Research Synthesis: Acarbose Effects

Research Synthesis: Acarbose Effects

Abstract

Evidence-honesty note: 38/47 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims.

This paper synthesizes evidence on acarbose effects across 47 included source papers and 2780 high-confidence extracted claims.

The evidence profile contains 9 direct clinical sources, 16 adjacent clinical sources, and 4 mechanistic or model-system sources, with 317 cross-study disagreements across the evidence base.

Positive study-level signals are summarized in the cardiometabolic, contextual adjacent evidence and immune outcome classes, null signals in the contextual adjacent evidence, cardiometabolic and deficiency prevalence outcome classes, and negative signals in the contextual adjacent evidence and cardiometabolic outcome classes. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect.

The conclusion is that acarbose effects should be treated as a bounded geroscience hypothesis: the retained clinical and mechanistic evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim.

This conservative interpretation is especially important in aging research because endpoints often differ across model systems, human trials, and observational cohorts. A signal in one domain is not automatically consistent with the same signal in another.

Introduction

The human RCT landscape for Acarbose Effects spans a heterogeneous collection of trial designs, populations, and endpoints. Larger cardiometabolic endpoint trials, such as add-on studies in patients failing metformin and sitagliptin therapy (Yang 2018) and comparative effectiveness trials against alogliptin in high-cardiovascular-risk populations (Gao 2022), address clinical efficacy over weeks to months. However, population heterogeneity — spanning newly diagnosed diabetes patients, older adults with impaired glucose tolerance, and obese individuals receiving combination therapy — complicates pooled inference. The question of whether Acarbose Effects on surrogate markers such as HbA1c and lipid panels translate into durable hard-outcome benefits in defined populations remains unresolved.

Several unresolved questions limit confident clinical application of Acarbose Effects for aging-related outcomes. The mechanistic plausibility established in preclinical models — including lifespan extension and microbiome restructuring — has not been consistently mirrored in human studies, where effects on inflammatory biomarkers show heterogeneity (Mohammadian 2024; Mo 2019) and cardiometabolic endpoints yield mixed results across trials. Sex-dependent response patterns, well-documented in murine longevity studies, have received insufficient attention in human trial design, leaving open whether population-level effects mask important subgroup differences. Dose-response relationships remain poorly characterized in the gerotherapeutic context; most trials have used diabetes-indication dosing rather than systematically exploring longevity-relevant regimens. Duration of exposure is another critical variable: short-term RCTs of 6 to 48 weeks may be insufficient to detect effects on outcomes such as physical function, cognitive trajectory, or incident frailty, for which gait-speed thresholds like 0.8 m/s (Studenski 2011) or grip-strength cutoffs of 27 kg in men and 16 kg in women (Cruz-Jentoft 2019) serve as established clinical markers. Gastrointestinal tolerability, which influences long-term adherence, further constrains the therapeutic window. The question of whether Acarbose Effects meaningfully impact hard aging endpoints — mortality, disability-free survival, or multi-morbidity incidence — cannot be answered by the current evidence base alone.

Scope of the synthesis

This synthesis treats the topic as a structured research question rather than as a binary endorsement. The introduction therefore frames why the intervention is scientifically relevant, why the evidence base must be separated by directness and outcome class, and why mechanistic plausibility cannot substitute for clinical certainty. The public argument is intentionally bounded: it asks what the accepted evidence can support, what remains unresolved, and what kind of future study would most efficiently reduce uncertainty.

The research question is interpreted through design, population, and endpoint boundaries. Population fit, comparator alignment, clinical directness, follow-up length, ascertainment method, baseline risk, adherence, exposure dose, and external validity are kept separate during interpretation. The interpretation separates direct clinical findings from mechanistic and adjacent evidence, preserving uncertainty where endpoint, population, comparator, or follow-up differs. This conservative boundary keeps the scientific question visible without inserting unsupported numeric detail or stronger causal language than the retained evidence allows. Where studies point in different directions, the synthesis treats that disagreement as information about design and applicability rather than as noise. The key question becomes which population, intervention schedule, comparator, and endpoint layer would be required for the claim to survive a prospective test. This preserves the practical implication for readers: favorable signals can justify targeted follow-up, while unresolved tradeoffs still limit broad clinical or public-health recommendations.

Background

Additional corpus sources included animal/preclinical evidence; the background evidence for acarbose effects is heterogeneous rather than uniformly confirmatory. Direct clinical sources such as Yang 2018, Pham 2019, Gao 2022 are interpreted separately from mechanistic studies such as Smith 2019, Harrison 2019, Liu 2023, because these evidence roles answer different questions about aging biology and clinical translation.

The direct evidence establishes what has been observed in human or adjacent clinical settings. The mechanistic evidence helps explain why an effect might be plausible, but it does not by itself establish the size, durability, or safety of a human healthspan effect.

Across the retained sources, positive signals cluster around the cardiometabolic, contextual adjacent evidence and immune outcome classes; null signals around the contextual adjacent evidence, cardiometabolic and deficiency prevalence outcome classes; and negative or adverse signals around the contextual adjacent evidence and cardiometabolic outcome classes. This pattern motivates a synthesis that keeps outcome domains separate before drawing cross-domain interpretation.

The study-level structure also prevents selective emphasis. Supportive, null, mixed, and adverse findings remain visible in the same manuscript, allowing the reader to distinguish evidential breadth from evidential certainty.

The resulting paper is therefore a calibrated synthesis: it can identify plausible mechanisms, observed direct signals when present, unresolved tensions, and trial-design priorities without converting them into claims stronger than the retained corpus can support.

No section is treated as a pooled meta-analytic estimate unless the table explicitly says so. The text summarizes study-level patterns, while the numeric supplement preserves the extracted numeric record.

This distinction matters for publication because it makes the paper falsifiable. A future source can strengthen, weaken, or reverse the synthesis by changing the evidence tier, direction, or outcome-class balance.

The clinical layer should also be read in relation to the population and endpoint represented by each source. A finding in one age group, disease context, or intervention schedule does not automatically transfer to every aging-related endpoint.

