Frailty and risk of cardiovascular disease and mortality.

BACKGROUND: Prospective cohort studies suggest that frailty is associated with an increased risk of incident cardiovascular disease (CVD) morbidity and mortality, but their mechanistic and developmental relations are not fully understood. We investigated whether frailty predicted an increased risk of incident nonfatal and fatal CVD among community-dwelling older adults.
METHODS: A population cohort of 5015 participants aged 55 years and above free of CVD at baseline was followed for up to 10 years. Pre-frailty and frailty were defined as the presence of 1-2 and 3-5 modified Fried criteria (unintentional weight loss, weakness, slow gait speed, exhaustion, and low physical activity), incident CVD events as newly diagnosed registered cases of myocardial infarction (MI), stroke, and CVD-related mortality (ICD 9: 390 to 459 or ICD-10: I00 to I99). Covariate measures included traditional cardio-metabolic and vascular risk factors, medication therapies, Geriatric Depression Scale (GDS), Mini-Mental State Exam (MMSE), and blood biomarkers (haemoglobin, albumin, white blood cell counts and creatinine).
RESULTS: Pre-frailty and frailty were significantly associated with elevated HR = 1.26 (95%CI: 1.02-1.56) and HR = 1.54 (95%CI:1.00-2.35) of overall CVD, adjusted for cardio-metabolic and vascular risk factors and medication therapies, but not after adjustment for GDS depression and MMSE cognitive impairment. The HR of association between frailty status and both CVD mortality and overall mortality, however, remained significantly elevated after full adjustment for depression, cognitive and blood biomarkers.
CONCLUSION: Frailty was associated with increased risk of CVD morbidity and especially mortality, mediated in parts by traditional cardio-metabolic and vascular risk factors, and co-morbid depression and associated cognitive impairment and chronic inflammation. Given that pre-frailty and frailty are reversible by multi-domain lifestyle and health interventions, there is potential benefits in reducing cardiovascular diseases burden and mortality from interventions targeting pre-frailty and early frailty population.

Introduction

Frailty is a common geriatric syndrome reflecting a state of reduced physiological reserve and increased vulnerability to the effects of stress [1]. The population prevalence of frailty and pre-frailty, defined using the Fried criteria is high, estimated at 17.4% and 49.3% respectively [2]. Frailty occurs in as much as 50% of older patients with cardiovascular disease (CVD) [3]. Both frail and pre-frail individuals compared to their robust counterparts have higher likelihoods of presenting with CVD, and vice versa [4]. Results from cross-sectional study supported an independent association between subclinical vasculopathy with muscle mass and strength, determinants of frailty [5]. However cross-sectional associations are unable to establish the causal relationship between the frailty and CVD. On the one hand, CVD has been shown to be an important predictor of the onset of frailty, and the presence of frailty in older adults with CVD increases the risk of falls, institutionalization, repeated hospitalization, and mortality [6-8]. On the other hand, frailty has been suggested as a risk factor for the development of CVD [9-11]. However, few prospective cohort studies [12-14] have investigated whether pre-frailty and frailty predict an earlier onset of CVD events and mortality.

Previous studies have reported the association of pre-frailty/frailty with incident hospitalization for heart failure and for overall CVD events, and not separately for non-fatal and fatal CVD events. Physical inactivity and slow gait (in one study [13]) and exhaustion (in two studies [13, 14]) were found to be significantly associated with the onset of CVD events. Depression is associated with cognitive impairment and both are well established comorbidities of frailty [15]. In turn, depression and inflammatory biomarkers are associated with CVD incidence [16]. The role of depression in explaining the association of frailty and CVD risk has not been investigated. The mechanistic and developmental relationships between frailty and CVD risk therefore remains not fully understood.

As frailty might be reversible if appropriately treated [17, 18], the timely detection and therapeutic interventions for frailty and the precursor pre-frailty may have a positive impact in terms of postponing or preventing onset of coronary heart disease, heart failure, stroke and mortality in older persons. The aim of the present prospective cohort study was to investigate the association of pre-frailty and frailty with the risk of developing CVD morbidity and mortality from 10-years follow-up in a cohort of community-dwelling older adults in an Asian population in Singapore.

Methods

Study population

The Singapore Longitudinal Ageing Study (SLAS) is a population-based study that recruited community-dwelling older adults (age>55) who were able to self-ambulate and with adequate cognitive capacity for participation in two separate recruitment waves. SLAS-1 recruited 2,800 older persons in the South-East Region in 2003–2004, and SLAS-2 recruited 3,200 individuals in the South Central and Western Region in Singapore in 2009–2013, each with 3 to 5 yearly follow-ups. The SLAS was approved by the National University of Singapore Institutional Review Board with all participants consented by written form. Full details of the study variables and data collection are described in previous studies [19, 20].

Study sample

In this study, we excluded participants with a confirmed diagnosis of acute myocardial infarction (MI) (n = 38) and stroke (n = 59) at baseline, subjects with self-reported history of atrial fibrillation, heart attack, and heart failure (n = 715) at baseline, and subjects with missing data on frailty and frailty components (n = 296). Our final sample size was 5,015, combining 2,426 participants from SLAS 1 and 2,589 from SLAS 2.

