Association of the Healthy Beverage Index With Metabolic Syndrome and Stroke in U.S. Adults: The National Health and Nutrition Examination Survey 2013-2014

doi:10.21203/rs.3.rs-936654/v1

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Association of the Healthy Beverage Index With Metabolic Syndrome and Stroke in U.S. Adults: The National Health and Nutrition Examination Survey 2013-2014

https://doi.org/10.21203/rs.3.rs-936654/v1

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Background: Whether beverage consumption pattern is associated with risk of metanolic syndrome (MetS) and stroke is of public health interest.

Methods: We examined the associations between the healthy beverage index (HBI) scores with prevalence of MetS and stroke among 4162 participants aged 20-80 years from the National Health and Nutrition Examination Survey (2013-2014) in U.S. Binary logistic regression was used to estimate odd ratios (OR) and 95% confidence intervals (95% CI), while adjusting for potential confounders and stratifying by gender.

Results: For the prevalence of stroke, there was an inverse association between HBI scores and stroke (adj. OR for Q4 versus Q1: 0.47; 95% CI: 0.27–0.81), and gender stratified models were similar. However, there was a positive association between HBI scores and MetS (adj. OR for Q4 versus Q1: 1.38; 95% CI: 1.46–1.79) in the total participants, and gender stratified models were different. Higher HBI scores was associated with lower prevalence of MetS in females (adj. OR for Q4 versus Q1: 0.95; 95% CI: 0.65–1.38), but with higher prevalence of MetS in males (adj. OR for Q4 versus Q1: 2.02; 95% CI: 1.37–2.99). Further analyses demonstrated differences in the association of SSBs, coffee and tea, alcohol, diet drinks and full-fat milk consumption with MetS and stroke in the total subjects, males and females.

Conclusions: Our findings suggested that the HBI scores was a negative association with stroke in a representative U.S. population, and the higher HBI scores was associated with increased prevalence of MetS in males but decreased in females, which may be caused by the same beverage may play a different role in the prevalence of MetS and stroke in the total participants,males and females.

Nutrition & Dietetics

The Healthy Beverage Index (HBI)

Beverage consumption pattern

Metabolic syndrome

Stroke

A previous study reported a prevalence of metabolic syndrome (MetS) 34.7% in adults, which remained stable from 2011 to 2016 [1]. As a cluster of interrelated metabolic anomalies, MetS includes central obesity, hypertriglyceridemia, reduced HDL cholesterol, hypertension and hyperglycemia [2]. MetS refers to a constellation of physiological co-incident and inter-related risk factors that place an individual at high risk of developing CVD and type 2 diabetes mellitus [3]. While lifestyle and genetic factors largely determine the risk for MetS and stroke, a growing body of evidence suggests that dietary factors, including intake of various beverages, are associated with an increased risk of MetS and stroke [4–6].

To date, extensive research has been done on the association between individual beverage consumption, particularly sugar-sweetened beverages (SSBs) and coffee and tea, with MetS and stroke [7–10]. However, few publications have examined the overall quality of daily beverage consumption within a broader context of evaluating all consumed beverages as a pattern [11, 12]. Therefore, the healthy beverage index (HBI) has been introduced as a holistic concept to evaluate the quality of overall beverage intake and its association with health-related outcomes in nutritional epidemiological studies [13].

Previous studies indicated that beverage consumption is association with cardiovascular, all-cause and causespecific mortality, coronary heart disease and etc [14–16]. However, results on the association of the HBI scores with MetS and stroke were inconsistent. SSBs consumption was associated with risk of MetS and stroke[17, 18], but other found no association was found for MetS and stroke in these two populations, respectively [19, 20]. Some studies reported that higher coffee intake was related to lower risk of MetS and stroke [21, 22], but others found no significant association [23].

In the current study, we evaluated the association between prevalence of MetS and stroke, and the HBI scores in the National Health and Nutrition Examination Survey (NHANES 2013–2014). We evaluated effect modification by gender for the association between the HBI scores and MetS and stroke.

Subjects

This study used data from National Health and Nutrition Examination Surveys (NHANES) between the years 2013 and 2014 for subjects 20 to 80 years of age. The NHANES is an on-going cross-sectional survey conducted by the National Center for Health Statistics (NCHS) in 2-year cycles, which designed to evaluate the health and nutritional status of adults and children in the United States. The NHANES employs a stratified, multistage probability sampling design and includes demographic, socioeconomic, dietary, health-related information, and examination and laboratory tests administered by highly trained medical personnel.

