Dataset Information

Vitamin D may not improve lipid levels: a serial clinical laboratory data study.

ABSTRACT:

Background

Vitamin D deficiency is highly prevalent and is associated with dyslipidemia and cardiovascular disease. The impact of correcting vitamin D deficiency on blood lipids, strong cardiovascular disease prognostic factors, is unknown.

Methods and results

To determine relationships between 25-hydroxyvitamin D levels and lipids, we analyzed 4.06 million deidentified patient laboratory test results from September 2009 through February 2011. We performed a cross-sectional study of this population to determine associations between 25-hydroxyvitamin D levels and lipids across clinically defined strata. We also conducted a retrospective cohort study of vitamin D deficient patients to investigate how changes in 25-hydroxyvitamin D levels relate to changes in lipid levels. After exclusions, 107 811 patients with serial testing were selected for cross-sectional analysis. Compared with vitamin D deficient patients (<20 ng/mL), those with optimal levels (≥30 ng/mL) had lower mean total cholesterol (-1.9 mg/dL; 95% confidence interval [95% CI], -1.2 to -2.7; P<0.0001), lower low-density lipoprotein cholesterol (-5.2 mg/dL; 95% CI, -4.5 to -5.8; P<0.0001), higher high-density lipoprotein cholesterol (4.8 mg/dL; 95% CI, 4.5-5.0; P<0.0001), and lower triglycerides (-7.5 mg/dL; 95% CI, -6.2 to -8.7; P<0.0001). For the retrospective cohort analysis, raising vitamin D levels from <20 to ≥30 ng/mL (n=6260), compared with remaining at <20 ng/mL (n=2332), was associated with a mean increase in total cholesterol (0.77 mg/dL; 95% CI, 0.18-1.36; P=0.01) and high-density lipoprotein cholesterol (0.42 mg/dL; 95% CI, 0.08-0.76; P=0.02) but nonsignificant changes in low-density lipoprotein cholesterol (0.32 mg/dL; 95% CI, -0.01 to 0.66; P=0.06) and triglycerides (0.04 mg/dL; 95% CI, -2.16 to 2.23 mg/dL; P=0.97).

Conclusions

Although vitamin D deficiency is associated with an unfavorable lipid profile in cross-sectional analyses, correcting for a deficiency might not translate into clinically meaningful changes in lipid concentrations; however, data from intervention trials are required to confirm these findings.

SUBMITTER: Ponda MP

PROVIDER: S-EPMC3713625 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Vitamin D may not improve lipid levels: a serial clinical laboratory data study.

Ponda Manish P MP Huang Xiaohua X Odeh Mouneer A MA Breslow Jan L JL Kaufman Harvey W HW

Circulation 20120620 3

<h4>Background</h4>Vitamin D deficiency is highly prevalent and is associated with dyslipidemia and cardiovascular disease. The impact of correcting vitamin D deficiency on blood lipids, strong cardiovascular disease prognostic factors, is unknown.<h4>Methods and results</h4>To determine relationships between 25-hydroxyvitamin D levels and lipids, we analyzed 4.06 million deidentified patient laboratory test results from September 2009 through February 2011. We performed a cross-sectional study ...[more]

PMID: 22718799

Similar Datasets

Project description:Recently, interest in supplementing vitamin D (Vit D) to improve aspects of health, mainly in human fertility, has emerged. Still, supplementation of Vit D above the minimum required levels has yet to be explored in cattle despite evidence for Vit D receptors in reproductive tissues. The objective of this study was to establish if a dose-response relationship exists between Vit D exposure and success of in vitro production (IVP) of embryos and, if acute supplementation of Vit D improves pregnancy rates during timed artificial insemination (TAI) of dairy cows. Cumulus-oocyte complexes (COCs) were obtained from ovaries acquired from a local abattoir and cultured in five different IVP treatments from three separate collections (Control, 50, 100, 150, and 200 ng/mL of 1,25(OH)2D3; n = 20-30 COCs/group). In Experiment 2, dairy breed cows (n = 100) were synchronized for TAI with the PresynchOvsynch protocol. Cows received 150,000 IU of Vit D (n = 48) or castor oil as control (n = 53) along with gonadotropin-releasing hormone (GnRH) 24 h before TAI. Serum samples were collected before and 24 h after treatment. A small cohort of cows (n = 4) received the same treatments in two separate cycles and follicular fluid (FF) was collected after 24 h for calcidiol (25OHD) analyses. Increased concentrations of Vit D resulted in decreased rates of maturation of COC (150 and 200 ng/mL vs. control and 50 ng/mL; P = 0.01). Supplementation with 50 ng/mL resulted in greater numbers of early blastocyst and blastocyst stage embryos (P < 0.009). Pregnancy at first breeding did not differ (P = 0.13) between groups, but serum 25OHD increased in treated females after 24 h (P = 0.002). The FF 25OHD levels were reflective of serum levels, however, the observed increase in the treatment cycle (P = 0.04) was parallel to an overall increase in serum 25OHD during the entire second cycle, likely due to increased environmental sunlight exposure (March, control vs. May, treatment). A similar increase in the serum 25OHD in the lactating commercial herd maintained in covered housing was not observed, although experiments were conducted during a similar timeframe. This herd had levels of 25OHD near the low end of sufficiency according to National Research Council (NRC) guidelines. We conclude mild Vitamin D supplementation with concentrations at the higher end of NRC guidelines can improve maturation rates of recovered COCs. However, longer term supplementation may be needed to appreciate any benefits on fertility.

