Abstract
Background
Increasing evidence suggests that abdominal fat distribution is a greater predictor of metabolic risk (MetS) than BMI. This study aimed to evaluate the 10-year cardiometabolic consequences of isolated abdominal obesity (IAO) and its association with MetS and health-related quality of life (HRQoL).
Methods
This longitudinal cohort study included 552 adults from the Turkestan region of Kazakhstan who underwent baseline assessment in 2014 and follow-up examination in 2024. Anthropometric, clinical, and laboratory parameters were measured, and the HRQoL was assessed by the SF-36 questionnaire.
Results
At baseline, 403 participants (73%) had IAO, and 149 (27%) did not. Participants with IAO were older (45 vs. 37 years, p < 0.001) and more frequently female (79%). They had higher body weight, waist circumference, BMI, blood pressure, and unfavorable metabolic parameters, including higher cholesterol, triglyceride, and glucose levels (all p < 0.05). Metabolic syndrome was more prevalent in the IAO group (30% vs. 1%, p < 0.001). After 10 years, IAO was observed in 472 participants: 97% of those with IAO at baseline remained obese, compared with 54% of those without baseline obesity who developed it (p < 0.001). Baseline IAO independently predicted incident metabolic syndrome (ORadj = 1.34; p = 0.041). Participants with persistent IAO had lower HRQoL scores in physical functioning, general health, and mental health than individuals without IAO (p < 0.05).
Conclusions
Persistent isolated abdominal obesity was associated with lower health-related quality of life and independently predicted metabolic syndrome over the 10-year follow-up period. These results highlight the importance of assessing abdominal adiposity in clinical practice.
Keywords: Isolated abdominal obesity, Metabolic syndrome, Cardiometabolic risk, Health-related quality of life, SF-36
Introduction
Obesity is a global public health problem and a major contributor to the increasing prevalence of cardiometabolic diseases. Body mass index (BMI) has been the most used index for assessing obesity. However, there is a growing body of evidence suggesting that fat distribution, particularly visceral fat accumulation, is more strongly and independently associated with cardiometabolic risk than obesity itself [1, 2].
There is an association between metabolically active visceral fat deposition in the abdominal cavity and the development of insulin resistance, chronic inflammation, and lipid dysregulation, which are hallmarks of metabolic syndrome (MetS) and major contributors to cardiovascular disease and diabetes [3]. More attention is being paid to Isolated Abdominal Obesity (IAO), defined as increased waist circumference in the presence of a normal BMI. BMI does not accurately reflect body fat distribution, and as a result, people with IAO are classified as normal weight even though they exhibit the same metabolic abnormalities as those with general obesity [4]. Previous studies on this topic have confirmed the association between IAO and excess visceral fat, insulin resistance, and components of MetS [4]. Therefore, BMI is not always an accurate measure of weight status and may therefore not be an accurate indicator of health risks.
Obesity can reduce health-related quality of life. According to the prospective cohort study, the persistence of IAO is associated with a longer duration of reduced health-related quality of life (HRQoL) [5, 6]. There is limited information on the long-term cardiometabolic consequences of IAO and on how these conditions affect individuals’ HRQoL. The purpose of this study was to evaluate the 10-year cardiometabolic effects of IAO, the incidence of metabolic syndrome, and the health-related quality of life.
Methods
Study design and population
This longitudinal cohort study was conducted at the Khoja Akhmet Yassawi International Kazakh-Turkish University and included participants from the Turkestan region of Kazakhstan. Baseline data were collected in 2014, and participants were followed for 10 years until the 2024 follow-up assessment. The study population consisted of adults who underwent standardized clinical examinations, anthropometric measurements, laboratory testing, and questionnaire-based assessments at baseline and follow-up visits. The study design enabled assessment of longitudinal changes in cardiometabolic parameters and evaluation of incident cardiometabolic outcomes over the observation period.