There were 5,476 participants aged 20 yeare and older from the years 2013-2014. Of these, we excluded those with missing information on body-mass index (BMI, calculated as weight [kg] divided by height [m] squared), race/ethnicity, educational level, smoking status, blood pressure, high density lipoprotein, glycohemoglobin, physical activity, family history of cardiovascular disease, family history of diabetes or sitting time. Meanwhile, we removed people who had daily energy intake < 500 kcal or > 4500 kcal and pregnant and/or lactating females. Finally, we used 4,162 samples to do the following analysis.

Metabolic Syndrome

MetS was defined according to the updated International Diabetes Federation (IDF) criteria [24] as follows: central obesity (waist circumference ≥102 cm for males or ≥88 cm for females) plus at least two of the following: 1) serum triglyceride level of >150 mg/dL,or current drug treatment for elevated TG; 2) HDL cholesterol level , <40 mg/dL for males and , <50 mg/dL for females, or current drug treatment for reduced HDL cholesterol; 3) fasting glucose level >100 mg/dL or use of antidiabetic medications (insulin or oral agents) or diatebes; or 4) systolic blood pressure >90 mmHg and/or diastolic blood pressure >140 mmHg, or use of antihypertensive medications.

Healthy Beverage Index (HBI)

HBI was used to assess the quality of beverage intake based on the Beverage Guidance System and recommalesdations from the Dietary Guidelines for Americans, which consisted of 10 components including water, SSBs, total beverage energy, unsweetened coffee and tea, and alcohol. A full description of the individual components can be found in Additional file 1: Tabel S1. The HBI score ranges from 0 to 100, with a higher score indicating better adherence to beverage guidelines and healthier beverage intake pattern [13].

Assessment of beverage intake, food intake and nutrients

Participants' food intake for two non-consecutive days through 24-hour dietary recall interviews were collected in the NHANES survey. The first dietary recall was conducted in-person and the second dietary recall was conducted 3-10 days later via telephone. Beverage intake and energy intake was estimated using the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS). The mean values of nutrient intakes for day one and day two of the 24 h dietary recall were used in analyses.

Covariates

Potential covariables were consisted of age (years), gender (male/female), race/ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other race), socioeconomic status (high/medium/low), the family history (no/either/both), BMI (under 18.5 kg/m²/18.5-25 kg/m²/25-30 kg/m²/over 30 kg/m²), physical activity (under 600 MET-mins/wks/600-3999 MET-mins/wks/4000-7999 MET-mins/wks/over 8000 MET-mins/wks), smoking status (current smokers/former smokers/never smokers), sitting time (under 4 h/day/4-6 h/day/6-8 h/day/over8 h/day), sleep duration (under 5 h/day/5-7 h/day/ 7-9 h/day/over 9 h/day), abdominal obesity (yes/no), DASH scores and non-HDL cholesterol (under 100 mg/dL/100-145 mg/dL/145-185 mg/dL/185-220 mg/dL/over 220 mg/dL).

Statistical analysis

We used mean ± SE or proportions to describe the characteristics of the study subjects by quartiles of HBI. We used binary logistic regression to estimate the associations of MetS and stroke prevalence with HBI scores, respectively. We used nested models to adjust for confounding that included: model 1, no adjustment; model 2, age, gender, race/ethnicity, socioeconomic status; model 3, age, gender, race/ethnicity, socioeconomic status, BMI, the family history of CVD or Diabetes, abdominal obesity; model 4, model 3 with the addition of smoking status, Non-HDL cholesterol, physical activity, sleep duration, sitting time and DASH scores. To access effect modification, we constructed stratified models to access modification by gender.

In order to assessed the sensitivity of our results, we performed several sensitivity analyses. We tested for effect modification by age, smoking status, physical activity and adequate intake (AI) using subgroup analysis. Meanwhile, sensitivity analyses were conducted among participants who did not drink tap water and who did not have diabetes mellitus (DM). And we did the same sensitivity analyses among males and females, respectively. All sensitivity analyses were conducted using the final adjustment model (model 4).

All statistical analyses were performed using SPSS 21.0 (SPSS Inc., Chicago, IL, USA). Graphic production was carried out by adopting R version 4.0.5 (The R Foundation for Statistical Computing, Vienna, Austria). Two-sided P <0.05 was considered statistically significant.

