📄 genetic and genomic medicine

Atypical energy-related symptoms define biologically distinct subtypes of major depressive disorder

This study demonstrates that major depressive disorder comprises biologically distinct subtypes defined by the directionality of neurovegetative symptoms, revealing that the "atypical" subtype (characterized by hypersomnia and weight gain) is genetically differentiated from the "typical" subtype by stronger heritability and specific associations with metabolic traits.

Original authors: Harder, A., Wang, R., Bergstedt, J., Huider, F., Kurvits, S., Thorp, J., Gong, T., Assary, E., Thijssen, A. B., Merola, G. P., Goula, A. A., Bentwood, S., Karlsson, R., Pasman, J. A., Fabbri, C., Hick

Published 2026-07-04

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Harder, A., Wang, R., Bergstedt, J., Huider, F., Kurvits, S., Thorp, J., Gong, T., Assary, E., Thijssen, A. B., Merola, G. P., Goula, A. A., Bentwood, S., Karlsson, R., Pasman, J. A., Fabbri, C., Hickie, I. B., Peyrot, W. J., Levinson, D. F., Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,, Potash, J. B., Shi, J., Medland, S. E., Mitchell, B. L., Kwong, A. S. F., Eley, T. C., Lewis, C. M., Hamilton, S. P., Martin, N. G., Boomsma, D. I., Lehto, K., Wray, N. R., Breen, G., Penninx, B. W. J. H., Milaneschi, Y., Marshall, S., Thomson, P. A., Lu, Y.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine Major Depressive Disorder (MDD) as a massive, crowded train station. For a long time, doctors and researchers have treated everyone waiting on the platform as if they are all going to the exact same destination. They see a ticket that says "Depression," and they assume the journey is identical for everyone.

However, this new study suggests that the station is actually split into three very different terminals, and the passengers in each terminal are traveling on different tracks with different engines.

Here is the breakdown of what the researchers found, using simple analogies:

1. The Three Different "Terminals"

The researchers looked at people who have depression but noticed that their physical symptoms (like sleep and appetite) pull in opposite directions. They sorted these people into three groups:

The "Heavy Sleeper & Big Eater" Group (AERS+): These are people who sleep too much (hypersomnia) and feel hungry or gain weight.
The "Insomniac & Dieter" Group (AERS-): These are people who can't sleep (insomnia) and lose their appetite or weight.
The "Mixed Bag" Group (Uncategorized): People who have depression but don't fit neatly into the first two categories (maybe they have one symptom but not the other, or their symptoms are mixed).

2. The Genetic "Engine" Difference

The study ran a massive genetic check-up (involving over 47,000 people in the "Heavy Sleeper" group and over 150,000 in the "Insomniac" group) to see if these groups were biologically different.

Think of depression as a car. The study found that the AERS+ group (Heavy Sleepers) has a very specific, powerful engine.

Stronger Engine: Their depression is driven by a smaller number of genetic "parts" (variants), but those parts have a very strong effect. It's like a high-performance sports car engine: fewer parts, but they pack a huge punch.
The "Fuel" Connection: This engine is deeply connected to the body's metabolism (how the body processes food and energy). Their genes are strongly linked to higher Body Mass Index (BMI), metabolic syndrome, and inflammation. It's as if their depression and their weight gain are fueled by the same biological fire.

In contrast, the AERS- group (Insomniacs) has a different engine.

Different Fuel: Their genes are linked to lower BMI and a different metabolic profile.
Less "Punch": Their genetic drivers are more spread out (more "polygenic"), meaning it takes a wider mix of many small genetic factors to cause their specific type of depression.

3. The "Uncategorized" Group is a Mix

The third group (Uncategorized) turned out to be a genetic "smoothie" of the other two. Their genetic makeup sits right in the middle, blending the traits of the Heavy Sleepers and the Insomniacs. However, they also showed a unique connection to cognitive traits (like education and IQ) that the other two groups didn't share as strongly.

