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Mortality risks of body mass index and energy intake trajectories in institutionalized elderly people: a retrospective cohort study

BMC Geriatrics volume 22, Article number: 85 (2022) Cite this article

Abstract

Background

Many factors can identify the mortality risks of institutionalized elderly people; among all such factors, body mass index (BMI) and energy intake (EI) can be employed as independent predictors. The objective of this study was to elucidate about the mortality risks and the trajectory of two parameters obtained from continuously monitored data.

Methods

This retrospective cohort study targeted 218 elderly people who were admitted to a nursing home for at least 6 months between 2007 and 2020 and passed away at the nursing home. BMI and EI (kcal) per body weight (BW) were continuously measured until death.

Results

BMI continued to decrease for 5 years until death. The rate of change of BMI significantly increased for 3 years before death (P = 0.004). In contrast, the rate of change of EI per BW significantly increased after 1 year before death (P < 0.001); in addition, 2 months before death, this rate of change significantly exceeded that of BMI (P = 0.007). In the four risk groups that were examined, a BMI of <18.0 + EI per BW of <29.2 and a BMI of <17.3 + EI per BW of <28.3 were significantly correlated with a high risk of death (log-rank test: P < 0.001, P = 0.002, respectively). There was no significant difference in the hazard ratio based on the age at the time of admission; however, when EI per BW was <23.8, the risk of death was significantly higher [hazard ratio = 4.36; 95% confidence interval: 2.31–8.24].

Conclusions

Elderly people in the current study presented a tendency toward decreasing BMI starting 60 months prior to death even if EI per BW remained constant. In the 1 year before death, EI per BW rapidly decreased. When the rate of change of EI per BW exceeded the rate of decrease of BMI, it was considered to be the point of no return when death was imminent. Our study showed that identifying mortality risks from the relationship between the trajectories of the two parameters that were continuously measured for several months to years is possible.

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Background

Although many families of elderly people and experts in the field of elderly care believe that palliation is an important goal for institutionalized elderly people, elderly people who pass away in a facility do not receive ideal palliative care [1]. It is possible that because advanced dementia and debility, when not specified otherwise, are not considered terminal conditions, care might not be directed toward optimum comfort until death is acknowledged as being imminent [2, 3]. Family members providing care are not receptive of the idea that their relatives are in the dying phase, whereas healthcare professionals do not communicate definitive evidence indicative of the approaching death. This is a hindrance to palliative care [4,5,6,7].

Therefore, it is extremely important to make an accurate risk assessment of the prognosis and imminent death. Mortality prediction and risk assessment of elderly people requires the analysis of many factors, such as blood parameters, the number of comorbidities, immune dysfunctions, and conducting statistical approaches [8,9,10]. However, such data collection and evaluation require skilled personnel, a multidisciplinary approach, and in some cases, high costs. Thus, the necessary data may not be available for institutionalized elderly people such as those admitted to a nursing home (NH) or in a homecare setting [7]. Factors for mortality prediction and risk assessment include specific causes and the number of comorbidities; however, institutionalized elderly people usually have multiple morbidities [11] but do not receive a routine workup, making it difficult to identify the true cause of their death [12].

Nutritional status is significantly or independently associated with mortality [13,14,15]. Therefore, the mortality risks associated with nutritional status could be used to predict mortality on their own. However, institutionalized elderly people with dementia experience mental and cognitive impairments, psychological conditions, and dysphagia, presenting complex nutritional problems in the early stage of death [16, 17]; thus, even if mortality was predicted, predicting the timing of death would be difficult.

Many studies have shown that a decrease in body weight (BW) and body mass index (BMI) increases the risk of mortality [18,19,20,21,22]. It has particularly been verified that unintentional weight loss in elderly people increases the risk of death [23]. According to our 5-year longitudinal study of 106 elderly people who were admitted to and died in an NH, even if the energy intake (EI) remained constant, BMI started to gradually decrease 5 years prior to death, and EI significantly decreased several months prior to death [24]. In other words, when the trajectory of EI rapidly decreases compared with the trajectory of decreasing BMI, it is reasonable to consider it as the “point of no return,” where death is imminent. An evaluation of the relationship between the two trajectories might be a useful tool in identifying mortality risks.

