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Updated: Apr 29, 2026

A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro
Published on: June 2, 2022
Contemporary Trends in Calcium Levels and Related Clinical Conditions: A Two Decade Analysis of U.S. National Health
Ethan Frank1, Eric Adjei Boakye2, Brendan C Stack3
1Molecular Biology, Brigham Young University, Provo, United States.
Abstract:
Ambulatory hypercalcemia is a proxy for primary hyperparathyroidism. Reports of an increased incidence of hypercalcemia and undiagnosed primary hyperparathyroidism in several electronic medical record studies have prompted a population-based trend analysis of serum calcium. Data from the 2000-2020 U.S. National Health and Nutritional Examination Survey were used to study the trend of serum calcium and related factors. The NHANES has contemporary insight into the ambulatory state of health in the large and diverse U.S. population. Joinpoint regression estimated yearly changes of serum calcium and related factors using annual percentage changes. Serum calcium levels increased by an average of 0.65 mg/dL/y from 2000 to 2004 and then decreased on average by 0.12 mg/dL annually from 2004 to 2020. Among women, serum calcium levels increased by an average of 0.69 mg/dL/y from 2000 to 2004 but then decreased on average by 0.13 mg/dL annually from 2004 to 2020. Among men, serum calcium levels increased by an average of 0.61 mg/dL/y from 2000 to 2004 and then remained stable. Trends of body mass index increased by an average of 0.49/y from 2014 to 2020. Ambulatory hypercalcemia is a proxy for primary hyperparathyroidism. Over 20 years in the U.S. National Health and Nutritional Examination Survey (2000-2020), calcium levels have been decreasing slightly since 2004 after an increase while body mass index has been increasing since 2014. These data conflict with reported observations of the undiagnosed and increased incidence of primary hyperparathyroidism. These data may ultimately serve to refine primary hyperparathyroidism data phenotype for machine learning deployed within an electronic medical record.
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