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Research Synthesis: Cold Exposure Brown Fat

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May 29, 2026

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OSF DOI: 10.17605/OSF.IO/5EKND

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Abstract

This synthesis tests the thesis that evidence for Cold exposure brown fat is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. This paper synthesizes cold exposure brown fat as an aging-related intervention across 37 included source papers and 1333 high-confidence extracted claims. The evidence profile contains no sources classified primarily as direct clinical evidence, 23 adjacent clinical sources, and 9 mechanistic or model-system sources, with 167 cross-study disagreements across the evidence base. Positive study-level signals concentrate in immune, null signals in contextual adjacent evidence, cardiometabolic, immune, and negative signals in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect. The conclusion is that cold exposure brown fat remains a bounded geroscience case: mechanistic plausibility and selected clinical signals justify further 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 does not automatically establish the same signal in another. The study-level stru

Review Summary

Evidence Transparency

Screening trace

Identified -> Screened -> Excluded with reasons -> Included

Identified: 37 candidate receipts.
Screened: 37 receipts after source retrieval, deduplication, and topic filtering.
Excluded with reasons: 0 recorded exclusions; no PRISMA full-text exclusion-stage filter was applied.
Included: 37 retained candidate receipts for evidence-map interpretation.

Included-studies preview

Study	Population	Intervention/exposure	Comparator	Endpoint	Effect	Risk of bias	Directness
Jaeckstein 2025	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Ma 2025	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Feng 2025	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Kwok 2024	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Lyons 2024	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Cal 2025	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Sarmiento-Ortega 2025	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable
Neal 2026	not extracted	not extracted	not extracted	not extracted	not extracted	not appraised in public preview	source-traceable

Downloadable sidecars

citation_traces.json claim_graph.json contradiction_map.json evidence_table.csv risk_of_bias.json

Reviewer-facing limitations

This is an agent-assisted evidence map, not a PRISMA-complete systematic review.
It is not PROSPERO-registered and should not be used as a clinical guideline or medical advice.
Empty sidecar fields mean not extracted, not evidence of absence.

Living Evidence Brief

Research Question

What does the current evidence establish about Cold Exposure Brown Fat and human geroscience? This synthesis tests the thesis that evidence for Cold exposure brown fat is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. This paper synthesizes cold exposure brown fat as an aging-related intervention across 37 included source papers and 1333 high-confidence extracted claims. The evidence profile contains no sources classified primarily as direct clinical evidence, 23 adjacent clinical sources, and 9 mechanistic or model-system sources, with 167 cross-study disagreements across the evidence base. Positive study-level signals concentrate in immune, null signals in contextual adjacent evidence, cardiometabolic, immune, and negative signals in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect. The conclusion is that cold exposure brown fat remains a bounded geroscience case: mechanistic plausibility and selected clinical signals justify further 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 does not automatically establish the same signal in another. The study-level stru

Search Summary

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-cold_exposure_brown_fat-v06-DAILY-2026-05-29T03-49-13Z-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: 2026-05-29.

Search strategy

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

Eligibility criteria

Sources whose primary content addresses cold exposure brown fat.
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 163 records in the receipt-candidate union, 43 were classified as source candidates and 37 were admitted as traceable synthesis sources. No additional records were excluded after final source admission.

source admission funnel

Admission bucket	n
Receipt candidate union	163
Classified source candidates	43
No extractable claims	45
None-only claim binding	10
Partial/none-only claim binding	50
Partial-only candidates	9
Strict high-confidence sources	6
Admitted final sources	37

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.

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, immune and inflammation, longevity, muscle function, 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.

Evidence Landscape

Outcome-class note: Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.

Outcome class	Corpus slice	Strongest signal	Directness	Main limitation
Contextual Adjacent Evidence	n=15; claims=469	null signal in 14/15 sources	11 indirect; 1 mechanistic; 3 review	limited corpus depth in this outcome class
Cardiometabolic	n=11; claims=236	null signal in 9/11 sources	8 indirect; 3 mechanistic	limited corpus depth in this outcome class
Immune	n=4; claims=380	null signal in 2/4 sources	2 indirect; 2 review	limited corpus depth in this outcome class
Immune and Inflammation	n=2; claims=74	null signal in 2/2 sources	1 indirect; 1 mechanistic	limited corpus depth in this outcome class
Safety and Comorbidity	n=2; claims=96	null signal in 2/2 sources	2 mechanistic	limited corpus depth in this outcome class
Dosing and Pharmacokinetics	n=1; claims=62	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating
Longevity	n=1; claims=11	null signal in 1/1 sources	1 indirect	single-source slice; hypothesis-generating
Muscle Function	n=1; claims=5	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating

Cardiometabolic Outcomes

The corpus includes 11 studies addressing cardiometabolic outcomes related to cold exposure and brown adipose tissue (BAT) activation, comprising 7 observational cohorts and 4 preclinical studies. The primary endpoints across these studies include BAT activation markers, body weight changes, metabolic parameters, and related inflammatory or bone density measures.

Quantitative findings from observational human studies demonstrate significant correlations between BAT-related parameters and metabolic markers. The detailed per-study endpoint evidence is presented in Table 2.

Mechanistically, the evidence points to several pathways through which BAT activation influences cardiometabolic health. In animal models, Eubacterium sp. The topical application of menthol, a pharmacological cold mimic, has been shown to induce cold sensitivity, adaptive thermogenesis, and BAT activation in mice (Sankina 2024). These mechanisms collectively support the biological plausibility linking BAT activation to metabolic improvements.

The evidence base reveals important tensions regarding the consistency and generalizability of cardiometabolic findings. While numerous studies report significant associations (Acosta 2019, Rosa 2024, Zhang 2025, Zhang 2025b), others present more nuanced or null findings. This suggests that in certain pathological states, the expected BAT-mediated metabolic benefits may be attenuated. Furthermore, the therapeutic development approach described in Cal 2025, involving a nitroalkene derivative of salicylate (SANA) that induces creatine-dependent thermogenesis, represents an alternative pharmacological strategy that does not rely on direct cold exposure. The heterogeneity in study designs—from human cohorts analyzing temperature rhythms to murine models of genetic obesity—highlights the need for careful interpretation when synthesizing evidence across different biological contexts and experimental paradigms.

Contextual Adjacent Evidence Outcomes

The corpus encompasses a heterogeneous collection of study designs investigating cold exposure and brown adipose tissue (BAT) biology. The primary endpoints vary widely, from BAT thermogenesis and fat fraction measured by infrared thermography or 18F-FDG-PET to gene expression profiles in adipose tissue. The population demographics span from pediatric goat models to older adults with comorbidities, reflecting the exploratory nature of this outcome class.

Quantitative findings across the corpus reveal significant associations and effects, though their directions and magnitudes are context-dependent. Cold exposure protocols produced significant reductions in supraclavicular BAT fat fraction (P < 0.001) in fasted young adults (Eenige 2025). Preclinical data from mouse models indicate that semaglutide and tirzepatide exert distinct effects on metabolic and inflammatory gene expression in BAT, with multiple genes reaching statistical significance (P < 0.001) (Ma 2025). Detailed per-study endpoint statistics are compiled in Table 2.

Mechanistically, the evidence points to BAT as a critical node in whole-body energy expenditure and metabolic health. Preclinical data suggest that cold exposure stimulates cross-tissue metabolic rewiring to fuel glucose-dependent thermogenesis in BAT (Cutler 2025). Furthermore, UCP1 expression in human BAT is inversely associated with cardiometabolic risk factors, suggesting a protective role (Kwok 2024). These mechanistic pathways are supported by transcriptomic analyses identifying key genes regulating BAT thermogenesis in developing goat kids (Li 2025).

Within the corpus, key tensions emerge regarding the consistency and translatability of BAT findings. The effect of habitual cold exposure in Arctic adults, as reviewed by Jensen 2025, presents a nuanced picture where some studies report stable supraclavicular skin temperature post-cooling (P < 0.001 for sternum decline), while others show variable BAT activation. The long-term health implications remain speculative; for example, BAT is proposed to mediate healthful longevity based on mouse transplantation studies, but human cohort data directly linking BAT activity to longevity endpoints are sparse (Zhang 2024). This underscores the gap between established mechanistic plausibility and definitive human clinical evidence.

Dosing and Pharmacokinetics Outcomes

Mechanistic preclinical evidence provides foundational insights into dose-response relationships relevant to brown adipose tissue (BAT) biology. Sarmiento-Ortega et al. (2025) examined the effects of minimal risk doses of cadmium exposure on BAT histological and functional alterations in a controlled Wistar rat model. The study design included a control group (n = 30) with access to cadmium-free water and experimental groups (n = 60) subdivided into two subgroups receiving defined cadmium doses. This preclinical framework allows for the systematic assessment of dose-dependent pathological changes in BAT, providing a translational basis for understanding toxicological thresholds. The work underscores the importance of precise dosimetry in animal models to establish the boundaries between physiological stressors and pathological insults to thermogenic adipose tissue.

The quantitative findings from this preclinical investigation demonstrate statistically significant histological and functional alterations in BAT following cadmium exposure at minimal risk doses. These consistent low p-values across different assessments indicate a robust dose-response effect where even minimal cadmium exposure induces measurable pathological changes in BAT. The data highlight the sensitivity of BAT to environmental toxicants and suggest that pharmacokinetic profiles of such exposures can drive significant tissue remodeling.

Mechanistically, the findings from Sarmiento-Ortega et al. (2025) implicate direct toxicological pathways in BAT dysfunction, offering a counterpoint to studies focusing on beneficial activators of thermogenesis. The preclinical data suggest that cadmium, a prevalent environmental toxicant, can induce histological damage and functional impairment in BAT at doses previously considered to pose minimal risk. This mechanistic substrate underscores the complexity of BAT regulation, where the organ is responsive not only to cold exposure and sympathetic activation but also to chemical insults. Understanding these adverse pharmacokinetic profiles is critical for a holistic view of BAT health in human populations exposed to environmental pollutants.

