Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study.

BACKGROUND: Whether a healthy lifestyle impacts longevity in the presence of multimorbidity is unclear. We investigated the associations between healthy lifestyle and life expectancy in people with and without multimorbidity. METHODS AND
FINDINGS: A total of 480,940 middle-aged adults (median age of 58 years [range 38-73], 46% male, 95% white) were analysed in the UK Biobank; this longitudinal study collected data between 2006 and 2010, and participants were followed up until 2016. We extracted 36 chronic conditions and defined multimorbidity as 2 or more conditions. Four lifestyle factors, based on national guidelines, were used: leisure-time physical activity, smoking, diet, and alcohol consumption. A combined weighted score was developed and grouped participants into 4 categories: very unhealthy, unhealthy, healthy, and very healthy. Survival models were applied to predict life expectancy, adjusting for ethnicity, working status, deprivation, body mass index, and sedentary time. A total of 93,746 (19.5%) participants had multimorbidity. During a mean follow-up of 7 (range 2-9) years, 11,006 deaths occurred. At 45 years, in men with multimorbidity an unhealthy score was associated with a gain of 1.5 (95% confidence interval [CI] -0.3 to 3.3; P = 0.102) additional life years compared to very unhealthy score, though the association was not significant, whilst a healthy score was significantly associated with a gain of 4.5 (3.3 to 5.7; P < 0.001) life years and a very healthy score with 6.3 (5.0 to 7.7; P < 0.001) years. Corresponding estimates in women were 3.5 (95% CI 0.7 to 6.3; P = 0.016), 6.4 (4.8 to 7.9; P < 0.001), and 7.6 (6.0 to 9.2; P < 0.001) years. Results were consistent in those without multimorbidity and in several sensitivity analyses. For individual lifestyle factors, no current smoking was associated with the largest survival benefit. The main limitations were that we could not explore the consistency of our results using a more restrictive definition of multimorbidity including only cardiometabolic conditions, and participants were not representative of the UK as a whole.
CONCLUSIONS: In this analysis of data from the UK Biobank, we found that regardless of the presence of multimorbidity, engaging in a healthier lifestyle was associated with up to 6.3 years longer life for men and 7.6 years for women; however, not all lifestyle risk factors equally correlated with life expectancy, with smoking being significantly worse than others.

Introduction

Multimorbidity, commonly defined as the presence of 2 or more long-term physical or mental health conditions [1,2], has recently become a major worldwide epidemic [3]. Considerable evidence exists on the increased prevalence and the negative impact that multimorbidity has on patients, family, carers, and healthcare systems [1]. Nevertheless, there is still limited research on approaches to self-managing multimorbidity [3,4]. People who engage in a healthy lifestyle, such as eating a balanced diet, exercising regularly, and avoiding smoking and excess alcohol consumption, have many health benefits, especially in terms of improved longevity [5-7]; in particular, a lower alcohol intake and greater levels of physical activity have been associated with proportionally larger effects on life expectancy in large observational studies [8,9]. However, whether and to what extent a healthy lifestyle impacts on longevity in people with multimorbidity is less clear. Clarifying this uncertainty may have important individual, clinical, and public health implications, in view of the rapidly increasing trends in the prevalence of multimorbidity [3].

Life expectancy estimates are easier to understand for both the public and healthcare professionals and have become a common metric for establishing public health priorities. To date, no study has explored the association of both individual and combined lifestyle factors such as smoking, diet, and alcohol intake with life expectancy, in relation to the presence of multimorbidity [3]. Only one study assessed the relationship of combined healthy lifestyle with life expectancy in people with one or more chronic conditions [10], while the remaining investigations included individuals from the general population, where the findings showed that a combined healthy lifestyle was associated with a life expectancy between 5.4 and 18.9 years longer compared to the unhealthiest group (S1 Table) [5-7,10-19]. Most of these studies used a combined score that did not account for the differential impact of each lifestyle factor on the risk of death while the magnitude of the association may vary across multiple lifestyle factors [7,20,21].

To clarify this uncertainty, we have investigated in a contemporary population the association between individual risk factors and a healthy lifestyle with life expectancy in relation to the presence of multimorbidity.

Methods

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist) following a pre-specified protocol [22]; local Institutional Review Board ethics approval was not necessary for this study.

Study population

We used data from the UK Biobank study (Application Number 14146). UK Biobank included 502,629 middle-aged (38–73 years) adults recruited from 22 sites across England, Wales, and Scotland with baseline measures collected between 2006 and 2010 and with data linked to mortality records [22]. Written informed consent was obtained prior to data collection; UK Biobank was approved by the National Health Service (NHS) National Research Ethics Service (16/NW/0274; ethics approval for UK Biobank studies) [23]. To minimise reverse causality, we excluded participants who died within the first 2 years of follow-up (n = 2,516) [24]. Participants who withdrew from the study (n = 91), whose age during follow-up was less than 45 years (n = 30), who had missing lifestyle data (n = 16,503), or who had missing covariate data (n = 2,549) were excluded from the analysis (S1 Fig).

Multimorbidity

UK Biobank collected self-reported medical information based on physician diagnosis. To define multimorbidity, 3 sources were used to select long-term cardiovascular, non-cardiovascular, or mental health conditions. The first included conditions from the Quality and Outcomes Framework (QoF), which reports the most common diseases in the UK [25]; the second is a large UK-based study, containing 40 of the recommended core disorders for any multimorbidity measure [1]; and the last is a systematic review on multimorbidity indices that included 17 conditions [26]. Based on these sources and the data available in UK Biobank, we selected a total of 36 chronic conditions: participants with 2 or more of these conditions were classified as having multimorbidity (S1 Text). Some of the diseases previously considered in the definitions of multimorbidity have not been included in this analysis as they have been used in the statistical modelling (i.e., obesity and alcohol, as body mass index is a model covariate and alcohol consumption is part of the lifestyle score); conversely, others (anaemia, meningitis, tuberculosis, and vestibular disorders) have been added as they were deemed clinically relevant. The combination of these 3 sources to identify the conditions has been adopted also in previous studies and [8,27], particularly by including the most common QoF diseases, enhances the generalisability of the results.

Mortality

Mortality data were obtained from the NHS Information Centre for participants from England and Wales and the NHS Central Register for participants from Scotland. Data for survivors were censored on 31 January 2016 for England and Wales and 30 November 2015 for Scotland.

Healthy lifestyle

Four well-known healthy lifestyle factors, based on national guidelines [28-32], were used in this study: leisure-time physical activity, smoking, diet (fruit and vegetables), and alcohol consumption; information on these factors was collected from an in-person baseline interview at the UK Biobank centre (http://biobank.ctsu.ox.ac.uk/crystal/search.cgi).

