"Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota"
https://pmc.ncbi.nlm.nih.gov/articles/PMC10963277/
Edit: Ah, HN submission 2 years ago: https://news.ycombinator.com/item?id=39592367
this is a great study if you are an overweight/obese adult without overt metabolic disease, willing and able to consume 90 g/d of starch supplement within a controlled diet, and living in Shanghai with similar baseline fiber intakes endemic to that population. it is extremely not generalizeable to you or even me though I fit more of those characteristics than I care to admit
stay skeptical of small studies like this, friend
But obviously, most people are a lot more interested in finding a magic food which does this rather than a proven calorie deficit, which is highly effective.
Baer, David J., et al. "Dietary fiber decreases the metabolizable energy content and nutrient digestibility of mixed diets fed to humans." The Journal of nutrition 127.4 (1997): 579-586.
Somehow American Heart Association and its European counterpart are in denial, and still pushing dinasour screening mechanism with very low accuracy for heart disease risk prediction.
The standard risk model for CVD based on PREVENT (US) and SCORE-2 (Europe) like parameters are very poor as reported in the recently published paper on the their accuracy performance by the Swedish team [1]. As all CVD risk stratification with cardiologist review (expert-in-the-loop), the most important accuracy metric is sensivity/recall (avoiding false negative that will escape review) of PREVENT and SCORE-2, 26% and 48%, respectively.
The paper alternative proposal increased the sensitivity to 58% by performing clustering instead of conventional regression models as practiced in the PREVENT and SCORE-2.
These type of models including the latest proposal performed very poorly as indicated by their otherwise excellent and intuitive display of graphical abstract results [1].
[1] Risk stratification for cardiovascular disease: a comparative analysis of cluster analysis and traditional prediction models:
https://academic.oup.com/eurjpc/advance-article/doi/10.1093/...
https://www.mayoclinic.org/tests-procedures/heart-scan/about...
https://www.mayoclinic.org/tests-procedures/ct-coronary-angi...
(Edit: This is intended to be sarcastic. I agree 100% with the comment)
(Edit 2: Added the smiley face)
You could say that it’s almost as if their model has a home-field advantage. Because of that fact alone, you can’t really conclude anything about the comparative performance of their models versus the existing ones from this paper.
Getting an ECG, EKG, TTE, CAC, carotid duplex US, lipid panel, CMP, TSH, 25-OH Vit D, B12 + folate were what my cardio recommended before appointment #2 on hypertension. Both of us are data guys.
BMI is easily misunderstood by people who know just enough to see that it’s imperfect, but not enough to understand why it’s still a valuable screening tool.
I’ve been in the “overweight” BMI range with low body fat before. It’s not too hard to get there if you’re lifting weights and paying attention to your diet consistently for years, but it takes a lot of work to get there. It doesn’t happen accidentally except for people who win some genetic lottery to build a lot of muscle and keep body fat low without trying.
Getting all the way to the obese BMI range while having healthy body fat is only happening for people with an extreme dedication to body building and diet (and let’s be honest, a lot of the people in this category are manipulating hormones too).
Yet whenever BMI comes up some people try to dismiss it as too flawed based on these possible edge cases. The edge cases for BMI exist, but that doesn’t mean it’s not useful. It’s a good general purpose screening tool with numbers that are available. If someone has more precise measurements available, those should be used instead. BMI is a really good first pass screener to determine if a closer look should happen.
Many active gym people have pretty high BMIs but fairly low fat (because muscle is dense), and unsurprisingly have better outcomes than the average person (if you ignore the share that uses/overuses anabolic steroids and co)
I often wonder far from the median I am in this regard. I was under the impression that it was pretty accurate for assessing populations, but fell apart very quickly at the individual level. How many "normal"/otherwise healthy people do fall outside BMI's numbers?
BMI has known biases in gender, age, and race. It misclassified Asians, women, elderly w sarcopenia, and people with high body fat to lean tissue ratio.
BMI
Waist circumference (WC)
Waist to hip ratio (WHR)
Subsequent risk of nine cardiovascular/mortality outcomes in >260,000 people followed for ~20 years
To make it even more useful they should have included DEXA scan bodyfat%.
