Abstract
Background
Although reported dietary supplement use is common in older adults, evaluations of dietary supplement use over the past 10 y are lacking.
Objective
This analysis determined changes in reported dietary supplement use in cognitively normal older adults (aged ≥ 55 y) using the National Alzheimer’s Coordinating Center data from 2015 to 2019 using a serial cross-sectional study design.
Methods
The first available visit for cognitively normal participants aged ≥ 55 y from 2015 to 2019 with a complete medication form was used, resulting in 9357 participants. Associations between visit year categories and reported use of dietary supplement categories/individual supplements were tested using categorical statistics. To determine whether the probabilities of reported supplement use changed in 2019 compared with those of 2015, z-scores and two-sided P values were used. Weighted analyses were used to confirm analytical findings.
Results
When comparing 2015 and 2019, the reported use of any dietary supplement decreased from 77.7% in 2015 to 71.0% in 2019 (P < 0.0001); any vitamin from 72.5% to 65.5% (P < 0.0001); any mineral from 39.2% to 30.4% (P < 0.0001); “other” nonvitamin/nonmineral supplements from 34.4% to 26.9% (P < 0.0001), calcium from 31.2% to 21.7% (P < 0.0001), multivitamins from 48.4% to 38.4% (P < 0.0001), potassium from 5.6% to 3.5% (P = 0.001), vitamin C from 13.0% to 9.2% (P = 0.0002), chondroitin from 6.0% to 4.1% (P = 0.006), glucosamine from 11.1% to 6.5% (P < 0.0001), and all omega fatty acids from 25.2% to 17.0% (P < 0.0001). Reported use increased for vitamin B7/biotin from 3.1% in 2015 to 5.8% in 2019 (P = 0.0003), melatonin from 3.1% to 5.8% (P = 0.0002), and turmeric from 1.2% to 4.7% (P < 0.0001).
Conclusion
Although the reported use of many major dietary supplement categories and individual supplements significantly decreased in older adults from 2015 to 2019, biotin, turmeric, and melatonin significantly increased. Because biotin may interfere with some laboratory tests, this may have important public health implications.
Keywords: dietary supplement, older adults, NACC, vitamins, mineral, herbal, supplement, dietary supplements
Introduction
The use of dietary supplements is intended to supplement the diet with vitamins, minerals, herbal/botanical products, or amino acids [[1], [2], [3]]. Dietary supplement use is common as 52% of adults aged ≥18 y reported their use during 2011–2012 [4]. Although United States adults most often reported taking dietary supplements to “improve” or “maintain” overall health [5, 6], interactions with co-administered medications may pose a health risk. Interactions may be pharmacokinetic, impacting the absorption, distribution, metabolism, or excretion of a co-administered drug. Alternatively, interactions may be pharmacodynamic, impacting how a co-administered drug exerts its mechanism of action within the body [7, 8]. In a systematic review that classified and quantified the types of interactions between dietary supplements and drugs from the previously performed studies and reviews, 882 interactions were identified [9]. Pharmacokinetic interactions accounted for 42.3% of all interactions, pharmacodynamic interactions for 40.1%, both mechanisms for 8.5%, and no identified mechanism for 9.1% [9].
Although the reported dietary supplement use in adults was stable during 1999–2000 and 2011–2012 [4], a closer examination of the data revealed an age-associated increase. Reported dietary supplement use increased with age from 1999–2000 (20–39: 43%; 40–64: 57%; ≥ 65: 63%) and during 2011–2012 (20–39: 40%; 40–64: 54%; ≥ 65: 72%) [4]. Although reported supplement use decreased in those aged 20–39 y and 40–64 y from 1999–2000 to 2011–2012, it significantly increased in those aged ≥65 y. This result demonstrated that older adults showed a very different trend from younger age groups in dietary supplement use across these periods. A related study using the same time frames and database found that the reported use of any medication or polypharmacy increased with age [10]. Although the change in reported medication use from 1999–2000 to 2011–2012 was similar between age groups, the change in reported polypharmacy using this same time frame was greatest for those aged ≥65 y (% increase in polypharmacy: 20–39: 2.4%; 40–64: 4.4%; ≥ 65: 16%) [10]. Together, these results demonstrate that reported use of dietary supplements and polypharmacy increased in those aged ≥ 65 y from 1999–2000 to 2011–2012. As the number of medications and dietary supplements increased, risk of potential interactions also increased. In a cross-sectional survey of older adults aged ≥ 65 y who reported the use of more than one medication, 33.6% (50/149) reported the use of a dietary supplement. Within dietary supplement users, 32.6% (16/50) were at risk of a potential adverse drug interaction [11], suggesting that risk of a potential interaction may be higher than appreciated.
Although a number of studies ascertained dietary supplement use in older adults, most reflect use from approximately 10 y ago. Whether reported dietary supplement use has changed in older adults, or how it has changed remains unclear. To obtain more recent insight and to add to what is known, the National Alzheimer’s Coordinating Center (NACC) database was used to assess the reported dietary supplement use from 2015 to 2019 in cognitively normal older adults aged ≥ 55 y using the first available visit with a complete medication form.
Methods
Inclusion/exclusion criteria
Cognitively normal participants aged ≥ 55 y from January 1, 2015 to December 31, 2019 with a complete/available medication form were included. Data from these 5 y were used as it was the most recent period not impacted by the COVID-19 pandemic. The first available visit for participants meeting the inclusion criteria was used for the analysis to maintain statistical independence. Any participant not meeting the inclusion criteria was excluded.
