Your metabolism has more to tell you than your cholesterol level

Why similar blood results can conceal different metabolic patterns, and what additional information an analysis of lipoproteins and metabolic markers can provide.

Two people compare their laboratory results. Both reports show the same LDL cholesterol level. At first glance, their lipid metabolism appears similar. But this value alone does not reveal how cholesterol in the blood is distributed among its transport particles.

In one person, a given amount of cholesterol may be carried by fewer transport particles, each containing more cholesterol. In another, it may be distributed among more particles, each carrying less. The measured level is similar, but the underlying pattern may differ.

These differences are one area of interest in metabolomics. This field studies small molecules that are produced, transformed or consumed during the body's metabolic processes. Examples include sugars, amino acids, fatty acids and other metabolic products. Individual laboratory values often reveal only part of how these substances interact.

A metabolome test measures a selection of these substances and brings them together in a single profile. This can provide additional information, but it replaces neither a medical diagnosis nor a clinician's assessment of individual laboratory values. What matters is therefore not simply how many parameters an analysis includes, but what conclusions can actually be drawn from them.

Contents

1. What metabolism really means
2. Cholesterol: why transport particles matter too
3. How a metabolome test builds a profile from a blood sample
4. How metabolism changes during the menopause transition
5. PCOS: when hormonal and metabolic processes overlap
6. Glucose and lipid metabolism are connected
7. The scales do not show every change
8. Diet does not affect all values in the same way
9. What a metabolic profile can and cannot tell you
10. References

What metabolism really means

In everyday conversation, metabolism is often reduced to a single question: is it fast or slow? What people usually mean is how easily someone gains or loses weight. Biologically, however, this view is too simplistic.

Metabolism encompasses numerous interconnected reactions. The liver processes nutrients and produces, among other things, components of blood lipids. Muscles take up glucose and use it to generate energy. Amino acids serve as building blocks for the body's proteins or enter other metabolic pathways. Hormones influence how these processes are regulated.

During these processes, measurable molecules known as metabolites are continuously produced and used up. Some serve as energy sources, while others are intermediate products of chemical reactions. Their concentrations can change with factors including diet, physical activity, time of day, medication use and health status.

The metabolome is the complete set of small molecules present in a biological system at a particular point in time. In practice, however, a test does not measure every substance in the body, but a defined selection. Which molecules can be measured depends on the measurement technology and the analytical method used.

A blood sample therefore captures only part of the body's metabolic activity. It shows the concentrations of the substances being analysed at the time the sample was taken. It does not directly show how quickly all metabolic processes are running or how well individual organs are functioning.

A comprehensive profile can reveal more relationships than a single laboratory value. What those relationships mean, however, depends on the specific parameter, the measurement method and the individual context.

Cholesterol: why transport particles matter too

Cholesterol is a component of cell membranes and is needed, among other things, to produce certain hormones. Because it does not readily dissolve in blood, it is transported in structures called lipoproteins. These particles consist of lipids and proteins and differ in size, composition and function.

The best-known classes include LDL, HDL and VLDL. A standard lipid profile measures total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides, among other parameters. These are established measures used in the medical assessment of lipid metabolism.

An extended lipoprotein profile can provide additional information. This includes the concentration of certain particles, their size distribution, and how cholesterol and triglycerides are distributed among different particle classes.

An analysis of the Multi-Ethnic Study of Atherosclerosis examined the relationship between LDL cholesterol, LDL particle number and subsequent cardiovascular events. In people whose LDL cholesterol level and LDL particle number differed substantially, particle number was more strongly associated with later risk than the cholesterol level alone.

This does not mean that particle number universally replaces every other measurement. Rather, it shows that the amount of cholesterol and the number of transport particles can provide different information. The study's findings support taking these differences into account when assessing risk in appropriate cases.

The common distinction between small, supposedly unfavourable LDL particles and large, supposedly harmless particles is also too simplistic. Particle size alone does not allow a complete assessment of lipid metabolism. Relevant factors include the concentration of atherogenic lipoproteins and other individual risk factors.

