Next Article in Journal
Prevalence and Relevance of Vitamin D Deficiency in Newly Diagnosed Breast Cancer Patients: A Pilot Study
Previous Article in Journal
Nicotinamide Adenine Dinucleotide Precursor Suppresses Hepatocellular Cancer Progression in Mice
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 6
10.3390/nu15061448
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals

by
Shasha Yu
1,†,
Bo Wang
1,†,
Guangxiao Li
2,
Xiaofan Guo
1,
Hongmei Yang
1 and
Yingxian Sun
1,*
1
Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang 110001, China
2
Department of Clinical Epidemiology, Institute of Cardiovascular Diseases, First Hospital of China Medical University, Shenyang 110001, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2023, 15(6), 1448; https://doi.org/10.3390/nu15061448
Submission received: 6 January 2023 / Revised: 13 March 2023 / Accepted: 15 March 2023 / Published: 17 March 2023
(This article belongs to the Section Geriatric Nutrition)
Browse Figures
Versions Notes

Abstract

:
In middle-aged and elderly individuals, the relationship between tea consumption and incident metabolic syndrome (MetS) is still unclear. Therefore, this study intends to figure out the relationship between tea-drinking frequency and MetS in rural middle-aged and older Chinese residents. In the Northeast China Rural Cardiovascular Health Study, 3632 middle-aged or older individuals (mean age 57 ± 8, 55.2% men) without MetS were included at baseline during 2012–2013 and were followed up on between 2015–2017. Participants showing differential tea consumption frequency were divided into the following classes: non-habitual tea drinkers, occasional tea drinkers, 1–2 times/day drinkers, and ≥3 times/day drinkers. Data showed that non-habitual tea drinking was more common among women. The frequency of tea consumption was higher in ethnic groups other than Han and among singles, as well as in concurrent smokers and drinkers and individuals with primary or lower educational status. The increasing tea consumption was in line with baseline elevations in body mass index, systolic and diastolic blood pressure, high-density lipoprotein cholesterol (HDL-C), and AST/ALT ratio. Multivariate logistic regression analysis confirmed that occasional tea drinking increased the incidence of low HDL-C [OR (95% CI): 1.268 (1.015, 1.584)], high waist circumference [OR (95% CI): 1.336 (1.102, 1.621)], and MetS [OR (95% CI): 1.284 (1.050, 1.570)]. In addition, 1–2 times/day tea drinking increased the cumulative incidence of high TG [OR (95% CI): 1.296 (1.040, 1.616)], high waist circumference [OR (95% CI): 1.296 (1.044, 1.609)] and MetS [OR (95% CI): 1.376 (1.030, 1.760)]. We demonstrated that regular tea consumption is correlated with a greater incidence of metabolic disorders and MetS. Our findings may help clarify the contradictory association reported between tea drinking and MetS development in middle-aged and older residents of rural China.

1. Introduction

The adverse effects of a combination of cardiovascular risk factors associated with metabolic syndrome (MetS) on the cardiovascular system are higher than those of the sum of the individual risk factors [1]. MetS encompasses insulin resistance, hypertension, dyslipidemia, and abdominal obesity [2]. Long-term studies have correlated MetS with an increased frequency of cardiovascular events and cardiovascular mortality. However, the prevalence of MetS has markedly increased in recent years. In China, the prevalence rate of MetS in 2001 was 13.7%, which increased to 24.2% in 2010–2012 [3,4]. According to the Adults Treatment Panel III criteria, the International Diabetes Federation criteria, and a harmonized definition, the prevalence rate of MetS in the overall rural population increased to 41.3%, 34.2%, and 44.1%, respectively, in 2017–2018 [5]. These prevalence rates were higher than those estimated by another study (20.5%) performed in the rural areas of China [6]. In China, the proportion of aging individuals has markedly increased, which is referred to as the silver tsunami [7]. According to the statistics from the Seventh National Population Census in 2020, the proportion of individuals aged > 60 years increased from 13.26% in 2010 to 18.70% in 2020, while that of individuals aged > 65 years increased from 8.9% in 2010 to 13.5% in 2020. As the proportion of middle-aged and elderly individuals in the population has increased, the risk factors in various age groups must be quantified to mitigate and manage MetS.
The Chinese were aware of the health-promoting and therapeutic benefits of tea 4000–5000 years ago. The health benefits of tea have been recorded in old medical texts, such as Shen Nong’s Herbal Classic. Tea has become one of the most consumed beverages in China, especially by middle-aged and elderly people. Previous studies have reported that frequent consumption of green tea exerts protective effects against cardiovascular diseases and metabolic disorders [8,9,10]. Additionally, various studies have reported that tea exerts several beneficial effects, such as antioxidant, anti-inflammatory, antibacterial, anticarcinogenic, antihypertensive, neuroprotective, cholesterol-lowering, and thermogenic effects [11,12]. The consumption of tea (Camelia sinensis L., green or black tea) is reported to decrease the risk of developing hypertension, dyslipidemia, diabetes, obesity, and MetS [13,14,15]. However, some studies have revealed a strong correlation between regular green tea consumption and an increased risk of developing MetS [16]. Therefore, the correlation between MetS and tea is controversial. Additionally, previous studies evaluating the effects of tea have only enrolled elderly participants. As both middle-aged and elderly subjects are keen on tea consumption, the correlation between tea consumption and MetS among middle-aged and elderly Chinese adults must be elucidated. Moreover, the effects of tea on subjects from rural areas have not been previously evaluated. Socioeconomic status (low annual income and educational status) and lifestyle habits (increased physical activity) are different among the inhabitants of rural areas, which may also affect the potential correlation between tea consumption and MetS. Previously, we demonstrated that village doctor-led interventions, including improving health knowledge, recommending healthy life habits, and monitoring the blood pressure (BP) of participants, markedly improved BP management among Chinese rural populations [17]. Understanding whether habitual tea consumption should be recommended to rural middle-aged and elderly individuals as an effective complementary non-pharmacological therapeutic approach for metabolic diseases is valuable and must be studied. Therefore, this study aimed to elucidate the correlation between tea consumption frequency and MetS in middle-aged and elderly residents of rural China to examine the effects of tea consumption on patients with metabolic disorders.

2. Materials and Methods

2.1. Study Design and Participants

A community-based prospective cohort research termed the Northeast China Rural Cardiovascular Health Study (NCRCHS) was conducted in rural Northeast China. The specific sample techniques and admittance requirements have previously been described in good detail [16]. Multi-stage, stratified, and random cluster sampling were used in the study design. From three respectively directional areas of Liaoning Province, Dawa from the eastern area, Zhangwu from the southern area, and Liaoyang from the northern area were selected in the first stage. A town from each area was chosen randomly for the following phase (a total of three towns). The final step involved selecting 8–10 rural communities at random from each town. There was a total of 26 rural villages. Each village’s eligible permanent residents (35 years of age or over) were sent an invitation to participate in the research. Pregnant women, people with malignant tumors, and people with mental illnesses were ruled out from the study [18]. The current study was certified by China Medical University’s ethics committee (Shenyang, China AF-SDP-07-1, 0-01). The baseline data for our study were based on the 2012–2013 survey results, and a total of 3988 participants who did not have MetS (older than 45 years) were enrolled. They were divided into two groups according to age: a middle-age category (between 45 and 60 years) and an elderly category (older than 60 years). The cohort was followed up from 2015 to 2017 and the median follow-up was 4.66 years. As a result of missing crucial data during the follow-up period, 356 participants were excluded. Eventually, 3632 participants were enrolled, and detailed data were gathered from them.

2.2. Study Variables

In light clothing and wearing no shoes, subjects’ height and weight were recorded. Utilizing non-elastic tape, the waist’s circumference was measured at the umbilicus. Body mass index (BMI) was calculated using the following equation: weight   kg height 2   m . An electronic standardized automated manometer (HEM-907; Omron, Tokyo, Japan) was used to measure participants’ blood pressure three times while they were seated after at least five minutes of rest. Participants who had been fasting for at least 12 h had their blood drawn in the morning. Low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), creatinine and uric acid were examined enzymatically.
An interview utilizing a standardized questionnaire was performed to gather comprehensive data on characteristics, lifestyle and dietary components, and medical history at the baseline. Tea consumption habit was divided into the following four groups: non-habitual tea drinkers, occasional tea drinkers, 1–2 times/day drinkers, and ≥3 times/day drinkers. To assess their dietary habits, participants were asked to recollect specifics of their food consumption during the previous year. Details on the typical weekly consumption of various foods were also requested on the questionnaire. The stated intake was roughly expressed in terms of grams per week. The following scale was used to evaluate vegetable consumption: rarely = 3, less than 1000 g = 2, more than 1000 g = 1, and more than 2000 g = 0. Meat consumption, including red meat, fish, and poultry, was evaluated using the following scale: rarely = 0, less than 250 g = 1, more than 250 g = 2, and more than 500 g = 3. A distinct diet score was assigned to each individual (meat consumption score plus vegetable consumption score, which ranges from 0–6). Lower diet scores implied adherence to the Chinese diet, while higher scores, which represented a greater meat intake and a lower vegetable intake, revealed more adherence to a Westernized diet. The same formulas for generating a diet were also employed in the ATTICA study [19]. Physical activity included occupational and leisure time physical activity. A detailed description of the methods for assessing physical activity has been presented elsewhere [20]. Participants self-reported their occupational physical activity according to the following three categories: light was physically very easy, sitting office work, e.g., secretary; moderate was work including standing and walking, e.g., store assistant; and active was work including walking and lifting, or heavy manual labour, e.g., industrial work, farm work. Self-reported leisure-time physical activity was classified into three categories: low was defined as almost completely inactive, e.g., reading, watching TV, or doing some minor physical activity but not of moderate or high level; moderate was doing some physical activity more than four hours a week, e.g., walking, cycling, etc.; and high was performing vigorous physical activity more than three hours a week, e.g., running, jogging, skiing, or regular exercise in competitive sports several times a week. Occupational and leisure-time physical activities were merged and regrouped into three categories: low was defined as subjects who reported light levels of both occupational and leisure-time physical activity; moderate was defined as subjects who reported moderate or high level of either occupational or leisure-time physical activity; and high was defined as subjects who reported a moderate or high level of both occupational and leisure-time physical activity.
MetS was defined following the unifying criteria set by the consensus of some major organizations in 2009 [21]. The coexistence of any three of the following listed five risk factors confirms metabolic syndrome diagnosis. 1. Raised blood pressure (therapy with antihypertensives in a patient with a history of hypertension is an alternative indicator). 2. High fasting glucose (an alternate indicator is medication used to treat high glucose): 100 mg/dL. 3. Reduced HDL-C levels (drug therapy for reduced HDL-C is an alternative indicator): less than 40 mg/dL (1.0 mmol/L) in men; less than 50 mg/dL (1.3 mmol/L) in women. 4. Men’s elevated waist circumference is more than 90 cm; women’s elevated waist circumference is more than 80 cm (Asians, Japanese, South and Central Americans). 5. Triglyceride elevation: 150 mg/dL (1.7 mmol/L) (drug treatment for triglyceride elevation is an alternative indication).

2.3. Statistical Analysis

Descriptive statistics were calculated for all the variables. Categorical variables were reported as numbers and percentages. Continuous variables were reported as mean values and standard deviations. Differences among categories were evaluated using t-test, ANOVA, non-parameter test, or the χ2-test as needed. After controlling for potential confounders, logistic regression analysis was conducted in order to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the related factors of MetS. Software SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analyses, and p values under 0.05 were regarded as statistically significant.

3. Results

The baseline characteristics of the enrolled participants according to sex are listed in Table 1. At baseline, the average age of the study cohort was 57.04 years. The proportion of females in the study cohort was 44.7%. Current smokers and alcohol consumers constituted 40.7% and 26.9%, respectively, of the study cohort. The number of current smokers and alcohol consumers among males was markedly higher than that among females. The proportion of married individuals among females (99.6%) was higher than that among males (98.4%). The frequency of primary and lower education among females (62.6%) was higher than that among males (47.3%). The duration of sleep varied between males and females. Females from rural areas tended to have a short sleep duration. The number of individuals with an annual income of >20,000 CNY/year among females (30.2%) was higher than that among males (28.6%). Additionally, the number of individuals involved in low levels of physical activity among females (41.6%) was higher than that among males (28.3%). The frequency of individuals with a diet score of >3 among females (40.2%) was higher than that among males (55.9%). This indicates that the frequency of individuals with western dietary habits among males was higher than that among females. Additionally, the levels of systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), estimated glomerular filtration rate (eGFR), body mass index (BMI), and uric acid were upregulated in males, whereas those of high-density lipoprotein-cholesterol (HDL-C) and aspartate transaminase (AST)/alanine transaminase (ALT) ratio were downregulated at baseline. Compared with those in females, the levels of SBP, FPG, DBP, BMI, and uric acid were higher and the levels of HDL-C, AST/ALT ratio, and eGFR were lower in males during follow-up. The cumulative incidence of MetS in the study cohort was 24.0% (22.2% and 25.8% in males and females, respectively; p = 0.203).
Table 2 shows the baseline characteristics of the study cohort according to the frequency of tea consumption. The frequency of non-habitual tea consumption among females was higher than that among males. Compared with that in the Han ethnicity (4.8%), the frequency of tea consumption was higher in ethnic groups other than Han (10.5%). The increased numbers of individuals with a diet score of >3 and smoking and alcohol consumption habits were consistent with the increased frequency of tea consumption. Furthermore, the frequency of tea consumption was high among unmarried participants. Participants with increased tea consumption frequency were less likely to have a primary and lower education. The frequency of tea consumption was inversely and significantly correlated with the sleep duration of ≤7 h/day. Similarly, the proportion of participants with an annual income of >20,000 CNY/year was the highest among those who consume tea ≥ 3 times/day. The incidence rates of low physical activity among participants with increased frequency of tea consumption was lower than that among non-habitual tea drinkers. The increased frequency of tea consumption was consistent with the increased levels of SBP, DBP, BMI, HDL-C, and AST/ALT ratio at baseline. The levels of triglyceride (TG), uric acid, and eGFR at baseline were different from those during follow-up. The SBP, DBP, and BMI levels during follow-up and baseline were positively correlated with tea consumption frequency.
Figure 1A shows the tea consumption frequency of the participants (61.2%, 19.5%, 16.4%, and 2.9% of the participants were non-habitual tea consumers, occasional tea consumers, consumed tea 1–2 times/day, and consumed tea ≥ 3 times/day, respectively). The frequency of metabolic diseases according to tea consumption is shown in Figure 1B. The cumulative incidence of high BP and TG significantly varied depending on the frequency of tea consumption.
As shown in Figure 2, multivariate logistic regression analysis after adjusting for potential confounders revealed that occasional tea consumption increased the incidence of low HDL-C [odds ratio (OR) = 1.268; 95% confidence interval (CI) = 1.015–1.584], high waist circumference [OR = 1.336; 95% CI = 1.102–1.621], and MetS [OR = 1.284; 95% CI = 1.050–1.570]. Additionally, tea consumption 1–2 times/day increased the cumulative incidence of high TG [OR = 1.296; 95% CI = 1.040–1.616], high waist circumference [OR 1.296; 95% CI = 1.044–1.609], and MetS [OR = 1.376; 95% CI = 1.030–1.760].
The study participants were subdivided into two categories according to age. As shown in Figure 3, occasional tea consumption was associated with an increased risk of developing MetS [OR = 1.510; 95% CI = 1.184–1.926], high waist circumference [OR = 1.372; 95% CI = 1.088–1.731], low HDL-C [OR = 1.345; 95% CI = 1.035–1.748], and high TG [OR = 1.445; 95% CI = 1.139–1.834] among the middle-aged group. Among the elderly group, tea consumption 1–2 times/day was correlated with an increased risk of developing MetS [OR = 1.565; 95% CI = 1.089–2.248] and high waist circumference [OR = 1.559; 95% CI = 1.075–2.261].

4. Discussion

This prospective study demonstrated that the risk of developing MetS among participants who occasionally consumed tea or consumed tea 1–2 times/day was higher than that among participants who non-habitually consumed tea. Additionally, the frequency of tea consumption was positively correlated with the levels of waist circumference and inversely correlated with the levels of HDL-C. These results revealed the contradicting effects of tea consumption on the metabolic health of rural middle-aged and elderly individuals.
In this study, habitual tea consumption was correlated with an increased risk of developing MetS. This is not consistent with the findings of several previous studies, which reported that tea consumption exerted beneficial effects on MetS and metabolic disorders [22,23]. Vernarelli et al. reported that Camellia sinensis consumption was inversely correlated with weight gain and other markers of MetS in the general population of the United States [24]. Similarly, a study conducted in a rural community in Taiwan reported that partially fermented tea consumption was inversely correlated with the incidence of MetS among elderly males, especially among those who consumed ≥240 mL of tea per day [23]. A cross-sectional study with adults from the United States revealed that the consumption of hot (brewed) tea rather than iced tea was inversely correlated with MetS [24]. In addition to findings from individual studies, systematic reviews and meta-analyses examining more than 26 randomized controlled trials (RCTs) revealed that tea consumption can alleviate the symptoms of MetS [25,26]. A systemic review of the results of eight studies demonstrated that frequent tea consumption exerts health-promoting effects and alleviates various metabolic disorders [14]. Daily consumption of green tea was demonstrated to exert beneficial health effects. Green tea is reported to inhibit lipid emulsification, suppress adipocyte differentiation, promote thermogenesis, and decrease appetite, improving systemic metabolism and decreasing fat mass [27]. Additionally, other molecules, such as theaflavins, catechins and their metabolites, and polyphenols have been implicated in the protective functions of tea against several metabolic disorders, including MetS, cardiovascular diseases, diabetes, and obesity [28]. Tea can also effectively reduce appetite, food consumption, and nutrient absorption in the gastrointestinal system, as well as alter fat metabolism [29].
In contrast to the positive impacts mentioned above, one study in England reported that the daily consumption of green tea along with high-dose caffeine did not exert therapeutic effects on metabolic disorders within 12 weeks [30]. The reasons for these conflicting results are unknown and may be related to the differential physiological and dietary conditions of different populations. According to one theory, tea extracts increase the serum uric acid levels in healthy individuals but downregulate their levels in individuals with hyperuricemia [31]. A systematic review and meta-analysis demonstrated that green tea consumption was positively correlated with the serum uric acid levels [32]. Additionally, increased uric acid levels were independently correlated with a high risk of incident MetS in the general population [33,34,35]. The potential underlying mechanism may involve uric acid-induced mitochondrial oxidative stress, which can alter AKT phosphorylation and modulate endothelial function. Moreover, uric acid is involved in the activation of the renin-angiotensin system [36,37,38,39]. According to a speculative hypothesis, the constituents of tea are complex, and some unknown constituents can upregulate the serum uric acid levels. Another possible reason may involve the dual effect of polyphenols on the serum uric acid levels. Tea extracts exert adverse effects on serum uric acid levels in healthy individuals but exert beneficial effects in individuals with hyperuricemia [31]. The findings of this study indicate that elderly and middle-aged participants who frequently consumed tea, especially those who consumed tea 1–2 times/day, were at risk to develop MetS. One study indicated that the probability of developing MetS was high among the elderly who frequently consume green tea [16]. These deleterious effects of tea consumption may be attributed to the increased excretion of urinary oxalic acid, which leads to the formation of renal stones and results in a burden on circulation [40]. However, information on tea consumption was obtained based on the recall by participants, which may have led to misclassification and information bias. This further complicates the elucidation of the correlation between tea consumption and MetS [16]. Moreover, most of these studies that confirmed the beneficial effects of tea on metabolic disorders involved participants who regularly and persistently consumed green tea or green tea extracts for ≥8–12 weeks [41,42]. Except for the regular and persistent consumption of tea or tea extracts, the amount of tea ingested should be warranted too. Recent RCTs demonstrated that the consumption of 458–886 mg of green tea catechins daily for 90 days decreased body fat in moderately overweight Chinese participants [43]. However, a recent RCT involving obese Caucasian females reported that the supplementation of 200 mg of green tea extract per day for 12 weeks did not affect various parameters, including BMI, energy and fat metabolism, insulin sensitivity, total or low-density lipoprotein (LDL)-cholesterol levels [44]. The use of small amounts of green tea extracts may not be sufficient to exert beneficial effects. Similar to the correlation between frequent tea consumption and MetS, some studies reported that the consumption of oolong and green tea increased the risk of developing type 2 diabetes (T2D) in Asian participants [45,46]. The beneficial effects of tea consumption on T2D were attributed to pesticide residues, which is supported by several epidemiological and experimental studies [46,47,48,49]. The quantity of tea consumption was correlated with the serum concentration of total organochlorine pesticides [50]. Some studies have reported a negative correlation between tea consumption and BP. For example, the consumption of green tea decoction was associated with a mild increase in BP and a marked decrease in heart rate in 76 females enrolled in an observational study [51]. Moreover, in a controversial trial with 29 elderly individuals, the consumption of green tea before lunch markedly increased SBP and DBP without affecting heart rate. Green tea was initially used to treat postprandial hypotension in elderly patients [52]. To explain tea-induced BP elevation, large amounts of green tea intake were speculated to be associated with an increased risk of developing hyperhomocysteinemia (HHcy). HHcy is demonstrated to be an independent risk factor for hypertension among residents of rural China [53,54]. The potential underlying mechanisms may involve green-tea-induced arteriolar constriction, renal dysfunction, and sodium reabsorption [55]. In addition to increasing the risk of hypertension, HHcy increases the risk of dyslipidemia. This can be partially explained by HHcy-associated hypomethylation, which results in lipid accumulation and the downregulation of the synthesis of phosphatidylcholine that is required for very low-density lipoprotein assembly and homeostasis [56]. Low HDL-C levels are attributed to the homocysteine-induced inhibition of enzymes or molecules involved in HDL particle assembly [57]. Previous studies have elucidated the correlation between HHcy, metabolic disorders, and MetS. However, the underlying mechanisms have not been elucidated. Several possible speculations have been proposed to offer some mechanistic insights. HHcy is associated with various pathological effects, including vascular damage, cytotoxicity [58], neuronal apoptosis [59], oxidative stress-induced DNA damage [60], alterations in DNA methylation [61,62], and endothelial nitric oxide production [63]. The role of homocysteine in the development of metabolic disease is unclear. Furthermore, we suggest that the contradictory findings may be attributed to the relatively high prevalence of drinking and smoking among habitual tea consumers. In China, tea consumption is a social event, and tea is the preferred drink among friends after alcohol consumption to relieve the discomforts associated with alcohol consumption. Similarly, the prevalence of smoking is high among Chinese tea consumers. These factors contributed to the increased rates of concurrent smoking and drinking among individuals consuming tea. Therefore, it must be verified if tea consumers in the study cohort constituted a small group of unhealthy subjects with a relatively high rate of smoking and alcohol consumption. These findings cannot be widely extrapolated to other populations because the study cohort was not representative. Further studies are needed to confirm the findings of this study. At baseline, the levels of SBP, DBP, BMI, and uric acid in habitual tea consumers were significantly higher than those in non-habitual tea consumers (Table 1). The combination of an unhealthy lifestyle (e.g., smoking cigarettes, drinking alcohol, and lack of leisure time exercise) and poor health status of tea drinkers relative to non-drinkers also supports this positive association [64,65,66].
In addition to poor lifestyle habits, such as smoking and drinking, dietary patterns may also be crucial factors regulating the development of MetS. Previous studies have demonstrated that the consumption of a vegetable-rich diet is associated with a decreased risk of developing metabolic disorders [67,68]. Moreover, prospective studies have reported an inverse correlation between chronic disease and plant protein intake. In contrast, the consumption of animal protein was positively correlated with the risk of developing hypertension, diabetes, and heart disease [20,69,70]. Potential mechanisms underlying these correlations have been listed below. First, the predominant amino acids vary between animal proteins and vegetable proteins. Glycine, which is predominant in animal proteins, increases BP and is associated with the risk of developing diabetes [71,72,73]. Second, the high sodium content of processed meats may be associated with hypertension and the risk of developing coronary heart disease [74,75]. In addition to high contents of sodium, nitrites in processed meats, which can be converted into nitrosamines, are toxic to pancreatic beta cells and increase the risk of diabetes in animals [76,77]. The blood nitrite concentrations can induce endothelial dysfunction [78] and insulin resistance [79] in humans. Third, advanced glycation end-products [80] or elevated inflammatory mediators [81] and gamma-glutamyltransferase [82] associated with high red meat intake can increase the risk of developing diabetes and coronary heart disease [83,84,85,86,87,88,89,90]. Finally, dietary iron, especially the heme-iron from red meat, is positively associated with diabetes, myocardial infarction, and fatal coronary heart disease [91,92,93,94,95,96,97]. Iron is responsible for catalyzing several cellular reactions, leading to the production of reactive oxygen species and induction of oxidative stress, which damages the pancreatic beta cells, increases the risk of developing diabetes, and exacerbates myocardial damage [98,99,100,101]. This study demonstrated that the increase in the frequency of tea consumption from non-habitual tea consumption to tea consumption for ≥3 times/day, western dietary habits, and increased meat consumption may affect the correlation between tea consumption and MetS. However, diet patterns were not significant factors after accounting for potential confounders. This may be related to the simplistic analysis of a diet pattern that only included the consumption of meat and vegetables. This study did not categorize animal protein into specific types because not all foods high in animal protein were equal. Red meat is positively correlated with metabolic risk factors, whereas fish and dairy are inversely correlated [102]. Further studies are needed to elucidate the relevance of dietary habits in the correlation between tea consumption and MetS.
Moreover, this study demonstrated that tea consumption was associated with MetS-related parameters (high TG, low HDL-C, and increased waist circumference). Xu et al. conducted a meta-analysis and reported that the consumption of green tea decreases the levels of LDL-C and total cholesterol but not those of HDL-C or TGs [103]. However, one systemic review of 14 unique RCTs reported that short-term (2–24 weeks) tea consumption did not significantly affect lipids in healthy or at-risk adults [104]. Recently, Huang et al. demonstrated that tea consumption delayed the aging-related decline in HDL concentrations after a six-year follow-up [19]. In this study, information on the tea consumption frequency in the past two weeks was obtained based on recall by the participants. This may have resulted in bias and consequently affected the conclusion of the study. Previous studies have indicated that the consumption of ≥3–4 cups of tea (600–900 mg tea catechins) alleviated MetS and decreased the risk of developing diabetes and cardiovascular diseases [13]. Therefore, the frequency of tea consumption may not be precise to establish the effect of tea on metabolic disorders. Several studies have demonstrated that tea consumption is frequently associated with decreased waist circumference [105]. These discrepant results may be due to the positive correlation between baseline BMI and tea consumption frequency. Tea consumption with meals decreases the absorption of non-heme iron, as tea contains tannins [106]. Black tea is reported to reduce the bioavailability of non-heme iron (Fe) by approximately 79–94%. Similarly, green tea catechins have a strong affinity for Fe because their infusions significantly reduce the bioavailability of Fe from food [11]. A recent quantitative meta-analysis examining 26 cross-sectional and case-control studies revealed that iron deficiency is significantly associated with overweight and obesity. This partially explains the reason for the positive correlation between tea consumption frequency and the risk of developing abdominal obesity [107].
Tea consumption is believed to be an effective habit with beneficial effects on hypertension, diabetes, dyslipidemia, obesity, and other metabolic disorders. Tea consumption is prevalent throughout China irrespective of rural or urban areas. However, the results of this study revealed that tea consumption increased the risk of MetS. This may be because of the increased prevalence of accompanying unhealthy lifestyle habits, such as smoking, drinking, and the increased levels of other parameters at baseline that increase the susceptibility to metabolic disorders. Therefore, non-pharmacological strategies must be carefully assessed before their application in rural inhabitants, especially middle-aged and elderly individuals.
Only middle-aged and geriatric Chinese individuals residing in Northeast China were eligible to participate in this study, which contributed to the inverse correlation between the incidence of MetS and the frequency of habitual tea consumption determined in this study. Therefore, the findings of this study may not be applicable to other populations with different demographics. Additionally, participants in this study were enrolled from rural Northeast China. This study cohort is not representative of the general population of China. Elderly individuals who frequently consumed tea in this study were a small group of non-healthy subjects with a high frequency of current smoking and alcohol consumption habits. Therefore, this is a non-representative group, which may lead to misclassification and information bias and may have distorted the effect estimates of tea consumption on MetS. Some bias was expected in the assessment because tea consumption was assessed based on self-reporting rather than through direct measurement. The potential presence of residual and unmeasured confounding factors, such as chronic illnesses, medication use, and malnutrition may increase the probability of developing MetS. Thus, the correlation reported in this study may not be accurate even though conditions were addressed for various potential confounders. This study only evaluated the frequency of tea consumption. However, information on the method used for tea preparation, the container used for tea consumption, the addition of sugar to tea, and the amount of tea consumed was not obtained. Thus, the description of the tea consumption behavior may be inaccurate. Fifth, the type of tea consumed may exert differential effects on metabolic disorders. Green tea is known to irritate the gastrointestinal tract, especially when ingested in large amounts on an empty stomach. Black tea is a gentle drink (less unpleasant) but its health benefits are limited. The composition and health benefits of dark tea, which is regarded as a mild drink, must be comprehensively investigated in the future. This study did not assess the specific types of tea consumed by the participants, which can affect the correlation between tea consumption and MetS. Additionally, comprehensive data on the intake of diets, such as dairy products, whole grains, fruits, or nuts were not available in this study. However, multivariate logistic regression analysis was performed after adjusting for some diet-related factors, including vegetable and meat consumption scores, which may partially mitigate the confounding effect of diet.

5. Conclusions

Our research concluded that among middle-aged and elderly rural Chinese participants, occasionally drinking and 1–2 times/day drinking tea is linked to a higher risk of developing MetS. Additional research is required to clarify the paradoxical association between tea consumption and MetS described in the literature and to identify potential mechanisms underlying the adverse effects of tea consumption.

Author Contributions

Conceptualization, Y.S.; methodology, X.G. and G.L.; software, G.L.; validation, S.Y., H.Y. and B.W.; formal analysis, S.Y.; investigation, S.Y., H.Y. and B.W.; resources, Y.S.; data curation, S.Y., H.Y. and B.W.; writing—original draft preparation, S.Y.; writing—review and editing, Y.S.; visualization, Y.S.; supervision, Y.S.; project administration, S.Y., H.Y., X.G., G.L. and B.W.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Ethics Committee of China Medical University (Shenyang, China AF-SDP-07-1, 0-01).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yao, F.; Bo, Y.; Zhao, L.; Li, Y.; Ju, L.; Fang, H.; Piao, W.; Yu, D.; Lao, X. Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017. Nutrients 2021, 13, 4475. [Google Scholar] [CrossRef]
  2. Özmen, M.; Yersal, Ö.; Öztürk, S.; Soysal, D.; Köseeoğlu, M. Prevalence of the metabolic syndrome in rheumatoid arthritis. Eur. J. Rheumatol. 2014, 1, 1–4. [Google Scholar] [CrossRef]
  3. Gu, D.; Reynolds, K.; Wu, X.; Chen, J.; Duan, X.; Reynolds, R.; Whelton, P.; He, J. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 2005, 365, 1398–1405. [Google Scholar] [CrossRef]
  4. Li, Y.; Zhao, L.; Yu, D.; Wang, Z.; Ding, G. Metabolic syndrome prevalence and its risk factors among adults in China: A nationally representative cross-sectional study. PLoS ONE 2018, 13, e0199293. [Google Scholar] [CrossRef]
  5. Du, Z.; Xing, L.; Liu, S.; Jing, L.; Tian, Y.; Zhang, B.; Yan, H.; Lin, M.; Yu, S.; Sun, Y. Prevalence and determinants of metabolic syndrome based on three definitions in rural northeast China. Public Health Nutr. 2020, 23, 3379–3386. [Google Scholar] [CrossRef] [PubMed]
  6. Ying, X.; Yang, S.; Li, S.; Su, M.; Wang, N.; Chen, Y.; Jiang, Q.; Fu, C. Prevalences of metabolic syndrome and its sex-specific association with socioeconomic status in rural China: A cross-sectional study. BMC Public Health 2021, 21, 2033. [Google Scholar] [CrossRef]
  7. Akimov, A.; Gemueva, K.; Semenova, N. The Seventh Population Census in the PRC: Results and Prospects of the Country’s Demographic Development. Her. Russ. Acad. Sci. 2021, 91, 724–735. [Google Scholar] [CrossRef]
  8. Tang, G.Y.; Meng, X.; Gan, R.Y.; Zhao, C.N.; Liu, Q.; Feng, Y.B.; Li, S.; Wei, X.L.; Atanasov, A.G.; Corke, H.; et al. Health Functions and Related Molecular Mechanisms of Tea Components: An Update Review. Int. J. Mol. Sci. 2019, 20, 6196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Xing, L.; Zhang, H.; Qi, R.; Tsao, R.; Mine, Y. Recent Advances in the Understanding of the Health Benefits and Molecular Mechanisms Associated with Green Tea Polyphenols. J. Agric. Food Chem. 2019, 67, 1029–1043. [Google Scholar] [CrossRef] [PubMed]
  10. Hartley, L.; Flowers, N.; Holmes, J.; Clarke, A.; Stranges, S.; Hooper, L.; Rees, K. Green and black tea for the primary prevention of cardiovascular disease. Cochrane Database Syst. Rev. 2013, 2013, CD009934. [Google Scholar] [CrossRef]
  11. Hayat, K.; Iqbal, H.; Malik, U.; Bilal, U.; Mushtaq, S. Tea and its consumption: Benefits and risks. Crit. Rev. Food Sci. Nutr. 2015, 55, 939–954. [Google Scholar] [CrossRef] [PubMed]
  12. Kim, T.; Jeong, G.; Yang, J.; Lee, K.; Kronbichler, A.; van der Vliet, H.; Grosso, G.; Galvano, F.; Aune, D.; Kim, J.; et al. Tea Consumption and Risk of Cancer: An Umbrella Review and Meta-Analysis of Observational Studies. Adv. Nutr. 2020, 11, 1437–1452. [Google Scholar] [CrossRef] [PubMed]
  13. Mahdavi-Roshan, M.; Salari, A.; Ghorbani, Z.; Ashouri, A. The effects of regular consumption of green or black tea beverage on blood pressure in those with elevated blood pressure or hypertension: A systematic review and meta-analysis. Complement. Ther. Med. 2020, 51, 102430. [Google Scholar] [CrossRef]
  14. Sirotkin, A.; Kolesárová, A. The anti-obesity and health-promoting effects of tea and coffee. Physiol. Res. 2021, 70, 161–168. [Google Scholar] [CrossRef] [PubMed]
  15. Kondo, Y.; Goto, A.; Noma, H.; Iso, H.; Hayashi, K.; Noda, M. Effects of Coffee and Tea Consumption on Glucose Metabolism: A Systematic Review and Network Meta-Analysis. Nutrients 2018, 11, 48. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Dong, X.; Wang, R.; Liu, J.; Ma, Q.; Pan, C. Habitual tea consumption and 5-year incident metabolic syndrome among older adults: A community-based cohort study. BMC Geriatr. 2021, 21, 728. [Google Scholar] [CrossRef]
  17. Sun, Y.; Mu, J.; Wang, D.; Ouyang, N.; Xing, L.; Guo, X.; Zhao, C.; Ren, G.; Ye, N.; Zhou, Y.; et al. A village doctor-led multifaceted intervention for blood pressure control in rural China: An open, cluster randomised trial. Lancet 2022, 399, 1964–1975. [Google Scholar] [CrossRef]
  18. Yu, S.; Guo, X.; Yang, H.; Zheng, L.; Sun, Y. An update on the prevalence of metabolic syndrome and its associated factors in rural northeast China. BMC Public Health 2014, 14, 877. [Google Scholar] [CrossRef] [Green Version]
  19. Huang, S.; Li, J.; Wu, Y.; Ranjbar, S.; Xing, A.; Zhao, H.; Wang, Y.; Shearer, G.C.; Bao, L.; Lichtenstein, A.H.; et al. Tea Consumption and Longitudinal Change in High-Density Lipoprotein Cholesterol Concentration in Chinese Adults. J. Am. Heart Assoc. 2018, 7, e008814. [Google Scholar] [CrossRef] [Green Version]
  20. Hernández-Alonso, P.; Salas-Salvadó, J.; Ruiz-Canela, M.; Corella, D.; Estruch, R.; Fitó, M.; Arós, F.; Gómez-Gracia, E.; Fiol, M.; Lapetra, J.; et al. High dietary protein intake is associated with an increased body weight and total death risk. Clin. Nutr. 2016, 35, 496–506. [Google Scholar] [CrossRef]
  21. Alberti, K.; Eckel, R.; Grundy, S.; Zimmet, P.; Cleeman, J.; Donato, K.; Fruchart, J.; James, W.; Loria, C.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar]
  22. Grosso, G.; Stepaniak, U.; Micek, A.; Topor-Mądry, R.; Pikhart, H.; Szafraniec, K.; Pająk, A. Association of daily coffee and tea consumption and metabolic syndrome: Results from the Polish arm of the HAPIEE study. Eur. J. Nutr. 2015, 54, 1129–1137. [Google Scholar] [CrossRef] [Green Version]
  23. Chang, C.-S.; Chang, Y.-F.; Liu, P.-Y.; Chen, C.-Y.; Tsai, Y.-S.; Wu, C.-H. Smoking, habitual tea drinking and metabolic syndrome in elderly men living in rural community: The Tianliao old people (TOP) study 02. PLoS ONE 2012, 7, e38874. [Google Scholar] [CrossRef] [PubMed]
  24. Vernarelli, J.A.; Lambert, J.D. Tea consumption is inversely associated with weight status and other markers for metabolic syndrome in US adults. Eur. J. Nutr. 2013, 52, 1039–1048. [Google Scholar] [CrossRef]
  25. Hursel, R.; Viechtbauer, W.; Westerterp-Plantenga, M.S. The effects of green tea on weight loss and weight maintenance: A meta-analysis. Int. J. Obes. 2009, 33, 956–961. [Google Scholar] [CrossRef] [Green Version]
  26. Phung, O.; Baker, W.; Matthews, L.; Lanosa, M.; Thorne, A.; Coleman, C. Effect of green tea catechins with or without caffeine on anthropometric measures: A systematic review and meta-analysis. J. Clin. Nutr. 2010, 91, 73–81. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Dinh, T.C.; Thi Phuong, T.N.; Minh, L.B.; Minh Thuc, V.T.; Bac, N.D.; Van Tien, N.; Pham, V.H.; Show, P.L.; Tao, Y.; Ngoc, V.T.N.; et al. The effects of green tea on lipid metabolism and its potential applications for obesity and related metabolic disorders—An existing update. Diabetes Metab. Syndr. 2019, 13, 1667–1673. [Google Scholar] [CrossRef]
  28. Shen, W.; Pan, Y.; Jin, B.; Zhang, Z.; You, T.; Qu, Y.; Han, M.; Yuan, X.; Zhang, Y. Effects of Tea Consumption on Anthropometric Parameters, Metabolic Indexes and Hormone Levels of Women with Polycystic Ovarian Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front. Endocrinol. 2021, 12, 736867. [Google Scholar] [CrossRef] [PubMed]
  29. Chieng, D.; Kistler, P. Coffee and tea on cardiovascular disease (CVD) prevention. Trends Cardiovasc. Med. 2021, 32, 399–405. [Google Scholar] [CrossRef] [PubMed]
  30. Janssens, P.L.; Hursel, R.; Westerterp-Plantenga, M.S. Long-term green tea extract supplementation does not affect fat absorption, resting energy expenditure, and body composition in adults. J. Nutr. 2015, 145, 864–870. [Google Scholar] [CrossRef] [Green Version]
  31. Peluso, I.; Teichner, A.; Manafikhi, H.; Palmery, M. Camellia sinensis in asymptomatic hyperuricemia: A meta-analysis of tea or tea extract effects on uric acid levels. Crit. Rev. Food Sci. Nutr. 2017, 57, 391–398. [Google Scholar] [CrossRef]
  32. Zhang, Y.; Cui, Y.; Li, X.A.; Li, L.J.; Xie, X.; Huang, Y.Z.; Deng, Y.H.; Zeng, C.; Lei, G.H. Is tea consumption associated with the serum uric acid level, hyperuricemia or the risk of gout? A systematic review and meta-analysis. BMC Musculoskelet. Disord. 2017, 18, 95. [Google Scholar] [CrossRef] [Green Version]
  33. Sumiyoshi, H.; Ohyama, Y.; Imai, K.; Kurabayashi, M.; Saito, Y.; Nakamura, T. Association of Uric Acid with Incident Metabolic Syndrome in a Japanese General Population. Int. Heart J. 2019, 60, 830–835. [Google Scholar] [CrossRef] [Green Version]
  34. Fu, Y.Q.; Yang, H.; Zheng, J.S.; Zeng, X.Y.; Zeng, W.; Fan, Z.F.; Chen, M.; Wang, L.; Li, D. Positive association between metabolic syndrome and serum uric acid in Wuhan. Asia Pac. J. Clin. Nutr. 2017, 26, 343–350. [Google Scholar]
  35. Zurlo, A.; Veronese, N.; Giantin, V.; Maselli, M.; Zambon, S.; Maggi, S.; Musacchio, E.; Toffanello, E.D.; Sartori, L.; Perissinotto, E.; et al. High serum uric acid levels increase the risk of metabolic syndrome in elderly women: The PRO.V.A study. Nutr. Metab. Cardiovasc. Dis. 2016, 26, 27–35. [Google Scholar] [CrossRef]
  36. King, C.; Lanaspa, M.A.; Jensen, T.; Tolan, D.R.; Sánchez-Lozada, L.G.; Johnson, R.J. Uric Acid as a Cause of the Metabolic Syndrome. Contrib. Nephrol. 2018, 192, 88–102. [Google Scholar]
  37. Mazzali, M.; Hughes, J.; Kim, Y.G.; Jefferson, J.A.; Kang, D.H.; Gordon, K.L.; Lan, H.Y.; Kivlighn, S.; Johnson, R.J. Elevated uric acid increases blood pressure in the rat by a novel crystal-independent mechanism. Hypertension 2001, 38, 1101–1106. [Google Scholar] [CrossRef] [Green Version]
  38. Sánchez-Lozada, L.; Soto, V.; Tapia, E.; Avila-Casado, C.; Sautin, Y.; Nakagawa, T.; Franco, M.; Rodríguez-Iturbe, B.; Johnson, R. Role of oxidative stress in the renal abnormalities induced by experimental hyperuricemia. Am. J. Physiol. Ren. Physiol. 2008, 295, F1134–F1141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Sánchez-Lozada, L.; Tapia, E.; López-Molina, R.; Nepomuceno, T.; Soto, V.; Avila-Casado, C.; Nakagawa, T.; Johnson, R.; Herrera-Acosta, J.; Franco, M. Effects of acute and chronic L-arginine treatment in experimental hyperuricemia. Am. J. Physiol. Renal. Physiol. 2007, 292, F1238–F1244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Gasińska, A.; Gajewska, D. Tea and coffee as the main sources of oxalate in diets of patients with kidney oxalate stones. Rocz. Panstw. Zakl. Hig. 2007, 58, 61–67. [Google Scholar] [PubMed]
  41. Wu, A.; Spicer, D.; Stanczyk, F.; Tseng, C.-C.; Yang, C.; Pike, M. Effect of 2-month controlled green tea intervention on lipoprotein cholesterol, glucose, and hormone levels in healthy postmenopausal women. Cancer Prev. Res. 2012, 5, 393–402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Suliburska, J.; Bogdanski, P.; Szulinska, M.; Stepien, M.; Pupek-Musialik, D.; Jablecka, A. Effects of green tea supplementation on elements, total antioxidants, lipids, and glucose values in the serum of obese patients. Biol. Trace Elem. Res. 2012, 149, 315–322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Wang, H.; Wen, Y.; Du, Y.; Yan, X.; Guo, H.; Rycroft, J.A.; Boon, N.; Kovacs, E.M.; Mela, D.J. Effects of catechin enriched green tea on body composition. Obesity (Silver Spring Md.) 2010, 18, 773–779. [Google Scholar] [CrossRef]
  44. Mielgo-Ayuso, J.; Barrenechea, L.; Alcorta, P.; Larrarte, E.; Margareto, J.; Labayen, I. Effects of dietary supplementation with epigallocatechin-3-gallate on weight loss, energy homeostasis, cardiometabolic risk factors and liver function in obese women: Randomised, double-blind, placebo-controlled clinical trial. Br. J. Nutr. 2014, 111, 1263–1271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Golozar, A.; Khademi, H.; Kamangar, F.; Poutschi, H.; Islami, F.; Abnet, C.; Freedman, N.; Taylor, P.; Pharoah, P.; Boffetta, P.; et al. Diabetes mellitus and its correlates in an Iranian adult population. PLoS ONE 2011, 6, e26725. [Google Scholar] [CrossRef] [Green Version]
  46. Hayashino, Y.; Fukuhara, S.; Okamura, T.; Tanaka, T.; Ueshima, H.; HIPOP-OHP Research Group. High oolong tea consumption predicts future risk of diabetes among Japanese male workers: A prospective cohort study. Diabet. Med. 2011, 28, 805–810. [Google Scholar] [CrossRef]
  47. Yang, J.; Mao, Q.X.; Xu, H.X.; Ma, X.; Zeng, C.Y. Tea consumption and risk of type 2 diabetes mellitus: A systematic review and meta-analysis update. BMJ Open 2014, 4, e005632. [Google Scholar] [CrossRef]
  48. Evangelou, E.; Ntritsos, G.; Chondrogiorgi, M.; Kavvoura, F.; Hernández, A.; Ntzani, E.; Tzoulaki, I. Exposure to pesticides and diabetes: A systematic review and meta-analysis. Environ. Int. 2016, 91, 60–68. [Google Scholar] [CrossRef]
  49. Ngwa, E.N.; Kengne, A.P.; Tiedeu-Atogho, B.; Mofo-Mato, E.P.; Sobngwi, E. Persistent organic pollutants as risk factors for type 2 diabetes. Diabetol. Metab. Syndr. 2015, 7, 41. [Google Scholar] [CrossRef] [Green Version]
  50. Lee, S.-A.; Dai, Q.; Zheng, W.; Gao, Y.-T.; Blair, A.; Tessari, J.; Ji, B.T.; Shu, X.-O. Association of serum concentration of organochlorine pesticides with dietary intake and other lifestyle factors among urban Chinese women. Environ. Int. 2007, 33, 157–163. [Google Scholar] [CrossRef]
  51. Ullah, N.; Rafique, N.; Nazir, A.; Anwar, S.; Altaf, N.; Ahmed, G. Effect of decoction of Camellia sinensis on blood pressure and heart rate. Med. Forum Mon. 2011, 22, 46–48. [Google Scholar]
  52. Son, J.T.; Lee, E. Effects of green tea ingestion on postprandial drops in blood pressure in older adults. J. Gerontol. Nurs. 2012, 38, 30–38. [Google Scholar] [CrossRef] [PubMed]
  53. Zhu, J.; Wang, W.; Xiong, Y.; Cooper, R.S.; Du Raza-Arvizu, R.; Cao, G.; Wang, Y.; Ji, P.; Bian, R.; Xu, J. The Association Between Tea Consumption and Hyperhomocysteine in Chinese Hypertensive Patients. Am. J. Hypertens. 2019, 32, 209–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Li, Z.; Guo, X.; Chen, S.; Zheng, L.; Yang, H.; Sun, G.; Yu, S.; Li, W.; Zhou, L.; Wang, J.; et al. Hyperhomocysteinemia independently associated with the risk of hypertension: A cross-sectional study from rural China. J. Hum. Hypertens. 2016, 30, 508–512. [Google Scholar] [CrossRef]
  55. Atif, A.; Rizvi, M.A.; Tauheed, S.; Aamir, I.; Majeed, F.; Siddiqui, K.; Khan, S. Serum homocysteine concentrations in patients with hypertension. Pak. J. Physiol. 2008, 4, 21–22. [Google Scholar]
  56. Lee, H.S.; In, S.; Park, T. The Homocysteine and Metabolic Syndrome: A Mendelian Randomization Study. Nutrients 2021, 13, 2440. [Google Scholar] [CrossRef]
