Morbid obesity in Taiwan: Prevalence, trends, associated social demographics, and lifestyle factors
Morbid obesity in Taiwan: Prevalence, trends, associated social demographics, and lifestyle factors
Heng-Cheng Chang 0 1 2 3
Hsin-Chou Yang 0 1 2
Hsing-Yi Chang 0 1 2
Chih-Jung Yeh 0 1 2
Hsin- Hung Chen 0 1 2
Kuo-Chin Huang 0 1 2
Wen-Harn Pan 0 1 2 3
0 MO in Taiwan
1 Data Availability Statement: Data of Nutrition and Health survey in Taiwan (NAHSIT): 1993-1996, 2005-2008 are available from Institute of Biomedical Sciences, Academia Sinica for researchers who meet the criteria for access to confidential data. Data requests may be sent to: Survey Research Data Archive
2 Editor: Yang-Ching Chen, Taipei City Hospital , TAIWAN
3 Graduate Institute of Life Sciences, National Defense Medical Center , Taipei, Taiwan , 2 Institute of Biomedical Sciences , Academia Sinica, Taipei, Taiwan , 3 Institute of Statistical Science , Academia Sinica, Taipei, Taiwan , 4 Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan, 5 School of Public Health, Chung Shan Medical University , Taichung, Taiwan , 6 Department of Nutrition and Health Science, Chang Jung Christian University , Tainan , Taiwan , 7 Department of Family Medicine, National Taiwan University Hospital and College of Medicine , Taipei , Taiwan
The prevalence of overweight and obesity together (BMI
24 kg/m2) was stabilized in the
recent two surveys, but that of MO (0.4%, 0.6%, to 1.4%) and obesity (BMI
(11.8%, 17.9%, to 22.0%) increased sharply. MO cases tended to have lower levels of
education, personal income, and physical activity. Furthermore, their dietary pattern featured
with a higher consumption frequency of red meat, processed animal products, and sweets/
sweetened beverage, but lower frequencies of fresh fruits, nuts, breakfast cereal, and dairy
FS-11, DOH-85-FS-11, DOH-86-FS-11,
DOH-94FS-6-4, and DOH102-HP-1703] and Health
Promotion Administration, Ministry of Health and
Welfare in Taiwan
Competing Interests: The authors have declared
that no competing interests exist.
This study documents a polarization phenomenon with smaller proportion of overweight
people at the center and higher proportions of normal weight and obesity subjects at two
extremes. MO was associated with low socioeconomic status and poor dietary pattern. The
obesogenic dietary pattern became more prevalent in later time.
Obesity is a major public health issue in the world. It has been estimated that there are
approximately 1.9 billion adults who are either overweight or obese (body mass index, BMI 25 kg/
m2). Among them, over 600 million are obese (BMI 30 kg/m2)[
]. According to World
Health Organization (WHO), this problem of energy imbalance may have contributed to an
estimated 3.4 million death each year including those resulted from cardiovascular disease,
type 2 diabetes mellitus, and cancers[
]. Quality of life of the obese may also be
compromised by conditions such as osteoarthritis, work disability, depression and sleep apnea [
The current worldwide prevalence of obesity in adults has been more than doubled in the
world since 1980[
].At the same time, the trend of obesity rates seems to be levelling off in
some developed countries since 2006[
].On the other hand, the prevalence of an extreme
phenotype, morbid obesity (MO) (BMI 40 kg/m2), is not only persistently rising, but also
expected to increase in an accelerating speed in the coming decades [
].Compared to those
whom were overweight and obesity, MO population suffer from even a shorter life expectancy,
greater severity of many comorbidity, and higher all-cause mortality rate[
associated medical cost and social economic burden are tremendous[
]. And yet weight control
measures are less efficient for MO except an extreme measure, bariatric surgery[
Taking a preventive standpoint, it is important to understand the trends and risk factors of
MO, in order to plan ahead for screening high risk youth, promoting healthy life style, and
building supportive environments. However, there are viewpoints such that severe obesity is
mainly genetic in origin, not due to the lifestyle and environmental factors[
]. Therefore, in
this study we aimed to take advantage of the data from Nutrition and Health Surveys in
Taiwan (NAHSIT) to document the MO prevalence trend from near zero to its abrupt appearance
and to assess the epidemiological characteristics of MO in Taiwan, an Asian region in a rapid
Materials and methods
Study designs and subjects
In this study, use was made of 3 waves of Nutrition and Health Survey in Taiwan (NAHSIT):
1993±1996, 2005±2008, and 2013±2014. The NAHSIT survey has been described elsewhere
]. In brief, the 3 surveys adopted stratified three-staged probability sampling scheme.
