Nordic Journal of Psychiatry
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ipsc20
Body mass index (BMI) and obesity in Nigerians
with schizophrenia
Oluyomi Esan & Arinola Esan
To cite this article: Oluyomi Esan & Arinola Esan (2021): Body mass index (BMI) and obesity in
Nigerians with schizophrenia, Nordic Journal of Psychiatry, DOI: 10.1080/08039488.2021.1926538
To link to this article: https://doi.org/10.1080/08039488.2021.1926538
Published online: 19 May 2021.
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NORDIC JOURNAL OF PSYCHIATRY
https://doi.org/10.1080/08039488.2021.1926538
ARTICLE
Body mass index (BMI) and obesity in Nigerians with schizophrenia
Oluyomi Esana and Arinola Esanb
a
Department of Psychiatry, University of Ibadan, Ibadan, Nigeria; bDepartment of Medicine, University College Hospital, Ibadan, Nigeria
ABSTRACT
ARTICLE HISTORY
Background: Few Nigerian studies have examined BMI in people with schizophrenia. The aims of the
present study were to assess the prevalence and distribution of obesity in Nigerians with schizophrenia and to examine the clinical correlates of BMI and obesity.
Methods: A total of 207 people with schizophrenia met the inclusion criteria and were evaluated for
BMI.The Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HDRS),
Social and Occupational Functioning Assessment Scale (SOFAS) were rated for all participants.
Anthropometric measures such as weight and height were taken using a standard protocol.
Results: The prevalence of obesity was 12.6%. The non-obese participants were made up of underweight 24 (11.7%), normal weight 118 (57%) and overweight 38 (18.4%). Compared to non-obese participants, obese participants were older, more educated, more likely to be employed, had higher
incomes, lower PANSS score (negative subscale), had fewer female participants, and better social and
occupational functioning (p < 0.05). BMI was positively correlated with age and monthly income. In
the adjusted model, age, gender and education were independently associated with obesity while
only age was associated with BMI.
Conclusion: The present study suggests that unlike in high-income countries, obese patients with
schizophrenia in Nigeria have better social and psychological functioning than non-obese patients.
Received 22 October 2020
Revised 30 April 2021
Accepted 3 May 2021
Introduction
Over the last five decades, obesity has emerged as a major
global public health problem [1]. Globally, in 2016, 11% of
men and 15% of women aged 18þ were obese (BMI 30 kg/
m2) [2]. Prevalence estimates of obesity ranging from 8.4%
to 36.0% have been reported in sub-Saharan Africa, with
higher estimates in women than men [3]. It has been projected that by 2030, globally, the number of obese adults
will approach 573 million individuals, 19.7% prevalence, if
the current secular trends are not addressed [4]. In lowincome countries, obesity is generally more frequent among
middle-aged adults from a wealthy and urban environment,
whereas, in high-income countries, it affects both genders
and all ages, but its prevalence is disproportionately greater
among disadvantaged groups [1]. Also, in low-income countries, the more affluent and/or those with higher educational
attainment are more likely to be obese, whereas, in middleincome countries, the more affluent/educated women are
less likely to be obese while for men the association is
largely mixed [5]. Obesity is associated with an increased risk
of several chronic medical conditions such as diabetes, dyslipidemia, mental and behavioural disorders, cardiovascular
disease, certain cancers, and osteoarthritis [6]. In terms of the
economic impact, obesity causes an increase in medical
spending, due to hospitalizations, prescription drugs, treating
obesity and obesity-related conditions [6,7].
CONTACT Oluyomi Esan
oluyomie@yahoo.com
ß 2021 The Nordic Psychiatric Association
KEYWORDS
Obesity; BMI;
schizophrenia; Nigeria
Obesity is common in patients with schizophrenia [8]. It is
a precarious problem in such patients because it raises the
risk of debilitating morbidity and mortality [6]. Many studies
have reported that obesity is associated with the severity of
psychopathology in patients with schizophrenia [9,10–12].
For example, Hui et al. [9] indicated that obesity correlates
with fewer symptoms in schizophrenia by demonstrating
significant inverse correlations between BMI and the Positive
and Negative Syndrome Scale (PANSS) -total, PANSS negative
symptom and general psychopathology subscales scores [9].
Likewise, an increase in BMI was found to be associated with
a decrease in the total PANSS score [13]. Patients with
schizophrenia are more likely to be obese than the general
population due to a combination of factors, including the
effects of antipsychotics, pretreatment genetic vulnerabilities,
psychosocial and socioeconomic risk factors, and unhealthy
lifestyles [14].
Geographical disparities exist in the prevalence of obesity,
obesity-related conditions and associated risk factors [15]. In
high-income countries, high socioeconomic status is inversely
related to adolescent adiposity [16]. However, the reverse is
true in low- and middle-income countries [17–19]. Globally,
the prevalence of obesity among patients with schizophrenia
is about 40–60% [20,21]. Only a few studies from Africa have
examined BMI in people with schizophrenia. In Nigeria, a
prevalence estimate of obesity of 7.3% was found in a study
of obesity and the use of antipsychotics among outpatients
Department of Psychiatry, University of Ibadan, Ibadan, Nigeria
2
O. ESAN AND A. ESAN
with schizophrenia. In the study, obesity was associated with
being female, younger age and comorbid hypertension [15].
Aims
The aims of the present study were to (i) assess the distribution and prevalence of obesity in Nigerian patients with
schizophrenia and (ii) to examine the sociodemographic and
clinical correlates of obesity.
Methods
The methods of the Multifaceted Dimensions of the Burden
of Bipolar Disorders in Ibadan, South-West Nigeria (Mulberry
Study) have been described in earlier reports [22]. Here, we
provide a brief account as well as the methods of the
related studies.
Study design and setting
The study was cross-sectional in design. It is a part of a
larger study (the Mulberry study) [22]. Participants were
recruited from the outpatient clinic of the Department of
Psychiatry, University College Hospital, Ibadan Nigeria, and
State Hospital, Adeoyo, Ibadan, Nigeria between 23 February
and 10 October 2018. The University College Hospital (UCH)
is a teaching Hospital located in Ibadan, Nigeria. It houses
the World Health Organization Collaborating Centre for
Research and Training in Mental Health, Neurosciences, Drug
and Alcohol Abuse. The State Hospital, Adeoyo, Ibadan is a
secondary health care centre with a department of psychiatry and manned by psychiatry specialists. These are the
only government hospitals with dedicated psychiatric services within Ibadan city, the capital of Oyo State, Southwest
Nigeria. Apart from serving Ibadan, the two hospitals also
receive referrals from other parts of Southwest Nigeria.
Sample/eligibility criteria
Consecutive and consenting stable patients attending the
follow-up outpatient clinics who met the criteria for DSM-IV
diagnostic criteria for a diagnosis of schizophrenia were
invited for possible recruitment into the study. The participants were patients already in the service of the hospital,
some for over 20 years. The diagnosis would have been validated by a consultant psychiatrist and written in the case
notes. Thereafter, during the study, the Structured Clinical
Interview for DSM-IV (SCID) was applied by the research
assistants for validation of the diagnosis before recruitment
into the study. Our target was to select all the patients with
schizophrenia in our service. Usually, patients, come one
after the other on Monday and Friday afternoons between
1pm and 5pm. So we approached them in the order in
which they arrived at the clinic (consecutive) and sought
their consent to participate in the study (consenting). We did
this until we were not getting new patients. From our
records, very few patients (eight in all) declined to participate in the study. One person declined on the condition that
we should pay a fee of thousands of Naira (Money). Another
declined on the basis that the results of the study would not
be made available, while others said that they were in a
hurry and would not be able to participate. Consequently,
we did a total population purposive sampling of all patients
that agreed to participate in the study. We did not keep
detailed records of the people that refused to participate in
the study, so we could not ascertain if they were statistically
significantly different from those who participated. For
recruitment into the current study, participants were required
to be between the ages of 18 and 55 years, should be able
to read and write and must have had at least 6 years of education, that is, primary school education in Nigeria.
Assessment
All assessments were performed by trained research assistants. Five trained research assistants, who had at least a university education, conducted the assessments. Research
assistants received a 4-week training including inter-rater
testing at the end of the 4-week training. The principal investigator O.E. conducted the training. One supervisor, who had
also received the same 4-week training but had participated
in similar studies in the past, monitored the data collection
and checked for completeness, dating, missing values as well
as the accuracy of the data collected.
Weight and height measurement
Weight was measured (in kilograms) using a beam balance
scale with subjects in light clothing and without shoes on.
Height was also measured (in meters) using a stadiometer
without the subjects wearing footwear, caps or other headgear. The head was kept in anatomical position and the
highest point was taken as the height. BMI was then calculated using the formula-BMI ¼ Weight/Height2 (kg/m2). A BMI
of 18.5–24.9 was taken as normal, BMI of 25–29.9 indicated
overweight and values >30 implied obesity.
Sociodemographic and clinical assessments
Important clinical and sociodemographic information was
obtained during the assessments from the participants, the
participants’ relatives and from participants’ case notes.
Sociodemographic and clinical information obtained included
age, age at onset of the first psychiatric episode, gender,
occupational status, marital status, numbers of years of education, family history of mental illness and medical
comorbidity.
The participants were also rated on the following
instruments:
1.
2.
3.
The 17-item Hamilton Depression Rating Scale
(HDRS) [23].
The Positive and Negative Syndrome Scale (PANSS) [24].
Social and Occupational Functioning Assessment Scale
(SOFAS-DSM-IV) [25].
NORDIC JOURNAL OF PSYCHIATRY
4.
Remission was defined by the Andreasen criteria for
remission [26].
Data analysis
Data analysis was carried out with the Statistical Package for
the Social Science (SPSS), version 22.0 for Windows. Data
analysis was conducted in three phases. Categorical variables
were summarized using frequencies and proportions.
Continuous variables were described using means and standard deviation if they were normally distributed or using
medians and interquartile ranges if not normally distributed.
The Chi-square test was used to assess the associations of
BMI/Obesity and categorical variables.
Pearson’s and spearman’s correlation analysis were used
to assess relationships between BMI and continuous variables
such as the age of the participant, age at onset of the first
episode, the number of years of education, the HDRS total
score, the PANSS subscale scores and the SOFAS score.
A multiple linear regression analysis model was created to
identify clinical variables that were independently associated
with BMI and to identify the strength of the effect that the
independent variables have on BMI. Binary logistic regression
analysis was carried out to determine factors that were independently associated with obesity. We did not include
3
income in the multiple regression analysis because it violated
some of the assumptions of the test.
All significant variables in Pearson’s correlation analysis
(Table 2) as well as categorical variables that were significantly associated (p < 0.05) with BMI and obesity were
included in the regression analyses (details of the included
variables are presented in Tables 1 and 2). Table 1 determines the variables in the adjusted model in Table 3, and
Table 2 determines the variables in the adjusted model in
Table 4.
Ethical considerations
All participants provided written informed consent. The study
was conducted following the guidelines laid down in the
Declaration of Helsinki. The protocol and procedures were
reviewed and approved by the Oyo State Research Ethical
Review Committee (AD13/479/746).
Results
Prevalence of obesity
A total of 207 participants with schizophrenia met the inclusion criteria for this study and were included in the data analysis. The prevalence of obesity was 12.6%. The non-obese
Table 1. Demographic and clinical characteristics of the study samplea.
Age (years)
Age at onset of first episode (years)
Education (years)
Gender, n (%)
Male
Female
Work status, n (%)
Employed
Unemployed
Family history of mental illness
Marital status, n (%)
Single/Never married
Married/Cohabiting
Divorced
Comorbid medical condition, n (%)
Absent
Present
Remission, n (%)
Absent
Present
Antipsychotic type, n (%)
Atypical antipsychotics only
Atypical þ Typical antipsychotics
Typical antipsychotics only
Monthly income (Naira)
BMI (Kg/m2)
Antipsychotic dose (mg/day) (haloperidol equivalents)
HDRS total score
PANSS (positive subscale)
PANSS (negative subscale)
PANSS (general subscale
PANSS (total score)
SOFAS
Non-obese participants (n ¼ 180)
37.2 ± 8.5
28.5 ± 7.9
12.8 ± 3.2
Obese participants (n ¼ 26)
44.2 ± 8.0
29.9 ± 8.0
14.7 ± 3.0
97 (53.0)
83 (46.1)
8 (30.8)
18 (69.2)
107 (59.4)
73 (40.6)
133 (73.9)
47 (26.1)
21 (80.8)
5 (19.2)
21 (80.8)
5 (19.2)
93 (51.7)
55 (30.6)
32 (17.8)
8 (30.8)
11 (42.3)
7 (26.9)
170 (94.4)
10 (5.6)
25 (96.2)
1 (3.8)
71 (39.7)
108 (60.3)
8 (30.8)
18 (69.2)
52 (35.1)
35 (23.6)
61 (41.2)
17002 ± 31861
22.22 ± 3.47
3.04 ± 5.61
5.96 ± 4.73
10.22 ± 4.23
12.94 ± 4.99
22.57 ± 5.35
45.73 ± 12.04
50.72 ± 13.46
7 (26.9)
5 (12.5)
14 (53.8)
48519 ± 51291
37.54 ± 13.29
3.88 ± 6.72
4.81 ± 5.26
10.15 ± 3.84
10.58 ± 4.32
21.69 ± 5.88
42.42 ± 10.22
56.34 ± 13.27
p-Value
<0.001
0.380
0.009
0.027
0.027
0.450
0.135
0.717
0.384
0.485
<0.001
<0.001
0.484
0.260
0.937
0.023
0.441
0.184
0.047
BMI: body mass index; PANSS: Positive and Negative Syndrome Scale; HDRS: Hamilton Rating Scale for Depression; SOFAS: Social and Occupational Functioning
Assessment Scale.
Data are presented as mean ± SD unless otherwise indicated.
Percentages may not sum up to 100 because of missing data.
Bold indicate significance at p-values <0.05.
a
4
O. ESAN AND A. ESAN
participants were made up of underweight 24 (11.7%), normal weight 118 (57%) and overweight 38 (18.4%).
Table 1 outlines a number of variables (and contrasts the
values for non-obese and obese patients) that were then
included in the adjusted regression model described in
Table 3, while Table 2 outlines a number of variables (and
contrasts them for non-obese vs. obese patients) that were
included in the adjusted regression model in Table 4.
Association between BMI and clinical characteristics in
participants
Correlation analysis indicated a significant positive association between BMI and age (p < 0.001), and between BMI
and Monthly income (Naira; p < 0.001; 1 Dollar ¼ 475 Naira)
and between BMI and age at onset of the first episode
(p ¼ 0.033) (Table 2).
Factors that were independently associated with BMI
Characteristics of the sample
Table 1 summarizes the demographic and clinical characteristics of the participants. Compared to non-obese participants,
obese participants were older, more educated, comprised fewer
female participants, had better social and occupational functioning, were more likely to be employed and had a higher
monthly income (all p < 0.05). There was no significant difference between obese participants and non-obese participants
concerning the antipsychotic type (typical vs. atypical þ typical
vs. typical) and the daily dose of antipsychotics (Table 1).
Table 2. Correlations between body mass index and clinical and demographic
characteristics.
Age of patient
Age at onset of first episode
Monthly income (Naira)
Antipsychotic dose (mg/day) (haloperidol equivalents)
HDRS total score
PANSS positive subscale
PANSS negative subscale
PANSS general subscale
PANSS total score
SOFAS score
r
p-Value
0.267
0.148
0.279
0.059
0.067
0.027
0.101
0.012
0.057
0.128
<0.001
0.033
<0.001
0.400
0.336
0.704
0.149
0.862
0.414
0.066
PANSS: Positive and Negative Syndrome Scale; HDRS: Hamilton Rating Scale
for Depression; SOFAS: Social and Occupational Functioning Assessment Scale.
Bold indicate significance at p-values <0.05.
Table 3. Associations with obesity using binary logistic regression.
95% C.I. for odds ratio
Age of patient
Gender
Female
Male
Education
Education
(years of education)
Work status
Unemployed
Employed
SOFAS score
PANSS (negative subscale)
p-Value
0.003
Odds ratio
1.08
Lower
1.03
Upper
1.13
0.020
1.00
0.32
0.12
0.84
0.018
1.21
1.03
1.41
0.253
0.883
0.370
1.00
1.85
1.00
0.95
0.64
0.96
0.85
5.34
1.04
1.06
SOFAS: Social and Occupational Functioning Assessment Scale; PANSS: Positive
and Negative Syndrome Scale.
Bold indicate significance at p-values <0.05.
Table 4. Associations with BMI using multiple regression analysis.
95% confidence interval
Age of patient
Age at onset of
first episode of psychosis
Beta
p-Value
Lower bound
Upper bound
0.261
0.017
0.001
0.835
0.085
0.130
0.340
0.161
Bold indicate significance at p-values <0.05.
A multiple regression analysis was run to determine factors
that were independently associated with BMI. The age of the
patient was significantly independently associated with BMI
(Table 4).
Factors that were independently associated with obesity
Using binary logistic regression analysis we found that age,
gender and education were independently associated with
obesity (Table 3).
Discussion
The present study has some key findings. First, we found a
prevalence estimate of obesity of 12.6% among the participants. Second, compared to non-obese participants, obese
participants were older, more educated, had fewer female
participants, better social and occupational functioning, more
likely to be employed and had fewer negative symptoms
(PANSS negative subscale) (p < 0.05). Third, BMI was positively correlated with age and monthly income. Fourth, in
the adjusted model, age was associated with BMI while age,
gender and education were independently associated
with obesity.
In our study, the prevalence estimate of obesity among
the participants (12.6%) was lower than those of several
reports from the general population in Nigeria. The prevalence estimates from Nigeria included 33.8% by Amole et al.
[27], 42% by Akarolo-Anthony et al. [28] and 22.3% by
Finkelstein et al. [29]. The prevalence of obesity was also
lower than the global prevalence of obesity among patients
with schizophrenia, which has been reported to between 40
and 60% [20,21]. The estimate of 40–60% is usually higher
than the prevalence of obesity in the corresponding general
population [21,30,31]. These discrepancies may be occasioned by the fact that whereas low socioeconomic status
has been associated with a higher prevalence of obesity in
high-income countries, in low-income countries, there is a
positive socioeconomic status-obesity relationship [32]. A
commonly mentioned cause of obesity is unhealthy food
choices [14,33]. This explanation is tenable only to the
degree that there is a choice. Severe poverty may limit such
choices, especially in people with severe mental illnesses like
schizophrenia. For example, the unemployment rate among
the participants in the current study was 40.6% which was
eight times higher than the national average of 4.9% [34].
Both unemployment and underemployment are major
NORDIC JOURNAL OF PSYCHIATRY
contributors to poverty. It is noteworthy that in the current
study, obese participants were significantly more educated,
had higher monthly income, had better social and occupational functioning and were more likely to be employed than
the non-obese participants. The aforementioned seems to
suggest that obese people with schizophrenia in Nigeria are
the more affluent and well off socially and economically. On
the contrary, obesity in high-income countries is inversely
correlated with affluence, it is those who hover around the
poverty line in high-income countries that appear to have
the highest rates of obesity [35].
We found that age, education and monthly income were
all associated with obesity. It has been suggested that as
affluence increases in low- and middle-income countries
such as Nigeria, it is expected that poverty-obesity patterns
would mirror those of high-income countries [6]. Education
is expected to offset the negative effects of increasing purchasing power in emerging obesogenic environments [6].
However, the protective influence of education is yet to be
seen in Nigeria, as revealed in the current study where both
more education and monthly income were directly associated with increased likelihood of obesity [6,36].
Several studies have reported that obesity is associated
with the severity of clinical symptoms in people with schizophrenia [9,37]. For example, in a report of the prevalence,
risk factors and clinical correlates of obesity in Chinese
patients with schizophrenia, Li et al. [37] found that obesity
was correlated with negative symptoms and total PANSS
score [37]. Similar to our study, Hui et al. [9] found that BMI
was inversely correlated with the PANSS negative symptom
and total PANSS score. Our report replicates these results in
showing significant associations between obesity and PANSSnegative symptoms scores [9].
In our study, age and monthly income were associated
with BMI. After regression analysis age was independently
associated with both BMI and obesity. Ageing, in the literature, is associated with abdominal obesity (specifically, an
increase in abdominal white adipose tissue) [38], fat deposition in skeletal muscles [39], certain hormonal changes [40],
decreased metabolism and decreased physical activity
[41,42]. All the aforementioned changes result in being overweight and obese if their obesogenic effects are
not addressed.
Our report should be interpreted in light of certain limitations. First, BMI was used as the only parameter for assessing
obesity. Using BMI as a sole indicator of obesity is not considered a very good indicator of cardiometabolic function.
The use of more sensitive measures such as waist circumference has been advocated [5,43]. Second, due to the crosssectional design of this study, causal inferences cannot be
drawn based on our results. Third, type 2 errors could
explain some of the non-significant results in our study. This
could have been avoided by increasing the sample size used
in the study. However, we were limited by the size of the
patient population with psychiatric disorders at the two hospitals where the study was conducted. These hospitals were
the only public hospitals with dedicated psychiatric services
5
in Ibadan. Also, the selection of the participants was not
based on random selection.
Although we did not include monthly income in our
regression analysis for BMI, in keeping with the trends in our
study, monthly income would be expected to have a significant impact on BMI. Specifically, to be positively associated
with BMI. Consequently, a higher proportion of high-income
earners would be expected to be overweight and obese.
Conclusion
Overall, the present study suggests that obese people with
schizophrenia in Nigeria may have better social and psychological functioning than their non-obese counterparts.
Disclosure statement
No potential conflict of interest was reported by the authors. The funder
did not play any role in the design, collection, screening, interpretation,
writing, and submission for publication of this study. The funder holds
no responsibility for the contents of this study.
Funding
This work was supported by a grant from the Tertiary Education Trust
Fund (TETFund National Research Fund).
Notes on contributors
Oluyomi Esan contributed to the conception and design of the work, to
the analysis of the results, critical revision of the article and the final
approval of the version to be published.
Arinola Esan contributed to the conception of the work, critical revision
of the article and the final approval of the version to be published.
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