新三篇vs 老三篇
تم الإنشاء في: ١٣ يوليو ٢٠٢٥
سؤال
- Three alternative introductions (all on HGI in critically ill atrial-fibrillation patients)
Introduction #1 (616 words)
Cardiovascular disease (CVD) continues to dominate global mortality statistics, accounting for almost one-third of all deaths worldwide. Among its myriad clinical manifestations, atrial fibrillation (AF) is the most common sustained arrhythmia and independently doubles the risks of heart failure, stroke and overall death. When AF occurs in the setting of critical illness, these hazards are magnified: contemporary registry data suggest that 10–15 % of intensive-care admissions manifest AF and that this subgroup’s short-term mortality is roughly twofold higher than that of their counterparts in sinus rhythm.
Glycaemic disturbance is a modifiable factor that may drive the poor outcomes observed in AF. Acute stressors—sympathetic surge, inflammatory cytokines, corticosteroid exposure—elevate circulating glucose early after ICU admission, whereas chronic metabolic control may vary widely among patients. Conventional markers such as fasting plasma glucose (FPG) and glycated haemoglobin (HbA1c) each capture only one facet of this complex picture. FPG reflects momentary glycaemia but ignores antecedent control, while HbA1c integrates the previous three months yet is blind to the violent swings that characterise critical illness.
The haemoglobin glycation index (HGI), calculated as the residual between observed HbA1c and the level predicted from concurrent FPG, reconciles these temporal extremes and quantifies intrinsic glycation propensity. High-positive HGI has been linked to endothelial dysfunction, oxidative stress and progressive atrial fibrosis—pathways that stabilise re-entrant circuits and encourage thrombo-embolic events. Conversely, markedly negative HGI may reflect shortened erythrocyte lifespan or occult haemolysis, both markers of severe systemic stress. Together, these observations suggest that HGI may exert a non-linear influence on prognosis, yet its role in the AF-ICU intersection remains unknown.
Several features of critical-care cardiology make this question pressing. First, AF therapies such as β-blockers, amiodarone and vasopressors can themselves perturb glucose handling, potentially shifting the HGI distribution. Second, perfusion-related anaemia and frequent transfusions may distort HbA1c values, further complicating risk assessment. Third, protocols targeting tight glucose ranges have given mixed results in the ICU; therefore, a biomarker that distinguishes harmful glycaemic variability from adaptive hyperglycaemia could refine therapeutic intensity.
Accordingly, we performed a retrospective cohort analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to examine the association between baseline HGI at ICU admission and subsequent all-cause mortality in adults with AF. We hypothesised a J-shaped relationship in which both high-positive and high-negative HGI values confer increased 30-day and one-year mortality independent of established prognostic covariates. To our knowledge, this is the first study to centre HGI on a critically ill AF cohort. By delineating the shape and strength of the HGI–mortality curve and exploring clinically relevant subgroups, our findings may lay the groundwork for precision glycaemic management strategies. Ultimately, bridging the current knowledge gap could integrate metabolic phenotyping into arrhythmia-care algorithms and thereby improve outcomes for a population in whom every marginal gain counts.
Introduction #2 (611 words)
Over the last two decades, cardiovascular disease (CVD) has stubbornly retained its place as the world’s foremost killer. Atrial fibrillation (AF)—the most common sustained arrhythmia—amplifies this burden by doubling the risks of stroke, heart-failure decompensation and death. When AF intersects with critical illness, the outlook worsens further: large registry analyses indicate that 12–15 % of adult intensive-care unit (ICU) admissions either present with or develop AF within the first day, and this subgroup experiences 30-day mortality rates approaching 25 %.
Metabolic dysregulation is a plausible yet under-investigated driver of this excess risk. Critical illness unleashes a surge of catecholamines and pro-inflammatory cytokines, induces peripheral insulin resistance and exposes patients to dextrose-containing fluids, vasopressors and corticosteroids—all of which disturb glucose homoeostasis. Clinicians commonly monitor FPG for real-time management and HbA1c for antecedent control, but each has blind spots: FPG is a fleeting snapshot, whereas HbA1c is slow to respond and can be confounded by anaemia, transfusion or altered erythrocyte turnover.
The haemoglobin glycation index (HGI) bridges these gaps by calculating the difference between observed HbA1c and the HbA1c predicted from concurrent FPG, thereby capturing individual propensity for non-enzymatic glycation beyond momentary glucose levels. Elevated HGI has been associated with nephropathy, coronary‐artery calcification, heart-failure readmission and all-cause mortality in diverse cardiovascular cohorts. Mechanistically, heightened glycation end-products promote oxidative stress, endothelial dysfunction and atrial structural remodelling—processes directly implicated in AF perpetuation. In parallel, glycaemic oscillations may disrupt autonomic balance and lengthen the QT interval, setting the stage for malignant arrhythmias and multi-organ injury.
Yet, the implications of HGI for critically ill adults with AF remain obscure. Landmark studies of glycaemic variability in the ICU have either excluded arrhythmias or aggregated AF with heterogeneous cardiac diagnoses, obscuring disease-specific signals. AF management frequently necessitates β-blockade, amiodarone loading and, in septic contexts, high-dose vasopressors—interventions that can independently modulate glucose utilisation and thus shift HGI. Furthermore, point-of-care HbA1c testing now allows real-time HGI calculation at the bedside, creating an opportunity for immediate clinical translation if the metric proves valuable.
Therefore, we undertook a retrospective cohort study using the publicly available MIMIC-IV database to explore whether admission HGI predicts 30-day and 365-day mortality in adults with AF. Interaction analyses tested whether the HGI–mortality relationship varies by diabetic status, severity-of-illness scores, septic shock, catecholamine need or renal replacement therapy. We hypothesised that both high-positive and high-negative HGI values would confer excess risk, reflecting a J-shaped exposure–response pattern.
Elucidating the prognostic power of HGI in this setting could sharpen risk stratification, inform resource allocation and generate hypotheses for interventional trials aimed at moderating glycaemic variability rather than pursuing uniformly strict glucose targets. Ultimately, integrating HGI into existing AF prognostic models—alongside CHA₂DS₂-VASc, HAS-BLED and SOFA scores—may yield a more holistic appraisal of cardiometabolic vulnerability and support nuanced decisions regarding anticoagulation, rate versus rhythm control and nutrition therapy. By illuminating the intersection of metabolic and electrophysiological derangements, the present investigation seeks to advance precision medicine for a growing and vulnerable critical-care population.
Introduction #3 (626 words)
Cardiovascular disease (CVD) claims more lives each year than any other pathology, and the inexorable rise of atrial fibrillation (AF) adds fuel to this epidemic. AF afflicts an estimated 60 million individuals globally and triples the risk of stroke while doubling all-cause mortality. The scenario is even grimmer inside the intensive care unit (ICU), where systemic inflammation, catecholamine excess and invasive procedures create a fertile milieu for both new-onset and pre-existing AF. Observational studies report that up to one-in-seven critically ill adults experience AF during their stay, with consequent prolongation of mechanical-ventilation time, length of stay and hospital costs.
Hyperglycaemia and glycaemic variability represent modifiable insults that may underlie these adverse outcomes. While FPG captures the immediate metabolic state and HbA1c conveys a three-month average, neither metric satisfactorily describes the dissonance that emerges when acute stress collides with chronic metabolic history. The haemoglobin glycation index (HGI)—calculated as the deviation between measured HbA1c and the value predicted from coincident FPG—addresses this deficit by quantifying individual susceptibility to protein glycation independent of prevailing glucose. In contrast to continuous-monitoring-derived indices, HGI is not vulnerable to perfusion-related artefacts that often compromise sensor accuracy in the ICU.
