统摄/总结/承接类的短语

“Discussion0909

Using the MIMIC-IV database, we evaluated associations between ICU-measured HGI and 30-, 90-, 180-, and 365-day all-cause mortality in patients with AF.

HGI showed a reverse J shaped association with all-cause mortality, and this pattern persisted after adjustment for demographics, vital signs, laboratory values, comorbidities, clinical scores, and medications.

Patients with low HGI had higher in-hospital mortality and longer ICU stays.

Below the inflection point, risk increased sharply as HGI decreased；above it, the curve plateaued, and further increases in HGI were associated with minimal additional risk.

These findings underscore the potential clinical relevance of HGI for risk evaluation and stratification and for informing management in ICU patients with atrial fibrillation.

Evidence from large cohorts and meta-analyses indicates that, even after accounting for conventional risk factors, diabetes confers an approximately 30–40% higher risk of incident atrial fibrillation.

Critically ill patients with atrial fibrillation often have complex clinical profiles and face elevated risks of adverse outcomes due to multiple interacting insults .

Admission blood glucose alone cannot capture chronic glycemic exposure because it does not reflect long-term fluctuations.

HbA1c is strongly correlated with mean glucose; however, mean glucose accounts for approximately 60–80% of the variance in HbA1c, leaving roughly 20–40% unexplained by glycemia—a residual that may have prognostic relevance for adverse outcomes .

HGI quantifies this residual component and is calculated as observed HbA1c minus glucose-predicted HbA1c.HGI provides a phenotypic index of glycation propensity and glycemic variability, especially relevant among ICU patients with AF.

The variation captured by HGI likely reflects factors beyond ambient glucose—including erythrocyte redox status, intrinsic glycation activity, genetic susceptibility of red cells, and hemoglobin oxygenation—which may underlie interindividual differences in the HbA1c–fasting glucose relationship.

Accumulating evidence indicates that higher HGI is associated with greater prevalence and incidence of microvascular complications—such as diabetic retinopathy and chronic kidney disease—independent of conventional glycemic measures.

In cardiovascular cohorts, elevated HGI is associated with increased risk of atherosclerotic events and may delineate subgroups that experience differential benefit or harm from intensive glucose-lowering strategies.

Beyond diabetes, higher HGI is associated with insulin resistance, carotid atherosclerosis, and nonalcoholic fatty liver disease in general-population and nondiabetic cohorts, suggesting that HGI captures broader metabolic risk.
Across acute-care settings—including stroke, heart failure, and critical illness—both high and low HGI have been associated with mortality or poor functional recovery, supporting a nonlinear relationship between glycation phenotype and prognosis.

In AF, HGI can be viewed as a composite phenotypic index that integrates glycation propensity and glycemic variability and is influenced by red cell turnover .

A coherent mechanistic framework implicates the advanced glycation end products (AGE)–receptor for AGE (RAGE) axis, together with RAAS activation and autonomic–metabolic stress, in driving oxidative injury and profibrotic TGF-β/Smad signaling.Epigenetically mediated metabolic memory sustains hyperglycemia induced signaling beyond episodes of transient hyperglycemia.

AGE-mediated collagen cross-linking stiffens the extracellular matrix and confers resistance to proteolysis, thereby limiting reverse remodeling.

In parallel, endothelial dysfunction and microvascular injury—exacerbated by recurrent stress hyperglycemia—promote nitric oxide depletion, a prothrombotic shift, and conduction heterogeneity [31,32].

Insulin resistance and inflamed epicardial adipose tissue add lipotoxic and mitochondrial stress, modulating CaMKII activity, ion-channel expression, and connexins.

Together, structural fibrosis (conduction slowing and spatial dispersion) and ionic remodeling (effective refractory period shortening and afterdepolarizations) supply both substrate and triggers for AF [33].

These processes converge into positive feedback loops linking glycemic dynamics, oxidative–fibrotic remodeling, and electrophysiologic instability, with the strength of evidence varying across steps.

Against this backdrop, between-study inconsistencies in the HGI likely arise from three domains: (1) non-glycemic determinants of HbA1c; (2) how predicted HbA1c and HGI are derived and modeled; and (3) population characteristics and treatment strategies [18].

Computational choices also matter: predicted HbA1c has been generated from heterogeneous regressions (e.g., fasting plasma glucose vs. mean glucose monitoring; cohort-specific vs. externally calibrated equations), and HGI has been analyzed as a continuous variable, by quantiles, or by dichotomization—decisions that can alter effect sizes, statistical significance, and even the direction of associations .

Notably, HGI derived from a single fasting glucose captures a different facet of glycemia than CGM-based estimates, a distinction has been highlighted in the DEVOTE secondary analysis.

In addition, differences in populations and glycemic strategies also shape HGI–outcome associations: in the ACCORD trial (intensive target HbA1c < 6.0%), intensive pharmacotherapy was associated with higher all-cause mortality and the trial was terminated early; by contrast, the ADVANCE trial (gliclazide modified-release regimen; target ≤ 6.5%) showed mainly microvascular benefits without excess mortality. Together with an ACCORD secondary analysis suggesting that harms clustered in high-HGI subgroups under intensive therapy, these contrasts illustrate how population and treatment strategy can shape observed risk patterns [34–36].

