Facts and Recommendations about Total Homocysteine Determinations: An Expert Opinion
Abstract
BACKGROUND: Measurement of plasma total homocysteine has become common as new methods have been introduced. A wide range of disorders are associated with increased concentrations of total homocysteine. The purpose of this review is to provide an international expert opinion on the practical aspects of total homocysteine determinations in clinical practice and in the research setting and on the relevance of total homocysteine measurements as diagnostic or screening tests in several target populations. METHODS: Published data available on Medline were used as the basis for the recommendations. Drafts of the recommendations were critically discussed at meetings over a period of 3 years. OUTCOME: This review is divided into two sections: (a) determination of homocysteine (methods and their performance, sample collection and handling, biological determinants, reference intervals, within-person variability, and methionine loading test); and (b) risk assessment and disease diagnosis (homocystinuria, folate and cobalamin deficiencies, cardiovascular disease, renal failure, psychiatric disorders and cognitive impairment, pregnancy complications and birth defects, and screening of elderly and newborns). Each of these subsections concludes with a separate series of recommendations to assist the clinician and the research scientist in making informed decisions. The review concludes with a list of unresolved questions.
Read the paper
Related studies
- Metabolic Syndrome: Updates on Pathophysiology and Management in 2021
- Environmental pollutants as risk factors for neurodegenerative disorders: Alzheimer and Parkinson diseases
- Antioxidants, inflammation and cardiovascular disease
- Application of Metabolomics in Alzheimer’s Disease
- Nutritional Importance of Carotenoids and Their Effect on Liver Health: A Review
- Nutritional essentiality of sulfur in health and disease
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.