Abstract
Background:
Life's Simple 7, the former construct of cardiovascular health (CVH) has been used to evaluate adverse non-communicable chronic diseases (NCDs). However, some flaws have been recognized in recent years and Life's Essential 8 has been established. In this study, we aimed to analyze the association between CVH defined by Life's Essential 8 and risk of 44 common NCDs and further estimate the population attributable fractions (PAFs) of low-moderate CVH scores in the 44 NCDs.
Methods:
In the UK Biobank, 170,726 participants free of 44 common NCDs at baseline were included. The Life's Essential 8 composite measure consists of four health behaviours (diet, physical activity, nicotine exposure, and sleep) and four health factors (body mass index, non-high density lipoprotein cholesterol, blood glucose, and blood pressure), and the maximum CVH score was 100 points. CVH score was categorized into low, moderate, and high groups. Participants were followed up for 44 NCDs diagnosis across 10 human system disorders according to the International Classification of Diseases 10th edition (ICD-10) code using linkage to national health records until 2022. Cox proportional hazard models were used in this study. The hazard ratios (HRs) and PAFs of 44 NCDs associated with CVH score were examined.
Results:
During the median follow-up of 10.85 years, 58,889 incident NCD cases were documented. Significant linear dose-response associations were found between higher CVH score and lower risk of 25 (56.8%) of 44 NCDs. Low-moderate CVH (<80 points) score accounted for the largest proportion of incident cases in diabetes (PAF: 80.3%), followed by gout (59.6%), sleep disorder (55.6%), chronic liver disease (45.9%), chronic kidney disease (40.9%), ischemic heart disease (40.8%), chronic obstructive pulmonary disease (40.0%), endometrium cancer (35.8%), lung cancer (34.0%), and heart failure (34.0%) as the top 10. Among the eight modifiable factors, overweight/obesity explained the largest number of cases of incident NCDs in endocrine, nutritional, and metabolic diseases (35.4%), digestive system disorders (21.4%), mental and behavioral disorders (12.6%), and cancer (10.3%); however, the PAF of ideal sleep duration ranked first in nervous system (27.5%) and neuropsychiatric disorders (9.9%).
Conclusions:
Improving CVH score based on Life's Essential 8 may lower risk of 25 common NCDs. Among CVH metrics, avoiding overweight/obesity may be especially important to prevent new cases of metabolic diseases, NCDs in digestive system, mental and behavioral disorders, and cancer.
Keywords: Life's Essential 8, Cardiovascular risk score, Non-communicable chronic disease, Population health management, Cohort analysis, Healthy lifestyle, UK Biobank
Introduction
As of 2010, the mortality of cardiovascular diseases (CVDs) had been declining for decades in the US, and the medical community expanded its attention from secondary and tertiary prevention to primary prevention targeting population health promotion. Thus, the American Heart Association (AHA) proposed a novel and feasible definition for cardiovascular health (CVH), Life's Simple 7, which includes diet, physical activity, smoking, body mass index (BMI), fasting blood glucose, total cholesterol, and blood pressure.[1] Each metric was ternary (poor, intermediate, or ideal), and ideal CVH was recognized as having ideal levels for all seven parameters.
Non-communicable chronic diseases (NCDs) are the leading cause of death globally, killing 41 million people every year (approximately 70% of all deaths), with CVDs, cancers, respiratory diseases, and diabetes mellitus (DM) ranking in the top four.[2] During the past 12 years, many studies have found an association of Life's Simple 7 with CVD, a wide variety of other chronic diseases, all-cause mortality, and cause-specific mortality.[3–6] Life's Simple 7 used to be considered a positive health-oriented construct widely accepted and used in diverse settings.[4] However, some flaws of Life's Simple 7 have been recognized in recent years, such as the relatively narrow scope of health in some CVH components (i.e., diet), limitations in metric quantification, the unavailability of risk equations for young individuals, and the needs for additional metrics of CVH.[7] Therefore, in 2022 the AHA introduced an enhanced approach to assessing CVH: Life's Essential 8.[7] This construct includes a new sleep metric, four updated metrics (diet, nicotine exposure, blood lipids, and blood glucose) and three previous metrics (physical activity, BMI, and blood pressure). To date, whether Life's Essential 8 is useful in predicting diverse NCD outcomes remains unknown, an outcome wide study is warranted.
In clinical and public health settings, clinicians help patients preserve or achieve CVH goals using shared decision-making methods.[8] When clinicians use motivational strategies to identify multiple metrics that need to be improved, it may be more helpful to focus on one major health threat at a time.[7] Thus, identifying the contribution order of the Life's Essential 8 metrics for a variety of NCDs is of great significance for clinical and public health strategies.
To fill this crucial research gap, using prospective multicenter cohort from the UK Biobank (UKB), the objectives of the current study were twofold. First, we examined the associations of the new CVH score with 44 common NCDs among 10 human system disorders including endocrine, nutritional, and metabolic diseases, mental and behavioral disorders, neuropsychiatric disorders, nervous system disorders, circulatory system disorders, respiratory system disorders, digestive system disorders, musculoskeletal system disorders, genitourinary system disorders, and cancer [Supplementary Table 1, http://links.lww.com/CM9/B697]. Second, population attributable fractions (PAFs) of low-moderate CVH and each ideal metric were calculated and ranked to determine the priority in associated system disorders and NCDs.
