Associations of psychosocial factors and cardiovascular health measured by Life’s Essential 8: The Atherosclerosis Risk in Communities (ARIC) study

Kennedy M Peter-Marske; Anna Kucharska-Newton; Eugenia Wong; Yejin Mok; Priya Palta; Pamela L Lutsey; Wayne Rosamond

doi:10.1371/journal.pone.0305709

. 2024 Jul 31;19(7):e0305709. doi: 10.1371/journal.pone.0305709

Associations of psychosocial factors and cardiovascular health measured by Life’s Essential 8: The Atherosclerosis Risk in Communities (ARIC) study

Kennedy M Peter-Marske ^1,^*, Anna Kucharska-Newton ¹, Eugenia Wong ¹, Yejin Mok ^2,³, Priya Palta ⁴, Pamela L Lutsey ⁵, Wayne Rosamond ¹

Editor: Mohammad Reza Mahmoodi⁶

PMCID: PMC11290690 PMID: 39083538

Abstract

Aims

Few studies investigate whether psychosocial factors (social isolation, social support, trait anger, and depressive symptoms) are associated with cardiovascular health, and none with the American Heart Association’s new definition of cardiovascular health, Life’s Essential 8 (LE8). Therefore, we assessed the cross-sectional associations of psychosocial factors with Life’s Essential 8 and individual components of Life’s Essential 8.

Methods

We included 11,311 Atherosclerosis Risk in Communities cohort participants (58% females; 23% Black; mean age 57 (standard deviation: 6) years) who attended Visit 2 (1990–1992) in this secondary data analysis using cross-sectional data from the ARIC cohort study. Life’s Essential 8 components included diet, physical activity, nicotine exposure, sleep quality, body mass index, blood lipids, blood glucose, and blood pressure. Life’s Essential 8 was scored per the American Heart Association definition (0–100 range); higher scores indicate better cardiovascular health. Associations of categories (high, moderate, and low) of each psychosocial factor with continuous Life’s Essential 8 score and individual Life’s Essential 8 components were assessed using multivariable linear regressions.

Results

11% of participants had high Life’s Essential 8 scores (80–100), while 67% and 22% had moderate (50–79) and low Life’s Essential 8 scores (0–49) respectively. Poor scores on psychosocial factor assessments were associated with lower Life’s Essential 8 scores, with the largest magnitude of association for categories of depressive symptoms (low β = Ref.; moderate β = -3.1, (95% confidence interval: -3.7, -2.5; high β = -8.2 (95% confidence interval: -8.8, -7.5)). Most psychosocial factors were associated with Life’s Essential 8 scores for diet, physical activity, nicotine, and sleep, but psychosocial factors were not associated with body mass index, blood lipids, blood glucose, or blood pressure.

Conclusion

Less favorable measures of psychosocial health were associated with lower Life’s Essential 8 scores compared better measures of psychosocial health among middle-aged males and females.

Introduction

Psychosocial factors such as social isolation, social support, trait anger, and depressive symptoms, have been associated with the incidence of a variety of cardiovascular diseases (CVDs) such as myocardial infarction and stroke, CVD mortality, and the progression of CVD [1–5]. The majority of past studies have focused on the relationships these four psychosocial factors with clinically recognized CVD events [1,2,4]. However, little is known about how they are related to cardiovascular health (CVH) as defined by the American Heart Association (AHA). In 2010, the AHA set a goal of improving CVH in all Americans, defining CVH as Life’s Simple 7 (LS7) [6], expanding the definition of CVH to include a spectrum of health, not limited to non-diseased and diseased states. CVH metrics created further opportunities for primary prevention of CVD through early risk identification and intervention. The LS7 metric has been widely used by CVD epidemiologists and researchers, and is prospectively associated with CVD and other health outcomes [7].

In 2022, the AHA released an updated definition of CVH called Life’s Essential 8 (LE8) [7]. This definition includes all health behaviors and factors (diet, physical activity, nicotine exposure, body mass index (BMI), blood lipids, blood pressure, and blood glucose) from LS7, and adds sleep as a new component [7]. Additionally, in comparison to the LS7, the LE8 updates the scoring of the 8 metrics to be continuous to better represent interindividual differences, and changes the definitions of component criteria to be in line with current clinical guidelines [7].

Although associations of social isolation, social support, trait anger, and depressive symptoms with individual components of CVH are relatively well studied [4,8], fewer prior studies have focused on the associations between these factors and the AHA’s CVH composite score. The majority of existing studies examined associations between depressive symptoms and CVH, while very few investigated associations of social isolation, social support, or trait anger with CVH [9–18]. To our knowledge, no studies have investigated the associations of social isolation, social support, trait anger, and depressive symptoms with CVH as defined by the new LE8 score. Examination of the associations of social isolation, social support, trait anger, and depressive symptoms with CVH may suggest targets to improve CVH before CVD manifests, or may indicate individuals in need of cardiovascular (CV) risk factor intervention based on their psychosocial factor profile.

Therefore, we used data from the Atherosclerosis Risk in Communities (ARIC) Study to investigate the cross-sectional associations of these four psychosocial factors with CVH defined by the AHA’s LE8, and with individual LE8 components.

Methods

Study design and population

The ARIC study is an ongoing community-based, prospective cohort study of middle-aged mostly White and Black men and women. Details of the ARIC study have been previously published [19]. Briefly, from 1987–1989, 15,792 men and women ages 45–64 years were recruited from four communities in the United States (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; and Washington County, MD) using various probability sampling methods described in detail elsewhere [20]. The baseline study visit (Visit 1, 1987–1989) was followed by additional in-person evaluations, along with yearly follow-up telephone calls and semi-annual follow-up calls starting in 2012. Protocols for all in-person visits were approved by the institutional review boards at the University of North Carolina at Chapel Hill, Wake Forest University, University of Mississippi Medical Center, University of Minnesota, and Johns Hopkins University. All participants provided written informed consent. Data were originally accessed for this analysis on September 11, 2022 and are can be requested through the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) website (https://biolincc.nhlbi.nih.gov/studies/aric/) after registering with the site.

Of the 14,348 ARIC cohort members who participated in Visit 2 (1990–1992), we excluded those with self-reported race other than Black or White (n = 42) and Black participants from Washington County, MD or the suburbs of Minneapolis, MN (n = 49) due to small samples sizes. We also excluded participants with missing social isolation, social support, trait anger, and depressive symptom data (n = 1,363), and those missing a component of CVH: diet (n = 256), physical activity (n = 4), nicotine exposure (n = 4), blood lipid measures (n = 132), blood pressure (n = 48), blood glucose measures (n = 54), BMI (n = 0), and sleep measures (n = 0). Those with prevalent coronary heart disease, myocardial infarction, and stroke, at Visit 2 were further excluded (n = 1,038), along with participants who were missing data on covariates of interest (n = 16). Our final analytic sample for analyses included 11,311 ARIC participants.

Psychosocial factors: Social isolation, social support, trait anger, and depressive symptoms

The four psychosocial factors were measured using self-administered questionnaires at ARIC Visit 2 (1990–1992). Social isolation addresses the number and frequency of interactions with social contacts, along with involvement in social networks and the community [21]. Social isolation was assessed using the Lubben Social Network Scale, a psychometrically valid questionnaire that has high internal reliability [22]. This questionnaire includes 10 questions on the self-assessed availability of social interactions that use a 0–5 rating scale with total scores ranging from 0–50. Previous studies have categorized this measure into levels of risk for social isolation: low risk (31–50), moderate risk (26–30), high risk (21–25), and socially isolated (0–20) [23]. Perceived social support, the extent and types of support available from existing relationships [24], was measured using an abbreviated, 16-item version of the Interpersonal Support Evaluation List. This questionnaire has good internal consistency, and is highly correlated with other measures of social support [25,26]. Total scores ranged from 0–48.

Anger, an emotion that arises from feelings of being treated unfairly and is accompanied by an agitated state [4], was measured using the Spielberger Trait Anger Scale. This questionnaire measures trait anger, and includes 10 questions that measure the frequency and intensity of symptoms of anger, particularly concepts such as hostility, and anger [27]. Response options are scored from 1–4, with total scores ranging from 10–40; previous studies have categorized this measure as low trait anger (10–14), moderate trait anger (15–21), and high trait anger (22–40) [28]. Depressive symptoms were measured using the Maastricht Vital Exhaustion Questionnaire [29]. We utilized 18 of the 21 questions as a measure of depressive symptoms, excluding three questions most related to sleep quality. Questions were scored as 0 (No), 1 (Don’t Know), and 2 (Yes), and overall summary scores ranged from 0–36 with higher scores indicating greater number of depressive symptoms.

Cardiovascular health

CVH was defined using the AHA’s new definition, the LE8 [7]. Measurement of the 8 CVH metrics (diet [30], physical activity [31], nicotine exposure, sleep, BMI, blood lipids, blood pressure, and blood glucose [32]), scoring details, and any deviations from LE8 definitions due to data availability are outlined in Table 1 (diet data summarized in S1 Table). Briefly, each LE8 component was calculated on a scale of 0–100. A measure of hours of sleep was not available; therefore, we used a derived measure of sleep quality from the Maastricht Vital Exhaustion Questionnaire. Total LE8 was calculated as the average of all LE8 health metrics, and ranged from 0–100 with higher scores indicating better CVH(7). The total summed LE8 score was categorized per AHA guidance as high (80≤ to ≤100), moderate (50≤ to <80), and low (0≤ to <50) CVH [7]. All LE8 components were measured at Visit 2, except diet and physical activity, which were measured at Visit 1 and are used as an approximation of these measures at Visit 2.

