Abstract
Background:
Declining mortality rates and an aging population have contributed to increasing rates of multimorbidity (MM) in the United States. MM is strongly associated with a decline in physical function. Obesity is an important risk factor for the development of MM, and its prevalence continues to rise. Our study aimed to evaluate the associations between obesity, MM, and rates of functional limitations in older adults.
Methods:
We analyzed body mass index (BMI) and self-reported comorbidity data from 7,261 individuals aged ≥ 60 years from the National Health and Nutrition Examination Surveys 2005–2014. Weight status was defined based on standard BMI categories. MM was defined as 2 or more comorbidities, while functional limitations were self-reported. Adjusted logistic regression quantified the association between standard BMI categories and MM. We also examined the difference in the prevalence of limitations between those with and without MM.
Results:
The overall proportion of individuals with concomitant MM and obesity was 27.0%. Compared to a normal BMI, older adults with obesity had higher odds of MM (Prevalence odds ratio 1.79, 95% CI 1.49, 2.12). Overall, 67.5% of patients with MM also reported a functional limitation, with rates of functional limitation increasing with increasing BMI. When evaluating functional limitations in those with MM by BMI class, 90% of patients classified as severely obese (BMI ≥ 40 kg/m2) with MM also had a concomitant functional limitation.
Conclusions:
Compared to normal weight status, obesity is associated with an increased burden of MM and functional limitation among older adults. Our results underscore the importance of identifying and addressing obesity, MM, and functional limitation patterns and the need for evidence-based interventions that address all three conditions in this population.
Keywords: Obesity, multimorbidity, older adults, physical function
INTRODUCTION
Chronic diseases are the leading cause of death and disability in the United States and the leading driver of health care costs.1 Multimorbidity (MM) has been defined as the co-occurrence of two or more chronic conditions.2 An aging population and declining mortality rates have contributed to rising rates of MM in the United States3, increasing from 45.7% in 1994 to 59.6% in 2014.3 These trends have profound consequences for both patients and the health care system. MM is associated with increased hospital admissions, longer lengths of stay, greater risk of nursing home placement, higher premature death rates, and lower quality of life.2,4
Obesity (body mass index, BMI: ≥ 30 kg/m2) is associated with many chronic conditions, including type 2 diabetes, hypertension, heart disease, obesity-related liver disease, chronic kidney disease, many neoplasms, and osteoarthritis, and thus directly impacting the prevalence of MM.5,6 The association between obesity and MM may be more robust in individuals categorized as having class II obesity [35–39.9kg/m2] or class III obesity (i.e. severe obesity [≥ 40 kg/m2]) as these patients are at an increased risk of having chronic conditions.7 While there has been a decrease in obesity-related mortality in recent years, likely due to declining cardiovascular mortality,8 there has been an increase in obesity-related disability.7 Before this change, obesity rates in older adults, were relatively low and overlooked;9,10 thus, less is known about the association between obesity and MM and their impact on function in individuals over age 60 years.
Our main objective was to assess the association between obesity and MM in older adults. Additionally, among those older adults with co-occurring obesity and MM, we sought to evaluate proportions of functional limitation, which gives insight into the impact of these comorbidities on quality of life in this population. The decrease in obesity-related mortality concurrent with an increase in obesity-related chronic diseases led us to hypothesize that there may be high rates of older adults categorized as having co-existent obesity and MM. While not well studied in the US, this association is robust across other populations. Studies in Canada and China have demonstrated a significant association between obesity in older adults and MM.11,12 MM is also strongly associated with a decline in physical function,13 cognitive status14 and leads to a lower quality of life.15 Therefore, we anticipate that increasing rates of obesity-related MM in older adults may be associated with reduced functional status. Studying this association in the older adult population while controlling for key covariates will help identify key sub-groups differences among older adults (i.e. BMI classification) that would benefit from targeted, evidence-based interventions that address obesity and MM.
MATERIALS & METHODS
Study Design
The Centers for Disease Control and Prevention routinely conduct the National Health and Nutrition Examination Surveys (NHANES), a cross-sectional survey that assesses disease-specific epidemiologic changes among the US population. NHANES data have informed policy changes over the past five decades and are currently released in two-year intervals. The survey oversamples older adults, racial, and ethnic demographic groups. All manuals, procedures, and data files are publicly available at http://www.cdc.gov/nchs/nhanes.html. The Office of Human Research Ethics at the University of North Carolina deems secondary analyses of de-identified public research as exempt.
