A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development and osteoporosis.

In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of disease, analysis of multi-view data with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets (GKMAHCE). This generalized semi-parametric (a mixed-effect linear model) approach includes the marginal and joint Hadamard product of features from different views of data. The proposed kernel machine approach considers multi-view data as predictor variables to allow a more thorough and comprehensive modeling of a complex trait. We applied GKMAHCE approach to both synthesized datasets and real multi-view datasets from adolescent brain development and osteoporosis study. Our experiments demonstrate that the proposed method can effectively identify higher-order composite effects and suggest that corresponding features (genes, region of interests, and chemical taxonomies) function in a concerted effort. We show that the proposed method is more generalizable than existing ones. To promote reproducible research, the source code of the proposed method is available at.

Identification and Functional Characterization of Metabolites for Bone Mass in Peri- and Postmenopausal Chinese Women.

CONTEXT: Although metabolic profiles appear to play an important role in menopausal bone loss, the functional mechanisms by which metabolites influence bone mineral density (BMD) during menopause are largely unknown. OBJECTIVE: We aimed to systematically identify metabolites associated with BMD variation and their potential functional mechanisms in peri- and postmenopausal women. DESIGN AND METHODS: We performed serum metabolomic profiling and whole-genome sequencing for 517 perimenopausal (16%) and early postmenopausal (84%) women aged 41 to 64 years in this cross-sectional study. Partial least squares regression and general linear regression analysis were applied to identify BMD-associated metabolites, and weighted gene co-expression network analysis was performed to construct co-functional metabolite modules. Furthermore, we performed Mendelian randomization analysis to identify causal relationships between BMD-associated metabolites and BMD variation. Finally, we explored the effects of a novel prominent BMD-associated metabolite on bone metabolism through both in vivo/in vitro experiments. RESULTS: Twenty metabolites and a co-functional metabolite module (consisting of fatty acids) were significantly associated with BMD variation. We found dodecanoic acid (DA), within the identified module causally decreased total hip BMD. Subsequently, the in vivo experiments might support that dietary supplementation with DA could promote bone loss, as well as increase the osteoblast and osteoclast numbers in normal/ovariectomized mice. Dodecanoic acid treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (eg,10, 100 µM). CONCLUSIONS: This study sheds light on metabolomic profiles associated with postmenopausal osteoporosis risk, highlighting the potential importance of fatty acids, as exemplified by DA, in regulating BMD.

Malnutrition and its associated factors among elderly Chinese with physical functional dependency.

OBJECTIVE: To assess the prevalence and to identify the associated factors of malnutrition among elderly Chinese with physical functional dependency. DESIGN: Face-to-face interviews using standardised questionnaires were conducted to collect demographic information, health-related issues and psychosocial status. Physical function was measured by the Barthel Index (BI), and nutrition status was assessed by the Mini Nutritional Assessment-Short Form. Multivariate binary logistic regression was used to assess associated factors of malnutrition. SETTING: China. PARTICIPANTS: A total of 2323 participants (aged ≥ 60 years) with physical functional dependency in five provinces in China were enrolled using a multistage cluster sampling scheme. RESULTS: The prevalence of malnutrition was 17·9 % (95 % CI 16·3, 19·4). Multivariable binary logistic regression revealed the independent risk factors of poor nutrition status were being female, older age, lower educational status, poor hearing, poor physical functional status, lack of hobbies, low religious participation, poor social support, lack of social participation and changes in social participation. The study found that the most significant independent risk factor for malnutrition was complete physical functional dependence (OR 4·46, 95 % CI 2·92, 6·82). CONCLUSIONS: The findings of the study confirm that malnutrition and the risk of malnutrition are prevalent in Chinese older adults with physical functional dependency. In addition to demographic and physical health-related factors, psychosocial factors, which are often overlooked, are independently associated with nutrition status in Chinese older adults with physical functional dependency. A holistic approach should be adopted to screen for malnutrition and develop health promotion interventions in this vulnerable population.

A Review of Integrative Imputation for Multi-Omics Datasets.

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.

The good, the bad, and the ugly of calcium supplementation: a review of calcium intake on human health.

