# Multidimensional apathy: A simple and inclusive clinical marker of youth mental health-A longitudinal study

Giulia Lafond-Brina, Anne Bonnefond

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Giulia Lafond-Brina, Anne Bonnefond. Multidimensional apathy: A simple and inclusive clinical marker of youth mental health-A longitudinal study. Journal of Affective Disorders, 2026, 393 (Pt A), pp.120259. 10.1016/j.jad.2025.120259. inserm-05516603

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Research paper

# Multidimensional apathy: A simple and inclusive clinical marker of youth mental health—A longitudinal study

Giulia Lafond-Brina<sup>a,b,*</sup>, Anne Bonnefond<sup>a,b</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Prevention

Prediction

Anhedonia

Psychiatry

Psychosis

Mood disorders

Depression

## ABSTRACT

**Background:** Early identification of youth at risk for psychiatric disorders is crucial to reducing the global mental health burden. This study explores whether multidimensional apathy phenotypes predict transitions to depressive or psychotic disorders in youth and could serve as clinical markers.

**Method:** A 4.5-year longitudinal study followed 470 young adults via online surveys assessing socio-demographic, medical, educational, and functional characteristics, alongside validated measures of apathy, self-esteem, depressive symptoms, and motivation. Binary logistic regressions identified predictors of depressive and psychotic disorders, and Chi-squared tests examined apathy stability over time.

**Results:** Depressive disorders were predicted by subclinical depressive symptoms (β = 5.672; *p* < 2.3 × 10⁻⁶), executive apathy (β = 2.229; *p* < 0.03), lower independence (β = −1.867; *p* < 0.07), alcohol consumption (β = 2.128; *p* < 0.06), and medical comorbidities (β = 1.899; *p* < 0.03, accuracy: 91.47 %). Psychotic disorders were predicted by low self-esteem (β = −0.19; *p* < 3.9 × 10⁻⁸), emotional apathy (β = 1.83; *p* < 0.006), absence of anxiety (β = −2.22; *p* < 0.003), psychoactive drug use (β = 1.13; *p* < 0.002), and family psychiatric history (β = 1.505; *p* < 0.03, accuracy: 76.81 %). Emotional and executive apathy showed strong temporal stability between 2020 and 2024 (*p* < 2.2 × 10⁻¹⁶).

**Conclusions:** Executive and emotional apathy predict transitions to depression and psychosis, respectively, at 4,5 years, supporting multidimensional apathy as a potential inclusive and easy-to-implement clinical marker for youth mental health.

## 1. Introduction

Youth mental health, as recently emphasized by The Lancet Psychiatry Commission, must be recognized as a public health priority (McGorry et al., 2024). Mental illness affects approximately 30 % of individuals aged 18 to 20, with the period from puberty to the mid-to-late twenties representing a critical time of vulnerability for the onset of psychiatric disorders (Auerbach et al., 2018). Notably, 63–75 % of mental disorders emerge before the age of 25, highlighting the urgent need for early detection and intervention strategies. Indeed, early interventions have been shown to reduce the incidence of mental disorders by improving long-term symptomatic and functional outcomes, lowering suicide rates following diagnosis, and causing a delayed or less disabling transition to psychosis (Fusar-Poli et al., 2017; McGorry et al., 2024; Thompson et al., 2015). Consequently, the need for appropriate care arises well before a formal diagnosis, which peaks around age 20.5

for schizophrenia and 19.5 for depressive disorders, but with only 47.4 % and 36.9 % of lifetime cases diagnosed by age 25, respectively (Solmi et al., 2022).

To advance research regarding the prediction of youth mental health disorders, identifying new and inclusive clinical markers—more cost-effective than genetic or functional biomarkers and more readily applicable in clinical practice—is crucial. Indeed, sudden psychiatric disorders are often preceded by early symptoms, which may serve as personalized clinical biomarkers (Schreuder et al., 2023). Clinically, these early symptoms appear during youth and manifest as (micro) phenotypes, that may fluctuate in severity and specificity throughout the transition to a full-blown disorder. However, they do not necessarily follow distinct trajectories leading to specific diagnoses such as schizophrenia or depression (McGorry et al., 2021). Conducting longitudinal studies with transdiagnostic models in emerging adulthood can therefore facilitate the understanding of these early symptoms and their

<footnote><sup>a</sup> INSERM, UMR-S 1329, Strasbourg, France</footnote>

<footnote><sup>b</sup> University of Strasbourg, France</footnote>

<footnote>* Corresponding author at: Laboratoire INSERM UMR-S1329, Pôle de Psychiatrie, 1 Place de l'Hôpital, 67000, Strasbourg, France.</footnote>

<footnote>E-mail address: lafond.brina@gmail.com (G. Lafond-Brina).</footnote>
trajectories.