Inclusion and exclusion criteria
Participants were eligible for inclusion if they were adults older than 18 years of age, had participated in the baseline examination in 2014, and had complete anthropometric, clinical, and laboratory measurements necessary to assess IAO and cardiometabolic risk factors. Additionally, participants were required to have follow-up data from the 2024 examination available. At baseline, individuals were excluded if they were unable to complete anthropometric measurements or clinical and laboratory data collection, or if they had severe chronic conditions that could substantially affect body composition, such as active malignancy or severe endocrine disorders. Women were excluded if they were pregnant. Participants who were lost to follow-up or lacked cardiometabolic outcome data at the 2024 assessment were also excluded. Figure 1 shows a detailed cohort set-up diagram.
Fig. 1.
Flow chart diagram of cohort-set-up
Definitions of outcomes
Isolated abdominal obesity was defined as abdominal obesity, with a waist circumference above the cut-off values for normal-weight individuals (BMI < 25.0 kg/
according to the World Health Organization [7]. In this study, IAO was defined as a waist circumference ≥ 80 cm in women and ≥ 94 cm in men. The waist-to-height ratio (WHtR) was calculated as waist circumference divided by height.
Metabolic syndrome was defined according to international criteria, requiring the presence of at least three of five core cardiometabolic perturbations: isolated abdominal obesity, hypertension, dyslipidemia, impaired glucose metabolism, and insulin resistance proxies [8].
Anthropometric and clinical measurements
Anthropometric measurements were obtained using standardized procedures. Body weight was measured to the nearest 0.1 kg, and height was measured to the nearest 0.5 cm. Body mass index was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the level of the umbilicus using a flexible measuring tape, while hip circumference was measured at the widest point of the buttocks. Blood pressure was measured using a calibrated sphygmomanometer after participants had rested in a seated position for at least five minutes. Systolic and diastolic blood pressure values were recorded during the examination.
Blood samples were collected after an overnight fast. Laboratory analyses included measurements of fasting plasma glucose, glucose concentration two hours after a standardized breakfast, insulin, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). All laboratory analyses were performed using standardized procedures in certified clinical laboratories.
Assessment of health-related quality of life
Health-related quality of life was assessed in 2024 using the Short Form-36 Health Survey (SF-36). The questionnaire evaluates eight domains of health status, including physical functioning, role limitations due to physical health, bodily pain, general health perception, vitality, social functioning, role limitations due to emotional problems, and mental health [9]. Each domain score ranges from 0 to 100, with higher scores indicating better perceived health-related quality of life.
Statistical analysis
Descriptive statistics were used to summarize the study population’s baseline characteristics. Continuous variables are presented as means and standard deviations, whereas categorical variables are presented as frequencies and percentages. The distribution of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of histograms. Variables with approximately normal distribution are presented as mean and standard deviation and were analyzed using parametric tests, whereas non-normally distributed variables are presented as median and interquartile range and were analyzed using non-parametric tests. Differences between groups were assessed using independent sample t-tests or Mann-Whitney U for continuous variables and chi-square or Fisher’s exact for categorical variables.
Longitudinal changes in cardiometabolic parameters between 2014 and 2024 were calculated as the difference between follow-up and baseline measurements. To examine factors associated with the development of metabolic syndrome during the follow-up period, multivariable logistic regression models were constructed. To analyze only incident cases, those with MetS were excluded. The regression models included isolated abdominal obesity as the main exposure variable and were adjusted for potential confounders, including age, gender, smoking status, alcohol consumption, baseline fasting glucose, and systolic blood pressure [10].
Health-related quality of life in 2024 was additionally compared between participants according to the persistence of isolated abdominal obesity during the follow-up period. Two groups were defined: individuals without isolated abdominal obesity at both baseline and follow-up, and those with persistent isolated abdominal obesity at both time points. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were performed using STATA 16.0.
Results
Baseline characteristics of the study population
A total of 552 participants were available for comparison between IAO (n = 403, 73%) and non-IAO (n = 149, 27%) participants (Table 1). Participants with IAO were significantly older than those without IAO (45 ± 11 vs. 37 ± 11 years, p < 0.001). Women constituted 77% of the cohort and were more common in the IAO group (79% vs. 71%, p = 0.047).
