# Effect of multimorbidity on health-related quality of life in adults aged 55 years or older: Results from the SU.VI.MAX 2 cohort

Valentin Walker, Christine Perret-Guillaume, Emmanuelle Kesse-Guyot, Nelly Agrinier, Serge Hercberg, Maria del Pilar Galan Hercberg, Karen Assmann, Serge Briancon, Christine Rotonda

<figure><img src="image_1.png" /></figure>

## To cite this version:

Valentin Walker, Christine Perret-Guillaume, Emmanuelle Kesse-Guyot, Nelly Agrinier, Serge Hercberg, et al.. Effect of multimorbidity on health-related quality of life in adults aged 55 years or older: Results from the SU.VI.MAX 2 cohort. PLoS ONE, 2016, 11 (12), pp.1-15. 10.1371/journal.pone.0169282. hal-01602844

HAL Id: hal-01602844

https://hal.science/hal-01602844

Submitted on 27 May 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

# Effect of Multimorbidity on Health-Related Quality of Life in Adults Aged 55 Years or Older: Results from the SU.VI.MAX 2 Cohort

Valentin Walker¹, Christine Perret-Guillaume¹,², Emmanuelle Kesse-Guyot³, Nelly Agrinier¹,⁴, Serge Hercberg³,⁵, Pilar Galan³, Karen E. Assmann³, Serge Briançon¹,⁴*, Christine Rotonda¹,⁴

1 EA4360 APEMAC, University of Lorraine, University Paris Descartes, Nancy, France, 2 Department of Gerontology, CHRU de Brabois, Nancy, France, 3 Equipe de Recherche en Epidémiologie Nutritionnelle, Centre d'Epidémiologie et Statistiques Paris Cité, Université Paris 13, Inserm (U1153), Inra (U1125), COMUE Sorbonne-Paris-Cité, Bobigny, France, 4 CIC-1433 Epidémiologie Clinique, Inserm, CHRU Nancy, Nancy, France, 5 Département de Santé Publique, Hôpital Avicenne, Bobigny, France

* serge.briancon@univ-lorraine.fr

<figure><img src="image_3.png" /></figure>

## Abstract

<figure><img src="image_4.png" /></figure>

OPEN ACCESS

**Citation:** Walker V, Perret-Guillaume C, Kesse-Guyot E, Agrinier N, Hercberg S, Galan P, et al. (2016) Effect of Multimorbidity on Health-Related Quality of Life in Adults Aged 55 Years or Older: Results from the SU.VI.MAX 2 Cohort. PLoS ONE 11(12): e0169282. doi:10.1371/journal.pone.0169282

**Editor:** Ali Montazeri, Iranian Institute for Health Sciences Research, ISLAMIC REPUBLIC OF IRAN

**Received:** September 5, 2016

**Accepted:** December 14, 2016

**Published:** December 29, 2016

**Copyright:** © 2016 Walker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability Statement:** The personal health data underlying the findings of our study are not publicly available due to legal restrictions related to data privacy protection. However, the data are available upon request to all interested researchers after authorization of the French "Commission nationale de l'informatique et des libertés." Interested researchers may contact Emmanuelle Kesse-Guyot (e.kesse@eren.smbh.univ-paris13.fr) for requests for data.

## Introduction

Multimorbid chronic diseases are usually considered separately in trials. Here, we aimed to describe overall multimorbidity patterns in adults aged 55 years or older and assess their effect on health-related quality of life (HRQoL).

## Methods

We used data for 5,647 participants included in the SUPplémentation en VItamines et Minér-aux AntioXydants 2 (SU.VI.MAX 2) population-based trial. HRQoL was assessed by the French versions of the Medical Outcome Study Short Form 36 and the Duke Health Profile. An exploratory factor analysis was used to determine multimorbidity patterns, and a multi-morbidity score for each resulting pattern was calculated. Adjusted multiple linear regres-sion was used to examine the association between the identified multimorbidity and HRQoL scores by gender and for each age group (55–59, 60–64, 65–69, ≥ 70 years).

## Results

More than 63% of the sample reported two or more chronic conditions (from 55.8% for those 55–59 years to 74.4% for those ≥ 70 years). Multimorbidity was more common among women than men (67.3% vs 60%). Two different multimorbidity patterns were identified. Pattern A was represented mainly by mental illness and bone impairments. Pattern B was represented mainly by cardiovascular and metabolic disorders. After adjusting for covari-ates, a high pattern A score was associated with reduced HRQoL for the physical and men-tal components of each HRQoL questionnaire, and a high pattern B score was associated with reduced HRQoL for only the physical component of each questionnaire. These multi-morbidity scores affected HRQoL differently by age group.
**Funding:** The SU.VI.MAX2 study (2003-2008) was funded by the French National Research Agency (n°ANR-05-PNRA-010), the French Ministry of Health, Médéric, Sodexo, Ipsen, MGEN and Pierre Fabre. Médéric and MGEN are French health insurance organizations, which are complementary to the National Health Insurance System. Ipsen and Pierre Fabre are private pharmaceutical companies. Sodexo, a food catering company, supported the study by organizing meetings between researchers and study participants. The funding bodies did not have any involvement in the design/conduct of or in data analysis/interpretation, or in writing/ approval of the manuscript. None of the funders had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Competing Interests:** The authors have declared that no competing interests exist.

## Conclusion

Our study used a novel methodological approach to account for multimorbidity patterns in determining the link with chronic conditions. These multimorbidity scores (counted and weighted) can be used in clinical research to control for the effect of multimorbidity on patients' HRQoL and may be useful for clinical practice.

## Clinical Trial Registration

Clinicaltrial.gov (number NCT00272428).

## Introduction

The European population is the most aged in the world, with 24% of the population 60 years or older. It is projected to remain the most aged population in the coming decades, with 34% of the population projected to be 60 years or older in 2050 [1]. This overall aging of the population is accompanied by a substantial increase in prevalence of chronic conditions. Two thirds of older adults in Europe who have reached retirement age have at least two chronic conditions [2,3].

This co-existence of multiple chronic conditions, defined as “multimorbidity”, is a common phenomenon in older people, and its occurrence increases with age [4]. Multimorbid chronic diseases are associated with increased rate of mortality and disability, reduced function levels, increased polypharmacy, poor health-related quality of life (HRQoL) and more health care utilization (costs, number of physician visits, length of hospital stay) [5-7]. In this context, health care should aim to increase the life span cost-efficiently while maintaining HRQoL and the ability to perform activities of daily life [8,9]. Most studies have shown impaired HRQoL by gender with the presence of many chronic diseases or with aging in older people [10-13].

When studying the impact of morbidities on HRQoL, morbidities are usually considered separately [2]. Most treatment plans and clinical guidelines target single diseases [14], but an effective intervention for one disease could be less effective or deleterious with the presence of coexisting conditions [15]. Regarding associations between morbidities, patterns of morbidities can be established. However, few studies are identifying patterns and potential factors underlying such associations [16-20]. The interest of these patterns is to consider the interrelations or the cumulative effect between different morbidities. Methodological approaches that consider such patterns that are well-adapted to the respective study populations are needed, as is the need to understand the patterns of disease combinations and their complexity. The identification and standardization of patterns of multimorbidity might help in organizing specific treatment strategies and system-wide initiatives to improve the care of people with various types and degrees of multimorbidity. However, more evidence on multimorbidity patterns is required.

We aimed to describe the multimorbidity patterns in adults aged 55 years or older by using national French data from the Supplémentation en VItamines et Minéraux AntioXydants 2 (SU.VI.MAX 2) study. We also aimed to assess the association between multimorbidity patterns and HRQoL among older people overall and by age and gender.
# Materials and Methods

## Study design

Our study is based on the data from SU.VI.MAX 2 study, which is an additional observational follow-up study, organized 5 years after the end of the initial SU.VI.MAX trial. The initial SU.VI.MAX trial was a randomized, double-blind, placebo-controlled primary prevention trial assessing the efficacy of a daily antioxidant supplementation in the incidence of cardiovascular disease and cancer. Eligibility criteria of SU.VI.MAX participants were described in previous publication [21-23]. This initial trial was launched in 1994-95 and had a planned follow-up of 8 years (until 2002).

The SU.VI.MAX and SU.VI.MAX 2 studies were approved by the Ethics Committee for Studies with Human Subjects of the Paris-Cochin Hospital (CCPPRB nos. 706 and 2364, respectively) and the Commission Nationale Informatique et Liberté (CNIL nos. 334641 and 907094, respectively). Written informed consent was obtained from all participants. Clinical Trial Registration at clinicaltrials.gov (number NCT00272428).