Data source
Data were obtained from the NACC Uniform Data Sets versions 1.0-3.0, with versions 2.0 and 3.0 used for analysis. The NACC began collecting prospective longitudinal data in September 2005 using standardized clinical evaluation forms from the Alzheimer’s Disease Research Centers in the United States. Participants range from being cognitively normal to having dementia. Cognitive status is determined at each visit by clinical diagnosis. Participants undergo a complete examination approximately annually. Data available include de-identified demographic data, neuropsychological testing scores, medical history, family history, medication use, and clinical diagnoses. Medication/supplement use is reported by a co-participant filling out the medications form for a participant at each visit based on the use of any supplement/medication within the past 14 d. Additional details about the dataset can be found elsewhere [[12], [13], [14]]. The study procedures and protocols were performed in accordance with the Declaration of Helsinki and were approved by the Southern Illinois University Edwardsville’s Institutional Review Board.
Supplement use definitions and tabulation
Supplement use was defined as the reported use of any vitamin/mineral or nonvitamin/nonmineral supplements (NVNM). Use of the term supplement in the absence of any further description indicates the use of any dietary supplement (i.e., ≥ 4 supplements). Topical or ophthalmologic use of any supplement was not included in the reported use. If the individual brand names of vitamins or supplements were used (e.g., Triple-Flex, Eyevites, Eye Vitamin and mineral, Prevagen, etc.), they were placed into broader categories to account for their use (cognitive enhancement supplements, other joint supplements, other eye vitamins).
For all individual dietary supplements, the total frequency of reported use for all drugs and dietary supplements for those aged ≥ 55 y from 2015 to 2019 was first tabulated. If the total frequency was ≥ 10 for an individual supplement, reported use of this individual supplement along with other names used for this very same dietary supplement, and dietary supplements specifically listing this supplement were used to tabulate the reported use. If a particular supplement listed more than one supplement in its name (e.g., calcium with vitamins D and K), the reported use of each supplement was tabulated (i.e., calcium, vitamin K, or vitamin D). As the NACC database occasionally listed multiple drugs under one drug category, this was accounted for in the analysis when looking for specific supplements (e.g., ubiquinone-vitamin E), along with misspellings. The inclusion of individual supplements or supplement categories into the tables was done only if there were over 100 reported users from 2015 to 2019 to increase the stability of the estimates.
Specific definitions for the reported use of vitamin, mineral, and nonvitamin/nonmineral supplements are found in the Supplemental Materials.
Statistics
All analyses used an α value of 0.05 to determine the statistical significance. Results were analyzed using Rstudio and SAS v9.4. To determine if the total proportions within a demographic category were similar, the chi-square test for equal proportions was used.
To determine if there was an association between a demographic variable and total reported supplement use across the study period, chi-square statistics were generally used. If both variables were ordinal, non-zero Cochran-Mantel-Haenszel statistics were used to test the associations. For any table with greater than two categories containing ≤ 5 individuals in a given category, a Monte-Carlo estimate of the exact P value was used. If there were zero counts in a category, categorical statistical tests were not conducted.
To determine if there was an association between supplement use and visit year within a particular demographic or between supplement use and the visit year, chi-square statistics were used.
To determine if there was a significant change from 2019 to 2015 for demographics, reported supplement use, or supplement use within the demographics, z-scores were calculated using the below equation. Probability 1 was the probability in 2019, and probability 2 was the probability in 2015. Two-sided P values were calculated using the standard normal distribution in R.
Weighting
The median of the total number of participants within each year from 2015 to 2019 was used for weighting (e.g., for those aged ≥ 55 y, the median sample size was 1152). Weights for a particular year were constructed by dividing the median sample size by the number of participants within that year (weights for those aged ≥ 55 y in 2015: 1152/4663=0.247). The weighted number of participants in each category was rounded to the next whole number.
Results
Within the dataset, there were 43,746 participants (Figure 1), with 19,932 participants aged ≥ 55 y from 2015 to 2019. Of these participants, 19,696 (98.8%) had at least one complete medication form. After taking the first available visit with a complete medication form, this resulted in 19,696 participants, with 9357 (47.51%) cognitively normal participants, resulting in a total sample size of 9357. A sub-analysis of these participants was performed using those aged ≥ 65 y, resulting in a total sample size of 7857 for the sub-analysis.
FIGURE 1.
The process for selecting participants for reported dietary supplement analysis using the National Alzheimer’s Coordinating Center database.
Demographic totals
When comparing the total proportions of participants by sex, age, race, or education demographic categories, the proportions were not similar (P < 0.0001 for each). The proportions were represented as percentages in the tables.
From 2015 to 2019, there was a significantly higher total percentage of female participants than the male participants (Table 1). The total age category percentages were lowest in those aged ≥ 85 y, followed by those aged 55–64 y, 75–84 y, and highest in those aged 65–74 y. The total race category percentages were lowest in the missing race, followed by Asian race, “other” race, Black race, and highest in the White race. The total education percentages were lowest in those missing education, followed by less than high school (HS), HS-some college, and highest in those with ≥ 4 y of college.
TABLE 1.
Demographic characteristics of NACC participants ≥ 55 y within each year from 2015 to 2019.