Taken together, heterogeneity in biology, analytics, and population/treatment strategies plausibly explains the divergent findings.

Consistent with this, multiple observational cohorts in critical-care and cardiovascular settings show a reverse-J association between HGI and outcomes, with higher mortality or complications at the low-HGI end (ICU heart failure, acute myocardial infarction, ischemic stroke, mixed ICU cohorts). Low HGI may index non-glycemic depression of HbA1c or overtreatment-related discordance, both of which can track heightened vulnerability despite apparently favorable HbA1c [37].

Across 30, 90, 180, and 365 days, adjusted spline curves consistently indicated a reverse J shaped association, with a nadir near HGI ≈ 0 and an inflection around −0.6().

Beyond the inflection, slopes were shallow and confidence bands wide, providing limited evidence for a monotonic increase.

Tests for the overall association and for nonlinearity were significant at the 30 and 365 day horizons;results at other time points were directionally consistent. (Report exact P values if available.)

Threshold models were concordant: below the inflection, each 1 unit increase in HGI (toward 0) was associated with a lower hazard, whereas above the inflection marginal effects were near null (95% CIs crossed 1.0).

Taken together, excess mortality clustered at the low HGI tail, with minimal incremental risk beyond the identified inflection.

Exploratory subgroup analyses—interpreted with multiplicity in mind—suggested effect modification consistent with biological heterogeneity [36].

Patients without chronic kidney disease or prior myocardial infarction displayed steeper risk gradients at higher HGI, whereas those with CKD or previous MI showed attenuated or reversed, likely because competing pathways dominate their prognosis[38,39].

The steeper high-HGI gradients in the strata without CKD or MI suggest that modifiable glycemic–vascular mechanisms are unmasked when irreversible structural disease is absent [40].

In established CKD, assay interference and uremic, inflammatory, and arrhythmic risks may attenuate the mortality association of HGI, rendering its prognostic signal less apparent[38].

After prior MI, scar burden, neurohormonal activation, and guideline-directed therapies often dominate outcomes, further attenuating HGI’s relative signal[39].

By contrast, patients without these fixed injuries may display greater endothelial plasticity and thrombo-inflammatory responsiveness to glycation stress[40].

Other prespecified subgroup analyses (BMI, diabetes, gender, and age) did not reveal statistically significant interactions with HGI and are not emphasized further .

At very low HGI, depression of HbA1c that is independent of ambient glycemia can accompany high physiologic stress .

Shortened erythrocyte lifespan during systemic inflammation, iron-restricted erythropoiesis with hepcidin upregulation and reticulocytosis, and uremic carbamylation can create a negative glycation gap, yielding deceptively low HbA1c despite substantial metabolic strain.

This hematologic/immunometabolic milieu—often co occurring with malnutrition–inflammation–catabolism—promotes mitochondrial dysfunction, dysregulated coagulation and vasoplegia, and susceptibility to secondary infection, phenomena poorly captured by glucose indices alone.

The net effect is a spuriously low HbA1c amid high stress, consistent with the disproportionate short-term mortality observed at the low-HGI tail.

When renal and myocardial reserve are preserved, chronically higher HGI plausibly reflects sustained glycation burden and glycemic volatility[45].

Activation of the AGE–RAGE axis augments NADPH oxidase–dependent oxidative stress and depletes nitric oxide bioavailability; glycocalyx injury and endothelial dysfunction tilt toward a prothrombotic, hypofibrinolytic state (elevated PAI-1, denser fibrin networks).

Superimposed sympathetic activation and oscillating glucose generate recurrent oxidative bursts that foster thrombo-inflammation and microvascular injury, while ionic remodeling (e.g., CaMKII signaling, ion-channel and connexin changes) may increase conduction heterogeneity relevant to AF.

This framework explains the modest upward slopes at higher HGI overall and the steeper gradients in subgroups where organ reserve is intact.

Collectively, the data suggest a reserve dependent model: low HGI often reflects illness and assay related depression of HbA1c and acute vulnerability, whereas high HGI indexes glycation mediated vascular risk chiefly when renal and myocardial reserves are intact.

We present this reserve-dependent framework as hypothesis-generating rather than definitive, inviting validation through prospective replications, quasi-experimental designs, and mechanistic interrogation.

Clinically, these data support individualized interpretation of HGI: identify and treat reversible causes of spuriously low HGI in the overall population, while considering dysglycemic and vascular pathways in subgroups where higher HGI retains prognostic salience.

Such heterogeneity motivates targeted evaluation rather than uniform intensification of glucose-lowering therapy.

Mechanistically anchored phenotyping—combining HGI with inflammation indices, renal function, and measures of glycemic variability—may help identify patients most likely to benefit from vascular-protective strategies.

Such approaches should be accompanied by vigilance for spuriously low HbA1c, ensuring that low HGI triggers investigation for anemia, hemolysis, and assay interference. Accordingly, our findings should be interpreted as hypothesis generating and require confirmation in independent, multicenter cohorts with standardized assessment of HbA1c modifiers.
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