Download pdf - Lipid trajectories as predictors of depressive symptoms: The Young Finns Study

Lipid trajectories as predictors of depressive symptoms: TheYoung Finns Study

Marko Elovainio, Laura Pulkki-Råback, Mika Kivimäki, Markus Jokela, Jorma Viikari, Olli T.Raitakari, Risto Telama, and Liisa Keltikangas-JärvinenNational Institute for Health and Welfare, P.O.Box 30, 00271 Helsinki, Finland, and, Departmentof Psychology, University of Helsinki, Finland, and, University College London, UK, MarkoElovainio, prof., University College London, UK, and Finnish Institute of Occupational Health,Helsinki, Finland, Mika Kivimäki, prof., Department of Psychology, University of Helsinki, Finland,Laura Pulkki-Råback, PhD., Markus Jokela, PhD., Liisa Keltikangas-Järvinen, prof., ResearchCentre of Applied and Preventive Cardiovascular Medicine and Department of ClinicalPhysiology, University of Turku, Turku, Finland, Jorma Viikari, prof., Olli T. Raitakari, prof., LIKES,University of Jyväskylä, Finland, Risto Telama,. prof.

AbstractObjective—The aim of this study was to identify common trajectories of lipid levels acrosschildhood and early adulthood life span.

Design—The sample was a subpopulation of 824 young adults (3 – 9 years at baseline in 1980)of the on-going population-based prospective Cardiovascular Risk in Young Finns Study. Lipidlevels were determined in 1980, 1983, 1986 and 2001.

Main outcome measures—Depressive symptoms were assessed using a modified version ofBeck’s Depression Inventory in 1992 and 2001.

Results—The two triglycerides trajectories (steeply vs moderately increasing) were differentlyrelated to depressive symptoms in adulthood. The trajectory showing steep increase over time wasassociated with higher level of depressive symptoms (mean 2.18, 95% CI 2.08 - 2.28 vs 1.99, 95%CI 1.95–2.04). This relationship persisted after adjustments for various risk factors. Thesetriglycerides trajectories accounted for part of the association between high BMI and depressivesymptoms.

Conclusion—A pattern of steeply increasing triglyceride levels throughout childhood andadulthood may be associated with increased the risk of depressive symptoms in adulthood. Thispattern may also be one link between obesity and depressive symptoms.

Keywordsdepression; CHD; psychosocial factors; lipids

Public health significance of depression has been emphasized due to the impact depressionhas on the quality of life and especially on the course and mortality associated with other

Correspondence concerning this article should be addressed to Marko Elovainio, National Institute for Health and Welfare, P.O. Box30, 00271 Helsinki, Finland, Tel. +358 20 610 7434, Fax +358 20 610 7485. [email protected]'s Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting,fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The AmericanPsychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscriptversion, any version derived from this manuscript by NIH, or other third parties. The published version is available atwww.apa.org/pubs/journals/HEA

NIH Public AccessAuthor ManuscriptHealth Psychol. Author manuscript; available in PMC 2012 April 4.

Published in final edited form as:Health Psychol. 2010 May ; 29(3): 237–245. doi:10.1037/a0018875.

NIH

-PA Author Manuscript

NIH


NIH


http://www.apa.org/pubs/journals/HEA

medical diseases (Kessler, Zhao, Blazer, & Swartz, 1997). A number of psychosocial factorssuch as disturbed family environment, predisposing personality traits, exposure to traumaticevents, low social support, recent stressful life events and work stress are suggested toincrease the risk of depressive symptoms(Bruce, 2002; Elovainio et al., 2006; Kendler,Neale, Kessler, Heath, & Eaves, 1993; Virtanen et al., 2008). It has also repeatedly beenshown that lipid metabolism may be associated with depression or depressive symptoms (J.M. Kim et al., 2006; Troisi, 2009).

The hypotheses on the biological mechanisms between cholesterol and mental health arebased on the findings that cholesterol forms part of cell membranes and is a component ofmyelin. The development, function and stability of synapses are also affected by cholesterol(Chattopadhyay & Paila, 2007; Chattopadhyay et al., 2007). It has been suggested thatserum cholesterol may directly influence brain lipids and the fluidity of the cell membrane,with secondary effects on serotonergic neurotransmission (Engelberg, 1992). It has alsobeen proposed that a decrease in serum total cholesterol or low density lipoproteincholesterol (LDL-c) would induce a relative increase in brain cell membrane fluidity withincreased presynaptic serotonin reuptake and decreased postsynaptic serotonin function(Diebold et al., 1998), which in turn could affect psychological states. It has been suggestedthat the lipid fraction associated with neuroendocrine indices of reduced serotonin functionis low high density lipoprotein cholesterol (HDL-c) rather than total cholesterol or LDL-c(Buydens-Branchey, Branchey, Hudson, & Fergeson, 2000). Very little is known, however,about the biological role of triglycerides. There are also preliminary findings suggesting thatthe same genetic factor (i.e., the APOE promoters and the serotonin transporterpolymorphism) contributes to lipid metabolism and psychiatric vulnerability (Elovainio etal., 2008; Fischer, Gruenblatt, Pietschmann, & Tragl, 2006).