Thus, this study aimed to understand the mortality risks by elucidating about the relationship between the trajectory of BMI and the trajectory of EI, which are parameters that could be routinely monitored for institutionalized elderly people, to determine whether they could be used to make an accurate mortality prediction and risk assessment for elderly people.

Methods

Study Design and Participants

The present study was a single-center retrospective cohort study. Baseline data collection was conducted from April 1, 2007 to February 29, 2020. The subjects were all elderly individuals (aged ≥65 years) who died during this period in “Junseien,” which is an NH for the Elderly, located in Kanagawa Prefecture in Japan. They were officially identified as being in a state requiring long-term care according to the Public Nursing Care Insurance Law. Residents of a facility covered by the Public Nursing Care Insurance, such as this NH, are required to be officially recognized as being at the care-need level 3 or above as per the Long-Term Care Insurance system.

The clinical picture of care-need level 3 individuals is as follows: difficulty in rising, moving, and transferring to and from a vehicle on their own, understanding the routine schedule [25], and remembering what they were doing prior to an interview. At the highest level, care-need level 5, in addition to the abovementioned conditions, includes dysphagia, disorientation, limited range of motion in the joints, motor paralysis, and other problems [26, 27].

All subjects received professional care assistance under the same living conditions from the time of admission. They consumed three main meals (breakfast, lunch, and dinner) that were managed as per their EI (kcal) and nutrient requirements by registered dieticians of the facility at a given time. All their meals were cooked and arranged at the facility. If they had difficulty in eating or swallowing, they received assistance.

The subjects included elderly individuals who continuously resided in the NH for at least 6 months and who passed away during their stay without receiving artificial nutrition and hydration (ANH). The exclusion criterion was elderly individuals who died after being hospitalized due to acute diseases or symptoms requiring continuous medical care that the NH could not provide.

Among the 267 individuals who passed away at the NH during the survey period, 218 met the inclusion criteria and were included in the present study.

Clinical Measurements

Nurses and trained caregivers measured the height of all elderly individuals to 0.1 cm and BW to 0.1 kg following a standard protocol [28] within 72 hours after admission and every month thereafter. For those with difficulty in standing up straight, the height was measured to 0.1 cm using a measuring tape and the weight was measured using a wheelchair scale.

A registered dietitian calculated the calorie count for each meal for each subject and placed the meal on an individual tray. Upon completion of each meal, the trained caregivers observed and recorded the proportion of meals consumed on a scale of 1–10. EI (kcal) was calculated daily by multiplying the proportion of the meal consumed by the nutritional value of each meal.

Statistical Analysis

The participants of this study were divided into four groups: 6 months to <12 months, 12 months to <36 months, 36 months to <60 months, and >60 months based on the number of months they survived following their admission. Because the age at admission and death, sex, height/weight at admission, BMI, mean EI per day 1 month after admission, and daily EI per BW, which also had an impact on survival [29, 30], were used to evaluate the energy consumption of the elderly people who stayed in a long-term care setting for a long period of time, we calculated the mean EI per BW and checked for statistically significant differences using one-way analysis of variance.

Furthermore, up to 60 months immediately before death, we calculated the mean monthly BMI and the mean daily EI per BW for every month. Later, we calculated the rate of change compared with the mean value 6 months ago. For each rate of change, we checked for statistically significant differences in the rate of change over 12 months (every 12 months from 60 months prior to death) using the paired t-test. This was followed by a verification of the change in the mean rate of change between the two parameters for each month during the 12 months prior to death.

Finally, based on BMI and EI per BW data from 12 months and 6 months prior to death, we divided the subjects into four groups at the time of admission, plotted Kaplan–Meier curves, and performed associated log-rank tests. Risk factors for mortality were assessed using the Cox proportional hazards regression model and associations were presented as hazard ratios (HRs) and 95% confidence intervals (CIs).

A P-value of <0.05 was considered statistically significant. All statistical analyses were performed using JMP Pro 15.2.0 (for Windows 10) (SAS Institute Inc., Cary, NC, USA).

Results

The baseline characteristics of the 218 elderly people are presented in Table 1. Subjects were divided into four groups: survival of 6 months to <12 months, 12 months to <36 months, 36 months to <60 months, and >60 months. There was a significant difference in the age at death, sex, height, BMI, EI, and EI per BW (Table 1).

Table 1 Baseline clinical characteristics of the 218 elderly people by the survival duration (months)
Full size table

The trends observed for the two parameters and the relationship between them were determined (Fig. 1).

Fig. 1
figure 1

Changes in the mean BMI and EI per BW 60 months prior to death

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Table 2 shows the difference in the rate of change of BMI and EI per BW for 60–48 months prior to death, 48–36 months prior to death, 36–24 months prior to death, 24–12 months prior to death, and 12 months to immediately prior to death. BMI continuously decreased for 60 months prior to death, but its decrease was significant from 36 months prior to death. There was no significant change in EI per BW until 12 months prior to death, following which there was a significant decrease in this parameter immediately before death.

Table 2 Difference in the rate of change in BMI and EI per BW every 12 months from 60 months prior to death
Full size table

We obtained the difference between the rates of change of the two parameters, BMI and EI per BW, for each month from 12 months prior to death in Table 3. After EI per BW began to decrease 7 months prior to death, and particularly in the 4 months prior to death, when the decrease in EI per BW exceeded that of BMI, we verified this change using the paired t-test. The mean rate of change of EI per BW that exceeded that of BMI 4 months prior to death continued to decrease and was significantly higher than that of BMI 2 months prior to death.

Table 3 Rate of change of BMI and EI per BW for each month from 12 months prior to death
Full size table

BMI and EI per BW were 18.0 and 29.2, respectively, at 12 months prior to death and 17.3 and 28.3, respectively, at 6 months prior to death. Based on these data, we divided subjects into four groups, namely, those whose scores were the same or above the mean and those whose scores were below the mean at the time of admission; plotted the Kaplan–Meier curves; and performed associated log-rank tests. In the group with BMIs of <18.0 and EIs per BW of <29.2 and the group with BMIs of <17.3 and EIs per BW of <28.3, the survival rate decreased significantly (Fig. 2).

Fig. 2
figure 2

Survival curves from the time of admission. a BMI ≥18.0 Groups with EI per BW ≥29.2 (n = 80) and of <29.2 (n = 52) b BMI <18.0 Groups with EI per BW ≥29.2 (n = 59) and of <29.2 (n = 27) c BMI ≥17.3 Groups with EI per BW ≥28.3 (n = 74) and of <28.3 (n = 77) d BMI <17.3 Groups with EI per BW ≥28.3 (n = 53) and <28.3 (n = 14)

Full size image

Finally, based on the sex and age at the time of admission and values obtained from each mean of BMI and EI per BW at 12, 6, and 2 months prior to death, we created four categories of BMI—<16.4, 16.4 to <17.2, 17.3 to <17.9, and ≥18—and four categories of EI per BW—<23.8, 23.8 to <28.2, 28.3 to <29.2, and ≥29.2—and obtained the HRs and their 95% CIs using the Cox proportional hazards model (Table 4).

Table 4 Characteristics of subjects and hazard ratios and 95% confidence intervals for each factor
Full size table

The results showed that the male subjects had an HR of 1.77 (95% CI: 1.24–2.53), indicating that the risk of death was 1.77 times significantly higher than that for the female subjects. The group with a BMI of <16.4 had an HR of 1.73 (95% CI: 1.17–2.55), indicating that compared with the group with a BMI of ≥18.0, the risk of death was 1.73 times significantly higher. The group with a BMI of 16.4 to <17.2 had an HR of 2.02 (95% CI: 1.25–3.26), indicating that the risk of death was 2.02 times significantly higher than that of the group with a BMI of ≥18.0.

When the EI per BW was <23.8 kcal/kg, the HR was 4.36 (95% CI: 2.31–8.24), indicating that the risk of death was 4.36 times significantly higher than that when EI per BW was ≥29.2 kcal/kg. Comparisons based on the age at the time of admission did not present significant HRs.

Discussion

To our knowledge, this is the first study to have deciphered the trajectories that longitudinally examined BMI for each month until death and calculated EI per BW from three daily meals during these months (up to 5 years prior to death) for elderly people who died at an NH.

The most notable finding in this study was that the changes of BMI and EI per BW showed distinctive trajectories over time as subjects came closer to their death.

BMI of elderly people who died at the NH gradually decreased from 60 months prior to death and continued to decrease until death. The mean BMI at 60 months prior to death was 20.5, but the mean BMI immediately prior to death was 16.0. In a cross-sectional survey, a BMI of 20.0 was considered a valid threshold for determining a high risk for short-term mortality in elderly individuals [21, 31]. Furthermore, the World Health Organization classifies adults aged ≥20 with BMIs <18.5 as being underweight. The mean BMI of the elderly subjects in this study was <20.0 at 50 months prior to death and was <18.5 at 17 months prior to death, clearly continuing to follow a decreasing trajectory. Therefore, by analyzing not only the specific value of BMI but also its trajectory for several months to years, the long-term mortality risk over a longer span (i.e., years) could be determined.

EI per BW of elderly people did not show any significant change in trend until 12 months prior to death. EI per BW began to decrease at 7 months prior to death, then followed an irreversible decreasing trend. Low EI per BW is implicated in mortality and is an independent predictor of all-cause mortality [32]. If BMI is useful in identifying the long-term mortality risk, a rapid decrease in the trajectory of EI per BW and a change in its value to <23.8 represents a short-term mortality risk within several months, which is a useful indicator.

The second important finding was that the trajectories of the changes in BMI and EI per BW do not decrease in parallel at similar rates of change.

At 60 months prior to death, BMI showed a decreasing trajectory, but EI per BW either continued to increase until 12 months prior to death or did not show continuous decrease, not presenting a notable difference in the rate of change. EI per BW for the subjects in the present study at 12 months prior to death was 29.2 kcal/BW, which was higher than 23.6–23.8 kcal/BW calculated based on the Harris–Benedict equation for calories that are necessary for each day [33]. In other words, despite the intake of the required amount of calories, BW continued to decrease. The rate of change of EI per BW was greater than BMI at 4 months prior to death and significantly more than the rate of change of BMI at 2 months prior to death. Therefore, following a long-term BMI loss and stable EI per BW, a relationship wherein the depletion of EI per BW increases faster than the declined in BMI indicates an imminent death.

In past studies, BMI was examined [31, 34, 35] as a factor related to mortality [36, 37] and malnutrition, and mortality was discussed based on BMI measured at a point in time. However, our study showed that by continuously measuring BMI and EI and examining the resulting trajectories and their relationship, instead of treating each parameter as a separate predictor, mortality risks could be identified with more accuracy. Risk assessment based on the relationship between the trajectories of BMI and EI per BW is an effective way of detecting mortality among institutionalized elderly individuals.

An interesting aspect of the present study is that by examining the relationship between the trajectories of the two parameters, it is possible to evaluate the time-imminent risk of death. When the rate of change of BMI significantly decreases while that of EI per BW does not, mortality risks tend to increase within 3 years. Under long-term BMI loss, when the nutritional depletion becomes evident and the rate of change of EI per BW significantly exceeds that of BMI, it is the point of no return and death will occur within 1–2 months. Assuming such a trajectory, we reckon that when BMI and EI per BW reach critical levels, such as 18.0 and 29.2 or 17.3 and 28.3, respectively, the chance of survival diminishes dramatically.

It is considered necessary to maintain a constant EI as a measure to prevent the decrease in weight and BMI of elderly people who are admitted to an NH [38, 39]. Specifically, in the case of an elderly person with dementia who has not expressed an intention to end life care, a question of whether to implement ANH or not puts pressure on families and the NH staff [40,41,42]. According to the results of the present study, when BMI decreases regardless of sufficient EI per BW, despite the deficiency in EI being supplemented by additional energy intake, such as ANH, the maintenance of body weight or BMI is predicted to be difficult. We consider palliation to be the most important goal for institutionalized elderly people, and a decrease in EI following a decrease in BMI must be accepted as a transitional phenomenon to death.

The present study has certain limitations. First, the sample size was slightly small and limited to Japanese people from a single NH; therefore, some results may only be applicable to the Japanese population. We also excluded elderly people who died within 6 months of being admitted to the NH; thus, the results of this study cannot be applied to elderly people with acute diseases requiring continuous medical care or acute symptoms for which the NH could not provide adequate care.

Furthermore, the present study targeted all elderly people who lived and died in the NH under the same living conditions for approximately 13 years without ANH. These elderly people mostly spent their time in a chair or on a bed and lacked the mobility or will to go to restrooms on their own. For 1 year prior to death, their BMIs dropped by a mean value of 2.0 accompanied by unintentional weight loss of approximately 5 kg; thus, all the subjects in the present study were considered to be frail elderly individuals [43]. Therefore, mortality risks for frail elderly individuals with advanced dementia and debility not otherwise specified could be identified using the present results.

Conclusion

Dying institutionalized elderly people may have constant EI per BW values but decreasing BMIs from approximately 60 months prior to their death. Once such individuals reach a period of several months before death, EI per BW rapidly decreases. When the rate of decrease of EI per BW exceeds that of BMI, it is considered the point of no return and that death is imminent. Therefore, longitudinal observations of trajectories of BMI and EI over several months to years and the analysis of the relationship between these trajectories could identify mortality risks.

Availability of data and materials

The data cannot be shared publicly because there was no such approval in the study protocol. The datasets used and analyzed during the study are available from the corresponding author upon request and subject to ethical approval.

Abbreviations

BW:

Body weight

BMI:

Body mass index

EI:

Energy intake

NH:

Nursing home

ANH:

Artificial nutrition and hydration

References

  1. Mitchell SL, Kiely DK, Hamel MB, Park PS, Morris JN, Fries BE. Estimating prognosis for nursing home residents with advanced dementia. JAMA. 2004;291:2734–40.

    Article  CAS  PubMed  Google Scholar 

  2. Mitchell SL, Kiel DK, Hamel MB. Dying with advanced dementia in the nursing home. Arch Intern Med. 2004;164:321–6.

    Article  PubMed  Google Scholar 

  3. Toscani F, Finetti S, Giunco F, Basso I, Rosa D, Pettenati F, et al. The last week of life of nursing home residents with advanced dementia: a retrospective study. BMC Palliat Care. 2019;18:117.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Van der Steen JT, Radbruch L, Hertogh CM, de Boer ME, Hughes JC, Larkin P, et al. White paper defining optimal palliative care in older people with dementia:A Delphi study and recommendations from the European Association for Palliative Care. Palliat Med. 2014;28:197–209.

    Article  PubMed  Google Scholar 

  5. Hebert RS, Schulz R, Copeland VC, Arnold RM. Preparing family caregivers for death and bereavement. Insights from caregivers of terminally ill patients. J Pain Symptom Manage. 2009;37:3–12.

    Article  PubMed  Google Scholar 

  6. Hebert RS, Dang Q, Schulz R. Preparedness for the death of a loved one and mental health in bereaved caregivers of patients with dementia: findings from the REACH study. J Palliat Med. 2006;9:683–93.

