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Vasant Hirani, Robert G Cumming, Vasi Naganathan, Fiona Blyth, David G Le Couteur, Benjumin Hsu, David J Handelsman, Louise M Waite, Markus J Seibel, Longitudinal Associations Between Vitamin D Metabolites and Sarcopenia in Older Australian men: The Concord Health and Aging in Men Project, The Journals of Gerontology: Series A, Volume 73, Issue 1, January 2018, Pages 131–138, https://doi.org/10.1093/gerona/glx086
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Abstract
To explore the associations between serum 25-hydroxyvitamin D (25D) and 1,25-dihydroxyvitamin D (1,25D) levels at baseline and incidence of sarcopenia over time in older Australian community-dwelling older men.
Of the 1,705 men aged ≥70 years (2005–2007) participating in the Concord Health and Ageing in Men Project, those without sarcopenia at baseline (n = 1,312 for 25D and n = 1,231 for 1,25D), 2 years (n = 1,024 for 25D and n = 956 for 1,25D), and 5-year follow-up (n = 709 for 25D and n = 663 for 1,25D) were included in the study. The main outcome measurement was the incidence of sarcopenia defined as appendicular lean mass adjusted for body mass index <0.789 and grip strength <26.0 kg. Serum 25D and 1,25D levels were measured at baseline by radioimmunoassay (Diasorin, Stillwater, MN) and categorized into quartiles as predictor variables. Covariates included age, income, season of blood collection, physical activity, vitamin D supplement and medication use, measures of health, serum parathyroid hormone (PTH), estimated glomerular filtration rate (eGFR), albumin, and white blood cell count.
In this study, incidence of sarcopenia was 3.9% in men at the 2-year follow-up and 8.6% at the 5-year follow-up. In adjusted analysis, men with vitamin D levels in the lowest quartiles (25D <40nmol/L; 1,25D <62 pmol/L) showed significant associations with increased odds of incident sarcopenia compared to those with vitamin D levels in the highest quartiles over 5 years. [25D: odds ratio (OR) 2.53 (95% confidence interval (CI) 1.14, 5.64) p = .02; 1,25D: OR 2.67 (95% CI 1.28, 5.60) p = .01]. After further adjustments for the respective other serum vitamin D measure, (either 25D or 1,25D), the association remained significant [25D: OR 2.40 (95% CI 1.02, 5.64) p = .04; 1,25D: OR 2.23 (95% CI 1.04, 4.80) p = .04].
Low serum 1,25D and 25D concentrations at baseline are independently associated with the incidence of sarcopenia over the subsequent 5 years. Although our data do not prove any causal relationship, it is conceivable that maintaining vitamin D sufficiency may reduce the incidence of sarcopenia in ageing men.
Introduction
Sarcopenia, an age-associated reduction in skeletal muscle mass and strength (1,2) is recognized as a major clinical problem in older people (3) and often coincides with hypovitaminosis D (4). Associations between low serum vitamin D levels and poor functional performance and increased risk of falls have been reported in various cohorts (5,6). With regards to sarcopenia, a Korean cross-sectional study suggested that sarcopenia is associated with low serum 25-hydroxyvitamin D (25D) levels in women but not in men (7). Another cross-sectional study reported that low serum 1,25D levels were significantly associated with low skeletal mass in younger women but found no consistent relationship between serum 1,25D or 25D levels and muscle mass or strength in older men and women (8). Prospective studies in older adults show associations between low serum 25D levels and poor muscle strength over 3 years (9), with low serum 25D levels increasing the risk of sarcopenia (ie, low muscle mass and poor grip strength) (10).
Since low vitamin D status and sarcopenia are important public health issues among older people due to their adverse impact on morbidity and mortality (4), a better understanding of the influence of vitamin D metabolites on muscle mass and strength is important clinically. While the diagnosis of vitamin D deficiency is made on the basis of serum 25D levels, 1,25D is the biologically active metabolite and reported to bind to the vitamin D receptor (VDR) in human muscle (11). Moreover, genotypic variations in the VDR have been associated with differences in muscle strength (12).
Therefore, the aims of this study are to examine whether low serum 25D and 1,25D levels at baseline are independently associated with the subsequent incidence of sarcopenia among older men aged ≥70 years, independent of other covariates and possible confounders.
Methods
Study Subjects
The Concord Health and Aging in Men Project (CHAMP) is an epidemiological study of a wide range of health issues in Australian men aged 70 years and over (13).
Participants in CHAMP were recruited from a well-defined urban geographical region (the Local Government Areas of Burwood, Canada Bay, and Strathfield) near Concord Hospital in Sydney, Australia. The sampling frame was the New South Wales Electoral Roll. Electoral registration is compulsory in Australia. The only exclusion criterion was living in a residential aged care facility. Eligible men were sent a letter describing the study and, if they had a listed telephone number, were telephoned about 1 week later. Of the 2,815 eligible men with whom contact was made, 1,511 participated in the study (54%). An additional 194 men aged 70 years or older living in the study area, who therefore met study eligibility criteria, volunteered to be in the study before receiving the invitation letter; these men had been told about the study by friends or knew about the study from media reports.
Data Collection
Baseline data were collected between January 2005 and June 2007. Men completed a questionnaire at home before coming to the study clinic at Concord Hospital. The clinic visit consisted of physical performance measures, biological measures, medication inventory, and neuropsychological testing. Data were collected by fully trained staff and the same equipment was used for all measurements and assessments, which were carried out in a single clinic. Two-year follow-up assessments were conducted between January 2007 and October 2009 and 5-year follow-up was conducted between January 2012 and October 2013, using the same measures as at baseline. Of the 1,705 subjects who completed the baseline assessments. 1,366 (79%) had 2-year follow-up assessments and 954 (55%) had 5-year follow-up assessments. Death was the main reason for non-participation at 2 years (99 deaths) and at 5 years (382 deaths). The other main reason for failure to attend the follow-up clinic visits was illness (n = 115 at 2 years and n = 186 at 5 years (see Figure 1).
Main Outcome Measures
Appendicular lean mass (ALM) and fat percentage
Whole-body dual energy X-ray absorptiometry (DXA) scans were acquired using the fan beam Discovery-W scanner (Hologic, Bedford, MA). ALM was calculated as the sum of lean mass of arms and legs (kg) (14). Fat percentage was calculated using bone, lean, and fat mass to estimate total fat mass divided by measured weight (kg) × 100.
Definitions of Low Muscle Mass
The FNIH Sarcopenia Project has derived cut-points from nine different studies with a broad representation of community dwelling older adults. The FNIH defines clinically relevant low lean mass as ALM: BMI ratio (ALMBMI) less than 0.789 for men (15).
