INTRODUCTION
Health-related quality of life (HRQoL) is a multidimensional concept encompassing the subjective perception of physical functioning and mental health. Globally, obesity poses a critical threat to public health, with its prevalence rapidly increasing and the associated burden growing [1,2]. Obesity is a factor linked to quality of life through various pathways that encompass both physical and mental health functioning [3-5]. However, the relationship between obesity and quality of life is inconsistent, with several studies demonstrating negative, positive, or no specific associations [4,6,7]. In addition, weight change, including both weight gain and loss, has been recognized as an important factor influencing quality of life [8,9]. In Asian populations, longitudinal studies investigating the relationship between weight change and quality of life have been relatively scarce compared to cross-sectional designs. Thus, the present study aimed to investigate how changes in body weight influence the quality of life, as measured by the 12-Item Short-Form Health Survey (SF-12), and determine whether these effects depend on baseline body mass index (BMI) among middle-aged and older adults in South Korea.
METHODS
1. Data and Study Participants
Data were retrieved from the population-based longitudinal study, which was part of the Korean Genome Epidemiology Study. The cohort initially included 10,030 participants aged 40 to 69 years, who underwent baseline health examinations and surveys in 2001–2002. These participants were then followed up biennially until 2020. For this study, we focused on 5,106 participants who had completed questionnaires regarding their HRQoL in both 2009–2010 and 2013–2014 (Figure 1). The study protocol was approved by the Institutional Review Board (IRB) of Bundang Seoul National University Hospital (IRB no., X-2308-846-905). The requirement for informed consent from individual patients was omitted because of the retrospective design of this study.
2. Assessment of Exposure Variables
Anthropometric measurements, including height and weight, were conducted by skilled study workers following the standard protocols. BMI was calculated as weight divided by height squared (kg/m2). We used the Asia-Pacific-specific guidelines, with BMI cut-off points of <18.5 kg/m2 for underweight, 18.5 to <23.0 kg/m2 for normal weight, 23.0 to <25.0 kg/m2 for overweight, and ≥25.0 kg/m2 for obesity [10,11]. Participants were categorized into one of three weight-change groups based on their patterns of weight change over the 4-year period: those who lost 3 kg or more, those who gained 3 kg or more, and participants whose weight change remained within 3 kg.
3. Assessment of Outcomes
The SF-12, a self-report questionnaire developed based on the 36-Item Short Form Survey (SF-36), was used to evaluate HRQoL [12]. The SF-12 comprises 12 questions, each rated on a 5-point Likert scale. The sum of the scores was transformed using a standardized scoring algorithm to fit within the 0 to 100 range. The total score encompasses eight subscales, namely physical functioning, role limitations due to physical problems, bodily pain, general health, vitality, social functioning, role limitations due to emotional problems, and mental health. Higher scores on these subscales indicate a better condition. These subscales were further divided into two components: the physical component summary (PCS) and the mental component summary (MCS).
4. Assessment of Potential Mediators
Information on socioeconomic status was collected via a questionnaire, which included details about educational attainment, monthly household income, and employment status. Educational attainment was categorized as either lower (high school or less) or higher attainment (college or above), considering that more than 90% of the population in Korea has been enrolled in high school since 1994 [13]. Monthly household income was divided into two groups: less than 3 and more than 3 million Korean won. In addition, employment status was classified as either employed or unemployed.
5. Assessment of Covariates
Participants provided information about their drinking, smoking, and exercise habits through a health-related behavior questionnaire. A current drinker was defined as drinking alcohol at least once a month. Smoking status was divided into two categories: never or former, and current smokers. A current smoker was defined as someone who has smoked at least 100 cigarettes in their lifetime and does so currently. Physical activity was divided into two categories: regular exercise (defined as engaging in physical activity for at least three episodes per week, with at least moderate intensity) and the inactive group.
We assessed medical history using a self-administered questionnaire, anthropometric measurements, and laboratory biochemical measurements. For defining blood pressure, we calculated the average of the last two of three measured values, considering both systolic and diastolic blood pressure. After at least 12 hours of fasting, blood tests were performed using a Hitachi 700-110 Chemistry Analyzer (Hitachi Co., Tokyo, Japan), and the plasma concentrations of fasting blood sugar, glycated hemoglobin, and postprandial 2-hour glucose level were evaluated. Participants whose laboratory biochemical measurements were consistent with diabetes mellitus, but who had not yet received a diagnosis, were also regarded as having diabetes mellitus [14]. Furthermore, participants diagnosed with coronary artery disease or myocardial infarction were categorized as having cardiovascular disease. Individuals were classified as having hypertension, diabetes mellitus, cardiovascular disease, or cancer (lung, stomach, liver, colon, pancreas, uterus, and breast) if they had received a diagnosis or were currently undergoing treatment for these conditions.
6. Statistical Analysis
The general characteristics of study participants were compared between the three weight-change groups according to obesity status using the analysis of variance for continuous variables and chi-square test for categorical variables. A multivariate logistic regression analysis was performed to determine the association between weight changes and the lowest quartile of PCS and MCS at year 4. Variables that showed statistical significance in the univariate analyses or those known to impact HRQoL were included in the adjusted models. The crude model was adjusted for baseline PCS and MCS, respectively. The following confounders were included in the adjusted model: age, sex, smoking, drinking habit, regular exercise, monthly household income, marital status, education attainment, employment, hypertension, diabetes mellitus, cardiovascular disease, and baseline cancer status. Furthermore, we performed generalized estimating equation analyses to assess the relationship between weight changes and changes in PCS and MCS scores over a 4-year period for each group, including underweight, normal weight, overweight, and obese participants. All statistical analyses were conducted using IBM SPSS Statistics ver. 26.0 (IBM Corp., Armonk, NY, USA), with a significance level set at P<0.05 for all tests.
RESULTS
1. Baseline Characteristics of Study Participants
Table 1 displays the baseline characteristics of participants in relation to changes in body weight over the 4-year follow-up period. Participants shared similar gender distributions; however, they exhibited differences in smoking and drinking habits and the presence of comorbidities such as hypertension and diabetes. Socioeconomic factors, including income and employment status, also varied among the groups. Notably, participants in the weight loss group tended to be older and had lower incomes. Weight gain was associated with a higher prevalence of smoking and a higher employment rate. In comparison to the stable weight group, weight loss was linked to a greater prevalence of hypertension and diabetes. Furthermore, participants who experienced weight loss of 3 kg or more reported considerably worse baseline physical functioning when compared to those with weight gain of 3 kg or more.
2. Characteristics of Study Participants across BMI Categories
Table 2 presents the characteristics of participants based on their baseline BMI. The mean age was 59.4±8.7 years in the normal BMI group, 58.8±8.1 years in the overweight group, 58.7±8.0 years in the obese group, and 64.2±9.4 years in the underweight group. Participants in the obese group exhibited a higher prevalence of comorbidities, including hypertension and diabetes. Regarding SF-12 components, participants in the obese group reported lower baseline PCS scores.
3. Relationship between Body Weight Changes and HRQoL with/without Covariate Adjustment
In the analysis presented in Table 3, weight gain of 3 kg or more was associated with poor MCS scores in the second round after adjusting for baseline mental functioning, lifestyle factors, socioeconomic status, and baseline comorbidities. When we stratified the analysis by baseline BMI, weight gain was associated with poor MCS scores in normal weight and overweight groups, but not in the obese and underweight groups.
When we examined the mean MCS scores using a generalized estimating equation, we found that normal-weight individuals who lost weight were associated with a greater increase (2.69 points; 95% confidence interval, 0.50–4.88) compared to normal-weight individuals who maintained their weight (Table 4). Weight loss was found to result in a similar increase in MCS scores regardless of baseline BMI, although these changes did not reach statistical significance. In contrast, weight gain appeared to be associated with lower MCS scores, except for underweight individuals who reported better MCS scores. Once again, the relationship between weight change and PCS was not statistically significant. As shown in Table 5, estimated differences in mean MCS scores were significant for specific groups: overweight weightlosers (-0.93 points; standard error [SE], 0.33; P<0.001), obese weightgainers (-0.81 points; SE, 0.30; P=0.01), and underweight weight-gainers (2.36 points; SE, 1.13; P=0.04), compared to weight maintainers in each respective weight group (Figure 2).
DISCUSSION
1. Main Findings
In this population-based study using SF-12 to assess HRQoL in community-dwelling middle-aged and older adults in South Korea, we found that those with a weight gain of 3 kg or more had a higher risk of poor mental functioning, especially in normal weight and overweight groups. In addition, weight loss in normal weight groups was associated with improved mental health function. Estimated differences in mean MCS scores between weight gainers/losers and maintainers in every weight group showed distinct patterns: weight gainers in obese groups showed worse functioning, whereas those in underweight groups reported better mental functioning. Interestingly, weight losers in the overweight group reported worse MCS scores.
Our results demonstrated that weight gain increased the risk of poor mental functioning. Furthermore, more importantly, the negative effect of weight gain on mental health remained the same only among normal weight and overweight groups. Our findings are consistent with those of several published studies. For instance, a cross-sectional study conducted among women aged 40–59 years in South Korea found that both non-obese and obese women who experienced weight gain reported higher levels of perceived stress than women who experienced weight loss [8]. Furthermore, weight gain among nonobese women and weight loss among obese women were associated with higher scores on the Patient Health Questionnaire-9. A previous longitudinal study conducted on a random sample of Stockholm County residents investigated the association between 8-year weight change and HRQoL, as measured by the EuroQol-5 Dimension (EQ-5D), based on baseline BMI. The study emphasized that heavy weight gain of 5% of body weight or more increased the risk of reporting impairment in HRQoL regardless of the baseline BMI category, whereas weight reduction had no significant impact [15]. Previous studies also found that compared to those with no weight change, the weight change regardless of obesity was associated with poorer mental health [8,16,17].