Methods
Ethical approval
We declare that all data are publicly available in the UKB repository.[9] The UKB received ethical approval from the UK National Health Service, National Research Ethics Service North West, National Information Governance Board for Health and Social Care in England and Wales, and Community Health Index Advisory Group in Scotland. All participants provided written informed consent. This study was approved by the UKB (No. 77740).
Study design and sample
The UKB is a population-based prospective cohort study, and detailed information has been described in a previous study.[9] Briefly, the UKB included more than 500,000 community-dwelling adults aged 37–73 years across the UK between 2006 and 2010 (https://www.ukbiobank.ac.uk/).
Among the 502,414 participants in the UKB, we excluded those with any history of NCDs before recruitment at baseline (n = 231,189) and those missing information for any variable used to calculate the CVH score (n = 100,499). Thus, a total of 170,726 participants were included in the main analysis [Supplementary Figure 1, http://links.lww.com/CM9/B697].
Exposure: CVH score
The CVH score was calculated based on the Life's Essential 8 metrics according to the AHA.[7] The eight metrics include four health behaviors (diet, physical activity, smoking, and sleep) and four health factors (BMI, non-high density lipoprotein, blood glucose, and blood pressure). An ordinal point scoring system for each metric, ranging from 0 to 100 points, was adopted. The self-reported daily intake of Mediterranean Eating Pattern for Americans (MEPA) eating patterns was adopted, and the criteria were modified according to the diet variables available in the UKB [Supplementary Table 2, http://links.lww.com/CM9/B697]. Physical activity was based on self-reported minutes of moderate or vigorous physical activity per week (≥150 min, 100 points; 120–149 min, 90 points; 90–119 min, 80 points; 60–89 min, 60 points; 30–59 min, 40 points; 1–29 min, 20 points; 0 min, 0 points). Smoking was based on the self-reported use of cigarettes (never smoking, 100 points; former smoking, 50 points; current smoking, 0 points). The measurement of sleep health was performed according to self-reported average hours of sleep per night (7 to <9 h, 100 points; 9 to <10 h, 90 points; 6 to <7 h, 70 points; 5 to <6 h or ≥10 h, 40 points; 4 to <5 h, 20 points; <4 h, 0 points). The categorization of BMI was <25.0 kg/m2 (100 points), 25.0–29.9 kg/m2 (70 points), 30.0–34.9 kg/m2 (30 points), 35.0–39.9 kg/m2 (15 points), and ≥40.0 kg/m2 (0 points). The blood lipid metric was based on non-high density lipoprotein cholesterol (mmol/L) (<3.36 mmol/L, 100 points; 3.37–4.11 mmol/L, 60 points; 4.12–4.89 mmol/L, 40 points; 4.90–5.66 mmol/L, 20 points; ≥5.67 mmol/L, 0 points; subtract 20 points if at the treated level). The blood glucose metric was determined by casual hemoglobin A1c (HbA1c) (%) (no history of diabetes and an HbA1c level <5.7, 100 points; no diabetes and an HbA1c level of 5.7–6.4, 60 points; diabetes with an HbA1c level <7.0, 40 points; diabetes with an HbA1c level of 7.0–7.9, 30 points; diabetes with an HbA1c level of 8.0–8.9, 20 points; diabetes with an HbA1c level of 9.0–9.9, 10 points; diabetes with an HbA1c level ≥10.0, 0 points). The blood pressure metric was defined by systolic and diastolic blood pressures (mmHg) (<120/<80, 100 points; 120–129/<80, 75 points; 130–139 or 80–89, 50 points; 140–159 or 90–99, 25 points; ≥160 or ≥100, 0 points; subtract 20 points if at the treated level). Detailed information is presented in Supplementary Table 3, http://links.lww.com/CM9/B697. The new CVH score ranges from 0 to 100 points, calculated as the unweighted average of all eight component metric scores. Based on the recommendation of the AHA, the following categorization of CVH status was adopted: 80–100 points indicated a high CVH score; 50–79 points indicated a moderate CVH score; and 0–49 points indicated a low CVH score.
Outcome: 44 NCDs
The outcomes of interest were 44 NCDs [Table 1]. NCD cases were identified from the "first occurrence defined by a 3-character the International Classification of Diseases 10th edition (ICD-10) Revision code" (category ID in the UKB: 1712). The diagnosis of NCDs was obtained by using linkage with death register, primary care, and hospital inpatient records. Detailed information regarding the linkage procedure is available online (https://biobank.ctsu.ox.ac.uk/crystal/exinfo.cgi?src=diag_ xtabs_HES). Similar to a previous outcome-wide study,[10] the 44 NCDs belong to 10 human system disorders including endocrine, nutritional, and metabolic diseases, mental and behavioral disorders, neuropsychiatric disorders, nervous system disorders, circulatory system disorders, respiratory system disorders, digestive system disorders, musculoskeletal system disorders, genitourinary system disorders, and cancer [Supplementary Table 1, http://links.lww.com/CM9/B697].
Table 1.