    Article  PubMed  Google Scholar 

  7. Cereda E, Zagami A, Vanotti A, Piffer S, Pedrolli C. Geriatric nutritional risk index and overall-cause mortality prediction in institutionalised elderly: a 3-year survival analysis. Clin Nutr. 2008;27:717–23.

    Article  PubMed  Google Scholar 

  8. Lukaszyk E, Bien-Barkowska K, Bien B. Identification of mortality risks in the advancement of old age: application of proportional hazard models based on the stepwise variable selection and the Bayesian model averaging approach. Nutrients. 2021;13:1098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Porock D, Oliver DP, Zweig S, Rantz M, Mehr D, Madsen R, et al. Predicting death in the nursing home: development and validation of the 6-month minimum data set mortality risk index. J Gerontol. 2005;60A:491–8.

    Article  Google Scholar 

  10. Sharifi F, Ghaderpanahi M, Fakhrzadeh H, Mirarefin M, Badamchizadeh Z, Tajalizadekhoob Y, et al. Older people’s mortality index: development of a practical model for prediction of mortality in nursing homes (Kahrizak Elderly Study). Geriatr Gerontol Int. 2012;12:36–45.

    Article  PubMed  Google Scholar 

  11. Finucane AM, Bone AE, Etkind S, Carr D, Meade R, Munoz-Arroyo R, et al. How many people will need palliative care in Scotland by 2040? A mixed-method study of projected palliative care need and recommendations for service delivery. BMJ Open. 2021;11:e041317.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Goldberg TH, Botero A. Causes of death in elderly nursing home residents. J Am Med Dir Assoc. 2008;9:565–7.

    Article  PubMed  Google Scholar 

  13. Shakersain B, Santoni G, Faxén-Irving G, Rizzuto D, Fratiglioni L, Xu W. Nutritional status and survival among old adults: an 11-year population-based longitudinal study. Eur J Clin Nutr. 2016;70:320–5.

    Article  CAS  PubMed  Google Scholar 

  14. Gentile S, Lacroix O, Durand AC, Cretel E, Alazia M, Sambuc R, et al. Malnutrition: a highly predictive risk factor of short-term mortality in elderly presenting to the emergency department. J Nutr Health Aging. 2013;17:290–4.

    Article  CAS  PubMed  Google Scholar 

  15. Gomes F, Emery PW, Weekes CE. Risk of malnutrition is an independent predictor of mortality, length of hospital stay, and hospitalization costs in stroke patients. J Stroke Cerebrovasc Dis. 2016;25:799–806.

    Article  PubMed  Google Scholar 

  16. Sanders CL, Wengreen HJ, Schwartz S, Behrens SJ, Corcoran C, Lyketsos CG, et al. Nutritional status is associated with severe dementia and mortality: the Cache County Dementia Progression Study. Alzheimer Dis Assoc Disord. 2018;32:298–304.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Murphy JL, Holmes J, Brooks C. Nutrition and dementia care: developing an evidence-based model for nutritional care in nursing homes. BMC Geriatrics. 2017;17:55.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Bamia C, Halkjaer J, Lagiou P, Trichopoulos D, Tjønneland A, Berentzen TL, et al. Weight change in later life and risk of death amongst the elderly: the European Prospective Investigation into Cancer and Nutrition-Elderly Network on Ageing and Health study. J Intern Med. 2010;268:133–44.

    Article  CAS  PubMed  Google Scholar 

  19. Lee IM, Paffenbarger RS Jr. Change in body weight and longevity. JAMA. 1992;268:2045–9.

    Article  CAS  PubMed  Google Scholar 

  20. Blair SN, Shaten J, Brownell K, Collins G, Lissner L. Body weight change, all-cause mortality, and cause-specific mortality in the Multiple Risk Factor Intervention Trial. Ann Intern Med. 1993;119:749–57.

