A comparison of heuristic and model-based clustering methods for dietary pattern analysis

Effect of dietary patterns on dental caries among 12-15 years-old adolescents: a cross-sectional survey

BACKGROUND

Several factors can influence the risk of dental caries, among which dietary factors have a significance impact on the occurrence of dental caries. The limitation of current studies is that they only focus on the influence of individual foods on the risk of dental caries. This study use cluster analysis to examine the relationship between dietary patterns and dental caries experience among adolescents aged 12-15.

METHODS

Based on data from the first oral epidemic survey in Shanxi Province, a cross-sectional study was conducted among 11,351 adolescents aged 12-15 in Shanxi Province through oral examination and questionnaires. The questionnaire included the intake frequency of seven types of food. Descriptive statistics, cluster analysis, and multinomial logistic regression were used to analyze the association between dietary patterns and dental caries experience.

RESULTS

The prevalence rate of caries was 44.57% and the mean DMFT score was 0.98 ± 1.49 in adolescents aged 12-15 in Shanxi Province. The caries rate was higher in females than males (X2 = 103.59, P < 0.001). Adolescents who grow up in one-child families have a lower caries risk than those who grow up in families with more than one child (OR:0.91; 95%CI:0.84-0.97). The dietary patterns of adolescents aged 12-15 can be divided into eight types, among which refreshments-rich diet (OR:1.47; 95%CI,1.22-1.77) can increase the risk of caries, while the coarse-grains-rich dietery pattern (OR:0.90; 95%CI, 0.79-0.97) has a lower caries risk.

CONCLUSIONS

Social determinants of health such as sex, family size and dietary patterns influence the risk of dental caries. Certain dietary patterns could increase or decrease the risk of caries. The government, school canteens and news media should take dietary pattern factors seriously.

A flexible data-driven audiological patient stratification method for deriving auditory profiles

For characterizing the complexity of hearing deficits, it is important to consider different aspects of auditory functioning in addition to the audiogram. For this purpose, extensive test batteries have been developed aiming to cover all relevant aspects as defined by experts or model assumptions. However, as the assessment time of physicians is limited, such test batteries are often not used in clinical practice. Instead, fewer measures are used, which vary across clinics. This study aimed at proposing a flexible data-driven approach for characterizing distinct patient groups (patient stratification into auditory profiles) based on one prototypical database (N = 595) containing audiogram data, loudness scaling, speech tests, and anamnesis questions. To further maintain the applicability of the auditory profiles in clinical routine, we built random forest classification models based on a reduced set of audiological measures which are often available in clinics. Different parameterizations regarding binarization strategy, cross-validation procedure, and evaluation metric were compared to determine the optimum classification model. Our data-driven approach, involving model-based clustering, resulted in a set of 13 patient groups, which serve as auditory profiles. The 13 auditory profiles separate patients within certain ranges across audiological measures and are audiologically plausible. Both a normal hearing profile and profiles with varying extents of hearing impairments are defined. Further, a random forest classification model with a combination of a one-vs.-all and one-vs.-one binarization strategy, 10-fold cross-validation, and the kappa evaluation metric was determined as the optimal model. With the selected model, patients can be classified into 12 of the 13 auditory profiles with adequate precision (mean across profiles = 0.9) and sensitivity (mean across profiles = 0.84). The proposed approach, consequently, allows generating of audiologically plausible and interpretable, data-driven clinical auditory profiles, providing an efficient way of characterizing hearing deficits, while maintaining clinical applicability. The method should by design be applicable to all audiological data sets from clinics or research, and in addition be flexible to summarize information across databases by means of profiles, as well as to expand the approach toward aided measurements, fitting parameters, and further information from databases.

A review of statistical methods for dietary pattern analysis

Background

Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately.

Methods

This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation.

Results

While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods’ performance in terms of reproducibility, validity, and ability to predict different outcomes.

Conclusion

Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.

Reproducibility and Validity of A Posteriori Dietary Patterns: A Systematic Review

The effective use of dietary patterns (DPs) remains limited. There is a need to assess their consistency over multiple administrations of the same dietary source, different dietary sources, or across different studies. Similarly, their generalizability should be based on a previous assessment of DP construct validity. However, to date, no systematic reviews of reproducibility and validity of a posteriori DPs have been carried out. In addition, several methodological questions related to their identification are still open and prevent a fair comparison of epidemiological results on DPs and disease. A systematic review of the literature on the PubMed database was conducted. We identified 218 articles, 64 of which met the inclusion criteria. Of these, the 38 articles dealing with reproducibility and relative and construct validity of DPs were included. These articles (published in 1999-2017, 53% from 2010 onwards) were based on observational studies conducted worldwide. The 14 articles that assessed DP reproducibility across different statistical solutions examined different research questions. Included were: the number of food groups or subjects; input variable format (as well as adjustment for energy intake); algorithms and the number of DPs to retain in cluster analysis; rotation method; and score calculation in factor analysis. However, we identified at most 3 articles per research question on DP reproducibility across statistical solutions. From another 15 articles, reproducibility of DPs over shorter (≤1 y) time periods was generally good and higher than DP relative validity (as measured across different dietary sources). Confirmatory factor analysis was used in 15 of the included articles. It provided reassuring results in identifying valid dietary constructs characterizing the populations under consideration. Based on the available evidence, only suggestive conclusions can be derived on reproducibility across different statistical solutions. Nevertheless, most identified DPs showed good reproducibility, fair relative validity, and good construct validity.

The Applications of Clustering Methods in Predicting Protein Functions

Background:

The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem.

Objective:

In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

Association between nutrient patterns and serum lipids in Chinese adult women: A cross-sectional study

AIM

To investigate the association between patterns of nutrient intake and serum lipids in Chinese women aged 18-80 years.

METHODS

In the present study, cross-sectional data were analysed from 2886 female participants aged 18-80 years from the China Health and Nutrition Survey wave 2009. Nutrient patterns were identified using factor analysis combined with cluster analysis based on the data of nutrient intake for three consecutive days. Multivariate linear regression models were used to estimate the association of nutrient patterns with serum lipids.

RESULTS

Four nutrient patterns were identified in Chinese adult women, which were the plant-based pattern, carbohydrate and animal fat pattern, plant fat and sodium pattern, and the animal-based pattern. Participants following different patterns varied significantly in sociodemographic characteristics, lifestyle behaviours and food consumption. Compared with the plant-based pattern, the carbohydrate and animal fat pattern was positively associated with low-density lipoprotein cholesterol (β = 4.57, 95% CI: 0.29-8.85, P = 0.036) and total cholesterol (β = 4.89, 95% CI: 0.34-9.44, P = 0.035). The corresponding rises for the animal-based pattern were 4.91 (95% CI: 0.99-8.82, P = 0.014) and 4.98 (95% CI: 0.82-9.15, P = 0.019), respectively.

CONCLUSIONS

Nutrient patterns with a high intake of animal fat and a low intake dietary fibre and with high intakes of animal fat, animal protein and cholesterol may increase the serum cholesterol in Chinese women.

Brain network profiling defines functionally specialized cortical networks

Neuroimaging research made rapid advances in the study of the functional architecture of the brain during the past decade. Many proposals endorsed the relevance of large-scale brain networks, defined as ensembles of brain regions that exhibit highly correlated signal fluctuations. However, analysis methods need further elaboration to define the functional and anatomical extent of specialized subsystems within classical networks with a high reliability. We present a novel approach to characterize and examine the functional proprieties of brain networks. This approach, labeled as brain network profiling (BNP), considers similarities in task-evoked activity and resting-state functional connectivity across biologically relevant brain subregions. To combine task-driven activity and functional connectivity features, principal components were extracted separately for task-related beta values and resting-state functional connectivity z-values (data available from the Human Connectome Project), from 360 brain parcels. Multiple clustering procedures were employed to assess if different clustering methods (Gaussian mixtures; k-means) and/or data structures (task and rest data; only rest data) led to improvements in the replication of the brain architecture. The results indicated that combining information from resting-state functional connectivity and task-evoked activity and using Gaussian mixtures models for clustering produces more reliable results (99% replication across data sets). Moreover, the findings revealed a high-resolution partition of the cerebral cortex in 16 networks with unique functional connectivity and/or task-evoked activity profiles. BNP potentially offers new approaches to advance the investigation of the brain functional architecture.

Strategic procedure in three stages for the selection of variables to obtain balanced results in public health research

Multidisciplinary research in public health is approached using methods from many scientific disciplines. One of the main characteristics of this type of research is dealing with large data sets. Classic statistical variable selection methods, known as "screen and clean", and used in a single-step, select the variables with greater explanatory weight in the model. These methods, commonly used in public health research, may induce masking and multicollinearity, excluding relevant variables for the experts in each discipline and skewing the result. Some specific techniques are used to solve this problem, such as penalized regressions and Bayesian statistics, they offer more balanced results among subsets of variables, but with less restrictive selection thresholds. Using a combination of classical methods, a three-step procedure is proposed in this manuscript, capturing the relevant variables of each scientific discipline, minimizing the selection of variables in each of them and obtaining a balanced distribution that explains most of the variability. This procedure was applied on a dataset from a public health research. Comparing the results with the single-step methods, the proposed method shows a greater reduction in the number of variables, as well as a balanced distribution among the scientific disciplines associated with the response variable. We propose an innovative procedure for variable selection and apply it to our dataset. Furthermore, we compare the new method with the classic single-step procedures.

Stability-based validation of dietary patterns obtained by cluster analysis

Background

Cluster analysis is a data-driven method used to create clusters of individuals sharing similar dietary habits. However, this method requires specific choices from the user which have an influence on the results. Therefore, there is a need of an objective methodology helping researchers in their decisions during cluster analysis. The objective of this study was to use such a methodology based on stability of clustering solutions to select the most appropriate clustering method and number of clusters for describing dietary patterns in the NESCAV study (Nutrition, Environment and Cardiovascular Health), a large population-based cross-sectional study in the Greater Region (N = 2298).

Methods

Clustering solutions were obtained with K-means, K-medians and Ward’s method and a number of clusters varying from 2 to 6. Their stability was assessed with three indices: adjusted Rand index, Cramer’s V and misclassification rate.

Results

The most stable solution was obtained with K-means method and a number of clusters equal to 3. The “Convenient” cluster characterized by the consumption of convenient foods was the most prevalent with 46% of the population having this dietary behaviour. In addition, a “Prudent” and a “Non-Prudent” patterns associated respectively with healthy and non-healthy dietary habits were adopted by 25% and 29% of the population. The “Convenient” and “Non-Prudent” clusters were associated with higher cardiovascular risk whereas the “Prudent” pattern was associated with a decreased cardiovascular risk. Associations with others factors showed that the choice of a specific dietary pattern is part of a wider lifestyle profile.

Conclusion

This study is of interest for both researchers and public health professionals. From a methodological standpoint, we showed that using stability of clustering solutions could help researchers in their choices. From a public health perspective, this study showed the need of targeted health promotion campaigns describing the benefits of healthy dietary patterns.

Electronic supplementary material

The online version of this article (doi:10.1186/s12937-017-0226-9) contains supplementary material, which is available to authorized users.