According to the geographic areas and the specific ethnic groups, Taiwan was divided into
several strata in the two earlier surveys. In the latest survey, the 20 strata were corresponding to
the 20 counties or metropolitan cities. And then, three-stage sampling was carried out in each
stratum. First stage was for the selection of primary sampling units (PSU: townships or city
districts) via the method of probability proportional to size. Next stage was to randomly choose
two initial households within each selected PSU. And the final stage was to do door to door
survey in two clusters, starting from each of the two initial households. The protocol and
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informed consent form have been approved by the Institutional Review Boards (IRB) of
Academia Sinica and National Health Research Institutes. A signed informed consent has been
obtained from every participant prior to the data collection.
The sample size was 3,071, 1,673 and 1,440 for those who aged equal to or above 19 years
old, respectively from the 3 surveys for the prevalence estimation. There were 16 MO (BMI
35 kg/m2) cases found in NAHSIT 2005±2008 and 23 MO cases in 2013±2014. For
epidemiological characteristics comparison, a total of 64 age (±3 years) and gender-matched normal
BMI (18.5±24 kg/m2) controls were selected in a 4 to 1 ratio [
] for the former survey and 92
controls selected the same way for the latter survey.
Data collection and derivation of variables of interest
In a face-to-face interview, data on socio-demographics (education, personal income, and
occupation), physical activity, and lifestyles (drinking, smoking, betel nut chewing and dietary
intake assessed by 24-hour recall and food frequency) were collected. Anthropometric and
biochemical profiles were assessed through physical examination.
To appraise energy expenditure from physical activity, we obtained metabolic equivalents
of task (METs) for each physical activity and weighted these METs with their corresponding
physical activity time [
]. The median MET-minutes per week were compared between
NW and MO. Further, we assessed and compared the percentage below the recommendation
of physical activity (450 MET-minutes per week) between NW and MO [
characterizing smoking, alcohol drinking and betel nut chewing habits, we have three categories for
each: namely ªnever-usersº for those who never established the habit, ªex-usersº for those who
had the habit but had quitted, and ªcurrent userº for those who continued with the habit.
Twenty four-hour dietary recall [
]was used to appraise mean dietary nutrient intake
levels. We assessed and compared median nutrient density (nutrient level divided by the total
kcal of an individual and multiplied by 2000) between MO cases and NW controls.
We used information on food frequency to search for dietary pattern associated with
obesity. From a total of 72 original food items, we combined them into 21 food categories
considering the food groups and nutrient density. And then reduced rank regression (RRR) [
applied to generate a linear combination of correlated food frequencies which maximized the
BMI variation explained. The factor loading value generated from RRR for each food category
(Table C in S1 File) was used to calculate factor loading value-weighted sum of the
consumption frequencies, i.e. the dietary pattern score. Since the higher the absolute factor loading
values, the greater the association with BMI; food categories with higher absolute factor loading
values were considered as influential and used to describe the dietary pattern.
The data was analyzed using SAS statistical software version 9.4 (SAS Inc., NC, USA). All
samples in each survey were weighted by individual sample weights to estimate the prevalence
rates of underweight, normal weight, overweight, and 3 classes of obesity.
For describing characteristics of the MO and matched NW, frequencies and proportions
are presented for categorical variables; so are mean (standard deviation, SD), median, or
interquartile range (25th to 75th percentiles) for continuous variables. The Chi-square test (for
proportion), and/or Mann-Whitney U test (for continuous variables with non-normal
distributions) were used when appropriate to test the differences between cases and controls.
