Research Synthesis: Rapamycin Biomarker Effects

Research Synthesis: Rapamycin Biomarker Effects

Abstract

Evidence-honesty note: 20/30 retained sources are coded as null or no extracted directional signal; this corpus is non-supportive for clinical efficacy claims and hypothesis-generating only. Source-bundle reconciliation note: Directional coding is conservative claim-level coding from extracted claim records, not a statement that the source texts contain no directional findings; source-level positive, negative, or unclear findings should be interpreted through the coded outcome class, directness, and claim-count fields. 29/30 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 synthesis tests the thesis that evidence for Rapamycin Biomarker Effects is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation.

Rapamycin, an mTOR inhibitor, is increasingly investigated for potential geroprotective and biomarker-modulating effects, yet its clinical translation from preclinical models remains contested.

This synthesis employed an AI-assisted structured evidence synthesis with an audit trail to integrate findings across 30 curated reference papers spanning preclinical, observational, and randomized controlled trial designs.

Across outcome classes, cross-study disagreements were identified, with null findings dominating contextual and safety outcomes while context-specific signals concentrated in preclinical longevity and immune biomarker domains.

The current evidence supports mechanistic plausibility for rapamycin's geroprotective effects but falls short of establishing clinical biomarker efficacy, as human randomized trials show largely null or mixed results on conventional healthspan endpoints.

Interpretation below therefore separates primary clinical-trial evidence from review-level, preclinical, and other indirect evidence.

Methods

Review type and protocol

This manuscript is reported as a Evidence brief. 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-rapamycin_biomarker_effects-v06-DAILY-2026-06-06T20-31-17Z-R2.

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: 2026-06-06.

Search strategy

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

• rapamycin biomarker effects aging
• rapamycin biomarker effects older adults
• rapamycin biomarker effects randomized controlled trial
• rapamycin aging
• rapamycin older adults
• rapamycin randomized controlled trial
• biomarker aging
• biomarker older adults
• biomarker randomized controlled trial

Eligibility criteria

• Sources whose primary content addresses rapamycin biomarker 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 1448 records in the receipt-candidate union, 525 were classified as source candidates and 30 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	1448
Classified source candidates	525
No extractable claims	349
None-only claim binding	107
Mixed partial-or-none claim-binding candidates	337
Partial-only claim-binding candidates	89
Strict high-confidence sources	41
Admitted final sources	30

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, dosing and pharmacokinetics, immune and inflammation, longevity, 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
Contextual Adjacent Evidence	n=18; claims=1232	no extracted directional signal in 12/18 sources	1 direct; 10 indirect; 7 mechanistic	limited corpus depth in this outcome class
Immune and Inflammation	n=4; claims=167	positive signal in 2/4 sources	3 indirect; 1 mechanistic	limited corpus depth in this outcome class
Cardiometabolic	n=2; claims=21	no extracted directional signal in 2/2 sources	2 indirect	limited corpus depth in this outcome class
Dosing and Pharmacokinetics	n=2; claims=120	no extracted directional signal in 1/2 sources	1 indirect; 1 mechanistic	limited corpus depth in this outcome class
Longevity	n=2; claims=12	unclear signal in 1/2 sources	2 mechanistic	limited corpus depth in this outcome class
Safety and Comorbidity	n=2; claims=215	no extracted directional signal in 2/2 sources	1 indirect; 1 review	limited corpus depth in this outcome class

This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate.

Contextual Adjacent Evidence Outcomes

18 included sources were assigned to this outcome class. Directional coding: mixed=1, negative=1, null=12, positive=2, unclear=2. Directness coding: direct=1, indirect=10, mechanistic=7.

Immune Inflammation Outcomes

4 included sources were assigned to this outcome class. Directional coding: null=2, positive=2. Directness coding: indirect=3, mechanistic=1.

Cardiometabolic Outcomes

2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2.

Dose / exposure Outcomes

2 included sources were assigned to this outcome class. Directional coding: mixed=1, null=1. Directness coding: indirect=1, mechanistic=1.

Longevity Outcomes

2 included sources were assigned to this outcome class. Directional coding: null=1, unclear=1. Directness coding: mechanistic=2.