Table 1. Scoring for Life’s Essential 8 according to Llyod-Jones et al. 2022 [7], measurement of Life’s Essential 8 metrics in the Atherosclerosis Risk in Communities (ARIC) Study, and modifications made to comply with data availability.

LE8 metric and definition	Measurement in ARIC	Score modifications for ARIC data
Diet: Population scoring using quantiles of DASH-style adherence or Healthy Eating Index: 100 = ≥ 95^th percentile (top/ideal) 80 = 75^th-94^th percentile 50 = 50^th-74^th percentile 25 = 25^th-49^th percentile 0 = 1^st-24^th percentile (bottom/least ideal)	A modified version of the 66-item Harvard food frequency questionnaire, which asks participants how frequently they eat a certain quantity of a specific food (eg. ½ cup serving of ice cream), with response options including: > 6 per day, 4–6 per day, 2–3 per day, 1 per day, 5–6 per week, 2–4 per week, 1 per week, 1–3 per month, and almost never.[30]	See S1 Table
Physical activity: Minutes of moderate-to-vigorous activity per week: 100 = ≥ 150 mins 90 = 120–149 mins 80 = 90–119 mins 60 = 60–89 mins 40 = 30–59 mins 20 = 1–29 mins 0 = 0 mins	Measured by the Baecke questionnaire, which assesses the participant’s yearly frequency of participating in sports (up to four entries) and their walking habits.[31] These logs were converted to metabolic equivalents (METs) per the Compendium of Physical Activities, and weekly minutes of moderate-to-vigorous physical activity was calculated by multiplying the duration of weekly physical activity by the number of months per year.	Same
Nicotine exposure: Combustible tobacco use or nicotine-delivery system, or secondhand smoke exposure: 100 = never smoker 75 = former smoker, quit ≥ 5 years 50 = former smoker, quit 1–5 years 25 = former smoker, quit < 1 year or currently using nicotine delivery system 0 = current smoker	Smoking status was measured at Visits 1 and 2 as current, former, or never smokers. At Visit 2, participants were asked for how long they had stopped smoking. Additionally, non-smokers were asked how many hours/week they were exposed to second-hand smoke at Visit 2.	Cigarette smoking status: 100 = never smoker 75 = former smoker, quit ≥ 3 years, or non-smoker in the upper quartile of hours/week of exposure to second-hand smoke 50 = former smoker, quit 1–3 years 25 = former smoker quit < 1 year 0 = current smoker
Sleep: Average hours of sleep per night: 100 = 7 to ≤ 9 hours 90 = 9 to ≤ 10 hours 70 = 6 to ≤ 7 hours 40 = 5 to ≤ 6 or > 10 hours 20 = 4 to ≤ 5 hours 0 = ≤ 4 hours	Sleep quality was assessed using the first three questions of the Maastricht Vital Exhaustion Questionnaire: 1) Do you often feel tired?, 2) Do you often have trouble falling asleep?, 3) Do you wake up repeatedly during the night? Responses were scored on a scale of 0–2, with total scores ranging from 0–6 and higher scores indicating poorer sleep quality.	Score on Maastricht Vital Exhaustion sleep-related questions: 100 = 0 90 = 1 70 = 2 60 = 3 40 = 4 20 = 5 0 = 6
Body mass index: BMI (kg/m²): 100 = < 25 kg/m² 70 = 25.0–29.9 kg/m² 30 = 30.0–34.9 kg/m² 15 = 35.0–39.9 kg/m² 0 = ≥ 40.0 kg/m²	BMI was calculated as weight (kg) / height squared (m²), where weight was measured using a balance beam scale, and height was measured in cm by a stadiometer.	Same
Blood lipids: Plasma total cholesterol and HDL cholesterol used to calculate non-HDL cholesterol (mg/dL): 100 = < 130 mg/dL 60 = 130–159 mg/dL 40 = 160–189 mg/dL 20 = 190–219 mg/dL 0 = ≥ 220 mg/dL If drug-treated level, subtract 20 points	Total cholesterol, high-density lipoproteins, and low-density lipoproteins were measured using standardized enzymatic methods on fasted blood samples. Cholesterol-lowering medication use within the past two weeks were taken by self-report and or from prescription bottles.	Same
Blood glucose: Fasting Blood Glucose (FBG) mg/dL or HbA1c (%): 100 = No history of diabetes and FGB < 100 (or HbA1c < 5.7) 60 = No diabetes and FBG 100–125 (or HbA1c 5.7–6.4) 40 = Diabetes with HbA1c < 7.0 30 = Diabetes with HbA1c 7.0–7.9 20 = Diabetes with HbA1c 8.0–8.9 10 = Diabetes with HbA1c 9.0–9.9 0 = Diabetes with HbA1c ≥ 10.0	Serum blood glucose and Hemoglobin A1c (HbA1c) were both measured as part of fasted blood samples at ARIC Visit 2, with HbA1c measured retrospectively as part of an ancillary study. Serum blood glucose levels were measured using the hexokinase/glucose-6-phosphate dehydrogenase procedure. HbA1c was measured using the Tosoh 2.2 Plus HPLC instrument and the Tosoh G7 HPLC instrument.[32] Intraclass correlation between these two instruments was 0.98.[32]	Same Diabetes defined as fasting blood glucose ≥ 126 mg/dL
Blood pressure: Systolic/diastolic blood pressures (mmHg) 100 = <120 / < 80 mmHg 75 = 120–129 / < 80 mmHg 50 = 130–139 or 80–89 mmHg 25 = 140–159 or 90–99 mmHg 0 = ≥ 160 or ≥ 100 mmHg Subtract 20 points if treated level	Sitting blood pressures (systolic and diastolic, mmHg) were measured 3 times after a 5 minute rest period, using a random zero sphygmomanometer; the final two measurements were averaged and used in analyses. Antihypertensive use within the past two weeks was taken by self-report and from prescription bottles.	Same

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Covariates

Biologic sex (male or female), self-reported race (Black or White), age (years), and education (< 12 years, 12 years, 12 < to < 16 years, and ≥ 16 years) were assessed at ARIC Visit 1. Age at Visit 1 was used to calculate age at Visit 2 by adding the time between visits to the participant’s age at Visit 1, rounding down to a whole number. Due to the sampling strategies, race and study-center are highly correlated in the ARIC cohort. Therefore, the variable race-center was developed as a 5-level categorical classification of race and field center (MN/White, MD/White, NC/White, NC/Black, MS/Black). Covariates for inclusion in models were identified based on prior literature and a priori scientific knowledge.

Statistical analysis

Tertiles of high, moderate, and low social support and depressive symptoms were computed, as there are no established categories for these measures. We produced descriptive statistics of demographic characteristics, psychosocial variables, and LE8 components in the overall analysis sample and stratified by levels of LE8 (high, moderate, low). Additionally, the prevalence of high, moderate, and low levels of LE8 were computed by high, moderate, and low levels of psychosocial factors. The cross-sectional associations between individual psychosocial factors with continuous overall LE8 score and continuous LE8 component scores were examined using multivariable linear regressions adjusted for sex, race-center, age, and education level. These covariates were identified a priori using substantive knowledge and prior literature. We assessed effect measure modification of these associations by sex and race by including interaction terms with psychosocial exposures. If p-values (2-sided) for interaction terms were < 0.05, models were stratified by modifying factors.

Although hours of sleep was not measured at Visit 2, self-reported typical hours of sleep per night was measured on a subset of 1,604 ARIC participants as a part of the Sleep Heart Health Study about 6 years (1996–1998) after Visit 2 [33,34]. Therefore, we conducted a sensitivity analysis using hours of sleep, as recommended by the LE8 scoring guidelines, instead of the modified sleep quality measure used for primary analyses among this sample. The correlation between hours of sleep per night and sleep quality-related questions from the Vital Exhaustion Questionnaire was assessed among the participants who have both measures. All analyses were repeated among this subsample of participants using hours of sleep to derive LE8. Another sensitivity analysis was conducted adjusting depressive symptom analyses for depression-related medications measured at Visit 2. As a further sensitivity analyses, we assessed the same associations of psychosocial factors with the odds of having a high LE8 score compared to moderate/low LE8 score using adjusted logistic regressions. All analyses were conducted in SAS version 9.4 (SAS Institute, Cary, NC, United States).

Results

Participant characteristics

Participants had a mean age of 56.8 (standard deviation (SD): 5.7) years, were 58% female, and 23% Black. Nineteen percent had less than a high school education, and the mean (SD) BMI was 27.9 (5.4) kg/m² (Table 2). The cohort consisted of 22% current smokers and 13% participants with diabetes. Mean (SD) minutes of weekly moderate-to-vigorous physical activity was 135 (154) minutes/week, while mean (SD) systolic blood pressure was 121 (19) mmHg. One percent were considered socially isolated, with another 4% at high-risk for social isolation. The prevalence of high trait anger was 7%. Mean (SD) social support score was fairly high at 37.5 (6.2) with a range of 4 to 48. Mean (SD) depressive symptoms score was relatively low at 7.9 (7.1) with a range of 0 to 36.