The RRR analysis was used to find dietary pattern associated with BMI and to calculate the
dietary pattern score as described above. We included in those food categories with absolute
factor loading values higher than 0.15 to show the meaning of dietary pattern. And then
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Spearman correlation was used to examine the correlation between the food frequency and the
dietary pattern score derived. Finally, logistic regression was used to assess the associations
between morbid obesity and various lifestyle and socioeconomic variables significantly
different between MO and NW, including: dietary pattern score, physical inactivity, betel nut
chewing, education and personal income.
Trends for prevalence rates of overweight, obesity and MO
According to 3 waves of the NAHSIT data from 1993±1996, 2005±2008 to the latest 2013±2014
(Fig 1), the percentage of overweight increased from 1993±1996 to 2005±2008, but stabilized
from 2005±2008 to 2013. However, both the prevalence rates of obesity (11.8%, 17.9%, to
22.1%) and MO (0.4%, 0.6%, to1.4%) increased sharply in Taiwan.
Socio-demographic and lifestyle characteristics of the MO
We found that MO status was significantly and negatively associated with the levels of
education (p = 0.0247) and income (p = 0.0372) and physical inactivity (< 450 MET-minutes per
week) (p = 0.0028), and the betel nut chewing habit (p = 0.0138) (Table 1). However, no
significant differences were found between MO cases and NW controls with respect to occupation
type, alcohol consumption, smoking status, and sleeping duration; although some unfavorable
trends for sleeping time and smoking can be seen for MO.
Fig 1. The BMI distribution (proportions of underweight, normal weight, overweight, and several obesity classes)
and median dietary pattern score by surveys.
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a BMI: body mass index.
b Mean (SD) for age and BMI; Median and interquartile range (25th -75th percentile) or %.
c The case and control group were compared with either Mann-Whitney U test(²) or Chi-square test(³).
Comparing dietary nutrient intake levels between MO and NW
The median caloric intake per day was higher in MO cases than in NW controls (Table 2).
However, the difference was not statistically significant (p = 0.4352). In terms of quality of the
diet, fat density was significantly higher; but carbohydrate, dietary fiber and calcium densities
were significantly lower in MO cases than in NW controls. Nevertheless, no significant
difference was found for nutrient densities of various water- or fat-soluble vitamins.
Dietary pattern associated with MO
The frequencies of these food categories were all significantly (p<0.05) correlated with the
dietary pattern score (Table 3). Therefore, this BMI associated dietary pattern was featured with
Median food intake frequency per month
Normal Weight (18.5 BMI<24) Morbid Obesity (BMI 35) P c
n = 156 n = 39
Loading value a
a The factor loading was calculated by reduced rank regression (RRR).
b The correlation coef®cient between food frequency and dietary pattern score was calculated by Spearman correlation.
c The case and control group were compared by using Mann-Whitney U test.
lower frequencies of fresh fruits and 100% juice; nuts; milk, yogurt, and cheese; breakfast
cereals; and beverage without sugar (coffee and tea); but higher frequencies of red meat; processed
seafood and meat products; ice pop, candy, and sweetened beverage.
For comparing frequency of single food item between MO and NW (Table 3), fresh fruits
and 100% juice (p = 0.0345) and nuts (p = 0.0209) were consumed significantly more by NW
controls than MO cases. In contrast, red meat (p = 0.0726) were consumed more by MO cases
than NW controls at a borderline significance level. Other items also showed the trends in line
with the dietary pattern, but significance was not achieved.
Finally, we examined the independent effect of dietary pattern score on MO status
(Table 4). In univariate analysis, we found that dietary pattern score, physical inactivity, and
low levels of education and income were associated with MO, respectively with age and sex
controlled. Betel nut chewing was not (data not shown). However, when both dietary pattern
factor and physical inactivity were included in the model (model 1), only dietary pattern score
was significant. If we further included in education and income variables (model 2), the
significance remained for the dietary pattern score with a significant trend (p for trend = 0.0002).
The median of dietary pattern score increased steadily from 1993±1996, to 2005±208, and to
2013±2014 (see Fig 1).
