​​Introduction

Regular monitoring of glycemic control, kidney function, blood pressure, and lipid levels is recommended in people with diabetes to assess disease progression and enable timely detection of complications and refine treatment needs. Current Danish national treatment guidelines for the management of type 1 diabetes (T1D) and type 2 diabetes (T2D) recommend regular follow-up assessments, with at least annual evaluations. At the same time, they emphasize that monitoring frequency should be tailored to individual clinical characteristics, treatment complexity, and patient needs rather than determined by a uniform schedule (1,2). Given substantial advances in diabetes management and the heterogeneity of people living with diabetes, it is plausible that some individuals could be screened less frequently without compromising clinical outcomes. Conversely, others may benefit from more frequent monitoring due to poor glycemic control, hypertension, dyslipidemia, diabetes-related complications, psychosocial vulnerability, or challenges with treatment adherence.

The aim of this review was therefore to provide an overview of the existing evidence regarding optimal screening intervals for glycated hemoglobin (HbA1c), blood pressure, estimated glomerular filtration rate (eGFR), albuminuria, and cholesterol in persons with diabetes.

​Methods

This review was based on a pragmatic literature search in PubMed/MEDLINE focusing on clinical guidelines, systematic reviews, and relevant primary studies. The identified literature was selected and evaluated in collaboration with Videnscenter for Diabetes at Steno Diabetes Center Copenhagen (SDCC). Key information was extracted, including recommended screening intervals, methodological approaches to risk stratification, and clinical outcome data. Literature search and data extraction were supported using Elicit (https://elicit.com), an AI-assisted research tool. The literature search was guided by the following question:

A total of 76 publications were identified through the literature search, and 11 publications were included following abstract screening for relevance to the research question.

In the following sections, the evidence base for each biomarker is presented separately and discussed in the context of clinical practice recommendations from the American Diabetes Association (ADA) (3).

​Results

Monitoring intervals for HbA1c

ADA recommendations

ADA recommends measuring HbA1c at least twice yearly in patients with stable glycemic control who are meeting treatment goals, and quarterly in patients whose therapy has changed or unmet glycemic targets. These recommendations are primarily based on expert consensus or clinical experience (3), underscoring the importance of maintaining target HbA1c levels to reduce the risk of diabetes-related complications that has been demonstrated in several landmark clinical trials (4,5).

Observational studies

Imai et al. conducted a study of 6,424 adults with T2D from Australian general practices, including patients with both stable and unstable glycemic control, followed over 5 years using electronic health record data (6). The researchers calculated an “adherence rate" based on the proportion of HbA1c tests performed within ADA guideline recommended intervals. They demonstrated that high adherence (>66%) compared to low adherence (≤33%) was associated with more stable or improved HbA1C levels over time, lower risk of chronic kidney disease (CKD), but no significant association with ischemic heart disease over time. The findings suggest a population-level association but should be interpreted cautiously. Patients who adhered to testing recommendations may also have differed in other ways (e.g., healthcare engagement, treatment adherence, or disease severity). 

Weiss et al. examined how often 112,572 adults with T2D received HbA1c testing (follow-up was 3 years) within a large US health insurance system, and whether testing frequency was associated with glycemic control and cardiovascular (CVD) outcomes (7). The study found that 32.9% of patients with baseline controlled HbA1c and 60.6% with uncontrolled baseline HbA1C did not receive HbA1c testing according to ADA guideline recommendations. The relationship between HbA1c testing frequency and glycemic control was inconsistent. While more frequent testing was significantly associated with cardiovascular events, this likely reflects more intensive monitoring of patients with greater disease severity and cardiovascular risk rather than a causal effect of testing frequency. 

Anjana et al. examined the association between scheduled diabetes care visits over time, glycemic burden, and incidence of microvascular complications in patients with T2D from a tertiary care center in India with up to 9 years of follow-up (8).  The main outcomes included glycemic burden, defined as cumulative exposure to HbA1c levels above target, and the development of complications such as retinopathy, nephropathy and neuropathy. The study suggested that continuity and regularity of diabetes management are important determinants of long-term complication risk. A key limitation was potential selection bias, as patients with regular follow-up may differ systematically from those receiving irregular care.

