Abstract
Background
Frailty can lead to an increase of mortality risk in chronic obstructive pulmonary disease (COPD). Anthropometric indices, such as body roundness index (BRI), A body shape index (ABSI), waist-to-weight Index (WWI), waist circumference (WC), and body mass index (BMI), were significantly positively associated with worse frailty at baseline. However, the associations between anthropometric indices and change of frailty index (FI) in long-term follow-up remained unknown.
Methods
Patients with COPD (pre-bronchodilator spirometry-defined forced expiratory volume in one second/ forced volume vital capacity < 0.70) from the English Longitudinal Study of Ageing from wave 2 were enrolled in this study. Anthropometric indices were measured in wave 2 (2004–2005), and a selection of 32 items was made to construct the FI index collected from wave 2 to 9. Three anthropometric indices group was categorized by the tertile of anthropometric indices: Q1 (low), Q2 (medium), Q3 (high). Pearson correlation analysis were used to explore association between anthropometric indices and baseline FI. We performed a linear mixed-effects model to explore an associations between anthropometric indices and change of FI at a 14-year follow-up.
Results
A total of 1,834 patients with COPD were enrolled in the study. Compared to the low anthropometric indices group, the high anthropometric indices group exhibited worse lung function and symptom scores at baseline. Anthropometric indices were significantly positively associated with worse FI at baseline. In a 14-year follow-up. We found that anthropometric indices were associated with faster decline in FI and the high anthropometric indices group exhibited faster decline in FI than those with the low anthropometric indices group.
Conclusion
Our findings showed that high anthropometric indices group experienced an increased risk of decline in FI, suggesting that higher adiposity may have a potential worse effect on frailty progression in patients with COPD over the long term.
Keywords: Anthropometric indices, Chronic obstructive pulmonary disease, Frailty, Spirometry, The english longitudinal study of ageing
Introduction
Chronic Obstructive Pulmonary Disease (COPD) is a common, preventable, and treatable chronic respiratory condition characterized by persistent respiratory symptoms and airflow limitation, and it represents a leading cause of morbidity and mortality worldwide [1]. As the disease progresses, COPD is often accompanied by various systemic manifestations, among which frailty, as a significant geriatric syndrome, has garnered increasing attention [2–4]. Frailty reflects a decline in physiological reserve and multisystem dysfunction, and its prevalence is notably higher in COPD patients compared with individuals without COPD [5]. It is strongly associated with pulmonary rehabilitation, pulmonary function decline, an increased risks of incident hospitalization and longer hospital stays, poor quality of life, and mortality [5–9]. Therefore, identifying modifiable factors associated with the onset and progression of frailty in COPD patients is crucial for early intervention and improving prognosis.
Anthropometric indices, such as body mass index (BMI) and waist circumference (WC), are commonly used simple tools for assessing body composition and nutritional status. In recent years, novel anthropometric indices such as the Body Roundness Index (BRI), A Body Shape Index (ABSI), waist-to-height ratio (WHtR) and the Waist-to-Weight Index (WWI) have gained prominence due to their ability to more accurately reflect body fat distribution and central obesity [10–14]. Cross-sectional studies have confirmed that these indices are significantly positively associated with the baseline frailty, meaning that higher index values typically indicate more severe frailty [15]. However, longitudinal evidence regarding the association between these anthropometric indices and frailty index (FI) progression over long-term follow-up in COPD patients is currently lacking. Given that both COPD and frailty are dynamically evolving clinical states, investigating their long-term association is essential for understanding the natural history of the disease and formulating long-term management strategies.
Utilizing data from the English Longitudinal Study of Ageing (ELSA), this study aims to thoroughly examine the longitudinal associations between various baseline anthropometric indices (including BRI, ABSI, WWI, WC, WHtR and BMI) and FI progression over a 14-year follow-up period in patients with COPD. We hypothesize that although higher anthropometric indices are associated with more severe frailty cross-sectionally, they may exert a different influence on the rate of frailty progression during long-term follow-up. The findings of this study are expected to provide new longitudinal epidemiological evidence for nutritional support, weight management, and frailty intervention in COPD patients.
