This 2015 Houston human study measured 575 metabolites in 72 biochemical pathways. The researchers used “nontargeted metabolomics” with next-generation gene sequencing to:
“Take account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy.”
The 80 subjects were 45 men and 35 women, average age of 54, in “..normal health with complete medical records and three-generation pedigrees.” The subjects all had college degrees, and were members or spouses of members of an upper-level socioeconomic organization.
The study’s range of 575 metabolites certainly cast a shadow over studies such as Running a marathon, cortisol, depression, causes, effects, and agendas that singled out 1 metabolite and tortured its data until it confessed a relationship that supported the preferred agenda.
Limitations of this study that weren’t mentioned by the researchers included:
- There were no specific target levels for each metabolite, which could lead to a misinterpretation that a “healthy” blood plasma level of a metabolite would always be the norm of the 80 subjects. This interpretation of each metabolite’s ideal level could be reinforced by the study calculating z-scores and P values of each individual’s measurement’s position within the cohort. The researchers stated:
“The identification of abnormal metabolic signatures was restricted by the relatively small number of subjects in the cohort.”
but that limitation wasn’t the flip side of omitted optimal levels.
- The metabolite measurements were mainly a one-time event although a series of measurements may have been more appropriate. Many of these metabolite levels vary with the time of day, what each individual had recently eaten, what each individual’s recent stress levels were, etc. This limitation may have been one of the sources for what the researchers noted:
“Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort.”
- There was no assessment of the relative contributions of epigenetic and genetic factors when discussing possible genetic impacts.
Regarding 1. above:
- It may be interesting to compare an individual to their peers and to other sources of information. However, when it comes time for “individualized therapy” because of a metabolic measurement that’s an outlier compared to these other sources, an individual’s history also matters.
- Each individual’s history could be used as a guide for optimal levels of some metabolites. For example, an optimal goal for “individualized therapy” for low testosterone levels of each of the 54-year old male subjects could be each individual’s previous higher levels of three decades earlier. It wouldn’t make sense for a 54-year old male to start testosterone therapy with a goal of raising his low levels to the non-therapeutic, low-level norm of other 54-year old males.
Regarding 2. above:
- As an example of a possible confounding factor – the time of day this study’s measurements were taken – a 2015 study Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans found an impact on several of the current study’s measured metabolites and biochemical pathways. Another 2015 study Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock found both a circadian and gender-specific impact on gut microbiota levels, albeit with mice. The current study partnered with a specialized company to analyze gut bacteria metabolites.
Regarding 3. above:
- As an example of unconsidered epigenetic factors, there was a discussion of acetaminophen metabolites because:
“The identification of at-risk populations could improve therapeutic options for individual patients and prevent adverse clinical outcomes.”
The researchers specifically compared and contrasted two subjects with the highest levels of acetaminophen metabolites, and concluded:
“These observations may suggest that volunteer 3976 was sensitive to acetaminophen-induced liver injury, whereas volunteer 3958 could tolerate acetaminophen well. This difference may relate to their cellular capability to maintain GSH [glutathione, one of our two primary endogenous antioxidants] levels in response to acetaminophen. We searched for a genetic basis of this variation in acetaminophen degradation/toxic metabolism without success.”
- The researchers shouldn’t have left the discussion hanging at this point. There’s no reason in 2015 for researchers to not investigate the contribution of epigenetic factors to:
“Take account of human individuality in genes, environment, and lifestyle.”
I was put off by the researchers statement:
“The volunteer’s cardiologist was informed of this observation to monitor possible drug interaction or toxicity.”
It appeared that the researchers bypassed one subject and informed the subject’s doctor directly when the subject was doing something the researchers considered detrimental to the subject’s health. I don’t know if the subject gave prior consent to be bypassed, though, because I didn’t see either study’s consent terms in the below linked material.
A few concluding questions:
- If it’s alright for personal health information to be transmitted without the consent of highly-educated, upper-level socioeconomic subjects, what can the rest of the population expect?
- Is “individualized therapy” best done through individual choices, or by forcing an individual to conform to expert opinion?
- Who is responsible for an individual’s physical and emotional health?
http://www.pnas.org/content/112/35/E4901.full “Plasma metabolomic profiles enhance precision medicine for volunteers of normal health”
http://www.pnas.org/content/110/42/16957.full “Personalized genomic disease risk of volunteers” (2013 original study with the same subjects)