ABSTRACT This study utilized NHANES data from 2007 to 2018 to investigate the correlation between frailty and the Conicity Index (CI) in individuals aged 60 and above in the United States.

The study used NHANES data from 2007 to 2018.CI was calculated as CI = wc / [0.109 × sqrt(bw / Height)]. Frailty was assessed by the frailty index (≥ 0.25). Weighted multivariate logistic

regression analysis, subgroup analyses, and interaction tests were used to investigate the connection between CI and the prevalence of frailty. Generalized additive modeling (GAM) was

employed to address any non-linear patterns, and the predictive capability of CI for frailty was evaluated by receiver operating characteristic (ROC) analysis. With a 69% rise in the

2.22,3.51; P < 0.001). The association between CI and prevalence of frailty was significant in all subgroups. In addition, statistically significant interactions were present in most

subgroups. When the CI > reached 1.35, the GAM model demonstrated a threshold effect and a significant nonlinear connection, with a 105% rise in the prevalence of frailty for every 0.1

unit increase in CI. In the male group, CI was a significantly greater indicator of the prevalence of frailty than both BMI and WC. According to this study, frailty in older persons is

substantially correlated with a higher CI. Although greater confirmation in large-scale prospective research is required, this study indicates that increased CI is a more reliable predictor

of the prevalence of frailty in older men and is significantly linked with its occurrence. SIMILAR CONTENT BEING VIEWED BY OTHERS COMBINED USE OF TWO FRAILTY TOOLS IN PREDICTING MORTALITY IN

OLDER ADULTS Article Open access 03 September 2022 IMPACT OF FRAILTY ON MORTALITY AND HEALTHCARE COSTS AND UTILIZATION AMONG OLDER ADULTS IN SOUTH KOREA Article Open access 01 December 2023

IS A HIGHER BODY MASS INDEX ASSOCIATED WITH LONGER DURATION OF SURVIVAL WITH DISABILITY IN FRAIL THAN IN NON-FRAIL OLDER ADULTS? Article Open access 15 November 2024 INTRODUCTION The state

of frailty, which is characterized by the deterioration of several physiological systems, greatly raises the risk of falls, incapacity, hospitalization, and even death1. As the global

population ages at an accelerated rate, frailty is becoming particularly prominent in the elderly population2. Understanding the metabolic basis of frailty is becoming increasingly important

to identify modifiable risk factors and develop targeted interventions3. It is commonly acknowledged that one of the main risk factors for frailty is obesity. However, traditional

assessments of body fat have mostly relied on metrics such as waist circumference (WC) or body mass index (BMI)4 but may have limitations when assessing the elderly population. These methods

do not accurately reflect the distribution of body fat, the distribution and quality of which may have a greater impact on the development of frailty5. The CI, a new obesity-related index,

is different from the traditional BMI in that CI combines weight, waist circumference, and height to more accurately reflect the distribution of fat, especially the effect of abdominal fat6.

According to previous research, CI exhibits high sensitivity in predicting risk for a number of illnesses, including metabolic diseases7, hypertension8, gallstones9, and urinary

incontinence in women10. In addition, studies have found a linear relationship between CI and mortality risk, suggesting that a high CI may increase mortality11, which further supports the

potential value of CI in health surveillance and risk assessment. While numerous studies have demonstrated a correlation between obesity and frailty, research addressing the relationship

between CI and frailty remains limited. Therefore, this study aims to improve our understanding of CI’s role in frailty screening and offer new support for early intervention in the elderly

population by evaluating its potential role in predicting frailty prevalence and examining its applicability in older U.S. adults using data from the National Health and Nutrition

Examination Survey (NHANES) from 2007 to 2018. MATERIALS AND METHODS DATABASE SOURCES AND SAMPLE SELECTION The NHANES is a nationwide cross-sectional research that assesses the general

health and nutritional status of noninstitutionalized U.S. adults using stratified multistage random sampling, which is carried out by the National Center for Health Statistics (NCHS).