  57. Obeid, R.; Herrmann, W. Homocysteine and lipids: S-adenosyl methionine as a key intermediate. FEBS Lett. 2009, 583, 1215–1225. [Google Scholar] [CrossRef] [Green Version]
  58. Yoshitomi, R.; Nakayama, K.; Yamashita, S.; Kumazoe, M.; Lin, T.-A.; Mei, C.-Y.; Marugame, Y.; Fujimura, Y.; Maeda-Yamamoto, M.; Kuriyama, S.; et al. Plasma Homocysteine Concentration is Associated with the Expression Level of Folate Receptor 3. Sci. Rep. 2020, 10, 10283. [Google Scholar] [CrossRef]
  59. Wang, X.-J.; Chen, W.; Fu, X.-T.; Ma, J.-K.; Wang, M.-H.; Hou, Y.-J.; Tian, D.-C.; Fu, X.-Y.; Fan, C.-D. Reversal of homocysteine-induced neurotoxicity in rat hippocampal neurons by astaxanthin: Evidence for mitochondrial dysfunction and signaling crosstalk. Cell Death Discov. 2018, 4, 50. [Google Scholar] [CrossRef] [Green Version]
  60. Endres, M.; Ahmadi, M.; Kruman, I.; Biniszkiewicz, D.; Meisel, A.; Gertz, K. Folate deficiency increases postischemic brain injury. Stroke 2005, 36, 321–325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Wang, W.-M.; Jin, H.-Z. Homocysteine: A Potential Common Route for Cardiovascular Risk and DNA Methylation in Psoriasis. Chin. Med. J. 2017, 130, 1980–1986. [Google Scholar] [CrossRef] [PubMed]
  62. Bednarska-Makaruk, M.; Graban, A.; Sobczy ńska-Malefora, A.; Harrington, D.J.; Mitchell, M.; Voong, K.; Dai, L.; Łojkowska, W.; Bochy ńska, A.; Ryglewicz, D.; et al. Homocysteine metabolism and the associations of global DNA methylation with selected gene polymorphisms and nutritional factors in patients with dementia. Exp. Gerontol. 2016, 81, 83–91. [Google Scholar] [CrossRef] [PubMed]
  63. McCully, K.S. Chemical pathology of homocysteine. IV. Excitotoxicity, oxidative stress, endothelial dysfunction, and inflammation. Ann. Clin. Lab. Sci. 2009, 39, 219–232. [Google Scholar] [PubMed]
  64. Li, X.H.; Yu, F.F.; Zhou, Y.H.; He, J. Association between alcohol consumption and the risk of incident type 2 diabetes: A systematic review and dose-response meta-analysis. Am. J. Clin. Nutr. 2016, 103, 818–829. [Google Scholar] [CrossRef] [Green Version]
  65. Pan, A.; Wang, Y.; Talaei, M.; Hu, F.B.; Wu, T. Relation of active, passive, and quitting smoking with incident type 2 diabetes: A systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015, 3, 958–967. [Google Scholar] [CrossRef] [Green Version]
  66. InterAct Consortium; Ekelund, U.; Palla, L.; Brage, S.; Franks, P.W.; Peters, T.; Balkau, B.; Diaz, M.J.T.; Huerta, J.M.; Agnoli, C.; et al. Physical activity reduces the risk of incident type 2 diabetes in general and in abdominally lean and obese men and women: The EPIC-InterAct study. Diabetologia 2012, 55, 1944–1952. [Google Scholar] [CrossRef] [Green Version]
  67. Cui, X.; Wang, B.; Wu, Y.; Xie, L.; Xun, P.; Tang, Q.; Cai, W.; Shen, X. Vegetarians have a lower fasting insulin level and higher insulin sensitivity than matched omnivores: A cross-sectional study. Nutr. Metab. Cardiovasc. Dis. 2019, 29, 467–473. [Google Scholar] [CrossRef] [PubMed]
  68. Cho, Y.A.; Kim, J.; Cho, E.R.; Shin, A. Dietary patterns and the prevalence of metabolic syndrome in Korean women. Nutr. Metab. Cardiovasc. Dis. 2011, 21, 893–900. [Google Scholar] [CrossRef] [PubMed]
  69. Kelemen, L.E.; Kushi, L.H.; Jacobs DRJr Cerhan, J.R. Associations of dietary protein with disease and mortality in a prospective study of postmenopausal women. Am. J. Epidemiol. 2005, 161, 239–249. [Google Scholar] [CrossRef]
  70. Alonso, A.; Beunza, J.J.; Bes-Rastrollo, M.; Pajares, R.M.; Martínez-González, M.A. Vegetable protein and fiber from cereal are inversely associated with the risk of hypertension in a Spanish cohort. Arch. Med. Res. 2006, 37, 778–786. [Google Scholar] [CrossRef]
  71. Stamler, J.; Brown, I.J.; Daviglus, M.L.; Chan, Q.; Miura, K.; Okuda, N.; Ueshima, H.; Zhao, L.; Elliott, P. Dietary glycine and blood pressure: The International Study on Macro/Micronutrients and Blood Pressure. Am. J. Clin. Nutr. 2013, 98, 136–145. [Google Scholar] [CrossRef] [Green Version]
  72. Stamler, J.; Brown, I.J.; Daviglus, M.L.; Chan, Q.; Kesteloot, H.; Ueshima, H.; Zhao, L.; Elliott, P.; INTERMAP Research Group. Glutamic acid, the main dietary amino acid, and blood pressure: The INTERMAP Study (International Collaborative Study of Macronutrients, Micronutrients and Blood Pressure). Circulation 2009, 120, 221–228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Wittenbecher, C.; Mühlenbruch, K.; Kröger, J.; Jacobs, S.; Kuxhaus, O.; Floegel, A.; Fritsche, A.; Pischon, T.; Prehn, C.; Adamski, J.; et al. Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am. J. Clin. Nutr. 2015, 101, 1241–1250. [Google Scholar] [CrossRef] [Green Version]
  74. Bibbins-Domingo, K.; Chertow, G.M.; Coxson, P.G.; Moran, A.; Lightwood, J.M.; Pletcher, M.J.; Goldman, L. Projected effect of dietary salt reductions on future cardiovascular disease. N. Engl. J. Med. 2010, 362, 590–599. [Google Scholar] [CrossRef] [Green Version]
  75. Smith-Spangler, C.M.; Juusola, J.L.; Enns, E.A.; Owens, D.K.; Garber, A.M. Population strategies to decrease sodium intake and the burden of cardiovascular disease: A cost-effectiveness analysis. Ann. Intern. Med. 2010, 152, 481–487. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Männistö, S.; Kontto, J.; Kataja-Tuomola, M.; Albanes, D.; Virtamo, J. High processed meat consumption is a risk factor of type 2 diabetes in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention study. Br. J. Nutr. 2010, 103, 1817–1822. [Google Scholar] [CrossRef] [PubMed]
  77. Tong, M.; Neusner, A.; Longato, L.; Lawton, M.; Wands, J.R.; de la Monte, S.M. Nitrosamine exposure causes insulin resistance diseases: Relevance to type 2 diabetes mellitus, non-alcoholic steatohepatitis, and Alzheimer’s disease. J. Alzheimers Dis. 2009, 17, 827–844. [Google Scholar] [PubMed]
  78. Kleinbongard, P.; Dejam, A.; Lauer, T.; Jax, T.; Kerber, S.; Gharini, P.; Balzer, J.; Zotz, R.B.; Scharf, R.E.; Willers, R.; et al. Plasma nitrite concentrations reflect the degree of endothelial dysfunction in humans. Free Radic. Biol. Med. 2006, 40, 295–302. [Google Scholar] [CrossRef]
  79. Pereira, E.C.; Ferderbar, S.; Bertolami, M.C.; Faludi, A.A.; Monte, O.; Xavier, H.T.; Pereira, T.V.; Abdalla, D.S. Biomarkers of oxidative stress and endothelial dysfunction in glucose intolerance and diabetes mellitus. Clin. Biochem. 2008, 41, 1454–1460. [Google Scholar] [CrossRef]
  80. Peppa, M.; Goldberg, T.; Cai, W.; Rayfield, E.; Vlassara, H. Glycotoxins: A missing link in the “relationship of dietary fat and meat intake in relation to risk of type 2 diabetes in men”. Diabetes Care 2002, 25, 1898–1899. [Google Scholar] [CrossRef] [Green Version]
  81. Azadbakht, L.; Esmaillzadeh, A. Red meat intake is associated with metabolic syndrome and the plasma C-reactive protein concentration in women. J. Nutr. 2009, 139, 335–339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Lee, D.H.; Steffen, L.M.; Jacobs, D.R., Jr. Association between serum gamma-glutamyltransferase and dietary factors: The Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am. J. Clin. Nutr. 2004, 79, 600–605. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Baranczewski, P.; Gustafsson, J.A.; Moller, L. DNA adduct formation of 14 heterocyclic aromatic amines in mouse tissue after oral administration and characterization of the DNA adduct formed by 2-amino-9H-pyrido[2,3-b]indole (AalphaC), analysed by 32P_HPLC. Biomarkers 2004, 9, 243–257. [Google Scholar] [CrossRef]
  84. Gertig, D.M.; Hankinson, S.E.; Hough, H.; Spiegelman, D.; Colditz, G.A.; Willett, W.C.; Kelsey, K.T.; Hunter, D.J. N-acetyl transferase 2 genotypes, meat intake and breast cancer risk. Int. J. Cancer 1999, 80, 13–17. [Google Scholar] [CrossRef]