Table 2. Sociodemographic, psychosocial characteristics, and health-related characteristics of the analysis sample at ARIC Visit 2 (1990–1992), stratified by Life’s Essential 8 cardiovascular health category; N = 11,311.

	N (%) or mean ± standard deviation
		Level of cardiovascular health
	All participants	Low (0–49)	Moderate (50–79)	High (80–100)
Total	11,311	2491 (22.0)	7530 (66.6)	1290 (11.4)
Study center
Forsyth County, NC	3081 (27.2)	507 (20.4)	2134 (28.3)	440 (34.1)
Jackson, MS	2310 (20.4)	931 (37.4)	1306 (17.3)	73 (5.7)
Suburbs of Minneapolis, MN	3239 (28.6)	485 (19.5)	2262 (30.0)	492 (38.1)
Washington County, MD	2681 (23.7)	568 (22.8)	1828 (24.3)	285 (22.1)
Female sex	6509 (57.6)	1475 (59.2)	4145 (55.1)	8889 (68.9)
Black (race)	2598 (23.0)	1027 (41.2)	1485 (19.7)	86 (6.7)
Age at Visit 2	56.8 ± 5.7	56.8 ± 5.6	56.9 ± 5.7	56.1 ± 5.6
Education
< 12 years	2164 (19.1)	862 (34.6)	1233 (16.4)	69 (5.4)
12 years	3570 (31.6)	796 (32.0)	2445 (32.5)	39 (25.5)
>12 years to < 16 years	991 (8.8)	205 (8.2)	685 (9.1)	101 (7.8)
≥ 16 years	4586 (40.5)	628 (25.2)	3167 (42.1)	791 (61.3)
Social isolation
Socially isolated (≤20)	140 (1.2)	46 (1.9)	82 (1.1)	12 (0.9)
High risk (21–25)	459 (4.1)	131 (5.3)	287 (3.8)	41 (3.2)
Moderate risk (26–30)	1471 (13.0)	377 (15.1)	945 (12.6)	149 (11.6)
Low risk (≥31)	9241 (81.7)	1937 (77.8)	6216 (82.6)	1088 (84.3)
Social support	37.5 ± 6.2	35.9 ± 6.7	37.7 ± 5.9	39.0 ± 5.5
Trait anger
High (22–40)	787 (7.0)	265 (10.6)	476 (6.3)	46 (3.6)
Moderate (15–21)	6268 (55.4)	1450 (58.2)	4166 (55.3)	652 (50.5)
Low (10–14)	4256 (37.6)	776 (31.2)	2888 (38.4)	592 (45.9)
Depressive symptoms	7.9 ± 7.1	11.2 ± 8.1	7.2 ± 6.6	5.1 ± 5.2
Diet score	41.4 ± 32.1	23.4 ± 26.8	43.6 ± 32.2	72.5 ± 25.4
Minutes of weekly MVPA	135 ± 154	36 ± 88	147 ± 153	254 ± 150
Smoking status
Never smoker	4635 (41.0)	713 (28.6)	3120 (41.4)	802 (62.2)
Former smoker	4233 (37.4)	772 (31.0)	3002 (39.9)	459 (35.6)
Current smoker	2443 (21.6)	1006 (40.4)	1408 (18.7)	29 (2.3)
Sleep score	69.5 ± 31.7	51.1 ± 35.0	72.7 ± 29.4	86.0 ± 20.5
Body mass index	27.9 ± 5.4	31.4 ± 6.3	27.4 ± 4.7	23.9 ± 2.7
Non-HDL cholesterol (mg/dL)	159 ± 42	178 ± 43	158 ± 40	131 ± 32
Diabetes	1490 (13.2)	756 (30.5)	714 (9.5)	20 (1.6)
HbA1c	5.7 ± 1.1	6.3 ± 1.7	5.6 ± 0.9	5.3 ± 0.4
Systolic blood pressure	121 ± 19	131 ± 20	120 ± 17	110 ± 12
Diastolic blood pressure	72 ± 10	76 ± 11	72 ± 10	67 ± 8

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N: Number; SD: Standard deviation; NC: North Carolina; MS: Mississippi; MN: Minnesota; MD: Maryland; MVPA: Moderate-to-vigorous physical activity; HDL: High-density lipoprotein; HbA1c: Hemoglobin A1c.

Compared to those with scores in the low LE8 category, the high LE8 category included more females, White participants, and those with higher education levels (Table 2). Additionally, those with high LE8 were less likely to be socially isolated/at high-risk of social isolation, less likely to have high trait anger, and had higher mean social support scores and lower mean depressive symptom scores than those in the moderate or low LE8 categories. The prevalence of high, moderate, and low overall LE8 levels also varied by levels of psychosocial factors, with high LE8 being more prevalent among low social isolation, low trait anger, low depressive symptoms, and high social support, compared to less favorable levels of the four psychosocial factors (Fig 1).

Psychosocial factors and cardiovascular health

Moderate and high levels of social isolation, trait anger, and depressive symptoms were incrementally associated with lower LE8 scores compared to low levels of these psychosocial factors (Fig 2). Similarly, low and moderate levels of social support, compared to high levels of social support, were incrementally associated with lower LE8 scores. The association between social isolation and LE8 was the smallest in magnitude, while depressive symptoms had the association of the greatest magnitude: being in the highest tertile of depressive symptoms was associated with having an LE8 score about 8 points lower than being in the lowest tertile of depressive symptoms (S2 Table). The associations between all four psychosocial factors and CVH were not modified by sex or race (data not shown).

When we explored individual components of LE8, high social isolation was associated with poor LE8 scores for diet, physical activity, nicotine use, and sleep quality, but was associated with a more favorable LE8 score for BMI (Table 3). Low social support, high trait anger, and high depressive symptoms were similarly associated with poor scores for diet, physical activity, nicotine use, and sleep quality. High levels of trait anger and depressive symptoms were associated with poor scores for BMI, and only the highest level of depressive symptoms was associated with having a poor score for blood glucose. None of the four psychosocial factors were associated with LE8 scores for blood lipids or blood pressure.

Table 3. Cross-sectional associations of psychosocial factors with cardiovascular health as defined by the American Heart Association’s Life’s Essential 8 definition at midlife, using multivariable linear regressions; The ARIC Study (1990–1992), N = 11,311.

	β (95% Confidence Interval)
	Diet	MVPA	Nicotine	Sleep	BMI	Lipids	Glucose	BP
Social isolation
Isolated/high	-2.62 (-5.19, -0.06)	-6.53 (-10.07, -2.98)	-11.17 (-14.18, -8.16)	-6.76 (-9.29, -4.23)	5.15 (2.72, 7.57)	2.28 (-0.27, 4.83)	0.06 (-1.99, 2.10)	2.30 (-0.33, 4.92)
Moderate	0.23 (-1.48, 1.94)	-2.88 (-5.24, -0.52)	-6.64 (-8.65, -4.64)	-2.99 (-4.68, -1.31)	1.60 (-0.19, 3.22)	0.96 (-0.74, 2.66)	1.12 (-0.24, 2.48)	0.01 (-1.74, 1.76)
Low	Referent	Referent	Referent	Referent	Referent	Referent	Referent	Referent
Social support
High	Referent	Referent	Referent	Referent	Referent	Referent	Referent	Referent
Moderate	-1.37 (-2.78, 0.05)	-4.92 (-6.87, -2.96)	-0.44 (-2.11, 1.22)	-5.80 (-7.18, -4.42)	-1.33 (-2.67, 0.02)	-0.08 (-1.49, 1.33)	0.89 (-0.25, 2.02)	0.06 (-1.39, 1.52)
Low	-4.50 (-5.87, -3.13)	-8.99 (-10.89, -7.10)	-5.89 (-7.50, -4.27)	-13.25 (-14.58, -11.91)	-1.17 (-2.47, 0.13)	0.46 (-0.91, 1.83)	-0.62 (-1.71, 0.48)	-0.18 (-1.59, 1.23)
Trait anger
High	-7.07 (-9.42, -4.71)	-3.91 (-7.17, -0.64)	-13.96 (-16.73, -11.19)	-17.04 (-19.34, -14.73)	-3.76 (-6.00, -1.53)	0.07 (-2.29, 2.42)	-1.50 (-3.38, 0.39)	-1.51 (-3.93, 0.91)
Moderate	-4.37 (-5.58, -3.17)	-1.90 (-3.57, -0.22)	-6.83 (-8.25, -5.41)	-7.42 (-8.60, -6.24)	-1.58 (-2.73, -0.44)	-1.04 (-2.24, 0.16)	-0.23 (-1.20, 0.74)	0.55 (-0.69, 1.79)
Low	Referent	Referent	Referent	Referent	Referent	Referent	Referent	Referent
Depressive symptoms
High	-6.32 (-7.79, -4.85)	-9.73 (-11.76, -7.70)	-7.80 (-9.53, -6.06)	-32.94 (-34.27, -31.62)	-4.49 (-5.89, -3.10)	-0.64 (-2.10, 0.83)	-2.45 (-3.64, -1.29)	-1.36 (-2.87, 0.15)
Moderate	-3.57 (-4.99, -2.15)	-4.15 (-6.10, -2.19)	-3.53 (-5.20, -1.86)	-11.82 (-13.10, -10.55)	-1.21 (-2.56, 0.13)	0.30 (-1.11, 1.71)	-0.36 (-1.49, 0.77)	-0.93 (-2.38, 0.53)
Low	Referent	Referent	Referent	Referent	Referent	Referent	Referent	Referent

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LE8: Life’s Essential 8 cardiovascular health metric; MVPA: Moderate-to-vigorous physical activity; BMI: Body mass index; BP: Blood pressure.

