Outcome of a Targeted Nutritional Intervention Among Older Adults With Early-Stage Alzheimer’s Disease: The Nutrition Intervention Study

Abstract

A 6-month dietary intervention program was designed for community-dwelling older adults with Alzheimer’s disease. Sixty-seven persons aged 70 years and above were recruited with their caregivers from six hospital memory and geriatric outpatient clinics, and allocated to intervention (n = 34 dyads) or control group (n = 33 dyads). Usual diet was assessed by a validated food frequency questionnaire and current diet by two nonconsecutive diet recalls or records corroborated by caregivers, at recruitment (T1) and exit from the study (T2). Intervention participants received targeted dietary recommendations; control participants received Canada’s Food Guide leaflets. The program was assessed using paired and independent t tests and nonparametric statistics. Fat intakes increased at T2 within intervention participants (54 ± 16 vs. 67 ± 23 g, p = .013), and there was a tendency for higher energy, protein, and calcium intakes at T2 within this group. Proportions with adequate protein intakes almost doubled from T1 to T2 in intervention group women (p = .028) but decreased in female controls (p = .030). Longer follow-up is necessary to determine persistence of benefits.

Keywords

nutrition older adults Alzheimer’s disease intervention caregiver

Introduction

Older adults with Alzheimer’s disease (AD) frequently have a poor diet and manifest food-related problems, even in early stages of the disease (Keller et al., 2008; Ogawa, 2014), leading to suboptimal intakes of energy and nutrients. Food safety issues and dietary behavior problems increase with the progression of AD and include eating foods past their expiry date and forgetting or refusing to eat (Keller et al., 2008). In previous research on the evolution of nutritional status over a 12-month period among individuals with early-stage AD compared with age- and gender-matched cognitively intact controls, we observed greater nutrient intakes from diet and supplements in the healthy controls, with significantly lower intakes among AD participants of energy, macronutrients, and calcium, iron, zinc, vitamins A and K and dietary fiber, as well as polyunsaturated fatty acids (Shatenstein, Kergoat & Reid, 2007).

A poor diet often leads to weight loss which heralds increased morbidity and mortality in older persons (Droogsma et al., 2014; Smith & Greenwood, 2008; Wallace & Schwartz, 1997). Because unintentional weight loss is a predictor of mortality in the elderly (Alibhai, Greenwood, & Payette, 2005; Ryan, Bryant, Eleazer, Rhodes, & Guest, 1995) and is often associated with AD (White, Pieper, Schmader, & Fillenbaum, 1996), a poor diet that is inadequate in energy and protein has potentially serious health consequences for these individuals. Consequences include weight loss (Keller et al., 2008), decreased resistance to infections, greater morbidity, poor quality of life, and institutionalization (Sergi, De Rui, Coin, Inelmen, & Manzato, 2013; Smith & Greenwood, 2008). Whatever the cause, weight loss in those with AD depletes both fat tissue and lean body mass (Wang, 2002), and the latter leads to lower muscle strength. In the presence of a poor diet, weight loss could also exacerbate existing oxidative tissue damage (Sergi et al., 2013). As a common measure of muscle strength in this clientele, grip strength has been found to be a long-term predictor of mortality in aging adults (Gale, Martyn, Cooper, & Sayer, 2007) and better predicts mobility impairment and mortality than muscle mass (Lauretani et al., 2014; Legrand et al., 2014); consequently, protein intakes above the current recommendations could help maintain muscle mass in older adults (Gaffney-Stomberg, Insogna, Rodriguez, & Kerstetter, 2009).

Older adults with AD are known to be at nutritional risk, but appropriate feeding assistance can prevent malnutrition in older individuals with AD. Indeed, almost 20 years ago, Franzoni, Frisoni, Boffelli, Rozzini, and Trabucchi (1996, page 1369) observed that “malnutrition is not an unavoidable correlate of dementia, which [suggests] that when appropriate feeding assistance is provided to the demented [sic], malnutrition might not develop.” However, diet is infrequently addressed in outpatient usual care although these individuals should be routinely assessed for nutritional risk and targeted for interventions (Burgener et al., 2008; Keller et al., 2008; Nourhashemi et al., 2005; Smith & Greenwood, 2008). While dietary assistance to community-dwelling older adults in early-stage AD could prevent or even reverse nutritional problems, and stabilize or improve nutritional status, thus delaying institutionalization, with benefits for the patient, his or her family, and the health care system, a recent review pointed to insufficient evidence on the best approach for nutrition intervention in this clientele (Droogsma et al., 2014).

Although food intakes among AD patients in institutions have been studied for some time (Spindler, Renvall, Nichols, & Ramsdell, 1996), institutionalized AD patients generally have more advanced disease than those living at home. In addition, their food intakes are dependent on institutional food services, and they are followed by health care professionals to ensure dietary and nutritional adequacy, providing therapeutic diets, nutritional supplements, and possibly tube feeding, as warranted. Keller et al. (2003) showed that greater dietitian time and menu changes fostered weight gain in institutionalized older adults with AD. However, we cannot extrapolate institutional care to community-dwelling individuals. Furthermore, in contrast to a captive institutionalized population, it is more difficult to assess diet and intervene in free-living individuals with AD. Still, although dietary assessment in a cognitively compromised population is challenging (Zuniga & McAuley, 2015), because short-term assessment methods such as repeated 24-hr recalls (24 hr) or diet records do not rely on the respondent’s long-term memory and can be completed at the time of the eating occasion, it is recommended that they be used among those with memory impairments (Carroll et al., 2012; Prentice et al., 2011).

We thus sought to determine whether targeted nutritional intervention could be effective in the community setting. The overall goal of the Nutrition Intervention Study (NIS) was to prevent weight loss and undernutrition among community-dwelling older adults with early-stage AD. Our specific objective was to develop, implement, and evaluate targeted nutrition intervention strategies for this vulnerable group. The present article reports on the intervention and its impact on food and nutrient intakes, body weight, and muscle strength within and between intervention and control participants.

