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连续血糖监测CGM用于非糖尿病人群健康管理是否有临床证据支持？请区分血糖波动发现、行为干预、体重管理、长期结局和商业化宣传边界，适合器械/数字健康企业评估。要求简短、有引用。
连续血糖监测 (CGM) 在非糖尿病人群健康管理中的应用有临床证据支持，但其有效性因具体目标和干预措施而异，且需区分血糖波动发现、行为干预、体重管理、长期结局以及商业化宣传的边界。
1. 血糖波动发现与心血管风险标记
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证据支持：
- CGM能够提供短期的血糖波动 (GV) 数据，这在没有糖尿病的个体中与心血管风险标记的关联正在被研究 。
- 在非糖尿病人群中，CGM可以识别亚临床的血糖失调，例如更年期和阻塞性睡眠呼吸暂停等疾病，并将较高的血糖波动与血压波动等心血管风险的替代指标联系起来 。
- 血糖波动在糖尿病前期人群中升高，可能与β细胞功能障碍有关 。
- 血糖波动与冠状动脉粥样硬化的发展相关，并可能预测心血管事件和2型糖尿病的发生 。
- 血糖波动可能是一个重要的风险衡量指标，即使在没有糖尿病的个体中也是如此 。
- 虽然在线资源将血糖“尖峰”描述为有害，但研究表明，大多数健康个体总体上保持正常的血糖水平，这使得这些“尖峰”的意义受到质疑 。不过，医疗和灰色文献都报告，血糖“尖峰”可能导致非糖尿病人群或人内皮细胞的内皮功能障碍、氧化应激和炎症 。长期频繁的血糖“尖峰”而非孤立的急性“尖峰”可能导致显著的健康结果 。
- 急性运动对某些CGM指标（如24小时平均血糖、平均血糖波动幅度、峰值血糖、4-6小时葡萄糖曲线下面积和目标范围内时间）产生了轻微、参数特异性的改善 。这些适度的变化表明了微妙的生理适应 。
- 减肥手术与较高的血糖波动相关，且根据胃肠道解剖重建的类型，产生了程序特异性的血糖波动模式 。
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商业化宣传边界： 尽管血糖波动与心血管风险标记存在关联，但目前直接证据表明CGM对硬性心血管终点的影响仍然有限 。 CGM可以识别高风险代谢表型，但未来长期试验应侧重于评估CGM引导的行为干预是否能提供成本效益和可持续的方法来增强非糖尿病人群的心血管预防策略，而非仅仅重申血糖波动与心血管风险之间的既定联系 。
2. 行为干预与生活方式改善
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证据支持：
- CGM在非糖尿病人群中的应用，显著改善了平均血糖，并增强了干预依从性 。
- 实时CGM (RT-CGM) 能够提供即时反馈，帮助用户调整食物选择和体育活动，具有作为生活方式改变和体重减轻行为修改工具的潜力，尤其是在糖尿病前期和2型糖尿病患者中 。
- 定性合成结果表明，CGM在修改短期饮食行为方面具有很高的可行性 。
- CGM被用于个性化运动时间，例如在个体餐后血糖峰值前开始步行，这显著降低了餐后血糖、胰岛素和C肽水平 。
- CGM还可作为激励工具，提高个体进行体育活动的意愿 。
- CGM在指导非糖尿病个体进行生活方式修改方面显示出前景，并改善了血糖结果，这些是心血管风险的关键替代指标 。
- CGM能够通过提供实时葡萄糖数据，展示饮食习惯和体育活动的影响，从而提高患者对生活方式修改的参与度和依从性 。
- 结合CGM和GLP-1受体激动剂（如司美格鲁肽）可以实现个性化治疗，CGM提供行为反馈，GLP-1受体激动剂解决高胰岛素血症和食欲失调问题 。
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商业化宣传边界： CGM对心血管风险因素的直接影响证据有限 。其效用在于优化体育活动时间、增强动机以及识别有风险的代谢表型 。
3. 体重管理
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证据支持：
- 一项荟萃分析显示，CGM的使用与体重指数（BMI）没有显著差异（SMD = -0.25, 95% CI -0.63 to 0.12; P = 0.19），表明其独立实现体重管理目标的能力有限 。
- 研究指出，CGM在没有伴随行为干预的情况下，不太可能独立实现体重管理目标 。
- 个性化的低血糖饮食，保持稳定的血糖水平，有助于减肥和管理（前）糖尿病以及偏头痛 。
