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Longitudinal association between cardiovascular risk factors and depression in young people: a systematic review and meta-analysis of cohort studies

Published online by Cambridge University Press:  25 June 2021

Anna B. Chaplin Open the ORCID record for Anna B. Chaplin [Opens in a new window] ,
Natasha F. Daniels Open the ORCID record for Natasha F. Daniels [Opens in a new window] ,
Diana Ples Open the ORCID record for Diana Ples [Opens in a new window] ,
Rebecca Z. Anderson ,
Amy Gregory-Jones ,
Peter B. Jones Open the ORCID record for Peter B. Jones [Opens in a new window]  and
Golam M. Khandaker Open the ORCID record for Golam M. Khandaker [Opens in a new window]
Anna B. Chaplin*
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, UK
Natasha F. Daniels
Affiliation:
School of Clinical Medicine, University of Cambridge, Cambridge, UK
Diana Ples
Affiliation:
School of Clinical Medicine, University of Cambridge, Cambridge, UK
Rebecca Z. Anderson
Affiliation:
Royal Liverpool University Hospital, Liverpool, UK Liverpool NHS Foundation Trust, Liverpool, UK
Amy Gregory-Jones
Affiliation:
School of Clinical Medicine, University of Cambridge, Cambridge, UK
Peter B. Jones
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, UK Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
Golam M. Khandaker
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, UK Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK MRC Integrative Epidemiology Unit, Population Health Science, University of Bristol, Bristol, UK Centre for Academic Mental Health, University of Bristol, Bristol, UK Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
*
Author for correspondence: Anna B. Chaplin, E-mail: ac2114@medschl.cam.ac.uk
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Abstract

Background

Depression is a common and serious mental illness that begins early in life. An association between cardiovascular disease (CVD) and subsequent depression is clear in adults. We examined associations between individual CVD risk factors and depression in young people.

Methods

We searched MEDLINE, EMBASE, and PsycINFO databases from inception to 1 January 2020. We extracted data from cohort studies assessing the longitudinal association between CVD risk factors [body mass index (BMI), smoking, systolic blood pressure (SBP), total cholesterol, high-density lipoprotein] and depression, measured using a validated tool in individuals with mean age of 24 years or younger. Random effect meta-analysis was used to combine effect estimates from individual studies, including odds ratio (OR) for depression and standardised mean difference for depressive symptoms.

Results

Based on meta-analysis of seven studies, comprising 15 753 participants, high BMI was associated with subsequent depression [pooled OR 1.61; 95% confidence interval (CI) 1.21–2.14; I2 = 31%]. Based on meta-analysis of eight studies, comprising 30 539 participants, smoking was associated with subsequent depression (pooled OR 1.73; 95% CI 1.36–2.20; I2 = 74%). Low, but not high, SBP was associated with an increased risk of depression (pooled OR 3.32; 95% CI 1.68–6.55; I2 = 0%), although this was based on a small pooled high-risk sample of 893 participants. Generalisability may be limited as most studies were based in North America or Europe.

Conclusions

Targeting childhood/adolescent smoking and obesity may be important for the prevention of both CVD and depression across the lifespan. Further research on other CVD risk factors including blood pressure and cholesterol in young people is required.

Keywords

Body mass indexcardiovascular risk factordepressiondepressive symptomsmeta-analysissmoking

Information

Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 3 , February 2023 , pp. 1049 - 1059
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re- use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Introduction

Depression is a common and serious mental illness with a lifetime risk of 10–20% (Hasin et al., Reference Hasin, Sarvet, Meyers, Saha, Ruan, Stohl and Grant2018). The majority of depression cases are established by age 24 (Kessler et al., Reference Kessler, McLaughlin, Green, Gruber, Sampson, Zaslavsky and Williams2010) and this condition is the leading cause of disability among children and young people (Polanczyk, Salum, Sugaya, Caye, & Rohde, Reference Polanczyk, Salum, Sugaya, Caye and Rohde2015). Following an initial depressive episode, the risk of recurrence is 60% (Holtzheimer & Mayberg, Reference Holtzheimer and Mayberg2011). Therefore, early-onset depression is associated with a longer period of risk for relapse as well as poor long-term outcomes (Holtzheimer & Mayberg, Reference Holtzheimer and Mayberg2011; Kessler, Reference Kessler2012). A better understanding of the aetiology of depression in young people is required to develop effective strategies for prevention and treatment (Niarchou, Zammit, & Lewis, Reference Niarchou, Zammit and Lewis2015).

