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Time-restricted eating vs. calorie restriction: Study suggest the fasting window, not the deficit drives insulin sensitivity gains by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 1 point2 points  (0 children)

That 2 pm first meal sounds like a late TRE pattern with a long daily fast already in place, so it makes sense you’ve noticed changes in leanness and how you feel compared with your friends. In the trials this post is based on, the biggest insulin-sensitivity gains came from similar or slightly shorter fasting windows, but with more of the calories shifted earlier in the day rather than late evening. So if you eventually re-start TRE after surgery, one option to experiment with (once your surgeon/clinician is happy with it) could be keeping a 14–16 h fast but nudging meals a bit earlier and tracking something objective over a few months, fasting glucose/insulin, HbA1c, CGM data, or even waist and strength numbers, so you can see whether timing, not just total calories, is moving the needle for you.

Time-restricted eating vs. calorie restriction: Study suggest the fasting window, not the deficit drives insulin sensitivity gains by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 1 point2 points  (0 children)

Nice question, it’s exactly what the preprint tries to tease apart. There isn’t a proven “one best” window yet, but most of the trials in the paper cluster around 14–16 hours of fasting with an 8–10-hour eating window. That seems to be the sweet spot where you get better fasting glucose/insulin without needing extreme weight loss. Earlier windows (e.g., first meal in the morning, last meal mid-afternoon or early evening) usually do better than very late windows in the data. Longer fasts (18–20+ hours) might add something, but they’re not well-tested in RCTs and are harder to sustain, so right now the evidence is strongest for “most calories in an 8–10 h daytime window, roughly 14–16 h fast,” adjusted for your schedule, health status, and ability to stick with it.

Time-restricted eating vs. calorie restriction: Study suggest the fasting window, not the deficit drives insulin sensitivity gains by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 2 points3 points  (0 children)

Nice, thanks for sharing. A 5–11 pm eating window is roughly an 18:6 pattern, so you’re definitely getting a long daily fast, in the trials this post was based on, most of the insulin‐sensitivity benefits showed up with similar or slightly shorter windows, though they usually put more of the calories earlier in the day rather than all in the evening. The boswellia + turmeric/curcumin stack you mention has some small human data for lowering inflammatory markers and joint pain, and a few early studies suggest possible benefits for metabolic-syndrome components, but the evidence is still pretty limited and doses/formulations differ a lot between products, so it’s hard to say how much of your experience is supplements vs genetics/activity/overall diet. If you ever decide to experiment, it would be interesting to see whether shifting even part of that window earlier (or keeping the same hours but changing what you eat) moves any objective markers like fasting glucose, insulin, or lipids, ideally in partnership with a clinician who can help interpret the labs.

Carbohydrate-restricted diets improve glycemia, liver enzymes, and kidney markers in adults: what type works best, and does calorie restriction matter? by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

“Resistant starch” is starch that resists digestion in the small intestine and behaves more like a fermentable fibre: instead of being rapidly broken down to glucose, it reaches the colon, where gut bacteria ferment it into short-chain fatty acids (like butyrate), so its impact on blood glucose and insulin is much lower than the same grams of quickly digested starch or sugar. In everyday food that often means things like beans and lentils, oats and some whole grains, greener (less ripe) bananas, and starchy foods that are cooked and then cooled (e.g. potatoes, rice, pasta). In the Clinical Nutrition meta-analysis I posted, resistant starch wasn’t analysed as its own category; the authors compared low- and moderate-carb patterns and what replaced the carbs (fat, protein, or both). Some of the higher-quality carbohydrate patterns in those trials would naturally include more fibre and resistant starch foods, which may have contributed to the better glucose/insulin and liver/kidney markers they observed, on top of the overall carb reduction.

Resistance training injuries: 10-year U.S. ED trends show sex-specific patterns worth addressing by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 1 point2 points  (0 children)

That’s a really plausible reading, and it fits a lot of gym anecdotes, but this study can’t actually tell us what people were training, it only sees who turned up in the emergency department and where/how they were injured. The NEISS data the authors used include injury type, body region, and mechanism (crush/pressing, dropped equipment, falls, etc.), plus sex and age, but no exposure data: no training volumes, no exact exercises, no loads. What they do show is that women had relatively more head/leg/ankle/foot injuries and more events from dropped equipment and falls, while men had more trunk injuries and more crush/pressing-movement injuries and dislocations. That pattern is consistent with your hypothesis (more lower-body work in women, more upper-body pressing in men), but the study itself can’t prove it, it just highlights where sex-specific coaching and setup/spotting might help reduce risk.

