Cited by 471Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and SenescenceAdvanced Glycation End Products research

Chronic inflammation and the hallmarks of aging

Open access
Baechle, Jordan J., Chen, Nan, Makhijani, Priya, Winer, Shawn, Furman, David, Winer, Daniel A.·2023·Molecular Metabolism

Abstract

BACKGROUND: Recently, the hallmarks of aging were updated to include dysbiosis, disabled macroautophagy, and chronic inflammation. In particular, the low-grade chronic inflammation during aging, without overt infection, is defined as "inflammaging," which is associated with increased morbidity and mortality in the aging population. Emerging evidence suggests a bidirectional and cyclical relationship between chronic inflammation and the development of age-related conditions, such as cardiovascular diseases, neurodegeneration, cancer, and frailty. How the crosstalk between chronic inflammation and other hallmarks of aging underlies biological mechanisms of aging and age-related disease is thus of particular interest to the current geroscience research. SCOPE OF REVIEW: This review integrates the cellular and molecular mechanisms of age-associated chronic inflammation with the other eleven hallmarks of aging. Extra discussion is dedicated to the hallmark of "altered nutrient sensing," given the scope of Molecular Metabolism. The deregulation of hallmark processes during aging disrupts the delicate balance between pro-inflammatory and anti-inflammatory signaling, leading to a persistent inflammatory state. The resultant chronic inflammation, in turn, further aggravates the dysfunction of each hallmark, thereby driving the progression of aging and age-related diseases. MAIN CONCLUSIONS: The crosstalk between chronic inflammation and other hallmarks of aging results in a vicious cycle that exacerbates the decline in cellular functions and promotes aging. Understanding this complex interplay will provide new insights into the mechanisms of aging and the development of potential anti-aging interventions. Given their interconnectedness and ability to accentuate the primary elements of aging, drivers of chronic inflammation may be an ideal target with high translational potential to address the pathological conditions associated with aging.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.molmet.2023.101755

Cites

418 of 418 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Chronic low-grade inflammation is a central driver of non-communicable diseases such as atherosclerosis, type 2 diabetes, neurodegeneration, autoimmune conditions, cancer, and ageing.

    “Chronic low-grade inflammation is a central driver of non-communicable diseases such as atherosclerosis, type 2 diabetes, neurodegeneration, autoimmune conditions, cancer, and ageing [48,49].”

    From Microalgae Bioactives for Functional Food Innovation and Health Promotion (2025) · cites this paper as [48]

    The Abstract (Background) describes a bidirectional relationship between chronic inflammation and cardiovascular disease, neurodegeneration, and cancer; the Introduction includes type 2 diabetes, and the Conclusions say chronic inflammation can drive aging and age-related diseases. The paper does not establish chronic low-grade inflammation as a central driver of every condition listed: its mention of autoimmune disease links it to an age-associated B-cell subset in mice, not directly to inflammation as a cause. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

1 retracted

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Baechle, J. J., Chen, N., Makhijani, P., Winer, S., Furman, D., & Winer, D. A.
Affiliations
  • Buck Institute for Research on Aging (US)
  • University Health Network (CA)
  • University of Toronto (CA)
  • University of Southern California (US)
Corresponding author
  • Furman, David
  • Winer, Daniel A.
Venue
Molecular Metabolism
Publisher
Elsevier BV
Published
2023-08
Volume
74
Pages
101755
ISSN
2212-8778 (ISSN-L)
OpenAlex
W4380854034
Cited by
471
References
418
Citations by year
2026: 193 · 2025: 157 · 2024: 107 · 2023: 13 · 2017: 1
FWCI
5.54
Type
journal article
Language
en
Open access
Locations
  • Molecular Metabolism (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health (5T32AG000266-23, P01AI153559, R01DK128435, U54AG075932)
  • Canadian Institutes of Health Research (PJT186165)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Advanced Glycation End Products research
Keywords
inflammaging, hallmarks of aging, chronic inflammation, dysbiosis, age-related diseases, frailty, neurodegeneration, cardiovascular disease, cancer, anti-aging interventions, geroscience
MeSH terms
Aged, Anti-Inflammatory Agents, Cardiovascular Diseases, Humans, Inflammation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review