Moreover, a longitudinal perspective allows the exploration of mechanisms that underpin mental ill health. This framework places people along a multidimensional continuum of health to illness, capturing elements of risk, onset, course, and prognosis. The current models of psychosis and mood disorder development suggest that they result from the complex interplay of genetic and environmental factors, which can either increase (risk factors) or decrease (protective factors) the likelihood of developing psychiatric disorders (Oliver et al., 2020). Among these, “non-purely genetic factors” encompass numerous influences, including socio-demographic variables, parental and perinatal antecedents, later-life factors and personal conditions (Radua et al., 2018). Concerning socio-demographic factors, it is important to note that in low-resource and middle-resource countries, mental healthcare and prevention systems for youth are typically absent or limited because global research propositions are often highly expensive (McGorry et al., 2024), even if poverty is a well-known risk factor of transition to psychiatric disorders (Radua et al., 2018).

Despite the numerous putative risk factors, the literature favors more non-specific genetic and “non-purely genetic” factors that transcend each categorical diagnosis. For example, several potential risk factors have been identified in genetics and imaging studies, but mostly on the psychiatric spectrum, independently of the diagnostic disorder (Andreou et al., 2023; Coleman et al., 2020; Hager and Keshavan, 2015). In clinical biomarker research, anhedonia, defined as a diminished capacity to experience pleasure and positive emotions, has consistently emerged as an early and transdiagnostic risk factor for both psychotic disorders (Oliver et al., 2020; Radua et al., 2018), and depressive disorders (Beard et al., 2016; Fried et al., 2016). Beyond its role as a core symptom of major depressive episodes, anhedonia is a robust predictor of mental illness onset, symptom severity, episode duration, and poor functional outcomes, including increased suicidality and social impairment, across both adolescent and adult populations (Auerbach et al., 2017; Gabbay et al., 2015; Gillissie et al., 2023; Liu et al., 2021; Serretti, 2023). Previous studies have also found that apathy, defined as a diminished goal-directed behaviors, is an early common risk factor for the development of psychotic and mood disorders (Luther et al., 2016; Schmidt et al., 2017; Strauss et al., 2018); being a transdiagnostic negative symptom, apathy is a considerably disabling symptom in psychiatry (Galderisi et al., 2018).

However, we believe that the exploration of multidimensional apathy, which is almost non-existent in psychiatry, could improve clinical guidance during emerging adulthood, by predicting specific psychiatric disorders. It is a timely subject since 27 % of youth at high risk of psychosis will transition to psychosis after four years, while the remaining 73 % will develop other mental disorders, especially in the mood-spectrum disorders (Salazar de Pablo et al., 2021). A multidimensional model of apathy has demonstrated its clinical relevance in neurology, by distinguishing three phenotypes—emotional, executive, and initiative—with specific symptoms (Levy and Dubois, 2006) underlying distinct networks (Pagonabarraga et al., 2015; Radakovic and Abrahams, 2018). Patients with emotional apathy suffer from an emotional blunting and indifference to negative information, while those with executive apathy suffer from a difficulty to plan actions and those with an initiative apathy have difficulty in initiating thoughts, actions, or emotions. We recently showed that the three phenotypes of apathy exist isolated in healthy young adults, with the same disabling consequences as the apathy symptom in disorders; moreover, each was associated with specific predictors and specific cognitive mechanisms in these subjects (Lafond-Brina et al., 2023, 2024; Lafond-Brina and Bonnefond, 2022).

Therefore, the current aim is to test if different phenotypes of multidimensional apathy could predict the transition to distinct psychiatric disorders in youth and if these phenotypes could even be clinical markers. Indeed, more than a risk factor, a clinical marker must be a stable, non-contextual trait that reveals underlying mechanisms of a

disorder and informs the development of more targeted treatments (Duka et al., 2011; Guan et al., 2024). Moreover, a clinical marker has to be inexpensive, simple, and possibly digital to compete with genetic and imaging markers and overcome the current global limitation of healthcare and disease prevention being accessible to only high and middleclass consumers (McGorry et al., 2024). Therefore, the clinical importance of investigating multidimensional apathy is threefold. First, the three phenotypes could potentially be used to improve and refine the prediction of transition to psychosis and depression in the general population, especially among people at risk of developing psychiatric disorders. Second, these risk factors may be clinical markers that can be potentially modifiable by preventive interventions. Third, these inclusive and inexpensive clinical factors are easy to measure over time using questionnaires and could be easily utilized for promoting worldwide mental health.

## 2. Method

### 2.1. Participants

In total, 624 young adults responded to the online survey about the evolution of mental health in young adults conducted in June 2024. Each respondent had previously participated in a 2020 survey focusing on mental health in university students, conducted on the entire student population of a French university, and had agreed to be contacted again by email at a later date. The results of this first survey, which included questionnaires on negative symptoms and their functional impact, are published in Lafond-Brina and Bonnefond (2022). After excluding incomplete responses, 470 participants constituted the total sample.