## Population and sampling

The SU.VI.MAX 2 participants were recruited through a postal campaign organized in 2007-2009 among all SU.VI.MAX participants. From the full initial SU.VI.MAX cohort (N = 13,017), 6,850 participants were agreed to participate in the SU.VI.MAX 2 study, and 5,925 participants were older than 55 years at enrollment and received a geriatric assessment [24,25]. Sociodemographic characteristics data, morbidities data and quality of life data were available for 5,647 aged 55 years or older for inclusion in the present analyses (Fig 1).

## Data collection

**Sociodemographic and comorbidities assessment.** Information on gender, date of birth, weight, size, employment status, education level, familial status, employment status and smoking status was collected.

In SU.VI.MAX 2, subjects reported their comorbidities by a self-administered questionnaire. Several chronic conditions were identified in 12 categories: cardiovascular diseases or other cardiovascular impairments; cancers; respiratory diseases or impairments; diseases or impairments related to the ears, nose, or throat; digestive diseases or impairments of digestive function; diseases or impairments related to bones and joints; urinary/genital diseases or impairments; endocrine or metabolic diseases; eye-related diseases or impairments; neurological/psychiatric diseases or impairments; infectious diseases; and other health problems. The year of diagnosis was collected, as was the presence of a medication or actual treatment for the condition. On the basis of the potential relevance of conditions for a participant's overall health status, one of the authors (CPG), a geriatrician, selected 19 chronic conditions: hypertension, heart failure, arrhythmias and palpitations, ischemic cardiovascular/vascular impairments, respiratory impairments, hearing impairments, ear, nose and throat impairments, digestive impairments, vertebral diseases, osteoporosis, arthritis and rheumatism, adenoma or prostatic hyperplasia, thyroid disease, diabetes, vision impairments, anxiety/depression, sleeping troubles, memory impairments and cancer. Multimorbidity was defined as having at least 2 of these 19 chronic conditions.

**Self-reported outcomes: HRQoL.** HRQoL was assessed (2007-2009) at enrollment by using the French version of the Medical Outcome Study Short Form 36 (SF-36) [26] and the French version of the Duke Health Profile (Duke) [27]. For each questionnaire, two main dimensions were retained: the physical component (SF-36: physical component summary
<figure><img src="image_5.png" /><figcaption>**Fig 1.** Enrollment and flow of participants in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) study and follow-up (SU.VI.MAX 2). <sup>1</sup> Doesn't met eligibility criteria. <sup>2</sup> Under 55 years. <sup>3</sup> Missing data about sociodemographic characteristics, morbidities and quality of life.</figcaption></figure>

doi:10.1371/journal.pone.0169282.g001

[PCS]; Duke: Phys) and the mental component (SF-36: mental component summary; Duke: Ment). The scores were linearized from 0 to 100 to compare scores between the questionnaires: (0: worst HRQoL, 100: best HRQoL).

**Statistical analysis.** We performed a descriptive analysis, reporting unweighted frequencies, proportions, and means (SD) by gender and age groups (55–59, 60–64, 65–69 and ≥ 70 years).

We performed an exploratory factor analysis of the 19 morbidities to determine multimorbidity patterns. We identified the tendencies of diseases to co-occur by selecting sets of variables with potentially common underlying causal factors. Factor analysis was used with a tetrachoric correlation matrix because conditions were expressed as binary variables [28]. The extraction of the multimorbidity patterns involved the principal factor method, and the number of factors to extract was determined by the scree-test [29], with minimal eigenvalue of 1.0 (Kaiser criterion). A condition with loading factor > 0.25 had greater importance in a pattern, which indicates a stronger association [30]. The Kaiser-Meyer-Olkin (KMO) method was used to estimate the adequacy of the data for our model on factor analysis. This parameter takes values between 0 and 1, which, with a greater goodness of fit, are close to 1. Cumulative variance of the sample was determined to describe the variance of the diagnostic data explained by the patterns. An oblique rotation (Oblimin) was applied to correlate factors with one another to obtain a better interpretation of the analysis factor. The results of this analysis could be interpreted as multimorbidity patterns (i.e., diseases that are non-randomly associated with each other).

For each participant, a multimorbidity score was calculated for every identified pattern. These individual scores corresponded to the sum of each loading factor from the factor analysis multiplied by the presence (= 1) or absence (= 0) of each condition. The mean of each multimorbidity score was calculated for every pattern. The higher the multimorbidity score, the greater the number and association of multimorbidities.
We hypothesized that multimorbidity scores' impact on HRQoL was different according to age and gender. Significant interactions between age groups and multimorbidity scores were found to be age and gender, and multimorbidity scores confirmed this hypothesis. So multiple linear regression was used to examine the association between multimorbidity and HRQoL scores (PCS and Phys; MCS and Ment) for each age group and by gender. Models were adjusted for other sociodemographic variables which p<0.2 in bivariate analyses. Analyses involved use of SAS 9.4. Two-sided p < 0.001 was considered statistically significant (after Bonferroni correction).

## Results

### Sample

Among the 5647 adults aged 55 years or older (51.5% women), the mean (SD) age was 63.2 years (4.9); 71.2% were retired and 66% declared a good general health status (data not shown). For participants 60–64 years old, 12.8% of women were working as compared with 18.7% of men (Table 1). For participants 55–59 years old, 36.0% of men were retired as compared with 23.6% of women. Among those ≥ 70 years old, 46.3% of men had a university education and 10.9% were single as compared with 36.9% and 35.3% of women, respectively. Among those 65–69 years, 29.6% of men never smoked as compared with 64.1% of women.

### Chronic conditions and prevalence of multimorbidity

More than 87% of the participants reported having at least one chronic condition, and more than 63% had at least two chronic conditions. The most frequent chronic diseases were arthritis/rheumatism, vision impairments and hypertension, with an overall prevalence of 41.0%, 40.5% and 23.9%, respectively (Table 2). For women, the most represented chronic conditions were arthritis and rheumatism (47.7%), vision impairments (43.3%), anxiety/depression (24.6%) and sleeping troubles (25.3%) (Table 2). For men, the most represented chronic conditions were vision impairments (37.5%), arthritis and rheumatism (33.8%) and hypertension (27.4%). The proportion of multimorbidity was greater for women than men (67.3% and 60.0%, respectively). For men and women, presence of an increasing number of chronic conditions increased with increasing age (Table 2). The proportion of each morbidity increased with age for men (hypertension– 55–59 years: 20.3%, ≥ 70 years: 32.6%; arthritis and rheumatism– 55–59 years: 22.7%, ≥ 70 years: 43.5%) and women (hypertension– 55–59 years: 16.9%, ≥ 70 years: 26.6%; arthritis and rheumatism– 55–59 years: 38.7%, ≥ 70 years: 60.9%), except for 4 conditions for which no increase was observed (hearing impairment, nose and throat impairments, digestive impairments, osteoporosis, sleeping troubles). The prevalence of only anxiety/depression tended to decrease with age for men (55–59 years: 15.3%, ≥ 70 years: 9.9%) and women (55–59 years: 26.9%, ≥ 70 years: 22.2%), but this decrease was greater for women. Other chronic conditions, such as sleeping troubles, had a higher frequency among women than men regardless of age group [men (55–59 years: 13.1%, ≥ 70 years: 14.6%) and women (55–59 years: 26.6%, ≥ 70 years: 27.2%)].

### HRQoL

HRQoL scores are in Table 3.