The primary tension within the dosing pharmacokinetics corpus pertains to the generalization from toxicological models to therapeutic contexts. The evidence from Sarmiento-Ortega et al. (2025) is derived exclusively from an animal model of toxicant exposure, which does not directly inform dosing for cold exposure or pharmaceutical BAT activation in humans. While this preclinical study provides high-quality evidence for the pathological effects of a specific cadmium dose regimen, its direct translation to human BAT physiology in the context of beneficial interventions remains limited. This represents a boundary condition: the current evidence profile for dosing in cold-exposure studies requires distinct human pharmacokinetic data, which is not addressed by this preclinical toxicology work.

Immune Outcomes

The evidence base for immune modulation by cold-activated brown adipose tissue (BAT) is derived from a combination of observational cohorts, mechanistic human studies, and a systematic review in a clinical population. Heimburger et al. (2022), a systematic review focused on patients with type 2 diabetes, reported positive effects on hepatic fat and BAT thermogenesis with significant p-values of P = 0.0005, P = 0.009, and P = 0.000072. Mendez-Gutierrez et al. (2024), another systematic review, concluded that cold exposure modulates potential brown adipokines in humans but found an unclear effect direction for immune outcomes, highlighting the complexity of the human response.

Mechanistically, the work by Feng et al. (2025) provides preclinical data suggesting a protective role for BAT in immune-related injury. Their model demonstrates that BAT-secreted Nrg4 suppresses ferroptosis in sepsis-induced liver injury, with BATectomy in mice exacerbating injury (n = 16 per group), and significant findings reported across multiple thresholds (P < 0.05, P < 0.01, P < 0.001). This preclinical evidence directly contrasts with the null findings from the human observational cohort by Jaeckstein 2025, creating a tension within the corpus. The agreement between the two null-effect human studies, Jaeckstein 2025 and Feng 2025 (in its human-relevant immune framing), stands against the positive effect suggested by the systematic review in diabetic patients (Heimburger 2022).

By contrast, the tension between the positive effect reported in the diabetic population review (Heimburger 2022) and the null or unclear findings in general adult cohorts (Jaeckstein 2025, Mendez-Gutierrez 2024) underscores the context-dependency of BAT's immune interactions. The observed disagreements, such as the null vs positive tension between Mendez-Gutierrez 2024 and Feng 2025, are non-orthogonal and reflect differences in study design (systematic review vs. observational cohort), population (diabetics vs. general adults), and the specific immune pathways under investigation. While mechanistic plausibility for immune modulation exists, as evidenced by the preclinical data from Feng 2025, the current human evidence is mixed, preventing a definitive conclusion on the net immune impact of cold exposure via BAT activation in the general population.

Immune and Inflammation Outcomes

The evidence for cold exposure and immune or inflammatory modulation is supported by two distinct lines of investigation from this curated corpus: a clinical proof-of-concept trial in human patients and a mechanistic mouse study. Buijze et al. (2019) conducted an observational cohort study examining an add-on training program that included breathing exercises, cold exposure, and meditation in 24 adults with moderately active axial spondyloarthritis, characterized by an ASDAS >2.1 and hs-CRP ≥5 mg/L. Xie et al. (2023) used a preclinical mouse model to investigate the role of CXCL13 in promoting thermogenesis through macrophage recruitment and inflammation inhibition in brown adipose tissue following cold stimulation. Both studies, despite their different designs and directness levels, converge on the immune-inflammation outcome class, providing a multi-layered view of potential mechanisms.

Quantitative findings from these studies present a profile of mixed significance and null effects. Preclinically, the mouse study by Xie et al. (2023) identified CXCL13 as an elevated chemokine in brown adipose tissue in response to cold and reported multiple statistically significant findings across its experimental analyses, with p-values ranging from P < 0.05 to P < 0.001. These data indicate that while the preclinical signal for a cold-induced, anti-inflammatory pathway in adipose tissue is robust, the translation to a measurable clinical anti-inflammatory effect in the human cohort studied was inconsistent.

Mechanistically, the preclinical data from Xie et al. (2023) provide a plausible biological substrate for the functional observations in the human trial. The mouse study demonstrates that cold exposure stimulates CXCL13 in brown adipose tissue, which in turn recruits M2 macrophages and inhibits inflammation, a pathway that promotes thermogenesis. This offers a direct mechanistic link between cold exposure and immune modulation within metabolically active tissue. The clinical RCT by Buijze et al. (2019), which combined cold exposure with other interventions, may be engaging this or related anti-inflammatory pathways, though the contribution of cold alone is confounded by the multimodal intervention design.

A key tension within this evidence base lies in the consistency of clinical translation. By contrast, the preclinical mouse model presents a coherent and statistically significant story of cold-induced anti-inflammation via CXCL13. The human observational cohort, however, reports a bifurcated set of results, with some endpoints meeting significance thresholds and others showing null or trend-level effects. This disagreement highlights the challenge of translating a potent, isolated mechanistic pathway observed in animal models into a clinically measurable outcome in humans, particularly within a complex, multi-component behavioral intervention. The boundary conditions for a clinical anti-inflammatory effect of cold exposure remain to be established.

Longevity Outcomes. The sole longitudinal evidence on cold exposure and aging-related outcomes comes from an observational cohort study examining the environmental toxicant bisphenol S (BPS) and its impact on brown adipose tissue (BAT) function. This study assessed the pathophysiological link between BPS exposure and accelerated aging through disruption of BAT-regulated energy metabolism, using a transcriptomic approach in a mouse model. The experimental design included a vehicle control group (n = 8) and a BPS-exposed group (n = 7), with transcriptome sequencing conducted on total RNA extracted from BATs. While this study provides mechanistic data on BAT disruption, it does not directly evaluate cold exposure as an intervention but rather examines a toxicant that impairs the same thermogenic pathway. The evidence therefore addresses aging biology indirectly through the lens of BAT dysfunction rather than through cold stimulation as a therapeutic modality.

Quantitative findings from this observational cohort are limited to transcriptomic endpoints rather than lifespan or validated aging biomarkers. The study reports no direct p-values or effect sizes linking BPS-mediated BAT disruption to longevity metrics in its available excerpts. The null effect direction for the longevity outcome class suggests that, within this evidence base, the study did not identify a statistically significant association between BAT-related pathophysiology and accelerated aging endpoints. This contrasts with the broader mechanistic literature suggesting BAT activation could influence metabolic health trajectories relevant to aging. The absence of robust clinical data means the longevity case for cold-induced BAT activation remains speculative at the human intervention level.

Safety and Comorbidity Outcomes

Preclinical investigations have examined how brown adipose tissue (BAT) responds to environmental stressors relevant to cold exposure paradigms. Lyons 2024 utilized highland deer mice as a model to assess thermogenic capacity under combined chronic cold and hypoxic conditions, a physiologically relevant stress combination. Translational relevance to humans remains uncertain. Migliaccio 2024 investigated BAT adaptation in male Wistar rats exposed to the environmental pollutant DDE and/or a high-fat diet, modeling a distinct comorbidity pathway (Migliaccio 2024). Translational relevance to humans remains uncertain. Together, these studies frame safety concerns around BAT function under chronic environmental stressors.

Mechanistically, the evidence suggests BAT plasticity is a key determinant of metabolic safety under stress. The mechanistic substrate underlying the functional finding in deer mice involves a substrate partitioning shift, where fatty acids are diverted to BAT for thermogenesis rather than to muscle for oxidative metabolism (Lyons 2024). By contrast, the preclinical data from Migliaccio 2024 indicate that exposure to the persistent organic pollutant DDE can disrupt BAT adaptation, a mechanistic pathway that could theoretically impair the thermogenic benefits of cold exposure in humans with similar environmental burdens. This tension within the preclinical corpus underscores that BAT's net effect on safety is not monolithic but depends on the specific comorbidity or environmental co-exposure.

A key tension within the corpus emerges from comparing these preclinical models. Both studies report null or complex findings for the overall safety outcome class, with Lyons 2024 showing a mix of significant and non-significant results and Migliaccio 2024 demonstrating strong effects of DDE on BAT (Lyons 2024; Migliaccio 2024). The disagreement is not in statistical direction but in the nature of the stressor: physiological (cold/hypoxia) versus toxicological (pollutant). This highlights a critical boundary condition for the safety of cold exposure interventions: the preclinical evidence is most favorable when considering physiological adaptation but raises caution when environmental toxicant exposures are present, a factor not typically controlled for in human trials.

Longevity Outcomes

Mechanistically, the Zhu 2025 data provide a relevant negative control: if environmental disruption of BAT accelerates aging pathways, this supports the biological plausibility that BAT activation (e.g., via cold exposure) could conversely attenuate them. Transcriptomic sequencing of BAT tissue in exposed mice would reveal dysregulated energy metabolism genes, aligning with preclinical data on BAT's role in metabolic homeostasis. However, this mechanistic substrate is derived from an observational toxicology model, not from a cold-exposure intervention trial. The pathway from BAT dysfunction to aging biology is thus established, but the reverse pathway—BAT activation via cold exposure conferring longevity benefits—lacks direct experimental validation. Preclinical data on BAT and lifespan remain sparse and largely restricted to rodent models without human translational studies.

Within the corpus, the longevity evidence profile is characterized by mechanistic plausibility coexisting with a near-complete absence of human RCT data. The Zhu 2025 observational cohort provides indirect support for the biological relevance of BAT in aging pathways through its toxicological disruption model, yet no parallel human cold-exposure trials report validated aging endpoints such as telomere length, epigenetic age clocks, or mortality. This evidentiary gap means the Cold exposure brown fat anti-aging case, as currently constituted, remains incomplete. The boundary conditions—dose, duration, and population characteristics for any putative longevity benefit—are entirely unestablished by intervention data. Future human studies directly testing cold exposure against aging biomarkers are needed to resolve this tension between mechanistic promise and clinical uncertainty.

Longevity remains a separate Results slice (n=1; claims=11; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.

Muscle Function Outcomes

Muscle Function Outcomes. Preclinical investigation by Alcala 2017 examined the effects of a high-fat diet (HFD) on brown adipose tissue (BAT) in a mouse model, focusing on parameters related to hypertrophy and cellular stress. The study quantified BAT mass and reported a statistically significant difference between obese and control animals.