Materials and methods
Study design and population
The English Longitudinal Study of Ageing (ELSA) is a nationally representative panel study focusing on the English population aged 50 and above. Established to chronicle the ageing process in 21st-century England, the study began in 2002 with follow-up assessments conducted biennially [16]. Data were gathered through computer-assisted personal interviews and self-completion questionnaires, supplemented by nurse visits for biomarker collection every four years. Spirometry tests were administered during Wave 2 (2004–2005), and data from wave 2 were used as the baseline for participant inclusion in the present analysis. Patients were excluded if they met any of the following criteria: (1) incomplete spirometry tests; (2) missing FI data at Wave 2; (3) missing data for any anthropometric indices (BRI, ABSI, WWI, WHtR, WC, BMI). This study was conducted according to the principles of the Declaration of Helsinki. All participants provided informed consent, and ELSA was approved by the London Multicenter Research Ethics Committee (MREC/01/2/91).
Spirometry tests
According to the study protocol [16], pre-bronchodilator spirometry was conducted using a Vitalograph Escort spirometer (Buckingham, UK) at wave 2 as baseline. Three maneuvers were attempted for each participant, with the quality of technique (satisfactory or not) being documented. FEV1/FVC ratio was obtained from the highest satisfactory FEV1 value and the highest satisfactory FVC value corresponding obtained from any single attempt. This suggestion was consistent with the ATS/ERS guidance [17]. COPD was defined as pre-bronchodilator FEV1/FVC < 0.70. The predicted value of lung function used Global Lung Function Initiative (GLI) 2022 [18]. COPD was graded according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria as follows: GOLD 1: FEV1% pred ≥ 80%; GOLD 2: 50% ≤ FEV1% pred < 80%; GOLD 3: 30% ≤ FEV1% pred < 50%; GOLD 4: FEV1% pred < 30%.
Definition of anthropometric indices
Physical indicators (Height, body weight, and WC) were measured by professional health technicians at wave 2. Anthropometric indices, such as BRI, ABSI, WWI, WC, WHtR and BMI, were measured in detail as follows [10–12]:
BMI= weight (kg) / height² (m²)
WWI= WC / (Weight^ (1/3) × Height^ (1/2))
BRI= 364.2 - 365.5 × √ (1 - (Waist Circumference / (2π)) ² / (0.5 × Height) ²)
ABSI = WC (m) / (BMI^(2/3) * Height(m)^(1/2)).
WHtR = WC (m)/ height (m)
Assessment of FI
In this study, the FI was constructed following a standardized procedure and encompasses major health domains. A set of 32 items was selected to build the FI [19], including variables such as comorbidities, physical function, disabilities, depression, and cognitive status. This FI has been validated in the CHARLS database as well as in other cohorts [20, 21]. With the exception of the 32nd item, each variable was dichotomized into 0 or 1 based on predefined cutoffs, where 0 indicates the absence of a deficit and 1 indicates its presence. The 32nd item is a continuous measure ranging from 0 to 1, with higher values reflecting poorer cognitive performance. For each participant, the 32-item FI score was calculated as the sum of existing health deficits divided by 32. The FI index was collected from wave 2 to wave 9 at a 14-year follow-up.
Covariates
In our analysis, a series of demographic variables were collected, including gender (male/female), age, education (Below high school/College or above/High school/Other), marital status (married or partnered/Never married/Separated/divorced/Widowed), and current residence (urban/rural). Health behaviors: smoking status (current smokers/ever smokers/never smokers), alcohol consumption status (ever drinking vs. never drinking). For clinical variables, the symptoms of wheezing and phlegm were determined by the questions“whether had attacks of wheezing or whistling in your chest during the last 12 months” and “whether usually brought up phlegm from your chest in the morning, during the day or at night in winter”. The modified Medical Research Council (mMRC) dyspnea scale was used to assess the severity of breathlessness and dyspnea was defined as the mMRC Grade ≥ 2 [22]. Psychological wellbeing was assessed using the CASP-19 quality of life questionnaire that consists of 19 items covering four theoretical domains, control, autonomy, self-realization and pleasure [23]. The history of hypertension, diabetes, stroke, high cholesterol, and congestive heart failure was based on self-reported physician diagnoses [24].
Statistical analysis
Statistical analysis of our study was performed using R software (Version 4.4.3). Continuous variables following a normal distribution were expressed as mean (standard deviation), while those with a skewed distribution were summarized as median (interquartile range). Categorical variables were presented as number (percentage). Three anthropometric indices group was categorized by the tertile of anthropometric indices: Q1 (low), Q2 (medium), Q3 (high). Differences in symptom score (mMRC scores and CASP-19 scores) across the anthropometric indices categories were assessed using one-way ANOVA or the Kruskal–Wallis test for continuous variables, as appropriate, with post-hoc pairwise comparisons conducted using the Tukey method for normally distributed data. We performed Pearson correlation analysis to explore an association between anthropometric indices and baseline FI.