Table 1.
Baseline sociodemographic and medical characteristics of patients (2014)
| Total (n = 552) |
No IAO (n = 149; 27%) |
IAO (n = 403; 73%) |
p-value | |
|---|---|---|---|---|
| Sociodedmographic and lifestyle | ||||
| Age (years), mean (SD) | 43 (11) | 37 (11) | 45 (11) | < 0.001 |
| Gender, n (%) | 0.047 | |||
| Female | 425 (77) | 106 (71) | 319 (79) | |
| Male | 127 (23) | 43 (29) | 84 (21) | |
| Marital status, n (%) | 0.022 | |||
| Single | 96 (17) | 35 (23) | 61 (15) | |
| Married | 456 (83) | 114 (77) | 342 (85) | |
| Education, n (%) | < 0.001 | |||
| Higher education | 374 (68) | 119 (80) | 255 (63) | |
| College degree | 178 (32) | 30 (20) | 148 (37) | |
| Disability, n (%) | 0.125 | |||
| No | 526 (95) | 145 (97) | 381 (95) | |
| Yes | 26 (5) | 4 (3) | 22 (5) | |
| Smoking, n (%) | 0.157 | |||
| No | 488 (88) | 127 (85) | 361 (90) | |
| Yes | 64 (12) | 22 (15) | 42 (10) | |
| Alcohol consumption, n (%) | 0.030 | |||
| No | 420 (76) | 123 (83) | 297 (74) | |
| Yes | 132 (24) | 26 (17) | 106 (26) | |
| Confirmed comorbid conditions | ||||
| Hypertension, n (%) | 215 (39) | 21 (14) | 194 (48) | < 0.001 |
| HF, n (%) | 48 (9) | 6 (4) | 42 (10) | 0.001 |
| AMI, n (%) | 19 (3) | 4 (3) | 15 (4) | 0.890 |
| COPD, n (%) | 116 (21) | 27 (18) | 89 (22) | 0.182 |
| Thyroid diseases, n (%) | 82 (15) | 18 (12) | 64 (16) | 0.499 |
| Hepatitis, n (%) | 87 (16) | 27 (18) | 60 (15) | 0.453 |
Patients without IAO had a higher educational level (80% vs. 63%, p < 0.001). Although alcohol use was higher in IAO patients (26% vs. 17%, p = 0.030), there was no significant difference in smoking status. Regarding comorbidities, hypertension (48% vs. 14%; p < 0.001) and heart failure (10% vs. 4%; p = 0.001) were significantly more prevalent among individuals with IAO. No significant differences were observed in the prevalence of acute myocardial infarction, COPD, thyroid diseases, or hepatitis.
As shown in Table 2, participants with IAO were significantly heavier (75 vs. 68 kg), had larger waist circumference (98.7 vs. 77.9 cm), waist-to-height ratio (0.61 vs. 0.48), larger hip circumference (110.3 vs. 95.6 cm), and higher body mass index (24.4 vs. 21.9 kg/
) with significant p-values for all.
Table 2.
Baseline anthropometric and clinical characteristics (2014)
| Total (n = 552) |
No IAO (n = 149; 27%) |
IAO (n = 403; 73%) |
p-value | |
|---|---|---|---|---|
| Antropometric measurers | ||||
| Height (cm) | 162 (8) | 163 (8) | 162 (8) | 0.447 |
| Weight (kg) | 71 (13) | 68 (7) | 75 (19) | < 0.001 |
| Waist circumferernce (cm) | 93.1 (13.2) | 77.9 (6.96) | 98.7 (10.3) | < 0.001 |
| WHtR (index) | 0.57 (0.08) | 0.48 (0.04) | 0.61 (0.07) | < 0.001 |
| Hip circumferernce (cm) | 106.4 (11.7) | 95.6 (6.08) | 110.3 (10.7) | < 0.001 |
BMI (kg/ ) |
23.1 (3.31) | 21.9 (2.94) | 24.4 (4.13) | 0.009 |
| Hemodynamics | ||||
| SBP (mmHg) | 118 (20) | 107 (16) | 123 (20) | < 0.001 |
| DBP (mmHg) | 78 (12) | 71 (10) | 81 (12) | < 0.001 |
| HR (bpm) | 77 (9) | 76 (10) | 77 (9) | 0.492 |
| Metabolic characteristics | ||||
| Total cholesterol (mmol/L) | 4.92 (0.91) | 4.65 (0.79) | 5.02 (0.93) | < 0.001 |
| LDL-C (mmol/L) | 2.79 (0.87) | 2.59 (0.82) | 2.86 (0.89) | 0.001 |
| HDL-C (mmol/L) | 1.49 (0.29) | 1.51 (0.27) | 1.48 (0.29) | 0.238 |
| Triglycerides (mmol/L) | 1.42 (0.53) | 1.20 (0.36) | 1.48 (0.56) | < 0.001 |
| Fasting glucose (mmol/L) | 5.18 (1.34) | 4.75 (0.63) | 5.34 (1.48) | < 0.001 |
| 2-hour glucose (mmol/L) | 5.24 (1.46) | 4.87 (0.99) | 5.37 (1.57) | < 0.001 |
| Metabolic Syndrome, n (%) | < 0.001 | |||
| No | 431 (78) | 147 (99) | 284 (70) | |
| Yes | 121 (22) | 2 (1) | 119 (30) | |
| Types of MS, n (%) | < 0.001 | |||
| MS with normoglycemia | 73 (13) | 2 (1) | 71 (18) | |
| MS with T2DM | 48 (9) | 0 | 48 (12) | |
Blood pressure was also higher in participants with IAO (p < 0.001), and heart rate did not differ. Metabolic parameters were less favorable among those with IAO, including higher total cholesterol, LDL cholesterol, triglycerides, fasting glucose, and 2-hour glucose (all p < 0.001). HDL cholesterol levels were comparable between groups. Metabolic syndrome was more prevalent in participants with IAO (30% vs. 1%, p < 0.001).
Cardiometabolic outcomes at 10-year follow-up
Cardiometabolic outcomes in 2024 according to IAO status in 2014 are shown in Table 3. At the follow-up visit, 472 (86%) of the participants had IAO. Of those with isolated abdominal obesity in 2014, 97% still had it in 2024, whereas of those without IAO in 2014, 54% developed it in 2024 (p < 0.001).
Table 3.
Cardiometabolic outcomes in 2024 according to isolated abdominal obesity status in 2014
| Total | Isolated abdominal obesity in 2014 | p-value | |||
|---|---|---|---|---|---|
| No (n = 149; 27%) |
Yes (n = 403; 73%) |
||||
| Cardiometabolic outcomes at 10-year follow-up (2024) | Isolated abdominal obesity, n (%) | < 0.001 | |||
| No | 80 (14) | 69 (46) | 11 (3) | ||
| Yes | 472 (86) | 80 (54) | 392 (97) | ||
| Metabolic Syndrome, n (%) | 0.002 | ||||
| No | 326 (59) | 104 (70) | 222 (55) | ||
| Yes | 226 (41) | 45 (30) | 181 (45) | ||
| Types of MS, n (%) | 0.007 | ||||
| MS with normoglycemia | 139 (25) | 26 (17) | 113 (28) | ||
| MS with T2DM | 87 (16) | 19 (13) | 68 (17) | ||
| Hypertension, n (%) | < 0.001 | ||||
| No | 322 (58) | 121 (81) | 201 (50) | ||
| Yes | 230 (42) | 28 (19) | 202 (50) | ||
Metabolic syndrome was identified in 226 (41%) participants. Metabolic syndrome was more frequently observed in participants with baseline isolated abdominal obesity (45% vs. 30%, p = 0.002). Normoglycemia in metabolic syndrome was observed in 25% of the total population (28% in the baseline isolated abdominal obesity group vs. 17% in the non-isolated abdominal obesity group, p = 0.005), and type 2 diabetes in metabolic syndrome was observed in 16% of the population (17% vs. 13%, p = 0.007).
Figure 2 shows ten-year changes in cardiometabolic parameters by baseline IAO status. Body weight increased in both groups, but to a greater extent in the non-IAO group (+ 4.55 kg vs. + 3 kg, p < 0.05). Waist circumference increased by a greater margin in the non-IAO group (+ 4.87 cm vs. + 2.09 cm, p < 0.001). The changes in WHtR were small; however, they differed significantly (p < 0.001). Body weight increased more markedly in the non-IAO group (+ 4.55 kg) than in the IAO group (+ 3.00 kg, p < 0.05). Changes in other metabolic parameters were generally small and modestly greater in participants with IAO.
Fig. 2.
Longitudinal changes in cardiometabolic parameters from 2014 to 2024 according to baseline IAO status (*p < 0.05; **p < 0.001)
Factors associated with incident MetS
Table 4 presents the results of logistic regression analysis examining factors associated with incident metabolic syndrome over the 10-year follow-up. In the unadjusted model, older age (OR = 1.09; 95%CI: 1.02–1.18; p = 0.024), smoking (OR = 1.24; 95% CI:1.16–1.42; p = 0.012), alcohol consumption (OR = 1.25; 95%CI: 1.01–1.52; p = 0.034), higher BMI (OR = 1.21; 95%CI: 1.03–1.48; p = 0.043), and isolated abdominal obesity (OR = 1.77; 95% CI: 1.16–2.71; p = 0.009) were associated with a higher risk of developing metabolic syndrome.
Table 4.
Multivariable logistic regression analysis of the association between baseline isolated abdominal obesity and incident metabolic syndrome at 10-year follow-up (n = 431)
| Unadjusted model | Adjusted model | |||
|---|---|---|---|---|
| OR [95% CI] | p-value | OR [95% CI] | p-value | |
| Age (years) | 1.09 [1.02–1.18] | 0.024 | 1.00 [0.99–1.01] | 0.594 |
| Gender [ref. Female] | 0.98 [0.61–1.57] | 0.919 | 1.21 [0.71–3.03] | 0.711 |
| Smoking [ref. no] | 1.24 [1.16–1.42] | 0.012 | 0.98 [0.63–1.74] | 0.819 |
| Alcohol consumption [ref. no] | 1.25 [1.01–1.52] | 0.034 | 1.13 [0.98–1.33] | 0.075 |
| BMI (kg/m2) | 1.21 [1.03–1.48] | 0.043 | 1.09 [0.78–1.31] | 0.214 |
| Fasting glucose | 0.86 [0.72–1.94] | 0.047 | 0.89 [0.41–1.13] | 0.326 |
| IAO [ref. no] | 1.77 [1.16–2.71] | 0.009 | 1.34 [1.15–2.53] | 0.041 |
*Adjusted model included age, gender, smoking status, alcohol consumption, body mass index, fasting glucose, and systolic blood pressure
After adjustment for potential confounders, baseline isolated abdominal obesity remained significantly associated with incident metabolic syndrome (adjusted OR = 1.34; 95% CI: 1.15–2.53; p = 0.041). None of the other variables, including age, sex, smoking status, alcohol consumption, BMI, or fasting glucose, remained statistically significant in the adjusted model. These findings indicate that isolated abdominal obesity at baseline independently predicts the development of metabolic syndrome over a 10-year period.
Health related quality of life based on IAO status
Figure 3 shows SF-36 domains in 2024 for participants free of IAO over the 10-year follow-up and those with persistent IAO. Participants with persistent IAO had lower health-related quality-of-life scores across all SF-36 domains compared with those without IAO over the 10-year follow-up. Compared with the remitting IAO group, the persistent IAO group had lower scores in the physical function (83 vs. 71), general health (72 vs. 65), and mental health (71 vs. 67) domains. All of these differences were statistically significant (p < 0.05). Scores for the role emotional were lower in the chronic IAO group (65 vs. 71), but the difference was small.
Fig. 3.