Calcium is an important integrative component of the human body and critical for human health. It has been well established that calcium intake is helpful in the prevention and treatment of osteoporosis, which has become one of the most serious public health problems across the world. However, community-dwelling adults with and without osteoporosis are rarely concerned or even not aware of the potential side effects of high or inappropriate doses of calcium intake. Some recent studies have revealed that excessive calcium intake might increase the risks of cardiovascular diseases. The purpose of this article was to review the health benefits, costs, and consequences of calcium supplementation on osteoporosis/osteoporotic fractures, cardiovascular events, kidney stones, gastrointestinal diseases, and other important diseases. In the end, we suggest that calcium supplementation should be prescribed and taken cautiously, accounting for individual patients' risks and benefits. Clearly, further studies are needed to examine the health effects of calcium supplementation to make any solid recommendations for people of different genders, ages, and ethnicities.

SNP rs11185644 of RXRA gene is identified for dose-response variability to vitamin D3 supplementation: a randomized clinical trial.

The level of serum 25-Hydroxyvitamin D [25(OH)D] has high heritability, suggesting that genes may contribute to variations in serum 25(OH)D level and vitamin D dose-response. As vitamin D deficiency has been linked to numerous diseases, understanding how genetic variation contributes to vitamin D dose-response is important for personalized vitamin D treatment and cost-effective disease prevention. To identify genetic variants responsible for vitamin D status and dose-response, we performed two vitamin D3 and calcium clinical supplementation trials in 2,207 postmenopausal Caucasian women. We examined the association of 291 SNPs with baseline serum 25(OH)D levels and 25(OH)D dose-response. Five SNPs, rs10500804 (P = 4.93 × 10-7), rs2060793 (P = 6.63 × 10-7), rs10741657 (P = 1.49 × 10-6), rs10766197 (P = 1.05 × 10-5) and rs11023380 (P = 7.67 × 10-5) in the CYP2R1 gene, as well as 6 SNPs, rs4588 (P = 7.86 × 10-7), rs2298850 (P = 1.94 × 10-6), rs1155563 (P = 6.39 × 10-6), rs705119 (P = 2.80 × 10-5), rs705120 (P = 1.08 × 10-4) and rs222040 (P = 1.59 × 10-4) in the GC gene were associated with baseline serum 25(OH)D levels. SNP rs11185644 near the RXRA was significantly associated with 25(OH)D dose-response (P = 1.01 × 10-4). Our data suggest that polymorphisms in the CYP2R1 and GC gene may contribute to variation in baseline serum 25(OH)D concentration, and that polymorphism rs11185644 may contribute to variation in 25(OH)D dose-response in healthy postmenopausal Caucasian women.

Tissue-specific pathway association analysis using genome-wide association study summaries.

MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: fzhxjtu@mail.xjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

DNA methylation levels of CYP2R1 and CYP24A1 predict vitamin D response variation.

Factors contributing to the variability of serum 25-hydroxyvitamin D [25(OH)D] in response to a given dose of vitamin D supplementation are largely unknown. We examined whether DNA methylation levels of Cytochrome P450 (CYP) enzymes (CYP2R1, CYP24A1, CYP27A1, and CYP27B1) are potential biomarkers predicting vitamin D response variation. We randomized 446 white postmenopausal women to a calcium and vitamin D (1100IU/day) intervention for at least 12 months. From these subjects, 18 with the highest 12-month increase in serum 25(OH)D were selected as "responders." Another 18 with the lowest 12-month increase in serum 25(OH)D were selected as "non-responders." DNA methylation levels between the groups were compared. To validate findings in the first study, association between DNA methylation levels and vitamin D response variation was studied in another 145 extended independent white postmenopausal women. In the first study, compared to non-responders, responders had significantly lower baseline DNA methylation levels in the promoter region of CYP2R1 (8% in the responders vs. 30% in the non-responders, P=0.004), and CYP24A1 (13% in the responders vs. 32% in the non-responders, P=0.001). In the validation study, for CYP2R1, baseline DNA methylation levels at eight CpG sites were negatively associated with 12-month increases in serum 25(OH)D (P<0.05). For CYP24A1, baseline DNA methylation levels at two CpG sites were also negatively associated with vitamin D response variation (r=-0.151, P=0.011; r=-0.131, P=0.025). These negative associations were consistent with the first study's results. Our findings indicate that baseline DNA methylation levels of CYP2R1 and CYP24A1 may predict vitamin D response variation. This article is part of a Special Issue entitled '16th Vitamin D Workshop'.