Linear regressions adjusted for sex, race-center, age, and education.

Social isolation: Low risk ≥ 31, 25 < moderate risk ≤ 30, high risk/socially isolated ≤ 25.

Social support: 4 ≤ low < 36, 36 ≤ moderate < 41, 41 ≤ high ≤ 48.

Trait anger: 10 ≤ low < 15, 15 ≤ moderate < 22, 22 ≤ high ≤ 40.

Depressive symptoms: 0 ≤ low < 4, 4 ≤ moderate < 10, 10 ≤ high ≤36.

Sensitivity analyses

Mean (SD) self-reported typical number of hours of sleep per night at approximately 6 years after Visit 2 was 7.3 (1.1) hours, with a range of 2.0 to 11.4 hours among the ARIC subsample that participated in the Sleep Heart Health Study (n = 1,604). The LE8 sleep score at Visit 2, defined using quality of sleep, showed a low degree of correlation with both duration of sleep (hours/night), and the sleep duration-based LE8 sleep score (Spearman correlation coefficients 0.12 and 0.14 respectively). Associations of psychosocial factors with overall LE8 score were closer to the null in this subsample (S3 Table). When overall LE8 scores were calculated using sleep duration instead of sleep quality, the magnitudes of association between psychosocial factors and LE8 were of an even smaller magnitude. The associations of each psychosocial factor with the LE8 sleep component specific score based on sleep duration were also closer to the null compared to those defined using sleep quality measures.

In another sensitivity analysis, there were 439 participants (3.9%) taking depression-related medications at Visit 2, and further adjustment for these medications in depressive symptom models did not alter estimates or inferences (data not shown). When assessing the odds of high LE8 scores, the presence and direction of associations agreed with analyses of LE8 modeled continuously.

Discussion

In cross-sectional analyses, social isolation, social support, trait anger, and depressive symptoms were modestly associated with having a 2 to 8 point lower CVH health score defined by AHA’s LE8. When comparing the least to most favorable levels of these psychosocial factors, LE8 scores were 2 to 8 points lower. The associations between the four psychosocial factors and LE8 score did not differ by sex or race. Overall, the prevalence of poor psychosocial factors in this sample was fairly low.

Social isolation, social support, trait anger, and depressive symptoms may impact biological mechanisms related to the immune, neuroendocrine, and CV system via the following proposed pathways [4,5,7,8,35–37]. Negative psychosocial factors, such as high levels of anger, depressive symptoms, social isolation, or lack of social support, may promote poor coping health behaviors, increase stress, and decrease tangible social support resources [4,5,8,24]. Furthermore, depressive symptoms and distress caused by social isolation or lack of social support may negatively impact sleep [38,39]. Peer influence from social support and social connectedness may also alter health behaviors such as physical activity participation, smoking, diet, alcohol intake, medication adherence, and other health seeking behaviors [4,5,8,36,37]. Lastly, social connections and social support may affect health through access to health information and resources [24,37]. Some of these mechanisms may be bidirectional, as negative psychosocial factors may increase the risk of physical health conditions such as CVD as well as mental health problems, which may exacerbate already poor psychosocial factors by limiting social participation and causing further distress, such as depression or anger [7,36].

Although there are many hypothesized pathways that connect psychosocial factors and CVH, few studies have investigated these associations with CVH defined by AHA’s metric LS7 or LE8 metric. Our study found modest cross-sectional associations of social isolation and social support with LE8 score. Our findings are in agreement with the previous two studies that assessed these associations [11,18]. Although our results suggested an incremental inverse association between trait anger and CVH, the only prior study that examined a related measure, hostility, found no association with CVH [11]. Differing results may be due to the fact that hostility is a different construct than trait anger, different scoring methods used to assess CVH (LS7 vs LE8), or different study populations. Overall, our study in conjunction with prior evidence suggests an association exists between social isolation, social support, trait anger, and CVH. This study was one of the first to investigate associations of social isolation, social support, and trait anger with individual components of CVH scores in a single population.

The results of this study add to the strong evidence for an association between greater depressive symptoms and lower CVH defined by the AHA LS7/LE8 metrics, as suggested by the majority of studies that examined depressive symptoms with LS7 cross-sectionally [9–16]. In addition to previous cross-sectional studies, three studies have investigated the prospective associations of depressive symptoms and CVH, finding depressive symptoms to predict worse LS7 scores prospectively and vice versa [40]. However, some of our findings differ from previous studies that investigated the association of depressive symptoms with individual CVH components, albeit previous studies used LS7 scoring while we used LE8. Based on the results of our study, prior CVH studies, and other epidemiological and mechanistic evidence, most evidence points to an association between depressive symptoms and CVH behavioral components such as diet [9,12,13], physical activity [9,10,12–14,16], and nicotine use [9,10,12–14,16,17,41]. Although this study did not find evidence of an association between depressive symptoms and CVH scores for BMI and blood glucose, multiple previous studies also suggest an association between depressive symptoms and CVH scores for BMI [9,12,14,16] and blood glucose [9,14,16,17], although these associations were typically smaller in magnitude than associations with the CVH behavioral factors. It is possible that we do not observe these associations due to the low prevalence of depressive symptoms in this cohort. Alternatively, differing results may be due to the use of a non-validated set of questions from the Maastricht Vital Exhaustion questionnaire to represent depressive symptoms in this study, as opposed to previous studies that have used more widely recognized measurement tools or a depression diagnosis.

Strengths and limitations

To our knowledge, this is the first study to examine psychosocial factors with CVH defined by the new LE8 metric. The LE8 improves upon past scoring as it offers a greater number of categories for each CVH metric, allowing for greater interindividual variation and for participants to get credit for moderate levels of CVH [7]. Additionally, the LE8 score incorporates sleep, which has been consistently associated with CVD outcomes, and scoring for other metrics was updated to match the most current public health and clinical guidelines [7]. Although a few prior studies have investigated the associations between psychosocial factors and CVH defined by LS7, the current study was likely able to better characterize the dose response relationship between psychosocial factors and CVH as the LE8 allows for greater granularity of CVH due to its scoring on a semi-continuous scale of 0–100. However, as these were cross-sectional data, we cannot infer causal or temporal relationships between psychosocial factors and CVH from this data.

This study was limited in that it did not measure hours of sleep at Visit 2, and instead used a derived measure of sleep quality. Sleep duration and quality are conceptualized as separate components and are typically assessed as separate exposures [42]. While there is overlap between those who have particularly short or long sleep durations and those with poor sleep quality, both aspects of sleep measurement may be important to consider in terms of public health since sleep duration needs can vary by individual [42]. This may explain the weak correlation between LE8 scores based on sleep duration and LE8 scores based on sleep quality. Additionally, the sleep quality questions may be inherently related to psychosocial factors since they were drawn from the measure of depressive symptoms, potentially inflating associations. Although the direction of associations of the four psychosocial factors with overall CVH score did not change when using LE8 based on sleep duration, further research is needed to determine the impact of defining LE8 sleep scores based on sleep duration or sleep quality. This study was also limited in that physical activity and diet were measured at Visit 1, approximately 3 years before Visit 2, which were used as an approximation of these measures at Visit 2. Lastly, self-reported measures such as diet, smoking status, and physical activity may be subject to recall and social desirability biases. This may increase measurement error and would likely cause results to be an underestimate of the true association due to potential underreporting of poor smoking, diet, or physical activity habits.

Conclusions

Results from this study expand on the previously established associations of psychosocial factors with CVD prevalence and incidence by assessing how they are associated with CVH and its components before clinical CVD manifests. This evidence suggests that those with the poorest psychosocial risk factors, particularly people with high depressive symptoms, may be an important group to target for CVD risk factor intervention, as they have the poorest CVH scores.

Supporting information

S1 Table. Details of diet scoring, comparing the foods included on the Life’s Essential 8 scoring by Lloyd-Jones et al.

2022 [7] and the scoring used in the Atherosclerosis Risk in Communities (ARIC) study based on diet item availability.

(DOCX)

pone.0305709.s001.docx^{(16.7KB, docx)}

S2 Table. Cross-sectional adjusted associations of psychosocial factors with cardiovascular health defined by the American Heart Association’s Life’s Essential 8 metric at ARIC Visit 2, using multivariable linear and logistic regressions; N = 11,311.

