Table 3:

Coefficients and p-values from linear regression models in which yield force was the dependent variable.

Model	Ref if not Rx	p-value	Add to Ref if Rx ^a	p-value	Slope	p-value	Slope if Rx ^b	p-value	Adj-R²
Rx + Body mass	+2.57	0.1388	−1.27	0.0020	+0.45	<0.0001	NI		0.414
Rx + Glucose	+21.58	<0.0001	−1.61	0.0015	−0.007	<0.0001	NI		0.293
Rx + HbA1c	+23.3	<0.0001	−5.3	0.0030	−0.8	<0.0001	−0.002	0.0130	0.257
Rx + Insulin ^c	+16.94	<0.0001	zero	0.6500	+0.19	0.0069	NI		0.063
Rx + P1NP ^d	+16.09	<0.0005	zero	0.246	+0.24	<0.0005	NI		0.309
Rx + CTX ^c	+18.12	<0.0001	zero	0.5131	−0.10	0.0128	NI		0.051
Rx + uCA/CR	+18.64	<0.0001	zero	0.5005	−0.001	<0.0001	−0.0002	0.0047	0.142
Rx + uPhos/CR	+17.95	<0.0001	zero	0.1287	−0.023	0.0005	NI		0.123
Rx + I_min/c_min	+2.2	0.3633	zero	0.9103	+99.7	<0.0001	NI		0.272
Rx + Ct.Ar	−8.3	0.0005	zero	0.7698	+33.3	<0.0001	NI		0.544
Rx + Ct.Th	−13	<0.0001	zero	0.6894	+146	<0.0001	NI		0.611
Rx + Ct.Po	+29.0	<0.0001	zero	0.5960	−4.1	<0.0001	NI		0.338

If zero, the contribution of the coefficient for treatment (Rx) to the prediction of yield force is negligible (not significant). Ref is the intercept term.

The interaction term was not included (NI) if it was not significant. When included, the coefficient of the slope is different between untreated mice and the CANA-treated mice.

The regression model explaining the variance in yield force is not robust (p<0.025 instead of p≤0.0006 like the other models)

P-values were generated from bootstrapped regression with 1000 replicates because residuals were heteroscedastic.