The incidence of 44 individual non-communicable chronic diseases by CVH score at baseline.
| NCDs | CVH score | ||
|---|---|---|---|
|
0–49 (n = 6385) |
50–79 (n = 129,226) |
≥ 80 (n = 35,115) |
|
| Lupus erythematosus | 5 (0.08) | 67 (0.05) | 11 (0.03) |
| Migraine | 68 (1.06) | 1169 (0.90) | 339 (0.97) |
| Rheumatoid arthritis | 75 (1.17) | 1011 (0.78) | 198 (0.56) |
| Lung cancer | 48 (0.75) | 500 (0.39) | 71 (0.20) |
| COPD | 237 (3.71) | 2075 (1.61) | 252 (0.72) |
| Thyroid cancer | 6 (0.09) | 100 (0.08) | 27 (0.08) |
| Kidney cancer | 26 (0.41) | 315 (0.24) | 45 (0.13) |
| Stomach cancer | 12 (0.19) | 147 (0.11) | 18 (0.05) |
| Diabetes | 840 (13.16) | 3964 (3.07) | 180 (0.51) |
| Gout | 246 (3.85) | 2062 (1.60) | 153 (0.44) |
| IBD | 46 (0.72) | 640 (0.50) | 116 (0.33) |
| CKD | 309 (4.84) | 3404 (2.63) | 427 (1.22) |
| Pancreas cancer | 17 (0.27) | 255 (0.20) | 38 (0.11) |
| CLD | 286 (4.48) | 2604 (2.02) | 342 (0.97) |
| Asthma | 208 (3.26) | 3032 (2.35) | 630 (1.79) |
| Multiple sclerosis | 3 (0.05) | 94 (0.07) | 39 (0.11) |
| Thyroid disorders | 217 (3.40) | 3348 (2.59) | 777 (2.21) |
| Depression | 277 (4.34) | 3555 (2.75) | 758 (2.16) |
| Diverticular disease | 850 (13.31) | 12,097 (9.36) | 2041 (5.81) |
| Esophagus cancer | 17 (0.27) | 201 (0.16) | 21 (0.06) |
| Malignant melanoma | 25 (0.39) | 725 (0.56) | 193 (0.55) |
| Bladder cancer | 20 (0.31) | 234 (0.18) | 35 (0.10) |
| Breast cancer | 106 (1.66) | 2119 (1.64) | 628 (1.79) |
| IHD | 654 (10.24) | 7881 (6.10) | 969 (2.76) |
| Anxiety disorder | 261 (4.09) | 3902 (3.02) | 901 (2.57) |
| Multiple myeloma | 14 (0.22) | 223 (0.17) | 40 (0.11) |
| Sleep disorders | 267 (4.18) | 1663 (1.29) | 181 (0.52) |
| Ovary cancer | 8 (0.13) | 236 (0.18) | 55 (0.16) |
| Dementia | 66 (1.03) | 1151 (0.89) | 204 (0.58) |
| Stroke | 134 (2.10) | 2141 (1.66) | 308 (0.88) |
| Prostate cancer | 148 (2.32) | 3016 (2.33) | 500 (1.42) |
| Non-Hodgkin lymphoma | 27 (0.42) | 482 (0.37) | 96 (0.27) |
| Heart failure | 197 (3.09) | 2082 (1.61) | 279 (0.79) |
| Oropharyngeal cancer | 5 (0.08) | 103 (0.08) | 18 (0.05) |
| Leukemia | 15 (0.23) | 333 (0.26) | 40 (0.11) |
| Colorectal cancer | 81 (1.27) | 1415 (1.09) | 250 (0.71) |
| Parkinson's disease | 22 (0.34) | 663 (0.51) | 135 (0.38) |
| Epilepsy | 31 (0.49) | 556 (0.43) | 101 (0.29) |
| Brain cancer | 7 (0.11) | 193 (0.15) | 54 (0.15) |
| AF | 414 (6.48) | 6198 (4.80) | 1030 (2.93) |
| Liver cancer | 10 (0.16) | 95 (0.07) | 14 (0.04) |
| Periodontal disease | 14 (0.22) | 160 (0.12) | 43 (0.12) |
| Endometrium cancer | 33 (0.52) | 326 (0.25) | 70 (0.20) |
| Schizophrenia | 3 (0.05) | 46 (0.04) | 6 (0.02) |
Data are presented as number of participants with the disease (incidence, %). AF: Atrial fibrillation; CKD: Chronic kidney disease; CLD: Chronic liver disease; COPD: Chronic obstructive pulmonary disease; CVH: Cardiovascular health score; IHD: Ischemic heart disease; IBD: Inflammatory bowel disease; NCDs: Non-communicable chronic diseases.
Statistical analyses
Data analyses were performed using IBM SPSS Statistics, Version 25 (IBM Corporation, Armonk, NY, USA) and R software, version 4.0.2 (R Project for Statistical Computing, www.r-project.org). Generally, a P value <0.05 indicated statistical significance (two-sided). However, to account for multiple comparisons for the primary analysis of the 10 systems and 44 NCDs, Bonferroni correction was used. P <0.005 (0.05/10) and P <0.0011 (0.05/44) were considered statistically significant.
We examined the association between CVH scores (high vs. low-moderate groups or per 10-point increment) and NCDs. Cumulative cases of NCDs were calculated during follow-up visits. The follow-up time was determined from the baseline date (date of attending the assessment center) to the first diagnosis of NCDs, death, or the censoring date (May 30, 2022), whichever came first. The Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) with age as the time scale. The model was adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), the Townsend index reflecting socioeconomic status (continuous), and alcohol consumption (continuous, g/day). The proportional hazard assumption was tested by the Schoenfeld residuals method and was satisfied. Additionally, we also used restricted cubic splines with five knots at the 5th, 27.5th, 50th, 72.5th, and 95th percentiles to reveal the non-linear associations of CVH scores with risk of NCDs. In preplanned exploratory analyses, we tested whether age (<60 years and ≥60 years), sex, education level, socioeconomic status (median Townsend index: -2.13), and alcohol consumption (median: 67.2 g/week) modified the association between CVH scores and the probability of NCDs in 10 system disorders using a log likelihood ratio test.
