Abstract
Background
As the pace of social aging accelerates, mitigating malnutrition has emerged as a pressing public health imperative. This study aimed to assess the association between age and malnutrition in Chinese oldest-old and centenarian populations.
Methods
This population-based survey investigated 1768 adults in 18 cities and counties using a standardized protocol. Face-to-face interview was conducted by a well-trained team using home-visiting method.
Results
Logistic regression analyses demonstrated a significantly inverse association between age and malnutrition. Each one-year increase in age was associated with a significantly reduced likelihood without malnutrition [model 1: odds ratio (OR) 0.91, 95%confidence interval (CI) 0.90–0.92, p < 0.001; model 2: OR 0.94, 95%CI 0.92–0.96, p < 0.001). Restricted Cubic Spline revealed a linear correlation between age and malnutrition (model 1: p-overall ≤ 0.001, p-nonlinear = 0.011; model 2: p-overall ≤ 0.001, p-nonlinear = 0.079). Subgroup analyses indicated interaction effects of cigarette smoking (p = 0.002), red meat consumption (p = 0.028), and vegetable consumption (p = 0.009) on malnutrition.
Conclusions
This study demonstrated a linear association between age and malnutrition in Chinese oldest-old populations and indicated interaction effects of cigarette smoking, red meat consumption, and vegetable consumption on malnutrition. Food consumption is related to nutritional status as a key element in age-malnutrition association. An in-depth understanding of food and nutrition in older adults could help establish evidence-based recommendations to increase survival rate.
Keywords: Centenarian, Food consumption, Geriatric Nutritional Risk Index, Malnutrition, Oldest-old
Background
Malnutrition is a prevalent and serious condition among older adults. Insufficient food and nutrient intake in older adults, which is associated with mortality rate and life quality, has aroused widespread concern all around the world [1]. Studies have revealed that 12.6% of older populations experienced malnutrition in China [2], with this proportion soaring to 53.0% among those aged 80 years and above [3]. Among community-dwelling older adults, 22.28% suffered from malnutrition in Guangzhou [4]. Moreover, a study involving 1001 older adults aged 65 years and above demonstrated that malnutrition is an independent predictor of mortality [5]. Approximately 25% of European older adults aged ≥ 65 years are malnourished [6]. For older people, malnutrition-associated adverse health outcomes are often more complex including frailty and mortality [7–10]. As the pace of social aging accelerates, mitigating malnutrition has emerged as a pressing public health imperative.
Both the 2015–2020 American Dietary Guidelines and 2016 Chinese Dietary Guidelines included the information on food diversity [11, 12]. A meta-analysis showed that greater adherence to the World Health Organization Dietary Guidelines was significantly associated with decreased mortality rate in older adults in the United States and Southern Europe [13]. Zutphen adults aged 65–84 years with high-quality food reported a 40% lower mortality rate and a 2.5-year longer life expectancy [14]. In China, fruit and vegetable intake has a positive correlation with nutritional status [15]. Data from the China Health and Nutrition Survey (CHNS) demonstrated that higher scores on the Plant-Based Diet Index were associated with a lower mortality rate [16]. Findings from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) suggested that adherence to the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet was associated with lower prevalence of depression and anxiety [17]. Furthermore, there is evidence that food and nutrition play an important role in the development of frailty, while the prevalence of frailty in older adult ranges from 4.0% to 59.1% [18, 19]. Frailty in older adults is related to many adverse health outcomes such as hospitalization and disability, which seriously affect the individuals’ life quality [20]. Food diversity may be a significant determinant of healthy aging.
As an indicator of nutritional status, Geriatric Nutritional Risk Index (GNRI) is widely used as to identify existed malnutrition and has been proven to be associated with long-term outcomes in older adults [21]. Studies have indicated that GNRI is a good predictor of morbidity and mortality and is indicative of complications during hospitalization [22, 23]. GNRI is being used more frequently with the growing aging populations in China. However, few studies have assessed the association between age and malnutrition in Chinese older adults. Therefore, this study aimed to assess the association between age and malnutrition in Chinese oldest-old and centenarian populations.
