My best guess is that SGLT2 inhibitors operate through multiple different off-target mechanisms to treat HFpEF, and they are complementary. Other off-target mechanisms that help treat HFpEF, found by AI:
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Ketoacidosis with SGLT2 inhibitors in routine clinical practice of type 2 diabetes: Scandinavian cohort and nested case–control study
Ketoacidosis risk with SGLT2 inhibitors varies greatly by patient characteristics and is not confined to early in treatment. Risk should be assessed throughout treatment, and patients should be instructed to pause treatment during acute illness and stress.
Paywalled paper: https://www.thelancet.com/journals/landia/article/PIIS2213-8587(26)00162-2/abstract
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More good news:
SGLT2 inhibitors reduce inflammatory cells linked to heart disease
For the first time in a human trial, a class of drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors, already known for their weight loss and glucose lowering benefits, has been shown to also reduce inflammatory cells in the blood known to cause cardiovascular disease. The findings were published in Circulation, a journal of the American Heart Association.
There had been some suggestive studies in animals with these medications indicating they might be modulating the immune system - affecting inflammation - but that hadn’t really been shown in humans. This study provides the first direct evidence of SGLT2 inhibitors altering human immune cells which may potentially reduce cardiovascular risk.
This finding is significant because it’s the first time this medication has been shown to have an immune effect at the cellular level with some specificity. If we understand how these medicines are reducing obesity-associated inflammation and heart disease and kidney disease, then we can identify the best patient for this treatment or discover other drug targets in that pathway that could really be beneficial."
Research Paper::
SGLT2 Inhibitor Empagliflozin Reduces Circulating Monocyte-Platelet Aggregates: A Pilot Study
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A summary of the paper:
SGLT2 Inhibitor Empagliflozin Reduces Circulating Monocyte-Platelet Aggregates: A Pilot Study
In a single-arm pilot, 12 weeks of empagliflozin (25 mg/day) given to women with obesity and prediabetes lowered the share of circulating monocyte-platelet aggregates (MPAs), cell complexes linked to vascular inflammation and cardiovascular disease. Single-cell RNA sequencing in eight participants showed the drop, and flow cytometry in seven supported it. A small historical diet-only group showed no change. Monocyte gene expression shifted toward oxidative phosphorylation and mTORC1 signaling, but a functional metabolic assay showed no change.
A class of diabetes drugs that keeps surprising cardiologists may have given up one more of its secrets. SGLT2 inhibitors such as empagliflozin were designed to push excess sugar out through the urine. Yet in large trials they cut heart failure admissions and cardiovascular deaths by more than their modest effects on glucose and weight can explain. Researchers have long suspected that the drugs also calm inflammation. What has been missing is a direct look at what they do to human immune cells in the bloodstream.
A team at Vanderbilt University Medical Center has now taken that look. They gave 25 mg of empagliflozin daily for 12 weeks to 16 women with obesity and prediabetes, and profiled blood immune cells from eight of them, one cell at a time, using single-cell RNA sequencing.
One cell population stood out. Monocyte-platelet aggregates, or MPAs, form when activated platelets latch onto monocytes, the white cells that go on to become the macrophages inside arterial plaque. These hybrids are raised in people with coronary disease and are regarded as a sensitive sign that platelets are switched on. After two weeks on the drug the share of MPAs had fallen, and by 12 weeks it had fallen further. The statistical model put the 12-week drop at 5 percentage points, which works out to roughly 40 percent lower odds that any given myeloid cell was stuck to a platelet.
A second method, flow cytometry, pointed the same way in seven participants. It also showed that the platelets still attached to monocytes carried less of an activation marker called CD62P. Four women from an earlier study who lost weight by dieting showed no such change, a hint that the effect is not simply a by-product of weight loss. Within the treated group, MPA levels did not track with weight or glucose.
The drug also shifted gene activity inside monocytes toward mitochondrial energy production and, less expectedly, toward mTORC1 growth signaling. A functional test of cell metabolism found no change, however, so the gene readout has not yet been matched by a measurable change in how the cells behave.
The caveats are substantial. There was no placebo group. The headline result rests on eight women, all White, averaging 61 years old. The diet comparison involved four people who started with different MPA levels. The cells had been frozen and thawed, and platelets readily stick to monocytes after blood leaves the body. Some of what was measured may therefore reflect how reactive the platelets were in the tube, not what was circulating. Nobody measured a clinical outcome.
