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Biological, Psychological, and Social Determinants of Depression: A Review of Recent Literature

Olivia remes, joão francisco mendes, peter templeton.

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Correspondence: [email protected]

Received 2021 Jul 27; Accepted 2021 Nov 12; Collection date 2021 Dec.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Depression is one of the leading causes of disability, and, if left unmanaged, it can increase the risk for suicide. The evidence base on the determinants of depression is fragmented, which makes the interpretation of the results across studies difficult. The objective of this study is to conduct a thorough synthesis of the literature assessing the biological, psychological, and social determinants of depression in order to piece together the puzzle of the key factors that are related to this condition. Titles and abstracts published between 2017 and 2020 were identified in PubMed, as well as Medline, Scopus, and PsycInfo. Key words relating to biological, social, and psychological determinants as well as depression were applied to the databases, and the screening and data charting of the documents took place. We included 470 documents in this literature review. The findings showed that there are a plethora of risk and protective factors (relating to biological, psychological, and social determinants) that are related to depression; these determinants are interlinked and influence depression outcomes through a web of causation. In this paper, we describe and present the vast, fragmented, and complex literature related to this topic. This review may be used to guide practice, public health efforts, policy, and research related to mental health and, specifically, depression.

Keywords: depression, major depressive disorder, risk factor, protective factor, determinant, review

1. Introduction

Depression is one of the most common mental health issues, with an estimated prevalence of 5% among adults [ 1 , 2 ]. Symptoms may include anhedonia, feelings of worthlessness, concentration and sleep difficulties, and suicidal ideation. According to the World Health Organization, depression is a leading cause of disability; research shows that it is a burdensome condition with a negative impact on educational trajectories, work performance, and other areas of life [ 1 , 3 ]. Depression can start early in the lifecourse and, if it remains unmanaged, may increase the risk for substance abuse, chronic conditions, such as cardiovascular disease, and premature mortality [ 4 , 5 , 6 , 7 , 8 ].

Treatment for depression exists, such as pharmacotherapy, cognitive behavioural therapy, and other modalities. A meta-analysis of randomized, placebo-controlled trials of patients shows that 56–60% of people respond well to active treatment with antidepressants (selective serotonin reuptake inhibitors, tricyclic antidepressants) [ 9 ]. However, pharmacotherapy may be associated with problems, such as side-effects, relapse issues, a potential duration of weeks until the medication starts working, and possible limited efficacy in mild cases [ 10 , 11 , 12 , 13 , 14 ]. Psychotherapy is also available, but access barriers can make it difficult for a number of people to get the necessary help.

Studies on depression have increased significantly over the past few decades. However, the literature remains fragmented and the interpretation of heterogeneous findings across studies and between fields is difficult. The cross-pollination of ideas between disciplines, such as genetics, neurology, immunology, and psychology, is limited. Reviews on the determinants of depression have been conducted, but they either focus exclusively on a particular set of determinants (ex. genetic risk factors [ 15 ]) or population sub-group (ex. children and adolescents [ 16 ]) or focus on characteristics measured predominantly at the individual level (ex. focus on social support, history of depression [ 17 ]) without taking the wider context (ex. area-level variables) into account. An integrated approach paying attention to key determinants from the biological, psychological, and social spheres, as well as key themes, such as the lifecourse perspective, enables clinicians and public health authorities to develop tailored, person-centred approaches.

The primary aim of this literature review: to address the aforementioned challenges, we have synthesized recent research on the biological, psychological, and social determinants of depression and we have reviewed research from fields including genetics, immunology, neurology, psychology, public health, and epidemiology, among others.

The subsidiary aim: we have paid special attention to important themes, including the lifecourse perspective and interactions between determinants, to guide further efforts by public health and medical professionals.

This literature review can be used as an evidence base by those in public health and the clinical setting and can be used to inform targeted interventions.

2. Materials and Methods

We conducted a review of the literature on the biological, psychological, and social determinants of depression in the last 4 years. We decided to focus on these determinants after discussions with academics (from the Manchester Metropolitan University, University of Cardiff, University of Colorado, Boulder, University of Cork, University of Leuven, University of Texas), charity representatives, and people with lived experience at workshops held by the University of Cambridge in 2020. In several aspects, we attempted to conduct this review according to PRISMA guidelines [ 18 ].

The inclusion and exclusion criteria are the following:

• - We included documents, such as primary studies, literature reviews, systematic reviews, meta-analyses, reports, and commentaries on the determinants of depression. The determinants refer to variables that appear to be linked to the development of depression, such as physiological factors (e.g., the nervous system, genetics), but also factors that are further away or more distal to the condition. Determinants may be risk or protective factors, and individual- or wider-area-level variables.
• - We focused on major depressive disorder, treatment-resistant depression, dysthymia, depressive symptoms, poststroke depression, perinatal depression, as well as depressive-like behaviour (common in animal studies), among others.
• - We included papers regardless of the measurement methods of depression.
• - We included papers that focused on human and/or rodent research.
• - This review focused on articles written in the English language.
• - Documents published between 2017–2020 were captured to provide an understanding of the latest research on this topic.
• - Studies that assessed depression as a comorbidity or secondary to another disorder.
• - Studies that did not focus on rodent and/or human research.
• - Studies that focused on the treatment of depression. We made this decision, because this is an in-depth topic that would warrant a separate stand-alone review.

Next, we searched PubMed (2017–2020) using keywords related to depression and determinants. Appendix A contains the search strategy used. We also conducted focused searches in Medline, Scopus, and PsycInfo (2017–2020).

Once the documents were identified through the databases, the inclusion and exclusion criteria were applied to the titles and abstracts. Screening of documents was conducted by O.R., and a subsample was screened by J.M.; any discrepancies were resolved through a communication process.

The full texts of documents were retrieved, and the inclusion and exclusion criteria were again applied. A subsample of documents underwent double screening by two authors (O.R., J.M.); again, any discrepancies were resolved through communication.

• a. A data charting form was created to capture the data elements of interest, including the authors, titles, determinants (biological, psychological, social), and the type of depression assessed by the research (e.g., major depression, depressive symptoms, depressive behaviour).
• b. The data charting form was piloted on a subset of documents, and refinements to it were made. The data charting form was created with the data elements described above and tested in 20 studies to determine whether refinements in the wording or language were needed.
• c. Data charting was conducted on the documents.
• d. Narrative analysis was conducted on the data charting table to identify key themes. When a particular finding was noted more than once, it was logged as a potential theme, with a review of these notes yielding key themes that appeared on multiple occasions. When key themes were identified, one researcher (O.R.) reviewed each document pertaining to that theme and derived concepts (key determinants and related outcomes). This process (a subsample) was verified by a second author (J.M.), and the two authors resolved any discrepancies through communication. Key themes were also checked as to whether they were of major significance to public mental health and at the forefront of public health discourse according to consultations we held with stakeholders from the Manchester Metropolitan University, University of Cardiff, University of Colorado, Boulder, University of Cork, University of Leuven, University of Texas, charity representatives, and people with lived experience at workshops held by the University of Cambridge in 2020.

We condensed the extensive information gleaned through our review into short summaries (with key points boxes for ease of understanding and interpretation of the data).

Through the searches, 6335 documents, such as primary studies, literature reviews, systematic reviews, meta-analyses, reports, and commentaries, were identified. After applying the inclusion and exclusion criteria, 470 papers were included in this review ( Supplementary Table S1 ). We focused on aspects related to biological, psychological, and social determinants of depression (examples of determinants and related outcomes are provided under each of the following sections.

3.1. Biological Factors

The following aspects will be discussed in this section: physical health conditions; then specific biological factors, including genetics; the microbiome; inflammatory factors; stress and hypothalamic–pituitary–adrenal (HPA) axis dysfunction, and the kynurenine pathway. Finally, aspects related to cognition will also be discussed in the context of depression.