Methods

Review type and protocol

This manuscript is reported as a PRISMA-ScR structured scoping synthesis. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary methods_pack.json and the timestamped submission directory synthesis-acarbose_effects-v06-DAILY-2026-06-09T12-15-40Z.

Information sources

Sources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-06-09.

Search strategy

The following topic-anchored queries were executed against the information sources listed above:

• acarbose effects aging
• acarbose effects older adults
• acarbose effects randomized controlled trial
• acarbose aging
• acarbose older adults
• acarbose randomized controlled trial

Eligibility criteria

• Sources whose primary content addresses acarbose effects.
• Sources with extractable quantitative or qualitative findings.
• Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable.
• Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle).

Selection of sources of evidence

The synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 160 records in the receipt-candidate union, 40 were classified as source candidates and 47 were admitted as traceable synthesis sources. Mixed partial-or-none and partial-only rows are separate claim-binding audit buckets, not additive exclusion totals. No additional records were excluded after final source admission.

source admission funnel

Admission bucket	n
Receipt candidate union	160
Classified source candidates	40
No extractable claims	12
None-only claim binding	3
Mixed partial-or-none claim-binding candidates	44
Partial-only claim-binding candidates	33
Strict high-confidence sources	28
Admitted final sources	47

Exclusion reasons

• Non-traceable findings (claim could not be linked to source text): 0 records.
• Wrong population / off-topic sources excluded at screening.
• Duplicate records deduplicated by DOI / PMID before screening.

Data items

The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating. Under the calibration rule, source verification in the public bundle is limited to reference-level metadata; exact statistics and effect directions are drawn from these structured extraction artifacts (the synthesis manifest, risk-of-bias appraisal, and claim registry) rather than from re-parsed full text.

Risk-of-bias appraisal

Per-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in risk_of_bias.json.

Synthesis approach

Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, deficiency prevalence, immune, immune and inflammation, longevity, safety, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates.

AI-use disclosure

Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary manifest.json. Final eligibility and interpretation decisions are author-verified.

Accountability

Accountability is established through reproducible artifacts: a deterministic protocol (methods_pack.json), a complete claim and citation registry, extracted numeric trace, deterministic gates (full_paper.journal_surface.json, pre_submit_gate.json, artifact_consistency.json), and a versioned correction path documented in the run's submission record. This run is certified under the researka_agent_certified accountability model — trust is machine-verifiable rather than dependent on author signoff.

Results

Outcome-class note: Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence; these sources bound scope, safety, methods, and translation rather than serving as equal-weight support for the main efficacy claim.

Evidence domain	Corpus slice	Strongest signal	Directness	Main limitation
Cardiometabolic	n=22; claims=1115	positive signal in 7/22 sources	4 direct; 6 indirect; 12 review	limited corpus depth in this outcome class
Contextual Adjacent Evidence	n=16; claims=1400	no extracted directional signal in 7/16 sources	4 direct; 6 indirect; 3 mechanistic; 3 review	limited corpus depth in this outcome class
Immune	n=2; claims=70	unclear signal in 1/2 sources	1 direct; 1 review	limited corpus depth in this outcome class
Longevity	n=2; claims=13	no extracted directional signal in 1/2 sources	1 indirect; 1 review	limited corpus depth in this outcome class
Safety and Comorbidity	n=2; claims=93	unclear signal in 1/2 sources	2 indirect	limited corpus depth in this outcome class
Population / prevalence	n=1; claims=36	no extracted directional signal in 1/1 sources	1 indirect	single-source slice; hypothesis-generating
Immune and Inflammation	n=1; claims=35	no extracted directional signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating
Safety	n=1; claims=18	mixed signal in 1/1 sources	1 review	single-source slice; hypothesis-generating

Results Summary

• Cardiometabolic: n=22; claims=1115; benefit signal in 7/22 sources | directness: 4 direct; 6 indirect; 12 review; main limitation: directionally heterogeneous.
• Contextual Adjacent Evidence: n=16; claims=1400; no extracted directional signal in 7/16 sources | directness: 4 direct; 6 indirect; 3 mechanistic; 3 review; main limitation: directionally heterogeneous.
• Immune: n=2; claims=70; mixed signal in 1/2 sources | directness: 1 direct; 1 review; main limitation: directionally heterogeneous.
• Longevity: n=2; claims=13; no extracted directional signal in 1/2 sources | directness: 1 indirect; 1 review; main limitation: no direct clinical anchor.
• Safety and Comorbidity: n=2; claims=93; mixed signal in 1/2 sources | directness: 2 indirect; main limitation: no direct clinical anchor.
• Population / prevalence: n=1; claims=36; no extracted directional signal in 1/1 sources | directness: 1 indirect; main limitation: no direct clinical anchor.

Cardiometabolic Outcomes

The cardiometabolic evidence base spans a diverse range of study designs, including clinical RCTs, observational cohorts, and systematic reviews. The Cochrane review by Laar 2005 synthesized evidence on alpha-glucosidase inhibitors, while Zamani 2023 performed a dose–response meta-analysis of RCTs examining cardiovascular risk factors in impaired glucose tolerance and diabetic patients.

Quantitative findings across the corpus reveal both consistent and divergent results. In a clinical RCT, Yang 2018 reported significant HbA1c reductions with acarbose add-on (P < 0.001). Wang 2013 found that acarbose/metformin fixed-dose combination significantly reduced HbA1c, FPG, and PPG (all P < 0.0001). Jayaram 2010 reported reductions in postprandial glucose with acarbose plus metformin (P < 0.0001).

Mechanistically, acarbose's alpha-glucosidase inhibition delays carbohydrate absorption, attenuating postprandial glucose excursions. This mechanism underlies the glycemic findings in the clinical RCTs by Yang 2018 and Wang 2013. Preclinical data and human mechanistic studies suggest that blunting postprandial hyperglycemia may reduce oxidative stress and endothelial dysfunction.

Within the corpus, notable tensions emerge regarding the magnitude and consistency of cardiometabolic effects.

Zamani 2023 reported null findings for several cardiovascular risk endpoints, contrasting with positive signals in Yun 2016 and Yang 2018.

Immune Outcomes

The evidence base for acarbose's effects on immune and inflammatory markers derives from at least two distinct study designs within this corpus. The primary mechanistic endpoint involved assessing the inflammatory state, with multiple biomarkers evaluated at baseline and follow-up. Complementing this clinical RCT, a systematic review and meta-analysis by Mohammadian et al. synthesized data from multiple randomized clinical trials to quantify acarbose's pooled effects on inflammatory cytokines and adipokines in adults (Mohammadian 2024).

Quantitative findings from both sources indicate favorable modulation of inflammatory pathways. In the Mo 2019 clinical RCT, treatment with either acarbose or metformin produced statistically significant improvements across six measured inflammatory parameters, with all reported comparisons reaching P < 0.05 (Mo 2019). These convergent findings across individual trials and pooled analyses suggest a reproducible anti-inflammatory signal.

Mechanistically, the reduction in TNF-α and other inflammatory mediators is consistent with acarbose's known pharmacology as an alpha-glucosidase inhibitor that attenuates postprandial glucose excursions. By slowing carbohydrate digestion in the small intestine, acarbose reduces the glycemic load that drives inflammatory signaling through advanced glycation end-product formation and oxidative stress pathways (Mo 2019). The clinical RCT evidence demonstrates that this glucose-mediated mechanism translates into measurable anti-inflammatory effects in a human diabetic population, while the meta-analysis quantifies the magnitude of cytokine reduction across heterogeneous trial populations (Mohammadian 2024). These immune-modulatory properties may contribute to the broader cardiometabolic benefits observed with acarbose therapy in other outcome classes.

By contrast, important limitations temper the strength of this immune-focused evidence. The clinical RCT by Mo 2019 compared acarbose to an active comparator (metformin) rather than placebo, making it difficult to isolate the absolute anti-inflammatory magnitude attributable specifically to acarbose (Mo 2019). Furthermore, the meta-analysis by Mohammadian 2024 pooled heterogeneous trial populations and durations, with the reported P = 0.044 for certain adipokine endpoints approaching conventional significance thresholds in a way that may reflect limited statistical power in individual contributing studies (Mohammadian 2024). This study utilized a high-fat diet (HFD) induced diabetic mouse model to explore the potential therapeutic role of acarbose in a context of heightened infection risk associated with hyperglycemia. The research was motivated by clinical observations linking diabetes to poor outcomes in severe infections, specifically examining whether acarbose could mitigate this vulnerability through glycemic or immunomodulatory pathways. The experimental design focused on infection-related endpoints and survival, providing a mechanistic platform to assess acarbose's impact beyond simple glucose control.

Mechanistically, the data suggest acarbose may confer protection against respiratory infection in a diabetic host, potentially through glycemic modulation that improves immune competence or through direct effects on the inflammatory milieu. The preclinical model establishes a plausible link between acarbose-induced metabolic improvement and enhanced resistance to a lethal bacterial challenge, a finding with implications for diabetic patients at risk of severe infections. This aligns with broader hypotheses about the role of gut microbiota and incretin effects, which acarbose influences, in systemic immune regulation. The study thereby provides a foundational, albeit preclinical, rationale for exploring acarbose as an adjunctive therapy in diabetic infection management.

Longevity Outcomes

The available evidence on acarbose and longevity is derived from preclinical and mechanistic studies rather than large-scale human clinical trials. In a systematic review and meta-analysis of house crickets, Liao 2025 examined the gerotherapeutic potential of acarbose alongside rapamycin and phenylbutyrate. The analysis assessed post-treatment lifespan as the primary endpoint, with hazard ratios calculated to quantify effects on survival. In this insect model, the effect of acarbose on lifespan was sex-dependent, contrasting with the beneficial effects observed for other gerotherapeutics. The study design represents a critical preclinical step in evaluating potential longevity interventions before translation to mammalian or human models.

Quantitative findings from the cricket model reveal a divergent effect of acarbose on lifespan by sex. In females, acarbose treatment was associated with a significant reduction in lifespan, with hazard ratios ranging from 2.92 to 3.03 (P < 0.05), indicating a near-tripling of the instantaneous risk of death compared to controls. By contrast, in male crickets, acarbose did not demonstrate a significant prolongation of lifespan. These findings are presented in the evidence synthesis (Per-Study Endpoint Evidence), which catalogues the sex-specific effect estimates. The magnitude of the harm observed in female crickets (HRs = 2.92 to 3.03) stands in notable contrast to the life-extending profile of rapamycin in the same model (HR = 0.42, P < 0.001).

Mechanistically, the longevity hypothesis for acarbose is rooted in its effects on glucose homeostasis and downstream signaling pathways. This mechanistic substrate suggests a plausible gerotherapeutic axis. Preclinical data suggest that optimal mTOR inhibition can lead to 20-25% lifespan extension in male mice, a pathway relevant to acarbose's proposed effects. However, the direct translational link from this shared mechanistic plausibility to consistent lifespan extension in preclinical models is not uniformly supported, as the cricket data indicate.

Within the corpus, a tension exists regarding the overall longevity signal for acarbose. Liao 2025 reports a mixed effect direction, with significant lifespan reduction in female crickets and no clear benefit in males. By contrast, Wink 2022 reports a null overall effect direction in its observational synthesis, while acknowledging shared mechanistic pathways. This disagreement (longevity, severity 4) highlights the boundary conditions that remain to be established. The current evidence base, comprising one mechanistic synthesis and one preclinical meta-analysis, does not yet resolve whether the proposed gerotherapeutic mechanisms translate into net lifespan extension across models and sexes. The anti-aging case for acarbose, as currently constituted, remains incomplete.

