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辽宁中医药大学 酸奶 衰老 研究 《营养学前沿》 2024年12月
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Yogurt consumption and risk of accelerated aging: A population-based study from the NHANES 2003-2006 Frontiers in Nutrition 2024
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Yogurt consumption and risk of accelerated aging DOI 10.3389/fnut.2024.1482980 authors publication date
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Yogurt consumption and risk of accelerated aging: A population-based study from the NHANES 2003-2006 authors Liaoning University of Traditional Chinese Medicine
1 Introduction
Aging is one of the risk factors for diabetes, coronary heart disease, cancer, and various neurological diseases. With the global progression of aging, the associated disease burden has become increasingly significant, making aging an important health issue that the medical community cannot ignore (1, 2). Therefore, finding ways to delay aging and reduce the resulting disease burden, thereby improving the quality of life and lifespan of the elderly, is a crucial challenge that the healthcare field must address.
Dietary therapy is a popular research topic in the field of aging (3, 4). This approach is more convenient, safer, and low-cost (3, 5). Researchers have long been committed to achieving the goal of preventing or treating diseases and delaying aging through dietary interventions. Fermented dairy products play an important role in this regard (6), and many studies have demonstrated the beneficial health effects of fermented dairy consumption. In a large-scale cohort study conducted in the Netherlands in 2011 (n = 120,825), a negative correlation between fermented dairy product intake and all-cause mortality was demonstrated among participants (7). Similarly, a 12-year cohort study by Sonestedt et al. (8) found a significant inverse association between fermented dairy intake and the incidence of cardiovascular disease. The group with the highest intake of fermented dairy had a 15% lower risk of cardiovascular disease compared to the group with the lowest intake (95% CI: 5–24%; p trend = 0.003).
As a type of fermented dairy product, yogurt is rich in nutrients and probiotics. Compared to milk, it contains higher levels of protein, vitamin B2, vitamin B12, calcium, magnesium, potassium, and zinc (9). It is also a component of the Mediterranean diet (10). Moreover, the probiotics in yogurt can promote lactose absorption and alleviate the symptoms of lactose intolerance (11), indicating that yogurt is more suitable for a wider population than milk. Yogurt has been shown in numerous studies to have various positive effects on human health (12). For example, studies have shown a negative correlation between yogurt consumption and the prevalence of hypertension (13) and diabetes (14). Two studies indicated that, among the elderly, yogurt consumers had better cognitive function compared to non-consumers, including better memory, executive function, and verbal fluency (15, 16). And a 2017 study from Ireland (n = 4,310) supported the association between yogurt consumption and bone health (17). This study demonstrated that higher yogurt intake was associated with a lower risk of osteoporosis and fractures, as well as better Timed Up and Go (TUG) performance. Subsequently, a 2021 study from Canada (n = 7,945) further confirmed these findings (18).
Phenotypic age acceleration was first proposed by Buendia et al. (13), and this metric is derived from nine commonly used clinical biomarkers along with chronological age. Phenotypic age is a measure that reflects an individual’s estimated mortality risk, corresponding to their expected age within the population. This metric is widely used in the literature as an indicator for identifying morbidity and mortality risk factors, evaluating intervention outcomes, and elucidating mechanisms of aging. Therefore, in this study, we used phenotypic age acceleration as a surrogate marker for aging in our analysis.
NHANES is a cross-sectional database conducted by U.S. agencies, collecting comprehensive health, nutrition, and lifestyle data. The study has been ongoing since 1999, and it is widely used for research in fields like public health and epidemiology due to its rich, representative dataset (19).
In summary, numerous studies have demonstrated a close association between yogurt and various diseases, as well as physical functions in the elderly, but whether yogurt itself is linked to aging remains unclear. Therefore, this study aims to explore the specific association between yogurt consumption and aging using data from the 2003–2006 NHANES, with the goal of providing clinical insights.
2 Methods
2.1 Study population
The individual data used in this study were sourced from the 2003–2006 NHANES database, including individuals who had complete dietary frequency questionnaire results, body mass index (BMI), and the biomarkers required for calculating phenotypic age (20). All participants in this study provided informed consent, and the study was approved by the Ethics Review Board of the National Center for Health Statistics.1 The specific NHANES codes for the data used in this study can be found in the Supplementary Table S1.
2.2 Definition of yogurt consumer
Data on yogurt consumption were obtained from the dietary frequency questionnaire, with the variable identifier FFQ0108: “How often did you eat yogurt (NOT including frozen yogurt)?” This variable contains 11 valid values, each representing a different frequency, where 1 corresponds to “never” and 11 corresponds to “two or more times per day.” We defined yogurt consumers as individuals with values ranging from 2 to 11 (i.e., those who consumed yogurt at least once per year).
[Yogurt consumption frequency meanings: (1) never, (2) 1–6 times per year, (3) 7–11 times per year, (4) 1 time per month, (5) 2–3 times per month, (6) 1 time per week, (7) 2 times per week, (8) 3–4 times per week, (9) 5–6 times per week, (10) 1 time per day, (11) 2 or more times per day].