3.1.1. Physical Health Conditions

Studies on physical health conditions—key points:

The presence of a physical health condition can increase the risk for depression

Psychological evaluation in physically sick populations is needed

There is large heterogeneity in study design and measurement; this makes the comparison of findings between and across studies difficult

A number of studies examined the links between the outcome of depression and physical health-related factors, such as bladder outlet obstruction, cerebral atrophy, cataract, stroke, epilepsy, body mass index and obesity, diabetes, urinary tract infection, forms of cancer, inflammatory bowel disorder, glaucoma, acne, urea accumulation, cerebral small vessel disease, traumatic brain injury, and disability in multiple sclerosis [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. For example, bladder outlet obstruction has been linked to inflammation and depressive behaviour in rodent research [ 24 ]. The presence of head and neck cancer also seemed to be related to an increased risk for depressive disorder [ 45 ]. Gestational diabetes mellitus has been linked to depressive symptoms in the postpartum period (but no association has been found with depression in the third pregnancy trimester) [ 50 ], and a plethora of other such examples of relationships between depression and physical conditions exist. As such, the assessment of psychopathology and the provision of support are necessary in individuals of ill health [ 45 ]. Despite the large evidence base on physical health-related factors, differences in study methodology and design, the lack of standardization when it comes to the measurement of various physical health conditions and depression, and heterogeneity in the study populations makes it difficult to compare studies [ 50 ].

The next subsections discuss specific biological factors, including genetics; the microbiome; inflammatory factors; stress and hypothalamic–pituitary–adrenal (HPA) axis dysfunction, and the kynurenine pathway; and aspects related to cognition.

3.1.2. Genetics

Studies on genetics—key points:

There were associations between genetic factors and depression; for example:

The brain-derived neurotrophic factor (BDNF) plays an important role in depression

Links exist between major histocompatibility complex region genes, as well as various gene polymorphisms and depression

Single nucleotide polymorphisms (SNPs) of genes involved in the tryptophan catabolites pathway are of interest in relation to depression

A number of genetic-related factors, genomic regions, polymorphisms, and other related aspects have been examined with respect to depression [ 61 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 ]. The influence of BDNF in relation to depression has been amply studied [ 117 , 118 , 141 , 142 , 143 ]. Research has shown associations between depression and BDNF (as well as candidate SNPs of the BDNF gene, polymorphisms of the BDNF gene, and the interaction of these polymorphisms with other determinants, such as stress) [ 129 , 144 , 145 ]. Specific findings have been reported: for example, a study reported a link between the BDNF rs6265 allele (A) and major depressive disorder [ 117 ].

Other research focused on major histocompatibility complex region genes, endocannabinoid receptor gene polymorphisms, as well as tissue-specific genes and gene co-expression networks and their links to depression [ 99 , 110 , 112 ]. The SNPs of genes involved in the tryptophan catabolites pathway have also been of interest when studying the pathogenesis of depression.

The results from genetics studies are compelling; however, the findings remain mixed. One study indicated no support for depression candidate gene findings [ 122 ]. Another study found no association between specific polymorphisms and major depressive disorder [ 132 ]. As such, further research using larger samples is needed to corroborate the statistically significant associations reported in the literature.

3.1.3. Microbiome

Studies on the microbiome—key points:

The gut bacteria and the brain communicate via both direct and indirect pathways called the gut-microbiota-brain axis (the bidirectional communication networks between the central nervous system and the gastrointestinal tract; this axis plays an important role in maintaining homeostasis).

A disordered microbiome can lead to inflammation, which can then lead to depression

There are possible links between the gut microbiome, host liver metabolism, brain inflammation, and depression

The common themes of this review have focused on the microbiome/microbiota or gut metabolome [ 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 ], the microbiota-gut-brain axis, and related factors [ 152 , 162 , 163 , 164 , 165 , 166 , 167 ]. When there is an imbalance in the intestinal bacteria, this can interfere with emotional regulation and contribute to harmful inflammatory processes and mood disorders [ 148 , 151 , 153 , 155 , 157 ]. Rodent research has shown that there may be a bidirectional association between the gut microbiota and depression: a disordered gut microbiota can play a role in the onset of this mental health problem, but, at the same time, the existence of stress and depression may also lead to a lower level of richness and diversity in the microbiome [ 158 ].

Research has also attempted to disentangle the links between the gut microbiome, host liver metabolism, brain inflammation, and depression, as well as the role of the ratio of lactobacillus to clostridium [ 152 ]. The literature has also examined the links between medication, such as antibiotics, and mood and behaviour, with the findings showing that antibiotics may be related to depression [ 159 , 168 ]. The links between the microbiome and depression are complex, and further studies are needed to determine the underpinning causal mechanisms.

3.1.4. Inflammation

Studies on inflammation—key points:

Pro-inflammatory cytokines are linked to depression

Pro-inflammatory cytokines, such as the tumour necrosis factor (TNF)-alpha, may play an important role

Different methods of measurement are used, making the comparison of findings across studies difficult

Inflammation has been a theme in this literature review [ 60 , 161 , 164 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 ]. The findings show that raised levels of inflammation (because of factors such as pro-inflammatory cytokines) have been associated with depression [ 60 , 161 , 174 , 175 , 178 ]. For example, pro-inflammatory cytokines, such as tumour necrosis factor (TNF)-alpha, have been linked to depression [ 185 ]. Various determinants, such as early life stress, have also been linked to systemic inflammation, and this can increase the risk for depression [ 186 ].

Nevertheless, not everyone with elevated inflammation develops depression; therefore, this is just one route out of many linked to pathogenesis. Despite the compelling evidence reported with respect to inflammation, it is difficult to compare the findings across studies because of different methods used to assess depression and its risk factors.

3.1.5. Stress and HPA Axis Dysfunction

Studies on stress and HPA axis dysfunction—key points:

Stress is linked to the release of proinflammatory factors

The dysregulation of the HPA axis is linked to depression

Determinants are interlinked in a complex web of causation

Stress was studied in various forms in rodent populations and humans [ 144 , 145 , 155 , 174 , 176 , 180 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 ].

Although this section has some overlap with others (as is to be expected because all of these determinants and body systems are interlinked), a number of studies have focused on the impact of stress on mental health. Stress has been mentioned in the literature as a risk factor of poor mental health and has emerged as an important determinant of depression. The effects of this variable are wide-ranging, and a short discussion is warranted.

Stress has been linked to the release of inflammatory factors, as well as the development of depression [ 204 ]. When the stress is high or lasts for a long period of time, this may negatively impact the brain. Chronic stress can impact the dendrites and synapses of various neurons, and may be implicated in the pathway leading to major depressive disorder [ 114 ]. As a review by Uchida et al. indicates, stress may be associated with the “dysregulation of neuronal and synaptic plasticity” [ 114 ]. Even in rodent studies, stress has a negative impact: chronic and unpredictable stress (and other forms of tension or stress) have been linked to unusual behaviour and depression symptoms [ 114 ].

The depression process and related brain changes, however, have also been linked to the hyperactivity or dysregulation of the HPA axis [ 127 , 130 , 131 , 182 , 212 ]. One review indicates that a potential underpinning mechanism of depression relates to “HPA axis abnormalities involved in chronic stress” [ 213 ]. There is a complex relationship between the HPA axis, glucocorticoid receptors, epigenetic mechanisms, and psychiatric sequelae [ 130 , 212 ].

In terms of the relationship between the HPA axis and stress and their influence on depression, the diathesis–stress model offers an explanation: it could be that early stress plays a role in the hyperactivation of the HPA axis, thus creating a predisposition “towards a maladaptive reaction to stress”. When this predisposition then meets an acute stressor, depression may ensue; thus, in line with the diathesis–stress model, a pre-existing vulnerability and stressor can create fertile ground for a mood disorder [ 213 ]. An integrated review by Dean and Keshavan [ 213 ] suggests that HPA axis hyperactivity is, in turn, related to other determinants, such as early deprivation and insecure early attachment; this again shows the complex web of causation between the different determinants.

