The Impact of Metabolic Syndrome on Bone Mass in Men: Systematic Review and Meta-Analysis.
Aleksandra RyłAleksandra SzylińskaKarolina Skonieczna-ŻydeckaTomasz MiazgowskiIwona RotterPublished in: Biomedicines (2023)
Studies to date have yielded conflicting results on associations between components of metabolic syndrome (MetS) and bone mineral density (BMD), particularly in men. This current systematic review and meta-analysis addresses the existing gap in the literature and aims to evaluate bone mineral density (BMD) at the femoral neck (FN) and lumbar spine (LS) in men diagnosed with MetS. The two study authors independently searched PubMed, Cinahl, Embase, and Web of Science up to 8 February 2022 for studies in English. The inclusion criteria were (i) diagnosis of MetS according to the NCEP-ATP III 2001 criteria; (ii) adult male demographic; (iii) analyzable data on BMD in at least two sites using dual-energy X-ray absorptiometry (DXA), and (iv) original observational studies. Case reports and non-English articles were excluded. We analyzed the results of seven studies providing data on bone density in men with MetS. Results: Based on random effect weights, the mean BMD of the femoral neck and lumbar spine were 0.84 and 1.02, respectively. The mean lumbar spine T-score was -0.92. In meta-regression analysis, the variances in mean BMD in the lumbar spine and femoral neck could not be significantly explained by BMI (lumbar BMD: Q = 1.10, df = 1, p = 0.29; femoral neck BMD: Q = 0.91, df = 1, p = 0.34). Our meta-analysis suggests normal bone mass in adult males with MetS. Due to the high heterogeneity in the seven analyzed studies and the lack of control groups in these studies, further research is needed to fully elucidate the associations between MetS and its components and BMD in men.
Keyphrases
- bone mineral density
- postmenopausal women
- body composition
- case control
- metabolic syndrome
- dual energy
- systematic review
- middle aged
- computed tomography
- public health
- big data
- electronic health record
- body mass index
- cardiovascular disease
- magnetic resonance imaging
- type diabetes
- minimally invasive
- artificial intelligence
- skeletal muscle
- machine learning
- uric acid
- case report
- adipose tissue
- single cell
- cardiovascular risk factors
- young adults