Project description:ObjectivesDeficiency of 25-hydroxyvitamin D (25[OH]D) is associated with increased risk for cardiovascular disease, perhaps mediated through dyslipidemia. Deficient 25(OH)D is cross-sectionally associated with dyslipidemia, but little is known about longitudinal lipid changes. The aim of this study was to determine the relationship of 25(OH)D deficiency to longitudinal lipid changes and risk for incident dyslipidemia.MethodsThis was a longitudinal community-based study of 13 039 participants from the ARIC (Atherosclerosis Risk in Communities) study who had 25(OH)D and lipids measured at baseline (1990-1992) and lipids remeasured in 1993 to 1994 and 1996 to 1998. Mixed-effect models were used to assess the association of 25(OH)D and lipid trends after adjusting for clinical characteristics and for baseline or incident use of lipid-lowering therapy. Risk for incident dyslipidemia was determined for those without baseline dyslipidemia.ResultsBaseline mean ± SD age was 57 ± 6 y and 25(OH)D was 24 ± 9 ng/mL. Participants were 57% women, 24% black. Over a mean follow-up of 5.2 y, the fully adjusted average differences (95% confidence interval [CI]) comparing deficient (<20 ng/mL) to optimal (≥30 ng/mL) 25(OH)D were: total cholesterol (TC) -2.40 mg/dL (-4.21 to -0.60), high-density lipoprotein cholesterol (HDL-C) -3.02 mg/dL (-3.73 to -2.32) and the ratio of TC to HDL-C 0.18 (0.11-0.26). Those with deficient compared with optimal 25(OH)D had modestly increased risk for incident dyslipidemia in demographic-adjusted models (relative risk [RR], 1.19; 95% CI, 1.02-1.39), which was attenuated in fully adjusted models (RR, 1.12; 95% CI, 0.95-1.32).ConclusionsDeficient 25(OH)D was prospectively associated with lower TC and HDL-C and a greater ratio of TC to HDL-C after considering factors such as diabetes and adiposity. Further work including randomized controlled trials is needed to better assess how 25(OH)D may affect lipids and cardiovascular risk.

Project description:BackgroundThe use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied.ObjectiveThe aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms-logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms-when applied to clinical laboratory datasets.MethodsWe applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5.ResultsThe experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency.ConclusionsThe XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

Project description:ObjectivesTo compare the findings of standard clinical assessments and of complementary clinical and laboratory methods for determining whether community-wide treatment for trachoma is warranted in a remote Queensland community.DesignThree cross-sectional screening surveys, 2019-2021, complemented by laboratory pathology testing.SettingSmall community in northwest Queensland with geographic and cultural ties to Northern Territory communities where trachoma persists.ParticipantsChildren aged 1-14 years; opportunistic screening of people aged 15 years or more.Main outcome measuresPrevalence of clinical signs of trachoma, Chlamydia trachomatis infection, ocular non-chlamydial infections, and seropositivity for antibodies to the C. trachomatis Pgp3 protein.ResultsDuring the three surveys, 73 examinations of 58 children aged 1-4 years, 309 of 171 aged 5-9 years, and 142 of 105 aged 10-14 years for trachoma were undertaken, as were 171 examinations of 164 people aged 15 years or more; 691 of 695 examinations were of Aboriginal or Torres Strait Islander people (99%), 337 were of girls or young women (48%). Clinical signs consistent with trachomatous inflammation-follicular were identified in 5-9-year-old children 23 times (7%), including in eleven with non-chlamydial infections and one with a C. trachomatis infection. One child (10-14 years) met the criteria for trachomatous scarring. Two of 272 conjunctival swab samples (all ages) were polymerase chain reaction-positive for C. trachomatis (0.7%). Two of 147 people aged 15 years or more examined in 2019 had trichiasis, both aged 40 years or more. Seven of 53 children aged 1-9 years in 2019 and seven of 103 in 2021 were seropositive for anti-Pgp3 antibodies.ConclusionsDespite the prevalence of clinical signs consistent with trachomatous inflammation-follicular among 5-9-year-old children exceeding the 5% threshold for community-wide treatment, laboratory testing indicated that childhood exposure to ocular C. trachomatis is rare in this community. Laboratory testing should be integrated into Australian trachoma guidelines.

Project description:BackgroundData standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods.ObjectiveWe aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology.MethodsWe developed a method called standardization algorithm for laboratory test-categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned.ResultsThe performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests).ConclusionsThe proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

Dataset Information

Vitamin D may not improve lipid levels: a serial clinical laboratory data study.

Background

Methods and results

Conclusions

Publications

Vitamin D may not improve lipid levels: a serial clinical laboratory data study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets