Nutrition Risk Is Associated with 3-Year Strength and Performance Indicators among Older Adults in the Canadian Longitudinal Study on Aging

Abstract

Objectives

Determine if nutrition risk, as measured by SCREEN-8 is predictive of 3-year strength and performance indicators among community-living older adults after adjusting for key demographic and health variables. Sex-stratified analyses were also determined.

Design

Cohort study with baseline and 3-year follow-up data from the Canadian Longitudinal Study on Aging (CLSA).

Participants

Participants 55 years and older at baseline were included (n = 22,502); those who reported nasogastric or abdominal tube feeding at either timepoint were excluded (n = 26). The final sample of participants available for analyses slightly varied depending on completion of the three outcome variables. List-wise deletion was used for nutrition risk and covariates to arrive at the sample available for analysis (n = 17,250).

Measurements

The valid and reliable SCREEN-8 tool was used to measure nutrition risk. The minimum and maximum score of SCREEN-8 is 0 and 48, respectively, with lower scores indicating greater nutrition risk. Baseline SCREEN-8 score was used in analyses. Grip strength, chair rise test time and gait speed assessed at the 3-year follow-up were the strength and performance outcomes. Criteria outlined by the European Working Group on Sarcopenia in Older People 2 were used to determine low performance for grip strength (<27 kg for males and <16 kg for females), chair rise test time (>15 seconds) and gait speed (≤0.8 m/s).

Results

Half of participants were female (49.4%) and mean age was 66.7 years (SD 7.9). Mean SCREEN-8 score was 39.2 (SD 6.0). Low grip strength, chair rise test performance and gait speed were found in 18.5%, 19.6% and 29.3% of participants, respectively. After adjusting for covariates (e.g., sex, age, education), SCREEN-8 score was significantly associated with grip strength (F = 11.21, p = .001; OR = 0.98, CI [0.97, 0.99]), chair rise time (F = 5.97, p = .015; OR = 0.99, CI [0.97, 0.997]), and gait speed (F = 9.99, p = .002; OR = 0.98, CI [0.97, 0.99]). Similar interpretation was seen in sex-stratified analyses, although chair rise time was not significant. Age, body mass index, Life Space Index Score and self-rated health were consistently associated with all outcome measures.

Conclusion

Nutrition risk, as measured by SCREEN-8, significantly predicted 3-year strength and performance measures. Greater nutrition risk is associated with an increased odds of low performance on grip strength, chair rise test, and gait speed. Future research should implement nutrition risk screening in primary care settings with subsequent assessment and treatment for at risk clients to determine if nutrition interventions implemented post screening can delay age-related losses in strength and performance.

Introduction

There has been substantial research on hospital malnutrition, demonstrating high prevalence at admission, especially for older adults (1, 2). With this recognition has come the understanding that malnutrition in the community also needs to be addressed (3). However, research on the nutritional state of older adults living in the community is less common (4). In the community, malnutrition, typically measured with the Mini-Nutritional Assessment, occurs in 0–25% of older adults, depending on location, levels of dependency or selection criteria for the sample (5). Nutrition risk, although poorly defined (3), has been described as the risk for malnutrition that is determined with a valid nutrition risk screening tool (6). Nutrition risk can be assessed in older adults with a variety of tools and has a higher prevalence at 34–55% (7, 8). A systematic review of European studies identified a pooled prevalence across tools and countries of 33% for combined moderate and high malnutrition risk (4). (Mal) nutrition risk is associated with hospitalization and mortality (7) and health care costs (9) thus making determining best practices to detect risk and reduce subsequent consequences a goal to support older adult health.

Malnutrition has been shown to overlap with the occurrence of sarcopenia (10, 11, 12, 13, 14). Prevalence of sarcopenia and malnutrition vary depending on definitions used and populations studied, but it is estimated that 10–44% of community-living older adults have sarcopenia (15), and a cross sectional study found that co-occurring nutrition risk and sarcopenia occurs among 35% of community-living older adults (10). Risk factors for sarcopenia among older adults include age, body mass index, cognitive impairment, physical activity, specific health conditions, social determinants of health like education, income and living alone, which are similar to the risk factors for malnutrition (10, 16, 17, 18). Some studies have identified that biological sex and/or gender may also contribute to an increased risk of sarcopenia and malnutrition, however findings are inconsistent on if men or women have increased risk (17, 19, 20).

The European Working Group on Sarcopenia in Older People 2 have created an algorithm for identifying, and quantifying sarcopenia diagnoses in practice (21). They recommend assessing muscle strength through grip strength or chair rise tests, confirming sarcopenia through determining muscle quantity or quality and assessing severity through physical performance like gait speed, Short Physical Performance Battery, timed-up and go and 400-metre walk (21). With an overlap of risk factors of malnutrition and sarcopenia (10, 13, 16, 17, 18, 22) understanding if nutrition risk which occurs before malnutrition can predict strength and performance measures could help to target assessment and interventions.