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商业化宣传边界： CGM应被定位为集成在结构化生活方式计划中的精准生物反馈工具，而非独立的体重管理解决方案 。
4. 长期结局
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证据支持：
- 目前关于CGM在非糖尿病人群中对测量准确性和血糖波动影响的证据仍然有限 。
- 直接证据表明CGM对传统心血管风险因素的改变是有限的 。
- 未来需要进行更大规模、更长期的、控制更好的试验来确定CGM在非糖尿病人群中对血糖指标改善的临床相关性 。
- 需要进一步研究来验证灰色文献中关于血糖“尖峰”影响健康的主张是否与同行评审的证据一致 。
- 目前没有随机对照试验评估CGM与GLP-1受体激动剂在无糖尿病肥胖症中的联合使用，这代表着一个关键的证据空白 。
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商业化宣传边界： 尽管CGM在短期行为改变和血糖优化方面显示出潜力，但其对长期健康结局（如心血管事件、2型糖尿病发病率或癌症风险）的直接、独立影响仍需更多高质量的长期研究来证实 。在缺乏此类证据的情况下，商业宣传应避免过度承诺长期健康益处。
5. 适合器械/数字健康企业评估的边界
- 精准生物反馈工具： CGM作为一种精准生物反馈工具，应被集成到结构化的生活方式计划中，而不是作为独立的解决方案进行推广 。
- 非侵入性技术： 机器学习方法结合非侵入性可穿戴设备数据进行血糖预测，有望解决CGM设备的侵入性、高成本和有限生命周期问题，这为非侵入性血糖监测提供了可能性 。
- 个性化干预： CGM在个性化运动时间、增强动机和识别高风险代谢表型方面具有实用性 。
- 数据标准化： 在非糖尿病肥胖管理中，CGM衍生指标尚未标准化，这限制了其解释价值，需要进一步研究 。
- AI整合： 未来，AI驱动的CGM分析可以预测个体对治疗的反应，从而改进干预措施并支持长期代谢健康 。
- 临床应用场景：
- 糖尿病前期人群： CGM在糖尿病前期人群中改善血糖控制的效果显著 。血糖波动在糖尿病前期人群中升高 。
- 围手术期血糖管理： 对于非糖尿病食管癌患者的术后高血糖管理，低碳水化合物肠内营养配方结合CGM监测血糖，正在进行一项随机探索性II期试验，以评估其临床意义 。这表明CGM在特定医疗干预下的血糖管理中具有潜在价值。
- 心血管预防： CGM在非糖尿病个体中用于指导生活方式干预以预防心血管疾病具有潜力 。
- 信息准确性： 灰色文献（如在线来源）对血糖“尖峰”的报告可能包含与医学研究不一致的额外效应，企业在宣传时需警惕潜在的误导信息，并强调基于同行评审证据的准确信息 。
- 成本效益与可持续性： 未来的研究应评估CGM引导的行为干预是否能提供成本效益和可持续的方法来增强心血管预防策略，而不仅仅是重申血糖波动与心血管风险之间的既定联系 。
总结：
CGM在非糖尿病人群中作为健康管理工具有一定的临床证据支持，尤其是在：
- 血糖波动发现： 能够识别和量化血糖波动，尤其在糖尿病前期人群中升高，并与心血管风险标记存在关联 。
- 行为干预： 作为一种生物反馈工具，可有效改善平均血糖，增强生活方式干预的依从性，并帮助用户调整饮食和运动习惯 。
然而，其在：
- 体重管理： 独立实现体重管理目标的效果不显著，需要与其他行为干预相结合 。
- 长期结局： 对硬性心血管终点、长期糖尿病预防或其他重大疾病结局的直接影响证据有限，需要更多长期、高质量的研究 。
- 商业化宣传： 应谨慎对待，避免过度承诺独立于生活方式干预或未经证实的长期健康益处。应将其定位为结构化生活方式计划中的辅助工具，并关注非侵入性技术、AI整合和数据标准化等未来发展方向 。
对于器械/数字健康企业而言，CGM的价值主要在于提供个性化的实时生物反馈，促进行为改变，并在特定高风险人群（如糖尿病前期、围手术期患者）中进行血糖优化。在推广时，应强调其作为辅助工具的定位，并避免超出现有临床证据的宣传，同时积极投资于长期研究和技术创新，以拓展其在非糖尿病人群中的确切临床价值。
References
1Continuous glucose monitoring in non-diabetic populations: a systematic review of observational and interventional studies with meta-analysis.PubMed
Xi Liao, Yongwei Li, Siwen Tang, et al.
BACKGROUND: Continuous glucose monitoring (CGM) has traditionally been used in diabetes management, but its application is expanding to non-diabetic populations for health promotion and metabolic optimization. However, the effectiveness of CGM in improving glycemic parameters, body weight, and behavioral outcomes in non-diabetic individuals remains unclear, limiting evidence-based implementation in clinical practice. OBJECTIVES: The aim of this study is to systematically evaluate the effects of CGM use on glycemic control, body weight, dietary behaviors, and adherence in non-diabetic populations compared to conventional monitoring or control interventions. METHODS: We applied comprehensive search strategies across the PubMed, Cochrane, Web of Science, and Embase databases to locate studies published until December 2025. Two reviewers independently screened titles and abstracts, assessed full texts, and extracted data. We performed random-effects meta-analyses for quantitative outcomes and qualitative synthesis for feasibility and behavioral outcomes. RESULTS: Twenty-three studies were included (7 randomized controlled trials, 1 cohort study, 7 prospective observational studies, 4 cross-sectional studies, and 4 clinical trials), comprising 1074 non-diabetic participants from 11 countries. Most studies targeted health promotion through lifestyle interventions with overlapping aims: glycemic optimization (9/23, 39%), weight management (9/23, 39%), dietary behavior modification (12/23, 52%), and adherence enhancement (5/23, 22%). Meta-analysis demonstrated that CGM use significantly improved mean blood glucose compared with controls (SMD = -0.54, 95% CI -1.02 to -0.07; P = 0.03), whereas no significant differences were observed for measurement accuracy (MD = -3.90 mg/dL, P = 0.40) or body mass index (SMD = -0.25, 95% CI -0.63 to 0.12; P = 0.19). Qualitative synthesis indicated that the impact of CGM on glycemic variability metrics was context-dependent. CGM use was associated with higher behavioral adherence and specific dietary modifications. Notably, CGM improved glycemic control in individuals with prediabetes, whereas no appreciable glycemic benefit was observed in healthy normoglycemic populations. CONCLUSIONS: These findings suggest that CGM use in non-diabetic populations may improve mean blood glucose and enhance intervention adherence, although evidence regarding its effects on measurement accuracy and glycemic variability remains limited. CGM demonstrates high feasibility in modifying short-term dietary behaviors; however, it is unlikely to achieve weight management goals independently without accompanying behavioral interventions. Therefore, CGM should be positioned as a precision biofeedback tool integrated within structured lifestyle programs.
2Behavior Modification in Prediabetes and Diabetes: Potential Use of Real-Time Continuous Glucose Monitoring.PubMed
Nicole Ehrhardt, Enas Al Zaghal
Real-time continuous glucose monitoring (RT-CGM) provides real time glucose readings to participants wearing the device. The ability to see changes in glucose has the potential to provide immediate feedback to users on food choices and physical activity. The National Diabetes Prevention Program is currently the only reimbursable intervention for diabetes prevention and weight loss. The purpose of this article is to review the CGM literature on measurements other than Hemoglobin A1c (HbA1c) changes and hypoglycemia and discuss RT-CGM potential use as a behavior modification tool for lifestyle changes and weight reduction in people with prediabetes and type 2 diabetes (T2D).
3Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis.PubMed
Anna Hjort, David Iggman, Fredrik Rosqvist
BACKGROUND & AIMS: Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. METHODS: Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. RESULTS: Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. CONCLUSION: GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.
4Use of Continuous Glucose Monitoring in Non-diabetic Individuals for Cardiovascular Prevention: A Systematic Review of Its Impact on Guiding Lifestyle Interventions.PubMed
Nour Ahmed, Mohamed Faisal Elzein Ali, Muhanad Noureldin Hamed Mohamed, et al.
Cardiovascular disease (CVD) is a leading global cause of mortality, with prevention through lifestyle modification being paramount. Continuous glucose monitoring (CGM), which provides real-time, dynamic glycemic data, is increasingly being explored in non-diabetic populations to guide lifestyle interventions. This systematic review aims to synthesize the evidence on the use of CGM in non-diabetic individuals for guiding lifestyle modifications and assess its impact on cardiovascular risk prevention. A systematic literature search was conducted in PubMed/MEDLINE, Embase, Scopus, Web of Science, and ClinicalTrials.gov from January 2020 to August 2025. Studies investigating CGM-guided lifestyle interventions in non-diabetic adults and reporting cardiovascular risk factors or metabolic outcomes were included. Study selection, data extraction, and risk of bias assessment (using Cochrane Risk of Bias 2 (RoB 2) for randomized controlled trials (RCTs) and Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) for non-randomized studies) were performed by independent reviewers. A narrative synthesis was conducted due to study heterogeneity. Seven studies were included. CGM was used to personalize exercise timing, such as initiating walking before an individual's postprandial glucose peak, which significantly reduced postprandial glucose, insulin, and C-peptide levels. CGM also served as a motivational tool, increasing readiness for physical activity. Observational studies demonstrated CGM's ability to identify subclinical dysregulation in conditions like menopause and obstructive sleep apnea, linking higher glycemic variability to surrogate markers of cardiovascular risk, such as blood pressure variability. The overall risk of bias was low for six studies and serious for one, primarily due to confounding. Direct evidence on changes in traditional cardiovascular risk factors was limited. CGM shows promise for personalizing lifestyle interventions and improving glycemic outcomes in non-diabetic individuals, which are key surrogates for cardiovascular risk. Its utility lies in optimizing physical activity timing, enhancing motivation, and identifying at-risk metabolic phenotypes. However, evidence of a direct impact on hard cardiovascular endpoints remains limited. Future long-term trials should focus not on reaffirming the established link between glycemic variability and cardiovascular risk but on evaluating whether CGM-guided behavioral interventions offer a cost-effective and sustainable approach to enhancing cardiovascular prevention strategies in non-diabetic populations.
5Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives.PubMed
F Picconi, A Di Flaviani, I Malandrucco, et al.
AIMS: The aim of this review is to focus on intra-day glucose variability (GV), specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in the non-diabetic population. DATA SYNTHESIS: The term GV has been used in the literature to express many different concepts; in the present review, we focus our attention on intra-day GV. In particular, we try to assess whether GV provides additional information on glycemic control beyond HbA1c, since GV seems to be incompletely expressed by HbA1c, particularly in patients with good metabolic control. Many different indexes have been proposed to measure GV, however at the moment no "gold standard" procedure is available. Evidence in vitro, in experimental settings and in animal studies, shows that fluctuating glucose levels display a more deleterious effect than constantly high glucose exposure. However, these findings are not completely reproducible in human settings. Moreover, the relationship between GV and cardiovascular events is still controversial. CONCLUSIONS: The term GV should be reserved to indicate intra-day variability and different indexes of GV should be used, depending on the metabolic profile of the population studied and the specific issue to be investigated. Self glucose monitoring or continuous glucose monitoring should be used for assessing glucose variability.
6Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning.PubMed
Xinyu Huang, Franziska Schmelter, Christian Seitzer, et al.
A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7-8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.
7Optimising obesity management: integrating continuous glucose monitoring with GLP-1 receptor agonists.PubMed
Pragati Gupta, Paolo Pozzilli
Obesity management is evolving with the integration of digital and pharmacological innovations. Continuous Glucose Monitoring (CGM) offers dynamic insights into glucose variability, while glucagon-like peptide-1 receptor agonists (GLP-1 RAs), such as semaglutide, demonstrate potent weight loss and glycaemic control - even in non-diabetic populations. The synergy of CGM and GLP-1RAs enables personalised treatment by capturing real-time metabolic responses, with CGM offering behavioural feedback and GLP-1 RAs addressing hyperinsulinaemia and appetite dysregulation. Yet, no randomised controlled trials have assessed their combined use in obesity without diabetes, representing a critical evidence gap. Furthermore, CGM-derived metrics remain unstandardised in this context, limiting their interpretive value. Looking ahead, AI-driven CGM analysis could predict individual responsiveness to therapy, refining interventions and supporting long-term metabolic health. Together, these tools offer a promising path to breaking the cycle of insulin resistance and obesity.
8A Scoping Review of Glucose Spikes in People Without Diabetes: Comparing Insights from Grey Literature and Medical Research.PubMed
Shira Avner, Timothy Robbins
BACKGROUND: The use of continuous glucose monitoring (CGM) has grown, extending its use to people without diabetes. CGM helps prevent hyperglycaemia-related complications in diabetes, however, its value in people without diabetes remains uncertain. Despite online sources framing glucose spikes as harmful, studies show that overall, most healthy individuals maintain normal glucose levels - therefore questioning the significance of these spikes. This project aims to examine whether glucose spikes affect the health of people without diabetes. By comparing the medical and grey literature, we aim to determine whether the grey literature aligns with the peer-reviewed medical literature, or whether it could cause harm through misinformation. METHODS: Population: people without diabetes and human endothelial cells; Concept: the effect of glucose spikes on health; Context: global studies and grey literature. Following the PRISMA-ScR guidelines, a systematic search was undertaken via Medline, Embase and Proquest. Fifty-nine sources were reviewed - 11 medical research papers and 48 grey literature sources. Excel spreadsheets were developed and piloted for the medical and grey literature respectively. Data was extracted and charted, and a narrative synthesis was formulated. RESULTS: Both the medical and grey literature reported glucose spikes can cause endothelial dysfunction, oxidative stress and inflammation in people without diabetes or human endothelial cells. However, the grey literature reported additional effects that is, increased risk of cancer and effects on mental health, energy, mood and sleep. CONCLUSIONS: Glucose spikes may impact the health of people without diabetes, but significant health outcomes likely stem from long-term frequent spikes rather than isolated acute spikes. Discrepancies between the medical and grey literature highlight potential for misinformation in the grey literature, although the author does not claim cited sources are misleading, nor does the absence of claims in medical literature mean grey literature is misinforming. Further research is needed to verify if grey literature claims align with peer-reviewed evidence, as hypothetically, misinformation could significantly impact consumer wellbeing.
9Glycemic variability and entero-pancreatic hormones signatures after different bariatric surgery procedures: a cross-sectional study.PubMed
Carolina B Lobato, Sofia S Pereira, Marta Guimarães, et al.
BACKGROUND/OBJECTIVES: Bariatric surgery changes food handling and entero-pancreatic endocrine dynamics. We aimed at understanding the influence of anatomical reorganization of the gastrointestinal tract induced by metabolic and bariatric surgery (BS) on glycemic variability and the extent to which glycemic variability reflects the underlying entero-pancreatic hormone dynamics. SUBJECTS: We performed a cross-sectional study on glycemic variability after four different BS procedures in comparison with non-operated matched controls (n = 8). The surgical groups were the classic Roux-en-Y gastric bypass (C-RYGB, n = 8), a modified long biliopancreatic limb RYGB (M-RYGB, n = 7), a single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S, n = 8) and a biliopancreatic diversion with duodenal switch (BPD-DS, n = 7). METHODS: Participants completed 14 days of intermittently scanned continuous glucose monitoring (isCGM). The surgical groups also underwent a mixed-meal test with hormone profiling. Our primary outcome was the mean absolute glucose change (MAG change) in the operated vs non-operated individuals. Additionally, we developed, validated and herein release an automated tool, Gluc4all, for personalized and automated continuous glucose monitoring data analysis, particularly relevant when evaluating the glycemic profile of individuals without diabetes. RESULTS: All surgical interventions were associated with an increase in the magnitude of postprandial glucose excursions, in anatomy-specific patterns (MAG change was 2.0-fold higher after C-RYGB and M-RYGB and 1.6-fold higher after SADI-S and BPD-DS than in non-operated controls). These isCGM findings matched the postprandial glucose, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1) and insulin profiles documented in the meal test. CONCLUSIONS: Overall, we show that BS interventions are associated with higher glycemic variability. Moreover, depending on the type of gastrointestinal anatomical reconstruction, BS yields procedure specific glycemic variability patterns. This might be due to faster glucose absorption, impaired amino acid absorption, and/or altered entero-pancreatic hormone profiles, including GLP-1 and insulin secretion.