Measurements

All-cause mortality and fatal CVD cases were obtained from the Death Registry data from Singapore National Registry of Diseases Office based on International Classification of Diseases (ICD). Fatal CVDs were identified using ICD 9 codes from 390 to 459 or ICD 10 codes from I00 to I99. Other CVD outcomes included 1) non-fatal MI, obtained from Singapore Myocardial Infarction Registry; 2) non-fatal stroke, obtained from Singapore Stroke Registry; 3) non-fatal CVD, defined as an inclusion of non-fatal MI and non-fatal stroke. Overall CVD included both fatal CVD and non-fatal CVD. Overall mortality includes all-cause of death cases. The follow-up time for this study started at the date of participants enrolment and ended in December 2017 for all the outcomes.

Frailty was defined according to Fried’s five criteria in the Cardiovascular Health Study [1]. Each domain (Shrinking, Low activity, Weakness, Exhaustion, Slowness) accounted for 1 point, and participants were categorized as frail (3–5 points), prefrail (1–2 points), or robust (0 point) based on the sum of all five items. The detailed frailty measurements were described in previous study [19] and summarized below.

Shrinking or weight loss: body mass index (BMI) of less than 18.5 kg/m2 and/or unintentional weight loss of ≥4.5 kg (10 pounds) in the past 6 months.

Weakness was defined as the lowest quintile of knee extension strength within sex and BMI strata in SLAS-2 participants. In SLAS-1 participants, this was defined as the lowest quintile of score of rising from chair test in the sitting position with arms folded, derived from the Performance Oriented Mobility Assessment (POMA) battery [21].

Slowness was defined as gait speed less than 0.8m/s from the fast gait speed test over 6 metres in SLAS2 participants. In SLAS2 participants, slowness was assessed by Tinetti POMA gait tests (subjects walked 6 meters and returned to the starting point quickly), which include 7 gait items—initiation of gait, step length and height, step symmetry, step continuity, path, trunk and walking stance. The total POMA gait score has a range from 0 to 12, and a score of less than 9 denotes impaired gait functioning.

Exhaustion was determined by the response of “not at all” to the question from SF-12 quality of life scale: “Do you have a lot of energy?”

Low activity was determined by self-report of “none” for participation in any physical activity (walking or recreational or sports activity).

One-point was assigned for the presence of each component, and the total score categorizes participants as frail (3–5 points), pre-frail (1–2 points), or robust (0 point).

Baseline covariates

Sociodemographic information included age, sex (male versus female), race (Chinese versus Non-Chinese), education (no education, 1–6 years primary and post-primary), housing (1–2 room, 3–5 room and high-end public/private housing), marital status (married versus single/divorced/widowed) and living status (alone versus not alone). Lifestyle behavior included smoking (current smoker versus non-smoke) and alcohol use (daily drinker versus non-daily drinker). Depressive symptoms were assessed by the Geriatric Depression Scale (GDS) score ≥5. Mini-Mental State Examination (MMSE) was used to categorize participants as cognitive impaired (MMSE score <24). Metabolic syndrome was defined according to the International Diabetes Federation including central obesity, raised triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL-C), hypertension and diabetes [22]. Raised low-density lipoprotein cholesterol (LDL-C) was defined as ≥3.4mmol/l [23]. Medication therapies included statin therapy, antiplatelet therapy, anticoagulant therapy. Other blood biomarkers contained hemoglobin (g/L), albumin (g/L), creatinine (umol/L) and white blood cell (WBC) (x10^9/L).

Statistical analysis

The analyses used means (SD) for continuous variables and proportions (N) for categorical variables of frailty, frailty domains, and covariates at baseline in the overall sample, and compared CVD versus non-CVD outcomes using two-sample t-tests and chi-square tests for significance tests. Hierarchical adjusted Cox proportional hazard models were used to estimate hazard ratios (HR) and their 95% confidence intervals (95% CIs) between frailty status and overall incident CVD, and between frailty status and overall mortality. Competing-risks survival regression models were performed to estimate sub-distribution hazard ratios (SHR) and their 95% CI between frailty status and frailty domains and other CVD outcomes described above. HR of incident CVD for frail versus robust, and prefrail versus robust were estimated first in the unadjusted Cox proportional hazard model. Covariates were included in Models 1 to 5 in sequential hierarchical order. Model 1: additionally adjusted for age and sex; Model 2: additionally for socio-demographics (race, education, housing); Model 3: additionally for smoking, alcohol, central obesity, raised TG, reduced HDL-C, diabetes, hypertension, raised LDL-C, statin therapy, antiplatelet therapy, anticoagulant therapy; Model 4: additionally, for GDS depression and MMSE; Model 5: additionally for blood biomarkers. The “time to event” was defined by the length of time between baseline and the first recorded CVD event. Sensitivity analysis excluding CVD cases within 1 year after baseline was performed. A two-sided p value of 0.05 was considered as statistically significant. All analysis was performed using Stata 13.0 (Stata Corporation, College Station, TX, USA).

Results

The mean age of the overall sample was 65.8 (SD = 7.6); nearly two-thirds were female (65.2%); and the majority were Chinese (90.6%); 19% were without an education; and 13.4% were living in lower-end 1–2 room apartments. In all, 3.7% of the participants were frail and nearly half (46.2%) were pre-frail. The prevalence of frailty domains was 26.4% for “Low activity”, 18.1% for “Weakness”, 11.4% for “Exhaustion”, 8.6% for “Shrinking”, and 4.5% for “Slowness”.

As shown in Table 1, participants with CVD events compared to those without differed significantly on baseline characteristics of frailty and frailty-related risk factors, showing higher baseline frequencies of pre-frailty and frailty and frailty domains, indexes of socioeconomic deprivation and isolation, depression and cognitive impairment, as well as established cardio-metabolic, vascular and inflammatory risk factors or markers: diabetes, hypertension, dyslipidemia, metabolic syndrome, as well as low albumin, high creatinine and white cell count.