Health-related quality of life (SF-36) in 2024 according to persistent isolated abdominal obesity status over the 10-year follow-up (*p < 0.05)
Discussion
This longitudinal cohort study investigated the long-term cardiometabolic consequences of isolated abdominal obesity and its association with health-related quality of life over a 10-year period. The findings demonstrate that individuals with isolated abdominal obesity at baseline exhibited a substantially less favorable cardiometabolic profile and had a higher risk of developing metabolic syndrome during follow-up. Moreover, persistent isolated abdominal obesity was associated with lower health-related quality-of-life scores across multiple SF-36 domains.
Isolated abdominal obesity and cardiometabolic risk
Participants with IAO had a cluster of cardiometabolic risk factors even in the presence of a normal or near-normal BMI. For example, they had significantly higher blood pressure, adverse lipid profiles, and higher fasting and postprandial glucose levels than non-IAO controls. These findings are consistent with growing evidence that fat deposition in central regions of the body is a stronger determinant of metabolic risk than fat deposited in peripheral sites [4]. Previous epidemiological studies have demonstrated that individuals with IAO have a significantly higher risk of cardiometabolic disease and mortality compared with individuals with normal BMI and normal waist circumference [11, 12].
Isolated abdominal obesity was identified as an independent predictor of metabolic syndrome in a cross-sectional study of a large cohort of obese adults [13]. According to the results of the current study, baseline IAO remained a strong and significant predictor of the metabolic syndrome after adjustment for potential confounding effects. In the MERLOT prospective cohort study, intra-abdominal fat area was a risk factor for MetS components, and the association remained significant after adjustment for BMI, even in non-obese individuals [14]. Isolated abdominal obesity was also confirmed to be a more reliable criterion for assessing the risk of developing MetS than BMI. The causal relationship between IAO and MetS may be mediated by visceral lipotoxicity-induced insulin resistance and atherogenesis [15].
Longitudinal changes in cardiometabolic parameters
The longitudinal analysis revealed that both groups experienced increases in body weight and waist circumference during the 10-year follow-up period. Interestingly, these increases were more pronounced among individuals without baseline IAO. This observation may reflect age-related shifts in fat distribution. Visceral fat progression is characterized by a redistribution from subcutaneous peripheral adipose toward visceral adipose tissue accumulation in the abdomen [16]. Several longitudinal studies have reported similar patterns, suggesting that aging is associated with gradual increases in abdominal fat even among individuals who initially present with normal adiposity profiles [17, 18].
Despite these increases in the non-IAO group, participants with baseline IAO maintained a generally less favorable metabolic profile throughout the follow-up period. This finding suggests that early visceral fat accumulation may initiate a long-term trajectory toward cardiometabolic dysfunction. Previous studies have similarly shown that early-life central adiposity is associated with persistent metabolic disturbances and increased risk of cardiometabolic diseases later in life [12]. A longitudinal cohort study in the US showed that a rapid increase in visceral fat area was associated with an increased risk of metabolic syndrome and type 2 diabetes over the next 15–25 years, driven by changes in circulating adipokines and increased intracellular lipid storage in the liver [19]. This trajectory suggests IAO may represent an early marker of long-term cardiometabolic deterioration and progression toward metabolic syndrome.
Isolated abdominal obesity and health-related quality of life
In addition to cardiometabolic risk factors, this study aimed to investigate the impact of persistent IAO on health-related quality of life. Compared with participants without IAO, those with persistent IAO had significantly lower SF-36 scores in physical functioning, general health perception, and mental health, indicating poorer physical and psychological well-being. These findings are consistent with previous studies indicating that obesity and IAO are associated with numerous adverse physical and psychological effects [20, 21].
Several factors have been proposed in the literature to explain the relationship between isolated abdominal obesity and lower HRQoL. For example, abdominal fat may restrict mobility and decrease physical performance, contributing to increased fatigue and weakness in activities of daily living and thereby decreasing physical function and vitality. People with IAO are at higher risk of developing chronic conditions such as hypertension and metabolic syndrome, all of which can affect self-reported general health [5, 21]. Obesity is also a highly stigmatised condition with negative social implications that can lead to adverse body image and low self-esteem, which are important components of mental health.