| Overall n (%) (95% CI) | 2015 n (%) (95% CI) | 2016 n (%) (95% CI) | 2017 n (%) (95% CI) | 2018 n (%) (95% CI) | 2019 n (%) (95% CI) | P value | 2019 vs. 2015 |
||
|---|---|---|---|---|---|---|---|---|---|
| % difference | P value | ||||||||
| Population | 9357 | 4663 | 1393 | 1152 | 1046 | 1103 | |||
| Sex | 0.693 | ||||||||
| Male | 3152 (33.7) (32.7, 34.6) |
1597 (34.2) (32.9, 35.6) |
468 (33.6) (31.1, 36.1) |
390 (33.9) (31.1, 36.6) |
337 (32.2) (29.4, 35.1) |
360 (32.6) (29.9, 35.4) |
-1.6 (-4.7, 1.5) | 0.306 | |
| Female | 6205 (66.3) (65.4, 67.3) |
3066 (65.8) (64.4, 67.1) |
925 (66.4) (63.9, 68.9) |
762 (66.1) (63.4, 68.9) |
709 (67.8) (64.9, 70.6) |
743 (67.4) (64.6, 70.1) |
1.6 (-1.5, 4.7) | 0.306 | |
| The age category | <0.00013 | ||||||||
| 55–64 y | 1500 (16.0) (15.3, 16.8) |
553 (11.9) (10.9, 12.8) |
285 (20.5) (18.3, 22.6) |
213 (18.5) (16.2, 20.7) |
218 (20.8) (18.4, 23.3) |
231 (20.9) (18.5, 23.3) |
9.1 (6.5, 11.7) | <0.0001 | |
| 65–74 y | 4154 (44.4) (43.4, 45.4) |
1786 (38.3) (36.9, 39.7) |
620 (44.5) (41.9, 47.1) |
633 (54.9) (52.1, 57.8) |
549 (52.5) (49.5, 55.5) |
566 (51.3) (48.4, 54.3) |
13.0 (9.7, 16.3) | <0.0001 | |
| 75–84 y | 2750 (29.4) (28.5, 30.3) |
1623 (34.8) (33.4, 36.2) |
363 (26.1) (23.8, 28.4) |
263 (22.8) (20.4, 25.3) |
246 (23.5) (20.9, 26.1) |
255 (23.1) (20.6, 25.6) |
-11.7 (-14.5, -8.8) | <0.0001 | |
| ≥ 85 y | 953 (10.2) (9.6, 10.8) |
701 (15.0) (14.0, 16.1) |
125 (9.0) (7.5, 10.5) |
43 (3.7) (2.6, 4.8) |
33 (3.2) (2.1, 4.2) |
51 (4.6) (3.4, 5.9) |
-10.4 (-12.0, -8.8) | <0.0001 | |
| Race | <0.0001 | ||||||||
| White | 7228 (77.2) (76.4, 78.1) |
3736 (80.1) (79.0, 81.3) |
1053 (75.6) (73.3, 77.9) |
859 (74.6) (72.0, 77.1) |
776 (74.2) (71.5, 76.8) |
804 (72.9) (70.3, 75.5) |
-7.2 (-10.1, -4.4) | <0.0001 | |
| Black | 1397 (14.9) (14.2, 15.7) |
665 (14.3) (13.3, 15.3) |
195 (14.0) (12.2, 15.8) |
188 (16.3) (14.2, 18.5) |
150 (14.3) (12.2, 16.5) |
199 (18.0) (15.8, 20.3) |
3.8 (1.3, 6.3) | 0.003 | |
| Asian | 294 (3.1) (2.8, 3.5) |
102 (2.2) (1.8, 2.6) |
65 (4.7) (3.6, 5.8) |
43 (3.7) (2.6, 4.8) |
34 (3.3) (2.2, 4.3) |
50 (4.5) (3.3, 5.8) |
2.3 (1.0, 3.6) | 0.0004 | |
| Other1 | 342 (3.7) (3.3, 4.0) |
140 (3.0) (2.5, 3.5) |
65 (4.7) (3.6, 5.8) |
45 (3.9) (2.8. 5.0) |
57 (5.4) (4.1, 6.8) |
35 (3.2) (2.1, 4.2) |
0.2 (-1.0, 1.3) | 0.770 | |
| Missing/Unknown | 96 (1.0) (0.8, 1.2) |
20 (0.4) (0.2, 0.6) |
15 (1.1) (0.5, 1.6) |
17 (1.5) (0.8, 2.2) |
29 (2.8) (1.8, 3.8) |
15 (1.4) (0.7, 2.0) |
0.9 (0.2, 1.6) | 0.010 | |
| Education | 0.0882 | ||||||||
| Less than HS | 250 (2.7) (2.3, 3.0) |
113 (2.4) (2.0, 2.9) |
39 (2.8) (1.9, 3.7) |
23 (2.0) (1.2, 2.8) |
41 (3.9) (2.7, 5.1) |
34 (3.1) (2.1, 4.1) |
0.7 (-0.5, 1.8) | 0.245 | |
| HS-some college | 2677 (28.6) (27.7, 29.5) |
1374 (29.5) (28.2, 30.8) |
415 (29.8) (27.4, 32.2) |
322 (28.0) (25.4, 30.5) |
266 (25.4) (22.8, 28.1) |
300 (27.2) (24.6, 29.8) |
-2.3 (-5.2, 0.7) | 0.130 | |
| ≥4 y of college | 6385 (68.2) (67.3, 69.2) |
3150 (67.5) (66.2, 68.9) |
934 (67.0) (64.6, 69.5) |
803 (69.7) (67.0, 72.4) |
734 (70.2) (67.4, 72.9) |
764 (69.3) (66.5, 72.0) |
1.7 (-1.3, 4.7) | 0.269 | |
| Missing/Unknown | 45 (0.5) (0.3, 0.6) |
26 (0.6) (0.3, 0.8) |
5 (0.4) (0.0, 0.7) |
4 (0.3) (0.0, 0.7) |
5 (0.5) (0.1, 0.9) |
5 (0.5) (0.1, 0.9) |
-0.1 (-0.6, 0.3) | 0.650 | |
HS, high school.
Includes Pacific Islanders, Alaska Natives, American Indians, Native Hawaiians, or multi-racial.
Monte-Carlo estimate of the exact P value was used.
Non-zero correlation CMH statistics were used.
The weighted analyses found similar results as the unweighted analyses for the significance of the findings and order of categories. However, the total percentages of participants in the demographic categories differed between the weighted and unweighted analyses. The large number of participants in 2015 in the unweighted analyses influenced some of the total percentages for demographics. The weighted analyses for some demographic categories showed a 0.1%–3.9% change, with the changes being most notable in the age categories of participants (Supplementary Table 4).