    Article  CAS  PubMed  Google Scholar 

  21. Sergi G, Perissinotto E, Pisent C, Buja A, Maggi S, Coin A, et al. An adequate threshold for body mass index to detect underweight condition in elderly persons: the Italian Longitudinal Study on Aging (ILSA). J Gerontol A Biol Sci Med Sci. 2005;60:866–71.

    Article  PubMed  Google Scholar 

  22. Mazza A, Zamboni S, Tikhonoff V, Schiavon L, Pessina AC, Casiglia E. Body mass index and mortality in elderly men and women from general population. The experience of Cardiovascular Study in the Elderly (CASTEL). Gerontology. 2007;53:36–45.

    Article  PubMed  Google Scholar 

  23. De Stefani FD, Pietraroia PS, Fernandes-Silva MM, Faria-Neto J, Baena CP. Observational evidence for unintentional weight loss in all-cause mortality and major cardiovascular events: a systematic review and meta-analysis. Sci Rep. 2018;8:1–1.

    Article  CAS  Google Scholar 

  24. Kawakami Y, Hamano J. Changes in body mass index, energy intake, and fluid intake over 60 months premortem as prognostic factors in frail elderly: a post-death longitudinal study. Int J Environ Res Public Health. 2020;17:1823.

    Article  PubMed Central  Google Scholar 

  25. Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc. 2005;53:522–7.

    Article  PubMed  Google Scholar 

  26. Yokobayashi, K, Matsushima M. Fujinuma Y, Tazuma S. Retrospective cohort study of the incidence and risk of fever in elderly people living at home: A pragmatic aspect of home medical management in Japan. Geriatr Gerontol Int 2013;13:887-893.

  27. Imahashi K, Kawagoe M, Eto F, Haga N. Clinical status and dependency of the elderly requiring long-term care in Japan. Tohoku J Exp Med. 2007;212:229–38.

    Article  PubMed  Google Scholar 

  28. NHS. Guidance on the Measurement and Estimation of Height and Weight. 2013. Available online: https://www.nhstaysidecdn.scot.nhs.uk/NHSTaysideWeb/idcplg?IdcService=GET_SECURE_FILE&Rendition=web&RevisionSelectionMethod=LatestReleased&noSaveAs=1&dDocName=prod_217927. Accessed 27 July 2021.

  29. Omura T, Tamura Y, Yamaoka T, Yoshimura Y, Sakurai T, Umegaki H, et al. Assessing the association between optimal energy intake and all-cause mortality in older patients with diabetes mellitus using the Japanese Elderly Diabetes Intervention Trial. Geriatr Gerontol Int. 2020;20:59–65.

    Article  PubMed  Google Scholar 

  30. Silver HJ, Wall R, Hollingsworth E, Pruitt A, Shotwell M, Simmons S. Simple kcal/kg formula is comparable to prediction equations for estimating resting energy expenditure in older cognitively impaired long term care residents. J Nutr Health Aging. 2013;17:39–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Veronese N, De Rui M, Toffanello ED, De Ronch I, Perissinotto E, Bolzetta F, 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;14:53–7.

    Article  PubMed  Google Scholar 

  32. Katano S, Yano T, Kouzu H, Ohori K, Shimomura K, Honma S, et al. Energy intake during hospital stay predicts all-cause mortality after discharge independently of nutritional status in elderly heart failure patients. Clin Res Cardiol. 2021;5:1–9.

    Google Scholar 

  33. Siervo M, Bertoli S, Battezzati A, Wells JC, Lara J, Ferraris C, et al. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin Nutr. 2014;33:613–9.

    Article  CAS  PubMed  Google Scholar 

  34. Buys DR, Roth DL, Ritchie CS, Sawyer P, Allman RM, Funkhouser EM, et al. 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 A Biol Sci Med Sci. 2014;69:1146–53.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Söderström L, Rosenblad A, Adolfsson ET, Saletti A, Bergkvist L. Nutritional status predicts preterm death in older people: a prospective cohort study. Clin Nutr. 2014;33:354–9.

