Monoterpenes in Your Beverage: A Flavoring Catalyst for Faster Aging?

doi:10.21203/rs.3.rs-5035701/v1

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Monoterpenes in Your Beverage: A Flavoring Catalyst for Faster Aging?

https://doi.org/10.21203/rs.3.rs-5035701/v1

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Background

Aging is characterized by the progressive deterioration of tissue structure and physiological functions. While the impact of sugar and artificial sweeteners in beverages on biological aging, mediated through increased body mass index (BMI) and obesity, has been well-documented, the potential effects of other food additives, particularly monoterpenes, have not been thoroughly explored. This study aimed to investigate the association between high exposure to monoterpenes in beverages and biological age acceleration.

Methods

Included in the current study were 1,217 adults from the National Health and Nutrition Examination Survey (NHANES) in 2013–2014. Beverage intake was assessed through the 24-hour dietary recall. Serum levels of three monoterpenes—limonene, α-pinene, and β-pinene were used to estimate monoterpene exposure. Biological age was assessed using phenotypic age (PA), with acceleration calculated as the difference from chronological age. Multiple statistical approaches, including linear regression, restricted cubic spline (RCS) models, quantile g-computation (Qgcomp), and Bayesian kernel machine regression (BKMR), were employed to analyze associations between beverage intake, monoterpene exposure and biological aging. Furthermore, mediation analyses were conducted to explore the mediated effects of monoterpenes and BMI on the association of beverage intake with PA acceleration.

Results

High beverage intake (β = 0.94, 95% CI: 0.26, 1.62) and increased exposure to monoterpenes, particularly limonene (β = 1.65, 95% CI: 0.55, 2.76) and β-pinene (β = 1.35, 95% CI: 0.53, 2.18), were associated with accelerated PA. In the RCS analyses, the effects of limonene and β-pinene on PA acceleration exhibited both linear and nonlinear. In the Qgcomp model, the mixed exposure of three monoterpenes had a significant positive relationship with PA acceleration (β = 0.25, 95% CI: 0.12, 0.37). Moreover, we observed antagonistic effects between limonene and each of α-pinene and β-pinene concerning the acceleration of biological aging in the BKMR model. Additionally, limonene and BMI were identified as parallel mediators of the relationship between beverage intake and PA acceleration.

Conclusion

The study provides novel insights into the detrimental effects of high monoterpene exposures in beverages on biological aging. These findings highlight the importance of considering a broader range of food additives in public health guidelines, as their impact on long-term health outcomes may be significant.

Monoterpenes

Limonene

α-Pinene

β-Pinene

Biological Aging

Beverage

Aging is characterized by the progressive deterioration of tissue structure and physiological functions within the body [1]. However, due to individual differences, aging does not progress at a uniform rate. Thus, biological aging is often used as an indicator, encompassing a range of factors including genetics, environmental exposures, lifestyle choices, and mental well-being [1]. In contrast to chronological aging, biological aging pertains to the intrinsic aging processes at the biological level [2]. Biological aging has become a key risk predictor of healthy longevity or mortality [3, 4]. A spectrum of methodologies has been introduced to quantify biological aging, from specific biomarkers such as telomere length to complex algorithms that amalgamate data from various 'omics' platforms, including proteomics, metabolomics, and epigenetics as well as other molecular analyses [5, 6]. Among these, the phenotypic age (PA), which integrates information across clinical metrics, has been demonstrated to be relatively reliable for predicting mortality and morbidity [7, 8].

Beverages, including soft drinks, carbonated beverages, sports drinks, energy drinks, and fruit-flavored drinks are the largest sources of sugars and low-calorie sweeteners [9]. Although no studies have reported a relationship between beverages and PA, previous research has suggested that beverage intake may shorten telomeres and accelerate aging [10]. And numerous studies have found that beverage intake is associated with an increase in body mass index (BMI), which can contribute to obesity [11, 12]. Obesity, in turn, can trigger oxidative stress and inflammation, both of which promote telomere shortening and aging acceleration [13]. Most studies attributed the detrimental effects of beverages on BMI and aging acceleration to their sugar and artificial sweeteners [11, 12, 14]. However, few studies have explored whether other food additives in beverages could have an impact on accelerating aging.