For leisure-time physical activity, participants were asked “In the last four weeks, did you spend any time doing the following: walking for pleasure, light DIY (do-it-yourself, i.e., home maintenance and improvement and gardening activities), heavy DIY (e.g., using heavy tools, weeding, lawn mowing, digging, carpentry), strenuous sports (i.e., sports that make you sweat or breathe hard), other exercises (e.g., swimming, cycling, keep fit, bowling); none of the above.” Participants could select more than one activity and were asked to quantify their participation by frequency (i.e., number of times in the previous 4 weeks) and duration. The intensity was expressed in terms of standardised metabolic equivalent of task (MET) values: 3.5 METs for walking for pleasure; 5.5 METs for heavy DIY; 8.0 METs for strenuous sports; 4.0 METs for other activities [33]. We did not include light DIY within our definition, since we were specifically investigating moderate to vigorous intensity physical activities. The total weekly leisure-time physical activity (MET-minutes/week) was calculated by multiplying the frequency, duration, and the MET values [33]. Regular physical activity was defined as meeting the current global health recommendations for physical activity (150 minutes of moderate activity or 75 minutes of vigorous activity or an equivalent combination) [28,32], which equated to ≥500 MET-minutes/week, or no regular physical activity (<500 MET-minutes/week). Smoking was categorised as not current smoker or current smoker at the time of assessment. A healthy diet was based on eating at least 5 portions of a variety of fruit and vegetables every day following the NHS guideline [29]. To calculate the portions, we used combined responses for fresh fruit (pieces), dried fruit (pieces), salad/raw vegetable (heaped tablespoons), and cooked vegetable (heaped tablespoons): these portions were grouped as ≥5 portions/day (meet fruit/vegetable guidelines) or <5 portions/day (do not meet fruit/vegetable guidelines). The UK Biobank asked participants for the number of pints of beer, glasses of wine, and measures of spirit consumed in the last week. Alcoholic drinks differ in the amount of alcohol content, therefore each drink was converted into equivalent standard units, where 1 unit contains 10 ml of ethyl alcohol [34]. The guidelines from the Office for National Statistics (ONS) were used as they report the most updated method of converting volumes to units [34]. Total weekly units of alcohol were calculated by adding the units of beer, wine, and spirits, and participants were grouped as reporting none/moderate alcohol consumption (0–14 units per week) or excess alcohol consumption (>14 units per week), based on the NHS guidelines [30].

A weighted healthy lifestyle score, combining the 4 risk factors, was computed (details are reported in the statistical analysis).

Confounders

All models were adjusted for ethnicity (white or non-white), socioeconomic status (measured using the Townsend deprivation index, which combines census data on housing, employment, and social class based on the postal code of participants), employment status (working, retired, or other [unemployed, looking after home and/or family, unable to work because of sickness or disability, unpaid/voluntary work, full/part time student, or did not answer]), body mass index calculated during the physical assessments, and total sedentary time estimated from the sum of self-reported hours spent watching television, using the computer, and driving during a typical day: values greater than 24 hours per day were excluded, and in those reporting over 16 hours sedentary time values were winsorized at 16 hours.

Statistical analysis

To account for potential differences in the association between each lifestyle factor and mortality risk, a weighted healthy lifestyle score was computed: β coefficients of each healthy lifestyle factor were estimated using a flexible parametric Royston-Parmar proportion-hazards model that included all 4 lifestyle factors and death as an outcome [35]. Participants were classified into 2 groups: 0 (no regular physical activity; current smoking; <5 portions/day of fruit/vegetable; excess alcohol intake) and 1 (regular physical activity; no current smoking; ≥5 portions/day of fruit/vegetable; none/moderate alcohol consumption). The binary lifestyle variables for each participant were then multiplied by the standardised weighted β coefficients, summed, and grouped in 4 ordered categories (further details are provided in S2 Text): very unhealthy (score 0–0.25; reference group), unhealthy (≥0.25–0.50), healthy (≥0.50–0.75), and very healthy (≥0.75–1).

Separate models were fitted for those with and those without multimorbidity, and for men and women. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) of all-cause mortality were calculated in complete-case analysis with age as time scale: estimates were firstly obtained for the lifestyle categories and then for each individual lifestyle factor. The calculation of years of life lost (i.e., difference in average life expectancy) involved a two-step process. First, residual life expectancy was estimated as the area under the survival curve up to 100 years old, conditional on surviving at ages 45 to 100 years old (1-year intervals); survival curves were predicted for each individual and averaged over individuals. Second, years of life lost and 95% CIs were calculated as the difference between the areas under 2 survival curves, between lifestyle categories and for each individual lifestyle factors. All analyses were adjusted for confounders (i.e., ethnicity, employment status and continuous effect for deprivation, BMI, and sedentary time).

We conducted 4 sensitivity analyses to assess the robustness of our results (S2 Table). In the first, we derived β coefficients using a random one-third of the dataset and estimated the weighted score in the remaining two-thirds (S2 Text); we also analysed data after imputing missing covariates (S3 Text). In a second sensitivity analysis, we re-performed all calculations using a continuous score obtained from continuous lifestyle variables (further details are reported in S4 Text). In the third, we used a more homogenous definition of multimorbidity, limited to cardiometabolic conditions (diabetes and cardiovascular diseases: stroke, myocardial infarction, heart failure, angina or peripheral vascular disease). In the fourth, we complemented our main results with analyses using a total score derived from the sum of each score, which therefore ranged from 0 (none of the “healthy” lifestyle factors present) to 4 (all present). Lastly, we estimated HRs and years of life lost in participants without multimorbidity who were matched to those with multimorbidity (further details are provided in S5 Text).

Stata version 16.0 was used to manipulate data and perform the survival analyses (stpm2 command) [35]. Results are reported with two-sided 95% CI.

Results

Baseline characteristics

In 480,940 participants, the 5 most prevalent chronic conditions for men were hypertension (29.6%), asthma (10.7%), cancer (6.3%), diabetes (5.8%), and angina (4.6%) and for women hypertension (22.7%), asthma (12.3%), cancer (9.8%), depression (6.7%), and migraine (4.2%); a total of 93,746 (19.5%) participants had multimorbidity (S3 and S4 Tables). Most participants were white (94.8%), with a median (range) age of 58 (38–73) years. Compared to participants without multimorbidity, those with multimorbidity were older (61 [54-65]) versus 57 [49-63] years, respectively) and more likely to live in deprived areas, be retired (45.4% versus 30.2%, respectively), and spend more time in sedentary activities (Table 1).

Table 1

Baseline characteristics of participants by multimorbidity status.

