Biotransformation profiles from a cohort of chronic fatigue women in response to a hepatic detoxification challenge, 2019, Erasmus et al

Andy

Retired committee member
Looks like a weird one.
Abstract
Chronic fatigue, in its various manifestations, frequently co-occur with pain, sleep disturbances and depression and is a non-communicable condition which is rapidly becoming endemic worldwide. However, it is handicapped by a lack of objective definitions and diagnostic measures. This has prompted the World Health Organization to develop an international instrument whose intended purpose is to improve quality of life (QOL), with energy and fatigue as one domain of focus. To complement this objective, the interface between detoxification, the exposome, and xenobiotic-sensing by nuclear receptors that mediate induction of biotransformation-linked genes, is stimulating renewed attention to a rational development of strategies to identify the metabolic profiles in complex multifactorial conditions like fatigue.

Here we present results from a seven-year study of a cohort of 576 female patients suffering from low to high levels of chronic fatigue, in which phase I and phase II biotransformation was assessed. The biotransformation profiles used were based on hepatic detoxification challenge tests through oral caffeine, acetaminophen and acetylsalicylic acid ingestion coupled with oxidative stress analyses. The interventions indicated normal phase I but increased phase II glucuronidation and glycination conjugation. Complementarity was indicated between a fatigue scale, medical symptoms and associated energy-related parameters by application of Chi-square Automatic Interaction Detector (CHAID) analysis.

The presented study provides a cluster of data from which we propose that multidisciplinary inputs from the combination of a fatigue scale, medical symptoms and biotransformation profiles provide the rationale for the development of a comprehensive laboratory instrument for improved diagnostics and personalized interventions in patients with chronic fatigue with a view to improving their QOL.
Paywall, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216298
Sci Hub, http://sci-hub.se/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216298

Selection criteria
In total, 673 women with complaints of fatigue were assessed for study eligibility. Of these, 576 were eligible; exclusion criteria for all cases were verified indications of diabetes, chronic high blood pressure, asthma, cancer, arthritis and some other minor clinical conditions, but retaining patients suggestive of fibromyalgia, depression or other affect syndromes. All eligible cases fully completed both questionnaires.
As I understand it ME was one of the "other affect syndromes".
 
I've been trying to get my head around this. Here's what I've worked out so far:

Female patients reporting to a clinic with chronic fatigue who didn't have recognised chronic conditions like heart disease, diabetes and asthma, but may have had things like fibromyalgia or depression (or ME/CFS) were asked to fill in detailed fatigue questionnaires that distinguished mental and physical fatigue, and subgrouped into 5 groups according to how high their mental and physical fatigue. They also filled in a medical symptoms questionnaire.

They were then tested for lots of biochemicals in their urine and blood.

Then they were given biochemical challenges consisting of measured doses of caffeine, acetaminophen (tylenol/paracetamol) and acetylsalicylic acid (aspirin). This was to test how well the liver detoxified these substances and by what pathways, tested using saliva collected at measured intervals after the challenges.

The outcome is less clear to me because my biochemistry knowledge and reading concentration are not up to it. It seems they found some relationship between biological test results and low versus high fatigue. A couple of metabolites measured were significantly different between low and high fatigue groups.

There is a lot of discussion about genetic variations in detoxification pathways (or something) and the suggestion that a mix of exposure to toxins and high ATP use in some detox pathways may be related to fatigue and that therefore personalised medicine approaches may be needed to see what specific treatments might help individuals according to their genetic and metabolic profiles.

I hope someone with more biochemistry knowledge than me will take a look at this and tell us whether it looks useful.

If someone else does a better job than me deciphering this, I'll delete my post!
 
I was hoping that someone would look at detox pathways. I wonder why no-one has ever wondered as to why ME patients cannot tolerate medications.


The following are interesting excerpts :


(a) Perform a comprehensive history to reasonably identify past and present xenobiotic exposures, while including clinical information similar to the present MSQ and PFS; (b) apply the present broad-based biochemical instrument as a first approximation on the biotransformation status of the individual case; (c) define a clinical approach to institute personal health-related treatment strategies directed towards the individual case. The approach proposed here is very similar to that suggested by Beger et al. [63], in which a metabolite profile, clinical and lifestyle data were combined in a predictive patient profile, resulting in better patient stratification which facilitates personalized treatment and minimize the risk of the traditional treatment-failure approach. This precision medicine approach is fast gaining ground and has been described in a number of recent publications [64][65][66][67] [68]. Incorporation of genomic data is part of our research unit’s future planning involving integration of genetic and metabolic profiles. According to Contrepois et al. [69], this approach and metadata (such as nutritional background, exercise and medical records) will allow the detection of early signs of aberrant biotransformation and will result in nutrition/lifestyle recommendations and efficient drug treatments and complements.

and

Moreover, the acetoaminophen challenge clearly indicated that phase II glucuronidation (Fig 5C) was significantly increased in the high fatigue group (FC = +1.23; p = 0.02), with practical significance (ES = 0.54). Phase II sulphation did not differ between the low and high fatigue groups (FC = +1.01), but Phase II mercapturation was somewhat increased (FC = +1.10) in the high fatigue group (Fig 5D and 5E, respectively)

The CHAID algorithm is an interesting addition which they used to identify segments of patients.

Once again, i think we are getting close.
 
I'm not sure how to interpret this one either. The reduction of total acylcarnitine in the "high fatigue" group does relate to previous studies and there does appear to be evidence of reduced fatty acid metabolism in CFS patients in other studies, though whether this is due to different activity patterns remains to be seen...

There was some amusing language related to their p-values "not statistically informative, although biochemically relevant" and "(p= 0.06), which may be interpreted as being practically significant".

I thought we didn't write like that in 2019, but I guess I was wrong.
 
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