Table 1

Baseline characteristics in overall sample and by CVD and non-CVD outcomes.

Baseline characteristics	Whole sample (n = 5015)		CVD (n = 423)		Non-CVD (n = 4,592)		P value
	Mean	± SD	Mean	± SD	Mean	± SD
	N	%	N	%	N	%
Age	65.8	± 7.6	71.2	± 8.9	65.4	7.2	<0.001
Sex							<0.001
Male	1747	34.8	203	48.0	1544	33.6
Female	3268	65.2	220	52.0	3048	66.4
Race							<0.001
Chinese	4541	90.6	354	83.9	4187	91.2
Others	470	9.4	68	16.1	402	8.8
Education							<0.001
No education	953	19.0	137	32.4	816	17.8
Primary (1–6 years)	1916	38.3	154	36.4	1762	38.4
Post-primary (> 6 years)	2139	42.7	132	31.2	2007	43.8
Housing type							<0.001
1–2 room	669	13.4	96	22.7	573	12.5
3–5 room	3408	68.1	256	60.7	3152	68.8
High end public/private housing	924	18.5	70	16.6	854	18.6
Single, divorced or widowed	1519	30.3	170	40.2	1349	29.4	<0.001
Living alone	569	11.4	61	14.5	508	11.1	0.038
Current smoking	402	8.03	55	13.0	347	7.6	<0.001
Alcohol drinking	178	3.6	26	6.2	152	3.3	0.003
Frailty status							<0.001
Robust	2513	50.1	157	37.1	2356	51.3
Prefrail	2317	46.2	228	53.9	2089	45.5
Frail	185	3.7	38	9.0	147	3.2
Shrinking	430	8.6	52	12.3	378	8.2	0.004
Low activity	1322	26.4	142	33.6	1180	25.7	<0.001
Weakness	907	18.1	133	31.4	774	16.9	<0.001
Exhaustion	572	11.4	64	15.1	508	11.1	0.012
Slowness	223	4.5	44	10.4	179	3.9	<0.001
Depressed (GDS>5)	275	5.5	37	8.8	238	5.2	0.002
Cognitive impaired (MMSE<24)	480	9.6	104	24.6	376	8.2	<0.001
Metabolic syndrome	1281	25.5	125	29.5	1156	25.2	0.048
Central obesity	2519	50.3	210	49.6	2309	50.3	0.825
Raised TG (>1.7)	1348	26.9	140	33.1	1208	26.3	0.003
Reduced HDL-C	1179	23.5	112	26.5	1067	23.2	0.132
Raised LDL-C	2140	45.0	184	45.8	1956	45.0	0.752
Hypertension	3638	72.5	369	87.2	3269	71.2	<0.001
Diabetes	1441	28.7	169	39.9	1272	27.7	<0.001
Haemoglobin (g/dL)	13.4	± 1.4	13.4	± 1.6	13.4	± 1.4	0.589
Albumin (g/L)	42.3	± 2.9	41.5	± 3.2	42.3	± 2.8	<0.001
Creatinine (umol/L)	73.8	± 34.1	89.9	± 58.4	72.3	± 30.5	<0.001
White blood cell (x10^9/L)	6.0	± 1.6	6.3	± 1.8	6.0	± 1.6	<0.001
Statin therapy	1251	25.0	103	24.4	1148	25	0.767
Antiplatelet therapy	186	3.7	30	7.1	156	3.4	<0.001
Anticoagulant therapy	8	0.2	1	0.2	7	0.2	0.679

Abbreviations: CVD, cardiovascular disease; GDS, Geriatric Depression Scale; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MMSE, Mini-Mental State Examination; TG, triglycerides.

We observed 423 CVD events from a total of 51,135.2 person-years (p-y) of follow-up observation; overall CVD incidence rate (IR): 8.3 per 100,000 p-y. Among 423 CVD cases, 155 were non-fatal MI, 164 were non-fatal strokes and 104 were fatal CVD. The estimated risks of CVD events overall from follow up observation according to baseline categories of robust, pre-frail and frail participants are shown in Table 2.

Table 2

Follow-up incidence rate of CVD events by baseline frailty status.

	Incident event N	Person-years (p-y) of observation	Incidence /1,000 p-y	(95% CI)
Overall CVD
Robust	157	26336.2	6.0	(5.08, 6.95)
Prefrail	228	23262.7	9.8	(8.59, 11.14)
Frail	38	1520.7	25.0	(17.90, 33.90)
Non-fatal MI
Robust	61	25870.4	2.4	(1.82, 3.01)
Prefrail	84	22334.9	3.8	(3.02, 4.63)
Frail	10	1310.2	7.6	(3.88, 13.61)
Non-fatal stroke
Robust	69	25796.8	2.7	(2.10, 3.36)
Prefrail	85	22253.6	3.8	(3.07, 4.70)
Frail	10	1289.5	7.8	(3.94, 13.83)
Non-fatal CVD
Robust	130	25591.5	5.1	(4.26, 6.01)
Prefrail	169	21998.9	7.7	(6.59, 8.91)
Frail	20	1271.7	15.7	(9.87, 23.85)
Fatal CVD
Robust	27	26082.5	1.0	(0.69, 1.48)
Prefrail	59	22626.5	2.6	(2.00, 3.34)
Frail	18	1331.1	13.5	(8.27, 20.96)

Abbreviations: CVD, cardiovascular disease; MI, Myocardial Infarction; CI, confidence interval.

