Abstract
Allostatic load (AL) is a comprehensive physiologic measure of the body's chronic stress response and is associated with physical and mental health risks. The function of DASH diet (Dietary Approaches to Stop Hypertension) model in the development of AL is unclear. The relationship between the DASH score and AL was examined in this study. 1,565 US adults from NHANES database 2017–2020 were selected for the study, and DASH dietary pattern was assessed using DASH score, and ≥4.5 indicated compliance with DASH diet. AL was calculated using 11 biomarkers, and a score >3 indicated high levels. The relationship between DASH score and AL was analysed using logistic regression. In this study, a high AL prevalence of 35.4% (n = 555) was found. In the unadjusted model, a high DASH score was associated with a low level of AL [OR = 0.458, 95% CI (0.306, 0.687), P < 0.001], and this relationship persisted in the adjusted model [OR = 0.473, 95% CI (0.310, 0.720), P < 0.001]. DASH score are negatively associated with AL, and low DASH score increase the risk of high AL, which can adversely affect physical and mental health.
1 Introduction
Multiple studies have demonstrated that stress increases physical and mental health risks, and this effect is seen in various groups such as pregnant women (Van den Bergh et al., 2020), adolescents (Hogberg, 2021), and middle-aged adults (Wickrama et al., 2021). As a comprehensive physiological measure of the body's chronic stress response, allostatic load outperforms traditional subjective stress and individual biomarkers in predicting adverse health outcomes (Wallace et al., 2013). Allostatic load (AL) is a composite index that indicates dysregulation of multiple systemic indicators of the organism, including neuroendocrine, immune, cardiovascular, and metabolic. This cumulative physiological dysregulation has pervasive effects on physical and mental health (McEwen, 1998), such as psychologically related disorders (depression and psychiatric disorders) (Petrova et al., 2024). The organism maintains homeostasis through non-homeostatic mechanisms; short-term non-homeostatic processes are adaptive, and when exposed to stress excessively or for a long period of time, the non-homeostatic systems become overactive or underfunctional, resulting in a dysfunction of physiological systems, leading to AL and its pathophysiological consequences (Juster et al., 2010; McEwen and Gianaros, 2011; Hodges et al., 2019). Allostatic load index (ALIs) can be used to assess AL levels (Carbone et al., 2022). AL is influenced by a variety of factors, and unhealthy diets, as a source of stress, have been shown to be associated with higher levels of AL (Rodriquez et al., 2018, 2021). Mellen et al. (2008) constructed a DASH (Dietary Approaches to Stop Hypertension) score, which may respond to a healthy eating score, with higher scores conforming to the DASH dietary pattern, representing higher quality diets and healthier diets (Harrington et al., 2013; Aghayan et al., 2021; Jayanama et al., 2021; Millar et al., 2021; Yisahak et al., 2021).
Despite the increasing number of relevant studies on DASH score and AL, the relationship between the two is still unclear. With the change of people's lifestyles and the updating of database data, more research is in order on the relationship of DASH diet and AL. In this research, by analysing the latest data publicly available in the NHANES database, we explored the relationship between DASH score and AL, which can provide a reference for lowering AL in order to improve the health level. We hypothesise that DASH diet has association with AL and that people who adhere to the DASH diet will have lower levels of AL (Table 1).
List of abbreviations in the manuscript
| Abbreviations | Full name |
| AL | Allostatic load |
| ALIs | Allostatic load index |
| DASH | Dietary Approaches to Stop Hypertension |
| PA | Physical activity |
| NHANES | National Health and Nutrition Examination Survey |
2 Methods
2.1 Research population
Data used in this study comes from The National Health and Nutrition Examination Survey (NHANES), a Centers for Disease Control and Prevention (CDC) cross-sectional survey. Project ethical approval was obtained from the Ethics Review Board of the National Center for Health Statistics, and all participants have signed a written informed consent form prior to the survey. The survey covered data related to NHANES 2017–2020, and participants aged ≥18 years were selected to exclude indicators of AL, general information, and missing dietary data. Finally, a total of 1,565 study participants were enrolled in this study.