In long-term follow-up, we performed a linear mixed-effects model to explore associations between anthropometric indices and change of FI index at a 14-year follow-up. We explored association of continuous anthropometric indices and anthropometric indices group with change of FI index. The adjusted covariates included age, race, sex, marital status, education, smoking status, drinking status, and baseline FEV1. The primary purpose of adjusting for baseline FEV1 is to control for disease severity, which is the strongest potential confounding factor, thereby ensuring that the observed association between anthropometric indices and changes in frailty reflects, as much as possible, the true relationship independent of baseline lung function status. All the P-values were two-sided. The statistical significance was defined as the P < 0.05.
Results
Baseline characteristics
Figure 1 shows flowchart of our study. 1045 patients were excluded due to lack of information about anthropometric indices, lung function indices and FI indices at wave 2 as baseline. 1834 patients with COPD were included in final data analysis. Table 1 illustrates characteristics of the participants. The mean age of the participants was 67.49 (SD 9.38) years. The cohort was nearly equally distributed by sex, with 919 (50.1%) males and 915 (49.9%) females. 901 (49.1%) were classified as ever smokers, while 409 (22.3%) were current smokers. A mean FEV1 of patients with COPD was 1.88 (SD 0.86) L. Respiratory symptoms were present as follows: phlegm (26.8%), wheeze (25.6%), and dyspnea (14.0%). The mean modified Medical Research Council (mMRC) dyspnea score was 0.59 (SD 0.94).
Fig. 1.
Flowchart of our study
Table 1.
Baseline clinical characteristics of patients with chronic obstructive pulmonary disease
| Value | Overall |
|---|---|
| Number | 1,834 |
| Age, year (mean (SD)) | 67.49 (9.38) |
| Sex, n (%) | |
| Male | 919 (50.1) |
| Female | 915 (49.9) |
| Race (%) | |
| White | 1806 (98.5) |
| Others race | 28 (1.5) |
| Education, n (%) | |
| Below high school | 829 (45.2) |
| College or above | 532 (29.0) |
| High school | 314 (17.1) |
| Other | 159 (8.7) |
| Marital status, n (%) | |
| Married or partnered | 1168 (65.3) |
| Never married | 93 (5.2) |
| Separated/divorced/Widowed | 529 (29.6) |
| Smoking status, n (%) | |
| current smokers | 409 (22.3) |
| ever smokers | 901 (49.1) |
| never smokers | 524 (28.6) |
| Drinking status, n (%) | |
| Ever drinkers | 1453 (88.7) |
| Never drinkers | 185 (11.3) |
| Hypertension, n (%) | |
| No | 1075 (58.6) |
| Yes | 759 (41.4) |
| Diabetes, n (%) | |
| No | 1718 (93.7) |
| Yes | 116 (6.3) |
| Stroke, n (%) | |
| No | 1756 (95.7) |
| Yes | 78 (4.3) |
| High cholesterol, n (%) | |
| No | 1505 (82.2) |
| Yes | 327 (17.8) |
| Congestive heart failure, n (%) | |
| No | 1824 (99.5) |
| Yes | 10 (0.5) |
| CASP-19 (mean (SD)) | 42.75 (8.60) |
| mMRC score (mean (SD)) | 0.59 (0.94) |
| phlegm (%) | |
| No | 1341 (73.2) |
| Yes | 492 (26.8) |
| wheeze (%) | |
| No | 1363 (74.4) |
| Yes | 470 (25.6) |
| Dyspnea (%) | |
| No | 1456 (86.0) |
| Yes | 237 (14.0) |
| FEV1, L (mean (SD)) | 1.88 (0.86) |
| FVC, L (mean (SD)) | 3.35 (1.26) |
| FEV1/FVC, % (mean (SD)) | 56.24 (14.07) |
| PEF, mL (mean (SD)) | 319.88 (145.07) |
| COPD grade, n (%) | |
| GOLD1 | 838 (45.7) |
| GOLD2 | 588 (32.1) |
| GOLD3 | 287 (15.6) |
| GOLD4 | 121 (6.6) |
| BRI (mean (SD)) | 4.91 (1.62) |
| WC (mean (SD)) | 94.85 (13.27) |
| WWI (mean (SD)) | 11.00 (0.74) |
| BMI (mean (SD)) | 27.11 (4.76) |
| ABSI (mean (SD)) | 0.08 (0.01) |
| WHtR (mean (SD)) | 0.57 (0.07) |
Data are presented as No. (%) and mean ± SD. NS normal spirometry
COPD Chronic Obstructive Pulmonary disease, BRI Body Roundness Index, ABSI A body shape index, WWI Waist-to-Weight Index, WC Waist Circumference, BMI Body Mass Index, WHTR the Waist-to-Height Ratio, FEV1, forced expiratory volume in one second, FVC Forced Volume Vital Capacity, PEF Peak Expiratory Flow, GOLD Global Initiative for Chronic Obstructive Lung Disease
Anthropometric indices and baseline FI
Figure 2 presents the correlation analysis between six anthropometric indices (BRI, WWI, WHtR, BMI, ABSI, WC) and the FI. All scatter plots demonstrate statistically significant positive correlations.