Demographics with year
No significant association was found between the visit year and sex, suggesting that the sex of participants remained similar over the study period. When comparing the percentage of participants within each sex between 2015 and 2019, no significant change was found (Table 1).
A significant association was found between the visit year and age category, suggesting that the age categories of participants changed over the study period. When comparing the percentage of participants within each age category between 2015 and 2019, the percentages significantly increased in those aged 55–64 y and 65–74 y in 2019, and significantly decreased in those aged 75–84 y and ≥ 85 y.
The race of participants was quantified using the NIH race definitions. A significant association was found between the visit year and race, suggesting that the race of participants changed over the study period. When comparing 2015 and 2019, “other” race participant percentages remained similar, percentages of Black, Asian, and missing race participants significantly increased in 2019, whereas percentages of White participants significantly decreased.
No significant association was found between the visit year and education category suggesting the education level of participants was similar over the study period. Similarly, no significant change was found in the percentages of participants within each education category between 2015 and 2019. The weighted analyses found similar results when compared with the unweighted analyses (Supplementary Table 4).
Demographics of total supplement use from 2015 to 2019
Reported use of any dietary supplement was aggregated across the entire study period (2015–2019) and denoted as total supplement use. It was then determined whether the total reported use of any supplement was associated with a demographic. A significant association was found between sex, age category, race category, and education category with total reported supplement use (Table 2).
TABLE 2.
Reported use of any dietary supplement in the past 14 d for NACC participants ≥ 55 y within demographic categories and each year from 2015 to 2019.
| Overall | Overall Supplement n (%) (95% CI) | 2015 n (%) (95% CI) | 2016 n (%) (95% CI) | 2017 n (%) (95% CI) | 2018 n (%) (95% CI) | 2019 n (%) (95% CI) | P value | 2019 vs. 2015 |
||
|---|---|---|---|---|---|---|---|---|---|---|
| % difference (95% CI) | P value | |||||||||
| Population | 9357 | 4663 | 1393 | 1152 | 1046 | 1103 | ||||
| Sex | <0.00011 | |||||||||
| Male | 3152 | 2190 (69.5) (67.9, 71.1) |
1165 (72.9) (70.8, 75.1) |
312 (66.7) (62.4, 70.9) |
273 (70.0) (65.4, 74.6) |
206 (61.1) (55.9, 66.3) |
234 (65.0) (60.1, 69.9) |
<0.0001 | -7.9 (-13.3, -2.6) | 0.004 |
| Female | 6205 | 4833 (77.9) (76.9, 78.9) |
2459 (80.2) (78.8, 81.6) |
715 (77.3) (74.6, 80.0) |
574 (75.3) (72.3, 78.4) |
536 (75.6) (72.4, 78.8) |
549 (73.9) (70.7, 77.1) |
0.0002 | -6.3 (-9.8, -2.9) | 0.0003 |
| The age category | <0.00011 | |||||||||
| 55–64 y | 1500 | 968 (64.5) (62.1, 67.0) |
373 (67.5) (63.5, 71.4) |
195 (68.4) (63.0, 73.8) |
130 (61.0) (54.5, 67.6) |
128 (58.7) (52.2, 65.3) |
142 (61.5) (55.2, 67.8) |
0.055 | -6.0 (-13.4, 1.4) | 0.113 |
| 65–74 y | 4154 | 3093 (74.5) (73.1, 75.8) |
1360 (76.1) (74.2, 78.1) |
451 (72.7) (69.2, 76.3) |
473 (74.7) (71.3, 78.1) |
405 (73.8) (70.1, 77.5) |
404 (71.4) (67.6, 75.1) |
0.157 | -4.8 (-9.0. -0.6) | 0.027 |
| 75–84 y | 2750 | 2175 (79.1) (77.6, 80.6) |
1311 (80.8) (78.9, 82.7) |
272 (74.9) (70.5, 79.4) |
215 (81.7) (77.1, 86.4) |
184 (74.8) (69.4, 80.2) |
193 (75.7) (70.4, 81.0) |
0.016 | -5.1 (-10.7, 0.5) | 0.075 |
| ≥ 85 y | 953 | 787 (82.6) (80.2, 85.0) |
580 (82.7) (79.9, 85.5) |
109 (87.2) (81.3, 93.1) |
29 (67.4) (53.4, 81.5) |
25 (75.8) (61.1, 90.4) |
44 (86.3) (76.8, 95.7) |
0.036 | 3.5 (-6.3, 13.4) | 0.482 |
| Race | <0.00011 | |||||||||
| White | 7228 | 5483 (75.9) (74.9, 76.8) |
2931 (78.5) (77.1 ,79.8) |
780 (74.1) (71.4, 76.7) |
641 (74.6) (71.7, 77.5) |
542 (69.8) (66.6, 73.1) |
589 (73.3) (70.2, 76.3) |
<0.0001 | -5.2 (-8.5. -1.9) | 0.002 |
| Black | 1397 | 1013 (72.5) (70.2, 74.9) |
495 (74.4) (71.1 ,77.8) |
145 (74.4) (68.2, 80.5) |
135 (71.8) (65.4, 78.2) |
111 (74.0) (67.0, 81.0) |
127 (63.8) (57.1, 70.5) |
0.053 | -10.6 (-18.1, -3.2) | 0.005 |
| Asian | 294 | 234 (79.6) (75.0, 84.2) |
89 (87.3) (80.8, 93.7) |
47 (72.3) (61.4, 83.2) |
36 (83.7) (72.7, 94.8) |
25 (73.5) (58.7, 88.4) |
37 (74.0) (61.8, 86.2) |
0.092 | -13.3 (-27.0, 0.5) | 0.059 |
| Other2 | 342 | 233 (68.1) (63.2, 73.1) |
97 (69.3) (61.6, 77.0) |
47 (72.3) (61.4, 83.2) |
25 (55.6) (41.0, 70.1) |
43 (75.4) (64.2, 86.7) |
21 (60.0) (43.7, 76.3) |
0.174 | -9.3 (-27.2, 8.7) | 0.310 |
| Missing/Unknown | 96 | 60 (62.5) (52.6, 72.4) |
12 (60.0) (38.1, 81.9) |
8 (72.3) (61.4, 83.2) |
10 (53.3) (27.6, 79.0) |
21 (72.4) (55.9, 89.0) |
9 (60.0) (34.8, 85.2) |
0.746 | 0.0 (-32.8, 32.8) | 1.000 |