Materials and methods
Study populations
The China Hainan Centenarian Cohort Study (CHCCS), a population-based survey, was conducted in 18 cities and counties in Hainan, China, from 2014 to 2017. In this study, 1768 Chinese oldest-old and centenarian adults over 80 years of age underwent a routine health investigation. Figure 1 shows age validation process and study population diagram. It was a challenge to recruit an entire sample of centenarians for an epidemiological study that included a detailed questionnaire and an extended examination. The Civil Affairs Bureau has the household register of the indigenous older populations and can provide a detailed list of centenarians throughout Hainan. With their critical support and help, we were able to have an interview with Hainan centenarians. To prevent participants from overstating their ages, an age verification process was conducted before participants were included in the study. Exclusion criteria were as follows: acute diseases, malignant tumors, severe trauma, or incomplete data. Subsequently, data from 1497 participants were used for further analyses. All participants provided informed consent for participation in the study and for their data to be used. The study was approved by the Ethics Committee of Hainan Hospital of Chinese PLA General Hospital (Sanya, Hainan; Number: 301HNLL-2016–01). The work described has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).
Fig. 1.
Age validation process and study population diagram
Data management
Face-to-face interview and biochemical tests were administered by the professional research team during a home visit. This interdisciplinary research team included internists, geriatricians, cardiologists, endocrinologists, nephrologists and nurses. Survey contents and procedures were systematically designed, and surveyors were strictly trained before investigation. Education was categorized into illiteracy and literacy. Work was categorized as physical work and mental work. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by calibrated desktop sphygmomanometer (Yuwell medical equipment and supply Co., Ltd., Jiangsu, China). Barthel index was used to evaluate activity of daily living (ADL) of older adults through 10 items (grooming, bathing, toilet use, bowel movement, urinary incontinence, dressing, feeding, stair climbing, transferring and walking). Higher Barthel index indicates better ADL. Based on recommendations from the World Health Organization, height was measured twice using a wall-mounted tape with participants standing without shoes, weight was measured twice on a digital scale while participants wore light clothing without shoes, and waist circumference (WC) was measured twice using a soft tape at the midpoint between the last rib and the iliac crest [24]. Body mass index (BMI) was calculated as weight (kg) divided by squared height (m2). Based on previous literature of original designer from American Society for Clinical Nutrition, GNRI was calculated from the following equations:
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Nutritional status was divided into two levels according to GNRI, and subjects with GNRI ≤ 98 were classified with malnutrition [25]. GNRI is a recommended indicator specially designed for evaluating older patients.
Food consumption
Data on food consumption were collected by face-to-face interview with questionnaires. Accompanied by close relatives, all participants were interviewed about their changes in the consumption of four foods over the past five years, including red meat, seafood, vegetables, and fruits. Consumption of red meat, seafood, vegetables, and fruits was categorized into three groups: decreased (≥ 30%), unchanged (< 30%), and increased (≥ 30%). Number of meals per day was classified as two types: ≤ 3 times/day, and > 3 times/day. Diet time was categorized as regular and irregular, depending on whether it was fixed.
Statistical analyses.
All data have been documented in EpiData 3.0 software. Mean and standard deviation were reported for continuous variables with normal distributions, and count and percentage were reported for categorical variables. Group comparisons for continuous variables with normal distributions were performed using analysis of variance. Group comparisons for categorical variables were performed using Chi-squared test. DAGitty tool (https://dagitty.net/dags.html#) was used to construct a Directed Acyclic Graph (DAG) and select the covariates associated with malnutrition. Logistic regression analyses explored the association between age and malnutrition, and performed subgroup and interaction analyses on categorical variables. Multiple models were constructed: model 1 was unadjusted, and model 2 was adjusted for education, BMI, WC, ADL, number of meals per day, diet time, red meat consumption, seafood consumption, vegetable consumption, and fruit consumption selected by DAG. Linear association was analyzed between age and malnutrition using Restricted Cubic Spline (RCS). Statistical significance was set at p < 0.05. Statistical analyses were performed using R software (version 4.2.2), along with MSTATA software (www.mstata.com).
Results
All participants were over 80 years old, with 69.4% females (1039 participants). After age was categorized into the quartiles, Table 1 presented the characteristics of participants stratified by age groups (Q1: 80–84, Q2: 84–97, Q3: 97–102, and Q4: 102–116 years). Significant differences were observed across age groups for most variables (all p < 0.05), including sex, education, work, SBP, DBP, BMI, WC, ADL, tea drinking, alcohol drinking, diet time, seafood consumption, fruit consumption, GNRI, and malnutrition. The oldest age group (Q4) showed the lowest anthropometric measures compared with younger groups. Lifestyle factors including ADL and diet time differed significantly across age groups (p < 0.05). Dietary patterns revealed age-related variations, with significant differences in seafood and fruit consumption (all p < 0.001). GNRI demonstrated a marked decline and more malnutrition happened with an increased age (all p < 0.001).
Table 1.
Characteristics of participants stratified by age groups
| Characteristics | Age | p | |||
|---|---|---|---|---|---|
| Q1 (n = 343) | Q2 (n = 402) | Q3 (n = 307) | Q4 (n = 445) | ||
| Sex, % | < 0.001 | ||||
| Male | 152(44.3) | 154(38.3) | 67(21.8) | 85(19.1) | |
| Female | 191(55.7) | 248(61.7) | 240(78.2) | 360(80.9) | |
| Ethnicity, % | 0.285 | ||||
| Han | 302(88.0) | 366(91.0) | 274(89.3) | 387(87.0) | |
| Non-Han | 41(12.0) | 36(9.0) | 33(10.7) | 58(13.0) | |
| Education, % | < 0.001 | ||||
| Illiteracy | 233(67.9) | 330(82.1) | 273(88.9) | 410(92.1) | |
| Literacy | 110(32.1) | 72(17.9) | 34(11.1) | 35(7.9) | |
| Work, % | < 0.001 | ||||
| Physical work | 111(32.4) | 123(30.6) | 149(48.5) | 197(44.3) | |
| Mental work | 232(67.6) | 279(69.4) | 158(51.5) | 248(55.7) | |
| SBP, mmHg | 148 ± 24 | 148 ± 24 | 154 ± 24 | 153 ± 24 | < 0.001 |
| DBP, mmHg | 83 ± 13 | 79 ± 12 | 76 ± 13 | 76 ± 13 | < 0.001 |
| BMI, kg/m2 | 21.2 ± 3.8 | 20.7 ± 3.6 | 18.3 ± 3.2 | 18.1 ± 3.6 | < 0.001 |
| WC, centimetre | 80 ± 10 | 78 ± 9 | 76 ± 8 | 75 ± 10 | < 0.001 |
| ADL | 96 ± 9 | 94 ± 12 | 82 ± 20 | 80 ± 20 | < 0.001 |
| Tea drinking, % | 0.005 | ||||
| Yes | 79(23.0) | 69(17.2) | 49(16.0) | 60(13.5) | |
| No | 264(77.0) | 333(82.8) | 258(84.0) | 385(86.5) | |
| Alcohol drinking, % | 0.043 | ||||
| Yes | 86(25.1) | 79(19.7) | 58(18.9) | 76(17.1) | |
| No | 257(74.9) | 323(80.3) | 249(81.1) | 369(82.9) | |
| Cigarette smoking, % | 0.129 | ||||
| Yes | 38(11.1) | 44(10.9) | 20(6.5) | 38(8.5) | |
| No | 305(88.9) | 358(89.1) | 287(93.5) | 407(91.5) | |
| Number of meals per day | 2.95 ± 0.35 | 2.96 ± 0.33 | 2.93 ± 0.40 | 2.90 ± 0.46 | 0.124 |
| Diet time, % | 0.006 | ||||
| Regular | 309(90.1) | 358(89.1) | 293(95.4) | 416(93.5) | |
| Irregular | 34(9.9) | 44(10.9) | 14(4.6) | 29(6.5) | |
| Red meat consumption, % | 0.335 | ||||
| Decreased | 112(32.7) | 134(33.3) | 106(34.5) | 159(35.7) | |
| Unchanged | 217(63.3) | 255(63.4) | 181(59.0) | 261(58.7) | |
| Increased | 14(4.1) | 13(3.2) | 20(6.5) | 25(5.6) | |
| Seafood consumption, % | < 0.001 | ||||
| Decreased | 107(31.2) | 122(30.3) | 132(43.0) | 223(50.1) | |
| Unchanged | 215(62.7) | 254(63.2) | 154(50.2) | 203(45.6) | |
| Increased | 21(6.1) | 26(6.5) | 21(6.8) | 19(4.3) | |
| Vegetable consumption, % | 0.082 | ||||
| Decreased | 86(25.1) | 108(26.9) | 80(26.1) | 144(32.4) | |
| Unchanged | 241(70.3) | 273(67.9) | 209(68.1) | 268(60.2) | |
| Increased | 16(4.7) | 21(5.2) | 18(5.9) | 33(7.4) | |
| Fruit consumption, % | < 0.001 | ||||
| Decreased | 145(42.3) | 170(42.3) | 166(54.1) | 257(57.8) | |
| Unchanged | 189(55.1) | 227(56.5) | 132(43.0) | 178(40.0) | |
| Increased | 9(2.6) | 5(1.2) | 9(2.9) | 10(2.2) | |
| GNRI | 103 ± 9 | 101 ± 9 | 93 ± 8 | 92 ± 10 | < 0.001 |
| Malnutrition, % | < 0.001 | ||||
| Yes | 110(32.1) | 155(38.6) | 237(77.2) | 336(75.5) | |
| No | 233(67.9) | 247(61.4) | 70(22.8) | 109(24.5) | |
Abbreviations: SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, WC waist circumference, ADL activity of daily living, GNRI geriatric nutritional risk index
As shown in Fig. 2, DAG selected the covariates associated with malnutrition. These covariates included education, BMI, WC, ADL, number of meals per day, diet time, red meat consumption, seafood consumption, vegetable consumption, and fruit consumption. Logistic regression analyses demonstrated a significantly inverse association between age and malnutrition (Table 2). Each one-year increase in age was associated with a significantly reduced likelihood without malnutrition [model 1: odds ratio (OR) 0.91, 95%confidence interval (CI) 0.90–0.92, p < 0.001; model 2: OR 0.94, 95%CI 0.92–0.96, p < 0.001). Participants in Q3 and Q4 exhibited significantly lower likelihood without malnutrition compared with the reference (Q1). RCS revealed a linear correlation between age and malnutrition (model 1: p-overall ≤ 0.001, p-nonlinear = 0.011; model 2: p-overall ≤ 0.001, p-nonlinear = 0.079; Fig. 3). As shown in Fig. 4, subgroup analyses indicated interaction effects of cigarette smoking (p = 0.002), red meat consumption (p = 0.028), and vegetable consumption (p = 0.009) on malnutrition.
Fig. 2.
Directed Acyclic Graph. Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; GNRI, Geriatric Nutritional Risk Index; BMI, body mass index; WC, waist circumference; ADL, activity of daily living
Table 2.
Logistic regression analyses between age and malnutrition
| Characteristic | Model 1 | Model 2 | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | p | OR | 95%CI | p | |
| Age | 0.91 | 0.90–0.92 | < 0.001 | 0.94 | 0.92–0.96 | < 0.001 |
| Q1 | — | — | — | — | ||
| Q2 | 0.75 | 0.56–1.02 | 0.066 | 0.96 | 0.62–1.48 | 0.853 |
| Q3 | 0.14 | 0.10–0.20 | < 0.001 | 0.27 | 0.16–0.44 | < 0.001 |
| Q4 | 0.15 | 0.11–0.21 | < 0.001 | 0.36 | 0.22–0.57 | < 0.001 |
| p for trend | < 0.001 | < 0.001 | ||||
Model 1: unadjusted;
Model 2: adjusted for education, body mass index, waist circumference, activity of daily living, number of meals per day, diet time, red meat consumption, seafood consumption, vegetable consumption, and fruit consumption
Abbreviations: OR odds ratio, CI confidence interval
Fig. 3.
Restricted Cubic Spline. A Unadjusted; B Adjusted for education, body mass index, waist circumference, activity of daily living, number of meals per day, diet time, red meat consumption, seafood consumption, vegetable consumption, and fruit consumption. Abbreviations: CI, confidence interval
Fig. 4.
Subgroup analyses and interaction effects. Abbreviations: OR, odds ratio; CI, confidence interval
Discussion
This study demonstrated a linear association between age and malnutrition in Chinese oldest-old populations and indicated interaction effects of cigarette smoking, red meat consumption, and vegetable consumption on malnutrition. Food consumption is related to nutrient intake and nutritional status as a key element in age-malnutrition association.
Study conducted in China showed that the prevalence of malnutrition among community-dwelling older adults aged ≥ 65 years was 36.4% in China [26]. Another study with a cohort of people aged ≥ 75 years, 2.2% were diagnosed with malnutrition, while 38.4% exhibited malnutrition [15]. Evidence indicates that malnutrition is higher with age increases [27]. This study specifically included older adults aged 80 years and above, including centenarians in Hainan, and showed a linear association between age and malnutrition in older populations. This age distribution is likely attributable to external factors such as famine or war. The individuals aged between 90 and 100 years may represent the survivors of such hardships, which could also explain an increased risk of malnutrition observed in this age distribution. These oldest-old and centenarian adults commonly face malnutrition and has more misconceptions about healthy dietary practice. This phenomenon is primarily attributed to objective physiological factors such as declining organ functions and complex socio-psychological elements [2, 28].
