Should Healthy People Take Metformin for Longevity?

I recently analyzed all the available RCTs on metformin use in healthy individuals and uncovered the following facts: 1) It shows zero improvement in insulin sensitivity; 2) It actually increases fasting blood glucose; 3) It blunts the health benefits of exercise. Based on these findings, it is reasonable to infer that metformin might actually increase the risk of new-onset diabetes in healthy people.

Furthermore, a post-hoc analysis of a large-scale diabetes prevention RCT confirms this suspicion. Even among non-diabetic individuals with impaired glucose tolerance, the lowest-risk subgroup on metformin saw a higher diabetes incidence rate (9.6%) compared to the placebo group (8.3%). While this specific trend did not reach statistical significance, the negative trajectory is clear. Conversely, the same low-risk subgroup assigned to lifestyle intervention achieved a drastically lower incidence rate (3.4%) than the placebo group (8.3%), with strong statistical significance.

Based on this, the debate over whether healthy people should take metformin is officially over.

Risk Stratification Range of Predicted Probability Observed Rate in Placebo Group (Events) Observed Rate in Metformin Group (HR) Observed Rate in Lifestyle Group (HR)
Quarter 1 (Lowest Risk) 1.1% - 9.5% 8.3% (19) 9.6% (HR: 1.07, 95% CI: 0.57 to 2.01) 3.4% (HR: 0.30, 95% CI: 0.12 to 0.75)
Quarter 2 (Medium-Low Risk) 9.5% - 15.1% 17.8% (37) 14.9% (HR: 0.79, 95% CI: 0.49 to 1.28) 8.6% (HR: 0.45, 95% CI: 0.26 to 0.79)
Quarter 3 (Medium-High Risk) 15.1% - 27.0% 29.1% (73) 24.4% (HR: 0.82, 95% CI: 0.57 to 1.18) 15.5% (HR: 0.43, 95% CI: 0.28 to 0.67)
Quarter 4 (Highest Risk) 27.0% - 99.8% 59.6% (140) 38.2% (HR: 0.44, 95% CI: 0.33 to 0.59) 31.3% (HR: 0.34, 95% CI: 0.25 to 0.46)

Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program

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To be honest, conducting this kind of analysis is quite tedious. You have to include as many RCTs as possible, including non-English papers and paywalled studies. Although I started this analysis hoping to find that metformin would benefit healthy people, unfortunately, the data clearly shows it is not suitable for individuals with completely normal metabolism.

Of course, the diabetes-related metrics I mentioned are just from one subgroup, which I used simply as a straightforward example to illustrate why it shouldn’t be taken. After comprehensively weighing all subgroups across various biological markers, I firmly believe there is absolutely no necessity for healthy individuals to take metformin. While I did uncover some fascinating insights during the process, they unfortunately didn’t change the ultimate conclusion—and this holds true for intermittent dosing as well.

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A summary of the paper:

Metformin Efficacy Evaporates in Low-Risk Pre-Diabetes: The Case for Precision Prevention

A post-hoc risk-stratified reanalysis of the landmark Diabetes Prevention Program clinical trial demonstrates that the preventative benefits of metformin are highly non-linear and concentrated almost exclusively within individuals at the highest quadrant of baseline diabetes risk. By applying an internal multivariable risk model to the trial data, researchers found that the bottom 75 percent of participants derived minimal to zero statistical benefit from metformin, with the lowest-risk quarter even showing a minor trend toward accelerated progression. In contrast, intensive lifestyle interventions yielded a powerful, consistent relative risk reduction across all risk tiers, signaling that universal pharmaceutical adoption for longevity or mild metabolic adjustments may be misguided.

For years, the longevity and biohacking communities have viewed metformin as a foundational therapeutic for metabolic optimization. This reputation was largely built on major clinical trials like the Diabetes Prevention Program, which reported that the drug slashed the average risk of progressing to type 2 diabetes by 31 percent. However, a critical reanalysis of the trial data published in The BMJ reveals that average statistics can mask a starkly different reality for the individual. The big idea driving this study is benefit-based tailored treatment, a paradigm shift showing that a patient’s baseline risk dictates how much absolute benefit they will actually receive from an intervention.

