clin Exp Allergy 2024; 54:145-147. [PMID: 37963601 DOI: 10.1111/cea.14421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/16/2023]
Affiliation(s)
- Erik Wilhelm Vinnes
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marie Bjørbak Alnaes
- Department of Occupational Medicine, Section for Allergology, Haukeland University Hospital, Bergen, Norway
| | - Torgeir Storaas
- Department of Occupational Medicine, Section for Allergology, Haukeland University Hospital, Bergen, Norway
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Velev J, LeBien J, Roche-Lima A. Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population. Sci Rep 2023; 13:17198. [PMID: 37821500 PMCID: PMC10567761 DOI: 10.1038/s41598-023-43830-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023] Open
Abstract
Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used in practice for the same analyte and the same range is used for all patients despite evidence that the values are gender, age, and ethnicity dependent. Moreover, the abnormal flags are rudimentary, merely indicating if a value is within the RI. At the same time, clinical lab data generated in the everyday medical practice contains a wealth of information, that given the correct methodology, can help determine the RIs for each specific segment of the population, including populations that suffer from health disparities. In this work, we develop unsupervised machine learning methods, based on Gaussian mixtures, to determine RIs of analytes related to chronic kidney disease, using millions of routine lab results for the Puerto Rican population. We show that the measures are both gender and age dependent and we find evidence for normal age-related organ function deterioration and failure. We also show that the joint distribution of measures improves the diagnostic value of the lab results.
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Affiliation(s)
- Julian Velev
- Department of Physics, University of Puerto Rico, San Juan, PR, 00925-2537, USA.
- Abartys Health, San Juan, PR, 00907-3913, USA.
| | - Jack LeBien
- Abartys Health, San Juan, PR, 00907-3913, USA
| | - Abiel Roche-Lima
- Center for Collaborative Research in Health Disparities - CCHRD, RCMI Program, Medical Science Campus, University of Puerto Rico, San Juan, PR, 00936-5067, USA
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Kim C, Chen B, Mohandas S, Rehman J, Sherif ZA, Coombs K. The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative's Mechanistic Pathways taskforce. eLife 2023; 12:e86043. [PMID: 37737716 PMCID: PMC10516599 DOI: 10.7554/elife.86043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative's Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes 'long COVID,' through contrasting objective and subjective narratives. Long COVID's prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.
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Affiliation(s)
- C Kim
- Department of Population Health, NYU Grossman School of MedicineNew YorkUnited States
| | - Benjamin Chen
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Sindhu Mohandas
- Department of Pediatrics, Division of Infectious Diseases, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Jalees Rehman
- Department of Biochemistry and Molecular Genetics, University of Illinois, College of MedicineChicagoUnited States
| | - Zaki A Sherif
- Department of Biochemistry & Molecular Biology, Howard University College of MedicineWashingtonUnited States
| | - K Coombs
- Department of Pandemic Equity, Vermont Center for Independent LivingMontpelierUnited States
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