Some cross-sectional research in the community setting has demonstrated an association between malnutrition or nutrition risk, or the proxy of weight loss, to be associated with frailty, performance, and strength measures (8, 23), but longitudinal studies are needed to demonstrate the temporal association between nutrition risk and these outcomes. SCREEN-8 is a nutrition risk tool that can be self- or interviewer administered and is based on questions posed to the older adult (55+ years of age), making it useful for large surveys that can be telephone administered (24). Use of SCREEN-8 in the Canadian Longitudinal Study of Aging (CLSA) provides the opportunity for further exploration of the predictive associations between nutrition risk and indicators of sarcopenia. The purpose of this study was to determine if nutrition risk, as measured by SCREEN-8, predicts 3-year follow-up grip strength, chair rise time, and gait speed, and to determine if these associations vary by sex.

Methods

CLSA Cohort

This analysis used baseline and follow-up 1 (year 3) data from the CLSA Comprehensive Cohort, a prospective, multisite study. Baseline data collection occurred from 2012–2015 and follow-up data collection from 2015–2018 (25, 26). Participants in this cohort live within a 50-kilometer radius of 11 major Canadian cities, and at baseline were at least 45 years old and could communicate in English or French. Individuals were not eligible if they were living in some remote regions, on First Nation Settlements or in the territories, if they were Canadian Armed Forces members or if at baseline they had cognitive impairment or were living in a long-term care home. The protocol, including ethics approval for the CLSA has previously been published (25, 26).

Present Study

The aim of this study was to determine if nutrition risk, as measured by SCREEN-8 is predictive of 3-year performance on grip strength, chair rise time and gait speed. Participants ages 55 years and older were included (n = 22,502). Participants who reported nasogastric or abdominal tube feeding at either baseline or follow-up were excluded (n = 26). This present study has been reviewed and received ethics clearance from a University of Waterloo Research Ethics Board (ORE#42598).

Measurements

SCREEN-8

Nutrition risk was measured using the SCREEN-8 tool (renamed from SCREEN-IIAB in 2019 https://www.olderadultnutritionscreening.com). This tool has demonstrated validity and reliability when used among community-living persons 55+ years (24). SCREEN-8 consists of eight questions regarding weight change, appetite, eating challenges (swallowing), meal preparation, and both fruit/vegetable and fluid intake. The resulting SCREEN-8 score can be interpreted by using either the numeric score, which will range from 0 to 48, where lower scores indicate greater nutrition risk, or the previously established cut-off of <38 to identify individuals at high nutrition risk (24). The numeric score was used in this analysis.

Covariates

Demographic, health and social determinants of health drawn upon from the Determinants of Malnutrition in Aged Persons model were adjusted for in analyses (16). All covariates were collected at baseline, primarily through interview-administered questionnaires. Sex (male, female), age, marital status (married/ living with a partner in a common-law relationship; single, never married or never lived with a partner; widowed; divorced or separated), education (less than post-secondary degree/ diploma, post-secondary degree/diploma), household income (<$50,000, $50,000–$99,999, $100,000–$149,999, ≥$150,000), self-rated health (excellent, very good, good, fair/poor), number of chronic conditions (0, 1, 2, 3, 4, 5, >5) and medications (polypharmacy dichotomized as ≤5, >5) were elicited. The Life Space Index (LSI) is a reliable and valid measure used to assess the frequency and extent of participants' mobility within and outside of their home (27). Higher LSI scores indicate increased life-space mobility. Body mass index (BMI) was calculated by measuring participant's height and weight and categorized using the GLIM malnutrition criteria and Centre for Disease Control and Prevention Categories. BMI categories were underweight (<20 kg/m² if <70 years, or <22 kg/m² if ≥70 years) (28), normal (≥20–24.9 kg/m² if <70 years, or ≥22–24.9 kg/m² if ≥70 years) (28, 29) overweight (≥25.0–29.9 kg/m²) (29), and obese (≥30.0 kg/m²) (29).

Strength and Performance Measures

The three outcome measures were grip strength, chair rise time and gait speed at the 3-year follow-up (26). A dynamometer (Tracker Freedom Wireless Grip) assessed grip strength using the participants' dominant hand, unless a contradiction (e.g., cast on arm/hand) was reported. The average of three trials was used in analysis. For the chair rise test, a chair without arm rests was used. Participants began by sitting in the chair and were asked to stand up and return to their sitting position while keeping their hands crossed over their chest. This task was completed 5 times and if a participant could not complete the chair rise for 5 repetitions, they were excluded from this analysis. The 4-metre walk test was used to assess gait speed and participants could complete the task with an assistive device. Participants who could not stand or walk without assistance from another person did not perform this task. The time to complete the 4-metre walk was determined when the participant completely crossed the 4-metre walk line, while walking at their typical pace.

The sarcopenia cut-offs developed by the European Working Group on Sarcopenia in Older People 2 were used to dichotomize grip strength, chair rise time and gait speed. Low grip strength for males and females were determined by <27 kg and <16 kg, respectively. Chair rise test time of >15 s for all five rises together was determined to be low performance. Gait speed was determined from the 4-metre walk task and a gait speed of ≤0.8 m/s was considered to be low performance (21).

Statistical Analysis

List-wise deletion was applied based on completion of SCREEN-8, covariates and outcomes. Descriptive statistics (frequency, percentages) were computed for predictors and each outcome variable, using the minimum sample size of participants with complete data for SCREEN-8 and included covariates (n = 17,250), as final models for the outcomes varied in sample size. Multivariable binary logistic regression was conducted to determine if SCREEN-8 predicted adequate or low performance on each of the three outcome measures while adjusting for covariates. In regression analyses, analytic and geographic weights from the CLSA were used to account for complex survey sampling using the SURVEYLOGISTIC procedure in SAS® Release: 3.81. Sex-stratified binary logistic regressions were also completed as prior research suggests potential differences. A p <.050 determined statistical significance.

Results

Half of participants were female (49.4%), and mean age was 66.7 years (SD 7.9). Most participants reported to be married/ living with a partner in a common-law relationship (69.1%), and/or have a post-secondary degree/diploma (76.7%). Just less than half of participants had a BMI in the overweight category (41.7%). Mean SCREEN-8 score was 39.2 (SD 6.0). Low performance on grip strength, the chair rise test and gait speed were found in 18.5%, 19.6%, and 29.3% of participants, respectively. Descriptive statistics for all participants, including sex-stratified reporting are in Table 1.

Table 1.

Descriptive Statistics of Predictor and Outcome Variables

	All		Females		Males
Baseline Predictors
	%	n	%	n	%	n
Sex
Female	49.4	8529	100.0	8529	-	-
Male	50.6	8721	-	-	100.0	8721
Marital Status
Married/living with a partner in a common-law relationship	69.1	11927	57.8	4929	80.2	6998
Single, never married or never lived with a partner	7.4	1278	8.5	729	6.3	549
Widowed	10.6	1827	15.9	1357	5.4	470
Divorced or separated	12.9	2218	17.8	1514	8.1	704
Education
Post-secondary degree/diploma	76.7	13238	74.2	6327	79.2	6911
Less than secondary school graduation	5.6	961	6.3	541	4.8	420
Secondary school graduation	9.7	1672	11.2	951	8.3	721
Some-post secondary education	8.0	1379	8.3	710	7.7	669
Household Income
<$50,000	30.5	5265	38.5	3280	22.8	1985
$50,000–$99,999	38.1	6573	36.4	3107	39.7	3466
$100,000–$149,999	17.9	3082	14.7	1250	21.0	1832
≥$150,000	13.5	2330	10.5	892	16.5	1438
BMI Category
Normal	24.5	4219	27.1	2315	21.8	1904
Low	4.4	765	6.6	565	2.3	200
Overweight	41.7	7186	36.2	3083	47.1	4103
Obese	29.4	5080	30.1	2566	28.8	2514
Self-Rated General Health
Excellent	21.3	3670	21.3	1817	21.3	1853
Very good	42.2	7276	43.4	3701	41.0	3575
Good	28.7	4960	27.6	2357	29.9	2603
Fair/Poor	7.8	1344	7.7	654	7.9	690
Chronic Conditions
0	11.9	2044	9.5	809	14.2	1235
1	19.9	3430	16.7	1421	23.0	2009
2	21.0	3627	20.4	1743	21.6	1884
3	16.7	2887	17.4	1483	16.1	1404
4	11.6	2003	13.0	1111	10.2	892
5	7.9	1355	8.8	753	6.9	602
≥6	11.0	1904	14.2	1209	8.0	695
Polypharmacy
≤5	62.6	10797	58.7	5008	66.4	5789
>5	37.4	6453	41.3	3521	33.6	2932
	Mean	SD	Mean	SD	Mean	SD
Age	66.7	7.9	66.4	7.9	66.9	7.9
Life Space Index	84.5	17.5	82.0	18.0	87.0	16.7
SCREEN-8	39.2	6.0	38.9	6.3	39.4	5.8
3-Year Follow-Up Outcomes
	%	n	%	n	%	n
Grip strength (n = 14,961)
Adequate	81.5	12191	81.5	5959	81.5	6232
Poor	18.5	2770	18.5	1352	18.5	1418
Chair Rise Time (n = 14,451)
Adequate	80.4	11624	80.0	5647	80.8	5977
Poor	19.6	2827	20.0	1410	19.2	1417
Gait speed (n = 15,223)
Adequate	70.7	10757	67.8	5060	73.4	5697
Poor	29.3	4466	32.2	2406	26.6	2060

n = 17,250 for the entire sample unless otherwise stated

Model effects for the binary logistic regression models testing the association between nutrition risk and covariates with the three outcome measures are reported in Table 2. The odds ratios for grip strength, chair rise test performance and gait speed models are in Tables 3, 4, and 5, respectively. For the total sample, SCREEN-8 was significantly associated with grip strength, chair rise test time and gait speed. For every one-point increase in SCREEN-8 score (indicating less nutrition risk), the odds of low performance on each outcome measure decreased. Sex-stratified analyses found similar associations between SCREEN-8 and grip strength and gait speed, but no sex-specific association was found with the chair rise stand. Increasing age was significantly associated with higher odds of low performance on all outcome measures, including across sex-stratified models.

Table 2.

Binary Logistic Regression Model Effects

	Grip Strength						Chair Rise Time						Gait Speed
	Alla		Femalesb		Malesc		Alld		Femalese		Malesf		Allg		Femalesh		Malesi
Effect	F value	p	F value	p	F value	p	F value	p	F value	p	F value	p	F value	p	F value	p	F value	p
Age	356.21	<.001	257.80	<.001	120.28	<.001	128.76	<.001	90.51	<.001	37.64	<.001	390.61	<.001	237.83	<.001	152.54	<.001
Sex	3.22	.073					0.01	.935					12.02	.001
Marital Status	1.78	.148	1.37	.250	4.36	.005	0.55	.647	0.41	.749	0.52	.671	0.98	.399	0.61	.610	0.90	.439
Education	0.21	.650	0.06	.803	0.04	.840	1.44	.230	0.00	.971	3.03	.082	14.09	<.001	6.18	.013	7.39	.007
Household income	7.07	<.001	1.22	.302	7.49	<.001	3.21	.022	2.04	.106	1.17	.321	1.68	.169	1.15	.327	0.77	.509
Body Mass Index	5.61	.001	2.61	.050	3.19	.023	6.80	<.001	2.11	.097	6.19	<.001	40.65	<.001	29.86	<.001	12.16	<.001
Self-Rated Health	8.69	<.001	8.82	<.001	2.08	.101	9.07	<.001	6.44	<.001	3.99	.008	20.70	<.001	13.81	<.001	9.18	<.001
Life Space Index Score	11.70	.001	10.05	.002	2.23	.135	5.21	.023	1.11	.293	3.87	.049	96.57	<.001	86.77	<.001	19.83	<.001
Number of chronic conditions	5.50	<.001	3.39	.003	2.96	.007	2.83	.009	6.07	<.001	0.50	.808	0.88	.507	1.19	.307	1.27	.267
Poly-pharmacy	5.18	.023	0.07	.793	9.74	.002	2.17	.141	0.01	.931	6.23	.013	11.52	.001	1.03	.311	16.07	<.001
SCREEN-8 score	11.21	.001	7.60	.006	4.60	.032	5.97	.015	3.02	.082	3.36	.067	9.99	.002	6.93	.009	4.19	.041

a. n = 14,961 max-rescaled R² = 0.20; b. n = 7,311, max-rescaled. R² = 0.24; c. n = 7,650, max-rescaled. R² = 0.17; d n = 14,451, max-rescaled. R² = 0.09; e. n = 7,057, max-rescaled. R² = 0.13; f. n = 7,394, max-rescaled. R² = 0.07; g. n = 15,223, max-rescaled. R² = 0.23; h. n = 7,466, max-rescaled. R² = 0.27; i. n= 7,757, max-rescaled. R² = 0.18; bolded values indicate statistical significance, p < .050

Table 3.

Odds Ratios for the Binary Logistic Regression Testing if Nutrition Risk is Associated with Low Grip Strength after Adjusting for Covariates

	Alla			Femalesb			Malesc
Effect	Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits
Age (numeric)	1.10	1.08	1.11	1.11	1.10	1.13	1.08	1.06	1.09
Sex
Male vs. female	1.14	0.99	1.31
Marital status
Single, never married or never lived with a partner	1.34d	1.04	1.74	1.14	0.83	1.56	1.57	1.05	2.33
Widowed	1.12	0.91	1.38	0.96	0.74	1.25	1.57	1.11	2.23
Divorced/separated	1.10	0.91	1.35	1.24	0.97	1.59	0.86	0.62	1.22
Married/Living with a partner in a common-law relationship	Reference
Education
Less than post-secondary degree/diploma	1.03	0.90	1.19	1.02	0.86	1.22	1.02	0.82	1.27
Post-secondary degree/diploma	Reference
Income
<$50,000	1.13	0.96	1.33	1.02	0.82	1.26	1.27	1.00	1.61
$100,000-$149,999	1.00	0.82	1.23	0.98	0.74	1.31	1.01	0.77	1.34
≥$150,000	0.66	0.53	0.81	0.73	0.51	1.03	0.60	0.46	0.79
$50,000–$99,999	Reference
Body Mass Index
Underweight	1.25	0.94	1.66	1.29	0.91	1.84	1.07	0.65	1.77
Overweight	0.77	0.65	0.92	0.83	0.66	1.05	0.69	0.53	0.90
Obese	0.83	0.69	1.01	0.86	0.68	1.10	0.77	0.57	1.04
Normal	Reference
Self- Rated General Health
Very good	1.23	1.02	1.49	1.47	1.15	1.88	1.02	0.77	1.34
Good	1.54	1.26	1.89	1.91	1.45	2.52	1.23	0.92	1.64
Fair/Poor	1.87	1.41	2.48	2.28	1.60	3.26	1.50	0.98	2.30
Excellent	Reference
Life space Index Score	0.99	0.99	0.999	0.99	0.99	0.999	1.00	0.99	1.00
Number of Chronic conditions
1	1.21	0.94	1.57	1.07	0.71	1.62	1.33	0.96	1.84
2	1.18	0.93	1.49	1.25	0.85	1.83	1.12	0.83	1.53
3	1.44	1.12	1.84	1.61	1.09	2.38	1.29	0.93	1.79
4	1.58	1.19	2.09	1.71	1.13	2.58	1.44	0.94	2.18
5	1.78	1.32	2.40	1.69	1.10	2.59	1.93	1.26	2.97
>6	2.07	1.54	2.79	2.05	1.35	3.11	2.16	1.37	3.42
0	Reference
Polypharmacy
Yes vs. no	1.18	1.02	1.35	1.03	0.85	1.24	1.38	1.13	1.69
SCREEN-8 score	0.98	0.97	0.99	0.98	0.97	0.99	0.98	0.96	0.998

a. n = 14,961 max-rescaled. R² = 0.20; b. n = 7,311, max-rescaled. R² = 0.24; c. n = 7,650, max-rescaled. R² = 0.17; d estimate is significant however the model effect was not significant; bolded values indicate statistical significance, p < .050. Low grip strength is defined as <27 kg for males, and <16 kg for females.

Table 4.

Odds Ratios for the Binary Logistic Regression Testing if Nutrition Risk is Associated with Low Chair Rise Test Performance after Adjusting for Covariates

	Alla			Femalesb			Malesc
Effect	Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits
Age (numeric)	1.05	1.04	1.06	1.07	1.05	1.08	1.04	1.03	1.05
Sex
Male vs. female	0.99	0.87	1.14
Marital status
Single, never married or never lived with a partner	1.17	0.91	1.50	1.07	0.75	1.54	1.24	0.85	1.79
Widowed	1.02	0.83	1.24	0.97	0.74	1.25	0.99	0.71	1.40
Divorced/separated	1.07	0.87	1.32	1.12	0.86	1.46	0.94	0.66	1.35
Married/Living with a partner in a common-law relationship	Reference
Education
Less than post-secondary degree/diploma	1.09	0.95	1.24	1.00	0.84	1.21	1.20	0.98	1.46
Post-secondary degree/diploma	Reference
Income
<$50,000	1.19	1.01	1.40	1.24	0.99	1.57	1.10	0.87	1.39
$100,000-$149,999	1.03	0.86	1.24	1.04	0.81	1.35	1.01	0.79	1.30
≥$150,000	0.83	0.68	1.01	0.81	0.60	1.10	0.83	0.64	1.07
$50,000-$99,999	Reference
Body Mass Index d
Underweight	0.86	0.63	1.17	0.79	0.54	1.17	1.05	0.63	1.75
Overweight	0.90	0.76	1.06	1.01	0.81	1.27	0.80	0.62	1.03
Obese	1.24	1.03	1.48	1.22	0.95	1.55	1.24	0.94	1.63
Normal	Reference
Self- Rated General Health
Very good	1.21	1.01	1.44	1.43	1.13	1.82	0.99	0.76	1.27
Good	1.52	1.25	1.84	1.71	1.30	2.26	1.30	0.99	1.69
Fair/Poor	1.85	1.41	2.44	2.14	1.45	3.14	1.63	1.10	2.40
Excellent	Reference
Life space Index Score	1.00	0.99	0.999	1.00	0.99	1.00	0.99	0.99	1.00
Number of Chronic conditions
1	1.01	0.80	1.27	1.07	0.74	1.55	0.96	0.72	1.29
2	0.95	0.76	1.19	0.89	0.63	1.27	1.00	0.76	1.33
3	0.90	0.71	1.14	0.87	0.60	1.27	0.94	0.70	1.27
4	0.97	0.75	1.27	0.81	0.56	1.19	1.24	0.84	1.81
5	1.14	0.86	1.50	1.26	0.83	1.90	0.98	0.66	1.45
≥6	1.45	1.09	1.94	1.87	1.25	2.80	0.94	0.60	1.49
0	Reference
Polypharmacy
Yes vs. no	1.11	0.97	1.28	0.99	0.81	1.21	1.28	1.06	1.56
SCREEN-8 score	0.99	0.97	0.997	0.99	0.97	1.00	0.99	0.97	1.00

a. n = 14,451, max-rescaled. R² = 0.09; b. n = 7,057, max-rescaled. R² = 0.13; c. n = 7,394, max-rescaled. R² = 0.07; d. The model effect of BMI in the model restricted to males was significant, however a significant difference was not apparent for the chosen referent group; bolded values indicate statistical significance, p < .050. Low chair rise test performance is defined as a test time >15s.

Table 5.

Odds Ratios for the Binary Logistic Regression Testing if Nutrition Risk is Associated with Low Gait Speed after Adjusting for Covariates

	Alla			Femalesb			Malesc
Effect	Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits		Odds Ratio	95% Confidence Limits
Age (numeric)	1.09	1.08	1.10	1.09	1.08	1.11	1.08	1.07	1.10
Sex
Male vs. female	0.81	0.72	0.91
Marital status
Single, never married or never lived with a partner	1.16	0.93	1.45	1.12	0.83	1.51	1.25	0.90	1.75
Widowed	1.09	0.91	1.30	1.15	0.92	1.44	0.91	0.68	1.22
Divorced/separated	0.98	0.83	1.16	1.02	0.82	1.26	0.93	0.70	1.25
Married/Living with a partner in a common-law relationship	Reference
Education
Less than post-secondary degree/diploma	1.27	1.12	1.43	1.22	1.04	1.42	1.31	1.08	1.59
Post-secondary degree/diploma	Reference
Income
<$50,000	1.14	1.00	1.31	1.15	0.96	1.38	1.13	0.92	1.39
$100,000-$149,999	1.15	0.96	1.38	1.11	0.87	1.42	1.17	0.91	1.50
≥$150,000	1.10	0.92	1.32	1.20	0.92	1.56	1.02	0.79	1.32
$50,000-$99,999	Reference
Body Mass Index
Underweight	0.88	0.67	1.16	0.99	0.72	1.37	0.64	0.39	1.05
Overweight	1.25	1.08	1.46	1.25	1.03	1.53	1.21	0.96	1.52
Obese	2.18	1.86	2.55	2.40	1.95	2.96	1.85	1.44	2.38
Normal	Reference
Self- Rated General Health
Very good	1.19	1.01	1.41	1.48	1.18	1.86	0.94	0.74	1.20
Good	1.63	1.36	1.95	2.04	1.58	2.61	1.28	0.99	1.65
Fair/Poor	2.32	1.82	2.97	2.70	1.89	3.85	1.97	1.41	2.77
Excellent	Reference
Life space Index Score	0.98	0.99	0.98	0.99	0.98	0.99	0.98	0.99	0.98
Number of Chronic conditions
1	1.02	0.82	1.26	0.80	0.58	1.10	1.25	0.94	1.66
2	1.03	0.84	1.27	0.83	0.62	1.12	1.23	0.93	1.63
3	0.97	0.79	1.20	0.74	0.54	1.01	1.24	0.93	1.63
4	1.08	0.85	1.36	0.74	0.54	1.01	1.55d	1.09	2.19
5	1.03	0.81	1.33	0.83	0.58	1.17	1.19	0.82	1.72
≥6	1.24	0.96	1.60	0.94	0.66	1.34	1.52d	1.03	2.23
0	Reference
Polypharmacy
Yes vs. no	1.24	1.09	1.40	1.09	0.92	1.29	1.45	1.21	1.73
SCREEN-8 score	0.98	0.97	0.99	0.98	0.97	0.996	0.98	0.97	0.999

a. n = 15,223, max-rescaled. R² = 0.23; b. n = 7,466, max-rescaled. R² = 0.27; c. n= 7,757, max-rescaled. R² = 0.18; d. estimate is significant however the model effect was not significant; bolded values indicate statistical significance, p < .050. Low gait speed is defined as <0.8 m/s.

