the ApoB thing finally clicked for me and I want to write it down
the ApoB thing finally clicked for me and I want to write it down — a position I have arrived at slowly and would like tested.
Took a baseline the week before starting purely because this board told me to. Best five minutes I have spent on this.
Tracked triglycerides against the weight trend for a year. The correlation was much weaker than I expected and that was worth knowing.
How to make a panel worth comparing, which is most of the value people leave on the table.
Standardise the conditions: same lab, same fasting state, same rough time of day, same point in the week, similar hydration. Take a baseline before you change anything. Repeat any surprising value before acting on it. Change one thing at a time if you want to attribute anything to it.
Do that and a year of panels is a trend. Skip it and a year of panels is a collection of unrelated mornings, which is what most of the alarmed posts here are actually describing.
Please do not ask me what dose you should be on. I genuinely do not know and neither does anyone else here.
best — the order this archive was captured in
Different laboratories use different assay platforms with different calibration. Comparing across labs adds a systematic offset that is invisible on the report.
Not convinced by the attribution. Weight loss alone moves that marker and you have no way to separate the two.
What did the repeat show?
Added ApoB to the panel after a thread here. It told me something the standard lipid panel had been hiding.
Compared two draws across two labs and spent a week worrying before finding out the assays were different.
one measurement is a point, two is a line, three is a trend
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baseline before you start is worth more than any later panel
baseline before you start is worth more than any later panel
Agreed — and standardising the draw conditions is the cheapest improvement available.
Time of day, fasting state, hydration and recent exercise all move common markers. Standardising the draw conditions is free and it removes most of the apparent variability.
I would not draw a line through two points, especially when the second was taken at a different time of day.
Substantial weight loss independently moves lipids, liver enzymes and several other markers. Attributing a change to a compound while losing weight is confounded by design.
That is a non-fasting value and the interval you are comparing it against is a fasting one.
weight loss itself moves several of these independently
hydration status moves a surprising number of markers
Disagree with reading anything into that. One value, one morning, and you changed two other things in the same window.
Did anything else change in that window — training, intake, hydration?
That comparison crosses two labs with different assays. The difference you are describing may be the method.
Yes. Repeat before you react. Almost every alarming single value I have posted here regressed on the repeat.
ApoB counts atherogenic particles rather than the cholesterol they carry, which is why it can diverge from LDL-C and why it is the addition most worth making.
Same view — ApoB if you add one thing. It answers a question the standard panel only gestures at.
Same view — ApoB if you add one thing.
laila_yilmaz is right that a reference interval is not a target. It is a description of a population.
Push back: a value just outside a reference interval is a common finding in healthy people. It is a reason to repeat.
Push back: a value just outside a reference interval is a common finding in healthy people.
Disagreeing with this bit: that change is confounded by the weight loss itself and cannot be attributed.
The confounding problem, stated plainly, because this board keeps stepping on it.
Substantial weight loss moves lipids, liver enzymes, insulin sensitivity markers and several others in its own right. If you are losing weight while taking something, any change in those markers has at least two candidate explanations and you cannot separate them from your own panel.
What you can do is standardise, take a baseline, keep the series long, and be honest in your posts about what is and is not attributable. The threads that say "this compound did X to my fasting insulin" almost never have the design to support the claim, mine included.
Yes — same lab, same fasting state, same rough time of day, or the comparison is doing nothing.
Why so many single values look alarming and turn out to be nothing.
A reference interval is built to contain about 95% of a healthy reference population, so one test in twenty falls outside one by construction. Add biological variation, assay imprecision, fasting state and time of day, and a genuinely stable person will produce occasional out-of-range results.
That is why repeat testing before acting is standard. Regression to the mean handles most of it. None of which means an out-of-range value should be ignored — it means it should be repeated and taken to somebody who can put it in context, which is emphatically not a ranked feed.
This. Reference ranges are population intervals, not targets, and conflating them causes a lot of unnecessary alarm here.
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