Association between body roundness index and risk of osteoarthritis: a cross-sectional study.

IF 3.9 2区医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Lipids in Health and Disease Pub Date : 2024-10-14 DOI:10.1186/s12944-024-02324-5

Xudong Wang, Zijian Guo, Meng Wang, Chuan Xiang

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Abstract

Background: The link between body roundness index (BRI) and osteoarthritis (OA) has yet to be validated. Our aim was to explore this connection between BRI and OA risk.

Methods: This cross-sectional study utilized the 1999-2018 National Health and Nutrition Examination Survey retrieved data. To assess the association between BRI and OA risk, we performed weighted multivariable regression analysis (MVRA), with smooth curve fitting for potential nonlinear association and subgroup analysis and interaction tests for relationships in specific subgroups. A 7:3 ratio was adopted for the random division of the acquired data into training and validation sets. Subsequently, least absolute shrinkage and selection operator regression, along with MVRA, were conducted for the training set to isolate variables for a prediction model. This model was visualized using the nomogram and was followed by evaluation. Finally, the validation set was utilized to validate the model.

Results: This study enrolled 12,946 individuals. Following the adjustment for all covariables, OA risk increased by 18% with every unit rise in BRI (odd ratio [OR] = 1.18; 95% confidence interval [CI]: 1.13-1.23; P < 0.0001). Upon regarding BRI as a categorical variable, it was divided into quartiles for subsequent analysis. In comparison to quartile 1, the risk of OA was increased in quartile 2 (OR = 1.58; 95% CI: 1.22-2.03; P = 0.0006), quartile 3 (OR = 1.83; 95% CI: 1.40-2.40; P < 0.0001) and quartile 4 (OR = 2.70; 95% CI: 1.99-3.66; P < 0.0001). Smooth curve fitting revealed no non-linear relationships. None of the subgroups showed a statistically significant interaction (all P > 0.05). After selecting the variables, a prediction model was developed. The prediction model exhibited favorable discriminatory power, high accuracy, and potential clinical benefits in training and validation sets.

Conclusions: The BRI was positively associated with OA risk. Our predictive model demonstrated that combining BRI with other easily accessible factors was helpful in assessing and managing high-risk OA groups.

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体圆指数与骨关节炎风险之间的关系：一项横断面研究。

背景：体圆指数（BRI）与骨关节炎（OA）之间的联系尚未得到验证。我们的目的是探讨BRI与OA风险之间的联系：这项横断面研究利用了 1999-2018 年全国健康与营养调查的检索数据。为了评估BRI与OA风险之间的关系，我们进行了加权多变量回归分析（MVRA），并对潜在的非线性关系进行了平滑曲线拟合，对特定亚组的关系进行了亚组分析和交互检验。采用 7:3 的比例将获得的数据随机分为训练集和验证集。随后，对训练集进行最小绝对收缩和选择算子回归，以及 MVRA，以分离出用于预测模型的变量。使用提名图对该模型进行可视化，然后进行评估。最后，利用验证集来验证模型：这项研究共招募了 12,946 人。在对所有协变量进行调整后，BRI 每上升一个单位，OA 风险就会增加 18%（奇数比 [OR] = 1.18；95% 置信区间 [CI]：1.13-1.23；P 0.05）。选定变量后，建立了一个预测模型。该预测模型在训练集和验证集中表现出良好的鉴别力、较高的准确性和潜在的临床益处：结论：BRI 与 OA 风险呈正相关。我们的预测模型表明，将 BRI 与其他容易获得的因素相结合有助于评估和管理高风险 OA 群体。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Lipids in Health and Disease 生物-生化与分子生物学

CiteScore

7.70

自引率

2.20%

发文量

122

审稿时长

3-8 weeks

期刊介绍： Lipids in Health and Disease is an open access, peer-reviewed, journal that publishes articles on all aspects of lipids: their biochemistry, pharmacology, toxicology, role in health and disease, and the synthesis of new lipid compounds. Lipids in Health and Disease is aimed at all scientists, health professionals and physicians interested in the area of lipids. Lipids are defined here in their broadest sense, to include: cholesterol, essential fatty acids, saturated fatty acids, phospholipids, inositol lipids, second messenger lipids, enzymes and synthetic machinery that is involved in the metabolism of various lipids in the cells and tissues, and also various aspects of lipid transport, etc. In addition, the journal also publishes research that investigates and defines the role of lipids in various physiological processes, pathology and disease. In particular, the journal aims to bridge the gap between the bench and the clinic by publishing articles that are particularly relevant to human diseases and the role of lipids in the management of various diseases.