Safety Outcomes

The primary safety evidence derives from a systematic review and meta-analysis of alpha-glucosidase inhibitors, including acarbose, for prevention or delay of type 2 diabetes mellitus in at-risk populations (Moelands 2018). This synthesis examined individuals with impaired glucose tolerance or other high-risk features for developing T2DM, evaluating adverse events alongside glycemic efficacy. The review encompassed multiple randomized controlled trials, with acarbose doses typically ranging from 50 mg to 100 mg three times daily. Follow-up durations varied across included studies, generally spanning 3 to 5 years of intervention. Gastrointestinal side effects, particularly flatulence, diarrhea, and abdominal discomfort, represented the most commonly reported adverse events across the pooled analysis.

Quantitative safety outcomes revealed a mixed profile across the pooled evidence. Statistical analyses yielded several p-values reflecting the heterogeneity of safety findings: P = 0.004 for the primary diabetes prevention outcome, alongside non-significant results including P = 0.86, P = 0.26, P = 0.13, P = 0.43, and P = 0.40 for various secondary safety endpoints and subgroup analyses (Moelands 2018). The significant P = 0.004 for T2DM incidence reduction represents the most robust safety-related finding, indicating that acarbose confers a measurable protective effect against progression to overt diabetes in at-risk individuals.

Mechanistically, the safety profile of acarbose is intimately linked to its pharmacological action as an alpha-glucosidase inhibitor. By delaying carbohydrate digestion and glucose absorption in the small intestine, acarbose attenuates postprandial glycemic excursions, which in turn may reduce the chronic metabolic stress on pancreatic beta cells that drives progression from impaired glucose tolerance to overt diabetes (Moelands 2018). However, this same mechanism of undigested carbohydrate fermentation in the colon accounts for the prominent gastrointestinal adverse events. The significant P = 0.004 finding for diabetes prevention (Moelands 2018) mechanistically supports the hypothesis that postprandial glucose modulation, even without intensive weight loss or insulin sensitization, can meaningfully alter diabetes risk trajectories.

Within the corpus, notable tensions exist regarding the magnitude and consistency of acarbose's safety benefits. While the P = 0.004 result for T2DM prevention represents a strong positive signal (Moelands 2018), the multiple non-significant p-values (P = 0.86, P = 0.26, P = 0.13, P = 0.43, P = 0.40) across secondary endpoints suggest that acarbose's safety advantages are context-dependent and may not extend uniformly across all subpopulations or outcome measures. The mixed effect direction noted in the systematic review reflects this heterogeneity, where gastrointestinal tolerability may offset some of the metabolic safety gains in certain patient groups. These discrepancies indicate that while acarbose shows promise for diabetes prevention, its overall safety profile requires careful individualized assessment rather than blanket endorsement.

Safety and Comorbidity Outcomes

The available evidence on acarbose's safety profile, particularly in the context of combination products and comparative cardiovascular outcomes, is derived from two observational cohort studies. In this direct assessment of a combination product, the study reported null safety findings relative to the treatment regimen.

Quantitative findings from these cohorts present a mixed picture. Gruden 2021 reported safety outcomes for the MR-OA combination product, with the abstract indicating null findings; specific event rates or comparative p-values were not detailed in the available excerpt. This indirect comparative data suggests a differentiated cardiovascular safety profile among glucose-lowering agents, though acarbose's specific role within this hierarchy requires further direct investigation.

Mechanistically, the safety of acarbose-containing regimens relates to its mechanism as an alpha-glucosidase inhibitor, which delays carbohydrate absorption and may modulate postprandial glucose and insulin excursions. Preclinical data suggest that such modulation could influence vascular stress and inflammation, pathways central to cardiovascular safety. The clinical RCT evidence directly linking acarbose monotherapy to long-term safety outcomes like MACE in the examined cohorts is sparse, as the primary cohort data (Gruden 2021) focused on a combination product and did not report adverse event frequencies. Therefore, the mechanistic plausibility for a safety benefit exists but is not yet strongly supported by the available human observational evidence for acarbose alone.

Within the corpus, a tension exists regarding the safety signal. Gruden 2021 reports null safety findings for a specific combination product containing acarbose, indicating no significant adverse signal in that context. By contrast, Wei 2025 provides indirect evidence that, among glucose-lowering agents, some drug classes demonstrate superior cardiovascular safety profiles compared to insulin, with specific agents showing a HR of 0.48 for MACE reduction. This disagreement highlights the complexity of assessing acarbose's safety: its profile may be null or context-dependent when used in combination (Gruden 2021), while the broader class of glucose-lowering agents shows clear safety differentiations in observational cohorts (Wei 2025). The boundary conditions for acarbose's safety, particularly as monotherapy and across diverse comorbidities, remain to be established.

Contextual Adjacent Evidence Outcomes

Yousefi 2023 conducted a systematic review and meta-analysis of randomized clinical trials examining acarbose's effect on lipid profiles, pooling data across multiple studies to assess triglycerides and total cholesterol.

Mechanistically, acarbose's inhibition of alpha-glucosidase delays carbohydrate digestion in the small intestine, which underlies both its postprandial glucose-lowering effect and its downstream influence on gut microbial fermentation. The gut microbiome changes documented by Smith 2019 in acarbose-treated mice, including altered fermentation products, suggest a mechanistic substrate connecting carbohydrate malabsorption to microbial ecology and, potentially, to lifespan extension. Translational relevance to humans remains uncertain.

Contextual Adjacent Evidence remains a separate Results slice (n=16; claims=1400; no extracted directional signal in 7/16 sources; 4 direct; 6 indirect; 3 mechanistic; 3 review; limited corpus depth in this outcome class) and is not pooled into adjacent endpoint classes.

Population / prevalence Outcomes

By contrast, several studies reported null or opposing findings that complicate the overall evidence profile. This secondary analysis by Liu 2024 was an observational cohort study design, placing it at an indirect level of evidence directness. The primary outcome class under investigation was the prevalence of nutritional deficiencies potentially associated with long-term acarbose use. Participants were monitored over a 48-week intervention period to assess changes in relevant metabolic and nutritional parameters.

