Mental and cardiometabolic disorders rank among the leading causes of burden of disease worldwide (Global Burden of Disease Collaborative Network, 2024). These disorders are highly multimorbid (i.e., the presence of 2 or more long-term conditions), and their relationship appears to be bidirectional (Nakada et al., 2023; Nuyen et al., 2021; Ronaldson et al., 2021). Although the aetiology and complex multimorbidity of mental and cardiometabolic disorders are not yet fully understood, current research suggests there are shared genetic, biological, and psychosocial mechanisms underlying these conditions, one of which is chronic low-grade inflammation (Goldfarb et al., 2022).
Longitudinal studies have shown that inflammation predicts subsequent mental and cardiometabolic health. The role of chronic low-grade inflammation in the development of cardiometabolic disease in adulthood is well established whereby studies report associations of inflammatory markers, such as interleukin-6 (IL-6) and C-reactive protein (CRP), with incident cardiovascular disease (Tahir & Gerszten, 2023). In addition, higher CRP levels in youth have been shown to predict metabolic syndrome in adulthood (Mattson et al., 2008). There is also a growing body of evidence that links inflammation to mental health disorders, for instance, higher IL-6 levels in childhood were associated with increased risks of psychosis, depression and negative psychotic symptoms in early adulthood (Edmonson-Stait et al., 2022; Khandaker et al., 2014; Perry et al., 2021).
Much of the existing literature investigating the relationship between inflammation and mental and cardiometabolic health, including some of the aforementioned studies, have included data from a single time point and/or a single outcome. Looking at longitudinal patterns of inflammation over time would allow one to answer questions on whether there are subgroups with distinct trajectories that may be linked to different risk factors or outcomes. Here, Palmer et al. (2024) examined associations between inflammation trajectories in childhood through adolescence and risks of cardiometabolic and mental disorders in early adulthood.
Inflammation is associated with cardiometabolic and mental disorders, but how changes in their trajectories over time affect our long-term health is less well understood.
Methods
This study used data from a UK birth cohort – the Avon Longitudinal Study of Parents and Children (ALSPAC).
Inflammation was measured using CRP levels (mg/dL) in non-fasting blood samples at ages 9, 15 and 17 years. The authors then sought to identify groups of individuals that displayed different patterns (i.e., trajectories) of CRP levels over time by using latent class growth analysis, adjusting for body mass index (BMI; kg/m2) at each time point.
The authors then examined the relationship between these different CRP trajectories with mental and cardiometabolic health outcomes at age 24 , using logistic and linear regression. The outcomes included:
- Psychotic disorder and psychotic experiences assessed using the Psychosis-Like Symptom Interview (PLIKSi).
- Depression (mild, moderate and severe) and generalised anxiety disorder using the computerised Clinical Interview Schedule – Revised (CIS-R; definitions are based on the International Classification of Diseases, 10th revision [ICD-10] criteria).
- Hypomania using the Hypomania Checklist-32.
- Glucose-insulin sensitivity using the Homeostasis Assessment Model (HOMA2) score.
These analyses were adjusted for other factors that may influence inflammation and/or the health outcomes (i.e., confounders), including biological sex at birth, ethnicity (white vs non-white), preterm birth, Family Adversity Index (total scores from pregnancy, ages 2 years and 4 years summed), parent-reported child health, and emotional symptoms at age 9 years assessed using the Strengths and Difficulties Questionnaire.
Results
A total of 6,556 participants were assigned to a group/trajectory class in the latent class growth analysis model. The optimal model identified three CRP trajectories:
- Reference group (n=6109; 93%) – persistently low CRP levels.
- Early peak group (n=197; 3%) – CRP levels peaked at at age 9 years.
- Late peak group (n=250; 4%) – CRP levels peaked at age 17 years.
Compared to the reference group, the early peak group was found to be at increased risk of psychotic disorders, psychotic experiences and severe depression, and also had higher HOMA2 scores at age 24 years. There was no strong evidence for associations with mild depression, hypomania or generalised anxiety disorder.
The study did not find any evidence for increased risk of any of the studied health outcomes in the late peak group.
Three distinct groups were identified that displayed different inflammation patterns over time and the risk for poor mental and cardiometabolic health varied between groups.
Conclusions
This study identified subgroups of individuals with distinct inflammation trajectories. In particular, those with high levels of inflammation in childhood had an increased risk of psychosis, depression and insulin resistance in early adulthood. These findings add to the existing literature on inflammation and mental and cardiometabolic health, and provides new insights on the potential influence of the developmental timing of inflammation on subsequent health outcomes.
High levels of inflammation in childhood may be linked to an increased risk of psychosis, depression and insulin resistance in early adulthood.
Strengths and limitations
This study has many notable strengths including:
- The use of data from ALSPAC, a large population-based cohort. ALSPAC is a rich data source with information across various domains of health and development from childhood through to adulthood.
- The longitudinal design which gives a better overview of inflammation profiles over time compared to studies that rely on a single measurement of inflammation.
- The use of latent class growth analysis to model CRP trajectories. This statistical approach can capture heterogeneity and identify subgroups within a population.
- Accounting for attrition bias. While dropout was high in ALSPAC (typical of all longitudinal cohort studies), the authors addressed the risk of this bias by accounting for missing data using inverse probability weighting.
However, this study also has its limitations:
- Lack of diversity – The data came from a volunteer cohort recruited from South West England, and there is an overrepresentation of more affluent groups and an underrepresentation of non-White minority ethnic groups compared with the general population (Boyd et al., 2013).
- Youth cohort – The age at which the outcomes were assessed (24 years) is potentially still quite young for changes relating to cardiometabolic disease to be observed; a better overview of the cardiometabolic health profiles of these subgroups may have been provided if more outcomes (e.g. blood pressure, blood lipids etc.) were included.
- Single inflammatory marker – Only one inflammatory marker has been included in this study, but the literature has shown differential associations of other inflammatory markers (e.g., IL-6, tumour necrosis factor-alpha or soluble urokinase plasminogen activator receptor) with health outcomes.
- Covariate adjustment – The authors adjusted for BMI at each time point in their latent class growth analysis. While the inclusion of covariates was commonly done in earlier studies, more recent simulation studies have shown that latent class models should be conducted without covariates, as covariate misspecification can lead to convergence on a model with the incorrect number of classes (Nylund-Gibson & Masyn, 2016). Current best practice guidelines encourage researchers to use what is called a three-step approach where the optimal model and number of classes are identified before adding covariates in the analysis (Weller et al., 2020).
This study provides a novel overview of inflammation trajectories over time using data from a large population-based cohort, however, the findings are limited in their generalisability and other methodological limitations need to be considered.
Implications for practice
This study sheds new light on the link between inflammation in childhood/ adolescence and health in early adulthood. The authors identified heterogeneous subgroups within the population presenting with distinct inflammation trajectories, and these subgroups were at different levels of risk for mental and cardiometabolic disorders. However, further research is required to better understand the underlying biological mechanisms before findings can be directly translated into policy or practice, as we still do not yet have a full understanding of the exact causes of such chronic low levels of inflammation and how best to intervene to prevent negative outcomes.
There is accumulating evidence to suggest that adverse childhood experiences can become “biologically embedded”, resulting in a proinflammatory state later in life (Miller et al., 2011). Studies have found that various childhood risk factors, such as low childhood socioeconomic status, exposure to adverse childhood experiences, and poor childhood health are associated with inflammation later in adulthood (Rasmussen et al., 2019). However, recent meta-analyses have indicated that the effects of adverse childhood experiences on inflammation are generally small (Chiang et al., 2022; Kuhlman et al., 2020).
In addition, these findings also highlight the potential relevance of the developmental timing of exposures like adverse childhood experiences, suggesting there may be “sensitive periods” where exposure to psychosocial stress may disrupt the normal development of various physiological systems and leave enduring effects on health. From a developmental neuroscience perspective, it is well established that there are periods of increased neurogenesis and neuroplasticity during development. During such periods, exposure to adversity can have profound impact on brain maturation, and changes on behaviour and health will depend on the developmental trajectories of the different brain regions (Heim & Binder, 2012).
Further studies investigating when/how adverse childhood experiences contribute to chronic low-grade inflammation, and the effects of moderating factors are important, as findings from these studies will be able to inform future prevention and intervention strategies.
Chronic low-grade inflammation may reflect an exposure to adverse childhood experiences, and the developmental timing of such exposures may be key to their relationship with later health outcomes.
Statement of interests
Ruby works on a similar line of research examining health and biomarker trajectories and their relationships with risk factors and health outcomes, but she has not had any personal involvement in this study.
Ruby is supported by the Tackling Multimorbidity at Scale Strategic Priorities Fund programme [grant number MR/W014416/1] delivered by the Medical Research Council and the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council.
She has no conflicts of interest to declare.
Links
Primary paper
Palmer ER, Morales-Muñoz I, Perry BI, et al. Trajectories of inflammation in youth and risk of mental and cardiometabolic disorders in adulthood. JAMA Psychiatry 2024 81(11) 1130-1137. https://doi.org/10.1001/jamapsychiatry.2024.2193
Other references
Boyd A, Golding J, Macleod J, et al. Cohort profile: the ‘Children of the 90s’ – the index offspring of the Avon Longitudinal Study of Parents and Children. International Journal of Epidemiology 2013, 42(1), 111-127. https://doi.org/10.1093/ije/dys064
Cassell A, Edwards D, Harshfield A, et al. The epidemiology of multimorbidity in primary care: a retrospective cohort study. British Journal of General Practice 2018, 68(669), e245-e251. https://doi.org/10.3399/bjgp18X695465
Chiang JJ, Lam PH, Chen E, et al. Psychological stress during childhood and adolescence and its association with inflammation across the lifespan: a critical review and meta-analysis. Psychological Bulletin 2022, 148(1-2), 27-66. https://doi.org/10.1037/bul0000351
Edmondson-Stait AJ, Shen X, Adams MJ, et al. Early-life inflammatory markers and subsequent psychotic and depressive episodes between 10 to 28 years of age. Brain, Behavior, & Immunity – Health 2022, 26, 100528. https://doi.org/10.1016/j.bbih.2022.100528
Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021). Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2024.