### Multimorbidity patterns

We identified two multimorbidity patterns according to the eigenvalues of the factor analysis and the results of the scree-test, which explained 62.2% of the total variance (Table 4).
<table><caption>Table 1. Description of participants aged 55 years or older in the SU.VI.MAX 2 study by gender and age group.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">TOTAL</th><th colspan="5">MALE (n = 2738, 48.5%)</th><th colspan="5">FEMALE (n = 2909, 51.5%)</th></tr><tr><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥70 years</th><th>Total</th><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥70 years</th><th>Total</th></tr></thead><tbody><tr><td>Sample</td><td>5647 (100)</td><td>497 (18.2)</td><td>1026 (37.5)</td><td>748 (27.3)</td><td>467 (17.1)</td><td>2738 (100)</td><td>978 (33.6)</td><td>976 (33.6)</td><td>635 (21.8)</td><td>320 (11.0)</td><td>2909 (100)</td></tr><tr><td>Age</td><td>63.2 (4.9)</td><td>58.0 (1.1)</td><td>61.8 (1.5)</td><td>67.0 (1.4)</td><td>71.3 (1.3)</td><td>64.2 (4.7)</td><td>57.2 (1.4)</td><td>61.7 (1.4)</td><td>66.9 (1.4)</td><td>71.3 (1.2)</td><td>62.4 (4.9)</td></tr><tr><td>BMI</td><td>25.7 (4.1)</td><td>26.2 (3.4)</td><td>26.6 (3.8)</td><td>26.5 (3.4)</td><td>26.5 (3.3)</td><td>26.5 (3.6)</td><td>24.6 (4.5)</td><td>25.0 (4.4)</td><td>25.2 (4.3)</td><td>26.0 (5.0)</td><td>25.0 (4.5)</td></tr><tr><td>Employment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Working</td><td>1188 (21.0)</td><td>281 (56.5)</td><td>192 (18.7)</td><td>20 (2.7)</td><td>5 (1.1)</td><td>498 (18.2)</td><td>544 (55.6)</td><td>125 (12.8)</td><td>17 (2.7)</td><td>4 (1.3)</td><td>690 (23.7)</td></tr><tr><td>Unemployed</td><td>436 (7.7)</td><td>37 (7.4)</td><td>28 (2.7)</td><td>4 (0.5)</td><td>3 (0.6)</td><td>72 (2.6)</td><td>203 (20.8)</td><td>128 (13.1)</td><td>24 (3.8)</td><td>9 (2.8)</td><td>364 (12.5)</td></tr><tr><td>Retired</td><td>4023 (71.2)</td><td>179 (36.0)</td><td>806 (78.6)</td><td>724 (96.8)</td><td>459 (98.3)</td><td>2168 (79.2)</td><td>231 (23.6)</td><td>723 (74.1)</td><td>594 (93.5)</td><td>307 (96.0)</td><td>1855 (63.8)</td></tr><tr><td>Education</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No education</td><td>36 (0.6)</td><td>2 (0.4)</td><td>0 (0)</td><td>7 (0.9)</td><td>7 (1.5)</td><td>16 (0.6)</td><td>3 (0.3)</td><td>4 (0.4)</td><td>7 (1.1)</td><td>6 (1.9)</td><td>20 (0.7)</td></tr><tr><td>Primary</td><td>375 (6.6)</td><td>18 (3.6)</td><td>56 (5.5)</td><td>68 (9.1)</td><td>50 (10.7)</td><td>192 (7.0)</td><td>41 (4.2)</td><td>57 (5.8)</td><td>50 (7.9)</td><td>35 (10.9)</td><td>183 (6.3)</td></tr><tr><td>Secondary</td><td>2387 (42.3)</td><td>185 (37.2)</td><td>387 (37.7)</td><td>298 (39.8)</td><td>194 (41.5)</td><td>1064 (38.9)</td><td>410 (41.9)</td><td>432 (44.3)</td><td>320 (50.4)</td><td>161 (50.31)</td><td>1323 (45.5)</td></tr><tr><td>University</td><td>2849 (50.5)</td><td>292 (58.8)</td><td>583 (56.8)</td><td>375 (50.1)</td><td>216 (46.3)</td><td>1466 (53.5)</td><td>524 (53.6)</td><td>483 (49.5)</td><td>258 (40.6)</td><td>118 (36.9)</td><td>1383 (47.5)</td></tr><tr><td>Family status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Single</td><td>955 (16.9)</td><td>52 (10.5)</td><td>101 (9.8)</td><td>90 (12.0)</td><td>51 (10.9)</td><td>294 (10.7)</td><td>189 (19.3)</td><td>196 (20.1)</td><td>163 (25.7)</td><td>113 (35.3)</td><td>661 (22.7)</td></tr><tr><td>Couple</td><td>4549 (80.6)</td><td>435 (87.5)</td><td>911 (88.8)</td><td>645 (86.2)</td><td>409 (87.6)</td><td>2400 (87.7)</td><td>752 (76.9)</td><td>743 (76.1)</td><td>457 (72.0)</td><td>197 (61.6)</td><td>2149 (73.9)</td></tr><tr><td>Living with another person</td><td>143 (2.5)</td><td>10 (2.0)</td><td>14 (1.4)</td><td>13 (1.7)</td><td>7 (1.5)</td><td>44 (1.6)</td><td>37 (3.8)</td><td>37 (3.8)</td><td>15 (2.4)</td><td>10 (3.1)</td><td>99 (3.4)</td></tr><tr><td>Smoking status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Never-smokers</td><td>2550 (45.2)</td><td>163 (32.8)</td><td>335 (32.7)</td><td>221 (29.6)</td><td>127 (27.2)</td><td>846 (30.9)</td><td>512 (52.4)</td><td>573 (58.7)</td><td>407 (64.1)</td><td>212 (66.3)</td><td>1704 (58.6)</td></tr><tr><td>Former smokers</td><td>2761 (48.9)</td><td>298 (60.0)</td><td>616 (60.0)</td><td>475 (63.5)</td><td>324 (69.4)</td><td>1713 (62.6)</td><td>399 (40.8)</td><td>341 (34.9)</td><td>209 (32.9)</td><td>99 (30.9)</td><td>1048 (36.0)</td></tr><tr><td>Current smokers</td><td>336 (5.9)</td><td>36 (7.2)</td><td>75 (7.3)</td><td>52 (7.0)</td><td>16 (3.4)</td><td>179 (6.5)</td><td>67 (6.9)</td><td>62 (6.4)</td><td>19 (3.0)</td><td>9 (2.8)</td><td>157 (5.4)</td></tr></tbody></table>

Data are mean (SD) or unweight frequencies (%). BMI, body mass index.

doi:10.1371/journal.pone.0169282.t001

The KMO value was 0.61, which was considered acceptable adequacy. Three conditions were less associated with the two patterns, A and B, because of factor loading < 0.25 (hearing impairment, vision impairments and cancer). With the threshold of 0.25, the variable respiratory impairments were correlated with both patterns (factor loading 0.26 and 0.30).

Pattern A included respiratory impairments; ear, nose and throat impairments; digestive impairments; vertebral diseases; osteoporosis; arthritis and rheumatism; thyroid disease; anxiety/depression; sleeping troubles and memory impairments.