Baseline Characteristics of Participants

In our study, among the 4,162 participants analyzed in the 2013–2014 NHANES data, 1,792 of the individuals demonstrated MetS, while 122 individuals demonstrated stroke. In both males and females, older age, high socioeconomic status, higher DASH scores, higher HDL-C, higher HEI-2015 scores, and higher bottled water, tap water and plain water intake were significantly associated with higher HBI scores, but with lower energy intake, physical activity, fluid intake, beverage energy intake and the percentages of exceeding AI (P < 0.05). In males, higher glycated haemoglobin (HbA1c) and the prevalence of diabetes, hypercholesterolemia and MetS were significantly associated with higher HBI scores (P < 0.05). In females, lower non-HDL cholesterol (Non-HDL-C) and diastolic blood pressure were significantly associated with higher HBI scores (P < 0.05). Meanwhile, people with higher HBI scores tended to never smoke (P < 0.05). No evident differences of sleep duration, sitting time and family history of diseases (diabetes and cardiovascular) were found in these four groups (Table 1,Fig. 1).

Table 1

Baseline Characteristics by sex and the Healthy Beverage Index (HBI)
	Males(n = 2017)^*					Females(n = 2145)^*
	HBI Q1 (20.00-55.90)	HBI Q2 (55.90–70.00)	HBI Q3 (70.00-88.70)	HBI Q4 (88.70–100.00)	P^b	HBI Q1 (20.00-55.90)	HBI Q2 (55.90–70.00)	HBI Q3 (70.00-88.70)	HBI Q4 (88.70–100.00)	P^b
Baseline^a
Ages,yrs	46.99 ± 0.71	47.0 ± 0.75	51.32 ± 0.78	49.92 ± 0.86	< 0.001	45.80 ± 0.81	47.18 ± 0.71	50.71 ± 0.71	51.36 ± 0.65	< 0.001
Race,%					< 0.001					< 0.001
Mexican American	83(13.32)	79(15.16)	59(12.50)	53(13.22)		47(11.27)	81(15.82)	70(13.06)	85(13.28)
Other Hispanic	50(8.03)	57(10.94)	35(7.42)	28(6.98)		40(9.59)	60(10.87)	62(11.57)	52(8.13)
Non-Hispanic White	309(49.60)	217(41.65)	213(45.13)	159(39.65)		184(44.12)	250(45.29)	253(47.20)	285(44.53)
Non-Hispanic Black	137(21.99)	107(20.54)	97(20.55)	59(14.71)		105(25.18)	113(20.47)	82(15.30)	100(15.63)
Other	44(7.06)	61(11.71)	68(14.41)	102(25.44)		41(9.83)	48(8.70)	69(12.87)	118(18.44)
Socioeconomic status ,%					< 0.001					0.041
Low	47(7.54)	35(6.72)	29(6.16)	17(4.24)		31(7.43)	43(7.79)	35(6.54)	41(6.41)
Medium	88(14.13)	39(7.49)	39(8.28)	30(7.48)		50(11.99)	53(9.60)	39(7.29)	42(6.56)
High	488(78.33)	447(85.80)	403(85.56)	354(88.28)		336(80.58)	456(82.61)	461(86.17)	557(87.03)
DASH	2.86 ± 0.05	3.08 ± 0.06	3.04 ± 0.05	3.17 ± 0.06	0.001	2.70 ± 0.06	2.94 ± 0.06	2.84 ± 0.05	2.92 ± 0.05	0.012
HEI-2015	52.60 ± 0.43	56.85 ± 0.51	58.64 ± 0.53	61.28 ± 0.58	< 0.001	53.77 ± 0.55	58.29 ± 0.51	59.64 ± 0.50	63.23 ± 0.44	< 0.001
Energy intake,kcal	2378.13 ± 31.45	2298.80 ± 34.43	2252.05 ± 35.15	2092.36 ± 35.25	< 0.001	1896.37 ± 33.66	1839.24 ± 27.18	1809.62 ± 27.47	1624.14 ± 22.29	< 0.001
Body mass index, kg/m²	28.23 ± 0.24	29.11 ± 0.27	28.25 ± 0.25	28.46 ± 0.28	0.048	29.28 ± 0.37	30.11 ± 0.34	29.46 ± 0.32	29.04 ± 0.28	0.095
Smoking status,%					< 0.001					< 0.001
Smoker	197(31.62)	102(19.58)	98(20.76)	24(5.99)		109(26.14)	124(22.46)	92(17.16)	59(9.22)
Former smoker	152(24.40)	159(30.52)	147(31.14)	126(31.42)		70(16.79)	84(15.22)	105(19.59)	138(21.56)
Never smoker	274(39.65)	260(49.90)	227(48.09)	251(62.59)		238(57.07)	344(62.32)	339(63.25)	443(69.22)
Sleep duration,h	6.80 ± 0.06	6.93 ± 0.06	6.90 ± 0.06	6.82 ± 0.06	0.358	6.93 ± 0.08	6.80 ± 0.06	6.82 ± 0.06	6.96 ± 0.05	0.141
Family history,%					0.232					0.153
Both	43(6.90)	26(4.99)	23(4.87)	19(4.74)		38(9.11)	52(9.42)	31(5.78)	42(6.56)
Diabetes or Cardiovascular	238(38.20)	177(33.97)	185(39.19)	159(39.65)		155(37.17)	209(37.86)	216(40.30)	269(42.03)
No History	342(54.90)	318(61.04)	264(55.93)	223(55.61)		224(53.72)	291(52.72)	289(53.92)	329(51.41)
Sitting time,mins	415.96 ± 7.85	411.09 ± 8.49	419.68 ± 8.84	435.46 ± 9.79	0.275	409.99 ± 10.23	415.12 ± 8.53	416.41 ± 8.22	416.04 ± 7.70	0.958
Physical activity ,mins/wk	4853.02 ± 289.30	4998.52 ± 327.76	3855.15 ± 281.06	3549.88 ± 261.11	0.001	2865.61 ± 295.07	2479.28 ± 178.39	2217.50 ± 181.92	2081.84 ± 138.61	0.031
HbAc1,%	5.61 ± 0.04	5.67 ± 0.04	5.81 ± 0.05	5.90 ± 0.06	< 0.001	5.63 ± 0.05	5.67 ± 0.05	5.68 ± 0.04	5.78 ± 0.05	0.104
Stroke,%	23(3.69)	12(2.30)	11(2.33)	7(1.75)	0.226	17(4.08)	13(2.36)	19(3.54)	20(3.13)	0.474
Tap water source,%					0.042					0.439
Community	454(72.87)	394(75.62)	362(76.69)	299(74.56)		284(68.11)	388(70.29)	403(75.19)	461(72.03)
Well or rain citern	63(10.11)	36(6.91)	34(7.20)	26(6.48)		32(7.67)	44(7.97)	33(6.16)	55(8.59)
Spring	6(0.96)	3(0.58)	12(2.54)	5(1.25)		6(1.44)	8(1.45)	6(1.12)	6(0.94)
Don’t drink tap water	100(16.05)	88(16.89)	64(13.56)	71(17.71)		95(22.78)	112(20.29)	94(17.54)	118(18.44)
Total beverage energy,kcal	561.53 ± 13.16	431.88 ± 13.31	313.77 ± 14.09	97.09 ± 3.99	< 0.001	430.60 ± 13.89	368.29 ± 11.09	214.61 ± 7.31	69.48 ± 2.44	< 0.001
Total beverage energy,%	23.75 ± 0.45	18.57 ± 0.45	13.70 ± 0.50	4.62 ± 0.17	< 0.001	23.03 ± 0.52	20.05 ± 0.48	12.16 ± 0.35	4.22 ± 0.13	< 0.001
Exceeding AI,%	204(32.74)	197(37.81)	122(25.85)	94(23.44)	< 0.001	166(39.81)	210(38.04)	137(25.56)	172(26.88)	< 0.001
* Tertile cut-offs are based on raw data.
^a Continuous variables are presented as mean (standard error). Categorical variables are presented as n (%).
^bDifferences of continuous or categorical variables between HBI groups were evaluated by using one-way ANOVA or chi-squared tests, respectively.

Beverage consumption pattern of the participants

The mean beverage consumption pattern of the participants by the quartiles of HBI scores was presented in Fig. 2. Though beverage intake pattern was different in subjects in different HBI quartiles, the top three fluid intakes were still water (tap water and bottled water), SSBs and coffee and tea. All kinds of beverages had distinct distribution in HBI Q1, HBI Q2, HBI Q3 and HBI Q4. The main fluid intake was water in HBI Q2, HBI Q3 and HBI Q4, but the main fluid consumption was SSBs in HBI Q1. As shown in Fig. 2, the mean beverage consumption pattern of the males and females were similar with total participants.

The mean energies from beverages are demonstrated in Fig. 2. According to the distribution of beverages energies in different HBI quartiles, the energies from beverages was mainly derived from SSBs, alcohol and full-fat milk except in HBI Q4 which top three were coffee and tea, full-fat milk and SSBs. Considering that the influence of the gender of participants, we also persented the mean energies from beverages by HBI scores and gender in Fig. 2. Compared with females, males had higher alcohol consumption in different HBI quartiles.

Associations of HBI scores with MetS and Stroke

Table 2 showed the associations of MetS and stroke prevalence with the HBI scores. After adjustment for potential covariates, higher HBI scores was associated with increased MetS prevalence in total participants. Compared with participants in the lowest quartile (Q1), the OR (95% CIs) for those in the second, third and upper quartiles were 0.98 (0.76, 1.25), 0.85 (0.66, 1.09) and 1.38 (1.06, 1.79) (P-value = 0.002, P-trend = 0.052) (Table 2). Gender stratified models exhibited clear associations. Consistent with the total participants, the higher HBI scores was associated with increased MetS prevalence with the OR (95%CIs) for males in the Q1, Q2, Q3 and Q4 being 1.00, 1.30 (0.92–1.84), 1.12 (0.78–1.60) and 2.02 (1.37–2.99) (P-value = 0.003, P-trend = 0.003), while the higher HBI scores was inveresely associated with reduced MetS prevalence with the OR (95%CIs) for females in the Q1, Q2, Q3 and Q4 being 1.00, 0.69 (0.48-1.00), 0.59 (0.41–0.86) and 0.95 (0.65–1.38) (P-value = 0.009, P-trend = 0.960) (Table 2). The HBI scores was inversely associated with stroke prevalence with the OR (95% CIs) for participants in the Q1, Q2, Q3, Q4 being 1.00, 0.55 (0.32–0.94), 0.55 (0.33–0.92) and 0.47 (0.27–0.81) (P-value = 0.024, P-trend = 0.008), however, this association disappeared when analysis was conducted among male and female participants (Table 2).

Table 2

**Odds ratios (95% confidence intervals) for MetS and stroke prevalence by tertiles of HBI** ^a
Model	HBI Q1 (20.00-55.90)		HBI Q2 (55.90–70.00)		HBI Q3 (70.00-88.70)		HBI Q4 (88.70–100.00)		P	P for trend
Model	OR	95%CI	OR	95%CI	OR	95%CI	OR	95%CI	P	P for trend
MetS
Overall
Model 1	1.00	Ref.	1.13	0.95–1.35	1.03	0.87–1.23	1.30	1.10–1.54	0.015	0.013
Model 2	1.00	Ref.	0.92	0.73–1.16	0.78	0.61–0.99	1.10	0.85–1.39	0.040	0.858
Model 3	1.00	Ref.	0.93	0.73–1.17	0.78	0.61-1.00	1.16	0.91–1.49	0.013	0.792
Model 4	1.00	Ref.	0.98	0.76–1.25	0.85	0.66–1.09	1.38	1.06–1.79	0.002	0.052
Males
Model 1	1.00	Ref.	1.26	0.99–1.61	1.10	0.85–1.42	1.44	1.11–1.87	0.035	0.023
Model 2	1.00	Ref.	1.34	1.03–1.74	1.00	0.76–1.31	1.51	1.14-2.00	0.006	0.046
Model 3	1.00	Ref.	1.21	0.87–1.69	1.00	0.71–1.41	1.69	1.17–2.43	0.021	0.028
Model 4	1.00	Ref.	1.30	0.92–1.84	1.12	0.78–1.60	2.02	1.37–2.99	0.003	0.003
Females
Model 1	1.00	Ref.	0.86	0.67–1.11	0.80	0.62–1.03	0.91	0.71–1.16	0.345	0.495
Model 2	1.00	Ref.	0.80	0.61–1.06	0.68	0.52–0.90	0.80	0.61–1.05	0.069	0.102
Model 3	1.00	Ref.	0.63	0.45–0.90	0.55	0.38–0.79	0.71	0.50–1.01	0.010	0.094
Model 4	1.00	Ref.	0.69	0.48-1.00	0.59	0.41–0.86	0.95	0.65–1.38	0.009	0.960
Stroke
Overall
Model 1	1.00	Ref.	0.60	0.36–0.99	0.77	0.47–1.24	0.67	0.40–1.10	0.189	0.187
Model 2	1.00	Ref.	0.56	0.33–0.95	0.59	0.36–0.98	0.50	0.30–0.84	0.035	0.013
Model 3	1.00	Ref.	0.57	0.33–0.96	0.60	0.36–0.99	0.50	0.30–0.84	0.038	0.015
Model 4	1.00	Ref.	0.55	0.32–0.94	0.55	0.33–0.92	0.47	0.27–0.81	0.024	0.008
Males
Model 1	1.00	Ref.	0.62	0.30–1.25	0.62	0.30–1.29	0.46	0.20–1.09	0.238	0.060
Model 2	1.00	Ref.	0.67	0.32–1.40	0.54	0.25–1.15	0.44	0.18–1.07	0.210	0.039
Model 3	1.00	Ref.	0.64	0.30–1.34	0.53	0.25–1.13	0.41	0.17-1.00	0.165	0.029
Model 4	1.00	Ref.	0.61	0.28–1.31	0.52	0.24–1.14	0.40	0.16–1.01	0.172	0.032
Females
Model 1	1.00	Ref.	0.57	0.27–1.18	0.87	0.44–1.69	0.76	0.39–1.47	0.480	0.739
Model 2	1.00	Ref.	0.51	0.24–1.09	0.65	0.32–1.29	0.55	0.28–1.08	0.255	0.180
Model 3	1.00	Ref.	0.54	0.25–1.15	0.70	0.34–1.41	0.58	0.29–1.16	0.341	0.248
Model4	1.00	Ref.	0.53	0.24–1.17	0.64	0.31–1.35	0.49	0.23–1.03	0.250	0.116
^a Model 1, no adjustmalest; Model 2, age, gender, race/ race/ethnicity, socioeconomic status; Model 3, age, gender, race/ race/ethnicity, socioeconomic status, BMI, the family history of CVD or Diabetes, abdominal obesity; Model 4, Model 3 with the addition of smoking stutas, Non-HDL cholesterol, physical activity, sleep duration, sitting time and DASH scores.

Among the HBI components, after adjusted for potential covariates, higher intakes of SSBs were associated with higher prevalence of stroke in total pariticipants and males. The OR (95% CI) for participants in the first, second, third and fourth quartiles were 1.00, 0.92 (0.48–1.77), 1.57 (0.97–2.56) and 2.19 (1.30–3.70) (P-trend = 0.002), 1.00, 0.38 (0.11–1.37), 2.64 (1.23–5.67) and 3.11 (1.40–6.95) (P-trend = 0.002), respectively. Higher intakes of coffee and tea were related to lower risk of MetS in females. The OR (95% CI) for participants in the first, second, third and fourth quartiles were 1.00, 1.65 (1.16–2.36), 1.05 (0.73–1.53) and 0.70(0.49-1.00) (P-trend = 0.014). Higher intakes of alcohol were related to lower risk of MetS in total subjects and females and lower risk of stroke in total subjects. The OR (95% CI) for participants in the The first and second dichotomies were 1.00 and 0.74 (0.61–0.90) (P-trend = 0.003), 1.00 and 0.62 (0.46–0.83) (P-trend = 0.002), 1.00 and 0.59 (0.35–0.99) (P-trend = 0.044), respectively. Higher intakes of diet drinks were associated with higher risk of MetS in total subjects and males. The OR (95% CI) for participants in the The first and second dichotomies were 1.00 and 1.39 (1.11–1.74) (P-trend = 0.004), 1.00 and 1.44 (1.04–1.98) (P-trend = 0.026), respectively. And higher full-fat milk consumption was associated with higher risk of MetS in total subjects and females. OR (95% CI) for participants in the first and second dichotomies were 1.00 and 1.31 (1.09–1.58) (P-trend = 0.005), 1.00 and 1.50 (1.15–1.95) (P-trend = 0.003), respectively. But no other significant associations were observed (Additional file 1: Tabel S2).