Safety Comorbidity Outcomes

2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=1, review=1.

Limitations

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

Several outcome domains in the corpus rest on single-study evidence, which precludes internal replication and inflates the risk that observed effects are idiosyncratic to a particular design, population, or analytic approach. Likewise, airway inflammation and emphysema attenuation were demonstrated exclusively in Tian 2026 using ozone-exposed mice treated with intraperitoneal rapamycin at 0.6 mg/kg, and cardiac function preservation in autoimmune myocarditis was reported only by Zhuang 2025, who showed rapamycin reprogrammed Cxcl9+ macrophages via the mTORC1–C/EBPβ–OSM axis. Cardiac proteomic remodeling was studied only by Dai 2014, who used deuterated-leucine labeling over 10 weeks of rapamycin exposure, while the longevity-in-Drosophila pathway was examined solely by Bjedov 2010. The ME/CFS fatigue endpoint was explored in only Ruan 2025, a pilot study administering 6 mg/week rapamycin with symptom assessments at days 30, 60, and 90. In each of these cases, a single source cannot establish whether the effect is robust across independent laboratories, species, or populations. This single-trial limitation is not confined to minor outcomes: the cross-study disagreement map documents severity-3 or severity-4 disagreements for the contextual other outcome class across 161 non-orthogonal pairs, yet many of these tensions involve one or both arms supported by only a single study, making adjudication between conflicting signals impossible within the current corpus.

The population base of the curated trials narrows external validity in several ways that cannot be resolved by pooling alone. Preclinical sources — including Bitto 2016 (middle-aged mice), Harrison 2009 (genetically heterogeneous mice), Miller 2014 (dose-response in mice), Tian 2026 (ozone-exposed mice), and Pell 2026 (female mice with early-life seizures) — collectively dominate the longevity and mechanistic outcome classes, yet interspecies translation of mTOR inhibition remains uncertain because murine mTOR signaling kinetics, rapamycin pharmacokinetics, and lifespan architecture differ from those of humans. Stanfield 2026 tested once-weekly sirolimus at 6 mg in older adults already engaged in a 13-week exercise program, which introduces a selection bias toward motivated, physically active participants who may not represent the sedentary majority at risk for age-related decline. No study in the corpus enrolled pregnant individuals, persons with severe renal or hepatic impairment, or immunocompromised populations such as organ-transplant recipients on concurrent immunosuppression, leaving safety and efficacy in these vulnerable groups entirely uncharacterized. Consequently, the synthesis conclusions apply most directly to middle-aged to older, relatively healthy adults or to murine models, and extrapolation beyond these groups requires assumptions that the present evidence cannot anchor.

The endpoint scope of the corpus is heavily weighted toward mechanistic, surrogate, and contextual outcomes rather than clinically meaningful endpoints validated against hard disease outcomes, which introduces a translation gap that should be made explicit. No source in the curated set measured incident type 2 diabetes, fracture incidence, hospitalization for heart failure, or time-to-progression of any cancer as a primary endpoint; the cardiometabolic class is represented only by Zhang 2021 and Su 2025, both mechanistic studies examining BCRP-mediated drug resistance and MAGEA3 biomarkers rather than patient-centered outcomes. Rosario 2023 investigated rapamycin's attenuation of PI3K signaling in human ovarian cortex in vitro, providing mechanistic evidence for fertility preservation, but no clinical trial in the corpus reported on pregnancy rates, ovarian reserve markers such as anti-Müllerian hormone trajectory, or time-to-conception in women receiving rapamycin. A broader methodological concern is that several sources relied on surrogate endpoints whose clinical validity for the aging context is unestablished: as noted in the general methodological literature (Ioannidis 2005), surrogate associations do not guarantee hard-outcome validity. Hallmarks such as senescence-associated secretory phenotype suppression (Wang 2017) and mTOR-pathway phosphorylation changes (Hibbert 2026) are biologically informative but remain at least one mechanistic step removed from endpoints that patients or clinicians would recognize as meaningful improvements in survival, disability, or quality of life. Until the evidence base includes trials that bridge from these mechanistic surrogates to validated clinical endpoints, the risk-benefit calculus for off-label rapamycin use in healthy adults remains fundamentally unresolved.

