Figuring out biology from complex data:

METABOLIC PHENOTYPES OF
DIABETIC KIDNEY DISEASE
Ville-Petteri Mäkinen	

Universit...
NATIONAL HEALTH AND NUTRITION	

EXAMINATION SURVEY 2005-2010, AGE ≥ 20

United States Renal Data System 2012 Annual Data R...
MORTALITY, AGING AND KIDNEY DISEASE

Age
Moody WE, Edwards NC, Chue CD et al. Heart 2013:365–372

Foley RN et al. Am J Kid...
TYPE 1 DIABETES
Autoimmune disease

No insulin secretion
High blood glucose
Complications
BLOOD GLUCOSE IN T1D

Meals, physical activity, sleep patterns etc. make it difficult to	

inject optimal amounts of insuli...
TYPE 1 DIABETES
Autoimmune disease

TYPE 2 DIABETES*
•

Insulin resistance (life style)

increased insulin demand	


Insuf...
Thorn et al. (2005) Diabetes Care 28:2019–2024
TYPE 1 DIABETES
Autoimmune disease

No insulin

SYSTEMIC
METABOLIC DISEASE
Life style and genetics

Insulin deficiency

Hig...
COMPLEX DISEASE CHALLENGE
• Combination

of environment and genes	


- network of inter-dependent causes.	


• Chronic

di...
COMPLEX DISEASE CHALLENGE
• Noisy

diagnoses and stochastic end-points	


- poor predictive performance.	


• Datasets

an...
OBJECTIVES
• Understand

the statistical patterns within a
cohort of patients with type 1 diabetes.	


• Typical

lipoprot...
• Four

traits:	


- hairiness	

- head size	

- eye size	

- mood.
Full data
- Easy interpretation	

- High resolution	

- Impractical for large datasets
Basic statistics
- Easy interpretat...
ORGANIZE SAMPLES IN 2D	

BASED ON SIMILARITY
SUMMARIZE LOCAL SAMPLES

N samples

K model phenotypes, K ≪ N
SUMMARY OF PHENOTYPIC DIVERSITY

Bar height indicates statistically normalized deviation
VISUALIZATION OF	

TRAIT VALUES

Eye size

Hairiness

Mood
Hairiness

TRAIT ASSOCIATIONS

Eye size
Cognitive decline and Alzheimer's
Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008

Lipoprotein structure an...
Diabetic complications
Mäkinen et al. Mol Syst Biol 4:167, 2008

Lipoprotein subclasses in type 1 diabetes
Kidney disease ...
STUDY DESIGN

The Finnish Diabetic Nephropathy Study	

Folkhälsan Research Center, Helsinki, Finland
DIABETES TYPE

Healthy C-peptide > 0.2 nmol/L
DIABETES MANAGEMENT

Target HbA1c < 7%
KIDNEY DISEASE

Healthy AER < 30 mg/24h

AER = albumin excretion rate
RAW DATA POINTS

Log AER

r = 0.25
−log P > 17

Glycated Hb (%)
AER (mg/24h)

300

30
10

HbA1c (%)

8.0

AER = albumin excretion rate

9.0
AER (mg/24h)

300

30
10

HbA1c (%)

8.0

AER = albumin excretion rate

9.0
AER (mg/24h)

300

30
10

HbA1c (%)

8.0

AER = albumin excretion rate

9.0
MODEL PHENOTYPES

AER (mg/24h)

300

30
10

HbA1c (%)

8.0

9.0

AER = albumin excretion rate
Biomarkers used in diagnoses were included in training set.
P = 0.66

−log P > 13

Sex differences were adjusted before analysis.

−log P > 20
−log P > 15

−log P > 12

−log P > 8
RAW DATA POINTS
Triglycerides (mmol/L)

r = −0.10
−log P > 8

Height (cm)
Triglycerides (mmol/L)

MODEL PHENOTYPES

Height (cm)
Wadén et al. (2005) Diabetes 28:2019–2024
Laser-treated
for eye disease
n = 1181

n = 268

Large vessel diseases
n = 343
Laser-treated
for eye disease
n = 1181

−log P > 30

n = 268

Large vessel diseases
n = 343

−log P > 22
MODEL PHENOTYPES

Prevalence of
vascular disease

Prevalence of retinopathy
FULL STUDY DESIGN

The Finnish Diabetic Nephropathy Study	

Folkhälsan Research Center, Helsinki, Finland
−log P > 30

−log P > 22

−log P > 6

−log P > 17

−log P > 28
All-cause mortality

MODEL PHENOTYPES

Urinary AER (mg/24h)
STANDARDIZED MORTALITY RATE
18.3

9.2

2.7
0.8
AER < 30

30 < AER < 300

AER > 300

End-stage	

renal disease

Groop et al...
−log P > 6

−log P > 17

P = 0.086

P = 0.036

−log P > 28

P < 0.001
TAKE-HOME MESSAGE?
• Carefully

investigate observed lipoprotein
characteristics.	


