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Association of serum 25-hydroxyvitamin D concentrations with risk of dementia among individuals with type 2 diabetes: A cohort study in the UK Biobank

['Tingting Geng', 'Department Of Nutrition', 'Food Hygiene', 'Hubei Key Laboratory Of Food Nutrition', 'Safety', 'Ministry Of Education Key Lab Of Environment', 'Health', 'State Key Laboratory Of Environmental Health', 'Incubating', 'School Of Public Health']

Date: 2022-01 

The UK Biobank study was approved by the National Information Governance Board for Health and Social Care in England and Wales, the Community Health Index Advisory Group in Scotland, and the North West Multi-centre Research Ethics Committee. All participants gave written informed consent. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline ( S1 Checklist ). Our prospective analysis plan is included in ( S1 Text ).

In the present study, we included 23,748 prevalent T2D cases at recruitment. Prevalent cases of T2D were identified using a UK Biobank algorithm that identified cases through multiple sources including self-report, trained health professional queried medical history, and medication history, which has been shown to be a reliable measurement, with 96% accuracy [ 21 ]. Overall, we included data from 13,486 participants who were ≥60 years old in the main analysis after excluding participants who had a previous diagnosis of dementia (N = 18), patients with incomplete information on serum 25(OH)D concentration (N = 2,096), and cases that were identified as involving death from dementia in the death registry (N = 12), to minimize the effect of VD on survival ( S1 Fig ).

The UK Biobank is a large population-based prospective cohort study for long-term study of genetic and lifestyle determinants of a wide range of common diseases of middle and old age. The design of the UK Biobank has been presented previously [ 20 ]. Briefly, it recruited more than 500,000 participants (aged 40–69 years) from 22 assessment centers across England, Scotland, and Wales between 2006 and 2010. Extensive phenotypic and genotypic data were collected at recruitment. Information on socio-demographics, habitual diet, lifestyle factors, and medical history was collected through touch-screen questionnaires; anthropometric data were obtained through physical measurements. Blood, urine, and saliva samples were also collected at baseline in all participants.

Blood samples were collected from consenting participants at recruitment, separated by components, and stored at UK Biobank (−80°C and liquid nitrogen) until analysis. Serum concentration of 25(OH)D (nmol/L) was measured using the chemiluminescent immunoassay method (DiaSorin Liaison XL). The assay’s analytical range was between 10 nmol/L and 375 nmol/L. The coefficient of variation for serum 25(OH)D ranged between 5.04% and 6.14%, and the results of the external quality assurance were 100%. Full details on assay performance have been published previously [ 22 ].

Dementia cases were ascertained using the algorithms provided by UK Biobank, which were generated based on electronic health records (EHRs) including hospital admissions and the death registry, using ICD-9 and ICD-10 codes ( S1 Table ). Medical history information on dementia-specific prescriptions including memantine, donepezil, galantamine, and rivastigmine was also used to identify prevalent dementia cases at baseline [ 23 ].

Information on height and body weight was collected during a nurse-led interview. Body mass index (BMI, kg/m 2 ) was calculated as body weight in kilograms divided by the square of height in meters. Physical activity was assessed using the short-form International Physical Activity Questionnaire (IPAQ) including walking and moderate and vigorous intensity activities. Total physical activity in metabolic equivalent minutes per week (MET-min/week) was computed from the IPAQ. Information on habitual diet and alcohol intake was captured by a touchscreen food frequency questionnaire at recruitment in all participants. A hypothesis-driven dietary pattern was computed based on 5 components to reflect the overall diet. Participants received points for consumption in 4 healthy food categories (fruits, vegetables, whole grains, and low-fat dairy) based on quintile (from 5 points in the highest quintile to 1 point in the lowest quintile). The category for unhealthy food (red and processed meat) was inversely scored. The overall diet score then was categorized into quintiles. Data on medication history were collected via interview. If the participants were not sure about the types of medications they were taking, they were asked to provide the medications they were taking later in the visit.

