Journal of Affective Disorders 109 (2008) 127–131www.elsevier.com/locate/jad

Research report

Increased rates of obesity in first-presentation adults with mooddisorders over the course of four-year follow-up

Valerie Taylor a, Kathryn Macdonald a, Margaret C. McKinnon a,Russell T. Joffe b, Glenda M. MacQueen a,⁎

a McMaster University, Hamilton, ON, Canadab University of Medicine and Dentistry New Jersey, Newark, NJ, USA

Received 12 June 2007; received in revised form 22 November 2007; accepted 5 December 2007

Available online 24 January 2008

Abstract

Background: Patients with mood disorders have higher rates of obesity than the general population. With respect to this, little isknown regarding how patient look like prior to treatment or the rates of change.Objective: To identify changes in the rates of obesity in never-treated patients with mood disorder over 4 years of follow-up.Methods: Sixty-six never-treated patients with mood disorders were evaluated via clinical interview, symptom assessment and bodymass index (BMI). Patients were followed 4 years. Population attributable risk (PAR%) was calculated.Results: Patients in underweight and normal weight groups fell by nearly 29%, with a corresponding increase in patients enteringoverweight and obese groups. Rates of PAR% increased to 16.0, a significant 5-point increase over baseline.Limitations: This study had a small sample size and the population was ethnically homogenous. BMI was used as a maker ofweight and not waist circumference.Conclusions: Over 4 years there was a significant increase in BMI and the risk conferred by obesity. Shift from normal weight tooverweight and obese is a significant risk for patients with a mood disorder and clinical programs should consider interventions thatmight ameliorate risk of this shift early in the course of the illness.© 2007 Elsevier B.V. All rights reserved.

Keywords: Obesity; Mood disorder; Morality; First episode

1. Introduction

Patients with severe mental illnesses have higherrates of obesity, hypertension, dyslipidemia, metabolicsyndrome and diabetes than the general population

⁎ Corresponding author. D104-F, Mood Disorders Program, Centre forMountain Health Services, St. Joseph's Healthcare, Hamilton, Ontario,Canada L8N 3K7. Tel.: +1 905 522 1155x35496; fax: +1 905 575 6029.

E-mail address: macqueng@mcmaster.ca (G.M. MacQueen).

0165-0327/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jad.2007.12.003

(Basu et al., 2004; Casey, 2004; Fagiolini et al., 2005;Heiskanen et al., 2006; Simon et al., 2006). Thismetabolic dysregulation and consequent vascular illnessaccount for much of the premature mortality associatedwith mood disorders (Cuijpers et al., 2004; Osby et al.,2001). While the link between mood disorders andmetabolic dysregulation appears established, little isknown about the temporal association between thesedisorders.Metabolic dysregulationmay antedate the onsetof a mood disorder; alternatively, it is possible that themood disorder antedates the metabolic dysregulation

Table 1Clinical and demographic characteristic of study sample

Characteristic Baseline Year 1 Year 2 Year 3 Year 4

(n=66) (n=46) (n=31) (n=29) (n=20)

n n n n nSex Male 38 26 15 15 8

Female 28 20 16 14 12Diagnosis UD 40 30 17 17 9

BD 26 16 14 12 11

Mean Mean Mean Mean MeanAge 28.7

(10.4)28.8(10.4)

28.9(11.0)

31.7(11.8)

30.5(12.3)

Ham-D17score

15.6(7.3)

6.8(6.3)

6.1(6.1)

6.9(6.7)

6.0(5.4)

YMS score 3.5(6.9) 0.1(0.5) 0.3(0.8) 0.7(1.8) 0.6(1.6)Number ofaffectiveepisodes

8.1(21.0)

9.8(25.8)

9.4(21.9)

7.8(20.6)

10.3(23.8)

Age at onsetof illness

19.7(18.0)

19.6(21.3)

18.2(23.3)

24.3(11.4)

23.6(11.6)

128 V. Taylor et al. / Journal of Affective Disorders 109 (2008) 127–131

whichmight then occur as a consequence of hypothalamicpituitary axis dysregulation, the effects of psychotropicmedications or in response to lifestyle factors.

When examining the temporal association betweenmood disorders andmetabolic dysregulation, there is someevidence that the psychiatric symptoms often emerge first;for example, depression is a risk factor for subsequentdiagnosis of diabetes (Musselman et al., 2003). If it is thecase that the mood disorder commonly emerges prior tometabolic dysregulation, then there is an opportunity tounravel the factors that increase risk of somatic disease andto reduce the risk of medical morbidity and prematuremortality in people with mood disorders. As an increase inweight is the first and most prevalent manifestation ofmetabolic dysregulation, weight gain is a simple butimportant way to identify peoplewithmood disorderswhoare at risk to develop metabolic syndrome. It also makessense to monitor weight changes early in treatmentbecause weight gain is potentially modifiable and earlyweight reduction should decrease the risk of emergentmetabolic dysregulation. This study was thereforedesigned to examine the baseline body mass index(BMI) of never-treated patients with mood disorders andto characterize changes in BMI, and the risk conferred bysuch change, over a four-year follow-up period.

2. Method

Sixty-six patients between the ages of 16 and 40[mean age=28.74(10.38); 28 female] admitted to theRegional Mood Disorders Program at St. Joseph'sCenter for Mountain Health Services with an episode ofmania or depression were included. All participants inthis study were properly informed and signed a writtenconsent and the study was approved by the ResearchEthics Board of St Joseph's Healthcare Hamilton.Demographic and clinical characteristics of the studysample are in Table 1.

Participants had a Structured Clinical Interview forDSM-IV (SCID; Spitzer and Williams, 1992) primarydiagnosis of major depressive disorder (MDD) orbipolar disorder (BD) and were able to provide informedconsent and to complete self-report measures. Exclusioncriteria included 1) previous treatment with psycho-tropic medication; 2) a history of a neurological disorderexcept migraines; 3) known medical illness such asthyroid disease; 4) a history of anorexia nervosa orbulimia nervosa as assessed by the SCID; 5) a prominentaxis-II diagnosis; or 6) a lifetime history of substancedependence or alcohol abuse within 6 months.

The Hamilton Depression Scale (Ham-D; Hamilton,1960), and the Young Mania Scale (YMS; Young et al.,

1978) were used to monitor mood weekly. BMI wasutilized as an accurate and reproducible measure forwhich epidemiological data indicate an increase inmortality with a BMI above 25 kg/m2 (Manson et al.,1987; Troiano et al., 1996; Vanltaillie and Lew, 1990;Vanltaillie, 1985; World Health Organization, 1995).Treatment followed published guidelines (AmericanPsychiatric Association, 2002; Parikh and Lam, 2001)and patients were assessed weekly until euthymic, andthen monthly. The prevalence of obesity using the BMIcategories of underweight (b18.49 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), obese (30–34.9 kg/m2) and morbidly obese (N35 kg/m2) adopted bythe World Health Organization was calculated at baselineand annually for 4 years (World Health Organization,1997).

Rates were calculated for i) underweight and normalweight; ii) overweight; and iii) obese andmorbidly obesepatients at baseline and longitudinally. Because not allpatients received assessment at each of the time points,changes were calculated for baseline and for final [meanduration=2.66 (1.78) years] BMI scores only. The pre-dicted increase in numbers of deaths of overweight andobese patients in this population was determined usingthe Population Attributable Risk percentage (PAR%).The PAR was calculated separately for overweight,obese and morbidly obese patients and then added,using the following equation: PAR=(∑[P(RR−1) /RR]⁎100), where P is the sample population prevalenceof overweight or obesity classes and RR is the relativerisk of mortality, obtained from previous studies of thesepopulations (Rockhill et al., 1998; Rothman, 1998).

129V. Taylor et al. / Journal of Affective Disorders 109 (2008) 127–131

Correlational analyses (Pearson's r; two-tailed) wereused to explore the relation between BMI and treatmentresponse, age, and duration of illness. Treatment res-ponse was measured as change scores over baseline andmost recent follow-up on the Ham-D, YMS and GAF.Changes in BMI over time were then analyzed using anunbalanced mixed-measures model with unstructuredcovariance matrices (Dixon, 1993). Specifically, weused a Wald test of significance of fixed effects andcovariates that yields a chi-square value which can beinterpreted against a standard distribution table. BMIscores at the yearly intervals were treated as the within-subjects variable. Sex and diagnosis (MDD or BD) wereincluded as between-subjects variables in this model.Where significant correlations emerged between BMIstatus and the age and duration of illness, these variableswere treated as covariates.

3. Results

Changes in prevalence rates for BMI categories areillustrated in Table 2. There was an upward shift in BMIcategory membership over time with the number ofpatients falling within the overweight and obese/morbidly obese groups increasing by 17 and 27%,respectively. Increases in PAR% over time in i) over-weight and in ii) obese and morbidly obese patients areillustrated in Table 2. There was a significant increase inPAR% over baseline and longitudinal follow-up (most

Table 2Percentage of participant falling within BMI categories and Par % a

Canada27 Studypatient

Studypatient

Age 18–34 Baseline Follow-up

Underweight and normal BMI≤24.99Total 54.30% 42.40% 30.30%Male 48.30% 26.30% 15.80%Female 60.60% 64.30% 50.00%

Overweight BMI 25–29.99Total 29.30% 34.90% 41.00%Male 33.80% 47.40% 50.00%Female 24.50% 17.90% 28.60%Par% 4.80% 3.40%

Obese and morbidly obese BMI≤30Total 16.40% 22.70% 28.80%Male 17.90% 26.30% 34.20%Female 14.90% 17.90% 21.40%Par% 5.90% 12.60%Total Par% BMIN25 10.70% 16.00%a PopulationAttributableRisk percentage (PAR%)PAR=(Σ[P(RR−1)/

RR]*100), where P is the sample population prevalence of overweight orobesity classes and RR is the relative risk of mortality.

recent measurement), where PAR% increased from 10.7to 13.5. Rates of PAR% varied at each of the yearlyintervals, reaching an apex at Year 4, where PAR%levels rose to 16.0, a 5-point increase over baseline.

There was a negative correlation between changes inHAM-D scores and changes in BMI scores overbaseline and longitudinal follow-up (r=− .43, pb .001)such that improvement in HAM-D scores (i.e., treatmentresponse) were associated with increases in BMI scoresover the measurement period. A similar finding emergedwhere there was a positive correlation between changesin GAF scores and changes in BMI scores over baselineand longitudinal follow-up (r=.38, pb .01); increases inGAF scores, again consistent with a positive treatmentresponse, were associated with increases in BMI scoresover the measurement period. Age at baseline measure-ment correlated positively with BMI scores at this timepoint (r=.28, pb .05). Duration of illness at baseline andchange in BMI scores over baseline and longitudinalfollow-up were marginally positively correlated (r=.29,p=.06). Hence, we included age and duration of illnessas covariates in the omnibus analyses.

There was a main effect of time on BMI scores whilecontrolling for age and illness duration; a Wald test ofsignificance of fixed effects and covariates yielded a χ2 (4)value of 21.21 (pb0.001); therewas a significant increasesin BMI scores over each of the annual assessment periods.There was also marginally significant main effect of sex,χ2(1)=3.27, p=.07 but no main effect of diagnosis( pN .05); women experienced slightly greater increases inBMI scores over time than men. No significant two- orthree-way interactions emerged between time, sex, anddiagnosis, however ( psN .05).

4. Discussion

This study examined the baseline rates and changeover time in overweight and obesity in patients withMDD and BD. Patients' weights were equivalent to themean BMI of Canadians at baseline. Thirty-four percentof men and 25% of women (age 18–34) wereoverweight, while 18% of men and 15% of womenwere obese in the Canadian Community Heath survey(Statistics Canada, 2004). In our sample, 34.85% of thepopulation was overweight at baseline, while 22.73%was obese (see Table 2 for a comparison).