Randomized trials

Wermeling et al. investigated whether 6-monthly monitoring of well-controlled patients with T2D was as effective and cost-effective as the standard 3-monthly monitoring approach in primary care in the Netherlands (9). Well-controlled patients were defined as having HbA1c ≤ 58 mmol/mol, systolic blood pressure ≤ 145 mmHg, and total cholesterol ≤ 5.2 mmol/L. A total of 2,215 patients were included, of whom those without a strong preference for monitoring frequency were randomized to either 3- or 6-monthly follow-up over 18 months. The primary outcome was the proportion of patients remaining within target levels for HbA1c, systolic blood pressure, and total cholesterol. The study demonstrated that 6-monthly monitoring was comparable to 3-monthly monitoring in well-controlled, non-insulin-treated patients with good cardiometabolic control and no preference regarding monitoring frequency.  

Studies modelling individualized intervals

Pedersen et al. (10) developed a machine learning approach to predict the optimal testing interval for monitoring HbA1c in persons with T2D. The study compared current practice where most persons are monitored every 3–6 months with a machine-learning approach predicting individualized intervals at 3, 6, 9 or 12-month based on patient-specific factors. They found that many patients with stable glycaemic control could potentially be monitored at 9- or 12-month intervals rather than 3- to 6-month, thereby reducing unnecessary testing and freeing healthcare resources.

Mukonda et al. (11) evaluated the effectiveness and cost-effectiveness of HbA1c test monitoring intervals of 3-, 4-, 6-monthly and annually in persons with T2D in South Africa using a Markov model with three health states (HbA1c <7%, HbA1c ≥ 7%, Dead). Compared with annual testing, more frequent monitoring increased both costs and quality-adjusted life years (QALYs). 6-monthly monitoring was found to be cost-effective, whereas increasing testing frequency to three-monthly intervals was not. These findings suggest that moderately frequent monitoring may provide an optimal balance between improved health outcomes and resource use, supporting a move away from fixed annual testing intervals.

Summary of evidence across studies

The evidence supports an individualized approach to HbA1c monitoring rather than a uniform testing interval for all people with T2D. ADA guidelines recommend testing at least twice yearly in patients with stable glycemic control and quarterly in those with treatment changes or suboptimal control, although these recommendations are based largely on expert consensus.

Observational studies suggest that adherence to recommended testing intervals is associated with better glycemic outcomes and lower complication risk, but these findings may be influenced by differences in patient engagement, disease severity, and treatment adherence.

Limited evidence from randomized studies indicates that less frequent monitoring may be safe in selected low-risk patients. Modelling studies similarly support risk-adapted testing, suggesting that longer intervals may be appropriate for stable patients, while more frequent monitoring should be targeted to those with poorer control, treatment changes, insulin use, or higher clinical risk.

Overall, current evidence supports HbA1c monitoring approximately every 6 months in stable, well-controlled patients with T2D and every 3 months in higher-risk or less stable patients. However, direct evidence from randomized trials remains limited, and little evidence is available for people with T1D or other forms of diabetes.

​Evidence on eGFR and albuminuria monitoring intervals

ADA recommendations

ADA recommends assessing kidney function using urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) at least annually in all individuals with T1D of ≥5 years' duration and in all individuals with T2D, regardless of treatment. In individuals with CKD, monitoring is recommended 1–4 times annually depending on the CKD stage (3). These recommendations are supported primarily by strong and consistent observational evidence demonstrating the clinical importance of kidney function monitoring in T2D and its influence on treatment decisions and prognosis (12,13). However, direct  evidence from randomized trials establishing optimal testing intervals for eGFR and UACR remains limited. 

Observational studies

Sundqvist et al. conducted an observational study in Finnish primary healthcare investigating the association between renal function screening (eGFR and albuminuria/proteinuria measurements) frequency and kidney function in patients with T2D (14). More frequent monitoring was associated with greater renal function decline; however, this likely reflected confounding by indication, as patients at higher risk of CKD progression were monitored more intensively. The design limits causal interpretation regarding optimal renal monitoring intervals. 

Studies modelling individualized intervals

Wu et al. used a Partially Observable Markov Decision Process (POMDP) to develop individualized CKD screening recommendations for people with T2D based on age, sex, race, and previous screening history (15). The model was designed as a decision-support tool to determine whether CKD screening should be performed at each three-month interval between ages 30 and 85, with the aim of optimizing the timing of screening according to an individual's risk of disease progression.

Compared with the current practice of uniform annual screening, the model generated individualized screening schedules based on age, sex, race, and previous screening history. Prior screening results were used to estimate an individual's risk of CKD progression, with patients showing evidence of renal impairment assigned shorter screening intervals and those with consistently normal findings monitored less intensively. Screening was generally recommended more frequently after age 40, increasing to every 3–6 months for many individuals aged 61–70 years, while Black individuals were assigned shorter intervals than White individuals. In simulation, this personalized strategy improved the timing of CKD detection and increased net monetary benefit by $1,000–$8,000 per patient compared with uniform annual screening.

Although the POMDP model demonstrated the potential benefits of individualized CKD screening intervals in simulation, it has not been implemented or prospectively evaluated in routine clinical practice. At present, the model should therefore be considered a decision-analytic framework illustrating the potential value of risk-based screening rather than a clinically adopted tool. 

Kessler et al. evaluated the cost-effectiveness of microalbuminuria screening for early CKD detection in individuals with diabetes using a validated microsimulation model (16,17). The study compared screening intervals of 1, 2, 5, and 10 years and assessed screening initiated from age 50 years onward. Screening every 2 years was identified as the most cost-effective strategy, with an incremental cost-effectiveness ratio of 54,000 CHF per QALY gained. These findings suggest that biennial UACR screening from age 50 years may provide an efficient approach for early CKD detection in people with diabetes. The findings should be interpreted in light of several limitations, including reliance on long-term microsimulation modelling, extrapolation of data from shorter-term studies, use of some non-Swiss epidemiological and cost inputs, and exclusion of cardiovascular and indirect societal costs.

Perkins et al. used data from the DCCT/EDIC studies to develop a risk-based albuminuria screening strategy for individuals with T1D (18). Screening intervals of 6 months, 1 year, or 2 years were assigned according to most recent HbA1c and albumin excretion rate (AER). Compared with standard annual screening, the personalized strategy reduced the time spent with undetected albuminuria by 34.9% while decreasing the number of screening tests by 20.4%. The resulting screening algorithm provides a simple, clinically interpretable approach for tailoring monitoring intensity according to individual risk and illustrates the potential of personalized screening to improve early detection while reducing testing burden. However, the model was derived from the DCCT/EDIC trial population and requires validation in broader real-world settings before routine implementation. 

Summary of evidence across studies

Current ADA guidelines recommend annual assessment of eGFR and UACR in all individuals with diabetes, with more frequent monitoring in those with established CKD. These recommendations are supported by extensive observational evidence linking kidney function measures to prognosis and treatment decisions, although direct evidence comparing alternative monitoring intervals is limited.

Observational and modelling studies suggest that annual monitoring is a reasonable default, while individualized, risk-based strategies may improve efficiency. Studies indicate that monitoring intervals can be tailored according to kidney function, albuminuria, glycemic control, CKD stage, and overall risk, potentially reducing unnecessary testing in low-risk individuals while maintaining closer surveillance for those at higher risk.

Taken together, the evidence supports annual kidney monitoring as a minimum standard, with more frequent monitoring for patients with albuminuria, reduced eGFR, poor glycemic control, or established CKD. Emerging evidence from prediction models and health-economic analyses suggests that monitoring frequency could be further individualized according to patient risk. However, most evidence comes from observational studies and modelling rather than direct randomized comparisons of monitoring intervals. All identified risk-based monitoring strategies were evaluated using observational data, retrospective analyses, or simulation modelling, and none have undergone prospective implementation and evaluation in routine clinical practice. 

Evidence on blood pressure monitoring intervals

ADA recommendations

ADA recommends that blood pressure should be measured at every routine clinical visit, or at least every 6 months. Individuals found to have elevated blood pressure without a diagnosis of hypertension (systolic blood pressure 120–129 mmHg and diastolic blood pressure <80 mmHg) should have blood pressure confirmed using multiple readings, including measurements on a separate day, to diagnose hypertension. This recommendation is supported by randomized trials including the UK Prospective Diabetes Study (UKPDS) (19), ACCORD (20), and ADVANCE (21), which demonstrated that blood pressure lowering drugs reduces cardiovascular and microvascular complications in people with T2D. Although these studies did not directly evaluate optimal blood pressure monitoring intervals, they provide strong indirect evidence supporting regular blood pressure assessment and treatment adjustment in diabetes care. 

Studies

We did not identify any studies on blood pressure monitoring intervals. 

Evidence on LDL cholesterol monitoring intervals

ADA recommendations

ADA guidelines recommend obtaining a lipid profile at diagnosis or initial evaluation in adults with diabetes, annually thereafter or more frequently if indicated, at least every 5 years in individuals <40 years of age not receiving lipid-lowering therapy, and at initiation of statins or other lipid-lowering therapy, 4–12 weeks after initiation or dose adjustment, and annually thereafter to monitor therapeutic response and adherence.

These recommendations are based on a combination of expert consensus, observational evidence, and randomized trial data. For individuals not receiving statins or other lipid-lowering therapies, annual lipid profile assessment is considered reasonable primarily to support cardiovascular risk stratification and treatment planning, although direct evidence establishing optimal testing intervals is limited. The recommendation for less frequent testing (at least every 5 years) in younger low-risk individuals largely reflects expert consensus and the relatively slower progression of lipid abnormalities in this population.

In contrast, recommendations for lipid testing after initiation or dose adjustment of statins or other lipid-lowering therapy are supported indirectly by randomized trials on statin use and lipid-management studies demonstrating the importance of achieving LDL cholesterol reduction and monitoring therapeutic response and adherence (22–25). However, even in this setting, evidence supporting specific monitoring intervals remains limited, and recommended intervals are based partly on clinical practice standards and expert consensus.

Studies

We did not identify any studies on LDL cholesterol monitoring intervals. 

Conclusion

Overall, the literature supports moving away from a one-size-fits-all approach toward screening intervals tailored to individual risk, disease progression, treatment intensity, and the presence of complications. This is consistent with current Danish recommendations, which emphasize individualized follow-up rather than uniform screening schedules.

Although evidence from randomized studies remains limited, observational and modelling studies suggest that risk-adapted screening strategies may improve efficiency by reducing unnecessary testing in lower-risk individuals while maintaining closer surveillance of those at higher risk of complications or disease progression. In several studies, personalized screening intervals were associated with reduced testing burden, improved cost-effectiveness, and earlier detection of disease progression compared with fixed annual screening approaches.

From a clinical perspective, the current evidence supports annual screening as a pragmatic minimum standard for many patients, with more frequent or less frequent screening considered according to factors such as glycaemic control, kidney function, albuminuria, treatment changes, and comorbidity burden. However, all identified risk-based screening strategies have been evaluated using observational data or modelling approaches and have not yet undergone prospective evaluation in routine clinical practice. Moreover, the existing models address isolated components of diabetes care, such as HbA1c or kidney function, rather than providing integrated recommendations across the range of biomarkers considered in routine diabetes follow-up, including glycaemic control, kidney function, blood pressure, and lipid levels. Future research should therefore focus on developing and prospectively validating optimal screening intervals in diverse real-world populations, with the aim of supporting more comprehensive and personalized screening approaches for people with diabetes. 

References

1.      Dansk Endokrinologisk Selskab. Nationale Behandlings Vejledninger - Type 1 Diabetes [Internet]. 2026. 

2.      Dansk Endokrinologisk Selskab. Nationale Behandlings Vejledninger - Type 2 Diabetes [Internet]. 2026. 

3.      American Diabetes Association Professional Practice Committee for Diabetes*, Bajaj M, McCoy RG, Balapattabi K, Bannuru RR, Bellini NJ, m.fl. 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes—2026. Diabetes Care. 1. januar 2026;49(Supplement_1):S183–215. doi:10.2337/dc26-S009

4.      UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. september 1998;352(9131):837–53. doi:10.1016/S0140-6736(98)07019-6

5.      The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus. N Engl J Med. 30. september 1993;329(14):977–86. doi:10.1056/NEJM199309303291401

6.      Imai C, Li L, Hardie RA, Georgiou A. Adherence to guideline-recommended HbA1c testing frequency and better outcomes in patients with type 2 diabetes: a 5-year retrospective cohort study in Australian general practice. BMJ Qual Saf. september 2021;30(9):706–14. doi:10.1136/bmjqs-2020-012026

7.      Weiss T, Edwards A, Lautsch D, Rajpathak S, Snow K. Hemoglobin A1C testing frequency among patients with type 2 diabetes within a US payer system: a retrospective observational study. Curr Med Res Opin. 26. august 2021;37(11):1859–66. Located at: Crossref. doi:10.1080/03007995.2021.1965562

8.      Anjana RM, Shanthirani CS, Unnikrishnan R, Mugilan P, Amutha A, Nair HD, m.fl. Regularity of follow-up, glycemic burden, and risk of microvascular complications in patients with type 2 diabetes: a 9-year follow-up study. Acta Diabetol. 25. december 2014;52(3):601–9. Located at: Crossref. doi:10.1007/s00592-014-0701-0

9.      Wermeling PR, Gorter KJ, Stellato RK, de Wit GA, Beulens JWJ, Rutten GEHM. Effectiveness and cost-effectiveness of 3-monthly versus 6-monthly monitoring of well-controlled type 2 diabetes patients: a pragmatic randomised controlled patient-preference equivalence trial in primary care (EFFIMODI study). Diabetes Obes Metab. september 2014;16(9):841–9. doi:10.1111/dom.12288

10.    Pedersen SM, Damslund N, Kjær T, Olsen KR. Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. PLOS ONE. 13. februar 2025;20(2):e0317722. Located at: Crossref. doi:10.1371/journal.pone.0317722

11.    Mukonda E, Lesosky M, Sithole S, van der Westhuizen DJ, Rusch JA, Levitt NS, m.fl. Comparing the effectiveness and cost-effectiveness of alternative type 2 diabetes monitoring intervals in resource limited settings. Health Policy Plan. 3. august 2024;39(9):946–55. Located at: Crossref. doi:10.1093/heapol/czae072

12.    Rossing P, Caramori ML, Chan JCN, Heerspink HJL, Hurst C, Khunti K, m.fl. Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease: an update based on rapidly emerging new evidence. Kidney Int. november 2022;102(5):990–9. doi:10.1016/j.kint.2022.06.013

13.    De Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, m.fl. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. 1. december 2022;45(12):3075–90. doi:10.2337/dci22-0027

14.    Sundqvist H, Heikkala E, Jokelainen J, Russo G, Mikkola I, Hagnäs M. Association of renal function screening frequency with renal function decline in patients with type 2 diabetes: a real-world study in primary health care. BMC Nephrol. 4. november 2022;23(1):356. doi:10.1186/s12882-022-02979-1

15.    Wu CC, Cao Y, Suen S chuan, Lin E. Examining chronic kidney disease screening frequency among diabetics: a POMDP approach. Health Care Manag Sci. 5. juni 2024;27(3):391–414. Located at: Crossref. doi:10.1007/s10729-024-09677-4

16.    Kessler R, Keusch GW, Szucs TD, Wittenborn JS, Hoerger TJ, Brügger U, m.fl. Health economic modelling  of the cost-effectiveness of microalbuminuria screening in Switzerland. Swiss Med Wkly. 29. januar 2012;142(0506):w13508. Located at: Crossref. doi:10.4414/smw.2012.13508

17.    Hoerger TJ, Wittenborn JS, Segel JE, Burrows NR, Imai K, Eggers P, m.fl. A Health Policy Model of CKD: 2. The Cost-Effectiveness of Microalbuminuria Screening. Am J Kidney Dis. marts 2010;55(3):463–73. doi:10.1053/j.ajkd.2009.11.017

18.    Perkins BA, Bebu I, de Boer IH, Molitch M, Zinman B, Bantle J, m.fl. Optimal Frequency of Urinary Albumin Screening in Type 1 Diabetes. Diabetes Care. 1. december 2022;45(12):2943–9. Located at: Crossref. doi:10.2337/dc22-1420

19.    UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. BMJ. 12. september 1998;317(7160):703–13. PubMed PMID: 9732337; PubMed Central PMCID: PMC28659.

20.    ACCORD Study Group, Cushman WC, Evans GW, Byington RP, Goff DC, Grimm RH, m.fl. Effects of intensive blood-pressure control in type 2 diabetes mellitus. N Engl J Med. 29. april 2010;362(17):1575–85. doi:10.1056/NEJMoa1001286 PubMed PMID: 20228401; PubMed Central PMCID: PMC4123215.

21.    Patel A. Effects of a fixed combination of perindopril and indapamide on macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE trial): a randomised controlled trial. The Lancet. september 2007;370(9590):829–40. doi:10.1016/S0140-6736(07)61303-8

22.    Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet. 6. juli 2002;360(9326):7–22. doi:10.1016/S0140-6736(02)09327-3 PubMed PMID: 12114036.

23.    Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HAW, Livingstone SJ, m.fl. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 21. august 2004;364(9435):685–96. doi:10.1016/S0140-6736(04)16895-5 PubMed PMID: 15325833.

24.    Cholesterol Treatment Trialists' (CTT) Collaboration, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, m.fl. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 13. november 2010;376(9753):1670–81. doi:10.1016/S0140-6736(10)61350-5 PubMed PMID: 21067804; PubMed Central PMCID: PMC2988224.

25.    Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, m.fl. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol. J Am Coll Cardiol. juni 2019;73(24):e285–350. doi:10.1016/j.jacc.2018.11.003​