Sensitivity analysis

We further conducted stratified analysis by age, BMI, smoking status, physical activity and adequate intake, and the above association of MetS or stroke prevalence and the HBI scores among HBI Q1, HBI Q2, HBI Q3 and HBI Q4 were unchanged(Additional file 2: Figure S1 and Additional file 2: Figure S2). Meanwhile, sensitivity analyses were conducted among participants who did not drink tap water and who did not have DM and the above association of MetS and stroke prevalence with the HBI scores in each group were unchanged (Additional file 2: Figure S1 and Additional file 2: Figure S2). Furthermore, when stratifying by gender, we found the significant association between HBI scores and MetS was changed (Additional file 2: Figure S1). Lastly, we conducted stratified analysis by age, BMI, smoking status, physical activity and adequate intake in males and females respectively, and the above association of MetS or Stroke prevalence and the HBI scores among HBI Q1, HBI Q2, HBI Q3 and HBI Q4 groups were unchanged (Additional file 2: Figure S1, Additional file 2: Figure S3 and Additional file 2: Figure S4).

In this nationally representative sample of US adults, we observed that higher HBI score was associated with higher MetS prevalence while higher HBI score was associated with lower stroke prevalence in the total participants. The associations between HBI score and stroke did not differ between males and females. However, among males, the higher HBI score was associated with higher MetS prevalence while higher HBI score was associated with lower MetS prevalence in females. Among the HBI components, higher consumption of SSBs increased the prevalence of stroke in total subjects and males, while higher diet drinks was associated with higher MetS. And coffee and tea consumption was inversely associated with MetS in females. The inverse relationship between alcohol consumption and MetS in total participants and females while alcohol intake was negetively associated with stroke in total participants. In addition, higher full-milk intake was associated with higher MetS in total participants and females.

The association between SSBs and MetS prevalence has been examined in a number of previous studies but the results were inconsistent. Several studies have evaluated the relationships between SSBs intake and cardiometabolic markers, which showed that the associations between SSBs intake and LDL cholesterol levels were significant positive [16, 25]. In another study conducted in a middle-aged and elderly Mediterranean population at high risk of CVD, the participants who consumed > 5 servings SSBs/wk during follow-up had a 43% higher risk of developing MetS than those who did not consume or rarely consumed SSBs [26]. A study from the Korean NHANES showed that the SSBs consumption was positively associated with obesity, MetS, in females after adjusting for potential covariates, while SSBs consumption was not associated with MetS among males [27]. In the present study, we found that higher SSBs consumption was no association with MetS in total subjects, males and females, whereas no statistically significant association was found in the IHHP study which is in line with our present result [28]. And our study found consumption of SSBs was inversely associated with stroke in total participants and males which is in line with some reported result [6, 29].

The relationship between coffee and tea consumption and MetS and stroke prevalence in previous studies is inconsistent. Some studies reported coffee consumption was non-linearly associated with a lower risk of incident stroke, with there was no apparent modification of this association by gender and the lowest CVD risk at 3 to 5 cups/d of coffee consumption [30, 31]. A meta-analysis found each cup (236.6 mL) increase in daily black or green tea consumption was associated with a 4% lower risk of stroke and the association was similar between males and females [32]. Our results showed that higher coffee and tea consumption was no association with stroke. For MetS, high versus low coffee consumption was associated with 9% lower risk [30]. Grosso et al reported after adjusting for potential confounding factors, both higher coffee and tea consumption were negatively associated with MetS [33]. In the present study, we found inverse association between coffee and tea consumption and MetS in females. And our study found consumption of coffee and tea was not associated with MetS in total participants and males which is in line with some reported result [34], [35] While some previous studies evaluating the association between coffee and tea consumption and MetS are somewhat conflicting, this could be due to differences in study population and method used to assess coffee and tea consumption and its related measures.

The relationship between alcohol consumption and the prevalence of Mets and stroke in previous studies is inconsistent. As reported previously, blood pressure and the concentrations (where available) of HDL cholesterol were positively associated with alcohol intake [36, 37]. Millwood et al reported that self-reported alcohol intake had a U-shaped association with moderate alcohol intake (about 100 g per week) was associated with a risk of stroke lower than that in non-drinkers or, particularly, ex-drinkers [36]. A meta-analysis found that alcohol consumption was associated with lower risk for coronary heart disease ,while the associations of alcohol intake with stroke incidence was closed to null [38]. Findings have also shown that increasing quantities of alcohol consumption were associated with a lower OR for the prevalence of the MetS in the overall population [39] while heavy alcohol consumption was associated with an increased risk of MetS and very light alcohol consumption seemed to be associated with a reduced risk of MetS [40]. Our results that higher alcohol consumption was associated lower MetS and stroke in total subjects.