    Article  PubMed  Google Scholar 

  36. Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of the BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet. 2018;6(12):944–53.

    Google Scholar 

  37. Iyen B, Weng S, Vinogradova Y, Akyea RK, Qureshi N, Kai J. Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK. BMC Public Health. 2021;21:576.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wirth R, Streicher M, Smoliner C, Kolb C, Hiesmayr M, Thiem U, et al. The impact of weight loss and low BMI on mortality of nursing home residents–Results from the nutrition day in nursing homes. Clin Nutr. 2016;35:900–6.

    Article  PubMed  Google Scholar 

  39. Volker D, Beck AM, Cederholm T, Cruz-Jentoft A, Goisser S, Hooper L, et al. ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin Nutr. 2019;38:10–47.

    Article  Google Scholar 

  40. Pasman HRW, Onwuteaka-Philipsen BD, Ooms ME, van Wigcheren PT, van der Wal G, Ribbe MW. Forgoing artificial nutrition and hydration in nursing home patients with dementia: patients, decision making, and participants. Alzheimer Dis Assoc Disord. 2004;18:154–62.

    Article  PubMed  Google Scholar 

  41. Pasman HRW, Onwuteaka-Philipsen BD, Kriegsman DMW, Ooms ME, Ribbe MW, van der Wal G. Discomfort in nursing home patients with severe dementia in whom artificial nutrition and hydration is forgone. Arch Intern Med. 2005;165:1729–35.

    Article  PubMed  Google Scholar 

  42. Pasman HRW, Onwuteaka-Philipsen BD, Kriegsman DMW, Ooms ME, Ribbe MW, Van der Wal G. Degree of discomfort following the decision to discontinue artificial nutrition and hydration in institutionalised psychogeriatric patients with severe dementia who no longer or scarcely eat or drink. Ned Tijdschr Geneeskd. 2006;150:243–8.

    CAS  PubMed  Google Scholar 

  43. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The authors would like to thank all the residents and the staff of the NH. The authors thank Crimson Interactive Pvt. Ltd. (Ulatus)—www.ulatus.jp—for their assistance in manuscript translation and editing.

Funding

This work was supported by the 2018–2021 Ministry of Education, Culture, Sports, Science and Technology Grant-in-Aid for Scientific Research by Young Scientists [Research Project Number: 18K17623]. This research was completed independently from the funder. It had no role in the conceptualization of this study, nor in its design, analysis, data interpretation, or manuscript writing.

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Authors and Affiliations

  1. Department of Nursing, Tokyo Ariake University of Medical and Health Sciences, Tokyo, Japan

    Yoshiaki Kawakami

  2. Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan

    Jun Hamano

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  1. Yoshiaki Kawakami
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  2. Jun Hamano
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Contributions

Y.K. obtained funding, designed and coordinated the study, performed data analysis, drafted the manuscript, and takes responsibility for the content. J.H. contributed to the design of the study and assisted with data analysis, supervising and drafting of the paper. All authors contributed to the intellectual content of the manuscript and have approved its final version. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yoshiaki Kawakami.

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Ethics approval and consent to participate

The study was planned and conducted in accordance with the principles of the Declaration of Helsinki, and the study protocol was approved by the Ethical Review Board of Tokyo Ariake University of Medical and Health Sciences (Project identification code: 256; date of approval: July 26, 2018). The requirement for informed consent was waived by the Ethics Committee of Tokyo Ariake University of Medical and Health Sciences because of the retrospective nature of the study and was not required for research that relied exclusively on secondary use of anonymous human biological materials as long as the process of data recording or dissemination of results does not generate identifiable information.

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Not applicable

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The authors declare that they have no competing interests.

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Kawakami, Y., Hamano, J. Mortality risks of body mass index and energy intake trajectories in institutionalized elderly people: a retrospective cohort study. BMC Geriatr 22, 85 (2022). https://doi.org/10.1186/s12877-022-02778-1

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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