Cardiovascular disease (CVD) is a leading cause of health-related disability worldwide (Roth et al., Reference Roth, Abate, Abate, Abay, Abbafati, Abbasi and Murray2018). There is evidence for bidirectional associations between CVD and depression in adults (Hiles et al., Reference Hiles, Baker, de Malmanche, McEvoy, Boyle and Attia2015; Inouye et al., Reference Inouye, Abraham, Nelson, Wood, Sweeting, Dudbridge and Samani2018; Khandaker et al., Reference Khandaker, Zuber, Rees, Carvalho, Mason, Foley and Burgess2019; Smolderen et al., Reference Smolderen, Spertus, Gosch, Dreyer, D'Onofrio, Lichtman and Krumholz2017). A substantial body of literature suggests that depression is a key risk factor for CVD in adults and that it may predict poor outcomes following a cardiac event (Barefoot & Schroll, Reference Barefoot and Schroll1996; Hiles et al., Reference Hiles, Baker, de Malmanche, McEvoy, Boyle and Attia2015; Inouye et al., Reference Inouye, Abraham, Nelson, Wood, Sweeting, Dudbridge and Samani2018; Khandaker et al., Reference Khandaker, Zuber, Rees, Carvalho, Mason, Foley and Burgess2019; Lippi, Montagnana, Favaloro, & Franchini, Reference Lippi, Montagnana, Favaloro and Franchini2009; Van der Kooy et al., Reference Van der Kooy, van Hout, Marwijk, Marten, Stehouwer and Beekman2007). CVD is also associated with subsequent depression in adults (Choi, Kim, Marti, & Chen, Reference Choi, Kim, Marti and Chen2014; Hare, Toukhsati, Johansson, & Jaarsma, Reference Hare, Toukhsati, Johansson and Jaarsma2014; Kendler, Gardner, Fiske, & Gatz, Reference Kendler, Gardner, Fiske and Gatz2009; Lippi et al., Reference Lippi, Montagnana, Favaloro and Franchini2009). However, studies of CVD risk and subsequent depression in young people are relatively less common. A clearer understanding of the association between CVD risk factors and depression in young people is required. Early detection and management of CVD risk factors may reduce risks for both CVD and depression subsequently during the life-course.

The World Health Organization defines young people as individuals aged 24 years or younger (WHO Study Group of Young People, 1986). Existing studies of CVD risk and depression in young people have often focused on individual risk factors such as body mass index (BMI) or smoking. In the past decade, a number of systematic reviews have highlighted an association between obesity in young people and depression across the lifespan (Hoare, Skouteris, Fuller-Tyszkiewicz, Millar, & Allender, Reference Hoare, Skouteris, Fuller-Tyszkiewicz, Millar and Allender2014; Mannan, Mamun, Doi, & Clavarino, Reference Mannan, Mamun, Doi and Clavarino2016; Mühlig, Antel, Föcker, & Hebebrand, Reference Mühlig, Antel, Föcker and Hebebrand2016; Sutaria, Devakumar, Yasuda, Das, & Saxena, Reference Sutaria, Devakumar, Yasuda, Das and Saxena2019). However, none of these studies specifically examined depression risk in young people. A recent systematic review of individuals aged 14–35 reported that childhood obesity is associated with approximately 50% increased risk of depression (Sutaria et al., Reference Sutaria, Devakumar, Yasuda, Das and Saxena2019). Another review reported that smoking in early life is associated with 73% increased risk of depression in young people (Chaiton, Cohen, O'Loughlin, & Rehm, Reference Chaiton, Cohen, O'Loughlin and Rehm2009). According to the Framingham study, other established CVD risk factors for adults include systolic blood pressure (SBP), total cholesterol, and high-density lipoprotein (HDL), in addition to smoking and BMI (Wilson et al., Reference Wilson, D'Agostino, Levy, Belanger, Silbershatz and Kannel1998; Wilson, Castelli, & Kannel, Reference Wilson, Castelli and Kannel1987). These CVD risk factors are all potentially modifiable and may be important in the aetiology and prevention of depression. CVD risk factors are increasingly being examined in young people; thus, a systematic review is required to summarise these findings.

We conducted a systematic review and meta-analysis of existing studies to quantify the longitudinal association of five key CVD risk factors (BMI, smoking, SBP, total cholesterol, and HDL) and depression in young people. These CVD risk factors were chosen for a number of reasons: (i) they are part of the Framingham Cardiovascular Risk Score for adults; (ii) they are potentially modifiable; and (iii) they remain relevant in the context of young people. Our outcome was depression (binary or continuous) assessed using a validated tool. We also performed a number of sensitivity analyses, for example by excluding studies that only looked at one gender or excluding studies based on quality assessment.

Methods

Search strategy and study selection

This study has been performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Details of the protocol were prospectively registered on PROSPERO (see https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020172460).

MEDLINE, EMBASE, and PsycINFO databases were searched to identify all relevant studies of the association between CVD risk factors and depression from database inception to 1 January 2020. The following keywords were used: ‘(cohort OR longitudinal OR prospective OR retrospective OR follow up stud*) AND depress* AND (adolescen* OR young person OR young people OR child* OR infant OR early adult OR youth* OR teen*) AND ((cardiovascular AND risk) OR total cholesterol OR high density lipoprotein OR hdl OR smok* OR bmi OR body mass index OR adiposity OR waist circumference OR body fat distribution OR skinfold thickness OR lipid accumulation product OR systolic blood pressure OR systolic bp OR sbp)’. See online Supplementary materials for the full search strategy. No language restriction was applied. The electronic search was complemented by hand-searching the reference lists of included studies. All titles and abstracts were examined to retrieve potentially relevant studies. ABC, NFD, AGJ, DP, and RZA applied inclusion/exclusion criteria and selected the final studies for this review.

Selection criteria

We included studies that: (i) had a longitudinal population-based cohort design (prospective or retrospective); (ii) included participants with a mean age of 24 years old or younger at follow-up; (iii) had at least one of the five CVD risk factors (BMI, smoking, SBP, total cholesterol, HDL) as the exposure at baseline; (iv) used a validated tool to measure depression (binary outcome or symptom score) at follow-up; and (v) reported effect estimate(s) for the association between CVD risk and subsequent depression. Studies were excluded if they did not have an unexposed group for a particular risk factor (e.g. experimental smoking used as the comparison group rather than no smoking), had depression as the exposure and the CVD risk factor as the outcome, or measured depression comorbid with another mental illness such as bipolar disorder or anxiety.

Data extraction

Data extraction was performed independently by ABC, NFD and DP, and disagreements were resolved by consensus. For each included study, we extracted the following data: (i) details of the cohort (country, name/setting, design, sample size, and follow-up length); (ii) assessment of exposure and outcome; (iii) age and sex of the included participants; and (iv) results of analysis (number of participants exposed at baseline, number of participants with depression at follow-up, adjusted/unadjusted effect estimates). When studies reported various methods for assessing the exposure or repeated measures of the exposure, we used the most comprehensive measure. For example, one study measured BMI eight times from birth to age 12 years (Wang, Leung, & Schooling, Reference Wang, Leung and Schooling2014). We chose the age 7 measure as the earliest age where BMI may be an appropriate measure of central adiposity to maximise the length of follow-up. In cases where there was more than one published report from the same population, we included the study with the larger sample size (Bares, Reference Bares2014; Duncan & Rees, Reference Duncan and Rees2005; Goodman & Capitman, Reference Goodman and Capitman2000; Munafò, Hitsman, Rende, Metcalfe, & Niaura, Reference Munafò, Hitsman, Rende, Metcalfe and Niaura2008). Some studies reported results where depression at baseline was adjusted for as well as analysis where baseline depression cases were removed. In such cases, we only included results where baseline depression was excluded to minimise reverse causality.

Data synthesis and meta-analysis

We performed separate meta-analyses for BMI, smoking, and SBP. Results from studies were pooled using the inverse variance method meaning that studies with larger sample sizes were given greater weight (Higgins et al., Reference Higgins, Thomas, Chandler, Cumpston, Li, Page and Welch2021; Schwarzer, Reference Schwarzer2007). Results for other CVD risk factors were summarised using a narrative review. Meta-analyses were performed separately for studies that reported beta estimates (continuous depressive symptoms outcome) or odds ratio (OR) (binary depression outcome). We used random-effect meta-analysis, which is appropriate where there is heterogeneity between the studies. Heterogeneity between studies was examined using the I 2 statistic.

We assessed publication bias by visual inspection of funnel plots, and by Egger's regression test for funnel plot asymmetry (mixed-effects meta-regression model). We considered a p value of <0.05 to indicate the existence of publication bias.

We assessed study quality using the Newcastle-Ottawa Scale for cohort studies (Stang, Reference Stang2010). We repeated analyses with only good/fair quality studies. We also repeated analyses after excluding studies with only female or male participants. Meta-analyses were carried out using the meta package version 4.11 in R version 3.6.1 (Schwarzer, Reference Schwarzer2007).