Although not consistent across outcomes or in sex-stratified models, other covariates associated with poorer performance included: lower education, lower income, poor self-rated health, decreased LSI, and polypharmacy. Sex was only associated with gait speed, with males having lower odds of low performance on this task; notably 32.2% of females and 26.6% of males had a poor performance on gait speed. Those with a lower education, had increased odds of a low gait speed, an association also found in sex-stratified models. Relative to participants with a BMI in the normal range, when looking at the total sample, overweight participants had lower odds of a low grip strength, and higher odds of low performance on gait speed, whereas obese participants had higher odds of low chair rise test and gait speed performance. Sex-stratified analyses found effects only for males and grip strength, but both groups for gait speed. Life Space Index was negatively associated with outcomes, including some sex stratified models, indicating that with a higher LSI score, participants had better performance on outcomes. An increasing number of chronic conditions was significantly associated with higher odds of low performance on grip strength and the chair rise test, but not gait speed; sex-stratified models showed similar associations for grip strength, but only females with more conditions had poor performance on the chair rise test. Participants currently taking 5 or more medications had higher odds of low grip strength and gait speed; this association was predominately a result of the males in the sample.

When further comparing final stratified models for males and females, a few notable differences were found. Only males who were single or widowed had increased odds of a low grip strength (Table 3). Across all categories of self-rated health, any females in the categories from ‘poor' to ‘very good' in comparison to the reference of ‘excellent', had increased odds of low grip strength, chair rise and gait speed performance, but only ‘fair/poor' self-rated health was associated with poor male performance on chair rise and gait speed. Income was not associated with outcomes in sex stratified analyses for low gait speed and chair rise stand. However, males with the highest income (≥$150,000) were less likely to have a low grip strength than those with incomes between $50,000–$99,999, and males in the lowest income bracket (<$50,000) had increased odds for this low performance.

Discussion

This study sought to determine if nutrition risk, as measured by SCREEN-8 was associated with 3-year function and performance measures among community-living older adults, including when models were sex-stratified. Results demonstrate that increased nutrition risk (i.e., lower SCREEN-8 scores) is associated with poorer grip strength, chair rise test performance and gait speed of community-living older adults ages 55+. However, sex-specific associations for nutrition risk on chair rise test time were not apparent. Further, this study demonstrates the validity of the SCREEN-8 tool for predicting 3-year strength and performance indicators among community-living older persons. As prior research has noted the association between nutritional status and functional outcomes (30) this association with SCREEN-8 corroborates the value of using this tool to determine nutrition risk.

Increasing age was strongly correlated with increased odds of low performance on all measures. Age is one of the strongest predictors of low functional performance and sarcopenia, likely due to declining skeletal muscle and strength (10, 17, 20). Education was only significantly associated with gait speed; however, household income was correlated with grip strength and chair rise test time. This demonstrates the intersectionality of the social determinants of health. Less years of formal education could reduce structured opportunities to learn and integrate healthful behaviours, and a lower household income could influence food purchasing behaviours (31, 32). Being underweight was not associated with performance on any of the three measures in our study, potentially due to its low prevalence (<5%), however being underweight has been previously found to be a risk factor for malnutrition and sarcopenia (32, 33). For chair rise test performance and gait speed, we found that a BMI considered to be overweight or obese was associated with poorer performance, while overweight persons, and specifically males, were more likely to have adequate grip strength. This finding is similar to a case control study which found that sarcopenic individuals were more likely to have a higher BMI and weight, however this same study also found that sarcopenic individuals had lower grip strength (34), which is dissimilar to findings from our current study around BMI and grip strength. Sarcopenic obesity, which is the co-occurrence of sarcopenia and obesity is estimated to occur among 10% of older adults globally and has been associated with accelerated functional decline (35). Therefore, it is critical that screening practices do not rely on an anthropometric trigger such as BMI, but rather all older adults, especially those who have experienced unintentional or rapid weight loss undergo screening and assessment for nutrition risk and sarcopenia (36).

Specific chronic conditions like dementia, cancer, and heart disease in addition to multimorbidity and polypharmacy have been associated with sarcopenia and nutrition risk (16, 18, 37, 38, 39). In this study, increasing multimorbidity was associated with low grip strength and chair rise test performance, and polypharmacy, specifically in males, was associated with outcomes. With increasing health complexity, these individuals may be more likely to have more frequent interactions with a primary care provider (40), which could serve as an opportunity to perform routine nutrition risk screening and if necessary, a referral to a dietitian or other healthcare provider. Adoption of an evidence-based screening and referral pathway like the Primary Care Nutrition pathway for Adults Aged 65+ (41) can help support early detection and intervention for nutrition challenges and reduce the development of malnutrition and its associated consequences, like a decrease in functional performance and muscle strength.

Greater mobility within and outside of the home was correlated with adequate performance on all measures. Previous studies have largely investigated functional performance and exercise (42), however we covaried for a life-space mobility measure due to its ability to quantify a continuum of mobility (43). Poor life-space mobility, which captures mobility in and outside the home has previously been correlated with nutrition risk measured using SCREEN-8, gait speed and grip strength (44). With respect to nutritional outcomes, poor mobility may hinder one's ability to go grocery shopping and prepare meals, which may lead to inadequate nutrition, and/or limited access to community care and programs. These challenges could contribute to subsequent muscle or weight loss and sarcopenia. Higher self-perceptions of general health were associated with adequate performance on all functional measures in our study, especially in females. A previous study using data from the CLSA found that higher self-perceptions of health were associated with greater life-space mobility and functional resilience, and inversely associated with multimorbidity (45).

Nutrition risk is associated with functional parameters used to assess for sarcopenia and determine its severity (21). While nutrition risk screening should not replace sarcopenia screening and assessment, it could be used alongside such screening to determine appropriate, individualized interventions and care planning. Interventions for sarcopenia are often based on exercise and sometimes combined with nutrition (46), but for individuals already at nutrition risk, they may more strongly benefit from nutritional support, or specific nutritional support paired with exercise. Routine nutrition risk screening for older adults in primary care is not only needed, but is also a recommended practice (47). Comparison of malnutrition screening tools used for older adult populations has been published (48). However, SCREEN-8 is an ideal screening tool because it was specifically designed for community-living older adults, captures upstream determinants of low food intake that could lead to malnutrition, and has demonstrated ability to predict mortality, hospitalization (7) and now functional performance. Future investigations should focus on how to successfully implement routine screening, assessment, and referral in clinical practice.

Strengths and Limitations

The CLSA is one of the largest prospective cohort studies that completes comprehensive data collection every three years for a minimum of 20 years, which allowed for modelling of numerous covariates of interest in this study. SCREEN-8 is a valid and reliable tool to measure nutrition risk among community-living persons 55 years and older (24), and previously established cut-offs for low functional performance that is widely accepted were used (21). This study included sex-specific analyses, which is aligned with recent calls to action to investigate sex-specific associations when researching health outcomes to advance equitable research and practice. Limitations of this work must also be acknowledged. As there was no measure of malnutrition in the CLSA, some participants scoring low with SCREEN-8 at baseline could have in fact been malnourished. All variables except for body mass index, and the three outcome measures were self-reported and may be subject to social desirability or recall bias. The CLSA cohort is predominantly white and from a higher socioeconomic status, and participants must be able to communicate in English or French and live near an urban academic centre, which excludes certain Canadians from participating (26). Although statistical weighting was used to adjust for complex survey sampling, results may not be generalizable to other populations. It is encouraged to consider these associations in future studies employing efforts to diversify recruitment and participation.

Conclusion

Findings from this study indicate that nutrition risk, as measured by SCREEN-8 can effectively predict strength and performance measures commonly used to assess for sarcopenia in older adults. As nutrition risk increases among community-living older adults, the odds of low grip strength, chair rise test performance and gait speed increase. Routine screening for nutrition risk, and conditions that impact function like frailty should be a priority in primary care to support older adult health through early detection and intervention. SCREEN-8 has demonstrated good reliability and predictive validity for hospitalization and mortality (7). Given the advancement in its predictive capacity of functional and healthcare outcomes (49), routinely using SCREEN-8 to identify nutrition risk in community-living adults presents an opportunity to support the health and wellbeing of older adults.

Funding Statement:

This secondary analysis was supported by the Canadian Consortium on Neurodegeneration in Aging (CCNA, Team 5). CCNA is supported by a grant from the Canadian Institutes of Health Research with funding from several partners.

Data Availability Statement:

Data are available from the Canadian Longitudinal Study on Aging (https://www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data.

Conflict of Interest:

HK is the originator of the SCREEN-8 instrument used in the CLSA to determine nutrition risk.

Ethical standards:

This present study has been reviewed and received ethics clearance from a University of Waterloo Research Ethics Board (ORE#42598).

Disclaimer:

The opinions expressed in this manuscript are the author's own and do not reflect the views of the Canadian Longitudinal Study on Aging.

Author Contributions:

HK conceptualized the study. VT performed data analyses. The first draft of the manuscript was written by VT and HK. VT and HK read and approved the final manuscript.