Muscle strength
Upper body muscle strength was assessed by hand grip strength using a Jamar dynamometer (Promedics, Blackburn, UK). Grip strength (kg) of the dominant hand (best of two trials) was used. Participants were dichotomized as grip strength <26.0 kg versus ≥26.0 kg (16,17).
Sarcopenia
Sarcopenia was defined as ALMBMI <0.789 and grip strength <26 kg (15). Based upon these criteria, participants were dichotomized as sarcopenic or non-sarcopenic.
Serum 25D and 1,25D
Fasting blood samples were collected from participants on the morning of their clinic visit. Serum 25D and 1,25D levels were measured by radioimmumoassay (RIA) using single batch reagents (DiaSorin Inc., Stillwater, MN) as described previously (18). The assay for 25D has a sensitivity of <3.75 nmol/L with an intra-assay precision of 7.6% and an inter-assay precision of 9.0%. The assay for 1,25D has a sensitivity of <5.2 pmol/L, an intra-assay precision of 7.7% and an inter-assay precision of 12.3%. All assays were carried out in duplicates. Of note, due to complete cross-reactivity of the antibody, the assays measure total circulating vitamin D levels, including both vitamin D2 and vitamin D3. The laboratory participates in regular quality assurance (QA) protocols, including the vitamin D external Quality Assessment Scheme (DEQAS). Regular QA checks were performed with any adjustment to assays as necessary.
Other Measures
Serum parathyroid hormone (PTH)
Serum levels of intact PTH were determined by a two-site chemiluminescent ELISA on an Immulite 1000 analyzer (Diagnostic Products, Los Angeles, CA) which measures the intact PTH molecule. The sensitivity of this assay is 1 pg/mL, and cross-reactivity to PTH fragments and related compounds is low. The assay has a typical intra-assay precision of 5.5%, an inter-assay precision of 7.9%, and the laboratory reference range is 23–66 pg/mL.
Renal function
Serum creatinine (Scr) levels were used to estimate glomerular filtration rate (eGFR), a measure of renal (kidney) function. We used the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations for men (19) as follows: Scr (μmol/L/mg/dL) = ≤80/≤ 0.9: eGFR = 141 × (Scr/0.9)-1.209 × (0.993)Age and Scr > 80/ > 0.9, eGFR = 141 × (Scr/0.9)-1.209 × (0.993)Age
All blood tests were performed using a MODULAR Analytics system (Roche Diagnostics, Castle Hill, Australia) at the Diagnostic Pathology Unit of Concord Hospital, a pathology service accredited by the National Australian Testing Authority. White blood cell analysis was performed by laser flow cytometry and was used as a continuous measure in the analyses. Serum albumin level was also used as a continuous measure.
Sociodemographic and economic measures
Sociodemographic variables included age and living arrangements (lives alone vs lives with others). Men were asked their country of birth, which enabled grouping into the categories of Australian-born, overseas-born from an English speaking country, and overseas-born from a non-English speaking country. Income was categorized as reliant on a government pension only versus other sources of income.
Lifestyle factors
Smoking status (never smoker, ex-smoker, current smoker) was assessed. Physical activity was measured using the Physical Activity Scale for the Elderly (PASE), a method that scores the level of physical activity in individuals aged 65 years or older (20).
Height (measured using the Harpenden Portable Stadiometer) and weight (measured using Wedderburn digital scales) were measured to determine Body Mass Index (BMI = weight/height2, with units kg/m2).Vitamin D supplement use was coded as “yes” if participants reported currently taking them. Supplements included ergocalciferol-D2, cholecalciferol-D3, alfacalcidol, and Ostevit-D (providing 25 µg/1000 IU of vitamin D).
Season
Season of blood sampling was categorized into summer (Dec–Feb), autumn (Mar–May), winter (Jun–Aug), and spring (Sept–Nov).
Health status
Data on medical conditions were obtained from a self-reported questionnaire in which participants reported whether a doctor or a health care provider had told them that they had any of the following diseases: diabetes, thyroid dysfunction, osteoporosis, Paget’s disease, stroke, Parkinson’s disease, epilepsy, hypertension, heart attack, angina, congestive heart failure, intermittent claudication, chronic obstructive lung disease, liver disease, cancer (excluding non-melanoma skin cancers), osteoarthritis, and gout. Depressive symptoms were evaluated by the Geriatric Depression Scale, short form (GDS) (21). A total of five or more depressive symptoms was considered indicative of possible depression.
All participants were screened for cognitive impairment using the mini–mental state examination (MMSE) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) (22,23). Participants who scored >26 on the MMSE or <3.6 on the IQCODE were classified as having no cognitive impairment. Those who scored 26 or below on the Mini-Mental State Examination or 3.6 or higher on the IQCODE were invited to have a detailed clinical assessment during a weekly consensus meeting attended by two geriatricians, a neurologist, and a neuropsychologist. At the end of these screening and clinical assessments, participants were categorized as having dementia, mild cognitive impairment, or cognitively intact by using all relevant information from the assessments, particularly the detailed cognitive assessment.
ADL disability
Physical disability was assessed by seven items from a modified version of the Katz ADL scale (24). ADLs were walking across a small room, bathing, grooming, dressing, eating, transferring from a bed to a chair, and using the toilet. ADL disability was defined as needing help with ≥1 activities on the Katz ADL scale.
Medication
Trained personnel conducted a medication inventory for each participant during the clinic visit. Participants were instructed to bring all their prescription and over-the-counter medications they were taking to the clinic visit for review. They were also asked whether they had taken any prescription or non-prescription medications during the past month. Details of all medications and prescription patterns were recorded. Reported medicines were coded using the Iowa Drug Information Service code numbers.
Statistical Analysis
Analysis was carried out using STATA v13 (Stata Corp., College Station, TX). Descriptive characteristics were expressed as means (SD) and percentages. Baseline descriptive characteristics were compared across categories using one-way ANOVA for continuous variables and Chi-square tests for categorical variables.
To study the longitudinal association between baseline 25D and 1,25D levels and the incidence of sarcopenia at 2 and 5 years of follow-up, we used generalized estimating equation (GEE) analyses (25) to predict population average association over time. With GEE analysis, the population average association between the longitudinally measured variables can be studied using longitudinal data simultaneously and adjusting for within person correlations caused by repeated measurement on each participant using robust estimation of the variances of the regression coefficients. In this analysis, we included measures at a maximum of two time points for sarcopenia (outcome), as we excluded men with sarcopenia at baseline (n = 130) in order to look at the incidence of sarcopenia at follow-up. Vitamin D metabolites were only measured at baseline. Models were initially unadjusted and then adjusted for potential confounders and covariates of clinical significance, measured at all time-points (except for PTH that was only measured at baseline). Variables of clinical significance were included as independent variables for the GEE analysis. Adjusted odds ratios were calculated from a series of models with adjustments for covariates such as demographic factors, season of blood sampling, vitamin D supplement intake, BMI, physical activity levels, presence of co-morbidities, depressive symptoms (as continuous variable), cognitive status, ADL disability, PTH and eGFR, no of medications, white blood cell count, and albumin, as well as further separate adjustments for the respective other serum vitamin D measure (either 25D or 1,25D). A time and vitamin D interaction term was included in our model. The goodness of fit of all the final adjusted models was assessed using the Hosmer–Lemeshow statistic.