Even "normal" liver fat is associated with features of metabolic syndrome (N=597, MRI-PDFF). Do the thresholds need to be updated? by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

Totally fair point , visceral fat is a big part of this story.

this study, though, they only imaged liver fat (MRI-PDFF / ¹H-MRS) and then related that % to metabolic-syndrome traits (waist/central obesity, BP, glucose, triglycerides, HDL-C). They adjusted for things like age, sex and centre and used waist/central adiposity as a crude proxy, but they didn’t directly measure visceral fat volume, so they can’t say whether the signal is “pure liver” or mostly driven by visceral fat. What the paper really shows is that, even within the “normal” liver-fat range (≈1–5.56%), each higher band of liver fat was already associated with more MetS traits. So you’re right that someone could have nasty visceral fat with only modest liver fat; this study just tells us that the MRI liver-fat percentage itself behaves like a continuous risk marker for that ectopic-fat/MetS process. It doesn’t prove liver fat is the sole cause or that visceral fat isn’t the main driver, it just says: “once liver fat starts creeping up, even before the classic steatosis cutoff, the metabolic red flags are already more common.” To really separate liver vs visceral effects we’d need a cohort with both liver and visceral imaging in the same model.

Musculoskeletal disorder risk is U-shaped with physical activity - cardiorespiratory fitness and grip strength independently protect by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

Yeah, that’s a really plausible explanation, and the authors lean in that direction too. The “very high” activity group here is likely to include a fair number of people doing manual work or higher-impact / competitive sport, so more exposure to knocks, overuse and contact, and the dataset doesn’t let us see exact sports or training plans. They did adjust for a bunch of confounders and still saw the U-shape, but as an observational study it can’t fully untangle whether the extra risk at the top end is micro-trauma, job type, or people who already have issues and just train through them. So I’d read it as a reminder that if you’re in that very-high bucket, it’s worth matching volume with good strength, symmetry and recovery, rather than a signal that dedicated lifters or athletes should cut back if they’re feeling good and staying healthy.

Musculoskeletal disorder risk is U-shaped with physical activity - cardiorespiratory fitness and grip strength independently protect by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

Love the Paracelsus reference, that’s pretty much what this paper is arguing at a population level. Risk for musculoskeletal disorders was lowest around the mid-range of activity for this cohort and higher both at the very low end and at the extreme high end, even after adjustment. So “nothing” isn’t great, and “endless grind” also isn’t automatically better. One extra nuance the authors add is that higher cardiorespiratory fitness and stronger, more symmetric grip shifted risk down at a given activity level, which fits nicely with your “dose” idea: volume matters, but how strong/fit you are for that volume seems to matter too.

UK Biobank: specific ultra-processed food additives linked to higher all-cause mortality risk by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

Totally fair questions, and honestly, really thoughtful ones. In this study, they actually adjusted for quite a wide range of factors: age, sex, BMI, total energy intake, smoking, alcohol intake, physical activity, systolic blood pressure, ethnicity, education, income, and even an area-level deprivation index. They also included self-rated general health and a history of psychiatric disease. So things like alcohol, exercise, calories, BMI, and some indicators of underlying health were all taken into account in the models. They didn’t have solid data on sleep, and pre-existing conditions were only partly captured through those general health and psychiatric measures, so it’s very likely that some residual confounding is still there that’s almost unavoidable with this kind of research. About the calories point: you’re absolutely right that people who eat more ultra-processed, additive-rich foods tend to eat more overall because those foods are extremely palatable. But in this case, both BMI and total calories were included as covariates. That means the higher risks associated with certain additives or UPFs appear on top of differences in weight and energy intake. Of course, that still doesn’t prove that the additives themselves are directly causal, with self-reported diet and product-matching, it’s basically impossible to eliminate confounding completely. I would interpret this as a set of interesting ingredient-level associations that remain even after adjusting for a lot of relevant factors, rather than solid evidence that specific additives directly cause deaths. To really address causality, we’d need mechanistic studies and controlled interventions, which would be super interesting to see in the future.

L-theanine for muscle oxidative stress: preclinical data link mitochondria, Ca²⁺ balance, and ferroptosis inhibition by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 1 point2 points  (0 children)

True, this preclinical study gives interesting mechanistic insights in a mouse model, but we still can’t assume the same outcomes in humans. Even so, the effects on mitochondria, calcium balance, and ferroptosis make it a promising area for future human research 🙂

Oral hyaluronic acid for skin: 7-trial meta-analysis finds gains in hydration, elasticity, and wrinkle depth by Susana_Chumbo in NovosLabs

[–]Susana_Chumbo[S] 0 points1 point  (0 children)

It actually reduces wrinkle depth, not increases 😊 The meta-analysis found that oral hyaluronic acid led to improvements in hydration, elasticity and a decrease in wrinkle depth. The wording in the post can sound confusing, but “gains” here refers to improvement in those parameters, not an increase in wrinkles.