Results

Electronic search identified 6616 studies. After removing duplicates, 4821 studies remained. After title and abstract screening, 197 (4.1%) potentially eligible studies were identified, of which 29 met the inclusion criteria and were included in the review (Albers & Biener, Reference Albers and Biener2002; Bares, Reference Bares2014; Beal, Negriff, Dorn, Pabst, & Schulenberg, Reference Beal, Negriff, Dorn, Pabst and Schulenberg2014; Boutelle, Hannan, Fulkerson, Crow, & Stice, Reference Boutelle, Hannan, Fulkerson, Crow and Stice2010; Chaiton, Cohen, Rehm, Abdulle, & O'Loughlin, Reference Chaiton, Cohen, Rehm, Abdulle and O'Loughlin2015; Chang et al., Reference Chang, Chang, Wu, Lin, Wu and Yen2017; Choi, Patten, Christian Gillin, Kaplan, & Pierce, Reference Choi, Patten, Christian Gillin, Kaplan and Pierce1997; Clark et al., Reference Clark, Haines, Head, Klineberg, Arephin, Viner and Stansfeld2007; Duncan & Rees, Reference Duncan and Rees2005; Eitle & Eitle, Reference Eitle and Eitle2018; Frisco, Houle, & Lippert, Reference Frisco, Houle and Lippert2013; Gage et al., Reference Gage, Hickman, Heron, Munafò, Lewis, Macleod and Zammit2015; Gomes et al., Reference Gomes, Soares, Menezes, Assunção, Wehrmeister, Howe and Gonçalves2019; Goodman & Whitaker, Reference Goodman and Whitaker2002; Hammerton, Harold, Thapar, & Thapar, Reference Hammerton, Harold, Thapar and Thapar2013; Hammerton, Thapar, & Thapar, Reference Hammerton, Thapar and Thapar2014; Marmorstein, Iacono, & Legrand, Reference Marmorstein, Iacono and Legrand2014; Monshouwer et al., Reference Monshouwer, Smit, Ruiter, Ormel, Verhulst, Vollebergh and Oldehinkel2012; Perry et al., Reference Perry, Khandaker, Marwaha, Thompson, Zammit, Singh and Upthegrove2020; Piumatti, Reference Piumatti2018; Pryor et al., Reference Pryor, Brendgen, Boivin, Dubois, Japel, Falissard and Côté2016; Raffetti, Donato, Forsell, & Galanti, Reference Raffetti, Donato, Forsell and Galanti2019; Ranjit et al., Reference Ranjit, Buchwald, Latvala, Heikkilä, Tuulio-Henriksson, Rose and Korhonen2019a, Reference Ranjit, Korhonen, Buchwald, Heikkilä, Tuulio-Henriksson, Rose and Latvalab; Rhew et al., Reference Rhew, Richardson, Lymp, McTiernan, McCauley and Stoep2008; Roberts & Duong, Reference Roberts and Duong2013; Rubio, Kraemer, Farrell, & Day, Reference Rubio, Kraemer, Farrell and Day2008; Wang et al., Reference Wang, Leung and Schooling2014; Zhang, Woud, Becker, & Margraf, Reference Zhang, Woud, Becker and Margraf2018). See Fig. 1 for study selection and Table 1 for characteristics of included studies.

Fig. 1.

PRISMA flow diagram for study selection.

Table 1.

Characteristics of studies included in systematic review

s.d., standard deviation; F,  female; M,   male; AI,  American Indian; W,  White; EPAD,  Early Prediction of Adolescent Depression study; ALSPAC,  Avon Longitudinal Study of Parents and Children; NS,  not significant; NM,  not mentioned; K-SADS,  Kiddie Schedule for Affective Disorders and Schizophrenia; SMFQ,  Short Mood and Feelings Questionnaire; MINI-5,  Mini International Neuropsychiatric Interview Version Five; DSM,  Diagnostic and Statistical Manual of Mental Disorders; CES-D,  Centre for Epidemiological Studies Depression; CAPA,  Child and Adolescent Psychiatric Assessment (Child Version); DAWBA,  Development and Wellbeing Assessment (Child Version); SCID,  Structured Clinical Interview for DSM-III-R; CIS-R,  Clinical Interview Schedule Revised; ICD-10,  International Statistical Classification of Diseases and Related Health Problems Tenth Revision; DISC IV-Y,  Diagnostic Interview Schedule for Children for direct administration to children or adolescents; WHO CIDI-3,  World Health Organisation Composite International Diagnostic Interview Version Three; CDI,  Children's Depression Inventory; PHQ,  Patient Health Questionnaire; MFQ,  Mood and Feelings Questionnaire; GBI,  General Behaviour Inventory; BDI,  Beck's Depression Inventory.

a Baseline sample only.

b Not included in meta-analysis. The four studies not included in BMI meta-analysis were excluded because they measured BMI as a continuous variable. The three studies not included in smoking meta-analysis were excluded because they did not report effect estimates comparable with the other studies.

All studies were prospective in design, except one which used a retrospective measure of depression at follow-up (Monshouwer et al., Reference Monshouwer, Smit, Ruiter, Ormel, Verhulst, Vollebergh and Oldehinkel2012). The majority of studies (55.2%) were rated as ‘good’ quality using the Newcastle-Ottawa Scale (online Supplementary Tables S1 and S2). Sex, age, parental education, race/ethnicity, baseline depression, and alcohol use were the most commonly used confounders (online Supplementary Table S3).