Participant Sample
Sample selection for this survey followed a multistage probability sampling design using three stages. Primary sampling units, classified as counties or contiguous counties, were selected, followed by segments within a unit. Segments are blocks or groups of blocks containing clusters of households. These households were further broken down via a household interview to select specific individuals for the study. Individuals then visited a mobile examination center that served as standardized sites for collecting individual objective measurements. The total sample included 27,711 individuals 18 years of age and older, of which 18,828 were <60 years old and excluded. Further exclusions for missing data totaled 1,622, resulting in a final analytic sample of 7,261 adults 60 years of age or older.
Key Study Measures
Individuals selected to participate in the NHANES study were asked to complete a medical conditions questionnaire, which evaluated for past diagnoses of chronic conditions such as asthma, diabetes, arthritis, gout, congestive heart failure, coronary heart disease, angina, stroke, thyroid disease, emphysema, chronic bronchitis, chronic obstructive pulmonary disease, liver disease, kidney disease, gallbladder conditions, and cancer. We categorized individuals who reported having two or more of these aforementioned chronic conditions as having MM.16
Standing and recumbent height were measured using a digital stadiometer, which was used to measure stature in patients aged 2 years and older and connected directly to their capturing data system. Weight was measured using a digital weight scale connected directly to the NHANES Integrated Survey Information System - its maximum weight was 200 kg. For patients greater than 200 kg, two weight scales were used. Individuals were then stratified according to standard BMI categories (underweight <18.5kg/m2, normal 18.5–24.9 kg/m2, overweight 25–29.9 kg/m2, obesity ≥30 kg/m2). Obesity status was then further defined as class 1 (BMI 30–34.9 kg/m2), class II (BMI 35–39.9kg/m2), and class III (BMI ≥40 kg/m2).
We identified self-reported functional limitations as conducted in our previous studies.17 We analyzed participant responses to questions from the physical functioning assessment questionnaire, which focused on the three different core domains of functional ability: physical limitations, basic activities of daily living (ADL), and instrumental activities of daily living (IADL). Physical limitations were evaluated by assessing the participant’s ability to perform basic physical tasks like walking, running, or standing up from a seated position. ADLs were assessed via an ability to complete tasks like grooming, bathing, and dressing. IADLs were evaluated by asking participants if they were able to do household chores or errands without assistance. Those identified as having a limitation in completing at least one activity within a specific domain were defined as having a deficit. Individuals with deficits in any of the three core domains were included in a larger group defined as having any functional limitation.
Other Relevant Covariates
We selected covariates relevant to obesity, MM, and functional limitations in older adults such as sex, race/ethnicity, education, marital status, smoking, and income. Each of these covariates were chosen on the basis of the research team’s previous work.18–20 All such information was gathered through the interview process, including age at the time of the interview, sex, and race/ethnicity (non-Hispanic white, non-Hispanic Black, Mexican American, other Hispanic, and other), education level (non-high school graduate, high school graduate/General Education Development qualification, some college or associate degree, and college graduate or above), marital status (single, married or living with a partner, widowed/divorced/separated), smoking status (never, current, former), and income as a percent of the federal poverty level.
Statistical Analysis
All analyses incorporated NHANES weighting, primary sampling unit, and stratum to account for the complex sampling procedures as per guidelines, making estimates representative of the US population. Descriptive statistics, including categorical data, are represented as counts with weighted percentages, and continuous data is presented as a weighted mean value ± standard error. We compared categories using a t-test of unequal variance or chi-squares for categorical variables. After adjusting for age, sex, race/ethnicity, marital status, education, and smoking status, we conducted logistic regression analyses to evaluate the association between BMI categories (referent = 18.5–24.9 kg/m2) and MM status (yes/no). We examined the association of the standard definition of BMI and MM in a similar model structure but limited to those with: a physical functional limitation, basic activities of daily living (ADL), or instrumental activities of daily living (IADL). To give insight on the relative impact that each covariate may have in adjustment we also conducted several additive models with MM as the outcome with presence or absence of each limitation separately (Supplementary Table 4). Along with bivariate models, multivariable models started with age, sex, race and the limitation being examined (model 1), with the addition of marital status and education (model 2), with the addition of smoking status (model 3), with the addition of BMI (model 4).A p-value <0.05 was considered statistically significant. All analyses were conducted using R 17.