Overall CVD

Compared to robust individuals, pre-frail and frail individuals were more likely to show higher risks of overall CVD. Adjusted for age, sex, education and housing type, pre-frailty-associated HR = 1.26 (95% CI: 1.02–1.56), frailty-associated HR = 1.82, (95% CI: 1.24–2.66) (Table 3). Including additional model covariates of vascular and cardio-metabolic risk factors resulted in no substantial alteration in the HR estimates: pre-frailty-associated HR = 1.26 (95% CI: 1.01–1.57), frailty-associated HR = 1.54 (95% CI: 1.00–2.35).

Table 3

Associations between frailty status at baseline and incidence of CVD events.

	Unadjusted model		Model 1		Model 2		Model 3		Model 4		Model 5
	SHR (95%CI)	P value	SHR (95%CI)	P value	SHR (95%CI)	P value	SHR (95%CI)	P value	SHR (95%CI)	P value	SHR (95%CI)	P value
Overall CVD
Robust	1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)
Prefrail	1.65 (1.35–2.02)	<0.001	1.36 (1.11–1.68)	0.004	1.26 (1.02–1.56)	0.031	1.26 (1.01–1.57)	0.038	1.24 (0.99–1.55)	0.053	1.22 (0.98–1.53)	0.074
Frail	4.31 (3.02–6.15)	<0.001	2.02 (1.38–2.96)	<0.001	1.82 (1.24–2.66)	0.002	1.54 (1.00–2.35)	0.047	1.35 (0.87–2.09)	0.184	1.30 (0.84–2.03)	0.243
Non-fatal MI
Robust	1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)
Prefrail	1.57 (1.13–2.19)	0.007	1.37 (0.98–1.91)	0.069	1.20 (0.86–1.70)	0.286	1.23 (0.87–1.75)	0.245	1.19 (0.84–1.70)	0.334	1.16 (0.81–1.67)	0.407
Frail	2.97 (1.51–5.82)	0.002	1.52 (0.73–3.18)	0.263	1.21 (0.59–2.48)	0.594	0.91 (0.40–2.07)	0.816	0.81 (0.35–1.91)	0.636	0.77 (0.32–1.86)	0.568
Non-fatal stroke
Robust	1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)
Prefrail	1.47 (1.08–2.02)	0.016	1.28 (0.93–1.78)	0.135	1.27 (0.92–1.77)	0.147	1.23 (0.87–1.75)	0.243	1.25 (0.88–1.78)	0.213	1.25 (0.87–1.79)	0.224
Frail	3.08 (1.56–6.06)	0.001	1.55 (0.74–3.26)	0.247	1.48 (0.70–3.10)	0.304	1.50 (0.68–3.31)	0.313	1.34 (0.57–3.13)	0.497	1.43 (0.61–3.38)	0.414
Non-fatal CVD
Robust	1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)
Prefrail	1.50 (1.19–1.88)	<0.001	1.30 (1.03–1.64)	0.027	1.21 (0.96–1.54)	0.112	1.21 (0.94–1.54)	0.135	1.18 (0.92–1.52)	0.182	1.18 (0.92–1.52)	0.200
Frail	2.91 (1.80–4.70)	<0.001	1.49 (0.88–2.53)	0.135	1.32 (0.79–2.20)	0.292	1.11 (0.63–1.96)	0.721	0.94 (0.52–1.73)	0.851	0.93 (0.50–1.73)	0.825
Fatal CVD
Robust	1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)		1 (Reference)
Prefrail	2.53 (1.61–3.99)	<0.001	1.91 (1.21–3.04)	0.006	1.83 (1.16–2.89)	0.009	1.79 (1.12–2.88)	0.016	1.76 (1.09–2.84)	0.021	1.70 (1.05–2.77)	0.032
Frail	13.5 (7.43–24.4)	<0.001	4.50 (2.30–8.83)	<0.001	3.88 (2.00–7.50)	<0.001	3.05 (1.49–6.27)	0.002	2.88 (1.38–6.00)	0.005	2.48 (1.14–5.37)	0.021

Abbreviations: CVD, cardiovascular disease; MI, Myocardial Infarction; CI, confidence interval.

Model 1: Adjusted for age, sex.

Model 2: Adjusted for Model 1 plus ethnicity, education, housing.

Model 3: Adjusted for Model 2 plus smoking, alcohol, central obesity, raised triglycerides, reduced high-density lipoprotein cholesterol, hypertension, diabetes, raised low-density lipoprotein cholesterol, statin therapy, antiplatelet therapy, anticoagulant therapy.

Model 4: Adjusted for Model 3 plus depression by Geriatric Depression Scale, cognitive impairment by Mini-Mental State Examination.

Model 5: Adjusted for Model 4 plus blood biomarkers (albumin, haemoglobin, white blood cell, creatinine).

Additional covariates of MMSE and GDS depression resulted in non-significant estimates of pre-frailty associated HR = 1.24 (95% CI: 0.99–1.55), frailty-associated HR = 1.35 (95% CI: 0.87–2.09). Additional covariates of blood biomarkers (albumin, creatinine, WBC, haemoglobin) resulted in further reduced and non-significant estimates of pre-frailty-associated HR = 1.22 (95% CI: 0.98–1.53), frailty-associated HR = 1.30, (95% CI: 0.84–2.03).