(DOCX)

pone.0305709.s002.docx^{(17.3KB, docx)}

S3 Table. Cross-sectional adjusted associations of psychosocial factors with cardiovascular health, defined by Life’s Essential 8, and Life’s Essential 8 sleep score, comparing scores defined using hours of sleep and sleep quality; N = 1,578.

(DOCX)

pone.0305709.s003.docx^{(20KB, docx)}

Acknowledgments

The authors thank the staff and participants of the ARIC study for their important contributions.

Data Availability

Data can be requested through the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) website (https://biolincc.nhlbi.nih.gov/studies/aric/) after creating an account and registering with the site. The data dictionary is available on this website. More information about the ARIC study can be found at https://aric.cscc.unc.edu/aric9/.

Funding Statement

The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). HbA1c data collection was supported by NIH/NIDDK grant R21 DK080294. The ARIC portion of the Sleep Heart Health Study was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53934 (University of Minnesota) and U01HL64360 (Johns Hopkins University). Kennedy M. Peter-Marske was supported by an National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute National Research Service Award (T32-HL007055). Pamela L. Lutsey was supported by NIH/NHLBII K24 HL159246. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Chin B, Cohen S. Review of the Association Between Number of Social Roles and Cardiovascular Disease: Graded or Threshold Effect? Psychosom Med. 2020;82(5):471–86. doi: 10.1097/PSY.0000000000000809 [DOI] [PubMed] [Google Scholar]
2.Everson-Rose SA, Lewis TT. Psychosocial factors and cardiovascular diseases. Annu Rev Public Health. 2005;26:469–500. doi: 10.1146/annurev.publhealth.26.021304.144542 [DOI] [PubMed] [Google Scholar]
3.Wulandari AN, Murti B, Tamtomo D. Meta-Analysis the Effects of Social Isolation and Loneliness on Cardiovascular Disease and Death in Elderly. Journal of Epidemiology and Public Health. 2023;8(1):121–37. [Google Scholar]
4.Suls J. Anger and the heart: perspectives on cardiac risk, mechanisms and interventions. Prog Cardiovasc Dis. 2013;55(6):538–47. doi: 10.1016/j.pcad.2013.03.002 [DOI] [PubMed] [Google Scholar]
5.Li X, Zhou J, Wang M, Yang C, Sun G. Cardiovascular disease and depression: a narrative review. Front Cardiovasc Med. 2023;10:1274595. doi: 10.3389/fcvm.2023.1274595 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121(4):586–613. doi: 10.1161/CIRCULATIONAHA.109.192703 [DOI] [PubMed] [Google Scholar]
7.Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, et al. Life’s Essential 8: Updating and Enhancing the American Heart Association’s Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association. Circulation. 2022:101161CIR0000000000001078. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Joynt KE, Whellan DJ, O’Connor CM. Depression and cardiovascular disease: mechanisms of interaction. Biological Psychiatry. 2003;54(3):248–61. doi: 10.1016/s0006-3223(03)00568-7 [DOI] [PubMed] [Google Scholar]
9.Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association’s definition in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PLoS One. 2012;7(12):e52771. doi: 10.1371/journal.pone.0052771 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Mathews L, Ogunmoroti O, Nasir K, Blumenthal RS, Utuama OA, Rouseff M, et al. Psychological Factors and Their Association with Ideal Cardiovascular Health Among Women and Men. J Womens Health (Larchmt). 2018;27(5):709–15. doi: 10.1089/jwh.2017.6563 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Veromaa V, Kautiainen H, Saxen U, Malmberg-Ceder K, Bergman E, Korhonen PE. Ideal cardiovascular health and psychosocial risk factors among Finnish female municipal workers. Scand J Public Health. 2017;45(1):50–6. doi: 10.1177/1403494816677661 [DOI] [PubMed] [Google Scholar]
12.Gaye B, Prugger C, Perier MC, Thomas F, Plichart M, Guibout C, et al. High level of depressive symptoms as a barrier to reach an ideal cardiovascular health. The Paris Prospective Study III. Sci Rep. 2016;6:18951. doi: 10.1038/srep18951 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Li Z, Yang X, Wang A, Qiu J, Wang W, Song Q, et al. Association between Ideal Cardiovascular Health Metrics and Depression in Chinese Population: A Cross-sectional Study. Sci Rep. 2015;5:11564. doi: 10.1038/srep11564 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Szlejf C, Suemoto CK, Santos IS, Brunoni AR, Nunes MA, Viana MC, et al. Poorer cardiovascular health is associated with psychiatric comorbidity: results from the ELSA-Brasil Study. Int J Cardiol. 2019;274:358–65. doi: 10.1016/j.ijcard.2018.06.037 [DOI] [PubMed] [Google Scholar]
15.Zeng Q, Dong SY, Song ZY, Zheng YS, Wu HY, Mao LN. Ideal cardiovascular health in Chinese urban population. Int J Cardiol. 2013;167(5):2311–7. doi: 10.1016/j.ijcard.2012.06.022 [DOI] [PubMed] [Google Scholar]
16.Zhang Z, Jackson S, Merritt R, Gillespie C, Yang Q. Association between cardiovascular health metrics and depression among U.S. adults: National Health and Nutrition Examination Survey, 2007–2014. Ann Epidemiol. 2019;31:49–56 e2. doi: 10.1016/j.annepidem.2018.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Langford AT, Butler M, Booth JN, Jin P, Bress AP, Tanner RM, et al. Stress and Depression Are Associated With Life’s Simple 7 Among African Americans With Hypertension: Findings From the Jackson Heart Study. Am J Hypertens. 2021;34(12):1311–21. doi: 10.1093/ajh/hpab116 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kieu C, Behforooz S, Wong N. Social isolation and cardiovascular health in US adults. Scripta Medica. 2020;51(1):9–14. [Google Scholar]
19.Wright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, et al. The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8. J Am Coll Cardiol. 2021;77(23):2939–59. doi: 10.1016/j.jacc.2021.04.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.The ARIC Investigators. The atherosclerosis risk in communities (ARIC) study: Design and objectives. American Journal of Epidemiology. 1989;129(4):687–702. [PubMed] [Google Scholar]
21.Valtorta NK, Kanaan M, Gilbody S, Hanratty B. Loneliness, social isolation and social relationships: what are we measuring? A novel framework for classifying and comparing tools. BMJ Open. 2016;6(4):e010799. doi: 10.1136/bmjopen-2015-010799 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Lubben JE, Blozik E, Gerhard G, Iliffe S, von Renteln Kruse W, Beck JC, et al. Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. The Gerontologist. 2006;46(4):503–13. doi: 10.1093/geront/46.4.503 [DOI] [PubMed] [Google Scholar]
23.Rubinstein RL, Lubben JE, Mintzer JE. Social Isolation and Social Support: An Applied Perspective. Journal of Applied Gerontology. 1994;13(1):58–72. [Google Scholar]
24.Berkman LF, Krishna A. Social Network Epidemiology. Social Epidemiology: Oxford University Press; 2014. [Google Scholar]
25.Heitzmann CA, Kaplan RM. Assessment of methods for measuring social support. Health Psychology. 1988;7(1):75–109. doi: 10.1037//0278-6133.7.1.75 [DOI] [PubMed] [Google Scholar]
26.Sarason BR, Shearin EN, Pierce GR, Sarason IG. Interrelations of Social Support Measures: Theoretical and Practical Implications. Journal of Personality and Social Psychology. 1987;52(4):813–32. [Google Scholar]
27.Spielberger CD. State-Trait Anger Expression Inventory. The Corsini Encyclopedia of Psychology 2010. [Google Scholar]
28.Garg PK, Claxton JNS, Soliman EZ, Chen LY, Lewis TT, Mosley T, et al. Associations of anger, vital exhaustion, anti-depressant use, and poor social ties with incident atrial fibrillation: The Atherosclerosis Risk in Communities Study. European Journal of Preventive Cardiology. 2021;28(6):633–40. doi: 10.1177/2047487319897163 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Appels A, Hoppener P, Mulder P. A questionnaire to assess premonitory symptoms of myocardial infarction. International Journal of Cardiology. 1987;17:15–24. doi: 10.1016/0167-5273(87)90029-5 [DOI] [PubMed] [Google Scholar]
30.Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, et al. Reproducibility and Validity of a Semiquantitative Food Frequency Questionnaire. American Journal of Epidemiology. 1985;122(1):51–65. doi: 10.1093/oxfordjournals.aje.a114086 [DOI] [PubMed] [Google Scholar]
31.Baecke JAH, Burema J, Frijters JER. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. The American Journal of Clinical Nutrition. 1982;36:936–42. doi: 10.1093/ajcn/36.5.936 [DOI] [PubMed] [Google Scholar]
32.Selvin E, Coresh J, Zhu H, Folsom A, Steffes MW. Measurement of HbA1c from stored whole blood samples in the Atherosclerosis Risk in Communities study. J Diabetes. 2010;2(2):118–24. doi: 10.1111/j.1753-0407.2010.00070.x [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lutsey PL, Misialek JR, Mosley TH, Gottesman RF, Punjabi NM, Shahar E, et al. Sleep characteristics and risk of dementia and Alzheimer’s disease: The Atherosclerosis Risk in Communities Study. Alzheimers Dement. 2018;14(2):157–66. doi: 10.1016/j.jalz.2017.06.2269 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Quan SF, Howard BV, Iber C, Kiley JP, Nieto FJ, O’Connor GT, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep. 1997;20(12):1077–85. [PubMed] [Google Scholar]
35.Ertel KA, Glymour MM, Berkman LF. Social networks and health: A life course perspective integrating observational and experimental evidence. Journal of Social and Personal Relationships. 2009;26(1):73–92. [Google Scholar]
36.Uchino BN. Social support and health: a review of physiological processes potentially underlying links to disease outcomes. J Behav Med. 2006;29(4):377–87. doi: 10.1007/s10865-006-9056-5 [DOI] [PubMed] [Google Scholar]
37.Hemingway H, Marmot M. Psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ. 1999;318:1460–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Pakpour AH, Griffiths MD, Ohayon MM, Brostrom A, Lin CY. Editorial: A Good Sleep: The Role of Factors in Psychosocial Health. Front Neurosci. 2020;14:520. doi: 10.3389/fnins.2020.00520 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Nordin M, Knutsson A, Sundbom E, Stegmayr B. Psychosocial factors, gender, and sleep. J Occup Health Psychol. 2005;10(1):54–63. doi: 10.1037/1076-8998.10.1.54 [DOI] [PubMed] [Google Scholar]
40.Ogunmoroti O, Osibogun O, Spatz ES, Okunrintemi V, Mathews L, Ndumele CE, et al. A systematic review of the bidirectional relationship between depressive symptoms and cardiovascular health. Prev Med. 2022;154:106891. doi: 10.1016/j.ypmed.2021.106891 [DOI] [PubMed] [Google Scholar]
41.Penninx BW. Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms. Neurosci Biobehav Rev. 2017;74(Pt B):277–86. doi: 10.1016/j.neubiorev.2016.07.003 [DOI] [PubMed] [Google Scholar]
42.Bin YS. Is Sleep Quality More Important Than Sleep Duration for Public Health? Sleep. 2016;39(9):1629–30. doi: 10.5665/sleep.6078 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0305709.r001

