Cholesterol-rich dietary pattern during early pregnancy and genetic variations of cholesterol metabolism genes in predicting gestational diabetes mellitus: a nested case-control study

BACKGROUND

Higher dietary cholesterol intake during pregnancy increases risk of gestational diabetes mellitus (GDM). However, no studies have investigated interindividual variability in cholesterol metabolism and the association of genetics and diet on GDM.

OBJECTIVE

; To prospectively evaluate the joint association of cholesterol-rich dietary patterns and polymorphisms of genes coding for cholesterol metabolism pathway proteins with GDM.

METHODS

A total of 1116 pregnant females from the Tongji Birth Cohort were enrolled. GDM was diagnosed according to a 75-g 2-h oral glucose tolerance test at 24-28 wk of gestation. Dietary data were collected by a validated food frequency questionnaire. The reduced-rank regression method was used to identify dietary patterns using dietary cholesterol as the response variable. Time-of-flight mass spectrometry was used for genotyping. The genetic risk score (GRS) for GDM was constructed with genetic variants in 28 cholesterol metabolism-related single-nucleotide polymorphisms (SNPs). Conditional logistic regression models were used to assess the odds ratio (OR) for GDM.

RESULTS

The cholesterol-rich dietary pattern was rich in livestock and poultry meat and eggs but lower in cereals. The multivariable-adjusted ORs for GDM were 1.24 (95% confidence interval: 1.06-1.44) per SD increment of cholesterol-rich pattern scores and 1.28 (1.09-1.49) per tertile GRS. The variants of the CYP7A1 rs3808607 G→T/rs8192871 G→A/rs7833904 A→T, as well as AGGG and TTGA haplotypes of 4 CYP7A1-spanning SNPs, were significantly associated with GDM. For the joint effect, the OR was 3.53 (1.71-7.31) in the highest categories of both dietary pattern scores and GRS compared with individuals with the lowest strata without significant interaction (P for interaction = 0.101).

CONCLUSIONS

Both a cholesterol-rich dietary pattern and genetic variants of cholesterol metabolism genes are associated with risk of GDM. Adherence to a cholesterol-rich dietary pattern during early pregnancy promotes the chance of GDM, especially in women with higher GRS.

CLINICAL TRIAL REGISTRY

This trial was registered at http://www.chictr.org.cn (Registration number: ChiCTR1800016908). URL: =https://www.chictr.org.cn/showprojEN.html?proj=28081.

Dimension reduction for integrative survival analysis

We propose a constrained maximum partial likelihood estimator for dimension reduction in integrative (e.g., pan-cancer) survival analysis with high-dimensional predictors. We assume that for each population in the study, the hazard function follows a distinct Cox proportional hazards model. To borrow information across populations, we assume that each of the hazard functions depend only on a small number of linear combinations of the predictors (i.e., "factors"). We estimate these linear combinations using an algorithm based on "distance-to-set" penalties. This allows us to impose both low-rankness and sparsity on the regression coefficient matrix estimator. We derive asymptotic results that reveal that our estimator is more efficient than fitting a separate proportional hazards model for each population. Numerical experiments suggest that our method outperforms competitors under various data generating models. We use our method to perform a pan-cancer survival analysis relating protein expression to survival across 18 distinct cancer types. Our approach identifies six linear combinations, depending on only 20 proteins, which explain survival across the cancer types. Finally, to validate our fitted model, we show that our estimated factors can lead to better prediction than competitors on four external datasets.

Association of maternal dietary patterns derived by multiple approaches with gestational diabetes mellitus: a prospective cohort study

We used two a priori diet scores [Mediterranean diet (aMed) and Diet Balance Index (DBI)] and two a posteriori approaches [principal components analysis (PCA) and reduced-rank regression (RRR)] to examine the association of maternal dietary patterns with risk of gestational diabetes mellitus (GDM) and blood glucose among 2202 pregnant women in the Tongji Birth Cohort. Compared to the highest quartile of the aMed and legumes-vegetables-fruits (derived by PCA) scores, the fasting blood glucose (FBG) levels were higher in the lower quartiles (p-trend < 0.05). Lower scores of the meats-eggs-dairy (derived by PCA) and eggs-fish patterns (derived by RRR; characterised by higher intakes of freshwater fish, eggs, and lower intakes of leafy and cruciferous vegetables and fruits) were associated with decreased FBG levels (p-trend < 0.05). Similarities were found across approaches that some dietary patterns were associated with FBG, but not with postprandial glucose and GDM risk.

Spectra in low-rank localized layers (SpeLLL) for interpretable time-frequency analysis

The time-varying frequency characteristics of many biomedical time series contain important scientific information. However, the high-dimensional nature of the time-varying power spectrum as a surface in time and frequency limits its direct use by applied researchers and clinicians for elucidating complex mechanisms. In this article, we introduce a new approach to time-frequency analysis that decomposes the time-varying power spectrum in to orthogonal rank-one layers in time and frequency to provide a parsimonious representation that illustrates relationships between power at different times and frequencies. The approach can be used in fully nonparametric analyses or in semiparametric analyses that account for exogenous information and time-varying covariates. An estimation procedure is formulated within a penalized reduced-rank regression framework that provides estimates of layers that are interpretable as power localized within time blocks and frequency bands. Empirical properties of the procedure are illustrated in simulation studies and its practical use is demonstrated through an analysis of heart rate variability during sleep.

