Diabetes and Related Traits

Medicine doctor touching electronic medical record on tablet. DNA

We aim to elucidate molecular mechanisms of metabolic diseases, using this information to refine subtype definitions for precision medicine.

About our Research

Our research focuses on the molecular analysis of traits related to metabolic diseases. We combine state-of-the-art laboratory and statistical methods in large human cohorts to study the complex interplay between molecular mechanisms, environment and lifestyle in the context of the onset and progression of disease.

Dr. Harald Grallert

Head of Research Group 'Diabetes and Related Traits', Scientist View profile

Qiuling Dong

PhD Student

Jiefei Niu

PhD Student

Dr. Sapna Sharma

Senior Scientist

Markéta Fuchs (née Rynesova)

PhD Student

Andreas Schmidt

Guest PhD Student

Homa Bazireh

PhD Student

Dr. Esienanwan Efiong

Postdoc

Dr. Elke Rodriguez

Senior Scientist

Aiman Farzeen

PhD Student

Chisom Soremekun

Visiting PhD Student

Sylwia Pietrasik

TA

Mutated DNA and molecules, scientific biotechnology, 3d rendering

Funded by DFG | 2023-2026

Precise characterization of metabolic risk loci using long-read NGS approach

In our project, we aim to analyze a T2D/prediabetes case-control cohort from the KORA FF4 study using a comprehensive genetic approach, incorporating Nanopore long read sequencing for fine mapping patient-specific risk loci.

Funded by BMBF | 2023-2028

NAKO - Expert Group Omics

Our role in this project is molecular phenotyping using OMICS Technologies. We are coordinating the NAKO expert group OMICS, which develops concepts for implementation of molecular phenotyping and its analysis. Currently, our focus is to isolate high-quality DNA and coordinating genomic data generation, applying Genotyping for all 200k, sequencing for a larger subsample (~25k), and methylation typing in 5k NAKO participants. Together with GHGA we implement long-term storage and access of molecular data for the scientific community. In addition, we support generation of other OMICS layers in close collaboration with partners from the NAKO expert group OMICS.

Funded by BMBF | Since 2009

DZD

In the framework of the DZD we contribute molecular epidemiological resources in our cohorts (KORA, NAKO) to identify new biomarkers for prediction or improved prevention of cardiometabolic diseases. These resources will be further developed to be used to validate findings in animal or cell models on human population level.

Funded by DAAD | 2023-2025

T2D in African populations

The study aims to explore the genetics of type-2 diabetes in individuals of African ancestry, using diverse statistical methodologies and leveraging genomic data from countries like Nigeria, Kenya, Ghana, South Africa, Malawi, and Uganda. By identifying genetic variants associated with type-2 diabetes and evaluating their cumulative impact on an individual's susceptibility, the research aims to pave the way for precision medicine interventions tailored to the unique genetic makeup of diverse populations.

Biomarker Discovery for Diagnostic and Prognostic Purposes

Funded by Alexander von Humboldt Foundation | 2023-2025

Diabetic Nephropathy

We aim to identify early precise biomarkers of renal insufficiency in diabetic nephropathy (DN) before apparent changes in kidney function occurs. The research is targeted at proteomic signatures associated with DN in diabetic cohorts and explores the inhibitory efficacy of Vernonia amygdalina leaves and Aloe vera var chinensis root extracts on MST1, an enzyme linked to DN progression. Positive results could enhance differential diagnosis and intervention in DN, potentially improving life expectancy for individuals with diabetes.

See all

2025 Diabetes Obes Metab

Niu J, Adam J, Skurk T, Seissler J, Dong Q, Efiong E, Gieger C, Peters A, Sharma S, Grallert H.

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort

2025 Diabetes Obes Metab

Dong Q, Xi Y, Brandmaier S, Fuchs M, Huemer MT, Waldenberger M, Niu J, [...], Gieger C, Thorand B, Peters A, Rospleszcz S, Grallert H.

Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort

2024 Diabetologia

Sharma S, Dong Q, Haid M, Adam J, [...], Adamski J, Pearson ER, Grallert H.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

2024 Diabetes Metab Res Rev

Ziegler D, Thorand B, Strom A, [...], Gieger C, Heier M, Meisinger C, Roden M, Peters A, Grallert H.

Association of transketolase polymorphisms with diabetic polyneuropathy in the general population: The KORA F4 study

2024 Nutr Metab Cardiovasc Dis

Sharma S, Subrahmanyam YV, Ranjani H, Sidra S, Parmar D, Vadivel S, Kannan S, Grallert H, [...], Jerzy A, Panchagnula V, Gokulakrishnan K.

Interaction between TCF7L2 polymorphism and dietary fat intake on high density lipoprotein cholesterol

2024 Nature

Suzuki K, Hatzikotoulas K, Southam L, [...], Grallert H, Cheng CY, Ghanbari M, [...], Voight BF, Morris AP, Zeggini E.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

2023 Nature

Lagou V, Jiang L, Ulrich A, […], Gieger C, Grallert H, Meisinger C, […], Kaakinen MA, Jones B, Prokopenko I; Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC).

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

2023 Sci Rep

Duschek E, Forer L, Schönherr S, Gieger C, Peters A, Kronenberg F, Grallert H, Lamina C.

A polygenic and family risk score are both independently associated with risk of type 2 diabetes in a population-based study

2023 Metabolites

Dong Q, Sidra S, Gieger C, Wang-Sattler R, Rathmann W, Prehn C, Adamski J, Koenig W, Peters A, Grallert H, Sharma S.

Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

2022 Nature

Yengo L, Vedantam S, Marouli E, Sidorenko J, [...], Gao Z, Gieger C, Grallert H, Peters A et al.

A saturated map of common genetic variants associated with human height

2022 Nature

Yengo L, Vedantam S, Marouli E, Sidorenko J, [...], Gao Z, Gieger C, Grallert H, Peters A et al.

A saturated map of common genetic variants associated with human height

2022 Nat Genet

Mahajan A, Spracklen CN, Zhang W, [...], Gieger C, Kriebel J, Meitinger T, Peters A, Strauch K, Thorand B, Zeggini E, Grallert H, Morris AP.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

2022 Nat Cardiovasc Res

Spielmann N, Miller G, Oprea TI, [...], Brandmaier S, Sharma S, Galter I, Östereicher MA, Zapf L, Mayer-Kuckuk P, Gilly A, Rayner NW, Zeggini E, Grallert H.

Extensive identification of genes involved in congenital and structural heart disorders and cardiomyopathy

2021 Nat Commun

Zaghlool SB*, Sharma S*, Molnar M, Matías-García PR, Elhadad MA, Waldenberger M, Peters A, Rathmann W, Graumann J, Gieger C, Grallert H*, Suhre K*.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Diabetes and Related Traits

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About our Research

People at Diabetes and Related Traits

Dr. Harald Grallert

Qiuling Dong

Jiefei Niu

Dr. Sapna Sharma

Markéta Fuchs (née Rynesova)

Andreas Schmidt

Homa Bazireh

Dr. Esienanwan Efiong

Dr. Elke Rodriguez

Aiman Farzeen

Chisom Soremekun

Sylwia Pietrasik

Our Projects

Precise characterization of metabolic risk loci using long-read NGS approach

NAKO - Expert Group Omics

DZD

T2D in African populations

Diabetic Nephropathy

Highlighted Publications

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Dr. Harald Grallert

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