Choudhary Pratik, Paldánius Päivi M, Salter John E, Lazaro-Pacheco Daniela, Cos Claramunt Francesc Xavier
Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Research Program for Clinical and Molecular Metabolism, Children's Hospital, University of Helsinki, Helsinki, Finland.
J Diabetes Sci Technol. 2025 Apr 12:19322968251331069. doi: 10.1177/19322968251331069.
Current diabetes screening methods are complex, inefficient, and inconvenient, requiring resource-intensive blood sampling. With the increasing prevalence of underdiagnosed type 2 diabetes mellitus (T2DM) worldwide, particularly in low-resource settings and underserved populations, affordable and sustainable mass-screening tools are crucial.
The accuracy and safety of the miniaturized near-infrared (NIR), full-spectrum spectroscopy Glyconics-DS System in detecting T2DM risk status was assessed by pooling data from two independent pilot studies: ANODE01 and ANODE02. Rapid NIR assessments of glycated nail keratin in 60 repeated spectral readings of fingernails from individuals with or without T2DM focused on detecting dichotomized diabetes risk status (glycated hemoglobin [HbA] <6.5%) based on chemometric prediction models, clinical specificity/sensitivity, and true/false positive outcomes. An HbA point-of-care assay served as an internal control.
Over 12 000 NIR spectral readings were collected in a female-dominant (58.5%), mostly non-smoking (80.0%), diverse cohort of 200 participants (n = 100 with/n = 100 without T2D). The selected chemometrics prediction model on a diagnostic HbA cut-off of 6.5% showed a specificity of 92.9% (95% confidence interval [CI] = 88.5-97.4) and a sensitivity of 34.2% (95% CI = 23.4-45.1), with 71.5% concordance. Chemometric predictions were consistent and reproducible with no relevant impact of anthropometric variables, concomitant conditions/medications, smoking status, and number of spectral assessments/nail or hand dominance on NIR assessment. No adverse events or suspected de novo T2D cases were reported.
This pooled analysis of two independent studies demonstrates the clinical feasibility and high specificity of rapid NIR spectral assessment of T2DM risk, with potential for screening, early detection, and sustainable management across health care settings.
当前的糖尿病筛查方法复杂、低效且不便，需要耗费大量资源进行血液采样。随着全球未确诊的2型糖尿病（T2DM）患病率不断上升，尤其是在资源匮乏地区和服务不足的人群中，价格合理且可持续的大规模筛查工具至关重要。
通过汇总两项独立的试点研究（ANODE01和ANODE02）的数据，评估了小型化近红外（NIR）全光谱光谱糖化血红蛋白检测系统（Glyconics-DS System）检测T2DM风险状态的准确性和安全性。对患有或未患有T2DM的个体的指甲进行60次重复光谱读数，快速近红外评估糖化指甲角蛋白，重点是基于化学计量学预测模型、临床特异性/敏感性以及真/假阳性结果来检测二分法糖尿病风险状态（糖化血红蛋白[HbA]<6.5%）。即时护理HbA检测用作内部对照。
在以女性为主（58.5%）、大多不吸烟（80.0%）的200名参与者（n = 100患有T2D/n = 100未患有T2D）的多样化队列中，收集了超过12000次近红外光谱读数。在诊断性HbA临界值为6.5%时，所选的化学计量学预测模型显示特异性为92.9%（95%置信区间[CI]=88.5 - 97.4），敏感性为34.2%（95%CI = 23.4 - 45.1），一致性为71.5%。化学计量学预测是一致且可重复的，人体测量变量、伴随疾病/药物、吸烟状态以及光谱评估次数/指甲或手的优势对近红外评估没有相关影响。未报告不良事件或疑似新发T2D病例。
这两项独立研究的汇总分析证明了快速近红外光谱评估T2DM风险的临床可行性和高特异性，具有在医疗保健环境中进行筛查、早期检测和可持续管理的潜力。