Monoterpenes are a class of volatile compounds consisting of a variety of 10-carbon skeleton molecules, including a wide range of oxygen-containing derivatives (monoterpenoids), such as alcohols, aldehydes, ketones, and carboxylic acids [15]. They are emitted by many kinds of plants and can also be widely found in various fruits, vegetables, spices, and herbs [16]. Due to their typical fragrance, monoterpenes, such as limonene, α-pinene, and β-pinene, were often used as food additives in beverages or cuisine to enhance flavor and aroma [17, 18].

Concerns are growing regarding the health impact of monoterpenes. Monoterpenes can be detected in human serum, and a positive correlation has been observed between exposure doses and serum concentrations [19, 20]. High exposures to monoterpenes have been found to irritate mucous membranes and be associated with asthma and dyslipidemia [21–23]. However, these studies mainly focus on inhalation exposure, and the information concerning monoterpene exposure on biological aging, especially for oral exposure, is very limited. Up until now, the underlying impact of monoterpene exposure on biological aging has not been explored. In the present study, we hypothesized that high exposure to monoterpenes in beverages are associated with PA acceleration and aimed to validate this hypothesis. Understanding the effects of exposure to monoterpene additives in beverages on biological aging has great significance for healthy life-span extension.

Study population

This study was conducted utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014 cycle. This cycle was selected because of the availability of the monoterpene exposure data. NHANES implemented a multistage stratified probability sampling design to obtain a weighted, representative sample of the United States population. All NHANES protocols have been approved by the National Center for Health Statistics Research Ethics Review Board (available on the web at: https://www.cdc.gov/nchs/nhanes/) and all participants gave informed consent.

Participants who were younger than 20 years old and were pregnant, or those with incomplete information on the serum monoterpenes, biological aging measurements, 24-hour dietary recall and covariates were excluded (Supplemental Figure S1).

Assessment of beverage intake

Consumption data for beverages was obtained from the 24-hour dietary recall, in which participants reported the types and amounts of all food and beverages consumed in the preceding 24 hour. On the same day as the blood sample for monoterpene analysis was collected, the 24-hour dietary recall data were recorded by an interviewer during the Mobile Examination Center (MEC) appointment. These data were labeled as "Day 1" to differentiate them from the dietary recall information collected on "Day 2", which was gathered via a phone interview conducted 3 to 10 days after the MEC appointment. Since monoterpenes can be rapidly eliminated from the body in approximately 24 hours [24], "Day 1" dietary recall was an appropriate time point to assess the relationship between beverage intake and serum monoterpene levels. In this study, beverages that might potentially contain monoterpene food additives included soft drinks, sports and energy drinks, fruit-flavored drinks, and carbonated water [25, 26]. However, 100% fruit juice, which naturally contains monoterpenes, was not included. According to the median, beverage intake was further categorized into low intake (< 124g/d), and high intake (≥ 124g/d).

Assessment of BMI and obesity

Weight and height were measured in MEC utilizing standardized techniques and equipment. The BMI was calculated as weight in kilograms divided by height in meters squared. BMI was further categorized into non-obesity (BMI < 30 kg/m²), and obesity (BMI ≥ 30 kg/m²) [27].

Assessment of monoterpene exposure

Numerous studies have indicated that monoterpenes are detectable in humans, with a positive correlation observed between exposure levels and serum concentrations [19, 20]. In this study, we utilized serum monoterpene levels as a proxy for monoterpene exposure. The serum concentration of three monoterpenes, including limonene, α-pinene, and β-pinene, were measured and recorded in NHANES 2013–2014. They were detected using the headspace solid-phase microextraction coupled to gas chromatography-tandem mass spectrometry (HS-SPME-GC-MS/MS) [28]. The lower limits of detection (LLODs) of limonene, α-pinene, and β-pinene were 0.162 ng/mL, 0.050 ng/mL, and 0.056 ng/mL, respectively. Samples with serum monoterpene levels below their respective LLODs were assigned an imputed value, calculated as the LLOD divided by the square root of 2. Detailed laboratory methodologies are accessible on the NHANES website. Serum monoterpene levels were further divided into three groups based on the tertiles: low, middle, and high, respectively.