To meet our objective, all participants of this sample who, in the initial survey, reported suffering from a psychiatric disorder and/or presented with moderate to severe depression were excluded. Depression severity was assessed using the 13-item short form of the Beck Depression Inventory-II (BDI-II-13), derived from the original 21-item version (Beck et al., 1996). The BDI-II-13 has been independently validated in both medical and student populations (Furlanetto et al., 2005; Leaune et al., 2022). A score greater than 13 was used as the threshold for moderate to severe depression, in line with prior validation studies. Participants with a BDI-II-13 score greater than 13 were thus excluded from the longitudinal analysis. Overall, 340 subjects constituted the sample of interest.

Fig. 1 represents the flowchart of the participants' inclusion, while Table 1 summarizes the demographic and neuropsychological characteristics of the total sample in 2020 and 2024 and the sample of interest in 2024.

All participants gave electronic informed consent and were not paid for their participation. The study was approved by the University review board (UNISTRA/CER/2020-13) and the French data protection authority (CNIL).

### 2.2. Variables of reference

The presence of a psychotic disorder in 2024 was determined based on participants' responses to the question: "Since the last survey in 2020, do you suffer, or have you suffered from a psychiatric disorder with psychotic symptoms such as schizophrenia or bipolar disorder?" Two response options were available: "yes" and "no". This question was designed to identify individuals reporting severe psychotic symptoms, including those associated with schizophrenia or bipolar disorder with psychotic features, without requiring formal diagnostic precision.

The presence of a depressive disorder in 2024 was determined based on the BDI-II-13 score. Following prior research conducted in comparable populations, a cut-off score of 13 was used to classify participants into two groups: (1) presence of moderate to severe depressive symptoms if the score was greater than 13; (2) absence of a depressive disorder if the score was inferior or equal to 13 (Furlanetto et al., 2005;
<figure><img src="image_4.png" /><figcaption>Fig. 1. Flowchart of participants' inclusion and selection process. The 13-item short form of the Beck Depression Inventory-II [BDI-II-13].</figcaption></figure>

Table 1

Demographic and neuropsychological characteristics of the 470 participants in 2020 and 2024. Values are given as the mean (standard deviation), the Dimensional Apathy Scale [DAS], the 13-item short form of the Beck Depression Inventory-II [BDI-II-13], the Rosenberg Self-Esteem Scale [RSE], the Temporal Experience of Pleasure Scale [TEPS].

<table><thead><tr><th></th><th>2020 February<br>n = 470</th><th>2024 June<br>n = 470</th><th>2024 June<br>n = 340</th></tr></thead><tbody><tr><td>Age (years)</td><td>20.3 (2.22)</td><td>24.9 (2.22)</td><td>25.3 (2.14)</td></tr><tr><td>Sex (% of males)</td><td>26.6</td><td>26.6</td><td>29.7</td></tr><tr><td>Level of education</td><td>Freshman: 29.1<br>%<br>Junior: 41.9 %<br>Senior: 28.9 %</td><td>Freshman: 5.8<br>%<br>Junior: 25.6 %<br>Senior: 68.7 %</td><td>Freshman: 5.6<br>%<br>Junior: 27.0 %<br>Senior: 67.3 %</td></tr><tr><td>DAS executive</td><td>11.1 (5.09)</td><td>10.7 (5.11)</td><td>9.5 (4.73)</td></tr><tr><td>DAS emotional</td><td>7.79 (4.35)</td><td>7.06 (4.16)</td><td>7.19 (4.23)</td></tr><tr><td>DAS initiative</td><td>10.10 (3.94)</td><td>9.93 (3.73)</td><td>9.71 (3.64)</td></tr><tr><td>BDI-II-13</td><td>8.60 (6.77)</td><td>8.16 (6.32)</td><td>6.53 (5.16)</td></tr><tr><td>RSE</td><td>27.2 (6.27)</td><td>28.2 (6.23)</td><td>29.6 (5.82)</td></tr><tr><td>TEPS Anticipatory<br>pleasure</td><td>44.8 (7.05)</td><td>44.9 (7.03)</td><td>45.4 (6.82)</td></tr><tr><td>TEPS Consummatory<br>pleasure</td><td>37.1 (5.89)</td><td>38.4 (5.78)</td><td>38.6 (5.79)</td></tr></tbody></table>

Leaune et al., 2022).

## 2.3. Regressors of interest

The regressors of interest, which can be grouped into five categories, were:

1) sociodemographic characteristics: gender, age, and level of education;

2) medical history: presence of non-psychiatric disorders (such as physical or sensory disability, thyroid disorder, neurological disease), antecedents of psychiatric disorder in first-degree relatives, consumption of legal psychoactive drugs (such as anxiolytics and sleeping pills), consumption of alcohol, consumption of cannabis, consumption of substances other than alcohol or cannabis;

3) education: current field studied at the university, number of repeated grades, number of skipped grades, and presence of professional- or university-related anxiety;

4) general functioning: type of accommodation, number of initiated contacts, presence of social isolation, presence of leisure, number of outings per month, daily fatigue, curiosity, independence, pleasure in daily life, realization of chores, realization of the grocery shopping, realization of the home cooking, and number of sedentary hours per day;

5) psychopathology: scores of four validated scale assessed multidimensional apathy, self-esteem, depressive symptoms, and motivation: DAS executive score, DAS emotional score, DAS initiative score, RSE score, BDI-II-13 score, TEPS anticipatory score and TEPS consummatory score respectively.