• Determine

overall measures that ar...
Triglycerides

LIPOPROTEIN LIPIDS

Cholesterol
SIMPLIFIED LIPOPROTEIN LIPIDS
Phenotype I

Phenotype II

Phenotype III

Lowest cholesterol

Lowest TG:C ratio

Highest TG:C ratio

End-stage renal disea...
Phenotype I	

 Phenotype II	

 Phenotype III	

Lowest cholesterol

Lowest TG:C ratio

Highest TG:C ratio

12 (11 - 13)
23....
Phenotype I	

 Phenotype II	

 Phenotype III	

Lowest cholesterol

Lowest TG:C ratio

Highest TG:C ratio

4% (2% - 6%)

2%...
SYSTEMIC
METABOLIC DISEASE
Life style and genetics

Insulin deficiency

Obesity
Metabolic stress

Diabetes

Vascular diseas...
Unhealthy	

life

Systemic	

metabolic	

stress

Insulin	

resistance

Obesity

Heart attack	

Stroke

Type 2
diabetes

Ag...
Full data
- Easy interpretation	

- High resolution	

- Impractical for large datasets
Basic statistics
- Easy interpretat...
ACKNOWLEDGMENTS
Folkhälsan Research Center
Aalto University

The FinnDiane Group
School of Science and Tech.
Prof Per-Henr...
Metabolic Phenotypes Of Diabetic Kidney Disease - Ville-Petteri Mäkinen
Metabolic Phenotypes Of Diabetic Kidney Disease - Ville-Petteri Mäkinen
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Metabolic Phenotypes Of Diabetic Kidney Disease - Ville-Petteri Mäkinen

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Diabetes, kidney disease and atherosclerosis often co-occur and interact in vulnerable individuals. By screening a large number of metabolites and other molecular traits, it is possible to investigate the emergent metabolic phenotypes that predict future clinical end-points, and thus better understand the combined genetic and environmental factors involved.

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Metabolic Phenotypes Of Diabetic Kidney Disease - Ville-Petteri Mäkinen

