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Journal of Primary Health Care Journal of Primary Health Care Society
Journal of The Royal New Zealand College of General Practitioners
RESEARCH ARTICLE (Open Access)

They’re sicker than we think: an exploratory study profiling the cardio-metabolic health in a sample of adults with pre-diabetes in Aotearoa New Zealand

Christine Barthow https://orcid.org/0000-0001-8308-4745 1 * , Sue Pullon https://orcid.org/0000-0003-0220-5010 2 , Mark Weatherall 1 , Jeremy Krebs 1
+ Author Affiliations
- Author Affiliations

1 Department of Medicine, University of Otago, Wellington, PO Box 7343, Wellington South 6242, New Zealand.

2 Department of Primary Health Care & General Practice, University of Otago, Wellington, PO Box 7343, Wellington South 6242, New Zealand.

* Correspondence to: Christine.Barthow@otago.ac.nz

Handling Editor: Felicity Goodyear-Smith

Journal of Primary Health Care 14(3) 221-228 https://doi.org/10.1071/HC22068
Published: 30 August 2022

© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of The Royal New Zealand College of General Practitioners. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

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.

Keywords: cardiometabolic risk factors, cardiovascular disease, glycated haemoglobin, metabolic syndrome, prediabetic state, primary health care, progression, renal disease, risk assessment, type 2 diabetes mellitus.


References

[1]  Ministry of Health. Annual Data Explorer 2019/20: New Zealand health survey. 2020. Available at https://minhealthnz.shinyapps.io/nz-health-survey-2019-20-annual-data-explorer/ [Accessed 10 November 2021]

[2]  Gu Y, Warren J, Kennelly J, et al. Incidence rate of prediabetes: an analysis of New Zealand primary care data. In: Georgiou A, Grain H, Schaper LK, editors. Driving Reform: Digital Health Is Everyone’s Business. IOS Press, Inc.; 2015. pp. 81–86.
| Crossref |

[3]  Ministry of Health. Living well with diabetes: a plan for people at high risk of or living with diabetes 2015-2020. 2015. Available at https://www.health.govt.nz/publication/living-well-diabetes

[4]  Messina J, Campbell S, Morris R, et al. A narrative systematic review of factors affecting diabetes prevention in primary care settings. PLoS One 2017; 12 e0177699
A narrative systematic review of factors affecting diabetes prevention in primary care settings.Crossref | GoogleScholarGoogle Scholar |

[5]  Kandula NR, Moran MR, Tang JW, et al. Preventing diabetes in primary care: providers’ perspectives about diagnosing and treating prediabetes. Clin Diabetes 2018; 36 59–66.
Preventing diabetes in primary care: providers’ perspectives about diagnosing and treating prediabetes.Crossref | GoogleScholarGoogle Scholar |

[6]  Piller C. Dubious diagnosis. Science 2019; 363 1026–1031.
Dubious diagnosis.Crossref | GoogleScholarGoogle Scholar |

[7]  Richter B, Hemmingsen B, Metzendorf M-I, et al. Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia. Cochrane Database Syst Rev 2018; 10 CD012661
Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia.Crossref | GoogleScholarGoogle Scholar |

[8]  McKinlay E, Hilder J, Hood F, et al. Uncertainty and certainty: perceptions and experiences of prediabetes in New Zealand primary care – a qualitative study. J Prim Health Care 2022; 14 138–145.
Uncertainty and certainty: perceptions and experiences of prediabetes in New Zealand primary care – a qualitative study.Crossref | GoogleScholarGoogle Scholar |

[9]  Coppell KJ, Mann JI, Williams SM, et al. Prevalence of diagnosed and undiagnosed diabetes and prediabetes in New Zealand: findings from the 2008/09 adult nutrition survey. N Z Med J 2013; 126 23–42.

[10]  Honigberg MC, Zekavat SM, Pirruccello JP, et al. Cardiovascular and kidney outcomes across the glycemic spectrum. J Am Coll Cardiol 2021; 78 453–464.
Cardiovascular and kidney outcomes across the glycemic spectrum.Crossref | GoogleScholarGoogle Scholar |

[11]  Yahyavi SK, Snorgaard O, Knop FK, et al. Prediabetes defined by first measured HbA1c predicts higher cardiovascular risk compared with HbA1c in the diabetes range: a cohort study of nationwide registries. Diabetes Care 2021; 44 2767–2774.
Prediabetes defined by first measured HbA1c predicts higher cardiovascular risk compared with HbA1c in the diabetes range: a cohort study of nationwide registries.Crossref | GoogleScholarGoogle Scholar |

[12]  Pylypchuk R, Wells S, Kerr A, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet 2018; 391 1897–1907.
Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study.Crossref | GoogleScholarGoogle Scholar |

[13]  DeBoer MD, Gurka MJ. Clinical utility of metabolic syndrome severity scores: considerations for practitioners. Diabetes, Metab Syndr Obes Targets Ther 2017; 10 65–72.
Clinical utility of metabolic syndrome severity scores: considerations for practitioners.Crossref | GoogleScholarGoogle Scholar |

[14]  Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome. Circulation 2009; 120 1640–1645.
Harmonizing the metabolic syndrome.Crossref | GoogleScholarGoogle Scholar |

[15]  Gurka MJ, Filipp SL, Musani SK, et al. Use of BMI as the marker of adiposity in a metabolic syndrome severity score: derivation and validation in predicting long-term disease outcomes. Metabolism 2018; 83 68–74.
Use of BMI as the marker of adiposity in a metabolic syndrome severity score: derivation and validation in predicting long-term disease outcomes.Crossref | GoogleScholarGoogle Scholar |

[16]  DeBoer MD, Filipp SL, Gurka MJ. Use of a metabolic syndrome severity Z score to track risk during treatment of prediabetes: an analysis of the diabetes prevention program. Diabetes Care 2018; 41 2421–2430.
Use of a metabolic syndrome severity Z score to track risk during treatment of prediabetes: an analysis of the diabetes prevention program.Crossref | GoogleScholarGoogle Scholar |

[17]  Barthow C, Hood F, Crane J, et al. A randomised controlled trial of a probiotic and a prebiotic examining metabolic and mental health outcomes in adults with pre-diabetes. BMJ Open 2022; 12 e055214
A randomised controlled trial of a probiotic and a prebiotic examining metabolic and mental health outcomes in adults with pre-diabetes.Crossref | GoogleScholarGoogle Scholar |

[18]  Barthow C, Hood F, McKinlay E, et al. Food 4 Health - He Oranga Kai: assessing the efficacy, acceptability and economic implications of Lactobacillus rhamnosus HN001 and β-glucan to improve glycated haemoglobin, metabolic health, and general well-being in adults with pre-diabetes: study protocol for a 2 × 2 factorial design, parallel group, placebo-controlled randomized controlled trial, with embedded qualitative study and economic analysis. Trials 2019; 20 464
Food 4 Health - He Oranga Kai: assessing the efficacy, acceptability and economic implications of Lactobacillus rhamnosus HN001 and β-glucan to improve glycated haemoglobin, metabolic health, and general well-being in adults with pre-diabetes: study protocol for a 2 × 2 factorial design, parallel group, placebo-controlled randomized controlled trial, with embedded qualitative study and economic analysis.Crossref | GoogleScholarGoogle Scholar |

[19]  Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28 412–419.
Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man.Crossref | GoogleScholarGoogle Scholar |

[20]  Statistics New Zealand. Ethnicity. 2018. Available at http://nzdotstat.stats.govt.nz/wbos/Index.aspx?DataSetCode=TABLECODE8338 [Accessed 27 October 2021]

[21]  Rush EC, Crook N, Simmons D. Optimal waist cutpoint for screening for dysglycaemia and metabolic risk: evidence from a Maori cohort. Br J Nutr 2009; 102 786–791.
Optimal waist cutpoint for screening for dysglycaemia and metabolic risk: evidence from a Maori cohort.Crossref | GoogleScholarGoogle Scholar |

[22]  Taylor RW, Brooking L, Williams SM, et al. Body mass index and waist circumference cutoffs to define obesity in indigenous New Zealanders. Am J Clin Nutr 2010; 92 390–397.
Body mass index and waist circumference cutoffs to define obesity in indigenous New Zealanders.Crossref | GoogleScholarGoogle Scholar |

[23]  Halim AA, Basu A, Kirk R. The prevalence of body mass index-associated chronic diseases in diverse ethnic groups in New Zealand. Asia Pac J Public Health 2019; 31 84–91.
The prevalence of body mass index-associated chronic diseases in diverse ethnic groups in New Zealand.Crossref | GoogleScholarGoogle Scholar |

[24]  Meredith-Jones K, Taylor R, Brown R, et al. Age and sex-specific visceral fat reference cutoffs and their association with cardio-metabolic risk. Int J Obes 2021; 45 808–817.
Age and sex-specific visceral fat reference cutoffs and their association with cardio-metabolic risk.Crossref | GoogleScholarGoogle Scholar |

[25]  Cervantes A, Singh RG, Kim JU, et al. Relationship of anthropometric indices to abdominal body composition: a multi-ethnic New Zealand magnetic resonance imaging study. J Clin Med Res 2019; 11 435–446.
Relationship of anthropometric indices to abdominal body composition: a multi-ethnic New Zealand magnetic resonance imaging study.Crossref | GoogleScholarGoogle Scholar |

[26]  Gurka MJ, Lilly CL, Oliver MN, et al. An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: a confirmatory factor analysis and a resulting continuous severity score. Metabolism 2014; 63 218–225.
An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: a confirmatory factor analysis and a resulting continuous severity score.Crossref | GoogleScholarGoogle Scholar |

[27]  Teng A, Blakely T, Scott N, et al. What protects against pre-diabetes progressing to diabetes? Observational study of integrated health and social data. Diabetes Res Clin Pract 2019; 148 119–129.
What protects against pre-diabetes progressing to diabetes? Observational study of integrated health and social data.Crossref | GoogleScholarGoogle Scholar |

[28]  Simons LA. An updated review of lipid‐modifying therapy. Med J Aust 2019; 211 87–92.
An updated review of lipid‐modifying therapy.Crossref | GoogleScholarGoogle Scholar |

[29]  Grundy SM, Stone NJ, Bailey AL, et al. AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: executive summary. J Am Coll Cardiol 2019; 73 3168–3209.
AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: executive summary.Crossref | GoogleScholarGoogle Scholar |

[30]  Grundy SM. Metabolic syndrome. In: Bonora E, DeFronzo RA, editors. Diabetes Complications, Comorbidities and Related Disorders, 2nd edn. Springer; 2020, pp. 71–107.
| Crossref |

[31]  Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346 393–403.
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.Crossref | GoogleScholarGoogle Scholar |

[32]  Tuomilehto J, Lindström J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344 1343–1350.
Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.Crossref | GoogleScholarGoogle Scholar |

[33]  Pan X-R, Li G-W, Hu Y-H, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. Diabetes Care 1997; 20 537–544.
Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance.Crossref | GoogleScholarGoogle Scholar |

[34]  Hamman RF, Wing RR, Edelstein SL, et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care 2006; 29 2102–2107.
Effect of weight loss with lifestyle intervention on risk of diabetes.Crossref | GoogleScholarGoogle Scholar |

[35]  Kriska AM, Rockette-Wagner B, Edelstein SL, et al. The impact of physical activity on the prevention of type 2 diabetes: evidence and lessons learned from the diabetes prevention program, a long-standing clinical trial incorporating subjective and objective activity measures. Diabetes Care 2021; 44 43–49.
The impact of physical activity on the prevention of type 2 diabetes: evidence and lessons learned from the diabetes prevention program, a long-standing clinical trial incorporating subjective and objective activity measures.Crossref | GoogleScholarGoogle Scholar |

[36]  Wild CEK, Rawiri NT, Willing EJ, et al. Determining barriers and facilitators to engagement for families in a family-based, multicomponent healthy lifestyles intervention for children and adolescents: a qualitative study. BMJ Open 2020; 10 e037152
Determining barriers and facilitators to engagement for families in a family-based, multicomponent healthy lifestyles intervention for children and adolescents: a qualitative study.Crossref | GoogleScholarGoogle Scholar |

[37]  Wild CEK, Rawiri NT, Willing EJ, et al. What affects programme engagement for Māori families? A qualitative study of a family‐based, multidisciplinary healthy lifestyle programme for children and adolescents. J Paediatr Child Health 2021; 57 670–676.
What affects programme engagement for Māori families? A qualitative study of a family‐based, multidisciplinary healthy lifestyle programme for children and adolescents.Crossref | GoogleScholarGoogle Scholar |

[38]  Tane T, Selak V, Hawkins K, et al. Māori and Pacific peoples’ experiences of a Māori-led diabetes programme. N Z Med J 2021; 134 79–89.