2.2 Covariate
A total of nine covariates were selected from the NHANES database based on a search and analysis of the relevant literature (Walubita et al., 2021; Bu and Li, 2023): gender, age, race, education level, marital status, monthly poverty index, drinking status, physical activity (PA), and sedentary behaviour. Race, education, and marital status were self-reported. The monthly poverty index was categorised as ≤1.30, 1.30–1.58, and >1.58. Drinking status was categorised as never drinkers, moderate drinkers (1–2 cups/day for men, 1 cup/day for women), and heavy drinkers (≥2 cups/day for men, ≥1 cup/day for women) as defined by the US Ministries of Health and Humanities and Agriculture in 2020. PA levels were categorised as “high” or “low” according to 2020 PA and Sedentary Behavior Guidelines from the World Health Organization (Bull et al., 2020), PA levels were categorised as “adequate” (moderate PA for ≥150 min per week or vigorous PA for at least 75 min per week) and “inadequate”, sedentary (low: ≤6 hours per day; high: >6 hours per day) behaviours.
2.3 DASH score
The DASH diet (Dietary Approaches to Stop Hypertension) was designed by a team of American researchers in the 1990s to prevent and manage hypertension in Americans, and is rich in fruits, vegetables, and low-fat dairy products, which reduce blood pressure, etc. (Chiu et al., 2016). In this study, DASH score of Mellen et al. (2008) was an assessment of adherence to the DASH dietary pattern in the study population. The DASH score was calculated on the basis of the target values for the nine nutrients (total fat, saturated fat, protein, fibre, cholesterol, calcium, magnesium, potassium, and sodium); for each nutrient, 1 score was given for achieving the specified target value, 0.5 score was given for achieving the intermediate target value, and 0 score was given for not meeting the target, for a total score of 0–9. Compliance with the DASH dietary pattern was defined as a total score of ≥4.5 (high: DASH score ≥4.5, low: DASH score <4.5) (Mellen et al., 2008).
2.4 Evaluation of ALIs
Based on previous literature (Cuevas et al., 2019; Carbone et al., 2022), the following biomarkers have been used as indicators for the assessment of ALIs: Body Mass Index (BMI), systolic blood pressure, diastolic blood pressure, waist-to-hip ratio, total cholesterol, high-density lipoproteins, low-density lipoproteins, fasting blood glucose, ultrasensitive C-reactive protein, triglycerides, and glycosylated haemoglobin, which are 11 physiological indicators representing the cardiovascular, metabolic, and physiologic changes in the immune system. AL levels were scored using the high-risk quartile method (Li et al., 2020), where the lower quartile of HDL was considered high risk, and the upper quartile was considered high-risk for all the remaining indices. Biomarker scores in the high-risk quartile were scored 1, otherwise 0. Finally, the total AL score was obtained by summing the scores of each biomarker, which were the nonstationary load indices (ALIs), with a total score of 0–11. The threshold used was the upper quartile 3 of the total AL score to categorise AL into high-level ALIs (AL > 3 points) and low-level ALIs (AL ≤ 3 points).
2.5 Statistical methods
SPSS 23.0 software was used for data processing and statistical analysis, and the test level was α = 0.05. Measurement data conforming to normal distribution were expressed as means and standard deviation (SD), and t-test was used for comparison between groups; count data were expressed as frequency and percentage, and chi-square test was used for comparison between groups. Binary Logistic regression was used to analyse the effect of DASH score on the level of AL.
3 Results and discussion
3.1 Research topic basic information
In total, 1,565 eligible subjects were enrolled, with 35.5% having high levels of ALIs, 52.4% male and 47.6% female, predominantly non-Hispanic whites (40.7%), predominantly college educated (37.0%), more than half married (60.7%), 59.0% having high levels of monthly poverty (>1.58), 81.8% drinking alcohol, 30.9% high daily sedentary behaviour, and 83.7% adequate physical activity. The differences in age, gender, education level, marital status, monthly poverty level index, alcohol consumption, and physical activity were statistically significant (P < 0.05) when comparing the study participants in the high ALIs group with the low ALIs group, as shown in Table 2.