3.1.6. Kynurenine Pathway

Studies on the kynurenine pathway—key points:

The kynurenine pathway is linked to depression

Indolamine 2,3-dioxegenase (IDO) polymorphisms are linked to postpartum depression

The kynurenine pathway was another theme that emerged in this review [ 120 , 178 , 181 , 184 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 ]. The kynurenine pathway has been implicated not only in general depressed mood (inflammation-induced depression) [ 184 , 214 , 219 ] but also postpartum depression [ 120 ]. When the kynurenine metabolism pathway is activated, this results in metabolites, which are neurotoxic.

A review by Jeon et al. notes a link between the impairment of the kynurenine pathway and inflammation-induced depression (triggered by treatment for various physical diseases, such as malignancy). The authors note that this could represent an important opportunity for immunopharmacology [ 214 ]. Another review by Danzer et al. suggests links between the inflammation-induced activation of indolamine 2,3-dioxegenase (the enzyme that converts tryptophan to kynurenine), the kynurenine metabolism pathway, and depression, and also remarks about the “opportunities for treatment of inflammation-induced depression” [ 184 ].

3.1.7. Cognition

Studies on cognition and the brain—key points:

Cognitive decline and cognitive deficits are linked to increased depression risk

Cognitive reserve is important in the disability/depression relationship

Family history of cognitive impairment is linked to depression

A number of studies have focused on the theme of cognition and the brain. The results show that factors, such as low cognitive ability/function, cognitive vulnerability, cognitive impairment or deficits, subjective cognitive decline, regression of dendritic branching and hippocampal atrophy/death of hippocampal cells, impaired neuroplasticity, and neurogenesis-related aspects, have been linked to depression [ 131 , 212 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 ]. The cognitive reserve appears to act as a moderator and can magnify the impact of certain determinants on poor mental health. For example, in a study in which participants with multiple sclerosis also had low cognitive reserve, disability was shown to increase the risk for depression [ 63 ]. Cognitive deficits can be both causal and resultant in depression. A study on individuals attending outpatient stroke clinics showed that lower scores in cognition were related to depression; thus, cognitive impairment appears to be associated with depressive symptomatology [ 226 ]. Further, Halahakoon et al. [ 222 ] note a meta-analysis [ 240 ] that shows that a family history of cognitive impairment (in first degree relatives) is also linked to depression.

In addition to cognitive deficits, low-level cognitive ability [ 231 ] and cognitive vulnerability [ 232 ] have also been linked to depression. While cognitive impairment may be implicated in the pathogenesis of depressive symptoms [ 222 ], negative information processing biases are also important; according to the ‘cognitive neuropsychological’ model of depression, negative affective biases play a central part in the development of depression [ 222 , 241 ]. Nevertheless, the evidence on this topic is mixed and further work is needed to determine the underpinning mechanisms between these states.

3.2. Psychological Factors

Studies on psychological factors—key points:

There are many affective risk factors linked to depression

Determinants of depression include negative self-concept, sensitivity to rejection, neuroticism, rumination, negative emotionality, and others

A number of studies have been undertaken on the psychological factors linked to depression (including mastery, self-esteem, optimism, negative self-image, current or past mental health conditions, and various other aspects, including neuroticism, brooding, conflict, negative thinking, insight, cognitive fusion, emotional clarity, rumination, dysfunctional attitudes, interpretation bias, and attachment style) [ 66 , 128 , 140 , 205 , 210 , 228 , 235 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 ]. Determinants related to this condition include low self-esteem and shame, among other factors [ 269 , 270 , 275 , 278 ]. Several emotional states and traits, such as neuroticism [ 235 , 260 , 271 , 278 ], negative self-concept (with self-perceptions of worthlessness and uselessness), and negative interpretation or attention biases have been linked to depression [ 261 , 271 , 282 , 283 , 286 ]. Moreover, low emotional clarity has been associated with depression [ 267 ]. When it comes to the severity of the disorder, it appears that meta-emotions (“emotions that occur in response to other emotions (e.g., guilt about anger)” [ 268 ]) have a role to play in depression [ 268 ].

A determinant that has received much attention in mental health research concerns rumination. Rumination has been presented as a mediator but also as a risk factor for depression [ 57 , 210 , 259 ]. When studied as a risk factor, it appears that the relationship of rumination with depression is mediated by variables that include limited problem-solving ability and insufficient social support [ 259 ]. However, rumination also appears to act as a mediator: for example, this variable (particularly brooding rumination) lies on the causal pathway between poor attention control and depression [ 265 ]. This shows that determinants may present in several forms: as moderators or mediators, risk factors or outcomes, and this is why disentangling the relationships between the various factors linked to depression is a complex task.

The psychological determinants are commonly researched variables in the mental health literature. A wide range of factors have been linked to depression, such as the aforementioned determinants, but also: (low) optimism levels, maladaptive coping (such as avoidance), body image issues, and maladaptive perfectionism, among others [ 269 , 270 , 272 , 273 , 275 , 276 , 279 , 285 , 286 ]. Various mechanisms have been proposed to explain the way these determinants increase the risk for depression. One of the underpinning mechanisms linking the determinants and depression concerns coping. For example, positive fantasy engagement, cognitive biases, or personality dispositions may lead to emotion-focused coping, such as brooding, and subsequently increase the risk for depression [ 272 , 284 , 287 ]. Knowing the causal mechanisms linking the determinants to outcomes provides insight for the development of targeted interventions.

3.3. Social Determinants

Studies on social determinants—key points:

Social determinants are the conditions in the environments where people are born, live, learn, work, play, etc.; these influence (mental) health [ 291 ]

There are many social determinants linked to depression, such as sociodemographics, social support, adverse childhood experiences

Determinants can be at the individual, social network, community, and societal levels

Studies also focused on the social determinants of (mental) health; these are the conditions in which people are born, live, learn, work, play, and age, and have a significant influence on wellbeing [ 291 ]. Factors such as age, social or socioeconomic status, social support, financial strain and deprivation, food insecurity, education, employment status, living arrangements, marital status, race, childhood conflict and bullying, violent crime exposure, abuse, discrimination, (self)-stigma, ethnicity and migrant status, working conditions, adverse or significant life events, illiteracy or health literacy, environmental events, job strain, and the built environment have been linked to depression, among others [ 52 , 133 , 235 , 236 , 239 , 252 , 269 , 280 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 , 313 , 314 , 315 , 316 , 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 , 336 , 337 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 ]. Social support and cohesion, as well as structural social capital, have also been identified as determinants [ 140 , 228 , 239 , 269 , 293 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 ]. In a study, part of the findings showed that low levels of education have been shown to be linked to post-stroke depression (but not severe or clinical depression outcomes) [ 299 ]. A study within a systematic review indicated that having only primary education was associated with a higher risk of depression compared to having secondary or higher education (although another study contrasted this finding) [ 296 ]. Various studies on socioeconomic status-related factors have been undertaken [ 239 , 297 ]; the research has shown that a low level of education is linked to depression [ 297 ]. Low income is also related to depressive disorders [ 312 ]. By contrast, high levels of education and income are protective [ 335 ].

A group of determinants touched upon by several studies included adverse childhood or early life experiences: ex. conflict with parents, early exposure to traumatic life events, bullying and childhood trauma were found to increase the risk of depression (ex. through pathways, such as inflammation, interaction effects, or cognitive biases) [ 161 , 182 , 258 , 358 , 362 , 380 ].

Gender-related factors were also found to play an important role with respect to mental health [ 235 , 381 , 382 , 383 , 384 , 385 ]. Gender inequalities can start early on in the lifecourse, and women were found to be twice as likely to have depression as men. Gender-related factors were linked to cognitive biases, resilience and vulnerabilities [ 362 , 384 ].