25D levels were categorized into lowest quartile (<40 nmol/L), second quartile (40–52.9 nmol/L), third quartile (53–68.9 nmol/L), and highest quartile (≥69nmol/L, the referent category). 1,25D levels were categorized into lowest quartile (<62 pmol/L), 2nd quartile (62–96.9 pmol/L), third quartile (97–145.9 pmol/L), and highest quartile (≥146pmol/L, the referent category) (26). Participants with renal disease [defined as an eGFR less than 30 mL/min/1.73m2 (27)] were excluded from the analyses (n = 24).
Ethics Approval and Informed Consent
All participants gave written informed consent. The study was approved by the Sydney South West Area Health Service Human Research Ethics Committee, Concord Repatriation General Hospital, Sydney, Australia.
Results
Baseline Characteristics of Participants
Characteristics of the study sample are summarized in Table 1 (Complete version of Table 1 can be found as online Supplementary Table) The mean age of the study population was 77.8 ± 4.6 years, mean PTH was 5.62 ± 2.32 pg/mL and the median (interquartile range) for eGFR was 71.3(29.4–100.0) mL/min/1.73 m2. When general characteristics were compared according to quartiles of serum 25D and 1,25D levels, most of them were not significantly different. The characteristics that were significant for quartiles of both 25D and 1,25D were season, poor self-rated health and eGFR levels (p < .05). Serum 25D and 1,25D levels correlated at r = 0.30 (p = .001). The incidence of sarcopenia was 3.9% at 2-year follow-up and 8.6% at 5-year follow-up.
Quartiles of vitamin D (nmol/L or pmol/L) . | . | Quartiles of 25D nmol/L . | Quartiles of 1,25D pmol/L . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Q1 (Lowest) <40.0 . | Q2 40–52.9 . | Q3 53–68.9 . | Q4 (Highest) ≥68.9 . | p valuea . | . | Q1 (Lowest) <62 . | Q2 62–96.9 . | Q3 97–145.9 . | Q4 (Highest) ≥146 . | p valuea . | |
N = 1024 | 208 | 255 | 286 | 275 | N = 956 | 218 | 228 | 240 | 270 | |||
Mean (SD)/N (%) | ||||||||||||
Age | .15 | .10 | ||||||||||
Mean (SD) | 77.8 (4.6) | 78.1 (4.7) | 77.4 (4.4) | 77.5 (4.5) | 78.1 (4.9) | 77.8 (4.6) | 78.2 (4.7) | 78.1 (4.6) | 77.3 (4.2) | 77.6 (4.7) | ||
Income | .20 | .57 | ||||||||||
Pension | 410 (40.4) | 78 (38.4) | 100 (39.7) | 107 (37.4) | 125 (45.6) | 384 (37.9) | 84 (42.9) | 95 (40.5) | 91 (39.1) | 114 (42.0) | ||
Country of birth | .06 | .45 | ||||||||||
Australia | 519 (50.7) | 88 (42.3) | 114 (44.7) | 152 (53.1) | 165 (60.0) | 485 (50.7) | 115 (52.8) | 110 (48.2) | 119 (49.6) | 141 (52.2) | ||
BMI kg/m2 | .80 | .79 | ||||||||||
Mean (SD) | 27.9 (3.7) | 28.4 (3.8) | 28.1 (3.8) | 28.1 (3.7) | 27.1 (3.3) | 28.0 (3.7) | 28.4 (3.8) | 27.6 (3.6) | 27.6 (3.5) | 27.9 (3.8) | ||
Take 25D supplements | .42 | .18 | ||||||||||
No | 960 (93.8) | 199 (95.7) | 236 (92.5) | 265 (92.7) | 260 (94.5) | 897 (93.8) | 198 (90.8) | 215 (94.3) | 226 (94.2) | 258 (95.6) | ||
Season | <.0001 | <.0001 | ||||||||||
Winter: June to August | 249 (24.3) | 78 (37.5) | 70 (27.5) | 62 (21.7) | 39 (14.2) | 243 (25.4) | 69 (31.7) | 71 (31.1) | 62 (25.8) | 41 (15.2) | ||
Smoking status | .29 | .83 | ||||||||||
Current smoker | 53 (5.1) | 12 (5.4) | 19 (7.5) | 8 (2.8) | 14 (5.1) | 51 (5.4) | 11 (5.1) | 9 (4.0) | 14 (5.8) | 17 (6.3) | ||
Self-rated general health | .03 | .004 | ||||||||||
Fair/poor/very poor | 248 (24.4) | 57 (27.8) | 69 (27.4) | 73 (25.5) | 49 (17.8) | 249 (24.9) | 73 (33.8) | 56 (24.8) | 47 (19.6) | 61 (22.8) | ||
Doctor diagnosed conditions | .42 | .01 | ||||||||||
≥4 conditions | 218 (21.4) | 48 (23.4) | 48 (19.0) | 68 (23.8) | 54 (19.6) | 205 (21.6) | 63 (29.3) | 43 (18.9) | 42 (17.5) | 57 (21.2) | ||
Dementia | .39 | .58 | ||||||||||
Yes | 27 (2.6) | 8 (3.8) | 6 (2.4) | 3 (1.0) | 10 (3.6) | 24 (2.5) | 6 (2.8) | 7 (3.1) | 2 (0.8) | 9 (3.3) | ||
Depression | .28 | .46 | ||||||||||
Yes | 88 (8.6) | 20 (9.8) | 28 (11.1) | 21 (7.3) | 19 (6.9) | 76 (8.0) | 21 (9.8) | 21 (9.3) | 16 (6.7) | 18 (6.7) | ||
PTH (pg/mL) | <.0001 | .66 | ||||||||||
Mean (SD) | 5.6 (2.2) | 6.1 (2.6) | 5.7 (2.3) | 5.4 (2.0) | 5.3 (2.1) | 5.6 (2.2) | 5.7 (2.4) | 5.5 (2.3) | 5.6 (2.2) | 5.5 (2.1) | ||
eGFR (mL/min/1.73 m2)b Median (IQR) | 71.3 (29.4–100.0) | 72.1 (32.1–100.0) | 72.2 (33.2–99.6) | 72.2 (29.9–99.6)) | 68.5 (29.4–98.2) | .02 | 71.3 (29.4–100.0) | 67.7 (31.8–100.0) | 71.3 (30.0–97.5) | 71.4 (30.2–99.6) | 73.7 (35.5–99.6) | <.0001 |
Quartiles of vitamin D (nmol/L or pmol/L) . | . | Quartiles of 25D nmol/L . | Quartiles of 1,25D pmol/L . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Q1 (Lowest) <40.0 . | Q2 40–52.9 . | Q3 53–68.9 . | Q4 (Highest) ≥68.9 . | p valuea . | . | Q1 (Lowest) <62 . | Q2 62–96.9 . | Q3 97–145.9 . | Q4 (Highest) ≥146 . | p valuea . | |
N = 1024 | 208 | 255 | 286 | 275 | N = 956 | 218 | 228 | 240 | 270 | |||
Mean (SD)/N (%) | ||||||||||||
Age | .15 | .10 | ||||||||||
Mean (SD) | 77.8 (4.6) | 78.1 (4.7) | 77.4 (4.4) | 77.5 (4.5) | 78.1 (4.9) | 77.8 (4.6) | 78.2 (4.7) | 78.1 (4.6) | 77.3 (4.2) | 77.6 (4.7) | ||
Income | .20 | .57 | ||||||||||
Pension | 410 (40.4) | 78 (38.4) | 100 (39.7) | 107 (37.4) | 125 (45.6) | 384 (37.9) | 84 (42.9) | 95 (40.5) | 91 (39.1) | 114 (42.0) | ||
Country of birth | .06 | .45 | ||||||||||
Australia | 519 (50.7) | 88 (42.3) | 114 (44.7) | 152 (53.1) | 165 (60.0) | 485 (50.7) | 115 (52.8) | 110 (48.2) | 119 (49.6) | 141 (52.2) | ||
BMI kg/m2 | .80 | .79 | ||||||||||
Mean (SD) | 27.9 (3.7) | 28.4 (3.8) | 28.1 (3.8) | 28.1 (3.7) | 27.1 (3.3) | 28.0 (3.7) | 28.4 (3.8) | 27.6 (3.6) | 27.6 (3.5) | 27.9 (3.8) | ||
Take 25D supplements | .42 | .18 | ||||||||||
No | 960 (93.8) | 199 (95.7) | 236 (92.5) | 265 (92.7) | 260 (94.5) | 897 (93.8) | 198 (90.8) | 215 (94.3) | 226 (94.2) | 258 (95.6) | ||
Season | <.0001 | <.0001 | ||||||||||
Winter: June to August | 249 (24.3) | 78 (37.5) | 70 (27.5) | 62 (21.7) | 39 (14.2) | 243 (25.4) | 69 (31.7) | 71 (31.1) | 62 (25.8) | 41 (15.2) | ||
Smoking status | .29 | .83 | ||||||||||
Current smoker | 53 (5.1) | 12 (5.4) | 19 (7.5) | 8 (2.8) | 14 (5.1) | 51 (5.4) | 11 (5.1) | 9 (4.0) | 14 (5.8) | 17 (6.3) | ||
Self-rated general health | .03 | .004 | ||||||||||
Fair/poor/very poor | 248 (24.4) | 57 (27.8) | 69 (27.4) | 73 (25.5) | 49 (17.8) | 249 (24.9) | 73 (33.8) | 56 (24.8) | 47 (19.6) | 61 (22.8) | ||
Doctor diagnosed conditions | .42 | .01 | ||||||||||
≥4 conditions | 218 (21.4) | 48 (23.4) | 48 (19.0) | 68 (23.8) | 54 (19.6) | 205 (21.6) | 63 (29.3) | 43 (18.9) | 42 (17.5) | 57 (21.2) | ||
Dementia | .39 | .58 | ||||||||||
Yes | 27 (2.6) | 8 (3.8) | 6 (2.4) | 3 (1.0) | 10 (3.6) | 24 (2.5) | 6 (2.8) | 7 (3.1) | 2 (0.8) | 9 (3.3) | ||
Depression | .28 | .46 | ||||||||||
Yes | 88 (8.6) | 20 (9.8) | 28 (11.1) | 21 (7.3) | 19 (6.9) | 76 (8.0) | 21 (9.8) | 21 (9.3) | 16 (6.7) | 18 (6.7) | ||
PTH (pg/mL) | <.0001 | .66 | ||||||||||
Mean (SD) | 5.6 (2.2) | 6.1 (2.6) | 5.7 (2.3) | 5.4 (2.0) | 5.3 (2.1) | 5.6 (2.2) | 5.7 (2.4) | 5.5 (2.3) | 5.6 (2.2) | 5.5 (2.1) | ||
eGFR (mL/min/1.73 m2)b Median (IQR) | 71.3 (29.4–100.0) | 72.1 (32.1–100.0) | 72.2 (33.2–99.6) | 72.2 (29.9–99.6)) | 68.5 (29.4–98.2) | .02 | 71.3 (29.4–100.0) | 67.7 (31.8–100.0) | 71.3 (30.0–97.5) | 71.4 (30.2–99.6) | 73.7 (35.5–99.6) | <.0001 |
Note: aANOVA for age, BMI, PTH, and eGFR and chi square for all other variables. bChronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.