  1. 1. Figuring out biology from complex data: METABOLIC PHENOTYPES OF DIABETIC KIDNEY DISEASE Ville-Petteri Mäkinen University of California, Los Angeles, USA Imperial College, London, UK South Australian Health and Medical Research Institute, Adelaide, AU
  2. 2. NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEY 2005-2010, AGE ≥ 20 United States Renal Data System 2012 Annual Data Report
  3. 3. MORTALITY, AGING AND KIDNEY DISEASE Age Moody WE, Edwards NC, Chue CD et al. Heart 2013:365–372
 Foley RN et al. Am J Kidney Dis 1998:S112–19
  4. 4. TYPE 1 DIABETES Autoimmune disease No insulin secretion High blood glucose Complications
  5. 5. BLOOD GLUCOSE IN T1D Meals, physical activity, sleep patterns etc. make it difficult to inject optimal amounts of insulin during the day.
  6. 6. TYPE 1 DIABETES Autoimmune disease TYPE 2 DIABETES* • Insulin resistance (life style)
 increased insulin demand Insufficient insulin response (genetics) • Vicious cycle of glucose and lipid toxicity • Chronic inflammation and end-organ damage • No insulin High blood glucose Complications *Any diabetes where an exact cause cannot be identified.
  7. 7. Thorn et al. (2005) Diabetes Care 28:2019–2024
  8. 8. TYPE 1 DIABETES Autoimmune disease No insulin SYSTEMIC METABOLIC DISEASE Life style and genetics Insulin deficiency High blood glucose Complications Obesity Metabolic stress Diabetes (type 2) Vascular diseases
  9. 9. COMPLEX DISEASE CHALLENGE • Combination of environment and genes - network of inter-dependent causes. • Chronic diseases, gradual development - ambiguous lines between health and disease. • Quantitative biology, qualitative end-points - diagnostic definitions used as risk factors.
  10. 10. COMPLEX DISEASE CHALLENGE • Noisy diagnoses and stochastic end-points - poor predictive performance. • Datasets and experiments are unique • Individual baseline state is unique - poor performance outside original study. - single time point is of limited value.
  11. 11. OBJECTIVES • Understand the statistical patterns within a cohort of patients with type 1 diabetes. • Typical lipoprotein characteristics associated with complications and mortality. • Conceptually simple framework that can handle large number of variables, discrete and continuous traits, and missing data.
  12. 12. • Four traits: - hairiness - head size - eye size - mood.
  13. 13. Full data - Easy interpretation - High resolution - Impractical for large datasets Basic statistics - Easy interpretation - Pre-defined groups - Impractical for complex data Regression and classification - Interpreted through parameters - High descriptive power - Danger of over-fitting
  14. 14. ORGANIZE SAMPLES IN 2D BASED ON SIMILARITY
  15. 15. SUMMARIZE LOCAL SAMPLES N samples K model phenotypes, K ≪ N
  16. 16. SUMMARY OF PHENOTYPIC DIVERSITY Bar height indicates statistically normalized deviation
  17. 17. VISUALIZATION OF TRAIT VALUES Eye size Hairiness Mood
  18. 18. Hairiness TRAIT ASSOCIATIONS Eye size
  19. 19. Cognitive decline and Alzheimer's Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008 Lipoprotein structure and composition Kumpula et al. J Lipid Res 51:431-439, 2010 Intima media thickness Wurtz et al 2010
  20. 20. Diabetic complications Mäkinen et al. Mol Syst Biol 4:167, 2008 Lipoprotein subclasses in type 1 diabetes Kidney disease progression Mäkinen et al. J Intern Med 273:383–395, 2013 Mäkinen et al. J Proteome Res 11:1782-1790, 2012
  21. 21. STUDY DESIGN The Finnish Diabetic Nephropathy Study Folkhälsan Research Center, Helsinki, Finland
  22. 22. DIABETES TYPE Healthy C-peptide > 0.2 nmol/L
  23. 23. DIABETES MANAGEMENT Target HbA1c < 7%
  24. 24. KIDNEY DISEASE Healthy AER < 30 mg/24h AER = albumin excretion rate
  25. 25. RAW DATA POINTS Log AER r = 0.25 −log P > 17 Glycated Hb (%)
  26. 26. AER (mg/24h) 300 30 10 HbA1c (%) 8.0 AER = albumin excretion rate 9.0
  27. 27. AER (mg/24h) 300 30 10 HbA1c (%) 8.0 AER = albumin excretion rate 9.0
  28. 28. AER (mg/24h) 300 30 10 HbA1c (%) 8.0 AER = albumin excretion rate 9.0
  29. 29. MODEL PHENOTYPES AER (mg/24h) 300 30 10 HbA1c (%) 8.0 9.0 AER = albumin excretion rate
  30. 30. Biomarkers used in diagnoses were included in training set.
  31. 31. P = 0.66 −log P > 13 Sex differences were adjusted before analysis. −log P > 20
