1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identifying characteristics and clinical conditions associated with hand grip strength in adults: the Project Baseline Health Study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Low hand grip strength (HGS) is associated with several conditions, but its value outside of the older adult population is unclear. We sought to identify the most salient factors associated with HGS from an extensive list of candidate variables while stratifying by age and sex. We used data from the initial visit from the Project Baseline Health Study (N = 2502) which captured detailed demographic, occupational, social, lifestyle, and clinical data. We applied MI-LASSO using group methods to determine variables most associated with HGS out of 175 candidate variables. We performed analyses separately for sex and age (< 65 vs. ≥ 65 years). Race was associated with HGS to varying degrees across groups. Osteoporosis and osteopenia were negatively associated with HGS in female study participants. Immune cell counts were negatively associated with HGS for male participants ≥ 65 (neutrophils) and female participants (≥ 65, monocytes; < 65, lymphocytes). Most findings were age and/or sex group-specific; few were common across all groups. Several of the variables associated with HGS in each group were novel, while others corroborate previous research. Our results support HGS as a useful indicator of a variety of clinical characteristics; however, its utility varies by age and sex.

          Related collections

          Most cited references127

          • Record: found
          • Abstract: not found
          • Article: not found

          mice: Multivariate Imputation by Chained Equations inR

            Bookmark
            • Record: found
            • Abstract: not found
            • Book: not found

            Multiple Imputation for Nonresponse in Surveys

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Multiple imputation by chained equations: what is it and how does it work?

              Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.
                Bookmark

                Author and article information

                Contributors
                kenneth.taylor@duke.edu
                megankcarroll@verily.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 April 2024
                18 April 2024
                2024
                : 14
                : 8937
                Affiliations
                [1 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Orthopaedic Surgery, , Duke University School of Medicine, ; Durham, NC USA
                [2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke Clinical Research Institute, , Duke University School of Medicine, ; Durham, NC USA
                [3 ]GRID grid.497059.6, Verily Life Sciences, ; South San Francisco, CA USA
                [4 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University School of Medicine, , Population Health Sciences, ; Durham, NC USA
                Article
                55978
                10.1038/s41598-024-55978-7
                11026445
                38637523
                f535f861-c749-42bf-a3aa-40f05c8e9e6c
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 November 2022
                : 29 February 2024
                Funding
                Funded by: Verily Life Sciences, San Francisco, CA
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                biomarkers,medical research,risk factors
                Uncategorized
                biomarkers, medical research, risk factors

                Comments

                Comment on this article