To uncover this hidden variation, researchers built a multivariable risk prediction model using baseline physiological data from over 3,000 participants. They factored in seventeen variables, including fasting blood sugar, hemoglobin A1c, family history, and waist measurements, to divide the cohort into four distinct quarters of ascending baseline risk.

The findings upend the conventional approach to preventative medicine. For participants placed in the highest-risk quarter, metformin was profoundly effective, delivering a dramatic drop in diabetes incidence over the 2.8-year tracking period. Yet, for the remaining 75 percent of the cohort, the clinical utility of the drug dropped off a cliff. In the second and third risk quarters, the benefits were marginal and statistically uncertain. Most revealingly, the individuals in the lowest-risk quarter experienced zero benefit from metformin. In fact, those taking the drug in this sub-group had a slightly higher rate of developing diabetes than those taking a placebo, though this trend did not achieve statistical significance.

Conversely, the study brought highly positive news for non-pharmaceutical interventions. The structured lifestyle modification program, which focused on modest weight loss and routine physical activity, achieved a uniform 58 percent relative risk reduction across all four quarters. While the absolute number of prevented cases was naturally higher in the top risk tier because they had more risk to mitigate, even the lowest-risk individuals gained substantial protection from lifestyle changes. This dichotomy indicates that while the human body universally responds to physical optimization, its response to targeted pharmaceutical enzymes depends entirely on the severity of the underlying pathology. For optimal health and longevity, automatic drug prescriptions based on a single borderline lab value should be retired in favor of sophisticated risk stratifications.

Context/Source

  • Paper Title: Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program
  • Access Status: Open Access (Distributed under CC BY-NC 4.0)
  • Lead Institutions: Department of Veterans Affairs Center for Clinical Management Research, University of Michigan, and Tufts Medical Center
  • Country: United States
  • Journal Name: BMJ (The BMJ)
  • Impact Evaluation: The impact score of this journal is 93.6, evaluated against a typical high-end range of 0–60+ for top general science, therefore this is an Elite impact journal.
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Furthermore, I want to avoid over-extrapolating on whether combining metformin with other drugs would alter its trajectory. Studies on multi-drug combinations in completely healthy individuals are incredibly scarce, let alone for biohackers who typically run complex stacks of prescription drugs and supplements.

This is exactly why frequent testing is so critical. In my opinion, the most vital investment for a biohacker isn’t the drugs, supplements, or health gadgets—it’s frequent, comprehensive lab work. Even then, it’s far from perfect. For instance, something like cancer risk is notoriously difficult to spot through routine blood markers.

Anyway, just a bit of rambling on my end. But as it stands, if you don’t overcomplicate things, the simple conclusion on metformin is just as I described.

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All I can say is I find myself hypoglycemic on both Telmisartan and Empagliflozin. I am unsure if most of the longevity community are just older folks in poor health that can actually take these.

Why did I bring up cancer risk? Because I’ve seen multiple cases where drugs that reduce cancer risk individually end up drastically increasing it when combined, especially when accounting for variables like sex, age, and ethnicity. Many of these findings are buried in obscure, paywalled papers—and the authors don’t even mention it in the main text; you have to run your own analysis to spot it.

To be honest, it wouldn’t surprise me at all if many biohackers fail to outlive the average US life expectancy. The field is just riddled with hidden pitfalls. I’d suggest not overthinking it, as it only breeds unnecessary anxiety. At some point, you just have to leave it up to fate.

Anti-aging is incredibly difficult for the youth, though seniors might have it a bit easier. The evidence we rely on is just too imperfect. If you blindly mess around with these interventions, you might honestly end up worse off than someone who simply exercises regularly and eats a clean diet.

My best advice for biohackers is to streamline your stack as much as possible—keep it as simple as possible, based strictly on RCTs that offer definitive conclusions. There’s no need to gamble with a biased mindset. If blindly stacking interventions actually worked, think about all the people out there with poor health who are already taking massive cocktails of different prescription drugs. Over time, that population must have exhausted almost every possible drug combination, much like a brute-force attack cracking a password. Yet, we haven’t seen a sudden surge of people breaking the 120-year age barrier. And that’s that.