Determinants can impact mental health outcomes through underpinning mechanisms. For example, harmful determinants can influence the uptake of risk behaviours. Risk behaviours, such as sedentary behaviour, substance abuse and smoking/nicotine exposure, have been linked to depression [ 226 , 335 , 355 , 385 , 386 , 387 , 388 , 389 , 390 , 391 , 392 , 393 , 394 , 395 , 396 , 397 , 398 , 399 , 400 , 401 ]. Harmful determinants can also have an impact on diet. Indeed, dietary aspects and diet components (ex. vitamin D, folate, selenium intake, iron, vitamin B12, vitamin K, fiber intake, zinc) as well as diet-related inflammatory potential have been linked to depression outcomes [ 161 , 208 , 236 , 312 , 396 , 402 , 403 , 404 , 405 , 406 , 407 , 408 , 409 , 410 , 411 , 412 , 413 , 414 , 415 , 416 , 417 , 418 , 419 , 420 , 421 , 422 , 423 , 424 , 425 , 426 , 427 , 428 ]. A poor diet has been linked to depression through mechanisms such as inflammation [ 428 ].

Again, it is difficult to constrict diet to the ‘social determinants of health’ category as it also relates to inflammation (biological determinants) and could even stand alone as its own category. Nevertheless, all of these factors are interlinked and influence one another in a complex web of causation, as mentioned elsewhere in the paper.

Supplementary Figure S1 contains a representation of key determinants acting at various levels: the individual, social network, community, and societal levels. The determinants have an influence on risk behaviours, and this, in turn, can affect the mood (i.e., depression), body processes (ex. can increase inflammation), and may negatively influence brain structure and function.

3.4. Others

Studies on ‘other’ determinants—key points:

A number of factors are related to depression

These may not be as easily categorized as the other determinants in this paper

A number of factors arose in this review that were related to depression; it was difficult to place these under a specific heading above, so this ‘other’ category was created. A number of these could be sorted under the ‘social determinants of depression’ category. For example, being exposed to deprivation, hardship, or adversity may increase the risk for air pollution exposure and nighttime shift work, among others, and the latter determinants have been found to increase the risk for depression. Air pollution could also be regarded as an ecologic-level (environmental) determinant of mental health.

Nevertheless, we have decided to leave these factors in a separate category (because their categorization may not be as immediately clear-cut as others), and these factors include: low-level light [ 429 ], weight cycling [ 430 ], water contaminants [ 431 ], trade [ 432 ], air pollution [ 433 , 434 ], program-level variables (ex. feedback and learning experience) [ 435 ], TV viewing [ 436 ], falls [ 437 ], various other biological factors [ 116 , 136 , 141 , 151 , 164 , 182 , 363 , 364 , 438 , 439 , 440 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 , 452 , 453 , 454 , 455 , 456 , 457 , 458 , 459 , 460 , 461 , 462 , 463 , 464 , 465 , 466 , 467 , 468 , 469 ], mobile phone use [ 470 ], ultrasound chronic exposure [ 471 ], nighttime shift work [ 472 ], work accidents [ 473 ], therapy enrollment [ 226 ], and exposure to light at night [ 474 ].

4. Cross-Cutting Themes

4.1. lifecourse perspective.

Studies on the lifecourse perspective—key points:

Early life has an importance on mental health

Stress has been linked to depression

In old age, the decline in social capital is important

Trajectories and life events are important when it comes to the lifecourse perspective. Research has touched on the influence of prenatal or early life stress on an individual’s mental health trajectory [ 164 , 199 , 475 ]. Severe stress that occurs in the form of early-life trauma has also been associated with depressive symptoms [ 362 , 380 ]. It may be that some individuals exposed to trauma develop thoughts of personal failure, which then serve as a catalyst of depression [ 380 ].

At the other end of the life trajectory—old age—specific determinants have been linked to an increased risk for depression. Older people are at a heightened risk of losing their social networks, and structural social capital has been identified as important in relation to depression in old age [ 293 ].

4.2. Gene–Environment Interactions

Studies on gene–environment interactions—key points:

The environment and genetics interact to increase the risk of depression

The etiology of depression is multifactorial

Adolescence is a time of vulnerability

A number of studies have touched on gene–environment interactions [ 72 , 77 , 82 , 119 , 381 , 476 , 477 , 478 , 479 , 480 , 481 ]. The interactions between genetic factors and determinants, such as negative life events (ex. relationship and social difficulties, serious illness, unemployment and financial crises) and stressors (ex. death of spouse, minor violations of law, neighbourhood socioeconomic status) have been studied in relation to depression [ 82 , 135 , 298 , 449 , 481 ]. A study reported an interaction of significant life events with functional variation in the serotonin-transporter-linked polymorphic region (5-HTTLPR) allele type (in the context of multiple sclerosis) and linked this to depression [ 361 ], while another reported an interaction between stress and 5-HTTLPR in relation to depression [ 480 ]. Other research reported that the genetic variation of HPA-axis genes has moderating effects on the relationship between stressors and depression [ 198 ]. Another study showed that early-life stress interacts with gene variants to increase the risk for depression [ 77 ].

Adolescence is a time of vulnerability [ 111 , 480 ]. Perceived parental support has been found to interact with genes (GABRR1, GABRR2), and this appears to be associated with depressive symptoms in adolescence [ 480 ]. It is important to pay special attention to critical periods in the lifecourse so that adequate support is provided to those who are most vulnerable.

The etiology of depression is multifactorial, and it is worthwhile to examine the interaction between multiple factors, such as epigenetic, genetic, and environmental factors, in order to truly understand this mental health condition. Finally, taking into account critical periods of life when assessing gene–environment interactions is important for developing targeted interventions.

5. Discussion

Depression is one of the most common mental health conditions, and, if left untreated, it can increase the risk for substance abuse, anxiety disorders, and suicide. In the past 20 years, a large number of studies on the risk and protective factors of depression have been undertaken in various fields, such as genetics, neurology, immunology, and epidemiology. However, there are limitations associated with the extant evidence base. The previous syntheses on depression are limited in scope and focus exclusively on social or biological factors, population sub-groups, or examine depression as a comorbidity (rather than an independent disorder). The research on the determinants and causal pathways of depression is fragmentated and heterogeneous, and this has not helped to stimulate progress when it comes to the prevention and intervention of this condition—specifically unravelling the complexity of the determinants related to this condition and thus refining the prevention and intervention methods.

The scope of this paper was to bring together the heterogeneous, vast, and fragmented literature on depression and paint a picture of the key factors that contribute to this condition. The findings from this review show that there are important themes when it comes to the determinants of depression, such as: the microbiome, dysregulation of the HPA axis, inflammatory reactions, the kynurenine pathway, as well as psychological and social factors. It may be that physical factors are proximal determinants of depression, which, in turn, are acted on by more distal social factors, such as deprivation, environmental events, and social capital.

The Marmot Report [ 291 ], the World Health Organization [ 482 ], and Compton et al. [ 483 ] highlight that the most disadvantaged segments of society are suffering (the socioeconomic context is important), and this inequality in resources has translated to inequality in mental health outcomes [ 483 ]. To tackle the issue of egalitarianism and restore equality in the health between the groups, the social determinants need to be addressed [ 483 ]. A wide range of determinants of mental health have been identified in the literature: age, gender, ethnicity, family upbringing and early attachment patterns, social support, access to food, water and proper nutrition, and community factors. People spiral downwards because of individual- and societal-level circumstances; therefore, these circumstances along with the interactions between the determinants need to be considered.

Another important theme in the mental health literature is the lifecourse perspective. This shows that the timing of events has significance when it comes to mental health. Early life is a critical period during the lifespan at which cognitive processes develop. Exposure to harmful determinants, such as stress, during this period can place an individual on a trajectory of depression in adulthood or later life. When an individual is exposed to harmful determinants during critical periods and is also genetically predisposed to depression, the risk for the disorder can be compounded. This is why aspects such as the lifecourse perspective and gene–environment interactions need to be taken into account. Insight into this can also help to refine targeted interventions.