Selected data from NHANES between 2007 and 2018 were used for the analysis of this study. First, information from 59,842 NHANES participants from 2007 to 2018 was taken into account. The

final sample consisted of 8,748 participants, excluding 47,932 persons younger than 60 years of age, 2,649 persons with unreliable frailty index assessments, 350 persons with missing or

outlier CI data, and 163 persons with missing covariates (Fig. 1). Informed consent forms were signed by all survey respondents, and the NHANES data were made publicly available. ASSESSMENT

OF FRAILTY A deficit accumulation model was used to measure frailty, and in order to be eligible, individuals had to answer at least 91% of the 49 questions on the Frailty Index (FI). The 49

diagnostic criteria that make up the FI encompass topics including cognitive function, physical performance, capacity to carry out everyday tasks, chronic illnesses, health status, and

laboratory testing. The final FI is calculated by dividing the total of the scores by the number of items12,13. Each criteria is rated based on severity, with 0 denoting no frailty and 1

denoting severe frailty. A FI value of 0.25 or greater than 14 is considered frail14. The complete set of criteria is given in Supplementary Table 1. ASSESSMENT OF CI Thorough training was

provided to all NHANES employees to guarantee measurement accuracy and uniformity. The exposure variable, CI, was computed as follows: CI = wc / [0.109 × sqrt(bw / Height)], with waist

circumference and height in meters, and body weight in kilograms6. Both continuous and categorical variables might be used to examine CI data. The CI values were analyzed by dividing them

into four groups (first quartile: 0.97 < CI ≤ 1.31; second quartile: 1.31 < CI ≤ 1.36; third quartile: 1.36 < CI ≤ 1.41; fourth quartile: 1.41 < CI ≤ 1.73). COVARIATES The study

controlled for a number of well-known covariates, such as age, gender, race, education level, marital status, PIR, diastolic and systolic blood pressure, energy intake, the Healthy Eating

Index 2015 (HEI-2015), smoking, alcohol use, and physical activity, in order to account for confounding variables. In addition, chronic diseases such as hypertension, high cholesterol,

diabetes, cardiovascular disease, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD) were also potential factors affecting frailty. PIR ≤ 1.3, 1.3 < PIR ≤ 3.5,

and PIR > 3.5 were used to classify specific income levels; smoking was defined as 100 cigarettes or more during one’s lifetime; and alcohol use was divided into five categories based on

current drinking status: never, former, heavy, moderate, and mild drinking15,16. Detailed categorization criteria are shown in Supplementary Table 2. Blood pressure was estimated by

averaging at least three consecutive standard measurements. Dietary information was gathered on the first day of the 24-h dietary recall trial. A person’s dietary compliance with the Dietary

Guidelines for Americans is assessed by the HEI-201517. Values vary from 0 to 100, with higher scores denoting higher-quality food and healthier eating practices. STATISTICAL ANALYSIS Each

statistical analysis took into account the complex sampling design of NHANES and applied the appropriate sampling weights. To avoid excessively large effect sizes, we created new data with a

tenfold CI for the analysis. Interpolated missing data for PIR, energy intake, HEI-2015, and alcohol use using the random forest method (missing values did not exceed 15%). Continuous

variables are shown as mean ± standard error (SE), whilst categorical data are presented as weighted percentages. Group differences were evaluated at baseline using t-tests and weighted

chi-square. The three weighted multivariate logistic regression models that were used to examine the relationship between CI and frailty were Model 1 (unadjusted), Model 2 (adjusted for age,

gender, race, and education level), and Model 3 (further adjusted for variables such as marital status, PIR, smoking, alcohol use, physical activity, SBP, DBP, HEI-2015, and energy intake).