RESULTS
Our final data set included 7,261 participants (54.3% females) (Table 1). Participants’ mean age was 70±7 years, mean BMI of 29±6 kg/m2, and the majority (80.8%) self-identified as non-Hispanic white. Over two-thirds of the population, n=4,965 (68.4%), were identified as having MM. The overall proportion of individuals with MM and obesity was 1,963 (27.0%). The proportion of adults with MM increased consistently with age, with 63.1%, 72.5%, and 75.2% of adults 60–69, 70–79, and 80+ years, respectively, classified as having MM. In our preliminary analyses, older adults with obesity were more likely to have MM (adjusted prevalence odds ratio 1.79, 95% CI 1.49, 2.12) (Figure 1). The odds of having MM increased with increasing BMI, with those with a BMI ≥40 kg/m2 having over four times the odds of having MM relative to those with normal BMI (Supplemental Table 2).
Table 1.
Baseline characteristics of the Study Cohort with and without Multimorbidity
| Characteristics | Overall Cohort | No MM | MM Present | P-value |
| N (% of total) | 7261 | 2296 (31.6) | 4965 (68.4) | |
| Age, years | <0.001 | |||
| 60–69 n (%) | 3579 (52.7) | 1321 (62.6) | 2258 (48.1) | |
| 70–79 n (%) | 2336 (31) | 641 (25.5) | 1695 (33.5) | |
| 80+ n (%) | 1346 (16.4) | 334 (11.9) | 1012 (18.4) | |
| BMI (kg/m2) | <0.001 | |||
| <18.5 | 103(1.4) | 37(0.5) | 66(0.9) | |
| 18.5–24.9 | 1825(25.1) | 631 (8.6) | 1194 (16.4) | |
| 25–29.9 | 2675(36.6) | 933 (12.8) | 1742 (23.9) | |
| ≥ 30 | 2658(36.6) | 695 (9.6) | 1963 (27.0) | |
| Female Sex n (%) | 3609 (54.3).61 | 989 (47.2) | 2620 (57.5) | <0.001 |
| Race and ethinicity n (%) | 0.15 | |||
| Non-Hispanic White | 3937 (80.8) | 1142 (79.9) | 2795 (81.3) | |
| Non-Hispanic Black | 1421 (7.8) | 459 (7.7) | 962 (7.9) | |
| Hispanic | 1465 (6.8) | 510 (7.3) | 955 (6.6) | |
| Other | 438 (4.5) | 185 (5.2) | 253 (4.3) | |
| Education n (%) | <0.001 | |||
| ≤ 12th grade | 2321 (21.1) | 669 (18.1) | 1652 (22.5) | |
| High school/GED | 1755 (24.9) | 529 (23.3) | 1226 (25.7) | |
| Some college/AA degree | 1743 (27.3) | 555 (26.8) | 1188 (27.6) | |
| > College graduate | 1431 (26.5) | 539 (31.6) | 892 (24.1) | |
| Poverty Income Ratio * | 0.02 | |||
| <1.0 | 1090 (8.5) | 301 (7.4) | 789 (9) | |
| >1.0 | 5550 (84.3) | 1779 (85.3) | 3771 (83.8) | |
| Marital Status n (%) | <0.001 | |||
| Single | 338 (3.6) | 101 (3.1) | 237 (3.8) | |
| Widowed, divorced, or separated | 2648 (32) | 771 (28.7) | 1877 (33.5) | |
| Married or living with a partner | 4272 (64.4) | 1423 (68.2) | 2849 (62.7) | |
| Smoking Status n (%) | 0.08 | |||
| Never smoker | 3514 (48.4) | 1150 (51.2) | 2364 (47.1) | |
| Former smoker | 2836 (40.3) | 853 (37.9) | 1983 (41.4) | |
| Current smoker | 905 (11.3) | 291 (10.9) | 614 (11.4) |
All values represent means ± standard errors or counts (weighted percentage)
p-values from chi-squared tests of distribution of age groups with the outcome using NHANES complex survey design weights.
GED – General Educational Development
AA degree- Associates degre
Figure 1: Adjusted* Association Between BMI Class And Having Multimorbidity.
* Adjusted for age group, sex, race, education level, marital status, and smoking status. Values represented as adjusted prevalence odds ratios and 95% confidence intervals.
There was an increasing proportion of functional limitation in patients with MM compared to those without MM (Table 2). There were higher rates of physical limitations (60.5 vs. 35.8%, p<0.001), basic (47.9 vs. 26.6%, p<0.001), and instrumental ADLs (26.6 vs. 16.7%, p < 0.001) among those with MM. Overall, 45% of patients without MM reported difficulty with any physical limitation, ADL, or IADL compared to 67.5% of patients with MM (p < 0.001). There was a statistically significant increase in rates of functional impairment with increasing BMI in patients with and without MM. Among patients with MM, obesity increased the odds of developing all forms of functional impairment (Figure 2)
Table 2.
Prevalence of Limitations by Multimorbidity and Body Mass Index category
| Overall | No Multimorbidity | Multimorbidity Present | |||||||||||
| Difficulty with | No MM | MM | p-value | <18.5 | 18.5–24.9 | 25–29.9 | ≥ 30 | p.value | <18.5 | 18.5–24.9 | 25–29.9 | ≥ 30 | p.value * |
| Physical Limitation, n (%) | 926 (35.8) | 3153 (60.5) | <0.001 | 13 (23) | 209 (28.4) | 358 (34.2) | 346 (45.3) | <0.001 | 43 (58.2) | 656 (51) | 1016 (55.4) | 1438 (71) | <0.001 |
| Basic ADL, n (%) | 685 (26.6) | 2569 (47.9) | <0.001 | 10 (18.4) | 164 (23.4) | 263 (25.5) | 248 (31.4) | <0.001 | 38 (51.2) | 549 (41.8) | 800 (43) | 1182 (55.9) | <0.001 |
| IADL, n (%) | 467 (16.7) | 1796 (32) | <0.001 | 13 (20.9) | 112 (14.3) | 190 (17.8) | 152 (17.1) | <0.001 | 29 (33.9) | 414 (29.8) | 565 (28.8) | 788 (36.1) | <0.001 |
| Any Limitation, n (%) | 1144 (45) | 3504 (67.5) | <0.001 | 17 (28.6) | 274 (37.5) | 450 (44.7) | 403 (53.2) | <0.001 | 47 (66.2) | 778 (61.1) | 1153 (63.4) | 1526 (75.2) | <0.001 |
p-values from chi-squared tests of distribution of age groups with the outcome using NHANES complex survey design weights.
All values represent means ± standard errors or counts (weighted percentages)
ADL – basic activity of daily living
IADL – Instrumental activities of daily living
MM - multimorbidity
Figure 2: Association of Body Mass Index and Multimorbidity By Type of Functional Limitation.
* Adjusted for age group, sex, race, education level, marital status, and smoking status
Finally, we evaluated the role of functional limitations in those with and without MM by BMI obesity class (Supplementary Table 3). Proportions of functional impairment were particularly high in those with MM, and class III obesity (BMI ≥ 40kg/m2), with 90% of patients with MM and a BMI ≥ 40kg/m2 having some form of functional limitation (Supplementary Table 3). Additive models did not reveal meaningful changes in covariate POR values (Supplementary Table 4).
DISCUSSION
Our results demonstrate that obesity is strongly associated with MM in older adults, and the strength of the association increases with increasing obesity severity. Additionally, we observed a positive relationship between increasing BMI and rates of functional impairment, with individuals with both MM and obesity experiencing high rates of functional impairment. These results further our understanding of the considerable public health impact of these co-existing disorders.