| Education | 0.00031 | |||||||||
| Less than HS | 250 | 163 (65.2) (59.3, 71.1) |
80 (70.8) (62.4, 79.2) |
22 (56.4) (40.7, 72.1) |
15 (65.2) (45.6, 84.8) |
23 (56.1) (40.8, 71.4) |
23 (67.6) (51.8, 83.5) |
0.346 | -3.1 (-21.0, 14.7) | 0.729 |
| HS-some college | 2677 | 1976 (73.8) (72.1, 75.5) |
1056 (76.9) (74.6, 79.1) |
301 (72.5) (68.2, 76.8) |
224 (69.6) (64.5, 74.6) |
183 (68.8) (63.2, 74.4) |
212 (70.7) (65.5, 75.8) |
0.005 | -6.2 (-11.8, -0.6) | 0.031 |
| ≥4 y of college | 6385 | 4847 (75.9) (74.9, 77.0) |
2467 (78.3) (76.9, 79.8) |
699 (74.8) (72.1, 77.6) |
604 (75.2) (72.2, 78.2) |
532 (72.5) (69.2, 75.7) |
545 (71.3) (68.1, 74.5) |
<0.0001 | -7.0 (-10.5, -3.5) | <0.0001 |
| Missing/Unknown | 45 | 37 (82.2) (70.6, 93.8) |
21 (80.8) (65.0, 96.5) |
5 (100.0) (100, 100) |
4 (100.0) (100, 100) |
4 (80.0) (43.5, 100.0) |
3 (60.0) (15.3, 100) |
NC3 | -20.8 (-66.3, 24.8) | 0.371 |
HS, high school.
P value reflects the association between the variable and total/overall supplement use from 2015-2019.
Includes Pacific Islanders, Alaska Natives, American Indians, Native Hawaiians, or multi-racial,
Statistical significance was not calculated (NC) due to zero counts for some categories.
The total reported supplement use was higher in women than in men. As age increased, the total reported use of any supplement increased. Total reported supplement use was lowest in those aged 55–64 y and highest in those ≥ 85. The total reported use of any supplement was lowest in participants missing race, followed by “other” race, Black race, and White race, as well as highest in Asian participants. As the number of years of education category increased, the total reported use of any supplement increased, being lowest in those with less than HS education and was highest in those with ≥ 4 y of college, when not considering the missing education category. Those with missing education had the highest total percentage of reported supplement use.
Changes in reported use of any dietary supplement from 2015 to 2019 within specific demographics
Within males and females, a significant association was found between the reported use of any supplement and visit year, suggesting it changed over the study period (Table 2). When comparing reported supplement use in 2015 and 2019 within each sex, it significantly decreased for each sex in 2019.
Within both the 75–84-y and ≥ 85-y age groups, a significant association was found between the reported use of any dietary supplement and visit year. When comparing the reported use of any supplement in 2019 and 2015 among those aged 75–84 y or ≥ 85 y, no significant change was found. This suggested that the reported use of any supplement within these age groups changed in years other than 2015 and 2019. For those aged ≥85 y, the reported use of any supplement decreased in 2017 compared to 2016 (% difference: -19.8%, 95% CI: -34.9, -4.6; P = 0.011), confirming that the significance was due to changes between other years. Because the changes in reported supplement use between 2015 and 2019 were of primary interest, changes between years other than 2015 and 2019 were not explored further.
For the White participants, a significant association was found between the reported use of any dietary supplement and visit year. Within Black, Asian, and “other” race, and missing race participants, no significant association was found between the reported use of any supplement and visit year. When comparing reported dietary supplement use in 2015 and 2019 among White participants, it significantly decreased in 2019.
Among those with HS-some college education or ≥ 4 y of college, a significant association was found between the reported use of any dietary supplement and visit year. When comparing reported dietary supplement use in 2015 and 2019 among those with HS-some college education or ≥4 y of college, it significantly decreased in 2019.
Changes in reported use of supplement categories from 2015 to 2019
Across the entire study period, 75.1% of participants reported the use of any dietary supplement (Supplementary Table 1). Significant associations were found between visit year and reported use of any supplement (Figure 2A) and ≥ 4 supplements. When comparing 2015 and 2019, the reported use of any supplement and ≥ 4 supplements significantly decreased in 2019.
FIGURE 2.
Reported use of A) any dietary supplement, B) any vitamin, C) any mineral, or D) any nonvitamin nonmineral supplement in the past 14 d in the National Alzheimer’s Coordinating Center participants aged ≥ 55 y within each year from 2015 to 2019 in weighted and unweighted analyses. ∗∗∗ P < 0.0001, ∗∗ P < 0.0100. All P values reflect two-sided P values obtained from z-scores comparing the proportions of reported use of supplement categories in 2019 compared to 2015. Because P values were obtained for both weighted (circles) and unweighted (squares) analyses, the highest P value was selected.
Within the vitamin and mineral supplement categories across the study period, the total reported use of any vitamin was higher than that of any mineral. A significant association was found between the visit year and the reported use of any vitamin or mineral, any vitamin (Figure 2B), any mineral (Figure 2C), and both a vitamin and mineral supplement (Supplementary Table 1). When comparing 2015 and 2019, the reported use of any vitamin/mineral, any vitamin, any mineral, or both vitamin and mineral all significantly decreased in 2019. All of the above associations and significant changes in the vitamin/mineral categories were held in the weighted analyses (Supplementary Table 5). Although a significant association was found with the reported use of any vitamin not including a multivitamin and visit year in the unweighted analysis, the significance of this association did not hold in the weighted analysis.
The top 3 reported NVNM supplement categories were “other” supplements, enzyme supplements, and herbal supplements. Although significant associations were found between visit year and the reported use of any NVNM supplement enzyme supplements and “other” supplements in the unweighted analyses (Figure 2D, Supplementary Table 1), the only association that held in the weighted analysis was with “other” supplements (Supplementary Table 5). When comparing 2015 and 2019, the reported use of “other” supplements significantly decreased in 2019, which also held in the weighted analysis (Supplementary Table 5). No significant association was found between the reported use of amino acid supplements, antioxidant supplements, or herbal NVNM supplements with visit year, suggesting that reported use was stable.
Change in reported use of individual supplements from 2015 to 2019
The top 3 individual vitamin supplements for total reported use was vitamin D (Supplementary Table 2, 45.9%), multivitamins (44.7%), and vitamin B12 (12.9%). The top 3 individual mineral supplements were calcium (27.9%), magnesium (4.5%), and potassium (4.4%). The same individual vitamin and mineral supplements made the top 3 spots in the weighted analysis (Supplementary Table 6).
For individual vitamins and minerals, no significant association was found between the visit year and the reported use of iron, magnesium, vitamin B3 (niacin), vitamin B6 (pyridoxine), vitamin B9 (folic acid), vitamin B12 (cobalamin), and vitamin D (Supplementary Table 2). A significant association was found between the visit year and the reported use of multivitamins (Figure 3A), calcium (Figure 3B), vitamin C (Figure 3C), vitamin B7 (biotin) (Figure 3D), potassium, and vitamin E. When comparing 2015 and 2019, the reported use of calcium, multivitamins, potassium, and vitamin C significantly decreased, whereas the use of biotin significantly increased in 2019. The significance of the associations and changes between 2015 and 2019 held for all the above analyses when using weighting (Supplementary Table 6). Although a significant association was found between reported vitamin E use and visit year in the unweighted analysis (Supplementary Table 2), the association did not hold in the weighted analysis (Supplementary Table 6).
FIGURE 3.
Reported use of A) multivitamins, B) calcium, C) vitamin C, or D) biotin supplements in the past 14 d in the National Alzheimer’s Coordinating Center participants aged ≥ 55 y within each year from 2015 to 2019 in weighted and unweighted analyses. ∗∗∗ P < 0.0001, ∗∗ P < 0.0100, ∗ P < 0.05. All P values reflect two-sided P values obtained from z-scores comparing the proportions of reported use of supplement categories in 2019 compared to 2015. Since P values were obtained for both weighted (circles) and unweighted (squares) analyses, the highest P value was selected. ∗∗∗ P < 0.0001, ∗∗ P < 0.0100. All P values reflect two-sided P values obtained from z-scores comparing the proportions of reported use of supplement categories in 2019 compared to 2015. Since P values were obtained for both weighted (circles) and unweighted (squares) analyses, the highest P value was selected.
The top 3 reported NVNM supplements across the study period were all in the omega fatty acid categories (Supplementary Table 3). When only allowing the top omega fatty acid supplement category in the top 3 NVNM supplements, the top 3 NVNM supplements were “all omega fatty acids” (23.2%), glucosamine (10.3%), and coenzyme Q10 (7.6%). The same supplements made the top 3 spots in the weighted analysis (Supplementary Table 7).
Within individual NVNM supplements, a significant association was found between the visit year and the reported use of all omega (Figure 4A), all omega no flax and omega-3s no flax, glucosamine (Figure 4B), melatonin (Figure 4C), turmeric (Figure 4D), and chondroitin (Figure 4, Supplementary Table 3). Although significant associations were also found between the visit year and the reported use of coenzyme Q10 and flax, the significance of these associations did not hold in the weighted analyses (Supplementary Table 7). When comparing 2015 and 2019, the reported use of melatonin and turmeric significantly increased in 2019, whereas for all 3 categories of omega fatty acid supplements, glucosamine, and chondroitin the use decreased in 2019. The significant differences found in the unweighted analyses between 2015 and 2019 were held in the weighted analyses.
FIGURE 4.
Reported use of A) all omega supplement categories, B) glucosamine, C) melatonin, or D) turmeric supplements in the past 14 d in the National Alzheimer’s Coordinating Center participants aged ≥ 55 y within each year from 2015 to 2019 in weighted and unweighted analyses.
Demographics and reported use of supplements in those aged ≥65 y from 2015 to 2019
Sub-analyses were also conducted in those aged ≥ 65 y from 2015 to 2019 with and without weighting for demographics (unweighted: Supplementary Table 8; weighted: Supplementary Table 12), supplement use categories (unweighted: Supplementary Table 9; weighted: Supplementary Table 13), and individual supplements (unweighted: Supplementary Tables 10 and 11; weighted: Supplementary Tables 14 and 15). For major supplement categories, all results remained similar except for a significant association found between the visit year and the reported use of enzyme supplements (Supplementary Table 9), which held with weighting (Supplementary Table 13). Reported use of enzyme supplements significantly increased in 2019 compared with that in 2015. This result also held with weighting.
For individual vitamins/minerals, all results remained similar with the exception that the association between visit year and reported folic acid and vitamin B6 became significant in the unweighted analyses (Supplementary Table 10). The significance of these results was lost in the weighted analysis (Supplementary Table 14). Additionally, the significant association between vitamin C and visit year was lost in the weighted analysis. Although the association between visit year and potassium was significant in both the weighted and unweighted analyses, when comparing 2015 and 2019, there was no longer a significant decline in 2019 in the weighted analysis (P = 0.073). This suggested that the significance of the association between visit year and reported potassium use was between years other than 2015 and 2019.
