Bioinformatician for The Predimed-plus Rct

**Descripción**:
The Barcelona Institute for Global Health (ISGlobal) is a cutting-edge institute addressing global public health challenges through research, translation into policy and education. ISGlobal has a broad portfolio in communicable and non-communicable diseases including environmental and climate determinants, and applies a multidisciplinary scientific approach ranging from the molecular to the population level. Research is organized in five programs: Climate, Air Pollution, Nature and Urban Health; Environment and Health over the Lifecourse; Global Viral and Bacterial Infections; Malaria and Neglected Parasitic Diseases and Maternal Child and Reproductive Health. ISGlobal is accredited with the Severo Ochoa distinction, a seal of excellence of the Spanish Science Ministry.

**Lugar**:
BARCELONA, CAMPUS MAR

**WHAT WE ARE LOOKING FOR**:
ISGlobal is seeking a bioinformatician who is interested in taking over and managing activities of the project PREDIMED-Plus RCT during 3 months. The project aims to prospectively assess the association between a wide range of environmental exposures and body composition in adults with overweight/obesity and metabolic syndrome from the PREDIMED-Plus RCT.
Specific body composition components seem to be the key to the development of obesity-associated chronic diseases. In particular, excess visceral fat and loss of muscle mass, which occurs with increasing age, but also with unhealthy lifestyle patterns, have been associated with a higher risk for a broad spectrum of health outcomes including cardiovascular disease and type 2 diabetes.
The recent study performed in the PREDIMED-Plus randomized controlled trial (RCT), demonstrated that the lifestyle intervention based on an energy-restricted Mediterranean diet and physical activity significantly reduced grams of visceral fat and percentage of total fat mass and increased percentage of lean mass at 3 years of follow-up of older adults with metabolic syndrome. More evidence on the topic comes from the observational studies in the PREDIMED-Plus cohort after one year of follow-up. In this sense, consumption of better carbohydrate quality was associated with concurrent favourable changes in visceral and overall fat accumulation. On the contrary, eating more ultra-processed foods was associated with an unfavourable body adiposity profile. Moreover, we also found that replacing sedentary time with physical activity at low intensities might be an appealing strategy for older adults to improve body composition (fat and muscle mass). Besides individual lifestyle habits (diet and physical activity), many other environmental exposures from the external exposome (such as air pollution, noise, built environment characteristics, etc) might also elicit an effect on body composition. In the current study, we seek to evaluate the potential role of these external exposome factors in the intervention effects over PREDIMED-Plus participants.

**TRAINING AND EXPERIENCE /QUALIFICATIONS**:

- A Bachelor's Degree in Biological Sciences, with a strong emphasis on Biochemistry or Biomedicine. Other relevant science degrees may also be considered.
- Proven experience analysing clinical and omics data from population-based cohort studies, with expertise in bioinformatics tools and data analysis.

**SPECIFIC REQUIREMENTS**:

- Proven experience in managing complex datasets, including data cleaning, imputation, normalization, and handling of missing data.
- Strong foundation in statistical analysis techniques, including regression analysis.
- Proficiency in R and Python programming for data analysis, with additional experience in other programming languages considered an asset.
- Familiarity with statistical software (e.g., SPSS).
- Competent in report writing, with proficiency in LaTeX for structured documents.

**KEY RESPONSIBILITIES**:

- Data Preparation and Exploratory Data Analysis (EDA): Conduct an extensive EDA to ensure data harmonization, cleaning, and preparation for analysis.
- Exposome-Wide Association Studies (ExWAS) for Single-Exposure Models: Perform ExWAS to evaluate single-exposure associations while correcting for multiple testing.
- Development of Multi-Exposure Models: Develop models to evaluate the simultaneous effects of multiple exposures, addressing co-exposure issues.
- Longitudinal Modelling of Time-Varying Exposures and Outcomes: Develop models to account for time-varying exposures and their relationship with outcomes over time.
- Results Reviewing and Sensitivity Analyses for Results Refinement: Perform sensitivity analyses to ensure robustness and revise results through re-analyses and subgroup testing.

**SPECIFIC DUTIES**:

- Clean data (imputation, outliers, duplicates, error checking, etc.).
- Transform data (z-score normalization by sex).
- Adjust for confounders (identification and residualization).
- Perform correlation analysis.
- Conduct cross-sectional and prospective ExWAS analyses with correction for mu