Another measurement is ApoB, short for apolipoprotein B. This protein is found on various lipoproteins that can contribute to the formation of deposits in artery walls. ApoB concentration provides information about the number of these atherogenic particles and can be useful in the medical assessment of lipid metabolism.

An extended profile is not automatically better than standard laboratory testing, however. Which additional parameters are useful depends on the specific question. Some questions can already be answered using established laboratory values, including ApoB.

How a metabolome test builds a profile from a blood sample

Nuclear magnetic resonance spectroscopy, or NMR, is a method for measuring characteristic signals from certain molecules in a sample. Suitable analytical procedures can then be used to derive the concentrations of selected metabolic parameters from these signals.

NMR-based analyses can capture different aspects of metabolism. Depending on the method, these include selected metabolites, lipids and lipoprotein parameters. For lipoproteins, for example, information about particle concentrations and size classes can be analysed. Which parameters are actually measured and included in the results report depends on the specific test profile.

This makes such an analysis different from a single laboratory value. Several measurements can be considered side by side and assessed in relation to one another. This can provide additional information, provided that the parameters are suitable for the question being asked and their significance has been sufficiently studied.

The method nevertheless has limitations. NMR does not automatically detect all metabolic products in the blood. The values derived from it also depend on the measurement and analytical procedures used. Moreover, a particular measurement is not the same as a diagnosis.

Scientific publications describe how NMR can be used to quantify lipoproteins and metabolites, and which analytical considerations need to be taken into account. This does not mean, however, that every commercial test has the same measurement accuracy or diagnostic validity. The specific procedures and analytical models must be evaluated separately.

The neotes Metabolome Test is offered as an analysis that considers selected metabolic parameters together. Exactly which parameters are measured and what conclusions can be drawn from them should be assessed using the current product description and the specific results report. Such a profile can provide an additional basis for discussion, but it does not replace medical interpretation.

How metabolism changes during the menopause transition

The menopause transition is often associated with changes to the menstrual cycle, hot flushes and sleep problems. Various metabolic parameters can also change during this stage of life. It is important to consider these changes in relation to ageing, the hormonal transition and other influencing factors.

A 2018 study examined women's metabolic profiles in relation to menopause. It identified changes in various lipoprotein and metabolic parameters, some of which could not be fully explained by ageing alone.

The SWAN-HDL Study examined 471 women during the menopause transition. The researchers looked not only at HDL cholesterol, but also at HDL subclasses, their lipid content and their functional properties. The findings show that these characteristics can follow different trajectories during the transition.

A rise in HDL cholesterol therefore does not automatically describe every change occurring in HDL particles. The concentration of the cholesterol being transported, the composition of the particles and certain functional properties are different measurements.

These studies do not establish that every woman experiences the same changes during the menopause transition. Rather, they show that different metabolic parameters can develop differently over the course of a life stage.

A lipoprotein profile can provide information about lipid transport. It does not, however, replace an assessment of hormone status, nor does it automatically measure the function of HDL particles. These require different tests and methods.

PCOS: when hormonal and metabolic processes overlap

Polycystic ovary syndrome, or PCOS, is a hormonal and reproductive health condition that can be associated with a range of metabolic characteristics. Its presentation varies from person to person.

One study compared 124 women with PCOS with 67 women in a control group. The researchers examined lipoprotein characteristics and the cholesterol efflux capacity of HDL, among other factors. This describes how effectively HDL can take up cholesterol from cells under the particular test conditions.

The study found lower HDL cholesterol efflux capacity in women with PCOS, alongside differences in other lipoprotein characteristics, including small LDL cholesterol particles and large VLDL particles. The findings illustrate that similar LDL cholesterol levels do not necessarily mean that all the lipoprotein characteristics being measured are the same.

The findings describe differences between the groups studied. They do not prove, however, that the measured particle characteristics were caused solely by PCOS, or that they can predict a particular health outcome for an individual. Nor can they be readily generalised to all women with PCOS.

In practice, it is also important to recognise that a lipoprotein profile cannot diagnose PCOS. Diagnosis relies on specific medical criteria and examinations. A metabolic analysis can add information about particular aspects, but cannot replace the diagnostic assessment.

Glucose and lipid metabolism are connected

Glucose and cholesterol usually appear in different sections of a laboratory report. In the body, however, their metabolic pathways are connected.

Insulin plays a central role. Among its functions, this hormone helps regulate glucose metabolism and influences other processes involved in nutrient processing and storage. When tissues become more or less sensitive to insulin, several metabolic parameters can change at the same time.

An analysis of the Women's Health Study followed 25,925 women without diabetes for a median period of around 20 years. A score based on NMR-derived lipoprotein parameters, the Lipoprotein Insulin Resistance Score, was associated with the subsequent development of type 2 diabetes.

The score incorporates various characteristics of VLDL, LDL and HDL particles, including their sizes and concentrations. The findings show that certain lipoprotein patterns can be associated with the later development of type 2 diabetes in population studies.

This does not mean that such a score reliably predicts disease in every individual. Nor is it equivalent to a direct measurement of insulin sensitivity. A score developed in one study also cannot simply be transferred to a different test or analytical model.

Individual amino acids are also investigated in metabolic research. What matters is not only which value is measured, but also the population, method and context in which the measurement is made. On its own, a single amino acid value does not allow a reliable conclusion about insulin resistance or a particular dietary pattern.

Considering several parameters together can reveal additional relationships. Interpreting them nevertheless requires scientifically validated analyses and information about the person being tested.

The scales do not show every change

People who exercise regularly may recognise this situation: endurance improves and movements become easier, but body weight barely changes. The scales measure body weight alone. They do not show the biochemical changes taking place at the same time.

The randomised STRRIDE study examined how different amounts and intensities of exercise affected blood lipoproteins in previously inactive, overweight adults. The study enrolled 111 people. The 84 participants who met the prescribed exercise requirements formed the basis of the main analysis.

The results showed changes in several lipoprotein parameters. In particular, a greater amount of exercise was associated with more extensive improvements in the lipoprotein profile. In the group studied, the changes were more closely related to the amount of activity than to exercise intensity or improvements in fitness.

This does not mean that exercise intensity is generally unimportant. The findings relate to the specific exercise programmes and measurements studied. They cannot be applied indiscriminately to every form of exercise, every person or every health goal.

Nor does the study establish that a blood profile can measure a person's fitness. A blood sample does not directly measure endurance or muscle strength. It provides information about the biochemical parameters being measured.

These measurements can offer an additional perspective when monitoring an exercise programme. They should nevertheless be considered alongside other information, such as exercise volume, physical performance and the conditions under which blood was taken.

Diet does not affect all values in the same way

With diet, too, there is rarely a single measurement that describes every change. Different nutrients can influence different metabolic pathways. Triglycerides, cholesterol and individual lipoprotein parameters may therefore respond differently.

A randomised crossover study in people with abdominal obesity examined how defined omega-3 and omega-6 fatty acid supplementation affected lipoprotein subclasses, standard blood lipids and apolipoproteins. The researchers found changes in the parameters studied, with effects differing according to the measurement.

The findings relate to the specific supplementation and study conditions examined. They cannot be generalised to every diet, every dietary supplement or every individual. In particular, they do not establish that a particular supplement regimen is advisable for everyone.

When assessing a dietary change, a specific question is therefore more useful than the general goal of optimising metabolism. What change was made? Which measurements should be considered? And under what conditions will they be measured again?

A change in a laboratory value does not automatically establish what caused it. Diet, physical activity, medication, medical conditions and other factors may all be involved at the same time.

What a metabolic profile can and cannot tell you

A more extensive metabolic profile can bring together different measurements. Its value lies primarily in providing additional information about selected biochemical processes. Whether that information is relevant to a specific question must be assessed individually.

