This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
I read post #89 twice before replying, because I had assumed the opposite.
HbA1c and glucose: HbA1c reflects average glucose over months; a spot glucose measurement reflects the moment. Trending glucose downward while HbA1c stays flat is a different picture than glucose going up with HbA1c.
This follows post #89 rather than contradicting it.
Reference intervals: constructed to contain the central 95% of a reference population, which means one in twenty healthy people falls outside one by definition. Add biological variation and analytical imprecision and the base rate of a meaningless flag is substantial.
A single value outside interval: usually uninformative on a single draw. What makes a result interesting: a trend across multiple draws, a magnitude well beyond the interval, a pattern that coheres with other values, or symptoms that fit.
Coming back to post #93, because the follow-up matters more than the original answer.
Two things before anyone answers the substance.
First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.
Biological variation: the person-to-person variation in a biomarker for a healthy person is larger than most people realize. Comparing your result to the reference interval is one thing; comparing your result today to your result from months ago is more sensitive to change.
A single value outside interval: usually uninformative on a single draw. What makes a result interesting: a trend across multiple draws, a magnitude well beyond the interval, a pattern that coheres with other values, or symptoms that fit.
post #97 answers the question as asked. The question underneath it is different.
Biological variation: the person-to-person variation in a biomarker for a healthy person is larger than most people realize. Comparing your result to the reference interval is one thing; comparing your result today to your result from months ago is more sensitive to change.
HbA1c and glucose: HbA1c reflects average glucose over months; a spot glucose measurement reflects the moment. Trending glucose downward while HbA1c stays flat is a different picture than glucose going up with HbA1c.
Lipid panel interpretation: total, LDL, HDL, triglycerides all on one panel. Reading them together is more informative than reading one value in isolation. An elevated triglyceride with low HDL is different from triglyceride elevation alone.
Worth separating two things that post #97 runs together.
Time of day: some biomarkers vary across the day. Cortisol in the morning differs from cortisol in the evening. Comparing results from different times of day is comparing things that are not the same.
Renal function markers: eGFR is estimated, not measured. It depends on creatinine, age, and weight. A small change in creatinine might not mean a real change in kidney function.
On post #101 — agreed on the reasoning, with one qualification.
Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.
post #105 answers the question as asked. The question underneath it is different.
Analytical imprecision: any measurement has an error margin. A small difference in consecutive tests is usually measurement noise, not a real change. Knowing the imprecision helps distinguish noise from signal.
Fasting state: some lab tests require fasting; others do not. Lipids on a fasting draw differ from lipids on a fed draw. Always note whether the test was fasting when comparing results.
Reference intervals: constructed to contain the central 95% of a reference population, which means one in twenty healthy people falls outside one by definition. Add biological variation and analytical imprecision and the base rate of a meaningless flag is substantial.
Liver enzymes: elevation does not specify cause. ALT and AST can rise from many things. Bilirubin helps narrow down the cause. Multiple markers together are more informative than one alone.
I read post #110 twice before replying, because I had assumed the opposite.
Reference intervals: constructed to contain the central 95% of a reference population, which means one in twenty healthy people falls outside one by definition. Add biological variation and analytical imprecision and the base rate of a meaningless flag is substantial.
Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.
Time of day: some biomarkers vary across the day. Cortisol in the morning differs from cortisol in the evening. Comparing results from different times of day is comparing things that are not the same.
post #116 answers the question as asked. The question underneath it is different.
Fasting state: some lab tests require fasting; others do not. Lipids on a fasting draw differ from lipids on a fed draw. Always note whether the test was fasting when comparing results.
Analytical imprecision: any measurement has an error margin. A small difference in consecutive tests is usually measurement noise, not a real change. Knowing the imprecision helps distinguish noise from signal.
A single value outside interval: usually uninformative on a single draw. What makes a result interesting: a trend across multiple draws, a magnitude well beyond the interval, a pattern that coheres with other values, or symptoms that fit.