Characteristics	With multimorbidity(n = 93,746)	Without multimorbidity(n = 387,194)
Age, median [IQR], y	61 [54–65]	57 [49–63]
Sex
Women	50,298 (53.7)	211,814 (54.7)
Men	43,448 (46.4)	175,380 (45.3)
Ethnicity
White	88,863 (94.8)	367,234 (94.8)
Non-white	4,883 (5.2)	19,960 (5.2)
Employment status
Working	34,438 (41.0)	239,910 (62.0)
Retired	42,538 (45.4)	116,820 (30.2)
Othera	12,770 (13.6)	30,464 (7.8)
Deprivation index,b mean [SD]	−0.9 [3.3]	−1.4 [3.0]
BMI, mean [SD], kg/m2	29.0 [5.5]	27.0 [4.5]
Sedentary behaviour,c mean [SD], h	5.4 [2.5]	5.0 [2.3]
Lifestyle factorsd
Regular physical activity	41,809 (44.6)	209,186 (54.0)
Not currently smoking	83,633 (89.2)	347,499 (89.8)
Healthy diet	36,734 (39.2)	145,946 (37.7)
None/moderate alcohol consumption	62,322 (66.5)	238,194 (61.5)
Healthy lifestyle categories
Very unhealthy	7,822 (8.3)	28,814 (7.4)
Unhealthy	2,291 (2.4)	10,881 (2.8)
Healthy	32,624 (34.8)	118,317 (30.6)
Very healthy	51,009 (54.4)	229,182 (59.2)

Shown are numbers (%) unless stated otherwise.

aOther = unemployed, student, volunteer, or missing.

bDeprivation = Townsend deprivation index was used as a measure of socioeconomic status, which combines census data on housing, employment, social class, and car availability based on the postal code of participants.

cSedentary = total number of self-reported hours spent watching television, using the computer, or driving.

dRegular physical activity: ≥500 MET-minutes/week; None/moderate alcohol consumption: 0 to 14 units of alcohol a week; Healthy diet: at least 5 portions of fruit and vegetables every day.

Abbreviations: BMI, body mass index; IQR, interquartile range; MET, metabolic equivalent of task

The lifestyle factors at baseline showed fewer participants with multimorbidity engaging in regular physical activity compared to those without multimorbidity (44.6% versus 54.0%, respectively) but slightly more reported a healthy diet (39.2% versus 37.7%) and consumed none or a moderate amount of alcohol (66.5% versus 61.5%). There was a similar proportion of participants who were not currently smokers (89.2% versus 89.8%). For the combined healthy lifestyle score, in participants with multimorbidity 8.3% were very unhealthy, 2.4% unhealthy, 34.8% healthy, and 54.4% very healthy; corresponding estimates in participants without multimorbidity were 7.4%, 2.8%, 30.6%, and 59.2% (Table 1).

During a mean follow-up of 7 (range, 2–9) years and 3.34 million person-years, 11,006 deaths were recorded. Compared to the reference group (very unhealthy), the adjusted HRs of mortality were lower in healthier groups in both men and women, ranging from HR 0.83 (95% CI 0.66–1.03; P = 0.096) to 0.40 (0.34–0.47; P < 0.001) in those with multimorbidity and from 0.84 (0.68–1.04; P = 0.111) to 0.35 (0.32–0.39; P < 0.001) in those without (Fig 1).

Fig 1

HRs of death by lifestyle score.

Models adjusted for ethnicity (white, non-white), working status (working, retired, other), deprivation (continuous), body mass index (continuous), sedentary time (continuous). CI, confidence interval; HR, hazard ratio; No., number.

Life expectancy rose as the level of healthy lifestyle increased (Table 2 and Fig 2). After covariate adjustments, at the age of 45 years in men with multimorbidity, an unhealthy score was associated with a gain of 1.5 (95% CI −0.3 to 3.3; P = 0.102) additional life years compared to very unhealthy; a healthy score with 4.5 (3.3–5.7; P < 0.001) years; and a very healthy score with 6.3 (5.0–7.7; P < 0.001) years. Corresponding estimates in women with multimorbidity were 3.5 (95% CI 0.7–6.3; P = 0.016), 6.4 (4.8–7.9; P < 0.001), and 7.6 (6.0–9.2; P < 0.001) years. In men without multimorbidity, an unhealthy score was associated with a gain of 2.8 (95% CI 1.5–4.1; P < 0.001) additional life years compared to very unhealthy, a healthy score with 5.7 (4.7–6.7; P < 0.001), and a very healthy score with 7.6 (6.5–8.6; P < 0.001) years. Corresponding estimates in women were 1.3 (95% CI −0.3 to 3.0; P = 0.111), 6.0 (4.9–7.2; P < 0.001), and 6.5 (5.4–7.6; P < 0.001) years. The pattern of results was similar at the age of 65 years (Table 2 and Fig 2).

Table 2

Years of life gained at age 45 and 65 years by lifestyle score.

Healthy lifestyle category	With multimorbidity				Without multimorbidity
	Men(n = 43,448)	P value	Women(n = 50,298)	P value	Men(n = 175,380)	P value	Women(n = 211,814)	P value
Years of life gained [95% CI], 45 y
Very unhealthy	Reference	-	Reference	-	Reference	-	Reference	-
Unhealthy	1.50 [−0.30 to 3.30]	0.102	3.48 [0.65 to 6.31]	0.016	2.77 [1.49 to 4.05]	<0.001	1.34 [−0.31 to 2.99]	0.111
Healthy	4.52 [3.30 to 5.73]	<0.001	6.36 [4.79 to 7.94]	<0.001	5.66 [4.65 to 6.66]	<0.001	6.03 [4.90 to 7.15]	<0.001
Very healthy	6.33 [4.98 to 7.69]	<0.001	7.59 [6.01 to 9.16]	<0.001	7.56 [6.47 to 8.64]	<0.001	6.49 [5.39 to 7.59]	<0.001
Years of life gained [95% CI], 65 y
Very unhealthy	Reference	-	Reference	-	Reference	-	Reference	-
Unhealthy	1.20 [−0.26 to 2.65]	0.106	2.94 [0.53 to 5.36]	0.017	2.39 [1.26 to 3.52]	<0.001	1.19 [−0.28 to 2.66]	0.112
Healthy	3.70 [2.67 to 4.73]	<0.001	5.43 [4.07 to 6.79]	<0.001	4.96 [4.05 to 5.88]	<0.001	5.42 [4.40 to 6.45]	<0.001
Very healthy	5.26 [4.09 to 6.42]	<0.001	6.50 [5.13 to 7.86]	<0.001	6.70 [5.69 to 7.70]	<0.001	5.85 [4.84 to 6.85]	<0.001

Models adjusted for ethnicity (white, non-white), working status (working, retired, other), deprivation (continuous), body mass index (continuous), sedentary time (continuous).

Abbreviation: CI, confidence interval

Fig 2

Years of life gained by lifestyle score.

Reference group is the very unhealthy group. Models adjusted for ethnicity (white, non-white), working status (working, retired, other), deprivation (continuous), body mass index (continuous), sedentary time (continuous).

Individual lifestyle factor

The associations between individual healthy lifestyle factors and survival are presented in Table 3. The largest survival difference was observed for the risk factor smoking: the adjusted mortality rate comparing non-current versus current smoker in participants with multimorbidity was 46% lower (HR 0.54 [95% CI 0.49–0.60; P < 0.001]) in men and 52% lower (HR 0.48 [0.42–0.55; P < 0.001]) in women; corresponding estimates in participants without multimorbidity were 0.45 (0.41–0.48; P < 0.001) and 0.44 (0.40–0.49; P < 0.001). At the age of 45 years, in participants with multimorbidity who do not currently smoke, the estimated life expectancy compared to those who smoke was 4.9 (95% CI 3.8–6.1; P < 0.001) years longer in men and 5.9 (4.6–7.3; P < 0.001) years longer in women; in those without multimorbidity, corresponding estimates were 5.9 (5.0–6.8; P < 0.001) and 5.8 (4.8–6.7; P < 0.001) years.

Table 3

Survival by individual lifestyle factor.

Healthy lifestyle factor	With multimorbidity				Without multimorbidity
	Men(n = 43,448)	P value	Women(n = 50,298)	P value	Men(n = 175,380)	P value	Women(n = 211,814)	P value
Regular physical activity
No: No. of deaths/participants	1,561 / 21,770		1,065 / 30,167		1,910 / 70,847		1,571 / 107,161
Yes: No. of deaths/participants	1,058 / 21,678		494 / 20,131		2,083 / 104,533		1,264 / 104,653
HR (95% CI), Yes vs. No (reference)	0.73 (0.67 to 0.79)	<0.001	0.77 (0.69 to 0.86)	<0.001	0.78 (0.73 to 0.83)	<0.001	0.87 (0.81 to 0.94)	<0.001
Years of life gained [95% CI], 45 y	2.49 [1.75 to 3.24]	<0.001	1.88 [1.08 to 2.68]	<0.001	1.80 [1.30 to 2.31]	<0.001	0.88 [0.39 to 1.36]	<0.001
Years of life gained [95% CI], 65 y	2.11 [1.47 to 2.76]	<0.001	1.62 [0.92 to 2.31]	<0.001	1.63 [1.17 to 2.08]	<0.001	0.79 [0.36 to 1.24]	<0.001
Smoking
Smoker: No. of deaths/participants	518 / 5,254		269 / 4,859		924 / 21,544		447 / 18,151
No current smoking: No. of deaths/participants	2,101 / 38,194		1,290 / 45,439		3,069 / 153,836		2,388 / 193,663
HR (95% CI), No vs. Yes (reference)	0.54 (0.49 to 0.60)	<0.001	0.48 (0.42 to 0.55)	<0.001	0.45 (0.41 to 0.48)	<0.001	0.44 (0.40 to 0.49)	<0.001
Years of life gained [95% CI], 45 y	4.94 [3.83 to 6.06]	<0.001	5.94 [4.61 to 7.27]	<0.001	5.88 [4.98 to 6.77]	<0.001	5.78 [4.83 to 6.72]	<0.001
Years of life gained [95% CI], 65 y	4.09 [3.13 to 5.04]	<0.001	5.09 [3.94 to 6.24]	<0.001	5.21 [4.38 to 6.03]	<0.001	5.21 [4.34 to 6.07]	<0.001
Healthy diet
No: No. of deaths/participants	1,789 / 28,903		912 / 28,109		2,867 / 121,358		1,574 / 119,890
Yes: No. of deaths/participants	830 / 14,545		647 / 22,189		1,126 / 54,022		1,261 / 91,924
HR (95% CI), Yes vs. No (reference)	0.93 (0.85 to 1.01)	0.072	0.91 (0.82 to 1.01)	0.065	0.88 (0.82 to 0.94)	<0.001	0.97 (0.90 to 1.05)	0.494
Years of life gained [95% CI], 45 y	0.61 [−0.06 to 1.28]	0.074	0.70 [−0.04 to 1.44]	0.063	0.90 [0.39 to 1.40]	0.001	0.17 [−0.31 to 0.64]	0.493
Years of life gained [95% CI], 65 y	0.51 [−0.05 to 1.08]	0.076	0.60 [−0.04 to 1.24]	0.066	0.81 [0.35 to 1.26]	0.001	0.15 [−0.28 to 0.58]	0.504
Alcohol consumption
Excess: No. of deaths/participants	1,157 / 20,612		281 / 10,812		2,156 / 91,566		704 / 57,434
None/moderate: No. of deaths/participants	1,462 / 22,836		1,278 / 39,486		1,837 / 83,814		2,131 / 154,380
HR (95% CI), None/moderate vs. Excess (reference)	1.09 (1.01 to 1.18)	0.029	1.15 (1.01 to 1.31)	0.041	0.95 (0.89 to 1.01)	0.123	1.03 (0.94 to 1.12)	0.560
Years of life gained [95% CI], 45 y	−0.69 [−1.32 to −0.06]	0.032	−0.98 [−1.90 to −0.05]	0.038	0.35 [−0.10 to 0.80]	0.127	−0.16 [−0.70 to 0.38]	0.573
Years of life gained [95% CI], 65 y	−0.58 [−1.12 to −0.05]	0.033	−0.84 [−1.64 to −0.04]	0.039	0.32 [−0.09 to 0.72]	0.121	−0.15 [−0.64 to 0.34]	0.560

Regular physical activity: ≥500 MET-minutes/week; Healthy diet: at least 5 portions of fruit and vegetables every day; None/moderate alcohol consumption: 0 to 14 units of alcohol a week. Model adjusted for ethnicity (white, non-white), working status (working, retired, other), deprivation (continuous), body mass index (continuous), sedentary time (continuous), and all other healthy lifestyle factors. The reference for years of life gained is the same used for HR.

Abbreviations: CI, confidence interval; HR, hazard ratio; MET, metabolic equivalent of task; ref, reference

Regular physical activity was associated with the second highest survival benefit. At the age of 45 years, regular physical activity was associated with 2.5 (95% CI 1.8–3.2; P < 0.001) years longer life expectancy in men and 1.9 (1.1–2.7; P < 0.001) in women with multimorbidity; in those without multimorbidity, corresponding estimates were 1.8 (1.3–2.3; P < 0.001) and 0.9 (0.4–1.4; P < 0.001) years. The years of life gained were smaller for alcohol consumption and healthy diet.

