Associations between Plant-Based Dietary Patterns and Sensory Impairments among Chinese Older Adults: Based on the Chinese Longitudinal Healthy Longevity Survey

The global elderly population is increasing, with projections suggesting that individuals aged 60 and above will constitute 22% of the world’s population by 2050 [1]. This has led to an increasing prevalence of sensory impairment (SI) [2], which mainly includes vision impairment (VI), hearing impairment (HI), and dual sensory impairment (DSI) [3]. Currently, a significant proportion of the world’s population suffers from VI (206 million individuals) and HI (1.57 billion individuals), with more than 60% of those over 50 years of age [4, 5]. As much as 2% of the worldwide population suffers from DSI, the majority of which belongs to the older age group [6] SI not only affects patients’ quality of life, but is also significantly associated with cognitive decline, depressive symptoms, suicidal ideation [7, 8], and an increased risk of death [9].

SI may be related to age and disease [10, 11]. Age-related hearing loss involves several auditory structures: degeneration of the mechanotransduction cochlear inner and outer hair cells; reduced function within the stria vascularis; and degeneration of the auditory nerve [12]. In addition, noise exposure and ototoxic drugs can independently cause hearing loss in individuals of any age [13]. Refractive errors, cataracts, glaucoma, and age-related macular degeneration (AMW) are all important contributors to VI, with glaucoma being an irreversible optic neuropathy that leads to loss of peripheral muscles and AMW involving pathological changes in the deep macular retina and peripheral chorioretinopathy, leading to loss of central vision [14]. In addition, early retinal deep vascular changes in the elderly may be indicative of subsequent developments in age-related central auditory processing [15]. However, a high-quality diet was associated with lower markers of inflammation [16] and markers of oxidative stress [17], as well as reduced endothelial dysfunction [16], which are associated with hearing loss and vision loss. Therefore, a higher-quality diet may reduce the prevalence of DSI in the population.

Among the multiple dietary patterns, plant-based diets have garnered significant attention due to their positive impacts on human health and planetary environmental sustainability [18]. These diets can be defined as those that primarily consist of plants but may also include animal products [19]. Because the nutritional quality of plant-based foods is not identical, Satija et al. [20] developed three indices for plant-based dietary patterns: the overall plant-based diet index (PDI), the healthy plant-based diet index (hPDI), and the unhealthy plant-based diet index (uPDI). These indices aim to assess the quality of plant-based food intake while also considering the nutritional value of animal foods consumed. As previous studies have revealed, the Mediterranean diet and plant-based diets can help prevent auditory vestibular dysfunction as well as vision loss due to cataracts, age-related macular degeneration (AMD), and diabetic retinopathy [21, 22]. Vegetarian diets and regular consumption of fish reduce the risk of age-related eye disease in adults, whereas consumption of red meat increases the risk [23]. High-fat diets, increased intake of carbohydrates and sugars, and high intake of beer and spirits may be associated with auditory impairment and hearing loss [21, 24‒26].

Studies have shown that following a healthy plant-based diet is linked to a reduced risk of various diseases and mortality, whereas unhealthy plant-based dietary patterns elevate the risk of disease as well as death [20, 27‒29]. However, the relationship between plant-based dietary patterns and SI in the elderly is unclear. In addition, Asian populations have a predominantly plant-based dietary pattern, whereas current studies on plant-based dietary patterns have mostly focused on Western populations. Therefore, we investigated the association between a set of plant-based dietary indices (PDI, hPDI, and uPDI) and SI in a group of older adults aged 65 years or older based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database to provide more evidence to support the dietary health of Asian populations.

The CLHLS is a follow-up survey of people aged 65 and above, organized by the Research Center for Healthy Aging and Development of Peking University. Follow-up has been conducted every 2–3 years since the 1998 baseline survey [30]. The multistage stratified cluster sampling method was used for sampling [31]. All randomly selected elderly people in the county and urban areas were voluntarily interviewed face-to-face by trained interviewers. CLHLS is the earliest-started and longest-sustained social science survey of its scope in China, which covers 23 provinces, municipalities, and autonomous regions across the country, and represents 85% of China’s elderly population. There was a consistently high level of data quality reported [32]. Before data collection, consent forms were signed by all respondents, and the CLHLS study was given the go-ahead by both Duke University and Peking University Research Ethics Boards (IRB 00001052-13074).

The study is based on data from the CLHLS for the year 2018. A total sample size of 15,771 participants aged 65 and over was recruited; participants lacking data on plant-based diet index (634), HI (77), and VI (201) were excluded. Ultimately, the study was attended by 14,859 individuals (Fig. 1).

A simplified food frequency questionnaire was used to collect dietary information from the participants [31]. Although the use of frequency can lack detailed quality, previous studies have shown this non-quantitative form of questionnaire to be reliable and valid [33].