Decision Letter 0

Mohammad Reza Mahmoodi

1 Apr 2024

PONE-D-23-24863Associations of psychosocial factors and cardiovascular health measured by Life’s Essential 8: the Atherosclerosis Risk in Communities (ARIC) StudyPLOS ONE

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Reviewer #2: Partly

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Reviewer #2: Yes

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Reviewer #1: Abstract

In the methods section, it would be useful to mention the study setting. Indicate whether primary or secondary data source was used. Also, would be useful to mention the categories of scores used in the analysis.

Background

Some grammatical errors that need proof-reading. E.g., No full stop on line 80, In line 88 full stop should come after the citation and this should be revised throughout the background and other parts of the manuscript.

Sentence in line 81 that ends with ‘CVD events’ needs to be supported by a reference.

In line 104 CV is not expanded anywhere in preceding text. Please expand the abbreviation.

Methods

Please add some brief information on the sampling strategy for the ARIC study.

In the covariate section, please provide further details how visit 1 variables were used to derive visit 2 estimates i.e. for age. For the race-center variable, please provide some context why MN and MD don’t have black and MS doesn’t have white.

Did you consider using ordinal logistic regression as the levels of cardiovascular health seem to be ordinal in nature? If so, why wasn’t ordinal considered. It would be good to have this as a sensitivity analysis to support the linear regression results.

The figures seems to be a bit blur. Please provide a clearer version.

Results

For table 2 it would be useful to provide some indications of statistical differences from hypothesis tests e.g. chi-squared for categorical variables and t-test/ANOVA for continuous. You can use * to indicate significance against the variables and add as notes below the table.

Reviewer #2: Tile: OK

Abstract: I think it is essential to show the descriptive measures of Life’s Essential 8 scores in each group of psychosocial factor assessments. In this case, it is essential to show the classification of these scores in more detail in material and method part of this section. It seems the stated conclusion is not supported by the evidences already provided in the result part.

Keywords: OK

Introduction: OK

Material and method: please notice to the following issues in this section:

- Please show how ethical issues have been dealt with and who has approved these issues in the context of the paper.

- For a better understanding of the setting of the study, please define the inclusion and exclusion criteria of the recruited participants in more detail, I think referring to the paper of the original study is not enough for such a comprehensive paper.

- Since this is a cross sectional study, I think it is necessary to define the sampling method as well as the method of sample weighting (if necessary) in more detail.

- Please show the reliability measure of the questionnaire proposed for social isolation (line 138). This is true for the anger and depressive symptoms questionnaires as well (lines 150-158).

- Please define how the proposed variable of the study have been measured (in terms of time the method of invitation of the participants in to the study).

- I think it is essential to define how covariates were selected to be entered to the multivariate regression model.

Results: considering the following comments is appreciated in this section:

- Please show the P values of the comparisons of different variable in table 2. It also not necessary to express the findings already shown in the table once again in the context of the paper. Generally speaking, showing the univariate comparing analysis in the LE8 groups is mandatory before multivariate analysis.

- Please complete the legend of different figures (e.g., the colors of figure 1).

Discussion: OK

References: If possible, please use more recent references.

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Reviewer #1: No

Reviewer #2: Yes: Babak Eshrati

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PLoS One. 2024 Jul 31;19(7):e0305709. doi: 10.1371/journal.pone.0305709.r002

Author response to Decision Letter 0

6 May 2024

Reviewer #1: Abstract

1. In the methods section, it would be useful to mention the study setting. Indicate whether primary or secondary data source was used. Also, would be useful to mention the categories of scores used in the analysis.

Thank you for this comment. We have added the following to the abstract:

“...in this secondary data analysis using cross-sectional data from the ARIC cohort study.”

The categories of LE8 scores were not used in multivariable analyses, so to clarify we have specified that we assessed continuous LE8 in the abstract methods section. We additionally added the categories of the psychosocial exposures (high, moderate, and low) to the methods section.

2. Background

Some grammatical errors that need proof-reading. E.g., No full stop on line 80,

The sentence referenced here ends at line 81, after listing a variety of cardiovascular-related diseases and their complications and progression.

3. In line 88 full stop should come after the citation and this should be revised throughout the background and other parts of the manuscript.

Thank you. This has been revised throughout the paper.

4. Sentence in line 81 that ends with ‘CVD events’ needs to be supported by a reference.

We thank the reviewer for this comment. References have been added for this sentence.

5. In line 104 CV is not expanded anywhere in preceding text. Please expand the abbreviation.

This has been fixed, thank you for noticing this error.

6. Methods

Please add some brief information on the sampling strategy for the ARIC study.

The ARIC study employed various sampling methods, of which have been previously described. It would take many sentences to fully describe this sampling, therefore we added a reference and statement to the methods reflecting this:

“...using various probability sampling methods described in detail elsewhere(19).”

7. In the covariate section, please provide further details how visit 1 variables were used to derive visit 2 estimates i.e. for age.

The following text has been added: “by adding the time between visits to the participant’s age at Visit 1, rounding down to a whole number.”

8. For the race-center variable, please provide some context why MN and MD don’t have black and MS doesn’t have white.

This is due to the sampling strategy of the original ARIC cohort. The following text has been revised to reflect this and give a bit more context: “Due to the sampling strategies, race and study-center are highly correlated in the ARIC cohort. Therefore, the variable race-center was developed as a 5-level categorical classification of race and field center (MN/White, MD/White, NC/White, NC/Black, MS/Black).”

9. Did you consider using ordinal logistic regression as the levels of cardiovascular health seem to be ordinal in nature? If so, why wasn’t ordinal considered. It would be good to have this as a sensitivity analysis to support the linear regression results.

We ran all models as logistic regressions estimating the odds of having high LE8 scores compared to the odds of having moderate/low LE8, as has been commonly done when assessing LS7 scores. Associations were in the same direction as linear regression results. We have chosen not to include these models because one of the major benefits of the LE8 score compared to the LS7 is the granularity of the measure and its ability to give individuals more “credit” for intermediate measures of cardiovascular health on a scale of 0 to 100 rather than categorizing them simply into three bins. However, to the reviewer’s recommendation, we have added this as a sensitivity analysis in the methods and results section in case readers wonder about this.

“As a further sensitivity analyses, we assessed the same associations of psychosocial factors with the odds of having a high LE8 score compared to moderate/low LE8 score using adjusted logistic regressions.”

“When assessing the odds of high LE8 scores, the presence and direction of associations agreed with analyses of LE8 modeled continuously.”

10. The figures seems to be a bit blur. Please provide a clearer version.

We thank the reviewer for this comment. We also noticed that the figures became blurry when converted to the pdf to send to reviewers, but they were not burry in the versions that we submitted to the journal. We will work with the journal to ensure that the final copy of figures published is clear and easy to read.

11. Results

We thank the reviewer for this suggestion. We did not perform statistical tests for the variables listed in the descriptive Table 2 since these are descriptive statistics of the sample characteristics, and not presented for hypothesis testing. This method is suggested here:

“Inferential measures such as standard errors and confidence intervals should not be used to describe the variability of characteristics, and significance tests should be avoided in descriptive tables.” - https://www.acpjournals.org/doi/10.7326/0003-4819-147-8-200710160-00010-w1

12. Reviewer #2: Tile: OK

Due to word count limits (300 words total, currently at 293), we are not able to elaborate extensively on how psychosocial factors were assessed in the abstract. We have clarified that these measures were assessed categorically, and the scale direction of the LE8 score.