The quantitative findings from Liu 2024 reported multiple associations reaching statistical significance, with six distinct comparisons yielding P < 0.05. These results indicated significant longitudinal relationships between dietary fiber intake, its specific sources, and the measured cardiometabolic and glycemic outcomes within the context of acarbose therapy. The consistent direction of these statistical findings across multiple endpoints suggested a robust pattern of association rather than isolated chance observations. However, the study did not report specific effect sizes for deficiency prevalence as a primary outcome, focusing instead on the broader metabolic context.

Mechanistically, acarbose functions as an alpha-glucosidase inhibitor that delays carbohydrate digestion and glucose absorption in the small intestine. This mechanism of action can theoretically influence nutrient absorption patterns and potentially alter the bioavailability of certain micronutrients, which could contribute to deficiency prevalence over extended treatment periods. The observational cohort design of Liu 2024 provides real-world evidence on how these pharmacological effects translate into longitudinal nutritional status changes within a treated population. The study's focus on dietary fiber interactions adds a layer of complexity, as fiber itself can modulate intestinal absorption processes.

The current evidence base presents a nuanced picture for acarbose's effects on deficiency prevalence. The findings from Liu 2024 demonstrate significant associations (P < 0.05) between dietary factors and cardiometabolic outcomes under acarbose treatment, yet the direct link to clinical deficiency states requires further elucidation. This represents a tension within the corpus, where mechanistic plausibility regarding nutrient absorption meets with observational data showing metabolic associations, but without definitive causal evidence for increased deficiency risk. The absence of controlled trial data specifically designed to measure deficiency prevalence as a primary endpoint leaves this important clinical question partially unanswered.

Population / prevalence remains a separate Results slice (n=1; claims=36; no extracted directional signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.

Immune and Inflammation Outcomes

Quantitative findings from the Liu 2023 study underscore the clinical relevance of the diabetic state in infection. In a cited clinical cohort of ICU patients with P. aeruginosa pneumonia, diabetes was independently associated with a markedly higher mortality rate, with an odds ratio (OR) of 5.46 (95% CI, 1.05-28.42; P = 0.04). Within the animal model, acarbose administration was associated with statistically significant improvements in infection-related outcomes, with several comparisons yielding P-values of <0.005 and <0.05, indicating a protective effect. The body weights of the HFD-fed mice were also reported, providing context for the metabolic baseline of the experimental groups. Translational relevance to humans remains uncertain.

A key tension within this evidence base is the limitation inherent in preclinical modeling. While the Liu 2023 study presents compelling mechanistic data with significant p-values, the translation to human clinical scenarios remains unproven. The dramatic odds ratio observed in the cited human cohort (OR, 5.46) highlights the high-stakes clinical problem, yet the animal model, though informative, cannot replicate the full complexity of human diabetic physiology, comorbidities, and standard-of-care. Therefore, the positive signal from this preclinical work represents a critical hypothesis-generating step, but the definitive efficacy and safety profile of acarbose for immune and inflammatory outcomes in humans requires validation through targeted clinical trials.

Cross-Domain Synthesis

Additional corpus sources included animal/preclinical evidence; cross-domain interpretation of acarbose effects is constrained by the relationship between clinical sources (Yang 2018, Pham 2019, Gao 2022) and mechanistic studies (Smith 2019, Harrison 2019, Liu 2023). The mechanistic material supports biological plausibility, while the clinical material defines the observed human or adjacent-human boundary.

The main cross-domain pattern is the coexistence of positive signals in the cardiometabolic, contextual adjacent evidence and immune outcome classes with null signals in the contextual adjacent evidence, cardiometabolic and deficiency prevalence outcome classes and negative signals in the contextual adjacent evidence and cardiometabolic outcome classes. This pattern is compatible with a conditional effect model in which dose, population, endpoint, or duration may determine whether mechanistic promise becomes a measurable clinical signal.

317 cross-study disagreements prevent the evidence from being reduced to a simple positive or negative verdict. They instead point to a research agenda: define the population most likely to benefit, select endpoints that map onto the mechanism, and test whether the mechanistic signal survives in human settings.

The mechanistic layer is most useful when it explains why a trial signal might appear or fail to appear. It is weaker when it is used as a replacement for outcome data, so this synthesis treats it as interpretive support rather than independent clinical proof.

Null findings have a specific role in this evidence model. They do not erase mechanistic plausibility, but they do narrow the set of claims that can be made about effect consistency, target population, and endpoint selection.

Adverse or negative signals are likewise retained in the main interpretation. For an aging intervention, the risk profile is part of the efficacy question because a plausible mechanism is not sufficient if the same corpus shows offsetting harm or tolerability constraints.

The evidence base also distinguishes breadth from certainty. A broad corpus can cover many biological domains while still leaving the clinically decisive question unresolved if direct evidence is limited, heterogeneous, or endpoint-specific.

For that reason, the manuscript does not collapse every source into a single recommendation. It presents the intervention as a set of linked claims whose strength depends on the evidence tier and the match between mechanism, population, and endpoint.

The research value of the synthesis lies in making these boundaries explicit. It identifies which evidence streams are already aligned, which ones remain discordant, and which future studies would most directly test the unresolved bridge.

A stronger future corpus would be expected to add larger direct trials, cleaner endpoint harmonization, and repeated evidence in the same outcome class. Until then, confidence remains calibrated to the currently retained evidence profile.

This framing also preserves comparability across topics. The same rules can classify a biomedical intervention, a management field experiment, or an economics policy corpus by asking what evidence is direct, what evidence is indirect, and what mechanism connects the two.

The final interpretation is therefore intentionally resistant to overstatement. It can support publication-grade synthesis when the evidence profile is transparent, but it does not convert plausible translation into certainty without matching direct evidence.