Previous studies have demonstrated that the rates of MM increase with both age and BMI category.3,22 Until recently, less was known about the interplay between obesity and MM in older adults.23 The pathophysiology behind each of these disorders has been described in the literature24,25. Obesity contributes to dysfunction in lipid and glucose metabolism. Additionally, increasing BMI results in myocellular, hormonal, and inflammatory changes that influence pathophysiology in multiple organ systems. These changes have been associated with the development of many chronic medical conditions. Pathways leading to MM in patients with obesity are distinct from those associated with normal aging. This led us to hypothesize that obesity in older adults would infer an additional risk for the development of MM. Unsurprisingly, we observed that in a representative sample of US older adults, the prevalence of MM increased with increasing age, and obesity rates were high among all age groups.20 Additionally, however, we found that obesity conferred an added risk for the development of MM, independent of age alone. The impact of obesity on MM was greatest at higher levels of BMI.
Our results demonstrated that increasing BMI is associated with loss of function in individuals with and without MM. We found that functional impairment was increased among all individuals with a BMI of > 30. Additionally, the impact increased considerably with each class of obesity. The impact of this relationship is evident when analyzing groups at opposite ends of this spectrum. Among individuals with no MM and class 1 obesity, 48.7% reported some limitation, while 90% of individuals with MM and class 3 obesity had at least one limitation (Supplemental Table 3). Previous studies have shown that both MM and obesity independently impact functional impairment.14,15,27 However, our study found a synergistic effect when they occurred together, furthering our understanding of the complex interplay between obesity, MM, and functional impairment in older adults.
There are strengths and limitations of the study design. We used a large, population-based cohort of older adults and relied on previously validated metrics to define MM, obesity, and functional impairments. Limitations include the use of self-reported measurements, specifically those that define functional limitations and comorbidities. Previous studies have found discrepancies between self-reported and objective measurements. Under-reporting or misreporting would bias our results towards the null, adding to the believability of these results. Second, the NHANES dataset is comprised of community-dwelling older individuals; therefore, results are not generalizable to institutionalized older adults with obesity and functional impairment where rates may be higher. Lastly, due to the cross-sectional study design, associations demonstrated herein cannot be used to make a causal inference.
Findings in this study have many potential implications for clinical practice and future research. As older adults with obesity continue to make up an ever-greater percentage of the total population, the conditions in this cohort are likely to impact the healthcare system. The findings of this study suggest that older adults with obesity are more likely to have MM and are also more likely to be functionally impaired than older adults without obesity. If this trend continues, the impact on the healthcare system is likely to be quite substantial as both functional impairment and MM lead to increased utilization22. However, the relationship between increasing BMI and the development of both MM and functional impairment suggests that any weight loss at any age may potentially decrease current multimorbidity burden along with the risk of future development of MM and functional impairment. There is strong evidence for implementing multi-component weight-loss interventions, with >5% total body weight loss being a threshold shown to improve long-term outcomes.23 We believe that further investigation is needed to determine if weight loss in older adults with obesity reduces the current burden of MM, decreases the future development of MM and improves overall function.
CONCLUSIONS
These results emphasize the role of obesity as a contributing factor to the burden of MM among older adults and underscore the importance of identifying and addressing obesity and MM via evidence-based interventions to decrease obesity prevalence. Further studies are needed to test if obesity prevention might be an effective route to mitigating the development of MM with age. Additionally, weight loss interventions tailored for older adults presenting with different patterns of MM may mitigate functional decline with age or lead to improved functional status.
Supplementary Material
Supplemental Table 1. Baseline characteristics of the Study Cohort with and without Multimorbidity
Supplemental Table 2. Associations between BMI and Multimorbidity
Supplementary Table 3. Rates of Limitations by Obesity Class.
Supplementary Table 4. Baseline Characteristic by Functional Limitation Class using Stepwise Additive Models for Adjustment
KEY POINTS.
In this prospective cohort study of individuals aged ≥ 60 years, the coexistence of multimorbidity and obesity increased the risk of functional limitation.
Among patients with multimorbidity and class 3 obesity ( BMI ≥ 40 kg/m2), 90% reported functional impairment.
WHY DOES THIS PAPER MATTER?
Failure to identify and treat multimorbidity and obesity in older adults may result in increased levels of functional impairment.
ACKNOWLEDGEMENTS
FINANCIAL DISCLOSURES:
The following agencies support this work:
JAB’s research reported in this publication was supported in part by the National Institute on Aging of the National Institutes of Health (K23 AG051681). ARK is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490. The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health. Dr. Batsis and Dr. Petersen hold equity in SynchroHealth LLC, a remote monitoring startup.
Sponsor’s role
JAB’s research reported in this publication was supported in part by the National Institute on Aging of the National Institutes of Health (K23 AG051681). The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health. This work was not sponsored by any other entities.
ABBREVIATIONS
- ADL
basic activities of daily living
- BMI
body mass index
- IADL
instrumental activities of daily living
- MM
multimorbidity
- NHANES
National Health and Nutrition Examination Survey
Footnotes
Work to be presented at the 2021 Gerontology Society of America’s Annual Scientific Meeting, Phoenix, Arizona. November 2021.
Conflicts of Interest
No personal or financial conflicts of interest
REFERENCES
- 1.Kochanek KD, Xu J, Arias E. Key Findings Data from the National Vital Statistics System How Long Can We Expect to Live?; 2019. https://www.cdc.gov/nchs/products/index.htm.
- 2.Navickas R, Petric VK, Feigl AB, Seychell M. Multimorbidity: What Do We Know? What Should We Do? Journal of Comorbidity. 2016;6(1):4–11. doi: 10.15256/joc.2016.6.72 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.K DE, X J, P CS. Multimorbidity Trends in United States Adults, 1988–2014. Journal of the American Board of Family Medicine : JABFM. 2018;31(4):503–513. doi: 10.3122/JABFM.2018.04.180008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health and quality of life outcomes. 2004;2:51. doi: 10.1186/1477-7525-2-51 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Pollack LM, Wang M, Leung MYM, Colditz G, Herrick C, Chang SH. Obesity-related multimorbidity and risk of cardiovascular disease in the middle-aged population in the United States. Preventive Medicine. 2020;139. doi: 10.1016/j.ypmed.2020.106225 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Musso G, Gambino R, Tabibian JH, et al. Association of Non-alcoholic Fatty Liver Disease with Chronic Kidney Disease: A Systematic Review and Meta-analysis. PLoS Medicine. 2014;11(7). doi: 10.1371/journal.pmed.1001680 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.P A, B K. Is the health burden associated with obesity changing? American journal of epidemiology. 2012;176(10):840–845. doi: 10.1093/AJE/KWS328 [DOI] [PubMed] [Google Scholar]
- 8.Lebenbaum M, Zaric GS, Thind A, Sarma S. Trends in obesity and multimorbidity in Canada. Preventive Medicine. 2018;116(August):173–179. doi: 10.1016/j.ypmed.2018.08.025 [DOI] [PubMed] [Google Scholar]
- 9.Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018 Key Findings Data from the National Health and Nutrition Examination Survey.; 2017. https://www.cdc.gov/nchs/products/index.htm.
- 10.Malenfant JH, Batsis JA. Obesity in the geriatric population – a global health perspective. Journal of Global Health Reports. 2019;3. doi: 10.29392/joghr.3.e2019045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hu F, Xu L, Zhou J, Zhang J, Gao Z, Hong Z. Association between overweight, obesity and the prevalence of multimorbidity among the elderly: Evidence from a cross-sectional analysis in Shandong, China. International Journal of Environmental Research and Public Health. 2020;17(22):1–10. doi: 10.3390/ijerph17228355 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Lebenbaum M, Zaric GS, Thind A, Sarma S. Trends in obesity and multimorbidity in Canada. Preventive Medicine. 2018;116:173–179. doi: 10.1016/j.ypmed.2018.08.025 [DOI] [PubMed] [Google Scholar]
- 13.Calderón-Larrañaga A, Vetrano DL, Ferrucci L, et al. Multimorbidity and functional impairment–bidirectional interplay, synergistic effects and common pathways. Journal of Internal Medicine. 2019;285(3):255–271. doi: 10.1111/joim.12843 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Caracciolo B, Gatz M, Xu W, Marengoni A, Pedersen NL, Fratiglioni L. Relationship of subjective cognitive impairment and cognitive impairment no dementia to chronic disease and multimorbidity in a nation-wide twin study. Journal of Alzheimer’s Disease. 2013;36(2):275–284. doi: 10.3233/JAD-122050 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kanesarajah J, Waller M, Whitty JA, Mishra GD. Multimorbidity and quality of life at mid-life: A systematic review of general population studies. Maturitas. 2018;109:53–62. doi: 10.1016/j.maturitas.2017.12.004 [DOI] [PubMed] [Google Scholar]