Low levels of HDL-c has repeatedly been found to be a risk factor for incident depression(Horsten, Wamala, Vingerhoets, & Orth-Gomer, 1997; J. M. Kim et al., 2006) as well as acorrelate of prevalent depression, mood disorders (Chen, Lu, Wu, & Chang, 2001;Dimopoulos et al., 2007; J. M. Kim, Stewart, Shin, & Yoon, 2004; Y. K. Kim & Myint,2004; Lehto et al., 2008; Vogelzangs et al., 2007) and bi-polar disorders (Sagud, Mihaljevic-Peles, Pivac, Jakovljevic, & Muck- Sleer, 2009). However, for total cholesterol and LDL-c,both lower and higher levels have been found to be associated with depression (Aijanseppaet al., 2002; Partonen, Haukka, Virtamo, Taylor, & Lonnqvist, 1999; Shin, Suls, & Martin,2008). Terao and others found more cases of depression among subjects having totalcholesterol levels between 4.8 mmol/l and 5.0 mmol/l compared to those having totalcholesterol levels higher than 6.1 mmol/l (Terao et al., 2000). Also null findings have beenreported (Blazer, Burchett, & Fillenbaum, 2002; Brown, 1995; Ergun et al., 2004). There isalso evidence suggesting positive association between high triglycerides and depression,bipolar disorders or affective disorders (Ergun et al., 2004; Glueck, Tieger et al., 1994;Lehto et al., 2008; Sagud et al., 2009). For instance, Almeida et al (Almeida et al., 2007)showed that high triglycerides, but not other lipids, may be associated with depression inolder men.

There are many potential reasons for these mixed findings. Cholesterol and triglyceridescirculate in various carrier proteins, each with unique features and are separated by theirdensity. LDL-c is the major carrier of cholesterol and is needed for the synthesis of steroidhormones and cell membranes. Elevated levels of LDL-c accumulate subendothelially inarteries, but HDL is responsible for the reverse cholesterol transport. The independent roleof triglycerides on various health outcomes is less clear due to the collinearity of elevatedtriglycerides with reduced HDL-c and other lipids. A recent review suggested, however, thatnon-fasting triglyceride levels may strongly predict the risk of cardiovascular events andother health outcomes (Abdel-Maksoud, Sazonov, Gutkin, & Hokanson, 2008). Most of the

Elovainio et al. Page 2

Health Psychol. Author manuscript; available in PMC 2012 April 4.

NIH


NIH


NIH


previous studies have examined only the effect of LDL-c or total cholesterol on depressivesymptoms although the effects may differ for other lipids, such as HDL-cholesterol ortriglycerides.

Furthermore, many of the previous studies have used clinical or elderly populations andcross-sectional designs. These studies presuppose that lipid levels have rather immediateimpact on mood or depressive symptoms. According to a previous study using the samecohort (Pulkki-Raback et al., 2009) depressive symptoms were associated with increasedrisk of the metabolic syndrome in adulthood and the metabolic syndrome in childhood, inturn, predicted higher levels of depressive symptoms in adulthood. The authors concludedthat process linking depressive symptoms with the components of metabolic syndrome maygo into both directions and may begin early in life. The authors also found a cross-sectionalassociation between triglycerides and depression in adulthood. The problem with this studywas the inability to model the change in metabolic syndrome components and especially theinability to model the unobserved heterogeneity in the population, in other words, differentlyshaped developmental trajectories of lipid levels, which was the particular aim of this study.There are age- and time-related patterns of lipid levels and thus it is reasonable to assumethat long term patterns of lipid levels over time may be more important for developingmental health problems than lipid levels at single points in time.

The current study examined patterns of lipid levels through childhood and early adulthoodand their associations with depressive symptoms in adulthood. We used a new statisticalapproach (Jones, Nagin, & Roeder, 2001; Marin, Chen, & Miller, 2008) to indentifycommon trajectories of LDL-c, HDL-c and triglycerides across childhood and earlyadulthood in a random sample of Finnish men and women. These trajectories were used topredict depressive symptoms in adulthood. The lipid levels trajectories were identified atfour time points during the 21-year follow-up.

MethodsStudy sample

The participants were derived from two samples of the on-going population based study of“Cardiovascular Risk in Young Finns“(Young Finns) (Raitakari et al., 2008). In thisprospective, epidemiological study, a randomized sample of 3596 healthy Finnish childrenand adolescents in age cohorts of 3, 6, 9, 12, 15, and 18 years have been followed since1980. During the sixth follow-up of the Young Finns in 2001 (21 years after the baseline),2229 participants were re-examined by measuring depressive symptoms using the modifiedBeck Depression Inventory. The sample of this study consisted of 824 boys and girls whowere 3, 6 or 9 years of age in 1980 and had complete follow-up data (i.e., lipids measured at1980, 1983, 1986 and 2001 and depression measured at 1992 and 2001). As 1765participants belonging to the three youngest age cohorts had lipids measured at baseline, thefinal sample was 47% of the baseline population.

Plasma lipidsAll measurements of lipid levels were performed in duplicate in the same laboratory. Allvenous blood samples were taken after 12 hours of fasting. Serum samples were storedfrozen at −20°C for no more than 6 months until analyzed. All lipid determinations wereperformed in duplicate in the same season (fall) and as simultaneously as possible. Frozenserum was used both in sample analyses and in the determination of method correctionequations. Standardized enzymatic methods were used for measuring levels of serum totalcholesterol, triglycerides, and high density lipoprotein cholesterol. Low density lipoproteincholesterol concentration was calculated by the Friedewald formula (Friedewald, Levy, &



NIH


NIH


NIH


Fredrickson, 1972). Similar methods were used in all study phases. Those with havingtriglycerides higher that 4 were excluded form the analyses (n=4).