Pattern B included hypertension, heart failure, arrhythmias and palpitations, ischemic cardiovascular/vascular impairments, respiratory impairments, adenoma or prostatic hyperplasia and diabetes.
<table><caption>Table 2. Prevalence of chronic diseases in adults aged 55 years or older in the SU.VI.MAX 2 study by gender and age group.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">TOTAL</th><th colspan="5">MALE (n = 2738, 48.5%)</th><th colspan="5">FEMALE (n = 2909, 51.5%)</th></tr><tr><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥ 70 years</th><th>Total</th><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥ 70 years</th><th>Total</th></tr></thead><tbody><tr><td>Hypertension</td><td>1347 (23.9)</td><td>101 (20.3)</td><td>253 (24.7)</td><td>243 (32.5)</td><td>152 (32.6)</td><td><strong>749 (27.4)</strong></td><td>165 (16.9)</td><td>196 (20.1)</td><td>152 (23.9)</td><td>85 (26.6)</td><td>598 (20.6)</td></tr><tr><td>Heart failure</td><td>81 (1.4)</td><td>5 (1.0)</td><td>11 (1.1)</td><td>18 (2.4)</td><td>19 (4.1)</td><td>53 (1.9)</td><td>9 (0.9)</td><td>5 (0.5)</td><td>8 (1.3)</td><td>6 (1.9)</td><td>28 (1.0)</td></tr><tr><td>Arrhythmias and palpitations</td><td>276 (4.9)</td><td>13 (2.6)</td><td>40 (3.9)</td><td>51 (6.8)</td><td>40 (8.6)</td><td>144 (5.3)</td><td>31 (3.2)</td><td>38 (3.9)</td><td>43 (6.8)</td><td>20 (6.3)</td><td>132 (4.5)</td></tr><tr><td>Ischemic cardiovascular/ vascular impairments</td><td>347 (6.1)</td><td>21 (4.2)</td><td>72 (7.0)</td><td>87 (11.6)</td><td>67 (14.4)</td><td>247 (9.0)</td><td>24 (2.5)</td><td>20 (2.1)</td><td>35 (5.5)</td><td>21 (6.6)</td><td>100 (3.4)</td></tr><tr><td>Respiratory impairments</td><td>292 (5.2)</td><td>31 (6.2)</td><td>43 (4.2)</td><td>38 (5.1)</td><td>26 (5.6)</td><td>138 (5.0)</td><td>44 (4.5)</td><td>39 (4.0)</td><td>40 (6.3)</td><td>31 (9.7)</td><td>154 (5.3)</td></tr><tr><td>Hearing impairments</td><td>913 (16.2)</td><td>68 (13.7)</td><td>172 (16.8)</td><td>168 (22.5)</td><td>134 (28.7)</td><td>542 (19.8)</td><td>99 (10.1)</td><td>107 (11.0)</td><td>99 (15.6)</td><td>66 (20.6)</td><td>371 (12.8)</td></tr><tr><td>Ear, nose and throat impairments</td><td>162 (2.9)</td><td>12 (2.4)</td><td>25 (2.4)</td><td>30 (4.0)</td><td>6 (1.3)</td><td>73 (2.7)</td><td>28 (2.9)</td><td>30 (3.1)</td><td>19 (3.0)</td><td>12 (3.8)</td><td>89 (3.1)</td></tr><tr><td>Digestive impairments</td><td>526 (9.3)</td><td>34 (6.8)</td><td>84 (8.2)</td><td>49 (6.6)</td><td>44 (9.4)</td><td>211 (7.7)</td><td>93 (9.5)</td><td>101 (10.4)</td><td>78 (12.3)</td><td>43 (13.4)</td><td>315 (10.8)</td></tr><tr><td>Vertebral diseases</td><td>430 (7.6)</td><td>30 (6.0)</td><td>50 (4.9)</td><td>42 (5.6)</td><td>37 (7.9)</td><td>159 (5.8)</td><td>75 (7.7)</td><td>84 (8.6)</td><td>70 (11.0)</td><td>42 (13.1)</td><td>271 (9.3)</td></tr><tr><td>Osteoporosis</td><td>477 (8.5)</td><td>8 (1.6)</td><td>12 (1.2)</td><td>14 (1.9)</td><td>5 (1.1)</td><td>39 (1.4)</td><td>120 (12.3)</td><td>152 (15.6)</td><td>107 (16.9)</td><td>59 (18.4)</td><td>438 (15.1)</td></tr><tr><td>Arthritis and rheumatism</td><td>2313 (41.0)</td><td>113 (22.7)</td><td>319 (31.1)</td><td>291 (38.9)</td><td>203 (43.5)</td><td><strong>926 (33.8)</strong></td><td>378 (38.7)</td><td>472 (48.4)</td><td>342 (53.9)</td><td>195 (60.9)</td><td><strong>1387 (47.7)</strong></td></tr><tr><td>Adenoma or prostatic hyperplasia</td><td>325 (5.8)</td><td>26 (5.2)</td><td>111 (10.8)</td><td>116 (15.5)</td><td>72 (15.4)</td><td>325 (11.9)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td></tr><tr><td>Thyroid disease</td><td>454 (8.0)</td><td>6 (1.2)</td><td>32 (3.1)</td><td>22 (2.9)</td><td>14 (3.0)</td><td>74 (2.7)</td><td>133 (13.6)</td><td>117 (12.0)</td><td>74 (11.7)</td><td>56 (17.5)</td><td>380 (13.1)</td></tr><tr><td>Diabetes</td><td>161 (2.9)</td><td>15 (3.0)</td><td>27 (2.6)</td><td>49 (6.6)</td><td>24 (5.1)</td><td>115 (4.2)</td><td>9 (0.9)</td><td>17 (1.7)</td><td>13 (2.1)</td><td>7 (2.2)</td><td>46 (1.6)</td></tr><tr><td>Vision impairments</td><td>2286 (40.5)</td><td>152 (30.6)</td><td>371 (36.2)</td><td>285 (38.1)</td><td>219 (46.9)</td><td><strong>1027 (37.5)</strong></td><td>397 (40.6)</td><td>400 (41.0)</td><td>282 (44.4)</td><td>180 (56.3)</td><td><strong>1259 (43.3)</strong></td></tr><tr><td>Anxiety / depression</td><td>1058 (18.7)</td><td>76 (15.3)</td><td>136 (13.3)</td><td>84 (11.2)</td><td>46 (9.9)</td><td>342 (12.5)</td><td>263 (26.9)</td><td>247 (25.3)</td><td>135 (21.3)</td><td>71 (22.2)</td><td><strong>716 (24.6)</strong></td></tr><tr><td>Sleeping troubles</td><td>1175 (20.8)</td><td>65 (13.1)</td><td>185 (18.0)</td><td>120 (16.0)</td><td>68 (14.6)</td><td>438 (16.0)</td><td>260 (26.6)</td><td>237 (24.3)</td><td>153 (24.1)</td><td>87 (27.2)</td><td><strong>737 (25.3)</strong></td></tr><tr><td>Memory impairments</td><td>334 (5.9)</td><td>13 (2.6)</td><td>39 (3.8)</td><td>39 (5.2)</td><td>30 (6.4)</td><td>121 (4.4)</td><td>48 (4.9)</td><td>62 (6.4)</td><td>64 (10.1)</td><td>39 (12.2)</td><td>213 (7.3)</td></tr><tr><td>Cancer</td><td>309 (5.5)</td><td>19 (3.8)</td><td>58 (5.7)</td><td>63 (8.4)</td><td>48 (10.3)</td><td>188 (6.9)</td><td>25 (2.6)</td><td>44 (4.5)</td><td>29 (4.6)</td><td>23 (7.2)</td><td>121 (4.2)</td></tr><tr><td>Number of chronic conditions</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0</td><td>721 (12.8)</td><td>111 (22.3)</td><td>174 (17.0)</td><td>86 (11.5)</td><td>44 (9.4)</td><td>415 (15.2)</td><td>141 (14.4)</td><td>101 (10.4)</td><td>48 (7.6)</td><td>16 (5.0)</td><td>306 (10.5)</td></tr><tr><td>1</td><td>1325 (23.5)</td><td>165 (33.2)</td><td>270 (26.3)</td><td>168 (22.5)</td><td>78 (16.7)</td><td>681 (24.9)</td><td>235 (24.0)</td><td>224 (23.0)</td><td>125 (19.7)</td><td>60 (18.8)</td><td>644 (22.1)</td></tr><tr><td>2</td><td>1315 (23.3)</td><td>109 (21.9)</td><td>256 (25.0)</td><td>162 (21.7)</td><td>112 (24.0)</td><td>639 (23.3)</td><td>230 (23.5)</td><td>247 (25.3)</td><td>144 (22.7)</td><td>55 (17.2)</td><td>676 (23.2)</td></tr><tr><td>3</td><td>1007 (17.8)</td><td>63 (12.7)</td><td>174 (17.0)</td><td>161 (21.5)</td><td>100 (21.4)</td><td>498 (18.2)</td><td>163 (16.7)</td><td>175 (17.9)</td><td>119 (18.7)</td><td>52 (16.3)</td><td>509 (17.5)</td></tr><tr><td>4+</td><td>1279 (22.7)</td><td>49 (9.9)</td><td>152 (14.8)</td><td>171 (22.9)</td><td>133 (28.5)</td><td>505 (18.4)</td><td>209 (21.4)</td><td>229 (23.5)</td><td>199 (31.3)</td><td>137 (42.8)</td><td>774 (26.6)</td></tr><tr><td>Multimorbidity</td><td>3601 (63.8)</td><td>221 (44.5)</td><td>582 (56.7)</td><td>494 (66.0)</td><td>345 (73.9)</td><td>1642 (60.0)</td><td>602 (61.6)</td><td>651 (66.7)</td><td>462 (72.8)</td><td>244 (76.3)</td><td>1959 (67.3)</td></tr></tbody></table>

Data are unweighted frequencies (n) and proportions (%).

doi:10.1371/journal.pone.0169282.t002
<table><caption>Table 3. Outcomes related to mental health, physical health and health-related quality of life (HRQoL) by gender and age groups.</caption><thead><tr><th rowspan="2"></th><th colspan="5">MALE</th><th colspan="5">FEMALE</th></tr><tr><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥ 70 years</th><th>Total</th><th>55-59 years</th><th>60-64 years</th><th>65-69 years</th><th>≥ 70 years</th><th>Total</th></tr></thead><tbody><tr><th>SF-36 (PCS)</th><td>52.2 (6.1)</td><td>50.8 (6.5)</td><td>50.1 (6.6)</td><td>49.3 (6.6)</td><td>50.6 (6.5)</td><td>50.9 (7.1)</td><td>50.4 (7.1)</td><td>48.3 (8.0)</td><td>46.9 (8.0)</td><td>49.7 (7.5)</td></tr><tr><th>SF-36 (MCS)</th><td>50.9 (9.5)</td><td>52.6 (7.9)</td><td>52.5 (7.8)</td><td>52.3 (7.5)</td><td>52.2 (8.2)</td><td>48.6 (9.6)</td><td>49.4 (9.3)</td><td>49.8 (9.5)</td><td>49.6 (9.9)</td><td>49.2 (9.5)</td></tr><tr><th>DUKE (Phys)</th><td>81.2 (16.9)</td><td>80.5 (17.0)</td><td>78.7 (17.0)</td><td>76.3 (17.9)</td><td>79.4 (17.2)</td><td>71.9 (19.0)</td><td>70.8 (19.2)</td><td>68.7 (19.6)</td><td>65.7 (21.0)</td><td>70.2 (19.5)</td></tr><tr><th>DUKE (Ment)</th><td>80.7 (19.1)</td><td>83.8 (16.9)</td><td>83.1 (16.4)</td><td>83.9 (16.7)</td><td>83.1 (17.2)</td><td>73.5 (20.5)</td><td>75.0 (20.0)</td><td>75.9 (18.9)</td><td>77.0 (18.8)</td><td>75.0 (19.9)</td></tr></tbody></table>

Data are mean (SD). SF-36, Medical Outcomes Study Short Form 36. DUKE, Duke Health Profile.