Measurements of biological aging

PA was used to estimate biological age in the current study, due to its thorough and practical applicability for implementation in NHANES [29–32]. In brief, PA is defined as the age at which an individual's mortality risk aligns with the average mortality hazard observed in the reference sample of NHANES [8]. We used the R package “BioAge” to train the PA algorithm through NHANES III data and to calculate the PA in the NHANES cycle from 2013 to 2014 [7]. We referred to the previous studies and included eleven biomarkers for the calculation of the biological aging measures: white blood cell count, lymphocyte percentage, mean cell volume, albumin, alkaline phosphatase, blood urea nitrogen, creatinine, uric acid, glycated hemoglobin, total cholesterol, and systolic blood pressure [2, 7, 33]. Because the C-reactive protein (CRP) being used in the PA algorithm from the original research [8] and its unavailability in the NHANES 2013–2014 cycle, we evaluated the impact of CRP on the PA algorithm and compared the difference between the PA calculation with and without CRP using data from the NHANES 2005 to 2010. We found that PA derived from the biomarker set without CRP was strongly correlated with that derived from the biomarker set with CRP (r = 0.9998, P < 0.001; Supplemental Figure S2), consistent with previous studies [34].

PA acceleration was calculated as the difference between PA and chronological age, where > 0 indicated a faster biological aging and < 0 indicated delayed biological aging.

Defining covariates

Covariates, including age, gender, race, education level, poverty income ratio (PIR), smoking status, alcohol drinking, and physical activity were collected by interview in NHANES. Age was adjusted as continuous covariates in the analysis. Gender was classified as “female” and “male”. Race was categorized into “non-Hispanic white”, and “others” (non-Hispanic black, Mexican American, other Hispanic, and other races). Educational level was categorized as “under high school” (including less than ninth grade and ninth-eleventh grades, which included 12th grade with no diploma), “high school or equivalent or above high school” (including high school grad/general educational development diploma or equivalent, some college or AA degree and college graduate or above). The PIR, defined as the ratio of a family's income to the poverty threshold, was utilized to categorize income levels into two groups: "below 1" and "1 and above" [35]. Participants were classified as "nonsmokers" if they had smoked fewer than 100 cigarettes throughout their lives or smoked more than 100 cigarettes during their lifetime but had ceased smoking. In contrast, "smokers" were those who smoked more than 100 cigarettes in life and smoked some days or every day. Regarding alcohol consumption, individuals were divided into “drinkers”, who had consumed at least 12 alcoholic drinks within one year, and “nondrinkers”, who had less than 12 alcoholic drinks in one year. Physical activity was categorized into “active” (moderate recreational activities ≥ 150 minutes/week or vigorous recreational activities ≥ 75 minutes/week) and “inactive” (moderate recreational activities < 150 minutes/week and vigorous recreational activities < 75 minutes/week).

Statistical analysis

The statistical analyses conducted in this study incorporated sample weights, clustering, and stratification due to the complex sampling design in NHANES.

Continuous variables were expressed as means (standard deviations) if they were normally distributed, while those without normal distribution were represented by median (interquartile ranges). Categorical variables were expressed as numbers (percentages). Numbers reflected the study sample, whereas percentages reflected the survey-weighted data.

Correlations among serum monoterpene levels, BMI and beverage intake were determined using Pearson correlations. A log transformation was applied to the serum monoterpene levels and beverage intake data, due to their non-normal distribution.

To investigate the associations between beverage intake, BMI and serum monoterpenes with PA acceleration, multiple linear regressions were used. Three models were utilized: crude model included no covariates, Model 1 adjusted for age, gender, race, education level, and PIR, whereas Model 2 adjusted for the covariates in Model 1 plus smoker/drinker, and physical activity. For the categorical variable analysis, the concentrations of three monoterpenes were separated into three tertiles. Restricted cubic spline (RCS) models were further employed to examine the non-linear associations. According to the previous study, we utilized 3 knots (10th, 50th, and 90th percentiles) in our study [23].

The quantile g-computation model (Qgcomp) and Bayesian kernel machine regression (BKMR) were applied to analyze the joint effects of mixed monoterpene exposure. Qgcomp is a statistical method that integrates g-computation and weighted quantile sum regression (WQS) [36]. In contrast to WQS, Qgcomp exhibits specific advantages in accommodating directional heterogeneity and non-linear or non-additive impacts of mixture components [36]. Qgcomp in this study was not only used to estimate the total effect of three monoterpenes on biological aging, but also was utilized to evaluate the relative contribution of the single monoterpene to the total effect. We complemented Qgcomp analyses with BKMR, which allowed us to explore the non-linear relationships and intricate interactions within the monoterpene mixture. In BKMR, posterior inclusion probabilities (PIP), which represent the probability that a certain chemical was included in the model and are used to quantify the relative importance of the single monoterpene, were calculated in the present study with 10,000 iterations through a Markov chain Monte Carlo algorithm [37, 38]. PIP values closer to 1 indicate greater importance, while values closer to 0 signify less importance[37, 38]. Moreover, we explored the joint effect of the monoterpene mixture on biological aging by reporting the changes in PA acceleration per 10th percentile increase or decrease from the median serum level of the monoterpene mixture. Additionally, we also investigated the dose-response relationships between single monoterpene and PA acceleration, holding the rest of the monoterpenes in the mixture at their 10th, 50th, and 90th percentile.