Epidemiological evidence indicates that better food quality is associated with a reduced mortality rate in older adults [29–31]. Previous studies have noted that weight tends to increase until the age of 50–60 years, and begins to decrease steadily for those over 65 years at the rate of 0.5% per year [32, 33]. More importantly, dramatic shift from protective to hazardous effects of low protein intake coincides with the beginning of weight loss. Some studies have shown that moderate and high protein intake may be necessary for older adults to reduce age-dependent weight loss [34–39]. Furthermore, previous study from yeast and mice models could partly explain the effects of protein intake on mortality rate based on the relationship between amino acid supply and deoxyribonucleic acid damage [35]. This study showed that participants with malnutrition were older than those without malnutrition, and food consumption had potential to promote decreased malnutrition in Chinese oldest-old and centenarian populations. This suggests that dietary health of older adults should be paid more attention to and further optimized as they grow older to supply good sources of high-quality protein, vitamins, and minerals [40–44].
Food diversity decreases with advancing age. There were changed body composition and declining gastrointestinal function, which may adversely affect nutrient digestion and absorption, leading to an increased malnutrition [45, 46]. Aging is commonly accompanied by decreased muscle and elevated osteoporosis, potentially restricting daily mobility and complicating food intake in older adults [47]. A study including 1856 adults aged ≥ 65 years reported that poor appetite was associated with lower food and nutrient intake and an independently increased mortality rate [48]. This study showed that red meat, seafood, vegetable and fruit consumption for the majority of older adults remained unchanged or decreased over the past five years. This may be related to body function, mental state, and tooth loss. Among 492,823 Americans aged 50–71 years, older adults with superior food diversity had a 12.28% reduced diseases and mortality [49]. This may be that food diversity is associated with the maintenance of muscle strength and the size of telomere length [50–52]. The CLHLS revealed that dietary pattern characterized by fruit and vegetable consumption was associated with a lower mortality [53]. This finding resembled dietary pattern among Hainan residents in this study, highlighting the need to keep a watchful eye on food diversity, especially in older adults, so as to promote healthy aging [54].
Limitations should be mentioned in this study. Firstly, it is possible insufficient to use only GNRI for representing nutritional status of older adults, and further researches should add other indicators to explain age and food in malnutrition. Secondly, there may be some exaggerations in age reporting among centenarian populations, including Chinese centenarians in this study. Thirdly, a quantitative evaluation of food intake was not performed, which made this research insufficiently detailed.
Conclusions
This study revealed a linear association between age and malnutrition among Chinese oldest-old adults, suggesting interaction effects of cigarette smoking, red meat consumption, and vegetable consumption on malnutrition. Moreover, food quality and diversity, determined by food consumption in terms of balance and variety, is an important regulator for age-malnutrition association of older adults. An in-depth understanding of food and nutrition in older adults could help establish evidence-based recommendations that have the potential to increase life quality and survival rate.
Acknowledgements
We appreciate all the staff for their continued cooperation and contribution in field work.
Authors’ contributions
All authors contributed to study design, project conduction, and manuscript writing.
Funding
This work was supported by grants from the Natural Science Foundation of Hainan Province (821QN389, 821MS117, 821MS115, 824MS173, 823MS161, 821MS112, 822MS198, 822MS193), the Joint Program on Health Science & Technology Innovation of Hainan Province (WSJK2024MS176, WSJK2024QN080, WSJK2025ZD223), the Hainan Clinical Medical Research Center Project (LCYX202106, LCYX202201, LCYX202303), and the Hainan Academician Workstation Foundation. The sponsors had no role in the design, conduct, interpretation, review, approval or control of this article.
Data availability
Datasets used or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee of Hainan Hospital of Chinese PLA General Hospital (Sanya, Hainan; Number: 301HNLL-2016–01). All participants provided informed consent for participation in the study and for their data to be used. The work described has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).
Consent for publication
Not applicable.
Competing interests
All authors have no competing interest.
Footnotes
Publisher’s Note
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Qiao Zhu, Shasha Guan, Qun Li, Yangrui Zheng and Xiaobing Wang co-first authors.
Contributor Information
Yali Zhao, Email: zhaoyl301@163.com.
Bing Zhu, Email: zhubing301@yahoo.com.cn.
Ping Ping, Email: pingping301@126.com.
Youchen Zhang, Email: zangyoujin123@163.com.
Shihui Fu, Email: xiaoxiao0915@126.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Datasets used or analysed during the current study are available from the corresponding author on reasonable request.