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Gupta A, Whiteley WN, Godec T, Rostamian S, Ariti C, Mackay J, Whitehouse A, Janani L, Poulter NR, Sever PS. Legacy benefits of blood pressure treatment on cardiovascular events are primarily mediated by improved blood pressure variability: the ASCOT trial. Eur Heart J 2024; 45:1159-1169. [PMID: 38291599 PMCID: PMC10984564 DOI: 10.1093/eurheartj/ehad814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/24/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND AND AIMS Visit-to-visit systolic blood pressure variability (BPV) is an important predictor of cardiovascular (CV) outcomes. The long-term effect of a period of blood pressure (BP) control, but with differential BPV, is uncertain. Morbidity and mortality follow-up of UK participants in the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure-Lowering Arm has been extended for up to 21 years to determine the CV impact of mean systolic blood pressure (SBP) control and BPV during the trial, and amongst those allocated to amlodipine- and atenolol-based treatment. METHODS Eight thousand five hundred and eighty hypertensive participants (4305 assigned to amlodipine ± perindopril-based and 4275 to atenolol ± diuretic-based treatment during the in-trial period (median 5.5 years) were followed for up to 21 years (median 17.4 years), using linked hospital and mortality records. A subgroup of participants (n = 2156) was followed up 6 years after the trial closure with a self-administered questionnaire and a clinic visit. In-trial mean SBP and standard deviation of visit-to-visit SBP as a measure of BPV, were measured using >100 000 BP measurements. Cox proportional hazard models were used to estimate the risk [hazard ratios (HRs)], associated with (i) mean with SBP and BPV during the in-trial period, for the CV endpoints occurring after the end of the trial and (ii) randomly assigned treatment to events following randomization, for the first occurrence of pre-specified CV outcomes. RESULTS Using BP data from the in-trial period, in the post-trial period, although mean SBP was a predictor of CV outcomes {HR per 10 mmHg, 1.14 [95% confidence interval (CI) 1.10-1.17], P < .001}, systolic BPV independent of mean SBP was a strong predictor of CV events [HR per 5 mmHg 1.22 (95% CI 1.18-1.26), P < .001] and predicted events even in participants with well-controlled BP. During 21-year follow-up, those on amlodipine-based compared with atenolol-based in-trial treatment had significantly reduced risk of stroke [HR 0.82 (95% CI 0.72-0.93), P = .003], total CV events [HR 0.93 (95% CI 0.88-0.98), P = .008], total coronary events [HR 0.92 (95% CI 0.86-0.99), P = .024], and atrial fibrillation [HR 0.91 (95% CI 0.83-0.99), P = .030], with weaker evidence of a difference in CV mortality [HR 0.91 (95% CI 0.82-1.01), P = .073]. There was no significant difference in the incidence of non-fatal myocardial infarction and fatal coronary heart disease, heart failure, and all-cause mortality. CONCLUSIONS Systolic BPV is a strong predictor of CV outcome, even in those with controlled SBP. The long-term benefits of amlodipine-based treatment compared with atenolol-based treatment in reducing CV events appear to be primarily mediated by an effect on systolic BPV during the trial period.
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Affiliation(s)
- Ajay Gupta
- William Harvey Research Institute, Queen Mary University of London, UK
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | | | - Thomas Godec
- William Harvey Research Institute, Queen Mary University of London, UK
| | - Somayeh Rostamian
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Cono Ariti
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Judith Mackay
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Andrew Whitehouse
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Leila Janani
- Clinical Trials Unit, Imperial College London, UK
| | | | - Peter S Sever
- National Heart & Lung Institute, Imperial College London, Room 333, ICTEM Building, Du Cane Road, London W12 0NN, UK
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Haycock PC, Borges MC, Burrows K, Lemaitre RN, Harrison S, Burgess S, Chang X, Westra J, Khankari NK, Tsilidis KK, Gaunt T, Hemani G, Zheng J, Truong T, O’Mara TA, Spurdle AB, Law MH, Slager SL, Birmann BM, Saberi Hosnijeh F, Mariosa D, Amos CI, Hung RJ, Zheng W, Gunter MJ, Davey Smith G, Relton C, Martin RM. Design and quality control of large-scale two-sample Mendelian randomization studies. Int J Epidemiol 2023; 52:1498-1521. [PMID: 38587501 PMCID: PMC10555669 DOI: 10.1093/ije/dyad018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/10/2023] [Indexed: 03/27/2024] Open
Abstract
Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).