Fig. 2.
The correlation analysis between six anthropometric indices (BRI, WWI, WHtR, BMI, ABSI, WC) and the frailty index
Anthropometric indices group and lung function, symptom score
Figure 3 shows difference in lung function among anthropometric indices group. High BRI and WHTR group had worse lung function (FEV1%pred, FVC%pred) than low group, but no differences were noted between medium and low group assessed by BRI and WHTR. High WWI and ABSI group had worse lung function than other groups. However, no differences in FEV1%pred were observed among BMI group. In symptom scores, all high anthropometric indices had higher mMRC and CASP-19 scores than other groups (Fig. 4).
Fig. 3.
Difference in lung function among anthropometric indices group
Fig. 4.
Difference in symptom scores among anthropometric indices group, CASP mean CASP-19
Anthropometric indices and decline in FI
We performed a linear mixed-effects model to explore associations between anthropometric indices and decline in FI at a 14-year follow-up. Our findings showed that faster decline in FI was associated with an increases of continuous BRI (β: −0.00148, 95%CI −0.00220 to −0.00076, P < 0.001), continuous ABSI (β: −0.45372, 95%CI −0.665 to −0.243, P < 0.001), continuous WWI (β: −0.00541, 95%CI −0.00698 to −0.00385, P < 0.001), and continuous WHtR (β: −0.03117, 95%CI −0.04710 to −0.01520, P < 0.001). After adjusting for age, race, sex, marital status, education, smoking status, drinking status, and baseline FEV1, similarity results were observed. Then, we analyzed associations between anthropometric indices group and decline in FI. High BRI group (β: −0.00557, 95%CI −0.00842 to −0.00271, P < 0.001), ABSI group (β: −0.00503, 95%CI −0.00789 to −0.00217, P < 0.001), WWI group (β: −0.00831, 95%CI −0.01120 to −0.00547, P < 0.001), and WHTR group (β: −0.00557, 95%CI −0.00842 to −0.00271, P < 0.001) were associated with faster decline in FI, but no associations between decline in FI and WC, BMI. After adjusting for age, race, sex, marital status, education, smoking status, drinking status, and baseline FEV1, similarity results were observed. (Table 2).
Table 2.