Previous studies have also reported inconsistent results regarding the relationship between obesity and the mental components of HRQoL [18,19]. In two large prospective cohorts of US women, the Nurses’ Health Study (in 1992–2000) and the Nurses’ Health Study (in 1992–2000), a modest relation was found between weight change over a 4-year period and HRQoL in the domains of mental functioning, generally remaining below clinically significant levels.4) In the Helsinki Health Study cohort, no statistically significant differences were observed in the changes in MCS scores between weight maintainers and weight gainers [6].
Weight loss is associated with improved mental health functioning in the normal weight group, whereas worse mean MCS scores were reported in the overweight group. Weight change was associated with worse HRQoL in a national sample of older adults in Spain [17]. Specifically, weight loss was linked to lower scores on several physical and mental scales of the SF-36 and a reduction in HRQoL among nonobese women who lost weight and among obese women who gained weight. The association between weight gain and reduction in HRQoL is consistent with our results, whereas the relationship between weight loss and worse mental functioning in non-obese women is inconsistent with our results regarding the relationship in normal weight groups. The explanation could be that we further divided the nonobese group into underweight, normal weight, and overweight groups, which could have led to discrepancies.
Some studies have reported that a weight change is associated with poor physical functioning, whereas our study found no association or clinically insignificant impacts on physical components. These discrepancies may arise from variations in ethnicity, BMI cutoff, statistical methodologies, and the inclusion of different confounding factors in the studies.
We do not have a clear biological explanation for the observation of worse mental HRQoL in weight gainers. Possibly, besides obesity, weight gain also contributes to the genesis of inflammation-mediated mental conditions such as depression and anxiety [20].
We presented further evidence for poor mental health functioning caused by weight changes using a longitudinal dataset from South Korea and provided specific insights into the critical role of weight changes on the mental components of HRQoL. To our knowledge, no previous study has assessed the association between body weight change and the physical and mental components of HRQoL while considering baseline BMI in a prospective design within a large community-dwelling sample in South Korea.
2. Strengths and Limitations of the Study
There are several strengths of our study. First, the longitudinal data were derived from a well-organized, nationally representative cohort study. The follow-up SF-12 results, which were not previously investigated cross-sectionally, were analyzed, allowing us to identify the temporal sequence. Second, weight and height were measured by trained personnel rather than self-reported. Self-reported anthropometric data were associated with misclassification, potentially underestimating the association of weight change with HRQoL. Third, this study is one of the few to compare underweight, overweight, and obese participants with the normal-weight population within the same study. Fourth, blood tests and physical examinations were used to define certain chronic diseases (e.g., hypertension and diabetes mellitus), reducing the potential for recall bias in identifying underlying comorbidities. Finally, our study assessed both PCS and MCS scores because body weight changes may have different impacts on physical functioning and mental health.
However, there are a few limitations to this study, including the following: first, HRQoL was determined based on self-reported questionnaires and subjective reports. Using generic HRQoL measurements rather than obesity-specific HRQoL measurements may not be sufficient to distinguish the association between weight change and other chronic diseases’ impact on HRQoL. Further studies are needed to investigate the underlying mechanisms of the complex relationship between body weight change and HRQoL. Finally, the retrospective design of our study presented limitations regarding the availability of data about intentional versus unintentional weight loss throughout the study periods. A cross-sectional study conducted among the community-dwelling older population aged 75 years and above in Korea indicated that both intentional and unintentional weight loss, regardless of initial weight status, were associated with diminished HRQoL as measured by the EQ-5D instrument in comparison to individuals with no change in weight [21]. Specifically, intentional weight loss contributed to increased levels of anxiety and depression, whereas unintentional weight loss was also related to a similar trend in anxiety and depression, constituting a notable portion of the mental component of HRQoL, without statistical significance. Furthermore, older adults experiencing unintentional weight loss are susceptible to being underweight in response to diseases and age-related changes. Several other variables known to influence unintentional weight loss, including socioeconomic status (including monthly household income, marital status, educational attainment, and employment status), health-related behaviors (smoking, alcohol consumption, and regular exercise), and medical histories (hypertension, diabetes, cardiovascular disease, and cancer), were considered [22]. Our analysis was conducted following adjustments for these factors to mitigate the potential impacts associated with the intentional or unintentional nature of weight loss.
3. Conclusion
Different patterns of relationships between weight changes and mental components of HRQoL were observed within each weight group. Hence, it is crucial to focus on the mental health of middle-aged and older adults when assessing changes in their body weight. Particularly, baseline obesity status may guide target-oriented approaches to support individuals’ mental health affected by weight changes.
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