  32. 32. −log P > 15 −log P > 12 −log P > 8
  33. 33. RAW DATA POINTS Triglycerides (mmol/L) r = −0.10 −log P > 8 Height (cm)
  34. 34. Triglycerides (mmol/L) MODEL PHENOTYPES Height (cm)
  35. 35. Wadén et al. (2005) Diabetes 28:2019–2024
  36. 36. Laser-treated for eye disease n = 1181 n = 268 Large vessel diseases n = 343
  37. 37. Laser-treated for eye disease n = 1181 −log P > 30 n = 268 Large vessel diseases n = 343 −log P > 22
  38. 38. MODEL PHENOTYPES Prevalence of vascular disease Prevalence of retinopathy
  39. 39. FULL STUDY DESIGN The Finnish Diabetic Nephropathy Study Folkhälsan Research Center, Helsinki, Finland
  40. 40. −log P > 30 −log P > 22 −log P > 6 −log P > 17 −log P > 28
  41. 41. All-cause mortality MODEL PHENOTYPES Urinary AER (mg/24h)
  42. 42. STANDARDIZED MORTALITY RATE 18.3 9.2 2.7 0.8 AER < 30 30 < AER < 300 AER > 300 End-stage renal disease Groop et al. (2009) Diabetes 58:1651-1658
  43. 43. −log P > 6 −log P > 17 P = 0.086 P = 0.036 −log P > 28 P < 0.001
  44. 44. TAKE-HOME MESSAGE? • Carefully investigate observed lipoprotein characteristics. • Determine overall measures that are - applicable to all lipoprotein subclasses - easy to relate to existing medical literature. • Test data. if these phenotypes are related to clinical
  45. 45. Triglycerides LIPOPROTEIN LIPIDS Cholesterol
  46. 46. SIMPLIFIED LIPOPROTEIN LIPIDS
  47. 47. Phenotype I Phenotype II Phenotype III Lowest cholesterol Lowest TG:C ratio Highest TG:C ratio End-stage renal disease at baseline 93% 5% 2% <1% 77% 12% 5% 4% 8% 7% 57% 27% Cholesterol (mmol/L) Triglycerides (mmol/L) HDL cholesterol (mmol/L) 3.9 0.76 1.22 5.0 0.81 1.82 5.3 2.11 1.06 Normal AER at baseline Microalbuminuria at baseline Macroalbuminuria at baseline Recommended cholesterol < 5.0 mmol/L Recommended triglycerides < 1.7 mmol/L Recommended HDL cholesterol > 1.1 mmol/L (men) Recommended HDL cholesterol > 1.3 mmol/L (women)
  48. 48. Phenotype I Phenotype II Phenotype III Lowest cholesterol Lowest TG:C ratio Highest TG:C ratio 12 (11 - 13) 23.4 (23.1 - 23.8) 0.67 (0.63 - 0.71) 124 (122 - 126) 76 (75 - 77) 19 (21 - 23) 23.8 (23.6 - 24.2) 0.66 (0.64 - 0.68) 131 (129 - 133) 80 (79 - 81) 30 (28 - 31) 25.6 (25.0 - 26.4) 0.69 (0.66 - 0.72) 145 (141 - 149) 84 (82 - 85) C-reactive protein (mg/L) 7.5 8.6 105 1.15 8.1 13 94 1.44 9.1 596 48 3.86 Serum adiponectin (mg/L) 9.3 16 18 Type 1 diabetes duration (years) Body-mass index (kg/m2) Insulin dose (IU/kg) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Hemoglobin A1c (%) Urinary albumin excretion (mg/24h) Estimated GFR (mL/min per 1.73m2)
  49. 49. Phenotype I Phenotype II Phenotype III Lowest cholesterol Lowest TG:C ratio Highest TG:C ratio 4% (2% - 6%) 2% (1% - 4%) 10% (4% - 20%) Progression from microalbuminuria <1% 15% (6% - 29%) 38% (18% - 57%) Progression from macroalbuminuria 1% (0 - 4%) 13% (3% - 26%) 42% (31% - 51%) <3% 6% (4% - 9%) 40% (32% - 47%) Progression from normal AER Deceased at follow-up Excess enrichment of triglycerides across every lipoprotein subclass is a part of a metabolic phenotype with high vascular risk in type 1 diabetes
  50. 50. SYSTEMIC METABOLIC DISEASE Life style and genetics Insulin deficiency Obesity Metabolic stress Diabetes Vascular diseases SCORING SYSTEM FOR METABOLIC SYNDROME Obesity • High blood glucose (or diabetes) • Abnormal blood lipids (TG and HDLC) • High blood pressure •
  51. 51. Unhealthy life Systemic metabolic stress Insulin resistance Obesity Heart attack Stroke Type 2 diabetes Aging Type 1 diabetes Unhealthy life Systemic metabolic stress Obesity Insulin resistance Heart attack Stroke Kidney disease
  52. 52. Full data - Easy interpretation - High resolution - Impractical for large datasets Basic statistics - Easy interpretation - Pre-defined groups - Impractical for complex data Regression and classification - Interpreted through parameters - High descriptive power - Danger for over-fitting
  53. 53. ACKNOWLEDGMENTS Folkhälsan Research Center Aalto University
 The FinnDiane Group School of Science and Tech. Prof Per-Henrik Groop Prof Kimmo Kaski Carol Forsblom Tomi Peltola Markku Lehto Lena M Thorn University of Oulu &
 Valma Harjutsalo University of Eastern Finland Prof Mika Ala-Korpela Pasi Soininen Tuulia Tynkkynen Antti Kangas
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