To estimate the contribution of low-moderate CVH scores and non-ideal levels of Life's Essential 8 metrics to the burden of NCDs, the hypothetical population PAF% was calculated using Levin's formula: PAFe = Pe(RRe-1)/(Pe[RRe-1] + 1), where Pe is the population prevalence of risk factor e and RRe is the relative risk of the outcome associated with exposure to risk factor e.[11] It was an estimate of the proportion of the NCDs in the study population during follow-up that theoretically would be prevented if all people were in the high CVH group or had ideal metric levels, assuming a causal relationship. We defined that the ideal levels of each metric were 100 points.
We conducted several sensitivity analyses. First, we restricted participants with incident NCDs diagnosed ≥1 year from baseline to perform the regression analyses. Second, we further adjusted for employment status (worked, retired, unemployed, and others) and C-reactive protein levels.
Results
Participant characteristics
Table 2 presents the baseline characteristics of the participants by low, moderate, and high CVH scores. Among a total of 170,726 participants, 89,971 (52.70%) were women, and the mean age was 55.6 years. The proportions of participants with low, moderate, and high CVH scores were 3.74%, 75.69%, and 20.57%, respectively. Overall, individuals with higher CVH scores were relatively younger, were more likely to be women, and had a higher education level, less alcohol intake, and better economic status. They also had lower C-reactive protein levels. Most characteristics were comparable between the samples with and without CVH data, but those with missing CVH data had a lower economic status reflected by Townsend deprivation scores [Supplementary Table 4, http://links.lww.com/CM9/B697].
Table 2.
Baseline characteristics of the participants according to CVH score.
| Characteristics | Total | CVH score | ||
|---|---|---|---|---|
| 0–49 | 50–79 | ≥ 80 | ||
| N | 170,726 | 6385 (3.74) | 129,226 (75.69) | 35,115 (20.57) |
| Sex | ||||
| Male | 47.30 (80,755/170,726) | 58.40 (3729/6385) | 50.41 (65,138/129,226) | 33.85 (11,888/35,115) |
| Female | 52.70 (89,971/170,726) | 41.60 (2656/6385) | 49.59 (64,088/129,226) | 66.15 (23,227/35,115) |
| Age (years) | 55.6 (8.1) | 56.6 (7.4) | 56.2 (8.0) | 53.0 (8.3) |
| Townsend deprivation index | -1.7 (2.8) | -1.2 (3.1) | -1.7 (2.8) | -1.8 (2.8) |
| Ethnicity | ||||
| White | 95.23 (162,575/170,726) | 95.13 (6074/6385) | 95.18 (122,998/129,226) | 95.41 (33,503/35,115) |
| Others | 4.77 (8151/170,726) | 4.87 (311/6385) | 4.82 (6228/129,226) | 4.59 (1612/35,115) |
| Education | ||||
| College or above | 37.25 (63,602/170,726) | 26.19 (1672/6385) | 35.45 (45,814/129,226) | 45.89 (16,116/35,115) |
| Others | 62.75 (107,124/170,726) | 73.81 (4713/6385) | 64.55 (83,412/129,226) | 54.11 (18,999/35,115) |
| Alcohol intake (g/week) | 82.1 (0, 204.4) | 112.0 (0, 321.8) | 89.6 (0, 218.4) | 61.6 (0, 145.6) |
| Employment status | ||||
| Worked | 64.75 (110,300/170,336) | 65.56 (4174/6367) | 63.20 (81,492/128,933) | 70.31 (24,634/35,115) |
| Retired | 29.18 (49,715/170,336) | 26.87 (1711/6367) | 31.14 (40,154/128,933) | 22.41 (7850/35,115) |
| Unemployed | 4.87 (8296/170,336) | 6.71 (427/6367) | 4.55 (5865/128,933) | 5.72 (2004/35,115) |
| Others | 1.18 (2023/170,336) | 0.86 (55/6367) | 1.10 (1422/128,933) | 1.56 (548/35,115) |
| C-reactive protein (mg/L) | 1.1 (0.6, 2.3) | 2.5 (1.4, 4.7) | 1.2 (0.6, 2.4) | 0.7 (0.4, 1.4) |
Data are given as the percentage (n/N) for categorical variables and the mean (standard deviation) (age and Townsend deprivation index) or median (lower quartile, upper quartile) (alcohol intake and C-reactive protein) for continuous variables. CVH: Cardiovascular health.