A number of interventions for depression have been developed or recommended, addressing, for example, the physical factors described here and lifestyle modifications. Interventions targeting various factors, such as education and socioeconomic status, are needed to help prevent and reduce the burden of depression. Further research on the efficacy of various interventions is needed. Additional studies are also needed on each of the themes described in this paper, for example: the biological factors related to postpartum depression [ 134 ], and further work is needed on depression outcomes, such as chronic, recurrent depression [ 452 ]. Previous literature has shown that chronic stress (associated with depression) is also linked to glucocorticoid receptor resistance, as well as problems with the regulation of the inflammatory response [ 484 ]. Further work is needed on this and the underpinning mechanisms between the determinants and outcomes. This review highlighted the myriad ways of measuring depression and its determinants [ 66 , 85 , 281 , 298 , 451 , 485 ]. Thus, the standardization of the measurements of the outcomes (ex. a gold standard for measuring depression) and determinants is essential; this can facilitate comparisons of findings across studies.

5.1. Strengths

This paper has important strengths. It brings together the wide literature on depression and helps to bridge disciplines in relation to one of the most common mental health problems. We identified, selected, and extracted data from studies, and provided concise summaries.

5.2. Limitations

The limitations of the review include missing potentially important studies; however, this is a weakness that cannot be avoided by literature reviews. Nevertheless, the aim of the review was not to identify each study that has been conducted on the risk and protective factors of depression (which a single review is unable to capture) but rather to gain insight into the breadth of literature on this topic, highlight key biological, psychological, and social determinants, and shed light on important themes, such as the lifecourse perspective and gene–environment interactions.

6. Conclusions

We have reviewed the determinants of depression and recognize that there are a multitude of risk and protective factors at the individual and wider ecologic levels. These determinants are interlinked and influence one another. We have attempted to describe the wide literature on this topic, and we have brought to light major factors that are of public mental health significance. This review may be used as an evidence base by those in public health, clinical practice, and research.

This paper discusses key areas in depression research; however, an exhaustive discussion of all the risk factors and determinants linked to depression and their mechanisms is not possible in one journal article—which, by its very nature, a single paper cannot do. We have brought to light overarching factors linked to depression and a workable conceptual framework that may guide clinical and public health practice; however, we encourage other researchers to continue to expand on this timely and relevant work—particularly as depression is a top priority on the policy agenda now.

Acknowledgments

Thank you to Isla Kuhn for the help with the Medline, Scopus, and PsycInfo database searches.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/brainsci11121633/s1 , Figure S1: Conceptual framework: Determinants of depression, Table S1: Data charting—A selection of determinants from the literature.

Appendix A.1. Search Strategy

Search: ((((((((((((((((“Gene-Environment Interaction”[Majr]) OR (“Genetics”[Mesh])) OR (“Genome-Wide Association Study”[Majr])) OR (“Microbiota”[Mesh] OR “Gastrointestinal Microbiome”[Mesh])) OR (“Neurogenic Inflammation”[Mesh])) OR (“genetic determinant”)) OR (“gut-brain-axis”)) OR (“Kynurenine”[Majr])) OR (“Cognition”[Mesh])) OR (“Neuronal Plasticity”[Majr])) OR (“Neurogenesis”[Mesh])) OR (“Genes”[Mesh])) OR (“Neurology”[Majr])) OR (“Social Determinants of Health”[Majr])) OR (“Glucocorticoids”[Mesh])) OR (“Tryptophan”[Mesh])) AND (“Depression”[Mesh] OR “Depressive Disorder”[Mesh]) Filters: from 2017—2020.

Ovid MEDLINE(R) and Epub Ahead of Print, In-Process, In-Data-Review & Other Non-Indexed Citations, Daily and Versions(R)

exp *Depression/

exp *Depressive Disorder/

exp *”Social Determinants of Health”/

exp *Tryptophan/

exp *Glucocorticoids/

exp *Neurology/

exp *Genes/

exp *Neurogenesis/

exp *Neuronal Plasticity/

exp *Kynurenine/

exp *Genetics/

exp *Neurogenic Inflammation/

exp *Gastrointestinal Microbiome/

exp *Genome-Wide Association Study/

exp *Gene-Environment Interaction/

exp *Depression/et [Etiology]

exp *Depressive Disorder/et

or/4-16   637368

limit 22 to yr = “2017–Current”

“cause* of depression”.mp.

“cause* of depression”.ti.

(cause adj3 (depression or depressive)).ti.

(caus* adj3 (depression or depressive)).ti.

Appendix A.2. PsycInfo

(TITLE ( depression OR “ Depressive Disorder ”) AND TITLE (“ Social Determinants of Health ” OR tryptophan OR glucocorticoids OR neurology OR genes OR neurogenesis OR “ Neuronal Plasticity ” OR kynurenine OR genetics OR “ Neurogenic Inflammation ” OR “ Gastrointestinal Microbiome ” OR “ Genome-Wide Association Study ” OR “ Gene-Environment Interaction ” OR aetiology OR etiology )) OR TITLE ( cause* W/3 ( depression OR depressive )).

Author Contributions

O.R. was responsible for the design of the study and methodology undertaken. Despite P.T.’s involvement in YPMH, he had no role in the design of the study; P.T. was responsible for the conceptualization of the study. Validation was conducted by O.R. and J.F.M. Formal analysis (data charting) was undertaken by O.R. O.R. and P.T. were involved in the investigation, resource acquisition, and data presentation. The original draft preparation was undertaken by O.R. The writing was conducted by O.R., with review and editing by P.T. and J.F.M. Funding acquisition was undertaken by O.R. and P.T. All authors have read and agreed to the published version of the manuscript.

This research was funded by The William Templeton Foundation for Young People’s Mental Health, Cambridge Philosophical Society, and the Aviva Foundation.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Error bars represent 95% CIs.

eFigure 1. Longitudinal Prevalence of High Loneliness by Birth Cohort and Sex Among US Young Adults Born 1972 Through 2001 and Measured From 1990 Through 2019

eFigure 2. Longitudinal Prevalence of High Self-Derogation by Birth Cohort and Sex Among US Young Adults Born 1972 Through 2001 and Measured From 1990 Through 2019

eFigure 3. Longitudinal Prevalence of Low Self-Esteem by Birth Cohort and Sex Among US Young Adults Born 1972 Through 2001 and Measured From 1990 Through 2019

eFigure 4. Prevalence of High Loneliness at Age 19/20 and Age 21-22 Stratified by Whether the Respondents Had High Depressive Symptoms at Age 18, by Birth Cohort

eFigure 5. Prevalence of High Self-Derogation at Age 19/20 and Age 21-22 Stratified by Whether the Respondents Had High Depressive Symptoms at Age 18, by Birth Cohort

eFigure 6. Prevalence of Low Self-Esteem at Age 19/20 and Age 21-22 Stratified by Whether the Respondents Had High Depressive Symptoms at Age 18, by Birth Cohort

eTable 1. Sample Size by Cohort and Wave

eTable 2. Demographics by Birth Cohort

eTable 3. Adjusted Odds Ratios Between Birth Cohort and Mental Well-Being at Ages 18, 19/20, and 21/22

eTable 4. Adjusted Odds Ratios Between Baseline Mental Health Predictors and Mental Health Outcomes at Ages 19/20 and Age 21/22

eTable 5. Interaction P values for Age 18 Predictor by Sex

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Keyes KM , Kreski NT , Patrick ME. Depressive Symptoms in Adolescence and Young Adulthood. JAMA Netw Open. 2024;7(8):e2427748. doi:10.1001/jamanetworkopen.2024.27748

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Depressive Symptoms in Adolescence and Young Adulthood

• 1 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
• 2 Institute for Social Research, University of Michigan, Ann Arbor

Question   To what extent have increases in adolescent depressive symptoms in recent birth cohorts persisted among young adults?

Findings   In this cohort study of 36 552 survey respondents, depressive symptoms at age 18 years were associated with depressive symptoms at ages 19 to 20 and 21 to 22 years, and this association did not vary by birth cohort. Increasing depressive symptoms in adolescence persisted into young adulthood, and a larger proportion of young adult symptoms in females was associated with symptoms during adolescence.

Meaning   Findings of this study suggest the need for primary prevention and mental health resources during the adolescent years.