4. Why This Matters (The "Mixing Problem")

The researchers argue that by lumping all these different people into one big "Depression" bucket, we have been missing the signal.

Imagine trying to understand the sound of a choir by listening to the whole room at once. If the bass singers are singing a low note and the sopranos are singing a high note, you just hear a muddy noise. But if you separate the bass section from the soprano section, you can hear the specific melody each group is singing.

By separating the "Heavy Sleepers" from the "Insomniacs," the researchers found:

Clearer Genetic Maps: They found 27 specific genetic locations (loci) that were previously hidden because the two groups were being averaged together.
Metabolic Clues: They confirmed that for the "Heavy Sleeper" group, the link to obesity and inflammation isn't just a side effect; it's written into their genetic code.

5. What the Study Does Not Say

It is important to stick to what the paper actually claims:

It does not say that being overweight causes depression, or that losing weight will cure it. It says the genetic blueprint for this specific type of depression is tied to metabolism.
It does not offer a new treatment or a new drug. It is a map-making study, not a construction project.
It does not claim this applies to everyone. The study only looked at people of European ancestry, so we don't know if these "terminals" look the same in other parts of the world.

The Bottom Line

This study suggests that "Depression" isn't just one thing. It's more like a category that contains different diseases. If you have the "Heavy Sleeper/Big Eater" type of depression, your biology is fundamentally different from the "Insomniac/Lost Appetite" type. Recognizing these differences helps scientists see the genetic roots of the disorder much more clearly, moving away from a "one-size-fits-all" view toward a more precise understanding of how the brain and body interact in different people.

Technical Summary: Atypical Energy-Related Symptoms Define Biologically Distinct Subtypes of Major Depressive Disorder

Problem Statement
Major Depressive Disorder (MDD) is a leading cause of global disease burden, yet therapeutic advances have stalled due to the disorder's profound heterogeneity. Current diagnostic criteria (DSM-5-TR) treat MDD as a unitary diagnosis, aggregating individuals with opposing neurovegetative symptom directions—specifically regarding sleep (insomnia vs. hypersomnia) and appetite/weight (loss vs. gain)—into a single phenotype. While historical clinical frameworks (e.g., DSM-III) distinguished between "melancholic" and "atypical" depression, and recent hypotheses suggest "atypical energy-related symptoms" (AERS) may index an immunometabolic subgroup, genetic characterizations of these subtypes have been limited by small, underpowered studies. The central question addressed is whether heterogeneous neurovegetative profiles reflect different presentations of a single disorder or partially distinct biological entities masked by syndromic aggregation.

Methodology
The authors conducted the largest genetic analysis to date of AERS-defined MDD subgroups using data from eight European ancestry cohorts, including the UK Biobank, Estonian Biobank, and the Psychiatric Genomics Consortium (PGC), totaling 124,348 MDD cases and 336,614 controls.

Phenotype Definition: MDD cases were stratified based on the directionality of symptoms during their worst lifetime episode:
- AERS+: Hypersomnia combined with increased appetite or weight gain.
- AERS-: Insomnia/hyposomnia combined with appetite or weight loss.
- Uncategorized: MDD cases not meeting criteria for either AERS+ or AERS-.
Genome-Wide Association Studies (GWAS): Three separate meta-analyses were performed for each subgroup ( $N_{eff}$ : 47,858 for AERS+, 156,624 for AERS-, and 215,828 for Uncategorized).
Genetic Architecture Analysis: The study employed LD Score Regression (LDSC) to estimate SNP-based heritability ( $h^2_{SNP}$ ) and genetic correlations ( $r_g$ ). Polygenicity was estimated using SBayesS.
Functional Annotation: Loci were annotated using eQTL colocalization (single-nucleus data from prefrontal cortex), stratified heritability enrichment (S-LDSC) across 256 cell types and 37 tissues, and colocalization with metabolic traits.
Causal Inference and Differentiation:
- Mendelian Randomization (MR): Two-sample MR was used to assess causal relationships between metabolic traits and AERS subtypes.
- Case-Case GWAS: A direct comparison between AERS+ and AERS- cases was performed to identify loci differentiating the subtypes.
- BMI Adjustment: Analyses were conditioned on BMI using mtCOJO to determine if genetic signals were independent of adiposity.