PCS, physical component summary; MCS, mental component summary; Phys, physical health; Ment, mental health.

doi:10.1371/journal.pone.0169282.t003

## Multimorbidity score

We established a pattern A and B score for each participant corresponding to the overall impact of these chronic conditions (mean [SD] score 0.73 [0.69] and 0.27 [0.40], respectively) (data not shown). The mean pattern A score for men was 0.55 [range: -0.29; 3.63] and for women 0.89 [range: -0.15; 4.31]. The mean pattern B score for men was 0.38 [range: -0.46; 2.70] and for women 0.17 [range: -0.54; 3.24].

Mean pattern A scores for men were 0.46, 0.54, 0.57 and 0.63 for ages 55-59, 60-64, 65-69 and ≥70, respectively, and for women 0.83, 0.87, 0.94 and 1.09, respectively. Mean pattern B scores for men were 0.25, 0.32, 0.46 and 0.52, respectively, and for women 0.13, 0.15, 0.22 and 0.27, respectively.

<table><caption>Table 4. Explanatory factor analysis for two multimorbidity patterns, A and B, for each condition.</caption><thead><tr><th>Conditions</th><th>Pattern A</th><th>Pattern B</th></tr></thead><tbody><tr><td>Hypertension</td><td>-0.01</td><td><strong>0.50</strong></td></tr><tr><td>Heart failure</td><td>0.10</td><td><strong>0.57</strong></td></tr><tr><td>Arrhythmias and palpitations</td><td>0.16</td><td><strong>0.32</strong></td></tr><tr><td>Ischemic cardiovascular impairments</td><td>-0.01</td><td><strong>0.52</strong></td></tr><tr><td>Respiratory impairments</td><td><strong>0.26</strong></td><td><strong>0.30</strong></td></tr><tr><td>Hearing impairment</td><td>0.16</td><td>0.17</td></tr><tr><td>Ear, nose and throat impairments</td><td><strong>0.39</strong></td><td>0.01</td></tr><tr><td>Digestive impairments</td><td><strong>0.34</strong></td><td>0.10</td></tr><tr><td>Vertebral diseases</td><td><strong>0.36</strong></td><td>0.10</td></tr><tr><td>Osteoporosis</td><td><strong>0.35</strong></td><td>-0.32</td></tr><tr><td>Arthritis and rheumatism</td><td><strong>0.44</strong></td><td>-0.04</td></tr><tr><td>Adenoma or prostatic hyperplasia</td><td>-0.15</td><td><strong>0.44</strong></td></tr><tr><td>Thyroid disease</td><td><strong>0.29</strong></td><td>-0.05</td></tr><tr><td>Diabetes</td><td>-0.14</td><td><strong>0.62</strong></td></tr><tr><td>Vision impairments</td><td>0.22</td><td>0.15</td></tr><tr><td>Anxiety / depression</td><td><strong>0.62</strong></td><td>-0.11</td></tr><tr><td>Sleeping troubles</td><td><strong>0.68</strong></td><td>-0.02</td></tr><tr><td>Memory impairments</td><td><strong>0.65</strong></td><td>0</td></tr><tr><td>Cancer</td><td>0.06</td><td>0.10</td></tr></tbody></table>

Pattern scores >0.25 are highlighted.

Kaiser-Meyer-Olkin (KMO) value: 0.613.

doi:10.1371/journal.pone.0169282.t004
<table><caption>Table 5. Impact of multimorbidity on HRQoL for males.</caption><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="8">MALE</th></tr><tr><th colspan="2">55-59 years</th><th colspan="2">60-64 years</th><th colspan="2">65-69 years</th><th colspan="2">≥ 70 years</th></tr><tr><th>Beta</th><th>P</th><th>Beta</th><th>P</th><th>Beta</th><th>P</th><th>Beta</th><th>P</th></tr></thead><tbody><tr><td rowspan="5">SF-36 (PCS)</td><td>Intercept</td><td>61.0</td><td>&lt;0.0001</td><td>64.5</td><td>&lt;0.0001</td><td>58.2</td><td>&lt;0.0001</td><td>56.8</td><td>&lt;0.0001</td></tr><tr><td>Pattern A Score</td><td>-2.29</td><td>&lt;0.0001</td><td>-3.49</td><td>&lt;0.0001</td><td>-2.39</td><td>&lt;0.0001</td><td>-2.95</td><td>&lt;0.0001</td></tr><tr><td>Pattern B Score</td><td>-3.29</td><td>&lt;0.0001</td><td>-3.36</td><td>&lt;0.0001</td><td>-2.39</td><td>&lt;0.0001</td><td>-1.95</td><td>0.0023</td></tr><tr><td>Adjusted Pattern A Score*</td><td>-1.9</td><td>&lt;0.0001</td><td>-3.0</td><td>&lt;0.0001</td><td>-2.1</td><td>&lt;0.0001</td><td>-2.7</td><td>&lt;0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>-2.3</td><td>0.0027</td><td>-1.6</td><td>0.0021</td><td>-1.8</td><td>0.0005</td><td>-1.3</td><td>0.0391</td></tr><tr><td rowspan="5">SF-36 (MCS)</td><td>Intercept</td><td>53.4</td><td>&lt;0.0001</td><td>54.3</td><td>&lt;0.0001</td><td>56.3</td><td>&lt;0.0001</td><td>55.1</td><td>&lt;0.0001</td></tr><tr><td>Pattern A Score</td><td>-5.39</td><td>&lt;0.0001</td><td>-4.07</td><td>&lt;0.0001</td><td>-3.13</td><td>&lt;0.0001</td><td>-4.38</td><td>&lt;0.0001</td></tr><tr><td>Pattern B Score</td><td>-0.96</td><td>0.4244</td><td>-1.35</td><td>0.0380</td><td>-0.91</td><td>0.1307</td><td>-0.92</td><td>0.21</td></tr><tr><td>Adjusted Pattern A Score*</td><td>-5.4</td><td>&lt;0.0001</td><td>-4.0</td><td>&lt;0.0001</td><td>-3.1</td><td>&lt;0.0001</td><td>-4.4</td><td>&lt;0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>/</td><td></td><td>-0.6</td><td>0.3607</td><td>-0.5</td><td>0.4128</td><td>/</td><td></td></tr><tr><td rowspan="5">Duke (Phys)</td><td>Intercept</td><td>112.4</td><td>&lt;0.0001</td><td>114.3</td><td>&lt;0.0001</td><td>105.4</td><td>&lt;0.0001</td><td>106.9</td><td>&lt;0.0001</td></tr><tr><td>Pattern A Score</td><td>-10.6</td><td>&lt;0.0001</td><td>-12.9</td><td>&lt;0.0001</td><td>-10.9</td><td>&lt;0.0001</td><td>-13.7</td><td>&lt;0.0001</td></tr><tr><td>Pattern B Score</td><td>-8.13</td><td>0.0001</td><td>-10.5</td><td>&lt;0.0001</td><td>-5.81</td><td>&lt;0.0001</td><td>-6.35</td><td>0.0003</td></tr><tr><td>Adjusted Pattern A Score*</td><td>-9.8</td><td>&lt;0.0001</td><td>-11.7</td><td>&lt;0.0001</td><td>-10.3</td><td>&lt;0.0001</td><td>-12.8</td><td>&lt;0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>-5.7</td><td>0.0047</td><td>-5.3</td><td>&lt;0.0001</td><td>-3.4</td><td>0.0057</td><td>-3.5</td><td>0.0280</td></tr><tr><td rowspan="5">Duke (Ment)</td><td>Intercept</td><td>92.8</td><td>&lt;0.0001</td><td>87.3</td><td>&lt;0.0001</td><td>88.4</td><td>&lt;0.0001</td><td>86.3</td><td>&lt;0.0001</td></tr><tr><td>Pattern A Score</td><td>-11.8</td><td>&lt;0.0001</td><td>-10.5</td><td>&lt;0.0001</td><td>-7.13</td><td>&lt;0.0001</td><td>-10.9</td><td>&lt;0.0001</td></tr><tr><td>Pattern B Score</td><td>-1.91</td><td>0.4262</td><td>-3.69</td><td>0.0077</td><td>-3.81</td><td>0.0024</td><td>-2.03</td><td>0.2154</td></tr><tr><td>Adjusted Pattern A score*</td><td>-11.5</td><td>&lt;0.0001</td><td>-10.3</td><td>&lt;0.0001</td><td>-6.9</td><td>&lt;0.0001</td><td>-10.9</td><td>&lt;0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>/</td><td></td><td>-1.6</td><td>0.2276</td><td>-2.9</td><td>0.0177</td><td>/</td><td></td></tr></tbody></table>

doi:10.1371/journal.pone.0169282.t005

## Association of multimorbidity scores and HRQoL

Tables 5 and 6 show the linear regression analysis of the association of multimorbidity and HRQoL scores by age groups for men and women, adjusted for covariates (BMI, professional status, education level, family status and smoking status). For men, after adjusting for covariates, increased pattern A score was associated with reduced HRQoL score for all four dimensions studied whatever the age group (Table 5). Increased adjusted pattern B score was associated with only Duke Phys dimension (60–64 years: -5.3; p<0.0001) and the association tended to decrease with age (55–59 years: -5.7; 60–64 years: -5.3; 65–69 years: -3.4; ≥70 years: -3.5).