The potential mediation effects of monoterpenes and BMI on the associations of beverages with PA acceleration were estimated by parallel mediation models. Mediation analyses applied the nonparametric bootstrap method with 1000 simulations. The direct effect (DE) indicated the effects of beverages on PA acceleration without a mediator. The indirect effect (IE) indicated the effects of beverage intake or BMI on PA acceleration through the mediator. The proportion of mediation was calculated by utilizing IE divided by total effect (TE).

A two-sided P value < 0.05 was considered statistically significant. R 4.3.1 software was used for all statistical analyses.

Participant characteristics

The NHANES 2013–2014 cycle encompassed 10,175 participants, 5,769 of whom aged ≥ 20 years. Individuals who were pregnant (n = 47) or had incomplete data regarding serum monoterpene levels (n = 4,197), biological measurements (n = 72), 24-hour dietary recall and covariates (n = 236) were excluded. Finally, 1,217 subjects were included in the current study. Distributions of age, gender, race, education level, PIR, smoker/drinker, physical activity, and BMI are presented in Table 1.

Table 1

Survey-weighted characteristics of adult participants
Variables	Total (n = 1217)
Age (years), mean (SD)	47.35(0.76)
Gender, n (%)
Female	596(51.97)
Male	621(56.77)
Race, n (%)
Non-Hispanic white	616(76.24)
Others	601(32.49)
Education, n (%)
Under high school	220(14.27)
High school or equivalent or above	997(94.47)
Poverty income ratio, n (%)
< 1	264(15.67)
≥ 1	953(93.06)
Smoker/drinker, n (%)
No	254(18.60)
Yes	963(90.14)
Physical activity, n (%)
Inactive	974(86.09)
Active	243(22.64)
Body mass index (kg/m²), mean (SD)	28.88(0.27)
Serum limonene (ng/mL), median (IQR)	1.15(0.70)
Serum α-pinene (ng/mL), median (IQR)	0.08(0.07)
Serum β-pinene (ng/mL), median (IQR)	0.07(0.05)
Beverage intake (g/d), median (IQR)	124.00(518.00)
SD: standard deviations; IQR: interquartile ranges; Continuous variables with normal distribution are reported as mean (SD), while those without normal distribution are represented by median (IQR). Categorical variables are reported as numbers (percentages). Numbers reflected the study sample, whereas percentages reflected the survey-weighted data.

Distributions of three serum monoterpenes

The serum concentrations of limonene, α-pinene, and β-pinene were presented as medians (interquartile ranges) of 1.15 (0.70) ng/mL, 0.08 (0.07) ng/mL, and 0.07 (0.05) ng/mL, respectively (Table 1). In the present investigation, the detection frequencies for these three serum terpenes were documented as 99.9%, 75.3%, and 74.9%.

Correlation analyses among serum monoterpene levels, BMI and beverage intake

Correlation analyses revealed significant associations between beverage intake with BMI and serum limonene (both P < 0.001). A moderate correlation was found between limonene and α-pinene (r = 0.33, P < 0.001), while a strong correlation was observed between α-pinene and β-pinene (r = 0.74, P < 0.001). Additional details on these correlations are provided in Supplemental Figure S3.

Multiple linear regression analyses to assess the association between beverage intake and BMI with biological aging

We found that beverage intake and BMI were significantly associated with PA acceleration (Table 2), after adjusting for all covariates. The β (95% CI) of beverage intake and BMI was 0.16 (0.06, 0.26) and 0.24 (0.19,0.29) for PA acceleration, respectively. Moreover, our results indicated that participants with high beverage intake (≥ 124g/day) or obesity (≥ 30kg/m²) experienced greater PA acceleration, compared to those with low beverage intake (< 124g/day) or non-obesity (< 30kg/m²).