Depressive symptomsDepressive symptoms were self-rated using a modified version of the Beck DepressionInventory (Beck, 1967). The questionnaire pins down negative mood, sadness, pessimism,indecisiveness and somatic problems such as insomnia and fatigue. The original BDI is a 21-item questionnaire that offers 4 alternative statements for each item. In the present study, theBDI was modified so that each statement represents the second mildest level of depressionin the original BDI. The participants were asked to choose between 1 (totally disagree) and 5(totally agree) (Elovainio et al., 2006). Cronbach’s α for the modified BDI in 1992 and 2001was .0.89 and 0.92. The Pearson correlation between depressive tendencies in 1992 and2001 was r=0.58 (p<0.0001).

Confounding and mediating factorsThe potential confounders were parental socioeconomic position, childhood body massindex and childhood C-reactive protein (CRP), all measured in 1980. Parentalsocioeconomic position was defined as family income and it was calculated as gross annualincome, ranging from 1 (corresponding to <USD 3,000) to 8 (corresponding to >USD22,000) (Kivimaki et al., 2006). These criteria correspond to the median income perhousehold in Finland which was 12 920 USD in 1983. Childhood serum high sensitive C-reactive protein (hsCRP) was analyzed by an automated analyzer (Olympus AU400,Olympus, USA) and a highly sensitive turbidimetric immunoassay kit ("CRP-UL"-assay,Wako Chemicals, Neuss, Germany). Detection limit of the assay was 0.06 mg/L. Physicalactivity was a sum index of five questions assessing the intensity, duration, and frequency ofsports.

The potential mediating factors were adulthood indicators of health behavior including bodymass index, alcohol consumption, smoking, physical activity and metabolic syndrome. Bodymass index was calculated at both times (1980 and 2001) as weight (kg)/[height]2. Heightwas measured with a wall-stated statiometer and weight with Seca scales. Alcoholconsumption was measured by one question (number of occasions per week when alcoholicbeverages are consumed more than six units during one day). Smoking status was assessedby a questionnaire. Those smoking on daily basis were classified as smokers. All measure shave been reported previously in Raitakari et al. (Raitakari et al., 2008). Metabolicsyndrome was defined following the criteria of National Institute of Health Adult TreatmentPanel III (NCEP). Metabolic syndrome was diagnosed as 3 or more of the followingconditions: waist ≥102 cm in men and ≥88 cm in women, serum triglycerides ≥1.695 mmol/l (150 mg/dl), HDL cholesterol <1.036 mmol/l (40 mg/dl) in men and <1.295 mmol/l (50mg/dl) in women, blood pressure ≥130 or ≥85 mmHg or treated, and plasma glucose ≥5.6mmol/l (100 mg/dl).

Statistical analysesStatistical calculations were done using SAS (version 9.1) statistical package. In the firstwave of analyses, we modelled trajectories of LDL-c, HDL-c and triglyceride levels acrosschildhood and adulthood by using a SAS procedure PROC TRAJ (Jones et al., 2001) thatseparates individuals into trajectory groups. TRAJ is a semiparametric, group-basedmodelling strategy that identifies clusters of individual trajectories. Model estimationproduces posterior probabilities of membership in each trajectory group for each participant.These probabilities are used to assign individuals to the trajectory group to which they aremost likely to belong. First, we determined the number of trajectories that best representedpatterns of lipids in our sample. The way of doing this was using the change in the Bayesian



NIH


NIH


NIH


information criteria (BIC) as an approximation to the log of the Bayes factor. Next, welooked at the parameter estimates to determine the shape (linear, quadratic, or cubic) of eachtrajectory. Finally, we examined the relationship between trajectory group membership anddepressive symptoms in adulthood in 2001 using analyses of variance. Depressivesymptoms measured at 1992 were treated as baseline measure of depressive symptoms. Thisset of analyses could be thought of as an inductive empirical approach based on what thedata reveal about the types of life-course patterns that are present in this sample.

ResultsThe sample characteristics are shown in Table 1. Almost half of the sample was lost duringthe follow-up. Compared to the baseline population, the participants in the present samplewere more often women (χ2 = 110.24, df = 1, p < 0.001). The other baseline differenceswere not statistically significant. Depressive symptoms slightly decreased from 1992 to2001

We tested the number of trajectories following the Bayesian Information Criterion (BIC)and continued to increase the number of trajectories as long as the BIC increased, indicatinga better model fit of the model: For LDL-c the BIC was −6969.0 for 1 group, −6181.4 for 2groups, and −5931.0 for 3 groups. For HDL-c the BIC was −1345.0 for 1 group, −841.6 for2 groups and −686.2 for 3 groups and -641.9 for 4 groups. For triglycerides the BIC was−2712.9 for 1 group, −1932.2 for 2 groups and −1768.4 for 3 groups. Because the fit did notmuch improve after 2 group solutions in LDL-c (group sizes 75%25%) and triglycerides(83%17%) we retained the 2 trajectory models. In HDL-c the fit improved after three groupsolution only marginally and to prevent the trajectory group sizes from getting too small, wedid not exceed the 4-group model. Both the 3- and 4-group models yielded similarinformation. In the interest of parsimony, we retained the 3-trajectory model (43%/48%/9%).

The shape of each trajectory was determined separately for LDL-c, HDL-c and triglyceridesby initially including linear, quadratic, and cubic parameters for both trajectories, and thendropping the nonsignificant ones. A parameter estimate divided by its standard error resultsin a t statistic, which was used to determine statistical significance. The shape of eachtrajectory was identified by the highest order term included in the model. In Model 1, linear,quadratic, and cubic parameters were included for each of the four trajectories. In LDL-c,HDL-c and triglycerides the cubic parameters were significant for all trajectories and thusModel 1 was adopted as our final model for these lipids (Table 2). In LDL-c the groups werecalled high (25%) and low (75%) and triglycerides groups were called moderatelyincreasing (83%) and steeply increasing (17%) trajectories. In HDL-c the trajectory groupswere called high (9%), medium (48%) and low (43%) (Figures 1–3).

As shown in Table 3, no statistically significant differences in baseline-adjusted depressivesymptoms between LDL-c or HDL-c trajectory groups were observed. However, the steeplyincreasing triglycerides trajectory group showed higher baseline-adjusted means ofdepressive symptoms than the moderately increasing group. Table 4 presents the effects ofvarious childhood confounders and adulthood mediators in the association betweentriglycerides trajectories and depressive symptoms. The association seemed to be quiterobust to adjustments for any childhood or adulthood factors or for their combination. Weadditionally adjusted the association between triglycerides trajectories and depressivesymptoms for triglyceride levels in 2001 and although it somewhat attenuated the effect, theassociation remained statistically significant (p=0.02).



NIH


NIH


NIH


We confirmed the results using linear regression analyses using triglycerides levels atbaseline and at follow-up predictors of depressive symptoms. The results showed thattriglycerides at both time points were associated with depressive symptoms in models wherebaseline depressive symptoms and other baseline variables were adjusted for, but only theeffect of baseline triglycerides (B=0.18, t(1)=2.61, p=0.009) was robust to adjustment forcombination of all childhood and adulthood factors. Using triglycerides change score(subtracting baseline from follow-up) produced comparable results. The association betweentriglyceride change and depressive symptoms was robust to adjustments for childhood andadulthood risk factors including metabolic syndrome (B=0.11, t(1)=2.48, p=0.013).

High childhood BMI (B=0.02, t(1)= 3.63, p<0.001) and greater increase of BMI fromchildhood to adulthood (B=0.01, t(1)=2.85, p=0.005) predicted increased depressivesymptoms, even when adjusted for other childhood and adulthood factors covariates excepttriglycerides. However, when triglycerides trajectory group was added to the model, neitherchildhood BMI (B=0.00, t(1)=1.67, p=0.095) nor BMI change from childhood to adulthood(B=0.01, t(1)=0.87, p=0.385) were associated with adulthood depressive symptoms.

DiscussionPrevious studies have reported mixed findings on the associations between LDL-c, HDL-c,triglycerides and depressive symptoms, but these studies have not taken into account theage-related changes in lipid trajectories and their effects on mental health problems. In this21-year follow-up of a randomly selected group of Finnish boys and girls aged 3 to 9 yearsat baseline a pattern of steeply increasing triglycerides levels from childhood to adulthoodwas associated with depressive symptoms in adulthood. This association was not explainedby confounding factors, such as childhood or adulthood health behaviours, earlier depressivesymptoms, or adulthood triglycerides. We observed no associations of LDL-c or HDL-ctrajectories with depressive symptoms.

High LDL- and total cholesterol levels have been shown to be associated with seriousphysical diseases, such atherosclerosis, coronary heart disease and diabetes, but they aresuggested to be associated with more favourable mental health outcomes (Shin, Suls &Martin, 2008). Although some studies suggest no association between cholesterol anddepression or depressive symptoms (Deisenhammer et al., 2004), several studies have foundlow or lowered LDL-cholesterol/total cholesterol to predict increased risk of depression andsuicidal behaviour (Borgherini, Dorz, Conforti, Scarso, & Magni, 2002; Terao et al., 2000).Early findings on the association between triglycerides and depressive symptoms, on theother hand, suggest that low triglycerides may be associated with less depressive symptoms(Glueck, Kuller et al., 1994; Glueck, Tieger et al., 1994). Recently some studies have foundsimilar positive associations between depression or depressive symptoms and elevatedtriglycerides but not with other lipids (Almeida et al., 2007; Glueck, Kuller et al., 1994;Glueck, Tieger et al., 1994; Toker, Shirom, Shapira, Berliner, & Melamed, 2005). Ourfindings are consistent with this evidence. We took into account age-related changes in lipidlevels and found triglycerides to have two clearly different-shaped trajectories (steep andmoderate increase) whereas trajectories of LDL-c and HDL-c showed group differences inaverage levels but not in the shapes of the age-related trajectories. Interestingly, only steeplyincreasing triglycerides trajectory was associated with depressive symptoms.

The importance of cholesterol in the function of neural system has been underlined. Barresand Smith suggested that cholesterol is needed to activate the signaling pathway that triggerssynaptogenesis (Barres & Smith, 2001). Engelberg (Engelberg, 1992) suggested a morespecific physiological mechanism that might explain the association between low LDL –cholesterol and total cholesterol and depressive symptoms. He concluded that low



NIH


NIH


NIH


membrane cholesterol decreases the number of serotonin receptors. Since membranecholesterol exchanges freely with cholesterol in the surrounding medium, a lowered serumcholesterol concentration may contribute to a decrease in brain serotonin, which then mayaffect mood. However, the role of triglycerides in physiological background of depressionremains unclear (Troisi, 2009).