“Life’s Essential 8 was scored per the American Heart Association definition (0-100 range); higher scores indicate better cardiovascular health.”

“Associations of categories (high, moderate, and low) of each psychosocial factor”

We also changed the directionality of the language in the conclusion to better match that of the results section: “Less favorable measures of psychosocial health were associated with lower Life’s Essential 8 scores compared better measures of psychosocial health among middle-aged males and females.”

13. Keywords: OK

Introduction: OK

Material and method: please notice to the following issues in this section:

- Please show how ethical issues have been dealt with and who has approved these issues in the context of the paper.

Please see the following text in the methods section:

“Protocols for all in-person visits were approved by the institutional review boards at the University of North Carolina at Chapel Hill, Wake Forest University, University of Mississippi Medical Center, University of Minnesota, and Johns Hopkins University. All participants provided written informed consent.”

14. For a better understanding of the setting of the study, please define the inclusion and exclusion criteria of the recruited participants in more detail, I think referring to the paper of the original study is not enough for such a comprehensive paper.

The inclusion/exclusion criteria for the ARIC study are as follows and is as described in this manuscript: “men and women ages 45-64 years were recruited from four communities in the United States (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; and Washington County, MD)”.

There were no other major inclusion or exclusion criteria from the general ARIC cohort, please see the new reference 19 for confirmation of this. From this original cohort, we applied many exclusion criteria which are outlined in the second paragraph of the methods section.

15. Since this is a cross sectional study, I think it is necessary to define the sampling method as well as the method of sample weighting (if necessary) in more detail.

Sample weighting was not applicable to this study as this was a community-based cohort. The sampling methods for each study site differed slightly. Detailing the sampling method would involve much description of which has already been published; therefore reference 19 and the following sentence have been added:

“Briefly, from 1987-1989, 15,792 men and women ages 45-64 years were recruited from four communities in the United States (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; and Washington County, MD) using various probability sampling methods described in detail elsewhere(19).”

16. Please show the reliability measure of the questionnaire proposed for social isolation (line 138). This is true for the anger and depressive symptoms questionnaires as well (lines 150-158).

The reliability of social isolation and trait anger have been previously published for different versions of these tools (using a different number of questions from the measures), therefore we did not report the reliability. Additionally, the internal consistency reported here for social support was for the 40-item measure, not the 16-item measure that is used in the present study, so this sentence has been revised to be more general:

“This questionnaire has good internal consistency, and is highly correlated with other measures of social support(24, 25).”

Depressive symptoms were derived using all but 3 questions from the Maastricht Vital Exhaustion questionnaire and therefore these 18 questions have not been validated as a measure of depressive symptoms. This was done so that we could incorporate the sleep-related items as a sleep measure in the LE8 score, and so that this measure would not be highly correlated with depressive symptoms since they are incorporated in the measure itself.

17. Please define how the proposed variable of the study have been measured (in terms of time the method of invitation of the participants in to the study).

Please see the following text outlining when participants were recruited and completed Visit 1: “The baseline study visit (Visit 1, 1987-1989) was followed by additional in-person evaluations, along with yearly follow-up telephone calls and semi-annual follow-up calls starting in 2012.”

The following text outlines how the psychosocial measures were administered and when: “The four psychosocial factors were measured using self-administered questionnaires at ARIC Visit 2 (1990-1992).”

For components of LE8 timing of measurement, please see the following sentence and Table 1 for details on their measurement: “All LE8 components were measured at Visit 2, except diet and physical activity, which were measured at Visit 1 and are used as an approximation of these measures at Visit 2.”

18. I think it is essential to define how covariates were selected to be entered to the multivariate regression model.

Thank you for this suggestion. The following sentence has been included in the methods section: “These covariates were identified a priori using substantive knowledge and prior literature.”

19. Results: considering the following comments is appreciated in this section:

20. Please complete the legend of different figures (e.g., the colors of figure 1).

Text for the figure legends includes the colors for figure 1:

“Fig 1. Prevalence of Cardiovascular Health (low/red, moderate/yellow, and high/green) by high/isolated, moderate, and low levels of psychosocial factors; N=11,311”

The following text has been added to clarify the figure legend for figure 2:

“Fig 2. Cross-sectional adjusted associations of psychosocial factors with Life’s Essential 8 using multivariable linear regressions, estimates represented as squares with 95% confidence interval bars; N=11,311”

21. Discussion: OK

References: If possible, please use more recent references.

Thank you for this comment. We have included Wulandari and Li as two more recent references of psychosocial factors and cardiovascular disease.

Attachment

Submitted filename: LE8 PLOS One Reviewer Comments.docx

pone.0305709.s004.docx^{(22.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0305709.r003

Decision Letter 1

Mohammad Reza Mahmoodi

5 Jun 2024

Associations of psychosocial factors and cardiovascular health measured by Life’s Essential 8: the Atherosclerosis Risk in Communities (ARIC) Study

PONE-D-23-24863R1

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #2: I think all of the comments are addressed by the distinguished authors and the paper can be accepted.

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Reviewer #2: Yes: Babak Eshrati

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PLoS One. doi: 10.1371/journal.pone.0305709.r004

Acceptance letter

Mohammad Reza Mahmoodi

24 Jun 2024

PONE-D-23-24863R1

PLOS ONE

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Details of diet scoring, comparing the foods included on the Life’s Essential 8 scoring by Lloyd-Jones et al.

2022 [7] and the scoring used in the Atherosclerosis Risk in Communities (ARIC) study based on diet item availability.

(DOCX)

pone.0305709.s001.docx^{(16.7KB, docx)}

(DOCX)

pone.0305709.s002.docx^{(17.3KB, docx)}

(DOCX)

pone.0305709.s003.docx^{(20KB, docx)}

Attachment

Submitted filename: LE8 PLOS One Reviewer Comments.docx

pone.0305709.s004.docx^{(22.6KB, docx)}

Data Availability Statement

[pone.0305709.ref001] 1.Chin B, Cohen S. Review of the Association Between Number of Social Roles and Cardiovascular Disease: Graded or Threshold Effect? Psychosom Med. 2020;82(5):471–86. doi: 10.1097/PSY.0000000000000809 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref002] 2.Everson-Rose SA, Lewis TT. Psychosocial factors and cardiovascular diseases. Annu Rev Public Health. 2005;26:469–500. doi: 10.1146/annurev.publhealth.26.021304.144542 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref003] 3.Wulandari AN, Murti B, Tamtomo D. Meta-Analysis the Effects of Social Isolation and Loneliness on Cardiovascular Disease and Death in Elderly. Journal of Epidemiology and Public Health. 2023;8(1):121–37. [Google Scholar]

[pone.0305709.ref004] 4.Suls J. Anger and the heart: perspectives on cardiac risk, mechanisms and interventions. Prog Cardiovasc Dis. 2013;55(6):538–47. doi: 10.1016/j.pcad.2013.03.002 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref005] 5.Li X, Zhou J, Wang M, Yang C, Sun G. Cardiovascular disease and depression: a narrative review. Front Cardiovasc Med. 2023;10:1274595. doi: 10.3389/fcvm.2023.1274595 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref006] 6.Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121(4):586–613. doi: 10.1161/CIRCULATIONAHA.109.192703 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref007] 7.Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, et al. Life’s Essential 8: Updating and Enhancing the American Heart Association’s Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association. Circulation. 2022:101161CIR0000000000001078. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref008] 8.Joynt KE, Whellan DJ, O’Connor CM. Depression and cardiovascular disease: mechanisms of interaction. Biological Psychiatry. 2003;54(3):248–61. doi: 10.1016/s0006-3223(03)00568-7 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref009] 9.Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association’s definition in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PLoS One. 2012;7(12):e52771. doi: 10.1371/journal.pone.0052771 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref010] 10.Mathews L, Ogunmoroti O, Nasir K, Blumenthal RS, Utuama OA, Rouseff M, et al. Psychological Factors and Their Association with Ideal Cardiovascular Health Among Women and Men. J Womens Health (Larchmt). 2018;27(5):709–15. doi: 10.1089/jwh.2017.6563 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref011] 11.Veromaa V, Kautiainen H, Saxen U, Malmberg-Ceder K, Bergman E, Korhonen PE. Ideal cardiovascular health and psychosocial risk factors among Finnish female municipal workers. Scand J Public Health. 2017;45(1):50–6. doi: 10.1177/1403494816677661 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref012] 12.Gaye B, Prugger C, Perier MC, Thomas F, Plichart M, Guibout C, et al. High level of depressive symptoms as a barrier to reach an ideal cardiovascular health. The Paris Prospective Study III. Sci Rep. 2016;6:18951. doi: 10.1038/srep18951 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref013] 13.Li Z, Yang X, Wang A, Qiu J, Wang W, Song Q, et al. Association between Ideal Cardiovascular Health Metrics and Depression in Chinese Population: A Cross-sectional Study. Sci Rep. 2015;5:11564. doi: 10.1038/srep11564 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref014] 14.Szlejf C, Suemoto CK, Santos IS, Brunoni AR, Nunes MA, Viana MC, et al. Poorer cardiovascular health is associated with psychiatric comorbidity: results from the ELSA-Brasil Study. Int J Cardiol. 2019;274:358–65. doi: 10.1016/j.ijcard.2018.06.037 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref015] 15.Zeng Q, Dong SY, Song ZY, Zheng YS, Wu HY, Mao LN. Ideal cardiovascular health in Chinese urban population. Int J Cardiol. 2013;167(5):2311–7. doi: 10.1016/j.ijcard.2012.06.022 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref016] 16.Zhang Z, Jackson S, Merritt R, Gillespie C, Yang Q. Association between cardiovascular health metrics and depression among U.S. adults: National Health and Nutrition Examination Survey, 2007–2014. Ann Epidemiol. 2019;31:49–56 e2. doi: 10.1016/j.annepidem.2018.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref017] 17.Langford AT, Butler M, Booth JN, Jin P, Bress AP, Tanner RM, et al. Stress and Depression Are Associated With Life’s Simple 7 Among African Americans With Hypertension: Findings From the Jackson Heart Study. Am J Hypertens. 2021;34(12):1311–21. doi: 10.1093/ajh/hpab116 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref018] 18.Kieu C, Behforooz S, Wong N. Social isolation and cardiovascular health in US adults. Scripta Medica. 2020;51(1):9–14. [Google Scholar]