Table 2

Multiple linear regression associations of beverage intake and BMI with PA acceleration
	Crude β (95% CI)	Model 1 β (95% CI)	Model 2 β (95% CI)
Beverage intake
Continuous^a	0.18(0.06,0.29)^**	0.16(0.07, 0.26)^**	0.16(0.06, 0.26)^**
Categorical
Low intake (< 124g/d)	Reference	Reference	Reference
High intake (≥ 124g/d)	1.05(0.29,1.81)^*	0.95(0.29, 1.61)^*	0.94(0.26, 1.62)^*
Body mass index
Continuous	0.22(0.17,0.27)^***	0.24(0.19,0.29)^***	0.24(0.19,0.29)^***
Categorical
Non-obesity (< 30kg/m²)	Reference	Reference	Reference
Obesity (≥ 30kg/m²)	2.72(1.86,3.58)^***	2.87(2.05, 3.69)^***	2.83(1.96, 3.70)^***
BMI: body mass index; PA: phenotypic age; ^:P < 0.05; ^:P < 0.01; ^**:P < 0.001; ^a: a log transformation was applied; Crude: no covariates were adjusted; Model 1: adjusted for age, gender, race, education level, poverty income ratio. Model 2: adjusted for the covariates in Model 1 plus smoker/drinker, physical activity.

Multiple linear regression analyses to assess the association between monoterpene exposures with biological aging

After adjusting for all covariates, we found that continuous serum limonene and β-pinene was positively related to PA acceleration (Table 3). The β (95% CI) of limonene and β-pinene was 1.50 (0.48, 2.52) and 0.95 (0.28, 1.61) for PA acceleration, respectively. The highest tertile of serum limonene and β-pinene showed positive associations with PA acceleration (limonene: β = 1.65, 95% CI: 0.55, 2.76; β-pinene: β = 1.35, 95% CI: 0.53, 2.18), compared with the lowest tertile. Furthermore, significant trends of limonene and β-pinene tertiles for PA acceleration were observed as well (both P for trend < 0.05). In contrast, serum α-pinene did not exhibit a significant linear relationship with PA acceleration.

Table 3

Multiple linear regression associations of serum monoterpenes with PA acceleration
	Crude β (95% CI)	Model 1 β (95% CI)	Model 2 β (95% CI)
Serum limonene (ng/mL)
Continuous	1.64(0.75,2.52)^**	0.16(0.07, 0.26)^**	1.50(0.48, 2.52)^*
Tertiles
Low	Reference	Reference	Reference
Middle	0.66(-0.18,1.51)	0.57(-0.36, 1.49)	0.60(-0.34, 1.55)
High	1.80(0.84,2.75)^**	1.65(0.61, 2.69)^*	1.65(0.55, 2.76)^*
P for trend	0.001	0.006	0.008
Serum α-pinene (ng/mL)
Continuous	0.54(-0.08,1.15)	0.55(-0.12, 1.22)	0.53(-0.16, 1.22)
Tertiles
Low	Reference	Reference	Reference
Middle	0.45(-0.43,1.32)	0.42(-0.56, 1.39)	0.38(-0.63, 1.39)
High	0.79(-0.14,1.71)	0.77(-0.31, 1.85)	0.77(-0.38, 1.92)
P for trend	0.089	0.134	0.145
Serum β-pinene (ng/mL)
Continuous	0.91(0.27,1.54)^*	0.91(0.26, 1.57)^*	0.95(0.28, 1.61)^*
Tertiles
Low	Reference	Reference	Reference
Middle	0.85(-0.24,1.93)	0.78(-0.20, 1.76)	0.80(-0.26, 1.86)
High	1.34( 0.60,2.07)^**	1.31(0.54, 2.08)^**	1.35(0.53, 2.18)^*
P for trend	0.002	0.004	0.005
PA: phenotypic age; ^:P < 0.05; ^*:P < 0.01; ^a: a log transformation was applied; Crude: no covariates were adjusted; Model 1: adjusted for age, gender, race, education level, poverty income ratio. Model 2: adjusted for the covariates in Model 1 plus smoker/drinker, physical activity.

RCS analyses to assess the association between monoterpene exposure and biological aging

The nonlinear associations between three serum monoterpenes and PA acceleration were examined by RCS analyses (Fig. 1). We found that serum limonene and β-pinene showed both linear and nonlinear relationships with PA acceleration (limonene: P for linearity < 0.001, P for nonlinearity = 0.009; β-pinene: P for linearity < 0.001, P for nonlinearity = 0.008). What is more, the significant linear trend and the marginally significant nonlinear trend between α-pinene and PA acceleration (P for linearity = 0.045, P for nonlinearity = 0.093) were also detected.