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Thank you for posting this. I am (I think) right on the borderline re: should I be taking metformin. I have been taking metformin for about 6 years. Fasting blood glucose ranged from 90 to 100 before metformin and now is still about the same – but – it had gone up when I started Repatha, at which time I increased from 500 to 1000 mg metformin. HA1C has ranged from about 5.6 to 5.9. I believe I am metabolically healthy, with low fasting insulin, BMI of 19.

So I am just below the official level of “prediabetes” . I mean to stay on Repatha, so if I quit Metformin, glucose will rise. But, I will not suffer the blunting-of-exercise effects. As a twee 76 year old with severe osteoporosis, I need all the muscle I can pack on (and am working hard on this and all the other lifestyle stuff).

Would appreciate views, suggestions.

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Should also note:

Metformin helps prevent or less the risk of fracture caused by osteoporosis. (My osteoporosis is severe and is my greatest health risk.) So: helps prevent fracture but as a mitochondrial toxin may contribute to sarcopenia.

Metformin helps prevent the progression of cancer because it reduces the availability of glucose to cancer cells.

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OK. How do you explain the results of Yang et al (2024) who gave metformin to macaques for 40 months and documented a large number of benefits?
I tend to discount mouse and rat studies quite a bit because “mice aren’t little people”. But, short of a RCT of metformin on humans, non-human primates come pretty close.
I was ready to give up on metformin, but, when the 2024 paper came out I shelved that idea.

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I thought I would have a look at that paper. I am not a fan of metformin I think the papers that suggested a health benefit for non-diabetics were generally subject to selection bias and there is good evidence against it.

Hence I have found the link downloaded the paper and had a chatGPT(5.5paid) analysis:

Looking at the analysis my basic first point is a question as to how replicable this study will end up as being.

https://www.cell.com/cell/fulltext/S0092-8674(24)00914-0

Paper

Yang Y. et al. “Metformin decelerates aging clock in male monkeys.” Cell 187 (2024): 6358–6378.

Overall summary

This study tested whether long-term metformin treatment can slow biological aging in healthy, non-diabetic male cynomolgus monkeys.

The investigators treated six older monkeys, initially 13–16 years old, with metformin 20 mg/kg daily for 1,200 days—about 3.3 years. Five untreated older monkeys completed the study after one control animal died of kidney failure. Separate young and middle-aged groups were used to define normal age-related trajectories.

The study combined:

  • behavioural cognitive testing;
  • MRI and CT imaging;
  • histology across multiple organs;
  • bulk RNA sequencing of 79 tissues;
  • DNA-methylation profiling;
  • plasma proteomics and metabolomics;
  • single-nucleus RNA sequencing of liver and frontal cortex;
  • experiments in human stem-cell-derived neurons.

The central conclusion is that metformin shifted numerous molecular, histological and functional measurements in an apparently younger direction, with particularly prominent effects in the brain and liver.


Main findings

1. Cognitive performance and brain structure

Metformin-treated monkeys performed better than untreated old monkeys in tests of:

  • delayed memory;
  • object discrimination learning;
  • reversal learning or cognitive flexibility.

MRI showed preservation of cortical thickness, particularly in frontal regions involved in executive function, working memory and reversal learning. Histology also suggested greater frontal cortical thickness.

Nine of 88 mapped brain regions showed significant preservation or apparent restoration of cortical thickness, with most located in the frontal lobe. The paper’s Figure 1, on pages 4–5, links these structural findings to the behavioural tests.

The authors therefore argue that metformin protects both the structure and function of the aging primate brain.

2. Broad transcriptomic effects across tissues

RNA sequencing was performed across 79 tissues from 11 organ systems. Aging was associated with:

  • increased inflammatory and innate-immune gene expression;
  • reduced extracellular-matrix maintenance, development and regenerative pathways;
  • increased apoptosis, fibrosis and oxidative-stress signatures;
  • reduced lipid metabolism, Wnt signalling and DNA-repair pathways.

Metformin shifted many of these age-associated patterns towards those of younger monkeys. The strongest transcriptomic “rescue scores” were observed in tissues including:

  • frontal cortex;
  • liver;
  • kidney;
  • lung;
  • skin;
  • skeletal muscle.