The most obvious confounder in our analyses was body mass index due to its strongassociations with both lipid levels and depressive symptoms, a finding also observed inprevious studies (Cortese, Faussard, Angriman, Pigaiani, & al., 2008). However, theassociations between triglycerides trajectories and depressive symptoms were quite robust toadjustments for childhood BMI. This is in line with recent studies suggesting that thedirection of the association between mental health and BMI may be dominantly from mentaldisorder to obesity rather than vice verse (Kivimäki et al., 2009). Moreover, the linkbetween steep increase in triglycerides and subsequent depressive symptoms was notexplained by markers of childhood systemic inflammation, or by adulthood health riskbehavior, both of which have been found to be associated with lipids and depressivesymptoms (Elovainio et al., 2006; Maes, 1999). In contrast, we found some evidence that theassociation of childhood BMI and weight gain from childhood to adulthood, with depressivesymptoms in adulthood attenuated when steeply increasing triglyceride levels were adjustedfor.

In the current study, we applied semi-parametric, group-based modeling. The approach isintended to complement two well-established methods for analyzing developmentaltrajectories - hierarchical modeling (Goldstein, Healy, & Rasbash, 1994) and latent growthcurve modeling (Bollen & Curran, 2006). These methods model variation in the parametersof developmental trajectories using continuous multivariate density function. The groupbased approach employs a multinomial modeling strategy and is especially useful formodeling unobserved heterogeneity in a population. In contrast to the homogeneous case, itis assumed that there are unobserved subpopulations differing in their parameter valuesdependent on time.

We additionally, however, performed latent growth curve analyses with intercept and slopelatent variables of triglycerides predicting depression at follow up. Latent factorsrepresenting intercept (baseline status) and slope (rate of change) components are extractedfrom the four observations of triglycerides across time, here identified as wave 1 (baseline),wave 2 and wave 3. Factor loadings of the latent intercept component to all threeobservation were fixed to 1, and the linear slope component was defined by fixing theparameters to 0 (baseline), 1 (wave 2), 2 (wave 3) and 3 (wave 4). The estimated variance ofthe intercept growth factor was 0.63 (SE 0.46), and the variance of the slope growth factorwas 0.17 (SE 0.09) indicating that there was significant individual heterogeneity around thegroup means for both initial status and rate of change. Furthermore, previous analysesindicated nonlinear growth curves in later triglycerides, thus, we freed the fourth factorloading to capture this nonlinear growth curve The fit indices suggested that the proposedmodel fit the data quite well Χ2 (5) = 15.12, p=0.009, RMSEA=0.053. In the second stage ofmodel development we tested whether the latent factors were associated with depressivesymptoms at follow up and found that there was a significant association between depressivesymptoms and triglyceride intercept (equation = 8.66, t=10.44) and slope (equation = 8.66,t=10.44). When the model was additionally adjusted for earlier depressive symptoms onlythe association between depressive symptoms and triglyceride slope remained statisticallysignificant (t=10.54).

In interpreting the present results, it is important to note some limitations. First, assignmentinto trajectory groups is probabilistic and some individuals had a high probability of



NIH


NIH


NIH


membership in the particular trajectory group, whereas others had a lower membershipprobability in that same trajectory group. Although this probabilistic assignment means thatthe trajectory groups should not be taken as real entities, any uncertainty in groupmembership is factored into the depressive symptoms comparisons. Second, the scale ofdepressive symptoms was not a measure of clinical depression and therefore our findingsshould be replicated using a standard measure of depression with clinical cut-off points. Ourmeasure of mild depression may better bring out subtle variations in depressive symptoms ina population of healthy young adults among whom rates of clinical depression are ratherlow. Even mild, sub-clinical, levels of depression may, however, be important as they havebeen suggested to be associated with increased CHD risk if they persist over time. Thevalidity of the modified BDI is supported by previous studies that have associated it withpsychosocial characteristics known to be associated with depression, such as child’s difficulttemperament (negative emotionality and low sociability), adulthood sentimentality andfatigability, and low social support and with cardiovascular risk factors such as C-reactiveprotein and pre-clinical atherosclerosis. Although we can never rule out the potentialresidual confounding, the fact that we did take into account the effect of earlier depressivesymptoms in the relationship between lipid trajectories and later depressive symptoms offersome support to the conclusion that our results are not explained by confounders known toimpact the level of depressive symptoms. Such factors would have already influenced thelevel of earlier depressive symptoms.

In conclusion, our results have to be considered as preliminary, and further research isneeded to confirm these findings. Our results suggest that relatively rapid increase oftriglycerides already at early age is associated with depressive symptoms in adulthood. Noevidence on association of any change patterns of LDL- or HDL-cholesterol on depressivewas found.

Abbreviations

LDL low-density lipoprotein

HDL high-density lipoprotein

CHD coronary heart disease

Young Finns Cardiovascular Risk in Young Finns

AcknowledgmentsThis study was financially supported by the Signe and Ane Gyllenberg’s Foundation (LK-J), the Academy ofFinland grants 111056, 209514, 123399 (LK-J), 123621 (LP-R) and 117604 (MK) and 7784 and 210283 (O.T.R),Emil Aaltonen Foundation (TL), Tampere University Hospital Medical Fund (TL), the Yrjö Jahnsson Foundationand Turku University Hospital Medical Funds (O.T.R and J.V.). ME was supported by the Work Environment Fundand Academy of Finland (128002). MK was supported by the National Heart, Lung, and Blood Institute(R01HL036310-20A2) and National Institute on Aging (R01AG034454-01), NIH, US; the Academy of Finland,Finland; and BUPA Foundation, UK.