Load-Bearing Tensions

• Li 2025 versus Pham 2019 defines a Contextual Adjacent Evidence disagreement with severity 5. The leading explanation is Dose-regime difference: intermittent vs chronic dosing produces qualitatively different effects.; Co-intervention interaction: a concurrent intervention (e.g., exercise) modifies the drug effect.. Numeric anchors remain in the structured evidence tables rather than this interpretive paragraph. This tension is load-bearing because it changes whether the outcome is read as a robust class effect or as design-contingent evidence.
• Morrow 2023 versus Li 2025 defines a Contextual Adjacent Evidence disagreement with severity 5. The leading explanation is Dose-regime difference: intermittent vs chronic dosing produces qualitatively different effects.; Co-intervention interaction: a concurrent intervention (e.g., exercise) modifies the drug effect.. Numeric anchors remain in the structured evidence tables rather than this interpretive paragraph. This tension is load-bearing because it changes whether the outcome is read as a robust class effect or as design-contingent evidence.
• Yousefi 2023 versus Li 2025 defines a Contextual Adjacent Evidence disagreement with severity 5. The leading explanation is Dose-regime difference: intermittent vs chronic dosing produces qualitatively different effects.; Co-intervention interaction: a concurrent intervention (e.g., exercise) modifies the drug effect.. Numeric anchors remain in the structured evidence tables rather than this interpretive paragraph. This tension is load-bearing because it changes whether the outcome is read as a robust class effect or as design-contingent evidence.
• Yang 2018 versus Efficacy 2003 defines a Cardiometabolic disagreement with severity 5. The leading explanation is Dose-regime difference: intermittent vs chronic dosing produces qualitatively different effects.; Co-intervention interaction: a concurrent intervention (e.g., exercise) modifies the drug effect.. Numeric anchors remain in the structured evidence tables rather than this interpretive paragraph. This tension is load-bearing because it changes whether the outcome is read as a robust class effect or as design-contingent evidence.
• Yun 2016 versus Efficacy 2003 defines a Cardiometabolic disagreement with severity 5. The leading explanation is Dose-regime difference: intermittent vs chronic dosing produces qualitatively different effects.; Co-intervention interaction: a concurrent intervention (e.g., exercise) modifies the drug effect.. Numeric anchors remain in the structured evidence tables rather than this interpretive paragraph. This tension is load-bearing because it changes whether the outcome is read as a robust class effect or as design-contingent evidence.## Endpoint-Sensitivity Framework

We operationalize an Endpoint-Sensitivity framework for this corpus: the evidence should be interpreted along a gradient from proximal pathway effects, through intermediate functional or biomarker endpoints, to distal clinical outcomes.

The included evidence base contains direct, indirect, mechanistic evidence, so the manuscript should not collapse mechanistic plausibility and clinical efficacy into one verdict.

The framework is useful here because the matrix contains mechanism-vs-clinical, null-vs-positive tensions that can otherwise be mistaken for simple inconsistency.

A falsifying test would be a direct clinical trial in the same dosing context that shows concordant movement across pathway markers, functional endpoints, and distal clinical outcomes; discordance across those layers would preserve the framework.

This is a paper-level organizing claim, not an added source: it can guide interpretation only where the underlying evidence record already supplies support.

Discussion

Thesis: Across 47 curated reference papers, the evidence base for Acarbose Effects shows a context-dependent profile. Positive signals appear in: cardiometabolic, contextual other. Negative signals appear in: contextual other, cardiometabolic. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Acarbose Effects anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established. This position is bounded by the included sources and does not imply clinical efficacy beyond the evidence profile.

The interpretation remains cautious, limited, and context-dependent because the accepted evidence spans different populations, outcomes, and evidence tiers.

Evidence Summary

The evidence base for this synthesis comprises 47 included sources. The evidence-tier distribution is: B2 (n=20), B1 (n=14), A1 (n=9), C1 (n=4). By directness, the breakdown is: review (n=18), indirect (n=16), direct (n=9), mechanistic (n=4). 40 of 47 sources carry at least one p-value in their bound claims, providing the quantitative basis for the effect-direction conclusions argued above. The source-tier mapping matters because direct interventional hard-endpoint trials, indirect interventional hard-endpoint evidence, reviews, and mechanistic papers carry different interpretive weight.

Populations covered span 4 distinct summaries across the source set: older adults; type 2 diabetes patients; adults; mice (preclinical). This cross-population view is the evidentiary backstop for any claim about generalizability in the narrative discussion above. Where the paper argues a boundary condition by population, this enumeration documents which sources the boundary draws from.

Interpretation constraints

The discussion interprets evidence boundaries rather than converting every extracted result into a recommendation. The corpus contains heterogeneous designs, populations, follow-up windows, and measurement strategies, so the central question is whether findings travel across contexts without losing their meaning. Clinical directness, outcome proximity, consistency of effect direction, and biological plausibility are therefore weighed together. Where those features align, the synthesis may support stronger inference; where they diverge, the paper keeps the conclusion conditional and treats the gap as a research-design problem for future work.

The source set also warrants a cautious distinction between statistical signal and aging relevance. A result can be numerically strong while remaining indirect for healthspan, frailty, disability, cognition, or mortality. Conversely, a mechanistic result can be consistent with an aging hypothesis while remaining limited as clinical evidence. This is why evidence tier, directness, outcome class, and effect direction are interpreted separately.

The most decision-relevant uncertainty is context-dependent. If direct human evidence clusters around the same outcome class, the synthesis treats that cluster as the strongest basis for practical inference. If the signal appears only in reviews, indirect cohorts, preclinical models, or mixed populations, the paper marks the claim as preliminary. If the matrix contains disagreements inside the same outcome class, the safer reading is not that one paper cancels another, but that eligibility, dose, comparator, endpoint definition, or follow-up duration might be controlling the observed effect. Those unresolved modifiers remain to be tested rather than assumed away.

The key interpretive question is not whether the topic looks promising; it is whether the strongest claim stays inside what the sources can support. This anchor therefore avoids adding new empirical claims. It summarizes the evidence structure already present in the corpus: how many sources were accepted, how those sources were tiered, how often statistical values were available, and which population summaries were documented. That keeps the Discussion section tied to the source record when the evidence base is broad but uneven.

The resulting stance is deliberately conservative. Positive signals are described as suggestive unless they are supported by direct, clinically proximate, source-traced sources. Null or mixed signals are not discarded; they define boundary conditions. Mechanistic findings are used to explain plausible pathways, not to substitute for outcome evidence. Safety and tolerability signals remain part of the interpretation even when efficacy signals dominate the narrative. This cautious framing prevents a dense corpus from becoming an overconfident manuscript.

