Introduction

Malnutrition can exacerbate individual vulnerability and incapacitation, thereby amplifying the burden of disease and mortality rates, especially among the older adults with chronic disease1. Common contributory factors to malnutrition include aging, acute and chronic diseases, insufficient dietary intake, sarcopenia, etc2,3,4,5. According to the Global Leadership Initiative on Malnutrition (GLIM) guidelines, a range of tools and methods are used for nutritional assessment, including body composition measurements, questionnaires, biomarkers, and medical devices such as computed tomography (CT), bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA)6.

BIA is widely used as an inexpensive, convenient, and non-invasive method for body composition analysis, by measuring the resistance and reactance of the entire body and specific body segments at designated frequencies7. In clinical trials, the prevailing practice involves single-frequency phase detection utilizing a 50 kHz single-frequency device with a minimum of 4 electrodes affixed at the wrist and ankle8. Phase angle (PhA), the most commonly used impedance parameter in clinical practice and a reliable indicator of nutritional status, is calculated using resistance (R) and reactance (Xc) in BIA8,9,10. PhA reflects the integrity of cellular health and membrane integrity and is an important parameter for assessing the state of cellular health11,12. A high PhA usually indicates good cell membrane integrity and function, while a low PhA may indicate reduced cell function and malnutrition13. In addition, PhA was also directly associated with a variety of nutritional indicators such as body mass index (BMI) and grip strength, which further supports its validity as a nutritional assessment tool14,15. Age differences may affect the PhA level in the body8. With advancing age, a reduction in muscle mass is associated with a decline in Xc, whereas a decrease in body water content is linked to an increase in R, ultimately resulting in a reduction in the PhA level. PhA gradually grows from birth to age 18, stabilizes between the ages of 19 and 48, and then gradually diminishes in older subjects and the elderly16,17.

Our study aimed to investigate the association between PhA and all-cause mortality in adults aged 18–49 years. Much of the existing literature has focused on older adults and chronically ill patients9,16,18,19,20, leaving a gap in understanding nutritional influences on mortality outcomes in younger, generally healthier populations. Additionally, the sample size was large in our study, enhancing the robustness of our findings and providing a reliable assessment of the impact of nutritional status on all-cause mortality in this demographic.

Materials and methods

Data sources and study population

The National Health and Nutrition Examination Survey (NHANES) is a nationwide survey administered by the National Center for Health Statistics (NCHS). NHANES employs a complex multistage probability sampling design, which divides the U.S. into primary sampling units that are further organized into strata and neighborhoods, from which households and participants are randomly selected, with each participant representing approximately 50,000 non-institutionalized civilians in the population. It aims to monitor the health and nutritional status of individuals across the United States, serving as a foundational resource for the development of health policies. The survey is conducted every two years with questionnaire interviews, physical and laboratory examinations of participants. Our study population was derived from three cycles of the NHANES (including 1999–2000, 2001–2002, 2003–2004). The detailed screening process is shown in Fig. 1. The exclusion criteria for the study included the following: (1) failure to complete BIA; (2) lack of data on resistance and reactance, making it impossible to calculate the PhA value; (3) participants younger than 18 years of age; (4) without mortality outcomes. This survey was approved by the NCHS Ethics Review Board and written informed consent has been obtained from all participants, specific details can be found at https://www.cdc.gov/nchs/nhanes/irba98.htm. All methods were performed in accordance with the relevant guidelines and regulations.

Fig. 1
figure 1

Flowchart for participant selection.

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Assessment of PhA

BIA is a method used to measure the electrical impedance of body tissues, enabling the assessment of fluid volumes, total body water, fat mass, and fat-free mass. In NHANES, BIA was administered to eligible survey participants 8–49 years of age. A portion of the participants were not eligible for the BIA examination, including (1) pregnant females; (2) individuals with amputated limbs; (3) those with artificial joints, pins, metal plates, or other types of metal objects in their bodies, pacemakers or automated defibrillators, coronary stents or metal suture material in the heart; (4) individuals weighing more than 300 pounds.