CVH and any NCD of the 10 system disorders
During the median follow-up of 10.85 years (approximately 1.9 million person-years), 58,889 NCD cases were documented . Using cubic spline analyses, the continuous CVH score presented inverse dose–response linear associations with any NCD in six systems (mental and behavioral disorders, neuropsychiatric diseases, circulatory system disorders, digestive system disorders, musculoskeletal system disorders and genitourinary system disorders) and curvilinear associations were observed in four systems (cancer; endocrine, nutritional, and metabolic diseases; nervous system disorders and respiratory system disorders) [Figure 1]. When the CVH score increased by 10 points, the risk of NCDs in each system decreased (all P <0.005), ranging from 8% to 40%, with endocrine, nutritional, and metabolic disorders (HR: 0.61, 95% CI: 0.60–0.63) and genitourinary system disorders (HR: 0.74, 95% CI: 0.72–0.77) ranking in the top two and cancer (HR: 0.92, 95% CI: 0.90–0.93) and neuropsychiatric disorders (HR: 0.88, 95% CI: 0.86, 0.90) ranking in the bottom two. Moreover, when the CVH score was considered categorical, compared with the low CVH score group, notably, the high CVH score group was associated with an 84% (HR: 0.16, 95% CI: 0.15–0.18) lower risk of NCDs in endocrine, nutritional, and metabolic systems and a 68% (HR: 0.32, 95% CI: 0.27–0.37) lower risk of NCDs in genitourinary systems but a 27% (HR: 0.73, 95% CI: 0.67–0.80) lower risk of cancer [Figure 2 and Supplementary Table 5, http://links.lww.com/CM9/B697].
Figure 1.
The association between the CVH score and non-communicable chronic disease among 10 system disorders. The restricted cubic spline model was adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), Townsend index (continuous), and alcohol consumption (continuous, g/day). CI: Confidence interval; CVH: Cardiovascular health; HR: Hazard ratio.
Figure 2.
The association of CVH scores with any non-communicable chronic disease in 10 system disorders. The Cox regression model was adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), Townsend index (continuous), and alcohol consumption (continuous, g/day). The following categorization of CVH status was adopted: 80–100 points, high CVH score; 50–79 points, moderate CVH score; and 0–49 points, low CVH score. CI: Confidence interval; CVH: Cardiovascular health; HR: Hazard ratio.
We further conducted stratified analyses by age, sex, education level, socioeconomic status, and alcohol consumption [Supplementary Table 6, http://links.lww.com/CM9/B697]. Here, we highlighted the results by age and sex. The associations were consistently strengthened in relatively younger participants (<60 vs. ≥60 years) in all 10 system disorders, especially endocrine, nutritional, and metabolic diseases, mental and behavioral disorders, neuropsychiatric diseases, and nervous, circulatory, and digestive system disorders (Pinteraction <0.005). Additionally, the associations were stronger in men with nervous system disorders and in women with endocrine, nutritional, and metabolic diseases and circulatory system disorders (Pinteraction <0.005).
PAFs of CVH and individual metric in any NCD of the 10 system disorders
PAFs were estimated for any NCD in individual system disorders that was significantly associated with low CVH scores [Figure 3]. When ordered from high to low sequences, the contribution of moderate-low CVH scores to NCDs in the 10 systems was as follows: endocrine, nutritional, and metabolic diseases, genitourinary, circulatory, digestive, respiratory, nervous, and musculoskeletal system disorders, mental and behavioral disorders, neuropsychiatric disorders, and cancer. These results suggested that, if causal, theoretically, 14.2% (95% CI: 10.7–17.6) to 49.5% (95% CI: 46.3–52.7) of the NCD cases in different human systems would not have occurred if all participants had a high CVH score.
Figure 3.
Multivariable-adjusted PAFs of high CVH scores for NCDs. Each box of the forest plot indicates the PAF (95% CI) for any NCD in 10 system disorders and individual NCDs, which can be interpreted as the proportional reduction in the population incidence that would have occurred during follow-up if all participants had CVH scores ≥80. The model was adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), Townsend index (continuous), and alcohol consumption (continuous, g/day). CI: Confidence interval; COPD: Chronic obstructive pulmonary disease; CVH: Cardiovascular health; HR: Hazard ratio; NCDs: Non-communicable chronic diseases; PAFs: Population attributable fractions.
More importantly, the PAFs of each Life's Essential 8 metric are presented in Table 3. Overweight/obesity (BMI ≥25 kg/m2) explained the largest number of cases of incident NCDs in four human systems, including endocrine, nutritional, and metabolic diseases (35.4%, 95% CI: 32.4–38.3), digestive system disorders (21.4%, 95% CI: 19.2–23.4), mental and behavioral disorders (12.6%, 95% CI: 8.9–16.2), and cancer (10.3%, 95% CI: 7.8–12.8). Ideal sleep duration, the newly added metric in Life's Essential 8, showed different PAFs in all 10 systems, ranking first in nervous system (27.5%, 95% CI: 25.5–29.5%) and neuropsychiatric disorders (9.9%, 95% CI: 8.0–11.7). In the circulatory system, the contribution order of the ideal metrics was as follows: blood pressure, BMI, blood lipids, nicotine exposure, sleep health, blood glucose, diet, and physical activity.
Table 3.
PAFs of non-ideal Life's Essential 8 metrics in 10 systems.