Over past four decades, global age-standardized prevalence of obesity (BMI 30 kg/m2)
increased from 3.2% to 10.8% in men, and from 6.4% to 14.9% in women [
]. And in Taiwan,
the prevalence trend of overweight or obesity (BMI 24 kg/m2) together has increased from
33.2%to 43.4% (1993±1996 to 2005±2008) and remained that level in latest survey. However,
the prevalence trend of obesity (BMI 27 kg/m2) was continuously increasing from 11.8%,
17.9%, and reached 22.0% in 2013±2014. Moreover, there was a noticeable increase in the MO
(BMI 35 kg/m2) prevalence in the same period, from 0.4% to more than one and half
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Total N = 175. 16 subjects (12 controls and 4 cases) were excluded due to missing values on FFQ.
a The dietary pattern score was the weighted sum of food frequencies, weighted by loading factors generated from RRR.
Univariate: age and sex-controlled. Model 1: dietary pattern scores and physical activity controlled for age and sex. Model 2: Model 1 adjusted further for
education level, and personal income.
percentage. However, prevalence of overweight decreased from 25.5% during the 2005±2008
survey to the recent 21.3%. These findings suggest that, although the prevalence stabilized for
overweight and obesity as a whole, the prevalence of obesity including the ªmorbid obesity" is
dramatically rising. Similar observations were also seen recently in European countries, US,
Sweden, and China [4, 12, 21±23]. It is likely that this phenomenon of BMI polarization in
Taiwan is due in part to the shifting to the desirable weight range for those who were at the lower
end of the overweight range, since the shrinkage of overweight proportion occurred clearly
after the nationwide campaign on "Losing 6 Million Tons" in 2011[
] and mostly health
conscious people participated in the movement. What happened at the other end of the spectrum
seems to show that the obesogenic environment continues to pull more toward BMI beyond
the obesity and the MO cut-points. We have demonstrated that MO cases have much worse
clinical chemistry and metabolic profiles than their non-obese counter parts(Table B in S1
File), similar to many other reports[
]. One should not overlook the threat of MO for health
and economics in the years to come [
] and it is crucial to understand the epidemiological
characteristics of the MO in order to make effective strategic plans.
We have found that MO subjects tended to be the under-privileged, i.e., with lower level of
education and income. Their low physical activity and poor dietary pattern are in part
associated with their socioeconomic status (SES). The relationship between SES and obesity has
been widely studied [27±29]. In the developing countries, obesity tends to be positively
associated with SES. However, in the developed countries, the directions of the association are
mixed. In addition, across different socioeconomic categories, the obesity prevalence seems to
rise with time in the group with lower education level, and remains stable or increases slightly
in the group with higher education level[
]. The association between MO and low SES
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may indicate that Taiwanese have experienced the nutrition transition. As economy
developed, the dietary pattern also changed from whole foods and plant foods-rich one into those
abundant with animal products and processed foods high in fats and sugar[
it would lead to obesity and diet-related non-communicable diseases[
]. This transition
has been observed in many countries in Asia too .