General data of subjects in the high ALIs group and the low ALIs group
| All (n = 1,565) | Low ALIs n = 1,010 (64.6%) | High ALIs n = 555 (35.4%) | x2 (t) value | P value | |
| Age, mean (SD) | 48.75 (16.64) | 46.02 (16.89) | 53.73 (14.96) | −9.312 | <0.001 |
| Gender, n (%) | 18.09 | <0.001 | |||
| Male | 820 (52.4) | 489 (48.4) | 331 (59.6) | ||
| Female | 745 (47.6) | 521 (51.6) | 224 (40.4) | ||
| Race, n (%) | 3.719 | 0.445 | |||
| Mexican American | 177 (11.3) | 114 (11.3) | 63 (11.4) | ||
| Other Hispanics | 138 (8.8) | 83 (8.2) | 55 (9.9) | ||
| Non-Hispanic whites | 637 (40.7) | 422 (41.8) | 215 (38.7) | ||
| Non-Hispanic blacks | 386 (24.7) | 239 (23.7) | 147 (26.5) | ||
| Other races | 227 (14.5) | 152 (15.0) | 75 (13.5) | ||
| Educational level, n (%) | 42.247 | <0.001 | |||
| Up to grade 9 | 54 (3.5) | 26 (2.6) | 28 (5.0) | ||
| Grades 9–11 | 142 (9.1) | 76 (7.5) | 66 (11.9) | ||
| Graduate from high school | 339 (21.7) | 195 (19.3) | 144 (25.9) | ||
| College | 580 (37.0) | 376 (37.2) | 204 (36.8) | ||
| University and above | 450 (28.7) | 337 (33.4) | 113 (20.4) | ||
| Marital status, n (%) | 20.385 | <0.001 | |||
| Married/living with partner | 950 (60.7) | 614 (60.8) | 336 (60.5) | ||
| Widowed/divorced/separated | 318 (20.3) | 178 (17.6) | 140 (25.2) | ||
| Never been married | 297 (19.0) | 218 (21.6) | 79 (14.3) | ||
| Monthly poverty level index, n (%) | 18.181 | <0.001 | |||
| ≤1.30 | 416 (26.6) | 243 (24.1) | 173 (31.2) | ||
| 1.30–1.58 | 225 (14.4) | 131 (12.9) | 94 (16.9) | ||
| >1.58 | 924 (59.0) | 636 (63.0) | 288 (51.9) | ||
| Drinking status, n (%) | 14.047 | 0.001 | |||
| Never | 284 (18.2) | 156 (15.4) | 128 (23.1) | ||
| Moderate drinkers | 669 (42.7) | 448 (44.4) | 221 (39.8) | ||
| Heavy drinker | 612 (39.1) | 406 (40.2) | 206 (37.1) | ||
| Sedentary behaviour, n (%) | 0.141 | 0.707 | |||
| Low | 1,082 (69.1) | 695 (68.8) | 387 (69.7) | ||
| High | 483 (30.9) | 315 (31.2) | 168 (30.3) | ||
| Physical activity, n (%) | 4.962 | 0.026 | |||
| Inadequate | 255 (16.3) | 149 (14.8) | 106 (19.1) | ||
| Adequate | 1,310 (83.7) | 861 (85.2) | 449 (80.9) |
ALIs = allostatic load index.
3.2 Characterisation of biomarkers for ALIs
Table 3 describes the range, mean, standard deviation, and threshold values for each biomarker, and the range, mean, and standard deviation for ALIs.
Characteristics and cutoff values of AL biomarkers
| Biomarker | Range | Average number | Standard deviation | Threshold value |
| 1. Cardiovascular system | ||||
| Systolic blood pressure, mmHg | 79.67, 210 | 122 | 17.61 | >131 |
| Diastolic blood pressure, mmHg | 41.33, 119 | 74.27 | 10.89 | >81.00 |
| total cholesterol, mg dL−1 | 79.00, 428 | 183 | 41.19 | >207 |
| Low density lipoprotein (LDL), mg dL−1 | 17.00, 357 | 108 | 36.11 | >130 |
| High density lipoprotein (HDL), mg dL−1 | 11.00, 187 | 54.00 | 16.23 | <42.00 |
| Triglyceride, mg dL−1 | 10.00, 398 | 104 | 63.19 | >130 |
| 2. Metabolic system | ||||
| BMI, kg m−2 | 15.50, 66.50 | 29.98 | 7.28 | >34.05 |
| Waist-to-hip ratio | 0.66, 1.19 | 0.94 | 0.82 | >0.99 |
| Fasting blood glucose, mg dL−1 | 66.00, 403 | 110 | 33.97 | >113 |
| Glycosylated haemoglobin, % | 4.10, 13.10 | 5.77 | 0.99 | >5.90 |
| 3. Immune system | ||||
| HsCRP, mg dL−1 | 0.11, 102 | 4.02 | 7.03 | >4.32 |
| ALIs | 0, 11 | 2.80 | 2.21 | |
ALIs = allostatic load index.
3.3 Binary logistic regression analysis of DASH score and AL
Binary logistic regression was used to analyse the relationship between high and low DASH score and AL. In the unadjusted model, DASH score correlated with AL (OR = 0.458, 95% CI: 0.306–0.687, P < 0.001), and after adjusting for age, gender, education, marital status, monthly poverty level index, alcohol consumption status, and physical activity, this correlation remained (OR = 0.473, 95% CI: 0.310–0.720, P < 0.001), as shown in Table 4.
Logistic regression analysis of DASH diet score and allostatic load index
| DASH score | Unadjusted model | Adjusted modela | ||
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Low | 1.000 | – | 1.000 | – |
| High | 0.458 (0.306–0.687) | <0.001 | 0.473 (0.310–0.720) | <0.001 |
aAdjusted for age, sex, education, marital status, monthly poverty level index, alcohol consumption status, physical activity.
3.4 Discussion
In total, 1,565 subjects participated in this research, 555 study subjects with high ALIs, and the incidence of high AL was 35.4%, which was lower than the 48.6% in the study data of Moore et al. obtained in 2015–2018 (Moore et al., 2021). Differences in study results may be due to the fact that the algorithms for ALIs have not been standardised, different biomarkers are included in ALIs, and different methods are used for calculating ALIs. Therefore, there is a need to establish a uniform and accurate method for assessing ALIs for comparison and analysis between studies. On the other hand, previous studies have pointed out that AL is influenced by factors such as socio-economic conditions (Cave et al., 2020), and, therefore, the incidence of high AL may vary in different periods. The Comprehensive Action Plan for Mental Health 2013–2030 emphasises the promotion of mental health and well-being for all and help prevent mental illness in at-risk populations for the improvement of mental health conditions. Chronic stress, as an important component affecting mental health, has been proved to affect physiological (Dai et al., 2020), psychological (Vargas et al., 2020), and cognitive (Akan et al., 2023) responses, which can adversely affect physical and mental health. Therefore, it is crucial to focus on chronic stress risk assessment in order to minimise the adverse health consequences of AL.
This study found that DASH score negatively correlated with AL in both models, and high DASH score decreased the occurrence of high levels of AL, which is in agreement with the findings of Beydoun et al. (2019). High levels of AL are caused by combined risk factors, and diet, as a stressor, may be an influential factor in the occurrence of high levels of AL (Dimitratos et al., 2021). On the one hand, the DASH dietary pattern is considered to be an effective dietary intervention for lowering blood pressure, and its richness in fruits, vegetables, and low-fat dairy products significantly reduces blood pressure, LDL, HDL, and cholesterol (Chiu et al., 2016), thus lowering ALIs. On the other hand, chronic activation of the HPA axis during periods of chronic stress can lead to prolonged effects of cortisol and subsequent appetitive responses, which may manifest as cravings for certain foods (Torres and Nowson, 2007). Ghrelin, a hormone that plays a key role in regulating appetite and influencing our eating habits, increases in concentration as the body is stressed (Bouillon-Minois et al., 2021). In cases of chronic stress or extreme levels of stress, cortisol and ghrelin may work together to lead to stressful eating habits (Piatkowska-Chmiel et al., 2023). It is evident that diet and chronic stress can interact with each other thus producing adverse consequences. Reducing chronic stress and improving mental health is still a serious issue, so close attention should be paid to the influencing factors that change the level of AL, and professional guidance should be provided in order to reduce chronic stress in various populations, and to help early detection and early prevention.
AL, as a compound physiological indicator for assessing chronic stress, plays an important role in the development of disease and mental health. Unhealthy diet as a stressor may lead to overweight AL if the DASH score is low, which may cause adverse health effects, and the present study may be a reference for early warning and prevention of high levels of AL. Strengths of this study is that it includes data from the publicly available, representative NHANES database, which has a large study sample size; and that ALIs, which are comprehensive multi-system markers, can objectively reflect chronic stress. However, this study was cross-sectional, and the inference of the causal relationship between DASH score and AL is limited; dietary data extracted through dietary recalls may have errors due to the influence of subjective factors. More prospective cohort studies with a large sample size should be conducted in the future to further investigate the association and potential mechanisms between DASH dietary pattern and AL.
4 Conclusions
DASH score are negatively associated with AL, and low DASH score increase the risk of high AL, which can adversely affect physical and mental health. Therefore, more attention should be paid to the key role of diet as a modifiable factor in AL to reduce the adverse health effects of chronic stress.
Acknowledgments
This paper was supported by the Natural Science Foundation of Anhui Province under the project “2108085MG242” and Graduate Youth Cultivation Program of School of Nursing, Anhui Medical University “hlqm12024003”.
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