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Background:
Systematic Review

The Obesity Paradox and Mortality in Older Adults: A Systematic Review

by
Moustapha Dramé
1,2,* and
Lidvine Godaert
1,3
1
EpiCliV Research Unit, Faculty of Medicine, University of the French West Indies, 97261 Fort-de-France, France
2
Department of Clinical Research and Innovation, University Hospitals of Martinique, 97261 Fort-de-France, France
3
Department of Geriatrics, General Hospital of Valenciennes, 59300 Valenciennes, France
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(7), 1780; https://doi.org/10.3390/nu15071780
Submission received: 12 March 2023 / Revised: 3 April 2023 / Accepted: 4 April 2023 / Published: 6 April 2023
(This article belongs to the Special Issue Nutrition Interventions for Healthy Ageing)
Browse Figure
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Abstract

:
“Obesity paradox” describes the counterintuitive finding that aged overweight and obese people with a particular disease may have better outcomes than their normal weight or underweight counterparts. This systematic review was performed to summarize the publications related to the obesity paradox in older adults, to gain an in-depth understanding of this phenomenon. PubMed©, Embase©, and Scopus© were used to perform literature search for all publications up to 20 March 2022. Studies were included if they reported data from older adults on the relation between BMI and mortality. The following article types were excluded from the study: reviews, editorials, correspondence, and case reports and case series. Publication year, study setting, medical condition, study design, sample size, age, and outcome(s) were extracted. This review has been registered with PROSPERO (no. CRD42021289015). Overall, 2226 studies were identified, of which 58 were included in this systematic review. In all, 20 of the 58 studies included in this review did not find any evidence of an obesity paradox. Of these 20 studies, 16 involved patients with no specific medical condition, 1 involved patients with chronic diseases, and 2 involved patients with type 2 diabetes mellitus. Seven out of the nine studies that looked at short-term mortality found evidence of the obesity paradox. Of the 28 studies that examined longer-term mortality, 15 found evidence of the obesity paradox. In the studies that were conducted in people with a particular medical condition (n = 24), the obesity paradox appeared in 18 cases. Our work supports the existence of an obesity paradox, especially when comorbidities or acute medical problems are present. These findings should help guide strategies for nutritional counselling in older populations.

1. Introduction

Obesity, usually defined by the body mass index (BMI), is considered a public health problem, and is associated with many diseases [1,2,3]. The prevalence of obesity is high in younger adults but also in older people [4], and evidence suggests that prevalence of obesity will continue to increase [5]. The term “obesity paradox” is used to describe the counterintuitive finding that aged overweight and obese people with a particular disease may have better outcomes than their normal weight or underweight counterparts. However, there is wide heterogeneity between studies regarding the association between obesity and mortality in older adults, depending on the diseases concerned, the presence or absence of a particular disease, or the BMI level considered [6,7,8]. In aged people, body composition tends to change, and body weight tends to decrease, and some authors have suggested that fatness could be healthy [9]. Thus, it is important to confirm whether an “obesity paradox” truly exists, with a view to adapting management policies for overweight or obese old people.
In this context, the objective of the study was to summarize the publications in the literature relating to the obesity paradox in older adults, to enhance our understanding of this phenomenon.

2. Methods

2.1. Literature Search

A preliminary check was made in PubMed©, Scopus©, Embase©, Prospero©, and the Cochrane Library© to ensure that no systematic reviews had previously been conducted on this specific topic.
A literature search was performed using PubMed©, Embase©, and Scopus© to cover all publications up to March 20, 2022. The search terms defined by the two researchers (LG, MD) included the following keywords in the title and/or the abstract: (“obesity paradox” OR “reverse epidemiology” OR “body mass index” OR BMI OR overweight OR obesity) AND (mortality OR death OR survival)). The search included studies in the French or English language and studies on human subjects, and excluded the following publication types: reviews, editorials, correspondence, and case reports and case series. A manual check was performed for potential additional studies. This systematic review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study was registered with PROSPERO (an International prospective register of systematic reviews) (number CRD42021289015), available at https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289015, accessed on 20 March 2023.

2.2. Study Selection

Study eligibility criteria were defined a priori by the two researchers (LG, MD) within the PICOS framework. Studies were eligible if they reported data on “obesity paradox” (using body mass index as a nutritional indicator). The population was restricted to studies that included persons 65 years or older, whatever their sex, ethnicity, or living place. The intervention (exposure) was a presence of overweight or obesity as defined by the baseline BMI value. The control was those who were underweight or a normal weight. The outcome was death, whatever the timepoint. When the study was not specifically conducted in older adults, only data concerning those aged 65 years or over were taken into account (provided that the information was available). Correspondence, editorials, reviews, basic science articles, and case reports and case series were excluded.

2.3. Data Extraction

The Covidence systematic review software© (Veritas Health Innovation, Melbourne, Australia), available at www.covidence.org, was used to perform data analysis. After elimination of duplicates, the two researchers (LG, MD) made a blind review of titles and abstracts of all articles. When there was disagreement about whether or not to include an article, they discussed the case until consensus was reached. Overlap between studies was verified. Data extraction was realised independently by the two researchers (LG, MD), using the same extraction form. The following data were extracted: publication year, study setting, medical condition, study design, sample size, age (mean or median and their statistical dispersion parameters, when available), and outcome(s). To check whether the obesity paradox was present or not, the following information was collected: outcome(s), BMI classes, type of analysis (whether multivariable or not), statistical estimates (Hazard ratio, Odds ratio, Rate ratio, Rates) and their respective 95% confidence intervals (95% CI), and the level of significance (p-values).

2.4. Quality Assessment

The Newcastle–Ottawa Scale (NOS) [10] was used to assess the quality of included studies. This scale is composed of three quality criteria: selection (4 points), comparability (2 points), and outcome assessment (3 points). This gives a total of between 0 and 9 points. Scores of 7 or more are considered high quality studies, scores of 5–6 as moderate quality, and scores below 5 as low quality. Disagreements in scoring were resolved by a joint review of the manuscript to reach consensus.
Where possible and appropriate, some parameters were calculated from available data (e.g., mean age and/or standard deviation, rate ratio, odds ratio, etc.).

3. Results

As shown in Figure 1, 2226 studies were identified by the literature search. Among these, 1285 duplicates were found and excluded. After checking titles and abstracts of the remaining 942 studies, 273 articles were included for full-text assessment. After full-text examination of these 273 studies, 215 were excluded for at least one of the following reasons: lack of relevant information, overlapping data, or inappropriate age of the study population. Thus, 58 studies were retained in this review.
Table 1 summarizes the characteristics of the studies included in the review. All studies were observational cohorts; 41 were prospective [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51] and 17 were retrospective [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68].
As shown in Table 2, 20 of the 58 studies included in this review did not find any evidence of an obesity paradox [17,27,28,29,36,39,42,43,46,47,49,50,53,56,59,62,63,65,68,69]. Of these 20 studies, 16 involved patients with no specific medical condition [17,28,29,36,39,42,43,46,47,49,50,53,56,62,65,69]. One involved patients with chronic diseases [59], and two involved patients with type 2 diabetes mellitus [27,63]. Of the 58 studies, 34 used the threshold of BMI ≥ 25.0 kg/m2 [11,12,14,16,19,20,21,22,24,26,30,31,32,34,38,40,41,44,45,51,52,54,55,57,58,60,66,67,68]. A further 10 studies used a threshold different from 25 kg/m2 and found evidence of the obesity paradox [13,18,23,25,33,35,37,48,61,64]. Regarding the time points, 9 studies looked at short-term mortality (less than 12-month mortality, ICU mortality, hospital mortality) [11,12,19,30,40,52,55,64,68]. All of these, except Yamamoto et al. [40] and Kananen et al. [68], found evidence of the obesity paradox. Of the 28 studies that examined longer-term mortality (time point ≥ 5 years) [13,14,15,20,22,27,28,32,34,36,37,38,39,42,44,45,46,49,53,56,57,58,59,60,61,62,63,66,67], 15 (54%) found evidence of the obesity paradox [13,14,20,22,32,34,37,38,44,45,57,58,60,61,66,67]. In the studies that were conducted in people with a particular medical condition (n = 24) [11,12,14,16,18,19,21,24,25,26,27,37,40,44,52,54,55,58,59,60,63,64,66,68], the obesity paradox appeared in 18 (75%) cases [11,12,14,16,18,19,21,24,25,26,37,40,44,52,54,55,58,60,64,66]. In the studies that were carried out among people with no specific medical condition (n = 34) [13,15,17,20,22,23,28,29,30,31,32,33,34,35,36,38,39,41,42,43,45,46,47,48,49,50,51,53,56,57,61,62,65,67], the obesity paradox appeared in 17 (50%) cases [22,23,30,31,32,33,34,35,38,41,45,48,51,57,61,67].
An appendix provides detailed information of the analyses and results of the relationship between BMI and mortality in aged adults. Of the analyses tested for the existence of an obesity paradox, 48 were adjusted for confounders, and 10 were unadjusted analyses (see Supplementary Materials).
The quality of the included studies, as assessed using the NOS, was considered high for all 58 studies (Table 3).

4. Discussion

In this systematic review of studies exploring the relationship between BMI and mortality in patients aged 65 years or older, 28 out of the 58 studies included observed longer survival in patients with a BMI ≥ 25 kg/m2 (the so-called obesity paradox) [11,12,14,16,19,20,21,22,24,26,30,31,32,34,38,40,41,44,45,51,52,54,55,57,58,60,66,67]. Among these 28 studies, 16 involved patients with a specific or acute medical condition [11,12,14,16,19,21,24,26,40,44,52,54,55,58,60,66]. Seven studies found improved survival in overweight and obese older people when focussing on short-term mortality [11,12,19,30,52,55,64,70]. One showed increased survival only in the oldest patients [25]. Two showed increased survival only in men [14,44]. Of the 23 studies that did not observe an obesity paradox [14,15,17,25,27,28,29,36,39,40,42,43,46,47,49,50,53,56,59,62,63,65,68], 7 involved populations selected according to the presence of a particular medical condition [14,25,27,40,59,63,68].
Nearly two-thirds of the studies included in this work report better survival in overweight or obese older people. Several factors may influence the relationship between obesity and survival in the older population, including age, degree of obesity, presence or absence of comorbidities, and occurrence of an acute event.
Regarding age, the studies in this review that failed to show better survival in overweight or obese individuals included populations that were, on average, younger than those demonstrating an obesity paradox. Wu et al. [25], in their study of the impact of age on the association between BMI and all-cause mortality in patients with atrial fibrillation, found better survival in overweight or obese patients aged 75 years or older but not in patients aged between 65 and 74 years. Observations made in older populations must therefore take into account the intrinsic characteristics of the survivors. For the same BMI, patient profiles can be different, and this profile can influence survival. For instance, body composition may differ due to ethnicity, sex, or advancing age [71,72]. BMI does not provide information on body composition, and is less correlated with percentage of body mass or fat mass index, especially in younger people [72]. Abdominal obesity has direct metabolic consequences (adipose tissue inflammation, dysglycaemia, alteration of blood pressure regulation, etc.). Conversely, subcutaneous fat accumulation in the hips, for example, appears to have benign effects on cardiovascular risk. Other indicators, such as waist circumference or waist-to-hip ratio, are strongly associated with higher mortality risk [73,74]. Taking only BMI into account does not make it possible to differentiate between these situations [9]. In all studies included in this work, BMI was defined as an obesity index. If obesity is defined by “body adiposity”, BMI level is probably not the best criterion [75]. The term “BMI paradox” may be more appropriate than “obesity paradox”, as suggested by Antonopoulos et al. [9].
Obesity is a factor associated with higher mortality in younger populations [76,77,78], but it is also associated with an increased risk of developing and dying from a number of diseases [3], such as cancer [79,80], Some authors point to the obesity-related cellular and immune changes that make obese people more vulnerable, including an increased risk of infections [1]. Older obese people could be considered constitutionally more robust as they have survived the risk factor of obesity into adulthood. The degree of obesity could also be a factor. In this review, not all authors differentiated between different classes of obesity. However, the positive effect on survival in cases of overweight and obesity was not found for morbid obesity (BMI ≥ 35.0 kg/m²) in 5 studies [11,32,57,58,66]. Furthermore, weight is not a reflection of body composition, in particular the muscle mass/fat mass ratio. Loss of muscle mass and strength (sarcopenia) is a factor associated with an increased risk of death. Tian et al. reported that obese people with sarcopenia have a higher risk of death than obese people without sarcopenia [81]. Obese people may be less frequently sarcopenic than non-obese people. In 1493 subjects aged 65 years or more (median age 74 ± 11 years), Sousa-Santos et al. [82] found a prevalence of 0.8% of obese sarcopenic individuals versus 11.6% of sarcopenic individuals of all BMI status.
The presence of a chronic pathology or an acute event may also influence survival. In this review, 20 studies [11,12,14,16,18,19,21,24,25,26,37,40,44,52,54,55,58,60,64,66] of the 38 which found a favourable effect of overweight or obesity on survival involved patients with a particular chronic condition or facing a specific medical event. This finding suggests that even moderately overweight older individuals with chronic disease or acute medical events have better survival. Obesity in older people with a chronic disease could be a sign of greater robustness or higher reserves (better appetite, less risk of undernutrition). Overweight or obese older subjects would be less undernourished than the general older population. Cereda et al. [83], in their meta-analysis of the prevalence of undernutrition in an older population, found a prevalence of undernutrition ranging from 3.1 to 29.4%, depending on the setting. Sousa-Santos et al. [84] showed that 6% of obese elderly subjects (BMI ≥ 30 kg/m2) were also undernourished or at risk of undernutrition. In the event of an acute event, obese elderly people may have a better chance of survival, particularly because of their greater functional reserves. This observation is also made in younger obese or overweight subjects. Akinnusi et al. [85] show in their meta-analysis of patients admitted to intensive care that obese subjects have a similar mortality to non-obese subjects. In 2013, the meta-analysis by Flegal et al. [76] confirmed in a population without any particular pathology that overweight people (BMI > 25 kg/m²) (all types of obesity and all ages) had a higher overall mortality rate, whatever the cause. However, mildly overweight people (BMI ≥ 25 and <30 kg/m²) had lower all-cause mortality than normal weight people (BMI < 25 kg/m²). Thus, this advantage was found regardless of age.
Several mechanisms could explain “obesity paradox”. Probably, there are “good adipose tissues” in elderly subjects. In the literature, overweight or obesity, defined by high level of BMI, is shown to have positive influence on prothrombotic factors, production of certain cytokines, or NT-proBNP levels. Adipokine produced by adipose tissue seems to be cardioprotective [86]. Obesity could have a protective effect against progression or consequences of some chronic diseases. High BMI could also reflect better nutritional status and adequate muscle reserves. Casas-Vara et al. [87] showed better nutritional status in overweight or obese elderly people with heart failure.
Our systematic review has limitations. Although the WHO has proposed thresholds for BMI, the authors used different thresholds in their respective studies. In addition, the outcomes were also different between the studies. This made it difficult to compare the studies, and precluded meta-analysis. The age variable was missing in 14.0% of cases (8/57).
However, this work covers a large number of studies, totalling more than 1,120,000 people aged 65 years or over, with varying medical conditions and in different settings. The follow-up time of the studies ranged from 30 days to 156 months (even though the majority of studies have a long-term follow-up). These differences in follow-up time may make comparison difficult. In addition, there is no information on BMI variation over time, especially for studies with long-term follow-up. Weight loss or gain between baseline measurement and death could have a significant impact. The fact that only studies conducted in subjects aged 65 years or older were selected gives a certain homogeneity to this systematic review in terms of population. Finally, all studies were evaluated for methodological quality using the NOS, and were found to be of high quality.

5. Conclusions

The findings of this systematic review are in favour of the existence of an obesity paradox, which could more specifically concern older subjects with a comorbidity and/or experiencing an acute event. Nevertheless, because BMI does not reflect body composition, the term “BMI paradox” would be more appropriate. The influence of the level of BMI remains unclear. These findings should help guide strategies for nutritional counselling in the older population.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15071780/s1, Table S1: Outcome and results of association between body mass index groups and mortality in aged adults (detailed information).

Author Contributions

L.G. and M.D. conceived and designed the study, prepared the material, collected the data, and performed the analysis. They wrote the first draft of the manuscript, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The APC was funded by tht University Hospitals of Martinique.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data could be made available on reasonable request at [email protected].

Acknowledgments

Thanks to Fiona Ecarnot for editorial assistance.

Conflicts of Interest

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

  1. Frydrych, L.M.; Bian, G.; O’Lone, D.E.; Ward, P.A.; Delano, M.J. Obesity and type 2 diabetes mellitus drive immune dysfunction, infection development, and sepsis mortality. J. Leukoc. Biol. 2018, 104, 525–534. [Google Scholar] [CrossRef] [PubMed]
  2. Liu, Z.; Sanossian, N.; Starkman, S.; Avila-Rinek, G.; Eckstein, M.; Sharma, L.K.; Liebeskind, D.; Conwit, R.; Hamilton, S. Adiposity and Outcome After Ischemic Stroke: Obesity Paradox for Mortality and Obesity Parabola for Favorable Functional Outcomes. Stroke 2021, 52, 144–151. [Google Scholar] [CrossRef] [PubMed]
  3. Powell-Wiley, T.M.; Poirier, P.; Burke, L.E.; Després, J.-P.; Gordon-Larsen, P.; Lavie, C.J.; Lear, S.A.; Ndumele, C.E.; Neeland, I.J.; Sanders, P.; et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2021, 143, e984–e1010. [Google Scholar] [CrossRef] [PubMed]
  4. Hales, C.M.; Carroll, M.D.; Fryar, C.D.; Ogden, C.L. Prevalence of Obesity and Severe Obesity Among Adults: United States. 2017–2018. NCHS Data Brief 2020, 360, 1–8. [Google Scholar]
  5. Ward, Z.J.; Bleich, S.N.; Cradock, A.L.; Barrett, J.L.; Giles, C.M.; Flax, C.; Gortmaker, S.L. Projected, U.S. State-Level Prevalence of Adult Obesity and Severe Obesity. N. Engl. J. Med. 2019, 381, 2440–2450. [Google Scholar] [CrossRef]
  6. Kwon, Y.; Kim, H.J.; Park, S.; Park, Y.G.; Cho, K.H. Body mass index-related mortality in patients with type 2 diabetes and heterogeneity in obesity paradox studies: A dose-response meta-analysis. PLoS ONE 2017, 12, e0168247. [Google Scholar] [CrossRef] [Green Version]
  7. Skinner, J.S.; Abel, W.M.; McCoy, K.; Wilkins, C.H. Exploring the “Obesity Paradox” as a Correlate of Cognitive and Physical Function in Community-dwelling Black and White Older Adults. Ethn. Dis. 2017, 27, 387–394. [Google Scholar] [CrossRef] [Green Version]
  8. Yamazaki, K.; Suzuki, E.; Yorifuji, T.; Tsuda, T.; Ohta, T.; Ishikawa-Takata, K.; Doi, H. Is there an obesity paradox in the Japanese elderly population? A community-based cohort study of 13,280 men and women. Geriatr. Gerontol. Int. 2017, 17, 1257–1264. [Google Scholar] [CrossRef] [Green Version]
  9. Antonopoulos, A.S.; Tousoulis, D. The molecular mechanisms of obesity paradox. Cardiovasc. Res. 2017, 113, 1074–1086. [Google Scholar] [CrossRef]
  10. Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Non-Randomised Studies in Meta-Analyses: The Ottawa Hospital. 2013. Available online: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 1 November 2022).
  11. Amin, R.M.; Raad, M.; Rao, S.S.; Musharbash, F.; Best, M.J.; Amanatullah, D.F. Survival bias may explain the appearance of the obesity paradox in hip fracture patients. Osteoporos. Int. 2021, 32, 2555–2562. [Google Scholar] [CrossRef]
  12. El Moheb, M.; Jia, Z.; Qin, H.; El Hechi, M.W.; Nordestgaard, A.T.; Lee, J.M.; Kaafarani, H.M. The Obesity Paradox in Elderly Patients Undergoing Emergency Surgery: A Nationwide Analysis. J. Surg. Res. 2021, 265, 195–203. [Google Scholar] [CrossRef] [PubMed]
  13. Lin, Y.-K.; Wang, C.-C.; Yen, Y.-F.; Chen, L.-J.; Ku, P.-W.; Chen, C.-C.; Lai, Y.-J. Association of body mass index with all-cause mortality in the elderly population of Taiwan: A prospective cohort study. Nutr. Metab. Cardiovasc. Dis. NMCD 2021, 31, 110–118. [Google Scholar] [CrossRef] [PubMed]
  14. Martinez-Tapia, C.; Diot, T.; Oubaya, N.; Paillaud, E.; Poisson, J.; Gisselbrecht, M.; Morisset, L.; Caillet, P.; Baudin, A.; Pamoukdjian, F.; et al. The obesity paradox for mid- and long-term mortality in older cancer patients: A prospective multicenter cohort study. Am. J. Clin. Nutr. 2020, 113, 129–141. [Google Scholar] [CrossRef] [PubMed]
  15. Lai, K.-Y.; Wu, T.-H.; Liu, C.-S.; Lin, C.-H.; Lin, C.-C.; Lai, M.-M.; Lin, W.-Y. Body mass index and albumin levels are prognostic factors for long-term survival in elders with limited performance status. Aging 2020, 12, 1104–1113. [Google Scholar] [CrossRef]
  16. Schneider, M.; Potthoff, A.-L.; Scharnböck, E.; Heimann, M.; Schäfer, N.; Weller, J.; Schaub, C.; Jacobs, A.H.; Güresir, E.; Herrlinger, U.; et al. Newly diagnosed glioblastoma in geriatric (65+) patients: Impact of patients frailty, comorbidity burden and obesity on overall survival. J. Neurooncol. 2020, 149, 421–427. [Google Scholar] [CrossRef]
  17. Nishida, M.M.; Okura, M.; Ogita, M.; Aoyama, T.; Tsuboyama, T.; Arai, H. Two-Year Weight Loss but Not Body Mass Index Predicts Mortality and Disability in an Older Japanese Community-Dwelling Population. J. Am. Med. Dir. Assoc. 2019, 20, 1654.e11–1654.e18. [Google Scholar] [CrossRef]
  18. Om, S.Y.; Ko, E.; Ahn, J.-M.; Kang, D.-Y.; Lee, K.; Kwon, O.; Lee, P.H.; Lee, S.-W.; Kim, H.J.; Kim, J.B.; et al. Relation of Body Mass Index to Risk of Death or Stroke in Patients Who Underwent Transcatheter Aortic Valve Implantation. Am. J. Cardiol. 2019, 123, 638–643. [Google Scholar] [CrossRef]
  19. Yoshihisa, A.; Sato, T.; Kajimoto, K.; Sato, N.; Takeishi, Y. Acute Decompensated Heart Failure Syndromes i. Heterogeneous impact of body mass index on in-hospital mortality in acute heart failure syndromes: An analysis from the ATTEND Registry. Eur. Heart J. Acute Cardiovasc. Care 2019, 8, 589–598. [Google Scholar] [CrossRef]
  20. Crotti, G.; Gianfagna, F.; Bonaccio, M.; Di Castelnuovo, A.; Costanzo, S.; Persichillo, M.; Iacoviello, L. Body Mass Index and Mortality in Elderly Subjects from the Moli-Sani Study: A Possible Mediation by Low-Grade Inflammation? Immunol. Investig. 2018, 47, 774–789. [Google Scholar] [CrossRef]
  21. De Palma, R.; Ivarsson, J.; Feldt, K.; Saleh, N.; Ruck, A.; Linder, R.; Settergren, M. The obesity paradox: An analysis of pre-procedure weight trajectory on survival outcomes in patients undergoing transcatheter aortic valve implantation. Obes. Res. Clin. Pract. 2018, 12, 51–60. [Google Scholar] [CrossRef]
  22. Kim, H.; Yoon, J.L.; Lee, A.; Jung, Y.; Kim, M.Y.; Cho, J.J.; Ju, Y.S. Prognostic effect of body mass index to mortality in Korean older persons. Geriatr. Gerontol. Int. 2018, 18, 538–546. [Google Scholar] [CrossRef] [PubMed]
  23. Lv, Y.-B.; Liu, S.; Yin, Z.-X.; Gao, X.; Kraus, V.B.; Mao, C.; Yuan, J.-Q.; Zhang, J.; Luo, J.-S.; Chen, H.-S.; et al. Associations of Body Mass Index and Waist Circumference with 3-Year All-Cause Mortality Among the Oldest Old: Evidence from a Chinese Community-Based Prospective Cohort Study. J. Am. Med. Dir. Assoc. 2018, 19, 672–678.e4. [Google Scholar] [CrossRef]
  24. de Souto Barreto, P.; Cadroy, Y.; Kelaiditi, E.; Vellas, B.; Rolland, Y. The prognostic value of body-mass index on mortality in older adults with dementia living in nursing homes. Clin. Nutr. 2017, 36, 423–428. [Google Scholar] [CrossRef] [PubMed]
  25. Wu, S.; Yang, Y.-M.; Zhu, J.; Wan, H.-B.; Wang, J.; Zhang, H.; Shao, X.-H. Impact of Age on the Association Between Body Mass Index and All-Cause Mortality in Patients with Atrial Fibrillation. J. Nutr. Heal. Aging 2017, 21, 1125–1132. [Google Scholar] [CrossRef]
  26. Flodin, L.; Laurin, A.; Lokk, J.; Cederholm, T.; Hedstrom, M. Increased 1-year survival and discharge to independent living in overweight hip fracture patients: A prospective study of 843 patients. Acta Orthop. 2016, 87, 146–151. [Google Scholar] [CrossRef] [Green Version]
  27. Kuo, J.F.; Hsieh, Y.T.; Mao, I.C.; Lin, S.D.; Tu, S.T.; Hsieh, M.C. The Association Between Body Mass Index and All-Cause Mortality in Patients With Type 2 Diabetes Mellitus: A 5.5-Year Prospective Analysis. Medicine 2015, 94, e1398. [Google Scholar] [CrossRef] [PubMed]
  28. Buys, D.R.; Roth, D.L.; Ritchie, C.S.; Sawyer, P.; Allman, R.M.; Funkhouser, E.M.; Locher, J.L. Nutritional risk and body mass index predict hospitalization, nursing home admissions, and mortality in community-dwelling older adults: Results from the UAB Study of Aging with 8.5 years of follow-up. J. Gerontol. Biol. Sci. Med. Sci. 2014, 69, 1146–1153. [Google Scholar] [CrossRef] [Green Version]
  29. Ford, D.W.; Hartman, T.J.; Do, C.S.; Wood, C.; Mitchell, D.C.; Erickson, P.; Bailey, R.; Smiciklas-Wright, H.; Coffman, D.L.; Jensen, G.L. Body mass index, poor diet quality, and health-related quality of life are associated with mortality in rural older adults. J. Nutr. Gerontol. Geriatr. 2014, 33, 23–34. [Google Scholar] [CrossRef]
  30. Lang, P.O.; Mahmoudi, R.; Novella, J.-L.; Tardieu, E.; Bertholon, L.-A.; Nazeyrollas, P.; Blanchard, F.; Jolly, D.; Dramé, M. Is obesity a marker of robustness in vulnerable hospitalized aged populations? Prospective, multicenter cohort study of 1306 acutely ill patients. J. Nutr. Health Aging 2014, 18, 66–74. [Google Scholar] [CrossRef]
  31. Lee, Y.; Kim, J.; Han, E.S.; Ryu, M.; Cho, Y.; Chae, S. Frailty and body mass index as predictors of 3-year mortality in older adults living in the community. Gerontology 2014, 60, 475–482. [Google Scholar] [CrossRef]
  32. Wu, C.Y.; Chou, Y.C.; Huang, N.; Chou, Y.J.; Hu, H.Y.; Li, C.P. Association of body mass index with all-cause and cardiovascular disease mortality in the elderly. PLoS ONE 2014, 9, e102589. [Google Scholar] [CrossRef] [Green Version]
  33. Chen, L.; Peng, L.; Liu, L.; Lin, M.; Lan, C.; Chang, P. Body mass index, health status, and mortality of older Taiwanese men: Overweight good, underweight bad, obesity neutral. J. Am. Geriatr. Soc. 2013, 61, 2233–2234. [Google Scholar] [CrossRef] [PubMed]
  34. Dahl, A.K.; Fauth, E.B.; Ernsth-Bravell, M.; Hassing, L.B.; Ram, N.; Gerstof, D. Body mass index, change in body mass index, and survival in old and very old persons. J. Am. Geriatr. Soc. 2013, 61, 512–518. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Nakazawa, A.; Nakamura, K.; Kitamura, K.; Yoshizawa, Y. Association between body mass index and mortality among institutionalized elderly adults in Japan. Environ. Health Prev. Med. 2013, 18, 502–506. [Google Scholar] [CrossRef] [Green Version]
  36. Takata, Y.; Ansai, T.; Soh, I.; Awano, S.; Nakamichi, I.; Akifusa, S.; Goto, K.; Yoshida, A.; Fujii, H.; Fujisawa, R.; et al. Body mass index and disease-specific mortality in an 80-year-old population at the 12-year follow-up. Arch. Gerontol. Geriatr. 2013, 57, 46–53. [Google Scholar] [CrossRef]
  37. Tseng, C.H. Obesity paradox: Differential effects on cancer and noncancer mortality in patients with type 2 diabetes mellitus. Atherosclerosis 2013, 226, 186–192. [Google Scholar] [CrossRef] [PubMed]
  38. Veronese, N.; De Rui, M.; Toffanello, E.D.; De Ronch, I.; Perissinotto, E.; Bolzetta, F.; D’Avanzo, B.; Cardin, F.; Coin, A.; Manzato, E.; et al. Body mass index as a predictor of all-cause mortality in nursing home residents during a 5-year follow-up. J. Am. Med. Dir. Assoc. 2013, 14, 53–57. [Google Scholar] [CrossRef] [PubMed]
  39. Woo, J.; Yu, R.; Yau, F. Fitness, fatness and survival in elderly populations. Age 2013, 35, 973–984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Yamamoto, M.; Mouillet, G.; Oguri, A.; Gilard, M.; Laskar, M.; Eltchaninoff, H.; Fajadet, J.; Iung, B.; Donzeau-Gouge, P.; Leprince, P.; et al. Effect of body mass index on 30- and 365-day complication and survival rates of transcatheter aortic valve implantation (from the FRench Aortic National CoreValve and Edwards 2 [FRANCE 2] registry). Am. J. Cardiol. 2013, 112, 1932–1937. [Google Scholar] [CrossRef]
  41. Zekry, D.; Herrmann, F.R.; Vischer, U.M. The association between the body mass index and 4-year all-cause mortality in older hospitalized patients. J. Gerontol. Biol. Sci. Med. Sci. 2013, 68, 705–711. [Google Scholar] [CrossRef] [Green Version]
  42. de Hollander, E.L.; Van Zutphen, M.; Bogers, R.P.; Bemelmans, W.J.; De Groot, L.C. The impact of body mass index in old age on cause-specific mortality. J. Nutr. Health Aging 2012, 16, 100–106. [Google Scholar] [CrossRef]
  43. Kvamme, J.M.; Holmen, J.; Wilsgaard, T.; Florholmen, J.; Midthjell, K.; Jacobsen, B.K. Body mass index and mortality in elderly men and women: The Tromso and HUNT studies. J. Epidemiol. Community Health 2012, 66, 611–617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Mihel, S.; Milanovic, S.M. Association of elevated body mass index and hypertension with mortality: The CroHort study. Coll. Antropol. 2012, 36, 183–188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Cereda, E.; Pedrolli, C.; Zagami, A.; Vanotti, A.; Piffer, S.; Opizzi, A.; Rondanelli, M.; Caccialanza, R. Body mass index and mortality in institutionalized elderly. J. Am. Med. Dir Assoc. 2011, 12, 174–178. [Google Scholar] [CrossRef] [PubMed]
  46. Berraho, M.; Nejjari, C.; Raherison, C.; El Achhab, Y.; Tachfouti, N.; Serhier, Z.; Dartigues, J.F.; Barberger-Gateau, P. Body mass index, disability, and 13-year mortality in older French adults. J. Aging Health 2010, 22, 68–83. [Google Scholar] [CrossRef]
  47. Han, S.S.; Kim, K.W.; Na, K.Y.; Chae, D.-W.; Kim, S.; Chin, H.J. Lean mass index: A better predictor of mortality than body mass index in elderly Asians. J. Am. Geriatr. Soc. 2010, 58, 312–317. [Google Scholar] [CrossRef]
  48. Kitamura, K.; Nakamura, K.; Nishiwaki, T.; Ueno, K.; Hasegawa, M. Low body mass index and low serum albumin are predictive factors for short-term mortality in elderly Japanese requiring home care. Tohoku J. Exp. Med. 2010, 221, 29–34. [Google Scholar] [CrossRef] [Green Version]
  49. Luchsinger, J.A.; Patel, B.; Tang, M.X.; Schupf, N.; Mayeux, R. Body mass index, dementia, and mortality in the elderly. J. Nutr. Health Aging. 2008, 12, 127–131. [Google Scholar] [CrossRef] [Green Version]
  50. Locher, J.L.; Roth, D.L.; Ritchie, C.S.; Cox, K.; Sawyer, P.; Bodner, E.V.; Allman, R.M. Body mass index, weight loss, and mortality in community-dwelling older adults. J. Gerontol. Biol. Sci. Med. Sci. 2007, 62, 1389–1392. [Google Scholar] [CrossRef] [Green Version]
  51. Takata, Y.; Ansai, T.; Soh, I.; Akifusa, S.; Sonoki, K.; Fujisawa, K.; Awano, S.; Kagiyama, S.; Hamasaki, T.; Nakamichi, I.; et al. Association between body mass index and mortality in an 80-year-old population. J. Am. Geriatr. Soc. 2007, 55, 913–917. [Google Scholar] [CrossRef]
  52. Danninger, T.; Rezar, R.; Mamandipoor, B.; Dankl, D.; Koköfer, A.; Jung, C.; Wernly, B.; Osmani, V. Underweight but not overweight is associated with excess mortality in septic ICU patients. Wien. Klin. Wochenschr. 2021, 134, 139–147. [Google Scholar] [CrossRef] [PubMed]
  53. Seino, S.; Kitamura, A.; Abe, T.; Taniguchi, Y.; Yokoyama, Y.; Amano, H.; Nishi, M.; Nofuji, Y.; Narita, M.; Ikeuchi, T.; et al. Dose-Response Relationships Between Body Composition Indices and All-Cause Mortality in Older Japanese Adults. J. Am. Med. Dir. Assoc. 2020, 21, 726–733.e4. [Google Scholar] [CrossRef]
  54. Tokarek, T.A.; Dziewierz, A.; Sorysz, D.; Bagienski, M.; Rzeszutko, Ł.; Krawczyk-Ożóg, A.; Kleczyński, P. The obesity paradox in patients undergoing transcatheter aortic valve implantation: Is there any effect of body mass index on survival? Kardiol. Pol. 2019, 77, 190–197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Keller, K.; Munzel, T.; Ostad, M.A. Sex-specific differences in mortality and the obesity paradox of patients with myocardial infarction ages > 70 y. Nutrition 2018, 46, 124–130. [Google Scholar] [CrossRef]
  56. Lee, S.H.; Kim, D.H.; Park, J.H.; Kim, S.; Choi, M.; Kim, H.; Park, Y.G. Association between body mass index and mortality in the Korean elderly: A nationwide cohort study. PLoS ONE 2018, 13, e0207508. [Google Scholar] [CrossRef] [Green Version]
  57. Cheng, F.W.; Gao, X.; Mitchell, D.C.; Wood, C.; Still, C.D.; Rolston, D.; Jensen, G.L. Body mass index and all-cause mortality among older adults. Obesity 2016, 24, 2232–2239. [Google Scholar] [CrossRef] [PubMed]
  58. Calabia, J.; Arcos, E.; Carrero, J.J.; Comas, J.; Valles, M. Does the obesity survival paradox of dialysis patients differ with age? Blood Purif. 2015, 39, 193–199. [Google Scholar] [CrossRef] [PubMed]
  59. Kim, N.H.; Lee, J.; Kim, T.J.; Kim, N.H.; Choi, K.M.; Baik, S.H.; Choi, D.S.; Pop-Busui, R.; Park, Y.; Kim, S.G. Body Mass Index and Mortality in the General Population and in Subjects with Chronic Disease in Korea: A Nationwide Cohort Study (2002–2010). PLoS ONE 2015, 10, e0139924. [Google Scholar] [CrossRef]
  60. Kubota, Y.; Iso, H.; Tamakoshi, A.; Group, J.S. Association of Body Mass Index and Mortality in Japanese Diabetic Men and Women Based on Self-Reports: The Japan Collaborative Cohort (JACC) Study. J. Epidemiol. 2015, 25, 553–558. [Google Scholar] [CrossRef] [Green Version]
  61. Shil Hong, E.; Khang, A.R.; Roh, E.; Jeong Ku, E.U.; An Kim, Y.E.; Min Kim, K.; Lim, S. Counterintuitive relationship between visceral fat and all-cause mortality in an elderly Asian population. Obesity 2015, 23, 220–227. [Google Scholar] [CrossRef]
  62. Clark, D.O.; Gao, S.; Lane, K.A.; Callahan, C.M.; Baiyewu, O.; Ogunniyi, A.; Hendrie, H.C. Obesity and 10-year mortality in very old African Americans and Yoruba-Nigerians: Exploring the obesity paradox. J. Gerontol. Biol. Sci. Med. Sci. 2014, 69, 1162–1169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Murphy, R.A.; Reinders, I.; Garcia, M.E.; Eiriksdottir, G.; Launer, L.J.; Benediktsson, R.; Gudnason, V.; Jonsson, P.V.; Harris, T.B. Adipose tissue, muscle, and function: Potential mediators of associations between body weight and mortality in older adults with type 2 diabetes. Diabetes Care 2014, 37, 3213–3219. [Google Scholar] [CrossRef] [Green Version]
  64. Yamauchi, Y.; Hasegawa, W.; Yasunaga, H.; Sunohara, M.; Jo, T.; Matsui, H.; Fushimi, K.; Takami, K.; Nagase, T. Paradoxical association between body mass index and in-hospital mortality in elderly patients with chronic obstructive pulmonary disease in Japan. Int. J. Chronic Obstr. Pulm. Dis. 2014, 9, 1337–1346. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Tsai, A.C.; Hsiao, M.L. The association of body mass index (BMI) with all-cause mortality in older Taiwanese: Results of a national cohort study. Arch Gerontol. Geriatr. 2012, 55, 217–220. [Google Scholar] [CrossRef] [PubMed]
  66. Lea, J.P.; Crenshaw, D.O.; Onufrak, S.J.; Newsome, B.B.; McClellan, W.M. Obesity, end-stage renal disease, and survival in an elderly cohort with cardiovascular disease. Obesity 2009, 17, 2216–2222. [Google Scholar] [CrossRef]
  67. Grabowski, D.C.; Ellis, J.E. High body mass index does not predict mortality in older people: Analysis of the Longitudinal Study of Aging. J. Am. Geriatr. Soc. 2001, 49, 968–979. [Google Scholar] [CrossRef]
  68. Kananen, L.; Eriksdotter, M.; Boström, A.; Kivipelto, M.; Annetorp, M.; Metzner, C.; Jerlardtz, V.B.; Engström, M.; Johnson, P.; Lundberg, L.; et al. Body mass index and Mini Nutritional Assessment-Short Form as predictors of in-geriatric hospital mortality in older adults with COVID-19. Clin. Nutr. 2022, 41, 2973–2979. [Google Scholar] [CrossRef]
  69. Lai, C.C.; Wang, C.Y.; Wang, Y.H.; Hsueh, S.C.; Ko, W.C.; Hsueh, P.R. Global epidemiology of coronavirus disease 2019: Disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. Int. J. Antimicrob. Agents 2020, 55, 105946. [Google Scholar] [CrossRef]
  70. Yaffe, K.; Fox, P.; Newcomer, R.; Sands, L.; Lindquist, K.; Dane, K.; Covinsky, K.E. Patient and caregiver characteristics and nursing home placement in patients with dementia. JAMA 2002, 287, 2090–2097. [Google Scholar] [CrossRef]
  71. Heymsfield, S.B.; Peterson, C.M.; Thomas, D.M.; Heo, M.; Schuna, J.M., Jr. Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review. Obes. Rev. 2016, 17, 262–275. [Google Scholar] [CrossRef] [Green Version]
  72. Jeong, S.M.; Lee, D.H.; Rezende, L.F.M.; Giovannucci, E.L. Different correlation of body mass index with body fatness and obesity-related biomarker according to age, sex and race-ethnicity. Sci Rep. 2023, 13, 3472. [Google Scholar] [CrossRef] [PubMed]
  73. Coutinho, T.; Goel, K.; de Sá, D.C.; Kragelund, C.; Kanaya, A.M.; Zeller, M.; Park, J.-S.; Kober, L.; Torp-Pedersen, C.; Cottin, Y.; et al. Central obesity and survival in subjects with coronary artery disease: A systematic review of the literature and collaborative analysis with individual subject data. J. Am. Coll. Cardiol. 2011, 57, 1877–1886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. de Koning, L.; Merchant, A.T.; Pogue, J.; Anand, S.S. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: Meta-regression analysis of prospective studies. Eur. Heart J. 2007, 28, 850–856. [Google Scholar] [CrossRef] [PubMed]
  75. Okorodudu, D.O.; Jumean, M.F.; Montori, V.M.; Romero-Corral, A.; Somers, V.K.; Erwin, P.J.; Lopez-Jimenez, F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: A systematic review and meta-analysis. Int. J. Obes. 2010, 34, 791–799. [Google Scholar] [CrossRef] [Green Version]
  76. Flegal, K.M.; Kit, B.K.; Orpana, H.; Graubard, B.I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA 2013, 309, 71–82. [Google Scholar] [CrossRef] [Green Version]
  77. Gao, F.; Wang, Z.J.; Shen, H.; Yang, S.W.; Nie, B.; Zhou, Y.J. Impact of obesity on mortality in patients with diabetes: Meta-analysis of 20 studies including 250,016 patients. J. Diabetes Investig. 2018, 9, 44–54. [Google Scholar] [CrossRef]
  78. Zhao, X.; Gang, X.; He, G.; Li, Z.; Lv, Y.; Han, Q.; Wang, G. Obesity Increases the Severity and Mortality of Influenza and COVID-19: A Systematic Review and Meta-Analysis. Front. Endocrinol. 2020, 11, 595109. [Google Scholar] [CrossRef]
  79. Golabek, T.; Bukowczan, J.; Szopinski, T.; Chlosta, P.; Lipczynski, W.; Dobruch, J.; Borowka, A. Obesity and renal cancer incidence and mortality--a systematic review of prospective cohort studies. Ann. Agric. Environ. Med. 2016, 23, 37–43. [Google Scholar] [CrossRef] [Green Version]
  80. Liu, X.; Ju, W.; Huo, C.; Zhang, S.; Wang, X.; Huang, K. Overweight and Obesity as Independent Factors for Increased Risk of Hepatocellular Cancer-Related Mortality: A Meta-Analysis. J. Am. Coll. Nutr. 2021, 40, 287–293. [Google Scholar] [CrossRef]
  81. Tian, S.; Xu, Y. Association of sarcopenic obesity with the risk of all-cause mortality: A meta-analysis of prospective cohort studies. Geriatr. Gerontol. Int. 2016, 16, 155–166. [Google Scholar] [CrossRef]
  82. Sousa-Santos, A.R.; Afonso, C.; Borges, N.; Santos, A.; Padrão, P.; Moreira, P.; Amaral, T.F. Sarcopenia and Undernutrition Among Portuguese Older Adults: Results from Nutrition UP 65 Study. Food Nutr. Bull. 2018, 39, 487–492. [Google Scholar] [CrossRef] [PubMed]
  83. Cereda, E.; Pedrolli, C.; Klersy, C.; Bonardi, C.; Quarleri, L.; Cappello, S.; Caccialanza, R. Nutritional status in older persons according to healthcare setting: A systematic review and meta-analysis of prevalence data using MNA((R)). Clin. Nutr. 2016, 35, 1282–1290. [Google Scholar] [CrossRef] [PubMed]
  84. Sousa-Santos, A.R.; Afonso, C.; Borges, N.; Santos, A.; Padrão, P.; Moreira, P.; Amaral, T.F. Sarcopenia, physical frailty, undernutrition and obesity cooccurrence among Portuguese community-dwelling older adults: Results from Nutrition UP 65 cross-sectional study. BMJ Open 2020, 10, e033661. [Google Scholar] [CrossRef] [PubMed]
  85. Akinnusi, M.E.; Pineda, L.A.; El Solh, A.A. Effect of obesity on intensive care morbidity and mortality: A meta-analysis. Crit. Care Med. 2008, 36, 151–158. [Google Scholar] [CrossRef] [Green Version]
  86. Donini, L.M.; Pinto, A.; Giusti, A.M.; Lenzi, A.; Poggiogalle, E. Obesity or BMI Paradox? Beneath the Tip of the Iceberg. Front. Nutr. 2020, 7, 53. [Google Scholar] [CrossRef]
  87. Casas-Vara, A.; Santolaria, F.; Fernandez-Bereciartua, A.; Gonzalez-Reimers, E.; Garcia-Ochoa, A.; Martinez-Riera, A. The obesity paradox in elderly patients with heart failure: Analysis of nutritional status. Nutrition 2012, 28, 616–622. [Google Scholar] [CrossRef]
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Figure 1. PRISMA flow diagram of the records included in the systematic review.
Figure 1. PRISMA flow diagram of the records included in the systematic review.
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Table
Table 1. Description of the studies included in the present systematic review.
Table 1. Description of the studies included in the present systematic review.
Author, YearCountryStudy DesignStudy SettingMedical ConditionSample SizeAge (Years)
Mean ± SD
Kananen, 2022 [68]SwedenRetrospective cohortHospital, GeriatricsCOVID-19140977 [65–104] ♣
Amin, 2021 [11]USAProspective cohortHospital, SurgeryHip fracture52,729x ± x
Danninger, 2021 [52]USARetrospective cohortHospital, ICUSepsis8707x ± x
El Moheb, 2021 [12]USAProspective cohortHospital, SurgeryEmergent surgery78,70475 ± x
Lin, 2021 [13]Taiwan Prospective cohortCommunityNone specific 81,22174 ± 6
Martinez-Tapia, 2021 [14]FranceProspective cohortHospital, GeriatricsCancer207181 ± 6
Lai, 2020 [15]TaiwanProspective cohortLTCFNone specific18279 ± 8
Schneider, 2020 [16]GermanyProspective cohortHospital, NeurosurgeryGlioblastoma11072 [65–86] ♣
Seino, 2020 [53]JapanRetrospective cohortCommunityNone specific 197772 ± 6 *
Nishida, 2019 [17]JapanProspective cohortCommunityNone specific122974 ± 5
Om, 2019 [18]KoreaProspective cohortHospital, CardiologyAortic stenosis37979 ± x *
Tokarek, 2019 [54]PolandRetrospective cohortHospital, CardiologyTAVI patients14782 [x–x] ♣
Yoshihisa, 2019 [19]JapanProspective cohortHospital, CardiologyAcute heart failure2410x ± x
Crotti, 2018 [20]ItalyProspective cohortCommunityNone specific497072 ± 5
De Palma, 2018 [21]SwedenProspective cohortHospital, CardiologyTAVI patients49283 ± 6
Keller, 2018 [55]GermanyRetrospective cohortHospital, CardiologyAMI122,60780 ± x
Kim, 2018 [22]KoreaProspective cohortCommunityNone specific170,63972 ± 5
Lee, 2018 [56]KoreaRetrospective cohortCommunityNone specific11,84472 ± 5
Lv, 2018 [23]ChinaProspective cohortCommunityNone specific436192 ± 8
de Souto Barreto, 2017 [24]FranceProspective cohortNursing homeDementia374186 ± 8
Wu, 2017 [25]ChinaProspective cohortHospital, EDAtrial fibrillation1321x ± x
Cheng, 2016 [57]USARetrospective cohortCommunityNone specific456574 ± 5
Flodin, 2016 [26]SwedenProspective cohortHospitalHip fracture84382 ± 7
Calabia, 2015 [58]SpainRetrospective cohortHospital, NephrologyHaemodialysis397875 ± 6
Kim, 2015 [59]KoreaRetrospective cohortCommunityChronic diseasesxx ± x
Kubota, 2015 [60]JapanRetrospective cohortCommunityT2DM16,304 #x ± x
Kuo, 2015 [27]TaiwanProspective cohortOutpatientsT2DMxx ± x
Shil Hong, 2015 [61]KoreaRetrospective cohortCommunityNone specific100076 ± 9
Buys, 2014 [28]USAProspective cohortCommunityNone specific125775 ± 7
Clark, 2014 [62]USA/NigeriaRetrospective cohortCommunityNone specific246677 ± 5 *
Ford, 2014 [29]USAProspective CohortCommunityNone specific299581 ± 4
Lang, 2014 [30]FranceProspective cohortHospital, EDNone specific130685 ± 6
Lee, 2014 [31]KoreaProspective cohortCommunityNone specific11,84473 ± 7
Murphy, 2014 [63]IcelandRetrospective cohortCommunityT2DM63777 [66–96] ♠
Wu, 2014 [32]TaiwanProspective cohortCommunityNone specific77,54173 ± 7
Yamauchi, 2014 [64]JapanRetrospective cohortHospital, PulmonologyCOPD263,94078 ± 7
Chen, 2013 [33]TaiwanProspective cohortVeteransNone specific125783 ± 5
Dahl, 2013 [34]SwedenProspective cohortCommunityNone specific88280 ± 6
Nakazawa, 2013 [35]Japan Prospective cohortNursing homeNone specific851084 ± 8
Takata, 2013 [36]JapanProspective cohortCommunityNone specific67580 ± 0
Tseng, 2013 [37]TaiwanProspective cohortCommunityT2DM34,825x ± x
Veronese, 2013 [38]ItalyProspective cohortNursing homeNone specific18181 ± 8
Woo, 2013 [39]ChinaProspective cohortCommunityNone specific400073 ± 5
Yamamoto, 2013 [40]FranceProspective cohortHospital, CardiologyTAVI patients307283 ± 7
Zekry, 2013 [41]Switzerland Prospective cohortHospital, GeriatricNone specific44485 ± 7
de Hollander, 2012 [42]NetherlandsProspective cohortCommunityNone specific198073 ± 2
Kvamme, 2012 [43]NorwayProspective cohortCommunityNone specific16,71173 ± 5
Mihel, 2012 [44]CroatiaProspective cohortCommunityHypertension2507x ± x
Tsai, 2012 [65]TaiwanRetrospective cohortCommunityNone specific2892x ± x
Cereda, 2011 [45]ItalyProspective cohortLTCFNone specific53384 ± 8
Berraho, 2010 [46]FranceProspective cohortCommunityNone specific364675 ± 7
Han, 2010 [47]KoreaProspective cohortCommunityNone specific87775 ± 8
Kitamura, 2010 [48]JapanProspective cohortHome careNone specific20584 ± 8
Lea, 2009 [66]USARetrospective cohortHospital, CardiologyAMI74,16777 ± x *
Luchsinger, 2008 [49]USAProspective cohortCommunityNone specific137278 ± 6
Locher, 2007 [50]USAProspective cohortCommunityNone specific98375 ± 7
Takata, 2007 [51]Japan Prospective cohortCommunityNone specific69780 ± 0
Grabowski, 2001 [67]USARetrospective cohortCommunityNone specific752777 ± 6
SD: Standard deviation; ICU: Intensive care unit; ED: Emergency department; TAVI: Transcatheter Aortic Valve Implementation; COPD: Chronic Obstructive Pulmonary Disease; AMI: Acute Myocardial Infarction; T2DM: Type 2 Diabetes Mellitus; LTCF: Long-term care facility. x: Missing information; #: Person-years; *: Pooled mean and/or standard deviation have been calculated with the information available in these articles; ♣: Median [range]; ♠: Mean [range].
Table
Table 2. Outcomes and association between body mass index group and mortality in aged adults.
Table 2. Outcomes and association between body mass index group and mortality in aged adults.
Author(s), YearAge
(Mean ±
SD)		Medical ConditionOutcomeObesity ParadoxBMI Thresholds # (kg/m2)
Kananen, 2022 [68]x ± xCOVID-19In-hospital mortalityNo18.5 < BMI < 25.0
Amin, 2021 [11]x ± xHip fracture30-day mortalityYesBMI ≥ 25.0
(No, if BMI > 40.0)
Danninger, 2021 [52]x ± xSepsisICU mortalityYesBMI ≥ 30.0
El Moheb, 2021 [12]75 ± xEmergent Surgery 30-day mortalityYesBMI ≥ 25.0
Lin, 2021 [13]74 ± 6None specific84-month mortalityYesBMI ≥ 24.0
Martinez-Tapia, 2021 [14]81 ± 6Cancer12-month mortality (men)YesBMI ≥ 30.0
12-month mortality (women)No
60-month mortality (men)YesBMI ≥ 30.0
60-month mortality (women)YesBMI ≥ 30.0
Lai, 2020 [15]79 ± 8None specific72-month mortalityNo
Schneider, 2020 [16]72 ± xGlioblastoma12-month mortalityYesBMI ≥ 30.0
Seino, 2020 [53]72 ± 6None specificAll-cause mortality (men)No
All-cause mortality (women)No
Nishida, 2019 [17]74 ± 5None specific36-month mortalityNo
Om, 2019 [18]79 ± xAortic stenosis12-month mortalityYesBMI ≥ 24.9
Tokarek, 2019 [54]82 ± xTAVI patients12-month survivalYesBMI ≥ 30.0
Yoshihisa, 2019 [19]x ± xAHFIn-hospital mortalityYesBMI ≥ 25.0
Crotti, 2018 [20]72 ± 5None specific68-month mortalityYesBMI ≥ 25.0
(No, if BMI > 30.0)
68-month CVD mortalityNo
68-month cancer mortalityNo
De Palma, 2018 [21]83 ± 6TAVI patients12-month mortalityYesBMI ≥ 25.0
50-month mortalityYesBMI ≥ 25.0
Keller, 2018 [55]80 ± xAMIIn-hospital mortalityYesBMI ≥ 30.0
Kim, 2018 [22]72 ± 5None specific60-month mortalityYesBMI ≥ 25.0
(No, if BMI > 27.5)
Lee, 2018 [56]72 ± 5None specific60-month mortalityNo
Lv, 2018 [23]92 ± 8None specific36-month mortalityYesBMI ≥ 18.5
De Souto Barreto, 2017 [24]86 ± 8Dementia18-month mortality (dementia)YesBMI ≥ 25.0
18-month mortality (without dementia)YesBMI ≥ 25.0
Wu, 2017 [25]x ± xAtrial fibrillation12-month mortality (65–74 years)No
12-month mortality (≥75 years)YesBMI ≥ 24.0
Cheng, 2016 [57]74 ± 5None specific132-month mortalityYesBMI ≥ 25.0
(No, if BMI ≥ 35.0)
DiabetesYesBMI ≥ 25.0
(No, if BMI ≥ 35.0)
HypertensionYesBMI ≥ 25.0
(No, if BMI ≥ 35.0)
DyslipidaemiaYesBMI ≥ 25.0
(No, if BMI ≥ 35.0)
Flodin, 2016 [26]82 ± 7Hip fracture12-month survivalYesBMI > 26.0
Calabia, 2015 [58]75 ± 6Haemodialysis 120-month mortalityYesBMI = 30.0–34.9
(No, if BMI = 27.5–29.9 or BMI ≥ 35.0)
Kim, 2015 [59]x ± xChronic diseases108-month mortalityNo
Kubota, 2015 [60]x ± xT2DM132-month ID mortalityYesBMI ≥ 25.0
Kuo, 2015 [27]x ± xT2DM66-month mortalityNo
Shil hong, 2015 [61]76 ± 9None specific72-month mortalityYesBMI ≥ 23.8
Buys, 2014 [28]75 ± 7None specific102-month mortalityNo
Clark, 2014 [62]77 ± 5None specific120-month mortality (Africans)No
120-month mortality (African Americans)No
Ford, 2014 [29]81 ± 4None specific40-month mortalityNo
Lang, 2014 [30]85 ± 6None specific6-week mortalityYesBMI ≥ 30.0
12-month mortalityYesBMI ≥ 25.0
24-month mortalityYesBMI ≥ 25.0
Lee, 2014 [31]73 ± 7None specific36-month mortalityYesBMI ≥ 25.0
(No, if BMI ≥ 30.0)
Murphy, 2014 [63]77 ± xT2DM84-month mortalityNo
Wu, 2014 [32]73 ± 7None specific60-month mortalityYesBMI ≥ 25.0
(No, if BMI ≥ 35.0)
60-month CVD mortality BMI ≥ 25.0
(No, if BMI ≥ 30.0)
Yamauchi, 2014 [64]78 ± 7COPDIn-hospital mortalityYesBMI ≥ 23.0
Chen, 2013 [33]83 ± 5None specific18-month mortalityYesBMI ≥ 23.0
Dahl, 2013 [34]80 ± 6None specific216-month mortalityYesBMI ≥ 25.0
(No, if BMI ≥ 30.0)
Nakazawa, 2013 [35]84 ± 8None specific12-month mortalityYesBMI ≥ 23.6
Takata, 2013 [36]80 ± 0None specific144-month mortalityNo
144-month CVD mortalityNo
144-month cancer mortalityNo
Tseng, 2013 [37]x ± xT2DM144-month mortalityYesBMI ≥ 23.0
Veronese, 2013 [38]81 ± 8None specific60-monthYesBMI ≥ 30.0
Woo, 2013 [39]73 ± 5None specific84-month mortalityNo
Yamamoto, 2013 [40]83 ± 7TAVI patients30-day mortalityNo
12-month mortalityYesBMI ≥ 25.0
Zekry, 2013 [41]85 ± 7None specific48-month mortalityYesBMI ≥ 30.0
de Hollander, 2012 [42]73 ± 2None specific120-month mortalityNo
Kvamme, 2012 [43]73 ± 5None specific12-month mortality (men)No
12-month mortality (women)No
Respiratory diseases12-month mortality (men)No
12-month mortality (women)No
CVD12-month mortality (men)No
12-month mortality (women)No
Cancer12-month mortality (men)No
12-month mortality (women)No
Mihel, 2012 [44]x ± xHypertension60-month mortality (men)YesBMI ≥ 30.0
60-month mortality (women)No
Tsai, 2012 [65]x ± xNone specific48-month mortality (65–74 y; men)No
48-month mortality (≥75 y; men)No
48-month mortality (65–74 y; women)No
48-month mortality (≥75 y; women)No
Cereda, 2011 [45]84 ± 8None specific72-month mortalityYesBMI ≥ 25.0
Berraho, 2010 [46]75 ± 7None specific156-month mortalityNo
Han, 2010 [47]75 ± 8None specific42-month mortalityNo
Kitamura, 2010 [48]84 ± 8None specific24-month mortalityYesBMI ≥ 17.1
Lea, 2009 [66]77 ± xAMI125-month mortalityYesBMI ≥ 25.0
(No, if BMI > 40.0)
Luchsinger, 2008 [49]78 ± 6None specific144-month mortalityNo
Locher, 2007 [50]75 ± 7None specific36-month mortalityNo
Takata, 2007 [51]80 ± 0None specific48-month mortalityYesBMI ≥ 25.0
48-month CVD mortalityNo
48-month cancer mortalityNo
Grabowski, 2001 [67]77 ± 6None specific96-month mortalityYesBMI ≥ 28.5
# BMI thresholds at which an obesity paradox was demonstrated. SD: Standard deviation; ICU: Intensive Care Unit; TAVI: Transcatheter Aortic Valve Implementation; COPD: Chronic Obstructive Pulmonary Disease; AHF: Acute Heart Failure; AMI: Acute Myocardial Infarction; T2DM: Type 2 Diabetes Mellitus; CVD: Cardiovascular disease; y, years. x: Missing information.
Table
Table 3. Quality assessment of the different studies included in this systematic review, using the Newcastle–Ottawa scale (NOS).
Table 3. Quality assessment of the different studies included in this systematic review, using the Newcastle–Ottawa scale (NOS).
Author, YearStudy DesignSelectionComparabilityOutcomeTotal ScoreQuality Rating
Kananen, 2022 [68]Retrospective cohort*********9High
Amin, 2021 [11]Prospective cohort*********9High
Danninger, 2021 [52]Retrospective cohort*********9High
El Moheb, 2021 [12]Prospective cohort*********9High
Lin, 2021 [13]Prospective cohort********8High
Martinez-Tapia, 2021 [14]Prospective cohort*********9High
Lai, 2020 [15]Prospective cohort*********9High
Schneider, 2020 [16]Prospective cohort*********9High
Seino, 2020 [53]Retrospective cohort*********9High
Nishida, 2019 [17]Prospective cohort*********9High
Om, 2019 [18]Prospective cohort********8High
Tokarek, 2019 [54]Retrospective cohort********8High
Yoshihisa, 2019 [19]Prospective cohort********8High
Crotti, 2018 [20]Prospective cohort*********9High
De Palma, 2018 [21]Prospective cohort********8High
Keller, 2018 [55]Retrospective cohort********8High
Kim, 2018 [22]Prospective cohort*********9High
Lee, 2018 [56]Retrospective cohort*********9High
Lv, 2018 [23]Prospective cohort*********9High
de Souto Barreto, 2017 [24]Prospective cohort*********9High
Wu, 2017 [25]Prospective cohort*********9High
Cheng, 2016 [57]Retrospective cohort*********9High
Flodin, 2016 [26]Prospective cohort*********9High
Calabia, 2015 [58]Retrospective cohort*********9High
Kim, 2015 [59]Retrospective cohort*********9High
Kubota, 2015 [60]Retrospective study*********9High
Kuo, 2015 [27]Prospective cohort********8High
Shil Hong, 2015 [61]Retrospective cohort*********9High
Buys, 2014 [28]Prospective cohort********8High
Clark, 2014 [62]Retrospective cohort*********9High
Ford, 2014 [29]Prospective cohort********8High
Lang, 2014 [30]Prospective cohort*********9High
Lee, 2014 [31]Prospective cohort*********9High
Murphy, 2014 [63]Retrospective cohort*********9High
Wu, 2014 [32]Prospective cohort*********9High
Yamauchi, 2014 [64]Retrospective cohort*********9High
Chen, 2013 [33]Prospective cohort********8High
Dahl, 2013 [34]Prospective cohort********8High
Nakazawa, 2013 [35]Prospective cohort*********9High
Takata, 2013 [36]Prospective cohort********8High
Tseng, 2013 [37]Prospective cohort*********9High
Veronese, 2013 [38]Prospective cohort********8High
Woo, 2013 [39]Prospective cohort*********9High
Yamamoto, 2013 [40]Prospective cohort*********9High
Zekry, 2013 [41]Prospective cohort*********9High
de Hollander, 2012 [42]Prospective cohort********8High
Kvamme, 2012 [43]Prospective cohort*********9High
Mihel, 2012 [44]Prospective cohort*******7High
Tsai, 2012 [65]Retrospective cohort*********9High
Cereda, 2011 [45]Prospective cohort********8High
Berraho, 2010 [46]Prospective cohort*********9High
Han, 2010 [47]Prospective cohort*********9High
Kitamura, 2010 [48]Prospective cohort*********9High
Lea, 2009 [66]Retrospective cohort*********9High
Luchsinger, 2008 [49]Prospective cohort*********9High
Locher, 2007 [50]Prospective cohort*********9High
Takata, 2007 [51]Prospective cohort*********9High
Grabowski, 2001 [67]Retrospective cohort*********9High
Each star is equal to one point. The sum of the stars gives the total score of the NOS. NOS score of ≥7 were considered as high quality studies, NOS score of 5–6 as moderate quality, and NOS Scores less than 5 as low quality.
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Dramé, M.; Godaert, L. The Obesity Paradox and Mortality in Older Adults: A Systematic Review. Nutrients 2023, 15, 1780. https://doi.org/10.3390/nu15071780

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Dramé M, Godaert L. The Obesity Paradox and Mortality in Older Adults: A Systematic Review. Nutrients. 2023; 15(7):1780. https://doi.org/10.3390/nu15071780

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Dramé, Moustapha, and Lidvine Godaert. 2023. "The Obesity Paradox and Mortality in Older Adults: A Systematic Review" Nutrients 15, no. 7: 1780. https://doi.org/10.3390/nu15071780

APA Style

Dramé, M., & Godaert, L. (2023). The Obesity Paradox and Mortality in Older Adults: A Systematic Review. Nutrients, 15(7), 1780. https://doi.org/10.3390/nu15071780

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