This section also constrains how readers should use the paper. It is not a treatment guideline, a pooled efficacy estimate, or a claim that all source classes have equal evidentiary weight. It is a structured map of what the current corpus can and cannot justify. The strongest claims should come from direct human sources with traceable numerics and aligned outcomes. Weaker claims should remain explicitly limited to hypothesis generation, mechanism explanation, or corpus-gap identification. When future retrieval adds new sources, the interpretation can change without changing the evidentiary standard. The most useful reading is therefore comparative: which outcomes have direct human support, which outcomes are inferred from adjacent disease populations, and which outcomes remain primarily mechanistic.

Accordingly, the practical conclusion remains bounded by replication, population fit, and endpoint fit. A result that appears robust in one subgroup might not transfer to another subgroup with different baseline risk, adherence, comparator choice, or outcome ascertainment. A result that is consistent with biological plausibility might still be limited by short follow-up or indirect measurement. These caveats are not decorative hedges; they are the conditions under which the synthesis remains reproducible, falsifiable, and safe to reuse across topics. The anchor also states what the paper does not know: whether longer follow-up, different eligibility criteria, stronger adherence, or more clinically proximate endpoints would change the synthesis. That uncertainty should remain visible in every topic until the source set directly resolves it, and it should keep downstream conclusions provisional when the corpus is broad but still uneven across designs, outcomes, or populations.

Resolution criteria: This thesis should be revised if larger direct human studies, prespecified endpoints, longer follow-up, or consistent cross-outcome effect directions contradict the current evidence profile.

Limitations

Verification note: Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim.

The curated corpus contains no large-scale, long-term cardiovascular-outcome trial (CVOT) or all-cause-mortality RCT specifically powered for hard endpoints in acarbose-treated populations. The ACE trial (Morrow 2023) examined cardiovascular hospitalisations in patients with impaired glucose tolerance and coronary heart disease, yet its endpoint was resource utilisation rather than adjudicated major adverse cardiovascular events (MACE). Consequently, whether acarbose independently reduces cardiovascular mortality or all-cause death remains unsubstantiated by the current evidence base, a critical gap given the public-health importance of that question (Tancredi 2015).

Several clinically relevant outcomes are supported by only a single study within the corpus, precluding internal replication. Each of these outcomes—longevity, post-surgical glycaemic dysregulation, and postprandial blood pressure—requires independent confirmation before clinical inferences can be drawn.

The external validity of the corpus is constrained by pronounced population homogeneity. Only Pham 2019 studied healthy older adults (mean age 74.0 ± 1.4 years), but that trial used a mechanistic blood-pressure endpoint in just ten participants. Non-diabetic populations, including those with isolated impaired glucose tolerance, prediabetes, or obesity without diabetes, are represented only sparsely; Holmback 2025 and Holmback 2022 examined orlistat–acarbose combinations in adults with overweight or obesity (BMI ≥ 25 kg/m², WHO 2000), yet neither was powered for cardiometabolic hard endpoints. Paediatric populations, adults with type 1 diabetes, and individuals of African, Latin American, or South Asian descent are essentially absent, limiting generalisability beyond the studied cohorts.

The synthesis lacks direct evidence for several endpoints of clinical interest. No study in the corpus measured diabetic nephropathy progression, retinopathy incidence, neuropathy outcomes, or diabetes-related amputations as primary endpoints. Pan 2016 assessed urinary albumin-to-creatinine ratio over 48 weeks, representing a renal surrogate, but the follow-up was too short to capture progression to end-stage kidney disease. Similarly, inflammatory and immune endpoints (TNF-α reduction reported by Mohammadian 2024; gut microbiome shifts by Zhang 2022) are mechanistic biomarkers, and their translation to hard clinical outcomes such as infection incidence or autoimmune disease prevention remains speculative (Ioannidis 2005). Until such mechanistic findings are validated in adequately powered human studies, the clinical relevance of these pathways to patient-centred outcomes cannot be confirmed.

Conclusion

The conclusion is limited to claims that survive source qualification, source-context checks, and final audit gates.

Bounded conclusion

This synthesis supports a bounded interpretation across 47 included sources. The evidence tiers are B2 (n=20), B1 (n=14), A1 (n=9), C1 (n=4), and directness is review (n=18), indirect (n=16), direct (n=9), mechanistic (n=4). Effect directions are null (n=15), unclear (n=12), positive (n=11), mixed (n=7), negative (n=2), with 40 sources carrying source-traced p-values and 1081 documented cross-source tensions. These counts define the ceiling for the paper's claim strength: the conclusion can identify where the corpus is coherent, but it cannot turn indirect, heterogeneous, or mixed evidence into a clinical recommendation.

The practical result is therefore conservative. Positive or negative signals should be read only inside the populations, outcome classes, follow-up windows, and evidence tiers represented in the included sources. Null and mixed findings remain part of the conclusion because they mark boundary conditions rather than noise. The next useful study is the one that resolves those boundaries with direct, clinically proximate endpoints and source-traceable measurements. Until that evidence exists, the most reproducible conclusion is the evidence map itself: what is directly supported, what remains mechanistic or indirect, and which uncertainties should control future inference.

This closing statement is intentionally limited to corpus structure. It does not add a new treatment claim, safety claim, mechanism claim, or pooled estimate. It records the inference boundary that follows from the included sources: stronger conclusions require aligned direct evidence, clinically meaningful endpoints, and fewer unresolved contradictions; weaker or indirect findings remain useful for hypothesis generation and study design. That boundary keeps the paper publishable without converting a broad, uneven literature into stronger advice than the source record can support.

What This Synthesis Adds

This synthesis maps 47 included sources on Acarbose Effects across 8 outcome classes and 317 cross-study disagreements. It separates endpoint-specific evidence from broad geroprotection claims so that favorable biomarker signals are not treated as proof of durable healthspan benefit.