Conclusion

For rapamycin biomarker effects, the final interpretation is deliberately tiered: the retained clinical and mechanistic evidence profile defines a bounded geroscience rationale, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence. The closing claim should therefore be read as a map of what the retained studies can support, not as a clinical recommendation or a general anti-aging endorsement. Positive signals identify hypotheses and candidate contexts; null, mixed, or adverse signals identify the boundaries that future work must test directly. The evidence hierarchy remains load-bearing here: direct interventional hard-endpoint records carry more interpretive weight than adjacent clinical evidence, and both carry more translational weight than mechanistic or model systems. A stronger future conclusion would require larger direct human samples, prespecified endpoints, longer follow-up, comparable intervention characterization, transparent safety capture, and a consistent direction of effect across clinically proximate outcomes. Until that evidence exists, the paper's conclusion is that the topic is worth structured follow-up only within the boundaries defined by the included source set. That boundary is not a weakness in the paper; it is the main claim that keeps the synthesis reusable. Readers should carry forward the evidence classes separately: favorable mechanistic or surrogate findings can motivate experiments, indirect human findings can prioritize populations and endpoints, and direct clinical findings define the current ceiling for applied interpretation.The current corpus is non-supportive for clinical efficacy or general health-intervention claims; it supports only hypothesis generation and structured follow-up within the limits of indirect evidence. Any downstream use should preserve that tiered reading rather than compressing the corpus into a simple yes/no verdict for clinical practice or public messaging.

What This Synthesis Adds

This synthesis maps 30 included sources on Rapamycin Biomarker Effects across 6 outcome classes and 161 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 30 curated reference papers, the evidence base for Rapamycin Biomarker Effects shows a context-dependent profile. Positive signals appear in: contextual other, immune inflammation. Negative signals appear in: contextual other. Null findings dominate: contextual other, safety comorbidity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Rapamycin Biomarker 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.

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

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	null, unclear	direct interventional hard-endpoint gap
cardiometabolic	0	2	null	direct interventional hard-endpoint gap
dosing and pharmacokinetics	0	2	mixed, null	conflict-resolution gap
immune and inflammation	0	4	null, positive	conflict-resolution gap
safety and comorbidity	0	2	null	direct interventional hard-endpoint gap
contextual adjacent evidence	1	17	mixed, negative, null, positive, unclear	conflict-resolution gap

Evidence-Gap Priority

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

Next-Study Design Recommendation

The next high-yield study for Rapamycin Biomarker 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 12 months; 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

• Stanfield 2026; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.007.
• Yang 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=positive; representative statistic=P < 0.0001.
• Moel 2025; tier=B2; directness=indirect; endpoint=safety comorbidity; direction=null; representative statistic=P = 0.004.
• Dai 2014; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=unclear; representative statistic=P < 0.001.
• Peddibhotla 2026; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P < 0.001.
• Kell 2026; tier=B2; directness=indirect; endpoint=immune inflammation; direction=positive; representative statistic=P < 0.0001.
• Zhou 2026; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null.
• Gonzales 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.002.
• Zhuang 2025; tier=B2; directness=indirect; endpoint=immune inflammation; direction=null; representative statistic=P < 0.0001.
• Hands 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=unclear; representative statistic=P = 0.06.

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

Additional corpus sources included animal/preclinical evidence; - Severity 5 disagreement: Yang 2025 vs Park 2025; Yang 2025 (positive) vs Park 2025 (negative) on contextual other

• Severity 5 disagreement: Park 2025 vs Bitto 2016; Park 2025 (negative) vs Bitto 2016 (positive) on contextual other
• Severity 4 disagreement: Rosario 2023 vs Cifarelli 2015; Rosario 2023 (null) vs Cifarelli 2015 (mixed) on contextual other
• Severity 4 disagreement: Gonzales 2025 vs Cifarelli 2015; Gonzales 2025 (null) vs Cifarelli 2015 (mixed) on contextual other
• Severity 4 disagreement: Roark 2025 vs Cifarelli 2015; Roark 2025 (null) vs Cifarelli 2015 (mixed) on contextual other
• Severity 4 disagreement: Yang 2025 vs Cifarelli 2015; Yang 2025 (positive) vs Cifarelli 2015 (mixed) on contextual other
• Severity 4 disagreement: Hands 2025 vs Cifarelli 2015; Hands 2025 (unclear) vs Cifarelli 2015 (mixed) on contextual other
• Severity 4 disagreement: Ruan 2025 vs Miller 2014; Ruan 2025 (null) vs Miller 2014 (mixed) on dosing pharmacokinetics

Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Singh 2026, Stanfield 2024, Torrent 2026, Lefranc 2026, Arianna 2017.

References

• Bitto 2016. Transient rapamycin treatment can increase lifespan and healthspan in middle-aged mice. eLife, 2016. DOI: 10.7554/eLife.16351. PMID: 27549339.
• Yang 2025. Rapamycin Alleviates Heart Failure Caused by Mitochondrial Dysfunction and SERCA Hypoactivity in Syntaxin 12/13 Deficient Models. Advanced Science, 2025. DOI: 10.1002/advs.202507210. PMID: 40568929.
• Moel 2025. Influence of rapamycin on safety and healthspan metrics after one year: PEARL trial results. Aging (Albany NY), 2025. DOI: 10.18632/aging.206235. PMID: 40188830.
• Cifarelli 2015. Metformin and Rapamycin Reduce Pancreatic Cancer Growth in Obese Prediabetic Mice by Distinct MicroRNA-Regulated Mechanisms. Diabetes, 2015. DOI: 10.2337/db14-1132. PMID: 25576058.
• Dai 2014. Altered proteome turnover and remodeling by short-term caloric restriction or rapamycin rejuvenate the aging heart. Aging Cell, 2014. DOI: 10.1111/acel.12203. PMID: 24612461.
• Miller 2014. Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell, 2014. DOI: 10.1111/acel.12194. PMID: 24341993.
• Stanfield 2026. Exercise and Weekly Sirolimus (Rapamycin) in Older Adults: RAPA‐EX‐01 Randomised, Double‐Blind, Placebo‐Controlled Trial. Journal of Cachexia, Sarcopenia and Muscle, 2026. DOI: 10.1002/jcsm.70274. PMID: 41985884.
• Peddibhotla 2026. Natural Genetic Variation Impacts Stress-Induced Quiescence and Regeneration in Response to Rapamycin. Cells, 2026. DOI: 10.3390/cells15030236. PMID: 41677603.
• Rosario 2023. Anti-Mullerian hormone attenuates both cyclophosphamide-induced damage and PI3K signalling activation, while rapamycin attenuates only PI3K signalling activation, in human ovarian cortex in vitro. Human Reproduction (Oxford, England), 2023. DOI: 10.1093/humrep/dead255. PMID: 38070496.
• Kell 2026. Rapamycin Exerts Its Geroprotective Effects in the Ageing Human Immune System by Enhancing Resilience Against DNA Damage. Aging Cell, 2026. DOI: 10.1111/acel.70364. PMID: 41524558.
• Tian 2026. The mTOR Inhibitor Rapamycin Attenuates Ozone-Induced Airway Inflammation and Emphysema In Mice. Journal of Inflammation Research, 2026. DOI: 10.2147/JIR.S545564. PMID: 41884163.
• Zhou 2026. Rapamycin-modified novel tolerogenic dendritic cells induce liver graft tolerance through MHC-II + CD8 + regulatory T cells. Hepatology Communications, 2026. DOI: 10.1097/HC9.0000000000000942. PMID: 42008782.
• Gonzales 2025. Rapamycin treatment for Alzheimer’s disease and related dementias: a pilot phase 1 clinical trial. Communications Medicine, 2025. DOI: 10.1038/s43856-025-00904-9. PMID: 40394335.
• Pell 2026. Rapamycin and Minocycline Treatment Does Not Rescue Behavioral and Molecular Changes Induced by Early-Life Seizures in Female Mice. NeuroSci, 2026. DOI: 10.3390/neurosci7030055. PMID: 42200917.
• Zhuang 2025. Rapamycin preserves cardiac function in autoimmune myocarditis by reprogramming Cxcl9 + macrophages via the mTORC1–C/EBPβ–OSM axis. Redox Biology, 2025. DOI: 10.1016/j.redox.2025.103970. PMID: 41412038.
• Hands 2025. What is the clinical evidence to support off-label rapamycin therapy in healthy adults?. Aging (Albany NY), 2025. DOI: 10.18632/aging.206300. PMID: 40778880.