Goldfarb M, De Hert M, Detraux J, et al. Severe mental illness and cardiovascular disease: JACC state-of-the-art review. JACC 2022, 80(9), 918-933. https://doi.org/10.1016/j.jacc.2022.06.017
Heim C & Binder EB. Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Experimental Neurology 2012, 233(1), 102-111. https://doi.org/10.1016/j.expneurol.2011.10.032
Khandaker GM, Pearson RM, Zammit S, et al. Association of serum interleukin 6 and C-reactive protein in childhood with depression and psychosis in young adult life: a population-based longitudinal study. JAMA Psychiatry 2014, 71(10), 1121-1128. https://doi.org/10.1001/jamapsychiatry.2014.1332
Kuhlman KR, Horn SR, Chiang JJ et al. Early life adversity exposure and circulating markers of inflammation in children and adolescents: a systematic review and meta-analysis. Brain, Behavior, and Immunity 2020, 86, 30-42. https://doi.org/10.1016/j.bbi.2019.04.028
Lamers FM, Milaneschi Y, Smit JH, et al. Longitudinal association between depression and inflammatory markers: results from the Netherlands Study of Depression and Anxiety. Biological Psychiatry 2019, 85(10), 829-837. https://doi.org/10.1016/j.biopsych.2018.12.020
Mattsson N, Rönnemaa T, Juonala M, et al. Childhood predictors of the metabolic syndrome in adulthood. The Cardiovascular Risk in Young Finns Study. Annals of Medicine 2008, 40(7), 542–552. https://doi.org/10.1080/07853890802307709
Miller GE, Chen E, & Parker KJ. Psychological stress in childhood and susceptibility to the chronic diseases of ageing: moving toward a model of behavioral and biological mechanisms. Psychological Bulletin 2011, 137(6), 959-997. https://doi.org/10.1037/a0024768
Nakada S, Ho FK, Celis-Morales C, et al. Individual and joint associations of anxiety disorder and depression with cardiovascular disease: A UK Biobank prospective cohort study. European Psychiatry 2023, 66(1):e54. https://doi.org/10.1192/j.eurpsy.2023.2425
Nuyen J, Bos EH, de Jonge P, et al. Longitudinal bidirectional associations between internalizing mental disorders and cardiometabolic disorders in the general adult population. Social Psychiatry & Psychiatric Epidemiology 2021, 56, 1611-1621. https://doi.org/10.1007/s00127-020-02007-3
Nylund-Gibson K & Masyn KE. Covariates and mixture modeling: results of a simulation study exploring the impact of misspecified effects on class enumeration. Structural Equation Modeling 2016, 23, 782-797. https://doi.org/10.1080/10705511.2016.1221313
Perry BI, Zammit S, Jones PB, Khandaker GM. Childhood inflammatory markers and risks for psychosis and depression at age 24: examination of temporality and specificity of association in a population-based prospective birth cohort. Schizophrenia Research 2021, 230, 69-76. https://doi.org/10.1016/j.schres.2021.02.008
Rasmussen LJH, Moffitt TE, Eugen-Olsen J et al. Cumulative childhood risk is associated with a new measure of chronic inflammation in adulthood. Journal of Child Psychology & Psychiatry 2019, 60, 199-208. https://doi.org/10.1111/jcpp.12928
Ronaldson A, Arias de la Torre J, Prina M, et al. Associations between physical multimorbidity patterns and common mental disorders in middle-aged adults: a prospective analysis using data from the UK Biobank. The Lancet Regional Health – Europe 2021, 8, 100149. https://doi.org/10.1016/j.lanepe.2021.100149
Tahir UA & Gerszten RE. Molecular biomarkers for cardiometabolic disease: risk assessments in young individuals. Circulation Research 2023, 132(12), 1663-1673. https://doi.org/10.1161/CIRCRESAHA.123.322000
Weller BE, Bowen NK, & Faubert SJ. Latent class analysis: a guide to best practice. Journal of Black Psychology 2020, 46(4), 287-311. https://doi.org/10.1177/0095798420930932
Zalli A, Jovanova O, Hoogendijk WJG, et al. Low-grade inflammation predicts persistence of depressive symptoms. Psychopharmacology 2016, 233, 1669-1678. https://doi.org/10.1007/s00213-015-3919-9