The strongest association was between an increase in both morbidity scores and reduced HRQoL measured by the DUKE questionnaire. This association remained strong after adjusting for covariates for the pattern A but not pattern B score (e.g., DUKE Ment, 55–59 years: adjusted pattern A score: -11.5, p<0.0001; adjusted pattern B score: not significant).

For women, the same results were found (Table 6). Increased adjusted pattern A score was associated with reduced HRQoL score for all four dimensions studied whatever the age group. Increased adjusted pattern B score was associated with some age groups for the SF-36 PCS (55–59 years: -3.9, p<0.0001; 60–64 years: -2.8, p<0.0001) and the DUKE Phys (55–59 years: -7.8, p<0.0001; ≥70 years: -9.0, p = 0.0006). This association tended to decrease for the SF-36 PCS (55–59 years: -3.9; 60–64 years, -2.8; ≥70 years: -2.3), whereas this decrease was maximal for the extreme age groups for the DUKE Phys (55–59 years: -7.8; ≤70 years: -9.0).

<footnote>* Linear regression model for each age class was adjusted for body mass index (BMI), professional status, education level, family status and smoking status.</footnote>
<table><caption>Table 6. Impact of multimorbidity on HRQoL for females.</caption><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="8">FEMALE</th></tr><tr><th colspan="2">55-59 years</th><th colspan="2">60-64 years</th><th colspan="2">65-69 years</th><th colspan="2">≥70 years</th></tr><tr><th>Beta</th><th>P</th><th>Beta</th><th>P</th><th>Beta</th><th>P</th><th>Beta</th><th>P</th></tr></thead><tbody><tr><td rowspan="5">SF-36 (PCS)</td><td>Intercept</td><td>57.4</td><td><0.0001</td><td>61.5</td><td><0.0001</td><td>64.4</td><td><0.0001</td><td>60.0</td><td><0.0001</td></tr><tr><td>Pattern A Score</td><td>-3.32</td><td><0.0001</td><td>-2.9</td><td><0.0001</td><td>-2.91</td><td><0.0001</td><td>-2.66</td><td><0.0001</td></tr><tr><td>Pattern B Score</td><td>-5.04</td><td><0.0001</td><td>-3.72</td><td><0.0001</td><td>-1.85</td><td><0.0343</td><td>-5.28</td><td><0.0001</td></tr><tr><td>Adjusted Pattern A score*</td><td>-3.1</td><td><0.0001</td><td>-2.8</td><td><0.0001</td><td>-2.8</td><td><0.0001</td><td>-2.3</td><td><0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>-3.9</td><td><0.0001</td><td>-2.8</td><td><0.0001</td><td>-0.9</td><td>0.2957</td><td>-2.3</td><td><0.0326</td></tr><tr><td rowspan="5">SF-36 (MCS)</td><td>Intercept</td><td>47.4</td><td><0.0001</td><td>40.9</td><td><0.0001</td><td>47.6</td><td><0.0001</td><td>54.5</td><td><0.0001</td></tr><tr><td>Pattern A Score</td><td>-3.79</td><td><0.0001</td><td>-4.22</td><td><0.0001</td><td>-3.85</td><td><0.0001</td><td>-4.67</td><td><0.0001</td></tr><tr><td>Pattern B Score</td><td>2.32</td><td><0.0227</td><td>-0.25</td><td>0.7819</td><td>1.25</td><td>0.2300</td><td>-2.18</td><td>0.0992</td></tr><tr><td>Adjusted Pattern A score*</td><td>-3.9</td><td><0.0001</td><td>-4.0</td><td><0.0001</td><td>-3.8</td><td><0.0001</td><td>-4.6</td><td><0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>1.6</td><td>0.1070</td><td>/</td><td>/</td><td>/</td><td>/</td><td>-0.5</td><td>0.7096</td></tr><tr><td rowspan="5">Duke (Phys)</td><td>Intercept</td><td>91.3</td><td><0.0001</td><td>103.7</td><td><0.0001</td><td>103.9</td><td><0.0001</td><td>105.0</td><td><0.0001</td></tr><tr><td>Pattern A Score</td><td>-11.1</td><td><0.0001</td><td>-11.6</td><td><0.0001</td><td>-10.5</td><td><0.0001</td><td>-10.5</td><td><0.0001</td></tr><tr><td>Pattern B Score</td><td>-11.6</td><td><0.0001</td><td>-7.34</td><td><0.0001</td><td>-6.33</td><td><0.0030</td><td>-17.2</td><td><0.0001</td></tr><tr><td>Adjusted Pattern A score*</td><td>-10.3</td><td><0.0001</td><td>-11.2</td><td><0.0001</td><td>-10.1</td><td><0.0001</td><td>-9.0</td><td><0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>-7.8</td><td><0.0001</td><td>-4.9</td><td><0.0034</td><td>-3.5</td><td>0.0704</td><td>-9.0</td><td><0.0006</td></tr><tr><td rowspan="5">Duke (Ment)</td><td>Intercept</td><td>80.3</td><td><0.0001</td><td>73.2</td><td><0.0001</td><td>96.9</td><td><0.0001</td><td>87.2</td><td><0.0001</td></tr><tr><td>Pattern A Score</td><td>-9.18</td><td><0.0001</td><td>-10.3</td><td><0.0001</td><td>-9.24</td><td><0.0001</td><td>-9.36</td><td><0.0001</td></tr><tr><td>Pattern B Score</td><td>1.28</td><td>0.5574</td><td>-2.22</td><td>0.2531</td><td>0.65</td><td>0.7526</td><td>-2.83</td><td>0.2621</td></tr><tr><td>Adjusted Pattern A score*</td><td>-9.1</td><td><0.0001</td><td>-9.9</td><td><0.0001</td><td>-9.0</td><td><0.0001</td><td>-9.4</td><td><0.0001</td></tr><tr><td>Adjusted Pattern B Score*</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr></tbody></table>

doi:10.1371/journal.pone.0169282.t006

## Discussion

In a sample of 5647 subjects aged 55 years or older, an exploratory factor analysis allowed for identifying two multimorbidity patterns: A and B. On exploring the cross-sectional association of individual multimorbidity and HRQoL scores (assessed by the SF-36 and DUKE questionnaires), pattern A explained more of the HRQoL score decrease than pattern B, with mean multimorbidity scores of 0.73 and 0.27 for the two patterns, respectively. HRQoL was better for men in general, and mental dimension scores tended to increase with age and physical dimension decreased.

The characteristics of the patterns we extracted were similar to those observed previously [31]. The most important conditions in pattern A concerned mental illness and bone impairments. Anxiety is associated with depression problems [32,33]. These conditions could induce sleep disorders, thereby causing some potential memory impairments that may explain the mental part of this pattern. Nevertheless, depression problems may increase with increasing pain and significant limitations in movement. Regarding the relationship between mental disorders and bone disorders, the World Mental Health Surveys, conducted across 17 countries, found greater risk of developing mood disorders and anxiety with presence of osteoarthritis [34]. Being in constant pain, with limited movement, may lead to doubts about the ability to be autonomous and thus a negative self-image inducing anxiety and depressive disorders.

Pattern B was found in previous studies [31]. Its composition is largely associated with cardiovascular and metabolic disorders. Blood vessels are involved in diabetes, so its presence in this pattern is justified. However, prostate impairments constitute a non-cardiovascular

<footnote>* Linear regression model for each age class was adjusted for BMI, professional status, education level, family status and smoking status.</footnote>
chronic condition, so their inclusion in this pattern is more questionable. Older men mainly have this pattern, so gender may explain the mix of prostate and cardiovascular impairments in this pattern without any relationship between them other than shared risk factors.