  85. Hansen, E.S. International Commission for Protection Against Environmental Mutagens and Carcinogens. ICPEMC Working Paper 7/1/2. Shared risk factors for cancer and atherosclerosis—A review of the epidemiological evidence. Mutat. Res. 1990, 239, 163–179. [Google Scholar] [CrossRef] [PubMed]
  86. Kanková, K. Diabetic threesome (hyperglycaemia, renal function and nutrition) and advanced glycation end products: Evidence for the multiple-hit agent? Proc. Nutr. Soc. 2008, 67, 60–74. [Google Scholar] [CrossRef] [Green Version]
  87. Santarelli, R.L.; Pierre, F.; Corpet, D.E. Processed meat and colorectal cancer: A review of epidemiologic and experimental evidence. Nutr. Cancer 2008, 60, 131–144. [Google Scholar] [CrossRef] [Green Version]
  88. Spiteller, G. Peroxyl radicals are essential reagents in the oxidation steps of the Maillard reaction leading to generation of advanced glycation end products. Ann. N. Y. Acad. Sci. 2008, 1126, 128–133. [Google Scholar] [CrossRef]
  89. Wakabayashi, K. International Commission for Protection Against Environmental Mutagens and Carcinogens. ICPEMC Working Paper 7/1/3. Animal studies suggesting involvement of mutagen/carcinogen exposure in atherosclerosis. Mutat. Res. 1990, 239, 181–187. [Google Scholar] [CrossRef]
  90. Wu, K.; Giovannucci, E.; Byrne, C.; Platz, E.A.; Fuchs, C.; Willett, W.C.; Sinha, R. Meat mutagens and risk of distal colon adenoma in a cohort of U.S. men. Cancer Epidemiol. Biomark. Prev. 2006, 15, 1120–1125. [Google Scholar] [CrossRef] [Green Version]
  91. van der, A.D.L.; Peeters, P.H.; Grobbee, D.E.; Marx, J.J.; van der Schouw, Y.T. Dietary haem iron and coronary heart disease in women. Eur. Heart J. 2005, 26, 257–262. [Google Scholar] [CrossRef] [Green Version]
  92. Lee, D.H.; Folsom, A.R.; Jacobs, D.R., Jr. Iron, zinc, and alcohol consumption and mortality from cardiovascular diseases: The Iowa Women’s Health Study. Am. J. Clin. Nutr. 2005, 81, 787–791. [Google Scholar] [CrossRef] [Green Version]
  93. Ascherio, A.; Willett, W.C.; Rimm, E.B.; Giovannucci, E.L.; Stampfer, M.J. Dietary iron intake and risk of coronary disease among men. Circulation 1994, 89, 969–974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Klipstein-Grobusch, K.; Grobbee, D.E.; den Breeijen, J.H.; Boeing, H.; Hofman, A.; Witteman, J.C. Dietary iron and risk of myocardial infarction in the Rotterdam Study. Am. J. Epidemiol. 1999, 149, 421–428. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Tzonou, A.; Lagiou, P.; Trichopoulou, A.; Tsoutsos, V.; Trichopoulos, D. Dietary iron and coronary heart disease risk: A study from Greece. Am. J. Epidemiol. 1998, 147, 161–166. [Google Scholar] [CrossRef] [Green Version]
  96. Jiang, R.; Ma, J.; Ascherio, A.; Stampfer, M.J.; Willett, W.C.; Hu, F.B. Dietary iron intake and blood donations in relation to risk of type 2 diabetes in men: A prospective cohort study. Am. J. Clin. Nutr. 2004, 79, 70–75. [Google Scholar] [CrossRef] [Green Version]
  97. Rajpathak, S.; Ma, J.; Manson, J.; Willett, W.C.; Hu, F.B. Iron intake and the risk of type 2 diabetes in women: A prospective cohort study. Diabetes Care 2006, 29, 1370–1376. [Google Scholar] [CrossRef] [Green Version]
  98. Rajpathak, S.N.; Crandall, J.P.; Wylie-Rosett, J.; Kabat, G.C.; Rohan, T.E.; Hu, F.B. The role of iron in type 2 diabetes in humans. Biochim. Biophys. Acta 2009, 1790, 671–681. [Google Scholar] [CrossRef] [PubMed]
  99. McCord, J.M. Oxygen-derived free radicals in postischemic tissue injury. N. Engl. J. Med. 1985, 312, 159–163. [Google Scholar]
  100. Ambrosio, G.; Zweier, J.L.; Jacobus, W.E.; Weisfeldt, M.L.; Flaherty, J.T. Improvement of postischemic myocardial function and metabolism induced by administration of deferoxamine at the time of reflow: The role of iron in the pathogenesis of reperfusion injury. Circulation 1987, 76, 906–915. [Google Scholar] [CrossRef] [Green Version]
  101. van der Kraaij, A.M.; Mostert, L.J.; van Eijk, H.G.; Koster, J.F. Iron-load increases the susceptibility of rat hearts to oxygen reperfusion damage. Protection by the antioxidant (þ)-cyanidanol-3 and deferoxamine. Circulation 1988, 78, 442–449. [Google Scholar] [CrossRef] [Green Version]
  102. Fardet, A.; Boirie, Y. Associations between food and beverage groups and major diet-related chronic diseases: An exhaustive review of pooled/meta-analyses and systematic reviews. Nutr. Rev. 2014, 72, 741–762. [Google Scholar] [CrossRef] [PubMed]
  103. Xu, R.; Yang, K.; Li, S.; Dai, M.; Chen, G. Effect of green tea consumption on blood lipids: A systematic review and meta-analysis of randomized controlled trials. Nutr. J. 2020, 19, 48. [Google Scholar] [CrossRef]
  104. Igho-Osagie, E.; Cara, K.; Wang, D.; Yao, Q.; Penkert, L.; Cassidy, A.; Ferruzzi, M.; Jacques, P.; Johnson, E.; Chung, M.; et al. Short-Term Tea Consumption Is Not Associated with a Reduction in Blood Lipids or Pressure: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J. Nutr. 2020, 150, 3269–3279. [Google Scholar] [CrossRef]
  105. Lin, Y.; Shi, D.; Su, B.; Wei, J.; Găman, M.; Macit, M.S.; Nascimento, I.B.; Guimaraes, N. The effect of green tea supplementation on obesity: A systematic review and dose-response meta-analysis of randomized controlled trials. Phytother. Res. 2020, 34, 2459–2470. [Google Scholar] [CrossRef] [PubMed]
  106. Zijp, I.M.; Korver, O.; Tijburg, L.B. Effect of tea and other dietary factors on iron absorption. Crit. Rev. Food Sci. Nutr. 2000, 40, 371–398. [Google Scholar] [CrossRef] [PubMed]
  107. Zhao, L.; Zhang, X.; Shen, Y.; Fang, X.; Wang, Y.; Wang, F. Obesity and iron deficiency: A quantitative meta-analysis. Obes. Rev. 2015, 16, 1081–1093. [Google Scholar] [CrossRef]
Nutrients 15 01448 g001a 550Nutrients 15 01448 g001b 550
Figure 1. Prevalence of different frequency of tea consumption among study subjects and the cumulative incidence of metabolic disorders and metabolic syndrome in different tea consumption groups. * means p < 0.05 between groups. MetS: metabolic syndrome; BP: blood pressure; HDL-C: high density lipoprotein cholesterol; TG: triglyceride.
Figure 1. Prevalence of different frequency of tea consumption among study subjects and the cumulative incidence of metabolic disorders and metabolic syndrome in different tea consumption groups. * means p < 0.05 between groups. MetS: metabolic syndrome; BP: blood pressure; HDL-C: high density lipoprotein cholesterol; TG: triglyceride.
Nutrients 15 01448 g001aNutrients 15 01448 g001b
Nutrients 15 01448 g002 550
Figure 2. Multivariate logistic regression analysis estimated the possible relationship between tea consumption and metabolic disorders, adjusting for possible confounders. Model 1 adjusted for age and gender. Model 2 adjusted for factors included in Model 1 plus ethnicity, current drinking and smoking status, marriage status, educational status, sleep duration, annual income, and physical activity. Model 3 adjusted for factors included in Model 2 plus BMI, eGFR, uric acid, AST/ALT and diet scores.
Figure 2. Multivariate logistic regression analysis estimated the possible relationship between tea consumption and metabolic disorders, adjusting for possible confounders. Model 1 adjusted for age and gender. Model 2 adjusted for factors included in Model 1 plus ethnicity, current drinking and smoking status, marriage status, educational status, sleep duration, annual income, and physical activity. Model 3 adjusted for factors included in Model 2 plus BMI, eGFR, uric acid, AST/ALT and diet scores.
Nutrients 15 01448 g002
Nutrients 15 01448 g003 550