Fatal CVD events

Consistent and robust estimates of association in all models were observed for fatal CVD. In the full model with all covariates (Table 3, Model 5), significant estimates remained: SHR = 1.70 (95% CI: 1.05–2.77) in prefrail group and SHR = 2.48 (95% CI: 1.14–5.37) in frail group.

Non-fatal CVD (including acute MI and stroke) rates were higher in pre-frail and frail individuals, based on small sample sizes, and the covariate-adjusted SHR were not statistically significant in Model 2, and not shown for additional covariate adjustments.

For individual components of frailty, significant associations in Model 4 (Table 4) were seen for weakness (HR = 1.36, 95% CI: 1.07–1.72), and shrinking (HR = 1.39, 95%CI: 1.01–1.89) with overall CVD events. Weakness, shrinking, and exhaustion showed significant associations with fatal CVD. There were no significant associations among non-fatal CVD outcomes.

Table 4

Association between frailty components at baseline and incidence of CVD events in the follow-up.

Frailty components	HR (95% CI)	P value
Overall CVD
Shrinking	1.39 (1.01–1.89)	0.041
Low activity	1.02 (0.82–1.27)	0.848
Weakness	1.36 (1.07–1.72)	0.011
Exhaustion	1.12 (0.83–1.50)	0.467
Slowness	1.01 (0.69–1.48)	0.968
Fatal CVD
Shrinking	1.98 (1.12–3.49)	0.018
Low activity	1.02 (0.64–1.62)	0.939
Weakness	1.79 (1.13–2.84)	0.013
Exhaustion	2.32 (1.44–3.74)	0.001
Slowness	1.54 (0.82–2.90)	0.182

Abbreviations: CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.

HR adjusted for age, sex, ethnicity, education, housing, smoking, alcohol, central obesity, raised triglycerides, reduced high-density lipoprotein cholesterol, hypertension, diabetes, raised low-density lipoprotein cholesterol, statin therapy, antiplatelet therapy, anticoagulant therapy, depression by Geriatric Depression Scale, cognitive impairment by Mini-Mental State Examination.

There was a total of 692 all-cause deaths over 50040.1 patient-years at risk, including 228 robust, 387 prefrail, and 77 frail participants. Compared with robust participants, prefrail and frail participants were both associated with higher risk of all-cause mortality. In the unadjusted model, increased risk for all-cause mortality was observed in both prefrail (vs robust, HR = 1.99, 95% CI:1.69–2.35, p<0.001) and frail (vs robust, HR = 7.49, 95% CI:5.78–9.72, p < 0.001). This significant association was consistent across all models with pre-frailty-associated HR = 1.40 (95% CI: 1.17–1.67, p<0.001), frailty-associated HR = 2.03 (95% CI:1.48–2.80, p<0.001) in Model 5 when all the covariate adjusted. All five frailty components except low activity showed significant associations with overall mortality: shrinking (HR = 1.51, 95% CI:1.19–1.91, p = 0.001), weakness (HR = 1.62, 95% CI:1.35–1.94, p<0.001), exhaustion (HR = 1.29, 95% CI:1.03–1.61, p = 0.028), slowness (HR = 1.53, 95% CI:1.16–2.02, p = 0.003).

In further sensitivity analyses, we excluded CVD cases within 1 year after baseline and found similar results.

Discussion

Our study, in agreement with previous studies showed that pre-frailty and frailty were associated with increased risks of overall CVD events [24], and frailty status was a significant predictor of all-cause mortality [4, 25]. However, previous studies have not reported the separate risks of non-fatal and fatal CVD events and did not control for the effects of depression. We observed in this study that pre-frailty and frailty were significantly associated with 1.3 and 1.7-fold increased risk of CVD overall, adjusted for sociodemographic, behavioral and cardio-metabolic and vascular risk factors, but not with subsequent adjustment for depression and cognitive impairment and blood biomarkers. However, pre-frailty and frailty were robustly associated respectively with 1.6-fold and 2.6-fold increased risk of fatal CVD in the fully adjusted model, whereas no significant associations were found for risk of non-fatal CVD events (acute MI or stroke).

Our study may provide clues to the mechanistic and developmental relationship by showing significant findings in the stepwise analysis after adjustment of traditional cardio-metabolic and vascular risk factors, medication therapies, depression, cognitive factors, and biomarkers. The results suggest that frailty clearly has a powerful influence in increasing the risk of dying from cardiovascular disease. Its significant HR after adjustment for cardio-metabolic and vascular risk factors was attenuated after adjustment for depression, cognitive impairment, and surrogate blood markers of chronic inflammation. This suggests that comorbid depression, and associated cognitive impairment and chronic inflammation, contributes to the increased CVD mortality risk among pre-frail and frail individuals.

We found that frailty was associated with a moderate (less than 50%) increased risk of non-fatal CVD incidence after adjustment for traditional CVD risk factors. This suggests that the role of frailty per se in promoting the development and clinical onset of cardiovascular disease is relatively subtle. Previous research showed that the presence of frailty among MI patients was significantly associated with increased CVD mortality [26, 27]. The population study by Veronese et al. 2017 [14] controlled for the presence of carotid intima media thickness, presence of carotid plaque and total coronary calcifications, and found that frailty (HR = 1.35; 95% CI: 1.05–1.74) remained significantly associated with CVD events overall, indicating that in the presence of subclinical atherosclerotic disease, it is an independent CVD risk factor. More prospective studies are needed to elucidate the longitudinal relationships between frailty measures and preclinical cardiovascular disease.

It thus appears that the frailty syndrome has a complex mechanistic link with the development of incipient CVD and with its final progression to fatal outcome. It is possible that frailty precipitates clinically overt CVD and/or accelerates disease progression from baseline subclinical atherosclerotic disease. The metabolic syndrome cluster of cardio-metabolic risk factors is well known to predict higher CVD [28] and stroke [29] risks, and has also been found to be associated with increased risk of incident frailty [30-32]. Independent inverse associations between subclincial measures of arterial disease with muscle mass and functional decline have also been reported in some studies [33, 34], but not in others [35].

Among component measures of frailty in this study, weakness showed significant association with the increased risk of overall CVD, fatal CVD, as well as all-cause mortality, which was in line with previously studies [24, 36–38]. As weakness was assessed by knee extension strength or POMA battery, which are both objective measurements for muscle strength, its strong predicting value for higher risk of CVD in our study suggested that preventions on muscle strength decline may potentially reduce the risk of CVD and mortality for older adults. Consistent with other studies [38, 39], slowness also presented higher risk of all-cause mortality in our study. However, we failed to find significant association between slowness and risk of CVD after adjusting for traditional CVD risk factors and medication therapies. Although study conducted by Veronese et al. [14] showed similar findings, some other studies [9, 13] concluded slow gait speed was a significant predictor for CVD. This inconsistency may be due to the different measurements and cutoffs for slowness definitions.

In this follow-up population without overt CVD at baseline, the prevalence of frailty (46%) and frailty (3.7%) is very high, but this is not exceptional, as it has been reported in many studies worldwide. Pre-frailty is a transitional precursor state of frailty, and both are reversible by multi-domain lifestyle and health interventions (nutritional, physical, cognitive interventions, polypharmacy de-prescription, vitamin D supplementation) [17, 18]. Further interventional studies should be conducted to evaluate the potential benefits of pre-frailty and frailty interventions to reduce the risk of CVD and mortality risks.

In this large prospective cohort study of community-dwelling middle-aged and older adults in an Asian population, case ascertainment of CVD events using computerized record linkage with the national registry of disease was accurate and complete. The sample was however still underpowered to detect significant associations for non-fatal CVD and especially stroke. A limitation is that non-fatal CVD included only acute MI and stroke and deaths from CVD included heart failure, but non-fatal cases of heart failure from hospitalization records were not ascertained. Another limitation is that low haemoglobin, low albumin, and white blood cell counts are non-specific indirect measures of inflammation, more specific established markers such as IL6 or TNF-alpha were not employed. Additionally, due to the small case number of fatal stroke and fatal MI, we were unable to further explore the relationship between frailty and the risk of fatal stroke and/or fatal MI specifically.

Conclusions

We demonstrated that pre-frailty and frailty were significantly associated with increased risks of incident CVD, and fatal CVD in particular. Given that they are reversible by early intervention, there are potential benefits in reducing CVD burden and mortality from interventions targeting pre-frailty and early frailty that should be further investigated in future clinical studies.

30 Dec 2021

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Reviewer #1: The authors examined the relationship between frailty and CVD events in prospective study. It is interesting topics and outcomes.

I have several comments.

It is interesting that frailty is associated with the occurrence of fatal CVD events. Which of the MI or stroke leads to fatal conditions? MI? Stroke? or both? Please add the results of examination the relationship between (1) frailty and fatal MI, and (2) frailty and fatal stroke.

Did the medication therapy such as antiplatelet therapy, anticoagulant therapy, and statin therapy influence the occurrence of CVD events?

I think that cognitive impairment is one of the important factor associated with frailty. Did the cognitive impairment or depression affect the CVD events including non-fatal MI, non-fatal stroke, fatal MI, fatal stroke, and fatal CVD?

I’m interested in the relationship between frailty and overall survival in your study population. Did the prefrail and frail affect overall mortality?

Reviewer #2: The authors have reported an association of pre-frailty and frailty with the risk of developing cardiovascular disease (CVD) morbidity and mortality over 10 years in a prospective cohort study of community-dwelling older adults in an Asian population. Although it is interesting, the present paper has several issues to be resolved as below.

1. What was the breakdown of cardiovascular diseases that were defined in this study? In particular, that of non-fatal cardiovascular diseases are unclear.

2. In Table 1, why raised or reduced low-density lipoprotein (LDL-C) was not shown? Isn’t the serum LDL-C a risk factor of CVD? Should several statistical analyses be adjusted for the serum levels of LDL-C? Moreover, the present study has lacked the statistical analysis adjusted for the medication history, especially for statin, antihypertensive and diabetes drugs.

3. Regarding the study subjects of 5,015, please show detail data of the follow-up periods of them.

4. In DISCUSSION, the authors have described as below: “Our study sheds light on the mechanistic and developmental relationship between… (page 16, line 1)”. However, the present study has only shown a relationship between pre-frailty/frailty and CVD outcomes. In addition, the frailty measurements to assess pre-frailty/frailty comprehensively was actually composed of unquantifiable measurement items of “Exhaustion” and “Low activity”. Isn’t the above description by the authors overestimated?

**********

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15 Jun 2022

Dear Reviewers,

Thank you very much for giving us the opportunity to submit a revised draft of our manuscript. We greatly appreciate the time and effort that you have dedicated to providing your valuable feedback and insightful comments on our manuscript. To address all the concerns raised, we have merged new variables in our dataset, redone our analysis, and revised our manuscript accordingly. We’ve tracked the changes in the manuscript.

Here is our point-by-point response to your comments and concerns. All the line numbers are based on “All Markup” viewing version.

Comments from Reviewer 1

Reviewer #1: The authors examined the relationship between frailty and CVD events in prospective study. It is interesting topics and outcomes. I have several comments.

1. It is interesting that frailty is associated with the occurrence of fatal CVD events. Which of the MI or stroke leads to fatal conditions? MI? Stroke? or both? Please add the results of examination the relationship between (1) frailty and fatal MI, and (2) frailty and fatal stroke.

Reply:

Thank you for pointing out that it would be interesting to explore the relationship specifically between frailty and fatal MI and/or stroke. Unfortunately, the number of fatal MI/stroke cases in our dataset was too small to perform the analysis: total number of fatal MI was 32 with only 1 frail case; and total number of fatal strokes was 10, all of which were prefrail cases. In this case, we added one more limitation in our discussion in Line 790-792:

“Additionally, due to the small case number of fatal stroke and fatal MI, we were unable to further explore the relationship between frailty and the risk of fatal stroke and/or fatal MI specifically.”

2. Did the medication therapy such as antiplatelet therapy, anticoagulant therapy, and statin therapy influence the occurrence of CVD events?

Reply:

Yes, we agree with this and we performed reanalysis by including antiplatelet therapy, anticoagulant therapy, and statin therapy into our dataset. Those covariates were added in model 3, model 4, and model 5. Our main findings remained the same after the re-analysis.

The method section had been revised accordingly in Line 189-190:

“Medication therapies included statin therapy, antiplatelet therapy, anticoagulant therapy.”

And Line 206-208:

“Model 3: additionally for smoking, alcohol, central obesity, raised TG, reduced HDL-C, diabetes, hypertension, raised LDL-C, statin therapy, antiplatelet therapy, anticoagulant therapy; ”

All the relevant results had been updated for Table 1, Table 3, Table 4, Abstract, and Results section.

3. I think that cognitive impairment is one of the important factor associated with frailty. Did the cognitive impairment or depression affect the CVD events including non-fatal MI, non-fatal stroke, fatal MI, fatal stroke, and fatal CVD?

Reply:

In our first manuscript version, we had already adjusted depression by Geriatric Depression Scale, and cognitive impairment by Mini-Mental State Examination in Model 4 (Table3) which showed significant results for fatal CVD but not non-fatal cases. Now in this updated version, after adding the new covariates mentioned above, the results remained similar (Table 3 Model 4).

4. I’m interested in the relationship between frailty and overall survival in your study population. Did the prefrail and frail affect overall mortality?

Reply:

Thank you for this suggestion. In our study, both prefrail and frail are significantly associated with higher risk of overall mortality.

We have included the method/analysis section about overall mortality in Line 197-199:

“Hierarchical adjusted Cox proportional hazard models were used to estimate hazard ratios (HR) and their 95% confidence intervals (95% CIs) between frailty status and overall incident CVD, and between frailty status and overall mortality.”

We added the results in the manuscript Line 626-636:

“There was a total of 692 all-cause deaths over 50040.1 patient-years at risk, including 228 robust, 387 prefrail, and 77 frail participants. Compared with robust participants, prefrail and frail participants were both associated with higher risk of all-cause mortality. In the unadjusted model, increased risk for all-cause mortality was observed in both prefrail (vs robust, HR = 1.99, 95% CI:1.69-2.35, p<0.001) and frail (vs robust, HR = 7.49, 95% CI:5.78-9.72, p < 0.001). This significant association was consistent across all models with pre-frailty-associated HR=1.40 (95% CI: 1.17-1.67, p<0.001), frailty-associated HR=2.03 (95% CI:1.48-2.80, p<0.001) in Model 5 when all the covariate adjusted. All five frailty components except low activity showed significant associations with overall mortality: shrinking (HR = 1.51, 95% CI:1.19-1.91, p=0.001), weakness (HR = 1.62, 95% CI:1.35-1.94, p<0.001), exhaustion (HR = 1.29, 95% CI:1.03-1.61, p=0.028), slowness (HR = 1.53, 95% CI:1.16-2.02, p=0.003).”

We added relevant discussion in Line 703-705:

“Our study, in agreement with previous studies showed that pre-frailty and frailty were associated with increased risks of overall CVD events[24], and frailty status was a significant predictor of all-cause mortality[4, 25]”

Comments from Reviewer 2

Reviewer #2: The authors have reported an association of pre-frailty and frailty with the risk of developing cardiovascular disease (CVD) morbidity and mortality over 10 years in a prospective cohort study of community-dwelling older adults in an Asian population. Although it is interesting, the present paper has several issues to be resolved as below.

1. What was the breakdown of cardiovascular diseases that were defined in this study? In particular, that of non-fatal cardiovascular diseases are unclear.

Reply:

Thank you for pointing this out. Non-fatal CVD cases included both non-fatal MI and non-fatal stroke which obtained from Singapore government registry data.

We had revised our method part to clarify the relevant definitions in Line 133-141:

“All-cause mortality and fatal CVD cases were obtained from the Death Registry data from Singapore National Registry of Diseases Office based on International Classification of Diseases (ICD). Fatal CVDs were identified using ICD 9 codes from 390 to 459 or ICD 10 codes from I00 to I99. Other CVD outcomes included 1) non-fatal MI, obtained from Singapore Myocardial Infarction Registry; 2) non-fatal stroke, obtained from Singapore Stroke Registry; 3) non-fatal CVD, defined as an inclusion of non-fatal MI and non-fatal stroke. Overall CVD included both fatal CVD and non-fatal CVD. Overall mortality includes all-cause of death cases.”

2. In Table 1, why raised or reduced low-density lipoprotein (LDL-C) was not shown? Isn’t the serum LDL-C a risk factor of CVD? Should several statistical analyses be adjusted for the serum levels of LDL-C?

Moreover, the present study has lacked the statistical analysis adjusted for the medication history, especially for statin, antihypertensive and diabetes drugs.

Reply:

Thank you for this suggestion. We agree that LDL-C and medical history should be adjusted in our analysis. We had redone the analysis by adding LDL-C and medication therapy (antiplatelet therapy, anticoagulant therapy, and statin therapy) in Model 3, Model 4, and Model 5. Our main findings remained the same after the re-analysis.

The method section had been revised accordingly in Line 188-190

“Raised low-density lipoprotein cholesterol (LDL-C) was defined as ≥3.4mmol/l[23]. Medication therapies included statin therapy, antiplatelet therapy, anticoagulant therapy.”

And Line 206-208:

“Model 3: additionally for smoking, alcohol, central obesity, raised TG, reduced HDL-C, diabetes, hypertension, raised LDL-C, statin therapy, antiplatelet therapy, anticoagulant therapy;”

All the relevant results had been updated for Table 1, Table 3, Table 4, Abstract, and Results section.

3. Regarding the study subjects of 5,015, please show detail data of the follow-up periods of them.

Reply:

We clarified the follow-up period in Line 139-141.

“The follow-up time for this study started at the date of participants enrolment and ended in December 2017 for all the outcomes.”

Participants’ enrollment time can be found in Line 118-121:

“SLAS-1 recruited 2,800 older persons in the South-East Region in 2003-2004, and SLAS-2 recruited 3,200 individuals in the South Central and Western Region in Singapore in 2009-2013, each with 3 to 5 yearly follow-ups.”

4. In DISCUSSION, the authors have described as below: “Our study sheds light on the mechanistic and developmental relationship between… (page 16, line 1)”. However, the present study has only shown a relationship between pre-frailty/frailty and CVD outcomes.

Reply:

Thank you for this comment. Yes, we investigated the relationship between frailty status and CVD but the results of stepwise analysis provided some clues on the mechanistic behind this relationship.

To make it clear, we had revised the manuscript in Line 714-716:

“Our study provides clues to the mechanistic and developmental relationship by showing significant findings in the stepwise analysis after adjustment of traditional cardio-metabolic and vascular risk factors, medication therapies, depression, cognitive factors, and biomarkers.”

In addition, the frailty measurements to assess pre-frailty/frailty comprehensively was actually composed of unquantifiable measurement items of “Exhaustion” and “Low activity”. Isn’t the above description by the authors overestimated?

Reply:

We acknowledge that “exhaustion” and “low activity” were self-reported measurement that may not be as accurate as other quantifiable objective measurements. Both measurements were integral components of the well-established Fried phenotype and defined accordingly. In this regard, we revise our manuscript by focusing our discussion on those objective measurements in Line 749-769.

“Among component measures of frailty in this study, weakness showed significant association with the increased risk of overall CVD, fatal CVD, as well as all-cause mortality, which was in line with previously studies[24, 36-38]. As weakness was assessed by knee extension strength or POMA battery, which are both objective measurements for muscle strength, its strong predicting value for higher risk of CVD in our study suggested that preventions on muscle strength decline may potentially reduce the risk of CVD and mortality for older adults. Consistent with other studies[38, 39], slowness also presented higher risk of all-cause mortality in our study. However, we failed to find significant association between slowness and risk of CVD after adjusting for traditional CVD risk factors and medication therapies. Although a study conducted by Veronese et al[14] showed similar findings, some other studies[9, 13] concluded slow gait speed was a significant predictor for CVD. This inconsistency may be due to the different measurements and cutoffs for slowness definitions.”

Sincerely,

Authors from manuscript [PONE-D-21-35749]

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

4 Jul 2022

20 Jul 2022

Dear Reviewers,

Thank you very much for taking your time to review our manuscript and for providing us with valuable comments. Please see our reply below:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reply to Reviewer #2:

Thank you very much for all your comments. We’ve revised the manuscript according to your comments.

2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

Reply to Reviewer #2:

We acknowledge that our manuscript may not be technically perfect. We have made every effort to minimize confounding bias in the data analysis. Our re-analysis of the data involves additional adjustment for potential confounding variables to make the interpretation of the results and conclusion reasonably sound.

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

Reviewer #1: Yes

Reviewer #2: N/A

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all the comments from the previous review.

I vote to accept this manuscript as authors have made significant changes to the questions posed to them.

Reviewer #2: Thank you for replies to my previous comments. Almost of those have been appropriately addressed by the authors; however, there is an issue to be revised in the text.

Although the authors have described in the term of DISCUSSION as follows “Our study provides clues to the mechanistic and development relationship by showing....”, it’s better to revise as follows “Our study may provide clues to the mechanistic and development relationship by showing....”.

Reply to Reviewer #2:

Thank you very much for this suggestion. We’ve revised the manuscript accordingly on Page 17:

“Our study may provide clues to the mechanistic and development relationship by showing....”

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Submitted filename: Responce to Reviewers.docx

Click here for additional data file.

21 Jul 2022

Frailty and Risk of Cardiovascular Disease and Mortality

PONE-D-21-35749R2

Dear Dr. Ng,

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9 Sep 2022

PONE-D-21-35749R2

Frailty and Risk of Cardiovascular Disease and Mortality

Dear Dr. Ng:

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