Inflammation Patterns in Childhood Linked to Adult Disorders

Mental and cardiometabolic disorders are among the top contributors to global disease burden, as reported by the Global Burden of Disease Collaborative Network (2024). These disorders frequently manifest as multimorbidity, where individuals suffer from two or more chronic conditions simultaneously. Research indicates a bidirectional relationship between mental health and cardiometabolic issues (Nakada et al., 2023; Nuyen et al., 2021; Ronaldson et al., 2021). While the underlying causes of these complex conditions remain elusive, emerging studies point towards shared genetic, biological, and psychosocial factors as common denominators, particularly highlighting the role of chronic low-grade inflammation (Goldfarb et al., 2022).

Longitudinal research has established a clear link between inflammation and the development of both mental and cardiometabolic health issues. Studies have identified that chronic low-grade inflammation is a significant predictor of cardiometabolic diseases in adults, with inflammatory markers like interleukin-6 (IL-6) and C-reactive protein (CRP) being associated with the onset of cardiovascular diseases (Tahir & Gerszten, 2023). Notably, elevated CRP levels during youth have been correlated with the emergence of metabolic syndrome later in life (Mattson et al., 2008). Additionally, a growing body of evidence links inflammation to mental health disorders, where higher IL-6 levels in childhood correlate with increased susceptibility to psychosis, depression, and negative psychotic symptoms in early adulthood (Edmonson-Stait et al., 2022; Khandaker et al., 2014; Perry et al., 2021).

Most existing studies exploring the connection between inflammation and mental as well as cardiometabolic health often rely on data collected from a single time point or focus on a single outcome. By examining longitudinal patterns of inflammation over time, researchers can investigate whether specific subgroups exhibit distinct trajectories that correlate with varying risk factors or health outcomes. In this context, Palmer et al. (2024) explored the associations between inflammation trajectories from childhood to adolescence and the subsequent risks of developing cardiometabolic and mental disorders during early adulthood.

Examining the Methodology of Inflammation Trajectories

This research utilized a dataset from a prominent UK birth cohort, known as the Avon Longitudinal Study of Parents and Children (ALSPAC). This extensive cohort study has been instrumental in uncovering various health and social outcomes over decades.

Inflammation levels were measured through CRP levels (mg/dL) derived from non-fasting blood samples taken at ages 9, 15, and 17 years. The researchers employed latent class growth analysis to identify groups of individuals displaying different patterns or trajectories of CRP levels over time, while adjusting for body mass index (BMI; kg/m²) at each measurement point.

Following this, the authors investigated the relationship between the identified CRP trajectories and various mental and cardiometabolic health outcomes at the age of 24. The outcomes evaluated included:

• Psychotic disorders and psychotic experiences measured through the Psychosis-Like Symptom Interview (PLIKSi).
• Depressive symptoms (ranging from mild to severe) and generalized anxiety disorder, assessed via the computerised Clinical Interview Schedule – Revised (CIS-R), following definitions set by the International Classification of Diseases, 10^th revision [ICD-10] criteria.
• Hypomania evaluated using the Hypomania Checklist-32.
• Glucose-insulin sensitivity assessed using the Homeostasis Assessment Model (HOMA2) score.

These analyses accounted for various factors that might influence inflammation and the associated health outcomes (i.e., confounders). These included biological sex at birth, ethnicity (white vs non-white), preterm birth, Family Adversity Index (total scores from pregnancy, ages 2 years and 4 years), parent-reported child health, and emotional symptoms at age 9 years, measured through the Strengths and Difficulties Questionnaire.

Results: Understanding Inflammation Patterns

A total of 6,556 participants were categorized into specific trajectory classes through latent class growth analysis. The optimal model revealed three distinct CRP trajectories:

1. Reference group (n=6109; 93%) – exhibiting persistently low CRP levels.
2. Early peak group (n=197; 3%) – displaying a peak in CRP levels at age 9 years.
3. Late peak group (n=250; 4%) – experiencing a peak in CRP levels at age 17 years.

When comparing the early peak group to the reference group, it was discovered that the early peak group faced significantly heightened risks of psychotic disorders, psychotic experiences, and severe depression, in addition to exhibiting higher HOMA2 scores at age 24. However, no compelling evidence indicated associations with mild depression, hypomania, or generalized anxiety disorder.

Interestingly, the research did not find any indications of increased risk for any of the evaluated health outcomes within the late peak group.

