The Mäkinen Group are focused on determining the molecular patterns of gene and protein expression that cause chronic and age-related conditions such as obesity, diabetes and cardiovascular disease.

As part of their research, the group develops and uses computational approaches to analyse the huge datasets generated by genomics, transcriptomics and metabolomics studies, looking for patterns linking genes and proteins to the physical characteristics of disease. 


By integrating epidemiological and clinical data from large population studies with genomic and gene/protein expression data, the group can create hypotheses of possible disease mechanisms which can then be tested and validated both in vitro and in live animal studies.

The Group combine the systematic analyses of multiple diseases and metabolic traits to get a detailed understanding of the molecular life paths of individuals and the context-dependent risk factors that predispose to morbidity in later life.


Three members of Makinen group looking at computer screens
  • Integrative genomics of Alzheimer's disease to uncover novel regulatory mechanisms of neuronal proteolysis. In particular, can we stop neurodegeneration by boosting lysosomal flux?
  • Genetic regulation of human metabolism is critical for understanding the risk factors for metabolic diseases in aging. What are the age-associated trajectories of circulating metabolites, and what are the genes that influence the concentrations in blood?
  • Big data in precision medicine to enable better prediction of acute events and subsequently improve long-term outcomes. In particular, how can we detect poor performing implants in national orthopaedics registries before significant impact to patient outcomes?
  • All the above areas require robust computational and statistical techniques. How to facilitate the discovery and biological interpretation of disease subtypes in a precision medicine framework, and how to leverage empirical data on gene regulatory networks to explain genetic associations and to uncover new clues on pathogenic processes?
Published In

Song G, Mutter S, Casey A, Sargeant T, Mäkinen V-P

Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease

Brain, 144:2711-2720. (2018)

Song G, Mutter S, Casey A, Mäkinen V-P

Numero: a statistical framework to define multivariable subgroups in complex population-based datasets

Int J Epidemiology, in press. (2018)

Mäkinen V-P

Body Mass Index in Children Validated by Metabolic and Fat Mass Profiling

Editorial comment: J Am Coll Cardiol. (2018)

Mutter S, Casey AE, Zhen S, Shi Z, Mäkinen V-P

Multivariable analysis of nutritional and socio-economic profiles shows differences in incident anemia for Northern and Southern Jiangsu in China

Nutrients, 21:9 (2017)

Couto Alves A, Valcarcel B, Mäkinen V-P, Morin-Papunen L, Sebert S, Kangas AJ, Soininen P, Das S, De Iorio M, Coin L, Ala-Korpela M, Järvelin MR, Franks S

Metabolic profiling of Polycystic Ovary Syndrome Reveals Interactions with Abdominal Obesity

 Int J of Obesity 41:1331-1340. (2017)

 Lithovius R, Toppila I, Harjutsalo V, Forsblom C, Groop PH, Mäkinen V-P

Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes

Diabetologia, 60:1234-1243. (2017)