The paper is a four-page research letter, and the authors call it a pilot. Its value is as a lead: a plausible, measurable route by which SGLT2 inhibitors might protect arteries. A randomized, placebo-controlled trial of 74 people with obesity and metabolic syndrome is already enrolling at Vanderbilt and should show whether the finding holds.
Actionable Insights
This paper does not justify starting empagliflozin for longevity or heart protection. It shows a change in a blood marker in eight people without a placebo comparison.
What it does offer:
- Size of the effect: MPAs fell by 3.4 percentage points at 2 weeks and 5.0 points at 12 weeks. In relative terms, the odds of a myeloid cell being bound to a platelet fell about 29 percent at 2 weeks and about 41 percent at 12 weeks. For a blood marker, that is a large shift, but small studies routinely overestimate effects.
- Speed: The change appeared within 2 weeks, before meaningful weight loss.
- Independence from weight and glucose: MPA levels did not track with either, and dieting alone did not reproduce the effect in four comparators.
- For people already prescribed an SGLT2 inhibitor: This adds a possible anti-platelet, anti-inflammatory mechanism to benefits already proven in outcome trials. In EMPA-REG OUTCOME, cardiovascular death fell from 5.9 to 3.7 percent over about three years in people with type 2 diabetes and established heart disease.
- For healthy, lean people: No data here apply. Participants had obesity and prediabetes.
Known risks of the drug class, including genital yeast infections, volume depletion, and rare ketoacidosis, are unchanged by this paper.
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A new panel presentation on a study comparing depagliflozin to metformin in diabetic patients:
Results in brief
During the follow-up period, a total of 846 events occurred, corresponding to approximately 40 % of the entire study population. Among participants treated with dapagliflozin, 425 events occurred, compared with 421 events among participants treated with metformin.
No statistically significant difference was observed when the different events were analysed separately or when different groups of participants were analysed, for example according to age, sex, or diabetes duration.
Overall, the SMARTEST study found no difference between dapagliflozin and metformin in preventing the complications investigated in the study among people with early type 2 diabetes. For participants who received either of the two medications, this means that neither medication was shown to be more effective than the other in preventing the complications investigated in the study.Serious adverse events, such as events leading to hospitalisation, were approximately equally common in the two groups. They occurred in 18.2 % of participants receiving dapagliflozin and 19.8% of those receiving metformin. However, more participants receiving dapagliflozin discontinued treatment: 9.5% compared with 4.9%. An important reason was genital fungal infections, which are a known side effect of dapagliflozin.
Overall, blood glucose, weight, and blood pressure developed similarly in the two groups, and participants were generally well treated throughout the study.
Full writeup:
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From the study RapAdmin linked:
“GSEA showed that 12-weeks of empagliflozin upregulated oxidative phosphorylation, mTORC1 signaling, PI3K/AKT/mTOR signaling, unfolded protein response, and MYC targets in all monocytes (Figure 1H), consistent with a coordinated metabolic reprogramming toward mitochondrial respiration and anabolic mTOR-driven growth signaling.”
This doesn’t square with this:
Empagliflozin protects against acute myocardial infarction by modulating the mTORC1─S6K pathway to drive metabolic reprogramming
https://ui.adsabs.harvard.edu/abs/2026JRRAS..1902321Z/abstract
“Mechanistically, empagliflozin suppressed mTORC1-S6K activity and concurrently activated the AMPK-PGC1α- Sirt1 axis, leading to increased myocardial NAD+ levels, enhanced Sirt1 deacetylase activity, and reduced oxidative stress and inflammation. Forced S6K activation prevented empagliflozin from stimulating this adaptive metabolic pathway, indicating reciprocal regulation between mTORC1-S6K and stress-responsive metabolic signaling. The cardioprotective effects of empagliflozin against AMI depend on functional inhibition of the mTORC1-S6K pathway, which enables activation of compensatory metabolic responses involving AMPK, PGC1α, and Sirt1.”
I think these guys need to do more work in this area, as it doesn’t square with everything I’ve read about SGLT2i impact on mTORC1.
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Completely different cell types, and insult types
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LOL, of course the effects of suppression vs upregulation are different, and of course the effect will be desirable or not depending on cell type. My question is how the same molecule (empagliflozin) biochemically both suppresses and upregulates the mTORC1 enzyme. I’m asking about the biochemical pathway, the mechanism in each case:
"### Summary
The pathways are mechanistically related (both center on the AMPK–mTOR metabolic axis), but they operate differently [yes of course, but HOW?] in heart muscle versus circulating immune cells. Empagliflozin suppresses hyperactive mTORC1 in ischemic cardiac tissue to prevent necrosis [yes of course, but HOW?], while prompting a healthy, homeostatic metabolic shift in circulating monocytes to dampen systemic vascular inflammation [yes of course, but HOW?]."