The study covered the most prevalent components of the daily diet of Chinese people with a total of sixteen different food kinds. All types of food were categorized into animal and plant categories based on their source. Healthy plant foods consist of whole grains, fresh vegetables, fresh fruits, vegetable oils, garlic, tea, nuts, and legumes. Less healthy plant foods included refined grains, salt-preserved vegetables, and sugar. Animal food consists of animal oils, eggs, fish, dairy products, and meat.

When calculating the PDI and hPDI, healthy plant foods were given a score of 5 to 1 based on the frequency of consumption (almost every day = 5, ≥1 time/week = 4, ≥1 time/month = 3, occasionally = 2, rarely or never = 1). Nevertheless, the inverse is true for the uPDI assignment (almost every day = 1, ≥1 time/week = 2, ≥1 time/month = 3, occasionally = 4, rarely or never = 5). Less healthy plant foods for PDI and uPDI were given a score of 5 to 1 based on the frequency of consumption (almost every day = 5, ≥1 time/week = 4, ≥1 time/month = 3, occasionally = 2, rarely or never = 1), the inverse is true for the hPDI assignment (almost every day = 1, ≥1 time/week = 2, ≥1 time/month = 3, occasionally = 4, rarely or never = 5). When calculating PDI, hPDI, uPDI for animal foods, a score of 1 to 5 is given based on the frequency of consumption (almost every day = 1, ≥1 time/week = 2, ≥1 time/month = 3, occasionally = 4, rarely or never = 5) [28] (please refer to online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000540611 for more details). The PDIs were calculated by summing the scores for the frequency of intake of the 16 foods in question, ranging from 16 to 80.

In the questionnaire, feedback was given by the investigator on the hearing function of the elderly. Responses were given with four options: (1) yes, no hearing aids; (2) yes, but need hearing aids; (3) partially, despite having hearing aids; and (4) no. The participants were asked if they had hearing aids or if they could not hear anything. If participants needed a hearing aid or were unable to hear anything, they were classified as hearing impaired (HI) [34].

Participants’ ability to see the circle on the card and determine the direction of the circle’s gap under the torchlight was used to measure their visual function. There were four choices: (1) could see and distinguish the notch in the circle; (2) could see but not distinguish the notch in the circle; (3) could not see; and (4) was blind. If a respondent was blind or could not see the gap, they were classed as having VI [34]. By definition, DSI is the co-occurrence of both hearing and VI [35].

Based on previous studies, we adjusted for potential covariates [36, 37]. Demographic characteristics included age, sex (male/female), body mass index (BMI) (<25/≥25), and residence (urban/rural). Among the socioeconomic traits were marital status (married cohabiting/other), Co-residence (solitary/non-solitary), education (illiterate/primary or secondary school and above), and economic status (affluent/non-affluent). Sleep duration (≤6 h/7–8 h/≥9 h), smoking status (current/previous/never), alcohol consumption (current/previous/never), diabetes (yes/no), cardiovascular disease (yes/no), hypertension (yes/no), and dementia (yes/no) are all factors that affect health behaviors (online suppl. Table 2).

Continuous variables represent mean ± standard deviation, categorical variables represent percent. Independent samples t tests for continuous variables and χ² tests for categorical variables were used to compare the baseline characteristics of the different SI groups. We grouped within the PDI, hPDI, and uPDI groups by quintiles into Q1, Q2, Q3, Q4, and Q5 groups (online suppl. Table 3). Logistic models and 2 types of regression models were developed to analyze the associations of PDI, hPDI, and uPDI with different SIs. Model 1 included age and gender; Model 2 included demographic characteristics, socioeconomic characteristics, and health behaviors. In addition, the risk of the disease per 10-point increase was estimated by using per10PDI, per10hPDI, and per10uPDI scores as continuous variables. Restricted triple spline analysis was applied to explore the dose-response relationship between plant dietary indices and SI. Stratified analyses were conducted by sex (male/female), age (<80/≥80) [38], BMI (<25/≥25), residence (urban/rural), marital status (married cohabiting/other), co-residence (solitary/non-solitary), educational attainment (illiterate/primary or secondary school and above), current alcohol consumption status (yes/no), and current smoking status (yes/no), and interactions between strata were analysed. Considering that multiple testing may lead to an increase in type I error, we used a Bonferroni correction and adjusted the significance level to p < 0.001. Statistical analyses were performed with SPSS 24.0 and R4.3.1, and a two-sided test of p < 0.05 was significant.

We included a total of 14,859 participants (6,492 males and 8,367 females) aged 65 years or older from CLHLS (2018). The mean age was 85.50 ± 11.60 years. 56.3% were female. The overall self-reported prevalence of VI, HI, and DSI was 36.4%, 26.8%, and 18.0%, respectively. PDI scores and hPDI scores may be higher, but uPDI scores may be lower, in the HI, VI, and DSI groups compared to the no SI group (p < 0.001) (Table 1).