Importance   Depressive symptoms have increased among US adolescents since 2010. It remains unclear as to what extent this increase will persist into young adulthood, potentially turning the youth mental health crisis into a young adult mental health crisis.

Objective   To test the association between birth cohort and adolescent depressive symptoms at ages 18, 19 to 20, and 21 to 22 years and changes in these symptoms by cohort.

Design, Setting, and Participants   This panel cohort study analyzed data from the Monitoring the Future longitudinal survey from 1990 to 2019, including birth cohorts from 1972 to 2001. Survey respondents were recruited from US high schools in 12th grade and were approximately aged 18 years (at baseline) through age 21 to 22 years (during mail and web follow-up). Data analysis was conducted from April to October 2023.

Exposure   Depressive symptoms score (>12 vs ≤12, with >12 representing top-decile scores) on a scale examining affective items (eg, “Life often seems meaningless”).

Main Outcomes and Measures   High (vs lower) depressive symptoms at ages 19 to 20 years and 21 to 22 years.

Results   The 36 552 respondents included 18 597 females (50.5%), and most reported having a parent who graduated from college (44.8%). Among females, 19.1% (95% CI, 16.7%-21.4%) of the most recent birth cohort (born: 1997-2001) had high depressive symptoms at age 18 years, higher than any previous birth cohort. While prevalence declined by age 21 to 22 years, it remained higher than previous cohorts at that age. Among males, 13.4% (95% CI, 11.2%-15.6%) of the most recent birth cohort had high depressive symptoms at age 18 years, and prevalence increased through young adulthood. Males with high baseline depressive symptoms had 10.24 (95% CI, 7.01-14.97) times the odds of symptoms at age 19 to 20 years and 6.20 (95% CI, 3.93-9.78) times the odds of symptoms at age 21 to 22 years. Females with high baseline depressive symptoms had 9.16 (95% CI, 6.57-12.76) times the odds of symptoms at age 19 to 20 years and 7.28 (95% CI, 4.92-10.78) times the odds of symptoms at age 21 to 22 years. The magnitude of the associations did not vary over time. Population attributable fractions indicated that the total proportion of young adult symptoms associated with depressive symptoms at age 18 years among females has increased; in the most recent birth cohort, 55.25% (95% CI, 38.11%-65.13%) of depressive symptoms at age 21 to 22 years were associated with symptoms at age 18 years.

Conclusions and Relevance   This panel cohort study found that increases in depressive symptoms in adolescence persisted into young adulthood, suggesting the need for primary prevention and mental health resources during the adolescent years.

Depressive symptoms have been increasing among US adolescents since approximately 2010. National estimates of major depressive episode prevalence among adolescents increased from 8.1% in 2009 to 15.9% in 2019, with increases concentrated among females and ranging from 11.4% to 23.4%. 1 Approximately 1 in 4 adolescent females reported seriously considering suicide and making a plan for suicide in 2021, 2 an increase from approximately 14% and 11%, respectively, in 2009. 3 Hospitalizations for intentional self-harm 4 and fatal suicide 5 , 6 also increased during the same time frame. While fatal suicide generally plateaued during the COVID-19 pandemic, 7 symptoms of mental health problems have continued to escalate. 2 , 8 - 11 Together, these patterns have been labeled as a teen mental health crisis in the US.

An open question is the extent to which increases in adolescent depressive symptoms in recent birth cohorts are associated with subsequent increases in young adult mental health problems as those cohorts transition into adulthood. In a given person, depressive symptoms at 1 developmental time point are typically associated with depressive symptoms at subsequent time points 12 , 13 ; thus, the population-wide increase in the prevalence of adolescent depressive symptoms will, based on historical data, affect later developmental time points, increasing the overall population burden in young adulthood. Yet the rapid increases in depressive symptoms in the past decade have been unprecedented; thus, whether these symptoms tend to resolve more frequently than in previous birth cohorts is unknown.

A relevant epidemiological question can be framed around the interaction between birth cohort and adolescent depressive symptoms. If adolescent depressive symptoms are developmentally limited (ie, more likely to resolve before adulthood than in previous birth cohorts), then we could expect an inverse interaction between birth cohort and these symptoms. That is, if there is an inverse interaction, then the association between adolescent and young adult depressive symptoms would have lower magnitude in recent birth cohorts than in previous birth cohorts. If there is no interaction between birth cohort and adolescent depressive symptoms, then these symptoms would be as much of a factor in young adult symptoms in recent birth cohorts as in previous birth cohorts. In the context of the rising prevalence of adolescent depressive symptoms, no interaction would portend a greater burden of young adult symptoms in recent years. Another potential scenario is a direct interaction, whereby the association between adolescent and young adult depressive symptoms increases in magnitude in recent birth cohorts. A direct interaction would imply that an increased prevalence of adolescent depressive symptoms makes these symptoms more persistent into young adulthood in recent than in previous birth cohorts, suggesting a higher mental health burden in this population and warranting stronger interventions.

In the present study, we used nationally representative longitudinal data collected from adolescence through young adulthood across cohorts spanning the past 30 years to test the association between birth cohort and adolescent depressive symptoms at ages 18, 19 to 20, and 21 to 22 years and changes in these symptoms by cohort. We examined the extent to which increases in depressive symptoms and other measures of mental well-being in adolescence persist into young adulthood and whether this persistence changes across birth cohorts given the dynamic temporal patterns in recent years.

Data for analysis were drawn from the Monitoring the Future (MTF) study, a longitudinal panel cohort study wherein a new prospective cohort began to be followed every year starting at age 18 years, selected from a nationally representative sample of high school seniors. The University of Michigan Institutional Review Board (IRB) approved the study, and the Columbia University IRB approved the analysis protocols. Passive (letter sent to parents/caregivers) or active (written) informed consent was obtained, per school policy, at baseline from parents of participants younger than 18 years or from participants 18 years or older. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

The pool of eligible respondents included those who participated in nationally representative samples of approximately 15 000 US high school seniors (12th grade), and we included data from those surveyed annually since 1990. 14 Among that pool, approximately 2450 students were randomly selected each year for longitudinal follow-up, with oversampling for students who reported drug use (statistical weights in analysis adjusted for oversampling). 15 Students selected for follow-up were randomly assigned to begin assessments either 1 year after baseline (modal age, 19 years) or 2 years after baseline (modal age, 20 years) and were then followed up every 2 years (either at 19 to 21 years or 20 to 22 years for the first 2 follow-up waves). In the 12th grade, respondents provided their name and contact information. This contact information was used for mailing invitation letters and sending emails and text messages. The MTF panel surveys were historically mailed paper surveys; however, starting in 2018, web-based surveys were introduced, with a paper option for those who preferred it. Participants were randomized to receive some subsets of measures, including mental health modules.

The present study included survey respondents who were queried about depressive symptoms and other measures of well-being. We included panel data for cohorts who were in the 12th grade from 1990 to 2019 (birth cohorts: 1972-2001) and followed up at ages 19 to 20 years (follow-up 1) and 21 to 22 years (follow-up 2) from 1991 to 2020 (with 36 552 participants queried about mental health). This population reflects approximately half of the total sample across birth cohorts.

Attrition from the study cohort increased across age and has increased across cohorts. The most important source of attrition was between baseline and first follow-up. Among the subset included in this analysis, the attrition range across study years was from 25.8% for those in the 12th grade in 1991 to 69.1% for those in the 12th grade in 2015. Similarly, by age 21 to 22 years, attrition for cohorts ranged from 33.4% for those in the 12th grade in 1991 to 68.7% for those in the 12th grade in 2015. We stratified by 5-year cohorts. Sample sizes by birth cohort and follow-up wave are provided in eTable 1 in Supplement 1 . We included attrition weighting in all analyses that calculated individual weights as the inverse of the probability of participation based on time-invariant characteristics measured at 12th grade. 15 , 16 Previous simulation analyses indicated that, while attrition varies by cohort, there is minimal resulting bias. 17

We examined 4 separate mental health scales: depressive affect, self-derogation, low self-esteem, and loneliness. Four items each were used to measure depressive affect, self-derogation, and low self-esteem, while 3 items were used to measure loneliness based on the extent to which respondents agreed or disagreed with certain statements. Example statements were as follows: “Life often seems meaningless” (depressive affect), “I take a positive attitude toward myself” (low self-esteem when reverse-coded), “I feel I do not have much to be proud of” (self-derogation), and “A lot of times I feel lonely” (loneliness). Response options for each item ranged from 1 (disagree) to 5 (agree). Items for each scale were summed to yield scores ranging from 4 to 20 (or from 3 to 15 for the loneliness scale), with higher scores indicating higher depressive affect, self-derogation, and loneliness, whereas a lower score on the self-esteem scale indicated worse self-esteem. Scale items were similar to existing scales with high reliability. 18 - 20 These scales have been operationalized and implemented similarly in other analyses of MTF data. 21 , 22

Due to symptom score non-normality, we created binary indicators. The main factor was depressive symptoms score at age 18 years (>12 vs ≤12, with >12 representing the top decile across all years or cohorts). Additional factors included self-derogation symptoms (>14 vs ≤14), self-esteem (≤11 vs >11), and loneliness symptoms (>13 vs ≤13) scores at age 18 years, with cutoff scores based on the top decile across all years or cohorts. Outcomes included the same mental health scale dichotomizations at ages 19 to 20 and 21 to 22 years.

Due to potential confounding, participants were queried about their sex (male vs female), race and ethnicity, and parental educational level at baseline (at least 1 parent was a college graduate vs no college). Until 2005, respondents selected 1 racial and ethnic category, including American Indian (Native American Indian), Asian American, Black or African American, Cuban American, Mexican American or Chicano, Other Latin American, Puerto Rican, White or Caucasian, or other. From 2005 onward, respondents could select multiple categories from the following: American Indian or Alaska Native, Asian American, Black or African American, Cuban American, Mexican American or Chicano, Native Hawaiian or Other Pacific Islander, Other Hispanic or Latino, Puerto Rican, or White. Across years, we harmonized the categories as follows: American Indian or Alaska Native, Asian or Pacific Islander, Black, Hispanic or Latino, non-Hispanic multiracial (based on reporting multiple racial identities), and White. For respondents surveyed before 2005, we also categorized as other those who selected that option. To account for potential response differences due to the COVID-19 pandemic, we included a dichotomous variable for data collection in 2020 vs all other years.

Analyses included descriptive graphs and logistic regression models conducted from April to October 2023 using survey procedures in SAS 9.4 (SAS Institute Inc). Descriptive prevalence estimates included sampling weights. Logistic regression models estimated the association between mental health at age 18 years and mental health at ages 19 to 20 and 21 to 22 years, incorporating attrition weights and controlling for race and ethnicity, parental educational level, birth cohort, and an indicator for data collection in 2020 vs previously. Interactions with birth cohort and sex were also tested. Two-sided P  < .05 indicated statistical significance.

Additionally, we estimated the population attributable fraction (PAF), which measures the total proportion of outcomes that can be attributed to the exposure and is calculated as follows:

PAF  =  p ( RR  − 1)/[ p ( RR  − 1) + 1],

where p is the exposure prevalence, and RR (relative risk) is the relationship between the exposure and outcome. 23 In this study, the exposure was depressive symptoms at age 18 years, and the outcomes were depressive symptoms at ages 19 to 20 and 21 to 22 years.

Population attributable fractions are community impact measures that change based on both the prevalence of exposure or independent variables and the magnitude of the association between exposure or independent variables and outcomes. The PAF increases as exposure prevalence increases, holding RR constant, and increases as RR increases, holding prevalence constant.

Of the 36 552 respondents who were followed-up longitudinally, 18 597 (50.5%) were females and 17 687 (48.8%) were males; 1.1% identified as American Indian or Alaska Native, 3.3% as Asian or Pacific Islander, 10.5% as Black, 14.0% as Hispanic or Latino, 66.0% as White, 1.8% as non-Hispanic multiracial, and 1.7% as other race and ethnicity; and 44.8% had a parent who graduated from college (eTable 2 in Supplement 1 ).

Among females ( Figure 1 ), 19.1% (95% CI, 16.7%-21.4%) of those in the most recent birth cohort (born 1997-2001) had high depressive symptoms at age 18 years, which was higher than any other previous birth cohort; while depressive symptom prevalence decreased to 12.1% (95% CI, 8.5%-15.7%) by age 21 to 22 years, it remained higher than any previous birth cohort at age 21 to 22 years. Among males ( Figure 1 ), 13.4% (95% CI, 11.2%-15.6%) of those in the most recent birth cohort (born 1997-2001) had high depressive symptoms at age 18 years, which was higher than any other previous birth cohort, and symptom prevalence increased to 15.6% (95% CI, 10.1%-21.0%) by age 21 to 22 years. Birth cohort patterns for loneliness, self-derogation, and low self-esteem are provided in eFigures 1, 2, and 3 in Supplement 1 .

Compared with the cohort born from 1972 to 1976, the cohort born from 1997 to 2001 had elevated depressive symptoms at age 18 years (adjusted odds ratio [AOR], 1.70; 95% CI, 1.38-2.11), 19 to 20 years (AOR, 1.91; 95% CI, 1.37-2.66), and 21 to 22 years (AOR, 2.35; 95% CI, 1.46-3.78) ( Table 1 ). Increases among recent birth cohorts were also evident across other domains of mental health and well-being (eTable 3 in Supplement 1 ), including self-derogation, low self-esteem, and (to a lesser extent) loneliness.

Table 2 shows the overall longitudinal associations of depressive symptoms across follow-up waves. Among males, those with high depressive symptoms at baseline had 10.24 (95% CI, 7.01-14.97) times the odds of high depressive symptoms at age 19 to 20 years and had 6.20 (95% CI, 3.93-9.78) times the odds of high depressive symptoms at age 21 to 22 years. Among females, those with high depressive symptoms at baseline had 9.16 (95% CI, 6.57-12.76) times the odds of high depressive symptoms at age 19 to 20 years and 7.28 (95% CI, 4.92-10.78) times the odds of high depressive symptoms at age 21 to 22 years. eTable 4 in Supplement 1 shows similarly poor mental health outcomes across follow-up waves as indicated by baseline depressive symptoms as well as baseline self-derogation, loneliness, and low self-esteem.

A critical question in this investigation was whether there were interactions between birth cohort and depressive symptoms at age 18 years; that is, whether the association between depressive symptoms at age 18 years and depressive symptoms at both age 19 to 20 years and age 21 to 22 years was changing across birth cohort. Table 2 shows no interaction among both females and males. There was no evidence of meaningful interactions between birth cohort and depressive symptoms at age 18 years compared with depressive symptoms at age 19 to 20 years or age 21 to 22 years.

Figure 2 shows follow-up depressive symptoms stratified by whether adolescents had high depressive symptoms at baseline. Unadjusted regression models did not find significant interactions between baseline depressive symptoms and sex (eTable 5 in Supplement 1 ); thus, Figure 2 grouped males and females together. Results indicated that the presence of high depressive symptoms at each follow-up were concentrated among those with high depressive symptoms at age 18 years. By the most recent birth cohort, among those with high depressive symptoms at baseline, approximately 45.6% had high depressive symptoms at age 19 to 20 years and 46.3% had high depressive symptoms at age 21 to 22 years. Comparatively, in previous birth cohorts, the prevalence of high depressive symptoms at follow-up waves among those with high depressive symptoms at baseline ranged from 16.5% to 34.6%. Analyses for loneliness, self-derogation, and low self-esteem are available in eFigures 4, 5, and 6 in Supplement 1 .