The quality of the collected raw frequency data was evaluated through an external Hydra modeling program provided by Xitron Technologies, Inc. The program measured R, Xc, and calculated the PhA value at each measured frequency. The PhA measured at 50 kHz is the most widely utilized9,21, calculated by the following formula: PhA (°) = arctangent (Xc/R) * (180/π)21.

All-cause mortality

NCHS has linked data from the NHANES survey with death certificate records obtained from the National Death Index (NDI). The Linked Mortality Files (LMF) have been regularly updated with mortality follow-up data through December 31, 2019. By referencing the participants’ unique respondent sequence number, we can ascertain their survival status. We accessed and downloaded relevant data at https://www.cdc.gov/nchs/data-linkage/mortality-public.htm.

Covariates

Our study incorporated sociodemographic variables, including age, gender, race, education level, marital status, and income information. Body measurements were collected, including fat-free mass, fat mass, and BMI. Information on participants’ comorbidities (including asthma, chronic obstructive pulmonary disease, cardiovascular disease, hypertension, diabetes, and cancer) and lifestyle habits, such as smoking and drinking, were also collected.

Statistical analysis

The weighted analysis was conducted following NHANES guidelines to account for the complex survey design, and to ensure that our estimates are representative of the US general population. The study population was stratified into two groups based on the median PhA value (the low PhA group: PhA < 6.96°; the high PhA group: PhA ≥ 6.96°). Continuous variables were presented as mean values and the first and third quartiles (Q1, Q3), while categorical variables were presented as percentages and 95% confidence intervals (CI). The survey-weighted linear regression was utilized for continuous variables, and the survey-weighted chi-square test was employed for categorical variables to evaluate differences. To initially assess the survival rate between the groups, Kaplan-Meier curves were plotted. Subsequently, we constructed several different Cox regression models: unadjusted model; Model 1 was adjusted for age, gender, race, education level, marital status, and income information; Model 2 was adjusted for age, gender, race, education level, marital status and income information, fat mass, fat-free mass, BMI, drinking and smoking; Model 3 was additionally adjusted for comorbidities based on Model 2. Additionally, stratified analyses were conducted across different demographic groups. Statistical analyses were conducted using R4.2.3 software and a two-tailed P < 0.05 was considered statistically significant.

Results

The baseline characteristics of participants in the low and high PhA groups (weighted) were detailed in Table 1, and the total population (unweighted and weighted) can be seen in Supplementary Table 1. In contrast to the low PhA group (PhA < 6.96°), individuals in the high PhA group (PhA ≥ 6.96°) were younger and had a higher proportion of males. The low and high PhA groups predominantly consisted of non-Hispanic white and individuals with higher levels of education. Compared to the low PhA group, the high PhA group had a higher fat-free mass, lower fat mass, and a higher BMI. Regarding comorbidities, statistically significant differences were observed between the two groups, except for asthma and diabetes. The average follow-up time for individuals in the high PhA group was 212.14 months, with a mortality rate of 3.98%. The low PhA group exhibited a mean follow-up time of 211.58 months, with a mortality rate of 5.07%.

Table 1 Characteristics of participants in the low and high phase angle groups, weighted.
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Kaplan-Meier survival plot and the log-rank test were used to compare the survival differences between the two groups (Fig. 2). The survival probability of the population in the high PhA group was higher than that of the population in the low PhA group (P = 0.047).

Fig. 2
figure 2

Kaplan-Meier curve of survival for populations in the low and high phase angle groups.

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The results of the Cox regression model were summarized in Table 2. In the analysis of continuous PhA, a statistically significant association with all-cause mortality was observed in the unadjusted model, showing a hazard ratio (HR) of 0.83 (95% CI: 0.71–0.98, P = 0.032). After adjustments, the risk of all-cause mortality decreaseed further in Model 1, Model 2, and Model 3, with HRs of 0.72 (95% CI: 0.58–0.88, P = 0.002), 0.66 (95% CI: 0.51–0.86, P = 0.002), and 0.73 (95% CI: 0.59–0.91, P = 0.005), respectively. For categorized PhA, individuals with low PhA (PhA < 6.96°) are treated as the reference group. In the unadjusted model, those with high PhA (PhA ≥ 6.96°) exhibited a HR of 0.78 (95% CI: 0.60–1.02, P = 0.074). This relationship strengthened in the adjusted models, with HRs of 0.66 (95% CI: 0.48–0.91, P = 0.011), 0.66 (95% CI: 0.45–0.98, P = 0.041), and 0.67 (95% CI: 0.46–0.98, P = 0.041) in Model 1, Model 2 and Model 3, respectively, indicating a statistically significant lower risk of all-cause mortality.

Table 2 Cox regression analyses of the association between phase angle and all-cause mortality, weighted.
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Finally, stratified analyses were performed (Fig. 3). Except for Mexican Americans and non-Hispanic whites and blacks, the negative association between PhA and all-cause mortality was statistically significant in other Hispanic (HR: 0.41, 95% CI: 0.18–0.94, P = 0.035) and other races (HR:0 .38, 95% CI: 0.22–0.66, P = 0.001). Additionally, there was a statistically significant negative association between PhA and all-cause mortality among those with lower level of education (HR: 0.72, 95% CI: 0.56–0.92, P = 0.009), without partner (HR: 0.75, 95% CI: 0.58–0.97, P = 0.026), and lower level of income (HR: 0.69, 95% CI: 0.52–0.92, P = 0.012). Moreover, PhA was similarly negatively associated with all-cause mortality in high adiposity (HR: 0.62, 95% CI: 0.45–0.85, P = 0.003) and overweight (HR: 0.62, 95% CI: 0.41–0.92, P = 0.019) and obese populations (HR: 0.65, 95% CI: 0.46–0.92, P = 0.015). Interestingly, we also observed an interaction between smoking status and PhA (P for interaction = 0.029), with a statistically significant negative association between PhA and all-cause mortality in the non-smokers (HR: 0.62, 95% CI: 0.53–0.88, P = 0.003) and no association in the smokers (HR: 1.04, 95% CI: 0.74–1.47, P = 0.813). As shown in Fig. 3, the negative association between PhA and all-cause mortality can be seen in terms of comorbidities.

Fig. 3
figure 3

Relationship between phase angle and all-cause mortality in different subgroups.

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Discussion

To our knowledge, this is the first study to investigate the association between PhA and all-cause mortality within a substantial sample of adults aged 18–49 years. Our study demonstrated a significant negative association between PhA and all-cause mortality in adults aged 18–49 years, and the negative association was stronger in non-smokers.

In our study, the mean age was 34 years, and the mean PhA was 6.86°. A meta-analysis of 249,844 participants showed that the mean PhA for men and women aged 18–38 years were 7.3° and 6.4°, respectively; over the age of 80 years, the values dropped to 5.3° and 5.4°, respectively16. Compared with the low PhA group, the high PhA group had a 33% lower risk of all-cause mortality, while the total population had a 27% lower risk of all-cause mortality for every 1° increase in the PhA value. Similarly, Young Eun Kwon et al. indicated that low PhA was significantly associated with mortality in older adults (mean age 83 years), with a 54% reduction in the risk of death for every 1° increase in PhA value22. In addition, lower individual-standardized PhA increased the risk of prolonged hospitalization by 7%, along with a 7.87-fold increase in the risk of death within 12 months23. Moreover, García-García, C et al. noted that PhA was one of the prognostic factors for cancer patients, and high PhA was significantly associated with a lower risk of mortality (HR:0.42, P = 0.014)24. In general, our study demonstrated that PhA is associated with all-cause mortality in adults aged 18–49 years.