Sensitivity analyses

The main results were confirmed in the first sensitivity analysis, using a third of the population to estimate the weighted score (S5 Table) or following imputation of missing data (S6 and S7 Tables). In the second sensitivity analysis, the pattern of the main results showing a similar benefit regardless of the presence of multimorbidity was confirmed when a continuous score was obtained from the entire population (S2 Fig and S8 Table), in one-third of the population (S3 Fig and S9 Table), or following imputation of missing data (S4 Fig and S10 Table). When the outcome was limited to cardiometabolic multimorbidity (third sensitivity analysis), the number of participants and events was significantly lower compared to multimorbidity defined using the main definition (3,804 versus 93,746 individuals), particularly women: this resulted in imprecise estimates of HR and years of life gained across groups defined by the weighted score (S11 Table). Similarly, very few participants and events were observed when investigating each lifestyle factor, yet the pattern was qualitatively similar to the main results indicating a greater relevance on life expectancy of physical activity and smoking compared to alcohol consumption and healthy diet (S12 Table). Although years of life gained were slightly greater comparing heathiest versus unhealthiest groups, the main results were largely confirmed using a score obtained from the sum of each “healthy” lifestyle and multimorbidity as outcome (S13 Table); however, imprecise or no estimates were obtained using the same score and cardiometabolic multimorbidity as outcome, due to very few participants and events (S14 Table). Lastly, the main results were confirmed in the cohort of participants without multimorbidity matched to those with multimorbidity (S5 Fig and S15 Table).

Discussion

Our results indicate that in participants with the healthiest lifestyle score, at 45 years the average life expectancy was about 7.6 years longer in men and 6.5 years longer in women compared to those reporting the lowest lifestyle score; conversely, the impact of multimorbidity was approximately 1-year difference: 6.3 years in men and 7.6 in women. These findings have relevant individual, clinical, and public health implications as the results suggest that a healthier lifestyle is similarly associated with longevity regardless of the presence of multimorbidity. Our results also confirmed that not all lifestyle risk factors are equal, and most of the reduction in life expectancy was related to smoking: at 45 years, current smokers had an estimated 5 to 6 years shorter life expectancy versus non-current smokers; in comparison, regular physical activity was associated with 1 to 2.5 longer life expectancy versus those not reporting physical activity, while uncertain and smaller associations were observed for healthy diet and alcohol intake.

To our knowledge, this is the first study to quantify whether the risk of death associated with individual and combined risk factors (accounting for their heterogeneous prognostic relevance) was dependent on the presence of multimorbidity. In terms of relative risk, a previous meta-analysis included 15 studies and found that a combination of at least 4 healthy lifestyle factors was associated with a 66% (95% CI 58%–73%) lower risk of mortality [21]. Our results for the healthiest group indicated a 60% lower risk of mortality compared to the unhealthiest group in people with multimorbidity and a 65% lower risk in those without multimorbidity. Moreover, when we used a similar score (count of lifestyle factors), our result indicated a risk reduction ranging from 66% to 71% in relation to sex and presence of multimorbidity, in noticeable agreement with the pooled meta-analytical estimate.

In our systematic search, we found 13 relevant studies, all of which showed a positive association between a healthy lifestyle and life expectancy (S1 Table) [5-7,10-19]. A study in Sweden stratified analyses by the presence of chronic conditions: comparing individuals with low (normal weight, never smoked, participation in at least one leisure activity, and a rich or moderate social network) versus high (overweight or underweight, current or former smokers, no participation in leisure activities, and a limited or poor social network) risk profile, differences in life expectancy were 4.7 years if they had one or more chronic conditions and 3 years if they had no chronic conditions [10]—, though this study population was small (n = 1,661) and included participants over the age of 75 years. Other studies included individuals from the general population and did not investigate differences by multimorbidity status. The results from the general population showed that a combined healthy lifestyle was associated with a longer life expectancy between 5.4 to 18.9 years, compared to the unhealthiest group.

Most of the estimates are higher compared to our study (ranging from 6.3 to 7.6 years), possibly because the definition of a healthy lifestyle was mainly based on non-weighted scores: greater differences comparing healthiest versus unhealthiest groups were indeed observed also in our study when using a non-weighted score. When each risk factor is first dichotomised (score 0: absent; score 1: present) and an overall score obtained as the sum of each score, an equivalent impact of the lifestyle factors on the risk of the outcome is assumed; while this approach has arguably a more immediate public health interpretation, the resulting associations may be larger when participants are grouped into “healthy” (all favourable lifestyle factors) versus “unhealthy” (all unfavourable lifestyle factors). However, it should be also noted that the close agreement between our estimates and those reported in 2 very recent studies (indicating differences in life expectancy between 7.1 and 9.4 years in women and 8.0 and 9.9 years in men comparing healthiest versus unhealthiest using non-weighted sum scores) [18,19] would suggest that, beyond the metric used to define the score, other factors are relevant as well. To our knowledge, only one study from Canada used the individual lifestyle mortality risks when predicting life expectancy [11,12].

The lifestyle factors chosen in this study were smoking and alcohol consumption, physical activity, and nutrition, as these health-related behaviours are related to several individual chronic diseases and are modifiable [3,20]. We found that not smoking had the largest impact on life expectancy for people with and without multimorbidity, similar to studies from the general population [5]. This emphasises the importance of smoking cessation. A healthy diet was defined as eating at least 5 portions of a variety of fruit and vegetables every day [29], as it has been suggested to have beneficial impact on health. A meta-analysis found that a high diet score that included fruit and vegetable intake was associated with a significant reduction in the risk of all-cause mortality, cardiovascular disease, cancer, and type 2 diabetes mellitus [36]. For alcohol intake, we found no meaningful difference in life expectancy: this could be a reflection of participants underreporting alcohol intake. Previous literature reports mixed results about alcohol consumption and risk of death, also quantified in terms of life expectancy [6,7,11].

Multimorbidity is a complex concept. The National Institute for Health and Care Excellence (NICE) UK has recently released guidelines for the assessment and management of people with multimorbidity: the key message from these guidelines is the individualised care [2]. However, whilst a tailored, individual approach mainly focuses on the management of pharmacological interventions, it remains unclear whether and to what extent unhealthy lifestyle behaviours are associated with a higher risk of death in patients with multimorbidity. In this respect, our study significantly contributes to the current evidence: in fact, by providing strong evidence using relative and absolute measures that a healthy lifestyle is equally important in people with and without multimorbidity, it suggests that public health recommendations about engaging in a healthy lifestyle to reduce the risk of developing chronic long-term conditions equally apply to patients who have already multimorbidity, confirming the importance of a healthy lifestyle throughout the entire lifespan. While multimorbidity is more prevalent in young and middle-aged adults living in the most socioeconomically deprived areas [1], where engaging in a healthy lifestyle could be more difficult, our study also found that certain lifestyle factors are more relevant than others; therefore, public health policies could focus on few, stronger risk factors (i.e., smoking) rather than on costly strategies addressing multiple risk factors. Similarly, when it is proven difficult to reduce all risk factors, individual decision of healthcare professionals may focus on stronger determinants of life expectancy, thus individualising the care of patients with multimorbidity in line with NICE guidance.