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Affiliation(s)
- Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Sean Harrison
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Xuling Chang
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat—National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Jason Westra
- Department of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center, IA, USA
| | - Nikhil K Khankari
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Tom Gaunt
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Therese Truong
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Team “Exposome, Heredity, Cancer and Health”, CESP, Villejuif, France
| | - Tracy A O’Mara
- Genetics and Computational Biology Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Medicine, Faculty of Health Sciences, University of Queensland, Brisbane, Australia
| | - Amanda B Spurdle
- Genetics and Computational Biology Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Medicine, Faculty of Health Sciences, University of Queensland, Brisbane, Australia
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia
| | - Susan L Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Brenda M Birmann
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Daniela Mariosa
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC), Lyon, France
| | - Christopher I Amos
- Dan L Duncan Comprehensive Cancer Center Baylor College of Medicine, Houston, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health and University of Toronto, Toronto, Canada
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), Lyon, France
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Richard M Martin
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
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Hernáez Á, Rogne T, Skåra KH, Håberg SE, Page CM, Fraser A, Burgess S, Lawlor DA, Magnus MC. Body mass index and subfertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study. Hum Reprod 2021; 36:3141-3151. [PMID: 34668019 PMCID: PMC8600658 DOI: 10.1093/humrep/deab224] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/10/2021] [Indexed: 01/29/2023] Open
Abstract
STUDY QUESTION What is the association between BMI and subfertility? SUMMARY ANSWER We observed a J-shaped relationship between BMI and subfertility in both sexes, when using both a standard multivariable regression and Mendelian randomization (MR) analysis. WHAT IS KNOWN ALREADY High BMI in both women and men is associated with subfertility in observational studies and this relationship is further substantiated by a few small randomized controlled trials of weight reduction and success of assisted reproduction. Women with low BMI also have lower conception rates with assisted reproduction technologies. STUDY DESIGN, SIZE, DURATION Cohort study (the Norwegian Mother, Father and Child Cohort Study), 28 341 women and 26 252 men, recruited from all over Norway between 1999 and 2008. PARTICIPANTS/MATERIALS, SETTING, METHODS Women (average age 30, average BMI 23.1 kg/m2) and men (average age 33, average BMI 25.5 kg/m2) had available genotype data and provided self-reported information on time-to-pregnancy and BMI. A total of 10% of couples were subfertile (time-to-pregnancy ≥12 months). MAIN RESULTS AND THE ROLE OF CHANCE Our findings support a J-shaped association between BMI and subfertility in both sexes using multivariable logistic regression models. Non-linear MR validated this relationship. A 1 kg/m2 greater genetically predicted BMI was linked to 18% greater odds of subfertility (95% CI 5% to 31%) in obese women (≥30.0 kg/m2) and 15% lower odds of subfertility (-24% to -2%) in women with BMI <20.0 kg/m2. A 1 kg/m2 higher genetically predicted BMI was linked to 26% greater odds of subfertility (8-48%) among obese men. Low genetically predicted BMI values were also related to greater subfertility risk in men at the lower end of the BMI distribution. A genetically predicted BMI of 23 and 25 kg/m2 was linked to the lowest subfertility risk in women and men, respectively. LIMITATIONS, REASONS FOR CAUTION The main limitations of our study were that we did not know whether the subfertility was driven by the women, men or both; the exclusive consideration of individuals of northern European ancestry; and the limited amount of participants with obesity or BMI values <20.0 kg/m2. WIDER IMPLICATIONS OF THE FINDINGS Our results support a causal effect of obesity on subfertility in women and men. Our findings also expand the current evidence by indicating that individuals with BMI values <20 kg/m2 may have an increased risk of subfertility. These results suggest that BMI values between 20 and 25 kg/m2 are optimal for a minimal risk of subfertility. STUDY FUNDING/COMPETING INTEREST(S) The MoBa Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Norwegian Ministry of Education and Research. This project received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No 947684). It was also partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. Open Access funding was provided by the Folkehelseinstituttet/Norwegian Institute of Public Health. D.A.L. is a UK National Institute for Health Research Senior Investigator (NF-SI-0611-10196) and is supported by the US National Institutes of Health (R01 DK10324) and a European Research Council Advanced Grant (DevelopObese; 669545). The funders had no role in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. D.A.L. receives (or has received in the last 10 years) research support from National and International government and charitable bodies, Roche Diagnostics and Medtronic for research unrelated to the current work. The rest of the authors declare that no competing interests exist. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Álvaro Hernáez
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain
| | - Tormod Rogne
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
- Department of Circulation and Medical Imaging, Gemini Center for Sepsis Research, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Anaesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Karoline H Skåra
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Mathematics, University of Oslo, Oslo, Norway
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Maria Christine Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
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Soares AG, Kilpi F, Fraser A, Nelson SM, Sattar N, Welsh PI, Tilling K, Lawlor DA. Longitudinal changes in reproductive hormones through the menopause transition in the Avon Longitudinal Study of Parents and Children (ALSPAC). Sci Rep 2020; 10:21258. [PMID: 33277550 PMCID: PMC7718240 DOI: 10.1038/s41598-020-77871-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/23/2020] [Indexed: 11/26/2022] Open
Abstract
We characterised changes in reproductive hormones-LH, FSH, SHBG and AMH-by chronological age and time around the menopause (reproductive age) in mid-life women and explored their associations with lifestyle and reproductive factors. We used data from 1608 women from a UK cohort who had repeat hormone measures and experienced a natural menopause. Multilevel models were used to assess: (i) changes in hormones (outcomes) by reproductive age and chronological age (these age variables being the key exposures) and (ii) associations of body mass index (BMI), smoking, alcohol intake, parity and age at menarche with changes in hormones by reproductive age. Both LH and FSH increased until ~ 5 and 7 years postmenopause, respectively, after which they declined, but not to premenopausal levels. SHBG decreased slightly until ~ 4 years postmenopause and increased thereafter. AMH decreased markedly before menopause and remained low subsequently. For all hormones, the best fitting models included both reproductive and chronological age. BMI, smoking and parity were associated with hormone changes; e.g., higher BMI was associated with slower increase in LH and FSH and decrease in AMH. Reproductive and chronological age contribute to changes in LH, FSH, SHBG and AMH across mid-life in women, and BMI, smoking and parity are associated with these hormone changes.