This pan-tissue atlas is one of the study’s most substantial contributions.

3. Histological markers of aging

Across several organs, metformin-treated monkeys showed lower levels of:

  • p21-positive putatively senescent cells;
  • fibrosis;
  • inflammatory-cell infiltration;
  • TNF-Îą, IL-1β, S100A8 and other inflammatory or SASP-associated markers;
  • lipid peroxidation marked by 4-HNE;
  • loss of H3K9me3;
  • expression of endogenous-retroviral proteins.

The treatment also appeared to preserve fast type II skeletal-muscle fibres.

These observations are consistent with reduced inflammation, senescence-associated signalling and tissue degeneration, although many are surrogate markers rather than direct demonstrations of rejuvenated function.

4. Biological-age clocks

The investigators constructed monkey aging clocks from:

  • DNA methylation;
  • tissue transcriptomes;
  • plasma proteins;
  • metabolites;
  • single-nucleus transcriptomes.

Reported age shifts included approximately:

  • −6.4 years for plasma protein age;
  • −6.1 years for frontal-lobe DNA-methylation age;
  • roughly −4 to −5 years in several liver, kidney, lung and tendon measurements;
  • −5.9 years for integrated frontal-lobe single-nucleus transcriptomic age;
  • −4.3 years for integrated liver single-nucleus transcriptomic age.

The often-repeated claim that metformin produced a “six-year regression” therefore refers to model-derived biological-age estimates, not to a demonstrated six-year increase in lifespan or healthspan.

5. Liver effects

Single-nucleus RNA sequencing suggested particularly strong effects in:

  • hepatocytes;
  • Kupffer cells;
  • T cells.

Metformin partly reversed age-associated changes involving:

  • lipid and amino-acid metabolism;
  • inflammatory signalling;
  • TGF-β signalling;
  • fibrosis.

Histology showed less lipid-droplet accumulation, fibrosis and inflammatory-marker expression.

6. Neuronal effects and Nrf2

The paper proposes that neuronal protection is mediated partly through Nrf2, a transcription factor controlling antioxidant and cellular-stress responses.

In cultured human stem-cell-derived neurons, metformin:

  • increased phosphorylated nuclear Nrf2;
  • raised expression of Nrf2 targets such as HO-1, NQO1, SOD3, GPX1 and GPX2;
  • lowered ROS and 4-HNE;
  • reduced senescence-associated β-galactosidase, protein aggregates, amyloid-β and IL-6;
  • preserved lamin B2.

Nrf2 knockdown substantially weakened the protective effects of metformin. Conversely, an activating Nrf2 E82G variant produced stronger protection than metformin, and metformin added little further benefit.

This supports the conclusion that Nrf2 is required for at least part of the observed in-vitro neuronal effect. It does not prove that Nrf2 is the dominant mechanism in the whole animal.


What is genuinely novel?

1. Long-duration intervention in healthy aging primates

The strongest novelty is the use of a prolonged pharmacological intervention in healthy, aging non-human primates rather than rodents or diabetic animals.

A 40-month trial is unusually long and translationally relevant. Cynomolgus monkeys are closer to humans than standard laboratory models in brain organisation, metabolism, lifespan and age-associated pathology.

2. System-wide, multi-tissue assessment

The study is much broader than a conventional metformin experiment. Profiling 79 tissues allowed the authors to examine whether treatment effects were:

  • systemic;
  • organ-specific;
  • cell-type-specific;
  • shared across different molecular layers.

The combination of imaging, behavioural testing, pathology, bulk transcriptomics, single-cell data, methylation, proteomics and metabolomics is an important methodological advance.

3. Cell-type-specific primate aging clocks

The creation of single-nucleus transcriptomic aging estimates for liver and frontal-cortex cell types is novel. It allowed the authors to propose that particular populations—such as excitatory neurons, microglia, hepatocytes and Kupffer cells—were more responsive than others.