Based on data availability, meta-analysis for BMI included 13 studies (Boutelle et al., Reference Boutelle, Hannan, Fulkerson, Crow and Stice2010; Chang et al., Reference Chang, Chang, Wu, Lin, Wu and Yen2017; Clark et al., Reference Clark, Haines, Head, Klineberg, Arephin, Viner and Stansfeld2007; Eitle & Eitle, Reference Eitle and Eitle2018; Frisco et al., Reference Frisco, Houle and Lippert2013; Gomes et al., Reference Gomes, Soares, Menezes, Assunção, Wehrmeister, Howe and Gonçalves2019; Goodman & Whitaker, Reference Goodman and Whitaker2002; Marmorstein et al., Reference Marmorstein, Iacono and Legrand2014; Monshouwer et al., Reference Monshouwer, Smit, Ruiter, Ormel, Verhulst, Vollebergh and Oldehinkel2012; Pryor et al., Reference Pryor, Brendgen, Boivin, Dubois, Japel, Falissard and Côté2016; Rhew et al., Reference Rhew, Richardson, Lymp, McTiernan, McCauley and Stoep2008; Roberts & Duong, Reference Roberts and Duong2013; Zhang et al., Reference Zhang, Woud, Becker and Margraf2018), and that for smoking included 11 studies (Albers & Biener, Reference Albers and Biener2002; Bares, Reference Bares2014; Beal et al., Reference Beal, Negriff, Dorn, Pabst and Schulenberg2014; Chaiton et al., Reference Chaiton, Cohen, Rehm, Abdulle and O'Loughlin2015; Choi et al., Reference Choi, Patten, Christian Gillin, Kaplan and Pierce1997; Clark et al., Reference Clark, Haines, Head, Klineberg, Arephin, Viner and Stansfeld2007; Duncan & Rees, Reference Duncan and Rees2005; Gage et al., Reference Gage, Hickman, Heron, Munafò, Lewis, Macleod and Zammit2015; Piumatti, Reference Piumatti2018; Raffetti et al., Reference Raffetti, Donato, Forsell and Galanti2019; Ranjit et al., Reference Ranjit, Buchwald, Latvala, Heikkilä, Tuulio-Henriksson, Rose and Korhonen2019a, Reference Ranjit, Korhonen, Buchwald, Heikkilä, Tuulio-Henriksson, Rose and Latvalab; Rubio et al., Reference Rubio, Kraemer, Farrell and Day2008; Zhang et al., Reference Zhang, Woud, Becker and Margraf2018) (online Supplementary Table S2). Meta-analysis for SBP included one study comprising two separate samples (Hammerton et al., Reference Hammerton, Harold, Thapar and Thapar2013). We found no studies of total cholesterol or HDL that met our inclusion criteria.

Meta-analysis of adjusted effect estimates

Longitudinal association between high BMI at baseline and risk of depression at follow-up

Based on seven studies reporting an adjusted OR, comprising a total of 15 753 participants, the pooled OR for depression at follow-up associated with high BMI (>25) at baseline was 1.61 (95% confidence interval (CI)   1.21–2.14) (Fig. 2). There was limited evidence of heterogeneity between studies (I 2 = 31%; 95% CI   0–71%; Cochran's Q = 8.7; p = 0.19). Separate meta-analysis of unadjusted effect estimates showed lower pooled results (online Supplementary Fig. S1).

Fig. 2.

Meta-analysis of longitudinal association between high BMI at baseline and subsequent depression in young people.

Longitudinal association between smoking at baseline and risk of depression at follow-up

Based on eight studies reporting an adjusted OR, comprising a total of 30 539 participants, the pooled OR for depression at follow-up associated with smoking at baseline was 1.73 (95% CI   1.36–2.20) (Fig. 3). There was evidence of heterogeneity between studies (I 2 = 74%; 95% CI   52–86%; Cochran's Q = 35.3; p < 0.01). Separate meta-analysis of unadjusted effect estimates showed higher pooled results (online Supplementary Fig. S2).

Fig. 3.

Meta-analysis of longitudinal association between smoking at baseline and subsequent depression in young people.

Longitudinal association between SBP at baseline and risk of depression at follow-up

One study examined associations of both low and high SBP with depression in two separate samples comprising a total of 5111 participants. Meta-analysis of these studies suggest depression at follow-up is associated with low SBP at baseline (OR   3.32; 95% CI   1.68–6.55), but not with high SBP at baseline (OR   0.82; 95% CI   0.55–1.22) (Fig. 4). There was some evidence of heterogeneity for high SBP (I 2 = 66%; 95% CI   0–92%; Cochran's Q = 3.0; p = 0.08) and little heterogeneity for low SBP (I 2 = 0%; 95% CI   0–0%; Cochran's Q = 0.04; p = 0.84).

Fig. 4.

Meta-analysis of longitudinal association between SBP at baseline and subsequent depression in young people. SBP, systolic blood pressure; EPAD, Early Prediction of Adolescent Depression study; ALSPAC, Avon Longitudinal Study of Parents and Children. *Subset of ALSPAC participants with mothers with recurrent depression. †EPAD participants have mother/father with recurrent depression.

Longitudinal association between high BMI, smoking at baseline and depressive symptoms at follow-up

Based on five studies reporting an adjusted beta estimate, comprising a total of 11 516 participants, the standardised mean difference (SMD) for an increase in depressive symptoms at follow-up associated with high BMI at baseline was 0.05 (95% CI –0.08–0.18) (online Supplementary Fig. S3). There was evidence of heterogeneity between studies (I 2 = 71%; 95% CI  34–88%; Cochran's Q = 17.5; p < 0.01).

Based on five studies reporting an adjusted beta estimate, comprising a total of 21 490 participants, the SMD for an increase in depressive symptoms at follow-up associated with smoking at baseline was 0.37 (95% CI   0.10–0.64) (online Supplementary Fig. S3). There was evidence of heterogeneity between studies (I 2 = 89%; 95% CI   78–94%; Cochran's Q = 44.8; p < 0.01).

Results for sensitivity analysis

After excluding three studies with only female participants (Boutelle et al., Reference Boutelle, Hannan, Fulkerson, Crow and Stice2010; Frisco et al., Reference Frisco, Houle and Lippert2013; Zhang et al., Reference Zhang, Woud, Becker and Margraf2018), the adjusted pooled OR for depression at follow-up for high BMI at baseline was 1.43 (95% CI   0.94–2.18) (Fig. 2). There was some heterogeneity between studies (I 2 = 47%; 95% CI   0–83%; Cochran's Q = 5.7; p = 0.13).

After excluding four studies with only female or male participants (Choi et al., Reference Choi, Patten, Christian Gillin, Kaplan and Pierce1997; Duncan & Rees, Reference Duncan and Rees2005; Rubio et al., Reference Rubio, Kraemer, Farrell and Day2008; Zhang et al., Reference Zhang, Woud, Becker and Margraf2018), the pooled adjusted OR for depression at follow-up for smoking at baseline was 1.23 (95% CI   1.02–1.49) (Fig. 3). There was little heterogeneity (I 2 = 11%; 95% CI   0–86%; Cochran's Q = 3.4; p = 0.34).

After excluding one study based on quality assessment (Duncan & Rees, Reference Duncan and Rees2005), the pooled adjusted OR for depression at follow-up for smoking at baseline was 1.48 (95% CI   1.21–1.80) (online Supplementary Fig. S4). There was some heterogeneity (I 2 = 38%; 95% CI   0–73%; Cochran's Q = 11.3; p = 0.12). For BMI, we did not exclude any studies based on quality assessment.

See online Supplementary Figs S5 and S6 for sensitivity analyses where the outcome of interest was depressive symptoms at follow-up.

Publication bias

Based on Egger's test and funnel plots, evidence for publication bias was not evident for studies reporting the adjusted association between high BMI and depression (Egger's test: p = 0.17; online Supplementary Fig. S7), smoking and depression (Egger's test: p = 0.80; online Supplementary Fig. S8), or high BMI and depressive symptoms (Egger's test: p = 0.35) (online Supplementary Fig. S9). Evidence for publication bias was present for studies reporting the adjusted association between smoking and depressive symptoms (Egger's test: p = 0.01) (online Supplementary Fig. S10).

Discussion

Depression and CVD are associated with each other in mid to late adulthood. Although depression is known to arise commonly in young people, the timing of the association with CVD risk is unclear and common risk factors for the two conditions raise the prospect of joint prevention. To the best of our knowledge, this is the first systematic review to consider the association between various CVD risk factors and subsequent depression in young people. We report four key findings: (i) BMI and smoking are the most well-studied risk factors for depression in this age group; (ii) both BMI and smoking at baseline are longitudinally associated with subsequent depression; (iii) smoking but not BMI is prospectively associated with depressive symptoms; and (iv) currently there is limited data on longitudinal associations of high SBP and cholesterol with subsequent depression in young people, which should be examined in future.

Our results suggest that obesity could be an important risk factor for depression in young people. The pooled OR of 1.61 for the association of high BMI and depression is remarkably similar to previous studies in adults. Previous meta-analyses have reported ORs of 1.51 and 1.70 for the prospective association between childhood high BMI and adult depression (Luppino et al., Reference Luppino, Wit, Bouvy, Stijnen, Cuijpers, Penninx and Zitman2010; Sutaria et al., Reference Sutaria, Devakumar, Yasuda, Das and Saxena2019). Another meta-analysis reported that obese adolescents had an 40% increased risk of experiencing depression as adults (Mannan et al., Reference Mannan, Mamun, Doi and Clavarino2016). A recent Mendelian randomisation study using data from 812 000 adult participants also found that fat mass could be a causal factor for depression (Speed, Jefsen, Børglum, Speed, & Østergaard, Reference Speed, Jefsen, Børglum, Speed and Østergaard2019). We did not find an association between high BMI and depressive symptoms score, indicating a possibly non-linear association between BMI and depression whereby association is restricted to only those with more severe symptoms.

Our findings also suggest that smoking could be a risk factor for depression in young people. The pooled OR of 1.73 for the association between smoking and depression is consistent with a previous meta-analysis in adults, which reported an OR of 1.62 (Luger, Suls, & Vander Weg, Reference Luger, Suls and Vander Weg2014). Similarly, a meta-analysis of nine cross-sectional and longitudinal studies found that adolescents exposed to second-hand smoking had increased odds of depression (Han, Liu, Gong, Ye, & Zhou, Reference Han, Liu, Gong, Ye and Zhou2019). However, current evidence from observational cohort studies and genetic Mendelian randomisation studies reports mixed findings regarding the association between smoking and depression, with some reporting an association (Wootton et al., Reference Wootton, Richmond, Stuijfzand, Lawn, Sallis, Taylor and Munafò2020) and others no association (Bjørngaard et al., Reference Bjørngaard, Gunnell, Elvestad, Davey Smith, Skorpen, Krokan and Romundstad2013; Taylor et al., Reference Taylor, Fluharty, Bjørngaard, Gabrielsen, Skorpen, Marioni and Munafò2014). Therefore, residual confounding or reverse causality remain viable explanations for the observed association between smoking and depression. Further longitudinal studies and genetic Mendelian randomisation studies are required to investigate this issue.

A number of potential mechanisms may be involved in the association of high BMI and subsequent depression, including low-grade systemic inflammation, hypothalamic–pituitary–adrenal axis (HPA) axis dysregulation, insulin resistance, and psychological distress. Inflammation is evident in around 25% of individuals with depression (Osimo, Baxter, Lewis, Jones, & Khandaker, Reference Osimo, Baxter, Lewis, Jones and Khandaker2019) and atypical depression is associated with inflammation and metabolic dysregulation (Lamers et al., Reference Lamers, Vogelzangs, Merikangas, de Jonge, Beekman and Penninx2013, Reference Lamers, Milaneschi, Vinkers, Schoevers, Giltay and Penninx2020). Adipose tissue also contains abundant inflammatory cytokines that are involved in fat metabolism (Heredia, Gómez-Martínez, & Marcos, Reference Heredia, Gómez-Martínez and Marcos2012). Similarly, the role of insulin in regulating adipocyte function contributes to the close link between insulin resistance and obesity (Kahn & Flier, Reference Kahn and Flier2000). Furthermore, melancholic depression, higher levels of abdominal fat, HPA axis hyperactivity, and cortisol dysregulation are inter-related (Incollingo Rodriguez et al., Reference Incollingo Rodriguez, Epel, White, Standen, Seckl and Tomiyama2015; Lamers et al., Reference Lamers, Vogelzangs, Merikangas, de Jonge, Beekman and Penninx2013, Reference Lamers, Milaneschi, Vinkers, Schoevers, Giltay and Penninx2020). Adverse childhood experiences are also one of the most robust risk factors for depression (Gardner, Thomas, & Erskine, Reference Gardner, Thomas and Erskine2019), and are associated with increased risk for obesity and metabolic dysregulation (Farr et al., Reference Farr, Ko, Joung, Zaichenko, Usher, Tsoukas and Mantzoros2015). Low-grade systemic inflammation may be important to the association between smoking and depression (Berk et al., Reference Berk, Williams, Jacka, O'Neil, Pasco, Moylan and Maes2013). Studies are required to investigate the complex mechanisms that may underlie the association between high BMI and depression.

Low SBP, but not high SBP, appeared to be associated with risk for depression in young people. However, our meta-analysis was based on only two cohorts at high risk for depression and further study is required. Low SBP appears to be associated with depression in young people at high-risk of depression but not in the general population (Hammerton et al., Reference Hammerton, Harold, Thapar and Thapar2013). Low SBP has also been associated with depression in cross-sectional and longitudinal studies of middle-aged and elderly adults (Hildrum et al., Reference Hildrum, Mykletun, Stordal, Bjelland, Dahl and Holmen2007; Huang, Su, Jiang, & Zhu, Reference Huang, Su, Jiang and Zhu2020). Conversely, higher SBP has been prospectively associated with fewer depressive symptoms in older adults with CVD risk factors (Herrmann-Lingen et al., Reference Herrmann-Lingen, Meyer, Bosbach, Chavanon, Hassoun, Edelmann and Wachter2018). In adult populations, the relationship between SBP and depression may be independent of a range of lifestyle factors, age, and sex (Hildrum et al., Reference Hildrum, Mykletun, Stordal, Bjelland, Dahl and Holmen2007; Huang et al., Reference Huang, Su, Jiang and Zhu2020).