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Affiliation(s)
- Ana Goncalves Soares
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Fanny Kilpi
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Scott M Nelson
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Paul I Welsh
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
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Dharmaraj P, Gorvin CM, Soni A, Nelhans ND, Olesen MK, Boon H, Cranston T, Thakker RV, Hannan FM. Neonatal Hypocalcemic Seizures in Offspring of a Mother With Familial Hypocalciuric Hypercalcemia Type 1 (FHH1). J Clin Endocrinol Metab 2020; 105:5801090. [PMID: 32150253 PMCID: PMC7096312 DOI: 10.1210/clinem/dgaa111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 03/03/2020] [Indexed: 12/28/2022]
Abstract
CONTEXT Familial hypocalciuric hypercalcemia type 1 (FHH1) is caused by loss-of-function mutations of the calcium-sensing receptor (CaSR) and is considered a benign condition associated with mild-to-moderate hypercalcemia. However, the children of parents with FHH1 can develop a variety of disorders of calcium homeostasis in infancy. OBJECTIVE The objective of this work is to characterize the range of calcitropic phenotypes in the children of a mother with FHH1. METHODS A 3-generation FHH kindred was assessed by clinical, biochemical, and mutational analysis following informed consent. RESULTS The FHH kindred comprised a hypercalcemic man and his daughter who had hypercalcemia and hypocalciuria, and her 4 children, 2 of whom had asymptomatic hypercalcemia, 1 was normocalcemic, and 1 suffered from transient neonatal hypocalcemia and seizures. The hypocalcemic infant had a serum calcium of 1.57 mmol/L (6.28 mg/dL); normal, 2.0 to 2.8 mmol/L (8.0-11.2 mg/dL) and parathyroid hormone of 2.2 pmol/L; normal 1.0 to 9.3 pmol/L, and required treatment with intravenous calcium gluconate infusions. A novel heterozygous p.Ser448Pro CaSR variant was identified in the hypercalcemic individuals, but not the children with hypocalcemia or normocalcemia. Three-dimensional modeling predicted the p.Ser448Pro variant to disrupt a hydrogen bond interaction within the CaSR extracellular domain. The variant Pro448 CaSR, when expressed in HEK293 cells, significantly impaired CaSR-mediated intracellular calcium mobilization and mitogen-activated protein kinase responses following stimulation with extracellular calcium, thereby demonstrating it to represent a loss-of-function mutation. CONCLUSIONS Thus, children of a mother with FHH1 can develop hypercalcemia or transient neonatal hypocalcemia, depending on the underlying inherited CaSR mutation, and require investigations for serum calcium and CaSR mutations in early childhood.
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Affiliation(s)
- Poonam Dharmaraj
- Department of Paediatric Endocrinology, Alder Hey Children’s NHS Foundation Trust, Liverpool, UK
| | - Caroline M Gorvin
- Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Current Affiliation: The current affiliation of C.M.G. is Institute of Metabolism and Systems Research, University of Birmingham, and Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK
| | - Astha Soni
- Department of Paediatric Endocrinology, Alder Hey Children’s NHS Foundation Trust, Liverpool, UK
| | - Nick D Nelhans
- Department of Paediatrics, Wrexham Maelor Hospital, Wrexham, UK
| | - Mie K Olesen
- Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Hannah Boon
- Oxford Molecular Genetics Laboratory, Churchill Hospital, Oxford, UK
| | - Treena Cranston
- Oxford Molecular Genetics Laboratory, Churchill Hospital, Oxford, UK
| | - Rajesh V Thakker
- Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Fadil M Hannan
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, UK
- Correspondence and Reprint Requests: Fadil Hannan, MBChB, DPhil, Nuffield Department of Women’s and Reproductive Health, Level 3, Women’s Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK. E-mail:
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