A positive association was seen between diet drinks and MetS in total participants and males. As reported previously, diet drinks consumption increases risk of MetS in mices [41] and in humans [26]. And in our study, higher full-fat milk consumption was a positive association with MetS prevalence in total subjects and females. A study reported that dairy fat and especially high-fat dairy food contribute to obesity and cardiometabolic risk [42]. Further studies are needed to validate these findings.

The gender-specific difference observed for the association of HBI scores with the prevalence of MetS and stroke needs to be interpreted with caution. Differences in Journal Pre-proof beverage and lifestyle factors between males and females, which may account for the gender difference in the HBI scores with MetS and stroke, have been documalested [27]. In the present study, we consistently observed statistically significant differences in the distribution of almost all these beverage and lifestyle factors between males and females, and the prevalence of MetS and stroke was different between males and females as well (Table 1,Table 2 and Fig. 1). In addition, in terms of the prevalence of MetS and stroke, it has been well-known that beverage consumption has different influence in males and females [15, 43, 44].

The positive association between higher HBI scores and the increased odds of having MetS in total participants may be the result of people having recently made changes to improve their beverage consumption pattern. This counterintuitive result would be particularly expected if these changes to beverage consumption pattern were recent and/or were not accompanied by other beneficial health behaviors (eg, decreasing total energy intake and increasing physical activity). It is also possible that the health effects of coffee may vary depending on the coffee consumption pattern, such as coffee type and amount of intake [45]. Although we control for total coffee consumption, we did not take coffee type into account.

There are limitations of this study. First, its cross-sectional nature means that causal associations could not be confirmed. Second, dietary and beverage data were based on a 24-hour recall questionnaire; this could be subject to recall bias and also might not be representative of a person’s regular diet and beverage pattern. Last, there was potential for unmeasured and residual confounding factors, which are a common problem in observational studies.

While these limitations exist, this study had several strengths. First, by evaluating HBI scores, we were able to evaluate the association between HBI scores and the prevalence of MetS and stroke in representative sample of the U.S. population, which allowed for the assessmalest of gender differences. Second, we were able to adjust for a variety of potential confounders, including lifestyle and sociodemographic factors. Finally, we evalued the association of beverage consumption pattern and the prevalence MetS and stroke other than only one type beverage.

In conclusion, our results indicated that the HBI scores was a negative association with stroke in a representative U.S. population, and the higher HBI scores was associated with increased prevalence of MetS in males but decreased in females. Further research is need ed to confirm the causal association between the HBI scores and MetS, stroke or other related chronic diseases in a prospective study or well-designed randomized controlled trials.

MetS: Metabolic syndrome; HBI: Healthy Beverage Index; OR: Odd Ratios; CI: Confidence Intervals; SSBs: Sugar-Sweetened Beverages; HDL: High-Density Lipoprotein; CVD: Cardiovascular Disease; NHANES: National Health and Nutrition Examination Survey; NCHS: National Center for Health Statistics; BMI: Body Mass Index; IDF: International Diabetes Federation; TG: Triacylglycerol; FNDDS: USDA Food and Nutrient Database for Dietary Studies; MET: Metabolic Equivalence of Energy; DASH: Dietary Approaches to Stop Hypertension; AI: Adequate Intake; DM: Diabetes Mellitus; HEI: Healthy Eating Index; HbA1c: Glycated Haemoglobin

Ethics approval and consent to participates

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Data used in the study is publicly available through the NHANES database (at https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx).

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by GA20C012 and 81973035 from the Applied Technology Research and Development Plan of Heilongjiang Province and the National Natural Science Foundation of China.

Authors’ contributions

All authors conceptualized the study. JL analyzed the data and drafted the manuscript. All authors contributed to designing the study, interpreting the data, and writing the manuscript. All authors have read and approved the submitted version.

Acknowledgements

We thank the investigators, the staff, and the participants of the National Health and Nutrition Examination Survey for their valuable contribution.

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Association of the Healthy Beverage Index With Metabolic Syndrome and Stroke in U.S. Adults: The National Health and Nutrition Examination Survey 2013-2014

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Abstract

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Background

Methods

Results

Baseline Characteristics of Participants

Beverage consumption pattern of the participants

Associations of HBI scores with MetS and Stroke

Sensitivity analysis

Discussion

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