The reason why low SBP has a potentially causal role in depression remains unclear. Neurons controlling blood pressure could be implicated in the association between SBP and depression. Neuropeptide Y, for example, reduces blood pressure and is involved in stress responses that have been linked to increased risk for depression, such as the HPA axis (Hildrum et al., Reference Hildrum, Mykletun, Stordal, Bjelland, Dahl and Holmen2007; Juruena, Bocharova, Agustini, & Young, Reference Juruena, Bocharova, Agustini and Young2018). Further research is required to understand the relationship between SBP and depression in young people, including potential mechanisms.

We found no studies assessing the association between either total cholesterol or HDL on risk for subsequent depression in young people. A meta-analysis of 30 cross-sectional studies reported that higher total cholesterol was associated with lower levels of depression in adults (Shin, Suls, & Martin, Reference Shin, Suls and Martin2008). Evidence from adults indicates both higher and lower HDL to be associated with increased risk for depression in adults. In a meta-analysis of 16 cross-sectional studies, high HDL was related to higher levels of depression, especially in women (Shin et al., Reference Shin, Suls and Martin2008). Conversely, a meta-analysis of 11 case-control studies reported that lower HDL levels may be associated with first-episode major depressive disorder in adults (Wei et al., Reference Wei, Cai, Liu, Liu, Wang, Tang and Wang2020). Given that abnormal HDL, LDL and triglyceride levels are increasingly common in adolescents [Centers for Disease Control and Prevention (CDC), 2010], effort should be made to study potential effects on mental health as well as physical health.

Strengths of this study include the systematic literature search which identified a large number of relevant studies comprising a total of 93 021 participants. We included studies considering the effect of various CVD risk factors on either binary or continuous measures of depression/depressive symptoms. We assessed the studies using the validated Newcastle-Ottawa Scale as well as conducting sensitivity analyses to examine the robustness of our findings. However, this study is not without limitations. First, the majority of studies came from North America and Europe, limiting the generalisability of the results to other parts of the world. The number of studies in each of the meta-analyses was also relatively small, which resulted in wide CIs for the pooled effect estimates, and reduced the statistical power to detect publication bias. There was a considerable amount of heterogeneity between studies, particularly studies of depressive symptoms. Sensitivity analyses revealed that sex explained heterogeneity in some of the meta-analyses. However, stratifying by sex decreased the sample size, and consequently, statistical power to detect an association. In future, studies with larger samples are required. Finally, the possibility of residual confounding by unidentified factors remains high so we are cautious in terms of any conclusion regarding causality. Although studies included in this meta-analysis controlled for various potential confounding effects, other factors may also explain these associations. Further research is needed to examine whether observed associations are likely to be causal. Since randomised controlled trials are neither feasible nor ethical for some of the exposures under investigation (e.g. smoking, obesity), genetic approaches to dealing with residual confounding, such as Mendelian randomisation, would be particularly useful.

In summary, we present evidence for a longitudinal association between CVD risk factors, namely high BMI and smoking, in childhood/adolescence and subsequent depression in young people. These risk factors could be important targets for the prevention of depression and CVD in young people and subsequently during the life course. Further study is needed to understand potential mechanisms for these associations as well as the relationship between other CVD risk factors, notably blood pressure and cholesterol and depression risk in young people.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291721002488.

Financial support

ABC is supported by the National Institute for Health Research (NIHR) CLAHRC RCF (MRR73-1795-00000) (https://www.nihr.ac.uk/), NIHR ARC East of England, the MQ: Transforming Mental Health (Data Science Award; grant code: MQDS17/40) (https://www.mqmentalhealth.org/home/). GMK acknowledges funding support from the Wellcome Trust (grant code: 201486/Z/16/Z) (https://wellcome.org/), MQ as above, the Medical Research Council (grant code: MC_PC_17213 and MR/S037675/1) (https://mrc.ukri.org/), and the BMA Foundation (J Moulton grant 2019) (http://www.bmafoundationmr.org.uk/). PBJ acknowledges funding from MQ, NIHR ARC East of England and the Medical Research Council, as above, and from NIHR PGfAR 0616-20003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Registration number: CRD42020172460.

Conflict of interest

None.

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Figure 0

Fig. 1. PRISMA flow diagram for study selection.

Figure 1

Table 1. Characteristics of studies included in systematic review

Figure 2

Fig. 2. Meta-analysis of longitudinal association between high BMI at baseline and subsequent depression in young people.

Figure 3

Fig. 3. Meta-analysis of longitudinal association between smoking at baseline and subsequent depression in young people.

Figure 4

Fig. 4. Meta-analysis of longitudinal association between SBP at baseline and subsequent depression in young people. SBP, systolic blood pressure; EPAD, Early Prediction of Adolescent Depression study; ALSPAC, Avon Longitudinal Study of Parents and Children. *Subset of ALSPAC participants with mothers with recurrent depression. †EPAD participants have mother/father with recurrent depression.

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