Quartiles of vitamin D (nmol/L or pmol/L) . | . | Quartiles of 25D nmol/L . | Quartiles of 1,25D pmol/L . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Q1 (Lowest) <40.0 . | Q2 40–52.9 . | Q3 53–68.9 . | Q4 (Highest) ≥68.9 . | p valuea . | . | Q1 (Lowest) <62 . | Q2 62–96.9 . | Q3 97–145.9 . | Q4 (Highest) ≥146 . | p valuea . | |
N = 1024 | 208 | 255 | 286 | 275 | N = 956 | 218 | 228 | 240 | 270 | |||
Mean (SD)/N (%) | ||||||||||||
Age | .15 | .10 | ||||||||||
Mean (SD) | 77.8 (4.6) | 78.1 (4.7) | 77.4 (4.4) | 77.5 (4.5) | 78.1 (4.9) | 77.8 (4.6) | 78.2 (4.7) | 78.1 (4.6) | 77.3 (4.2) | 77.6 (4.7) | ||
Income | .20 | .57 | ||||||||||
Pension | 410 (40.4) | 78 (38.4) | 100 (39.7) | 107 (37.4) | 125 (45.6) | 384 (37.9) | 84 (42.9) | 95 (40.5) | 91 (39.1) | 114 (42.0) | ||
Country of birth | .06 | .45 | ||||||||||
Australia | 519 (50.7) | 88 (42.3) | 114 (44.7) | 152 (53.1) | 165 (60.0) | 485 (50.7) | 115 (52.8) | 110 (48.2) | 119 (49.6) | 141 (52.2) | ||
BMI kg/m2 | .80 | .79 | ||||||||||
Mean (SD) | 27.9 (3.7) | 28.4 (3.8) | 28.1 (3.8) | 28.1 (3.7) | 27.1 (3.3) | 28.0 (3.7) | 28.4 (3.8) | 27.6 (3.6) | 27.6 (3.5) | 27.9 (3.8) | ||
Take 25D supplements | .42 | .18 | ||||||||||
No | 960 (93.8) | 199 (95.7) | 236 (92.5) | 265 (92.7) | 260 (94.5) | 897 (93.8) | 198 (90.8) | 215 (94.3) | 226 (94.2) | 258 (95.6) | ||
Season | <.0001 | <.0001 | ||||||||||
Winter: June to August | 249 (24.3) | 78 (37.5) | 70 (27.5) | 62 (21.7) | 39 (14.2) | 243 (25.4) | 69 (31.7) | 71 (31.1) | 62 (25.8) | 41 (15.2) | ||
Smoking status | .29 | .83 | ||||||||||
Current smoker | 53 (5.1) | 12 (5.4) | 19 (7.5) | 8 (2.8) | 14 (5.1) | 51 (5.4) | 11 (5.1) | 9 (4.0) | 14 (5.8) | 17 (6.3) | ||
Self-rated general health | .03 | .004 | ||||||||||
Fair/poor/very poor | 248 (24.4) | 57 (27.8) | 69 (27.4) | 73 (25.5) | 49 (17.8) | 249 (24.9) | 73 (33.8) | 56 (24.8) | 47 (19.6) | 61 (22.8) | ||
Doctor diagnosed conditions | .42 | .01 | ||||||||||
≥4 conditions | 218 (21.4) | 48 (23.4) | 48 (19.0) | 68 (23.8) | 54 (19.6) | 205 (21.6) | 63 (29.3) | 43 (18.9) | 42 (17.5) | 57 (21.2) | ||
Dementia | .39 | .58 | ||||||||||
Yes | 27 (2.6) | 8 (3.8) | 6 (2.4) | 3 (1.0) | 10 (3.6) | 24 (2.5) | 6 (2.8) | 7 (3.1) | 2 (0.8) | 9 (3.3) | ||
Depression | .28 | .46 | ||||||||||
Yes | 88 (8.6) | 20 (9.8) | 28 (11.1) | 21 (7.3) | 19 (6.9) | 76 (8.0) | 21 (9.8) | 21 (9.3) | 16 (6.7) | 18 (6.7) | ||
PTH (pg/mL) | <.0001 | .66 | ||||||||||
Mean (SD) | 5.6 (2.2) | 6.1 (2.6) | 5.7 (2.3) | 5.4 (2.0) | 5.3 (2.1) | 5.6 (2.2) | 5.7 (2.4) | 5.5 (2.3) | 5.6 (2.2) | 5.5 (2.1) | ||
eGFR (mL/min/1.73 m2)b Median (IQR) | 71.3 (29.4–100.0) | 72.1 (32.1–100.0) | 72.2 (33.2–99.6) | 72.2 (29.9–99.6)) | 68.5 (29.4–98.2) | .02 | 71.3 (29.4–100.0) | 67.7 (31.8–100.0) | 71.3 (30.0–97.5) | 71.4 (30.2–99.6) | 73.7 (35.5–99.6) | <.0001 |
Quartiles of vitamin D (nmol/L or pmol/L) . | . | Quartiles of 25D nmol/L . | Quartiles of 1,25D pmol/L . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Q1 (Lowest) <40.0 . | Q2 40–52.9 . | Q3 53–68.9 . | Q4 (Highest) ≥68.9 . | p valuea . | . | Q1 (Lowest) <62 . | Q2 62–96.9 . | Q3 97–145.9 . | Q4 (Highest) ≥146 . | p valuea . | |
N = 1024 | 208 | 255 | 286 | 275 | N = 956 | 218 | 228 | 240 | 270 | |||
Mean (SD)/N (%) | ||||||||||||
Age | .15 | .10 | ||||||||||
Mean (SD) | 77.8 (4.6) | 78.1 (4.7) | 77.4 (4.4) | 77.5 (4.5) | 78.1 (4.9) | 77.8 (4.6) | 78.2 (4.7) | 78.1 (4.6) | 77.3 (4.2) | 77.6 (4.7) | ||
Income | .20 | .57 | ||||||||||
Pension | 410 (40.4) | 78 (38.4) | 100 (39.7) | 107 (37.4) | 125 (45.6) | 384 (37.9) | 84 (42.9) | 95 (40.5) | 91 (39.1) | 114 (42.0) | ||
Country of birth | .06 | .45 | ||||||||||
Australia | 519 (50.7) | 88 (42.3) | 114 (44.7) | 152 (53.1) | 165 (60.0) | 485 (50.7) | 115 (52.8) | 110 (48.2) | 119 (49.6) | 141 (52.2) | ||
BMI kg/m2 | .80 | .79 | ||||||||||
Mean (SD) | 27.9 (3.7) | 28.4 (3.8) | 28.1 (3.8) | 28.1 (3.7) | 27.1 (3.3) | 28.0 (3.7) | 28.4 (3.8) | 27.6 (3.6) | 27.6 (3.5) | 27.9 (3.8) | ||
Take 25D supplements | .42 | .18 | ||||||||||
No | 960 (93.8) | 199 (95.7) | 236 (92.5) | 265 (92.7) | 260 (94.5) | 897 (93.8) | 198 (90.8) | 215 (94.3) | 226 (94.2) | 258 (95.6) | ||
Season | <.0001 | <.0001 | ||||||||||
Winter: June to August | 249 (24.3) | 78 (37.5) | 70 (27.5) | 62 (21.7) | 39 (14.2) | 243 (25.4) | 69 (31.7) | 71 (31.1) | 62 (25.8) | 41 (15.2) | ||
Smoking status | .29 | .83 | ||||||||||
Current smoker | 53 (5.1) | 12 (5.4) | 19 (7.5) | 8 (2.8) | 14 (5.1) | 51 (5.4) | 11 (5.1) | 9 (4.0) | 14 (5.8) | 17 (6.3) | ||
Self-rated general health | .03 | .004 | ||||||||||
Fair/poor/very poor | 248 (24.4) | 57 (27.8) | 69 (27.4) | 73 (25.5) | 49 (17.8) | 249 (24.9) | 73 (33.8) | 56 (24.8) | 47 (19.6) | 61 (22.8) | ||
Doctor diagnosed conditions | .42 | .01 | ||||||||||
≥4 conditions | 218 (21.4) | 48 (23.4) | 48 (19.0) | 68 (23.8) | 54 (19.6) | 205 (21.6) | 63 (29.3) | 43 (18.9) | 42 (17.5) | 57 (21.2) | ||
Dementia | .39 | .58 | ||||||||||
Yes | 27 (2.6) | 8 (3.8) | 6 (2.4) | 3 (1.0) | 10 (3.6) | 24 (2.5) | 6 (2.8) | 7 (3.1) | 2 (0.8) | 9 (3.3) | ||
Depression | .28 | .46 | ||||||||||
Yes | 88 (8.6) | 20 (9.8) | 28 (11.1) | 21 (7.3) | 19 (6.9) | 76 (8.0) | 21 (9.8) | 21 (9.3) | 16 (6.7) | 18 (6.7) | ||
PTH (pg/mL) | <.0001 | .66 | ||||||||||
Mean (SD) | 5.6 (2.2) | 6.1 (2.6) | 5.7 (2.3) | 5.4 (2.0) | 5.3 (2.1) | 5.6 (2.2) | 5.7 (2.4) | 5.5 (2.3) | 5.6 (2.2) | 5.5 (2.1) | ||
eGFR (mL/min/1.73 m2)b Median (IQR) | 71.3 (29.4–100.0) | 72.1 (32.1–100.0) | 72.2 (33.2–99.6) | 72.2 (29.9–99.6)) | 68.5 (29.4–98.2) | .02 | 71.3 (29.4–100.0) | 67.7 (31.8–100.0) | 71.3 (30.0–97.5) | 71.4 (30.2–99.6) | 73.7 (35.5–99.6) | <.0001 |
Note: aANOVA for age, BMI, PTH, and eGFR and chi square for all other variables. bChronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.
Longitudinal Analyses
GEE analyses of the associations between baseline 25D and 1,25D levels and incidence of sarcopenia over time are shown in Tables 2 and 3. Serum levels in the lowest quartiles of both metabolites 25D (level <40.0nmol/L) and 1,25D (levels <62 pmol/L) were significantly associated with increased odds of incident sarcopenia compared to serum levels in the highest quartiles. Thus, for serum 25D levels below 40.0nmol/L the OR for the incidence of sarcopenia was 2.53 [95% confidence interval (CI) 1.14, 5.64] p = .02. For serum 1,25D level below 62 pmol/l the OR for sarcopenia was 2.67 (95% CI 1.28, 5.60) p = .01) after adjustment for covariates (Tables 2 and 3, Model 3). After further adjustment for either 25D or 1,25D (Tables 2 and 3, Model 4), associations of 25D and 1,25D with incident sarcopenia remained significant (25D: OR 2.40 (95% CI 1.02, 5.64) p = .04; 1,25D: OR 2.23 (95% CI 1.04, 4.80) p = .04). The association between 25D and 1,25D at baseline and sarcopenia at 5-year follow-up varied by time [vitamin D measure and time interaction: 25D β coefficient −0.90 (95% CI −1.28, 0.51, p < .0001) and for 1,25D β coefficient −0.84 (95% CI −1.23, −0.45, p < .0001)].
N = 709 . | Serum 25D Lowest Quartile <40.0 nmol/L . | Serum 25D Second Quartile 40–52.9 nmol/L . | Serum 25D Third Quartile 53–68.9 nmol/L . | Serum 25D Fourth Quartile ≥68.9 nmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.55 (1.33,4.90) p = .005 | 1.75 (0.91,3.39) p = .10 | 1.33 (0.69,2.58) p = .39 | 1 |
Model 2* | 2.48 (1.27,4.85) p = .01 | 1.90 (0.97,3.73) p = .10 | 1.43 (0.74,2.78) p = .29 | 1 |
Model 3* | 2.53 (1.14,5.64) p = .02 | 1.90 (0.89,4.10) p = .10 | 1.51 (0.73,3.12) p = .27 | 1 |
Model 4* | 2.40 (1.02,5.64) p = .04 | 1.86 (0.83,4.15) p = .13 | 1.47 (0.68,3.16) p = .33 | 1 |
N = 709 . | Serum 25D Lowest Quartile <40.0 nmol/L . | Serum 25D Second Quartile 40–52.9 nmol/L . | Serum 25D Third Quartile 53–68.9 nmol/L . | Serum 25D Fourth Quartile ≥68.9 nmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.55 (1.33,4.90) p = .005 | 1.75 (0.91,3.39) p = .10 | 1.33 (0.69,2.58) p = .39 | 1 |
Model 2* | 2.48 (1.27,4.85) p = .01 | 1.90 (0.97,3.73) p = .10 | 1.43 (0.74,2.78) p = .29 | 1 |
Model 3* | 2.53 (1.14,5.64) p = .02 | 1.90 (0.89,4.10) p = .10 | 1.51 (0.73,3.12) p = .27 | 1 |
Model 4* | 2.40 (1.02,5.64) p = .04 | 1.86 (0.83,4.15) p = .13 | 1.47 (0.68,3.16) p = .33 | 1 |
Note: *Model 1 = unadjusted, Model 2 = adjusted for age, Model 3 = Model 2 plus adjusted for season, income, smoking status, physical activity, vitamin D supplement use no of comorbidities, depressive symptoms, dementia, ADL disability, no of medications, white cell count, albumin, PTH eGFR, Model 4 = Model 3 plus 1,25D.