Anthropometric indices and decline in FI in patients with chronic obstructive pulmonary disease
| Unadjusted Coefficient | p-value | Adjusted Coefficient | p-value | |
|---|---|---|---|---|
| Continuous BRI | −0.00148 (−0.00220, −0.00076) | < 0.001 | −0.00159 (−0.00235 to −0.00083) | < 0.001 |
| Q1 | Reference | - | Reference | - |
| Q2 | −0.00029 (−0.00314 to −0.00257) | 0.843 | 0.00082(−0.00215 to 0.00379) | 0.590 |
| Q3 | −0.00557 (−0.00842 to −0.00271) | < 0.001 | −0.00544(−0.00845 to −0.00245) | < 0.001 |
| Continuous BMI | −0.00008 (−0.00034 to 0.00017) | 0.523 | −0.00015 (−0.00041 to 0.00011) | 0.253 |
| Q1 | Reference | - | Reference | - |
| Q2 | 0.00089 (−0.00198 to 0.00376) | 0.543 | 0.00125 (−0.00175 to 0.00424) | 0.416 |
| Q3 | −0.00151 (−0.00437 to 0.00136) | 0.303 | −0.00150 (−0.00452 to 0.00151) | 0.329 |
| Continuous ABSI | −0.45372 (−0.665 to −0.243) | < 0.001 | −0.45971 (−0.683 to −0.236) | < 0.001 |
| Q1 | Reference | - | Reference | - |
| Q2 | −0.00056 (−0.00342 to 0.00230) | 0.700 | −0.00051 (−0.00349 to 0.00247) | 0.736 |
| Q3 | −0.00503 (−0.00789 to −0.00217) | < 0.001 | −0.00557 (−0.00856 to −0.00258) | < 0.001 |
| Continuous WC | −0.00005 (−0.00014 to 0.00004) | 0.2457 | −0.00009 (−0.00018 to 0.000005) | 0.062 |
| Q1 | Reference | - | Reference | - |
| Q2 | 0.00168 (−0.00119 to 0.00454) | 0.251 | 0.00265 (−0.00034 to 0.00564) | 0.083 |
| Q3 | −0.00147 (−0.00434 to 0.00139) | 0.314 | −0.00273 (−0.00572 to 0.00027) | 0.075 |
| Continuous WWI | −0.00541 (−0.00698 to −0.00385) | < 0.001 | −0.00524 (−0.00692 to −0.00356) | < 0.001 |
| Q1 | Reference | - | Reference | - |
| Q2 | −0.00269 (−0.00553 to 0.00015) | 0.064 | −0.00254 (−0.00550 to 0.00042) | 0.093 |
| Q3 | −0.00831 (−0.01120 to −0.00547) | < 0.001 | −0.00817 (−0.01110 to −0.00519) | < 0.001 |
| Continuous WHtR | −0.03117 (−0.04710, −0.01520) | < 0.001 | −0.03330 (−0.05020 to −0.01640) | < 0.001 |
| Q1 | Reference | - | Reference | - |
| Q2 | −0.00029 (−0.00314 to 0.00257) | 0.843 | 0.00082 (−0.00215 to 0.00379) | 0.590 |
| Q3 | −0.00557 (−0.00842 to −0.00271) | < 0.001 | −0.00545 (−0.00845 to −0.00245) | < 0.001 |
BRI Body Roundness Index, ABSI A Body Shape Index, WWI Waist-to-Weight Index, WC Waist Circumference, BMI Body Mass index, WHtR the Waist-to-Height Ratio, bold P mean <0.05
The adjusted covariates included age, race, sex, marital status, education, smoking status, drinking status, and baseline FEV1
Discussion
This longitudinal study investigated the association between various anthropometric indices and frailty progression in a large cohort of patients with COPD over a 14-year follow-up period. First, all six anthropometric indices showed significant positive correlations with the baseline FI. Second, participants classified into the high groups of certain indices (particularly BRI, WWI, WHtR, and ABSI) exhibited poorer lung function and more severe respiratory symptoms at baseline compared to those in lower groups. Third, and most importantly, longitudinal analysis revealed that higher baseline values of BRI, ABSI, WWI, and WHtR were significantly associated with a faster rate of FI increase over time, indicating an accelerated decline in health status. These associations remained significant after adjusting for a comprehensive set of potential confounders, including sociodemographic factors, lifestyle habits, and baseline lung function. In contrast, traditional indices like BMI and WC did not show a significant association with the progression of frailty.
The baseline analysis further supports the clinical relevance of these indices. Patients in the high BRI, WHtR, WWI, and ABSI groups had significantly worse lung function (FEV1%pred and FVC%pred) and higher symptom burden (mMRC and CASP-19 scores). This finding indicates that adverse body composition profiles are linked to greater disease severity at baseline. The longitudinal finding that these same indices predict a steeper trajectory of frailty suggests a vicious cycle: adverse body composition may contribute to more severe disease expression, which in turn accelerates the aging process and the accumulation of health deficits, leading to functional decline.
The positive cross-sectional correlations between anthropometric indices and baseline FI align with existing literature linking body composition to health status [15, 25–27]. These findings suggested that both overall adiposity and fat distribution patterns are interrelated with the multidimensional burden of frailty, even at a single time point. However, the longitudinal findings provide deeper insights. The significant association of indices like BRI, ABSI, WWI, and WHtR with an accelerated decline in FI underscores their potential utility as prognostic markers for health deterioration in COPD. These indices arguably offer a more nuanced reflection of body composition than BMI [28–30]. BRI and WHtR are indicators of central obesity, which is strongly associated with visceral adiposity and metabolic dysfunction [31, 32]. WWI, which adjusts waist circumference for weight, is proposed to reflect body fat and muscle mass simultaneously [33]. ABSI was specifically designed to quantify abdominal adiposity relative to height and weight, independent of BMI [34]. The fact that these indices predicted frailty progression, while BMI and WC did not, strongly suggests that the distribution of body fat—particularly abdominal or visceral fat—may be a more critical determinant of functional decline than overall obesity or simple waist measurement in the COPD population. This is biologically plausible, as visceral adipose tissue is metabolically active and secretes pro-inflammatory cytokines that can exacerbate systemic inflammation, a key driver of frailty and multimorbidity [35, 36]. This chronic inflammatory state may synergize with the pathophysiological processes of COPD, leading to a more rapid accumulation of health deficits.