ReferencesAbdel-Maksoud M, Sazonov V, Gutkin SW, Hokanson JE. Effects of modifying triglycerides and

triglyceride-rich lipoproteins on cardiovascular outcomes. Journal of Cardiovascular Pharmacology.2008; 51(4):331–351. [PubMed: 18427276]

Aijanseppa S, Kivinen P, Helkala EL, Kivela SL, Tuomilehto J, Nissinen A. Serum cholesterol anddepressive symptoms in elderly Finnish men.[see comment]. International Journal of GeriatricPsychiatry. 2002; 17(7):629–634. [PubMed: 12112160]



NIH


NIH


NIH


Almeida OP, Flicker L, Norman P, Hankey GJ, Vasikaran S, van Bockxmeer FM, et al. Association ofcardiovascular risk factors and disease with depression in later life. American Journal of GeriatricPsychiatry. 2007; 15(6):506–513. [PubMed: 17158633]

Barres BA, Smith SJ. Neurobiology. Cholesterol--making or breaking the synapse. Science. 2001;294(5545):1296–1297. [PubMed: 11701918]

Beck, A. Depression. Clinical, experimental and theoretical aspects. New York, Harper & RowPublishers, 1967. New York: Harper & Row Publishers; 1967.

Blazer DG, Burchett BB, Fillenbaum GG. APOE epsilon4 and low cholesterol as risks for depressionin a biracial elderly community sample. American Journal of Geriatric Psychiatry. 2002; 10(5):515–520. [PubMed: 12213685]

Bollen, KA.; Curran, PJ. Latent curve models: A structural equation perspective. Hoboken, NewJersey: Wiley-Interscience; 2006.

Borgherini G, Dorz S, Conforti D, Scarso C, Magni G. Serum cholesterol and psychological distress inhospitalized depressed patients. Acta Psychiatrica Scandinavica. 2002; 105(2):149–152. [PubMed:11939964]

Brown SL. Cholesterol concentrations and depression in elderly people. Annals of Medicine. 1995;27(2):141–142. [PubMed: 7632405]

Bruce ML. Psychosocial risk factors for depressive disorders in late life. Biol Psychiatry. 2002; 52(3):175–184. [PubMed: 12182924]

Buydens-Branchey L, Branchey M, Hudson J, Fergeson P. Low HDL cholesterol, aggression andaltered central serotonergic activity. Psychiatry Res. 2000; 93(2):93–102. [PubMed: 10725526]

Chattopadhyay A, Paila YD. Lipid-protein interactions, regulation and dysfunction of braincholesterol. Biochemical & Biophysical Research Communications. 2007; 354(3):627–633.[PubMed: 17254551]

Chattopadhyay A, Paila YD, Jafurulla M, Chaudhuri A, Singh P, Murty MR, et al. Differential effectsof cholesterol and 7-dehydrocholesterol on ligand binding of solubilized hippocampal serotonin1Areceptors: implications in SLOS. Biochemical & Biophysical Research Communications. 2007;363(3):800–805. [PubMed: 17904101]

Chen CC, Lu FH, Wu JS, Chang CJ. Correlation between serum lipid concentrations andpsychological distress. Psychiatry Research. 2001; 102(2):153–162. [PubMed: 11408054]

Cortese S, Faussard B, Angriman M, Pigaiani Y, et al. The relationship between body size anddepressive symptoms in adolescents. Journal of Pediatrics. 2008:1–5.

Deisenhammer EA, Kramer-Reinstadler K, Liensberger D, Kemmler G, Hinterhuber H, FleischhackerWW. No evidence for an association between serum cholesterol and the course of depression andsuicidality. Psychiatry Research. 2004; 121(3):253–261. [PubMed: 14675744]

Diebold K, Michel G, Schweizer J, Diebold-Dorsam M, Fiehn W, Kohl B. Are psychoactive-drug-induced changes in plasma lipid and lipoprotein levels of significance for clinical remission inpsychiatric disorders? Pharmacopsychiatry. 1998; 31(2):60–67. [PubMed: 9562210]

Dimopoulos N, Piperi C, Salonicioti A, Psarra V, Mitsonis C, Liappas I, et al. Characterization of thelipid profile in dementia and depression in the elderly. Journal of Geriatric Psychiatry &Neurology. 2007; 20(3):138–144. [PubMed: 17712096]

Elovainio M, Keltikangas-Jarvinen L, Pulkki-Raback L, Kivimaki M, Puttonen S, Viikari L, et al.Depressive symptoms and C-reactive protein: the Cardiovascular Risk in Young Finns Study.Psychological Medicine. 2006; 36(6):797–805. [PubMed: 16623962]

Elovainio M, Puttonen S, Pulkki-Raback L, Kivimaki M, Viiri LE, Lehtimaki T, et al. Association ofthe apolipoprotein E gene, its promoter polymorphisms and haplotypes with depressive symptoms.The Cardiovascular Risk in Young Finns Study. Neuropsychobiology. 2008; 58(2):91–96.[PubMed: 18843194]

Engelberg H. Low serum cholesterol and suicide. Lancet. 1992; 339(8795):727–729. [PubMed:1347593]

Ergun UG, Uguz S, Bozdemir N, Guzel R, Burgut R, Saatci E, et al. The relationship betweencholesterol levels and depression in the elderly. International Journal of Geriatric Psychiatry. 2004;19(3):291–296. [PubMed: 15027046]



NIH


NIH


NIH


Fischer P, Gruenblatt E, Pietschmann P, Tragl KH. Serotonin transporter polymorphism and LDL-cholesterol. Molecular Psychiatry. 2006; 11(8):707–709. [PubMed: 16868569]

Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoproteincholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18(6):499–502. [PubMed: 4337382]

Glueck CJ, Kuller FE, Hamer T, Rodriguez R, Sosa F, Sieve-Smith L, et al. Hypocholesterolemia,hypertriglyceridemia, suicide, and suicide ideation in children hospitalized for psychiatricdiseases. Pediatric Research. 1994; 35(5):602–610. [PubMed: 8065845]

