Genetic evidence for behavioral preference factors on fatigue: A Mendelian randomization study of chronotype, morning alertness... 2026 Luo et al

Andy

Andy

Senior Member (Voting rights)
Full title: Genetic evidence for behavioral preference factors on fatigue: A Mendelian randomization study of chronotype, morning alertness, and outdoor exposure

Abstract
Fatigue is a multifactorial condition influenced by environmental, behavioral, genetic, and disease-related factors. While observational studies have identified key contributors like circadian disruption, sleep disturbances, and genetic predisposition, the causal relationships remain unclear. Mendelian randomization (MR) offers a robust approach to overcome limitations of traditional studies and establish causal links between modifiable behavioral preferences and fatigue.

This study employed univariate and multivariate MR (MVMR) analyses using publicly available genome-wide association study (GWAS) summary statistics to investigate the potential causal relationships between these behavioral preference factors and the risk of fatigue. We obtained GWAS summary statistics for relevant variables from the Neale Lab and MRC Integrated Epidemiology Unit (MRC-IEU) databases. After systematic screening of multiple domains (internal microenvironment, indoor environment parameters, and natural environment characteristics), the inverse variance weighted (IVW) method served as the primary analysis, complemented by sensitivity analysis (heterogeneity test, pleiotropy analysis, leave-one-out analysis, and MR-PRESSO) to evaluate result robustness.

Using GWAS data from 32 traits (1645,048 participants), we identified three significant behavioral preference-related determinants of fatigue: chronotype, ease of getting up in the morning, and time spent outdoors in summer. The MR results demonstrated: Protective effects against fatigue associated with greater ease of getting up in the morning (OR = 0.991, 95%CI 0.987–0.995; P < .001) and longer summer outdoor exposure (OR = 0.996, 95%CI 0.992–1.000; P = .030); Elevated fatigue risk,linked to evening chronotype (OR = 1.003, 95%CI 1.001–1.005; P = .013). MVMR analysis showed that after jointly incorporating variables, the impact of ease of getting up in the morning on fatigue remained significant (OR = 0.987, 95%CI: 0.980–0.995, P = .002). Sensitivity analyses confirmed the robustness of these findings: although significant heterogeneity was detected for ease of getting up in the morning (Cochran’s Q test P < .05), no evidence of horizontal pleiotropy (MR-Egger intercept P > .05) or outlier SNPs (MR-PRESSO) was found, and results were consistent across multiple MR methods.

These findings provide genetic evidence supporting causal relationships between modifiable behavioral preference factors and fatigue. Specifically, greater ease of getting up in the morning and longer summer outdoor exposure may reduce fatigue risk, while evening chronotype increases susceptibility.

Open access
 
It [fatigue] reduces work efficiency by up to 75%,[2] and exacerbates underlying diseases, including an increased risk of Hashimoto’s thyroiditis (10-20%) and immune dysfunction in ME/chronic fatigue syndrome patients (CFS) patients (25% showing deficiencies in total immunoglobulins or immunoglobulin subclasses, 15% lacking mannose-binding lectin, and 25% demonstrating polyclonal immunoglobulin proliferation),[3] and is associated with elevates mortality risk (CFS patients show an all-cause standardized mortality ratio of 1.14; 95% CI = 0.65–1.85).
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Fatigue arises from complex interactions among environmental, behavioral, genetic, and disease-related factors, with existing research identifying multiple contributors, including circadian rhythm disruption (manifested through reduced light exposure, irregular activity patterns, delayed melatonin secretion, and disrupted body temperature rhythms),[9] overcrowded spaces, environmental stressors (54.9% of CFS patients report light sensitivity that exacerbates fatigue, while 62.8% experience noise-induced fatigue aggravation),[10] occupational pressures (77.3% of healthcare workers developed burnout during the COVID-19 pandemic),[11] and sleep deprivation (sleep disruption and non-restorative sleep affect 95% of ME/CFS patients
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For example, it remains unclear whether cortisol reduction in CFS patients is a cause or consequence of the condition.[14]
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"Have trouble getting up in the morning? Just get up easily get in the morning, dummy."

Also clearly the way to avoid falling from on high is to not crash into the ground. Clearly a modifiable behavior, just ease gently into the ground instead of very rapidly. Damn, solving difficult problems is really easy when you don't bother with things such as the time symmetry and its pesky relationship with cause and effect.
 
It's a long-running gag in sports communities to make jokes like "why doesn't the team score more goals than their opponents, are they stupid?" and this assertion is basically this but somehow serious.

Simple-but-difficult problems can't simply be turned into complex-but-easy problems, not when it changes the nature and the entire chain of causality. Or, well they can't be while expecting useful results out of it.
 
rvallee said:
Also clearly the way to avoid falling from on high is to not crash into the ground. Clearly a modifiable behavior, just ease gently into the ground instead of very rapidly.
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Or better still, miss the ground completely. Per Douglas Adams —

“There is an art to flying, or rather a knack. The knack lies in learning how to throw yourself at the ground and miss. ... Clearly, it is this second part, the missing, that presents the difficulties.”
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In addition to the points already made, applying mendelian randomization (MR) in this situation seems questionable. I don't know a ton about the details of MR, but my understanding is an ideal MR situation would look like this:
  1. You have an SNP that increases a person's tendency to smoke (the 'exposure').
  2. You are very confident the SNP is not directly associated with risk of lung cancer (the 'outcome') via a non-smoking mechanism (i.e. perhaps you magically have data from an identical population where smoking was unavailable and confirmed no association).
  3. You test the association of the smoking SNP with lung cancer. If you do find an association, this is evidence that smoking (the exposure) causes lung cancer (the outcome).

The assumption in step 2 is the weak point I've seen people question in other GWAS-based MR studies. It's especially sketchy when the exposure and the outcome seem likely biologically related, as here. In this study I believe their process was:
  1. Find SNPs associated in a GWAS with 'ease of getting up in the morning' (i.e P < 5×10−8).
  2. Of those SNPs, remove the ones that were found to be individually associated with fatigue in a different GWAS (with P < 5×10−8).
  3. Take the remaining set of 'ease of getting up in the morning' SNPs and see if they are *collectively* associated with fatigue via another statistical test.
Naturally, the concern is that all they've done is remove the SNPs with the strongest association to fatigue, and that the remaining SNPs still directly (biologically) cause fatigue to a lesser extent -- one that becomes significant when they are added together. The tests they ran in step 3 are meant to help a bit with this issue, but (from my understanding) they basically just weaken the SNPs-not-associated-to-the-outcome assumption from step 2 to something a bit more manageable (e.g. the SNPs can be associated with the outcome, but they have to have both positive and negative associations that cancel each other out). I haven't read all the details of the paper, but I am skeptical they could know any of the necessary assumptions hold for these SNPs they just pulled out of GWAS data.

Some articles discussing these issues (probably there are better sources though): [1] [2] [3]
 
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