While threshold effects and turning points were examined using linear regression models, potential nonlinear connections were assessed using GAM. Furthermore, interaction tests and subgroup

analysis have been performed out. ROC analysis was used to examine the predictive power of CI, BMI, and WC for frailty. DeLong tests were used to look for statistically significant changes

in the ROC analysis findings. Sensitivity analyses consisted, among other things, of removing all missing covariates and further adjusting for hypertension, high cholesterol, cardiovascular

disease, diabetes, COPD, CKD, cholesterol-lowering, and antidiabetic medication use. P-values below 0.05 were regarded as statistically significant. All statistical analyses were conducted

using Empower States (version 4.2) and R software (version 4.2). RESULTS BASELINE CHARACTERISTICS OF PARTICIPANTS Table 1 displays the overall demographics of the 8,748 participants in the

study, whose mean age was 69.48 ± 6.81 years.The sample was very well divided, with 49.94% of the participants being female and 50.06% being male. 13.57 ± 0.80 was the mean CI. As CI grew,

the prevalence of frailty rose noticeably. The basic characteristics of the groups differed significantly depending on whether they were frailty or not. MULTIPLE LOGISTIC REGRESSION ANALYSIS

Table 2 provides a summary of the weighted multivariate logistic regression analysis’s results. For every 0.1 unit increase in CI (95% CI: 1.66,2.00; P < 0.001), the prevalence of

frailty rose 1.82 times in model 1 (unadjusted); in model 2 (adjusted for age, sex, race, and education), the OR was 1.91 (95% CI: 1.73,2.11; P < 0.001). The prevalence of frailty

increased 69% in fully adjusted model 3 for every 0.1 unit rise in CI (OR: 1.69, 95% CI: 1.53,1.86; P < 0.001). Additionally, Table 2 shows that when CI was classified, the prevalence of

frailty was significantly higher in the group with the greatest CI than in the group with the lowest CI (OR = 2.79, 95% CI: 2.22,3.51; P < 0.001). NONLINEAR ANALYSIS The association

between CI and the prevalence of frailty was further assessed using GAM, and the findings showed a significant nonlinear connection (Fig. 2, Table 3). Segmented regression analysis provided

more evidence for the presence of a threshold effect. Furthermore, a threshold effect and a significant nonlinear association were seen in both males and females. SUBGROUP ANALYSIS Subgroup

analyses were carried out to investigate the association in various groups, accounting for factors such age, gender, race, education, marital status, BMI, PIR, BMI, HEI-2015, physical

activity, smoking, and alcohol use. Further supporting the idea that CI is a risk factor for frailty development in older Americans is the analysis’s findings, which revealed a significant

correlation between CI and the prevalence of frailty in all subgroups with statistically significant interactions in the majority of subgroups (Table 4). Results of the subgroup analysis

were adjusted for all covariates except the effect modifier. SENSITIVITY ANALYSIS A number of sensitivity analyses were performed to ensure the accuracy of the findings. These included

deleting all missing covariates and adjusting for hypertension, high cholesterol, diabetes, cardiovascular disease, COPD, CKD, cholesterol-lowering, and antidiabetic medication use. Good

stability of the data was indicated by the sensitivity analyses, which continuously revealed a substantial connection between CI and the prevalence of frailty (Table 5). ROC ANALYSIS ROC

analysis was used to evaluate the predictive power of CI, BMI, and WC for the prevalence of frailty. The findings indicated that the three did not significantly differ from the whole

population, and the predictive ability was poorly demonstrated in the female population. However, when it came to predicting the prevalence of frailty in the male population, CI performed

noticeably better than BMI and WC(Fig. 3, Table 6). DISCUSSION This study assessed the relationship between CI and frailty risk in US seniors aged 60 and above using NHANES data from

2007–2018. The results showed a significant and independent correlation between a higher prevalence of frailty and CI. Frailty prevalence increased by 69% for every 0.1 unit increase in CI

in fully adjusted models. The group with the greatest CI had a significantly higher prevalence of frailty than the group with the lowest CI (OR = 2.79, 95% CI: 2.22,3.51; P < 0.001).

Through GAM analysis, it was clarified that there was a nonlinear relationship between CI and frailty, with a 105% increase in frailty risk for every 0.1 unit increase in CI when CI > 

1.35. CI considerably surpassed BMI and WC in predicting the prevalence of frailty in the male population, according to the results of the ROC curve study, which further demonstrated the

advantages of applying CI in differentiating high-risk populations. It should be noted that the absolute differences in AUC values among the three were relatively small. Therefore, the

clinical or practical significance of this difference remains unclear and needs to be further validated in larger samples and multicenter studies. The current results suggest that CI has

some potential as an indicator of abdominal fat distribution, but it is not yet sufficient to replace existing commonly used indicators, and future studies should focus on its practical

value in different populations and specific health outcomes. Subgroup and sensitivity analyses were also performed in this study to increase the robustness of the findings. After removing

the missing covariates, hypertension, high cholesterol, cardiovascular disease, diabetes, COPD, CKD, cholesterol-lowering, and antidiabetic medication use, there were no significant

associations between CI and the prevalence of frailty, which demonstrated the strong stability of the study results. In subgroup analyses, the association between CI and prevalence of

frailty was significant in all subgroups, with statistically significant interactions in most subgroups, suggesting that CI has good applicability and predictive power across a wide range of

older populations. Subgroup analyses showed a higher prevalence of frailty in men, and in heavy drinkers, suggesting that both groups may be more sensitive to the negative effects of

abdominal fat accumulation. Men tend to store fat in the form of visceral fat, which has higher pro-inflammatory properties18, and heavy alcohol consumption may lead to metabolic

disturbances and nutritional imbalances that exacerbate the risk of frailty19. These findings emphasize the importance of identifying high-risk groups in the context of the individual and

targeting interventions. Recent years have seen a surge in studies on the connection between frailty and metabolic health, body fat distribution, and obesity. Yuan et al. found a U-shaped

association between frailty and both BMI and WC, as well as a high correlation between abdominal obesity and frailty through a systematic review and meta-analysis20. Our findings, which

demonstrated a strong association between CI and the prevalence of frailty, further corroborated this hypothesis. Furthermore, WC has been shown to be a more reliable indicator of older

persons’ risk of frailty than BMI21,22. Our study further demonstrates that WC is more accurate than BMI in both male and female populations. More importantly, CI is more accurate than WC in

predicting frailty in older adults. According to He et al., obesity that was metabolically unhealthy considerably sped up the development of frailty, while obesity that was metabolically

healthy had less of an impact23. Our study also showed that individuals with higher CI are usually accompanied by poorer metabolic health status, increasing the risk of frailty prevalence.

In addition, our study shows a threshold effect and nonlinear association between CI and frailty for the first time, indicating that in both male and female populations, the prevalence of

frailty is significantly higher when the CI is greater than 1.35. This threshold may correspond to a physiologic transition from compensation to imbalance. After a certain level of CI,

abdominal fat may have entered a dysfunctional state characterized by a high release of pro-inflammatory cytokines, adipose tissue hypoxia, and mitochondrial dysfunction24, leading to a

markedly increased systemic inflammatory response. At the same time, muscle mass loss, worsening insulin resistance, hormonal disturbances and elevated levels of oxidative stress are more

pronounced25. Thus, when the CI exceeds the threshold of 1.35, the above pathologic processes may act synergistically to accelerate the onset of frailty. Although this threshold may be

variable in different populations, our findings suggest that CI has potential stratification significance in identifying at-risk individuals. Abdominal fat accumulation raises the risk of

frailty through a number of pathophysiologic mechanisms. One metabolically active tissue that can secrete a number of pro-inflammatory factors, including interleukin-6 and tumor necrosis

factor-alpha, is abdominal fat. These factors cause a systemic, chronic, low-grade inflammation that speeds up the loss of muscle mass and strength, which leads to the development of

frailty26. One of the primary causes of weakness is believed to be this inflammatory condition. People who have a high CI are more likely to have larger reserves of belly fat and are more

vulnerable to the harmful consequences of the inflammatory response. In addition to affecting muscle mass, this persistent low-grade inflammation may hasten the deterioration of other

systems by causing oxidative stress27. The development of insulin resistance is intimately associated with obesity, which in turn directly contributes to metabolic illnesses including

diabetes and hyperlipidemia, which in turn hasten the onset of frailty28. Because abdominal fat is accurately reflected by CI, we found that CI was a better predictor of frailty than BMI in

this study. Additionally, abdominal obesity is linked to a number of hormonal imbalances, such as abnormalities in leptin29, insulin-like growth factor30, and sex hormone-binding globulin31.

Obese people tend to have lower levels of IGF-1 and increased leptin resistance32, which may worsen the condition of frailty. These hormones have significant impacts on muscle mass and bone

strength in addition to being strongly linked to energy metabolism and fat distribution. Also, abdominal obesity is strongly associated with cardiovascular disease risk. By causing harm to

the cardiovascular system, higher CI may hasten the onset of frailty33. Through the indirect effects of decreased physical activity, changed dietary patterns, and heightened chronic

inflammation, excessive obesity is frequently linked to higher levels of psychological issues like anxiety and depression34, which may hasten the onset of frailty35. The ROC analysis in this

study found that CI was a better predictor of frailty than BMI and WC in men and relatively weaker in women. The likely reason for this is that men are more likely to accumulate visceral

fat18, and elevated CI tends to directly reflect abnormal accumulation of visceral fat, which in turn leads to a more pronounced systemic inflammatory response and metabolic disturbances,

mechanisms that are strongly associated with the development of frailty. In contrast, women more commonly exhibit increased subcutaneous fat, which has lower metabolic activity and

inflammatory potential, and CI may not adequately reflect the underlying risk of frailty in women. In addition, estrogen levels in women may be protective against inflammation and fat

distribution36, thus weakening the association between CI and frailty. This result suggests that the moderating role of gender factors on fat distribution patterns and pathophysiologic

mechanisms should be considered when assessing the risk of frailty. Because the study is based on NHANES data, which has a large and nationally representative sample size, its conclusions

are more reliable. The reliability of the results was improved by the study’s adjustment for a number of possible confounders, such as age, gender, race, and lifestyle characteristics. The

results’ robustness was further supported by subgroup analyses, which revealed a strong correlation between CI and frailty in all groups. The study also had limitations. As there is no

uniform international classification standard for CI. This study uses quartiles for grouping, aiming to achieve internal risk stratification based on the study sample. However, the external

comparability of this method is limited. In the future, large-scale cohort studies should be relied upon to establish normative ranges and clinical classification boundaries for CI to

enhance its practical value in disease screening and risk prediction. The data came from a cross-sectional survey, so it was not possible to prove a causal relationship between CI and

frailty; further longitudinal research is needed to confirm this; even after controlling for a number of variables, there may be potential confounders (e.g., genetic factors, etc.) that were

missed; the study population was an older U.S. population, and further validation is needed to determine whether the findings apply to other regions or ethnicities. CONCLUSION This study

showed a strong positive correlation between CI and frailty in older Americans, emphasizing the significance of taking metabolic parameters into account when developing prevention and

treatment plans for frailty. Future research should examine how well strategies to lower CI and slow the onset of frailty work. These findings demonstrate the potential of CI as a screening

and management tool for frailty; however, larger prospective studies are needed for further confirmation. DATA AVAILABILITY Data is provided within the manuscript or supplementary

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to all participants and investigators of the NHANES. FUNDING The Scientific Research Program of Sichuan medical and health care promotion institute,KY2022SJ0396 AUTHOR INFORMATION AUTHORS

AND AFFILIATIONS * Department of Sports Medicine, Sichuan Provincial Orthopedics Hospital, Chengdu, China Jie Xu, Jiaming Cui & Xiaobing Luo * Department of Emergency Medicine, Nanchong

Hospital of Traditional Chinese Medicine, Nanchong, China Meng Chen Authors * Jie Xu View author publications You can also search for this author inPubMed Google Scholar * Meng Chen View

author publications You can also search for this author inPubMed Google Scholar * Jiaming Cui View author publications You can also search for this author inPubMed Google Scholar * Xiaobing

Luo View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.X. and M.C. designed the research. J.X. collected, analyzed the data, and drafted the

manuscript. JM.C., and XB. L. revised the manuscript. All authors contributed to the article and approved the submitted version. CORRESPONDING AUTHOR Correspondence to Xiaobing Luo. ETHICS

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frailty in older Americans: the NHANES cross-sectional study, 2007–2018. _Sci Rep_ 15, 17857 (2025). https://doi.org/10.1038/s41598-025-02455-4 Download citation * Received: 22 January 2025

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KEYWORDS * Conicity index * Obesity * Cross-sectional study * Frailty * NHANES