High HGI has been implicated in accelerated atherogenesis, cardiac autonomic neuropathy and adverse neurological recovery after stroke, whereas markedly negative HGI values may reflect shortened erythrocyte lifespan, occult haemolysis or massive transfusion, each a surrogate of profound systemic stress. Collectively, these observations suggest a potentially non-linear association wherein both tails of the HGI distribution carry heightened risk. Yet, the bulk of evidence arises from community cohorts or stable coronary populations; the critical-care landscape, particularly among patients with AF, remains largely uncharted.
Several mechanistic pathways justify a dedicated exploration of HGI in this context. Advanced glycation end-products foster atrial structural remodelling, increase interstitial fibrosis and impair ion-channel function, all of which perpetuate AF. Glycaemic swings influence autonomic tone and myocardial substrate utilisation, thereby modulating arrhythmogenic thresholds. Therapies commonly employed for AF in the ICU—amiodarone, β-blockers, digoxin, systemic anticoagulation—carry metabolic footprints that may interact with HGI-mediated pathways. Ignoring these interactions risks overlooking a readily measurable marker that could enhance risk stratification.
The resource-intensive nature of critical care underscores the need for pragmatic, low-cost tools to guide therapeutic intensity. HGI requires only two laboratory values routinely obtained at admission, rendering it accessible even in low-resource settings. If proven meaningful, HGI could refine triage decisions, signal the need for continuous glucose monitoring or prompt early endocrinology consultation.
Accordingly, the present study leverages the expansive MIMIC-IV database to investigate the relationship between admission HGI and all-cause mortality at 30 days and one year among adult ICU patients with AF. We posit a J-shaped association such that both elevated and depressed HGI values independently predict mortality after adjusting for demographics, comorbidities, illness-severity indices and treatment variables. Restricted cubic-spline analyses will visualise non-linearity and help distinguish safe from hazardous HGI ranges, information that could readily inform protocolised insulin titration tables. Secondary objectives include evaluating the incremental prognostic value of HGI over established scoring systems, delineating subgroup interactions by diabetic status and vasoactive medication use, and mapping dose–response curves to identify putative therapeutic targets.
By clarifying whether HGI embodies more than a statistical curiosity, this work aims to bridge the gap between metabolic research and bedside arrhythmia management. Positive findings could catalyse interventional trials that test personalised glucose-modulation strategies tailored to HGI phenotypes, thereby moving the needle toward precision critical-care cardiology. Ultimately, an evidence-based appreciation of HGI may open a new therapeutic window in which clinicians can temper both arrhythmic and metabolic volatility to improve outcomes for a fragile yet growing patient population.请帮我翻译成中文，然后对比一下这三段introduction的英文原文上是否有哪些区别并汇总，告诉我哪个最好，你如果是编辑你是否对这三个introduction有意见
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引言 1
心血管疾病（CVD）仍是全球死亡率最高的病因，约占全部死亡的三分之一。其中，房颤（AF）是最常见的持续性心律失常，可将心力衰竭、卒中及全因死亡风险提高一倍。当 AF 发生于危重病情背景下，这些危害被进一步放大：当代登记研究显示，重症监护病房（ICU）中约 10–15% 的患者出现 AF，此亚组的短期死亡率约为窦性心律患者的两倍。
血糖紊乱是驱动 AF 不良结局的可调节因素之一。应激状态下的交感神经兴奋、炎性细胞因子释放以及糖皮质激素使用，会在 ICU 入院早期升高血糖，而患者的慢性代谢控制则差异巨大。传统指标如空腹血糖（FPG）与糖化血红蛋白（HbA1c）各自仅反映复杂血糖谱的一面：FPG 捕捉瞬时血糖，却忽视既往控制；HbA1c 综合近三个月水平，却无法反映重症期剧烈波动。
血红蛋白糖化指数（HGI）计算公式为实测 HbA1c 与基于同期 FPG 预测值之差，可同时整合长期与短期信息，量化个体固有糖化倾向。高 HGI 与内皮功能障碍、氧化应激及心房纤维化相关，这些途径稳定折返激动并促发血栓栓塞事件；而 HGI 显著降低则可能提示红细胞寿命缩短或隐匿性溶血，均为严重系统应激标志。由此推测，HGI 对预后可能呈非线性影响，但在 AF 合并危重症这一交点中的作用尚未厘清。
危重症心脏病学的若干特点令该问题尤为迫切。首先，β 受体阻滞剂、胺碘酮和血管活性药等 AF 治疗本身可扰乱血糖，潜在改变 HGI 分布。其次，灌注相关贫血及频繁输血会扭曲 HbA1c 值，使风险评估更为复杂。再次，靶向严格血糖管理的 ICU 方案结论不一，若能有一项生物标志物区分有害波动与适应性高血糖，则可优化治疗强度。
因此，本研究利用 MIMIC-IV 数据库开展回顾性队列分析，评估 ICU 入院基线 HGI 与 AF 患者后续全因死亡的关联。我们假设 HGI 与 30 天及 1 年死亡率呈 J 形关系，高正值与高负值均增加风险且独立于既往预测因素。该研究首次聚焦危重 AF 患者的 HGI，通过描绘 HGI-死亡曲线形态并探讨临床相关亚组，有望为精准血糖管理奠定基础，将代谢表型纳入心律失常治疗算法，为高度脆弱人群争取边际获益。
引言 2
过去二十年，CVD 顽固地占据全球首位死因。AF 作为最常见的持续性心律失常，进一步加重负担：可使卒中、心衰及死亡风险翻倍。当 AF 遇上危重症，预后更差：大型注册研究提示，成人 ICU 患者中 12–15% 在首日出现或新发 AF，其 30 天死亡率接近 25%。
代谢失调被视为此额外风险的合理但研究不足的驱动因素。危重症触发儿茶酚胺及炎性细胞因子激增、诱导外周胰岛素抵抗，加之含葡萄糖液体、血管活性药及糖皮质激素共同作用，严重扰乱血糖稳态。临床常用 FPG 实时管理血糖、用 HbA1c 评估既往控制，但两者各有盲区：FPG 只是瞬时状态，HbA1c 反应迟缓且易受贫血、输血及红细胞周转影响。
HGI 通过计算实测 HbA1c 与基于同期 FPG 预测值之差，弥补此缺口，捕捉超过瞬时血糖的个体糖化倾向。高 HGI 已与肾病、冠脉钙化、心衰再住院及全因死亡相关。机制上，糖化终产物促进氧化应激、内皮功能障碍及心房结构重塑，均与 AF 维持密切相关；而血糖波动亦可扰乱自主神经平衡、延长 QT 间期，诱发恶性心律失常及多器官损伤。
然而，HGI 对危重 AF 成年人的影响尚不清楚。ICU 血糖波动的里程碑研究要么排除心律失常，要么将 AF 与异质性心脏诊断合并分析，掩盖特异信号。AF 管理常需 β 阻滞剂、胺碘酮负荷及败血症情境下的大剂量血管活性药，这些干预可独立改变葡萄糖利用，从而影响 HGI。此外，即时 HbA1c 床旁检测已可实现实时 HGI 计算，若其价值得到验证，便可立即临床应用。
因此，我们采用公开的 MIMIC-IV 数据库开展回顾性队列研究，探讨入院 HGI 是否预测 AF 患者的 30 天及 365 天死亡率。并行交互分析检验 HGI-死亡关系是否受糖尿病状态、病情严重度评分、感染性休克、儿茶酚胺需求或肾替代治疗影响。我们假设 HGI 与预后呈 J 形曲线，高正值与高负值均增加风险。
阐明 HGI 在此场景下的预测力，可强化风险分层、指导资源配置，并为以调节血糖波动而非一律严格血糖为目标的干预试验生成假设。将 HGI 与 CHA₂DS₂-VASc、HAS-BLED 及 SOFA 等评分结合，或能更全面评估心脏-代谢脆弱性，支持抗凝、控速/复律及营养治疗的个体化决策。通过揭示代谢与电生理错配，本研究旨在推动危重心脏病学的精准医疗。
引言 3
CVD 每年导致的死亡人数高居榜首，而 AF 的持续增加更使这一流行趋势雪上加霜。全球约有 6000 万人患 AF，其可使卒中风险增至 3 倍、全因死亡率增至 2 倍。ICU 内部情形更不乐观：系统炎症、儿茶酚胺过量及侵入性操作为既往或新发 AF 提供温床。观察研究报道，约七分之一危重成人在住院期间出现 AF，导致机械通气时间、住院天数及成本增加。
高血糖及血糖波动是可调节的损伤因素。FPG 反映即时代谢状态，HbA1c 表示三个月平均，但二者都无法描述急性应激与慢性代谢史碰撞产生的不一致。HGI 由实测 HbA1c 减去基于同时 FPG 预测值得到，可量化不依赖当前血糖的个体蛋白糖化倾向；与连续监测指标不同，HGI 不受 ICU 中低灌注伪影影响。
高 HGI 与加速动脉粥样硬化、心脏自主神经病变及卒中后神经恢复不良相关；而高度负值可能提示红细胞寿命缩短、隐匿溶血或大量输血，均为系统性重应激的替代指标。这提示 HGI 两端均可能高危，但现有证据多来自社区或稳定冠心病人群，危重 AF 情景几近空白。
多条机制通路支持在此环境中深入研究 HGI。糖化终产物促心房结构重塑、纤维化增加及离子通道功能障碍，助长 AF；血糖波动影响自主神经张力及心肌底物利用，改变致心律失常阈值。ICU 常用的 AF 抗心律失常和血流动力学治疗带来的代谢足迹，也可能与 HGI 交互。忽视这些交互将错过一项可改善风险分层的易得指标。
重症护理资源昂贵，亟需务实、低成本工具指导治疗强度。HGI 仅需两项入院常规化验即可计算，即便在资源有限地区亦可实施。若其有意义，可优化分诊、提示需连续血糖监测或及早内分泌会诊。
本研究利用 MIMIC-IV 数据库，探讨入院 HGI 与 AF 成人 ICU 患者 30 天及 1 年全因死亡关系。我们提出 J 形假设：HGI 高低两端均独立增加死亡风险，且与人口学、共病、病情指数及治疗因素校正后仍存。限制性三次样条拟合将可视化非线性，帮助界定安全及危险 HGI 区间。次要目标包括评估 HGI 对预测模型的增量价值、分析糖尿病状态及血管活性药使用的交互，并绘制剂量-反应曲线挖掘潜在干预目标。
通过澄清 HGI 是否仅是统计学好奇心，本研究旨在弥合代谢研究与床旁心律失常管理之间的鸿沟。如结果积极，可催生按 HGI 表型个体化血糖调控策略的干预试验，推动精准危重心脏病学。
三段英文原文的差异汇总
| 方面 | 引言 1 | 引言 2 | 引言 3 |
|---|---|---|---|
| 首段切入 | 全球 CVD 死亡 → AF 在 ICU 中的高发和高危 | 全球 CVD → AF 负担 → ICU 中 AF 高死亡率 | 全球 CVD → AF 患病人数 → ICU 环境造就高发生率 |
| 数据/比例 | ICU AF 占 10–15%，对照窦律死亡风险×2 | ICU AF 12–15%，30天死亡≈25% | ICU AF 约1/7，强调住院成本 |
| 血糖/代谢背景展开 | 对 FPG、HbA1c 局限的阐述较详细，突出二者“时间维”缺陷 | 着力描述危重症导致的代谢混乱；强调 HbA1c 受贫血等影响 | 更重视“急性应激 vs 慢病史”的失配概念 |
| HGI 机制论证 | 正负两端均提出并解释潜在机制 | 强调高 HGI 临床关联与电生理机制；加入血糖波动的心律影响 | 融入更多机制（自主神经、血糖波动），强调 HGI 测量优势 |
| 研究空缺陈述 | 明确指出 AF-ICU-HGI 三重交集缺乏研究 | 指出既往研究排除/混合 AF | 强调危重 AF 相关证据“几近空白” |
| 临床意义/应用 | 关注优化血糖管理策略、纳入代谢表型 | 强调风险分层、资源配置、结合现有评分 | 强调低成本可行性、指导分诊、生成干预假设 |
| 方法与假设描述 | 提出回顾性队列 + J 形假设；简要提到亚组分析 | 更详细列出交互变量及实时 HGI 可能 | 描述样条拟合、增量价值分析等更技术化 |
| 写作风格 | 逻辑严谨，语句紧凑，信息密度高 | 语言较流畅，侧重临床相关性，略带科普色彩 | 词汇丰富，修辞较多，叙事性最强 |
哪一版最好？
若从 学术期刊版面 的角度评估：
-
引言 1：
- 优点：结构清晰，“问题—差距—解决方案”链条完整；对 FPG/HbA1c 局限解释到位；篇幅最紧凑；结尾明确提出研究假设和创新点。
- 缺点：行文略显密集，部分长句可能影响可读性；缺少对实时 HGI 测量可行性的讨论。
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引言 2：
- 优点：叙事流畅，对危重代谢背景和床旁应用（即时 HbA1c）有实用角度；列出交互分析，使方法透明。
- 缺点：与引言 1 内容重叠较多，个别句子略口语化；对 HGI 负值的生理意义着墨较少。
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引言 3：
- 优点：写作生动，机制阐述丰富，论证 HGI 临床可负担；方法部分介绍多种统计技术，显示研究深度。
- 缺点：叙事冗长，修辞偏多，可能超出部分期刊对引言字数/密度的要求；关键信息稍显分散。
综合考虑（逻辑性、信息密度、篇幅控制、读者易读性）—— 引言 1 最平衡，最符合多数医学期刊的引言期待：
- 聚焦缺口：明确 HGI 在 AF-ICU 领域的空白；
- 临床背景 与 方法假设 相衔接；
- 字数适中，复述较少。
编辑意见与改进建议
| 共性改进 | 具体建议 |
|---|---|
| 避免数据重复 | 三篇都引用“10–15% ICU 患者有 AF”等，正式稿选用一次即可。 |
| 缩短长句 | 多处 50+ 字英文长句，建议拆分，以提高可读性并减少语法风险。 |
| 精简修辞 | 第 3 版“雪上加霜、温床”等比喻可删，以符合集医学术写作简洁客观的风格。 |
| 统一术语 | 如 “β-blockers” 与 “β 受体阻滞剂” 等，首次出现括注中英文，后文统一英文或缩写。 |
| 突出创新点 | 在结尾用 1–2 句强调研究独到之处（首个将 HGI 纳入 AF-ICU 预后模型），避免与已有研究混淆。 |
如果我是编辑，我会选择 以引言 1 为基底，吸收引言 2 强调的 床旁即时 HGI 计算可行性 与引言 3 对 统计方法（样条、增量价值） 的简要说明，再压缩多余修辞，用约 550–600 词完成最终引言，以兼顾深度与精炼。
سؤال
“Introduction
Over the past few decades, cardiovascular disease has ranked as the world’s leading cause of death. In 2021, 20.5 million people died from cardiovascular disease (CVD), accounting for about one-third of all deaths worldwide [1]. As a common CVD, atrial fibrillation (AF) is associated with a significantly increased risk of all-cause mortality, heart failure (HF), hospitalization, and thromboembolic events [2, 3]. The conditions of critically ill patients admitted to intensive care units (ICUs) are complex and a variety of causes may be involved. A study revealed that AF occurred in around 14% of all patients who were admitted into ICU [4]. However, there are lacking studies which demonstrate the prognosis of critically ill patients with AF. Therefore, it is vital to identify and avoid risk factors to reduce complications and mortality in critically ill patients with AF.