This study has several limitations. Firstly, participants from the UK Biobank were volunteers with slightly higher representation from affluent groups; therefore, participants may not be completely representative of the UK population [37]. While the evidence of low generalisability of UK Biobank is documented [38], participants need not be representative of the “target” populations when estimating relative risk factor associations, as expected from a theoretical point of view [39,40] and empirically demonstrated specifically for UK Biobank [41]. Absolute estimates, conversely, are related to the mortality rates in the sample population: as mortality rates in UK Biobank are lower than those in the general population [38] and the relative estimates are applicable to the general population, the differences in years of life quantified in our analyses are likely smaller than those in the general population, further underlining the significant potential benefit of a healthy lifestyle. Second, although participants who died within the first 2 years of follow-up were excluded to reduce the risk of reverse causation [24], it is still possible that participants with multimorbidity may generally be less well, which could result in unhealthy lifestyle behaviours and a higher mortality rate, or adherence to a healthier lifestyle may be associated to a greater adherence to medications. Third, the lifestyle factors were assessed at a single time point, which did not take into account lifestyle changes before or after assessment, and the study was limited to mortality end point. Fourth, lifestyle behaviours are all self-reported measures, which could lead to inaccurate responses, although most large epidemiological studies rely on self-reported questionnaires; however, self-reported physical activity has been found to have a moderate correlation with objective accelerometer measures [42]. Fifth, we did not include other healthy lifestyle factors that could also have an independent association such as sleep duration, other dietary variables (including red or processed meat consumption), or sedentary time. However, in our analyses, we did adjust for sedentary time. Sixth, there is currently no standard definition of multimorbidity [3]. We defined multimorbidity as the presence of 2 or more chronic conditions among 36 conditions that are the core entities in several multimorbidity measures [1,26,27]. Although some studies used a larger number of conditions, we opted for 2 or more as this is the most common approach [3]. Moreover, we searched among the most common QoF diseases to enhance the generalisability of the results. It is also worth noting that, in a previous study, using 2 different methods to define multimorbidity (accounting for the frequency of comorbidities and for self-reported overall health—a proxy of disease severity) showed consistent results regardless of the definition used [8]. Yet we recognise that it remains unclear to what extent the number of conditions or some clusters of multimorbidity modify the association between healthy lifestyles and life expectancy [27]. In the attempt to define a more coherent and homogeneous group of conditions designating multimorbidity, we also explored associations in participants with cardiometabolic multimorbidity. However, we could not consistently compare the results across the 2 definitions because there were very few participants with cardiometabolic multimorbidity: in our analysis, 5.4% had stroke or ischaemic heart disease, whereas in 2017 in the UK, the prevalence ranged from 6.6% in the age group 45–54 years to 21.6% in the age group 65–74 years [43]. Therefore, whether lifestyle factors and an overall healthier lifestyle is differently associated with life expectancy in relation to the pathophysiological characteristics of the chronic conditions should be explored in further studies. Finally, this was an observational study, and causality cannot be demonstrated.

The overall large sample size, which allowed estimations of the life expectancy by multimorbidity status and sex, is a strength of this study. Another major strength is the reporting of relative as well as absolute measures; absolute measures, particularly how many years of additional life could be gained due to a healthy lifestyle, are easy to interpret and could motivate individuals when considering a lifestyle change. Additionally, we used a weighted healthy lifestyle score as main exposure and complemented the main analysis with sensitivity investigations employing a non-weighted score to assess the robustness of our result, enhance the public health message, and facilitate the comparison with previous literature. Lastly, we based our healthy lifestyle factors on recommended national guidelines for the general population, although personalised lifestyle programs should also consider an individual patient’s characteristics and comorbidities [44].

In conclusion, our findings suggest that engaging in a healthy lifestyle could significantly improve life expectancy regardless of the presence of multimorbidity.

List of the 36 chronic conditions included within the definition of multimorbidity.

(DOCX)

Click here for additional data file.

Weighted healthy lifestyle score.

(DOCX)

Click here for additional data file.

Missing lifestyle and covariate data.

(DOCX)

Click here for additional data file.

Continuous weighted healthy lifestyle score.

(DOCX)

Click here for additional data file.

Matching.

(DOCX)

Click here for additional data file.

Previous studies investigating combined lifestyle factors and life expectancy.

(DOCX)

Click here for additional data file.

Summary of main and sensitivity analyses.

(DOCX)

Click here for additional data file.

Most to least prevalent chronic conditions, by sex.

(DOCX)

Click here for additional data file.

Number of participants by total number of chronic conditions.

(DOCX)

Click here for additional data file.

Survival using the weighted score obtained from a random one-third of the population.

(DOCX)

Click here for additional data file.

Survival using the weighted score following imputation of missing data.

(DOCX)

Click here for additional data file.

Survival using individual lifestyle factor following imputation of missing data.

(DOCX)

Click here for additional data file.

Survival using the continuous weighted lifestyle score (CIs).

CI, confidence interval

(DOCX)

Click here for additional data file.

Survival using the continuous weighted lifestyle score obtained from a random one-third of the population (CIs).

CI, confidence interval

(DOCX)

Click here for additional data file.

Survival using the continuous weighted lifestyle score following imputing missing data (CIs).

CI, confidence interval

(DOCX)

Click here for additional data file.

Survival using weighted score by cardiometabolic multimorbidity.

(DOCX)

Click here for additional data file.

Survival using individual lifestyle factor by cardiometabolic multimorbidity.

(DOCX)

Click here for additional data file.

Survival using number of healthy lifestyle risk factors (score 0–4) by multimorbidity.

(DOCX)

Click here for additional data file.

Survival using number of healthy lifestyle risk factors (score 0–4) by cardiometabolic multimorbidity.

(DOCX)

Click here for additional data file.

Survival in the matched cohort.

(DOCX)

Click here for additional data file.

Flow chart of participants included in the study.

(DOCX)

Click here for additional data file.

Estimated residual life expectancy using the continuous weighted lifestyle score.

(DOCX)

Click here for additional data file.

Estimated residual life expectancy using the continuous weighted lifestyle score obtained from a random one-third of the population.

(DOCX)

Click here for additional data file.

Estimated residual life expectancy using the continuous weighted lifestyle score following imputation of missing data.

(DOCX)

Click here for additional data file.

Years of life gained in the matched cohort.

(DOCX)

Click here for additional data file.

STROBE Checklist.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology

(DOCX)

Click here for additional data file.

14 Apr 2020

Dear Dr Chudasama,

Thank you for submitting your manuscript entitled "Healthy Lifestyle and Life Expectancy in people with Multimorbidity: a UK Biobank prospective study" for consideration by PLOS Medicine.

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11 May 2020

Dear Dr. Chudasama,

Thank you very much for submitting your manuscript "Healthy Lifestyle and Life Expectancy in people with Multimorbidity: a UK Biobank prospective study" (PMEDICINE-D-20-01260R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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a. Please include the study design, population demographics (eg. age range, sex), and dates during which the study data were collected.

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a. Citations in the main text should come before punctuation, e.g., "... multimorbidity measures [1,21,24].

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----

Comments from the reviewers:

Reviewer #1: I confine my remarks to statistical aspects of this paper.

There is one major problem that, unfortunately, means that all the analysis has to be redone.

The authors have categorized every continuous variable. This is a mistake. Categorizing continuous variable increases both type I and type II error, it also introduces a kind of magical thinking - i..e. that something amazing happens right at the cutpoint. Frank Harrell, in *Regression Modelling Strategies* listed 11 problems that categorizing independent variables can cause and summed up "nothing could be more disastrous". I wrote a blog post demonstrating some of these problems graphically https://medium.com/@peterflom/what-happens-when-we-categorize-an-independent-variable-in-regression-77d4c5862b6c

All the variables should be left continuous. Splines can be used to look for nonlinearities.

These changes would affect all of the subsequent write up, so I will wait for a revision to do a review of those parts.

Peter Flom

Reviewer #2: This paper reports data on lifestyle and life expectancy in multimorbidity from the UK Biobank. This is an important topic and the dataset is sufficiently large to address the study question. I have the following comments for the author to consider:

1. The design is likely to introduce reverse causation bias in particularly because the authors have chosen a very broad definition for multimorbidity (any two or more of the 36 health conditions which vary in terms of severity). For example, a multimorbidity case with 2+ severe and disabling diseases may limited ability to exercise unlike another multimorbidity case with 2 mild health conditions; the association of physical inactivity with life expectancy will be overestimated in this case as the baseline difference in mortality risk between the two cases is not accounted for (ie the participant with 2+ severe conditions has a higher risk of dying independently of physical activity). The authors' attempt to reduce this kind of bias by excluding the first years of follow-up is a good but only partial solution. For this reason, I suggest they run a sensitivity analysis using a more homogeneous definition for multimorbidity - e.g. by looking the associations of lifestyle factors &score with life expectancy in participants with cardiometabolic multimorbidity (ie a combination of cvd and diabetes).

2. The description of multimorbidity definition seems insufficient. It remains unclear how the authors decided which 36 chronic conditions they included in the definition. Why 36 rather than some other quantity and why these specific diseases? Is this a new definition or used also previously? Multimorbidity is a key variable in this paper, so the rationale for the definition should be clear.

3. The authors use a weighted lifestyle score which may introduce circularity bias. To obtain the weights, the authors first compute beta coefficients for each dichotomised lifestyle factor-mortality association. Then they construct a weighted lifestyle score by taking the sum of dichotomised lifestyle factors multiplied by the beta coefficient obtained from the mortality analysis. With this weighted lifestyle score, they estimate differences in life expectancy between those with higher and lower weighted lifestyle score - the main study question. These differences are expected because the weights for the exposure were based on information (ie mortality) from the outcome (life expectancy) - hence the circularity. I suggest that the authors run complementary analyses using a simple sum of dichotomised lifestyle factors as the exposure (range from 0 to 4). Unike the weighted lifestyle score, this indicator will allow comparison of the present findings to those from other studies in the field and it is not subject to circularity.

4. The authors have previously published on physical activity and life expectancy in multimorbidity using UK Biobank (BMC Med 2019) - this study should be noted in the introduction. The same in the description of the assessment of physical activity in this paper - did the authors use the same operationalision? Are the findings on physical activity and life expectancy the same as in the previous paper?

5. Further details are needed on how winsorizing was done as there are many options.

6. How the cut points for 'very unhealthy', 'unhealthy' etc for the lifestyle score categories were chosen?

7. I am surprised by the prevalence of chronic conditions. Why cancer is more common than diabetes in men? Why cancer is more common than depression in women? Are there figures correct; sat least, they seem not to correspond to those observed in the general population. If correct, some discussion is needed on the reliability of measuring diseases using self-reports in the UK Biobank.

8. Discussion, first para. Two main findings are described. However, I do not think the comparison of lifestyle score vs multimorbidity in terms of which is more strongly associated with life expectance is meaningful. With such a broad definition of multimorbidity (any 2+ conditions from a list 36 diseases), the reduction in life expectancy is heavily affected by the specific distribution of the 36 conditions in this highly-selected study population. The finding is by no means generalisable. Thus, I would drop that from the synopsis of the main findings. The other main finding is that "not all lifestyle risk factors are equal" - this has long been known and has been well documented, so I suggest the authors also drop that point. In my opinion, the main finding of this study is that a heathy lifestyle is equally important in term of life expectancy for people with and without multimorbidity. This is a novel and surprising finding which the author should highlight more as it shows how important these factors are for the prognosis/outcome of multimorbidity.

9. Discussion, 2nd para. Here results from a supplementary analysis of unweighted lifestyle score (the sum of lifestyle risk factors) would allow a more direct comparison for other studies.

10. Limitations section. A bit more discussion on generalisability is needed. The 5% response rate in the UK Biobank is exceptionally low by any standards. Selection has been shown to have affected disease prevalence in the cohort. But the key issue here is whether selection is likely to have affected associations between lifestyle and life expectancy. There are studies comparing risk factor-disease outcome associations in UK Biobank and studies with conventional response rates which could help to evaluate this.

11. Several recent studies have examined lifestyle scores in relation to disease-free life expectancy and the results are well in agreement with the current figures on life expectancy - approximately 10+-2 years difference between people with the healthiest versus unhealthiest lifestyle factors (e.g. Zaninotto et al Sci Rep 2020, Nyberg et al JAMA Intern Med, Li et al BMJ 2020). The authors might consider highlighting this close agreement in results across health span and life span.

12. Final paragraph of the discussion, the last sentence. It is well-known that risk factors are not equally strongly associated with life expectancy or mortality - highlighting this as a main conclusion makes this paper look quite non-innovative. I suggest dropping the last sentence.

Reviewer #3: In this study, the authors determined the effect of adherence to a healthy lifestyle on life expectancy in adults with and without known co-morbidities. Using the data from U.K. Biobank, the authors concluded that regardless of the presence of multimorbidity, engaging in a healthier lifestyle is associated with up to 8 years longer life. The study is of great public health importance.

I have the following comments and suggestions:

1. Given that these results are of great interest to public health, I suggest that scoring of adherence to a healthy lifestyle should be defined differently and simplified. For each lifestyle factor studied, the participants get a score of 1 if they met the healthy definition and 0 if they not. Then, sum these 4 scores and create an overall index of healthy lifestyle ranging from 0-4, with higher scores indicating a healthier lifestyle. The advantage of this simple score is that we get a sense of risk reduction or increased life expectancy if the population shifts to a healthier lifestyle (e.g., from 2 to 4 healthy lifestyle factors). Weighing the score is a sound method that the authors clearly explain in the Discussion, but does not align well with the public health interest.

2. The group with multimorbidity is very heterogeneous, while the group without co-morbidity is homogenous. The study population with multimorbidity included adults with less life-threatening conditions such as glaucoma, hypertension, sinusitis, rheumatoid arthritis, and those with diseases such as heart disease, stroke, cancer, dementia that are leading causes of death. I suggest authors create subgroups of people with multimorbidity and take into account the severity of the disease. Or, authors may apply a weighted score to account for the disease severity.

3. The authors compare people with and without multimorbidity regarding the effect of lifestyle factors and life expectancy, but how different are those groups concerning sample size, demographic, lifestyle, and other clinical factors? To have a fair comparison between groups, the authors should match people with and without multimorbidity and conduct the analysis.

4. The authors report more than 50% of the study population as very healthy according to the weighted score. In the U.S., about 5% of the study population met the overall healthy lifestyle (Li et al. BMJ 2020; 368).

Any attachments provided with reviews can be seen via the following link:

[LINK]

8 Jun 2020

Submitted filename: R1_Response to reviewers.pdf

Click here for additional data file.

30 Jun 2020

Dear Dr. Chudasama,

Thank you very much for submitting your manuscript "Healthy Lifestyle and Life Expectancy in people with Multimorbidity: a UK Biobank study" (PMEDICINE-D-20-01260R2) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent once more to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will still not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Jul 21 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

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Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from the editors:

1. Please respond to the comments of reviewer #3, specifically relating to an additional matching/specificity analysis.

2. Abstract:

a. Line 53: Given the high p value for this particular result, please rephrase the following to say that the association was not significant: “At 45 years, in men with multimorbidity an unhealthy score was associated with a gain of 1.5 (95% CI: -0.3, 3.3; P=0.572) additional life…”

b. In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology more clearly, and use the word “limitation(s)”.

c. Line 65: Please replace “contributed to” with “correlated with”.

3. Please remove spaces in your citation callouts, eg. “…mental health conditions [1,2],…”

4. Please move the Ethics statement from page 21 to the Methods section.

5. Please move the Data statement from page 21 to the submission form.

6. Please provide more access details (eg. URL, DOI, or issue/page nos.) for references 12, 16, and 34.

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Comments from the reviewers:

Reviewer #1: The authors have addressed my concerns and I now recommend publication

Peter Flom

Reviewer #3: I thank the authors for responding to my comments.

I noted that when the analysis was focused on individuals with diabetes, heart disease and stroke, authors reported "imprecise HR and years of life gained estimates" due to the limited number of people with events. Does this indicate a presence of selection bias in the study? The authors are studying life expectancy, but the number of people with life-threatening diseases such as heart disease and stroke does not allow authors to conduct a rigorous analysis among this group? The authors may compare the prevalence of the cardiovascular disease in the UK biobank with national statistics in the UK.

Concluding that the effect of lifestyle factors is similar in people with and without multimorbidity is simplified, in my opinion. First, there is no analysis to compare head-to-head the role of lifestyle factors on life expectancy between the groups with and without multimorbidity. The similarities or differences in life expectancy in each group could be explained by other factors and not necessarily to the adherence to a healthy lifestyle. For this reason, I recommended that authors conduct a matching analysis (as a sensitivity analysis), in which they will select people with multimorbidity with similar lifestyle scores and other characteristics (age, gender, BMI, social status) to people without multimorbidity. As a result of matching, they will create two "identical" groups in terms of sample size, lifestyle, and other characteristics, but different from the presence of multimorbidities. Then, in each group, separately, they will compare the role of adherence to a healthy lifestyle in life expectancy. Second, there is no information about the severity of diseases and the role of medications in people with multimorbidities. It could be that those who adhere to a healthier lifestyle could have less severe conditions and proper adherence to medication, suggesting that these findings could be attributed to disease severity and treatment management, not lifestyle factors adherence. Third, from the public health perspective, the guidelines that authors based on the lifestyle score (e.g., physical activity >150 minutes/week of moderate activity or 75 minutes of vigorous activity) are for primary prevention of chronic diseases such as cardiovascular disease (e.g., heart disease and stroke) and not secondary prevention. For example, according to the American Heart Association/American Stroke Association, physical activity recommendations for stroke survivors should be customized for each individual and should promote low- to moderate-intensity aerobic activity (Stroke. 2014;45:2532-2553).

Any attachments provided with reviews can be seen via the following link:

[LINK]

6 Jul 2020

Submitted filename: R2_Response to reviewers.pdf

Click here for additional data file.

21 Jul 2020

Dear Dr. Chudasama,

Thank you very much for re-submitting your manuscript "Healthy Lifestyle and Life Expectancy in people with Multimorbidity: a UK Biobank study" (PMEDICINE-D-20-01260R3) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by one reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jul 28 2020 11:59PM.

Sincerely,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

1. Please update the Title to: “Healthy Lifestyle and Life Expectancy in people with Multimorbidity in the UK Biobank: a longitudinal cohort study”

2. Abstract:

a. Line 43: Please use square brackets when nesting inside other brackets: “…(median age of 58 years [range 38-73], 46% male, 95% white)…”

b. At line 55, please adapt the text to "... significantly associated with a gain of 4.5 life years ...".

c. Please add another limitation to the end of the Methods and Findings subsection, eg. participants not being representative of the UK as a whole.

d. At line 64, please begin the sentence with "In this analysis of data from the UK Biobank, we found that ..." or similar.

e. Rather than "up to 8 years" at line 65, we suggest quoting the observed maxima for men and women (i.e., 6.3 and 7.6 years).

3. Methods: Please cite the prospective protocol on line 146 for clarity.

4. Tables 2, 3: Please include p values for all relevant results.

5. Please upload the STROBE S1 Checklist as a separate file.

----

Comments from Reviewers:

Reviewer #3: Thank you for addressing my comments. I have no additional comments or suggestions.

Any attachments provided with reviews can be seen via the following link:

[LINK]

11 Aug 2020

Submitted filename: Request from editors & production.pdf

Click here for additional data file.

18 Aug 2020

Dear Dr Chudasama,

On behalf of my colleagues and the academic editor, Dr. Sanjay Basu, I am delighted to inform you that your manuscript entitled "Healthy Lifestyle and Life Expectancy in people with Multimorbidity in the UK Biobank: a longitudinal cohort study" (PMEDICINE-D-20-01260R4) has been accepted for publication in PLOS Medicine.

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org