Table 3 shows the PAFs for the proportion of high depressive symptoms at ages 19 to 20 and 21 to 22 years attributable to high depressive symptoms at age 18 years. The results indicated that among these birth cohorts, the proportions of high depressive symptoms at ages 19 to 20 and 21 to 22 years were highly associated with depressive symptoms at age 18 years. In earlier cohorts, PAFs were typically larger for males; for later cohorts, PAFs were typically larger for females. While there is no formal statistical test for trend in PAFs, we found that among females born from 1972 to 1976, 35.28% (95% CI, 19.42%-50.14%) of depressive symptoms at age 21 to 22 years were associated with depressive symptoms at age 18 years, whereas among females born from 1997 to 2001, 55.25% (95% CI, 38.11%-65.13%) of depressive symptoms at age 21 to 22 years were associated with depressive symptoms at age 18 years. Overall, in the most recent cohort born from 1997 to 2001, 39.38% (95% CI, 30.74%-46.37%) of high depressive symptoms at age 19 to 20 years were associated with depressive symptoms at age 18 years, and 43.44% (95% CI, 30.98%-52.44%) of high depressive symptoms at age 21 to 22 years were associated with depressive symptoms at age 18 years.

Depressive symptoms and other indicators of mental health and well-being are increasing in US adolescents and young adults. The magnitude of the association between having depressive symptoms at age 18 years and having depressive symptoms at ages 19 to 20 and 21 to 22 years has remained stable across birth cohorts, yet prevalence across all ages is increasing by cohort. Thus, the increases in youth mental health problems in the US are shifting to young adults as populations age. By the most recent birth cohort, nearly half of high depressive symptoms at age 21 to 22 years were associated with high symptoms in adolescence; thus, early intervention and prevention are critical to reduce the burden on both adolescent and adult populations.

Previous studies examining the development of depressive symptoms from adolescence to young adulthood have shown that, in a given individual, symptom prevalence is associated with time points yet is often heterogeneous across the lifespan. 9 - 11 Ongoing studies often follow cohorts of similar age at baseline across time, and thus a broader historical lens is often not possible with existing cohorts. Data from other national longitudinal sources, such as the National Longitudinal Study of Adolescent to Adult Health, have shown that depressive symptoms typically decline after adolescence and into young adulthood 24 and then accelerate, particularly for females, at the start of midlife. 24 Data from the present study were generally consistent with this pattern, especially for females, revealing decreases in depressive symptoms after age 18 years. However, this study adds to the literature by showing a substantial cohort variation in depressive symptom trajectory, with mean levels of depressive symptoms increasing in the most recent birth cohorts, across the age span of adolescence to young adulthood.

The PAFs indicate that, across all cohorts, adolescent depressive symptoms explain a substantial proportion of young adult depressive symptoms and that the proportions explained appear to be increasing for females. The PAF is an important component of evaluating the implications of increasing depressive symptoms by cohort 25 , 26 because it has 2 central components: magnitude of associations and prevalence. It increases as prevalence increases, even when the relationship between exposure and outcome remains steady. Given that we did not observe an interaction between birth cohort and depressive symptoms at age 18 years, the reason that the PAF increased for females was that prevalence at age 18 years was increasing. Thus, without considering PAFs, we might erroneously conclude that the public health burden of these symptoms is remaining stable for females since the magnitude of association is not changing. But the public health burden is increasing for females because the exposure prevalence is increasing, even while the magnitude of the relationship is stable across cohorts. In other words, in the case of adolescent to adult depressive symptoms across birth cohorts, the public health burden for females is increasing due to the increase in depressive symptoms at age 18 years.

Increases in adolescent, and now young adult, mental health symptoms indicate a growing need for additional infrastructure for treatment and services. Available data indicate that there are substantial gaps in availability of trained specialist mental health practitioners across the US 27 as well as access challenges even in areas in which practitioners are concentrated. Psychiatrists are less likely to accept insurance than other clinicians, 28 creating financial barriers for most US families. Financial barriers are among the most frequently cited reasons for unmet mental health care needs among people with psychiatric distress. 29 Medicaid remains an important payer of mental health services in the US for individuals eligible for coverage, but from 2010 to 2015—a period of the Affordable Care Act implementation and Medicaid expansion in many states—the proportion of psychiatrists who accepted Medicaid decreased. 30 Primary care physicians and pediatricians are increasingly a central source of mental health care for US adolescents and adults, 31 but the lack of specialized training for more complex mental health challenges may continue the treatment gaps. Rates of emergency department use for mental health care among Medicaid recipients differ up to 8-fold across US regions, 32 suggesting access gaps. While expansion of teletherapy and telehealth, as well as the launch of the 3-digit mental health crisis line, may be leveraged to expand mental health care access, it is clear that primary prevention, ideally before upcoming cohorts reach adulthood, is also urgently needed. Beyond the expansion of the psychiatric workforce, school- and family-based interventions for adolescents to learn evidence-based stress-coping strategies are also critical to improving population mental health. 33

The underlying factors associated with increases in depressive symptoms among adolescents remain under investigation. Substantial resources have been placed in identifying the role of digital technology and social media in accelerating mental health problems among youth, although thus far meta-analyses and systematic reviews have generally found small to null effect sizes, 34 - 38 although measurement challenges in identifying appropriate exposure constructs make it difficult to identify both the risks and benefits of digital and social media use. 39 Other hypotheses have included interactions between historical patterns in biological risk factors (eg, earlier maturation and puberty) 40 and social risk factors (eg, political polarization and environmental instability limiting future outlooks). 41 , 42 As new cohorts of adolescents emerge and existing cohorts transition into adulthood, it is unlikely that the value of these novel social factors will abate; thus, without investment into prevention and intervention, further acceleration of mental health problems may be expected.

This study is subject to limitations. Depressive symptoms were self-reported rather than ascertained using diagnostic instruments or clinical assessment. While self-reported depressive symptoms are typically reliable indicators of psychological distress, these measures have not been validated with clinical cut points for probable depression, and thus we do not know the extent to which the patterns represented here would generalize to clinical depression. We also cannot separate patterns in willingness to report depressive symptoms from true change; given that all depression diagnoses and symptoms were based on self-report (even to a clinician), there was no way to distinguish true change from willingness to report. However, the patterns observed in the MTF data were similar to those observed in other data sources, including those with diagnostic instruments 1 and administrative records of outcomes such as suicidal self-injury, 4 reducing the possibility that the patterns observed here were not meaningful for population mental health. Furthermore, the MTF panel has substantial attrition across follow-up waves, although statistical weights adjusted for the association of attrition with study estimates. Additionally, the baseline MTF survey is conducted only among adolescents in school, and thus we lacked data on individuals who left high school or were absent on the day of the survey. We anticipated that respondents who were not in high school likely had higher levels of depressive symptoms and other mental health problems; thus, the estimates here are likely to be conservative.

The findings of this cohort study suggest that the current increase in depressive symptoms among US adolescents is swiftly shifting to an increase in young adult depressive symptoms as adolescents move into adulthood. There was significant persistence in depressive symptoms from ages 18 years to 21 to 22 years, with the recent birth cohort reporting the most stable elevated symptoms across age compared with previous birth cohorts. Given this persistence, reducing the onset of depressive symptoms through primary prevention and mental health resources during the adolescent years is critical.

Accepted for Publication: June 18, 2024.

Published: August 14, 2024. doi:10.1001/jamanetworkopen.2024.27748

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Keyes KM et al. JAMA Network Open .

Corresponding Author: Katherine M. Keyes, PhD, Mailman School of Public Health, Columbia University, 722 W 168th St, Room 724, New York, NY 10032 ( [email protected] ).

Author Contributions: Prof Keyes and Mr Kreski had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Keyes, Kreski.

Critical review of the manuscript for important intellectual content: Patrick.

Statistical analysis: Keyes, Kreski.

Obtained funding: Keyes, Patrick.

Supervision: Keyes.

Conflict of Interest Disclosures: Prof Keyes reported receiving personal fees from Lieff Cabraser and Motley Rice and for expertise in pharmaceutical litigation outside the submitted work. Dr Patrick reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was funded by grants R01-DA048853 (Prof Keyes), R01 DA001411, and R01-DA016575 (Dr Patrick) from the National Institute on Drug Abuse.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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