Frailty does not cause all frail symptoms: United States Health and Retirement Study

Yi-Sheng Chao; Chao-Jung Wu; June Y T Po; Shih-Yu Huang; Hsing-Chien Wu; Hui-Ting Hsu; Yen-Po Cheng; Yi-Chun Lai; Wei-Chih Chen

doi:10.1371/journal.pone.0272289

. 2022 Nov 2;17(11):e0272289. doi: 10.1371/journal.pone.0272289

Frailty does not cause all frail symptoms: United States Health and Retirement Study

Yi-Sheng Chao ^1,^*, Chao-Jung Wu ², June Y T Po ³, Shih-Yu Huang ^4,⁵, Hsing-Chien Wu ⁶, Hui-Ting Hsu ⁷, Yen-Po Cheng ⁷, Yi-Chun Lai ⁸, Wei-Chih Chen ^9,^*

Editor: George Vousden¹⁰

PMCID: PMC9629634 PMID: 36322566

Abstract

Background

Frailty is associated with major health outcomes. However, the relationships between frailty and frailty symptoms haven’t been well studied. This study aims to show the associations between frailty and frailty symptoms.

Methods

The Health and Retirement Study (HRS) is an ongoing longitudinal biannual survey in the United States. Three of the most used frailty diagnoses, defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were reproduced according to previous studies. The associations between frailty statuses and input symptoms were assessed using odds ratios and correlation coefficients.

Results

The sample sizes, mean ages, and frailty prevalence matched those reported in previous studies. Frailty statuses were weakly correlated with each other (coefficients = 0.19 to 0.38, p < 0.001 for all). There were 49 input symptoms identified by these three models. Frailty statuses defined by the three models were not significantly correlated with one or two symptoms defined by the same models (p > 0.05 for all). One to six symptoms defined by the other two models were not significantly correlated with each of the three frailty statuses (p > 0.05 for all). Frailty statuses were significantly correlated with their own bias variables (p < 0.05 for all).

Conclusion

Frailty diagnoses lack significant correlations with some of their own frailty symptoms and some of the frailty symptoms defined by the other two models. This finding raises questions like whether the frailty symptoms lacking significant correlations with frailty statuses could be included to diagnose frailty and whether frailty exists and causes frailty symptoms.

Introduction

Frailty is a syndrome and can be diagnosed with composite criteria that consist of various frailty symptoms [1–3]. Frailty is often characterized by age-related symptoms, such as declines in physical and cognitive functioning. It has been considered significant for the prediction of major health outcomes, such as falls, surgical outcomes, and mortality [2, 3]. By aggregating information from multiple symptoms, frailty index scores can be assigned to individuals [2, 3]. Frailty status can then be derived by applying theoretical thresholds to frailty index scores [2, 3]. Three of the most commonly used frailty indices require 4 to 70 input domains or symptoms for the diagnosis of frailty [2].

Ideally, pathological changes or underlying health conditions are expected to cause or lead to significant increases in symptom occurrence. The increased frequency of symptom development can be used to make diagnoses that serve as proxy measures to the pathological changes{please add new reference: DOI:10.1038/s41598-022-14826-2}. For example, frailty has been recognized as a cause of disability, independent of clinical conditions [4]. Other researchers also found that frailty can lead to symptoms, particularly mental symptoms [5, 6] and fatigue [7]. Frailty has been confirmed using multiple frailty symptoms and considered a diagnosis that represent low physical reserve [8]. However, the effects of frailty on the development of frailty symptoms (those used to diagnose frailty) have not been well discussed. Instead, frailty has been described and defined differently [1]. Some studies have shown that how frailty is diagnosed seems far from ideal and lacks pathological confirmation [2]. Additionally, researchers have confirmed notable differences in the frail patients identified between the three frailty models [1].

In other words, whether frailty causes frailty symptoms remains unclear and whether frailty should cause frailty symptoms has not been explicitly declared in various, often conflicting, theories of frailty [2]. Causal relationships can be established through different approaches [9], such as using the Bradford-Hill Criteria [10]. Among all requirements in the Bradford-Hill Criteria, the strengths of associations between frailty and frailty symptoms are important and can be used to assess the impact of frailty prevention programs on frailty treatment and to understand the mechanisms that cause frailty. For example, it has been suggested that cognitive impairment plays an important role for frailty diagnosis and mortality among frail patients [3]. The confirmation of the causal relationship between frailty and cognitive function has the potential for intervention development. Without extensive reviews of the relationships between frailty and its symptoms, how frailty may influence frailty symptoms is an important question that is left unanswered. This study aims to assess the effect of frailty on the occurrence of frailty symptoms using a cohort that have been used to compare three of the most used frailty indices.

Methods

The Health and Retirement Study (HRS) follows Americans aged 50 years and over every 2 years [1, 2, 11, 12]. The 2004 wave HRS data were used to compare frailty indices defined by 3 models: the Functional Domains Model by Strawbridge et al. [13], the Burden Model by Rockwood et al. [14, 15], and the Biologic Syndrome Model by Fried et al. [4]. Frailty symptoms or input variables were used to defined various domains defined by the 3 models [2, 3, 16]. When individuals presented enough numbers of frailty symptoms in a domain, these individuals might be considered to have a deficit in this domain for the Functional Domain Model and the Biologic Syndrome Model [2]. For example, the weight loss domain in the Biologic Syndrome Model asked individuals whether they had body mass index (BMI) less than 18.5 kg/m² or whether they lost weight for 10% or more, compared to two years ago [1]. This domain required information on weights, heights, BMI, and weights two years ago [1].

In the Burden Model, one symptom represented a single domain and the presence of a symptom suggested the occurrence of a deficit [1, 2]. The frailty indices were the numbers of deficits identified using 4, 70, and 5 domains defined by the 3 frailty models, respectively [1]. Frailty statuses could be diagnosed when individuals had 2, 18 (70 times 0.25), or 3 deficits according to the 3 models, respectively [2]. The details of the frailty symptoms and input variables were published elsewhere (https://doi.org/10.1371/journal.pone.0197859.s002) [2]. The names and definitions of the input variables, frailty symptoms, and domains are listed in Tables 1–3. There were 10, 26, and 14 variables (frailty symptoms, input variables, or domains) identified for the 3 models, respectively. In total, there were 57 variables required to produce the frailty indices defined by the 3 models. In addition, there were 4 bias variables induced by the 4 domains in the Functional Domains Model (Table 1), 1 bias variable induced by the Burden Model (Table 2), and 4 bias variables induced by the Biologic Syndrome Model (Table 3) [2].

Table 1. Frailty status defined by the Functional Domains Model and its associations with frailty symptoms.

HRS variables	Definitions	N with symptoms for binomial variables	Odds ratios (95% CIs	Mean (SD)	Correlation coefficients (95% CIs)
r7agey_b	Age at interview (years)			73.92 (7.59)	0.26 (0.24 to 0.27)^***
r7bmi	Self-reported body mass index = kg/m2			26.68 (5.31)	-0.02 (-0.04 to 0)
r7cogtot	Total cognition summary score			20.53 (6.08)	-0.41 (-0.42 to -0.39)^***
r7dizz	Physical functioning: Dizziness as persistent problem	1577	7.9 (7.03 to 8.87)^***	0.14 (0.35)	0.36 (0.35 to 0.38)^***
r7eye	Sensory problems: Fair or poor eyesight despite use of corrective lenses			2.95 (1.01)	0.44 (0.42 to 0.45)^***
r7fall	fallen down last 2 years			0.9 (2.88)	0.29 (0.27 to 0.31)^***
r7frailim1	Frailty index: Functional Domains Model			0.98 (0.93)	0.85 (0.84 to 0.85)^***
r7frailim1cat	Frailty status: Functional Domains Model (outcome of this table)	3059	Not applicable	0.28 (0.45)	1 (1 to 1)^***
r7hear	Sensory problems: fair or poor hearing despite use of hearing aides			2.87 (1.12)	0.37 (0.35 to 0.39)^***
r7lift	some difficulty in lift/carry 10lbs	3631	8.85 (8.05 to 9.72)^***	0.33 (0.47)	0.46 (0.45 to 0.48)^***
r7memopr	Proxy memory rating			1.39 (1.18)	0.33 (0.31 to 0.35)^***
r7wchange	Weight in wave 2002 minus weight in wave 2004 (%)			0.01 (0.08)	-0.04 (-0.05 to -0.02)^***
ragender	Male = 0; female = 1	6385	1.32 (1.21 to 1.44)^***	0.57 (0.49)	0.06 (0.04 to 0.08)^***
Domains and other frailty symptoms identified by the other 2 models
r7actsum	Summary scores of physical activities			33.45 (9.11)	0.31 (0.29 to 0.33)^***
r7arthrcat	Binomial: Arthritis	7693	1.84 (1.67 to 2.03)^***	0.69 (0.46)	0.12 (0.1 to 0.14)^***
r7bathcat	Binomial: Problems with bathing	1264	8.28 (7.27 to 9.42)^***	0.11 (0.32)	0.34 (0.32 to 0.36)^***
r7cancrcat	Binomial: Malignant disease	1978	1.03 (0.92 to 1.15)	0.18 (0.38)	0.01 (-0.01 to 0.02)
r7cogimpair	Impaired cognition based on performance-based scores or proxy assessment	999	23.09 (19.14 to 27.85)^***	0.09 (0.29)	0.41 (0.4 to 0.43)^***
r7deprescat	Binomial: Feeling sad, blue, depressed	2011	3.54 (3.2 to 3.91)^***	0.18 (0.39)	0.24 (0.22 to 0.26)^***
r7diabscat	Binomial: History of diabetes mellitus	292	1.38 (1.08 to 1.77)*	0.03 (0.16)	0.02 (0.01 to 0.04)^**
r7dresscat	Binomial: problem getting dressed	1419	6.01 (5.34 to 6.76)^***	0.13 (0.33)	0.31 (0.29 to 0.32)^***
r7effort	everything an effort	2932	3.96 (3.62 to 4.34)^***	0.26 (0.44)	0.29 (0.27 to 0.31)^***
r7fall_cat1	More than 1 falls	3640	3.6 (3.3 to 3.93)^***	0.33 (0.47)	0.28 (0.26 to 0.3)^***
r7fall_cat2	More than 2 falls	1929	6.71 (6.04 to 7.46)^***	0.17 (0.38)	0.36 (0.35 to 0.38)^***
r7frail1_1	Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds	4383	27.81 (24.63 to 31.42)^***	0.39 (0.49)	0.61 (0.6 to 0.62)^***
r7frail1_2	Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2	866	9.35 (7.97 to 10.96)^***	0.08 (0.27)	0.3 (0.29 to 0.32)^***
r7frail1_3	Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating			0.09 (0.29)	0.42 (0.4 to 0.43)^***
r7frail1_4	Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides			0.42 (0.49)	0.59 (0.58 to 0.6)^***
r7frail3_2	Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.	4025	3.99 (3.66 to 4.35)^***	0.36 (0.48)	0.3 (0.29 to 0.32)^***
r7frail3_3	Frequency of three intensities of activity, lowest quintile (stratified according to sex)	2872	4.11 (3.75 to 4.51)^***	0.26 (0.44)	0.3 (0.28 to 0.32)^***
r7frail3_4	Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same	5474	1.44 (1.33 to 1.57)^***	0.49 (0.5)	0.08 (0.06 to 0.1)^***
r7frail3_5	Grip strength: Weakest 20% (stratified according to sex and BMI)	2529	1.43 (1.3 to 1.57)^***	0.23 (0.42)	0.07 (0.05 to 0.09)^***
r7frailim1	Frailty index: Functional Domains Model			0.98 (0.93)	0.85 (0.84 to 0.85)^***
r7frailim2	Frailty index: Burden Model			5.02 (2.83)	0.46 (0.44 to 0.47)^***
r7frailim2cat	Frailty status: Burden Model			0.45 (0.5)	0.38 (0.36 to 0.4)^***
r7frailim3	Frailty index: Biologic Syndrome Model			1.14 (1.08)	0.35 (0.3 to 0.39)^***
r7frailim3cat	Frailty status: Biologic Syndrome Model			0.12 (0.33)	0.3 (0.26 to 0.34)^***
r7going	Could not get going	2682	3.41 (3.11 to 3.73)^***	0.24 (0.43)	0.25 (0.24 to 0.27)^***
r7grip	Grip strength, largest value			30.19 (16.32)	-0.07 (-0.09 to -0.06)^***
r7gripl	Grip strength, left hand			26.63 (16.29)	-0.07 (-0.09 to -0.05)^***
r7gripr	Grip strength, right hand			28.74 (16.14)	-0.1 (-0.12 to -0.08)^***
r7headac	Headache	817	3.05 (2.64 to 3.53)^***	0.07 (0.26)	0.15 (0.13 to 0.17)^***
r7heartcat	Binomial: Cardiac problems	3631	2.15 (1.97 to 2.35)^***	0.33 (0.47)	0.17 (0.15 to 0.18)^***
r7height	Self-reported height in meters			1.68 (0.1)	-0.09 (-0.11 to -0.07)^***
r7hibp	had high blood pressure since last interview	6791	1.49 (1.36 to 1.62)^***	0.61 (0.49)	0.08 (0.07 to 0.1)^***
r7ltactx	Frequency of light physical activity			2.87 (1.2)	0.29 (0.28 to 0.31)^***
r7lungcat	Binomial: Lung problems	1345	2.02 (1.79 to 2.27)^***	0.12 (0.33)	0.11 (0.09 to 0.13)^***
r7mdactx	Frequency of moderate physical activity			3.13 (1.37)	0.29 (0.27 to 0.31)^***
r7memryscat	Binomial: Memory changes	345	7.46 (5.86 to 9.48)^***	0.03 (0.17)	0.18 (0.16 to 0.2)^***
r7mobila	Some difficulty in mobility /05			1.36 (1.59)	0.43 (0.42 to 0.45)^***
r7muscle	Musculoskeletal problems	386	1.3 (1.05 to 1.62)^*	0.03 (0.18)	0.02 (0 to 0.04)^*
r7psychcat	Binomial: Depression	1799	2.82 (2.55 to 3.13)^***	0.16 (0.37)	0.19 (0.17 to 0.21)^***
r7psychscat	Binomial: Changes in general mental functioning	246	3.6 (2.78 to 4.64)^***	0.02 (0.15)	0.1 (0.08 to 0.12)^***
r7seizure	Seizures, generalized	Not available
r7sleeprcat	Binomial: Sleep changes	3102	2.26 (2.07 to 2.47)^***	0.28 (0.45)	0.17 (0.15 to 0.19)^***
r7strokcat	Binomial: Cerebrovascular problems	1139	3.36 (2.97 to 3.81)^***	0.1 (0.3)	0.19 (0.17 to 0.21)^***
r7strokecat	Binomial: History of stroke	1283	3.39 (3.01 to 3.82)^***	0.12 (0.32)	0.2 (0.18 to 0.22)^***
r7stroks	Had stroke since last interview	255	3.31 (2.58 to 4.25)^***	0.02 (0.15)	0.09 (0.08 to 0.11)^***
r7tired	Tiredness all the time	1	Not applicable	0 (0.01)	-0.01 (-0.02 to 0.01)
r7toiltcat	Binomial: Toileting problems	903	6.9 (5.95 to 8)^***	0.08 (0.27)	0.27 (0.26 to 0.29)^***
r7underw	Underweight in wave 2004 (%)	313	7.94 (6.15 to 10.25)^***	0.03 (0.17)	0.18 (0.16 to 0.19)^***
r7urine	Urinary incontinence	2710	2.35 (2.15 to 2.58)^***	0.24 (0.43)	0.18 (0.16 to 0.19)^***
r7vgactx	Frequency of vigorous physical activity			4.24 (1.24)	0.2 (0.18 to 0.22)^***
r7walkt	Slowness: Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			5.09 (19.08)	0.11 (0.1 to 0.13)^***
r7walkt15	Time to walk 15 feet			9.54 (35.77)	0.11 (0.1 to 0.13)^***
r7weight	Self-reported weight in kilograms			76.03 (17.43)	-0.06 (-0.08 to -0.04)^***
Bias variables
Biases induced by the Functional Domains Model
r7frail1_1res	Bias induced by Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds			0 (0.29)	0.35 (0.34 to 0.37)^***
r7frail1_2res	Bias induced by Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2			0 (0.25)	0.2 (0.19 to 0.22)^***
r7frail1_3res	Bias induced by Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating			0 (0.15)	-0.03 (-0.05 to -0.01)^**
r7frail1_4res	Bias induced by Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides			0 (0.2)	0.02 (0.01 to 0.04)^*
Biases induced by the Burden Model
r7frail2res	Bias induced by Summary of proxy memory rating and total cognition summary score			0 (0.04)	-0.14 (-0.16 to -0.11)^***
Biases induced by the Biologic Syndrome Model
r7frail3_2res	Bias induced by Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.			0 (0.17)	0.01 (-0.04 to 0.06)
r7frail3_3res	Bias induced by Frequency of three intensities of activity, lowest quintile (stratified according to sex)			0 (0.31)	0.1 (0.05 to 0.15)^***
r7frail3_4res	Bias induced by Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			0 (0.49)	0.16 (0.12 to 0.21)^***
r7frail3_5res	Bias induced by Grip strength: Weakest 20% (stratified according to sex and BMI)			0 (0.34)	0.05 (0 to 0.09)

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n = 11,113; frailty n (%) = 3,059 (27.53%); mean age = 74.92 years; female % = 57.46%.

BMI = body mass index; CES-D = Center for Epidemiological Studies Depression; HRS = Health and Retirement Study.

* = p < 0.05

** = p < 0.01

*** = p < 0.001.

Table 3. Frailty status defined by the Biologic Syndrome Model and its associations with frailty symptoms.

HRS	Definitions variables	N with symptoms for binomial variables	Odds ratios (95% CIs	Mean (SD)	Correlation coefficients (95% CIs)
r7agey_b	Age at interview (years)			76.05 (7.36)	0.23 (0.19 to 0.28)^***
r7bmi	Self-reported body mass index = kg/m2			26.41 (4.91)	-0.04 (-0.09 to 0.01)
r7cogtot	Total cognition summary score			21.44 (4.79)	-0.22 (-0.26 to -0.17)^***
r7effort	Everything an effort	258	5.56 (4.04 to 7.66)^***	0.16 (0.36)	0.28 (0.23 to 0.32)^***
r7frailim3	Frailty index: Biologic Syndrome Model			1.14 (1.08)	0.73 (0.7 to 0.75)^***
r7frailim3cat	Frailty status: Biologic Syndrome Model (outcome of this table)	203	Not applicable	0.12 (0.33)	1 (1 to 1)^***
r7going	Could not get going	306	6.43 (4.7 to 8.8)^***	0.19 (0.39)	0.31 (0.27 to 0.36)^***
r7gripl	Grip strength, left hand			24.85 (13.1)	-0.28 (-0.32 to -0.23)^***
r7gripr	Grip strength, right hand			27.38 (13.46)	-0.32 (-0.36 to -0.28)^***
r7height	Self-reported height in meters			1.69 (0.1)	-0.12 (-0.17 to -0.08)^***
r7mdactx	Frequency of moderate physical activity			2.93 (1.3)	0.41 (0.37 to 0.45)^***
r7memopr	Proxy memory rating (1 to 6)			1 (0)	Not applicable for uniform values
r7stroks	had stroke since last interview (1 = no, 2 = yes)			1 (0)	Not applicable for uniform values
r7vgactx	Frequency of vigorous physical activity			4.18 (1.26)	0.21 (0.16 to 0.26)^***
r7walkt	Slowness: Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			4.14 (13.29)	0.42 (0.38 to 0.46)^***
ragender	Male = 0; female = 1	898	2.61 (1.88 to 3.64)^***	0.55 (0.5)	0.14 (0.1 to 0.19)^***
Domains and frailty symptoms identified by the other 2 models
r7actsum	Summary scores of physical activities			32.41 (8.61)	0.37 (0.33 to 0.41)^***
r7arthrcat	Binomial: Arthritis	1140	2.4 (1.63 to 3.52)^***	0.69 (0.46)	0.11 (0.06 to 0.16)^***
r7bathcat	Binomial: Problems with bathing	88	6.75 (4.3 to 10.59)^***	0.05 (0.23)	0.23 (0.18 to 0.28)^***
r7cancrcat	Binomial: Malignant disease	300	1.28 (0.89 to 1.83)	0.18 (0.39)	0.03 (-0.02 to 0.08)
r7cogimpair	Impaired cognition based on performance-based scores or proxy assessment	45	5.09 (2.75 to 9.42)^***	0.03 (0.16)	0.14 (0.09 to 0.19)^***
r7deprescat	Binomial: Feeling sad, blue, depressed	0	Not applicable	0	Not applicable for uniform values
r7diabscat	Binomial: History of diabetes mellitus	42	1.7 (0.77 to 3.72)	0.03 (0.16)	0.03 (-0.02 to 0.08)
r7dizz	Physical functioning: Dizziness as persistent problem	176	2 (1.34 to 2.98)^**	0.11 (0.31)	0.09 (0.04 to 0.13)^***
r7dresscat	Binomial: problem getting dressed	120	3.64 (2.39 to 5.54)^***	0.07 (0.26)	0.16 (0.11 to 0.2)^***
r7eye	Sensory problems: Fair or poor eyesight despite use of corrective lenses			2.85 (0.97)	0.13 (0.09 to 0.18)^***
r7fall	fallen down last 2 years			0.75 (2.34)	0.11 (0.06 to 0.16)^***
r7fall_cat1	More than 1 falls	513	2.2 (1.63 to 2.96)^***	0.31 (0.46)	0.13 (0.08 to 0.18)^***
r7fall_cat2	More than 2 falls	242	2.34 (1.65 to 3.31)^***	0.15 (0.35)	0.12 (0.07 to 0.17)^***
r7frail1_1	Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds	573	3.3 (2.44 to 4.46)^***	0.35 (0.48)	0.2 (0.15 to 0.24)^***
r7frail1_2	Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2	94	8.49 (5.49 to 13.14)^***	0.06 (0.23)	0.27 (0.23 to 0.32)^***
r7frail1_3	Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating	45	5.09 (2.75 to 9.42)^***	0.03 (0.16)	0.14 (0.09 to 0.19)^***
r7frail1_4	Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides	645	2.24 (1.66 to 3.02)^***	0.39 (0.49)	0.13 (0.09 to 0.18)^***
r7frail3_2	Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.	441	9.93 (7.13 to 13.84)^***	0.27 (0.44)	0.38 (0.34 to 0.42)^***
r7frail3_3	Frequency of three intensities of activity, lowest quintile (stratified according to sex)	329	17.58 (12.47 to 24.78)^***	0.2 (0.4)	0.49 (0.45 to 0.52)^***
r7frail3_4	Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same	724	27.06 (14.95 to 48.96)^***	0.44 (0.5)	0.38 (0.34 to 0.42)^***
r7frail3_5	Grip strength: Weakest 20% (stratified according to sex and BMI)	290	12.72 (9.18 to 17.64)^***	0.18 (0.38)	0.44 (0.4 to 0.48)^***
r7frailim1	Frailty index: Functional Domains Model			0.83 (0.8)	0.31 (0.26 to 0.35)^***
r7frailim1cat	Frailty status: Functional Domains Model	340	5.91 (4.34 to 8.06)^***	0.21 (0.41)	0.3 (0.26 to 0.34)^***
r7frailim2	Frailty index: Burden Model			4.17 (2.07)	0.27 (0.22 to 0.32)^***
r7frailim2cat	Frailty status: Burden Model			0.33 (0.47)	0.19 (0.14 to 0.24)^***
r7frailim3	Frailty index: Biologic Syndrome Model			1.14 (1.08)	0.73 (0.7 to 0.75)^***
r7grip	Grip strength, largest value			28.31 (13.69)	-0.32 (-0.36 to -0.27)^***
r7headac	Headache	74	1.11 (0.56 to 2.2)	0.05 (0.21)	0.01 (-0.04 to 0.06)
r7hear	Sensory problems: fair or poor hearing despite use of hearing aides			2.88 (1.09)	0.11 (0.06 to 0.16)^***
r7heartcat	Binomial: Cardiac problems	562	1.83 (1.36 to 2.46)^***	0.34 (0.47)	0.1 (0.05 to 0.15)^***
r7hibp	Had high blood pressure since last interview	987	1.51 (1.11 to 2.07)^**	0.6 (0.49)	0.06 (0.02 to 0.11)**
r7lift	Some difficulty in lift/carry 10lbs	440	6.83 (4.98 to 9.35)^***	0.27 (0.44)	0.32 (0.28 to 0.37)^***
r7ltactx	Frequency of light physical activity			2.71 (1.06)	0.25 (0.2 to 0.29)^***
r7lungcat	Binomial: Lung problems	165	1.84 (1.21 to 2.79)^**	0.1 (0.3)	0.07 (0.02 to 0.12)**
r7memryscat	Binomial: Memory changes	20	1.79 (0.59 to 5.4)	0.01 (0.11)	0.03 (-0.02 to 0.07)
r7mobila	Some difficulty in mobility /05			1.12 (1.38)	0.37 (0.33 to 0.41)^***
r7muscle	Musculoskeletal problems	57	0.53 (0.19 to 1.47)	0.03 (0.18)	-0.03 (-0.08 to 0.02)
r7psychcat	Binomial: Depression	165	2.38 (1.6 to 3.54)^***	0.1 (0.3)	0.11 (0.06 to 0.16)^***
r7psychscat	Binomial: Changes in general mental functioning	17	1.53 (0.43 to 5.36)	0.01 (0.1)	0.02 (-0.03 to 0.06)
r7seizure	Seizures, generalized	0	Not applicable		Not applicable for uniform values
r7sleeprcat	Binomial: Sleep changes	346	1.74 (1.25 to 2.41)^**	0.21 (0.41)	0.08 (0.03 to 0.13)^***
r7strokcat	Binomial: Cerebrovascular problems	110	2.65 (1.68 to 4.18)^***	0.07 (0.25)	0.11 (0.06 to 0.15)^***
r7strokecat	Binomial: History of stroke	139	2.56 (1.68 to 3.88)^***	0.08 (0.28)	0.11 (0.06 to 0.16)^***
r7tired	Tiredness all the time	0	Not applicable		Not applicable for uniform values
r7toiltcat	Binomial: Toileting problems	60	6.07 (3.56 to 10.35)^***	0.04 (0.19)	0.18 (0.14 to 0.23)^***
r7underw	Underweight in wave 2004 (%)	36	9.72 (4.95 to 19.09)^***	0.02 (0.15)	0.2 (0.15 to 0.24)^***
r7urine	Urinary incontinence	360	2.14 (1.56 to 2.94)^***	0.22 (0.41)	0.12 (0.07 to 0.17)^***
r7walkt15	Time to walk 15 feet			7.76 (24.91)	0.42 (0.38 to 0.46)^***
r7wchange	Weight in wave 2002 minus weight in wave 2004 (%)			0 (0.06)	0.05 (0 to 0.1)^*
r7weight	Self-reported weight in kilograms			75.75 (16.87)	-0.1 (-0.15 to -0.05)^***
Bias variables
Bias variables induced by the Functional Domains Model
r7frail1_1res	Bias induced by Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds			0 (0.29)	0.03 (-0.02 to 0.08)
r7frail1_2res	Bias induced by Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2			-0.03 (0.22)	0.21 (0.17 to 0.26)^***
r7frail1_3res	Bias induced by Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating			0 (0.16)	-0.02 (-0.06 to 0.03)
r7frail1_4res	Bias induced by Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides			0 (0.21)	0 (-0.05 to 0.05)
Bias variables induced by the Burden Model
r7frail2res	Bias induced by Summary of proxy memory rating and total cognition summary score			0 (0.01)	-0.21 (-0.27 to -0.16)^***
Bias variables induced by the Biologic Syndrome Model
r7frail3_2res	Bias induced by Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.			0 (0.17)	0.08 (0.03 to 0.13)^***
r7frail3_3res	Bias induced by Frequency of three intensities of activity, lowest quintile (stratified according to sex)			0 (0.31)	0.34 (0.3 to 0.38)^***
r7frail3_4res	Bias induced by Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			0 (0.49)	0.3 (0.26 to 0.34)^***
r7frail3_5res	Bias induced by Grip strength: Weakest 20% (stratified according to sex and BMI)			0 (0.34)	0.26 (0.22 to 0.31)^***

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n = 1,642; frailty n (%) = 540 (32.89%); mean age = 77.05 years; female % = 54.69s%. HRS = Health and Retirement Study.

BMI = body mass index; CES-D = Center for Epidemiological Studies Depression; HRS = Health and Retirement Study.

* = p < 0.05

** = p < 0.01

*** = p < 0.001.

Table 2. Frailty status defined by the Burden Model and its associations with frailty symptoms.

HRS variables	Definitions	N with symptoms for binomial variables	Odds ratios (95% CIs	Mean (SD)	Correlation coefficients (95% CIs)
r7agey_b	Age at interview (years)			77.43 (6.46)	0.19 (0.17 to 0.22)^***
r7arthrcat	Binomial: Arthritis	5457	4.45 (3.97 to 4.99)^***	0.71 (0.45)	0.3 (0.28 to 0.32)^***
r7bathcat	Binomial: Problems with bathing	1091	28.44 (21.95 to 36.86)^***	0.14 (0.35)	0.4 (0.39 to 0.42)^***
r7cancrcat	Binomial: Malignant disease	1489	2.12 (1.89 to 2.38)^***	0.19 (0.39)	0.15 (0.13 to 0.17)^***
r7cogtot	Total cognition summary score			19.69 (6.27)	-0.25 (-0.27 to -0.23)^***
r7deprescat	Binomial: Feeling sad, blue, depressed	1480	5.55 (4.87 to 6.32)^***	0.19 (0.39)	0.31 (0.29 to 0.33)^***
r7diabscat	Binomial: History of diabetes mellitus	174	3.07 (2.21 to 4.26)^***	0.02 (0.15)	0.08 (0.06 to 0.1)^***
r7dresscat	Binomial: problem getting dressed	1159	18.97 (15.34 to 23.48)^***	0.15 (0.36)	0.4 (0.38 to 0.42)^***
r7fall	Fallen down last 2 years			0.99 (2.89)	0.3 (0.28 to 0.32)^***
r7frailim2	Frailty index: Burden Model			5.02 (2.83)	0.8 (0.8 to 0.81)^***
r7frailim2cat	Frailty status: Burden Model (outcome of this table)	3442	Not applicable	0.45 (0.5)	1 (1 to 1)^***
r7headac	Headache	514	5.28 (4.25 to 6.58)^***	0.07 (0.25)	0.19 (0.17 to 0.21)^***
r7heartcat	Binomial: Cardiac problems	2817	4.01 (3.64 to 4.42)^***	0.37 (0.48)	0.32 (0.3 to 0.34)^***
r7hibp	Had high blood pressure since last interview	4809	2.81 (2.55 to 3.1)^***	0.62 (0.48)	0.24 (0.22 to 0.26)^***
r7lungcat	Binomial: Lung problems	928	4.41 (3.77 to 5.16)^***	0.12 (0.33)	0.23 (0.2 to 0.25)^***
r7memopr	Proxy memory rating			1.45 (1.29)	0.23 (0.21 to 0.25)^***
r7memryscat	Binomial: Memory changes	303	5.95 (4.43 to 8)^***	0.04 (0.19)	0.15 (0.13 to 0.17)^***
r7mobila	Some difficulty in mobility /05			1.51 (1.66)	0.56 (0.55 to 0.58)^***
r7muscle	Musculoskeletal problems	258	3.61 (2.73 to 4.78)^***	0.03 (0.18)	0.11 (0.09 to 0.13)^***
r7psychcat	Binomial: Depression	1230	7.74 (6.63 to 9.04)^***	0.16 (0.37)	0.33 (0.31 to 0.35)^***
r7psychscat	Binomial: Changes in general mental functioning	188	15.02 (8.84 to 25.5)^***	0.02 (0.15)	0.15 (0.13 to 0.17)^***
r7seizure	Seizures, generalized	0	Not applicable	0	Not applicable for uniform values
r7sleeprcat	Binomial: Sleep changes	2185	5.05 (4.53 to 5.63)^***	0.28 (0.45)	0.35 (0.33 to 0.37)^***
r7strokcat	Binomial: Cerebrovascular problems	923	11.45 (9.34 to 14.03)^***	0.12 (0.32)	0.32 (0.3 to 0.34)^***
r7strokecat	Binomial: History of stroke	1062	10.42 (8.68 to 12.51)^***	0.14 (0.34)	0.34 (0.32 to 0.36)^***
r7tired	Tiredness all the time	1	Not applicable	0 (0.01)	0.01 (-0.01 to 0.03)
r7toiltcat	Binomial: Toileting problems	759	44.18 (29.79 to 65.52)^***	0.1 (0.3)	0.35 (0.33 to 0.36)^***
r7urine	Urinary incontinence	2075	6.41 (5.71 to 7.19)^***	0.27 (0.44)	0.38 (0.36 to 0.4)^***
ragender	Male = 0; female = 1	4534	1.69 (1.54 to 1.85)^***	0.59 (0.49)	0.13 (0.1 to 0.15)^***
Domains and other frailty symptoms identified by the other 2 models
r7actsum	Summary scores of physical activities			34.44 (8.92)	0.35 (0.33 to 0.37)^***
r7bmi	Self-reported body mass index = kg/m2			26.1 (5.07)	0.07 (0.05 to 0.1)^***
r7cogimpair	Impaired cognition based on performance-based scores or proxy assessment	884	4.71 (4 to 5.54)^***	0.11 (0.32)	0.23 (0.21 to 0.25)^***
r7dizz	Physical functioning: Dizziness as persistent problem	1163	3.93 (3.42 to 4.5)^***	0.15 (0.36)	0.23 (0.21 to 0.25)^***
r7effort	Everything an effort	2153	4.59 (4.12 to 5.11)^***	0.28 (0.45)	0.33 (0.31 to 0.35)^***
r7eye	Sensory problems: Fair or poor eyesight despite use of corrective lenses			3 (1.03)	0.26 (0.24 to 0.28)^***
r7fall_cat1	More than 1 falls	2761	5.61 (5.06 to 6.21)^***	0.36 (0.48)	0.39 (0.37 to 0.41)^***
r7fall_cat2	More than 2 falls	1499	6.94 (6.06 to 7.95)^***	0.19 (0.4)	0.35 (0.33 to 0.37)^***
r7frail1_1	Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds	3290	6.02 (5.45 to 6.64)^***	0.43 (0.49)	0.42 (0.4 to 0.44)^***
r7frail1_2	Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2	635	1.97 (1.67 to 2.32)^***	0.08 (0.27)	0.09 (0.07 to 0.11)^***
r7frail1_3	Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating			0.11 (0.32)	0.23 (0.21 to 0.25)^***
r7frail1_4	Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides			0.45 (0.5)	0.22 (0.2 to 0.24)^***
r7frail3_2	Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.	2977	4.72 (4.28 to 5.21)^***	0.39 (0.49)	0.36 (0.34 to 0.38)^***
r7frail3_3	Frequency of three intensities of activity, lowest quintile (stratified according to sex)	2226	4.89 (4.39 to 5.45)^***	0.29 (0.45)	0.34 (0.32 to 0.36)^***
r7frail3_4	Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same	3853	1.37 (1.25 to 1.5)^***	0.5 (0.5)	0.08 (0.06 to 0.1)^***
r7frail3_5	Grip strength: Weakest 20% (stratified according to sex and BMI)	1840	1.4 (1.26 to 1.55)^***	0.24 (0.43)	0.07 (0.05 to 0.09)^***
r7frailim1	Frailty index: Functional Domains Model			1.08 (0.96)	0.43 (0.41 to 0.45)^***
r7frailim1cat	Frailty status: Functional Domains Model	2434	5.57 (5.01 to 6.19)^***	0.32 (0.46)	0.38 (0.36 to 0.4)^***
r7frailim2	Frailty index: Burden Model			5.02 (2.83)	0.8 (0.8 to 0.81)^***
r7frailim3	Frailty index: Biologic Syndrome Model			1.22 (1.11)	0.28 (0.23 to 0.33)^***
r7frailim3cat	Frailty status: Biologic Syndrome Model			0.14 (0.35)	0.19 (0.14 to 0.24)^***
r7going	could not get going	2026	4.64 (4.15 to 5.18)^***	0.26 (0.44)	0.32 (0.3 to 0.34)^***
r7grip	Grip strength, largest value			29.49 (15.93)	-0.08 (-0.1 to -0.06)^***
r7gripl	Grip strength, left hand			25.89 (15.72)	-0.07 (-0.09 to -0.04)^***
r7gripr	Grip strength, right hand			27.99 (15.37)	-0.11 (-0.13 to -0.09)^***
r7hear	Sensory problems: fair or poor hearing despite use of hearing aides			2.97 (1.13)	0.16 (0.14 to 0.18)^***
r7height	Self-reported height in meters			1.68 (0.1)	-0.1 (-0.13 to -0.08)^***
r7lift	Some difficulty in lift/carry 10lbs	2862	5.88 (5.31 to 6.51)^***	0.37 (0.48)	0.4 (0.39 to 0.42)^***
r7ltactx	Frequency of light physical activity			2.99 (1.24)	0.3 (0.28 to 0.32)^***
r7mdactx	Frequency of moderate physical activity			3.24 (1.41)	0.34 (0.32 to 0.36)^***
r7stroks	Had stroke since last interview	208	13 (8.09 to 20.88)^***	0.03 (0.16)	0.15 (0.13 to 0.18)^***
r7underw	Underweight in wave 2004 (%)	270	1.68 (1.31 to 2.14)^***	0.04 (0.18)	0.05 (0.03 to 0.07)^***
r7vgactx	Frequency of vigorous physical activity			4.35 (1.19)	0.23 (0.21 to 0.25)^***
r7walkt	Slowness: Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			5.2 (19.49)	0.12 (0.1 to 0.15)^***
r7walkt15	Time to walk 15 feet			9.75 (36.55)	0.12 (0.1 to 0.15)^***
r7wchange	Weight in wave 2002 minus weight in wave 2004 (%)			0.01 (0.08)	0.04 (0.02 to 0.07)^***
r7weight	Self-reported weight in kilograms			73.91 (16.73)	0 (-0.02 to 0.03)
Bias variables
Bias variables induced by the Functional Domains Model
r7frail1_1res	Bias induced by Dizziness as persistent problem, > = 2 falls in previous 2 years, or difficulty lifting 10 pounds			0.01 (0.3)	0.19 (0.17 to 0.21)^***
r7frail1_2res	Bias induced by Weight in wave 2002 minus weight in wave 2004! 10% of weight in wave 2002 or body mass index o18.5 kg/m2			0 (0.26)	0.15 (0.13 to 0.17)^***
r7frail1_3res	Bias induced by Mild to severe cognitive impairment on performance-based measure or according to proxy and interviewer rating			0 (0.17)	-0.01 (-0.03 to 0.01)
r7frail1_4res	Bias induced by Fair or poor eyesight despite use of corrective lenses or fair or poor hearing despite use of hearing aides			0 (0.21)	0.01 (-0.01 to 0.03)
Bias variables induced by the Burden Model
r7frail2res	Bias induced by Summary of proxy memory rating and total cognition summary score			0 (0.04)	-0.06 (-0.08 to -0.04)^***
Bias variables induced by the Biologic Syndrome Model
r7frail3_2res	Bias induced by Yes to either of two CES-D items: (i) Felt that everything I did was an effort in last week. (ii) Could not get going in last week.			0 (0.17)	-0.04 (-0.09 to 0.01)
r7frail3_3res	Bias induced by Frequency of three intensities of activity, lowest quintile (stratified according to sex)			0 (0.32)	0.12 (0.07 to 0.17)^***
r7frail3_4res	Bias induced by Time to walk 8 ft, converted to time to walk 15 ft. Cutoff criteria according to sex and height remain the same			0.03 (0.49)	0.09 (0.03 to 0.14)^**
r7frail3_5res	Bias induced by Grip strength: Weakest 20% (stratified according to sex and BMI)			0.01 (0.35)	0.03 (-0.03 to 0.08)

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n = 7,713; frailty n (%) = 6,755 (87.58%); mean age = 78.43 years; female % = 58.78%.

BMI = body mass index; CES-D = Center for Epidemiological Studies Depression; HRS = Health and Retirement Study.

* = p < 0.05

** = p < 0.01

*** = p < 0.001.

Statistical analyses

The associations between the frailty statuses and their frailty symptoms were determined with odds ratios and correlation coefficients. Odds ratios were the ratios of the odds of developing symptoms occurred among frail individuals, compared to the odds among those not frail [17]. Odds ratios were applicable to binomial variables [17]. Odds ratios equaling 1 suggest that the two groups have similar risks of developing symptoms [17]. The processing to transform non-binomial variables to binomial variables were according to the recommendations by the authors of the Burden Model [18]. Pearson’s correlation coefficients were used to assess the associations between frailty statuses defined by the 3 models and frailty symptoms or input variables or domains or bias variables [19]. Correlation coefficients ranged from -1 to 1, representing completely opposite information and identical information between 2 variables, respectively. We hypothesized that 1) frailty statuses were not associated with symptom incidence (odds ratio = 1); 2) frailty statuses were not correlated with frailty symptoms or input variables of the frailty indices (correlation coefficient = 0). Correlation coefficients between 0 and 0.10, 0.10 and 0.39, 0.40 and 0.69, 0.70 and 0.89, and 0.90 and 1.00 were interpreted as negligible, weak, moderate, strong, and very strong correlations, respectively [20]. P values were adjusted for multiple comparison using false discovery rates [21]. Two-tailed P values that were less than 0.05 were considered statistically significant. All statistical analyses were conducted within R environment (v4.0.4) [22] and RStudio (v1.4.1106) [23]. This secondary data analysis was approved by the ethics review committee at the Centre Hospitalier de l’Université de Montréal.

Results

There were 11,113, 7,713, and 1,642 HRS participants analyzed for the frailty indices defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model in Tables 1–3, respectively [2]. The numbers of frail patients were 3,059 (27.53%), 3,442 (44.63%), and 203 (12.36%), respectively [2]. The mean ages were 74.92, 78.43, and 77.05 years, respectively. The proportions of females were 57.46%, 58.78%, and 54.69%, respectively.

Frailty symptom development based on frailty status

In Tables 1–3, the associations between frailty status (yes or no) and symptom development are shown using odds ratios and correlation coefficients. Overall, most of frailty symptoms were significantly associated with frailty statuses. However, frailty statuses defined by the three models were not significantly associated with all frailty symptoms or input variables or domains. The frailty symptoms or input variables or domains that were not significantly associated with frailty statuses are described below. The correlation coefficients between the three frailty statuses ranged from 0.19 to 0.38 (weak correlations, p < 0.001 for all).

In Table 1, the frailty status defined by the Functional Domains Model was not significantly correlated with one input variable, BMI (correlation coefficients = -0.02, 95% CI = -0.04 to 0).

Among the frailty symptoms or input variables or domains identified by the other two models, two symptoms, malignant disease and tiredness all the time, were not significantly associated with this frailty status (p of correlations > 0.05 for both symptoms). Among the bias variables, one bias variable that was induced by having one of two Center for Epidemiologic Studies Depression (CES-D) items was not significantly associated with this frailty status (correlation p > 0.05).

In Table 2, the frailty status defined by the Burden Model was assessed for the associations with frailty symptoms, input variables, and domains. One input symptom, tiredness all the time, was not significantly associated with this frailty status (p of correlation > 0.05). One symptom identified by the other two models, self-reported weight, was not significantly correlated with this frailty status (p > 0.05). Among the bias variables, four were not significantly correlated with this frailty status (p > 0.05 for all).

In Table 3, the frailty status defined by the Biologic Syndrome Model was assessed for the associations with frailty symptoms, input variables, and domains. BMI was not significantly correlated with this frailty status (p > 0.05). Because the values of two symptoms, proxy memory rating and history of stroke, were the same for frail and non-frail HRS participants for this frailty index, their correlations with this frailty status could not be assessed. Six symptoms defined by the other two models, history of malignant disease, diabetes mellitus, headache, memory change, musculoskeletal problems, and change in general mental functioning, were not significantly correlated with this frailty status (p > 0.05 for all).

Correlations with bias variables

In Tables 1–3, the correlations with bias variables are shown for the three frailty indices. Each frailty status was significantly associated with the bias variables induced by their own diagnostic criteria. The frailty statuses defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were significantly correlated with four, one, and four bias variables induced by their own models, respectively (p < 0.05 for all). In addition, the frailty status defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were significantly correlated with three, four, and two bias variables induced by the other two models, respectively (p < 0.05 for all).

Discussion

Strengths of the associations are one of the Bradford-Hill criteria to assess whether a disease causes symptoms or outcomes [10]. Frailty has been promising in establishing causal relationships with major health outcomes, such as mortality and falls, based on frailty’s significant associations with them [2]. However, whether frailty should cause frailty symptoms has not been declared in the theories of frailty and whether frailty causes frailty symptoms have not been well studied. In this study using the HRS data, three of the most used frailty diagnoses fail to demonstrate significant correlations with some of the frailty symptoms of their own or those defined by the other two frailty diagnoses. When frailty lacks significant associations with frailty symptoms, this suggests frailty diagnoses are made based on so-called frailty symptoms, some of which frailty may not cause them. This needs serious discussion and examination.

Why frailty fails to be significantly associated with frailty symptoms?

Frailty diagnoses do not fully support the models of their own by showing insignificant correlations with some of their own frailty symptoms or input variables. One reason may be that frailty researchers did not recognize the importance of causation between frailty and frailty symptoms. The authors of the Burden Model recommended selecting frailty symptoms based on the associations between the candidate symptoms and two factors: age and general health status [18], rather than selecting the symptoms that cause or are caused by frailty. For this model, the so-called frailty symptoms are, in fact, age-related and general health-related variables.

Second, for the Burden Model that requires a large number of frailty symptoms [18], some symptoms may not present in the population at all and are used for frailty diagnosis regardless. For example, in the HRS cohort, since we did not identify any patients with generalized seizures and it was impossible to determine the association between this symptom and the frailty status defined by this model. Including frailty symptoms that do not present in a population can underestimate the prevalence rate of frailty defined by the Burden Model. This is because the diagnostic threshold of this model is proportional to the total number of frailty symptoms used [18]. When a symptom that should not be included in the diagnostic criteria is included, the diagnostic threshold increases with the total number of frailty symptoms. If the symptom, generalized seizures, is excluded from the diagnostic criteria, the diagnostic threshold can decrease and it is likely that more HRS participants can qualify the diagnosis of frailty defined by the Burden Model.

The third reason is that the diagnostic criteria of frailty have been made so complicated that biases have been introduced and interfered the relationships between frailty and frailty symptoms. The biases are produced by censoring the sum of multiple symptoms and dichotomizing continuous variables [2]. These biases are so important that each of the frailty indices defined by the three models is significantly associated with the biases created by their own or the other two models. In fact, the biases generated by the diagnostic criteria of the Biologic Syndrome Model explain this model’s frailty index better than its frailty symptoms [2]. When the frailty indices better represent the biases, these indices are less likely to have significant associations with their frailty symptoms.

The last reason is the correlations between frailty symptoms have been neglected. The correlations between symptoms, symptom prevalence, and the design of the diagnostic criteria (whether biases are created and integrated to the diagnosis) are the three major determinants of the prevalence of the diagnosis [24]. Neglecting the importance of symptom correlations can lead to major errors. For example, when five highly correlated variables with the same means (correlation coefficients = 1) are summed to create an index, this index is not very different from five times any one of the five input variables [25]. When two completely opposite variables with mean values of 0 (correlation coefficient = -1) are summed, the derived index contains only 0 [25]. The frailty indices defined by the three frailty models consist of frailty symptoms of various correlations with each other. Some of the frailty symptoms in the three models may be highly correlated with each other. It takes only four frailty symptoms to explain more than 54% of the variances of the three frailty indices that use 9 or more frailty symptoms [2]. This issue becomes more problematic for frailty diagnoses made based on a large number of frailty symptoms. The frailty status defined by the Burden Model requires at least 30 symptoms for diagnosis and 70 symptoms have often been used [18]. It took only 11 frailty symptoms to explain more than 90% of the variances of the frailty index [2]. The frailty status defined by this model is not significantly associated with two of its frailty symptoms in this study. The role of insignificant symptoms, seizure and tiredness, in the frailty status defined by the Burden Model haven’t been discussed in previous studies [18].

Causation?

In addition to the strengths of associations, the evidence to support the causal relationship between frailty and frailty symptoms seems limited. The pathological changes that are considered related to frailty include sarcopenia, heart disease, and lung disease, depending on the frailty models [2]. In this study, grip strength, cardiac problems, and lung problems were significantly correlated with the three frailty indices, respectively. However, the biological and pathological evidence that support the causal relationship between frailty and the frailty symptoms of the other organ systems seems insufficient [26].

Assumptions

The different patterns of the insignificance between frailty symptoms and the 3 frailty statuses indicated conflicting views on frailty. The 3 frailty models have major discrepancies in the underlying assumptions, including theoretical frameworks, age thresholds, the selection of frailty symptoms, and the design of diagnostic criteria [2]. Subsequently, the differences in these assumptions between frailty models can be shown with the symptoms that best explained frailty statuses [2]. We found the frailty symptoms or input variables that had the largest correlation coefficients with the three frailty statuses were different. The three symptoms that have the largest significant correlation coefficients with the frailty indices defined by the Functional Domain Model, the Burden Model, and the Biologic Syndrome Model are some difficulties in lifting 10 pounds, some difficulty in mobility, and slowness measured by time to walk 8 feet, respectively. The paths from non-frailty to frailty vary depending on the models used.

Logic challenges

These results highlight logic challenges. Frail patients are not more likely to have certain frailty symptoms, but these symptoms are necessary to make these diagnoses. It is unclear whether the symptoms that frailty is insignificantly correlated with can be called “frailty” symptom or used for frailty diagnosis. When excluding these symptoms from being used for the diagnosis of frailty, the prevalence of frailty will likely decrease for the Functional Domain Model and the Biologic Syndrome Model and may increase or decrease for the Burden Model. Whether the updated indices will become insignificantly associated with other frailty symptoms is unclear. If the frailty symptoms that are not significantly associated with frailty should be excluded, many of the published frailty prevalence rates are likely to be overestimated or biased for the reason describe above. We have not identified any studies explicitly examine the significance of the associations between the frailty statuses and frailty symptoms they defined in their own models. We will continue exploring the causal relationship between frailty and frailty symptoms using other data sets in the future.

Limitations

This study has strengths in using a publicly accessible database that has been investigated in previous studies [1, 2]. The demographic characteristics reported in this study matched those reported [1]. However, there are several limitations to this study. There are other statistical and epidemiological measures of association that can be tested to demonstrate the strengths of associations, including Chi-squared statistics and risk ratios [27, 28]. Odds ratios are adequate for cross-sectional studies to approximate risk ratios [17]. However, odds ratios can over- or under-estimate effect sizes if the underlying risk ratios are greater or less than 1, respectively [29]. Other measures of association will be explored in the future. Moreover, there are other factors influencing the correlations between frailty statuses and frailty symptoms, such as demographic characteristics. These factors can be adjusted using techniques, such as multiple regression [30, 31]. Lastly, this study used cross-sectional data and longitudinal follow-up of the strengths of the associations between frailty and frailty symptoms might help to answer important questions, such as whether the insignificant associations are transient, whether frailty predicts major outcomes better if insignificant frailty symptoms are discarded, and whether the biases induced by the frailty diagnostic criteria predict outcomes better than frailty symptoms. This will need to be explored in future research.

Conclusion

The frailty diagnoses defined by three models were assessed for their correlations with frailty symptoms of their own, those defined by the other two models, and bias variables using odds ratios and correlation coefficients. Frailty diagnoses lack significant correlations with some of their own frailty symptoms and some of the frailty symptoms defined by the other two models. This suggests that frail patients are not more likely to have certain frailty symptoms using any of the three frailty models. This finding raises questions like whether frailty symptoms lacking significant correlations with frailty statuses could be included to diagnose frailty and whether frailty exists and causes frailty symptoms. Further research to assess the causal relationships between frailty and frailty symptoms is needed and planned.

Data Availability

The HRS data produced by the RAND Center for the Study of Aging can be accessed via the University of Michigan site (https://hrs.isr.umich.edu/data-products). The authors do not have special access to the HRS data.

Funding Statement

This study was supported by Taipei Veterans General Hospital in the form of a grant awarded to WCC (V111B-024). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

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PLoS One. doi: 10.1371/journal.pone.0272289.r001

Decision Letter 0

Adrian Stuart Wagg

10 May 2022

PONE-D-21-33729Frailty does not cause all frail symptoms: United States Health and Retirement StudyPLOS ONE

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Reviewer #1: It is always to see research done to appreciate and explore the concept of fitness of frailty and to explore which aspects of this phenomenon can potentially be intervened on.

However; I am not sure if I agree with the premise of this paper, which seems to assume that each variable that makes up a model of measurement of physiological reserves or “frailty”, ought to correlate with the entire model or measuring tool that has been described. I am also not entirely surprised to see that there are some variables, when assessed individually, that do not correlate with the models or measures of frailty.

Comments:

1. “Frailty is a geriatric syndrome….”. This is not true. Frailty is a concept/phenomenon that has been shown to exist across the numeric age spectrum. Although it is increasingly prevalent with increasing numeric age, it is not an exclusive phenomenon in those who are of a particular older numeric age.

2. Frailty is a term used to describe the amount or degree of physiological reserves an individual has. Such that, the more amount of physiological reserves an individual has, the more resilient he or she is in the way of any “stressors” (e.g. disease, interventional treatments) that they face. It takes less degree of stressor to destabilize an individual’s homeostasis, if they have less reserves or if they are frail. Fit patients are less likely to decompensate medically and functionally in the way of stressors, unlike frail individual, because they more physiological reserves.

Fitness and frailty are used to describe the entire set of reserves in an individual. It is akin to trying to determine the degree of fibrotic tissue or liver reserves or cirrhosis in an individual with liver disease. However, “frailty” is more complex, because it is an attempt describe and measure reserves in an entire individual

3.regarding the line: “ the causal relationships between frailty and frailty symptoms can be confirmed based on previously published criteria.” I am not convinced that these criteria or any other attempts to measure or describe the concept of physiological reserves or frailty, has demonstrated any casual relationship between frailty or any “frailty symptoms as is suggested by this paper.

4. Instead, the ways in which various measures or tools that are used to assess frailty, use a multitude of variables that include:

a. Contributing factors (e.g. various disease processes, comorbidities)

b. Phenotypic markers (e.g. sarcopenia, and measures of sarcopenia – e.g. grip strength)

c. Sequalae of being fit or frail (e.g. degree or function or dysfunction, number or falls, low mood)

To what extent an individual’s reserves are diminished cannot accurately be determined by any single variable captured by a tool. Simply having the presence of heart failure, does not automatically reflect that someone is frail. However, in someone with long standing severe heart failure, it is conceivable that they may become deconditioned after some time (partially reflected by sarcopenia, and it’s measures) , and that after some time, that degree of sarcopenia or deconditioning may lead to loss of strength and decline in functioning. It is conceivable that after some time, that they may have low mood or depression as a result of their inability to function to their previous extent.

At the same time, it is also conceivable that a disorder such as a depression may not only be a resulting factor of being frail, but it may also be a contributing factor. For example, someone with a primary psychiatric diagnosis of major depressive disorder, with otherwise manageable heart failure, may not adhere to their medications while depressed. They may also not be physically active. This may worsen their heart failure. This may in turn lead to them to becoming frail, if their issues are not managed.

5. Single variables may also be mild, early, or transient (potentially cured) in nature, such that their

existence at a particular cross-section (when the frailty measure is use) may not be enough to cause a degree of diminished physiological reserves to be frail. This is conceivable for the variables assessed in this study, For example, malignant disease or history of, tiredness all of the time, memory change and headache, which did not correlate with frailty status. Some variables, when they become severe, may draw out other variables that are part of a model or measurement tool for frailty/reserves.

In that regard, a single variable may not capture the extent of diminished reserves, as perhaps a tool or measure that encompasses multiple variables does or a tool that captures the enduring effects the things that can contribute to frailty do (e.g. permanent loss of function). As such, the interaction of the variables also plays an important role.

6. Other specific aspects of the paper

a. It would be easier to label the measure or models as they are commonly described e.g. Rockwood’s “frailty index” or “Fried model” or “Phenotypic model”. Otherwise, it does get confusing to keep track of which is what with newer ways of describing them.

b. The paper by Strawbridge et al. that was published in 1998 lays out variables or items that are more commonly reflected in those who are frail versus non frail. I am not sure if it was intended to be a “model” or a measure of reserves, in the way that the other two were. Also, has this approach been validated in various settings as has the other two models?

c. The use of the term “frailty symptoms”, in my opinion, over simplifies what the variables in the various measures of physiological reserves/fitness-frailty are attempting to capture. (see point 4 above).

d. “it has been suggested that cognitive impairment plays an important role for frailty diagnosis and mortality among frail patients”. Cognitive impairment, if it does not go beyond erosion of domain deficits, that is if there is no resulting functional deficits (which would then be dementia) may reflect some degree of diminishment physiological reserves, but it may not reach the “threshold” for reflecting frailty in the way moderate to severe might. At the same time, there are individuals who have enough of their physiological reserves eroded (due to other processes or variables) that they have become “frail”, but who still have their cognitive abilities intact.

Overall:

Areas of improvements or cons of the article: There is an over simplification of the concept of frailty reflected by terms such as: “diagnose frailty”, “symptoms of frailty” and “causality” .

I am also not entirely convinced on the premise of the article which seems to be that the individual variables that make up the various tools ought to be reflective the extent of physiological reserves or frailty and be as reflective of the ability of the various measuring models or tools (that capture a variety of variables).

Items/variables used in the various models or measuring tools of assessing physiological reserves/fitness-frailty do not necessarily reflect "symptoms of frailty"(see point 4). In addition, variables that reflect degree of physiological reserves (either as contributing factors, phenotypic manifestations or resulting factors) can either be mild or severe or transient, and can draw in other variables given the nature of the variable.

As such, a more nuanced approach is required to assess which variables that reflect frailty, can be "targeted" to help management strategies. For example: in this article, malignancy or having a history of malignancy did not correlate with being frail. Once could conceive that if an early malignant process were to progress towards metastasis, that perhaps at some point, a patient would be more “frail” towards the end of the process than at the start. It would be worthwhile trying to stop the malignancy nonetheless, as it would be important to try to stop or modify other contributing factors to frailty, before someone has already become frail.

Positives: Exploring models or measures of “reserves” to determine their strengths and weakness.

There is also, I believe, an unintended, but important purpose that can be derived from this article, even though it has not been stated: in an effort to choose a measuring tool or model for “physiological reserves” or frailty, there may be value in having a tool that is practical to use in particular settings. That practically may be achieved with a tool that perhaps has the least amount of variables, while still being effective.

Reviewer #2: Please refer to the attached document for my comments. Overall, I think this an interesting and valuable study, which has highlighted an important issue. All my comments are all focused on spelling and grammar only.

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PLoS One. 2022 Nov 2;17(11):e0272289. doi: 10.1371/journal.pone.0272289.r002

Author response to Decision Letter 0

15 Jun 2022

Reply to the reviewers

Reviewer #1: It is always to see research done to appreciate and explore the concept of fitness of frailty and to explore which aspects of this phenomenon can potentially be intervened on.

Authors’ reply: thank you for the comments and all your time and efforts to help improve this manuscript. We appreciated your inputs and were happy that you were fine with the fact that not all input variables were significantly associated with frailty. We were not taking a position for whether frailty should be significantly associated with frailty symptoms or not. In the Introduction, we added that we were uncertain whether frailty should cause frailty symptoms. Different people will have very different opinions about the causal relationship between them. We presented the results only to demonstrate the lack of association between frailty and some frailty symptoms. We greatly appreciated the opportunity to have this in-depth conversation with an experienced frailty researcher like you.

Comments: 1. “Frailty is a geriatric syndrome....”. This is not true. Frailty is a concept/phenomenon that has been shown to exist across the numeric age spectrum. Although it is increasingly prevalent with increasing numeric age, it is not an exclusive phenomenon in those who are of a particular older numeric age.

Authors’ reply: thank you for the correction. We agreed and removed “geriatric”. We did find that more than 10% of the populations younger than the age thresholds of 3 commonly used frailty models were considered frail in a previous study (page 7, Table 1 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). Frailty has been found prevalent in many age groups.

Authors’ reply: thank you for the introduction to the frailty theory. We agreed that physiological reserves might have been the target that various frailty models aimed to measure. However, many might fail to measure the physiological reserves they hoped to quantify. We interpreted 3 commonly used frailty indices using frailty symptoms and found frailty indices could be understood as frailty symptoms plus different weights on them, rather than physiological reserves. Our findings suggested that it took few frailty symptoms to explain frailty indices (page 8, Table 1 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). For example, the frailty index defined by the Burden model by Rockwood et al. (2007) that required 70 variables to measure physiological reserve could be mostly explained by 4 frailty symptoms: 1) History of stroke; 2) Depression (clinical impression); 3) Impaired mobility; 4) Urinary incontinence (R-squared = 0.667). This is to say that determining the degree of frailty an individual (frailty index range: 0 to 1 based on 70 variables) has is not very different from asking whether this individual has a history of stroke, depression, impaired mobility, and urinary incontinence. This is just one of the 3 frailty models. The other frailty models had their own frailty symptoms that best explained them. We were uncertain about whether these 4 variables represented the physiological reserve that a frailty index aimed to measure. We agreed that frailty is complex, but the measurement of frailty is adding more complexity for the understanding of frailty.

Authors’ reply: thank you. We agreed that causation could be conceived very differently. We revised the texts to show there are different ways to establish causal relationships. We thought the diversity in the understanding in causation very important and even did a survey to understand how people thought about the causal relationship between diagnosis and symptoms (Table 3 in https://www.frontiersin.org/articles/10.3389/fpsyt.2022.860487/full). Some (7.14%) agreed with you that the causation of symptoms by diagnoses could be confirmed by including the symptoms in the diagnostic criteria. However, many more valued other approached more for causal relationships. 28.57% and 57.14% considered strengths of associations and pathological evidence, respectively, important to confirm causation between diagnoses and symptoms.

4. Instead, the ways in which various measures or tools that are used to assess frailty, use a multitude of variables that include:

a. Contributing factors (e.g. various disease processes, comorbidities) b. Phenotypic markers (e.g. sarcopenia, and measures of sarcopenia – e.g. grip strength) c. Sequalae of being fit or frail (e.g. degree or function or dysfunction, number or falls, low mood)

Authors’ reply: thank you very much for the introduction to frailty symptom classifications. We agreed that frailty symptoms might represent different relationships with frailty. However, the diagnostic criteria of 3 commonly used frailty indices (the Functional Domain model proposed by Strawbridge et al. (1998), the Burden model by Rockwood et al. (2007), and the Biological Syndrome model by Fried et al. (2004)) were not designed properly and distorted the relationships between frailty and frailty symptoms (see the visualization in Fig 2 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). In the same article, we noticed that some frailty symptoms were weighted much more than the magnitude we expected from reading the diagnostic criteria. Single frailty symptoms could be in fact much more important than the others, ie explaining most of the frailty index variances. For example, the Burden model adopted too many variables related to cardiovascular disease and the single frailty symptom that best interpreted the frailty index was a history of stroke (Table 1 in page 8, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). This overemphasis of single symptoms due to the distortion created by the diagnostic criteria contradicted the heart failure example. We found having a history of stroke could significantly predict frailty status defined by the Burden Model.

5. Single variables may also be mild, early, or transient (potentially cured) in nature, such that their existence at a particular cross-section (when the frailty measure is use) may not be enough to cause a degree of diminished physiological reserves to be frail. This is conceivable for the variables assessed in this study, For example,

malignant disease or history of, tiredness all of the time, memory change and headache, which did not correlate with frailty status. Some variables, when they become severe, may draw out other variables that are part of a model or measurement tool for frailty/reserves.

Authors’ reply: thank you for the hypotheses for the lack of significant associations between frailty symptoms and frailty. They are great research topics for longitudinal data. However, our previous analysis of the same data revealed reasons why some frailty symptoms were not significantly associated with frailty indices. The diagnostic criteria of 3 frailty indices were overly complicated and encouraged inadequate data processing. There were 2 main mechanisms that aggregated information from input symptoms and introduced biases to the diagnoses: censoring of multiple variables and dichotomization of continuous variables (see the visualization in Fig 2 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). In this article, we reproduced the 3 frailty indices created by other researchers and derived the bias variables that were created by information censoring of multiple variables or dichotomization of continuous variables. There were 4, 1, and 5 sources of biases identified for the 3 frailty models (Table 1 in the same article). These bias variables were used to interpret frailty indices, compared to frailty symptoms. The results were incredible to many. The frailty index defined by Fried’s Biologic Syndrome Model was better explained by biases (information undesired, produced by inadequate data processing) than by its input frailty symptoms (Table 1 in the above-mentioned article). This clearly showed that biases explained the information in 3 commonly used frailty indices well, sometimes better than frailty symptoms. The data processing embedded in the complicated diagnostic criteria for the diagnosis of frailty not only created bias variables, but also weakened the connection between frailty symptoms and frailty indices. This fact could be supported by the R-squared of frailty symptoms and bias variables to interpret frailty indices, as well as the area under ROCs of frailty symptoms and bias variables to predict binominal frailty statuses (Table 1 in the article). For the Rockwood’s Burden Model, it aggregated too many variables to create a frailty index. Imaging summing 69 cardiovascular disease related symptoms (diastolic hypertension, systolic hypertension, history of hypertension, history of abnormal lipid profile,…) and 1 unrelated symptom (eg tiredness) into an index, this index could be very well explained by 1 or 2 CV symptoms because these CV symptoms were highly correlated and having 1 or 2 of the CV symptoms could explain much of the variances of the new index. However, the unrelated symptoms, tiredness, would be very likely to have insignificant association with the index because this index, in fact, represented CV symptoms. In Table 1 in the above-mentioned article, using the Health and Retirement Study, the frailty index defined by the Burden Model could be explained by 11 frailty symptoms, R-squared > 0.9. Using many correlated symptoms for this model could lead to the insignificant association between frailty index and some frailty symptoms.

Some transient effects or other mechanisms you hypothesized might partly explain the lack of significant associations between some frailty symptoms and frailty indices in longitudinal analysis. However, the evidence to show the inadequate data processing embedded in the frailty diagnostic criteria was strong even in cross-sectional data and existing in 3 commonly used frailty indices.

6. Other specific aspects of the paper a. It would be easier to label the measure or models as they are commonly described e.g. Rockwood’s “frailty index” or “Fried model” or “Phenotypic model”. Otherwise, it does get confusing to keep track of which is what with newer ways of describing them.

Authors’ reply: thank you for the comment. We agreed that frailty models were named or called very differently. In a previously published article (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859), we followed the terminology that Cigolle et al. (2009) used to describe 3 frailty models and analyze the Health and Retirement Study. Because the results were not supportive to the 3 frailty models and there were a lot of critiques to these models, we thought it better to label these models without directly naming the researchers.

Authors’ reply: thank you for this point. We searched for some time and found only 1 article (Cigolle et al. 2009: https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/j.1532-5415.2009.02225.x) that used patient-level data and compared more than 2 frailty models. We followed Cigolle’s terminology and called Strawbridge et al.’s frailty definition as “the Functional Domains Model”. We thought Cigolle et al. were satisfied with the validity of this model and its agreement with Rockwood’s and Fried’s models.

Authors’ reply: thank you for the clarification. We agreed that these symptoms might represent different components of frailty or physiological reserves, as you pointed out here and above. We began to call these input variables as frailty symptoms in our syndrome mining article (https://www.nature.com/articles/s41598-020-60869-8), because we did not find evidence to show these symptoms could be categorized in ways you described above. We tried all possible combinations of any 4 frailty symptoms to find the patterns of significant frailty indices. These symptoms were ordered by the effect sizes of predicting mortality. We found the key to produce a statistically significant frailty index with a large effect size for mortality prediction is to add a frailty symptom with a large effect size, “cognitive impairment”, and 3 insignificant frailty symptoms with near-zero regression coefficients. Creating a significant frailty index is simple with a high success rate, > 99%, if you have many age-related symptoms. Creating a significant frailty index does not seem to be related to the categories that researchers use to group frailty symptoms.

Authors’ reply: thank you for the comment. We agreed that cognitive impairment may interact with other frailty symptoms. However, we found cognitive impairment was an independent risk factor for mortality because it lacked significant interactions with other frailty symptoms defined by the Functional Domains Model (https://www.nature.com/articles/s41598-020-58782-1). This finding was based on a survival analyses we conducted based on frailty symptoms and the interactions between these symptoms. The interactions between frailty symptoms did not significantly predict mortality and we concluded the frailty symptoms defined by Functional Domains Model independently predicted mortality (Figure 7 in https://www.nature.com/articles/s41598-020-58782-1).

Overall: Areas of improvements or cons of the article: There is an over simplification of the concept of frailty reflected by terms such as: “diagnose frailty”, “symptoms of frailty” and “causality” .

Authors’ reply: thank you for the introduction to frailty theory. We used terms that we used in previous publications that aimed to interpret frailty. For example, we diagnosed frailty in an article that interpreted frailty with frailty symptoms (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). We defined frailty symptoms in a syndrome mining article (https://www.nature.com/articles/s41598-020-60869-8). We discussed causality between symptoms and diagnoses in another article (https://www.frontiersin.org/articles/10.3389/fpsyt.2022.860487/full). We added these references to the texts. Hope this works.

Authors’ reply: thank you for the comment. This is philosophical. This is a hypothesis-generating manuscript. In the Abstract and Conclusion, we left questions like whether the insignificant associations should be considered for frailty symptom selection. We revised the texts to make this point clear and ensure that this manuscript did not claim that symptom-based diagnoses “should cause” symptoms. We want this manuscript to describe the fact that frailty was not significantly associated with some frailty symptoms (consistent with the terminology in a previous publication: https://www.nature.com/articles/s41598-020-60869-8). We already explored the reasons why frailty did not have significant associations with frailty symptoms in another publication (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). The reasons were described in detail and added to the Discussion. When frailty symptoms were aggregated into frailty indices, information from the symptoms was top-censoring or dichotomized and biases were introduced to frailty indices.

Authors’ reply: thank you for rephrasing the comment in Point 5. We hope the reply to Point 5 also addresses this comment well. We also added longitudinal analysis as one of the future projects in the Limitations.

Positives: Exploring models or measures of “reserves” to determine their strengths and weakness.

Authors’ reply: thank you for the comment. We think frailty diagnoses could be improved in various ways, for example using a scale we created based on patient perspectives without using composite diagnostic criteria (https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-019-4206-3). We agreed that frailty measures could be simplified and already proved this point in a previous publication (Table 1 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197859). More than 54.1% of the variances of the 3 frailty indices could be explained by 4 frailty symptoms. It took 11 frailty symptoms to explain more than 90% of the variances of the frailty index defined by Rockwood’s Burden Model (that usually took 70 variables). The 3 frailty indices were too complicated and their diagnostic criteria introduced biases that distorted the relationships between frailty indices and frailty symptoms. We appreciated all your time and efforts to help improve this manuscript. The conversation with an experienced frailty expert is a great learning experience for us. Thank you.

Authors’ reply: thank you for the comment and the detailed review. We appreciated your input. Corrections were made based on your comments.

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PLoS One. doi: 10.1371/journal.pone.0272289.r003

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18 Jul 2022

Frailty does not cause all frail symptoms: United States Health and Retirement Study

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PLoS One. doi: 10.1371/journal.pone.0272289.r004

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24 Oct 2022

PONE-D-21-33729R1

Frailty does not cause all frail symptoms: United States Health and Retirement Study

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Data Availability Statement

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PERMALINK

Frailty does not cause all frail symptoms: United States Health and Retirement Study

Yi-Sheng Chao

Chao-Jung Wu

June Y T Po

Shih-Yu Huang

Hsing-Chien Wu

Hui-Ting Hsu

Yen-Po Cheng

Yi-Chun Lai

Wei-Chih Chen

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Table 1. Frailty status defined by the Functional Domains Model and its associations with frailty symptoms.

Table 3. Frailty status defined by the Biologic Syndrome Model and its associations with frailty symptoms.

Table 2. Frailty status defined by the Burden Model and its associations with frailty symptoms.

Statistical analyses

Results

Frailty symptom development based on frailty status

Correlations with bias variables

Discussion

Why frailty fails to be significantly associated with frailty symptoms?

Causation?

Assumptions

Logic challenges

Limitations

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Adrian Stuart Wagg

Roles

Author response to Decision Letter 0

Decision Letter 1

George Vousden

Roles

Acceptance letter

George Vousden

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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