Insulin resistance (IR) has been linked to AF in previous study [5]. As a side effect of IR, the body secretes excessive insulin as compensation for a reduction in insulin’s efficiency to increase glucose uptake and utilization. This results in hyperinsulinemia, which maintains blood glucose levels. IR is the typical feature of metabolic syndrome and type 2 diabetes [6]. The current gold standard for evaluating IR is the hyperinsulinemic euglycemic clamp (HEC), which is applicable to varieties type of populations [7]. Thus, the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index is the most accurate, but the technique requires special equipment and skilled technicians, is expensive and time-consuming, requires multiple blood sampling during the test, is difficult to be accepted by patients, is currently only used for scientific research, and cannot be applied on a large scale in the clinic, and the method of this test is to determine the body’s sensitivity to exogenous insulin, which has the problem of bioefficacy. Nowadays, the triglyceride-glucose (TyG) index was proposed to be a simple surrogate marker of IR, and it has been proved to predict the prognosis of cardiovascular disease, including coronary heart disease (CHD), HF, acute myocardial infarction, stroke and hypertension [8–10]. More importantly, recent studies have shown that the TyG index, when combined with obesity indicators such as body mass index (BMI), waist-to-height ratio (WtHR), and waist circumference (WC), could significantly improve the validity of assessing IR [11]. There have been reported that a combination of TyG and BMI could perform better in accordance with HOMA-IR for IR assessment [12], and demonstrated that TyG-BMI index may be closely associated with the prevalence of hypertension, cardiovascular outcomes in patients with coronary artery disease, and non-alcoholic fatty liver disease [13–15]. However, there still lacks studies on the influence of the TyG-BMI index on the outcomes of patients with AF, especially those who were critically ill patients with AF.
Therefore, the purpose of the current study was to assess the potential impact of the baseline TyG-BMI index at admission to ICU on all-cause mortality in critically ill patients with AF. The finding of this study may help to explore new methods for early identification and improvement of prognosis in critically ill patients with AF.”
“
Introduction
Cardiovascular disease is the most common cause of mortality and morbidity worldwide, with a substantial portion of this burden borne by low- and middle-income countries [1]. Acute coronary syndrome is often the first clinical manifestation of cardiovascular disease. In 2019, there were an estimated 5.8 million new cases of ischemic heart disease in the 57 ESC member countries. Ischemic heart disease is the most common cause of CVD death, accounting for 38% of all CVD deaths in females and 44% in males [1, 2]. The in-hospital mortality rates for unselected patients suffering from ST-elevation Myocardial Infarction (STEMI) across the national registries of European Society of Cardiology (ESC) member countries range from 4 to 12% [3]. While the reported one-year mortality rate among STEMI patients enrolled in angiographic registries is around 10% [4, 5].
Acute coronary syndrome (ACS) itself may give rise to hyperglycemia due to catecholamine-induced stress [1]. Stress hyperglycemia ratio, which is divided admission blood glucose (ABG) measurement by HbA1c, is a novel biomarker of acute hyperglycemic state [6]. SHR has been shown to be significantly associated with poor patient prognosis in several cardiovascular disease studies. In the context of different cardiovascular diseases such as acute coronary syndrome, myocardial infarction (MI), myocardial infarction with non-obstructive coronary artery disease (MINOCA) lesions, critically ill patients with atrial fibrillation and chronic total occlusion (CTO), SHR has been found to be associated with an increased risk of major cardiovascular and cerebrovascular events (MACCEs) [7–15].
Patients with ACS and hyperglycemia on admission to hospital have a higher risk of death than patients with ACS without hyperglycemia, irrespective of diabetes status [16]. Glucose lowering is important in order to prevent microvascular complications in patients with diabetes mellitus [1]. While the risk of hypoglycemia-related events when using intensive insulin therapy should not be neglected [1], it is best to attempt moderately tight glycemic control while avoiding hypoglycemia in the early hours of ACS [16]. Moderate hyperglycemia constitutes an adaptive and protective response to acute illnesses, J-shaped relationship between stress hyperglycemia ratio and 90-day and 180-day mortality in patients with acute myocardial infarction has been reported in a previous study [17], we hypothesis that appropriate SHR can help the moderately glycemic control in patients with ACS.
Atrial fibrillation is the most frequent supraventricular arrhythmia in patients with ACS. AF may be pre-existing, first-time detected, or of new-onset during ACS management [1]. The prevalence of atrial fibrillation in patients with ACS ranges from approximately 2-23% [18]. Patients with AF have a greater number of comorbidities compared with patients without AF and are at higher risk of complications [1]. In patients with acute myocardial infarction (AMI), the risk of new onset of AF is significantly higher than in the general population by approximately 60-77% [19]. Atrial fibrillation confers an increased risk of mortality in patients hospitalized for acute myocardial infarction, the risk of mortality was 2-fold higher in patients with AF than in those in sinus rhythm [20].
SHR is significantly associated with 1-year and long-term all-cause mortality, which indicates a J-shaped correlation between SHR and all-cause mortality [21]. While AMI patients with AF may have a nearly 2-fold mortality increase than those without AF. In critically ill patients with AF, higher levels of the SHR index are significantly associated with an increased risk of all-cause mortality at 30 days, 90 days, 180 days, and 365 days [15]. However, the exact role of SHR in AMI patients with or without AF has not been investigated at this time. Meanwhile, the J-shaped or U-shaped correlation between SHR and all-cause mortality means that: lower SHR increases the risk of all-cause mortality, but which factors affect the mortality in lower SHR patients remains unknown. Therefore, our study investigated the role of SHR in AMI patients with or without AF, and whether AF affects the mortality in lower SHR patients.”
“
Introduction
Currently, the global burden of coronary artery disease (CAD) remains a critical global public health problem that requires attention, despite increasing improvements in preventive measures and treatment options [1]. Among critically ill patients in the intensive care unit (ICU), patients with coronary artery disease are likely to require treatment in the ICU after cardiac surgery, which is one of the main reasons for admission to the ICU; moreover, the mortality rate due to exacerbation of chronic cardiovascular disease within 1 year is 16.1%, which is the second leading cause of death after malignant tumours [2, 3]. Prognostic management of patients with critical coronary artery disease is a current medical priority that requires attention; however, few current studies have evaluated the prognosis of patients with critical coronary artery disease.
Diabetes is one of the underlying diseases of coronary heart disease, and studies have revealed that patients with diabetes have a significantly higher risk of diffuse coronary atherosclerosis and fatal coronary heart disease than patients without diabetes [4–6]. A previous cohort study showed that metabolic syndrome is a key risk factor for an increased risk of death from CAD, and that diabetes is one of the key components of metabolic syndrome [7]. Several studies have shown that stabilized and regulated blood glucose levels have long-lasting benefits for CAD patients [8, 9]. Glycosylated hemoglobin (HbA1c), utilized in the diagnosis and management of diabetes mellitus, reflects an individual’s average blood glucose over a three-month period and is currently the most commonly used surrogate marker of the effectiveness of glucose-lowering interventions [10, 11]. However, there is evidence that HbA1c is consistently higher or lower than fasting plasma glucose (FPG) levels in some populations [12]. There are several independent effects of the mean erythrocyte lifespan, differences in cell membrane glucose transmembrane gradients, enzyme abnormalities, and genetic factors on HbA1c [13–15].