| Rank | Cancer | Endocrine, nutritional, and metabolic diseases | Mental and behavioral disorders | Neuropsychiatric diseases | Nervous system disorders | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | |||||
| 1 | BMI | 10.3 (7.8–12.8) | BMI | 35.4 (32.4–38.3) | BMI | 12.6 (8.9–16.2) | Sleep health | 9.9 (8.0–11.7) | Sleep health | 27.5 (25.5–29.5) | ||||
| 2 | Blood pressure | 7.9 (2.9–12.9) | Blood glucose | 32.2 (30.8–33.7) | Sleep health | 10.7 (9.0–12.5) | BMI | 8.3 (4.5–12) | BMI | 15.7 (11.7–19.6) | ||||
| 3 | Nicotine exposure | 5.9 (4.3–7.5) | Blood pressure | 17.8 (11.9–23.6) | Nicotine exposure | 10.2 (7.9–12.6) | Blood pressure | 5.9 (–1.1 to 12.8) | Diet | 7.2 (–4.5 to 18.6) | ||||
| 4 | Diet | 4.3 (-2.8 to 11.4) | Blood lipids | 12.9 (6.9–18.9) | Physical activity | 5.1 (2.8–7.5) | Nicotine exposure | 4.8 (2.3–7.2) | Nicotine exposure | 7.1 (4.6–9.6) | ||||
| 5 | Physical activity | 3.2 (1.6–4.7) | Diet | 10.7 (2.2–19.0) | Blood glucose | 2.2 (1.1–3.3) | Physical activity | 3.8 (1.2–6.3) | Physical activity | 6.5 (4.0–9.0) | ||||
| 6 | Blood glucose | 2.1 (1.4–2.9) | Sleep health | 7.1 (5.6–8.5) | Diet | 0 | Blood glucose | 0.2 (–0.9 to 1.2) | Blood glucose | 1.7 (0.6–2.9) | ||||
| 7 | Sleep health | 0.6 (–0.5 to 1.7) | Nicotine exposure | 6.5 (4.6–8.4) | Blood pressure | 0 | Blood lipids | 0 | Blood lipids | 0.5 (–7.0 to 8.1) | ||||
| 8 | Blood lipids | 0 | Physical activity | 5.3 (3.4–7.2) | Blood lipids | 0 | Diet | 0 | Blood pressure | 0 | ||||
| Rank | Circulatory system disorders | Respiratory system disorders | Digestive system disorders | Musculoskeletal system disorders | Genitourinary system disorders | |||||||||
| Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | Items | HR (95% CI) | |||||
| 1 | Blood pressure | 35.0 (30.8–39.1) | Nicotine exposure | 20.0 (17.6–22.3) | BMI | 21.4 (19.2–23.4) | Blood pressure | 20.6 (6.5–33.8) | Blood pressure | 35.6 (26.7–43.9) | ||||
| 2 | BMI | 19.4 (17.1–21.6) | BMI | 16.2 (12.5–19.9) | Blood pressure | 13.3 (9.1–17.4) | BMI | 17.2 (9.7–24.5) | BMI | 32.2 (27.8–36.5) | ||||
| 3 | Blood lipids | 14.1 (9.7–18.5) | Diet | 8.4 (–2.1 to 18.7) | Blood lipids | 10.2 (6.1–14.3) | Diet | 16.9 (–3.3 to 35.9) | Diet | 15.6 (3.9–26.9) | ||||
| 4 | Nicotine exposure | 6.5 (5.0–7.9) | Sleep health | 7.2 (5.5–8.9) | Diet | 8.0 (1.9–14) | Nicotine exposure | 12.6 (7.8–17.4) | Blood lipids | 9.9 (0.1–19.4) | ||||
| 5 | Sleep health | 4.3 (3.4–5.3) | Blood glucose | 5.2 (4–6.4) | Nicotine exposure | 7.9 (6.5–9.2) | Sleep health | 8.0 (4.4–11.6) | Sleep health | 7.5 (5.4–9.5) | ||||
| 6 | Blood glucose | 3.8 (3.1–4.5) | Physical activity | 3.4 (1.1–5.7) | Sleep health | 5.1 (4.1–6.1) | Physical activity | 3.1 (–1.8 to 7.9) | Blood glucose | 7.1 (5.6–8.7) | ||||
| 7 | Diet | 2.4 (-4.1 to 8.7) | Blood pressure | 0 | Physical activity | 3.5 (2.2–4.9) | Blood glucose | 1.9 (–0.5 to 4.3) | Nicotine exposure | 4.2 (1.3–7.1) | ||||
| 8 | Physical activity | 0.8 (–0.5 to 2.1) | Blood lipids | 0 | Blood glucose | 3.2 (2.6–3.9) | Blood lipids | 0 | Physical activity | 4.2 (1.4–6.9) | ||||
Data in parentheses are 95% CIs. The non-ideal levels of each metric were defined as less than 100 points. Models were adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), Townsend index (continuous), and alcohol consumption (continuous, g/day) and were mutually adjusted for individual risk factors. Individual risk factors with a negative PAF% were not included in the analyses; the negative PAF% was truncated at a lower limit of 0, as this is the lowest threshold to determine an association with increased risk. BMI: Body mass index; CIs: Confidence intervals; HR: Hazard ratio; PAFs: Population attributable fractions.
CVH and the 44 individual NCDs
In restricted cubic spline models, the overall inverse dose–response associations between CVH scores and individual NCDs were significant for 25 NCDs, namely migraine, kidney cancer, diabetes, gout, sleep disorders, esophageal cancer, chronic liver disease (CLD), ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), endometrial cancer, diverticular disease, inflammatory bowel disease (IBD), stroke, heart failure, lung cancer, depression, asthma, rheumatoid arthritis, colorectal cancer, thyroid disorders, anxiety disorders, atrial fibrillation (AF), bladder cancer, and breast cancer (P <0.0011). Among them, all presented linear associations, except diabetes, which presented a curvilinear association [Supplementary Figure 2, http://links.lww.com/CM9/B697].