By contrast with the psychopathological constructs assessed using validated self-report questionnaires, the first four categories of predictors were developed specifically for this longitudinal survey to screen relevant dimensions in a large student population. Although these items are not based on standardized scales, their full content has been previously published in Lafond-Brina et al. (2022). In Supplementary Data 1 and 2, a description of the qualitative regressors of interest of the five categories, and a description and interpretation of the four validated scales that assessed psychopathology are detailed.

## 2.4. Statistical analysis

The percentage of subjects in the sample that had a psychotic disorder, a depressive disorder, or both in 2024 were calculated. Moreover, binary logistic regression analyses were used to analyze the predictors of the presence of a psychotic or depressive disorders. All predictor variables were entered into the model either as continuous or categorical variables. Categorical variables were dummy coded via model.matrix() in R, which generates k-1 binary indicators per factor using treatment coding, ensuring appropriate reference contrasts and avoiding multicollinearity in the model design. Using a mixed stepwise selection, we aimed to determine which variables in the survey most influenced the variable of reference due to the Akaike Information Criterion (AIC) —smaller AIC values indicate better the model fit, using the fewest possible variables. Furthermore, R-square values (the percentage variation of the reference explained by the regressor of interest) and the variance inflation factor (VIF), which measures the amount of multicollinearity in the regressors of interests, were calculated—higher VIF
indicates higher possibility that multicollinearity exists. In binary logistic regression, VIF values above 2.5 may be a cause for concern (Senaviratna and Cooray, 2019). A confusion matrix was then proposed to determine the accuracy, the Cohen's kappa, the sensitivity, the specificity, and the positive and negative predictive values of the models.

Two complementary analyses were proposed. To test the stability of the multidimensional apathy trait, since some intra-subjects' variations in questionnaires can be clinically insignificant, a Chi-squared test was used to determine whether the proportion of subjects remaining in the same category (subclinical apathy, or lack of apathy) in 2024 than in 2020 was significantly different from the expectations. To explore the cross-sectional relevance of apathy phenotypes, we conducted post hoc comparisons of executive, emotional, and initiation apathy scores between participants with and without a diagnosis of psychotic or depressive disorders in 2020. Due to non-normal distributions and unequal group sizes, non-parametric Wilcoxon-Mann-Whitney tests were used. Effect sizes were estimated using rank-biserial correlation (r) with 95 % confidence intervals.

To assess the robustness of our findings, we also conducted penalized logistic regression using LASSO with 10-fold cross-validation on standardized predictors. This approach identified baseline depressive symptoms (BDI-II-13 score in 2020) as the sole retained predictor across all models. However, due to shared variance, clinically relevant dimensions such as self-esteem, multidimensional apathy, and anhedonia were not selected. In contrast, stepwise regression allowed for their independent contribution to be tested, supporting their value as trans-diagnostic predictors. Full LASSO results are provided in Supplementary Data 3.

# 3. Results

## 3.1. Predictors of psychiatric disorders

### 3.1.1. Characterization of the sample with a psychotic disorder, a depressive disorder, or both in 2024

Overall, 74 subjects developed a psychotic disorder between 2020 and 2024, while 42 subjects developed a moderate to severe depressive disorder between 2020 and 2024. Only 25 developed both a psychotic disorder and a moderate to severe depressive disorder. Thus, only 33.78 % of participants with a psychotic disorder also presented a concomitant depressive disorder.

### 3.1.2. Predictors of psychotic disorders

In a binary logistic regression model with the presence/absence of a psychotic disorder as the reference, RSE score (β = -0.193), consumption of legal psychoactive drugs (β = 1.127), presence of professional- or university-related anxiety (β = -2.222), DAS emotional score (β = 1.828), presence of leisure (β = -1.774), antecedents of psychiatric disorder in first-degree relatives (β = 1.505), and consumption of substances other than alcohol or cannabis (β = 1.726) explained 21.8 % of the variance in the presence/absence of a psychotic disorder (X²(6) = 45.3; p < 0.001; AIC = 318.75; VIF = [1.03; 1.58]). Table 2 depicts the statistical characteristics of the predictors. The confusion matrix analysis revealed an overall accuracy of 76.81 % (95 %CI: 72.72%–80.55 %), with a highly significant deviation from the no-information rate (p = 8.547 × 10⁻⁷). The Cohen's kappa was 0.44, and sensitivity was high at 89.46 %, while specificity was lower at 51.59 %. The positive predictive value was 78.65 %, and the negative predictive value was 71.05 %.

### 3.1.3. Predictors of depressive disorders

In a binary logistic regression model with the presence/absence of a depressive disorder as the reference, subclinical BDI-II-13 score (β = 5.672), level of education (being junior in university; β = 3.681), presence of non-psychiatric disorders (β = 1.899), DAS executive score (β =

<table><caption>**Table 2**

Predictors in 2020 of a psychotic disorder in 2024.</caption><thead><tr><th></th><th>b</th><th>SE</th><th>95%CI</th><th>t</th><th>p-value</th></tr></thead><tbody><tr><td>RSE score</td><td>-0.140</td><td>0.026</td><td>[-0.19,-0.06]</td><td>-5.497</td><td>p < 3.9<br/>× 10<sup>-8</sup></td></tr><tr><td>Consumption of legal<br/>psychoactive drugs</td><td>1.363</td><td>0.436</td><td>[-0.55,2.25]</td><td>3.13</td><td>p < 0.002</td></tr><tr><td>Presence of<br/>professional- or<br/>university-related<br/>anxiety</td><td>-0.949</td><td>0.300</td><td>[-1.67,-0.24]</td><td>-2.97</td><td>p < 0.003</td></tr><tr><td>DAS emotional score</td><td>0.084</td><td>0.028</td><td>[0.02,0.16]</td><td>2.75</td><td>p < 0.006</td></tr><tr><td>Presence of leisure</td><td>-0.865</td><td>0.262</td><td>[-1.52,-0.21]</td><td>-2.39</td><td>p < 0.02</td></tr><tr><td>Antecedents of<br/>psychiatric disorder<br/>in first-degree<br/>relatives</td><td>0.781</td><td>0.256</td><td>[-0.03,1.52]</td><td>2.18</td><td>p < 0.03</td></tr><tr><td>Consumption of<br/>substances other<br/>than alcohol or<br/>cannabis</td><td>1.497</td><td>0.487</td><td>[0.27,2.69]</td><td>2.15</td><td>p < 0.03</td></tr></tbody></table>

Note: b = unstandardized regression coefficient; SE = standard error; 95 % CI = 95 % confidence interval; t = test statistic.

Positive b values indicate an increase in the log-odds of the presence of a psychotic disorder as the predictor increases, while negative b values indicate a decrease. The 95 % CI provides a range in which the true coefficient is expected to fall with 95 % confidence, while p-values below 0.05 suggest statistical significance.

2.229), consumption of alcohol (β = 2.128), and independence (β = -1.867) explained 53.1 % of the variance in the presence/absence of a depressive disorder (X²(5) = 66.9; p < 0.001; AIC = 205.06; VIF = [1.00; 1.15]). Table 3 depicts the statistical characteristics of the predictors. The confusion matrix analysis revealed an overall accuracy of 91.47 % (95 %CI, 87.98%–94.21 %), with a significant deviation from the no-information rate (p < 0.003). The Cohen's kappa was 0.52, and sensitivity was high at 97.99 %, while specificity was lower at 45.24 %. The positive predictive value was 92.70 %, and the negative predictive value was 76.00 %.

### 3.1.4. Predictors of a psychotic disorder with a concomitant depressive disorder

In a binary logistic regression model with the presence/absence of both a psychotic and a depressive disorder as the reference, dynamism (β = -3.728), presence of non-psychiatric disorders (β = 3.904), pleasure in daily life (β = -6.799), age (β = -5.508), absence of realization

<table><caption>**Table 3**

Predictors in 2020 of a depressive disorder in 2024.</caption><thead><tr><th></th><th>b</th><th>SE</th><th>95%CI</th><th>t</th><th>p-value</th></tr></thead><tbody><tr><td>Subclinical BDI-II-13 score</td><td>0.240</td><td>0.051</td><td>[0.15,0.35]</td><td>4.73</td><td>p < 2.25<br/>× 10<sup>-6</sup></td></tr><tr><td>Being junior in university</td><td>1.607</td><td>0.535</td><td>[0.30,2.71]</td><td>2.99</td><td>p < 0.003</td></tr><tr><td>Presence of non-psychiatric disorders</td><td>1.335</td><td>0.622</td><td>[0.12,2.55]</td><td>2.15</td><td>p < 0.03</td></tr><tr><td>DAS executive score</td><td>1.147</td><td>0.547</td><td>[-0.14,2.23]</td><td>2.10</td><td>p < 0.03</td></tr><tr><td>Consumption of alcohol</td><td>0.136</td><td>0.073</td><td>[-0.05,0.29]</td><td>1.87</td><td>p < 0.06</td></tr><tr><td>Independence</td><td>-0.841</td><td>0.480</td><td>[-1.49,0.11]</td><td>-1.75</td><td>p < 0.07</td></tr></tbody></table>

Note: b = unstandardized regression coefficient; SE = standard error; 95 % CI = 95 % confidence interval; t = test statistic.