It is crucial to understand the nutrient vectors or food vectors contributing to obesity or
MO for developing effective health policies. Dietary pattern analysis for obesity or for
incremental BMI gained much attention recently [
]. Most of the dietary pattern studies [38±40]
employed principle component (or factor) analysis and obtained healthy (prudent) vs. western
(animal protein) patterns or traditional vs. modern patterns. Western, modern, and animal
protein pattern concurred with higher risk, but healthy and traditional ones with lower risk of
obesity. Only a few studies in Greece[
] and in Australia [
] applied RRR method in recent
years, identifying a pattern featured with increased sweets/SSB and red/processed meat, but
lowered fruits and vegetables. Our study is the first studying Asians. Our findings on nutrient
density from 24-hour recall and on food pattern analysis with food frequency questionnaire
are consistent between each other and with the previous mentioned RRR studies. According to
the 24-hour recall, the MO subjects tend to consume a diet high in fat; but low in calcium,
fiber and carbohydrate. From dimension reduction analysis of the food frequency data, the
diet of Taiwanese MO was composed of less fresh fruit, nuts, breakfast cereals, and dairy
products; but of more red meat, processed animal products, and sweets/sweetened beverage. These
findings on diet are consistent with the obesogenic dietary pattern discovered previously, i.e.,
abundant with animal products and processed foods high in fat and simple sugar [32, 34±43],
although percent energy from carbohydrate is relatively less in the MO subjects of our study. It
is interesting to observe that some not commonly consumed food items such as nuts, breakfast
cereal and dairies are beneficial in Taiwan. High consumption frequency of sugar-sweetened
beverages (SSBs) has been associated with some adverse health consequences, i.e., obesity,
metabolic syndromes, gout, and non-alcoholic fatty liver disease [
]. Our data showed that it is
the dietary pattern with less nutrient dense foods and more SSBs which made the
contributions. Although it is probably hard to tease apart the contributions from lack of nutrient-dense
foods, our findings suggest that the key problem may be accessibility and affordability of the
healthy food (wholegrain cereals, fruits and vegetables) rather than availability of foods in
]. We were able to show that median of BMI-associated dietary pattern
score increased steadily from 1990's to 2010's (0.8 !1.0 !1.6), indicating a shift toward
obesogenic dietary pattern. Effective public health measures are required to educate people and to
build healthy eating environment.
We also found that low physical activity was significantly associated with MO. Although, it
was hard to separate the effect of diet and physical activity, there are large declines in level of
physical activity and increases in sedentary behavior globally[
]. Lack of sufficient physical
activity has been viewed as a major crisis of public health[
], including data from Taiwan
 and China . Unfortunately, we could not examine the trend of physical inactivity due
to different questionnaires employed in three surveys.
This is the first study portraying MO epidemiology in an Asian population, providing not
only the descriptive statistics but also socio-demographic and lifestyle determinants of the
MO. However, there are some limitations in this study. First of all, the sample size of MO and
the power of the study are relatively small. We did not observe an effect from sleeping
duration, and caloric intake due to low statistical power contributed by small sample size, relatively
small effect and large variation of the variables. Nevertheless, our study provides a first glance
on epidemiological characteristics of MO in an Asian population. Secondly, some of the
discovered associations between MO and epidemiological characteristics are cross-sectional in
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nature. Cautions should be taken to interpret the findings, in particularly on the negative
association between physical activity and MO status. Although physical inactivity may contribute
to positive energy balance leading to obesity, MO subjects may also have difficulty to carry out
physical activity. Furthermore, genetic factors may contribute to the development of obesity
and morbid obesity. Individual with the genetic susceptibility may be more so influenced[
by obesogenic environment[
] such as the accessibility of processed and high-energy-dense
foods, and desk-constrained works as a consequence of modernization and social
development. The risk factors of MO found in this study were exactly what resulted from the
obesogenic environments. However, it is lacking genetic epidemiology study on MO, especially in
Asian populations. Further research on genetics of MO in Asian populations is needed. We
could not investigate the genetic and environmental interactions in this study, since genetic
component was not included in the IRB approval.
In summary, this study illustrates how MO and desirable weight polarized recently in
Taiwan. In addition, comprehensive epidemiological characteristics of MO have been studied.
MO primarily appears among those who had lower education and personal income. They
were physically inactive and tend to consume a nutrient-poor dietary pattern which are high
in red meat/processed animal products and sweets/sweeten beverage, but less in fruits, nuts,
and vegetables. Our results point to the role of poor lifestyle and associated obesogenic
environmental factors in contributing to the development of MO in those of the underprivileged,
which have important implications in developing national health policy.
S1 File. Definition and Database.
The authors wish to thank all the participants as well as the research staffs involved in the data
Conceptualization: HCC WHP.
Data curation: HYC CJY WHP.
Formal analysis: HCC.
Investigation: HCC HCY WHP.
Methodology: HCC HCY WHP.
Project administration: WHP.
Supervision: HCY WHP.
Validation: HCC HCY WHP.
Visualization: HCC WHP.
Writing ± original draft: HCC.
Writing ± review & editing: HCC HCY HHC KCH WHP.
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