Dunkler D(1-3), Dehghan M(1), Teo KK(1-4), Heinze G(3), Gao P(1), Kohl M(1-3), Clase CM(4), Mann JFE(2,5), Yusuf S(1,4), Oberbauer R(3,6)

1- Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada
2- Universitaetsklinikum Erlangen, Department of Nephrology, Germany
3- Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Austria
4- McMaster University, Hamilton, Ontario, Canada
5- Schwabing General Hospital, and KfH Kidney Center, Munich, Germany
6- KH Elisabethinen Linz, Austria, and Department of Internal Medicine III, Medical University of Vienna, Austria

Webtable 1:

Description of food items in the Food Frequency Questionnaire. These examples are not exhaustive.
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Webfigure 1:

Single variable models adjusted with known confounders.
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Webtable 2:

Assumed protein content per serving size and conversion between servings and gram based on
USDA United State Department of Agriculture National Nutrient database for standard reference.
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Webfigure 2:

Single variable model with mAHEI adjusted with known confounders, separated for
participant’s albuminuria status at baseline.
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Webtable 3:

Changes in the number of participants with new micro- or macro-albuminuria at study end
when the minimum increase in UACR between baseline and 5 years follow-up measurement is changed.
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Webfigure 3:

Multivariable model adjusted with known confounders.
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Webtable 4:

Clinical and nutrition characteristics of participants with type 2 diabetes mellitus, separated by
the three outcome states at 5.5 years of follow-up-. The number of participants with available data; median,
first and third quartiles (IQR) or frequencies and percentages are given.
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Webfigure 4:

Comparison of estimates of multivariable models adjusted with known confounders after 5.5
years of follow-up (Webtable 7) and after 2 years of follow-up (Webtable 13).
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Webtable 5:

Distribution of the three outcome states at the 5.5 year follow-up status separated by normo-
and micro-albuminuria at baseline.
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Webtable 6:

Distribution of the two types (albuminuria and GFR) of renal outcomes.
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Webtable 7:

Single variable models adjusted with known confounders.
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Webtable 8:

Multivariable model adjusted with known confounders.
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Webtable 9:

Single variable models adjusted with the extended set of confounders 1.
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Webtable 10:

Multivariable model adjusted with the extended set of confounders 1.
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Webtable 11:

Single variable models adjusted with the extended set of confounders 2.
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Webtable 12:

Multivariable model adjusted with the extended set 2 of confounders.
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Webtable 13:

Multivariable logistic model adjusted with known confounders.
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Webtable 14:

Single variable model with mAHEI and multivariable model adjusted with known confounders.
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Webtable 15:

Single variable model with mAHEI and multivariable model adjusted with the extended set of confounders 1.
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Webtable 16:

Single variable model with mAHEI and multivariable model adjusted with the extended set of confounders 2.
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Webtable 17:

Single variable model with mAHEI and multivariable model adjusted with known confounders.
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Webtable 18:

Single variable model with mAHEI and multivariable model adjusted with the extended set of confounders 1.
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Webtable 19:

Single variable model with mAHEI and multivariable model adjusted with the extended set of confounders 2.
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Webtable 20:

Combined Renal Outcome: Multinomial logit model including only variables from the set of known confounders.
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Webtable 21:

Combined Renal Outcome: Multinomial logit model including only variables from the set of extended confounders 1.
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Webtable 22:

Combined Renal Outcome: Multinomial logit model including only variables from the set of extended confounders 2.
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