Measuring, comparing and interpreting phenotypic selection on floral scent

Natural selection on floral scent composition is a key element of the hypothesis that pollinators and other floral visitors drive scent evolution. The measure of such selection is complicated by the high-dimensional nature of floral scent data and uncertainty about the cognitive processes involved in scent-mediated communication. We use dimension reduction through reduced-rank regression to jointly estimate a scent composite trait under selection and the strength of selection acting on this trait. To assess and compare variation in selection on scent across species, time and space, we reanalyse 22 datasets on six species from four previous studies. The results agreed qualitatively with previous analyses in terms of identifying populations and scent compounds subject to stronger selection but also allowed us to evaluate and compare the strength of selection on scent across studies. Doing so revealed that selection on floral scent was highly variable, and overall about as common and as strong as selection on other phenotypic traits involved in pollinator attraction or pollen transfer. These results are consistent with an important role of floral scent in pollinator attraction. Our approach should be useful for further studies of plant-animal communication and for studies of selection on other high-dimensional phenotypes. In particular, our approach will be useful for studies of pollinator-mediated selection on complex scent blends comprising many volatiles, and when no prior information on the physiological responses of pollinators to scent compounds is available.

LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK

In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimensional features, and have access to a large number of outcomes (phenotypes)-lifestyle measures, biomarkers, and disease outcomes. We are hence led to fit sparse reduced-rank regression models, using computational strategies that allow us to scale to problems of this size. We use a scheme that alternates between solving the sparse regression problem and solving the reduced rank decomposition. For the sparse regression component we propose a scalable iterative algorithm based on adaptive screening that leverages the sparsity assumption and enables us to focus on solving much smaller subproblems. The full solution is reconstructed and tested via an optimality condition to make sure it is a valid solution for the original problem. We further extend the method to cope with practical issues, such as the inclusion of confounding variables and imputation of missing values among the phenotypes. Experiments on both synthetic data and the UK Biobank data demonstrate the effectiveness of the method and the algorithm. We present multiSnpnet package, available at http://github.com/junyangq/multiSnpnet that works on top of PLINK2 files, which we anticipate to be a valuable tool for generating polygenic risk scores from human genetic studies.

Cross-Sectional, Short-, Medium-, and Long-Term Effects of Dietary Pattern on Frailty in Taiwan

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Association of Testosterone-Related Dietary Pattern with Testicular Function among Adult Men: A Cross-Sectional Health Screening Study in Taiwan

Diets could play an important role in testicular function, but studies on how adherence to the dietary patterns influences human testicular function in Asian countries are scarce. Herein, we examined the association between testosterone-related dietary patterns and testicular function among adult men in Taiwan. This cross-sectional study recruited 3283 men who attended a private medical screening program from 2009 to 2015. Testosterone-related dietary pattern was generated by the reduced rank regression (RRR) method. The association between adherence to quartile of dietary pattern scores with sex hormones (testosterone, follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2)) and sperm quality (sperm concentration (SC), total sperm motility (TSM), progressive motility (PRM), and normal sperm morphology (NSM)) were examined by multivariable linear regression. Hemoglobin (β = 0.57, p < 0.001), hematocrit (β = 0.17, p = 0.002), triglyceride (β = −0.84, p < 0.001), HDL-cholesterol (β = 3.58, p < 0.001), total cholesterol to HDL-cholesterol ratio (β = −0.78, p < 0.001), and uric acid (β = −10.77, p < 0.001) were highly correlated with testosterone levels. Therefore, these biomarkers were used to construct a testosterone-related dietary pattern. Highest adherence (Q4) to dietary pattern scores were negatively associated with lower testosterone in the pooled analysis (β = −0.89, p = 0.037) and normal-weight men (β = −1.48, p = 0.019). Likewise, men in the Q4 of the dietary pattern had lower SC (β = −5.55, p = 0.001) and NSM (β = −2.22, p = 0.007) regardless of their nutritional status. Our study suggesting that testosterone-related dietary pattern (rich in preserved vegetables or processed meat or fish, deep-fried foods, innards organs, rice or flour products cooked in oil, and dipping sauce, but low in milk, dairy products, legumes, or beans, and dark or leafy vegetables) was associated with a poor testicular function.

Sequential Co-Sparse Factor Regression

In multivariate regression models, a sparse singular value decomposition of the regression component matrix is appealing for reducing dimensionality and facilitating interpretation. However, the recovery of such a decomposition remains very challenging, largely due to the simultaneous presence of orthogonality constraints and co-sparsity regularization. By delving into the underlying statistical data generation mechanism, we reformulate the problem as a supervised co-sparse factor analysis, and develop an efficient computational procedure, named sequential factor extraction via co-sparse unit-rank estimation (SeCURE), that completely bypasses the orthogonality requirements. At each step, the problem reduces to a sparse multivariate regression with a unit-rank constraint. Nicely, each sequentially extracted sparse and unit-rank coefficient matrix automatically leads to co-sparsity in its pair of singular vectors. Each latent factor is thus a sparse linear combination of the predictors and may influence only a subset of responses. The proposed algorithm is guaranteed to converge, and it ensures efficient computation even with incomplete data and/or when enforcing exact orthogonality is desired. Our estimators enjoy the oracle properties asymptotically; a non-asymptotic error bound further reveals some interesting finite-sample behaviors of the estimators. The efficacy of SeCURE is demonstrated by simulation studies and two applications in genetics.