Method

Design, Participants, Recruitment

The NIS has been described in detail elsewhere (Shatenstein, Kergoat, Reid, & Chicoine, 2008). Briefly, it was a 6-month quasi-experimental pre–post dietary intervention conducted from 2006 to 2008. Participants and their caregiver were recruited as dyads from six university-affiliated Montreal hospitals having outpatient memory or geriatric clinics, which were designated for logistical reasons as intervention and control institutions, respectively, based on their vocation as memory or general geriatric outpatient clinics. NIS was powered on recruiting 35 participants per group (intervention and control) to detect a 5% loss of body weight (key dependent variable, with 80% power, α = .05) within individuals, as well as differences between groups.

Participants were eligible if they were aged 70 years or older, in early-stage AD (Stage 3-4 Reisberg/ Functional Assessment Staging Test (FAST) scale; Reisberg, Ferris, Mony, de Leon, & Crook, 1982), or had an Mini-Mental State Examination (MMSE) score of 22/30 or greater (Folstein, Folstein & McHugh, 1975) were able to provide informed consent (determined by asking participants what the study was about after reading the consent form), had no significant weight loss in the previous year (≥4.5 kg in previous 6 months or ≥2.2 kg in previous month; Omran & Morley, 2000), were in good physical health, had a willing caregiver (typically their spouse or an adult child), and spoke and understood French or English. Exclusion criteria included Class III or more severe heart failure, chronic obstructive pulmonary disease requiring home oxygen therapy or oral steroids, cancer treated by radiation therapy, chemotherapy or surgery in the 5 years prior to enrolment (basal skin cell carcinoma admissible), inflammatory digestive disease, or any other chronic illness likely to interfere with diet or participation in the study. Individuals with severe visual or auditory problems which could interfere with communication or evidence of clinical depression were also excluded, as were those with a baseline body mass index (BMI) < 18.5 as this indicates an existing state of malnutrition associated with debilitating health conditions and possible early signs of underlying disease (Visscher et al., 2000). Patients fulfilling inclusion criteria were identified by clinic personnel (coordinators, nurses, or physicians) using an eligibility checklist. They were given a written document describing the study and those who agreed to participate were referred to our team and contacted by the NIS coordinator who scheduled a meeting at their clinic. Participants were recruited as participant–caregiver dyads and both parties signed informed consent prior to assessment. The study flowchart is shown in Figure 1. The study was approved by the Institutional Review Board (IRB) of the Institut universitaire de gériatrie de Montréal and the IRB in each of the other five participating institutions.

Figure 1.

Nutrition intervention study flowchart.

Study Procedures

With the agreement of each clinic, consenting patients’ medical charts were reviewed prior to interview to confirm eligibility and extract information on medical history, recent nutrition-related biological parameters, medication use, prescribed therapeutic diets, and physical limitations. The 2- to 3-hr interview took place at entry (T1) and exit (T2) from the study with both intervention and control participants. Information on sociodemographics, general health, medication use, health perception, and physical activity was collected by a questionnaire adapted from a patient intake instrument used in our institution. Functional status (Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL)) was queried (Lawton & Brody, 1969), visual analogue scales were used to qualify hunger and appetite (Lennie, Neidig, Stein, & Smith, 2001), and a Health Events Journal (Means, Swan, Jobe, & Esposito, 1991; Schwarz & Sudman, 1994) traced ongoing health issues. Anthropometric and physical function measures were obtained by the study coordinator using a standardized approach with participants dressed in street clothes, without shoes. These included body weight (beam balance) and height (stadiometer), measurement of waist and hip circumferences using standard procedures with a flexible tape, and grip strength using a vigorimeter (Martin Dynanometer vigorimeter, Tuttlingen, Germany) and the mean of the highest score on three trials was recorded (Laukkanen, Heikkinen, & Kauppinen, 1995; Webb, Newman, Taylor, & Keogh, 1989). BMI (weight (kg) / height (m²)) and waist:hip ratio (WHR) were calculated. Weight history was assessed using an adapted Silhouette method (Must, Willett, & Dietz, 1993) and nutrition risk was determined by the Elderly Nutritional Screening tool (ENS©; Payette, Gray-Donald, Cyr, Coulombe, & Bouner, 1996; Payette, Guigoz, & Vellas, 1999). As in previous research with this clientele (Shatenstein, Kergoat & Reid, 2007), usual diet was assessed using a validated 78-item interviewer-administered semiquantitative food frequency questionnaire (FFQ; Shatenstein, Nadon, Godin, & Ferland, 2005), with caregiver assistance or as proxy respondent. Current diet was assessed by two nonconsecutive 24-hr or diet record collected by telephone from participants/caregivers in the month following the baseline/exit interview, using the USDA (United States Department of Agriculture) five-step Multiple Pass Method (Conway, Ingwersen, Vinyard, & Moshfegh, 2003; Moshfegh, Borrud, Perloff, & LaComb, 1999). Detailed instructions and supporting documents were distributed to participants and caregiver for reporting diet. Use of dietary and nutritional supplements, holistic or herbal preparations, modified food textures and consistencies, therapeutic diets, and general health status was recorded.

Several novel techniques were developed to ensure accuracy in collecting 24 hr/diet record from participants. Caregivers were actively involved and the flexible approach reflected the cognitive constraints of participants and the caregiver burden. The dietitian called the participant several times on the 24 hr/diet record day and asked the participant to describe the food on his or her plate and in the refrigerator and pantry. The caregiver provided or corroborated the participant’s dietary report as needed. Thus, the participant could (a) provide dietary information independently, (b) it was obtained by assisted interview, or (c) the caregiver replied for the participant. If the participant had been ill or was hospitalized or could not come to the memory clinic or research center for interviews, the dietitian conducted a home or hospital visit and observed the participant, his food-preparation and/or eating abilities (see the appendix). We also referred to the FFQ responses to note usual dietary habits that could explain food intakes reported in the 24 hr/diet record. At T2, the study dietitian reviewed the T1 interview and examined the coherence of the individual’s stated likes and dislikes and the typical daily food intakes in light of the reported 24 hr/diet record, and assessed changes in meal and snacking patterns. Finally, body weight stability or changes were also used in assessing the plausibility of dietary reports. All measures and assessments were done by the same trained professional clinical research dietitian with extensive experience working with this clientele.

NIS Program

The foundation of the NIS program is based on a comprehensive nutrition intervention model as described in Shatenstein et al. (2008). It uses clinical dietetics principles to help the individual with AD eat well, prevent weight loss and/or maintain a healthy body weight. Based on consensus reached in a focus group with six expert dietitians (Shatenstein, Reid & Kergoat, 2007), it was designed to provide plain-language dietary advice in the form of a maximum of three dietary objectives, bolstered by frequent follow-up and aided by accessible nutrition education resources. This approach reflects Locher et al.’s (2009) recommendations to include personalized messages and concentrate on familiar and liked foods.

The intervention participant’s food and nutrient problems were central to the diet plan. Diet teaching was directed at both members of the intervention dyad who received feedback and preliminary dietary advice immediately after the baseline (T1) assessment along with instructions on keeping diet records. Details of the intervention followed by mail: Each intervention dyad received a plastified 8-1/2 × 11-inch colorful poster with magnets for affixing it to the refrigerator illustrating the three dietary objectives, as well as related consumer-oriented educational materials providing tips on good food sources of the targeted nutrients and advice on adjusting favorite dishes to increase energy and protein intakes. These documents were assembled using resources prepared for nutrition professionals and the public by national and provincial dietitians’ associations. We also consulted the Canadian Nutrient File (Health Canada, 1982) to identify the best food sources of target nutrients; a consumer protection magazine produced by the Québec provincial government provided good food tips, and tested recipes were obtained from reliable sources. Intervention participants were telephoned within a week of mailing to respond to their questions and clarify the recommendations as needed.

A copy of the participant’s dietary assessment was sent to the family practitioner and the clinic physician. Memory or geriatric clinic staff (nurse, social worker) and public access community services were contacted to provide further referrals as needed for the participant and caregiver. Additional resources offered to the dyad included a “SOS Dietitian” telephone service available 24/7, and a dedicated nutrition page placed on our institutional website with FAQ on diet and nutrition for older adults. Participant dyads were telephoned by the dietitian at 3 to 6 week intervals, according to their needs. Finally, the participant’s home scale was calibrated to the memory/geriatric clinic scale to ensure accurate weighing of the participant at regular intervals. The Control group received general information on food and eating as Canada’s Food Guide (CFG) leaflets (Health Canada, 2007).

Data Handling and Analyses

Questionnaire responses, anthropometrics and grip strength measurements, the hunger and appetite scales, and the ENS© tool were entered into Microsoft Excel© spreadsheets as collected. Diet records and 24-hr analyses were carried out using CANDAT dietary analysis software (version 6.0, 2005, Godin, London, ON, Canada), based on the then-current 2001 Canadian Nutrient File (CNF) (Health Canada, 1982) and CANDAT output files were exported to Excel for data handling. The FFQ was entered using Microsoft Access© software for customized data entry and analysis, with algorithms developed by our study team to compute energy, nutrient and CFG food group values from the instrument, also based on the same version of the CNF (Health Canada, 1982). Adequacy of participants’ daily energy and protein intakes was assessed as energy intakes of 30 kcal/kg body weight (BW) / day (d) (Payette & Ferry, 2007), and protein intakes of both 0.8 g protein/kg BW/day and 1.0 g protein/kg BW/day (Gaffney-Stomberg et al., 2009; Gray-Donald et al., 2014), respectively.

Statistical analyses were carried out as “intention-to-treat.” Independent and paired t tests and nonparametric Wilcoxon signed rank tests were used to assess within- and between-group differences in outcome variables and results were corrected for multiple comparisons using a Bonferroni adjustment. SPSS (version 15, SPSS Inc, Chicago, IL) was used for statistical analyses. Results are presented as means ± SD or as frequency counts and percentages, unless stated otherwise.

Results

Sixty-seven participants with a diagnosis of early-stage AD were recruited with their caregiver from the six participating clinics and allocated to the intervention (n = 34 dyads) or control (n = 33 dyads) groups. Those who refused to participate cited lack of interest or that they felt that it would be too much of a burden. The groups resembled one another except for living situation: More intervention group participants lived alone compared to those in the control group (17.6% vs. 3%, p < .05), while virtually all control participants lived with others, usually their spouse.

In both groups, participants’ mean age was 79 to 80 years, average MMSE score was 24 to 25/30 indicating mild cognitive impairment (Folstein, Folstein, & McHugh, 1975), some 32% to 33% of participants in both groups had a university education, and nutrition risk was moderate, as shown by mean ENS scores of 3.36 ± 1.69 and 3.25 ± 1.4, intervention and control participants, respectively. All participants qualified their appetite as moderately good. Among women in both groups, grip strength was similar to normative data published for older adults, but fell a little below the norms (Desrosiers, Bravo, Hébert, & Dutil, 1995) among men in both study groups. At a mean of 25 to 26 (SD = 4), BMI was in the normal range for older adults. There were no gender-based differences in baseline characteristics (Table 1).

Table 1.

Participant Characteristics at Entry Into the Study (T1).

Participant characteristics	Group		p value
	Intervention (n = 34)	Control (n = 33)
	n (%)
Sex
Male	11 (23)	17 (52)	ns
Female	23 (67)	16 (48)
Living situation
Alone	6 (17.6)	1 (3.0)	.05
With others	28 (82.4)	32 (97.0)
Education level
Primary	2 (5.9)	5 (15.2)	ns
Secondary	16 (47.1)	11 (33.3)
College	5 (14.7)	6 (18.2)
University	11 (32.4)	11 (33.3)
Nutrition risk ENS©/13^a	3.36 ± 1.69	3.25 ± 1.40	ns
Men	3.09 ± 1.14	3.00 ± 1.41
Women	3.50 ± 1.92	3.50 ± 1.40
Caregiver relation to participant
Spouse	22 (64.7)	28 (84.8)	ns
Adult child	7 (20.6)	4 (12.1)
Sibling	2 (5.9)	1 (3.0)
Other^b	3 (8.8)	0
M ± SD
Age (years)	79.7 ± 4.9	80.0 ± 5.3	ns
MMSE (/30)^c	25 ± 1.9	24 ± 2.8	ns
Appetite (/10)^d	6.6 ± 2.4	6.3 ± 2.1	ns
Grip strength, right hand (kPa)^e
Men	64.8 ± 4.5	58.8 ± 5.0	ns
Women	52.3 ± 3.6	50.6 ± 3.9
Waist:hip ratio
Men	0.96 ± 0.01	0.97 ± 0.01	ns
Women	0.86 ± 0.01	0.88 ± 0.02
BMI (kg/m²)^f	25 ± 4	26 ± 4	ns
Men	26 ± 2	27 ± 4
Women	24 ± 5	26 ± 5
Caregiver age (years)	71.4 ± 10.7	73.6 ± 11.1	ns

Note. Pearson chi-square test.

Elderly Nutritional Screening; 0-2 = low risk; 3-5 = moderate risk; 6+ = high risk (Payette, Guigoz, & Vellas, 1999).

Friend, neighbor.

Folstein, Folstein, & McHugh (1975).

Lennie, Neidig, Stein, & Smith (2001); no significant difference at T1 or T2 between/within subjects (data not shown).

Mean of best score on three trials (Laukkanen, Heikkinen, & Kauppinen, 1995).

Body mass index (p < .05 intervention vs. controls; no significant differences by gender stratification).

The following were the most frequent recommendations made by the dietitian to intervention participants, in decreasing order of frequency. The majority of participants (70%) were told to monitor their weight regularly, more than 45% were advised to eat more dairy foods rich in calcium and leafy green vegetables for vitamin K (45.5%), and increase dietary (or supplemental) sources of vitamin D (42.4%), while smaller proportions were advised to increase energy intakes (~35%), eat good food sources of folic acid (33.3%) or high protein foods (27.3%), and adjust their food intakes to eat several small meals and snacks at more frequent intervals (24.2%). There was some variability in gender-specific priorities. For example, more than 70% of men were advised to increase food sources of calcium and vitamin D, while among women, half were simply told to increase energy intakes by eating foods they enjoyed, some 30% to 40% were advised to increase consumption of protein-rich foods, and to eat smaller meals more frequently. In addition to these recommendations, and in line with their individual needs, we also advised participants to continue taking their vitamin-mineral supplements, eat foods providing essential fatty acids, get more sunshine, eat at regular times, drink sufficient fluids, eat vitamin E-rich foods, continue taking dietary supplements (e.g., Boost© or Ensure©), eat foods rich in iron, eat in restaurants or with others more frequently, and do more exercise.

There were no differences between intervention and control groups in energy or macronutrient intakes at either T1 or T2 (Table 2) and all participants met guidelines for energy and macronutrient Dietary Reference Intakes (DRIs) for their age group and gender. However, within the intervention group, fat intakes at T2 relative to T1 were greater (54 ± 16 vs. 67 ± 23 g, p = .013) and the latter was reflected by higher, albeit not statistically significant intakes in most lipid fractions at T2 in this group. There was also a tendency for increased energy and protein intakes over the 6-month intervention within the intervention group. There were no differences within controls from T1 to T2 in energy or macronutrient intakes.

Table 2.

Macronutrient Intakes Within Intervention and Control Groups^a at Entry (T1) and After 6 Months (T2).

Nutrient	Group and period
	Intervention		p value	Controls		p value
	T1 (n = 32)	T2 (n = 32)	p value	T1 (n = 28)	T2 (n = 28)	p value	DRI^b

			M ± SD				EAR^b AMDR	RDA/AI^b
Energy (Kcal)	1,631 ± 388	1,780 ± 43	ns	1,689 ± 511	1,678 ± 393	ns
Protein (g)	72 ± 27	78 ± 23	ns	73 ± 24	71 ± 26	ns	10%-35%	56/46
Fat (g)	54 ± 16	67 ± 23	.013	62 ± 27	60 ± 17	ns	20%-35%
Carbohydrates (g)	215 ± 51	218 ± 58	ns	206 ± 59	212 ± 58	ns	45%-65%	130
Total dietary fiber (g)	18 ± 7	18 ± 7	ns	17 ± 7	19 ± 6	ns	—	—
Dietary cholesterol (mg)	245 ± 136	250 ± 151	ns	230 ± 124	214 ± 113	ns	—	—
Saturated fat (g)	17.5 ± 7.7	22.3 ± 10.5	ns	20.5 ± 11.8	19.5 ± 6.8	ns	—	—
Mono-unsaturated fat (g)	20.5 ± 7.2	25.0 ± 9.2	ns	22.9 ± 10.1	23.4 ± 8.5	ns	—	—
Polyunsaturated fat (g)	9.5 ± 3.9	12.5 ± 6.5	ns	11.7 ± 7.5	10.9 ± 4.7	ns	10%	—
Omega-6 fatty acids (g)	6.85 ± 3.40	9.46 ± 5.74	ns	8.27 ± 5.60	7.32 ± 3.31	ns	—	5-10
Omega-3 fatty acids (g)	1.20 ± 0.74	1.38 ± 0.88	ns	1.65 ± 1.34	1.08 ± 0.60	ns	—	0.6-1.2
DHA (g)^c	0.24 ± 0.38	0.25 ± 0.34	ns	0.27 ± 0.48	0.43 ± 0.49	ns	—	—
EPA (g)^d	0.12 ± 0.20	0.12 ± 0.17	ns	0.12 ± 0.21	0.22 ± 0.25	ns	—	—

Note. Paired t tests corrected by Bonferroni adjustment.

Participants with complete data.

DRI = Dietary Reference Intakes; AMDR = Acceptable Macronutrient Distribution Ranges; EAR = estimated average requirement; RDA = Recommended Dietary Allowance; AI = Adequate Intake, adults aged 70+ years; Dietary Reference Intakes, Institute of Medicine, National Academy of Sciences (2006).

Docosahexanoic acid.

Eicosapentanoic acid.

Intakes of selected micronutrients in both groups are shown in Table 3. No differences in nutrient intakes were detected between groups at either T1 or T2. Within the intervention group calcium intakes from T1 to T2 appeared to increase (659 ± 323 vs. 826 ± 324 mg) along with a tendency for greater vitamin D intakes, but neither change was statistically significant. Within controls, intakes of folates appeared higher at T2 relative to T1 (272 ± 95 vs. 308 ± 103 µg), but this was not statistically significant. Neither group met DRIs for magnesium, potassium, zinc, vitamin D, Vitamin E, pantothenic acid, folates or vitamin K, either at the group (Estimated Average Requirements [EAR]) or individual (Recommended Dietary Allowances [RDA] or Adequate Intakes [AI]) levels, according to recommendations available at the time of the study.

Table 3.

Micronutrient Intakes Within Intervention and Control Groups^a at Entry (T1) and After 6 Months (T2).

Nutrient	Group and period
	Intervention		p value	Controls		p value	DRI^b
	T1 (n = 32)	T2 (n = 32)	p value	T1 (n = 28)	T2 (n = 28)	p value	DRI^b

			M ± SD				EAR^b m/f	RDA/AI^b m/f
Calcium (mg)	659 ± 323	826 ± 324	ns	757 ± 518	777 ± 585	ns	1,200	1,200
Iron (mg)	12.0 ± 4.1	12.5 ± 5.0	ns	12.4 ± 4.4	11.7 ± 3.6	ns	6/5	8
Magnesium (mg)	292 ± 99	297 ± 81	ns	287 ± 93	313 ± 108	ns	350/265	420/320
Phosphorus (mg)	1,174 ± 423	1,244 ± 371	ns	1,183 ± 443	1,208 ± 543	ns	580	700
Potassium (mg)	3,070 ± 1,012	2,981 ± 852	ns	2,891 ± 770	3,100 ± 1,039	ns	—	4.7 g
Sodium (mg)	2,194 ± 717	2,416 ± 938	ns	2,778 ± 1,247	2,297 ± 830	ns	—	1.2 g
Zinc (mg)	9.1 ± 3.7	10.2 ± 4.0	ns	9.6 ± 3.9	8.8 ± 3.6	ns	9.4/6.8	11/8
Copper (mg)	1.51 ± 1.15	1.48 ± 1.34	ns	1.27 ± 0.46	1.33 ± 0.43	ns	0.7	900 ug
Manganese (mg)	3.71 ± 1.65	3.45 ± 1.41	ns	3.74 ± 1.55	4.10 ± 1.31	ns	—	2.3/1.8
Selenium (µg)	81 ± 32	81 ± 27	ns	80 ± 28	91 ± 36	ns	—	55
Vitamin A (ER) (µg)	1,868 ± 1,758	1,558 ± 1,886	ns	1,221 ± 840	1,522 ± 926	ns	625/500	900/700
Vitamin D (µg)	3.27 ± 3.75	3.41 ± 3.24	ns	2.56 ± 2.41	2.68 ± 3.05	ns	15	15
α-tocopherol equivalents—total activity (mg)^c	3.50 ± 1.98	4.39 ± 2.40	ns	3.43 ± 1.87	4.05 ± 1.85	ns	12	15
Vitamin C (mg)	128 ± 74	131 ± 82	ns	122 ± 51	150 ± 87	ns	75/60	90/75
Thiamin (mg)	1.48 ± 0.56	1.63 ± 0.88	ns	1.54 ± 0.67	1.40 ± 0.51	ns	1.0/0.9	1.2/1.1
Riboflavin (mg)	1.60 ± 0.53	1.75 ± 0.60	ns	1.65 ± 0.86	1.63 ± 0.77	ns	1.1/0.9	1.3/1.1
Niacin (mg)	20.0 ± 8.1	19.6 ± 6.9	ns	19.1 ± 6.7	18.9 ± 6.5	ns	12/11	16 /14
Pantothenic acid (mg)	4.64 ± 1.63	4.59 ± 1.57	ns	4.43 ± 1.53	4.55 ± 1.64	ns	—	5
Vitamin B₆ (mg)	1.84 ± 0.64	1.82 ± 0.62	ns	1.76 ± 0.65	1.88 ± 0.64	ns	1.4/1.3	1.7/1.5
Folates (µg)	281 ± 120	286 ± 157	ns	272 ± 95	308 ± 103	ns	320	400
Vitamin B₁₂ (µg)	4.4 ± 5.3	5.1 ± 6.2	ns	4.0 ± 2.3	5.7 ± 6.1	ns	2	2.4
Vitamin K (µg)	75 ± 75	82 ± 61	ns	93 ± 76	104 ± 80	ns	—	120/90

Note. Paired t tests corrected by Bonferroni adjustment.

Participants with complete data.

DRI = Dietary Reference Intakes; AMDR = Acceptable Macronutrient Distribution Ranges; EAR = Estimated average requirement; RDA = Recommended Dietary Allowance; AI = Adequate Intake, adults aged 70+ years; Dietary Reference Intakes, Dietary Reference Intakes, Institute of Medicine, National Academy of Sciences (2006).

Alpha-tocopherol equivalents (total activity) is calculated for 45.12% of foods and thus provides best reflection of vitamin E intakes (Canadian Nutrient File, Health Canada, 1982).

The proportions of participants achieving energy adequacy were similar in both groups at T1 (Table 4). However, there was improvement within the intervention group as a whole at T2, largely due to improvements in energy intakes among women, with 40.9% achieving energy adequacy at T2.

Table 4.

Proportions Within Intervention and Control Participants^a Meeting Criteria For Adequacy of Energy and Protein Intakes at Entry (T1) and After 6 Months (T2).

Component	Time	Intervention (n = 33)		p value	Control (n = 29)		p value

		n (%)
		Males	Females		Males	Females
		11 (33)	22 (67)		15 (52)	14 (48)
Energy adequacy 30 kcal/kg	T1	2 (20.0)	7 (31.8)	ns	1 (6.7)	4 (30.8)	ns
Energy adequacy 30 kcal/kg	T2	2 (20.0)	9 (40.9)	ns	2 (13.3)	4 (30.8)	ns
Protein adequacy 0.8 g/kg	T1	10 (100)	15 (68.2)	ns	13 (86.7)	10 (76.9)	ns
Protein adequacy 0.8 g/kg	T2	9 (90)	20 (90.9)	ns	12 (80.0)	10 (76.9)	ns
Protein adequacy 1.0 g/kg	T1	9 (81.8)	10 (45.5)	.028	11 (68.8)	9 (64.3)	.030
Protein adequacy 1.0 g/kg	T2	8 (80.0)	18 (81.1)		11 (68.8)	6 (46.2)

Note. Pearson chi-square test, paired samples.

Participants with complete data.

Likewise, protein adequacy was similar between groups at both T1 and T2. However, within intervention group participants, there was an increase in proportions achieving protein adequacy over the 6-month intervention period at both the 0.8 g/kg BW/d and the 1.0 g/kg BW/d levels of protein intakes (p = .028). This was due to improved protein intakes among female intervention participants, with 91% and 81% of intervention group women attaining protein adequacy at the 0.8 and 1 g protein/kg BW/d, respectively, at T2, compared with 68% and 46% (p = .030), respectively, at T1. In contrast, protein adequacy within the control group remained unchanged over the 6-month intervention at the 0.8 g/kg BW/d level, and decreased from T1 to T2 at the 1.0 g/kg BW/d level, attributable to a drop in protein intakes among female controls (p = .030; Table 4).

BMI was stable over the study period among both groups of participants. WHR increased within intervention group women from T1 to T2 (0.85 ± 0.01 vs. 0.89 ± 0.02, p = .015), bringing these women to approximately the same WHR as control group women. Within both groups and in both sexes, grip strength decreased at T2 (p = .04). Finally, while nutrition risk scores showed some improvement (i.e., decreased) among intervention participants, they worsened (increased) in controls, but differences from T1 to T2 did not reach statistical significance between groups or within either group (Table 5).

Table 5.

Nutrition-Related Parameters Within Intervention and Control Participants at Baseline (T1) and After 6 Months (T2).

Characteristics	Intervention		p value	Control		p value
Characteristics	T1	T2	p value	T1	T2	p value
BMI (kg/m²)
Men	26 ± 2	26 ± 2	ns	27 ± 4	27 ± 3	ns
Women	24 ± 5	24 ± 5	ns	25 ± 4	24 ± 4	ns
Waist:hip ratio
Men	0.96 ± 0.01	0.98 ± 0.01	ns	0.97 ± 0.02	0.97 ± 0.02	ns
Women	0.85 ± 0.01	0.89 ± 0.02	.05	0.87 ± 0.02	0.90 ± 0.02	ns
Grip strength, right hand (kPa)
Men	64.8 ± 14.8	57.5 ± 21.2	.04	59.1 ± 21.6	50.9 ± 19.3	.04
Women	52.6 ± 14.5	43.8 ± 15.4	.04	50.4 ± 15.9	40.9 ± 16.5	.04
Nutrition risk (/13)^a	3.4 ± 1.7	3.2 ± 2.0	ns	3.3 ± 1.4	3.5 ± 1.7	ns

Note. Paired t tests.

Elderly Nutritional Screening© (Payette, Guigoz, & Vellas, 1999): 0-2 = low risk; 3-5 = moderate risk; 6+ = high risk.

Discussion

The present article reports on a targeted dietary intervention carried out in a group of community-dwelling older adults in the early stages of AD, a vulnerable geriatric population. As suggested in our earlier analyses and by others, the active participation of the caregiver was essential to the delivery of the intervention (Sergi et al., 2013; Silva, Kergoat, & Shatenstein, 2013) and to ensure access to and consumption of a healthy diet (Shatenstein et al., 2008), which could allow the individual to remain at home for as long as possible (Sergi et al., 2013). The study’s strength lies in its demonstration of the feasibility of effecting beneficial, albeit modest, dietary change in this vulnerable population group, and provides empirical support for our previous work relating the family caregiver’s appreciation of the intervention (Silva et al., 2013), thus complementing our earlier qualitative research report.

Dietary recommendations and close monitoring of participants in the intervention group over the 6-month study period led to small increases in intakes and more varied food consumption overall, resulting in greater lipid intakes, improvement in the adequacy of their protein intakes, higher energy, and increased consumption of good dietary sources of calcium although the latter increases did not reach statistical significance. Individualized recommendations were made to intervention participants and their caregivers in line with the three or four priorities that had emerged from dietary assessment at entry into the study. They also underscored the primary dietary deficiencies observed in this group, notably in foods providing calcium (dairy) vitamin K (vegetables), folates (fruits, fortified grain products), and protein (both plant and animal sources). We had advised NIS intervention participants to eat more dairy products and increase their energy and protein intakes, and provided them with lists of good food sources of the different nutrients. Indeed, increasing food sources of calcium was one of the most widespread recommendations in the intervention group. However, despite their better intakes of calcium-rich foods, they still did not meet the DRIs, which argues for supplementation to address the challenge of meeting their calcium requirements.

The targeted dietary intervention also led to significantly increased adequacy of protein intakes from T1 to T2 among women in the intervention group. These salutary increases were in contrast to the deterioration in protein adequacy levels among women in the control group at exit from the study. While 0.8 g protein/kg BW/day is the current recommended intake level, it has been suggested that protein intakes of 1.0 or even 1.2 g/Kg BW/day are necessary to improve nitrogen balance during resting metabolism, benefit bone (Gaffney-Stomberg et al., 2009) and preserve muscle function, particularly in sedentary, but otherwise healthy older adults (Ferrando et al., 2010). Although there have been concerns about potential detrimental effects of high protein intakes on bone health, renal, neurological, and cardiovascular function, these are generally unfounded (Ferrando et al., 2010). In fact, many of these factors are improved in older adults who ingest elevated quantities of protein. It even appears that an intake of 1.5 g protein/kg/day—about 15% to 20% of total energy intake—evenly spaced throughout the day, is a reasonable target for optimizing protein intake for overall health and physiological function among for older individuals (Berner, Becker, Wise, & Doi, 2013; Breen & Phillips, 2011; Gaffney-Stomberg et al., 2009; Wolfe, Miller, & Miller, 2008). Indeed, a recent workshop held by the European Society for Clinical Nutrition and Metabolism (ESPEN) has recommended protein intakes of 1.2 to 1.5 g/kg/day for malnourished or at-risk older individuals (Deutz et al., 2014). Clearly, the increased intakes of dairy products also provided intervention group participants with an excellent additional source of protein, while the decrease in protein adequacy among control women was a sign of unchecked deterioration in their nutritional status.

Dietitian advice also focused on increasing consumption of foods providing vitamins K and D, as well as energy and dietary fiber and these recommendations were as frequent as those addressing calcium. However, intervention participants’ intakes of fiber, vitamin K and folates remained stable over the study and no significant increases in these nutrients were detected after 6 months. This suggests that not all recommendations were implemented within the intervention group. While this could be due to our limiting the number of dietary recommendations to a maximum of three, it also reflect reports from caregivers who informed us that it was difficult to effect dietary change in their family members in line with some dietary advice. For example, they reported that it was harder to introduce a variety of vegetables into their family member’s diet than to add cheese, nuts, and seeds (Silva et al., 2013). It should also be noted that simply encouraging participants to eat was a major challenge and was a recommendation in and of itself. Maintenance of stable body weight among intervention participants attests to modest success in achievement of this recommendation.

Although not statistically significant, nutrition risk showed a tendency to decrease over the 6-month period among intervention participants. The tool we used, the ENS© (Payette et al., 1999), may not have sufficient sensitivity to detect changes in nutrition risk in such a short time. Still, the intervention did show benefit for certain aspects of food consumption behavior addressed by this screening instrument (such eating a food from each of the food groups at breakfast) and body weight stability. This is of particular interest in light of the fact that participants in the intervention group increased their energy intakes, which has been shown to decrease nutrition risk in institutionalized older adults (Keller et al., 2003).

One of the main objectives of the targeted nutrition intervention was to help participants maintain a healthy body weight. While this was achieved in the intervention group overall and mean BMI in the whole control group decreased over the 6-month intervention period, the difference between groups in weight stability disappeared when gender-specific analyses were carried out at exit from the study. However, we observed a significant increase in WHR among intervention participants over the 6-month study period. This may be due to the significantly higher energy intakes among women in this group, mainly because of their greater fat consumption over the course of the study. The issue of WHR in older adults is somewhat controversial. We are unaware of a potentially negative impact for these individuals resulting from increased WHR, because it is unclear whether general adiposity, or a larger waist or WHR carry the same health risks for older adults compared to middle-aged adults. For example, in a body composition study of 4,000 older men and women aged 65 years and above, greater adiposity appeared to favor survival, and the authors speculated that mild overweight and even central obesity could be protective in older individuals (Auyeung et al., 2010). Because BMI does not measure central adiposity (Auyeung et al., 2010) and overestimates health risks in those aged 75 years and above (Price, Uauy, Breeze, Bulpitt, & Fletcher, 2006), Auyeung et al. (2010) have suggested that BMI and WHR cutoffs applied to older adults should be revised. While Price et al. (2006) have suggested that relative abdominal obesity as measured by a high WHR is predictive of increased mortality, others have found no relationship between higher WHR and chronic disease markers such as insulin resistance in older men or women (Lee, Glickman, Dengel, Brown, & Supiano, 2005).

However, intervention participants maintained their weight, and the increase in WHR at the end of the study brought them to the same measurement as that of control participants. Indeed, a study conducted among older men showed little relationship between mortality and increased adiposity as BMI, WHR, or waist circumference even after adjustment for lifestyle characteristics (Wannamethee et al., 2007); consequently, these observations provide some reassurance that the increased WHR in NIS participants was benign. On the contrary, in this study, underweight men had very high mortality rates (Wannamethee et al., 2007) in contrast to a later study that observed that WHR better predicted mortality in high-functioning older adults than BMI (Srikanthan et al., 2009). Thus, the question of where to set the cutoff for a “high” WHR in older adults remains unanswered and should be the focus of further studies on anthropometric norms for older adults.

Finally, despite increased protein intakes in the intervention group, grip strength—as a measure of muscle strength—deteriorated over the course of the intervention. In fact, grip strength decreased significantly in both groups at the 6-month exit assessments. Several authors have shown that increasing protein intakes above current recommendations has a favorable impact on muscle, whether from diet alone (Ferrando et al., 2010; Wolfe et al., 2008) or by combining a protein-enriched diet with strength training (Holm et al., 2008). It is possible that our participant follow-up was not sufficiently long to observe benefits for muscle strength from increased protein intakes. It is also conceivable that the increase in protein intakes was not large enough to foster maintenance of grip strength over the 6-month period. In fact, some investigators have shown that short-term protein intakes of 1.6 g/kg/BW/day could be necessary to show gains in muscle strength (Gaffney-Stomberg et al., 2009). Our anecdotal observations on the difficulties inherent in inducing older adults with early-stage AD to simply eat more would likely preclude this level of protein intake by food alone. It is also possible that increased intakes of vitamin D may be necessary to show a favorable effect of protein on lean body mass, because it has been shown that among both active and sedentary individuals aged 60 years and above, 25(OH)D concentrations between 40 and 94 nmol/L are associated with better lower extremity muscle function than are concentrations lower than 40 nmol/L (Bischoff-Ferrari et al., 2004). While we provided participants and caregivers with information on increasing vitamin D intakes, there was no significant change in this nutrient over the course of the intervention. This speaks to the importance of supplementation to ensure adequate plasma vitamin D levels, as well as the need for better communication and closer collaboration between nutrition professionals and physicians.

The study had limits related, in part, to its design. Because of budget constraints, a single research dietitian carried out all tasks. While having only one research dietitian ensures consistency and fidelity in study procedures, this interviewer was not blinded to group status and although she was rigorously trained and experienced, she could have inadvertently been more attentive to intervention participants. Furthermore, because diet is composed of numerous nutrients which are assessed simultaneously, it can be challenging to assess the significance of change in individual, frequently intercorrelated nutrients over the course of an intervention. It should also be noted that, while true for both intervention and control groups, participation in dietary assessment heightens awareness of diet which could potentially lead to change in food choice and consumption. Moreover, the control group was aware of the broad recommendations of the CFG, and could have had access to the vast array of diet and cognition-related health information directed at the general public. It should also be mentioned that some caregivers in the control group contacted the study dietitian during the course of the study to ask pointed questions about their family member’s diet. For professional ethics reasons, the dietitian provided them with general information, taking care not include strategies for improving dietary intakes as with the intervention group. Thus, because they had agreed to take part in a nutrition study it is possible that both the control participants and their caregivers were more interested in food and nutrition than others, and made efforts to eat well on their own. It is also possible that the simple fact of meeting a dietitian on two occasions could have had a beneficial impact on their dietary habits by making them more aware of what they ate. Finally, despite our innovative and attentive approach to collection of accurate food consumption data using validated tools and caregiver assistance, some dietary measurement error was inevitable (McNeill, Winter, & Jia, 2009), particularly given the cognitive limitations of the participants (Pope et al., 2007).

Conclusion

In conclusion, this study has shown that we can intervene and effect modest dietary change in older adults with AD and improve their food and nutrient intakes, thereby providing potential benefits to their overall health and well-being as well as reassurance to their caregivers. Longer follow-up may be necessary to determine whether intakes of other nutrients would improve, and to assess the persistence of the benefits of nutrition intervention on body weight and muscle strength. However, such an undertaking must involve the affected individual’s caregiver. It also requires intense attention from a nutrition professional to the person’s specific individual needs as well as continuous follow-up and interaction with other members in the health care and social services sectors. If we are to keep individuals with AD at home longer, we must assess and track diet from diagnosis and involve the caregiver and the health care and social services team in this process, thus providing all older adults with the quality nutrition care they need (Bernstein & Munoz, 2012).

One could question whether this type of “first-class” service is feasible within the ever-tightening budget constraints of our health care systems. However, considering the rapid aging of populations worldwide, and the expected escalation in the number of people affected by AD, we should instead ask whether we can afford not to intervene in this clientele, just as Donini et al. (2010) commented on intervention to prevent the anorexia of aging. The dilemma of how best to maintain good nutrition in older adults, and especially those with AD, is likely to be the subject of much debate in the years to come. It argues for systematic nutritional evaluation of older adults and stepped-up efforts to increase inter-professional collaboration.

Footnotes

Appendix

Additional Measures Taken to Obtain Dietary Intake Data From Intervention and Control Participants at Baseline (T1) and After 6 Months (T2).

Nature of measure	Group and timen (%)
	Intervention		Control
	T1 (n = 34)	T2 (n = 33)	T1 (n = 33)	T2 (n = 30)
Multiple phone calls throughout the dietary data collection day	2 (12.5)	2 (16.7)	1 (7.1)	—
Verification of information using a variety of dietetic strategies during phone calls	6 (37.5)	4 (33.3)	8 (57.1)	3 (37.5)
Caregiver provided information or corroborated participant’s report	7 (43.8)	5 (41.7)	5 (35.7)	5 (62.5)
Information obtained from seniors’ residence personnel	1 (6.3)	—	—	—
Information obtained from nurse during participant’s hospitalization	—	1 (8.3)	—	—
Total	16 (47)	12 (36.4)	14 (42.4)	8 (26.7)

Acknowledgements

The authors gratefully thank the study participants and their caregivers for their enthusiastic participation and generous allocation of time, as well as the partner hospitals, clinic physicians and staff, and the study personnel, who are all professional dietitians with great concern for and devotion to the well-being of older adults with Alzheimer’s disease.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Nutrition Intervention Study was funded by the Alzheimer Society of Canada, Grant 07 71.

Author Biographies

Bryna Shatenstein, PhD, PDt, is a dietitian, full professor, and researcher in methodological, clinical, and applied nutrition at the Département de nutrition, Université de Montréal and Centre de recherche, Institut universitaire de gériatrie de Montréal.

Marie-Jeanne Kergoat, MD, is a geriatrician, head of specialized services and the Memory Clinic at the Institut universitaire de gériatrie de Montréal, and professor in the Département de médecine at Université de Montréal.

Isabelle Reid, MSc, DtP, is a dietitian and research professional with many years of experience working with older adults with Alzheimer’s disease at the Centre de recherche, Institut universitaire de gériatrie de Montréal.

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