Previous studies found that chronic obesity is associated with a long-term decline in HRQoL in the physical domain [22, 23]. However, few studies have investigated the long-term effects of isolated abdominal obesity on HRQoL. This study contributes to the body of research showing that chronic IAO has long-term negative impacts not only on metabolic health but also on individuals’ well-being.
Strengths and limitations
This study has several strengths and limitations. To date, there are no longitudinal studies examining the relationship between isolated abdominal obesity and changes in cardiometabolic risk factors and quality of life measures in an obese population. This study is a major longitudinal study with a 10-year follow-up. A further strength of this study is the use of anthropometric and laboratory measurements that were carried out in a standardised manner. The impact of IAO on the quality of life was also assessed using validated patient-reported measures, including the SF-36.
However, this study has several limitations. Participants were recruited from one region, which may not be representative of other populations. In addition, several potentially important confounding factors, including physical activity, dietary patterns, and socioeconomic status, were not available in the dataset and therefore could not be included in the adjusted analyses. Residual confounding related to these factors cannot be excluded. Moreover, health-related quality of life was measured only at the follow-up examination, limiting the ability to assess longitudinal changes in HRQoL. Finally, although multivariable models adjusted for several confounders, residual confounding cannot be entirely excluded.
Conclusion
Isolated abdominal obesity is associated with unfavourable cardiometabolic changes and is an independent predictor of metabolic syndrome over a 10-year period. Temporal persistence of IAO was associated with poorer HRQoL across several dimensions, particularly those related to physical function and general health. Our study supports clinical and research recommendations to consider body fat distribution in relation to BMI and to intervene early to reduce central fat deposits and prevent further cardiometabolic risks.
Authors’ contributions
Conceptualization, G.N. and L.N.; methodology, G.N.; software, Y.S.; validation, G.A. and M.T.; formal analysis, D.A.; investigation, K.S.; resources, M.T.; data curation, Y.S.; writing—original draft preparation, L.N.; writing—review and editing, G.N. and G.I.; visualization, Y.S.; supervision, G.N.; project administration, G.N.; funding acquisition, G.N. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR24992814).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the local Ethics Committee of Khoja Akhmet Yassawi International Kazak-Turkish University (Protocol No. 30, May 30, 2024). Ethical guidelines were followed during the study in accordance with the Declaration of Helsinki. All participants provided written informed consent before the study began.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Reference
- 1.Geraci C, Geraci G, Buonauro A, Morello V, La Rocca F, Esposito R. Gaps in Current Cardiometabolic Risk Assessment: A Review Supporting the Development of the CORE Indicator Model. J Clin Med. 2026;15(2):617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sun Y, Lin X, Zou Z, Zhou Y, Liu A, Li X, et al. Association between visceral fat area and metabolic syndrome in individuals with normal body weight: insights from a Chinese health screening dataset. Lipids Health Dis. 2025;24(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Jiang K, Luan H, Pu X, Wang M, Yin J, Gong R. Association between visceral adiposity index and insulin resistance: a cross-sectional study based on US adults. Front Endocrinol. 2022;13:921067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Sung K, Lee SH, Lim S. Beyond body mass index: new criteria for a holistic approach to clinical obesity. Diabetes Metabolism J. 2025;49(2):165–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chen SH, Chen SC, Lai YP, Chen PH, Yeh KY. Abdominal obesity and hypertension are correlated with health-related quality of life in Taiwanese adults with metabolic syndrome. BMJ Open Diabetes Res Care. 2020;8(1). 10.1136/bmjdrc-2019-000947. [DOI] [PMC free article] [PubMed]
