Observational Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Cardiol. Jul 26, 2022; 14(7): 411-426
Published online Jul 26, 2022. doi: 10.4330/wjc.v14.i7.411
Vitamin d deficiency and metabolic syndrome: The joint effect on cardiovascular and all-cause mortality in the United States adults
Longjian Liu, Saishi Cui, Stella L Volpe, Nathalie S May, Deeptha Sukumar, Rose Ann DiMaria-Ghalili, Howard J Eisen
Longjian Liu, Saishi Cui, Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States
Stella L Volpe, Department of Human Nutrition, Foods, and Exercise, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
Nathalie S May, Department of Medicine, College of Medicine, Drexel University, Philadelphia, PA 19102, United States
Deeptha Sukumar, Department of Nutrition Sciences, College of Nursing and Health Professions, Drexel University, Philadelphia, PA 19102, United States
Rose Ann DiMaria-Ghalili, Department of Graduate Nursing, College of Nursing and Health Professions, Drexel University, Philadelphia, PA 19102, United States
Howard J Eisen, Division of Cardiology, Heart and Vascular Institute, Pennsylvania State University, Hershey, PA 17033, United States
Author contributions: Liu L conceptualized the study and analysis designs, and performed the data analysis and drafted the manuscript; Cui S performed the machine learning analysis. Volpe SL, May NS, Sukumar D, DiMaria-Ghalili RA, Cui S, and Eisen H critically reviewed the study design and analysis methods, and carefully reviewed the results and edited the manuscript. All authors contributed to the study and approved the submission.
Institutional review board statement: The study was reviewed and approved by Drexel University Institutional Review Board (Approval No. 2105008546).
Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Longjian Liu, MD, MSc, PhD, Doctor, Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, 3215 Market Street, Philadelphia, PA 19104, United States. ll85@drexel.edu
Received: August 30, 2021
Peer-review started: August 30, 2021
First decision: April 7, 2022
Revised: April 25, 2022
Accepted: June 17, 2022
Article in press: June 17, 2022
Published online: July 26, 2022
Processing time: 324 Days and 0.1 Hours
Abstract
BACKGROUND

The long-term impact of vitamin D deficiency and metabolic syndrome (MetS) on cardiovascular disease (CVD) and all-cause mortality are still a matter of debate.

AIM

To test the hypotheses that lower serum 25 hydroxyvitamin D [25(OH)D] concentrations (a marker of vitamin D level) and MetS have a long-term impact on the risk of CVD and all-cause mortality, and individuals with vitamin D deficiency can be identified by multiple factors.

METHODS

A sample of 9094 adults, 20 to 90 years of age, who participated in the Third National Health and Nutrition Examination Survey (NHANES III, 1988 to 1994) were followed through December 2015 was analyzed. The associations of serum 25(OH)D concentrations and MetS with CVD and all-cause mortality were analyzed longitudinally using Cox regression models. Classification and regression tree (CART) for machine learning was applied to classify individuals with vitamin D deficiency.

RESULTS

Of 9094 participants, 30% had serum 25(OH)D concentrations < 20 ng/mL (defined as vitamin D deficiency), 39% had serum 25(OH)D concentrations between 20 to 29 ng/mL (insufficiency), and 31% had serum 25(OH)D concentrations ≥30 ng/mL (sufficiency). Prevalence of MetS was 28.4%. During a mean of 18 years follow-up, vitamin D deficiency and MetS were significantly associated with increased risk of CVD and all-cause mortality. Subjects with both vitamin D deficiency and MetS had the highest risk of CVD mortality (HR = 1.77, 95%CI: 1.22-2.58) and all-cause mortality (HR = 1.62, 95%CI: 1.26-2.09), followed by those with both vitamin D insufficiency and MetS for CVD mortality (HR = 1.59, 95%CI: 1.12-2.24), and all-cause mortality (HR = 1.41, 95%CI: 1.08-1.85). Meanwhile, vitamin D sufficiency significantly decreased the risk of CVD and all-cause mortality for those who even had MetS. Among the total study sample, CART analysis suggests that being non-Hispanic Black, having lower serum folate level, and being female were the first three predictors for those with serum 25(OH)D deficiency.

CONCLUSION

Vitamin D deficiency and MetS were significantly associated with increased risk of CVD and all-cause mortality. There was a significant joint effect of vitamin D deficiency and MetS on the risk of mortality. Findings of the CART analysis may be useful to identify individuals positioned to benefit from interventions to reduce the risk of CVD and all-cause mortality.

Keywords: Joint effect; Serum 25 hydroxyvitamin D concentration; Metabolic syndrome; Cardiovascular and all-cause mortality; Cox model and machine learning

Core Tip: To investigate the long-term effect of vitamin D deficiency and metabolic syndrome on the risk of cardiovascular disease and all-cause mortality using a nationally representative sample. Standard measurements of the study exposures, co-variables and outcomes are processed. Multivariate Cox's proportional hazards regression analysis was used to prospectively test the associations between the exposures and outcomes. Classification and regression tree for machine learning was applied to classify subjects with higher risk of lower serum vitamin D concentrations.