Workshop di Chemiometria 2011 - Alessandria

Recognition of Metabolic Patterns in Kidney

Transplanted patients by

Chemometrics Elaboration of NMR Profile Data

Dott. Marco CalderisiCentro NMRDipartimento di Chimica, Università di Siena

My tutorsDott. Antonio Vivi

Prof. Marina Cocchi

Workshop di Chemiometria 2010, 4-5 Febbraio

UNIVERSITA’ DEL PIEMONTE ORIENTALE “A. AVOGADRO”

PhD Student Marco Calderisi

Centro NMR, Università di Siena

NMR CentreBruker DRX Avance 600MHz

Bruker DRX Avance 500MHz





Projects

Recognition of metabolic patterns in kidney transplanted patients

Autism: identification od metabolic pattern to find diagnostic specific subgroups





Projects

Recognition of metabolic patterns in kidney transplanted patients

Autism: identification od metabolic pattern to find diagnostic specific subgroups





Goal of the work

Identification of the dynamic biopatterns related to the recovery progress of kidney transplant patients.

Hypotesis:The graft kidney recovery can be considered a discrete some-stages progress

Metabolic profiles show an evolution with respect to the initial graft recovery period





Kidney transplantation

50 years after the first successful kidney transplantation there are still many obstacles:

immunological rejection

ischemia /reperfusion injury

damages from drugs





Index

NMR Spectra

Data pretreatment

Spectras alignment

Scaling

First experimental step

NMR data analysis

Clinical data analysis





Index

NMR Spectra

Data pretreatment

Spectras alignment

Scaling

First experimental step

NMR data analysis


Second experimental step

NMR data analysis





NMR Spectra

Urine samplepH=2,532768 points





NMR spectra pretreatment

1) “light” binning (32768 --> 16384)

2) baseline correction (weighted least square - II order polynomial)





NMR spectra pretreatment

1) “light” binning (32768 --> 16384)

2) baseline correction (weighted least square - II order polynomial)





Spectra alignment

1. “Hand” alignment (TSP zero shift)

2. By means of a specific algorithm

COW

ICOSHIFT (work in progress)





Original signal (TSP)





Spectra alignment


Hand (TSP zero shift)





Spectra alignment


Hand (TSP zero shift) Icoshift





Spectra alignment


Hand (TSP zero shift) Icoshift COWWorkshop di Chemiometria 2010, 4-5 Febbraio




Spectra alignment

Original signal (Creatinine)





Allineamento






Allineamento

hand (TSP zero shift)






Allineamento

Icoshifthand (TSP zero shift)






Allineamento

COWIcoshifthand (TSP zero shift)

Exclusion of uninteresting regionsExclusion of water region (4.79-5.21 δ ppm)

and spectra extreme regions (from 16k to 12k)





Exclusion of uninteresting regionsExclusion of water region (4.79-5.21 δ ppm)

and spectra extreme regions (from 16k to 12k)





Normalization

Normalization (unit area, unit lenght, probabilistic quotient normalization* on raw data or normalized data)

*Anal. Chem. 2006, 78, 4281-4290





Normalization


*Anal. Chem. 2006, 78, 4281-4290





Normalization


*Anal. Chem. 2006, 78, 4281-4290





Scaling

Centering, scaling, and transformations: improving the biological information content of metabolomics dataRobert A van den Berg, Huub C J Hoefsloot, Johan A Westerhuis, Age KSmilde and Mariët Jvan der WerfBMC Genomics 2006, 7, 1-42





Scaling

Centering, scaling, and transformations: improving the biological information content of metabolomics dataRobert A van den Berg, Huub C J Hoefsloot, Johan A Westerhuis, Age KSmilde and Mariët Jvan der WerfBMC Genomics 2006, 7, 1-42

Range scaling (modified)





Autoscaling Range scaling

Pareto scaling Range scaling (modified)





Scaling

Sviluppo del lavoroStep 1:18 patients were sampled during a recovery period of 2 weeks.Lack of an underlying experimental design (missing data and uneven sampling). Clinical data analysis.





Sviluppo del lavoroStep 1:18 patients were sampled during a recovery period of 2 weeks.Lack of an underlying experimental design (missing data and uneven sampling). Clinical data analysis.

Step 2:27 patients were sampled all along the hospital recovery period (from 5 to 40 days) and during the first follow up. Systematic sampling.





Step 118 patients were sampled during a recovery period of 2 weeks

Lack of an underlying experimental design (missing data and uneven sampling)





Elaboration:

1. Unsupervised analysis (PCA) on each single patient

2. Supervised analysis (OPLS-DA) on each single patient

3. PCA on predictive OPLS-DA loadings

[All spectra normalized to unit area and Pareto scaled]





Step 1





Step 1

Positive correlation to successful recovery

Negative correlation to successful recovery

Anomalie

Patients 1317 and 1324 do not show a similar effect as the others

Patient 1325 indicates only one stage





Step 1

diuresis (volume of urine produced daily)

dialysis (number of dialysis to which the patient undergo)

therapy (seven different type of drugs and their amount given to each patient)

glucose (record of the value of glucose, monitored based on a non-systematic plan)


Goal: visualize hidden information from clinical data





Step 1

Conclusion

From clinical data analysis, results that patients 1317, 1324,

1325 were subjected to an high number of dialysis, six,

compared to the others

Patient 1325, which shows no trajectory at all in the model,

is the one who have a anomalous values of glucose during the

first days after the transplantation





Step 1

27 patients were sampled all along the hospital recovery period (from 5 to 40 days) and during the first follow up

systematic sampling (one sample a day) [clinical data available on day 0, 1, 2, 3, 4, 7, 14, 21, 30, 60, ...]

so far 15 patients (170 samples) have been analyzed





Step 2

Elaboration:

Unsupervised analysis (PCA) on the whole dataset

Supervised analysis (PLS-DA) on the whole dataset

[All spectra normalized to unit area and centered (or Pareto scaled or

robust range scaled)]





Step 2

Unsupervised analysis (PCA), first 3 patient (Pareto scaling)





Step 2






Step 2






Step 2






Step 2


Post-operation

Pre-discharge

Follow-up





Step 2

Unsupervised analysis (PCA), 6 patient (Pareto scaling)





Step 2






Step 2


Post-operation

Pre-discharge

Follow-up





Step 2


3318_12

3317_7

3317_8 Post-operation

Pre-discharge

Follow-up





Step 2

Unsupervised analysis (PCA), 15 pazienti (Pareto scaling)





Step 2

Post-operation





VIP

scor

esStep 2

Pre-discharge





VIP

scor

esStep 2

Follow-up





VIP

scor

esStep 2

Conclusion

NMR spectra has biochemical (metabolites) and therapy information

The presented model describes concisevely the metabolic covarying biopatterns related to the recovery progress of kidney transplant patients

NMR spectra plus chemometric elaboration could be used to identify patients that do not recover as they are supposed to





Further study on signals alignment and signals interpretation

Analyze more patients in order to verify, validate and improve the results so far achieved

Single patient trajectory study





Things to do

Alignment

Hand Icoshift





Pareto scaling

Alignment

Manuale Icoshift





Pareto scaling

Individual trajectory and …





Pareto scaling

…improve class boundaries





Class 1 or 2?

Pareto scaling

Signal identification

Class 1 : post-intervention Classe 3- follow-up





δ ppm δ ppm

Pareto scaling

Thank you for your attention!!!





Technology

Workshop di Chemiometria 2011 - Alessandria