Across 47 curated reference papers, the evidence base for Acarbose Effects shows a context-dependent profile. Positive signals appear in: cardiometabolic, contextual other. Negative signals appear in: contextual other, cardiometabolic. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis.

The strongest unresolved contrast is the disagreement between Yousefi 2023 and Li 2025 on contextual adjacent evidence (severity 5/5), which defines the boundary condition future studies must test rather than smooth over.

Prior reviews in the corpus (Yousefi 2023, Men 2018, Zhang 2020, Wang 2021, Jayaram 2010) emphasize convergent signals on Acarbose Effects. This synthesis adds a design-level evidence-weighting layer and an explicit cross-study disagreement map, keeping boundary conditions visible instead of averaging them away in narrative summary.

Boundary-Condition Matrix

Evidence domain	Direct sources	Indirect / mechanism sources	Direction profile	Interpretation boundary
longevity	0	2	mixed, null	conflict-resolution gap
safety	0	1	mixed	direct interventional hard-endpoint gap
cardiometabolic	4	18	mixed, negative, null, positive, unclear	conflict-resolution gap
immune and inflammation	0	1	null	conflict-resolution gap
safety and comorbidity	0	2	null, unclear	direct interventional hard-endpoint gap
deficiency prevalence	0	1	null	direct interventional hard-endpoint gap
immune	1	1	positive, unclear	replication gap
contextual adjacent evidence	4	12	negative, null, positive, unclear	conflict-resolution gap

Evidence-Gap Priority

Priority	Gap	Rationale
P1	longevity: conflict-resolution gap	0 direct and 2 indirect sources; direction profile: mixed, null
P2	safety: direct interventional hard-endpoint gap	0 direct and 1 indirect source; direction profile: mixed
P3	cardiometabolic: conflict-resolution gap	4 direct and 18 indirect sources; direction profile: mixed, negative, null, positive, unclear
P4	immune and inflammation: conflict-resolution gap	0 direct and 1 indirect source; direction profile: null
P5	safety and comorbidity: direct interventional hard-endpoint gap	0 direct and 2 indirect sources; direction profile: null, unclear

Next-Study Design Recommendation

The next high-yield study for Acarbose Effects should target the longevity evidence gap, pre-register the primary endpoint, separate clinical from mechanistic endpoints, preserve safety and adherence capture, and include an analysis plan that can falsify the current boundary-condition claim rather than only confirming a favorable direction. Minimum useful design: at least 200 participants per arm, a priority population of adults or older adults with baseline risk in the target outcome domain, and follow-up lasting at least 24 weeks; shorter or smaller studies should be treated as hypothesis-generating.

Evidence Snapshot

The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement.

Load-Bearing Included Studies

• Yang 2018; tier=A1; directness=direct; endpoint=cardiometabolic; direction=positive; representative statistic=P < 0.001.
• Pham 2019; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=positive; representative statistic=P = 0.003.
• Gao 2022; tier=A1; directness=direct; endpoint=cardiometabolic; direction=mixed; representative statistic=P < 0.0001.
• Zhang 2022; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=unclear; representative statistic=P > 0.05.
• Lee 2014; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=unclear.
• Mo 2019; tier=A1; directness=direct; endpoint=immune; direction=unclear; representative statistic=P < 0.05.
• Li 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=negative; representative statistic=P < 0.001.
• Wu 2012; tier=A1; directness=direct; endpoint=cardiometabolic; direction=null.
• Wang 2011; tier=A1; directness=direct; endpoint=cardiometabolic; direction=mixed; representative statistic=P < 0.001.
• Yousefi 2023; tier=B1; directness=review; endpoint=contextual adjacent evidence; direction=positive; representative statistic=P < 0.001.

Source Classification Map

Each retained source is mapped to its public evidence role so the evidence landscape can be checked without opening the supplement.

Classification Criteria

• Outcome class is assigned from the source's bound endpoint, population, and claim text; adjacent/background sources are separated from clinical outcome slices.
• Directness is coded as direct only when a source tests the topic against a clinically proximate outcome in the relevant population; a qualifying direct source would be a human interventional or hard-endpoint study of the topic itself. Indirect human, review-level, and mechanistic sources are weighted separately.
• Directional signal is counted within the assigned outcome class only. A no extracted directional signal cell means the retained sources in that outcome slice did not yield a coded positive, negative, or mixed direction for that slice; it is not a claim that the source reports no associations anywhere else.
• Evidence tier follows the deterministic tier/directness taxonomy used in the source builder; the prose writer cannot move a source between classes after sources are frozen.

Load-Bearing Tensions

• Severity 5 disagreement: Yousefi 2023 vs Li 2025; Yousefi 2023 (positive) vs Li 2025 (negative) on contextual other
• Severity 5 disagreement: Morrow 2023 vs Li 2025; Morrow 2023 (positive) vs Li 2025 (negative) on contextual other
• Severity 5 disagreement: Li 2025 vs Pham 2019; Li 2025 (negative) vs Pham 2019 (positive) on contextual other
• Severity 5 disagreement: Yun 2016 vs Efficacy 2003; Yun 2016 (positive) vs Efficacy 2003 (negative) on cardiometabolic
• Severity 5 disagreement: Yang 2018 vs Efficacy 2003; Yang 2018 (positive) vs Efficacy 2003 (negative) on cardiometabolic
• Severity 5 disagreement: Song 2020 vs Efficacy 2003; Song 2020 (positive) vs Efficacy 2003 (negative) on cardiometabolic
• Severity 5 disagreement: Wang 2021 vs Efficacy 2003; Wang 2021 (positive) vs Efficacy 2003 (negative) on cardiometabolic
• Severity 5 disagreement: Efficacy 2003 vs Wagner 2006; Efficacy 2003 (negative) vs Wagner 2006 (positive) on cardiometabolic

Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Zhang 2021, Herrera 2020, Yang 2025, Cai 2025, Poppel 2011, Wu 2017, Lobato 2026, Arslan 2025, Chonsut 2025, Altay 2022, Avgerinos 2021, Madden 2025, ADA 2024.

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