The lack of a significant association between BMI groups and FEV1%pred at baseline, and no association of BMI and WC with frailty progression, is particularly noteworthy. It reinforces the concept of the “obesity paradox” in respiratory medicine, where a higher BMI has sometimes been associated with better survival in COPD, potentially due to nutritional reserve [37, 38]. Our results suggest that this paradox may mask underlying risks. A patient with a normal or overweight BMI could still have a high-risk body composition profile that would be captured by indices like WWI or ABSI but missed by BMI alone. Due to its inability to accurately measure body fat mass or distribution, reliance on BMI alone frequently fails to capture true health risks, resulting in misclassification [39, 40]. Therefore, relying solely on BMI may lead to an incomplete assessment of risk for functional decline in this patient group.
Several limitations of this study should be considered. First, while we adjusted for a wide range of covariates, the possibility of residual confounding from unmeasured factors cannot be entirely ruled out. Secondly, the generalizability of our findings may be limited. The study cohort consisted of English participants aged 50 and above, primarily of White ethnicity. Therefore, our results require further validation in more diverse populations, including different ethnicities, younger age groups with COPD, and healthcare settings outside the UK. Thirdly, we assessed the anthropometric indices only at baseline; future studies with repeated measurements of body composition are needed to understand how dynamic changes in these indices over time influence frailty trajectories. Finally, the definition of COPD was based on pre-bronchodilator pulmonary function measurements. Although this aligns with common practices in large epidemiological cohorts, it differs from the clinical gold standard (post-bronchodilator FEV1/FVC < 0.70). This discrepancy may lead to misclassification of COPD patients, such as potentially including some individuals with pronounced airway reversibility or missing some with mild conditions.
In conclusion, this study demonstrates that specific anthropometric indices—BRI, WWI, WHtR, and ABSI—are not only cross-sectionally associated with frailty and disease severity but are also significant longitudinal predictors of an accelerated rate of frailty progression in patients with COPD. These indices, which reflect central obesity and body composition, appear to be superior to traditional measures like BMI and WC for identifying patients at risk for faster health status decline. These findings highlight the importance of moving beyond simple weight and height measurements in the clinical management of COPD. Incorporating these easily calculable indices into routine assessment could help identify a high-risk subgroup of COPD patients who may benefit from targeted interventions, such as personalized nutritional support and physical activity programs designed to improve body composition, with the ultimate goal of mitigating frailty progression and preserving functional independence.
Acknowledgements
Thanks to ELSA cohort participants and subjects for their contributions.
Artificial intelligence involvement
Non artificial intelligence involvement.
Abbreviations
- COPD
Chronic obstructive pulmonary disease
- BRI
Body roundness index
- ABSI
A body shape index
- WWI
Waist-to-weight Index
- WC
Waist circumference
- BMI
Body mass index
- FEV1
Forced expiratory volume in one second
- FVC
Forced volume vital capacity
- FI
Frailty index
Authors’ contributions
K.J.W conceived and designed the study. J.H.L. K.H.X had written manuscript, B.Y.L, B.C.L, R.H, J.H.L, and Z.C had collected data, All authors read and approved the final manuscript.
Funding
None.
Data availability
The datasets analysed are available upon reasonable request and with permission of the corresponding authors.
Declarations
Ethics approval and consent to participate
This study was conducted according to the principles of the Declaration of Helsinki. All participants provided informed consent, and ELSA was approved by the London Multicenter Research Ethics Committee (MREC/01/2/91).
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.
Jiahui Lei and Keheng Xiang contributed equally to this work.