We have observed a reduction in the proportion of anxiety and depression with increasing of age. The onset of physical illness common in older persons has been shown to increase pro- portion of depression and anxiety [35]. However, the literature showed that studies that have examined the incidence of anxiety or depression across the life span have inconclusive results. Often, anxiety or depression measures depend on cohort characteristics such as age, cultural background. . . [35]. Our sample is constituted of healthy voluntary participants who may rep- resent a healthy cohort bias.

In our sample, cancer did not seem a component of multimorbidity patterns. Our sample was relatively young, with a low proportion of cancer.

The impact on HRQoL was greater with pattern A than B multimorbidity score. The great- est effects were found with the Duke Health Profile. A high pattern A score was associated with a lower score in both mental and physical dimensions of HRQoL and a high pattern B score was essentially associated with a lower score in the physical dimension. Indeed, no signif- icant association was found between mental HRQoL score and pattern B score. Pattern B is related to cardiovascular and metabolic disorders. The literature showed that cardiovascular diseases were associated with a reduced in physical and mental dimensions of HRQoL [36]. In addition, a strong association was found between depression and cardiovascular diseases [37]. So we could have expected in our study a significant impact of pattern B multimorbidity score on mental HRQoL. In the absence of this result, we can make the hypothesis that our method allows us to better identify participants with cardiovascular pathology in absence of psycholog- ical disorders. In fact, anxiety/depression condition is represented in pattern A.

According to our analysis by age groups and gender, decrease in HRQoL was associated more with the DUKE than the SF-36, especially for pattern A score. This result was expected, considering that the Duke Health Profile has more items oriented toward mental health than the SF-36, which is a more general questionnaire. Several studies have shown that among all morbidities, mental disorders have the highest impact on HRQoL [10,38]. Nevertheless, other studies did not account for these conditions in HRQoL assessment [39]. Our results highlight the importance of considering mental disorders in HRQoL studies.

Many studies have shown decreased HRQoL with increasing number of chronic conditions [31,40,41]. With the methodology we used, we accounted for not only the number of morbidit- ties but also their association in the population, which allowed for measuring the effect of mor- bidities on HRQoL as accurately as possible and comparing participants with each other by age group and gender.

Our approach allowed us to account for the interaction between chronic conditions and determine the multimorbidity status of each participant in contrast to studies that conducted a latent class analysis, seeking to consolidate clustered participants, which were unable to account for the complexity of the possibilities. This type of study incurs misclassification error and the model can be applied to only a limited number of participants [42]. When studies ana- lyze morbidities individually, they can consider only a limited number of morbidity interac- tions [10]. Also it is not necessary to apply a method dedicated to multimorbidity for a specific population because our method allows for self-determination of a multimorbidity pattern.

Another strength of our study is the sample size. In addition, more than 5000 participants received a geriatric consultation. Nevertheless, our study could feature an underreporting of some diseases/conditions because of the self-administered questionnaires. Moreover, to assess the multimorbidity measure, we did not use a validated instrument such as the Cumulative Ill- ness Rating Scale [43] or the Duke Severity Illness Checklist [44], both of which cannot be
used for an existing cohort. Finally, we did not assess the severity of conditions and did not use
an exhaustive list of conditions.

The results of our study of a relatively healthy sample, including a low prevalence of mor-
bidities and high HRQoL, suggest that multimorbidity affects HRQoL differently depending
on gender or age. Nonetheless, our study is a novel use of multimorbidity patterns to test the
impact of multimorbidity on HRQoL. We found two patterns, which were clinically recogniz-
able and theoretically plausible. Further investigations and research in older populations
should consider multimorbidity patterns to confirm these findings.

**Conclusions and Future Research**

Our analysis of more than 5,000 participants of 55 years and older, revealed two multimorbid-
ity patterns which were clinically recognizable and theoretically plausible. These two identified
patterns affected both HRQoL, notably a strong association was found between a multimor-
bidity pattern related to mental illness and deteriorated bone health (pattern A)—and a
decrease in physical and mental HRQoL. The multimorbidity pattern related to cardiovascular
and metabolic disorders (pattern B) seems to have no impact on mental HRQoL. The strength
of these associations differed according to age groups.

Our study is a new integrating approach of accounting for multimorbidity patterns in
studying HRQoL in healthy population. Indeed, available multimorbidity indices were based
on specific outcomes, such as mortality, costs, or function, and therefore may not address a
patient's overall condition [45]. In addition, they were validated on very specific populations
and then it is difficult to apply them to other populations. The multimorbidity scores we iden-
tified (counted and weighted) can be used in clinical research to control for the effect of multi-
morbidity on patients' HRQoL and may be useful for clinical practice.

To conclude, the results of this study could lead to a deeper understanding of the associa-
tion of multimorbidity and HRQoL. Nevertheless, this method should be deepened through
further studies to integrate the severity of conditions and to enrich the methodology.

**Acknowledgments**

We thank Younes Esseddik, Gwenaël Monot, Paul Flanzy, Yasmina Chelghoum, and Than
Duong Van (computer scientists), and Nathalie Arnault, Véronique Gourlet, Fabien Szabo,
Laurent Bourhis, and Stephen Besseau (statisticians) for their technical contribution to the SU.
VI.MAX study.

**Author Contributions**

**Conceptualization:** VW CPG SB CR.

**Formal analysis:** VW CPG CR.

**Methodology:** VW CPG CR.

**Project administration:** CPG SB CR.

**Resources:** EKG SH PG KA.

**Software:** VW CR.

**Supervision:** CPG SB CR.

**Validation:** VW CPG SB CR.

**Writing – original draft:** VW CPG CR.
Writing - review & editing: VW CPG EKG NA SH PG KA SB CR.

References

1. United Nations, Department of Economic and Social Affairs, Population Division [Internet]. 2015. http://www.un.org/en/development/desa/population/publications/pdf/popfacts/PopFacts_2014-4Rev1.pdf

2. Kirchberger I, Meisinger C, Heier M, Zimmermann A-K, Thorand B, Autenrieth CS, et al. Patterns of Multimorbidity in the Aged Population. Results from the KORA-Age Study. Thiem U, editor. PLoS ONE. 2012; 7: e30556. doi: 10.1371/journal.pone.0030556 PMID: 22291986

3. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: A systematic review of the literature. Ageing Res Rev. 2011; 10: 430-439. doi: 10.1016/j.arr.2011.03.003 PMID: 21402176

4. Van den Akker M, Buntinx F, Metsemakers JF, Roos S, Knottnerus JA. Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol. 1998; 51: 367-375. PMID: 9619963

5. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001; 54: 661-674. PMID: 11438406

6. Calderón-Larrañaga A, Poblador-Plou B, González-Rubio F, Gimeno-Feliu LA, Abad-Díez JM, Prados-Torres A. Multimorbidity, polypharmacy, referrals, and adverse drug events: are we doing things well? Br J Gen Pract. 2012; 62: 821-826.

7. Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide. Ann Fam Med. 2012; 10: 134-141. doi: 10.1370/afm.1363 PMID: 22412005

8. Sprangers MA, de Regt EB, Andries F, van Agt HM, Bijl RV, de Boer JB, et al. Which chronic conditions are associated with better or poorer quality of life? J Clin Epidemiol. 2000; 53: 895-907. PMID: 11004416

9. Guthrie B, Payne K, Alderson P, McMurdo MET, Mercer SW. Adapting clinical guidelines to take account of multimorbidity. BMJ. 2012; 345: e6341-e6341. doi: 10.1136/bmj.e6341 PMID: 23036829

10. Garin N, Olaya B, Moneta MV, Miret M, Lobo A, Ayuso-Mateos JL, et al. Impact of Multimorbidity on Disability and Quality of Life in the Spanish Older Population. Glymour MM, editor. PLoS ONE. 2014; 9: e111498. doi: 10.1371/journal.pone.0111498 PMID: 25375890

11. Lawson KD, Mercer SW, Wyke S, Grieve E, Guthrie B, Watt GC, et al. Double trouble: the impact of multimorbidity and deprivation on preference-weighted health related quality of life a cross sectional analysis of the Scottish Health Survey. Int J Equity Health. 2013; 12: 67. doi: 10.1186/1475-9276-12-67 PMID: 23962150

12. Schäfer I, Hansen H, Schön G, Höfels S, Altiner A, Dahlhaus A, et al. The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC Health Serv Res. 2012; 12: 89. doi: 10.1186/1472-6963-12-89 PMID: 22471952

13. Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies. Scuteri A, editor. PLoS ONE. 2014; 9: e102149. doi: 10.1371/journal.pone.0102149 PMID: 25048354

14. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. Jama. 2005; 294: 716-724. doi: 10.1001/jama.294.6.716 PMID: 16091574

15. Tinetti ME, Studenski SA. Comparative Effectiveness Research and Patients with Multiple Chronic Conditions. N ENGL J MED. 2011; 2478.