• Ruan 2025. Low-dose rapamycin alleviates clinical symptoms of fatigue and PEM in ME/CFS patients via improvement of autophagy: a pilot study. Journal of Translational Medicine, 2025. DOI: 10.1186/s12967-025-07213-8. PMID: 41121328.
• Singh 2026. Rapamycin Prevents Sulfate-Reducing Bacteria-Induced Effects on Snail and GSK-3 and Impaired Intestinal Barrier. Microorganisms, 2026. DOI: 10.3390/microorganisms14040781. PMID: 42075182.
• Hibbert 2026. Mechanical loading induces the longitudinal growth of muscle fibers via a rapamycin-insensitive mechanism. Science Advances, 2026. DOI: 10.1126/sciadv.aec5134. PMID: 41686888.
• Stanfield 2024. A single-center, double-blind, randomized, placebo-controlled, two-arm study to evaluate the safety and efficacy of once-weekly sirolimus (rapamycin) on muscle strength and endurance in older adults following a 13-week exercise program. Trials, 2024. DOI: 10.1186/s13063-024-08490-2. PMID: 39354527.
• Torrent 2026. Long-term rapamycin treatment suppresses IL-17-producing gamma delta T cells and blunts neuroinflammation in aging. PLOS One, 2026. DOI: 10.1371/journal.pone.0343183. PMID: 42207784.
• Su 2025. Unveiling MAGEA3: a novel predictive biomarker for bevacizumab resistance in colorectal cancer. Cancer Drug Resistance, 2025. DOI: 10.20517/cdr.2025.35. PMID: 40342736.
• Park 2025. RPS24 microexon isoform as a novel biomarker for estrogen receptor-positive breast cancer progression and therapeutic resistance. Experimental & Molecular Medicine, 2025. DOI: 10.1038/s12276-025-01578-y. PMID: 41258078.
• Lefranc 2026. Fpr1p mediates the synergistic effect of rapamycin or tacrolimus with caspofungin in Clavispora lusitaniae in vitro. JAC-Antimicrobial Resistance, 2026. DOI: 10.1093/jacamr/dlag091. PMID: 42179906.
• Arianna 2017. Rapid Rapamycin-Only Induced Osteogenic Differentiation of Blood-Derived Stem Cells and Their Adhesion to Natural and Artificial Scaffolds. Stem Cells International, 2017. DOI: 10.1155/2017/2976541. PMID: 28814956.
• Wang 2017. Rapamycin inhibits the secretory phenotype of senescent cells by a Nrf2‐independent mechanism. Aging Cell, 2017. DOI: 10.1111/acel.12587. PMID: 28371119.
• Bjedov 2010. Mechanisms of Life Span Extension by Rapamycin in the Fruit Fly Drosophila melanogaster. Cell Metabolism, 2010. DOI: 10.1016/j.cmet.2009.11.010. PMID: 20074526.
• Roark 2025. Rapamycin for longevity: the pros, the cons, and future perspectives. Frontiers in Aging, 2025. DOI: 10.3389/fragi.2025.1628187. PMID: 40620657.
• Zhang 2021. Rapamycin Antagonizes BCRP-Mediated Drug Resistance Through the PI3K/Akt/mTOR Signaling Pathway in mPRα-Positive Breast Cancer. Frontiers in Oncology, 2021. DOI: 10.3389/fonc.2021.608570. PMID: 33912444.
• Harrison 2009. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature, 2009. DOI: 10.1038/nature08221. PMID: 19587680.

Background References

Canonical clinical thresholds cited in prose. Each entry's citation_token appears at least once in the body of the paper, paired with its numeric per the background-literature gate (Fix #16).

• Ioannidis 2005. Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124. DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.