Figure 3. Multivariate logistic regression analysis estimated the possible relationship between tea consumption and metabolic disorders according to age categories [middle-aged group (between 45 to 60 year) and older-age group (more than 60 year)], adjusted for age, gender, race, current drinking and smoking status, marriage status, educational status, sleep duration, annual income, physical activity, BMI, eGFR, uric acid, AST/ALT and diet scores.
Figure 3. Multivariate logistic regression analysis estimated the possible relationship between tea consumption and metabolic disorders according to age categories [middle-aged group (between 45 to 60 year) and older-age group (more than 60 year)], adjusted for age, gender, race, current drinking and smoking status, marriage status, educational status, sleep duration, annual income, physical activity, BMI, eGFR, uric acid, AST/ALT and diet scores.
Nutrients 15 01448 g003
Table
Table 1. Characteristics of study participants according to gender.
Table 1. Characteristics of study participants according to gender.
Baseline CharacteristicsTotal
(n = 3632)		Men
(n = 2005)		Women
(n = 1627)		p-Value *
Age, years57.04 ± 8.2958.15 ± 8.5755.67 ± 7.71<0.001
Han ethnicity3417 (94.1)1891 (94.3)1526 (93.8)0.276
Current smoking (yes)1480 (40.7)1167 (58.2)313 (19.2)<0.001
Current drinking (yes)978 (26.9)923 (46.0)55 (3.4)<0.001
Married3593 (98.9)1972 (98.4)1621 (99.6)<0.001
Primary and lower education1966 (54.1)948 (47.3)1018 (62.6)<0.001
Sleep duration (h/day) <0.001
≤71893 (52.2)963 (48.1)930 (57.3)
7–8991 (27.3)575 (28.7)416 (25.6)
8–9482 (13.3)300 (15.0)182 (11.2)
>9261 (7.2)165 (8.2)96 (5.9)
Annual income (CNY/year) 0.025
≤5000454 (12.5)277 (13.8)177 (10.9)
5000–20,0002111 (58.2)1154 (57.6)957 (58.9)
>20,0001064 (29.3)572 (28.6)492 (30.2)
Physical activity <0.001
Low1234 (34.3)562 (28.3)672 (41.6)
Moderate647 (18.0)350 (17.6)297 (18.4)
Severe1719 (47.8)1073 (54.1)646 (40.0)
Diet score (>3)1776 (48.9)1121 (55.9)655 (40.2)<0.001
SBP, mmHg139.93 ± 22.76142.57 ± 22.61136.67 ± 22.53<0.001
DBP, mmHg80.65 ± 11.1682.34 ± 11.0978.57 ± 10.88<0.001
BMI, kg/m223.43 ± 3.0823.54 ± 2.8523.31 ± 3.340.026
HDL-C, mmol/L1.54 ± 0.391.52 ± 0.421.57 ± 0.35<0.001
TG, mmol/L1.14 ± 0.631.14 ± 0.671.14 ± 0.580.524
FPG, mmol/L5.57 ± 1.065.67 ± 1.175.43 ± 0.88<0.001
eGFR, mL/min per 1.73 m292.45 ± 13.6392.98 ± 13.0691.79 ± 14.270.009
Uric acid, μmoI/L279.53 ± 77.18313.33 ± 75.16237.87 ± 56.52<0.001
AST/ALT1.18 (0.95, 1.43)1.14 (0.90, 1.41)1.22 (1.00, 1.45)<0.001
Follow-up Characteristics
SBP, mmHg135.99 ± 21.60139.14 ± 21.34132.10 ± 21.29<0.001
DBP, mmHg80.01 ± 11.6181.85 ± 11.6877.74 ± 11.12<0.001
BMI, kg/m223.64 ± 3.5524.28 ± 3.2822.86 ± 3.72<0.001
HDL-C, mmol/L1.46 ± 0.381.44 ± 0.391.48 ± 0.370.005
TG, mmol/L1.41 ± 1.101.39 ± 1.211.44 ± 0.960.221
FPG, mmol/L5.60 ± 1.285.75 ± 1.485.41 ± 0.95<0.001
eGFR, mL/min per 1.73 m291.24 ± 12.2490.55 ± 12.0592.07 ± 12.41<0.001
Uric acid, μmoI/L284.10 ± 75.39314.60 ± 73.93246.50 ± 58.26<0.001
AST/ALT1.36 (1.09, 1.68)1.33 (1.04, 1.65)1.39 (1.14, 1.70)<0.001
MetS1188 (24.0)572 (22.2)616 (25.8)0.203
Data are expressed as the mean ± SD or as n (%). * Student’s t test and χ2 tests were used, as appropriate, to compare differences between males and females. SD Standard deviation, CNY Chinese Yuan, SBP Systolic blood pressure, DBP Diastolic blood pressure, BMI Body mass index, HDL-C High-density lipoprotein cholesterol, TG Triglycerides, FPG Fasting plasma glucose, Metabolic syndrome.
Table
Table 2. Characteristics of study participants according to tea consumption habits.
Table 2. Characteristics of study participants according to tea consumption habits.
Non-Habitual Tea Drinkers
(n = 2223)		Occasional Drinkers
(n = 708)		1–2 Times/Day
(n = 596)		≥3 Times/Day
(n = 105)		p-Value !
Baseline Characteristics
Gender (women)1216 (54.7)232 (32.8)165 (27.7)14 (13.3)<0.001
Age, years57.12 ± 8.3256.43 ± 8.2457.41 ± 8.27 #57.33 ± 7.900.143
Han ethnicity2116 (95.2)667 (94.2)540 (90.6)94 (89.5)<0.001
Current smoking (yes)749 (33.7)322 (45.5)335 (56.2)74 (70.5)<0.001
Current drinking (yes)412 (18.5)247 (34.9)265 (44.5)54 (51.4)<0.001
Marriage status (yes)2204 (99.1)705 (99.6)581 (97.5)103 (98.1)0.001
Primary and lower education1290 (58.0)345 (48.7)289 (48.5)42 (40.0)<0.001
Sleep duration (h/day) <0.001
≤71252 (56.4)354 (50.2)248 (41.6)39 (37.1)
7–8545 (24.5)199 (28.2)207 (34.7)40 (38.1)
8–9267 (12.0)99 (14.0)104 (17.4)12 (11.4)
>9157 (7.1)53 (7.5)37 (6.2)14 (13.3)
Annual income (CNY/year) 0.001
≤5000273 (12.3)72 (10.2)97 (16.3)12 (11.4)
5000–20,0001279 (57.6)414 (58.5)363 (60.9)55 (52.4)
>20,000668 (30.1)222 (31.4)136 (22.8)38 (36.2)
Physical activity 0.019
Low803 (36.5)226 (32.1)177 (29.9)28 (27.2)
Moderate379 (17.2)124 (17.6)120 (20.3)24 (23.3)
Severe1021 (46.3)353 (50.2)294 (49.7)51 (49.5)
Diet score (>3)1028 (46.2)360 (50.8)331 (55.5)57 (54.3)<0.001
SBP, mmHg137.47 ± 21.96139.54 ± 21.25 *148.59 ± 24.84 ^,#145.62 ± 24.30 &,$<0.001
DBP, mmHg79.76 ± 10.8481.15 ± 10.88 *82.75 ± 11.87 ^,#84.13 ± 12.97 &,$<0.001
BMI, kg/m223.15 ± 3.1523.67 ± 2.94 *24.10 ± 2.85 ^,#24.04 ± 3.03 &,$<0.001
HDL-C, mmol/L1.49 ± 0.351.54 ± 0.38 *1.70 ± 0.46 ^,#1.65 ± 0.52 &,$<0.001
TG, mmol/L1.14 ± 0.621.20 ± 0.731.07 ± 0.53 ^,#1.11 ± 0.560.006
FPG, mmol/L5.56 ± 0.985.59 ± 1.025.54 ± 1.155.61 ± 1.910.844
eGFR, mL/min per 1.73 m290.68 ± 13.3493.89 ± 14.58 *97.10 ± 12.33 ^,#93.81 ± 12.62 &,**<0.001
Uric acid, μmoI/L272.72 ± 74.19292.76 ± 84.63 *284.84 ± 75.12 ^304.36 ± 80.14 &,**<0.001
AST/ALT1.15 (0.93, 1.40)1.17 (0.92, 1.42)1.25 (1.03, 1.50)1.31 (1.00, 1.62)<0.001
Follow-up Characteristics
SBP, mmHg135.20 ± 21.12135.48 ± 20.73139.26 ± 23.73 ^,#137.54 ± 21.600.001
DBP, mmHg79.40 ± 11.3280.26 ± 11.0581.65 ± 12.85 ^,#81.77 ± 13.06 &<0.001
BMI, kg/m223.34 ± 3.6024.07 ± 3.47 *24.15 ± 3.34 ^24.35 ± 3.49 &<0.001
HDL-C, mmol/L1.46 ± 0.371.45 ± 0.401.44 ± 0.391.50 ± 0.420.437
TG, mmol/L1.36 ± 0.941.48 ± 1.36 *1.51 ± 1.33 ^1.48 ± 0.940.007
FPG, mmol/L5.58 ± 1.285.58 ± 1.195.69 ± 1.35 ^5.62 ± 1.460.235
eGFR, mL/min per 1.73 m291.52 ± 12.3391.65 ± 11.8590.36 ± 11.97 ^87.40 ± 13.47 &,$,**0.001
Uric acid, μmoI/L274.77 ± 72.80296.00 ± 76.47 *299.45 ± 76.41 ^314.16 ± 83.52 &,$<0.001
AST/ALT1.36 (1.09, 1.69)1.31 (1.05, 1.60)1.42 (1.12, 1.71)1.42 (1.10, 1.73)0.001
MetS557 (25.1)196 (27.7)166 (27.9)26 (24.8)0.359
Data are expressed as the mean ± SD or as n (%). ! ANOVA test and χ2 tests were used, as appropriate, to compare differences between different tea consumption categories. * means p < 0.05 between Non-habitual tea drinkers vs. occasional drinkers. ^ means p < 0.05 between Non-habitual tea drinkers vs. 1–2 times/day. & means p < 0.001 between Non-habitual tea drinkers vs. ≥3 times/day. # means p < 0.05 between occasionally drinking vs. 1–2 times/day. $ means p < 0.05 between occasionally drinking vs. ≥3 times/day. ** means p < 0.05 between 1–2 times/day vs. ≥3 times/day. SD Standard deviation. CNY Chinese Yuan, SBP Systolic blood pressure, DBP Diastolic blood pressure, BMI Body mass index, HDL-C High-density lipoprotein cholesterol, TG Triglycerides, FPG Fasting plasma glucose, MetS Metabolic syndrome.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yu, S.; Wang, B.; Li, G.; Guo, X.; Yang, H.; Sun, Y. Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals. Nutrients 2023, 15, 1448. https://doi.org/10.3390/nu15061448

AMA Style

Yu S, Wang B, Li G, Guo X, Yang H, Sun Y. Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals. Nutrients. 2023; 15(6):1448. https://doi.org/10.3390/nu15061448

Chicago/Turabian Style

Yu, Shasha, Bo Wang, Guangxiao Li, Xiaofan Guo, Hongmei Yang, and Yingxian Sun. 2023. "Habitual Tea Consumption Increases the Incidence of Metabolic Syndrome in Middle-Aged and Older Individuals" Nutrients 15, no. 6: 1448. https://doi.org/10.3390/nu15061448

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop