Symmetric dimethylarginine concentrations in dogs with hypothyroidism before and after treatement with levothyroxine

Development and internal validation of diagnostic prediction models using machine-learning algorithms in dogs with hypothyroidism

Introduction

Hypothyroidism can be easily misdiagnosed in dogs, and prediction models can support clinical decision-making, avoiding unnecessary testing and treatment. The aim of this study is to develop and internally validate diagnostic prediction models for hypothyroidism in dogs by applying machine-learning algorithms.

Methods

A single-institutional cross-sectional study was designed searching the electronic database of a Veterinary Teaching Hospital for dogs tested for hypothyroidism. Hypothyroidism was diagnosed based on suggestive clinical signs and thyroid function tests. Dogs were excluded if medical records were incomplete or a definitive diagnosis was lacking. Predictors identified after data processing were dermatological signs, alopecia, lethargy, hematocrit, serum concentrations of cholesterol, creatinine, total thyroxine (tT4), and thyrotropin (cTSH). Four models were created by combining clinical signs and clinicopathological variables expressed as quantitative (models 1 and 2) and qualitative variables (models 3 and 4). Models 2 and 4 included tT4 and cTSH, models 1 and 3 did not. Six different algorithms were applied to each model. Internal validation was performed using a 10-fold cross-validation. Apparent performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC).

Results

Eighty-two hypothyroid and 233 euthyroid client-owned dogs were included. The best performing algorithms were naive Bayes in model 1 (AUROC = 0.85; 95% confidence interval [CI] = 0.83-0.86) and in model 2 (AUROC = 0.98; 95% CI = 0.97-0.99), logistic regression in model 3 (AUROC = 0.88; 95% CI = 0.86-0.89), and random forest in model 4 (AUROC = 0.99; 95% CI = 0.98-0.99). Positive predictive value was 0.76, 0.84, 0.93, and 0.97 in model 1, 2, 3, and 4, respectively. Negative predictive value was 0.89, 0.89, 0.99, and 0.99 in model 1, 2, 3, and 4, respectively.

Discussion

Machine learning-based prediction models were accurate in predicting and quantifying the likelihood of hypothyroidism in dogs based on internal validation performed in a single-institution, but external validation is required to support the clinical applicability of these models.

Alterations in serum concentrations of visfatin and betatrophin in dogs with hypothyroidism

BACKGROUND

Hypothyroidism in dogs is associated with obesity and altered lipid and carbohydrate metabolism. The adipokines, visfatin, and betatrophin, affect glucose tolerance. Betatrophin is involved in lipid regulation.

HYPOTHESIS

Visfatin and betatrophin serum concentrations are altered in hypothyroid dogs.

ANIMALS

Dogs with naturally occurring hypothyroidism (n = 25) and healthy dogs (n = 25).

METHODS

Insulin, visfatin, and betatrophin serum concentrations were measured in all dogs and 19 of the hypothyroid dogs after 30 days of thyroxine treatment. Body condition score (BCS) was determined (1-9 scale).

RESULTS

Visfatin concentrations were lower in hypothyroid compared with healthy dogs (mean, 95% confidence interval [CI]; 2.0 ng/mL, 1.2-3.3 vs 5.1 ng/mL, 3.3-7.8; P = .004) and increased post-treatment (3.1 ng/mL, 1.9-4.9 vs 2.6 ng/mL, 1.6-4.1; P = .05). Betatrophin concentrations were lower in lean to normal (body condition score [BCS], 3-5) hypothyroid dogs compared to lean to normal healthy dogs (52 pg/mL, 9-307 vs 597 pg/mL, 216-1648; P = .03), but were not different between overweight (BCS, 6-9) hypothyroid and healthy dogs (341 pg/L, 168-695 vs 178 pg/mL, 77-415; P = .26), and decreased post-treatment in overweight dogs (206 pg/mL, 87-488 vs 268 pg/mL, 112-640; P = .004). Visfatin concentrations were higher in overweight compared with lean to normal dogs (4.7 ng/mL, 3.3-6.6 vs 2.2 ng/mL, 1.2-4.2; P = .04). Betatrophin concentrations were positively correlated with BCS (r = .47, P = .02) and insulin concentrations (r = .48, P = .03) in hypothyroid dogs and negatively correlated with BCS (r = -.47, P = .02) and thyroid stimulating hormone concentrations (r = -.56, P = .01) in healthy dogs.

CONCLUSIONS AND CLINICAL IMPORTANCE

Hypothyroidism in dogs is associated with alterations in visfatin and betatrophin concentrations that partially resolve with thyroxine treatment.

Hyperthyroid cats and their kidneys: a literature review

Hyperthyroidism and chronic kidney disease (CKD) are common diseases of geriatric cats, and often occur concurrently. Thus, a thorough understanding of the influence of thyroid function on renal function is of significant value for all feline practitioners. Among other effects, hyperthyroidism causes protein catabolism and increases renal blood flow and glomerular filtration rate (GFR). These effects render traditional renal markers insensitive for the detection of CKD in cats with uncontrolled hyperthyroidism. Furthermore, the development of iatrogenic hypothyroidism with over treatment of hyperthyroidism can be detrimental to renal function and may negatively affect long-term survival. This review discusses important diagnostic considerations of feline hyperthyroidism, as well as key treatment modalities, with an emphasis on the use of radioiodine and the importance of post treatment monitoring of thyroid and renal parameters. In Australia, a common curative treatment for cats with benign hyperthyroidism (i.e. thyroid hyperplasia or adenoma) is a fixed dose of orally administered radioiodine, regardless of the serum total thyroxine concentration at the time of diagnosis. This review discusses the long term outcomes of this standard of care in comparison with current, relevant research literature from around the world. Finally, this review explores the use of symmetric dimethylarginine (SDMA) in assessing renal function before and after treatment in hyperthyroid cats. SDMA correlates well with GFR and creatinine in non-hyperthyroid cats, but our understanding of its performance in hyperthyroid cats remains in its infancy.