Over the course of the follow-up period, the meanBMI in our population increased significantly to 28.0with an increase in prevalence rates of 41% and 28.8%for overweight and obese, respectively. This studypopulation is young, with a mean age of 27, whichmakes this comparison against the general population

130 V. Taylor et al. / Journal of Affective Disorders 109 (2008) 127–131

more striking as weight increases with age. Given thistrend, and with no active intervention, this populationwould move from a BMI category of overweight toobese in less than 5 years, a change that is likely toconfer a negative impact on health (National Institutesof Health National Heart Lung and Blood Institute,1998). In fact, based on population attributable risk, thenumber of obesity-related deaths in Canada in 2000 was9.3% (Katzmarzyk and Ardern, 2004). Using thisapproach, the risk of obesity-related death in our patientpopulation is over 16%.

Notably, patients with the greatest degree of clinicalimprovement were those with the largest increase inBMI. While it is possible that this simply reflects areturn to normal appetite as the mood disorderimproved, this seems unlikely. It may also be that thisis the result of biological factors such as leptin, whichare important factors in determining appetite and weightand which may also have interactions with keybrain regions (Lu et al., 2006) and neurotransmitters(Leibowitz and Alexander, 1998) implicated in depres-sion. Women gained more weight than men during thefollow-up period. This is consistent with data from otherstudies; in the CATIE trial, women with schizophreniaappeared to be particularly vulnerable to central obesityand metabolic syndrome (McEvoy et al., 2005) andAllison et al. found that women but not men withschizophrenia had higher BMI scores than age-matchedU.S. norms (Allison et al., 1999). The reasons for thisassociation between sex and weight gain are unknown,but include the possibility that sex hormones such asprolactin may moderate the effects of medications onweight (Windgassen et al., 1996).

Somewhat surprisingly, we did not find an associationbetween diagnosis and change in BMI. From a symptomprofile, individuals with BD spend most of the time inwhich they are ill in a depressed state (Judd et al., 2002),perhaps rendering both groups equally vulnerable to theincreases in appetite (Carter et al., 1994; Stunkard et al.,1990) and decreases in physical activity (Cassidy et al.,2004) associated with some forms of depression. Weexpected that patients treated for BD would have greaterexposure to medications associated with weight gainthan patients with MDD who were treated almostexclusively with SSRIs, which are generally consideredto have a better side effect profile than other psychotropicmedications (Croft et al., 1999; de Jonghe and Swinkles,1992; Michelson et al., 1999). Recent work hashighlighted the fact that SSRI's are in fact associatedwith increases in obesity, however (Raeder et al., 2006)and specific conclusions regarding the effects ofmedication require a larger sample size.

Relatively little is known about the prevention andtreatment of overweight and obesity on a population-wide basis (Douketis et al., 1999; Jeffery, 2001). On anindividual level structured programs can produce long-term weight loss of about 5% to 10% of starting weightwhich, while modest, is associated with decreased risk ofco-morbid physical illness (Wing et al., 2001). The rapidrate with which our patients experienced an increase inweight emphasizes that early prevention must be aprimary focus of programs aimed at improving physicalhealth in patients with psychiatric illnesses. Mentalhealth professionals thus have a critical role in avertingor delaying patients' progression to metabolic syndrome,diabetes and cardiovascular disease. Intervention cannotbe reserved for patients with chronic, established illnessas the trajectory towards obesity appears to emergeproximate to the time treatment that is initiated in youngpeople first presenting with a mood disorder.

Role of funding sourceFunding for this study was provided by the Canadian Institutes of

Health Research. The CIHR had no further role in the study design; inthe collection of data; in the writing of the report; and in the decision tosubmit the paper for publication.

Conflict of interestAll authors declare that they do not have conflicts of interest.

Acknowledgements

We are grateful to the patients and their families fortheir assistance. We thank Cathy Preete, Gurpreet Jaswaland Benjamin Doxtdator for their assistance in thepreparation of this manuscript and Laura Garrick, HelenBegin, Cindy D'Amico, Scott Simons and Tana Pati forassistance with patient scheduling and testing. This studywas supported by a research grant from the CanadianInstitutes of Health Research to Glenda MacQueen.

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