This merely doubles down declaratively on the assertions repeatedly - without explanation. If someone says “2+2=5”, and you ask for an explanation it doesn’t help to say “because 2+2=5”.
I can go through each paragraph showing this.
Examples:
“Both studies evaluate the mTORC1 (mechanistic target of rapamycin complex 1) signaling hub and its cross-talk with AMPK and cellular metabolic stress , but they examine its role in two distinct cell types:[…]”
But I’m not asking whether the role of mTORC1 can be different in distinct cell types - of course it can. I’m asking how the empagliflozin molecule does the opposite in two cell types.
And what is the explanation in that table? That empagliflozin suppresses in one and upregulates in another. Facepalm. This is mere repetition.
" 2. Why isn’t this a contradiction?
The divergent responses reflect the tissue-specific and context-dependent functions of mTOR signaling: […]"
And… we have more explanations of the effects of mTORC1 suppression in one vs mTORC1 upregulation in the other. Well of course the effects of mTORC1 upregulation are different from suppression - I’m not asking about that. I’m asking how the molecule (empagliflozin) accomplishes both.
For example, it could say something along the lines: the reason empagliflozing suppresses mTORC1 in cell type #1 is because it acts on an intermediate molecule “X” which suppresses mTORC1 while in cell type #2 is because it acts on an intermediate molecule “Y” which acts in the opposite way and upregulates mTORC1. And then there is an explanation of how “X” and “Y” bind to empagliflozin and being either present or absent in a different cell type has a differential effect. Why does this matter? Because it’s the whole point of my post: it cannot be that it’s a direct effect since if it was direct, then empagliflozin would bind in a particular way to mTORC1 and wouldn’t suddenly bind chemically differently in a different cell - the chemistry of neither changes so it binds the same. The only way this could be the case is if there is some chain, something inbetween empagliflozin and mTORC1 that depending on what it is, has a differential effect - it would be a mediated, not direct effect - and if so, I’d like to see the biochemical basis for this, because the moment you introduce complex chains there is room for error in assigning which molecule is responsibe for what - this is why I thought more work needs to be done here by the researchers for me to buy their assertions. But of course, AI misses all that and rather hamfistedly repeats assertions not understanding what it is saying.
This is why I am super careful about using AI to analyze papers. It misses out on nuance and fundamental understanding of the questions asked. AI can be useful to round up papers, but for analysis I kick it into the trash.
EDIT: if you want proof of why AI analysis is often dogshit, you can take a look at a thread I just created citing two studies. One study shows how certain mTOR inhibitors show no life extension (and actual shortening at higher doses).The other study looks at AMPK activators (which btw. can be a pathway to mTORC1 inhibition), and shows something that I have been expounding upon in this and previous post - it is critically important whether the inhibition/upregulation is done directly by the drug (empagliflozin, metformin etc.) or is mediated by other steps - metformin, which is an indirect AMPK activator shows no life extending by itself, whereas drugs which are direct activators do in the models shown. Since I have reason to say empa does not directly suppress and upregulate mTORC1 (as I argued above), it can only do so in some indirect way, at which point a lot of ambiguity is introduced - and these effects need to be clearly demonstrated. I’ll link to that thread below, which includes my quote:
“Note the emphasis on direct activation vs more unclear indirect activation as in metformin. The direct activators show unambiguously benefits in lifespan extension (which metformin by itself does not). Worth keeping in mind - it’s not just about mediated suppression or activation, because you are introducing intermediate steps which might decisively affect the outcome (as in a recent discussion of empagliflozin both upregulating and suppressing mTORC1 in different cell types - why the question of direct bonding vs indirect is so critical).”
In summary, as so often, AI analysis is wholly inadequate, unable as it is, to even grasp the questions being asked. My very frequent experience with AI is that you really have to know the subject extremely well, because that allows you to spot where AI is going wrong and off the rails missing critical nuance which obviates its usefulness as a reliable analytic. But it also means that if you don’t already know the subject extremely well, you cannot have any confidence that AI is not producing dog caca. So, AI is just a tool - and as any tool is useful when used appropriately. At this point in time, it is IMHO completely unreliable as an analytical methodology and for mission critical applications unusable (not a chance I’d rely on AI for medical advice!), at least at this point in its technological development (hopfully vastly improving in the future); it can be very useful in hypothesis generation, material gathering, image analysis etc. - and that’s what I would limit myself to at this point in time. YMMV.
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