Positive b values indicate an increase in the log-odds of the presence of a depressed disorder as the predictor increases, while negative b values indicate a decrease. The 95 % CI provides a range in which the true coefficient is expected to fall with 95 % confidence, while p-values below 0.05 suggest statistical significance.
of home cooking (β = 10.405), and TEPS consummatory score (β = -3.569) explained 42.8 % of the variance in the presence/absence of both a psychotic and a depressive disorder as the reference (X²(5) = 40.2; p < 0.001; AIC = 154.12; VIF = [1.08; 1.50]). Table 4 displays the statistical characteristic of the predictors. The confusion matrix analysis revealed an overall accuracy of 95.59 % (95 %CI: 92.83 %-97.51 %), with a significant deviation from the no-information rate (p < 0.009). The Cohen's kappa was 0.59, and sensitivity was high at 99.37 %, while specificity was lower at 48.00 %. The positive predictive value was 96.01 %, and the negative predictive value was 85.71 %.

## 3.2. Complementary analyses

### 3.2.1. Longitudinal intra-subject evolution of apathy categorization (apathy vs no apathy)

The Chi-squared test for the given probabilities yielded a highly significant result for executive categorization (X² = 162.08, df = 1, p < 2.2 × 10⁻¹⁶), indicating a significant tendency of subjects to remain in the same DAS executive category between 2020 and 2024.

The Chi-squared test for the given probabilities yielded a highly significant result for emotional categorization (X² = 243.07, df = 1, p < 2.2 × 10⁻¹⁶), indicating a significant tendency of subjects to remain in the same DAS emotional category between 2020 and 2024.

The Chi-squared test for the given probabilities yielded a highly significant result for initiative categorization (X² = 196.63, df = 1, p < 2.2 × 10⁻¹⁶), indicating a significant tendency of subjects to remain in the same DAS initiative category between 2020 and 2024.

### 3.2.2. Relevance of apathy predictors in young adults suffering from mental health disorders in 2020

Wilcoxon-Mann-Whitney tests revealed that participants with psychotic disorders in 2020 displayed significantly higher levels of executive apathy (p < 0.001, r = 0.19), emotional apathy (p < 0.001, r = 0.32), and initiative apathy (p < 0.005, r = 0.13) compared to healthy young adults. Similarly, participants with depressive disorders showed significantly higher executive apathy (p < 0.001, r = 0.29) and initiative apathy (p < 0.001, r = 0.26), but not emotional apathy (p > 0.20, r = 0.06).

## 4. Discussion

This longitudinal study, which followed a cohort of 470 young adults followed for four years, revealed for the first time that apathy, in a

<caption>**Table 4**</caption>

Predictors in 2020 of a co-occurrence of psychotic and depressive disorders in 2024.

<table><thead><tr><th></th><th>b</th><th>SE</th><th>95 %CI</th><th>t</th><th>p-value</th></tr></thead><tbody><tr><td>Dynamism</td><td>-1.286</td><td>0.712</td><td>[-2.74,0.09]</td><td>-1.81</td><td>p < 1.59<br/>× 10<sup>-4</sup></td></tr><tr><td>Presence of non-psychiatric disorders</td><td>2.259</td><td>0.849</td><td>[0.62,4.00]</td><td>2.66</td><td>p < 5.93<br/>× 10<sup>-4</sup></td></tr><tr><td>Pleasure in daily life</td><td>-1.782</td><td>0.632</td><td>[-3.11,-<br/>0.59]</td><td>-2.82</td><td>p < 8.32<br/>× 10<sup>-4</sup></td></tr><tr><td>Age</td><td>-0.396</td><td>0.176</td><td>[-0.61,-<br/>0.08]</td><td>-2.24</td><td>p < 0.02</td></tr><tr><td>Absence of realization of home cooking</td><td>3.313</td><td>1.179</td><td>[1.17,5.86]</td><td>2.81</td><td>p < 0.03</td></tr><tr><td>TEPS consummatory score</td><td>-0.095</td><td>0.055</td><td>[-0.01,0.21]</td><td>1.74</td><td>p < 0.04</td></tr></tbody></table>

Note: b = unstandardized regression coefficient; SE = standard error; 95 % CI = 95 % confidence interval; t = test statistic.

Positive b values indicate an increase in the log-odds of the presence of a co-occurrence of psychotic and depressed disorders as the predictor increases, while negative b values indicate a decrease. The 95 % CI provides a range in which the true coefficient is expected to fall with 95 % confidence, while p-values below 0.05 suggest statistical significance.

multidimensional conception, is an early predictor of interest for the specific transition to psychosis or depressive disorders in emerging adulthood. By highlighting that the emotional and executive phenotypes of apathy are predictive of the transition to psychosis and depression, respectively, at 4.5 years, our results have significant implications on the issue of youth mental health. Indeed, if clinical predictors of psychiatric disorders in youth have already been identified (predictors that were also found in the present study), none, to date, has been able to specifically predict the transition to a psychotic or depressive disorder and to meet all the criteria of a good clinical biomarker.