For individual NVNM supplements, all results remained similar with the exception that the association between visit year and coenzyme Q10 became significant in the unweighted (Supplementary Table 11) and weighted analysis (Supplementary Table 15). When comparing 2015 and 2019, the reported use of coenzyme Q10 significantly increased in 2019 in both the unweighted and weighted analyzes.
Discussion
The goals of this study were to 1) obtain estimates of reported supplement use in cognitively normal older adults aged ≥55 y from 2015 to 2019 and 2) determine whether the reported use of supplement categories or individual supplements remained similar, increased, or decreased in 2019 compared to that in 2015.
In this study, 75.1% of those aged ≥ 55 y and 77.1% of those aged ≥ 65 y reported the use of any supplement from 2015 to 2019. In those aged ≥ 55 y and ≥ 65 y, the reported use of any supplement in 2015 was 77.7% and 79.1 %, respectively, and 71.0% and 73.5% in 2019, respectively. The reported use of any supplement significantly decreased in 2019 compared to 2015 in both the unweighted and weighted analyses for both age groups. Studies in older adults using NHANES found that the reported use of any dietary supplement was 72% (95% CI: 69%, 75%) in those aged ≥65 y from 2011 to 2012 (4), 69.7% (± 1.4%, SEM) in those aged ≥ 60 y from 2011 to 2014 [15], and 83% in those aged > 70 y from 2015 to 2018 [16]. In those aged 51–70 y and > 70 y, the reported dietary supplement use was similar during 2015 to 2018 when compared with that during 2011 to 2014, suggesting that dietary supplement use was stable across these 2 periods [16]. This current study reports that dietary supplement use significantly decreased in those aged ≥ 55 y and ≥ 65 y in 2019 compared with that in 2015. However, there are a number of important qualifiers when comparing the results of this study to the results of others. Because NACC data are not nationally representative, the results will reflect the characteristics of NACC participants. Within this study, 66.3% of NACC participants in this study were female and 96.8% had ≥ HS education. These percentages were much higher than those for older adults aged ≥60 y in the general United States population from 2011 to 2014, as 52% were female and 71.7 % had ≥ HS education [15]. In this study, dietary supplement use increased with female sex and increased education, which is consistent with findings from another evaluation using older adults [15]. Thus, it would be expected for this study to observe higher dietary supplement use comparing studies in older adults using nationally representative data. However, this was not observed. Although this study found 75.6% (weighted) of those aged ≥ 65 y reported dietary supplement use from 2015 to 2019, another study using nationally representative data from 2015 to 2018 found 83% of older adults aged > 70 y reported dietary supplement use [16]. One possibility to explain this finding, is that dietary supplement use has declined in 2019 in older adults who are female and/or highly educated. If this is the case, this trend may take additional time to show in older adults using nationally representative data given the comparatively low percentage of females and education attained.
For those aged ≥55 y, reported use of ≥ 4 supplements, most vitamin/mineral supplement categories, and “other” supplements significantly decreased in 2019 compared with that in 2015 in both the unweighted and weighted analyses. When comparing the percentage decline between the major supplement categories (any vitamin, any mineral, or any NVNM supplement) from 2015 to 2019, they were similar. This suggested that the decline in dietary supplement use was not explained by one particular supplement category, but they all contributed.
For individual vitamin/mineral supplements in those aged ≥ 55 y, reported use of biotin significantly increased in 2019 compared with that in 2015, whereas calcium, multivitamins, potassium, and vitamin C significantly decreased. This result with biotin was similar to another study in which ≥1 mg/d of biotin significantly increased from 0.3% during 1999–2000 to 4.7% during 2015–2016 for those aged ≥ 60 y [17]. Although the dose of biotin could not be ascertained in our study, this study shows that the reported biotin use was significantly higher in 2019 than that in 2015 NACC participants, showing that it continues to increase. This has important public health implications. High intake of biotin can interfere with biotin/streptavidin-based immunoassays used for clinical laboratory testing, leading to incorrect test results [[18], [19], [20]]. Although the Food and Nutrition Board recommends 30 μg/d of biotin for adequate intake, dietary supplements marketed for strengthening hair and nails commonly contain 5–10 mg of biotin [21]. Moreover, high pharmacological doses of oral biotin (8.2 and 89.1 μmol) are absorbed nearly completely with ∼100% bioavailability [22]. In outpatient settings in 2017, self-reported biotin use was 7.7% (149/1944), with 22% of biotin users (34/149) taking ≥ 5 mg biotin/d [23]. In patients visiting the emergency room (ER) in 2017, 7.4% (107/1442) of all patients had plasma biotin concentrations ≥ 10 ng/mL [23], a concentration that can interfere with laboratory tests. Of ER patients with biotin levels ≥ 10 ng/mL, biotin use was documented for only 1.9% (2/107), with multivitamin use documented for 30.8% (33/107). This may implicate multivitamin formulations in the elevated biotin levels, or may suggest that biotin use is underreported by patients in ER settings. High-dose biotin supplements (10 mg/d for a week) have been shown to interfere with 39% of biotin/streptavidin-based immunoassays with no impact on nonbiotinylated assays [18]. Of the assays high-dose biotin intake interfered with, test results were falsely high for 63% and falsely low for 27%. For some assays, high biotin intake falsely decreased thyroid-stimulating hormone, parathyroid hormone, and N-terminal pro b-type natriuretic peptide used to diagnose congestive heart failure. addition, high biotin intake in some assays falsely increased free tri-iodothryronine (T3), total T3, and free thyroxine. Biotin has also interfered with biotin/streptavidin-based troponin assays to diagnose myocardial infarctions [24]. After the Food and Drug Administration received a report that one patient taking high levels of biotin died following a falsely low troponin test result, they issued safety communication to increase awareness of this issue [25]. Although many manufacturers successfully mitigated biotin interference in their troponin assays, some manufacturers have yet to address this in 2019 [26]. Given clinical laboratory testing has attempted to minimize biotin interference on some assays, concern may still be warranted given biotin use in older adults has increased.