The rs1142345 in TPMT Affects the Therapeutic Effect of Traditional Hypoglycemic Herbs in Prediabetes.

Therapeutic interventions in prediabetes are important in the primary prevention of type 2 diabetes (T2D) and its chronic complications. However, little is known about the pharmacogenetic effect of traditional herbs on prediabetes treatment. A total of 194 impaired glucose tolerance (IGT) subjects were treated with traditional hypoglycemic herbs (Tianqi Jiangtang) for 12 months in this study. DNA samples were genotyped for 184 mutations in 34 genes involved in drug metabolism or transportation. Multinomial logistic regression analysis indicated that rs1142345 (A > G) in the thiopurine S-methyltransferase (TPMT) gene was significantly associated with the hypoglycemic effect of the drug (P = 0.001, FDR P = 0.043). The "G" allele frequencies of rs1142345 in the healthy (subjects reverted from IGT to normal glucose tolerance), maintenance (subjects still had IGT), and deterioration (subjects progressed from IGT to T2D) groups were 0.094, 0.214, and 0.542, respectively. Binary logistic regression analysis indicated that rs1142345 was also significantly associated with the hypoglycemic effect of the drug between the healthy and maintenance groups (P = 0.027, OR = 4.828) and between the healthy and deterioration groups (P = 0.001, OR = 7.811). Therefore, rs1142345 was associated with the clinical effect of traditional hypoglycemic herbs. Results also suggested that TPMT was probably involved in the pharmacological mechanisms of T2D.

Factors predicting vitamin D response variation in non-Hispanic white postmenopausal women.

OBJECTIVE: It is well documented that there is wide variation in the response of serum 25-hydroxyvitamin D [25(OH)D] to a given dose of vitamin D supplementation. Understanding factors affecting the response variation is important for identifying subjects who are susceptible to vitamin D deficiency or toxicity. This study aimed to evaluate potential predictors for vitamin D response variation. DESIGN AND PARTICIPANTS: A total of 1179 non-Hispanic white postmenopausal women were enrolled into a 4-yr calcium and vitamin D (1100 IU/d) clinical trial. Among them, serum 25(OH)D level of 1063 subjects were measured at both baseline and after 12 months treatment. Vitamin D response was computed for these 1063 subjects as the difference in levels of serum 25(OH)D concentration at the end of a 12-month vitamin D treatment compared with baseline. Stepwise linear regression was used to identify predictors of vitamin D response variation. RESULTS: Increase in vitamin D intake, baseline serum 25(OH)D level, baseline blood collection season, baseline serum calcium level, and baseline body mass index were predictors of vitamin D response variation. These five factors explained 46.8% of the vitamin D response variation in the 1063 subjects. The first three factors [increase in vitamin D intake, baseline serum 25(OH)D level, baseline blood collection season] remained as predictors in the 392 subjects with trial vitamin D supplementation. For the first time, our study indicated that season is an important prediction factor for vitamin D response variation. Subjects who started vitamin D treatment in a cold season (autumn and winter) achieved a significantly higher serum 25(OH)D increase than those started in a hot season (summer) (P < 0.001). CONCLUSION: Our study suggests that the increase in vitamin D supplementation, baseline serum 25(OH)D level, and the season when initiating the vitamin D supplementation can partially predict vitamin D response variation in non-Hispanic postmenopausal women.

The imprinted gene neuronatin is regulated by metabolic status and associated with obesity.