The hemoglobin glycation index (HGI) quantifies changes in the relationship between HbA1c and plasma glucose concentration [16]. The HGI was defined as the difference between the observed HbA1c and the predicted HbA1c in a linear regression equation fitted according to FPG [17]. Several studies have shown that HGI predicts the risk of diabetes complications, including mortality, cardiovascular disease, and microvascular complications [17, 18]. In the above studies, a high HGI was strongly associated with major adverse cardiovascular events in the experimental population. However, several studies have shown that patients in the subgroup with low HGI have a higher risk of adverse outcomes [18, 19]. Glycaemic control in critically ill CHD patients under intensive care is particularly important for their survival [20]. Previous studies have shown that the HGI can be a relatively intuitive reflection of glycaemic variability in patients [21]. There are relatively few studies on glycaemic variability in patients with critical coronary artery disease. Exploring the correlation between HGI and the prognosis of patients with critical coronary artery disease is beneficial for understanding the significance of the relationship between glycaemic variability and long-term survival. Additionally, HGI is related not only to HbA1c, which can reflect the long-term glycaemic control of patients but also to the immediate FPG of patients. Enhancing the management of patients’ HGI also improves their short-term glycaemic control. Evaluating whether HGI is a valid prognostic risk stratifier for patients with critical CHD may help identify patients at high risk of all-cause mortality who are candidates for early surveillance and intervention. In contrast, few previous studies have evaluated the relationship between HGI and prognosis in patients with critical coronary heart disease. Therefore, in the present study, the Medical Information Mart for Intensive Care IV (MIMIC-IV) was used to construct linear regression equations to calculate the HGI and analyse the correlation between the HGI and adverse outcomes in patients with critical coronary artery disease.”
这三篇有出现刚刚你说的上三篇出现的语病或者阅读不适吗？
الفكرة
الإجابة
下面按 篇章顺序 简要点评语病、用词或可读性问题，并指出与上一轮提到的常见毛病（长句过多、重复 / 赘述、逻辑跳跃、术语混用等）的对应之处。整体上三篇并无严重语法错误，但仍存在若干学术英文写作常见“槽点”，建议在投稿前做针对性精修。
① TyG-BMI 与危重 AF 预后
| 位置（摘句） | 主要问题 | 建议 |
|---|---|---|
| “there are lacking studies which demonstrate…” | 主谓搭配：“there are lacking studies” 用法生硬 | 改为 “few studies have demonstrated…” |
| HEC/HOMA-IR 段 | 概念混淆：HOMA-IR 通常被视为简便替代，而非“最准确” | 明确：HEC = gold standard；HOMA-IR = widely used surrogate |
| “varieties type of populations” | 词形/搭配 | “…across various populations” |
| 整段介绍 TyG-BMI 与 IR | 长句堆叠、逻辑跳跃，读者须反复咀嚼 | 拆句；用“一句一观点”串联：先交代 TyG → 再引入 BMI → 最后说明研究空缺 |
| “there have been reported that…” | 语态混乱 | 改为 “it has been reported that…” |
| 结尾目的句 | 信息重复：与开头“vital to identify risk factors”含义重叠 | 精简或合并，突出 研究重点=TyG-BMI 与死亡 |
② SHR、AMI ± AF
| 位置 | 主要问题 | 建议 |
|---|---|---|
| 首段多组流行病学数字 | 细节过密、部分数字与主题（SHR）关联度弱 | 精选一两条关键统计，其余可在讨论中引用 |
| “Acute coronary syndrome is often the first clinical manifestation of cardiovascular disease.” | 概念不严谨：ACS 并非所有 CVD 的首发表现 | 可改 “For many patients, ACS is the first manifestation of CAD.” |
| 多处 “While …” 从句 | 悬空副词从句：后文缺主句或逻辑突兀 | 确保 while- 句之后紧跟对照主句 |
| “we hypothesis that…” | 拼写：hypothesis→hypothesize | “…we hypothesize that…” |
| SHR 与死亡 J 型关系段 | 证据链跳跃，未区分糖尿病 vs 非糖尿病 | 明确文献出处，或在方法中注明将分层分析 |
| 末段“lower SHR increases risk… remains unknown” | 自相矛盾/表述含糊：为何低 SHR 危险却要“适度控制” | 衔接：解释过度降糖→缺能效应或低血糖风险 |
③ HGI 与危重 CAD
| 位置 | 主要问题 | 建议 |
|---|---|---|
| 首句两次 “global” | 重复 | 精简：“CAD remains a critical public-health burden.” |
| “…patients with coronary artery disease are likely to require treatment in the ICU after cardiac surgery, which is one of the main reasons for admission to the ICU.” | 赘述：ICU×2；逻辑含糊 | 拆句并澄清“cardiac surgery 是 ICU 主要入科原因之一” |
| 多次使用 “current” “currently” | 冗余 | 删除多余限定 |
| HbA1c 偏高/偏低原因段 | 长串并列影响易读性 | 考虑用分号或列表列举原因 |
| “HGI … predicts the risk of diabetes complications, including mortality, cardiovascular disease, and microvascular complications” | 措辞不精确：mortality 属于结果，非“糖尿病并发症” | 细分类别，或改为 “including cardiovascular and microvascular events and all-cause mortality” |
| 方法陈述 | 略显啰嗦：多次说明“少有研究” | 一处强调研究空白即可；留下字数介绍创新统计方法/研究设计 |
与上一轮提到的“常见毛病”对照
| 常见问题 | ① TyG-BMI | ② SHR | ③ HGI |
|---|---|---|---|
| 超长句 | 仍多 | 多 | 多 |
| 概念/术语混用 | HEC vs HOMA-IR | ACS/CVD 范围 | HbA1c vs 并发症 |
| 数据/信息堆砌 | 适中 | 偏多 | 适中 |
| 重复 & 冗词 | 少量 | 少 | 明显 |
| 逻辑衔接 | 尚可 | 部分跳跃 | 尚可 |
总体建议
- 精简与聚焦：保留能直接支持研究假设的数字；其余可移至讨论。
- 拆分复合长句：英文医学期刊推荐 ≤ 30 词/句可读性最高。
- 统一术语与概念链：先定义（TyG-BMI / SHR / HGI），再叙述机制 → 证据 → 空缺。
- 避免“current/nowadays”等口头化副词，用精确时间或被动语态写法。
- 检查拼写与语法细节（如 hypothesize、patients were critically ill 等）。
结论：三篇稿件较前一批已显著收敛修辞，但仍建议按以上要点做语言润色与逻辑压缩，特别是长句拆分和概念澄清，可显著提升可读性与专业度。
سؤال
那你给我把我最初发给你的三篇的版本按照后面这三篇你认为在语法上不错的来修改润色：“总体建议
精简与聚焦：保留能直接支持研究假设的数字；其余可移至讨论。
拆分复合长句：英文医学期刊推荐 ≤ 30 词/句可读性最高。
统一术语与概念链：先定义（TyG-BMI / SHR / HGI），再叙述机制 → 证据 → 空缺。
避免“current/nowadays”等口头化副词，用精确时间或被动语态写法。
检查拼写与语法细节（如 hypothesize、patients were critically ill 等）。”你既然给了总体建议，甚至这个建议就是你写完总结给我的，那你给我改改
الفكرة
الإجابة
Below are streamlined, grammar-checked versions of your original three introductions.
I kept every statistic or detail that directly supports the study aim, broke very long sentences into shorter units (≈ ≤ 30 words), removed redundant wording, and used consistent terminology: first define the haemoglobin-glycation index (HGI) and then discuss mechanism → evidence → knowledge gap.
Revised Introduction #1
Cardiovascular disease (CVD) remains the leading global cause of death, accounting for one-third of all deaths in 2021. Atrial fibrillation (AF), the most common sustained arrhythmia, doubles the risks of heart failure, stroke, and all-cause mortality. In intensive-care units (ICUs) AF is even more threatening: 10–15 % of admissions manifest AF, and their short-term mortality is about twice that of patients in sinus rhythm.
Glycaemic disturbance is one modifiable factor that may drive this excess risk. Acute stressors—sympathetic activation, inflammatory cytokines, corticosteroid exposure—raise glucose soon after ICU admission, whereas long-term metabolic control varies widely. Conventional markers capture only parts of this picture. Fasting plasma glucose (FPG) reflects momentary glycaemia but ignores prior control; glycated haemoglobin (HbA1c) averages the past three months yet misses rapid swings typical of critical illness.
The haemoglobin-glycation index (HGI) solves this temporal mismatch. It is calculated as the difference between observed HbA1c and the HbA1c predicted from concurrent FPG, quantifying each patient’s intrinsic glycation propensity. High-positive HGI correlates with endothelial dysfunction, oxidative stress, and atrial fibrosis, which stabilise re-entry circuits and promote thrombo-embolism. Markedly negative HGI may signify shortened erythrocyte lifespan or occult haemolysis, both markers of severe systemic stress. These findings imply a non-linear, J-shaped relationship between HGI and prognosis, but the association has never been examined in critically ill adults with AF.
Several aspects of critical-care cardiology make this question urgent. β-blockers, amiodarone, and vasopressors can themselves alter glucose handling and shift the HGI distribution. Perfusion-related anaemia and frequent transfusions distort HbA1c, complicating risk assessment. Clinical trials of tight glucose control in the ICU have produced mixed results, so a biomarker that separates harmful variability from adaptive hyperglycaemia could refine therapy.
We therefore performed a retrospective cohort study in the Medical Information Mart for Intensive Care IV database. Our primary objective was to test whether admission HGI predicts 30-day and one-year all-cause mortality in adults with AF. We hypothesised a J-shaped association in which both high-positive and high-negative HGI values confer excess risk independent of established covariates. Clarifying this relationship could integrate metabolic phenotyping into AF care pathways and improve outcomes in a vulnerable population.
Revised Introduction #2
Cardiovascular disease still tops global mortality statistics. Atrial fibrillation, its most frequent sustained arrhythmia, doubles the risks of stroke, heart-failure decompensation, and death. In ICUs the outlook is worse: 12–15 % of adult admissions either present with or develop AF within 24 hours, and nearly one-quarter of these patients die within 30 days.
Metabolic dysregulation is a plausible yet understudied driver of this excess risk. Critical illness triggers catecholamine release, pro-inflammatory cytokines, peripheral insulin resistance, and exposure to dextrose-containing fluids, vasopressors, or corticosteroids, all of which disrupt glucose homeostasis. Clinicians track FPG for real-time management and HbA1c for antecedent control, but each has blind spots. FPG is a transient snapshot, whereas HbA1c changes slowly and is confounded by anaemia, transfusion, and altered erythrocyte turnover.
The haemoglobin-glycation index bridges these gaps by subtracting predicted from observed HbA1c, thereby capturing glycation propensity beyond momentary glucose. Elevated HGI predicts nephropathy, coronary calcification, heart-failure readmission, and mortality. Mechanistically, advanced glycation end-products promote oxidative stress, endothelial dysfunction, and atrial remodelling—all integral to AF maintenance. Glycaemic oscillations also disturb autonomic balance and prolong the QT interval, setting the stage for malignant arrhythmias and multi-organ injury.
Despite these links, the prognostic value of HGI in critically ill adults with AF is unknown. Landmark ICU studies of glycaemic variability have excluded arrhythmias or aggregated AF with heterogeneous cardiac diagnoses, obscuring disease-specific signals. AF treatment in the ICU requires β-blockade, amiodarone loading, and often high-dose vasopressors; each intervention can alter glucose utilisation and thereby modulate HGI. Point-of-care HbA1c testing now enables bedside HGI calculation, making immediate translation possible if the metric proves useful.
We analysed the MIMIC-IV database to determine whether admission HGI predicts 30-day and 365-day mortality in adults with AF. Interaction tests assessed whether diabetic status, illness-severity scores, septic shock, catecholamine use, or renal-replacement therapy modify the association. We hypothesised a J-shaped relationship in which both tails of the HGI distribution increase mortality. Demonstrating prognostic utility could sharpen risk stratification, guide resource allocation, and support trials that target glycaemic variability rather than uniformly strict glucose control.
Revised Introduction #3
Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs.
Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU.
High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent.
Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests.
We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.
سؤال
翻译成中文，顺带告诉我这三篇各自的英文字数
Python
pythonintro1 = """Cardiovascular disease (CVD) remains the leading global cause of death, accounting for one-third of all deaths in 2021. Atrial fibrillation (AF), the most common sustained arrhythmia, doubles the risks of heart failure, stroke, and all-cause mortality. In intensive-care units (ICUs) AF is even more threatening: 10–15 % of admissions manifest AF, and their short-term mortality is about twice that of patients in sinus rhythm. Glycaemic disturbance is one modifiable factor that may drive this excess risk. Acute stressors—sympathetic activation, inflammatory cytokines, corticosteroid exposure—raise glucose soon after ICU admission, whereas long-term metabolic control varies widely. Conventional markers capture only parts of this picture. Fasting plasma glucose (FPG) reflects momentary glycaemia but ignores prior control; glycated haemoglobin (HbA1c) averages the past three months yet misses rapid swings typical of critical illness. The haemoglobin-glycation index (HGI) solves this temporal mismatch. It is calculated as the difference between observed HbA1c and the HbA1c predicted from concurrent FPG, quantifying each patient’s intrinsic glycation propensity. High-positive HGI correlates with endothelial dysfunction, oxidative stress, and atrial fibrosis, which stabilise re-entry circuits and promote thrombo-embolism. Markedly negative HGI may signify shortened erythrocyte lifespan or occult haemolysis, both markers of severe systemic stress. These findings imply a non-linear, J-shaped relationship between HGI and prognosis, but the association has never been examined in critically ill adults with AF. Several aspects of critical-care cardiology make this question urgent. β-blockers, amiodarone, and vasopressors can themselves alter glucose handling and shift the HGI distribution. Perfusion-related anaemia and frequent transfusions distort HbA1c, complicating risk assessment. Clinical trials of tight glucose control in the ICU have produced mixed results, so a biomarker that separates harmful variability from adaptive hyperglycaemia could refine therapy. We therefore performed a retrospective cohort study in the Medical Information Mart for Intensive Care IV database. Our primary objective was to test whether admission HGI predicts 30-day and one-year all-cause mortality in adults with AF. We hypothesised a J-shaped association in which both high-positive and high-negative HGI values confer excess risk independent of established covariates. Clarifying this relationship could integrate metabolic phenotyping into AF care pathways and improve outcomes in a vulnerable population.""" len(intro1.split()) 351
Python
pythonintro2 = """Cardiovascular disease still tops global mortality statistics. Atrial fibrillation, its most frequent sustained arrhythmia, doubles the risks of stroke, heart-failure decompensation, and death. In ICUs the outlook is worse: 12–15 % of adult admissions either present with or develop AF within 24 hours, and nearly one-quarter of these patients die within 30 days. Metabolic dysregulation is a plausible yet understudied driver of this excess risk. Critical illness triggers catecholamine release, pro-inflammatory cytokines, peripheral insulin resistance, and exposure to dextrose-containing fluids, vasopressors, or corticosteroids, all of which disrupt glucose homeostasis. Clinicians track FPG for real-time management and HbA1c for antecedent control, but each has blind spots. FPG is a transient snapshot, whereas HbA1c changes slowly and is confounded by anaemia, transfusion, and altered erythrocyte turnover. The haemoglobin-glycation index bridges these gaps by subtracting predicted from observed HbA1c, thereby capturing glycation propensity beyond momentary glucose. Elevated HGI predicts nephropathy, coronary calcification, heart-failure readmission, and mortality. Mechanistically, advanced glycation end-products promote oxidative stress, endothelial dysfunction, and atrial remodelling—all integral to AF maintenance. Glycaemic oscillations also disturb autonomic balance and prolong the QT interval, setting the stage for malignant arrhythmias and multi-organ injury. Despite these links, the prognostic value of HGI in critically ill adults with AF is unknown. Landmark ICU studies of glycaemic variability have excluded arrhythmias or aggregated AF with heterogeneous cardiac diagnoses, obscuring disease-specific signals. AF treatment in the ICU requires β-blockade, amiodarone loading, and often high-dose vasopressors; each intervention can alter glucose utilisation and thereby modulate HGI. Point-of-care HbA1c testing now enables bedside HGI calculation, making immediate translation possible if the metric proves useful. We analysed the MIMIC-IV database to determine whether admission HGI predicts 30-day and 365-day mortality in adults with AF. Interaction tests assessed whether diabetic status, illness-severity scores, septic shock, catecholamine use, or renal-replacement therapy modify the association. We hypothesised a J-shaped relationship in which both tails of the HGI distribution increase mortality. Demonstrating prognostic utility could sharpen risk stratification, guide resource allocation, and support trials that target glycaemic variability rather than uniformly strict glucose control.""" len(intro2.split()) 339
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder c... High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over establis... len(intro3.split())
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = """Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs. Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU. High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. Data for critically ill adults with AF are almost nonexistent. Mechanistic pathways strengthen the case for investigation. Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests. We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology.""" len(intro3.split()) 375
Python
pythonintro3 = ( "Cardiovascular disease remains the leading cause of death, and the rising prevalence of atrial fibrillation fuels the trend. " "AF affects about 60 million people worldwide, triples stroke risk, and doubles all-cause mortality. " "The ICU environment—systemic inflammation, catecholamine surges, and invasive procedures—further increases AF incidence: up to one-seventh of critically ill adults develop AF, extending ventilation time, length of stay, and costs.\n\n" "Hyperglycaemia and glycaemic variability are modifiable insults that may underlie these adverse outcomes. " "FPG captures the immediate metabolic state, and HbA1c conveys a three-month average, yet neither describes the discordance created when acute stress collides with chronic history. " "The haemoglobin-glycation index, defined as observed minus predicted HbA1c (from concurrent FPG), quantifies glycation susceptibility independent of prevailing glucose and is not affected by the perfusion artefacts that hinder continuous glucose monitors in the ICU.\n\n" "High HGI has been linked to accelerated atherosclerosis, cardiac autonomic neuropathy, and poor neurological recovery after stroke, whereas very low HGI may indicate shortened erythrocyte lifespan, occult haemolysis, or massive transfusion—each a marker of severe systemic stress. " "Together these findings suggest that both extremes of HGI carry risk, yet evidence comes mainly from community or stable coronary cohorts. " "Data for critically ill adults with AF are almost nonexistent.\n\n" "Mechanistic pathways strengthen the case for investigation. " "Advanced glycation end-products promote atrial fibrosis and ion-channel dysfunction, sustaining AF. " "Glycaemic swings alter autonomic tone and myocardial substrate use, shifting arrhythmogenic thresholds. " "Treatments common in the ICU—amiodarone, β-blockers, digoxin, anticoagulation—have metabolic footprints that may interact with HGI. " "Ignoring these interactions risks overlooking an inexpensive marker available from two routine laboratory tests.\n\n" "We therefore used MIMIC-IV to study the association between admission HGI and 30-day and one-year all-cause mortality in adult ICU patients with AF. " "We posited a J-shaped curve in which both high and low HGI values independently predict death after adjustment for demographics, comorbidities, severity scores, and treatments. " "Restricted cubic splines visualised non-linearity and helped define safe versus hazardous HGI ranges. " "Secondary aims were to quantify the incremental prognostic value of HGI over established scores, explore interactions by diabetic status and vasoactive medication use, and map dose–response curves to identify potential therapeutic targets. " "Clarifying whether HGI is more than a statistical curiosity could open a new window for personalised glucose management in critical-care cardiology." ) len(intro3.split()) 375
Python
pythonintro1 = ( "Cardiovascular disease (CVD) remains the leading global cause of death, accounting for one-third of all deaths in 2021. " "Atrial fibrillation (AF), the most common sustained arrhythmia, doubles the risks of heart failure, stroke, and all-cause mortality. " "In intensive-care units (ICUs) AF is even more threatening: 10–15 % of admissions manifest AF, and their short-term mortality is about twice that of patients in sinus rhythm.\n\n" "Glycaemic disturbance is one modifiable factor that may drive this excess risk. " "Acute stressors—sympathetic activation, inflammatory cytokines, corticosteroid exposure—raise glucose soon after ICU admission, whereas long-term metabolic control varies widely. " "Conventional markers capture only parts of this picture. " "Fasting plasma glucose (FPG) reflects momentary glycaemia but ignores prior control; glycated haemoglobin (HbA1c) averages the past three months yet misses rapid swings typical of critical illness.\n\n" "The haemoglobin-glycation index (HGI) solves this temporal mismatch. " "It is calculated as the difference between observed HbA1c and the HbA1c predicted from concurrent FPG, quantifying each patient’s intrinsic glycation propensity. " "High-positive HGI correlates with endothelial dysfunction, oxidative stress, and atrial fibrosis, which stabilise re-entry circuits and promote thrombo-embolism. " "Markedly negative HGI may signify shortened erythrocyte lifespan or occult haemolysis, both markers of severe systemic stress. " "These findings imply a non-linear, J-shaped relationship between HGI and prognosis, but the association has never been examined in critically ill adults with AF.\n\n" "Several aspects of critical-care cardiology make this question urgent. " "β-blockers, amiodarone, and vasopressors can themselves alter glucose handling and shift the HGI distribution. " "Perfusion-related anaemia and frequent transfusions distort HbA1c, complicating risk assessment. " "Clinical trials of tight glucose control in the ICU have produced mixed results, so a biomarker that separates harmful variability from adaptive hyperglycaemia could refine therapy.\n\n" "We therefore performed a retrospective cohort study in the Medical Information Mart for Intensive Care IV database. " "Our primary objective was to test whether admission HGI predicts 30-day and one-year all-cause mortality in adults with AF. " "We hypothesised a J-shaped association in which both high-positive and high-negative HGI values confer excess risk independent of established covariates. " "Clarifying this relationship could integrate metabolic phenotyping into AF care pathways and improve outcomes in a vulnerable population." ) len(intro1.split()) 351
Python
pythonintro2 = ( "Cardiovascular disease still tops global mortality statistics. " "Atrial fibrillation, its most frequent sustained arrhythmia, doubles the risks of stroke, heart-failure decompensation, and death. " "In ICUs the outlook is worse: 12–15 % of adult admissions either present with or develop AF within 24 hours, and nearly one-quarter of these patients die within 30 days.\n\n" "Metabolic dysregulation is a plausible yet understudied driver of this excess risk. " "Critical illness triggers catecholamine release, pro-inflammatory cytokines, peripheral insulin resistance, and exposure to dextrose-containing fluids, vasopressors, or corticosteroids, all of which disrupt glucose homeostasis. " "Clinicians track FPG for real-time management and HbA1c for antecedent control, but each has blind spots. " "FPG is a transient snapshot, whereas HbA1c changes slowly and is confounded by anaemia, transfusion, and altered erythrocyte turnover.\n\n" "The haemoglobin-glycation index bridges these gaps by subtracting predicted from observed HbA1c, thereby capturing glycation propensity beyond momentary glucose. " "Elevated HGI predicts nephropathy, coronary calcification, heart-failure readmission, and mortality. " "Mechanistically, advanced glycation end-products promote oxidative stress, endothelial dysfunction, and atrial remodelling—all integral to AF maintenance. " "Glycaemic oscillations also disturb autonomic balance and prolong the QT interval, setting the stage for malignant arrhythmias and multi-organ injury.\n\n" "Despite these links, the prognostic value of HGI in critically ill adults with AF is unknown. " "Landmark ICU studies of glycaemic variability have excluded arrhythmias or aggregated AF with heterogeneous cardiac diagnoses, obscuring disease-specific signals. " "AF treatment in the ICU requires β-blockade, amiodarone loading, and often high-dose vasopressors; each intervention can alter glucose utilisation and thereby modulate HGI. " "Point-of-care HbA1c testing now enables bedside HGI calculation, making immediate translation possible if the metric proves useful.\n\n" "We analysed the MIMIC-IV database to determine whether admission HGI predicts 30-day and 365-day mortality in adults with AF. " "Interaction tests assessed whether diabetic status, illness-severity scores, septic shock, catecholamine use, or renal-replacement therapy modify the association. " "We hypothesised a J-shaped relationship in which both tails of the HGI distribution increase mortality. " "Demonstrating prognostic utility could sharpen risk stratification, guide resource allocation, and support trials that target glycaemic variability rather than uniformly strict glucose control." ) len(intro2.split()) 339
الفكرة
الإجابة
译文与字数统计
引言 1 （英文 351 词）
心血管疾病（CVD）依旧是全球首要死因，2021 年占全部死亡病例的三分之一。房颤（AF）是最常见的持续性心律失常，可使心力衰竭、卒中和全因死亡风险增加一倍。在重症监护病房（ICU）内，AF 的威胁更甚：10–15 %的入院患者出现 AF，其短期死亡率约为窦性心律患者的两倍。
血糖紊乱是导致上述过度风险的可调节因素之一。交感神经激活、炎症细胞因子及糖皮质激素暴露会在 ICU 入院初期迅速升高血糖，而患者的长期代谢控制差异很大。常用指标仅能捕捉局部信息：空腹血糖（FPG）反映瞬时水平，糖化血红蛋白（HbA1c）代表过去三个月平均，却忽视急性波动。
血红蛋白糖化指数（HGI）弥合了这一本质差异。HGI 为实测 HbA1c 减去基于同期 FPG 预测值，量化个体固有的糖化倾向。HGI 升高与内皮功能障碍、氧化应激及心房纤维化相关，可稳定折返环并促进血栓形成；而 HGI 明显降低可能提示红细胞寿命缩短或隐匿性溶血，均代表严重系统应激。这些发现暗示 HGI 与预后之间可能呈 J 形曲线，但在危重 AF 成人中尚未得到验证。
重症心脏病学的多种特征令该问题尤为紧迫。β 受体阻滞剂、胺碘酮和血管活性药可改变葡萄糖代谢并移动 HGI 分布；灌注相关贫血和频繁输血会扭曲 HbA1c；而 ICU 严格血糖控制试验结果不一，急需能区分有害波动与适应性高血糖的生物标志物。
基于此，我们利用 MIMIC-IV 数据库开展回顾性队列研究，检验入院 HGI 是否可预测 AF 成人的 30 天和 1 年全因死亡。我们假设 HGI 与死亡呈 J 形关系，即高正值和高负值均独立增加风险。阐明此关联有望将代谢表型整合进 AF 治疗路径，改善这一脆弱人群的预后。
引言 2 （英文 339 词）
心血管疾病仍居全球死亡之首。房颤作为最常见的持续性心律失常，可使卒中、心衰失代偿及死亡风险翻倍。在 ICU 中，形势更为严峻：12–15 %的成人入院 24 小时内即存在或新发 AF，近四分之一患者在 30 天内死亡。
代谢失衡可能是该过度风险的驱动因素之一，却研究不足。危重病激活儿茶酚胺和炎症反应，诱导胰岛素抵抗，并暴露于含葡萄糖液体、血管活性药及糖皮质激素，均可破坏葡萄糖稳态。FPG 用于实时监测，HbA1c 用于回顾控制，但两者各有盲区：FPG 为瞬时快照，HbA1c 受贫血、输血和红细胞周转影响。
HGI 通过实测 HbA1c 减去预测值来弥补这一缺陷，捕捉超越瞬时血糖的糖化倾向。HGI 升高与肾病、冠状动脉钙化、心衰再入院及死亡相关。糖基化终产物诱发氧化应激、内皮功能障碍与心房重塑，而血糖波动扰乱自主神经并延长 QT 间期，促进恶性心律失常。
然而，HGI 在危重 AF 成人中的预后价值尚未探明。既往 ICU 血糖研究排除了心律失常或将 AF 与其他心脏疾病混合分析。ICU 中 AF 治疗需 β 阻滞剂、胺碘酮和高剂量血管活性药，这些都会改变葡萄糖利用并影响 HGI。床旁 HbA1c 测试如今可实时计算 HGI，一旦验证有效即可临床应用。
我们分析 MIMIC-IV 数据库以评估入院 HGI 对 AF 成人 30 天及 365 天死亡的预测作用，并检验糖尿病状态、病情评分、感染性休克、儿茶酚胺使用与肾替代治疗的交互影响。我们提出 HGI 与死亡呈 J 形关系，极端高低值均增加风险。若证实其预测力，可强化风险分层、优化资源配置，并支持以血糖波动为靶点的干预试验。
引言 3 （英文 375 词）
心血管疾病仍是全球首要死因，而房颤的流行加剧了这一趋势。全球约 6000 万人受 AF 影响，卒中风险增至 3 倍，全因死亡率增至 2 倍。ICU 环境中的系统炎症、儿茶酚胺激增与侵入性操作进一步推高 AF 发生率：多达七分之一危重成人在住院期间出现 AF，延长通气时间、住院天数并增加费用。
高血糖及血糖波动是可调节损伤因素，可能解释上述不良结局。FPG 反映即时代谢状态，HbA1c 表示三个月平均，却无法描述急性应激与慢性病史碰撞产生的不一致。血红蛋白糖化指数（HGI）定义为实测 HbA1c 减去基于同期 FPG 的预测值，可独立于当前血糖量化糖化易感性，并不受 ICU 中低灌注伪影影响。
HGI 升高与动脉粥样硬化加速、心脏自主神经病变及卒中后神经恢复不良相关；而极低 HGI 可能提示红细胞寿命缩短、隐匿溶血或大量输血，均为严重系统应激标志。这提示 HGI 两端均可能危险，但现有证据主要来自社区或稳定冠心病人群，危重 AF 数据几乎空白。
多条机制通路强化了研究必要性：糖基化终产物导致心房纤维化与离子通道功能障碍，维持 AF；血糖波动改变自主神经张力和心肌底物利用，调整致心律失常阈值。ICU 常用的胺碘酮、β 阻滞剂、地高辛及抗凝治疗均会影响代谢，与 HGI 潜在交互。忽视这些作用可能错过一项仅需两项常规化验即可获得的经济指标。
因此，我们利用 MIMIC-IV 数据库评估入院 HGI 与 AF 危重成人 30 天及 1 年全因死亡的关联。假设 HGI 与死亡呈 J 形曲线，极高与极低值在校正人口学、合并症、病情评分及治疗后仍独立预测死亡。限制性三次样条用于可视化非线性并界定安全区间；次要目标包括评估 HGI 的增量预测价值、分析糖尿病状态及血管活性药使用的交互，并绘制剂量-反应曲线寻找潜在干预靶点。阐明 HGI 的真实意义或可开启个体化血糖管理的新窗口。
英文原文字数
- 引言 1：351 词
- 引言 2：339 词
- 引言 3：375 词