Table 2 shows the results of analyzing the logistic model. In model 1, adjusted for sex and age, each 10-point increase in PDI was related to a lower prevalence of VI (OR 0.83, 95% CI: 0.78–0.89), HI (OR 0.84, 95% CI: 0.77–0.91), and DSI (OR 0.75, 95% CI: 0.69–0.83), and each 10-point increase in hPDI was also linked to SI. After further adjustment for other confounders and chronic diseases, these associations were somewhat attenuated, and the associations of each 10-unit increase in PDI with VI (OR 0.85, 95% CI: 0.78–0.93), HI (OR 0.87, 95% CI: 0.79–0.96) and DSI (OR 0.78, 95% CI: 0.69–0.87) remained significant. Furthermore, the correlation with VI (OR 0.82, 95% CI: 0.82–0.97) remained significant only for each 10-unit increase in hPDI. In contrast, in both models 1 and 2 we observed that each 10-point increase in uPDI was related to an increasing prevalence of VI (OR 1.20, 95% CI: 1.10–1.28), HI (OR 1.13, 95% CI: 1.04–1.24), and DSI (OR 1.25, 95% CI: 1.13–1.38).

To assess the potential dose-response relation of plant-based dietary patterns and SI, we assigned PDIs a 5-quintile score. In model 2, those with PDI in the highest quintile had a significantly lower risk of VI (OR 0.78, 95% CI: 0.67–0.90, p_trend <0.001), HI (OR 0.83, 95% CI: 0.70–0.99, p_trend <0.001), and DSI (OR 0.62, 95% CI: 0.51–0.77, p_trend <0.001). Furthermore, individuals who ranked in the highest quintile for hPDI exhibited a 25% reduced risk of VI disease. In contrast, high uPDI was found to be linked to an increased risk of VI (OR 1.37, 95% CI: 1.17–1.61, p_trend <0.001), HI (OR 1.36, 95% CI: 1.12–1.65, p_trend <0.001), and DSI (OR 1.56, 95% CI: 1.25–1.95, p_trend <0.001) (Table 2).

Figures 2-4 illustrate the results of the subgroup analyses, where we observed that per10PDI, per10hPDI, and per10uPDI were significantly associated with VI, HI, and DSI in subgroups defined by gender, age, BMI, type of place of residence, marital status, cohabitation status, educational attainment, and drinking and smoking status and that no significant interactions were comparing the classes (p_interaction >0.001). Restricted cubic spline analysis was used to investigate the dose-response relationship between PDIs and SI. After adjusting for covariates, we found that only hPDI showed a nonlinear relationship with VI (p_{for nonlinearity} = 0.001). The others were linear relationships (p_{for nonlinearity} >0.05) (Fig. 5).

The study confidently asserts that a 10-unit increase in PDI results in a reduced risk of VI, HI, and DSI. Additionally, an increase of 10 points in hPDI results in a decrease in the risk of VI. It is worth noting, however, that an increase of 10 units in uPDI leads to an increase in the risk of VI, HI, and DSI.

Our study found that the hPDI was negatively associated with VI, specifically, for every 10-point increase in the hPDI, the risk of developing VI decreased. In contrast, uPDI was positively associated with VI, specifically, the risk of developing VI increased with every 10-point increase in uPDI. Recent reviews have shown that Mediterranean and plant-based diets – high in fruits, vegetables, beans, whole cereals, and nuts and low in animal products and processed foods – effectively protect against cataracts, AMD, and diabetic retinopathy [39]. Chiu et al. [40] reported that in addition, many studies have shown that adherence to Eastern dietary patterns, the Mediterranean diet [41], and the Healthy Eating Index [42] can prevent AMD. However, Western dietary patterns that are unhealthy, which are characterized by high in red meat, saturated fat, processed foods, confectionery, desserts, and sugary drinks may accelerate the progression of cataracts [43] and AMD [44]. Cataracts and AMD are widely acknowledged as the main reasons for vision loss in the elderly [45, 46]. HPDI is a dietary pattern that emphasizes the consumption of nutritious plant foods and restricts the intake of animal foods, while uPDI is the opposite approach. Thus, our results are consistent with previous studies. Regarding the possible mechanisms, hPDI contains high levels of unsaturated fatty acids and antioxidants like vitamins and flavonoids, which have been proven to have anti-inflammatory benefits [47, 48]. Oxidative stress and inflammation have been implicated as common pathogenic mechanisms in age-related eye diseases, according to numerous reports [49‒52]. In addition, hPDI has a high fiber content, which significantly influences the diversity and homeostasis of the intestinal flora structure [53]. Dysbiosis of gut flora has been demonstrated to correlate with immune and inflammation-mediated eye diseases [54].

Our study also found that PDI was negatively associated with HI, with a 10-point increase in PDI resulting in a decreased risk of developing HI. However, uPDI was positively associated with HI, with a 10-point increase in uPDI resulting in an increased risk of HI. To date, several reports on the link between diet and HI have been published. In the USA, a Healthy Eating Index was linked to a lower likelihood of persistent tinnitus in cross-sectional analyses [55]. Another study showed that a diet high in fruit, vegetables, and meat and low in fat was related to a reduced risk of hearing loss [56]. A plant-based diet positively impacts hearing function through several mechanisms. Oxidative stress and systemic inflammation have been implicated in age-related hearing loss [57]. PDI emphasizes a high intake of vegetables, fruit, whole grains, nuts, and beans, which are important sources of age-protecting chemicals [16, 58]. On the other hand, a high-quality diet may prevent vascular damage and reduce blood flow to the cochlea by promoting favorable lipid distribution, improving the function of the endothelium, lowering the level of blood pressure, and reducing inflammation. Neuroinflammation and neurodegenerative diseases of nerve fibers and the central auditory pathway may also be reduced by a healthy diet [59, 60]. In contrast, higher consumption of carbohydrates [24], fat [61, 62]^, and cholesterol [63] has been associated with hearing loss. This may be because excessive carbohydrate, fat, and cholesterol intake increases the likelihood of cardiovascular and cerebrovascular disease, and similar mechanisms affect cochlear blood flow [64]. In addition, changes in the gut microbiota that may result from a chronic high-fat diet may trigger a systemic immune reaction that impairs the blood-labyrinthine barrier (BLB) permeability of the inner ear, leading to cochlear inflammation and hearing loss [65].

In summary, the risk of developing DSI decreases by 10 points for every 10-point increase in PDI, while the risk increases by 10 points for every 10-point increase in uPDI. Although the precise mechanisms underlying the relationship between PDIs and DSI remain unclear, several studies have indicated that inflammation and oxidative stress may be associated with vision and hearing loss. Consequently, antioxidant components and antioxidant effects in plant diets may partly explain the protective effect of PDI against DSI. Furthermore, alterations in the composition of the gut microbiota may also be implicated in the pathogenesis of VI and HI. High-fat diets have been associated with gut microbiota dysbiosis, which may represent a potential mechanism by which uPDI is associated with an increased risk of DSI.

The study has several strengths. First, as far as we know, this is the first study using national-representative data to evaluate the association of plant dietary patterns with VI, HI, and DSI in Chinese adults aged 65 years and older. Second, the CLHLS is a nationally representative, standardized survey with generalizable results and a high degree of external validity. Despite the many strengths of this study, we need to be cautious in interpreting the results due to the following limitations. First, measurement errors in self-reported diet, SI, and other information are inevitable, which may lead to errors in association estimates. Second, while we adjusted for many relevant confounding factors, we cannot exclude unknown or residual confounding. Third, causal inference is limited by the cross-sectional design of this study. Finally, it is worth noting that large sample sizes can make even small effects statistically significant, so we should be more careful in interpreting the results to avoid over-interpretation and misrepresentation. However, there are some benefits to large sample sizes! They help reduce sampling error, leading to more accurate estimates and more stable results. This is the reason why most of the best papers nowadays choose to use large sample size data for their research. In addition, our results need to be validated by further large-scale cohort studies.

The increase in PDI and hPDI correlates with a reduced prevalence of one or more SIs. Conversely, an increase in uPDI is associated with an elevated prevalence of multiple SIs. Our study findings emphasize the significance of plant-based food quality, advocating for adherence to a plant-based dietary pattern while reducing the intake of less healthy plant foods and animal-based products.

The data from the CLHLS survey already obtained ethical approval and informed consent and was approved by the Research Ethics Committees of Duke University and Peking University (IRB00001052-13074). The authors affirmed that human research participants provided informed consent for publication. This article does not contain any studies with animals performed by any of the authors. This study was not formally registered, and the analysis plan was not formally pre-registered.

The authors have declared no conflict of interest.

The funders had no role in the design of the manuscript.

Xingxu Song, Zhong Tian, and Kexin Jiang: data curation, conceptualization, methodology, software, formal analysis, validation, writing – original draft preparation, and writing – review and editing. Kai He: data curation, conceptualization, and writing – review and editing. Yuhan Huang, Chengxiang Hu, and Xue He: resources and investigation. Lina Jin and Yuchun Tao: writing – review and editing, supervision, conceptualization, and project administration.

Xingxu Song, Zhong Tian, and Kexin Jiang contributed equally to this work as co-first authors.Lina Jin and Yuchun Tao have contributed equally as co-senior authors.

The data that support the findings of this study are openly available in CLHLS at https://opendata.pku.edu.cn/dataverse/CHADS, Reference No. IRB00001052-13074.