Qgcomp and BKMR analyses to assess the combined association between the mixed monoterpene exposure and biological aging

In the Qgcomp model (Fig. 2), the mixed exposure of three monoterpenes had a significant positive relationship with PA acceleration (β = 0.25, 95% CI: 0.12, 0.37, P = 0.001). The positively associated monoterpenes with PA acceleration were limonene (positive weights = 0.52) and β-pinene (positive weights = 0.48), while α-pinene (negative weights = 1) was negatively associated with PA acceleration.

According to BKMR analyses, Fig. 3 showed the overall effect of the three monoterpenes on PA acceleration. We discovered a significantly positive association between exposure to monoterpene mixtures and the acceleration of PA. The estimated β and 95% CI of monoterpene mixture exposure on PA acceleration is 0.13 (0.02, 0.23), 0.28 (0.06, 0.49), 0.48 (0.11, 0.85), 0.87 (0.20, 1.55) at their respective 60th, 70th, 80th and 90th percentiles, compared with the 50th percentile. Table 4 presented the PIP values assigned to the serum monoterpenes, with limonene (PIP = 0.89) playing the most crucial role in the combined effects on PA acceleration, followed by β-pinene (PIP = 0.80) as the second most significant contributor.

Table 4

PIPs from BKMR for the associations between serum terpenes and phenotypic age acceleration in adults
	PIPs
	α-Pinene	β-Pinene	Limonene
Phenotypic Age Acceleration	0.49	0.80	0.89
PIPs: posterior inclusion probabilities. BKMR: Bayesian kernel machine regression. Adjusted for age, gender, race, education level, poverty income ratio, smoking/drinking, physical activity.

In bivariable analysis, we also explored the associations between individual monoterpene exposures and PA acceleration while fixing another monoterpene exposure at the 10th, 50th, and 90th quantiles (and holding the other monoterpene to its median level). The results were shown in Fig. 4. Since the slopes were different between α-pinene and PA acceleration, β-pinene and PA acceleration while fixing limonene at the 50th, and 90th quantiles, potential interactions might exist between α-pinene and limonene as well as β-pinene and limonene. The detrimental effects of limonene on PA acceleration could be attenuated by α-pinene or β-pinene.

Mediation analyses to assess the effects of BMI and monoterpene exposure on the association between beverage intake and biological aging

According to the parallel mediation analyses, BMI and limonene had significant mediated effects on the association between beverage intake and PA acceleration (Fig. 5). The mediated proportion of BMI and limonene was 24.5% and 7.2%, respectively (both P < 0.05). However, α-pinene and β-pinene did not manifest a significant mediated effect on the relationship between beverage intake and PA acceleration.

For the first time, we examined whether high beverage intake and high monoterpene exposure were positively associated with PA acceleration in humans. Consistent with our initial hypothesis, participants with high beverage intake had greater PA acceleration than those with low beverage intake. Moreover, higher exposure to monoterpenes, especially limonene and β-pinene, were individually or jointly related to more PA acceleration. We observed that the effects of limonene and β-pinene on PA acceleration exhibited both linear and non-linear patterns. In addition, there also existed potential interactions between α-pinene and limonene as well as β-pinene and limonene. Furthermore, we found that BMI and limonene were identified as parallel mediators for the association between beverage intake and PA acceleration.

Previous research has indicated that beverage intake may shorten telomeres and accelerate aging [10]. Numerous studies have linked the negative impact of beverage intake on biological aging acceleration to the sugar and artificial sweeteners in beverages, which lead to increased BMI and obesity [11, 12, 14]. Excessive sugar consumption can cause an energy surplus, while artificial sweeteners might increase cravings for sweetness, potentially resulting in higher energy intake [39, 40]. The excessive energy intake is strongly linked to elevated BMI and obesity, which has been found to compromise genomic integrity, impair mitochondrial function, accumulate intracellular macromolecules, weaken immunity, and enhance systemic inflammation, ultimately leading to biological aging acceleration [41]. However, it is worth noting that the relationship between beverage intake and PA acceleration has not been explored until now. This is the first study to observe the association between beverage intake and PA acceleration. In addition, our findings showed that beverage might accelerate aging by increasing BMI, which were similar with previous studies. What's more, it is important to emphasize that limited research has examined the potential role of other food additives in beverages beyond sugar and artificial sweeteners in accelerating the biological aging process.

Monoterpenes are a group of naturally occurring VOCs typically found in many species of vegetation [42]. Previous studies have indicated that diet and inhalation are the two primary ways of monoterpene exposure for adults [19]. Monoterpenes, such as limonene, α-pinene, and β-pinene, were commonly used as food flavoring essence in beverages [17, 18]. Despite the widespread use of monoterpenes, epidemiological and clinical evidence on the human health associated with oral monoterpene exposure is virtually limited.

Several clinical studies have suggested the potential health benefits of monoterpenes for humans. For example, elderly people can benefit from incorporating monoterpenes, such as limonene, into a RISTOMED diet, which has been shown to result in improved depression symptoms, enhanced quality of life, and decreased levels of glucose, insulin, homeostasis model assessment-insulin resistance (HOMA-IR), and fibrinogen [43, 44]. Recently, limonene was also found to selectively attenuate delta-9-tetrahydrocannabinol (THC)-induced anxiogenic effects [45] and increase the efficacy of standard therapy in irritable bowel syndrome (IBS) and functional dyspepsia (FD) patients [46]. Chios mastiha essential oil and Mastiha Oil, which are rich in monoterpenes, such as α-pinene, and β-pinene, have been reported to upregulate human antioxidant capabilities, improve blood lipid profile, systolic blood pressure, obesity and alanine aminotransferase [47, 48]. Based on the research findings mentioned above, monoterpenes might help delay aging, since antioxidant capabilities, depression, anxiety, glucose and lipid metabolism, systolic blood pressure, and intestinal disorders were all related to aging [7, 8, 49, 50]. However, our present results, which suggested that high exposures to monoterpenes might accelerate aging, seemed to be in discordance with these studies.

Although this is the first epidemiological study discovering the unfavorable influence of monoterpene exposures on biological aging, high monoterpene exposures have been linked to dyslipidemia, asthma, impaired pulmonary function, and dermatitis [19, 21–23]. Furthermore, recent studies have found that monoterpenes, such as limonene, α-pinene, and β-pinene could be rapidly oxidized by hydroxyl and ozone radicals, producing various peroxides, which can cause oxidative stress [16]. Additionally, studies have indicated that monoterpenes can increase the formation of secondary organic aerosol (SOA)-induced reactive oxygen species (ROS) and cytotoxicity [51]. Oxidative stress has been found to contribute to the erosion of telomeres, DNA damage and accelerated aging [52]. Therefore, we speculated that oxidative stress might be the potential mechanism by which monoterpenes cause biological age acceleration.

The discrepancies between some previous studies and our findings may be attributed to methodological differences. Most of the previous research used mixtures, such as essential oils or combined interventions, in which the interference of other factors could not be excluded. Additionally, the levels of monoterpenes applied in these studies frequently surpassed those encountered under normal conditions. Furthermore, the majority of existing studies were conducted in vitro or were limited to short-term effects. As a result, the effects of chronic exposure to high levels of monoterpenes on human health remain a subject of debate.

Moreover, we found beverages might be one of the sources of serum limonene exposure and this study is the first to reveal a significant mediating effect of limonene on the relationship between beverage intake and PA acceleration, independent of BMI. Our findings underscore the complexity of dietary impact on biological aging, suggesting that the constituents of what we consume extend beyond the commonly scrutinized sugars and artificial sweeteners. The present research highlights the importance of considering a broader range of food additives, particularly flavoring agents like limonene, which are usually overlooked in concerns about dietary health. These additives, traditionally regarded as harmless, may have subtle but meaningful influence on the acceleration of biological aging. Therefore, it is imperative that future dietary guidelines and public health policies take the potential effects of these substances into account, as they may play a key role in shaping long-term health outcomes.

In addition, though β-pinene exposure was associated with PA acceleration, the mediating effect of β-pinene on the relationship between beverage intake and PA acceleration was not statistically significant. The reason behind this might be that limonene is much more widely used in beverages compared to β-pinene, since the citrus flavor of limonene is more aligned with consumer expectations for a wide range of beverages, whereas the flavor of β-pinene is more specific and less versatile [53] .

Furthermore, an antagonistic effect on PA acceleration in our research was found to exist between α-pinene and limonene as well as β-pinene and limonene. Literature directly exploring the synergistic or antagonistic effect of limonene, α-pinene, and β-pinene on humans was very limited. Only a small number of studies have indicated limonene could inhibit attraction to α-pinene in two specific insects and limonene showed an antagonistic effect with β-pinene on larvicidal activity [54, 55]. Further investigation is needed on the antagonistic effect.

This study has several strengths. Firstly, this study represents a significant advancement in examining the association between monoterpene exposures and biological aging in humans. Our current findings filled an important gap in the existing knowledge. Secondly, this is the first study to explore the relationship between beverage intake and PA acceleration. Thirdly, our results for the first time found limonene had a significant mediating effect on the association of beverage intake and biological aging acceleration, independent of BMI. Fourthly, our analyses evaluated the individual influence of monoterpenes, the combined effects of monoterpene mixtures as well as the interaction between different monoterpenes, providing a thorough understanding of their impact on biological aging. Lastly, we not only assessed for linear but also for nonlinear relationship of monoterpene exposures with biological aging.

Nevertheless, there are certain limitations in our research. First, our study was cross-sectional, which was unable to determine a causal association between monoterpene exposures and biological aging. Future cohort studies and randomized controlled trials were needed to validate the temporality and causality. Second, the utilization of single time-point serum samples for the quantification of serum monoterpenes may not accurately capture chronic exposure levels. Future studies could address this concern by incorporating repeated measurements over a specific period. Third, we only explored three monoterpenes, disregarding the broader spectrum of monoterpenes. The omission might neglect the intricate impacts of complex monoterpene exposures on biological aging. Further studies are therefore warranted to explore the impact of mixtures of multiple monoterpene exposures and potential confounding variables. Fourthly, we found the association between α-pinene and PA acceleration in linear regression was not significant, whereas using RCS analyses revealed a significant linear relationship. Reasons for this discrepancy might be related to the model flexibility. RCS models are more flexible than linear regression, allowing them to fit a broader range of data patterns. Compared with linear regression, RCS is more likely to capture subtle trends and variations, which might make the linear association significant. Given the contradictory results, the relationship between α-pinene and PA acceleration should be interpreted with caution, and further investigation is warranted. Finally, the underlying mechanisms through which monoterpenes lead to the biological age acceleration remain unclear. Further investigation is necessary to clarify the potential pathways involved.

This study represents a pioneering effort in examining the association between monoterpene exposures and biological aging in humans. Our findings indicated that higher beverage intake and increased exposure to monoterpenes, particularly limonene and β-pinene, were associated with accelerated PA. These results suggested that the detrimental effects of beverages on biological aging are not limited to sugars and artificial sweeteners but also include other additives, such as flavoring agents. Additionally, the study highlights the complex interactions between different monoterpenes, such as the antagonistic effects between limonene and both α-pinene and β-pinene, which may influence the aging process. The significant mediating role of limonene in the relationship between beverage intake and biological aging, independent of BMI, underscores the need for further research and consideration of food additives in public health policies.

Ethics approval and consent to participant

All participants provided written informed consent, and study procedures were approved by the National Center for Health Statistics Research Ethics Review Board (Protocol Number: Protocol #2005-06 and Protocol #2011-17).

Consent for publication

The manuscript is approved by all authors for publication.

Competing interests

The authors declare no competing interests.

Funding

This work was supported by National Natural Science Foundation of China (No.82204060), Shanghai Municipal Health System Project (No.202140517), Huadong Hospital Clinical Research Center Project (LCZX2204).

Author Contribution

A.Z.: conceptualization, methodology, formal analysis, writing-original draft, and visualization. Z.L.: conceptualization, methodology.X.G: writing-review and editing, and supervision. J.W: conceptualization, project administration, writing-review and editing, and supervision.

Acknowledgement

The authors thank all participants and all investigators who contributed to the NHANES data. Thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People's Hospital, Fudan University) for his work on the NHANES database. His outstanding work, nhanesR package and webpage, makes it easier for us to explore NHANES database.

Data Availability

Data is provided within the website: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx

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Monoterpenes in Your Beverage: A Flavoring Catalyst for Faster Aging?

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study population

Assessment of beverage intake

Assessment of BMI and obesity

Assessment of monoterpene exposure

Measurements of biological aging

Defining covariates

Statistical analysis

Results

Participant characteristics

Distributions of three serum monoterpenes

Correlation analyses among serum monoterpene levels, BMI and beverage intake

Multiple linear regression analyses to assess the association between monoterpene exposures with biological aging

RCS analyses to assess the association between monoterpene exposure and biological aging

Discussion

Conclusions

Declarations

Ethics approval and consent to participant

Consent for publication

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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