Compared with low CVH scores, high CVH scores were significantly associated with a lower risk of 22 of the 44 NCD (P <0.0011) [Figure 4 and Supplementary Table 7, http://links.lww.com/CM9/B697]. Very notably, the risk of diabetes decreased by 95% (HR: 0.05, 95% CI: 0.04–0.06) in the high CVH score group. Further analysis using competing risk models did not significantly attenuate these results [Supplementary Table 8, http://links.lww.com/CM9/B697]. Moreover, we found that higher CVH score was significantly associated with lower risk of developing multiple NCD outcomes [Supplementary Table 9, http://links.lww.com/CM9/B697]. Regarding the PAF, the contribution of low CVH scores to the 22 NCDs were shown in Figure 3: diabetes (80.3%, 95% CI: 77.3–83.0), gout (59.6%, 95% CI: 52.9–65.4), sleep disorder (55.6%, 95% CI: 48.8–61.7) [Figure 3].
Figure 4.
Associations between the CVH score and 44 individual non-communicable chronic diseases. The bold number indicates P <0.0011 (0.05/44). The Cox regression model was adjusted for age at baseline, sex, ethnicity (White/others), education level (university or college degree/others), Townsend index (continuous), and alcohol consumption (continuous, g/day). CI: Confidence interval; HR: Hazard ratio; COPD: Chronic obstructive pulmonary disease; CVH: Cardiovascular health; HR: Hazard ratio.
The PAFs of each Life's Essential 8 metric in the 22 NCDs are presented in Supplementary Table 10, http://links.lww.com/CM9/B697. Overweight/obesity (BMI over 25 kg/m2) accounted for the largest number of cases in nine NCDs including diabetes, gout, chronic liver disease, asthma, thyroid disorders, depression, diverticular disease, kidney cancer and endometrium cancer. The PAFs of non-ideal blood pressure ranked top in breast cancer, ischemic heart disease, stroke, heart failure, atrial fibrillation, rheumatoid arthritis, inflammatory bowel disease, and chronic kidney disease. Analyses stratified by age, sex, education level, socioeconomic status, and alcohol consumption are shown in Supplementary Table 11, http://links.lww.com/CM9/B697. The results were rather similar to those in previous system disorders. Significant interactions were mainly observed in the age and sex categories.
Sensitivity analyses
First, we restricted participants with incident NCDs diagnosed ≥1 year from baseline. Second, we further adjusted employment status (worked, retired, unemployed, and others) and C-reactive protein levels. These two conditions did not significantly attenuate the associations between CVH score and NCDs [Supplementary Tables 12–15, http://links.lww.com/CM9/B697].
Discussion
In this large-scale cohort with an approximately 11-year follow-up time, our comprehensive analyses found that: (1) there were significant inverse associations of Life's Essential 8 CVH scores (continuous or categorical) with any NCD in the 10 human systems and at least 22 types of the 44 NCDs; (2) low-moderate CVH scores were responsible for the largest proportion of incident cases in endocrine, nutritional, and metabolic diseases among the 10 system disorders, and they also explained more than 50% of new cases of diabetes, gout, and sleep disorder; (3) among the eight modifiable factors, a BMI ≥25 kg/m2 explained the largest number of cases of NCDs in 6 system disorders, and a non-ideal sleep duration showed the highest PAF in nervous system and neuropsychiatric disorders. These findings provide comprehensive information about the utility of the new CVH score for predicting diverse health outcomes and could help further energize individual and population health promotion strategies and reduce the ongoing substantial burden of NCDs.
Our study systematically evaluated the association of Life's Essential 8 metrics with incident NCDs and further provided a novel insight into their priority in a variety of NCDs. Many studies have indicated an inverse relationship between Life's Simple 7 scores and the risks of CVD[12] and non-CVD,[13] for example, diabetes,[14] COPD,[15] CKD,[16] and dementia[17]. In a multiethnic study with participants aged 45–84 years, an optimal score was associated with a 20–49% decreased risk of non-CVDs, with COPD being the first factor and cancer being the last factor.[13] However, Life's Simple 7 has limited sensitivity to changes in individuals or populations.[18] As three levels of each factor, only high Life's Simple 7 scores (5–7 points on a 7-point scale) were associated with a significantly decreased risk of CD,[12] while moderate scores using Life's Essential 8 (50–79 points on a 100-point scale) have already indicated a protective association in our study. Additionally, updated scores arise by taking lifestyle changes and the requirements of the latest guidelines into account.[19] In a study covering 229,976 participants, the author indicated that three biological metrics (blood pressure, cholesterol, and glucose) of Life's Simple 7 scores, but no other lifestyle scores, were associated with dementia risk.[20] However, we found that the PAF of ideal sleep duration ranked at the top of Life's Essential 8 metrics in nervous system and neuropsychiatric disorders. Overall, the scope of Life's Simple 7 has been relatively narrow in recent and prior research, and its applications on the outcomes were scattered across individual diseases, systems, and study populations. A comprehensive assessment of overall NCDs was lacking. Our work fills this gap and provides reliable evidence using the new CVH metrics based on a large sample size.