N = 709 . | Serum 25D Lowest Quartile <40.0 nmol/L . | Serum 25D Second Quartile 40–52.9 nmol/L . | Serum 25D Third Quartile 53–68.9 nmol/L . | Serum 25D Fourth Quartile ≥68.9 nmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.55 (1.33,4.90) p = .005 | 1.75 (0.91,3.39) p = .10 | 1.33 (0.69,2.58) p = .39 | 1 |
Model 2* | 2.48 (1.27,4.85) p = .01 | 1.90 (0.97,3.73) p = .10 | 1.43 (0.74,2.78) p = .29 | 1 |
Model 3* | 2.53 (1.14,5.64) p = .02 | 1.90 (0.89,4.10) p = .10 | 1.51 (0.73,3.12) p = .27 | 1 |
Model 4* | 2.40 (1.02,5.64) p = .04 | 1.86 (0.83,4.15) p = .13 | 1.47 (0.68,3.16) p = .33 | 1 |
N = 709 . | Serum 25D Lowest Quartile <40.0 nmol/L . | Serum 25D Second Quartile 40–52.9 nmol/L . | Serum 25D Third Quartile 53–68.9 nmol/L . | Serum 25D Fourth Quartile ≥68.9 nmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.55 (1.33,4.90) p = .005 | 1.75 (0.91,3.39) p = .10 | 1.33 (0.69,2.58) p = .39 | 1 |
Model 2* | 2.48 (1.27,4.85) p = .01 | 1.90 (0.97,3.73) p = .10 | 1.43 (0.74,2.78) p = .29 | 1 |
Model 3* | 2.53 (1.14,5.64) p = .02 | 1.90 (0.89,4.10) p = .10 | 1.51 (0.73,3.12) p = .27 | 1 |
Model 4* | 2.40 (1.02,5.64) p = .04 | 1.86 (0.83,4.15) p = .13 | 1.47 (0.68,3.16) p = .33 | 1 |
Note: *Model 1 = unadjusted, Model 2 = adjusted for age, Model 3 = Model 2 plus adjusted for season, income, smoking status, physical activity, vitamin D supplement use no of comorbidities, depressive symptoms, dementia, ADL disability, no of medications, white cell count, albumin, PTH eGFR, Model 4 = Model 3 plus 1,25D.
N = 663 . | Serum 1,25D Lowest Quartile <62 pmol/L . | Serum 1,25D Second Quartile 62–96.9 pmol/L . | Serum 1,25D Third Quartile 97–145.9 pmol/L . | Serum 1,25D Fourth Quartile ≥146 pmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.27 (1.28,4.01) p = .01 | 0.95 (0.48,1.87) p = .88 | 0.75 (0.38,1.48) p = .41 | 1 |
Model 2* | 2.01 (1.12,3.61) p = .02 | 0.88 (0.44,1.73) p = .71 | 0.78 (0.38,1.54) p = .47 | 1 |
Model 3* | 2.67 (1.28,5.60) p = .01 | 1.08 (0.49,2.41) p = .85 | 1.03 (0.47,2.25) p = .95 | 1 |
Model 4* | 2.23 (1.04,4.80) p = .04 | 1.01 (0.45,2.26) p = .98 | 0.97 (0.45,2.10) p = .93 | 1 |
N = 663 . | Serum 1,25D Lowest Quartile <62 pmol/L . | Serum 1,25D Second Quartile 62–96.9 pmol/L . | Serum 1,25D Third Quartile 97–145.9 pmol/L . | Serum 1,25D Fourth Quartile ≥146 pmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.27 (1.28,4.01) p = .01 | 0.95 (0.48,1.87) p = .88 | 0.75 (0.38,1.48) p = .41 | 1 |
Model 2* | 2.01 (1.12,3.61) p = .02 | 0.88 (0.44,1.73) p = .71 | 0.78 (0.38,1.54) p = .47 | 1 |
Model 3* | 2.67 (1.28,5.60) p = .01 | 1.08 (0.49,2.41) p = .85 | 1.03 (0.47,2.25) p = .95 | 1 |
Model 4* | 2.23 (1.04,4.80) p = .04 | 1.01 (0.45,2.26) p = .98 | 0.97 (0.45,2.10) p = .93 | 1 |
Note: *Model 1 = unadjusted, Model 2 = adjusted for age, Model 3 = Model 2 plus adjusted for season, income, smoking status, physical activity, vitamin D supplement use, no of comorbidities, depressive symptoms, dementia, ADL disability, no of medications, white cell count, albumin, and PTH, eGFR, Model 4 = Model 3 plus 25D.
N = 663 . | Serum 1,25D Lowest Quartile <62 pmol/L . | Serum 1,25D Second Quartile 62–96.9 pmol/L . | Serum 1,25D Third Quartile 97–145.9 pmol/L . | Serum 1,25D Fourth Quartile ≥146 pmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.27 (1.28,4.01) p = .01 | 0.95 (0.48,1.87) p = .88 | 0.75 (0.38,1.48) p = .41 | 1 |
Model 2* | 2.01 (1.12,3.61) p = .02 | 0.88 (0.44,1.73) p = .71 | 0.78 (0.38,1.54) p = .47 | 1 |
Model 3* | 2.67 (1.28,5.60) p = .01 | 1.08 (0.49,2.41) p = .85 | 1.03 (0.47,2.25) p = .95 | 1 |
Model 4* | 2.23 (1.04,4.80) p = .04 | 1.01 (0.45,2.26) p = .98 | 0.97 (0.45,2.10) p = .93 | 1 |
N = 663 . | Serum 1,25D Lowest Quartile <62 pmol/L . | Serum 1,25D Second Quartile 62–96.9 pmol/L . | Serum 1,25D Third Quartile 97–145.9 pmol/L . | Serum 1,25D Fourth Quartile ≥146 pmol/L (Referent Category) . |
---|---|---|---|---|
Model 1* | 2.27 (1.28,4.01) p = .01 | 0.95 (0.48,1.87) p = .88 | 0.75 (0.38,1.48) p = .41 | 1 |
Model 2* | 2.01 (1.12,3.61) p = .02 | 0.88 (0.44,1.73) p = .71 | 0.78 (0.38,1.54) p = .47 | 1 |
Model 3* | 2.67 (1.28,5.60) p = .01 | 1.08 (0.49,2.41) p = .85 | 1.03 (0.47,2.25) p = .95 | 1 |
Model 4* | 2.23 (1.04,4.80) p = .04 | 1.01 (0.45,2.26) p = .98 | 0.97 (0.45,2.10) p = .93 | 1 |
Note: *Model 1 = unadjusted, Model 2 = adjusted for age, Model 3 = Model 2 plus adjusted for season, income, smoking status, physical activity, vitamin D supplement use, no of comorbidities, depressive symptoms, dementia, ADL disability, no of medications, white cell count, albumin, and PTH, eGFR, Model 4 = Model 3 plus 25D.
Discussion
In this epidemiological study amongst community-dwelling older men living in Australia, we demonstrate a significant relationship between serum vitamin D levels and the incidence of sarcopenia over 2 and 5 years of follow-up. The relationship remained after adjusting for potential confounders or the alternate vitamin D metabolite (25D or 1,25D), suggesting independent effects of 25D and 1,25D on sarcopenia. Our study is the first to investigate longitudinal associations between the biologically active form of vitamin D, 1,25D, and sarcopenia using the FNIH cut points in older community dwelling men.
