Introduction Type 2 diabetes mellitus (T2DM) is a highly prevalent and potentially preventable condition associated with significant health, social, and economic costs. The detection and management of pre-diabetes is an important opportunity to prevent or delay the onset of T2DM and associated morbidities; however, its importance is controversial as the health risks associated with pre-diabetes are poorly understood. Aim To understand the cardio-metabolic health profile of a sample of adults with pre-diabetes in Aotearoa New Zealand. Methods Secondary analyses of baseline data from all 153 adults recruited to an intervention trial for adults with pre-diabetes were carried out. A profile of cardio-metabolic risk was measured by describing the proportion with metabolic syndrome (MetS) calculated using Adult Treatment Panel III criteria, which includes blood pressure, lipids, and obesity in addition to glycaemic measures. The severity of MetS was calculated as MetS Z-scores. Subgroup analyses for sex, ethnicity and glycated haemoglobin (HbA1c ) were performed. Results Overall, 74% of this study population had MetS, and the proportion varied according to ethnicity and HbA1c level. The severity of MetS was highly variable, with MetS-Z-scores ranging from -1.0 to 2.8. Although mean MetS Z-scores differed according to ethnicity and HbA1c level, all subgroups included individuals with widely differing severity of MetS, suggesting likely quite different risks for progression to diabetes or cardiovascular disease across the range of pre-diabetes defined by HbA1c . Discussion Single biochemical markers of glycaemia are insufficient to ascertain overall cardio-metabolic risk when prioritising clinical efforts for those with pre-diabetes, particularly in primary care, where the potential for preventing or delaying the onset of type 2 diabetes mellitus (T2DM) is significant. Findings indicate the importance of attending to all cardio-metabolic risk factors when caring for people with pre-diabetes. The development of tools using multiple relevant variables and predicting a comprehensive range of outcomes would improve timely risk stratification and treatment effect monitoring of pre-diabetes populations.