Considering that most PAFs of high CVH scores and individual metrics were significant in the 10 systems, Life's Essential 8 could be used not only to assess the risk of CVD but also as a valuable indicator instructing and promoting health for the whole body. From our results, high CVH scores accounted for the greatest benefit on endocrine, nutritional, and metabolic diseases. The Diabetes Prevention Program demonstrated that the incidence of type 2 diabetes could be reduced by 58% over 3 years by an intensive lifestyle intervention, including a weight reduction of at least 7% and the achievement of moderate-intensity physical activity.[21] The most direct impact of healthy lifestyle changes is to improve metabolic indicators and further benefit endocrine and cardiovascular disorder amelioration.[22,23] However, it is also worth noting that Life's Essential 8 may work for neoplasms. The PAF of a high CVH score was just 14.2% for cancer, which was much lower than that of 49.5% for metabolic diseases. However, this does not mean that primary prevention can be ignored for cancer as all cancer types were calculated together and effects might be diluted. Cancer in different systems had various etiologies, and risk factors vary. In lung cancer, nicotine exposure accounts for 49.1% of the cases, which is in accordance with the close relationship between smoking and lung cancer as nicotine can induce the expression of embryonic stem cell factor Sox2, which is indispensable for self-renewal and maintenance of stem cell properties in lung cancer.[24] When it comes to kidney cancer and endometrium cancer, BMI ranks first among the eight individual metrics. These results can be explained by chronic tissue hypoxia and the secretion of adipokines induced by visceral fat resulting in increased proliferation and tumorigenesis.[25] Furthermore, adipose tissue is a source of mesenchymal stem cells, which can be recruited to support tumor growth and progression.[26] Given the fact that cancer continues to be a leading cause of mortality, following the up-to-date goals in Life's Essential 8 is something that should be widely known and practiced for the public.
Presenting the contributions (i.e., PAFs) of individual metrics for a variety of NCDs in the 10 systems in one study population has important implications, which has been quite rare in previous studies. In our findings, the intra-NCD PAFs of the eight metrics could be compared and ranked. The PAF of overweight/obesity ranked first among the nine NCDs, ranging from 61.7% (diabetes) to 16.5% (thyroid disorders). Many previous studies reported PAF estimates ranging from -0.2% to 8% for all cancers, 7% to 44% for CVD incidence, and 3% to 83% for diabetes; however, their definitions of exposures, outcomes, and populations had considerable variability.[27] Combined with previous evidence, we understand that maintaining a normal BMI probably provides great benefits against a variety of NCDs.[28] Although we observed that physical activity and diet had much lower PAFs than BMI for metabolic diseases, the two may be key success factors for reaching the weight goal and maintaining it for a long time.[29,30] Furthermore, ideal sleep duration (7 to <9 h), a newly added metric, had meaningful PAFs in at least 18 NCDs, which were not always lower than those of the traditional seven components. Sleep health is a complex construct including duration, chronotype, regularity, excessive daytime sleepiness, etc. A multidimensional sleep pattern has been found to be associated with an increased risk of CVD and DM.[31,32] Studies on other sleep metrics and interventions to improve CVH through improved sleep health warrant further investigation.
In stratified analyses, an important finding that is worth mentioning is that the associations between CVH and any NCD were consistently strengthened in relatively young participants (<60 vs. ≥60 years) in all 10 system disorders and were especially significant for 6 systems (Pinteraction <0.005). A previous study also found that the impact of traditional cardiovascular risk factors generally declined with age.[33] Thus, we speculate that preserving high CVH scores in younger individuals may provide much more benefit than preserving scores in older individuals. Promoting health by starting earlier is applicable not only in maternal–fetal–pediatric timing[34] but also in the middle-aged population.
Our study had notable strengths. With the prospective cohort design, large sample size, and relatively long follow-up duration, this study had modest statistical power to perform outcome-wide analyses. Most importantly, this is the first study to assess the utility of Life's Essential 8 in public health settings and the contribution order of 8 metrics for 10 human system disorders. Some limitations of this study should be acknowledged. First, due to the observational nature of this study, a causal relationship could not be demonstrated, and residual confounding owing to unmeasured factors might still be possible. Second, similar to many large epidemiological studies, healthy lifestyle information, such as diet and physical activity, was obtained through self-reporting, which may cause recall bias. Third, Life's Essential 8 metrics were acquired only at baseline, and changes due to a long follow-up time were not accounted for in this study. Last, the UKB cohort was restricted to volunteers of European ancestry, mostly White British individuals, and the representation of the general population could be limited, with people from poor socioeconomic backgrounds being underrepresented. Therefore, further research is required to ascertain whether these findings are generalizable to other populations.
In conclusion, in this large population-based study, we utilized the new CVH construct, Life's Essential 8, for predicting diverse health outcomes, including 44 NCDs among 10 human system disorders. A higher CVH score was associated with a lower risk of 22 NCDs, indicating that positive and actionable strategies could be adopted to measure and modify CVH in a variety of NCDs. Overweight/obesity explained the largest number of cases of NCDs in six system disorders, suggesting that focusing on improving one major health factor at a time, rather than too many factors at once, may provide benefits in patients with various NCDs.
Data availability statement
The data underlying this article are available in UK Biobank, at https://www.ukbiobank.ac.uk/.
Funding
This study was supported by grants from Science and Technology Commission of Shanghai Municipality (No. 19140902400), Shanghai Municipal Health Commission (No. 2022XD017), Clinical Research Plan of SHDC (No. SHDC2020CR4006), and Shanghai Municipal Human Resources and Social Security Bureau (No. 2020074).
Conflicts of interest
None.
Supplementary Material
Footnotes
How to cite this article: Yu YT, Sun Y, Yu YF, Wang YY, Chen C, Tan X, Lu YL, Wang NJ. Life’s Essential 8 and risk of non-communicable chronic diseases: Outcome-wide analyses. Chin Med J 2024;137:1553–1562. doi: 10.1097/CM9.0000000000002830
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article are available in UK Biobank, at https://www.ukbiobank.ac.uk/.