References
- 1.Papaioannou AI, Hillas G, Loukides S, Vassilakopoulos T. Mortality prevention as the centre of COPD management. ERJ Open Res. 2024. 10.1183/23120541.00850-2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Yang M, Liu Y, Zhao Y, Wang Z, He J, Wang Y, et al. Association of frailty with Patient-Report outcomes and major clinical determinants in patients with acute exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2024;19:907–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Feng Z, Li G, He Q, Sun N, Li T, Han Q, et al. Associations of socioeconomic status and phenotypic frailty with incident COPD: findings from UK biobank participants. Chest. 2025;167(5):1321–32. [DOI] [PubMed] [Google Scholar]
- 4.Marengoni A, Vetrano DL, Manes-Gravina E, Bernabei R, Onder G, Palmer K. The relationship between COPD and frailty: a systematic review and meta-analysis of observational studies. Chest. 2018;154(1):21–40. [DOI] [PubMed] [Google Scholar]
- 5.Lee SY, Nyunt MSZ, Gao Q, Gwee X, Chua DQL, Yap KB, et al. Co-occurrence of physical frailty and COPD and association with disability and mortality: Singapore longitudinal ageing study. Chest. 2022;161(5):1225–38. [DOI] [PubMed] [Google Scholar]
- 6.Maddocks M, Kon SS, Canavan JL, Jones SE, Nolan CM, Labey A, et al. Physical frailty and pulmonary rehabilitation in COPD: a prospective cohort study. Thorax. 2016;71(11):988–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kennedy CC, Novotny PJ, LeBrasseur NK, Wise RA, Sciurba FC, Benzo RP. Frailty and clinical outcomes in chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2019;16(2):217–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hanlon P, Guo X, McGhee E, Lewsey J, McAllister D, Mair FS. Systematic review and meta-analysis of prevalence, trajectories, and clinical outcomes for frailty in COPD. NPJ Prim Care Respir Med. 2023;33(1):1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Weng SC, Hsu CY, Wu MF, Lee WH, Lin SY. The impact of frailty status on pulmonary function and mortality in older patients with chronic obstructive pulmonary disease. J Nutr Health Aging. 2023;27(11):987–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Shafran I, Krakauer NY, Krakauer JC, Goshen A, Gerber Y. The predictive ability of ABSI compared to BMI for mortality and frailty among older adults. Front Nutr. 2024;11:1305330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Pan X, Liu F, Fan J, Guo Q, Guo M, Chen Y, et al. Association of body roundness index and a body shape index with obstructive sleep apnea: insights from NHANES 2015–2018 data. Front Nutr. 2024;11:1492673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Xu C, Wu X. Association between four anthropometric indices with age-related macular degeneration from NHANES 2005–2008. Lipids Health Dis. 2025;24(1):11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ichikawa T, Okada H, Nakajima H, Yoneda M, Ushigome E, Hamaguchi M, et al. Anthropometric indices for predicting incident cardiovascular disease: a 13-year follow-up study in a Japanese population. Am J Clin Nutr. 2025. 10.1016/j.ajcnut.2025.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Eng PC, Teo AE, Leow MKS, Tai ES, Khoo CM. Body roundness index (BRI) and obesity-related anthropometrics: relationship to visceral adiposity, insulin sensitivity index and cardiometabolic risk. Diabetes Obes Metab. 2025;27(10):5554–65. [DOI] [PubMed] [Google Scholar]
- 15.Jayanama K, Theou O, Godin J, Mayo A, Cahill L, Rockwood K. Relationship of body mass index with frailty and all-cause mortality among middle-aged and older adults. BMC Med. 2022;20(1):404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Steptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: the English longitudinal study of ageing. Int J Epidemiol. 2013;42(6):1640–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al. Standardization of spirometry 2019 update. An official American Thoracic Society and European Respiratory Society technical statement. Am J Respir Crit Care Med. 2019;200(8):e70–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Bowerman C, Bhakta NR, Brazzale D, Cooper BR, Cooper J, Gochicoa-Rangel L, et al. A Race-neutral approach to the interpretation of lung function measurements. Am J Respir Crit Care Med. 2023;207(6):768–74. [DOI] [PubMed] [Google Scholar]
- 19.Sun L, Deng G, Lu X, Xie X, Kang L, Sun T, et al. The association between continuing work after retirement and the incidence of frailty: evidence from the China health and retirement longitudinal study. J Nutr Health Aging. 2024;28(12):100398. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.He D, Wang Z, Li J, Yu K, He Y, He X, et al. Changes in frailty and incident cardiovascular disease in three prospective cohorts. Eur Heart J. 2024;45(12):1058–68. [DOI] [PubMed] [Google Scholar]