Several points are important:

• A snapshot remains a snapshot. The values describe the substances analysed at the time the blood was taken. They do not capture every process in the body.
• Reference ranges are not personal targets. They indicate how measurements compare with a particular reference group. They do not automatically establish which value would be optimal for an individual.
• More measurements do not automatically mean greater diagnostic certainty. An additional parameter is only useful if its significance for the question being asked has been sufficiently studied.
• Research findings do not automatically apply to every test. Studies involving specific NMR platforms, populations and analytical models do not, on their own, establish the diagnostic performance of a different commercial test.
• Abnormal values need context. Medication, medical conditions, diet and physical exertion can influence results. Comparable sampling conditions and a consistent analytical method are important for repeat measurements over time.

Scientific studies of lipoproteins and metabolites show that further patterns can lie behind familiar laboratory values. They provide insights into relationships between metabolic parameters and different life stages. They do not, however, establish that a single metabolic profile can diagnose diseases or reliably predict their development in an individual.

The neotes Metabolome Test takes the approach of presenting selected metabolic information in a single profile. Exactly which parameters are measured and what medical conclusions can be drawn from them must be assessed using the relevant test documentation and the scientific validation of the measurement and analytical procedures. The individual's medical history, any further investigations and professional interpretation are also important.

Ultimately, the aim is not to collect as many numbers as possible. What matters is which question a measurement answers, what uncertainty remains, and whether the additional information is genuinely helpful in its particular context.

References

1. Otvos, J. D., Mora, S., Shalaurova, I., Greenland, P., Mackey, R. H. & Goff, D. C., Jr. (2011). Clinical implications of discordance between low-density lipoprotein cholesterol and particle number. Journal of Clinical Lipidology, 5(2), 105–113. DOI: 10.1016/j.jacl.2011.02.001
2. Wang, Q., Ferreira, D. L. S., Nelson, S. M., Sattar, N., Ala-Korpela, M. & Lawlor, D. A. (2018). Metabolic characterization of menopause: cross-sectional and longitudinal evidence. BMC Medicine, 16, Article 17. DOI: 10.1186/s12916-018-1008-8
3. El Khoudary, S. R., Chen, X., Nasr, A. et al. (2021). HDL (High-Density Lipoprotein) Subclasses, Lipid Content, and Function Trajectories Across the Menopause Transition: SWAN-HDL Study. Arteriosclerosis, Thrombosis, and Vascular Biology, 41(2), 951–961. DOI: 10.1161/ATVBAHA.120.315355
4. Roe, A., Hillman, J., Butts, S. et al. (2014). Decreased cholesterol efflux capacity and atherogenic lipid profile in young women with PCOS. The Journal of Clinical Endocrinology & Metabolism, 99(5), E841–E847. DOI: 10.1210/jc.2013-3918
5. Harada, P. H. N., Demler, O. V., Dugani, S. B. et al. (2017). Lipoprotein insulin resistance score and risk of incident diabetes during extended follow-up of 20 years: The Women's Health Study. Journal of Clinical Lipidology, 11(5), 1257–1267.e2. DOI: 10.1016/j.jacl.2017.06.008
6. Kraus, W. E., Houmard, J. A., Duscha, B. D. et al. (2002). Effects of the Amount and Intensity of Exercise on Plasma Lipoproteins. The New England Journal of Medicine, 347(19), 1483–1492. DOI: 10.1056/NEJMoa020194
7. Grytten, E., Laupsa-Borge, J., Bohov, P. et al. (2021). Changes in lipoprotein particle subclasses, standard lipids, and apolipoproteins after supplementation with n-3 or n-6 PUFAs in abdominal obesity: A randomized double-blind crossover study. Clinical Nutrition, 40(5), 2556–2575. DOI: 10.1016/j.clnu.2021.03.040
8. Loo, R. L., Lodge, S., Kimhofer, T. et al. (2020). Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples. Journal of Proteome Research, 19(11), 4428–4441. DOI: 10.1021/acs.jproteome.0c00537