Also, BMI becomes somewhat biased at height extremes because body mass doesn't scale exactly with height². Humans aren't geometrically scaled copies of one another and empirical scaling exponents are often somewhere between 2 and 3. Conventional BMI tends to read relatively high in very tall people and relatively low in very short people. But changing the exponent would only fix one relatively small limitation of BMI
For better height adjusted replacement for BMI itself, one option is Trefethen’s BMI
WHR and WC is positively correlated to bodyfat% but this may get distorted for strongmen or sumo wrestler who tend to have much higher than average lean mass, may also have higher WC and WHR but difference maybe waist to shoulder ratio, they tend to have much bigger and powerful shoulders.
what's interesting is, for sumo wrestlers specifically, WC still correlates strongly with BF% one study reported r ≈ 0.86
There is a category in fitness called "skinny fat" where you are at low bodyweight (so low BMI) but your fat mass is relatively higher when compared to lean mass, so higher bodyfat%
Many skinny fat people refuse to believe they carry higher bodyfat% because they think they've low bodyweight so they can't possibly carry higher fat, which is wrong.
If you are interested in knowing more about bodyfat, this may help you: https://aretecodex.pages.dev/knowledge/measure/bodyfat
How does one determine if one has an excess of visceral fat?
If I'm reading that right, it sounds like obesity (and therefore BMI) is still a better predictor for all-cause mortality. Perhaps waist circumference is better at predicting cardiovascular risk but BMI is still useful.
Cutting saturated fat to under 15g per day and increasing intake of viscose fibre (e.g. beans) will reduce your LDL particle count in a few weeks, which reduces your CVD hazard ratio. You can measure your LDL and look up the papers yourself. Statins will reduce it a lot too (potentially with side effects). Replace solid fats like butter with liquid fats like olive oil.
Literally any amount of regular exercise, including walking, will decrease CVD HR. The more the better (up until quite a large amount e.g. professional athlete). The more your heart is steadily pumping during exercise the better. Every bit helps reduce CVD risk. Movement is medicine.
For the love of God do not smoke. Literally one of the surest ways to die a horrible death. Stopping smoking reduces CVD risk by a large amount.
Do not give yourself diabetes by eating vast amounts of sugar. If you are doing this, stop. Not having diabetes decreases CVD risk.
Other factors you probably can't change so focus on these.
Doctors, if I got anything wrong please correct me.
Thirty years after we learned that abdominal fat distribution matters, large-scale longitudinal evidence shows that waist measurements meaningfully reclassify cardiovascular risk beyond BMI alone.
For example, I’m 45yo/178cm/93kg and am obese by BMI measurement. However, my body fat is 20% (Dexa), VO2 of 50 (lab) & have the aerobic fitness to run a half marathon after work and not care.
I’m not surprised that you need other metrics like hip/waist ratio, measured body fat, visceral fat, etc to better understand the composition of someone’s body and how it might relate to health outcomes like heart disease.
(previously at https://news.ycombinator.com/item?id=45857053)
They need to figure out a way to reliably Measure OXLDL.
just got statin at 44 :(
i am not fat and workout ( although diet can use some improvment)
BMI was never meant to be used as a diagnostic measure. BMI is just a rough filter for large data sets, and entirely dependent on the average height and habits of that population.
Anyone taller than about 6'3" could tell you the recommended weight according to their BMI has always been absurdly low. If it's a printed chart on the wall, they might not even be on it.
And these aren't merely hypothetical bodybuilder edge cases. A systematic review found BMI had only ~50% sensitivity for detecting obesity when compared with body-fat reference methods—i.e. it missed roughly half the people classified as obese by adiposity.
More importantly, the 2025 Lancet Commission on clinical obesity explicitly recommended that BMI be used only as a population-level risk surrogate or screening tool, not as an individual measure of health. For individual assessment they recommend actually confirming excess adiposity with waist measurements or direct body-fat measurement.
Which is basically what this study is demonstrating again: where the fat is contains substantially more useful cardiovascular information than the number you get from dividing someone's mass by the square of their height.