10Effects of exercise interventions on 24-h continuous glucose profiles in non-diabetic populations: A systematic review and meta-analysis.PubMed
Bingqing Yang, Fenghua Sun, Eric Tsz-Chun Poon, et al.
AIMS: Emerging evidence suggests that glycemic variability may be relevant to metabolic health, even in people without diabetes. This study aims to assess the effects of exercise on 24-h continuous glucose profiles in the non-diabetic population. MATERIALS AND METHODS: Adhering to PRISMA guidelines, PubMed, Web of Science, Embase, and CNKI databases were searched up to August 25, 2025. The meta-analysis was performed on the 24-h mean blood glucose (MBG), mean amplitude of glycemic excursion (MAGE), peak blood glucose (MAX), 4-6-h incremental area under the glucose curve (iAUC), and time in range (TIR). Risk of bias was assessed using RoB2 and ROBINS-I V2. Subgroup analyses considered population, age, study design, and exercise type. RESULTS: Twenty-three studies (18 RCTs, five non-RCTs; 408 participants) were included. Exercise significantly reduced MBG (weighted mean difference [WMD] = -0.17 mmol/L), MAGE (WMD = -0.27 mmol/L), MAX (WMD = -0.63 mmol/L), and 4-6-h iAUC (standardized mean difference [SMD] = -0.60), while increasing TIR (WMD = 6.22%), compared to non-exercise or usual activity controls. Other outcomes showed no significant changes. Subgroup analyses indicated that age influenced MBG, while population and exercise type affected MAGE. Risk of bias showed 38.9% RCTs as low risk, 61.1% RCTs with some concerns, and all non-RCTs at moderate risk. CONCLUSIONS: Acute exercise produced minor, parameter-specific improvements in certain CGM metrics (MBG, MAGE, MAX, TIR, and 4-6-h iAUC). These modest changes indicate subtle physiological adaptations. Larger, longer, and better-controlled trials are needed to determine their clinical relevance.
11Skin autofluorescence is associated with glycemic variability in type 2 diabetes patients.PubMed
Eui Seok Shin, Hyun Joo Jung, Jindong Kim, et al.
Type 2 diabetes mellitus (T2DM) is a chronic disorder characterized by insulin resistance and hyperglycemia. To prevent diabetic complications, blood glucose levels should be maintained within a target range. While glycated hemoglobin (HbA1c) is widely used to assess long-term glycemic control, it does not provide information on glycemic variability (GV), which contributes to diabetes-related complications. Measuring skin autofluorescence (SAF) is a non-invasive method for measuring advanced glycation end-products (AGEs) in skin tissue and the accumulation of AGE is accelerated under hyperglycemic conditions. However, the relationship between SAF and GV in T2DM is not explored. This study examines the correlation between SAF and GV in T2DM patients and address the potential of SAF as a biomarker to alleviate the risk of diabetic complications. Fifty T2DM patients and fifty non-diabetic controls were recruited. SAF was measured using an AGE reader, and GV was assessed through continuous glucose monitoring (CGM) for 14 days. CGM metrics, including Mean Amplitude of Glycemic Excursions (MAGE) and Coefficient of Variation (CV), were analyzed. Serum AGE levels were measured via ELISA. Bivariate and multivariate associations between SAF and GV were examined using Pearson correlation analysis and multiple linear regression. T2DM patients exhibited higher SAF and GV compared to controls. SAF was positively correlated with GV metrics, including CV and MAGE, in diabetic patients, even after adjusting for confounding factors. No significant correlation was found between GV and serum AGE levels both in normal and T2DM subjects. SAF correlates with GV in T2DM, suggesting its potential as a non-invasive biomarker for glycemic fluctuations and associated risk. Unlike serum AGEs, SAF reflects localized AGE accumulation, which may be a complementary biomarker to HbA1c in assessing glycemic variability related with diabetes complication. Further research, such as longitudinal study, is needed to confirm its clinical utility in predicting long-term outcomes.