Of all individual vitamins, multivitamins showed the greatest decline (10%) in reported use in 2019 compared to 2015. Although the most common reason for using multivitamin/minerals (MVM) or multivitamins from 2007 to 2010, was “to improve overall health” [5], whether multivitamin use is beneficial remains unclear. One of the challenges with comparing results between studies with multivitamins is that multivitamins can vary greatly in their supplement composition, and in their amounts. A meta-analysis of randomized controlled trials (RCTs) and prospective cohort studies from 1970 to 2016 found multivitamin/multimineral (MVM) supplements had no significant effect on stroke incidence or mortality from cardiovascular disease, congestive heart disease, or stroke [27]. An RCT in adults aged ≥ 55 y found that supplementation with a specific MVM for 12 wks significantly increased vitamin C and zinc levels but had no significant effect on calcifediol or any measure of immune function [28].
Of all minerals, calcium showed the greatest decline in reported use (9.6%) in 2019 compared with that in 2015. Although it remains unclear why reported calcium use decreased, the most common reason listed for using calcium supplements was “to improve bone health” [5]. Although improving bone health remains a top reason for using dietary supplements in older adults [15], the perceived benefit of calcium supplementation by clinicians and patients may be declining as more recent studies have failed to find positive results. A relatively recent meta-analysis used 33 RCTs investigating supplementation with calcium, vitamin D, or both compared with placebo on risk of hip, vertebral, non-vertebral, or total fractures in community-dwelling adults aged 50 y and older [29]. Risk of fracture was similar with vitamin D, vitamins plus calcium, and calcium when compared to placebo, suggesting a lack of benefit. However, a previous meta-analysis provides an additional perspective. This earlier meta-analysis used 29 RCTs of calcium, vitamin D, or both compared with placebo on risk of fracture in adults aged 50 y and older [30]. Although calcium plus vitamin D significantly reduced risk of fracture by 13% (RR=0.87, 95% CI: 0.77–0.97), this effect was lost with calcium alone, suggesting that vitamin D supplementation was necessary to observe a benefit. When performing subgroup analyses, a significantly greater treatment effect was found for those that were institutionalized, if the dietary intake of calcium was low, if participants were ≥ 70 y of age, the dose of calcium was ≥ 1200 mg, vitamin D was ≥ 800 IU, serum 25-hydroxyvitamin D was low, and treatment compliance was ≥ 80% [30]. In the later meta-analysis, the dose of calcium was classified as ≥ 900 mg or < 900 mg [29]. This was lower than the older meta-analysis (≥ 1200 mg calcium, < 1200 mg) [30], which may have impacted the results. Although the results of these meta-analyses may suggest that community -dwelling older adults may not benefit from calcium and vitamin D supplementation on risk of fracture, there were benefits on fracture in institutionalized patients, when compliance rates were high, the dose of calcium was ≥ 1200 mg and the dose of vitamin D was ≥ 800 IU. Whether compliance rates impacted the results in the later meta-analysis remains unclear. The earlier meta-analysis noted that many RCTs had low compliance rates [30]. When trials with compliance rates ≥ 80% were analyzed separately, risk reduction doubled, suggesting the benefit of calcium and/or vitamin D supplementation was underestimated due to poor compliance rates.
For individual NVNM supplements, the reported use of turmeric and melatonin significantly increased from 2015 to 2019, whereas chondroitin, flax, glucosamine, and all categories of omega fatty acid supplements significantly decreased. Although no change was found in the reported use of coenzyme Q10 in those aged ≥ 55 y in 2019 compared with that in 2015, it significantly increased in those aged ≥ 65 y.
The results of this study have several limitations. NACC data have no specific information on the dose of the supplement and/or whether participants used these supplements on a regular basis. Second, participant supplement use is reported by the co-participant. Although cognitively normal older adults were selected as they could more accurately verify with co-participants which dietary supplements were taken, self-report is also prone to bias. Another limitation is that this data is not nationally representative, which limits external validity. As the specific vitamins, minerals or NVNM supplements present in a multivitamin could not be ascertained, the reported use of individual supplements will be underestimated. The selection of participants using the inclusion criteria was originally problematic as 49% of all participants fell within 2015. This made statistical inference questionable as there were smaller errors in 2015 compared to other years. Use of the weighting maintained the percentages within each year facilitated comparisons for statistical inference and maximized the number of participants.
In conclusion, the reported use of any supplement, any vitamin, or any mineral all declined in 2019 compared to that in 2015, whereas the reported NVNM use was stable in those aged ≥ 55 y. Reported use of individual supplements, including calcium, multivitamins, and many others significantly decreased, whereas turmeric, melatonin, and biotin significantly increased.
Author disclosures
The author reports no conflicts of interest.
Data Availability
The analytic code will be made available upon request. Data described in the manuscript will not be made available by the author because it belongs to the NACC. The data can be requested from the NACC.
Acknowledgments
The NACC database was funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded Alzheimer’s Disease Research Centers: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). KS had responsibility for all parts of the manuscript. Alzheimer’s Disease Research Centers collected the data. The NACC provided the data. NACC and KS approved the final manuscript.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.tjnut.2023.04.004.
Appendix A. Supplementary methods and data
The following are the Supplementary methods and data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The analytic code will be made available upon request. Data described in the manuscript will not be made available by the author because it belongs to the NACC. The data can be requested from the NACC.