[pone.0305709.ref019] 19.Wright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, et al. The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8. J Am Coll Cardiol. 2021;77(23):2939–59. doi: 10.1016/j.jacc.2021.04.035 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref020] 20.The ARIC Investigators. The atherosclerosis risk in communities (ARIC) study: Design and objectives. American Journal of Epidemiology. 1989;129(4):687–702. [PubMed] [Google Scholar]

[pone.0305709.ref021] 21.Valtorta NK, Kanaan M, Gilbody S, Hanratty B. Loneliness, social isolation and social relationships: what are we measuring? A novel framework for classifying and comparing tools. BMJ Open. 2016;6(4):e010799. doi: 10.1136/bmjopen-2015-010799 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref022] 22.Lubben JE, Blozik E, Gerhard G, Iliffe S, von Renteln Kruse W, Beck JC, et al. Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. The Gerontologist. 2006;46(4):503–13. doi: 10.1093/geront/46.4.503 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref023] 23.Rubinstein RL, Lubben JE, Mintzer JE. Social Isolation and Social Support: An Applied Perspective. Journal of Applied Gerontology. 1994;13(1):58–72. [Google Scholar]

[pone.0305709.ref024] 24.Berkman LF, Krishna A. Social Network Epidemiology. Social Epidemiology: Oxford University Press; 2014. [Google Scholar]

[pone.0305709.ref025] 25.Heitzmann CA, Kaplan RM. Assessment of methods for measuring social support. Health Psychology. 1988;7(1):75–109. doi: 10.1037//0278-6133.7.1.75 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref026] 26.Sarason BR, Shearin EN, Pierce GR, Sarason IG. Interrelations of Social Support Measures: Theoretical and Practical Implications. Journal of Personality and Social Psychology. 1987;52(4):813–32. [Google Scholar]

[pone.0305709.ref027] 27.Spielberger CD. State-Trait Anger Expression Inventory. The Corsini Encyclopedia of Psychology 2010. [Google Scholar]

[pone.0305709.ref028] 28.Garg PK, Claxton JNS, Soliman EZ, Chen LY, Lewis TT, Mosley T, et al. Associations of anger, vital exhaustion, anti-depressant use, and poor social ties with incident atrial fibrillation: The Atherosclerosis Risk in Communities Study. European Journal of Preventive Cardiology. 2021;28(6):633–40. doi: 10.1177/2047487319897163 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref029] 29.Appels A, Hoppener P, Mulder P. A questionnaire to assess premonitory symptoms of myocardial infarction. International Journal of Cardiology. 1987;17:15–24. doi: 10.1016/0167-5273(87)90029-5 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref030] 30.Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, et al. Reproducibility and Validity of a Semiquantitative Food Frequency Questionnaire. American Journal of Epidemiology. 1985;122(1):51–65. doi: 10.1093/oxfordjournals.aje.a114086 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref031] 31.Baecke JAH, Burema J, Frijters JER. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. The American Journal of Clinical Nutrition. 1982;36:936–42. doi: 10.1093/ajcn/36.5.936 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref032] 32.Selvin E, Coresh J, Zhu H, Folsom A, Steffes MW. Measurement of HbA1c from stored whole blood samples in the Atherosclerosis Risk in Communities study. J Diabetes. 2010;2(2):118–24. doi: 10.1111/j.1753-0407.2010.00070.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref033] 33.Lutsey PL, Misialek JR, Mosley TH, Gottesman RF, Punjabi NM, Shahar E, et al. Sleep characteristics and risk of dementia and Alzheimer’s disease: The Atherosclerosis Risk in Communities Study. Alzheimers Dement. 2018;14(2):157–66. doi: 10.1016/j.jalz.2017.06.2269 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref034] 34.Quan SF, Howard BV, Iber C, Kiley JP, Nieto FJ, O’Connor GT, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep. 1997;20(12):1077–85. [PubMed] [Google Scholar]

[pone.0305709.ref035] 35.Ertel KA, Glymour MM, Berkman LF. Social networks and health: A life course perspective integrating observational and experimental evidence. Journal of Social and Personal Relationships. 2009;26(1):73–92. [Google Scholar]

[pone.0305709.ref036] 36.Uchino BN. Social support and health: a review of physiological processes potentially underlying links to disease outcomes. J Behav Med. 2006;29(4):377–87. doi: 10.1007/s10865-006-9056-5 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref037] 37.Hemingway H, Marmot M. Psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ. 1999;318:1460–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref038] 38.Pakpour AH, Griffiths MD, Ohayon MM, Brostrom A, Lin CY. Editorial: A Good Sleep: The Role of Factors in Psychosocial Health. Front Neurosci. 2020;14:520. doi: 10.3389/fnins.2020.00520 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0305709.ref039] 39.Nordin M, Knutsson A, Sundbom E, Stegmayr B. Psychosocial factors, gender, and sleep. J Occup Health Psychol. 2005;10(1):54–63. doi: 10.1037/1076-8998.10.1.54 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref040] 40.Ogunmoroti O, Osibogun O, Spatz ES, Okunrintemi V, Mathews L, Ndumele CE, et al. A systematic review of the bidirectional relationship between depressive symptoms and cardiovascular health. Prev Med. 2022;154:106891. doi: 10.1016/j.ypmed.2021.106891 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref041] 41.Penninx BW. Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms. Neurosci Biobehav Rev. 2017;74(Pt B):277–86. doi: 10.1016/j.neubiorev.2016.07.003 [DOI] [PubMed] [Google Scholar]

[pone.0305709.ref042] 42.Bin YS. Is Sleep Quality More Important Than Sleep Duration for Public Health? Sleep. 2016;39(9):1629–30. doi: 10.5665/sleep.6078 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Associations of psychosocial factors and cardiovascular health measured by Life’s Essential 8: The Atherosclerosis Risk in Communities (ARIC) study

Kennedy M Peter-Marske

Anna Kucharska-Newton

Eugenia Wong

Yejin Mok

Priya Palta

Pamela L Lutsey

Wayne Rosamond

Roles

Abstract

Aims

Methods

Results

Conclusion

Introduction

Methods

Study design and population

Psychosocial factors: Social isolation, social support, trait anger, and depressive symptoms

Cardiovascular health

Table 1. Scoring for Life’s Essential 8 according to Llyod-Jones et al. 2022 [7], measurement of Life’s Essential 8 metrics in the Atherosclerosis Risk in Communities (ARIC) Study, and modifications made to comply with data availability.

Covariates

Statistical analysis

Results

Participant characteristics

Table 2. Sociodemographic, psychosocial characteristics, and health-related characteristics of the analysis sample at ARIC Visit 2 (1990–1992), stratified by Life’s Essential 8 cardiovascular health category; N = 11,311.

Fig 1. Prevalence of cardiovascular health (low/red, moderate/yellow, and high/green) by high, moderate, and low levels of psychosocial factors; N = 11,311.

Psychosocial factors and cardiovascular health

Fig 2. Cross-sectional adjusted associations of psychosocial factors with Life’s Essential 8 using multivariable linear regressions, estimates represented as squares with 95% confidence interval bars; N = 11,311.

Table 3. Cross-sectional associations of psychosocial factors with cardiovascular health as defined by the American Heart Association’s Life’s Essential 8 definition at midlife, using multivariable linear regressions; The ARIC Study (1990–1992), N = 11,311.

Sensitivity analyses

Discussion

Strengths and limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Mohammad Reza Mahmoodi

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Author response to Decision Letter 0

Decision Letter 1

Mohammad Reza Mahmoodi

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Acceptance letter

Mohammad Reza Mahmoodi

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Associated Data

Supplementary Materials

Data Availability Statement

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