Key Findings and Implications

This research successfully identified subgroups of individuals characterized by unique inflammation trajectories. Specifically, those exhibiting elevated inflammation levels in childhood were found to have an increased risk of developing psychosis, depression, and insulin resistance as they transitioned into early adulthood. These results contribute valuable insights to the existing body of literature on the connections between inflammation and both mental and cardiometabolic health while emphasizing the potential significance of the timing of inflammation during developmental stages on subsequent health outcomes.

Strengths and Limitations of the Study

This study is characterized by several notable strengths:

• Utilization of ALSPAC data, a comprehensive population-based cohort that provides extensive information across various health and developmental domains from childhood through adulthood.
• Longitudinal design offering a comprehensive view of inflammation profiles over time, surpassing studies reliant on single measurements.
• Employment of latent class growth analysis to effectively model CRP trajectories, allowing for the identification of heterogeneous subgroups within the population.
• Addressing attrition bias. Although dropout rates in ALSPAC are typical for longitudinal cohort studies, the authors mitigated this bias by employing inverse probability weighting to account for missing data.

Despite these strengths, the study does have limitations:

• Lack of diversity – The data is derived from a volunteer cohort based in South West England, leading to an overrepresentation of more affluent groups and an underrepresentation of non-White minority ethnic populations when compared to the national demographics (Boyd et al., 2013).
• Youth cohort limitations – Assessing outcomes at age 24 may be relatively young for observing significant changes related to cardiometabolic diseases; including additional outcomes such as blood pressure and blood lipids could enhance understanding of these subgroups’ cardiometabolic health.
• Single inflammatory marker limitations – The study focused exclusively on one inflammatory marker, while existing literature indicates that other markers (e.g., IL-6, tumor necrosis factor-alpha, soluble urokinase plasminogen activator receptor) may show differential associations with health outcomes.
• Adjustment for covariates – Although the authors adjusted for BMI at each measurement point in the latent class growth analysis, recent simulations suggest that latent class models should ideally be conducted without covariates, as incorrect covariate specification can lead to errors in identifying the correct number of classes (Nylund-Gibson & Masyn, 2016). Current best practices advocate a three-step approach where the optimal model and class count are determined prior to covariate inclusion (Weller et al., 2020).

Practical Implications and Future Research Directions

This study offers valuable insights into the connection between childhood inflammation and health outcomes in early adulthood. The identification of heterogeneous subgroups exhibiting distinct inflammation trajectories suggests that these groups face varying levels of risk for mental and cardiometabolic disorders. However, additional research is essential to unravel the underlying biological mechanisms, as a comprehensive understanding of the causes behind chronic low-grade inflammation is still lacking. This knowledge is crucial for developing effective interventions aimed at preventing adverse health outcomes.

There is growing evidence indicating that adverse childhood experiences can become “biologically embedded,” potentially leading to a proinflammatory state later in life (Miller et al., 2011). Various childhood risk factors, such as low socioeconomic status, exposure to adverse experiences, and poor health, have been linked to inflammation in adulthood (Rasmussen et al., 2019). However, recent meta-analyses suggest that the effects of these adverse experiences on inflammation are generally modest (Chiang et al., 2022; Kuhlman et al., 2020).

Furthermore, these findings underscore the importance of the timing of adverse exposures, suggesting the existence of “sensitive periods” when exposure to psychosocial stress can disrupt physiological development and have lasting health ramifications. Developmental neuroscience firmly supports the idea that there are critical periods of neurogenesis and plasticity during which adversity can significantly alter brain development, with potential long-term implications for behavior and health depending on the maturation of different brain regions (Heim & Binder, 2012).

Future studies should focus on elucidating the timing and mechanisms by which adverse childhood experiences contribute to chronic low-grade inflammation, along with identifying moderating factors to inform effective prevention and intervention strategies.

Disclosure of Interests

Ruby is engaged in similar research focusing on health and biomarker trajectories, along with their associations with various risk factors and health outcomes; however, she has had no personal involvement in this particular study.

She is supported by the Tackling Multimorbidity at Scale Strategic Priorities Fund programme [grant number MR/W014416/1], which is delivered by the Medical Research Council and the National Institute for Health Research, in collaboration with the Economic and Social Research Council and the Engineering and Physical Sciences Research Council.

Ruby has no conflicts of interest to declare.

Essential References for Further Reading

Primary Research 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

Additional 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.