PhA is an indicator measured by BIA and is commonly used to assess the nutritional status and mortality risk of individuals8,25. The results of our study indicate a significant association between PhA and all-cause mortality in adults aged 18–49 years. The lower in PhA, the higher the risk of all-cause mortality. In adults, lower PhA is associated with an increased subsequent risk of dying prematurely and cardiovascular disease26. This finding may be related to the sensitivity of PhA to malnutrition. Studies indicate that PhA is sensitive to changes in body composition and can detect the shift from intracellular to extracellular water early in disease-associated malnutrition27,28. Malnutrition is typically accompanied by a shift in intracellular to extracellular water, which results in a reduction in cell mass. These changes can be reflected by alterations in PhA29. Therefore, PhA, as a marker of nutritional status, may account for some of the observed association with mortality.

In subgroup analyses, we found that the relationship between PhA and all-cause mortality was consistent across different subgroups, including sociodemographic variables, body measurements, comorbidities, and lifestyle habits. It’s worth noting that the negative association between PhA and all-cause mortality existed among non-smokers (P = 0.003) and a significant interaction was observed between PhA and smoking status (P for interaction = 0.029). To the best of our knowledge, this study is the first to indicate that smoking status may have an impact on the association between PhA and all-cause mortality. Nevertheless, the mechanisms underlying the association remain unclear and need more research. Some studies have pointed out that the PhA value is lower in smokers due to oxidative stress and inflammation disrupting the normal cell structure30,31,32. The PhA value was lower in smokers than in non-smokers (6.6 ± 0.13° vs. 7 ± 0.06°, P = 0.038)30. Cellular damage caused by unhealthy lifestyle, and smoking-induced systemic oxidative stress and inflammation are the main causes of low PhA. Harmful substances in tobacco, such as nicotine, can disrupt mitochondrial endoplasmic reticulum crosstalk, interfering with normal redox signaling in the body and thus leading to dysfunctions in cell structure and function31. In addition, smoking leads to low-grade inflammation and makes inflammatory factors rise significantly32. Overall, our study demonstrated that the all-cause mortality of non-smokers may decrease with the increase of PhA in adults aged 18–49 years.

In clinical practice, PhA is a simple, non-invasive, and easily obtainable measurement with significant utility; regular monitoring can help identify individuals at risk of nutritional deficiencies or declining health25,33. Additionally, PhA can assist in identifying nutritional health issues across diverse populations, particularly in resource-limited or low-income settings8. Integrating PhA assessment into public health policies can enhance health monitoring and intervention strategies, ultimately reducing the burden of malnutrition-related diseases and lowering mortality rates25.

Strengths and limitations

There are some strengths in our study. Firstly, our study population was characterized by a large sample size and complex sampling, ensuring representation and enhancing the reliability of our conclusions. Secondly, we adjusted for as many confounders as possible in constructing the regression model, including sociodemographic variables, body measurements, comorbidities, and lifestyle habits, and also performed stratified analyses to demonstrate the robustness of our findings. Thirdly, our study benefited from its foundation on a high-quality research study with a prolonged follow-up duration, reinforcing the veracity and dependability of our conclusions. However, several limitations warrant acknowledgment. Firstly, NHANES facilitated a single measurement of PhA in the study population, thus potentially overlooking the significance of examining dynamic trends in the PhA. Secondly, information on the presence of underlying diseases and smoking status is based on self-reporting by respondents in the questionnaire. Even though the questionnaire was based on a procedural approach and was also administered by well-trained staff, recall bias on the part of respondents could not be ruled out. Thirdly, we focused primarily on individuals aged 18 to 49 years. This age group generally tends to be healthier, which may limit our ability to fully capture the relationship between nutritional status and all-cause mortality across different age groups. Future research could benefit from including a broader age range to gain a more comprehensive understanding of the relationships we investigated.

Conclusion

In adults aged 18–49 years, there was a significant negative association between PhA and all-cause mortality. The negative association was stronger in the non-smoking population. It is therefore important to monitor and manage PhA in order to reduce the risk of all-cause mortality. Further studies are required to validate these findings and determine how PhA can be optimally utilised in clinical practice to enhance quality of life.