16. Garin N, Olaya B, Perales J, Moneta MV, Miret M, Ayuso-Mateos JL, et al. Multimorbidity Patterns in a National Representative Sample of the Spanish Adult Population. Scuteri A, editor. PLoS ONE. 2014; 9: e84794. doi: 10.1371/journal.pone.0084794 PMID: 24465433

17. Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol A Biol Sci Med Sci. 2016; 71: 205-214. doi: 10.1093/gerona/glv128 PMID: 26419978

18. Diaz E, Poblador-Pou B, Gimeno-Feliu L-A, Calderón-Larrañaga A, Kumar BN, Prados-Torres A. Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway. Chamberlain AM, editor. PLOS ONE. 2015; 10: e0145233. doi: 10.1371/journal.pone.0145233 PMID: 26684188

19. Clerencia-Sierra M, Calderón-Larrañaga A, Martínez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, et al. Multimorbidity Patterns in Hospitalized Older Patients: Associations among
Chronic Diseases and Geriatric Syndromes. PloS One. 2015; 10: e0132909. doi: 10.1371/journal.pone.0132909 PMID: 26208112

20. Schäfer I, von Leitner E-C, Schön G, Koller D, Hansen H, Kolonko T, et al. Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions. Ross JS, editor. PLoS ONE. 2010; 5: e15941. doi: 10.1371/journal.pone.0015941 PMID: 21209965

21. Hercberg S, Galan P, Preziosi P, Roussel AM, Arnaud J, Richard MJ, et al. Background and rationale behind the SU.VI.MAX Study, a prevention trial using nutritional doses of a combination of antioxidant vitamins and minerals to reduce cardiovascular diseases and cancers. SUpplementation en Vitamines et Minéraux AntioXydants Study. Int J Vitam Nutr Res Int Z Vitam- Ernahrungsforschung J Int Vitaminol Nutr. 1998; 68: 3–20.

22. Hercberg S, Galan P, Preziosi P, Bertrais S, Mennen L, Malvy D, et al. The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals. Arch Intern Med. 2004; 164: 2335–2342. doi: 10.1001/archinte.164.21.2335 PMID: 15557412

23. Hercberg S, Kesse-Guyot E, Druesne-Pecollo N, Touvier M, Favier A, Latino-Martel P, et al. Incidence of cancers, ischemic cardiovascular diseases and mortality during 5-year follow-up after stopping antioxidant vitamins and minerals supplements: A postintervention follow-up in the SU.VI.MAX Study. Int J Cancer. 2010; 127: 1875–1881. doi: 10.1002/ijc.25201 PMID: 20104528

24. Kesse-Guyot E, Fezeu L, Jeandel C, Ferry M, Andreeva V, Amieva H, et al. French adults' cognitive performance after daily supplementation with antioxidant vitamins and minerals at nutritional doses: a post hoc analysis of the Supplementation in Vitamins and Mineral Antioxidants (SU.VI.MAX) trial. Am J Clin Nutr. 2011; 94: 892–899. doi: 10.3945/ajcn.110.007815 PMID: 21775560

25. L'étude SU.VI.MAX 2—Comportement alimentaire et qualité du vieillissement—Projet COMPALIMAGE [Internet]. https://www.agropolis.fr/pdf/agrumes-12-08/14-Jeandel.pdf

26. Leplège A, Ecosse E, Verdier A, Perneger TV. The French SF-36 Health Survey: translation, cultural adaptation and preliminary psychometric evaluation. J Clin Epidemiol. 1998; 51: 1013–1023. PMID: 9817119

27. Briancon S, Alla F, Mejat E, Guillemin F, Villemot J-P, Mertes P-M, et al. Mesure de l'incapacité fonctionnelle et de la qualité de vie dans l'insuffisance cardiaque. Adaptation transculturelle et validation des questionnaires de Goldman, du Minnesota et de Duke. Arch Mal Coeur Vaiss. 1997; 90: 1577–1585. PMID: 9587437

28. Kubinder K. On artificial results due to using factor analysis for dichotomous variables. Psychol Sci. 2003; 45: 103–10.

29. Cattell RB. The Scree Test For The Number Of Factors. Multivar Behav Res. 1966; 1: 245–276.

30. Prados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, González-Rubio F, Poncel-Falcó A, et al. Multimorbidity Patterns in Primary Care: Interactions among Chronic Diseases Using Factor Analysis. Gagnier JJ, editor. PLoS ONE. 2012; 7: e32190. doi: 10.1371/journal.pone.0032190 PMID: 22393389

31. Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014; 67: 254–266. doi: 10.1016/j.jclinepi. 2013.09.021 PMID: 24472295

32. Lamers F, van Oppen P, Comijs HC, Smit JH, Spinhoven P, van Balkom AJLM, et al. Comorbidity patterns of anxiety and depressive disorders in a large cohort study: the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry. 2011; 72: 341–348. doi: 10.4088/JCP.10m06176blu PMID: 21294994

33. Alonso J, Angermeyer MC, Bernert S, Bruffaerts R, Brugha TS, Bryson H, et al. 12-Month comorbidity patterns and associated factors in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr Scand Suppl. 2004; 28–37. doi: 10.1111/j.1600-0047.2004.00328.x PMID: 15128385

34. He Y, Zhang M, Lin EHB, Bruffaerts R, Posada-Villa J, Angermeyer MC, et al. Mental disorders among persons with arthritis: results from the World Mental Health Surveys. Psychol Med. 2008; 38: 1639. doi: 10.1017/S0033291707002474 PMID: 18298879

35. Campbell KE, Dennerstein L, Tacey M, Szoeke CE. The trajectory of negative mood and depressive symptoms over two decades. Maturitas. 2017; 95: 36–41. doi: 10.1016/j.maturitas.2016.10.011 PMID: 27889051

36. Swenson JR, Clinch JJ. Assessment of quality of life in patients with cardiac disease: the role of psycho-somatic medicine. J Psychosom Res. 2000; 48: 405–415. PMID: 10880662

37. van Marwijk HWJ, van der Kooy KG, Stehouwer CDA, Beekman ATF, van Hout HPJ. Depression increases the onset of cardiovascular disease over and above other determinants in older primary care
patients, a cohort study. BMC Cardiovasc Disord. 2015; 15: 40. doi: 10.1186/s12872-015-0036-y
PMID: 25962398

38. Arokiasamy P, Uttamacharya U, Jain K, Biritwum RB, Yawson AE, Wu F, et al. The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal? BMC Med. 2015; 13: 178. doi: 10.1186/s12916-015-0402-8 PMID: 26239481

39. Hunger M, Thorand B, Schunk M, Döring A, Menn P, Peters A, et al. Multimorbidity and health-related quality of life in the older population: results from the German KORA-Age study. Health Qual Life Outcomes. 2011; 9: 53. doi: 10.1186/1477-7525-9-53 PMID: 21767362

40. Wang R, Yan Z, Liang Y, Tan EC, Cai C, Jiang H, et al. Prevalence and Patterns of Chronic Disease Pairs and Multimorbidity among Older Chinese Adults Living in a Rural Area. PloS One. 2015; 10: e0138521. doi: 10.1371/journal.pone.0138521 PMID: 26394368

41. Roberts KC, Rao DP, Bennett TL, Loukine L, Jayaraman GC. Prevalence and patterns of chronic disease multimorbidity and associated determinants in Canada. Health Promot Chronic Dis Prev Can Res Policy Pract. 2015; 35: 87-94.

42. Whitson HE, Johnson KS, Sloane R, Cigolle CT, Pieper CF, Landerman L, et al. Identifying Patterns of Multimorbidity in Older Americans: Application of Latent Class Analysis. J Am Geriatr Soc. 2016; 64: 1668-1673. doi: 10.1111/jgs.14201 PMID: 27309908

43. Hudon C, Fortin M, Vanasse A. Cumulative Illness Rating Scale was a reliable and valid index in a family practice context. J Clin Epidemiol. 2005; 58: 603-608. doi: 10.1016/j.jclinepi.2004.10.017 PMID: 15878474

44. Parkerson GR Jr, Broadhead E, Tse C-KJ. The Duke Severity of Illness Checklist (DUSOI) for measurement of severity and comorbidity. J Clin Epidemiol. 1993; 46: 379-393. PMID: 8483003

45. Diederichs C, Berger K, Bartels DB. The Measurement of Multiple Chronic Diseases—A Systematic Review on Existing Multimorbidity Indices. J Gerontol A Biol Sci Med Sci. 2011; 66A: 301-311.