Glueck CJ, Tieger M, Kunkel R, Hamer T, Tracy T, Speirs J. Hypocholesterolemia and affectivedisorders. American Journal of the Medical Sciences. 1994; 308(4):218–225. [PubMed: 7942980]

Goldstein H, Healy MJ, Rasbash J. Multilevel time series models with applications to repeatedmeasures data.[see comment]. Statistics in Medicine. 1994; 13(16):1643–1655. [PubMed:7973240]

Horsten M, Wamala SP, Vingerhoets A, Orth-Gomer K. Depressive symptoms, social support, andlipid profile in healthy middle-aged women.[erratum appears in Psychosom Med 1998 May-Jun;60(3)257]. Psychosomatic Medicine. 1997; 59(5):521–528. [PubMed: 9316185]

Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimatingdevelopmental trajectories. Sociological methods & research. 2001; 20:374–393.

Katainen S, Räikkönen K, Keltikangas-Järvinen L. Adolescent temperament, perceived social support,and depressive tendencies as predictors of depressive tendencies in young adulthood. EuropeanJournal of Personality. 1999; 13(3):183–207.

Kendler KS, Neale MC, Kessler RC, Heath AC, Eaves LJ. Major depression and phobias: the geneticand environmental sources of comorbidity. Psychol Med. 1993; 23(2):361–371. [PubMed:8332653]

Kessler RC, Zhao S, Blazer DG, Swartz M. Prevalence, correlates, and course of minor depression andmajor depression in the National Comorbidity Survey. Journal of Affective Disorders. 1997; 45(1–2):19–30. [PubMed: 9268772]

Kim JM, Stewart R, Kim SW, Yang SJ, Shin IS, Yoon JS. Vascular risk factors and incident late-lifedepression in a Korean population. British Journal of Psychiatry. 2006; 189:26–30. [PubMed:16816302]

Kim JM, Stewart R, Shin IS, Yoon JS. Vascular disease/risk and late-life depression in a Koreancommunity population. British Journal of Psychiatry. 2004; 185:102–107. [PubMed: 15286060]

Kim YK, Myint AM. Clinical application of low serum cholesterol as an indicator for suicide risk inmajor depression. Journal of Affective Disorders. 2004; 81(2):161–166. [PubMed: 15306143]

Kivimaki M, Smith GD, Elovainio M, Pulkki L, Keltikangas-Jarvinen L, Talttonen L, et al.Socioeconomic circumstances in childhood and blood pressure in adulthood: the cardiovascularrisk in young Finns study. Annals of Epidemiology. 2006; 16(10):737–742. [PubMed: 16843680]

Kivimäki M, Lawlor DA, Singh-Manoux A, Batty GD, Ferrie JE, Shipley MJ, et al. Common mentaldisorder and obesity -- Insight from 4 repeat measures over 19 years: The prospective Whitehall IIcohort study. British Medical Journal. 2009; 339:b3765. [PubMed: 19808765]

Lehto SM, Hintikka J, Niskanen L, Tolmunen T, Koivumaa-Honkanen H, Honkalampi K, et al. LowHDL cholesterol associates with major depression in a sample with a 7-year history of depressivesymptoms. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2008; 32(6):1557–1561. [PubMed: 18583011]

Maes M. Major depression and activation of the inflammatory response system. Advances inExperimental Medicine & Biology. 1999; 461:25–46. [PubMed: 10442165]

Marin TJ, Chen E, Miller GE. What do trajectories of childhood socioeconomic status tell us aboutmarkers of cardiovascular health in adolescence? Psychosomatic Medicine. 2008; 70(2):152–159.[PubMed: 18256337]

Partonen T, Haukka J, Virtamo J, Taylor PR, Lonnqvist J. Association of low serum total cholesterolwith major depression and suicide.[see comment]. British Journal of Psychiatry. 1999; 175:259–262. [PubMed: 10645328]



NIH


NIH


NIH


Pulkki-Raback L, Elovainio M, Kivimaki M, Mattsson N, Raitakari OT, Puttonen S, et al. Depressivesymptoms and the metabolic syndrome in childhood and adulthood: a prospective cohort study.Health Psychology. 2009; 28(1):108–116. [PubMed: 19210024]

Raitakari OT, Juonala M, Rönnemaa T, Keltikangas-Järvinen L, Räsänen L, Pietikäinen M, et al.Cohort profile: The Cardiovascular risk in young Finns study. International Journal ofEpidemiology. 2008:1–7.

Sagud M, Mihaljevic-Peles A, Pivac N, Jakovljevic M, Muck-Sleer D. Lipid levels in female patientswith affective disorders. Psychiatry Research. 2009; 168:218–221. [PubMed: 19560828]

Shin JY, Suls J, Martin R. Are cholesterol and depression inversely related? A meta-analysis of theassociation between two cardiac risk factors. Annals of Behavioral Medicine. 2008; 36(1):33–43.[PubMed: 18787911]

Terao T, Iwata N, Kanazawa K, Takano T, Takahashi N, Hayashi T, et al. Low serum cholesterollevels and depressive state in human dock visitors. Acta Psychiatrica Scandinavica. 2000; 101(3):231–234. [PubMed: 10721872]

Toker S, Shirom A, Shapira I, Berliner S, Melamed S. The association between burnout, depression,anxiety, and inflammation biomarkers: C-reactive protein and fibrinogen in men and women.Journal of Occupational Health Psychology. 2005; 10(4):344–362. [PubMed: 16248685]