Key Results

Genetic Loci: The study identified 27 genome-wide significant loci across the three subtypes (4 for AERS+, 10 for AERS-, and 13 for Uncategorized).
Heritability and Polygenicity: AERS+ exhibited higher SNP-based heritability (10.9%) and lower polygenicity (1.7% of SNPs with non-zero effects) compared to AERS- (7.9%; 2.9%) and Uncategorized (8.6%; 5.3%). This suggests AERS+ is driven by fewer, larger-effect variants.
Genetic Correlation: The AERS+ and AERS- subtypes showed a moderate genetic correlation ( $r_g = 0.64$ ), indicating they are distinct but related. The Uncategorized group showed an intermediate genetic profile, positioned between AERS+ and AERS- in geometric space (GDIS), except for cognitive traits where it showed stronger correlations.
Metabolic Differentiation: A primary axis of differentiation was metabolic biology.
- AERS+: Showed strong positive genetic correlations with BMI ( $r_g = 0.39$ ), metabolic syndrome, waist circumference, type 2 diabetes, and inflammatory markers (e.g., GlycA, CRP).
- AERS-: Showed weak negative correlations with these same metabolic traits.
- Causal Direction: MR analyses indicated bidirectional causal relationships between AERS+ and metabolic traits (BMI, waist circumference, metabolic syndrome), whereas AERS- showed effects in the opposite direction.
Functional Insights:
- AERS+ loci included FTO and NEGR1 (nearby LINC02796), genes strongly associated with BMI. However, two AERS+ loci remained significant after conditioning on BMI, suggesting independence from simple adiposity signals.
- Cell-type enrichment was specific to the central nervous system for all subtypes, with AERS- showing strong enrichment in hippocampal CA1-3 neurons.
Case-Case Analysis: The direct comparison of AERS+ vs. AERS- identified four loci (FTO, ADCY3, TMEM18, NEGR1), all known obesity genes. Even after adjusting for BMI, the case-case phenotype retained significant genetic correlations with loneliness, neuroticism, and MDD, indicating that the subtypes differ in both metabolic and psychosocial dimensions.

Significance and Claims
The paper claims that the directionality of neurovegetative symptoms indexes meaningful genetic heterogeneity within MDD. The findings support the hypothesis that "atypical energy-related symptoms" (AERS+) define a biologically distinct subgroup characterized by a specific immunometabolic profile, rather than simply representing high-BMI individuals with depression.

Key claims include:

Subtyping Validity: Aggregating opposing symptom directions obscures subtype-specific genetic signals and inflates estimates of polygenicity. Stratifying by symptom direction reveals distinct genetic architectures.
Metabolic Axis: Metabolic biology is a central, though not exclusive, axis of differentiation. AERS+ is genetically linked to a pro-atherogenic and pro-inflammatory state, suggesting a potential "immunometabolic depression" pathway.
Clinical Implications: The results argue for a precision-psychiatry approach where metabolic risk factors are prioritized for specific depression presentations. The authors suggest that future prevention, stratification, and treatment-response studies (e.g., investigating GLP-1 receptor agonists) should explicitly track symptom directionality rather than relying solely on binary MDD definitions.
Limitations: The authors acknowledge limitations including the restriction to European ancestry, the cross-sectional nature of the data preventing definitive causal inference, potential recall bias in retrospective symptom reporting, and the lack of treatment exposure data in the subtype definition.

The study concludes that treating MDD as a genetically homogeneous entity may mask subtype-specific signals, and that preserving symptom-level detail in clinical and biobank settings is essential for refining these distinctions.