Using restriction fragment differential display (RFDD) technology, we have identified the imprinted gene neuronatin (Nnat) as a hypothalamic target under the influence of leptin. Nnat mRNA expression is decreased in several key appetite regulatory hypothalamic nuclei in rodents with impaired leptin signaling and during fasting conditions. Furthermore, peripheral administration of leptin to ob/ob mice normalizes hypothalamic Nnat expression. Comparative immunohistochemical analysis of human and rat hypothalami demonstrates that NNAT protein is present in anatomically equivalent nuclei, suggesting human physiological relevance of the gene product(s). A putative role of Nnat in human energy homeostasis is further emphasized by a consistent association between single nucleotide polymorphisms (SNPs) in the human Nnat gene and severe childhood and adult obesity.

Improvement of mapping accuracy by unifying linkage and association analysis.

It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests (FBATs) capture only partial information, leading to inefficiency. For example, FBATs will fail to detect even very tight linkage in the case where no allelic association exists, while a violation of the assumption of linkage equilibrium will result in biased estimation and reduced efficiency in linkage mapping. In this article, by using a data augmentation technique and the EM algorithm, we propose a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach is expected to have greater estimation accuracy and power. Monte Carlo simulations support our theoretical expectations and demonstrate that our new methodology: (1) is more powerful than either FBATs or classic linkage analysis; (2) can unbiasedly estimate genetic parameters regardless of whether association exists, thus remedying the bias and less precision of traditional linkage analysis in the presence of association; and (3) is capable of identifying tight linkage alone. The new approach also holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association study and pedigree-based linkage analysis into a coherent framework. Furthermore, our method is numerically stable and computationally efficient, as compared to existing parametric methods that use the simplex algorithm or Newton-type methods to maximize high-order multidimensional likelihood functions, and also offers the computation of Fisher's information matrix. Finally, we apply our methodology to a genetic study on bone mineral density (BMD) for the vitamin D receptor (VDR) gene and find that VDR is significantly linked to BMD at the one-third region of the wrist.

Patterns of linkage disequilibrium and haplotype distribution in disease candidate genes.

BACKGROUND: The adequacy of association studies for complex diseases depends critically on the existence of linkage disequilibrium (LD) between functional alleles and surrounding SNP markers. RESULTS: We examined the patterns of LD and haplotype distribution in eight candidate genes for osteoporosis and/or obesity using 31 SNPs in 1,873 subjects. These eight genes are apolipoprotein E (APOE), type I collagen alpha1 (COL1A1), estrogen receptor-alpha (ER-alpha), leptin receptor (LEPR), parathyroid hormone (PTH)/PTH-related peptide receptor type 1 (PTHR1), transforming growth factor-beta1 (TGF-beta1), uncoupling protein 3 (UCP3), and vitamin D (1,25-dihydroxyvitamin D3) receptor (VDR). Yin yang haplotypes, two high-frequency haplotypes composed of completely mismatching SNP alleles, were examined. To quantify LD patterns, two common measures of LD, D' and r2, were calculated for the SNPs within the genes. The haplotype distribution varied in the different genes. Yin yang haplotypes were observed only in PTHR1 and UCP3. D' ranged from 0.020 to 1.000 with the average of 0.475, whereas the average r2 was 0.158 (ranging from 0.000 to 0.883). A decay of LD was observed as the intermarker distance increased, however, there was a great difference in LD characteristics of different genes or even in different regions within gene. CONCLUSION: The differences in haplotype distributions and LD patterns among the genes underscore the importance of characterizing genomic regions of interest prior to association studies.

Role of genetics in osteoporosis.

Osteoporosis is a disease characterized by fragile bones and high susceptibility to low-trauma fractures. It is a serious health problem, especially in elderly women. Bone mineral density (BMD) has been employed most commonly as the index for defining and studying osteoporosis. BMD has high genetic determination, with heritability ranging from 50 to 90%. Various gene-mapping approaches have been applied to identify specific genes underlying osteoporosis, largely using BMD as the study phenotype. We review here the genetic determination of osteoporosis as defined by BMD and discuss a fundamental issue we encounter in genetic research in osteoporosis: the choice of phenotype(s) to study. We briefly summarize and discuss advantages and disadvantages of various approaches used in genetic studies of osteoporosis. Finally, we review and discuss the current status for mapping and identification of genes for osteoporosis. We focus on linkage studies in humans and quantitative trait loci mapping in mice to supplement the already extensive reviews of association studies made by many investigators for candidate genes.