### 4.1. Multidimensional apathy, as a specific predictor of psychiatric disorders, could offer new targeted therapeutic strategies

Indeed, our results show that emotional apathy can significantly impact the likelihood of developing a psychotic disorder within 4.5 years. The presence of a psychotic disorder, such as schizophrenia or bipolar disorder with psychotic features, was based on self-reported diagnoses. Among the predictors highlighted, emotional apathy and the absence of anxiety were the strongest (standardized coefficients β of 1.83 and -2.222, respectively). Notably, as shown in the initial survey of the same population in 2020, these two predictors are strongly correlated (Lafond-Brina and Bonnefond, 2022). Indeed, in young adults, lower anxiety, when accompanied by indifference toward this anxiety, was associated with greater severity of emotional apathy. Consequently, measures of emotional apathy and lack of anxiety may represent the same clinical difficulty and could therefore be under-pinned by similar cognitive processes (Lafond-Brina et al., 2024). Similarly, the results of the present study reveal that executive apathy significantly influences the likelihood of developing a depressive disorder within 4.5 years. The presence of a depressive disorder was based on self-reported responses to the BDI-II-13 scale. Using the odds ratio of its b coefficient (1.147), our results show that individuals with higher levels of executive apathy are approximately 3.15 times more likely to experience a depressive disorder within 4.5 years than individuals without executive apathy.

So far, apathy—when defined as a reduction in goal-directed behavior—has been present since the earliest descriptions of untreated youth at risk of schizophrenia or depression (Bleuler, 1911; Delay, 1946; Kraepelin, 1919) and is recognized as one of the most prevalent symptoms in psychiatric disorders (Galderisi et al., 2018; Strauss and Cohen, 2017). However, this classical evaluation of apathy has not been able to specifically predict psychiatric disorders in cross-sectional studies of high-risk youth (Schmidt et al., 2017; Strauss et al., 2018). Contrarily, our results suggest that a finer assessment of the specific domain impacted by apathy—whether cognitive, emotional, or related to auto-activation (Robert et al., 2018)—makes such prediction possible. Although the multidimensional conception of apathy is rarely applied in psychiatry to our knowledge, it is well established that a continuum of abnormalities in goal-oriented behaviors underlies different phenotypes of apathy, each associated with distinct neural and cognitive alterations (Lafond-Brina et al., 2024; Levy and Dubois, 2006; Pagonabarraga et al., 2015). More specifically, orbitofrontal and subgenual cingulate dysfunction contribute to emotional apathy, which, on a more functional level, is associated with a “disliking” impairment, i.e. an indifference to negative information. Executive apathy, however, is associated with ventrolateral prefrontal cortex and posterior parietal dysfunction and an impairment of proactive control mode, corresponding to an anticipatory engagement of cognitive control processes. This knowledge makes this predictor an excellent potential clinical marker since the use of targeted therapies, a major point for improving outcomes in at-risk youth, becomes a real possibility. Encouraging results are already supporting this direction. For example, a recent study showed that rTMS stimulation of the orbitofrontal cortex could represent a promising therapeutic alternative in young adults with schizophrenia, by revealing a significant reduction of psychotic and negative
symptoms in patients with drug-naïve, first-episode schizophrenia (Hu et al., 2023). Concerning depressive disorders, the first, tenuous argument arose from an fMRI study which showed that a digital intervention specifically designed to enhance cognitive control processes was effective in reducing depressive and apathetic symptoms, as well as in normalizing activity of the cognitive control network (Gunning et al., 2021). In contrast, a meta-analysis of cognitive remediation targeting a broad range of executive functions in individuals with depression found no significant reduction in the severity of depressive symptoms (Mokhtari et al., 2023).

## 4.2. Emotional and executive apathy as promising clinical markers

In addition to promising treatment strategies, emotional and executive phenotypes of apathy, unlike the other predictors identified, meet all the criteria of a good clinical biomarker. Indeed, starting with familial and personal antecedents, the most frequent predictor of the transition to a psychiatric disorder identified in the literature, our results showed that antecedents in first-degree relatives predict psychotic disorders, whereas personal medical antecedents predict depressive disorders. However, no study, to our knowledge, has shown that these antecedents were able to predict the transition toward a specific psychiatric disorder (Chen et al., 2019; Cheng et al., 2018; Faridi et al., 2009; Le et al., 2020; Lindsey et al., 2009). Moreover, by showing a strong association between antecedents in first-degree relatives and both psychosis and resistant depression, genetics studies have confirmed the non-specificity of an antecedent in first-degree relatives (Andreou et al., 2023; Cheng et al., 2024; Coleman et al., 2020). However, this phenotype of resistant depression usually emerges in the thirties and could therefore have been missed in our sample. Consequently, although a history of psychiatric disorders in first-degree relatives is a potential predictor of both psychosis and resistant depression, our findings could only reflect its association with psychosis. Furthermore, substances intake, specifically the consumption of alcohol and of both psychoactive medication and illicit drugs, have been identified as predictors of depressive and psychotic disorders, respectively. However, the risks that they may be more predictive of comorbidities should be emphasized. Indeed, substance abuse disorders and sleep disorders are frequent comorbidities of both psychotic and depressive disorders (Bagautdinova et al., 2023; Christensen et al., 2023; Cohen et al., 2024; El-Hage et al., 2023; Fekih-Romdhane et al., 2022; Reeve et al., 2019; Ulrichsen et al., 2025; Zanini et al., 2015; Zhang et al., 2022). Although no direct sleep measures were included in the survey, the use of legal psychoactive drugs could serve as an indirect indicator of sleep disorders, as these substances are primarily used for sleep management among emerging adults in France (Batisse et al., 2021; Lacroix et al., 2024). Moreover, the survey does not provide information on the type and frequency of drug use, which prevents exploration of the potential dose-response relationship between the consumption of these substances and the risk of developing a psychotic disorder.

Finally, some psychological factors measured by questionnaires, such as a lack of self-esteem and subclinical depressive symptoms, have also been identified as predictors of psychotic and depressive disorders, respectively. However, the longitudinal follow-up at four years showed that the emotional and executive phenotypes of apathy were stable in time, whereas self-esteem and subclinical depressive symptoms were not. In addition to this temporal stability, our previous results regarding young adults showed that emotional and executive apathy were stable before and during the COVID-19 pandemic, revealing a contextual stability, unlike self-esteem and depressive symptoms (Lafond-Brina and Bonnefond, 2022). Additionally, emotional and executive apathy phenotypes were already elevated at baseline in individuals with psychotic and depressive disorders, respectively, reinforcing their value as early, specific, and stable clinical markers in youth. These findings further support the view that multidimensional apathy is not only predictive of distinct psychiatric trajectories, but also clinically detectable prior to

disease onset, making it a promising target for screening and stratified prevention in youth mental health. Multidimensional apathy also seems to be an inclusive marker, showing cultural stability across young and older adults from various countries (Ang et al., 2017; Yi et al., 2024). On the contrary, the non-stability over time, contexts and cultures for self-esteem and depressive symptoms has been confirmed by several studies (Erol and Orth, 2011; Ettman et al., 2020; Medda et al., 2022; Orth et al., 2018). Besides its stability, multidimensional apathy can also be easily integrated into clinical practices. Indeed, validated scales for assessing its intensity, like DAS, are available for use with youth from diverse countries (Ang et al., 2017; Kawagoe et al., 2020; Lafond-Brina and Bonnefond, 2022). Furthermore, this symptom can also be objectively measured using (digital) behavioral markers (Godefroy et al., 2021; Kluge et al., 2018; Sezer et al., 2024). Behavioral measures offer the added advantage of requiring less metacognitive effort, which is particularly relevant since youth at psychiatric risk often exhibit reduced self-awareness of their symptoms compared to their healthy peers (Clark et al., 2018).

Some limitations of this study should be acknowledged. Firstly, some personality measures such as those captured by the Big Five model are missing. Indeed, previous research has shown significant links between personality dimensions, especially neuroticism, and vulnerability to affective disorders (Wilks et al., 2020). Future studies should therefore explore whether multidimensional apathy provides incremental predictive value beyond personality-related variance. Secondly, our findings, based on self-reported symptomatology and transdiagnostic categories, need to be confirmed by future longitudinal studies involving structured clinical assessments conducted by psychiatrists. Such designs would provide a more robust understanding of the evolution of psychiatric symptoms in youth and help clarify diagnostic trajectories, particularly distinctions between primary depressive disorders, bipolar disorder, and schizophrenia-spectrum psychosis. Finally, as highlighted by Solmi et al. (2022), the diagnostic lag observed in youth underscores the need for predictive models that can identify at-risk individuals before formal clinical recognition.

In conclusion, identifying young persons at very early stages of psychiatric diseases, such as psychotic or mood disorders, is crucial to mitigate the growing worldwide impact of mental health disorders, one of the most common worldwide causes of disability. Our longitudinal results suggest that multidimensional apathy is a simple, easy-to-implement and inclusive candidate for clinical markers, which will help advance youth mental health prediction and personalize the strategies to target for improving the efficacy of preventive interventions. By stipulating “candidate,” we anticipate this topic will receive active consideration by the field, because, to date, not enough data are available to fully evaluate its status as a specific clinical marker to identify youth at risk of psychotic or depressive disorders.

### CRediT authorship contribution statement

**Giulia Lafond-Brina:** Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Anne Bonnefond:** Writing – review & editing, Validation, Supervision, Methodology, Data curation, Conceptualization.

### Transparency declaration

The corresponding author, G.L-B, affirm that this manuscript is an honest, accurate, and transparent account of the study reported and that no important aspects have been omitted. There were no discrepancies from the planned study.

### Funding statement

This research received no specific grant from any funding agency,
commercial or not-for-profit sectors.

Declaration of competing interest

Research support

This research received no external financial or non-financial support.

Relationships

There are no additional relationships to disclose.

Patents and Intellectual property

There are no patents to disclose.

Other activities

There are no additional activities to disclose.

Acknowledgments

None.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jad.2025.120259.

Data availability

The data that support the findings of this study are available from the
corresponding author, G.L-B, upon reasonable request. The data are not
publicly available because they could compromise the privacy of
research participants.

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