Mechanistically, this hypertrophy was accompanied by markers of increased cellular stress. The preclinical data from Alcala 2017 linked the observed BAT expansion to elevated inflammation and oxidative stress, suggesting a potential maladaptive response to the HFD-induced obese state. These findings provide a mechanistic substrate for understanding how metabolic overload may compromise BAT function, moving beyond simple mass increase to include qualitative changes in tissue physiology.

The evidence from this preclinical model suggests a complex relationship between cold exposure-linked tissue and metabolic stress. While hypertrophy of BAT is often considered a potential adaptive response, the concurrent rise in inflammation and oxidative stress observed by Alcala 2017 points toward a context-dependent outcome. This tension underscores that increased BAT mass alone, as measured in this mouse study, may not equate to improved functional capacity under conditions of dietary obesity.

Key Findings

Outcome-class note: Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.

Outcome class	Corpus slice	Strongest signal	Directness	Main limitation
Contextual Adjacent Evidence	n=15; claims=469	null signal in 14/15 sources	11 indirect; 1 mechanistic; 3 review	limited corpus depth in this outcome class
Cardiometabolic	n=11; claims=236	null signal in 9/11 sources	8 indirect; 3 mechanistic	limited corpus depth in this outcome class
Immune	n=4; claims=380	null signal in 2/4 sources	2 indirect; 2 review	limited corpus depth in this outcome class
Immune and Inflammation	n=2; claims=74	null signal in 2/2 sources	1 indirect; 1 mechanistic	limited corpus depth in this outcome class
Safety and Comorbidity	n=2; claims=96	null signal in 2/2 sources	2 mechanistic	limited corpus depth in this outcome class
Dosing and Pharmacokinetics	n=1; claims=62	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating
Longevity	n=1; claims=11	null signal in 1/1 sources	1 indirect	single-source slice; hypothesis-generating
Muscle Function	n=1; claims=5	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating

Cardiometabolic Outcomes

Contextual Adjacent Evidence Outcomes

Dosing and Pharmacokinetics Outcomes

Immune Outcomes

Immune and Inflammation Outcomes

Safety and Comorbidity Outcomes

Longevity Outcomes

Longevity remains a separate Results slice (n=1; claims=11; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.

Muscle Function Outcomes

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 of 37 reference papers is dominated by observational cohorts, preclinical animal models, and mechanistic studies, with an absence of large-scale, long-duration randomized controlled trials (RCTs) that directly measure cold-exposure interventions against hard clinical endpoints such as all-cause mortality, major cardiovascular events, or incident type 2 diabetes. Without RCT evidence, it is impossible to determine whether deliberately activating BAT through cold exposure, pharmacological mimics, or dietary interventions causes durable reductions in disease incidence or mortality, or merely correlates with healthier metabolic phenotypes. Consequently, the synthesis cannot adjudicate whether BAT activation is a causal therapeutic target or an epiphenomenon of metabolic health, a question that only adequately powered, long-term RCTs can resolve. The corpus also lacks dedicated safety trials assessing adverse effects of chronic cold exposure protocols, which are essential before any clinical recommendation can be made.

Several key outcome domains in this synthesis rest on single studies, creating a single-trial generalization risk where findings cannot be replicated or cross-validated within the curated corpus. Each of these represents a distinct immune pathway with a single source, making it impossible to determine whether the observed immune effects are robust phenomena or study-specific artifacts. Similarly, the longevity claim—that BAT transplantation extends lifespan in mice (Zhang 2024)—derives from a single preclinical source and has no corroborating human evidence in the corpus. The safety and comorbidity domain is supported by only two preclinical sources (Lyons 2024 examining cold hypoxia in deer mice, Migliaccio 2024 examining pollutant exposure in Wistar rats), neither of which directly models human cold-exposure safety. This pervasive single-source dependency means that effect sizes, dose-response relationships, and mechanistic pathways identified in one study cannot be statistically triangulated, leaving the synthesis vulnerable to false-positive conclusions drawn from isolated findings. The cross-study disagreement map further reveals cross-study disagreements across outcome classes, but the resolution of these tensions requires replication studies that are not present in the current corpus.

The population profile of the enrolled studies limits generalizability to several clinically important groups. The synthesis therefore cannot determine whether BAT-targeted interventions are effective, safe, or feasible in the patient subgroups with the greatest disease burden.

The endpoint scope of the corpus is heavily skewed toward mechanistic and surrogate markers, with minimal representation of clinically meaningful hard endpoints. Most sources report PET-derived BAT activity (18F-FDG uptake), UCP1 expression levels, infrared thermography signals, or transcriptomic changes—proxies whose relationship to downstream health outcomes is assumed rather than validated. As Ioannidis 2005 cautions, surrogate endpoint associations do not guarantee hard-outcome validity, and the corpus contains no source that directly links measured BAT activation to reduced incidence of myocardial infarction, stroke, cancer, or death in a prospective human cohort. The cardiometabolic outcome class, which includes the largest number of sources (approximately one-third of the corpus), reports associations with fat distribution, inflammatory markers, and metabolic gene expression, but none of these constitute endpoints recognized by regulatory agencies for cardiovascular risk reduction. The immune outcome class shows the only positive signal in the synthesis (Heimburger 2022 reporting GIP effects on BAT thermogenesis in type 1 diabetes, P = 0.0005), yet this single systematic review's directness is classified as positive while the remaining immune sources (Jaeckstein 2025, Feng 2025) report null or mechanistic findings, creating unresolved tension. Safety endpoints are virtually absent: only two preclinical sources (Lyons 2024, Migliaccio 2024) address adverse outcomes, and neither models human-relevant chronic cold-exposure safety profiles. The synthesis therefore cannot assess the risk-benefit ratio of BAT-activating interventions, a prerequisite for any clinical guideline recommendation. Additionally, no source in the corpus measures patient-reported outcomes such as quality of life, functional capacity, or treatment adherence during cold-exposure protocols.

A substantial portion of the corpus addresses mechanistic pathways connecting BAT to systemic metabolism, but these mechanistic insights have not been translated into clinical evidence through interventional studies. These mechanistic findings suggest clinically relevant applications (hepatoprotection, bone health, pharmacological BAT activation) that are not tested in any human trial within the corpus. Similarly, Xie 2023 identifies CXCL13 as a chemokine promoting thermogenesis via M2 macrophage recruitment and inflammation inhibition in BAT (P < 0.05), a pathway with potential anti-inflammatory therapeutic implications, yet no human study in the corpus examines this axis. The gap between mechanism and clinic is further widened by the predominance of preclinical models using acute cold challenges (e.g., Cutler 2025 examining cold-induced cross-tissue metabolic rewiring in chow-fed mice) that do not model the chronic, repeated cold exposures that would characterize any real-world intervention. Translational relevance to humans remains uncertain. The corpus thus contains a rich mechanistic substrate suggesting BAT's therapeutic potential, but the absence of translational human trials means these mechanisms cannot be leveraged for evidence-based clinical practice. Until dose-finding, safety, and efficacy trials bridge this mechanism-to-clinic gap, the synthesis can only describe biological plausibility, not clinical utility.

Gaps Identified

Thesis: Across 37 curated reference papers, the evidence base for cold exposure brown fat shows a context-dependent profile. Positive signals appear in: immune. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces 167 non-orthogonal tensions across outcome classes — see Cross-Domain Synthesis. The cold exposure brown fat 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 cold exposure brown fat evidence base is best interpreted as conditionally supportive rather than definitive. The evidence base contains no sources classified primarily as direct clinical evidence and 9 mechanistic sources, so the strongest claims concern where signals converge and where translation remains uncertain.

Additional corpus sources included animal/preclinical evidence; positive sources (Heimburger 2022) are important, but they must be read alongside null sources (Jaeckstein 2025, Ma 2025, Feng 2025) and negative sources (the retained evidence base). This comparison keeps the discussion from converting selected favorable findings into a generalized anti-aging conclusion.

The practical implication is a calibrated research position. Cold Exposure Brown Fat may justify further targeted testing when the mechanistic rationale, clinical endpoint, and population risk profile align, but the present corpus does not justify claims that ignore the null or adverse parts of the evidence base.

The favorable evidence should therefore be read as endpoint-specific rather than global. Signals in immune can justify continued mechanistic and clinical follow-up, but they do not cancel null results in contextual adjacent evidence, cardiometabolic, immune or adverse results in no dominant outcome class. That distinction is especially important for aging claims, where a short-term biomarker shift is not equivalent to a durable improvement in function, disability, morbidity, or survival.

The most useful next trial would make this boundary explicit: predefine the endpoint layer, preserve clinically relevant function while testing metabolic benefit, track adherence over long enough follow-up to detect decay, and report null or negative results with the same prominence as favorable signals. A study designed this way would test the tradeoff directly instead of asking readers to infer it across heterogeneous populations, comparators, and outcome definitions.

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.

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 can 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 interpretation calibrates confidence, clinical meaning, generalizability, and unresolved study-design needs. 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.

Confidence calibration

The most cautious reading is that the evidence may support a bounded and context-dependent interpretation, but it might not generalize across populations, endpoints, doses, or follow-up windows without additional direct tests. The pattern suggests biological plausibility where it is consistent with the retained sources, yet it appears qualified by uncertainty, limited directness, and preliminary evidence in several domains. A cautious interpretive stance is therefore warranted: what remains to be established is whether the observed signals travel cleanly from mechanism or adjacent evidence into the target clinical or organizational outcome.

Resolution criteria: The thesis would be reinforced by adequately powered trials with pre-specified clinical endpoints, ≥2-year follow-up, intention-to-treat and per-protocol analyses, and concurrent biomarker plus functional measurement. It would be falsified by replicated null findings on those endpoints or by demonstration that any short-term benefit reverses on intervention withdrawal.

Conclusion

The final interpretation is deliberately tiered. Cold Exposure Brown Fat has a biologically plausible geroscience rationale and selected clinical signals, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence.

The strongest interpretation is that positive signals in immune coexist with null signals in contextual adjacent evidence, cardiometabolic, immune and negative signals in no dominant outcome class. That profile supports further targeted research and careful hypothesis refinement, not unqualified clinical or public-health claims.

The current corpus may support cold exposure brown fat as a general health or lifestyle intervention where otherwise indicated, but does not justify marketing it as a standalone geroprotective or anti-aging intervention with proven hard-longevity effects. The safer translation path is a registered trial that specifies the endpoint layer in advance, pairs dosing with monitoring for metabolic and immune safety, and reports null or adverse signals with the same visibility as favorable results.

In animal/preclinical evidence, future work should prioritize studies that connect mechanistic studies (Ma 2025, Lyons 2024, Sarmiento-Ortega 2025) to direct clinical outcomes represented by the retained evidence base. Until that bridge is stronger, cold exposure brown fat remains a promising but bounded geroscience case whose most useful contribution is to define the next trial rather than to justify current clinical adoption.

The decisive unresolved question is not whether the intervention can move selected biomarkers or pathway markers, but whether those changes improve durable human function without offsetting harm, adherence failure, or loss in another clinically relevant domain. That question should set the bar for future claims, clinical translation, future study design, and any public recommendation.

Research Synthesis: Cold Exposure Brown Fat

Abstract

This paper synthesizes cold exposure brown fat as an aging-related intervention across 37 included source papers and 1333 high-confidence extracted claims.

The evidence profile contains no sources classified primarily as direct clinical evidence, 23 adjacent clinical sources, and 9 mechanistic or model-system sources, with 167 cross-study disagreements across the evidence base.

Positive study-level signals concentrate in immune, null signals in contextual adjacent evidence, cardiometabolic, immune, and negative signals in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect.

The conclusion is that cold exposure brown fat remains a bounded geroscience case: mechanistic plausibility and selected clinical signals justify further 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 does not automatically establish the same signal in another.

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.

Introduction

As global life expectancy continues to rise, the burden of age-related chronic diseases—cardiovascular disorders, metabolic syndrome, and immune dysfunction—has become the central challenge for modern healthcare systems. The question of whether interventions targeting fundamental aging biology can compress morbidity and extend healthspan, rather than merely treating individual diseases, has gained urgent clinical and policy significance. Brown adipose tissue (BAT), long recognized as a thermogenic organ, has emerged as a potential mediator of systemic metabolic and inflammatory homeostasis, raising the question of whether Cold exposure brown fat activation might modulate age-related physiological decline. Evidence suggests that UCP1 expression in human brown adipose tissue is inversely associated with cardiometabolic risk factors (Kwok 2024), yet the translational relevance of this association to hard clinical endpoints remains uncertain. Furthermore, the recent demonstration that Cold exposure brown fat may be linked to longevity pathways (Zhang 2024) has intensified interest, even as the boundary conditions for such effects are poorly defined. This synthesis therefore asks: what is the current state of evidence that Cold exposure brown fat can modulate aging-relevant outcomes, and where do critical gaps remain?

The geroscience hypothesis posits that targeting the core hallmarks of aging—mitochondrial dysfunction, chronic inflammation, and metabolic dysregulation—offers a more efficient therapeutic strategy than addressing individual age-related pathologies in isolation. Within this framework, Cold exposure brown fat represents a compelling but complex intervention class, as brown adipose tissue activation appears to engage multiple aging-relevant pathways simultaneously. Preclinical evidence indicates that BAT-derived extracellular vesicles can regulate hepatocyte mitochondrial activity (Zhang 2025b), while cold exposure itself stimulates cross-tissue metabolic rewiring to fuel glucose-dependent thermogenesis in brown adipose tissue (Cutler 2025). The logic of repurposing or mimicking cold exposure as a geroprotective strategy rests on its capacity to simultaneously modulate energy expenditure, inflammation, and insulin sensitivity, though the magnitude and durability of these effects in humans remain uncertain. Moreover, the observation that brown adipose tissue plays a central role in systemic inflammation-induced sleep responses (Szentirmai 2018) suggests that Cold exposure brown fat may intersect with circadian and neuroimmune axes relevant to aging. Whether this mechanistic plausibility translates into clinically meaningful healthspan extension is the central question that the current evidence base must address.

The biological rationale for Cold exposure brown fat as a therapeutic target rests on its capacity to induce non-shivering thermogenesis and metabolic activation, yet this tissue is subject to significant degeneration and environmental vulnerability. Purinergic adipocyte-macrophage crosstalk has been shown to promote degeneration of thermogenic brown adipose tissue (Jaeckstein 2025), suggesting that chronic inflammation may undermine the very organ cold exposure seeks to activate. Simultaneously, environmental pollutants such as cadmium can induce histological and functional alterations in BAT at minimal risk doses (Sarmiento-Ortega 2025), raising concerns about the confounding effects of real-world exposures on Cold exposure brown fat function. Furthermore, preclinical work demonstrates that semaglutide and tirzepatide exert distinct effects on metabolic and inflammatory gene expression in brown adipose tissue of mice fed a high-fat, high-fructose diet (Ma 2025), suggesting that even established metabolic drugs may modulate Cold exposure brown fat in heterogeneous ways. The question of whether pharmacological or behavioral cold exposure strategies can reliably engage BAT in diverse human populations, and whether such engagement confers net clinical benefit, therefore remains open.

The human evidence landscape for Cold exposure brown fat interventions spans a heterogeneous mix of observational cohorts, mechanistic studies, and a small number of controlled trials, but large-scale randomized clinical trials with hard endpoints are notably absent. However, most available human data derive from acute exposure paradigms in young, lean volunteers; for example, cold exposure and thermoneutrality were found to similarly reduce supraclavicular brown adipose tissue fat fraction in fasted young lean adults (Eenige 2025), a finding that complicates straightforward interpretations of BAT activation. A proof-of-concept trial in axial spondyloarthritis demonstrated that an add-on training program involving breathing exercises, cold exposure, and meditation attenuated inflammation, with significant reductions in hs-CRP (Buijze 2019), though the contribution of BAT-specific mechanisms was not isolated. The heterogeneity in study designs, populations, and endpoints suggests that the clinical translation of Cold exposure brown fat remains in its early stages, and the question of whether BAT activation per se drives observed benefits, as opposed to confounded or pleiotropic effects, is unresolved.

Several critical questions remain unresolved regarding the translation of Cold exposure brown fat biology into clinical benefit. The mechanistic distinction between BAT activation and broader cold-stress responses is poorly delineated; for instance, cold exposure modulates potential brown adipokines in humans, but only FGF21 is consistently associated with BAT volume (Mendez-Gutierrez 2024), suggesting that most measured circulating factors may be epiphenomenal. Population specificity further complicates the picture, as UCP1 expression in BAT is inversely associated with cardiometabolic risk factors (Kwok 2024), yet the determinants of this association—age, sex, ethnicity, comorbidity burden—are not well characterized across studies. The dose-response and duration dimensions of Cold exposure brown fat exposure remain largely unexplored in humans; most studies employ acute or short-term protocols, and whether chronic, habitual cold exposure yields cumulative benefits or adaptive tolerance is unclear. Preclinical work in Arctic adults suggests that habitual cold exposure may preserve BAT activity (Jensen 2025), but direct evidence in non-Arctic, older, or metabolically compromised populations is sparse. The question of whether Cold exposure brown fat interventions carry meaningful risks—such as cardiovascular strain, immune perturbation, or environmental pollutant interactions—also requires systematic evaluation before clinical recommendations can be made.

In animal/preclinical evidence, this synthesis aims to address the cross-domain tensions inherent in the Cold exposure brown fat evidence base by applying structured evidence weighting that separates mechanistic plausibility from clinical efficacy claims. Across the curated literature, positive signals appear primarily in immune and inflammatory outcomes, while null or conflicting findings dominate in cardiometabolic and contextual domains, yielding a context-dependent evidence profile. The identification of cross-study disagreements across outcome classes underscores the fragmented nature of the field and the need for explicit adjudication frameworks. By separating mechanistic studies—such as those demonstrating that CXCL13 promotes thermogenesis via M2 macrophage recruitment and inflammation inhibition in BAT (Xie 2023)—from clinical outcome studies, this synthesis seeks to clarify where Cold exposure brown fat biology offers genuine translational promise versus where extrapolation has outpaced evidence. The contribution of this work is therefore not to adjudicate whether Cold exposure brown fat extends healthspan, but to map the evidence landscape rigorously, identify the specific boundary conditions under which benefit or harm may emerge, and delineate the trial designs and endpoints most likely to resolve current uncertainties. In doing so, we aim to inform both the design of future clinical investigations and the responsible communication of Cold exposure brown fat science to researchers, clinicians, and the public.

Background

In animal/preclinical evidence, the background evidence for cold exposure brown fat is heterogeneous rather than uniformly confirmatory. Direct clinical sources such as the retained evidence base are interpreted separately from mechanistic studies such as Ma 2025, Lyons 2024, Sarmiento-Ortega 2025, 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 immune; null signals around contextual adjacent evidence, cardiometabolic, immune; and negative or adverse signals around no dominant outcome class. This pattern motivates a synthesis that keeps outcome domains separate before drawing cross-domain interpretation.

The resulting paper is therefore a calibrated synthesis: it can identify plausible mechanisms, direct clinical signals, 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.

Evidence Context

The evidence context combines established clinical use, adjacent human evidence, animal or cellular mechanisms, and open translational questions. Separating those evidence types prevents later sections from collapsing unlike forms of support into a single verdict. The central research problem remains whether mechanistic plausibility and source-traced findings converge strongly enough to justify further clinical testing while keeping patient-facing claims conservative.

Methods

Review type and protocol

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: 2026-05-29.

Search strategy

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

Eligibility criteria

Sources whose primary content addresses cold exposure brown fat.
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

source admission funnel

Admission bucket	n
Receipt candidate union	163
Classified source candidates	43
No extractable claims	45
None-only claim binding	10
Partial/none-only claim binding	50
Partial-only candidates	9
Strict high-confidence sources	6
Admitted final sources	37

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

Risk-of-bias appraisal

Synthesis approach

AI-use disclosure

Accountability

Results

Outcome-class note: Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.

Outcome class	Corpus slice	Strongest signal	Directness	Main limitation
Contextual Adjacent Evidence	n=15; claims=469	null signal in 14/15 sources	11 indirect; 1 mechanistic; 3 review	limited corpus depth in this outcome class
Cardiometabolic	n=11; claims=236	null signal in 9/11 sources	8 indirect; 3 mechanistic	limited corpus depth in this outcome class
Immune	n=4; claims=380	null signal in 2/4 sources	2 indirect; 2 review	limited corpus depth in this outcome class
Immune and Inflammation	n=2; claims=74	null signal in 2/2 sources	1 indirect; 1 mechanistic	limited corpus depth in this outcome class
Safety and Comorbidity	n=2; claims=96	null signal in 2/2 sources	2 mechanistic	limited corpus depth in this outcome class
Dosing and Pharmacokinetics	n=1; claims=62	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating
Longevity	n=1; claims=11	null signal in 1/1 sources	1 indirect	single-source slice; hypothesis-generating
Muscle Function	n=1; claims=5	null signal in 1/1 sources	1 mechanistic	single-source slice; hypothesis-generating

Cardiometabolic Outcomes

Contextual Adjacent Evidence Outcomes

Dosing and Pharmacokinetics Outcomes

Immune Outcomes

Immune and Inflammation Outcomes

Safety and Comorbidity Outcomes

Longevity Outcomes

Longevity remains a separate Results slice (n=1; claims=11; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.

Muscle Function Outcomes

Cross-Domain Synthesis

The most persistent cross-domain tension in the cold exposure and brown adipose tissue (BAT) literature lies between the robust mechanistic evidence for thermogenic activation and the near-universal null signal on cardiometabolic endpoints in human observational studies. Preclinical work by Sankina 2024 demonstrates that topical menthol, a pharmacological cold mimic, induces adaptive thermogenesis and BAT activation in mice, while Mercer 1984 shows that even in genetically obese (ob/ob) mice, cold exposure can activate BAT thermogenesis via mechanisms involving insulin signaling and GDP binding. This mechanistic plausibility is further supported by Cutler 2025, who reports that cold exposure stimulates cross-tissue metabolic rewiring to fuel glucose-dependent thermogenesis in BAT (P < 0.05). However, when translated to human populations, the cardiometabolic evidence base collapses into a consistent null pattern. The boundary condition likely involves species-specific differences in BAT depot abundance and sympathetic nervous system regulation: adult humans retain substantially less functional BAT than mice, and the metabolic contribution of activated human BAT to whole-body energy expenditure may be insufficient to meaningfully alter cardiometabolic risk markers. Resolving this tension would require large-scale, long-duration human RCTs that measure hard cardiometabolic endpoints (e.g., incident cardiovascular events, HbA1c trajectories) rather than surrogate markers like 18F-FDG uptake or skin temperature, with pre-specified analyses stratifying by baseline BAT volume.

Another critical tension emerges between the immune-modulatory narrative — where the most positive human signals appear — and the biological plausibility of BAT as a direct immune regulator. Mendez-Gutierrez 2024, a systematic review, finds that cold exposure modulates potential brown adipokines in humans, but only FGF21 is consistently associated with BAT volume, and the immune-outcome direction remains unclear. The tension is thus between BAT as a source of protective immune signals (Nrg4, CXCL13-mediated M2 polarization) and BAT as a site where immune activation — particularly purinergic macrophage infiltration — accelerates thermogenic tissue loss. The boundary condition may depend on the acute versus chronic nature of immune stimulation: acute cold-induced immune activation may be beneficial, while chronic inflammatory infiltration may be degenerative. Evidence to resolve this would include longitudinal human studies tracking BAT volume alongside serial immune biomarkers, distinguishing adaptive immune engagement from maladaptive inflammation.

Another cross-domain tension concerns the gap between preclinical longevity claims and the available human evidence for BAT-mediated healthspan extension. Zhang 2024 reviews the role of BAT in mediating healthful longevity, noting that BAT transplantation increases longevity and exercise performance in mice with disruption of RGS14, and references a recent clinical cohort suggesting BAT activity may correlate with longevity markers. Zhu 2025 provides mechanistic evidence that pathophysiologically relevant bisphenol S exposure accelerates aging by disrupting BAT-regulated energy metabolism in mice (transcriptome sequencing, n = 8 vehicle control, n = 7 BPS-exposed). These preclinical findings suggest a causal link between BAT function and aging trajectories. However, the human evidence is restricted to observational and cross-sectional designs with no longitudinal mortality or healthspan endpoints. The boundary condition here is clear: model-organism lifespan extension through BAT transplantation or genetic manipulation cannot be directly extrapolated to human longevity because (a) the magnitude of BAT's contribution to total energy expenditure in adult humans is far smaller, (b) human BAT volume declines substantially with age, and (c) the transplant paradigm is not clinically translatable. Zhang 2024's longevity claim, while intriguing at the model-organism level, remains unsubstantiated in human populations. Resolution would require prospective cohort studies with mortality as the primary endpoint, quantifying BAT activity via validated imaging and adjusting for confounders such as BMI, physical activity, and cardiometabolic comorbidity.

Another tension involves the conflict between surrogate BAT-activity markers and the broader contextual evidence questioning whether these surrogates meaningfully track with clinical benefit. These findings support the view that BAT activity markers (FDG uptake, UCP1 expression, thermographic signatures) serve as valid surrogates for cardiometabolic health. Yet the tension arises when these surrogate associations are examined against evidence that the surrogates themselves may be unstable or context-dependent. The boundary condition is that surrogate markers may be informative for within-individual longitudinal tracking but unreliable for cross-sectional risk prediction or population-level inference. Ioannidis 2005 cautions that surrogate associations do not guarantee hard-outcome validity — a principle directly applicable here. Resolving this tension demands intervention trials that pair BAT-activity surrogates with hard clinical endpoints, quantifying whether changes in BAT imaging markers predict downstream cardiometabolic events.

A fifth, often-overlooked tension lies between the environmental safety literature and the therapeutic BAT-activation narrative, raising the question of whether BAT stimulation is inherently beneficial or can be co-opted by pathological processes. These findings imply that BAT is a responsive tissue that can be perturbed by exogenous toxicants, and that the same metabolic pathways targeted by therapeutic cold exposure may also be disrupted by environmental stressors. The tension here is between BAT activation as a health-promoting intervention and BAT as a vulnerability window to environmental insults. The boundary condition may relate to the nature of the stimulus: endogenous cold exposure may activate protective pathways (e.g., UCP1-mediated uncoupling), while exogenous chemical stressors may trigger maladaptive responses (e.g., inflammation, mitochondrial dysfunction). Szentirmai 2018 adds that BAT plays a central role in systemic inflammation-induced sleep responses in mice, further illustrating that BAT's role extends beyond simple energy expenditure into complex systemic regulation. This cross-domain conflict matters because if BAT-activation therapies move toward clinical implementation, safety profiling must account for potential interactions with environmental exposures that simultaneously target BAT. Resolution would require mechanistic studies distinguishing adaptive from maladaptive BAT activation pathways, and epidemiological data on whether individuals with higher BAT activity are differentially susceptible to environmental toxicant exposure.

Metabolic-Functional Tradeoff Framework

We operationalize a Metabolic-Functional Tradeoff 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 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 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

Interpretation constraints

Confidence calibration

Limitations

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

Conclusion

What This Synthesis Adds

This synthesis maps 37 included sources on Cold exposure brown fat across 8 outcome classes and 167 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.

Prior reviews in the corpus (Shojaei 2025, Heimburger 2022, Mendez-Gutierrez 2024) emphasize convergent signals on Cold exposure brown fat. 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

Outcome class	Indirect / mechanism sources	Direction profile	Interpretation boundary
cardiometabolic	11	null, unclear	direct clinical gap
immune	4	null, positive, unclear	direct clinical gap
longevity	1	null	direct clinical gap
muscle function	1	null	direct clinical gap
contextual adjacent evidence	15	null, unclear	direct clinical gap
dosing and pharmacokinetics	1	null	direct clinical gap
immune and inflammation	2	null	direct clinical gap
safety and comorbidity	2	null	direct clinical gap

Evidence-Gap Priority

Priority	Gap	Rationale
P1	cardiometabolic: direct clinical gap	0 direct and 11 indirect sources; direction profile: null, unclear
P2	immune: direct clinical gap	0 direct and 4 indirect sources; direction profile: null, positive, unclear
P3	longevity: direct clinical gap	0 direct and 1 indirect source; direction profile: null
P4	muscle function: direct clinical gap	0 direct and 1 indirect source; direction profile: null
P5	contextual adjacent evidence: direct clinical gap	0 direct and 15 indirect sources; direction profile: null, unclear

Next-Study Design Recommendation

The next high-yield study for Cold exposure brown fat should target the cardiometabolic 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.

Structured Evidence Tables

The following tables present the structured evidence summary referenced throughout this paper. Numbers live in the tables; prose references them. Tables 1-3 cover included studies, per-study endpoint evidence, and cross-domain tensions; Table 4 is a supplemental design-level evidence weighting heuristic; Table 5 surfaces the underlying per-paper numeric index.

Table 1: Included Studies

Citation	Design	Tier	N	Population	Endpoint	Direction	Directness	Trial ID	Representative p-value	n claims
Jaeckstein 2025	Observational	B2	—	adults	immune	null	indirect	—	P = 0.0006	281
Ma 2025	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	contextual other	null	mechanistic	—	P < 0.001	95
Feng 2025	Observational	B2	—	adults	immune	null	indirect	—	P < 0.001	82
Kwok 2024	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.001	72
Lyons 2024	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	safety comorbidity	null	mechanistic	—	P < 0.0001	67
Cal 2025	Observational	B2	—	adults	cardiometabolic	null	indirect	—	—	63
Sarmiento-Ortega 2025	Preclinical (animal/in vitro)	C1	—	adults	dosing pharmacokinetics	null	mechanistic	—	P < 0.0001	62
Neal 2026	Observational	B2	—	older adults	contextual other	null	review	—	P < 0.001	59
Eenige 2025	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.001	43
Ishida 2025	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.001	42
Buijze 2019	Observational	B2	—	adults	immune inflammation	null	indirect	—	P = 0.040	40
Acosta 2019	Observational	B2	—	adults	cardiometabolic	null	indirect	—	p ≤ 0.01	36
Xie 2023	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	immune inflammation	null	mechanistic	—	P < 0.001	34
Zhang 2024	Observational	B2	—	adults	contextual other	null	indirect	—	—	33
Rosa 2024	Observational	B2	—	adults	cardiometabolic	null	indirect	—	P < 0.000	29
Migliaccio 2024	Preclinical (animal/in vitro)	C1	—	adults	safety comorbidity	null	mechanistic	—	P < 0.0001	29
Furuuchi 2024	Observational	B2	—	adults	contextual other	null	indirect	—	P = 0.021	29
Zhang 2025	Observational	B2	—	adults	cardiometabolic	null	indirect	—	P < 0.0001	28
Gao 2025	Observational	B2	—	adults	cardiometabolic	null	indirect	—	—	23
Cutler 2025	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.05	22
Shojaei 2025	Review / meta-analysis	B1	—	—	contextual other	null	review	—	P < 0.001	21
Zou 2026	Observational	B2	—	adults	cardiometabolic	null	indirect	—	P < 0.001	21
Gong 2025	Observational	B2	—	adults	cardiometabolic	null	indirect	—	P < 0.001	15
Zhang 2025b	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	cardiometabolic	null	mechanistic	—	P < 0.0001	13
Yoneshiro 2025	Observational	B2	—	adults	contextual other	unclear	indirect	—	—	12
Yamada 2022	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.001	12
Zhu 2025	Observational	B2	—	adults	longevity	null	indirect	—	—	11
Heimburger 2022	Review / meta-analysis	B1	—	type 2 diabetes patients	immune	positive	review	—	P = 0.000072	11
Li 2025	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.01	10
Jensen 2025	Observational	B2	—	—	contextual other	null	review	—	P < 0.001	10
Mendez-Gutierrez 2024	Review / meta-analysis	B1	—	adults	immune	unclear	review	—	—	6
Ostarijas 2025	Observational	B2	—	adults	contextual other	null	indirect	—	P < 0.2	6
Alcala 2017	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	muscle function	null	mechanistic	—	P = 0.003	5
Szentirmai 2018	Observational	B2	—	adults	cardiometabolic	null	indirect	—	—	5
Chen 2024	Observational	B2	—	adults	contextual other	null	indirect	—	—	3
Sankina 2024	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	cardiometabolic	unclear	mechanistic	—	—	2
Mercer 1984	Preclinical (animal/in vitro)	C1	—	mice (preclinical)	cardiometabolic	unclear	mechanistic	—	—	1

Table 2: Per-Study Endpoint Evidence

Additional corpus sources included animal/preclinical evidence; | Endpoint | Study | p/CI | Direction | Directness | Tier | Interpretation | | --- | --- | --- | --- | --- | --- | --- | | immune | Jaeckstein 2025 | P = 0.0055 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Jaeckstein 2025 | P = 0.0212 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Jaeckstein 2025 | P = 0.0303 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Jaeckstein 2025 | P = 0.0006 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Jaeckstein 2025 | P = 0.0007 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Jaeckstein 2025 | P = 0.0255 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.01 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Ma 2025 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune | Feng 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Kwok 2024 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Kwok 2024 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Kwok 2024 | P = 0.11 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Kwok 2024 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Kwok 2024 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Kwok 2024 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P = 0.02 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P = 0.007 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P < 0.05 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P < 0.05 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Lyons 2024 | P > 0.05 | null summary | mechanistic | C1 | reported statistic; source summary remains null | | cardiometabolic | Cal 2025 | — | null | indirect | B2 | no significant effect on cardiometabolic | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | p ≤ 0.05 | null summary | mechanistic | C1 | reported statistic; source summary remains null | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | dosing pharmacokinetics | Sarmiento-Ortega 2025 | P = 0.0075 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Neal 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Neal 2026 | P = 0.650 | null summary | review | B2 | reported statistic; source summary remains null | | contextual other | Neal 2026 | P = 0.152 | null summary | review | B2 | reported statistic; source summary remains null | | contextual other | Neal 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Neal 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Neal 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Eenige 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Eenige 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Eenige 2025 | P = 0.55 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Eenige 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Eenige 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Eenige 2025 | P = 0.92 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Ishida 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ishida 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ishida 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ishida 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ishida 2025 | P = 0.031 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Ishida 2025 | P = 0.020 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune inflammation | Buijze 2019 | P = 0.040 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune inflammation | Buijze 2019 | P = 0.406 | null summary | indirect | B2 | reported statistic; source summary remains null | | immune inflammation | Buijze 2019 | P = 0.044 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune inflammation | Buijze 2019 | P = 0.064 | null summary | indirect | B2 | reported statistic; source summary remains null | | immune inflammation | Buijze 2019 | P = 0.182 | null summary | indirect | B2 | reported statistic; source summary remains null | | immune inflammation | Buijze 2019 | P = 0.268 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | p ≤ 0.011 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | p ≤ 0.02 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | p ≤ 0.01 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | p ≤ 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | p ≤ 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Acosta 2019 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | immune inflammation | Xie 2023 | P < 0.05 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | immune inflammation | Xie 2023 | P < 0.01 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | immune inflammation | Xie 2023 | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | immune inflammation | Xie 2023 | P < 0.05 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | immune inflammation | Xie 2023 | P < 0.01 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Zhang 2024 | — | null | indirect | B2 | no significant effect on contextual other | | cardiometabolic | Rosa 2024 | P < 0.005 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Rosa 2024 | P < 0.000 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Rosa 2024 | P < 0.000 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Rosa 2024 | P < 0.000 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Rosa 2024 | P < 0.000 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | safety comorbidity | Migliaccio 2024 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Migliaccio 2024 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | safety comorbidity | Migliaccio 2024 | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Furuuchi 2024 | P = 0.029 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Furuuchi 2024 | P = 0.029 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Furuuchi 2024 | P = 0.329 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Furuuchi 2024 | P = 0.958 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Furuuchi 2024 | P = 0.021 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Furuuchi 2024 | P = 0.994 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Zhang 2025 | P < 0.0001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Gao 2025 | — | null | indirect | B2 | no significant effect on cardiometabolic | | contextual other | Cutler 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P < 0.001 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P < 0.05 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P < 0.01 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P = 0.02 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P = 0.03 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | contextual other | Shojaei 2025 | P < 0.01 | significant statistic | review | B1 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zou 2026 | P > 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null | | cardiometabolic | Gong 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Gong 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Gong 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.01 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.0001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.001 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Zhang 2025b | P < 0.01 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | contextual other | Yoneshiro 2025 | — | unclear | indirect | B2 | unclear effect on contextual other | | contextual other | Yamada 2022 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Yamada 2022 | P = 0.022 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Yamada 2022 | P = 0.035 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Yamada 2022 | P = 0.031 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | longevity | Zhu 2025 | — | null | indirect | B2 | no significant effect on longevity | | immune | Heimburger 2022 | P = 0.0005 | positive summary | review | B1 | reported statistic; source summary remains positive | | immune | Heimburger 2022 | P = 0.009 | positive summary | review | B1 | reported statistic; source summary remains positive | | immune | Heimburger 2022 | P = 0.000072 | positive summary | review | B1 | reported statistic; source summary remains positive | | contextual other | Li 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Li 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Li 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Li 2025 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Li 2025 | P > 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null | | contextual other | Li 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P = 0.015 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P = 0.021 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P = 0.026 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | contextual other | Jensen 2025 | P = 0.037 | significant statistic | review | B2 | significant statistic; source-level direction remains null | | immune | Mendez-Gutierrez 2024 | — | unclear | review | B1 | unclear effect on immune | | contextual other | Ostarijas 2025 | P < 0.2 | null summary | indirect | B2 | reported statistic; source summary remains null | | muscle function | Alcala 2017 | P = 0.003 | significant statistic | mechanistic | C1 | significant statistic; source-level direction remains null | | cardiometabolic | Szentirmai 2018 | — | null | indirect | B2 | no significant effect on cardiometabolic | | contextual other | Chen 2024 | — | null | indirect | B2 | no significant effect on contextual other | | cardiometabolic | Sankina 2024 | — | unclear | mechanistic | C1 | unclear effect on cardiometabolic | | cardiometabolic | Mercer 1984 | — | unclear | mechanistic | C1 | unclear effect on cardiometabolic |

Table 3: Cross-Domain Tensions

Additional corpus sources included animal/preclinical evidence; | Tension kind | Severity | source A | source B | Outcome class | Summary | Practical implication | | --- | --- | --- | --- | --- | --- | --- | | null vs positive | 3 | Mendez-Gutierrez 2024 | Feng 2025 | immune | Mendez-Gutierrez 2024 (unclear) vs Feng 2025 (null) on immune | null vs positive (notable) | | null vs positive | 3 | Mendez-Gutierrez 2024 | Jaeckstein 2025 | immune | Mendez-Gutierrez 2024 (unclear) vs Jaeckstein 2025 (null) on immune | null vs positive (notable) | | agreement | 1 | Mercer 1984 | Sankina 2024 | cardiometabolic | Mercer 1984 (unclear) vs Sankina 2024 (unclear) on cardiometabolic | agreement (minor) | | null vs positive | 3 | Mercer 1984 | Rosa 2024 | cardiometabolic | Mercer 1984 (unclear) vs Rosa 2024 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Gong 2025 | cardiometabolic | Mercer 1984 (unclear) vs Gong 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Zhang 2025b | cardiometabolic | Mercer 1984 (unclear) vs Zhang 2025b (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Cal 2025 | cardiometabolic | Mercer 1984 (unclear) vs Cal 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Gao 2025 | cardiometabolic | Mercer 1984 (unclear) vs Gao 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Zhang 2025 | cardiometabolic | Mercer 1984 (unclear) vs Zhang 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Zou 2026 | cardiometabolic | Mercer 1984 (unclear) vs Zou 2026 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Szentirmai 2018 | cardiometabolic | Mercer 1984 (unclear) vs Szentirmai 2018 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Mercer 1984 | Acosta 2019 | cardiometabolic | Mercer 1984 (unclear) vs Acosta 2019 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Rosa 2024 | cardiometabolic | Sankina 2024 (unclear) vs Rosa 2024 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Gong 2025 | cardiometabolic | Sankina 2024 (unclear) vs Gong 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Zhang 2025b | cardiometabolic | Sankina 2024 (unclear) vs Zhang 2025b (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Cal 2025 | cardiometabolic | Sankina 2024 (unclear) vs Cal 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Gao 2025 | cardiometabolic | Sankina 2024 (unclear) vs Gao 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Zhang 2025 | cardiometabolic | Sankina 2024 (unclear) vs Zhang 2025 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Zou 2026 | cardiometabolic | Sankina 2024 (unclear) vs Zou 2026 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Szentirmai 2018 | cardiometabolic | Sankina 2024 (unclear) vs Szentirmai 2018 (null) on cardiometabolic | null vs positive (notable) | | null vs positive | 3 | Sankina 2024 | Acosta 2019 | cardiometabolic | Sankina 2024 (unclear) vs Acosta 2019 (null) on cardiometabolic | null vs positive (notable) | | agreement | 1 | Xie 2023 | Buijze 2019 | immune inflammation | Xie 2023 (null) vs Buijze 2019 (null) on immune inflammation | agreement (minor) | | agreement | 1 | Rosa 2024 | Gong 2025 | cardiometabolic | Rosa 2024 (null) vs Gong 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Zhang 2025b | cardiometabolic | Rosa 2024 (null) vs Zhang 2025b (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Cal 2025 | cardiometabolic | Rosa 2024 (null) vs Cal 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Gao 2025 | cardiometabolic | Rosa 2024 (null) vs Gao 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Zhang 2025 | cardiometabolic | Rosa 2024 (null) vs Zhang 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Zou 2026 | cardiometabolic | Rosa 2024 (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Szentirmai 2018 | cardiometabolic | Rosa 2024 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Rosa 2024 | Acosta 2019 | cardiometabolic | Rosa 2024 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Chen 2024 | Kwok 2024 | contextual other | Chen 2024 (null) vs Kwok 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Zhang 2024 | contextual other | Chen 2024 (null) vs Zhang 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Furuuchi 2024 | contextual other | Chen 2024 (null) vs Furuuchi 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Eenige 2025 | contextual other | Chen 2024 (null) vs Eenige 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Ostarijas 2025 | contextual other | Chen 2024 (null) vs Ostarijas 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Ishida 2025 | contextual other | Chen 2024 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Chen 2024 | Yoneshiro 2025 | contextual other | Chen 2024 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Chen 2024 | Li 2025 | contextual other | Chen 2024 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Cutler 2025 | contextual other | Chen 2024 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Jensen 2025 | contextual other | Chen 2024 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Ma 2025 | contextual other | Chen 2024 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Shojaei 2025 | contextual other | Chen 2024 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Neal 2026 | contextual other | Chen 2024 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Chen 2024 | Yamada 2022 | contextual other | Chen 2024 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Lyons 2024 | Migliaccio 2024 | safety comorbidity | Lyons 2024 (null) vs Migliaccio 2024 (null) on safety comorbidity | agreement (minor) | | agreement | 1 | Kwok 2024 | Zhang 2024 | contextual other | Kwok 2024 (null) vs Zhang 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Furuuchi 2024 | contextual other | Kwok 2024 (null) vs Furuuchi 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Eenige 2025 | contextual other | Kwok 2024 (null) vs Eenige 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Ostarijas 2025 | contextual other | Kwok 2024 (null) vs Ostarijas 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Ishida 2025 | contextual other | Kwok 2024 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Kwok 2024 | Yoneshiro 2025 | contextual other | Kwok 2024 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Kwok 2024 | Li 2025 | contextual other | Kwok 2024 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Cutler 2025 | contextual other | Kwok 2024 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Jensen 2025 | contextual other | Kwok 2024 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Ma 2025 | contextual other | Kwok 2024 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Shojaei 2025 | contextual other | Kwok 2024 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Neal 2026 | contextual other | Kwok 2024 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Kwok 2024 | Yamada 2022 | contextual other | Kwok 2024 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Furuuchi 2024 | contextual other | Zhang 2024 (null) vs Furuuchi 2024 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Eenige 2025 | contextual other | Zhang 2024 (null) vs Eenige 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Ostarijas 2025 | contextual other | Zhang 2024 (null) vs Ostarijas 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Ishida 2025 | contextual other | Zhang 2024 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Zhang 2024 | Yoneshiro 2025 | contextual other | Zhang 2024 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Zhang 2024 | Li 2025 | contextual other | Zhang 2024 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Cutler 2025 | contextual other | Zhang 2024 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Jensen 2025 | contextual other | Zhang 2024 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Ma 2025 | contextual other | Zhang 2024 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Shojaei 2025 | contextual other | Zhang 2024 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Neal 2026 | contextual other | Zhang 2024 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2024 | Yamada 2022 | contextual other | Zhang 2024 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Eenige 2025 | contextual other | Furuuchi 2024 (null) vs Eenige 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Ostarijas 2025 | contextual other | Furuuchi 2024 (null) vs Ostarijas 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Ishida 2025 | contextual other | Furuuchi 2024 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Furuuchi 2024 | Yoneshiro 2025 | contextual other | Furuuchi 2024 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Furuuchi 2024 | Li 2025 | contextual other | Furuuchi 2024 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Cutler 2025 | contextual other | Furuuchi 2024 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Jensen 2025 | contextual other | Furuuchi 2024 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Ma 2025 | contextual other | Furuuchi 2024 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Shojaei 2025 | contextual other | Furuuchi 2024 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Neal 2026 | contextual other | Furuuchi 2024 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Furuuchi 2024 | Yamada 2022 | contextual other | Furuuchi 2024 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Ostarijas 2025 | contextual other | Eenige 2025 (null) vs Ostarijas 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Ishida 2025 | contextual other | Eenige 2025 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Eenige 2025 | Yoneshiro 2025 | contextual other | Eenige 2025 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Eenige 2025 | Li 2025 | contextual other | Eenige 2025 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Cutler 2025 | contextual other | Eenige 2025 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Jensen 2025 | contextual other | Eenige 2025 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Ma 2025 | contextual other | Eenige 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Shojaei 2025 | contextual other | Eenige 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Neal 2026 | contextual other | Eenige 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Eenige 2025 | Yamada 2022 | contextual other | Eenige 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Gong 2025 | Zhang 2025b | cardiometabolic | Gong 2025 (null) vs Zhang 2025b (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Cal 2025 | cardiometabolic | Gong 2025 (null) vs Cal 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Gao 2025 | cardiometabolic | Gong 2025 (null) vs Gao 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Zhang 2025 | cardiometabolic | Gong 2025 (null) vs Zhang 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Zou 2026 | cardiometabolic | Gong 2025 (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Szentirmai 2018 | cardiometabolic | Gong 2025 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gong 2025 | Acosta 2019 | cardiometabolic | Gong 2025 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Ishida 2025 | contextual other | Ostarijas 2025 (null) vs Ishida 2025 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Ostarijas 2025 | Yoneshiro 2025 | contextual other | Ostarijas 2025 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Ostarijas 2025 | Li 2025 | contextual other | Ostarijas 2025 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Cutler 2025 | contextual other | Ostarijas 2025 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Jensen 2025 | contextual other | Ostarijas 2025 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Ma 2025 | contextual other | Ostarijas 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Shojaei 2025 | contextual other | Ostarijas 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Neal 2026 | contextual other | Ostarijas 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Ostarijas 2025 | Yamada 2022 | contextual other | Ostarijas 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Ishida 2025 | Yoneshiro 2025 | contextual other | Ishida 2025 (null) vs Yoneshiro 2025 (unclear) on contextual other | null vs positive (notable) | | agreement | 1 | Ishida 2025 | Li 2025 | contextual other | Ishida 2025 (null) vs Li 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Cutler 2025 | contextual other | Ishida 2025 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Jensen 2025 | contextual other | Ishida 2025 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Ma 2025 | contextual other | Ishida 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Shojaei 2025 | contextual other | Ishida 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Neal 2026 | contextual other | Ishida 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Ishida 2025 | Yamada 2022 | contextual other | Ishida 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Yoneshiro 2025 | Li 2025 | contextual other | Yoneshiro 2025 (unclear) vs Li 2025 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Cutler 2025 | contextual other | Yoneshiro 2025 (unclear) vs Cutler 2025 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Jensen 2025 | contextual other | Yoneshiro 2025 (unclear) vs Jensen 2025 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Ma 2025 | contextual other | Yoneshiro 2025 (unclear) vs Ma 2025 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Shojaei 2025 | contextual other | Yoneshiro 2025 (unclear) vs Shojaei 2025 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Neal 2026 | contextual other | Yoneshiro 2025 (unclear) vs Neal 2026 (null) on contextual other | null vs positive (notable) | | null vs positive | 3 | Yoneshiro 2025 | Yamada 2022 | contextual other | Yoneshiro 2025 (unclear) vs Yamada 2022 (null) on contextual other | null vs positive (notable) | | agreement | 1 | Zhang 2025b | Cal 2025 | cardiometabolic | Zhang 2025b (null) vs Cal 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025b | Gao 2025 | cardiometabolic | Zhang 2025b (null) vs Gao 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025b | Zhang 2025 | cardiometabolic | Zhang 2025b (null) vs Zhang 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025b | Zou 2026 | cardiometabolic | Zhang 2025b (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025b | Szentirmai 2018 | cardiometabolic | Zhang 2025b (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025b | Acosta 2019 | cardiometabolic | Zhang 2025b (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Li 2025 | Cutler 2025 | contextual other | Li 2025 (null) vs Cutler 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Li 2025 | Jensen 2025 | contextual other | Li 2025 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Li 2025 | Ma 2025 | contextual other | Li 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Li 2025 | Shojaei 2025 | contextual other | Li 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Li 2025 | Neal 2026 | contextual other | Li 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Li 2025 | Yamada 2022 | contextual other | Li 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Cutler 2025 | Jensen 2025 | contextual other | Cutler 2025 (null) vs Jensen 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Cutler 2025 | Ma 2025 | contextual other | Cutler 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Cutler 2025 | Shojaei 2025 | contextual other | Cutler 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Cutler 2025 | Neal 2026 | contextual other | Cutler 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Cutler 2025 | Yamada 2022 | contextual other | Cutler 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Feng 2025 | Jaeckstein 2025 | immune | Feng 2025 (null) vs Jaeckstein 2025 (null) on immune | agreement (minor) | | null vs positive | 3 | Feng 2025 | Heimburger 2022 | immune | Feng 2025 (null) vs Heimburger 2022 (positive) on immune | null vs positive (notable) | | agreement | 1 | Jensen 2025 | Ma 2025 | contextual other | Jensen 2025 (null) vs Ma 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Jensen 2025 | Shojaei 2025 | contextual other | Jensen 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Jensen 2025 | Neal 2026 | contextual other | Jensen 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Jensen 2025 | Yamada 2022 | contextual other | Jensen 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Cal 2025 | Gao 2025 | cardiometabolic | Cal 2025 (null) vs Gao 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Cal 2025 | Zhang 2025 | cardiometabolic | Cal 2025 (null) vs Zhang 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Cal 2025 | Zou 2026 | cardiometabolic | Cal 2025 (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Cal 2025 | Szentirmai 2018 | cardiometabolic | Cal 2025 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Cal 2025 | Acosta 2019 | cardiometabolic | Cal 2025 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gao 2025 | Zhang 2025 | cardiometabolic | Gao 2025 (null) vs Zhang 2025 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gao 2025 | Zou 2026 | cardiometabolic | Gao 2025 (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gao 2025 | Szentirmai 2018 | cardiometabolic | Gao 2025 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Gao 2025 | Acosta 2019 | cardiometabolic | Gao 2025 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Ma 2025 | Shojaei 2025 | contextual other | Ma 2025 (null) vs Shojaei 2025 (null) on contextual other | agreement (minor) | | agreement | 1 | Ma 2025 | Neal 2026 | contextual other | Ma 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Ma 2025 | Yamada 2022 | contextual other | Ma 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Zhang 2025 | Zou 2026 | cardiometabolic | Zhang 2025 (null) vs Zou 2026 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025 | Szentirmai 2018 | cardiometabolic | Zhang 2025 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zhang 2025 | Acosta 2019 | cardiometabolic | Zhang 2025 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Shojaei 2025 | Neal 2026 | contextual other | Shojaei 2025 (null) vs Neal 2026 (null) on contextual other | agreement (minor) | | agreement | 1 | Shojaei 2025 | Yamada 2022 | contextual other | Shojaei 2025 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | null vs positive | 3 | Jaeckstein 2025 | Heimburger 2022 | immune | Jaeckstein 2025 (null) vs Heimburger 2022 (positive) on immune | null vs positive (notable) | | agreement | 1 | Neal 2026 | Yamada 2022 | contextual other | Neal 2026 (null) vs Yamada 2022 (null) on contextual other | agreement (minor) | | agreement | 1 | Zou 2026 | Szentirmai 2018 | cardiometabolic | Zou 2026 (null) vs Szentirmai 2018 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Zou 2026 | Acosta 2019 | cardiometabolic | Zou 2026 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) | | agreement | 1 | Szentirmai 2018 | Acosta 2019 | cardiometabolic | Szentirmai 2018 (null) vs Acosta 2019 (null) on cardiometabolic | agreement (minor) |

Table 4 (supplemental): Design-Level Evidence Weighting Heuristic

Per-domain grades are derived from each study's evidence tier (A1/A2/B1/B2/C1/C2) — they capture design-level limitations, NOT a formal per-paper risk-of-bias assessment from the source text. Domains follow design-family categories for randomized, observational, animal, and systematic-review evidence; n/a indicates the domain is not meaningful for that design (e.g. blinding for an observational cohort). The Weight in synthesis column is the qualitative weighting the synthesis applies to each source — derived from tier × directness × overall RoB.

Citation	Tier	Tool	Allocation	Blinding	Attrition	Outcome measurement	Reporting	Confounding control	Generalizability	Overall RoB	Weight in synthesis	Effect direction notes
Jaeckstein 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Ma 2025	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Feng 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Kwok 2024	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Lyons 2024	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Cal 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Sarmiento-Ortega 2025	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Neal 2026	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Eenige 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Ishida 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Buijze 2019	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Acosta 2019	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Xie 2023	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Zhang 2024	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Rosa 2024	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Migliaccio 2024	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Furuuchi 2024	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Zhang 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Gao 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Cutler 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Shojaei 2025	B1	AMSTAR-2 (review)	unclear	unclear	unclear	unclear	moderate	moderate	moderate	unclear	supporting (synthesis evidence)	primary endpoint did not reach significance
Zou 2026	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Gong 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Zhang 2025b	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Yoneshiro 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	signed claims without significance signal
Yamada 2022	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Zhu 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Heimburger 2022	B1	AMSTAR-2 (review)	unclear	unclear	unclear	unclear	moderate	moderate	moderate	unclear	supporting (synthesis evidence)	positive effect — see Tables 1/2
Li 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Jensen 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Mendez-Gutierrez 2024	B1	AMSTAR-2 (review)	unclear	unclear	unclear	unclear	moderate	moderate	moderate	unclear	supporting (synthesis evidence)	signed claims without significance signal
Ostarijas 2025	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Alcala 2017	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	primary endpoint did not reach significance
Szentirmai 2018	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Chen 2024	B2	ROBINS-I	n/a	n/a	moderate	moderate	moderate	high	moderate	moderate	contextual (observational signal)	primary endpoint did not reach significance
Sankina 2024	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	signed claims without significance signal
Mercer 1984	C1	SYRCLE (animal)	low	n/a	low	moderate	moderate	n/a	high	low	hypothesis-generating (preclinical mechanism)	signed claims without significance signal

Table 5 (supplemental): Per-Paper Numeric Index

Top-N quantitative claims per paper — the underlying corpus numerics that power Q2 trace and Q9 density. One row per (paper × claim) tuple, prioritised by claim type (p-value > percentage > ratio > unit-value).

Additional corpus sources included animal/preclinical evidence; | Citation | Section | Type | Value | Units | | --- | --- | --- | --- | --- | | Ma 2025 | results | p-value | P < 0.01 | — | | Ma 2025 | results | mean ± SD | 37.21 ± 2.83 | — | | Ma 2025 | results | mean ± SD | 28.65 ± 4.18 | — | | Ma 2025 | results | mean ± SD | 27.87 ± 4.03 | — | | Ma 2025 | results | p-value | P < 0.01 | — | | Shojaei 2025 | results | p-value | P < 0.01 | — | | Heimburger 2022 | abstract | p-value | P = 0.0005 | — | | Heimburger 2022 | abstract | percentage | 3.1% | % | | Heimburger 2022 | abstract | unit value | 8 years | years | | Heimburger 2022 | abstract | mean ± SD | 26 ± 8 | — | | Heimburger 2022 | abstract | mean ± SD | 23.8 ± 1.8 | — | | Mendez-Gutierrez 2024 | abstract | unit value | 0.98 ng/mL | ng/mL | | Mendez-Gutierrez 2024 | abstract | mean ± SD | 0.58 ± 0.98 | — | | Mendez-Gutierrez 2024 | abstract | mean ± SD | 19.63 ± 46.2 | — | | Mendez-Gutierrez 2024 | abstract | mean ± SD | 33.72 ± 55.13 | — | | Mendez-Gutierrez 2024 | abstract | mean ± SD | 1.98 ± 3.56 | — | | Sankina 2024 | abstract | percentage | 10% | % | | Sankina 2024 | abstract | unit value | 15 days | days | | Mercer 1984 | abstract | unit value | 35 days | days |

Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: WHO 2000.

References

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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).

WHO 2000. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. WHO Technical Report Series 894. 2000. PMID: 11234459.
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.

Proof Trail

Decision: AcceptLiving evidence briefGate failures: 0

Topic: research

Author: Dominic Lynch

Author ORCID: 0009-0005-4286-8363

Institution: not supplied

ROR: not supplied

RAiD: not supplied

OSF DOI: 10.17605/OSF.IO/5EKND

AI co-writer: agent-v3-full-paper

Reviewer: reviewer-panel

AI disclosure: Agent-generated artifact reviewed by Researka; not a clinical guideline or human-authored journal article.

Integrity check: not recorded

Published: May 29, 2026

Provenance chain: Available → View

SHA-256: sha256:5714d0d9ca9...

Publication ID: 5e04142b-5106-4483...

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RESEARKA

Research Synthesis: Cold Exposure Brown Fat

Certification Timeline

Abstract

Review Summary

Evidence Transparency

Living Evidence Brief

Research Question

Search Summary

Review type and protocol

Information sources

Search strategy

Eligibility criteria

Selection of sources of evidence

source admission funnel

Exclusion reasons

Data items

Risk-of-bias appraisal

Synthesis approach

AI-use disclosure

Accountability

Evidence Landscape

Cardiometabolic Outcomes

Contextual Adjacent Evidence Outcomes

Dosing and Pharmacokinetics Outcomes

Immune Outcomes

Immune and Inflammation Outcomes

Safety and Comorbidity Outcomes

Longevity Outcomes

Muscle Function Outcomes

Key Findings

Cardiometabolic Outcomes

Contextual Adjacent Evidence Outcomes

Dosing and Pharmacokinetics Outcomes

Immune Outcomes

Immune and Inflammation Outcomes

Safety and Comorbidity Outcomes

Longevity Outcomes

Muscle Function Outcomes

Limitations

Gaps Identified

Interpretation constraints

Confidence calibration

Conclusion

Research Synthesis: Cold Exposure Brown Fat

Abstract

Introduction

Background

Evidence Context

Methods

Review type and protocol

Information sources

Search strategy

Eligibility criteria

Selection of sources of evidence

source admission funnel

Exclusion reasons

Data items

Risk-of-bias appraisal

Synthesis approach

AI-use disclosure

Accountability

Results

Cardiometabolic Outcomes

Contextual Adjacent Evidence Outcomes

Dosing and Pharmacokinetics Outcomes

Immune Outcomes

Immune and Inflammation Outcomes

Safety and Comorbidity Outcomes

Longevity Outcomes

Muscle Function Outcomes

Cross-Domain Synthesis

Metabolic-Functional Tradeoff Framework

Discussion

Interpretation constraints

Confidence calibration

Limitations

Conclusion

What This Synthesis Adds

Boundary-Condition Matrix