12Non-Invasive Continuous Glucose Monitoring in Patients Without Diabetes: Use in Cardiovascular Prevention-A Systematic Review.PubMed
Filip Wilczek, Jan Gerrit van der Stouwe, Gloria Petrasch, et al.
Continuous glucose monitoring (CGM) might provide immediate feedback regarding lifestyle choices such as diet and physical activity (PA). The impact of dietary habits and physical activity can be demonstrated in real time by providing continuous data on glucose levels and enhancing patient engagement and adherence to lifestyle modifications. Originally developed for diabetic patients, its use has recently been extended to a non-diabetic population to improve cardiovascular health. However, since data in this population are scarce, the effect on cardiovascular outcomes is unclear. CGM may offer potential benefits for cardiovascular prevention in healthy individuals without diabetes. The aim of this systematic review is to evaluate the use of CGM in healthy non-diabetic individuals, focusing on its potential to guide lifestyle interventions in the context of cardiovascular prevention, which may ultimately reduce cardiovascular risk.
13Study protocol: The effect of a low-carbohydrate enteral nutrition formula on postoperative hyperglycemia in non-diabetic patients with esophageal cancer: A randomized exploratory phase II trial (ENLICHE study).PubMed
Masayoshi Terayama, Yu Imamura, Toru Kitazawa, et al.
BACKGROUND: Postoperative hyperglycemia in diabetic patients is a widely known risk factor for postoperative infectious complications (PICs) after esophagectomy; however, the significance of glycemic control in non-diabetic patients is less clear. In diabetic patients, early postoperative management of esophagectomy favors low-carbohydrate enteral nutrition (EN) over standard EN to suppress the risk of glycemic spike. Our single-center, randomized phase II trial seeks to test the hypothesis that low-carbohydrate EN can suppress hyperglycemia in non-diabetic patients who undergo esophagectomy. Herewith we present the study protocol. METHODS: A total of 50 patients will be enrolled and randomly assigned (1:1 ratio) to standard or low-carbohydrate EN. Randomization will be stratified by operation time (≥560 vs. < 560 min) and HbA1c (6.0-6.4% vs. < 6.0%). Both EN formula will be fed according to the following protocol: 400 mL/24 h on postoperative day (POD)-1; 800 mL on POD-2; 1200 mL on POD-3 and 1600 mL from POD-4 to POD-8. On POD-9, oral food intake will be initiated. A continuous glucose monitoring (CGM) device will be used to monitor blood glucose levels from POD-1 to -8. The primary outcome is the mean time-in-range (TIR) across the 48 h from POD-1 to -2. TIR is defined as the percentage-time that blood glucose remains within the targeted range of 70-180 mg/dL. The primary analysis will calculate the least squares mean difference in TIR over the 48 h (POD-1 to -2) between the two groups, with p-values calculated to test the null hypothesis that the mean difference between the groups is zero. The secondary outcomes will be as follows: 1) the incidence of PICs and/or other adverse events within 30 days after esophagectomy or during the hospital stay; 2) the number of cases requiring any dose alteration in EN formula during monitoring; 3) the number of cases requiring interventions for hyperglycemia or hypoglycemia; 4) the rates in change of nutritional indicators, such as serum albumin, prealbumin, and total protein levels, during the post-surgical hospital stay (vs. those values on the day of admission); and 5) the following CGM indices in relation to the incidence rate of PICs within 30 days after esophagectomy: the mean values for time-above-range (TAR), area under the curve (AUC), and TIR for each POD or from POD-1 to -8. TAR is defined as the percentage of time of a patient is recorded as having hyperglycemia (>blood glucose level of 180 mg/dL), and is indicative of the frequency and duration of hyperglycemia. AUC, which identifies periods of hyperglycemia and provides a comprehensive picture of glucose variability and control in diabetes management, is defined as the area under the curve over blood glucose level of 180 mg/dL on CGM monitoring. DISCUSSION: This study is the first to investigate the impact of a low-carbohydrate EN formula on hyperglycemic control during perioperative nutritional management of esophageal cancer. These results will help to outline whether glycemic control should be also considered for non-diabetic patients during hospital care. TRIAL REGISTRATION: This trial has been registered in the Japanese Registry of Clinical Trials (jRCTs031240081).