4. Direct evidence of primate neuroprotection

Previous metformin geroscience work relied heavily on rodents, epidemiology or metabolic disease populations. Here, metformin was associated with:

  • better cognition;
  • preservation of cortical thickness;
  • reduced neuronal pathology;
  • improved myelin measures;
  • younger neuronal transcriptomic profiles.

The convergence of behavioural, structural and molecular observations is more convincing than any one of these outcomes alone.

5. Nrf2 as a mechanistic link

Metformin is conventionally discussed in relation to:

  • mitochondrial complex I inhibition;
  • changes in cellular energy state;
  • AMPK activation;
  • mTOR suppression;
  • insulin and glucose metabolism.

The identification of an Nrf2-dependent, cell-autonomous neuronal effect adds a potentially important mechanism. The knockdown and activating-mutation experiments make this component stronger than a purely correlative pathway analysis.


Critical appraisal

Strengths

Broad convergence of measurements

A major strength is that the result does not depend on one clock or biomarker. Cognitive tests, MRI, histology, transcriptomics and inflammatory measurements generally point in the same direction.

Clinically relevant dose and duration

The monkey dose was chosen to approximate human therapeutic exposure, making the work more relevant than studies using extremely high experimental concentrations.

Healthy rather than diabetic animals

Because the monkeys were not diabetic and metformin did not materially lower glucose or body weight, the findings cannot easily be attributed only to correction of hyperglycaemia or obesity.

Mechanistic intervention

The Nrf2 knockdown and gain-of-function experiments go beyond association and provide evidence of mechanistic necessity and sufficiency within the cultured-neuron model.


Major limitations

1. Very small number of animals

The central comparison involved approximately:

  • six metformin-treated old monkeys;
  • initially six old controls, with only five completing the study;
  • six young controls;
  • only three middle-aged controls.

This is a very small study for an intervention generating thousands of molecular measurements. Individual-animal differences can exert disproportionate effects, particularly for cognition, imaging and aging-clock estimates.

The large number of cells, tissue samples or molecular features must not be confused with a large number of independent experimental subjects. The monkey, not each cell or tissue sample, is the primary experimental unit.

2. Male-only study

All animals were male. Metformin responses may differ by sex because of differences in:

  • sex hormones;
  • body composition;
  • hepatic metabolism;
  • mitochondrial biology;
  • immune aging;
  • pharmacokinetics.

The results cannot automatically be generalized to female monkeys or women.

3. Cross-sectional reference groups

The young and middle-aged animals were distinct cohorts, rather than the treated monkeys being followed from youth through old age.

Therefore, the aging trajectories used to construct the clocks combine:

  • true age-related change;
  • cohort effects;
  • individual differences;
  • possible differences in early environment or life history.

A longitudinal untreated cohort with serial tissue sampling would be stronger, although clearly difficult in primates.

4. Limited evidence of actual healthspan or lifespan extension

The paper demonstrates changes in intermediate outcomes, but not:

  • reduced mortality;
  • longer lifespan;
  • delayed onset of diagnosed disease;
  • reduced frailty;
  • sustained benefit after metformin withdrawal.

Only a limited set of functional outcomes was assessed, principally cognition. Most other conclusions rest on molecular or histological proxies.

Thus, “slowing aging” is a plausible interpretation, but “extending healthspan or lifespan” remains unproven.

5. Biological-age regression may be overstated

The reported four-to-six-year rejuvenation figures sound more precise than the data justify.

These values depend on:

  • the selected molecular features;
  • model structure;
  • a small training population;
  • calibration against chronological age;
  • extrapolation from cross-sectional age groups;
  • the assumption that movement towards a younger molecular profile is beneficial.

An aging clock can show a younger estimate because a drug directly changes its constituent biomarkers, even when the drug has not altered the underlying rate of organismal aging.

Consequently, it is safer to say that metformin shifted the clocks towards younger reference profiles, rather than literally making the animals six biological years younger.

6. Risk of model overfitting and non-independence

ElasticNet regularisation and leave-one-out validation help, but the overall dataset is still small relative to the number of possible transcriptomic, methylomic and proteomic predictors.

Some clock evaluations may also be partly circular:

  1. features are selected because they vary with age;
  2. metformin reverses some of those features;
  3. the same or closely related features are then used to conclude that age has been reversed.