Troisi A. Cholesterol in coronary heart disease and psychiatric disorders: same or opposite effects onmorbidity risk? Neuroscience & Biobehavioral Reviews. 2009; 33(2):125–132. [PubMed:18824194]

Virtanen M, Pentti J, Vahtera J, Ferrie JE, Stansfeld SA, Helenius H, et al. Overcrowding in hospitalwards as a predictor of antidepressant treatment among staff. American Journal of Psychiatry.2008; 165:1482–1486. [PubMed: 18676590]

Vogelzangs N, Suthers K, Ferrucci L, Simonsick EM, Ble A, Schrager M, et al. Hypercortisolemicdepression is associated with the metabolic syndrome in late-life.[see comment].Psychoneuroendocrinology. 2007; 32(2):151–159. [PubMed: 17224244]



NIH


NIH


NIH


Figure 1.Developmental trajectories of LDL-cholesterol level from childhood to adulthood among824 men and women



NIH


NIH


NIH


Figure 2.Developmental trajectories of HDL-cholesterol level from childhood to adulthood among824 men and women



NIH


NIH


NIH


Figure 3.Developmental trajectories of triglyceride level from childhood to adulthood among 824men and women



NIH


NIH


NIH


NIH


NIH


NIH



Table 1

Sample characteristics (N=824)

Variable Mean (SE) N (%)

In childhood

Age (range 3–9 years) 5.9 (2.5)

Sex

Women 487 (59)

Men 336 (41)

Body-mass index in childhood (kg/m2) 15.9 (1.8)

C-reactive protein (mg/L)a −1.9 (1.3)

Family’s annual income a 4.9 (1.9)

In adulthood

Body-mass index in adulthood (kg/m2) 24.4 (4.3)

Smoking

No 615 (80)

Yes 159 (20)

Alcohol consumptionc 2.5 (1.3)

Physical activity d 9.9 (2.4)

Metabolic syndrome (NCEP)

No 627 (76)

Yes 31 (4)

Missing 166 (20)

Depressive symptoms in 1992 2.2 (0.6)

Depressive symptoms in 2001 2.0 (0.7)

aLogarithmic

bRange from 1 (less than 15000FIM) to 8 (more than 100 000)

cRange 1–6

dPhysical activity index


NIH


NIH


NIH



Table 2

Model selection for the shape of the LDL, HDL and Triglycerides trajectories

Trajectory 1 Trajectory 2 Trajectory 3

Model and parameter Parameterestimate

Parameterestimate

Parameterestimate

BIC

HDL –cholesterol trajectories −532.8

Intercept 6.12 6.62 22.2

Linear 0.12*** 0.10*** 0.11***

Quadratic −0.01*** −0.01*** −0.01***

Cubic 0.00*** 0.00*** 0.00***

LDL−cholesterol trajectories −6181.4

Intercept −0.00 22.8 NA

Linear −0.02 −0.01* NA

Quadratic −0.01*** −0.01*** NA

Cubic 0.00*** −0.01*** NA

Triglycerides-trajectories −1918.6

Intercept −12.9 −94.4 NA

Linear −0.04*** −0.08*** NA

Quadratic 0.00*** 0.01*** NA

Cubic −0.00*** −0.00*** NA

Function tested is Cubic


NIH


NIH


NIH



Table 3

Means (95% CI) of depressive symptoms in the LDL, HDL and triglyceride trajectory groups.

Adjusted for baselinedepressive symptoms

Adjusted for baseline depressivesymptoms, age and sex

Trajectory group Mean (95% CI ) p-value Mean (95% CI ) p-value

LDL

Low (N=550) 2.00 (1.96, 2.06) 2.01 (1.96, 2.06)

High (N=273) 2.06 (1.99, 2.13) 0.206 2.05 (1.98, 2.12) 0.335

HDL

Low (N=340) 2.04 (1.98, 2.11) 2.04 (1.98, 2.11)

Medium (N=417) 2.00 (1.95, 2.06) 2.00 (1.95, 2.06)

High (N=66) 2.08 (1.94, 2.22) 0.442 2.07 (1.93, 2.21) 0.450

Triglycerides

Moderate increase (N=692) 2.00 (1.95, 2.04) 1.99 (1.95, 2.04)

Steep increase (N=131) 2.18 (2.08, 2.27) 0.001 2.18 (2.08, 2.28) 0.001


NIH


NIH


NIH



Table 4

Means and 95% Cl of depressive symptoms in 2 triglyceride trajectory groups (moderate and steep increase)with depressive symptoms. All models adjusted for depressive symptoms in 1992, age and sex)

Moderate increasegroup

Steep increase group

Adjusted in addition to baselinedepressive symptoms, age and sex

Mean (95% Cl) Mean (95% Cl) p-value

Parental socioeconomic position 2.00 (1.95 – 2.04) 2.19 (2.08 – 2.29) <0.001

Childhood BMI 2.00 (1.96 – 2.04) 2.18 (2.08 – 2.28) 0.001

Childhood CRP 1.96 (1.91 – 2.01) 2.20 (2.10 – 2.30) <0.001

Adulthood metabolic syndrome 1.96 (1.91 – 2.01) 2.19 (2.07 – 2.30) <0.001

Adulthood health behaviour (BMI,physical activity, smoking andalcohol consumption)

2.00 (1.95 – 2.04) 2.19 (2.09 – 2.30) 0.001

All 1.97 (1.92 – 2.01) 2.21 (2.10 – 2.33) <0.001