- 16.King DE, Xiang J, Pilkerton CS. Multimorbidity trends in United States adults, 1988–2014. Journal of the American Board of Family Medicine. 2018;31(4):503–513. doi: 10.3122/jabfm.2018.04.180008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Batsis JA, Mackenzie TA, Lopez-Jimenez F, Bartels SJ. Sarcopenia, sarcopenic obesity, and functional impairments in older adults: National Health and Nutrition Examination Surveys 1999–2004. Nutrition Research. 2015;35(12):1031–1039. doi: 10.1016/j.nutres.2015.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Batsis JA, Mackenzie TA, Lopez-Jimenez F, Bartels SJ. Normal-Weight Obesity and Disability in Older Adults: Data from the National Health and Nutrition Examination Study 1999–2004. Journal of the American Geriatrics Society. 2016;64(6):1367–1368. [DOI] [PubMed] [Google Scholar]
- 19.Batsis JA, Barre LK, Mackenzie TA, Pratt SI, Lopez-Jimenez F, Bartels SJ. Variation in the prevalence of sarcopenia and sarcopenic obesity in older adults associated with different research definitions: Dual-energy X-ray absorptiometry data from the National Health and Nutrition Examination Survey 1999–2004. Journal of the American Geriatrics Society. 2013;61(6):974–980. [DOI] [PubMed] [Google Scholar]
- 20.Crow RS, Lohman MC, Titus AJ, et al. Mortality Risk Along the Frailty Spectrum: Data from the National Health and Nutrition Examination Survey 1999 to 2004. Journal of the American Geriatrics Society. 2018;66(3):496–502. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bunn AG. A dendrochronology program library in R (dplR). Dendrochronologia. 2008;26(2):115–124. doi: 10.1016/j.dendro.2008.01.002 [DOI] [Google Scholar]
- 22.Pollack LM, Wang M, Leung MYM, Colditz G, Herrick C, Chang SH. Obesity-related multimorbidity and risk of cardiovascular disease in the middle-aged population in the United States. Preventive Medicine. 2020;139. doi: 10.1016/j.ypmed.2020.106225 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity Among Adults and Youth: United States, 2015–2016 Key Findings Data from the National Health and Nutrition Examination Survey.; 2015. Accessed May 10, 2021. https://www.cdc.gov/nchs/data/databriefs/db288_table.pdf#1.
- 24.Redinger Professor R The Pathophysiology of Obesity and Its Clinical Manifestations. Vol 3.; 2007. [PMC free article] [PubMed] [Google Scholar]
- 25.Wetterling T Pathogenesis of multimorbidity—what is known? Zeitschrift fur Gerontologie und Geriatrie. 2021;54(6):590–596. doi: 10.1007/s00391-020-01752-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018 Key Findings Data from the National Health and Nutrition Examination Survey.; 2017. https://www.cdc.gov/nchs/products/index.htm.
- 27.Calderón-Larrañaga A, Vetrano DL, Ferrucci L, et al. Multimorbidity and functional impairment–bidirectional interplay, synergistic effects and common pathways. Journal of Internal Medicine. 2019;285(3):255–271. doi: 10.1111/joim.12843 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Frølich A, Ghith N, Schiøtz M, Jacobsen R, Stockmarr A. Multimorbidity, healthcare utilization and socioeconomic status: A register-based study in Denmark. PLoS ONE. 2019;14(8). doi: 10.1371/journal.pone.0214183 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Batsis JA, Gill LE, Masutani RK, et al. Weight Loss Interventions in Older Adults with Obesity: A Systematic Review of Randomized Controlled Trials Since 2005. Journal of the American Geriatrics Society. 2017;65(2):257–268. doi: 10.1111/jgs.14514 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Table 1. Baseline characteristics of the Study Cohort with and without Multimorbidity
Supplemental Table 2. Associations between BMI and Multimorbidity
Supplementary Table 3. Rates of Limitations by Obesity Class.
Supplementary Table 4. Baseline Characteristic by Functional Limitation Class using Stepwise Additive Models for Adjustment