Statistical analysis

Each participant’s person-years were calculated from the date of recruitment to the date of reported dementia diagnosis, death, or loss to follow-up, or 28 February 2018, whichever occurred first. As 16 individuals were diagnosed with both AD and VD, we calculated the person-years for AD and VD separately. We imputed missing values (<5.3%) using multiple imputation by chained equations with 20 imputations. Given that few participants had serum 25(OH)D ≥ 75 nmol/L in the current study population, serum vitamin D status was categorized into 3 groups: severely deficient (<25 nmol/L), moderately deficient (25 to <50 nmol/L), and insufficient and above (≥50 nmol/L), according to the Endocrine Society Clinical Practice Guidelines [24]. Distributions of the baseline characteristics were compared across the categories of the serum 25(OH)D concentration.

We used multivariable Cox proportional hazards regression models to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between serum 25(OH)D concentration and risk of all-cause dementia, AD, and VD. The relationships between serum 25(OH)D level and risk of the outcomes were first evaluated on a continuous scale using restricted cubic spline analysis. We selected the number of knots based on the values of the Akaike information criterion to fit the best-approximating model, chose the lowest value of serum 25(OH)D as reference, and tested for linearity by Wald test.

In Model 1, we adjusted for the following potential confounders: age at recruitment (continuous, years), sex (male, female), education (college or university degree, A/AS levels or equivalent or O levels/General Certificate of Secondary Education [GCSE] or Certificate of Secondary Education or equivalent, National Vocational Qualification or Higher National Diploma or Higher National Certificate or equivalent or other professional qualifications, none of the above), socio-economic status (Townsend deprivation index, continuous), ethnicity (White, Asian, Black, Mixed), blood collection season (Dec–Feb, Mar–May, Jun–Aug, Sep–Nov), sun-exposure time in summer (continuous, hours/day), and APOE ε4 genotype (carrier, non-carrier). In Model 2, we further adjusted for BMI (continuous, kg/m2), alcohol intake (never or special occasions, monthly to weekly, daily), smoking status (never, past, current), physical activity (continuous, MET-hours/week), healthy diet score (in quintiles), sleep duration (≤6, 7–8, ≥9 hours/day), and multivitamin intake (yes, no). In Model 3, we further adjusted for diabetes duration (continuous, years); concentrations of hemoglobin A1c (HbA1c; continuous, mmol/mol), total cholesterol (TC; continuous, mmol/L), triglycerides (TGs; continuous, mmol/L), low-density lipoprotein cholesterol (LDL-C; continuous, mmol/L), and C-reactive protein (CRP; continuous, mg/L); medication for diabetes (none, only oral medicine, insulin and others), medication for hypertension and cholesterol (yes, no); and history of hypertension, cardiovascular disease, cancer, and depression (yes, no). The median values were assigned to each category of serum 25(OH)D concentration to test for linear trends.

We also stratified the analyses by sex (male, female), APOE ε4 genotype (carrier, non-carrier), BMI (≤30, >30 kg/m2), diabetes duration (≤7, >7 years), and smoking status (never, ever). The multiplicative interactions between serum vitamin D status and the stratification factors on the risk of dementia were tested using the likelihood ratio test by including an interaction term.

Several sensitivity analyses were conducted. First, to minimize the possibility of reverse causality in the observed associations, we repeated the analyses excluding participants with less than 2 years of follow-up. Second, as kidney function may influence circulating vitamin D levels and cognitive function, estimated glomerular filtration rate (eGFR) was further adjusted for. Third, as frailty is a potential risk factor of dementia, we further adjusted for walking pace and hearing impairment. Fourth, serum calcium was adjusted for, and finally, vitamin D supplements were additionally adjusted for. Fifth, we also repeated the analysis categorizing serum 25(OH)D values as severely deficient (<25 nmol/L), moderately deficient (25 to <50 nmol/L), insufficient (50 to <75 nmol/L), and sufficient (≥75 nmol/L). In addition, we performed the main analyses in the full cohort including individuals with or without T2D (≥60 years old; N = 192,862). Finally, to further clarify the potential effect modification of T2D, we also tested the association of vitamin D with risk of outcomes stratified by T2D status. The last 4 sensitivity analyses were requested by the reviewers, and were not included in the analysis plan.

All analyses were performed using SAS version 9.4 (SAS Institute) and Stata statistical software, release 14.0 (StataCorp, College Station, Texas), and a 2-sided P < 0.05 was set as the threshold for statistical significance.

[END]

[1] Url: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003906

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