- 6.Leith A, Harrison L, da Rocha Fernandes JD, Hagelund LM, Lübker C, Capucci S. Impact of Body Mass Index and Comorbidities on Health-Related Quality of Life and Work Productivity in People With Overweight or Obesity: A Survey-Based Study in the United Kingdom. J Health Econ Outcomes Res. 2025;12(2):32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Rubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025;13(3):221–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Purnell JQ. Definitions, Classification, and Epidemiology of Obesity. In: Endotext. MDText.com, Inc., South Dartmouth (MA); 2000. PMID: 25905390. [PubMed]
- 9.Jenkinson C, Coulter A, Wright L. Short form 36 (SF36) health survey questionnaire: normative data for adults of working age. BMJ. 1993;306(6890):1437–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Chang S, Chang Y, Wu L. Gender differences in lifestyle and risk factors of metabolic syndrome: Do women have better health habits than men? J Clin Nurs. 2019;28(11–12):2225–34. [DOI] [PubMed] [Google Scholar]
- 11.Qin H, Hu G, Liang Z, Jin X, Chen X, Su B, et al. Association of longitudinal trajectories of general and abdominal adiposity during middle age with mental health and well-being in late life: A prospective analysis. Psychiatry Res. 2024;335:115863. [DOI] [PubMed] [Google Scholar]
- 12.Ahmed KY, Aychiluhm SB, Thapa S, Tegegne TK, Ketema DB, Kassa ZY, et al. Cardiometabolic outcomes among adults with abdominal obesity and normal body mass index. JAMA Netw open. 2025;8(10):e2537942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ahmed KY, Mondal UK, Huda MM, Aychiluhm SB, Newman J, Thapa S, et al. Normal-weight central obesity and cardiometabolic disorders among Aboriginal and Torres Strait Islander Australians. BMC Med. 2025;23(1):106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Nakao YM, Miyawaki T, Yasuno S, Nakao K, Tanaka S, Ida M et al. Intra-abdominal fat area is a predictor for new onset of individual components of metabolic syndrome: MEtabolic syndRome and abdominaL ObesiTy (MERLOT study). Proceedings of the Japan Academy, Series B. 2012;88(8):454–61. 10.2183/pjab.88.454. [DOI] [PMC free article] [PubMed]
- 15.Zhang X, Wang Y, Li Y, Gui J, Mei Y, Yang X, et al. Four-years change of BMI and waist circumference are associated with metabolic syndrome in middle-aged and elderly Chinese. Sci Rep. 2024;14(1):10220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Szulc P, Duboeuf F, Chapurlat R. Age-related changes in fat mass and distribution in men—the cross-sectional STRAMBO study. J Clin Densitometry. 2017;20(4):472–9. [DOI] [PubMed] [Google Scholar]
- 17.Kuk JL, Saunders TJ, Davidson LE, Ross R. Age-related changes in total and regional fat distribution. Ageing Res Rev. 2009;8(4):339–48. [DOI] [PubMed] [Google Scholar]
- 18.Baarts RB, Jensen MR, Hansen OM, Haddock B, Prescott E, Hovind P, et al. Age-and sex‐specific changes in visceral fat mass throughout the life‐span. Obesity. 2023;31(7):1953–61. [DOI] [PubMed] [Google Scholar]
- 19.Reis JP, Hankinson AL, Loria CM, Lewis CE, Powell-Wiley T, Wei GS, et al. Duration of abdominal obesity beginning in young adulthood and incident diabetes through middle age: the CARDIA study. Diabetes Care. 2013;36(5):1241–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Bosomworth NJ. Normal-weight central obesity: Unique hazard of the toxic waist. Can Fam Physician. 2019;65(6):399–408. [PMC free article] [PubMed] [Google Scholar]
- 21.Jayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar S. Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies. BMJ. 2020;370. 10.1136/bmj.m3324. [DOI] [PMC free article] [PubMed]
- 22.Jepsen R, Aadland E, Robertson L, Kolotkin RL, Andersen JR, Natvig GK. Physical activity and quality of life in severely obese adults during a two-year lifestyle intervention programme. J Obes. 2015;2015(1):314194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jepsen R, Aadland E, Andersen JR, Natvig GK. Associations between physical activity and quality of life outcomes in adults with severe obesity: a cross-sectional study prior to the beginning of a lifestyle intervention. Health Qual Life Outcomes. 2013;11(1):187. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.