External validation in a completely independent monkey cohort would be needed to establish that these clocks predict meaningful future outcomes.

7. Multiple-comparison burden

The study examined:

  • 79 tissues;
  • many cell types;
  • thousands of genes;
  • numerous pathways;
  • multiple histological markers;
  • dozens of brain regions;
  • several aging clocks.

Although adjusted statistics were used for many omics analyses, such a vast analytical search space increases the risk of selective emphasis on favourable findings. Some reported regional or histological results rely on nominal (p<0.05), where more stringent correction would be appropriate.

8. Possible pseudoreplication

Single-nucleus analyses generate many metacells or cellular observations from very few monkeys. Treating these as fully independent observations would artificially narrow confidence intervals.

The paper appears to aggregate cells in parts of its analysis, but the effective sample size remains the number of animals. Stronger hierarchical models explicitly nesting cells within animal would make the inference more secure.

9. Nrf2 mechanism is only partially established in vivo

The Nrf2 experiments are strongest in cultured neurons. In the monkeys, Nrf2 activation correlates with metformin treatment, but the investigators did not block Nrf2 in vivo.

Therefore, the evidence supports:

Metformin can protect cultured neurons through an Nrf2-dependent mechanism.

It does not fully establish:

Nrf2 is responsible for the systemic or brain-wide anti-aging effects of metformin in primates.

AMPK, mitochondrial complex I, mTOR, insulin signalling, autophagy, inflammatory regulation and altered one-carbon metabolism may all contribute.

10. Culture concentration needs careful interpretation

The neuronal experiments used 5 ÎźM metformin, which is described as a low concentration and is more physiologically plausible than the millimolar concentrations often used in cell culture.

Nevertheless, extracellular culture concentration does not directly establish the concentration reached in particular human neuronal compartments during ordinary oral dosing. Cellular uptake depends on transporter expression and tissue pharmacokinetics.

11. Cognitive testing could be vulnerable to expectancy or handling effects

The article states that older monkeys were randomly assigned, but the paper would be more persuasive with clearer reporting of:

  • blinding of behavioural assessors;
  • blinding during MRI segmentation;
  • blinding of histological quantification;
  • pre-specified primary cognitive endpoints;
  • handling and training equivalence.

In a small behavioural study, even subtle differences in testing or animal-handler interaction can matter.

12. Mortality imbalance cannot be interpreted

One control monkey died from kidney failure, while none of the treated animals died. The numbers are far too small to infer a survival benefit, and excluding the deceased animal from end-stage tissue analyses could introduce survivorship bias.

13. Safety assessment is underpowered

The study reports no major abnormalities in weight, glucose, blood counts or urine parameters. However, six treated animals are insufficient to detect uncommon adverse effects.

Human concerns such as:

  • vitamin B12 depletion;
  • gastrointestinal intolerance;
  • lactic acidosis in susceptible individuals;
  • effects in renal impairment;
  • loss of exercise adaptation in some contexts;

cannot be resolved by this experiment.


Interpretation of the Nrf2 finding

The Nrf2 result fits a coherent model:

  1. neuronal aging lowers effective Nrf2 activity;
  2. antioxidant and detoxification genes decline;
  3. ROS and lipid peroxidation rise;
  4. protein aggregation, nuclear-envelope disruption and inflammatory signalling increase;
  5. metformin restores Nrf2 activity and partially reverses these changes.

However, Nrf2 activation is not necessarily synonymous with reversal of aging. Nrf2 is fundamentally a stress-response pathway. A younger molecular profile could reflect improved damage resistance rather than a resetting of the underlying aging process.

The distinction is important:

  • Geroprotection: reduces damage or preserves function while treatment continues.
  • Rejuvenation: reverses accumulated age-related damage or permanently restores a younger state.
  • Slower aging rate: changes the future slope of deterioration.

This experiment supports geroprotection most strongly. It provides suggestive, but not definitive, evidence for rejuvenation or a lower intrinsic rate of aging.


Bottom-line assessment

This is an ambitious and important proof-of-concept study. It provides some of the strongest experimental evidence so far that clinically relevant long-term metformin exposure can produce broad geroprotective effects in a healthy non-human primate.

Its most convincing findings are:

  • preservation of frontal cortical structure;
  • improved cognitive test performance;
  • broad reduction in inflammatory and degenerative tissue signatures;
  • consistent shifts towards younger molecular profiles;
  • an Nrf2-dependent protective effect in cultured human neurons.

Its weakest aspect is the quantitative claim of several years of biological-age reversal. Those figures derive from newly developed clocks trained on very small cohorts and should be viewed as descriptive molecular shifts, not literal measures of rejuvenation.

A fair conclusion is:

Long-term metformin treatment appears to preserve several aspects of brain and systemic function in aging male monkeys and shifts many molecular markers towards younger states. The study supports metformin as a candidate geroprotective drug, but it does not yet prove lifespan extension, durable rejuvenation, or equivalent benefits in humans.

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As I said…a male monkey study is good enough for me. (I’m a male monkey!)
I don’t know why the AI kept stressing that metformin “didn’t increase lifespan”…that wasn’t the object of the study. The object of the study was to show the tissue-level changes metformin causes in aged, male monkeys. I think it achieved that with flying colours.

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A lot depends on how many better options there are. I personally believe it would be harmful, but also i have a number of interventions with good evidence

Do you mind expanding on the reasons why you think metformin is harmful yet you posted a study (above) on primates (closest to humans) that showed significant positive effects. I’m a bit confused. Genuine question btw, not trying to be smart…

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I have posted the detailed arguments on other threads.

If you follow the thread above you will see I posted the study because it was referenced without a link.

https://www.rapamycin.news/search?expanded=true&q=metformin%20%40John_Hemming

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Agree. It was over long ago.

What would you/anyone suggest (if not metformin) for keeping FG in check (i.e… someone who has FG in the 98-105 range, not horrible and not good either)?

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Kelman, picking up on your point-- FG in the 98-105 range --not good or horrible –

What if FG is about 90, but insulin is low, metabolically healthy.
But metformin has pleiotropic effects – helps prevent cancer progression by reducing glucose available to cancer cells – and is part of the Care Oncology Protocol. And metformin can suppress sclerostin, which when it is too high (as it is for me) causes suppression of bone formation causing osteoporosis and also can contribute to Alzheimers!

So, I am basically healthy and glucose is higher than I would like but not too alarming. But I take metformin for (breast) cancer recurrence prevention and its potential to suppress sclerostin.

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Not aware of those benefits but good to know. Take it daily, little to no effect on FG and no side effects.

For lowering fasting blood glucose (FPG) specifically, the ranking is:

1. SGLT2 inhibitors — strongest FPG effect. By blocking renal glucose reabsorption they produce continuous glucosuria that works independent of meals, so fasting glucose drops meaningfully (roughly 20–35 mg/dL as monotherapy). Rating: strong.

2. Imeglimin — moderate FPG effect. Its main actions—reduced hepatic gluconeogenesis plus glucose-dependent insulin secretion and improved insulin sensitivity—directly target the fasting state. In the TIMES trials FPG fell roughly 15–20 mg/dL. Rating: moderate.

3. Acarbose — weakest FPG effect. As an α-glucosidase inhibitor it slows intestinal carbohydrate absorption, so its action is overwhelmingly on post-prandial glucose. Fasting glucose barely moves (a few mg/dL at most). Rating: weak.

Quick summary: SGLT2 inhibitors > imeglimin > acarbose for fasting glucose.

Two caveats worth noting: if you instead ranked by postprandial glucose, acarbose would move up substantially; and overall HbA1c reductions for all three are fairly comparable (~0.5–1.0%), because they hit different components of glycemia. Magnitudes vary by dose, baseline glucose, and background therapy.

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Thanks, actually been doing 12.5mg Empa, 50mg acarbose with main meal, and 500mg metformin yet my FG has been stubbornly stuck on high normal 96-104 while my HbA1c has been somewhat better (right at the border of optimal and normal) @ 5.2 . I will substitute metformin with Imeglimin. and try 25mg Empa and up acarbose to 100mg with main meal just to see if it makes a difference.

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