- 21.He D, Qiu Y, Yan M, Zhou T, Cheng Z, Li J, et al. Associations of metabolic heterogeneity of obesity with frailty progression: results from two prospective cohorts. J Cachexia Sarcopenia Muscle. 2023;14(1):632–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.He D, Sun Y, Gao M, Wu Q, Cheng Z, Li J, et al. Different risks of mortality and longitudinal transition trajectories in new potential subtypes of the preserved ratio impaired spirometry: evidence from the english longitudinal study of aging. Front Med (Lausanne). 2021;8:755855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hyde M, Wiggins RD, Higgs P, Blane DB. A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19). Aging Ment Health. 2003;7(3):186–94. [DOI] [PubMed] [Google Scholar]
- 24.Li X, Lu L, Chen Y, Liu B, Liu B, Tian H, et al. Association of atherogenic index of plasma trajectory with the incidence of cardiovascular disease over a 12-year follow-up: findings from the ELSA cohort study. Cardiovasc Diabetol. 2025;24(1):124. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Jia S, Huo X, Sun L, Yao Y, Chen X. The association between the weight-adjusted-waist index and frailty in US older adults: a cross-sectional study of NHANES 2007–2018. Front Endocrinol (Lausanne). 2024;15:1362194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Liu S, Pan X, Chen B, Zeng D, Xu S, Li R, et al. Association between healthy lifestyle and frailty in adults and mediating role of weight-adjusted waist index: results from NHANES. BMC Geriatr. 2024;24(1):757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Cataltepe E, Ceker E, Fadiloglu A, Gungor F, Karakurt N, Ulger Z, et al. Weight-adjusted waist index: an anthropometric measure for frailty in older adults. Eur Geriatr Med. 2024;15(6):1731–8. [DOI] [PubMed] [Google Scholar]
- 28.Dhana K, Kavousi M, Ikram MA, Tiemeier HW, Hofman A, Franco OH. Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality. J Epidemiol Community Health. 2016;70(1):90–6. [DOI] [PubMed] [Google Scholar]
- 29.Zhang C, Xiao L, Lu H, Deng H. Predictive efficacy of waist-related indices for sarcopenic obesity incidence in Chinese older adults: a nationwide cohort study. BMC Geriatr. 2025;25(1):637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chen Y, Wang C, Sun Q, Ye Q, Zhou H, Qin Z, et al. Comparison of novel and traditional anthropometric indices in Eastern-China adults: which is the best indicator of the metabolically obese normal weight phenotype? BMC Public Health. 2024;24(1):2192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86. [DOI] [PubMed] [Google Scholar]
- 32.Rico-Martín S, Calderón-García JF, Sánchez-Rey P, Franco-Antonio C, Martínez Alvarez M, Sánchez Muñoz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: a systematic review and meta-analysis. Obes Rev. 2020;21(7):e13023. [DOI] [PubMed] [Google Scholar]
- 33.Kim KJ, Son S, Kim KJ, Kim SG, Kim NH. Weight-adjusted waist as an integrated index for fat, muscle and bone health in adults. J Cachexia Sarcopenia Muscle. 2023;14(5):2196–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Kajikawa M, Maruhashi T, Kishimoto S, Yamaji T, Harada T, Saito Y, et al. A body shape index as a simple anthropometric marker of abdominal obesity and risk of cardiovascular events. J Clin Endocrinol Metab. 2024;109(12):3272–81. [DOI] [PubMed] [Google Scholar]
- 35.Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Trayhurn P. Hypoxia and adipose tissue function and dysfunction in obesity. Physiol Rev. 2013;93(1):1–21. [DOI] [PubMed] [Google Scholar]
- 37.Tenda ED, Henrina J, Setiadharma A, Felix I, Yulianti M, Pitoyo CW, et al. The impact of body mass index on mortality in COPD: an updated dose-response meta-analysis. Eur Respir Rev. 2024. 10.1183/16000617.0261-2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Sun Y, Milne S, Jaw JE, Yang CX, Xu F, Li X, et al. BMI is associated with FEV(1) decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials. Respir Res. 2019;20(1):236. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Baumgartner RN, Heymsfield SB, Roche AF. Human body composition and the epidemiology of chronic disease. Obes Res. 1995;3(1):73–95. [DOI] [PubMed] [Google Scholar]
- 40.Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr Today. 2015;50(3):117–28. [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 analysed are available upon reasonable request and with permission of the corresponding authors.