Alan Light, Kathleen Light en collega's onderzochten de expressie van 34 genen in mensen met

ME/CVS (Fukuda) en/of fibromyalgie (33, 15, resp. 79), depressie (73) en gezonde mensen (61).

Op basis van onderlinge samenhang werden de 34 genen in vier "factoren" onderverdeeld.

Aan de aanduiding van de vier groepen (zie hieronder) moet niet al te veel waarde worden gehecht,

omdat bijvoorbeeld afweersysteem-gerelateerde genen in groep 1, 2 en 3 voorkomen.

Bij mensen met ME/CVS (Fukuda) was de expressie van de genen in groep 1 en 3 verhoogd,

terwijl de expressie van de genen uit groep 1 en 3 bij depressie juist afgenomen was.

Klik op onderstaande afbeelding voor de genen in de vier factoren (en hyperlinks):

Volgens dr. Alan Light (e-mail correspondentie) zouden lopende studies, o.m.

genexpressie na inspanning, eerdere bevindingen (klik hier en hier) in grote lijnen bevestigen.

Gene expression factor analysis to differentiate pathways linked to

fibromyalgia, chronic fatigue syndrome, and depression in a diverse patient sample.

Arthritis Care Res (Hoboken). 2015 Jun 19. doi: 10.1002/acr.22639. doi: 10.1002/acr.22639.

Iacob E, Light AR, Donaldson GW, Okifuji A, Hughen RW, White AT, Light KC.

Objective:

To determine if independent candidate genes

can be grouped into meaningful biological factors and

if these factors are associated with the diagnosis of

chronic fatigue syndrome (CFS) and fibromyalgia (FMS)

while controlling for co-morbid depression, sex, and age.

Methods:

We included leukocyte mRNA gene expression from a total of 261 individuals including

• healthy controls (n=61),
• patients with FMS only (n=15),
• CFS only (n=33),
• co-morbid CFS and FMS (n=79), and
• medication-resistant (n=42) or
• medication-responsive (n=31) depression.

We used Exploratory Factor Analysis (EFA) on 34 candidate genes to determine factor scores

and regression analysis to examine if these factors were associated with specific diagnoses.

Results:

EFA resulted in four independent factors with minimal overlap of genes between factors

explaining 51% of the variance.

We labeled these factors by function as:

1. Purinergic and cellular modulators;
2. Neuronal growth and immune function;
3. Nociception and stress mediators;
4. Energy and mitochondrial function.

Regression analysis predicting these biological factors

using FMS, CFS, depression severity, age, and sex

revealed that greater expression in Factors 1 and 3

was positively associated with CFS and

negatively associated with depression severity (QIDS score),

but not associated with FMS.

Conclusion:

Expression of candidate genes can be grouped into meaningful clusters, and

CFS and depression are associated with the same 2 clusters

but in opposite directions when controlling for co-morbid FMS.

Given high co-morbid disease and interrelationships between biomarkers,

EFA may help determine patient subgroups in this population based on gene expression.

PMID: 26097208

http://www.omicsonline.org/open-access/objective-evidence-of-postexertional-malaise-in-myalgic-encephalomyelitis-and-chronic-fatigue-syndrome-2161-0673-1000159.pdf