There are plausible biological pathways for a role of vitamin D metabolites with muscle mass and strength. Current evidence suggests that 25D may be taken up and stored by skeletal muscle (28) and there is the possibility that there are direct effects of vitamin D on muscle strength (29). However, the evidence is not always consistent as some cross-sectional studies find associations between serum 25D levels and poor muscle function (30) while others do not (31). In a review of published studies, Annweiler and colleagues (6) discuss the reasons for the divergence in study findings, some of which may be due to methodological differences, including a lack of consideration of confounding influences in some studies. Our results regarding the relationship between serum 25D and the incidence of sarcopenia are comparable to those of another longitudinal study amongst older men and women which was over a shorter, 3-year follow-up (10).
A number of processes may underlie the association between vitamin D levels and the incidence of sarcopenia. One possible explanation is that individuals with functional or health problems such as sarcopenia are more likely to have low vitamin D levels due to the lack of time spent outdoors and therefore lack of UV exposure (32). However, the above associations remained after adjusting for comorbidities and physical activity at the three time points, making this possibility less likely.
The novel aspect of our study is the demonstration that low serum 1,25D levels at baseline are associated with the incidence of sarcopenia over time. Of note, studies so far have not investigated the association between serum 1,25D concentrations and sarcopenia in older people despite plausible biological concepts that would suggest such associations. While the potential mechanisms that link 1,25D status to muscle function are complex, the presence of the VDR in muscle tissue support a physiological role of vitamin D in muscle function (33). This is further substantiated by the observation that polymorphisms in the VDR gene are related to differences in muscle strength (34) and that vitamin D deficiency is associated with decreased muscle strength (35). In addition, VDR in muscle tissue is a nuclear receptor that binds 1,25D with high affinity and stimulates its actions to regulate protein synthesis. The direct influence of 1,25D on calcium homeostasis is also believed to impact contractile properties of muscle cells (36).
There are established physiological reasons for the age-related decline in 1,25D levels which are in line with our findings that show mean 1,25D levels decline with age although this was not found for 25D levels. Therefore, older men may be at risk of 1,25D deficiency due to a progressive decline in renal vitamin D metabolism (37). However, 1,25D levels are also determined by a range of other age-related factors, including impaired VDR function or reduced receptor expression (38).
The main strengths of our study are that it involves a large, representative sample of community-dwelling older Australian men aged 70 and over with longitudinal data. Recent studies only includes 25D as the preferred assay, related to the fact that 1,25D levels can be normal or even elevated in the presence of secondary hyperparathyroidism. We have the advantage of having data on 1,25D levels in addition to a wide range of data that has allowed us to investigate and adjust for a number of important variables in examining associations between the two baseline vitamin D measures and incidence of sarcopenia over a 5-year period.
Our study shows that only a small proportion of men (6.5%) were taking vitamin D supplements. Clinical trials on vitamin D supplementation and effects on muscle strength, have been inconclusive. A recent systematic review and meta-analysis of seventeen randomized controlled trials suggested that vitamin D supplementation did not have a significant effect on muscle strength in vitamin D replete adults, but a significant improvement in strength was observed in the trials in which the mean starting level of 25D was 25 nmol/L or below (39).
We used GEE to examine the longitudinal relationship between baseline low vitamin D metabolites and incidence of sarcopenia over 5 years. The most important advantage of a technique like GEE in describing longitudinal relationships is that all available data are used, which increases the power to detect relationships. GEE also takes into account the time-varying nature of both the outcome and the exposure (this was only measured in this study at baseline). Another advantage is that it can also be used if participants have unequal numbers of observations and/or unequally spaced time intervals between observations (29). Both situations occur frequently in epidemiological cohort studies. Longitudinal studies are subject to attrition (loss at follow-up), due to non-participation or mortality. GEE analysis methodology however is robust with regard to data missing at random in longitudinal analyses. As we lack clinical data for the men who refused to participate in the study, we are unable to provide a direct comparison between participants and non-participants in the study endpoints. Men who participated in CHAMP are considered to be a healthier group since there are able to attend the clinic at Concord hospital so may be more likely to participate in the study. Men without follow-up data showed no significant differences by age, BMI, no of co-morbidities but had significantly poorer self-reported health, frailty, and depressive symptoms compared with those to attended at follow-up.
We used DEXA to measure body composition which has an advantage in the ability to estimate total and sub compartments of lean mass and we used the recently developed FNIH criteria for the assessment of sarcopenia derived from nine large studies among community dwelling older people that are readily generalizable to our study population. Currently there are very few studies that have used this definition for sarcopenia in the general population (15).
We have the advantage of having data on a range of clinical measures including PTH that have allowed us to investigate and adjust for these variables in examining associations between the two vitamin D measures and sarcopenia. Another advantage is that we have three time point data collected on muscle mass, muscle strength, and other covariates so that we could look at the 25D and 1,25D and associations with incidence in sarcopenia over time. The age distribution of the men in the CHAMP study is similar to that of the census population (13) and the prevalence of self-reported disease in CHAMP participants is very similar to that found in a recent Australian national telephone survey of men’s health (40).
There are some study limitations. We did not obtain data on time spent outdoors or sunscreen use or other sun protection behaviors (eg, Wearing hats, sunglasses, clothing), which is important since UVB exposure is the primary source of vitamin D. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation was used since it is preferred to the Modification of Diet in Renal Disease equation but both are shown to have limitations in determining renal function (41). In addition, creatinine is associated with muscle mass, and therefore a biased measure of renal function among older adults, especially when there may be changes in body composition. Associations between 25D and sarcopenia have only been shown in women in some studies in this area (7), however since our study was among men only, we were unable to confirm these findings. We only had baseline 25D and 1,25D levels, and this single measure may not reflect long-term levels at follow up.
Conclusion
Our findings suggest important links between the two measures of vitamin D and incidence of sarcopenia, but the findings do not prove causality. It is important to note that our findings are of clinical importance as it highlights whether correction of vitamin D deficiency through appropriate interventions, could influence the incidence of sarcopenia over time. The independent association between 25D and 1,25 D and sarcopenia suggests that they may influence sarcopenia through different biological mechanisms and pathways.
Supplementary Material
Supplementary data is available at Journals of Gerontology Series A: Biological Sciences and Medical Sciences online.
Funding
The views expressed are those of the authors, not of the funders. Data analysis and interpretation were carried out by the authors independently of the funding sources based on the available data. The corresponding author had full access to the survey data and had final responsibility for the decision to submit for publication. The funding body played no role in the formulation of the design, methods, subject recruitment, data collection, analysis, or preparation of this paper. The CHAMP study is funded by the National Health and Medical Research Council (project grant number 301916) and the Ageing And Alzheimer’s Institute.
Conflicts of Interest
The authors declare that they have no conflict of interest.
Acknowledgments
We thank all the staff working on CHAMP and the participants in the project.
References