2.3 Phenotypic accelerated aging
The nine biomarkers required to calculate phenotypic age acceleration include albumin, creatinine, glucose, C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The calculation of phenotypic age acceleration is based on the residuals of phenotypic age after adjusting for chronological age through linear regression. Participants with phenotypic age acceleration greater than 0 were defined as experiencing accelerated aging, while those with phenotypic age acceleration less than 0 were defined as experiencing decelerated aging. The detailed calculation method can be found in the correction associated with the paper (21).
2.4 Covariates
In this study, we also selected the following covariates based on references to other literature related to aging. These include sex, race, education level, marital status, poverty income ratio (PIR), BMI, serum cotinine levels, alcohol consumption status, self-reported hypertension, self-reported coronary heart disease, self-reported diabetes, self-reported cancer, low-density lipoprotein (LDL), and total cholesterol (TCHOL) levels. The PIR was used as a proxy for economic status, and serum cotinine levels were used as a proxy for smoking status (22, 23). For marital status, we categorized “Married” and “Living with partner” as “Non-single,” and “Widowed,” “Divorced,” “Separated,” and “Never married” as “Single.”
2.5 Statistical analysis
The weighted analysis in this study was conducted using the special weights derived from the dietary frequency questionnaire—WTS_FFQ—and these weights were appropriately calculated based on the survey cycles. To minimize errors arising from uneven group distribution in cross-sectional data, all participants were divided into two groups based on yogurt consumption—consumers and non-consumers. After propensity score matching, analyses were conducted, including multivariable logistic regression for the association between yogurt consumption and phenotypic age acceleration, subgroup analysis, RCS analysis, and analysis of the association between yogurt consumption and overweight status. All analyses in this study were performed under weighted conditions.
The R packages used in this paper include survey (4.4.2), gtsummary (2.0.2), and MatchIt (4.4.5). After completing the statistical analysis, forestploter (1.1.2) was used to create forest plots. The statistical analyses in this study were conducted using R version 4.4.1.
3 Results
3.1 The characteristics of participants before and after PSM
The characteristics before and after matching are shown in Table 1. A total of 4,056 participants were included in this study, with 2,635 yogurt consumers (65%) and 1,421 non-consumers (35%). Before matching, the average age of the yogurt consumption group was 42.00 ± 18.62 years, with a higher proportion of females (60%) and 40% males. The average BMI was 27.64 ± 6.76, and 21% of the group exhibited phenotypic age acceleration. In contrast, the non-yogurt consumption group had an average age of 46.29 ± 20.09 years, with a higher proportion of males (61%) and 39% females. The average BMI was 28.38 ± 7.61, and 30% of the group exhibited phenotypic age acceleration. The results regarding gender and socioeconomic status (PIR index) were consistent with previous studies, showing that yogurt consumers tend to have a higher proportion of females and individuals with higher socioeconomic status (24, 25).
Table 1. Characteristics of full and propensity score-matched cohorts by yogurt consumer.
After matching, the average age of the yogurt consumption group was 44.60 ± 18.77 years, with 57% males and 43% females. The average BMI was 27.94 ± 6.60, and 23% of the group exhibited phenotypic age acceleration. In the matched non-yogurt consumption group, the average age was 45.56 ± 20.31 years, with 57% males and 43% females. The average BMI was 28.36 ± 7.68, and 29% of the group exhibited phenotypic age acceleration.
Before matching, there were significant differences between the two groups in terms of age, PIR, BMI, serum cotinine levels, sex, race, education, alcohol consumption, coronary heart disease, and hypertension. After PSM, the two groups only differed in PIR, education, and diabetes.
It is important to note that we used PSM to eliminate potential bias due to groupings in the cross-sectional data. However, one limitation of this study is that, even after PSM, significant differences remained between the two groups in terms of PIR index and education level. Given that individuals with better economic conditions and higher education levels may have healthier dietary conditions and habits, caution is needed when interpreting our study’s conclusions.
3.2 Analysis of risk factors for accelerated aging
According to the results of the multivariate logistic regression analysis shown in Figure 1, yogurt consumers had a lower risk of accelerated aging compared to non-consumers [OR (95%) = 0.544 (0.354–0.836), p = 0.020]. This result is consistent with the conclusions mentioned in the introduction, indicating that yogurt consumption may have a protective effect on human health.
Figure 1. Forest plot of accelerated aging risk factors. PIR, poverty impact ratio; BMI, body mass index; LDL, low-density lipoprotein; TCHOL, total cholesterol.
PIR, BMI, serum cotinine levels, alcohol consumption, and diabetes were all significantly associated with accelerated aging. PIR is used as a proxy for socioeconomic status, serum cotinine levels as a proxy for smoking, and alcohol consumption—these three variables have been confirmed in multiple studies to be associated with aging. Additionally, the close relationship between BMI and accelerated aging has also been validated by numerous studies (26, 27). Furthermore, since one of the nine biomarkers required for calculating phenotypic age acceleration is blood glucose, and the diabetes data in this study are based on self-reported information, there may be bias in the results regarding the association between diabetes and accelerated aging.
3.3 Subgroup analysis
Subsequently, we investigated the association between yogurt consumption and accelerated aging across different subgroups. We categorized age into three groups: “<30,” “30–59,” and “≥60,” and divided PIR into two groups: “<1” and “≥1.” BMI was categorized into “normal or underweight (<25),” “overweight (25–30),” and “obese (≥30).” Log2 cotinine was divided into three groups: “<0.05,” “0.05–3,” and “≥3” (15). And the previous groupings, including sex, race/ethnicity (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, Other race), education level (less than high school, high school or equivalent, more than high school), marital status (non-single, single), alcohol consumption (yes, no), diabetes (yes, no), high blood pressure (yes, no), coronary heart disease (yes, no), and cancer (yes, no). We also explored the interaction between these subgroups and yogurt consumption. All p-values were adjusted using false discovery rate (FDR) correction to ensure reliability, and the results are shown in Figure 2.
Figure 2. Forest plot of subgroup analysis of the association between yogurt consumption and phenotypic accelerated aging. PIR, poverty impact ratio; BMI, body mass index. All models were adjusted for the following covariates: sex, age (<30, 30–59, ≥60), log2 cotinine (<0.05, 0.05–3, ≥3), LDL, TCHOL, PIR (<1, ≥1), BMI (normal or underweight, overweight, obesity), race/ethnicity (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, Other race), education level (less than high school, high school or equivalent, more than high school), marital status (non-single, single), alcohol consumption (yes, no), diabetes (yes, no), high blood pressure (yes, no), coronary heart disease (yes, no), and cancer (yes, no).
The results of the subgroup analysis indicate that the association between yogurt consumption frequency and accelerated aging varies significantly across different groups. A significant association was observed only in individuals aged 30–59, males, Non-Hispanic Whites, those with a PIR index ≥1, those who are underweight or of normal weight, individuals with log2 cotinine levels ≥3 ng/mL, those with a high school or equivalent education, single individuals, alcohol drinkers, those with hypertension, non-cardiovascular disease patients, non-diabetic individuals, and non-cancer individuals. We also found a significant interaction between PIR index and yogurt consumption. We speculate that this association may be due to the relatively better access to healthcare and nutritional resources in the group with PIR ≥1.
3.4 Nonlinear association
We used RCS to explore the potential nonlinear association between yogurt consumption frequency and accelerated aging and conducted analyses both before and after PSM. The results from both analyses were consistent (Figure 3), indicating a U-shaped relationship between yogurt consumption frequency and the risk of accelerated aging. This suggests that an appropriate frequency of yogurt consumption may help delay aging.
Figure 3. Restricted cubic spline models for the relationship between yogurt intake frequency and the risk of Phenotypic accelerated aging. All models were adjusted for the following covariates: sex, age, BMI, log2 cotinine, LDL, TCHOL, PIR, race/ethnicity (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, Other race), education level (less than high school, high school or equivalent, more than high school), marital status (non-single, single), alcohol consumption (yes, no), diabetes (yes, no), high blood pressure (yes, no), coronary heart disease (yes, no), and cancer (yes, no).
However, due to the lack of specific yogurt intake data in our study, the relevance of consumption frequency alone, without considering the amount consumed, is limited and requires further verification. A recent study (24) indicated a similar J-shaped association between yogurt consumption and hepatic steatosis (HS) and provided a recommended intake of 0.4 cups per day, which is approximately 95 mL/day (1 cup = 237 mL). But this study was also based on cross-sectional NHANES data, so the results should be interpreted with caution.
3.5 Yogurt consumption with overweight status
Previous studies have shown a close association between yogurt consumption and BMI (or overweight status) (26–29), and there is also a strong link between BMI (or overweight status) and accelerated aging (30–33). In other words, yogurt consumption is associated with lower BMI, while higher BMI is associated with accelerated aging. Considering our findings, we have reason to infer that yogurt consumption may help delay aging, with BMI potentially playing an important mediating role in this process. Therefore, we conducted an association analysis between yogurt consumption and overweight status (defined as BMI >25). The results indicate that there is no significant association between yogurt consumption (yogurt consumer or non-consumer) and overweight status. However, there is a negative correlation between the frequency of yogurt consumption and overweight status. We also performed a nonlinear analysis of yogurt consumption frequency and overweight status. Since the PSM grouping in this study was based on whether individuals were yogurt consumers, to reduce potential bias from this grouping, we analyzed the results both before and after PSM matching, and the findings remained consistent. As shown in Table 2 and Figure 4.
Table 2. Association between yogurt consumption and overweight status.
Figure 4. Restricted cubic spline models for the relationship between yogurt intake frequency and the risk of overweight status. OR, odds ratio; CI, confidence interval. All models were adjusted for the following covariates: sex, age, log2 cotinine, LDL, TCHOL, PIR, race/ethnicity (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, Other race), education level (less than high school, high school or equivalent, more than high school), marital status (non-single, single), alcohol consumption (yes, no), diabetes (yes, no), high blood pressure (yes, no), coronary heart disease (yes, no), and cancer (yes, no).
The results of the RCS analysis indicate that there is primarily a negative linear association between yogurt consumption frequency and the risk of being overweight. However, since the PSM grouping i ...
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